The Impact of COVID-19 on India and the Global Order: A Multidisciplinary Approach 9811684723, 9789811684722

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Mousumi Dutta Zakir Husain Anup Kumar Sinha   Editors

The Impact of COVID-19 on India and the Global Order A Multidisciplinary Approach

The Impact of COVID-19 on India and the Global Order

Mousumi Dutta · Zakir Husain · Anup Kumar Sinha Editors

The Impact of COVID-19 on India and the Global Order A Multidisciplinary Approach

Editors Mousumi Dutta Economics Department Presidency University Kolkata, West Bengal, India

Zakir Husain Economics Department Presidency University Kolkata, West Bengal, India

Anup Kumar Sinha Heritage Business School Kolkata, West Bengal, India

ISBN 978-981-16-8471-5 ISBN 978-981-16-8472-2 (eBook) https://doi.org/10.1007/978-981-16-8472-2 © The Editor(s) (if applicable) and The Author(s), under exclusive license to Springer Nature Singapore Pte Ltd. 2022 This work is subject to copyright. All rights are solely and exclusively licensed by the Publisher, whether the whole or part of the material is concerned, specifically the rights of translation, reprinting, reuse of illustrations, recitation, broadcasting, reproduction on microfilms or in any other physical way, and transmission or information storage and retrieval, electronic adaptation, computer software, or by similar or dissimilar methodology now known or hereafter developed. The use of general descriptive names, registered names, trademarks, service marks, etc. in this publication does not imply, even in the absence of a specific statement, that such names are exempt from the relevant protective laws and regulations and therefore free for general use. The publisher, the authors and the editors are safe to assume that the advice and information in this book are believed to be true and accurate at the date of publication. Neither the publisher nor the authors or the editors give a warranty, expressed or implied, with respect to the material contained herein or for any errors or omissions that may have been made. The publisher remains neutral with regard to jurisdictional claims in published maps and institutional affiliations. This Springer imprint is published by the registered company Springer Nature Singapore Pte Ltd. The registered company address is: 152 Beach Road, #21-01/04 Gateway East, Singapore 189721, Singapore

Introduction

The Backdrop The number 2020 is usually associated with near-perfect vision for human beings. The same number, when it represented a calendar year, turned out to be almost blind vision for humanity. The COVID-19 pandemic came so suddenly and so fast that it caught all nations by surprise, and it became virtually impossible to see what the future would look like when the pandemic tapered off. It has been more than a year since the fateful March 13 2020 when the World Health Organization (WHO) declared a global pandemic. We are still in the dark about exactly when the pandemic might end and what the ultimate human toll would be. COVID19, a group of viruses affecting human beings through zoonotic transmission, was first observed in the Hubei province of China in December 2019. The virus spread swiftly and widely killing thousands and infecting millions. Doctors did not know what the best line of treatment was: which medications would work the best and what would be the most efficient containment strategy. The genome of the virus had been sequenced in January 2020 and there was a scramble for experimenting with vaccines that could provide immunity to millions of people. The virus killed and maimed people, hammered economies into recession, high unemployment, and lower incomes. People across the world were losing lives and livelihoods with no light visible at the end of the proverbial tunnel. It created panic, despair, anger, and a sense of helplessness. The policy responses were equally arbitrary: the trend was to impose lockdowns to prevent the spread of the infection. This forced people to stay at home, work from home, continue education from home and entertain oneself at home. Different countries had different experiences—economic costs, lives lost, the stress on the healthcare system, and the psychological costs people suffered from the massive disruption caused by a tiny virus which, according to medical science, is not even a living being. By December 2020 several vaccines had come into the market. However, there are still no definite signs of abatement. A ferocious second wave swept the world and experts fear the imminent arrival of a third wave. The toll keeps mounting and by July 8, 2021 the total global toll was according to the Johns

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Hopkins website: cases 185575443, deaths 4010743 and vaccine doses administered at 3356909595 (as on February 10, 2022 the figures are cases 403224417, deaths 5777230 and vaccines doses administered 10123468259). COVID disrupted societies in many different ways. In almost every country of the world the healthcare system came under severe strain with lack of beds and other live-saving equipment. Healthcare personnel were also in short supply. Doctors and nurses experienced stressful and long duty hours. Governments had difficulty managing the pandemic on three distinct counts. The first was the need to bolster healthcare infrastructure in a very short span of time. The second was to manage the economic effects of the pandemic: the lockdowns and associated unemployment and bankruptcies. Large fiscal and monetary interventions were needed with their own implications for budgetary health. In many cases, the value of the government support to the economy was as large as 10 per cent of national income. The third challenge the state had to face was how to ensure that citizens did not get too restless and defy the protocols of disease management such as wearing of protective masks, keeping social distancing and avoiding events that would allow crowds to congregate. With the availability of vaccines, another challenge became clear: the government had to manage the logistics of supply and the distribution of vaccines according to geographies and demography. No one is clear what the after-effects of the pandemic will be when the disease is controlled and tamed. What would be the lasting effect on health, education, economy and social psychology? Amidst this on-going confusion and great unpredictability, the disease plays out its unknown trajectory. People have started to reflect on the state of the world during COVID, and what might it look like after COVID. The current volume is an effort to chart out in situ the impact of a deeply disturbing moment in world’s history which has touched every aspect of human life across the world. None of us living now has actually seen anything of this magnitude and scale in our lifetimes. Assessing its full impact will not be easy. Much will be written and analyzed later for decades to come. The collection of essays, mostly written in the first quarter of 2021, represents an early attempt to assess the impact of the global pandemic, and explore the challenges it has thrown up for the economy and the society.

Setting the Context The impact of human activities on the environment, and the resultant backlash, has been well documented. In Part I of this volume, Dipesh Chakrabarty situates the pandemic in this discourse. He argues that—in contrast to slowly occurring processes like climate change, global warming, rising sea levels, resource depletion—COVID has had an immediate impact on human society. Pointing out the link between anthropocentric activities and the increasing onset of zoonotic transmission of diseases in recent years, the chapter notes that, during the lockdown, we have been able to scale down our destructive activities, with a consequent relief for nature. We had a momentary glimpse of how clean and undisturbed nature could look like. The question of

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how to moderate our activities, while meeting the basic needs of the world’s growing population, will emerge as a major challenge in the post-COVID world.

Disruptions in the Economy Part II focuses on the impact of COVID on the Indian economy and society. India’s response to the second wave of the pandemic has been widely criticized in recent times (Lancet, 2021). The sudden announcement of a national level lockdown with little preparation, along with the failure to comprehend the need to provide vulnerable sections with a safety net, was no less inept, leading to a humanitarian crisis. In their exhaustive study, Debraj Ray and S. Subramanian examine the tragic consequences of this “tough and timely decision”. The chapter is a valuable historic record of the initial reactions of economists to the pandemic and a discussion of the early policy measures. Although the chapter is dated in the sense that it covers the period of the tragedy that occurred in April and May 2020, it is our misfortune that the warnings made by the authors about the need to scale up the health infrastructure went unheeded and merely set the stage for another costly calamity. The importance of revamping the health setup is reiterated by Maitreesh Ghatak and Ramya Raghavan. Challenging the notion that there is a trade-off between health and economic outcomes, they have shown—based on a cross-country analysis comparing India with several developed and developing countries —that countries which have invested in public health have not only managed to control the disease better but also minimized the adverse economic impact on the population. This has an important lesson for India, a country that has historically failed to recognize the importance of health, and has performed particularly poorly on this count in the context of the pandemic. A major reason for the failure of India’s management of the COVID crisis has been a poor data capturing system. Although the need to collect statistics on cases and deaths as an integral part of public health policies was recognized as early as 1592 in London, such historical lessons were ignored by the COVID management group in India. Studies have asserted that there is a huge volume of under-reporting of COVID cases and deaths in India (Mukherjee et al., 2021; Lancet, 2021). One of the major reasons why cases were under-reported was the poor levels of testing, coupled with the over-reliance on Rapid Antigen Tests that notoriously yielded false negatives. Anirban Banerjee et al. have examined the associated hypothesis that the decline in cases during the first wave may have been due to poor testing. Results of the VAR model indicate that there were differential impacts across states. The under-reporting might have occurred in Uttar Pradesh and Maharashtra, though not in Kerala.

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The Creation of Vulnerabilities The pandemic has created and increased the vulnerability levels of many sections of the population. This issue is examined in Part III. The case of the migrant worker has drawn the attention of the media and the public. Amitabh Kundu and Yogesh Kumar examine the plight of migrant workers in the face of the pandemic. Despite an increasing inter-state migration rate since the 1990s, governments had failed to prepare a database of such workers. The workers were already residing in precarious living conditions. When the lockdown was announced, there were no means to either provide for their basic needs or to control their return migration and they slipped further into vulnerability. The consequences were felt not only by the workers but by people living in the sources of origin of these workers where the pandemic was transmitted by the hapless migrants. Past pandemics had been known to create panic and a sense of threat to individual security, which is manifested in anxiety, insomnia and alcoholism (Morganstein et al., 2017). COVID-19 is no exception. In the absence of vaccination during the first wave of COVID-19 governments relied on measures like social distancing, quarantining of affected persons, restrictions on travel, containment zones, and lockdowns to control the spread of the pandemic. Such measures reduced social interaction, leading to behavioral changes (Brooks et al., 2020; Galea et al., 2020). The unpredictable and uncertain nature of the pandemic (Ibrahimagi´c et al., 2020), lack of information about appropriate treatment (Shelef and Zalsman, 2020), conflicting messages from the authorities (Pfeifferbaum and North, 2020), high mortality (Shelef and Zalsman, 2020), stigma (Brooks et al., 2020) and overrun of medical facilities (Pfefferbaum and North, 2020; Thombs et al., 2020) created a mass fear of COVID (Dubey et al., 2020). “Coronaphobia” interacted with social isolation, infringement of personal freedom, financial losses and economic stress (Dubey et al., 2020; Pfefferbaum and North, 2020) to generate frustration, boredom, insomnia, anxiety, stress, depression, and other such symptoms of mental distress (Izaguirre-Torres and Siche, 2020). In developing countries, like India, the problem is accentuated by the social stigma associated with mental health problems and the low priority placed by policymakers, making the issue critical. In their chapter, Soumitra Sankar Datta et al. investigate this issue in detail. Starting with an analysis of how the pandemic has created stress the authors examine the societal response. The study also explores how to facilitate recovery. They warn that a reduction in the already low allocation to mental health may create a crisis. In the final chapter of this section, Anindita Chakrabarti et al. examine the vulnerability of Black and Ethnic Minorities (BAME) in Great Britain, a community with a greater risk of COVID-19 disease burden than the general population. Their analysis, based on data from the Middle Layer Super Output Area (MSOA) in England, reveals considerable heterogeneity within the Black and Ethnic minority (BAME) community, with localities inhabited by “Black-Caribbean”, “Black-African”, “Indian” and “Chinese” recording significantly higher mortality rates due to COVID. The spatial

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analysis also indicates that areas adjacent to neighborhoods with a high BAME population also exhibit high mortality rates, possibly due to the spread of harmful behavior through social networks.

The Shock That Shook the World In Part IV, we shift from our focus on India to adopt an international perspective. The macroeconomic scenario has, since the sub-prime crisis of 2007–08, been persistently dismal, leading economists to argue that stagnation is the new normal (Summers, 2013). Given this background, the pandemic has been an additional shock that has acted to further slowdown the recovery of economies from the earlier crisis. Seppo Honkapohja and Kaushik Mitra’s paper uses a nonlinear New Keynesian model with imperfect knowledge and learning to study the impact of the COVID shocks. The authors consider two types of negative shocks: (i) persistent shock of pessimism to expectations about aggregate output, and (ii) persistent negative shock to total factor productivity. They show how this adds to the stagnation pressure. The study calls for increased government expenditure on consumption goods to counter the demand shock, while investment in increasing productivity is required to combat the supply-side effects of the pandemic. The bleak picture of the economy portrayed by Honkapohja and Mitra is not, however, reflected in the financial sector. There is a total disconnect between developments in the real sector and the movement of financial indicators in both developed and developing countries. As India reeled from the second wave of the pandemic, the stock indices touched new heights. Partha Ray and Parthapratim Pal argue that possible reasons for the phenomenon lie in the quantitative easing and the monetary and fiscal stimulus measures introduced in many developed countries. Possibly a substantial part of the economic stimulus packages flowed to the stock markets. Such developments pose questions about the fairness of the system, the design of the stimulus packages, and create apprehension about a rise in global inequality. In this context, identifying the winners and losers of the corporate sector is an important task. This analysis has been attempted by Sudipto Dasgupta et al. using data from India and the USA. The authors argue that, in terms of share prices, firms at both ends of the tail have benefitted, the story is different when we consider income and sales. While firms in the top quartile have performed well in both 2019 and 2020, those in the bottom quartile have performed poorly in both these years. The authors argue that the performance of the Small and Medium Enterprises is likely to be similar to that of the latter group of firms. While such developments are not necessarily bad—large firms like Google and Amazon have, for instance, hired heavily to reduce the impact of the pandemic—countries with weak institutional structures will provide big firms with the opportunities to play a predatory role with devastating consequences.

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The Institutional Response Part V examines the institutional challenges posed by the pandemic. The foremost of these challenges is the government response. The reaction of governments to the pandemic has been a major issue of concern and has drawn widespread attention. China’s early success in controlling the pandemic using its authoritarian structure is well known. The political response in democracies has varied across countries and has met with different levels of success. In this context, Biju Paul Abraham distinguishes between populist and pragmatic governments. While Taiwan and New Zealand have followed a pragmatic approach, tailoring their response to fit in with an increasing scientific understanding of COVID-19 and garnering political support for an effective response to the crisis. On the other hand, populist leaders in the USA, India and Brazil focussed more on maintaining or even increasing their support base than on effectively tackling the pandemic. Another important issue has been that of vaccines. In 2020, the major challenge was to develop vaccines. Related issues like pricing, access and patent protection were recognized as important areas for potential disputes between and within countries. While the main debate centered on vaccines, access to medical products required for treatment and prevention of COVID also mattered. These issues are examined by Sudip Chaudhuri. He starts by examining the implications for a patent waiver for providing incentives to innovation and creation of manufacturing capacity, before examining possible alternatives to patent waiver such as voluntary initiatives. While there have been some voluntary initiatives, feasible alternatives exist within the provisions of the Trade Related Intellectual Property Rights Scheme (TRIPS). Chaudhuri argues that these alternatives have not been utilized, mainly because of the multinational companies (MNCs) and developed countries. In this situation, patent waiver remains the only alternative before the developing countries to ensure equality in access to vaccines. Schumpeter viewed business cycles as a process of creative destruction, which saw the entry of inefficient firms during boom periods, and their exit in times of slumps. Cyclical processes lead to cyclical changes in market concentration and power, social welfare, and inequality. Such recurring changes can be quite complex and are influenced by the institutional structure. The response of firms to the economic recession caused by the pandemic has been investigated by Anindya Sundar Chakrabarti and Chirantan Chatterjee. Firms have reacted by either leaving the market, temporarily stopping production, or reducing output levels. Simultaneously, there has been a collapse in global trade, reformulation of supply chains reducing global linkages, and reduction in cross-country linkages. Along with the heterogeneous impact on labor market outcomes, such developments can have a potentially disruptive impact on local economies. Sustaining supply chains has been a major problem during the pandemic, especially with the lockdowns and other restrictions on movement imposed in an effort to contain COVID. Parikshit Ghosh and Vaibhav Ojha point out that the latter measures were introduced by different countries in keeping with the spread of the pandemic in

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the country. Given the sequential nature of supply chain links, it has a major impact on the economy. The impact of COVID manifested in a series of shocks over a long period, leading to persistent disruption in supply chains. Producers tend to adapt by focussing on cost reduction, and outsourcing beyond optimal levels. In such a situation, the global integration process will either have to be checked or controlled through a global coordination mechanism.

Towards a Post-COVID World In the concluding section, we return to our starting point. Amitava Krishna Dutt points out that the focus of the literature has been on the impact of the pandemic measured in terms of the substantial losses in GDP, per capita income, and employment, leading to large scale poverty and inequality. Comparatively, little research has been undertaken on welfare levels, which can be measured using indicators like happiness levels. Dutt argues that the pandemic can lead to a reassessment of the meaning and ways to attain happiness. If we can rethink new, less techno-dependent and materialistic means of being happy, then it can lead to the emergence of a new society. Dutt’s paper leads us to open-ended questions about when the pandemic might end, how will the world change post-COVID if at all, and would human societies keep on inflicting damage to the natural environment? The pandemic, its zoonotic transmission origins, and its astonishingly rapid spread among human populations across different geographies have indicated beyond doubt that environmental problems are firstly global in nature, secondly can have differential impacts, and thirdly not all of the problems lie in an indefinite future. Maybe we will learn to take problems like climate change, loss of bio-diversity, degradation of the fertility of the top-soil a little more urgently. These are no longer issues that will hurt future generations. They can destroy us with a surprising swiftness against which human ingenuity and technological innovations will appear completely impotent.

References Brooks, S. K., Webster, R. K., Smith, L. E., Woodland, L., Wessely, S., Greenberg, N., Rubin, G. J. (2020). The psychological impact of quarantine and how to reduce it: Rapid review of the evidence. Lancet, 395, 912–920. doi: 10.1016/S0140-6736(20)30460-8 Dubey, S., Biswas, P., Ghosh, R., Chatterjee, S., Dubey, M. J., Chatterjee, S., Lahiri, D., Lavie, C. J. (2020). Psychosocial impact of COVID-19. Diabetes and Metabolic Syndrome: Clinical Research and Reviews, 14, 779–788. doi: 10.1016/j.dsx.2020.05.035 Galea, S., Merchant, R. M., Lurie, N. (2020). The mental health consequences of COVID-19 and physical distancing. JAMA Internal Medicine, 180, 817. doi: 10.1001/jamainternmed.2020.1562 ´ Kuni´c, S., Kalabi´c, Z., Smajlovi´c, D., Dostovi´c, Z., Tupkovi´c, E. (2020). Ibrahimagi´c, O. C., Comment on an article: “COVID-19 disease will cause a global catastrophe in terms of mental health: A hypothesis.” Medical Hypotheses, 143, 110154. doi: 10.1016/j.mehy.2020.110154 Izaguirre-Torres, D., Siche, R. (2020). Covid-19 disease will cause a global catastrophe in terms of mental health: A hypothesis. Medical Hypotheses, 143, 109846. doi:10.1016/j.mehy.2020. 109846

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Lancet Editorial. 2021. “India’s COVID-19 emergency”. Lancet, 397(10286), 8 May 2021. doi: https://doi.org/10.1016/S0140-6736(21)01052-7 Morganstein, J. C., Ursano, R. J., Fullerton, C. S., Holloway, H. C. (2017). Pandemics: Health emergencies. In R. J. Ursano, S. C. Fullerton, L. Weisaeth, B. Raphael (Eds.), Textbook of Disaster Psychiatry (pp. 270–283). Cambridge: Cambridge University Press Mukherjee, B., S. Purkayastha, M. Salvatore, and S. Mishra. (2021). Under-reporting does hurt the Covid fight. The Hindu, 4 May 2021. Accessed from https://www.thehindu.com/opinion/lead/ under-reporting-does-hurt-the-covid-fight/article34474676.ece on 2 July 2021 Pfefferbaum, B., North, C. S. (2020). Mental health and the Covid-19 pandemic. The New England Journal of Medicine, 383, 510–512. doi:10.1056/NEJMp2008017 Schumpeter, J. (1942). Capitalism, socialism, and democracy. New York: Harper & Bros Shelef, L., Zalsman, G. (2020). [THE PSYCHOLOGICAL IMPACT OF COVID-19 ON MENTAL HEALTH - LITERATURE REVIEW]. Harefuah 159, 320–325 Thombs, B. D., Bonardi, O., Rice, D. B., Boruff, J. T., Azar, M., He, C., Markham, S., Sun, Y., Wu, Y., Krishnan, A., Thombs-Vite, I., Benedetti, A., (2020). Curating evidence on mental health during COVID-19: A living systematic review. Journal of Psychosomatic Research, 133, 110113. doi: 10.1016/j.jpsychores.2020.110113 WHO (2020) Virtual press conference on COVID-19—11 March 2002. Accessed from https:// www.who.int/docs/default-source/coronaviruse/transcripts/who-audio-emergencies-corona virus-press-conference-full-and-final-11mar2020.pdf?sfvrsn=cb432bb3_2 on 5/5/2020

Contents

Setting the Context Zoonotic Pathogens, Human Life, and Pandemic in the Age of the Anthropocene . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . Dipesh Chakrabarty

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Disruptions in the Indian Economy India’s Lockdown: An Interim Report . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . Debraj Ray and S. Subramanian

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The COVID-19 Shock and the Indian Economy—A Cross-Country Comparative Analysis . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . Maitreesh Ghatak and Ramya Raghavan

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Data as Guide to Policy: Bills of Mortality of 17th Century and COVID-19 of 21st Century . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . Anirban Banerjee, Manisha Chakrabarty, and Subhankar Mukherjee

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Creation of Vulnerabilities Regional Disparity, Migration and Pandemic: Issues of Labour Market Integration and Future of Cities . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 101 Amitabh Kundu and Yogesh Kumar How Can We Facilitate Psychological Recovery Following the COVID-19 Pandemic? . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 127 Soumitra S. Datta, Arnab Mukherjee, and Raka Maitra How Susceptible is the Black and Ethnic Minority (BAME)? An Analysis of COVID-19 Mortality Pattern in England . . . . . . . . . . . . . . 151 Anindita Chakrabarti, Kausik Chaudhuri, and Jose Martin Lima

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The Shock that Shook the World Effects from Pandemic and Stagnation . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 171 Seppo Honkapohja and Kaushik Mitra Festival of Death: Global Stock Markets During the Pandemic . . . . . . . . . 189 Partha Ray and Parthapratim Pal COVID-19 and The Corporate Sector: Winners, Losers, and What Lies Ahead . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 211 Sudipto Dasgupta, Jayati Sarkar, Subrata Sarkar, and Jiali Yan The Institutional Response Populists, Pragmatists, and Pandemics: Explaining the Variance in Response of Democracies to Sars-COVID-19 . . . . . . . . . . . . . . . . . . . . . . . 241 Biju Paul Abraham Patent Protection and Access to COVID-19 Medical Products in Developing Countries . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 267 Sudip Chaudhuri Pandemic, Market Structure, and Institutions . . . . . . . . . . . . . . . . . . . . . . . . 285 Anindya S. Chakrabarti and Chirantan Chatterjee Integration Without Coordination: Revisiting Globalization in the Light of the Pandemic . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 297 Parikshit Ghosh and Vaibhav Ojha Towards a Post COVID-19 World The COVID-19 Pandemic and Happiness . . . . . . . . . . . . . . . . . . . . . . . . . . . . 313 Amitava Krishna Dutt

About the Editors

Mousumi Dutta is currently Professor and Head of the Economics Department, Presidency University, Kolkata. She is Gold Medalist in M.Sc. (Economics) from Calcutta University and completed her doctoral degree on “Economics of conservation of built heritage: the case of Kolkata” from the same University. She has been Principal or Co Investigator in projects funded by The International Growth Center, London School of Economics; Rosa Luxemburg Society, Berlin, ICSSR, and UGC. Professor Dutta has worked extensively on the built environment, health and gender issues and published in journals like Tourism Management, Journal of Cultural Heritage, Social Indicators Research, and Journal of International Development. Professor Dutta has also published books on gender and reproductive health. She is also Co-Editor of a book on Opportunities and Challenges in Development. She has also presented her work in Stockholm University, Gutenburg University, Corvinus University, Leeds University, Winchester University, Liverpool University, Cardiff University, and Shanghai University. Zakir Husain is Professor in the Economics Department, Presidency University, Kolkata. A graduate from Presidency College, he passed his Masters from Calcutta University; his Ph.D., also from Calcutta University, was on community management of natural resources. He uses econometric tools and methods to study issues related to exclusion and discrimination in gerontology, education, demography, and health. He has been Faculty in Institute of Economic Growth and Indian Institute of Technology Kharagpur, Senior Consultant in the Prime Minister’s High Level Committee to prepare report on status of Muslims, and Member of West Bengal Planning Board. He has presented in several Universities in Asia and Europe and has been involved with projects funded by The World Bank, Rosa Luxemburg Society, The Indian Growth Centre, Cancer Research UK, Niti Ayog, etc., as one of the investigators. Anup Kumar Sinha is currently Chief Mentor, Heritage Business School and Chairman Bandhan Bank Limited. He was Professor of Economics at IIM Calcutta where he taught economics from 1991 to 2016 when he retired. He was educated at Presidency College Calcutta, University of Rochester, New York, and University of xv

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Southern California, Los Angeles, from where he received his Ph.D. He has taught at Presidency College, University of Calcutta, Indian Statistical Institute, and held visiting appointments at University of Southern California, Washington University in St. Louis, and Curtin University of Technology at Perth and Kyoto University. His academic interests including publications are in the areas of macroeconomic policy, development strategies, and sustainable development and ethics. He has served three terms on the Board of Governors IIM Calcutta and had also served as Dean of the Institute during 2003–2006. In 2004 and 2005, he received the Best Faculty Award from the Alumni of IIM Calcutta. In 2012, 2014, and 2015, he was chosen as the Most Popular Teacher by outgoing MBA students.

Setting the Context

Zoonotic Pathogens, Human Life, and Pandemic in the Age of the Anthropocene Dipesh Chakrabarty

Abstract This article locates the pandemic from the larger point of view of the systematic anthropogenic damage caused to the natural environment. Humans have been used to experiencing typhoons and firestorms and floods for a fairly long time, many of which are the results of climate change and global warming. When the WHO declared COVID-19 as a pandemic, the UN pointed out that this was part of many changes which our planet must get prepared for. With the wildlife habitats getting spoiled and lost, the frequency of new zoonotic pathogens has increased. In other words, the transmission of viruses from animals to humans has increased and is likely to increase even more in the near future. Experts have talked about the rise of new forms of pathogens which will cause new forms of public health hazards. The pandemic is one of a set of problems that can only be tackled by changing lifestyles and through international cooperation to comprehensively reduce environmental damage. Keywords Anthropocene · COVID-19 · Zoonotic transmission

1 Nature and The Pandemic It is important to situate the COVID-19 pandemic within the discourse or politics of the Anthropocene. Such an Anthropocene perspective may well reveal new possibilities and opportunities for human change. The pandemic is indeed connected to the period in global history that Earth System scientists and their collaborators have designated “The Great Acceleration”— c.1950 to now. But unlike, say, the deadly typhoons, landslides, or firestorms that we have now come to expect as deleterious effects of anthropogenic climate change, the pandemic surely caught humanity by surprise. Nevertheless, the moment the World Health Organization (WHO) declared COVID-19 to be a pandemic, United Nations (UN) officials in their section on Environment immediately drew a connection between deforestation, destruction of D. Chakrabarty (B) Lawrence A. Kimpton Distinguished Service Professor of History, South Asian Languages and Civilizations, and the College, The University of Chicago, Chicago, USA e-mail: [email protected] © The Author(s), under exclusive license to Springer Nature Singapore Pte Ltd. 2022 M. Dutta et al. (eds.), The Impact of COVID-19 on India and the Global Order, https://doi.org/10.1007/978-981-16-8472-2_1

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D. Chakrabarty

wildlife habitat, growing human affluence, and the pandemic. Some of them even went so far as to describe the pandemic as nature’s “warning” to humans. They pointed out—and others have too—that in the last twenty years or so, seventy-five percent of new human infectious diseases for humans have been of the zoonotic kind, i.e., they originated when a bacteria or virus jumped species, moving from wild animals to humans. This movement of viruses and bacteria from animal or bird bodies to humans has been hastened by the destruction of wildlife habitat, thanks to the increasing pace of deforestation due to mining, logging, road building, conversion of forests to farmland, expansion of human habitations, illegal trade in wildlife products, and so on. These propositions are also supported by the research of virologists like Nathan Wolfe (see his book The Viral Storm) and science writers such as David Quammen (the author of Spillover). The chain of causation shows the connection between the Anthropocene and the pandemic: increased access to cheap and plentiful energy leads to greater human numbers and prosperity that in turn results in increasing human demand for development and consumption in those regions of the planet that are the interface between humans and wild animals. All these fit nicely, if sadly, into the narrative of Great Acceleration that underpins the Anthropocene hypothesis. The pandemic has brought to focus many new aspects of contemporary human living. Some of the most apparent are (a) that when humans are forced to scale back their presence and activities, the air becomes cleaner, the sky is bluer, and birds and animals have the chance to return to the cities that displaced them; (b) that a world based on ubiquitous travel actually leaves us vulnerable to future pandemics of this kind; (c) that we may be ready to inhabit a world with less travel and more online presence; and, most broadly, (d) that humans are the biggest spoilers of this planet’s environment. It is likely that the pandemic will induce changes in political and economic thought to accommodate the emerging normative and practical challenges. For one thing— and assuming that global leaders of the economy are not going to mend their ways, at least in the short run—it produces an immediate need for creating global institutions for predicting, if possible, the emergence of future pandemics and for overseeing their global management. Some economists have also suggested the launching of an International Solidarity Fund that will make it possible for the richer nations of the world to help poorer nations deal better with the devastating impact of the pandemic on their economies, so that the poor of the world are not forced to make a horrible choice between dying of hunger and dying from the pandemic. As the pandemic recedes, as it inevitably will, it may also present us with a moment when a global carbon tax could be introduced, in order to preserve the benefits of the enforced lowering of greenhouse gas emissions during the shutdown period. This may also be the period to take seriously the kind of proposals for re-wilding much of the planet that Edward O. Wilson makes in his book Half-Earth for the preservation of global diversity.

Zoonotic Pathogens, Human Life, and Pandemic in the Age …

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2 Impact on South Asia and India I have wished for a long time that the South Asian Association for Regional Cooperation (SAARC) would evolve into a European Union-like formation with free flows of capital, labor, ideas, and people across the boundaries of the nations that constitute SAARC. This would, of course, require nations to overcome the mistrust and suspicion that for all too long have characterized their relationships. But this seems to be the only way that India, Pakistan, and Bangladesh, for instance, can forget the trauma of the 1947 Partition and that lasting—well, at least, longer-lasting—peace may be achieved in Kashmir. This would allow the region to deal better with the various impacts of climate change including coastal cyclones. Also, China needs to be part of this conversation. The glaciers of the Himalayas feed the river systems of several Asian countries, from Pakistan to Vietnam. Yet there are no multilateral treaties or organizations to look after these glaciers. Every country thinks of rivers passing through its territory as its own resources. Overall, I think that this region ought to think of itself as a region and to create common policies, shared funds, and multilateral authorities to deal with climate change, water scarcity, cyclones, tsunamis, and other “natural” disasters. One of the saddest sights coming out of India during this pandemic has been the condition of the so-called “migrant workers”. When the country was shut down with only four hours of notice, these workers lost their income and were thrown out of their residences by landlords/slumlords unwilling to let them stay for free. The workers had to suffer for months an untold number of indignities and harassments. Many died on the desperate journey back “home”. The pandemic revealed an open secret about the Indian labor force: that in a country where around ninety per cent of its laborers are engaged in the informal sector, the critical fact about these laborers’ lives is that they are migrants. We all knew this but no one ever—at least in the policymaking circles—seems to have given it a conscious thought. All the glitter of upper-middle-class and upper-class India depends on the labor of these migrant workers. Apart from that, there are problems of under testing, and hence likely underreporting of the incidence of COVID-19 across nations in South Asia. The demographic factors—a largely young and a largely rural population—work in the region’s favor in a pandemic that has preyed on the elderly and rampaged in dense cities. But the requirement of physical distancing is very difficult to enforce or practice in a region where lives are lived in an intensely social fashion, not simply because cities are crowded—that is, of course, a problem—but also by cultural choice. Then there is the prevalence of poverty and relative ignorance of medical knowledge among the citizenry. These subaltern classes are usually the forgotten members of the body politic. Yet helping them to deal with the pandemic is critical to the region’s success in managing it. I think that is the crucial task facing the administration in the region. But the task is complicated, in the case of India, by the problems of the country’s federal political structure.

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3 Looking Ahead: A Question that Needs an Answer The current moment belongs not only to the global history of humans, it also represents a moment in the history of biological life on this planet when humans are acting as the amplifiers of a virus whose host reservoir may, for millions of years, have been some bats in China. Bats are an old form of mammal; they have been around for about fifty million years (compared to our 300,000). In the Darwinian history of life, all forms of life seek to increase their chances of survival. The Novel Coronavirus has, thanks to the demand for exotic meat in China, jumped species and has now found a wonderful agent in humans that allows it spread worldwide. Why? Because humans are very social creatures. We now exist in very large numbers on a crowded planet, and most of us are extremely mobile. That, in short, is the history of our globalization. And, of course, the virus is helped tremendously by the fact that people can be infectious before they are symptomatic. So from the point of view of the pathogen, jumping to human hosts has been a great move. Humans may win their battle against the virus—I really hope they do—but the virus has already won the war. This is no doubt an episode in the Darwinian history of life. And the changes it causes will be momentous both in our global history and in the planetary history of biological life. The really critical and fundamental question we have to ask is: do we, as humans, want to continue expanding an economy that keeps increasing for us the risk of zoonotic diseases? Nathan Wolfe, the virologist I mentioned before, suggests that epidemics caused by viral and bacterial transfer between humans and domesticated animals reached a kind of equilibrium about 5000 years ago. He says most recent infections with the potential to cause epidemic or pandemic diseases have been due to increasing human contact with wild animals. Wild animals do not seek us out; we go and destroy their habitat, forcing them to come into contact with us. Many Earth System scientists, evolutionary biologists, and Anthropocene scholars have been reminding us that the global economy is destroying biodiversity and that, on human scales of time, biodiversity is a non-renewable resource that is critical to the flourishing of all life, including ours. It is time we debated the kind of civilization humans would want to live in. The Cold War battle between capitalism and socialism is well and truly dead. But that does not mean that the question of debating capitalism has lost any of its significance. The alternative to present-day capitalism does not have to be Maoist or Leninist socialism. How to remain modern and democratic and yet not destroy or completely dominate the order of life on the planet remains a critical question as humans contemplate their future on a planet they have taken for granted for far too long. Acknowledgements This essay is a slightly edited version of the interview given by the author to the Toynbee Prize Foundation in 2020.

Zoonotic Pathogens, Human Life, and Pandemic in the Age …

References Quammen, D. (2013). Spillover: Animal Infections and the Next Human Pandemic. Norton. Wilson, E. (2016). Half Earth: Our Planet’s Fight for Life. Norton. Wolfe, N. (2011). The Viral Storm: The Dawn of a New Pandemic Age. Penguin Books.

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Disruptions in the Indian Economy

India’s Lockdown: An Interim Report Debraj Ray and S. Subramanian

Authors’ Note, May 2021. The first version of this paper was published in May 2020 as a National Bureau of Economic Research working paper, and a subsequent version was published in The Indian Economic Review, a few months later, in September. At the time of publication, the Covid-19 case trends had just started their (transitory) downward trend, one that persisted well into the early part of 2021. That decline was puzzling to many, and almost certainly due to collective precautions taken by a large fraction of Indian citizens. For it was quite unclear, even then, that India’s 2020 lockdown had achieved little else but a sense of real awareness among a large (but alas, not universal) section of the public. Once that public guard was let down, as it was bound to be in the afterglow of the Government’s ill-timed and ill-advised celebratory air, the lack of anything else—expanded medical capacity, widespread vaccine capabilities, oxygen supplies, tracking and tracing facilities—became all too shockingly obvious.

This report is based on several scattered items of information in an environment of relatively scanty hard data, regarding an event-in-progress. The objective is to put together, as an “interim report,” some of the work being done by journalists and scholars, in the expectation that some overall picture will begin to emerge regarding India’s efforts to come to terms with the pandemic. We thank Bishnupriya Gupta, Rajeswari Sengupta, and Lore Vandewalle for helpful comments. Our efforts are dedicated to the memory of Hari Vasudevan: eminent historian, wide-ranging scholar, the gentlest of souls, who left us all too soon. Hari died in Kolkata of Covid-19 on May 10, 2020. D. Ray (B) New York University, New York, USA e-mail: [email protected] University of Warwick, Warwick, UK S. Subramanian Independent Researcher (formerly, Madras Institute of Development Studies), Chennai, India © The Author(s), under exclusive license to Springer Nature Singapore Pte Ltd. 2022 M. Dutta et al. (eds.), The Impact of COVID-19 on India and the Global Order, https://doi.org/10.1007/978-981-16-8472-2_2

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We do not add anything to the paper, save this brief foreword. Our Interim Report was not only a chronicle of a disaster then unfolding, but has turned out to be one of a tragedy foretold. We fervently wish it had been otherwise. With regard to the 2020 lockdown, we (and many others) emphasized the obvious: the need for “…the widespread provision of adequate health and medical facilities, adequate protection for the elderly, and transfers to those severely affected by the lockdown…”, as also continued insistence on “…easy-to-implement measures such as mask-wearing, physical distancing, and the avoidance of crowded gatherings…” But above all, we noted that “…the coronavirus pandemic has brought into sharp relief the importance of having a solid country-wide health infrastructure which can respond quickly and flexibly to a health crisis—and India’s weak preparedness for such contingencies.” Without utilizing the opportunity to build that capacity, a lockdown is pretty much useless, for when all is said and done, it is only a measure that buys time by flattening the dreaded curve. A lockdown per se does nothing for the total number of infections and perhaps even the total number of deaths. What it does do is draw these out over time. In a related essay written for the Boston Globe (Ray and Subramanian, 2020), we suggested that “…lockdowns are not supposed to be punitive. They are meant to provide time, among other things, to create, deploy, and allocate medical resources; to prepare and train a vast support network of health care and contact-tracing personnel; to rearrange commercial, infrastructural, and educational resources to meet a new era; and to psychologically prepare society for the long haul.” Otherwise, as we wrote in our Interim Report, “…the brutal enforcement of a lockdown with none of these accompanying measures can only worsen outcomes for the poorest and most vulnerable among the population.” India’s 2020 lockdown came nowhere near meeting even a fraction of these requirements. Today we face a second wave of the epidemic, caused in varying proportions by a new mutant of the virus, by increased complacency and laxness in the routine use of personal protective equipment, and by mind-defying gatherings of people, in their thousands, for election rallies and religious observances—in encouraging both of which the State has had a leading role to play. The second wave we are witnessing is one in which the daily infection rate peaked at over four times the largest daily infection rate in the first wave, with mortality rates rising in tandem with infection rates. While the wave of new cases is beginning to recede yet again, the pain it has inflicted on millions of families can never be forgotten. The tragic want of anything like a National Plan to deal with the crisis is evident in the near-complete collapse of the country’s health system, with hopelessly over-stretched hospitals having to cope with frighteningly minuscule supplies of beds, oxygen, ventilators and vaccine. Our Interim Report of “then” would appear to cover the case of “now”—only, with a many-fold increase in the gravity of its assessment. It is this dismal sentiment that keeps us from saying much more on the subject than we have already done in the paper that appears in this volume.

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1 Prologue Our goal is to provide an interim report on the Indian lockdown provoked by the covid19 pandemic. While our main themes—ranging from the philosophy of lockdown to the provision of relief measures—transcend the Indian case, our context is deeply India-specific in several senses that we hope will become clear through the article. A fundamental theme that recurs throughout our writing is the enormous visibility of covid-19 deaths worldwide, now that sensitivities and anxieties regarding the pandemic have been honed to an extreme sharpness. Governments everywhere are propelled to respect this visibility, developing countries perhaps even more so than their developed counterparts. In advanced economies, the cost of achieving this reduction in visible deaths is “merely” a dramatic reduction in overall economic activity, coupled with a far-reaching relief package to partly compensate those who bear such losses. But for India, a developing country with great sectoral and occupational vulnerabilities, this dramatic reduction is more than economics: it means lives lost. These lost lives, through violence, starvation, indebtedness, and extreme stress, both psychological and physiological, are invisible, in the sense that they are—and will continue to be—diffuse in space, time, cause, and category. They will blend into the surrounding landscape; they are not news, though the intrepid statistician or economist will pick them up as the months go by. It is this conjunction of visibility and invisibility that drives the Indian response. The lockdown meets all international standards so far; the relief package none.

2 The Lockdown On January 30, 2020, a brief press release issued by the Ministry of Health and Family Welfare in India read: One positive case of novel coronavirus patient, of a student studying in Wuhan University, has been reported in Kerala. The patient has tested positive for novel coronavirus and is in isolation in the hospital. The patient is stable and is being closely monitored.

By February 3, this had risen to three cases, all students at Wuhan University. An ominous silence then prevailed for all of February, until March 4 when a further 22 cases came to light. It took till March 6th for the Ministry of Health and Family Welfare to launch its covid-19 awareness program (see Somdeep Sen’s report Sen (2020) on the timeline leading to the lockdown announcement). Matters accelerated after that, as the infection moved into community transmission: by mid-March, there were over a hundred confirmed cases and over 1000 by the end of March. Countries all over the world were entering lockdown. On March 11, the World Health Organization stated that covid-19 represented a global pandemic. The pressure was on. The Indian government appeared to be located, to put it baldly, between a rock and a hard place. The preceding months and weeks had been far from placid. The

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Citizenship Amendment Act, which sought to grant citizenship to all refugees of nonMuslim origin from Pakistan, Bangladesh, and Afghanistan, had met with sustained protest around the country. The Act and the groundswell of response had generated recurrent tensions. One of these protest sites was threatened by a Hindu nationalist BJP leader and subsequently attacked, leading to communal riots in Delhi starting February 23 that left 53 people dead, of whom more than two-thirds were Muslims. These riots coincided, perhaps not coincidentally, with Donald Trump’s visit to India on February 24th–25th. Most recently, on March 16, the Congress Government had lost its majority in the state of Madhya Pradesh, and the BJP was about to install a government in that state (on March 23rd). So India was behind the curve. To be scrupulously fair, it wasn’t far behind the responses of many Western countries, but that is neither here nor there. Kerala, with its recent history of dealing with the Nipah outbreak, had already been getting its act together. In a report Nair (2020) filed on March 29th, the columnist Supriya Nair writes: “Since February, opposition politicians have been warning of the approaching risks. The southern state of Kerala, the first to start widespread testing and quarantine measures, has prevented uncontrolled outbreak within its borders for several weeks.” In an interview Thapar (2020) with Karan Thapar for The Wire on March 19th, the epidemiologist Ramanan Laxminarayan, Director of the Center for Disease Dynamics, Economics and Policy in Washington, D. C., “…disagree[d] with the Indian Council of Medical Research’s official stand that India is still in Stage 2 of the epidemic and has not entered Stage 3 [community transmission].” A morning-to-evening 14-hour “Janata Curfew”—a Curfew of the People—was announced for March 22nd, at the end of which, at 9 p.m., people were invited to assemble on their balconies and together bang their pots and pans as a gesture of appreciation for functionaries involved in running essential services. This preceded a full lockdown that was announced on March 24th for a period of 3 weeks, till April 14th. On April 14th, the lockdown was extended for another 19 days, till May 3rd, and then extended again till May 18, with provision for relaxation of the shutdown for selected agricultural businesses, cargo transportation, and sale of farming supplies, from April 20th, in those areas of the country in which the infection was perceived to have been contained. (At the time of writing, a further extension of the lockdown appears to be extremely likely.) On April 16th, the government resorted to a three-fold classification of districts—the so-called red zones assessed as hotspots with quick case-doubling times, orange zones which were assessed as having some infection, and green zones which were assessed as being free of infection in the last 21 days.1 Activities such as personal travel by rail, air or metro (with some exceptions), hospitality services such as restaurants, schools and colleges, cinema halls and sports complexes, and large gatherings are prohibited in all zones. Some manufacturing and industrial activities remain open in all zones. Then there are gradations that depend on the particular zone.

1

Government of India information; https://static.mygov.in/rest/s3fs-public/mygov_158884465 251307401.pdf.

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According to some views, the government’s abrupt declaration of a lockdown without a gradual leading up to it was perhaps a symptom of panic under pressure. Vivek Menezes (2020) describes the moment in an “Opinion” piece for The Guardian written on April 8th: At 8 pm on 24 March, Prime Minister Narendra Modi announced that India would shut down in four hours. As he spoke, chaos erupted. Panicked mobs besieged the shops. Then, as buses and trains were cancelled, millions of migrant workers took to the roads on foot, streaming toward home in scenes that recall the partition photographs of Margaret Bourke-White.

After weeks of dithering, India had just been served with four hours of notice. In contrast, the government did nothing for all of February by way of testing, tracing, and quarantining. A Kerala-type preliminary operation would have prepared the ground for a much mellower transition to lockdown. For many citizens, the Prime Minister’s announcements were frustratingly short on detail with respect to concrete steps that would be taken by the government to alleviate distress during the lockdown: a report by The Wire (2020) on the announcement made on March 24th states: “The lack of details in Modi’s speech …resulted in an almost nationwide spike in panic …As soon as the speech ended, neighbourhoods and markets across the country saw a sudden rise in traffic and footfalls as people rushed to stock up on supplies, with concerns about social distancing—the goal of the lockdown in the first place—temporarily being ignored.” There were similar reactions to the lockdown extensions. An April 14th report Scroll Staff (2020) by The Scroll on the lockdown-extension announcement observes: “Several Twitter users were indignant about Modi failing to make any mention of plans to rejuvenate a falling economy, help the poor, or boost India’s attempts to fight the novel coronavirus.” But as the lockdown settled, the cessation of normal activity was remarkably comprehensive. In the initial phase, apart from essential services (principally banks, ATMs, petrol bunks, and emergency services), and shops selling or home-delivering medicines and food, vegetables and dairy products, other services and activities were stopped. In fact, the police enforced the shutdown with a surprising excess of enthusiasm: to begin with, even “essential commodities” could not be transported through the supply chain, as both agricultural mandis and urban distribution points for food items were shut down in several states. The lockdown decree did allow for the movement of essential commodities, but as Sudha Narayanan has observed, “the word ‘essential’ comes from the Essential Commodities Act, [and] there is no good reason to expect …the police to know what these are”Narayanan (2020). Since April 20, there has been some easing as described above. We will return to supply chains in Sect. 7.3. In summary, a mammoth population of 1.3 billion people has been restricted to their homes, and transport services, schools, factories, and business establishments have been closed. This state of affairs will continue into the proximate future. If nothing else—though we will need to see what else—this in itself is an achievement (if that word can be employed neutrally), the implementation of which could not have been foreseen.

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3 The Philosophy of Lockdown India’s lockdown is an instance of the dominant global policy response to the covid-19 pandemic. It entails the observance of physical distancing and isolation in a context of widespread, across-the-board suspension of normal human mobility and economic activity. With some exceptions in severity (such as Sweden where the government has been reluctant to impose measures other than guidelines, or Belarus, where there don’t seem to be guidelines either), this is very much the model that has been adopted by economically advanced European, North American and English-speaking countries like Australia and New Zealand. Indeed, India’s lockdown, now to run for an overall two months and more at the time of writing, is a prime example of the global approach, and the stringency of its implementation is reputedly unsurpassed by the record of any other country. The normative philosophy for any policy is a social externality relative to some welfare objective. By this criterion, the core externality underlying an epidemic is obvious—individuals will generally underemphasize the negative contagion that their actions impose on others. But ours is not an exercise in welfare economics: rather, we seek to understand the positive, not normative, philosophy underlying the response to the epidemic. The externality notion is also useful in this context, but we additionally need to ask: an externality relative to which viewpoint or welfare function? The dominant policy response has been dictated by a largely epidemiological view of the problem. This is hardly surprising as the medical profession has been at the vanguard of those advising governments on the matter. A standard epidemiological model is pretty mechanical. An interactive stochastic process takes individuals through “states”: susceptive, infected, recovered, and so on. An array of parameters that govern contagion, fatality, recovery, or re-infection can be fed into these equations, which can then be solved (sometimes analytically, more often numerically) to generate estimates of a time path of infections and fatalities. Because this baseline model is devoid of behavioral responses, an enormous externality is present, by definition. This reliance on mechanical “parameters” is easy to criticize. Every behavioral social scientist would know that even without a government to urge them along, individuals will react to an epidemic, adjusting their own behavior to avoid infection—at the very least out of narrow self-interest. The extent to which they react will, in turn, influence the “parameters” of the model, thereby generating nonlinear responses that will go some way toward “flattening the curve” on its own. That said, this more sophisticated variant of the epidemiological policy view would still say that spontaneous reactions are still not enough, and intervention is needed. Even the modified behavioral models would not necessarily yield socially optimal outcomes.2

2

There has been a veritable epidemic of papers on behavioral models of epidemiology, some more explicit than others in modeling individual responses, and all studying optimal lockdowns under various scenarios. For a tiny sample, see (pre-pandemic) Geoffard and Philipson (1996), Fenichel

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The lockdown response is further bolstered by the fact that organized services form a large share of the occupational structure of economically advanced societies, so there is substantial scope to move work activities online. If we factor in the demographic age distribution in these societies, and the concentration of resources and lobbying power in the upper reaches of that age distribution, it is not hard to see why the epidemiological view has come to dominate the debate, though not without substantial opposition. That opposition comes from a motley crew of interest groups. There are libertarians, mask-spurners, and freedom junkies whose only goal is to exercise their right to free choice without heed to the consequences of their actions. There are religious groupings of all stripes who feel that human injunctions to fight the coronavirus are powerless—or unnecessary, or worse, a heresy—in the shadow of an omniscient God. And there are immensely powerful corporate and business interests, which not only feed off the freedom-loving/god-fearing/libertarian mishmash, but also inject the more insidious (and yet more serious) argument that the price of life has its limits and that human souls can be placed on the same weighing scale as goods and services, at some possibly large but finite price. Support for the lockdown is, therefore, seen as emanating from those who are high-minded enough to understand that life must be valued above commodities, those who understand the need for regulation to combat unwanted externalities. In contrast, those who would leave their societies unregulated are viewed with emotions ranging from amused contempt to outright suspicion and high outrage. It is a bit of a caricature, but only a bit, that supports the lockdown is associated with being “left,” or “progressive,” while those in favor of rapid relaxation are associated with being to the “right,” or “individual-choice-oriented.” There is just one uncomfortable hole in the above seemingly tidy argument. It is that the motley crew of libertarians and capitalists are beginning to be joined by an increasingly desperate and vocal working class who live in unceasing fear of their livelihoods never returning. For this reason and others, a nuanced reading of these contrasting philosophies reveals doubts: doubts that are significantly heightened as we switch our focus to developing countries. While welfare economics is conducted in the pristine glow of a single, undisputed objective function, positive political economy is not: whose welfare is often a far more important question that the textbook criteria of market failures relative to some universally accepted social welfare function.

4 Lives Versus Lives: The Visible and the Invisible The covid-19 pandemic has created a laser focus on lives lost from the virus. No other epidemic in living memory has done this in economically advanced societies, with the possible exception of HIV-AIDS. Not Ebola, nor SARS, not MERS, not (2013) and Fenichel et al. (2011), and (post-pandemic) Farboodi, Jarosch and Shimer (2020), Garibaldi, Moen, and Pissarides (2020), and Jones, Philippon and Venkateswaran (2020).

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Table 1 Tests, Cases and Deaths for Selected Countries. Notes: Figures accurate as of May 10, 2020. Figures in the first three columns for India (May 12) are from https://www.covid19india.org/, and elsewhere from Wikipedia; see https://en.wikipedia.org/wiki/COVID-19_testing and https:// en.wikipedia.org/wiki/Template:COVID-19_pandemic_data Country Tests [1] Confirmed [2] Deaths [3] Case Fatality Tests Rate [3/2] & (per million) U.S. Spain U.K. Italy Germany France India

8,412,095 2,467,761 1,728,443 2,514,234 2,755,770 724,574 1,759,579

1,334,280 223,578 215,260 218,268 170,979 138,421 74,219

79,254 23,822 31,587 26,478 7510 26,230 2,415

5.94 10.65 14.67 12.13 4.39 18.95 3.25

25.628 52,805 25,589 41,654 33,142 10,811 1,300

even the H1N1 pandemic has generated this level of red alert; and certainly not malaria or dengue, those stalwart relics safely locked away in the developing world. It isn’t that these advanced societies haven’t had first-hand experiences in some of these epidemics. But SARS-CoV-2 is different—it comes with a combination of contagiousness and fatality risk that has now taken over a quarter of a million lives worldwide, and this time the developed world accounts for the vast bulk of these deaths. The international visibility of covid-19 deaths, heightened by their predominance in “the West,” plays no small role in our story. The visibility of covid-19 deaths is greatly bolstered by the epidemiological perspective described in the previous section. It is further heightened by two additional considerations. The first is the lack of information. The initial response to the pandemic has been reactive testing, with only ill—often desperately ill—patients being tested and others being told to wait until their symptoms worsened. This approach does not serve (and is not intended to serve) any statistical purpose: its sole purpose is to confirm positive cases among severely ill patients and then isolate and treat them. However, as a consequence, while we might have a pretty good idea of covid-19 deaths—though there are issues here to be resolved3 —we only have rough bounds on covid-19 infections. Table 1 displays country-level statistics on tests, positive cases, and the case fatality rate (CFR), which is the death rate from known positive cases. These rates are frighteningly high, often—as in the case of Spain, Italy or the United Kingdom—reaching well into double digits. For the world as a whole, we

3

Excess deaths have been a feature of this pandemic period, leading to the possibility that we may be undercounting covid-19 deaths; see, for instance, Galeotti, Hohmann and Surico (2020). The opposing argument has also been made: that we may be ascribing too many deaths to covid-19, simply because individuals dying of other morbidities may have been found to be infected; see, for instance, Lee (2020).

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have around 4.7m cases with around 310,000 deaths (as of May 17, 2020), a CFR of around 6.7%. It is not uncommon for commentators even today, well into the pandemic, to use these rates as rough proxies for the true fatality rate from covid-19, but of course, that is a far cry from the truth.4 The last column of Table 1 shows that there are wide inter-country variations in the number of tests conducted per million population, with France, the UK, and the USA lagging behind among the advanced countries, and India revealing a severely depressed record of testing intensity, with additional peculiar features to which we return below; see Sect. 5. But the point is that we are very far from universal testing. Every country predominantly tests the very ill, with the obvious outcome that the CFR is nowhere close to the true death rate from covid-19. The fact of the matter is that the public does not know what the mortality rate from covid-19 is. Indeed, no one knows, and we won’t know in a definitive way until our samples cease to be biased, where the word is invoked not in the context of treatment but in the sense of access to statistical knowledge. This brings into relief the usefulness of conducting tests on completely unbiased and random national samples, a point widely appreciated at a very early stage by a multitude of statisticians and economists. At the time of writing, the samples we have may be too small to yield statistical confidence, or too prone to bias (such as asking for volunteers or sampling from a biased sub-population, such as shoppers). In “natural experiments” such as the covid-19 infection on the Princess Diamond cruise ship, age distributions are not well-represented either in composition or in size. This situation will change very quickly over the coming weeks or months. What we do know without a doubt is that infection mortality rates are orders of magnitude lower than the case fatality rates reported in Table 1, and there is every likelihood that these rates will be somewhere between 0.5 and 0.8%, higher than the rate of 0.1% commonly ascribed to the flu, but not stratospherically so. For more discussion, see Sect. 5. A corollary of the above argument is that we also do not know the contagion rate for the virus.5 We can (tentatively) treat the total number of covid deaths as accurate information, but if we do not know what the total number of infections is, it is not possible to factorize deaths/population into the accounting expression: Deaths/ population = [Deaths/in f ections] ∗ [I n f ections/ population], (1) where the first term on the right would reflect the true fatality rate, and the second would be a proxy for how contagious the disease is. The missing culprit is “infections,” on which we do not yet have accurate data.

4

Even months into the crisis, there is confusion among observers and certainly the general public regarding how deadly covid-19 really is; see, for instance, Caroline Chen’s report (2020) in ProPublica. 5 The word “contagious” is invoked relative to some non-behavioral baseline. As already noted, behavioral reactions by the population would alter the effective pace of propagation of the disease.

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For instance, compare a mildly contagious but highly fatal disease to a highly contagious but flu-mortality-like illness. Both could multiply out to the same death/population number, but they would affect our behavior in very disparate ways— your authors, for instance, would probably be terrified of the first scenario and more accepting of the second. But from an overall public health perspective, the two scenarios are very similar; if anything, given the behavioral reactions just outlined, the highly contagious but mildly fatal illness could wreck the public health system to a greater degree than the low-contagion high fatality disease. Any competent epidemiologist knows (or should know) these things, but is deeply concerned that the public—left to its own selfish devices—will not do enough to protect society. That concerned epidemiologist would love to say that both contagion and fatality rates are high, and often does. She might focus, for instance, on the exponential growth of reported cases (contagion? not really) and simultaneously on the case fatality rate (the death rate? not really). She might say that a great majority of the afflicted are young—which will be generally true as long as a great majority of the population is young, or that the disease causes irreversible damage even if it does not kill. All of this has enough partial truth to it to carry the ring of truth and represents a well-meaning attempt to drag individual behavior toward the social optimum. Therefore, the vast majority of the public remains imperfectly informed about some salient features of covid-19, even to the extent that such information may be available.6 The one feature that is made highly visible is that we are locked in mortal combat with a killer disease. The second consideration that works to highlight the visibility of epidemic deaths is the view that a disease such as covid-19 claims lives in a predatory manner that the suspension of economic activity cannot. In short, a welfare contest between lives and the disruptions caused by a cessation or reduction in economic activity should be no contest at all. This no-contest view is soundly supported by the epidemiologists. No contest echoes any Government which has thrown its weight behind a lockdown. For instance, New York Governor Andrew Cuomo, whose empathetic daily briefings have gained a wide following, and not just in New York, had this to say: How much is a human life worth? That is the real discussion that no one is admitting, openly or freely. That we should. To me, I say the cost of a human life, a human life is priceless. Period.

Is this a reasonable position? For many, even in advanced economies, the answer is actually in the negative. An economist could estimate the value of life for you in twenty different ways, ranging from private decisions to cross a busy street, or expenditure on airline safety, or the extent of public smoking bans. Each of these would return a finite price of life. 6

Certainly, with a public that seeks out its own independent information and, in particular, is fully and intelligently informed about aggregate deaths, the epidemiologist cannot successfully manipulate both contagion and fatality rates, but even here there is no incentive for a government policing a lockdown to credibly reveal the correct statistics to the public. This is not a conspiracy theory. It is a simple fact that can be associated even with benevolent (if paternalistic) governance.

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We would like to be clear that we do not accept this counter-position. It is indeed true that our actions often reveal an implicit price on life that’s finite. We do that every time we get on an airplane. We do realize that prolonging the life of a loved one may not be sensible at any cost. But very often these costs are not economic. We might get on a plane because I (correctly) attach infinitesimally low probabilities to a fatal crash. We might drive a car or cross a busy street because we (incorrectly) attach arbitrarily high probability to being a better-than-average driver, or to being nimble enough to avoid getting run over. However, these mind games so beloved to revealed-preference economists do not prevent us from attaching extremely high value to otherwise healthy life, especially when confronted only with the calculus of monetary loss. Ingenuous though his words might sound, that is why we would agree with Governor Cuomo. Faced with a lockdown that destroys some economic value and protects lives, we would take the lockdown. Or, to be absolutely clear about this before we enter darker waters: in our opinion, the first-best (or “unconstrained”) approach to tackling the viral epidemic is a fully implemented lockdown which is also accompanied by a comprehensive package of welfare measures designed to compensate for the negative impact on human lives of such a lockdown. We believe that this is a clear enough articulation of an ethical position that one might, in principle, adopt. But this position-in-principle inevitably also provokes the pragmatic question: what if the State is unable to implement the first-best option just outlined—for reasons that could range from financial constraints to lack of expertise to ignorance to incompetence? Even worse, what if the State were unwilling to do so—for reasons of being unprepared to make the effort to overcome the shortfalls in State capacity just mentioned, or, additionally, unprepared to disturb the settled weight of vested interests by engaging in redistributive policy? (The notion of “redistributive policy” is here interpreted broadly as an orientation tilting in favor of alleviating the burden of the poor laboring classes at the possible cost of some benefit—fiscal or healthrelated—to the relatively affluent classes.) In the remainder of this essay, we will demonstrate just how important these questions are in the Indian context. The problem is that in India, and without the shadow of a doubt, an economic lockdown will entail the widespread loss of life. There are lives that will be lost by lockdown-induced conditions of starvation, ill-health, violence, a rise in indebtedness, and persistent loss of incomes and livelihoods. Without sustained and comprehensive protection to those at risk, the entire philosophical question of whether a human life has finite value or not is far less relevant. In India especially—and in poor countries more generally—it is not a question of lives versus economics, it is a question of lives versus lives. Or more pertinently, it is a question of which lives have greater visibility. The lives that are lost through violence, starvation, indebtedness, and extreme stress, are invisible, in the sense that they will diffuse through category and time. Someone will die of suicide. A woman will be killed in an episode of domestic violence. The police might beat a protestor to death. The deaths will occur not just now, but months and years from now, as mounting starvation, indebtedness, and chronic illnesses take their collective toll. As Hari Vasudevan wrote on April 22:

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D. Ray and S. Subramanian Starvation is peculiar. It does not kill quickly. Gandhi showed that. So have many others. But, ultimately, it kills surely. Perhaps, under the relaxation of the lockdown, a hungry family will go back to work, having been unable to move more than a hundred kilometers, and now terrified of a homecoming fraught with the suspicion of contagion …Vasudevan (2020).

The non-covid deaths from a lockdown will blend into the surrounding landscape of ill-health and death from a multitude of categories; their very diffusiveness makes them not news, though they will reappear under careful record-keeping by statisticians, public-interest activists or economists. The website https://thejeshgn. com/projects/covid19-india/non-virus-deaths/ tracks lockdown-related deaths not ascribed to covid-19. Deaths are tracked in national and local newspapers in English and in several (but not all) Indian languages. The categories into which deaths now primarily fall are suicide, migration-related accidents, violence (domestic and otherwise), exhaustion, financial distress, and hunger. The tracking is laudable but necessarily incomplete, and this incompleteness is bound to increase as the newspaper reports fail to make the connection—as they invariably and understandably will— between each death in question and the lockdown that caused it. Neither will these deaths carry the same urgent exponential signature as the initial trajectory of covid19 deaths will display, even though they have spiked days after the lockdown. But over time, they will surely mount, just as they will surely slide under the radar of a world eager for well-defined events. These lost lives are invisible, but are just as ascribable to covid-19. We will argue that it is this conjunction of visibility and invisibility that drives the Indian response: unexpectedly and often brutally efficient in enforcing a lockdown, so as to gain international kudos, while verbally exaggerated and yet silently deficient on the relief that is so desperately needed for those whose livelihoods are at stake. We are seeing a Central State intervention that is geared maximally to achieving visibility, rather than the less optically dramatic but equally important act of substantive amelioration. It is the international political economy with domestic consequences to boot, as we shall see below when we review the changed fiscal relationships that are beginning to emerge between the Indian Center and the Indian States, the new amendments to labor laws that are swiftly being put into place, the clauses that incentivize corporate donations to flow toward the Center, and the wholly inadequate definition and implementation of the relief packages. In short, the Indian Government is succumbing to the overriding temptation of implementing all the trappings of a stringent lockdown without bothering overmuch about delivering compensating welfare relief. The former strategy is amenable to propaganda, touting measures, directed to the prevention of covid-related deaths. In contrast, the latter approach possesses less immediate advertisement-value, and is more difficult, plodding, and laborious to implement. It looks unlikely to ever be. In what follows, we will selectively describe some of the many practical suggestions that have been made by various commentators on what sort of relief package might be dictated in a time of lockdown and beyond, actual State performance in this regard, some consequences and correlates of State policy, and the social attitudes of the ruling classes; and we shall end with a possibly reasonable pragmatic alternative that we proposed in our earlier writing. We hope to be able to locate our assessment

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within the framework described in this section, one that highlights the relatively easy rewards to State intervention that are to be had from a policy that prioritizes visibility above other goals.

5 The Progression of Covid-19 in India: A Brief Account Figure 1, taken from the website Our World in Data, at https://ourworldindata.org/, locates India with respect to other countries in the progression of the disease. The figure shows how the disease has progressed, measured in days since the 100th confirmed case. Panel A shows this for India relative to some other advanced economies, and Panel B does the same for India relative to some other developing countries. (The light gray lines show all countries.) Nothing here suggests, despite the zeal of lockdown enforcement, that India is headed for any unique destination. With populations taken into account, and barring the development of widespread testing or a vaccine, it suggests that the Indian curve will soon overtake most other countries in terms of absolute numbers—although this is tempered by the low CFR; see below. Given the enormous population density of Indian megacities and even its smaller towns, as well as the population clusters (e.g., in slums) within the cities, this is not surprising—if anything, it points to the relative stringency of the lockdown measures that have been adopted in the country. Figure 2 does a bit more in terms of percentages. Panel A shows that while India has a similar time trajectory on the aggregate, it has a lower case fatality rate: at just a shade over 3%, it is far lower than the world average of around 7%. One suspects (correctly, as we will argue below) that this has to do with the age distribution of the population. This somewhat optimistic scenario is tempered by Panel B, which suggests that India, with its low testing rate, is surely undercounting the cases and

Fig. 1 Time Path of Cases Since the 100th Confirmed Case of Covid-19; India and Selected Countries. Source Our World in Data, https://ourworldindata.org/

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Fig. 2 Case fatality rates (Panel A), and tests and cases per million (Panel B); India and selected countries. Source Our World in Data, https://ourworldindata.org/

most likely the deaths as well. Taken together, while Panels A and B do not say much about the trend of the disease in India—Figure 1 is more relevant for that— they do suggest that the multiplicative constant that anchors the overall trajectory of progression has yet to fully reveal itself. Now let us go a bit deeper into the Indian experience. As of May 17, 2020, India is recording just short of 100,000 cases and a shade over 3,000 dead, a case fatality rate of 3.16%. Table 2 shows the pattern of cases and deaths across several Indian states and territories that together account for the vast majority of confirmed incidences (94617 of the 95,698 cases and 3,012 of the 3,025 deaths). Just Maharashtra, Gujarat, Tamil Nadu, and Delhi account for 2/3 of both cases and deaths—this regional clustering is, of course, only to be expected of a highly contagious disease and seen the world over. These numbers don’t tell us anything about how truly widespread the disease is, nor how the lockdown is affecting its incidence, because the tests are as they should be at this stage—reactive in nature and not designed for statistical inference. Testing is, therefore, overwhelmingly concentrated on those who are seriously ill and in need of medical assistance, and is being used—as in other countries at this stage of the curve—for confirmatory diagnostic purposes. That said, some features of this table are of interest. First, states such as Tamil Nadu and Kerala are doing extremely well in that they are registering very low case fatality rates. This is not surprising in the case of Kerala, even with its relatively low testing rate. Kerala has a history of battling the Nipah epidemic, and the first outbreaks of covid-19 also occurred there, as we’ve already noted. Kerala’s secret has never been high-tech, but rather reliable medical facilities, supreme common sense in matters such as contact tracing and quarantine, and a highly educated population that understands the need for such measures. As the Economist observes (2020): Kerala tamed Nipah within a month, adopting an all-hands approach that included districtwide curfews, relentless contact- tracing and the quarantine of thousands of potential carriers. Kerala has used the same simple, cheap tools to fight covid-19, with similarly stellar results.

That article was written on May 9. Eight days later, the number of deaths still has not changed. May it stay that way. But we know that Kerala has many emigrants to

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Table 2 Cases, Deaths and Tests per Million by Indian States and Territories. Notes: Numbers as of May 17, 2020. Source https://www.covid19india.org/ Region

Cases [1]

% 1-Week Growth

Deaths [2]

CFR (%) [2/1]

Tests per m.

Maharashtra Gujarat

33,053 11,380 11,224

7.64 3.56 6.04

1,198 659 79

3.62 5.79 0.70

2,243 2,114 4,316

9,755 5,202 4,977

4.52 4.88 3.90

148 131 248

1.52 2.52 4.98

6,853 3,002 1,263

4,464

4.84

112

2.51

765

2,677

3.92

238

8.89

887

2,380

1.06

50

2.10

4,577

1,964 1,551 1,320

0.92 2.78 12.05

35 34 8

1.78 2.19 0.61

1,735 518 383

1,183

5.53

13

1.10

6,130

1,147 910 828

5.04 2.59 12.35

37 14 4

3.23 1.54 0.48

2,210 2,721 2,089

602

2.38

4

0.66

1,282

95,698

5.5

3,025

3.16

1,606

Tamil Nadu Delhi Rajasthan Madhya Pradesh Uttar Pradesh West Bengal Andhra Pradesh Punjab Telangana Bihar Jammu and Kashmir Karnataka Haryana Odisha Kerala All-India

the Middle East, and infection rates are high there. They will be returning on the first available flights, once those flights are permitted. But the State Government understands this and is preparing for it the best they can. “We are training up school teachers,” says the Health Minister of Kerala, K.K. Shailaja. Tamil Nadu is a success story along another dimension—they are testing at a relatively high rate. They have the third highest case count in the country—assuredly, in part, because of the testing. 80% of Tamil Nadu’s 11,000+ cases are asymptomatic: another sure sign that the state is testing aggressively. The numbers are dramatically higher than those for Kerala, but Tamil Nadu holds a comparable CFR of 0.70%, on par with Kerala’s 0.66%. It has preemptively extended its lockdown beyond the current Central guidelines. Again, tracking and tracing is of a very higher order, and a home quarantine option is available, with regular monitoring.

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The state of Odisha, one of the poorest in the country, is another stand-out in terms of readiness, but with inadequate facilities relative to its competence and understanding. Like Kerala, Odisha understands disasters—it faced down the deadly 1999 cyclone. With its limited resources, Odisha nevertheless became the first state in India to incentivize self-declaration of the disease, and is testing well above the national average. Odisha incentivized their quarantine scheme with an offer of INR 15,000, and has managed to contain the deaths from covid-19 to an admirable degree. That might change, as migrants continue to flood back home. Odisha has registered a significant recent spike in confirmed cases, as Table 2 shows. Despite comparably low mortality rates in Bihar, it is extremely doubtful that the same steps are being taken there. Bihar has an abysmally low rate of testing and very poor health facilities. Ordinarily, one would expect that to translate into a very high CFR, so overall Bihar’s statistics do not hang together in any consistent way. One can only presume that the virus had not been firmly seeded there. That is about to change. Most alarming is the recent spike in cases, which—as in the case of Odisha—is very likely due to returning migrants from the megacities. (For more remarks, see Sect. 7.4.) In the coming weeks and months, Bihar will be a potential hotspot for the virus, unless quarantining facilities rise to the occasion. Similar worries beset (or should beset) the states of West Bengal and Uttar Pradesh, both of which also display very low rates of testing. West Bengal, in particular, stands out quite dramatically in its extremely high case fatality rate. Again, this could be the outcome of low testing or poor deployment of health facilities—at this stage, we cannot tell. But there is little doubt that the state, with the megacity of Kolkata at its core, is a potential flashpoint for the disease. Returning now to India as a whole and its comparison with the world at large, one feature that stands out is the relatively low CFR of 3.16%. This is way short of the world average, which hovers around 6.7%. Notice that India is low on the world testing scale—see Fig. 2 Panel B—which would suggest that it would essentially be testing the more serious cases. But that would generally move the CFR upwards. On the other hand, given that the Indian population is younger than all economically advanced economies, that would bring the CFR down. It is possible to tentatively reconcile these opposing tendencies if we have some ideas of covid-19 incidence by age group. The only information we could find on this is reported by Statista.com for a sample of 2,344 patients.7 Table 3 reports this case distribution for India on Row 1, recalls India’s population share by age in Row 2, and displays the impact of covid-19 on each age group in Row 3 (Row 1/ Row 2). As expected, the incidence is higher among individuals of working age. The remaining rows record CFRs by the same age groups from different countries, and use these, weighted by the numbers in Row 1, to “predict” the overall Indian CFR. The results are striking: each of these predictions drastically understates the actual Indian CFR of 3.16%. That number may look small, but corrected for age it is actually extremely high. Just why this is the case remains to be seen. One possibility, 7

See https://www.statista.com/statistics/1110522/india-number-of-coronavirus-cases-by-agegroup/.

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Table 3 Counterfactual Indian CFRs. Numbers in the final column report Indian CFR that would be predicted by the age-based CFRs of different countries, using India’s age distribution of cases in Row 1. Row 3 shows how different ages are impacted relative to population share. Sources: Case distribution from sample of 2,344 Indian patients reported by Statistia, https://www.statista. com/statistics/1110522/india-number-of-coronavirus-cases-by-age-group/, Population age distribution from 2011 Indian Census, CFRs by age from Wikipedia, https://en.wikipedia.org/wiki/ Mortality_due_to_COVID-19#Mortality_by_age Age group

11–20 9.7 20.9 0.5 0.2

21–30 21.9 17.6 1.3 0.2

31–40 22.9 14.4 1.6 0.2

41–50 16.3 11.1 1.5 0.4

51–60 13.1 7.3 1.8 1.3

61–70 8.7 5.3 1.7 3.6

71–80 2.6 2.4 1.1 8.0

81+ 0.6 0.9 0.6 14.8

CFR N’lands 0

0.3

0.1

0.2

0.5

1.5

7.6

23.2

30

CFR Italy

0.2

0

0.1

0.4

0.9

2.6

10

24.9

30.0

CFR Spain

0.3

0.4

0.3

0.3

0.5

1.3

4.4

13.2

20.3

CFR S. Kor

0

0

0

0.1

0.2

0.7

2.5

9.7

22.2

Case % [1] Pop % [2] Impact [1/2] CFR China

0–10 4.2 19.8 0.2 0

Implied CFR India

0.95 1.81 2.29 1.28 0.74 CFR India 3.16

as already mentioned, is that testing is very low, so that only the more desperate cases are recorded, which naturally have high mortality associated with them. The other possibility is that these are truly high fatality rate coming from lack of proper care conditional on illness. While we may safely rule out the latter hypothesis in states like Kerala, the jury is still out on the other states. In the absence of adequate health infrastructure, a stringent lockdown can only go so far. That noted, we can be quite confident in stating that these rates far exceed the true or infection fatality rate (IFR), which is deaths divided by infections, including infections that are mild or asymptomatic. Studies of infection fatality rates are on the increase as antibody testing becomes more widely available, but many of these are problematic in one way or the other, and given the policy implications that could hang on this data, discussion around these efforts can become extremely contentious. One meta-study of published research Meyerowitz-Katz and Merone (2020) provides an average point estimate of 0.75%, with a pretty wide confidence interval (at the 95% level) ranging from 0.49% to 1.01%.These numbers are likely to undergo substantial adjustment as serological tests continue to acquire greater ease and precision, and as

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the sample size (as well as sampling methods8 ) continue to improve. As an admittedly rough rule of thumb, if India’s CFR were to be scaled down by the same factor as the world’s CFR of 6.7% must be scaled in order to reach the average point estimate of 0.75%, then India’s IFR likely stands at around 0.35%, which is higher than that of the flu (widely thought to be around 0.1%) but not inordinately higher, even with the suspicion cast on health infrastructure. In any case, as already stated, these estimates await serious empirical confirmation. These optimistic adjustments regarding the IFR notwithstanding, the coronavirus pandemic has brought into sharp relief the importance of having a solid countrywide health infrastructure that can respond quickly and flexibly to a health crisis—and India’s weak preparedness for such contingencies. This is reflected in the country’s poor record of testing, which has been seriously hampered by insufficient access to testing kits, especially of the type that permit rapid serological tests—even admitting that serological tests are of greater importance in later scenarios such as lockdown exit (or even research), whereas PCR testing is the more urgent from the point of view of treatment. The availability of indigenous technology is restricted to a very small number of domestic companies. The government has, therefore, had to import rapid testing kits, by placing orders with multinational companies for such kits with enhanced pack-sizes allowing for more tests per kit Jayakumar and Sharma (2020). The experience here has been an unhappy one. A report filed in the Economic Times of April 27th Teena Thacker (2020) indicates that around the middle of April the government suspended its countrywide plan of conducting tests employing imported Chinese rapid testing kits, as these were allegedly faulty; and that all orders for such kits have been withdrawn. Chinese exporters have responded with denials and counter-charges of incorrect timing in the use of the kits. Testing has been further compromised by the paucity of epidemiologists, who have an important role to play in surveillance, testing, and identification. In a report, dated April 20th, Anoo Bhuyan (2020) points out that “more than a quarter of India’s 736 districts have no district-level epidemiologists and 11 states have no state-level epidemiologist either, according to the letter from India’s health ministry…” The letter, written on April 7th by India’s Health Secretary and addressed to the states of the Indian Union, directs them to hire, on “a war footing,” epidemiologists who are described as being “a crucial element in the effective management of …pandemics like COVID-19.” Briefly, limited resources and their poor deployment have been a feature of state intervention in tackling the coronavirus epidemic. A zealously enforced lockdown isn’t everything.

8

With the obvious lacunae seen in sampling and statistical procedures for some of these studies, we would recommend that JPAL organize a crash course for epidemiologists.

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6 Relief Measures 6.1 Introduction Despite the extreme and emergent nature of the corona crisis, it is nevertheless quite remarkable that several detailed and practical measures for relief and rehabilitation have been provided, at short notice, by several commentators. We cannot possibly hope to cite all, or even most, of these contributions, let alone review them, but it is also the case that these proposed measures have an enormous overlap. After all, at some level, what needs to be done isn’t rocket science. An enormous section of the Indian population will be hit very hard by the lockdown, and adequate transfers need to be provided to tide them over this difficult period. But the devil, as always, lies in the details. As discussed by us in an earlier joint contribution with Lore Vandewalle (2020), vulnerability to a lockdown is accentuated in India by three specific structural features of the population. The first has to do with the ubiquity of casual labor, accounting for well over 20% of all Indian households—such individuals are particularly vulnerable. The second is the preponderance of informal production—well over half of India’s GDP is produced in the informal sector—and these are activities which cannot be easily taken online. Many of these are based on personalized relationships with other Indian households, but those relationships—domestic work, for instance—require physical interaction. Third, median household savings are low, and inadequate to take an estimated 38% of all households through even a 21-day lockdown (we are currently on our way to two months) if all their employment dries up. The above considerations demand that a redistributive plan must be a first-order accompaniment to a lockdown. There is an absolute necessity to make transfers, to protect the most vulnerable of the population from going under. But apart from the distributive aspects of any relief plan, there are, in addition, macroeconomic angles—ranging from the propping up of demand (largely through fiscal means), the management of supply-side issues (largely through monetary policy), and also the maintenance of fiscal balance across Center and States. Every relief plan must be evaluated according to how it fares on both the distributive and macroeconomic dimensions. We go into more detail below.

6.2 Distributive Considerations The fundamental questions with targeted transfers are these: how do we reach the vulnerable households, and what form should these transfers take? A few years ago, this would have been a hopeless question, because targeting by economic characteristics was practically non-existent. Fast forward to today and the situation has changed somewhat; just how much will be discussed below. Very few individuals still file income taxes, it is true. But we do have some lists that continue to exhibit

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some degree of correlation with economic need, though the extent of that correlation continues to be debatable. Several lists are potentially available for making targeted transfers to the population at large: 1. JDY. The most recent of these come from the bank accounts opened under Jan Dhan Yojana (JDY), a financial inclusion program started in late 2014. Around 380 million JDY accounts exist today, over half of which are owned by women. The vast majority of these accounts are in public sector banks, and around 60 million accounts appear to be held in regional rural banks. 2. PDS The second is the list that is available, under the public distribution system, for the distribution of foodgrain, sugar, and kerosene at ration shops, or the PDS list for short. The PDS list, as it now exists, came into being under the National Food Security Act of 2013. It covers approximately 2/3 of the population: around 75% in the rural sector and around half of the urban population possesses uniform entitlement of 5 kg of foodgrain per person per month, with a household lower bound of 35 kg for a sub-list of the poorest households. 3. NREGA Then there is a list of individuals who have job cards under the National Rural Employment Guarantee Scheme, which guarantees up to 100 days per year of rural wage employment for all households with individuals above 18 years of age and willing to work. The scheme was rolled out in 1995 and has been extended to comprehensively cover rural India. A couple of points are to be noted: this is an exclusively rural list, and approximately 140 million individuals are signed up for job cards. 4. BPL The BPL (“below poverty line”) list is meant to be a comprehensive list of all Indian families that are economically disadvantaged. The identification of the poor under this list has not been an easy task, and the criteria used for BPL membership vary from state to state, and across rural and urban areas. Members are eligible for various benefits, such as cooking fuel. For this reason, membership on the lists has become a political issue and there are significant errors of inclusion. Moreover, the vulnerable in this crisis clearly transcend the relatively demanding and narrow class of those in poverty. Both PDS and NREGA lists are linked imperfectly to bank accounts. Some Indian states—but not all—appear to have a comprehensive mapping between PDS recipients and bank accounts they might possess. Likewise, an amendment to the Employment Guarantee Act in 2011 requires that all job card holders under NREGA must have a linked bank account, but several states have continued to make payments in cash. JDY list-members do not have this problem by definition: they are defined by their bank accounts. How correlated they might be to people in need is another matter altogether. Several observers have provided wide-ranging wishlists for the distributive portion of a relief plan. Reetika Khera Khera (2020) advocates the implementation of cash transfers, employing existing transfer schemes and National Electronic Funds Transfer. She also recommends in-kind transfers, to cover enhanced rations and an

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expansion of the PDS to include essential commodities such as oil and soap, while relaxing requirements of proof of eligibility by way of the cumbersome (and now potentially dangerous) Aadhaar-based biometric identification procedure. This is accompanied by suggestions of home delivery of food for children, the provision of shelter for migrant workers in urban areas, the opening of community kitchens, and the adoption of measures for controlling the prices of essentials. On the health front, Khera advocates public hygiene education, the regulation of private health agencies, and extensive testing for covid-19. Further prescriptions, which also take account of the medium- and long-term, are available in an elaborate list prepared by a group of academics from Ashoka University—this is available in a two-part article in The Wire by Abhinash Borah et al. Borah et al. (2020). Parikshit Ghosh (2020) advances the cause of extensive testing for covid-19 beyond the lockdown; and to make quarantining a feasible proposition, he recommends the grant of a “quarantine allowance,” citing the case of Odisha, which has provided for an incentive of Rs.15,000 for a two-week stay in quarantine. Apart from this, he also advocates a social safety net in the form of a quasi-universal basic income, to be paid out over the next six months. In this context, a novel aspect of Ghosh’s suggestion is that he advocates cash transfers via the PDS, an idea perhaps well worth exploring and to which we briefly return below. We also note here a manifesto Caravan (2020) with 635 signatories, to the Finance Minister’s announcement of a relief package (to be described and discussed below), seeking—among other measures—enhanced rations, implementation of minimum wages for NREGA workers, advance and increased pension payments, enhanced cash transfers via the JDY scheme, cash transfers to construction workers, assistance to pregnant women and mothers, expansion of coverage of farmers under the Prime Minister’s Kisan scheme, and a moratorium on all debts incurred after January 1st, 2020. All of this is worthwhile advice, and while some of the detailed prescriptions made may be no more than counsels of perfection, they would not run to more than percentage points of GDP measured in the single digits—an expense well worth considering, as several other countries have. Indeed, some of this counsel could be viewed as fundamental and non-negotiable, such as universal access to the public distribution system at this extraordinary time. There is, in fact, wide and rather obvious consensus on this matter; see, for instance, Amartya Sen, Raghuram Rajan, and Abhijit Banerjee, who write Sen et al. (2020): The correct response is to issue temporary ration cards—perhaps for six months—with minimal checks to everyone who wants one and is willing to stand in line to collect their card and their monthly allocations. The cost of missing many of those who are in dire need vastly exceeds the social cost of letting in some who could perhaps do without it.

This is by no means a new observation. The Supreme Court made it in 2016: In the States in which drought has been declared or might be declared in the future, all households should be provided with their monthly entitlement of food grains in terms of the National Food Security …regardless of whether they fall in the category of priority household or not …The requirement of a household having a ration card is directed to be

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D. Ray and S. Subramanian substituted by an appropriate identification or proof of residence that is acceptable to the State Government.9 [Emphasis ours.]

6.3 Macroeconomic Considerations There are features of the current crisis which may be viewed as a “supply-side failure.” The most important of these have to do with the failure of supply chains. We have already mentioned the disruption of the agricultural supply chain, coming on the heels of mandi closures, which choked off the wholesale buying points in the chain. As a result, many farmers found themselves with harvests that they could not sell in the ongoing lockdown. Other choke points include production: there have been shortages of agricultural workers during harvests both due to the fear of the pandemic and also for fear of police reactions. While there are features of the retailing chain that are specific to agriculture, it is not hard to imagine other supply processes being damaged in the same way in the manufacturing sector. For smaller firms without access to liquidity to tide them over, this could be a death blow. It is important to note, however, that these are no ordinary supply bottlenecks. The injection of liquidity per se will not get rid of them. What is needed, in contrast, is an environment that will safely permit labor to work, or buying points to operate, without fear either of the virus or of unsympathetic law-enforcers entrusted with implementing the lockdown. Both these fears can be ameliorated to some degree by proper planning. In an early article Iyer and Krishnamurthy (2020), Yamini Iyer and Mekhala Krishnamurthy have advanced a useful conceptual apparatus for dealing with the epidemic, covering the issues of how to manage the movement of persons, the movement of food, the movement of funds and schemes, and the functioning of supply chains for agricultural commodities. The last mentioned item is of crucial importance in mitigating the severe costs of a generalized and long-drawn-out lockdown. Quite apart from its obvious role in catering to household needs on the consumption side, it is central if agrarian incomes are to be protected, especially with the crush of returning migrants from the urban areas. Sudha Narayanan Narayanan (2020) offers pragmatic advice on the freeing-up of mandis and all markets for agricultural produce, on managing post-harvest output, on decentralizing the procurement of foodgrain, and on avoiding unreasonable restrictions on the transport of commodities so as to minimize supply chain failures in an environment where “[m]any of the logistical disruptions have come from an ‘overzealous bureaucracy’ and police overreach in enforcing the lockdown.” Again, these are not classical supply-side problems. Expanding liquidity directly to companies, especially micro, small and medium enterprises (MSMEs), is certainly important. It will serve to prevent these companies from going under. MSMEs pro9

See Directions 30 (3 and 4) of the Supreme Court Judgment in Swaraj Abhiyan v. Union of India, Writ Petition (Civil) No. 857 of 2015, available at https://www.legitquest.com/case/swarajabhiyan--ii-v-union-of-india-others/9B0E4.

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duce around 30% of Indian GDP, are spatially distributed almost equally in rural and urban areas, and contribute significantly to manufacturing, trade, and other services. Equally important, MSMEs claim a major share of employment in India, accounting for over 110 m jobs.10 One can only hope that direct support to MSMEs will protect that employment as well, though it is unclear to us that it will; see, for instance, Sect. 7.5. At the same time, we should not expect an aggregate injection of liquidity, especially via the banking system, to have any substantive effect in combating the economic slowdown. Such monetary easing via banks will generally be unhelpful from the supply side. As Rajeshwari Sengupta and Harsh Vardhan (2020) have pointed out, banks have effectively become highly risk-averse because of a large fraction of non-performing loans (leading in part to the punishment of banks by the Government and the Reserve Bank of India), as well as the fact that a rising proportion of firms are highly over-leveraged. Commercial banks have been increasingly engaged in retail lending, while public sector banks have been passing the buck by lending on to non-bank financial companies (NBFCs). A liquidity easing to banks by the Reserve Bank of India is, therefore, highly unlikely to be passed along in terms of new credit to firms. Banks are likely to hoard that liquidity. Indeed, given that firms are facing a demand crisis, it is likely that the portfolio mix of loan demand will shift even more strongly away from productive investment to shorter-term sustenance, leaving banks even more unwilling to lend to firms. In contrast, one might expect a substantial fiscal stimulus to serve to reverse, at least in part, a demand crisis that has been ongoing before the covid-19 lockdown and now is further exacerbated by additional drops in demand layered on by the heightened uncertainty of the virus. The role of fiscal policy is further heightened by the monetary impotence highlighted in the previous paragraph. In this context, it has to be noted that the distributive transfers of the previous section and fiscal stimuli go hand in hand. In short, the most direct macroeconomic response to this should be fiscal rather than monetary—interventions on the spending side, and transfers can add to that response. It may be that the Indian government is reluctant to spend directly, but then there is a case for making transfers to deserving, targeted, vulnerable groups, and letting them shore up the demand side, to the extent possible, as a serendipitous byproduct. If the government is unwilling to engage in direct deficit spending to shore up the demand side, the least it can do is give the money away to those who need it the most, and wait for those funds to return as aggregate demand. There is a final macroeconomic aspect to any relief package, and that concerns the relationship between the Center and the States. It should be noted that health is a State subject, and yet the States have been facing a funding shortage that intensifies with each passing day of the lockdown. For one thing, revenues from the new goods and services (GST) tax have declined in March to under Rs 1 tr, as business shutdowns took their inevitable toll on sales tax revenues, the drop since March 2019 coming in at 8.4%. Under GST law, States are supposed to be reimbursed by the Center 10

This information has been taken from the Annual Report of the Government of India’s Ministry of Micro, Small and Medium Enterprises; see https://msme.gov.in/sites/default/files/MSME-AR2017-18-Eng.pdf.

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for revenue shortfalls in the first five years of GST implementation, which started in 2017, but there are arrears still to be released. Meanwhile, the Center has been advantaged by a drop in oil prices, which have been mopped up as Central revenue by virtue of an oil tariff hike. That raises the specter of excessive centralization of funds which the Center must do its best to avoid, but which it actually seems to be doing its worst to promote. Fund transfers to the States have three important aspects. First, the States need the funds and will spend them. That will have demand-side effects which are welcome given the existence of a widespread demand crisis. Even money spent on health professionals and equipment will translate into income. Second, the incentive for the States to exit the lockdown will become better aligned to those of the Center. As matters stand now, given the severe shortage of resources, no State will want to exit the lockdown, fearing that it will have a spiraling epidemic on its hands. And finally, the decision rights over funds have to match the informational reality on the ground—States and local governments have a far better knowledge of local conditions, and can react to them in more flexible ways than any one-size-fits-all that the Center might adopt.

6.4 The Indian Government’s Relief Plan 1 It was only after the first lockdown of March 25th was implemented that the Finance Minister announced a covid-19 relief package on March 27th. In light of a new package just released at the time of this writing, we will refer to this as Plan 1. The overall package was valued at Rs. 1.7 trillion (or around US 22.5 billion dollars). Under Plan 1, these earmarked funds were to be deployed for the following purposes Economic Times Bureau (2020): 1. Rs.500 per month, for 3 months, to an estimated 200 million JDY female account holders; 2. An additional 5kg of wheat or rice per person on the PDS list, and 1kg of pulses per PDS household, for 3 months; 3. Enhanced “rural employment guarantee” daily wages, from Rs.182 to Rs.202, presumably available for job card holders on the NREGA list. 4. A cash transfer of Rs.2000 to 87 million farmers under the PM-Kisan scheme; 5. Free Liquefied Petroleum Gas cylinders for 86 million Ujjwala scheme beneficiaries (who are all BPL families) for 3 months; 6. An ex gratia payment of Rs.1000 to poor senior citizens, widows and disabled persons. 7. Medical insurance of Rs.5 million for health workers fighting covid-19. 8. Collateral-free loan of up to Rs 2 million for female self-help groups. In addition, for the organized sector, Plan 1 envisaged government contributions to Provident Funds for those employees earning under Rs 15,000 per month in companies with fewer than 100 employees, and permits a non-refundable advance of

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75% of the Provident Fund, or 3 months’ wages, whichever is lower. For construction workers, Plan 1 directs States to provide relief under the Construction Workers’ Welfare Fund, which would be made available to the States. The Plan also makes available unallocated funds under the District Mineral Fund to be used by the States for supplementing medical testing and screening. Overall, Plan 1 is less than 1% of India’s GDP. This compares very unfavorably with the allocations made by economically advanced countries. According to Anderson et al. (2020), the “immediate fiscal impulse [of] additional government spending (such as medical resources, keeping people employed, subsidizing SMEs, public investment) and foregone revenues (such as the cancelation of certain taxes and social security contributions)” is of the order of 9.1% of GDP for the USA, 6.9% for Germany, and 4.5% for the UK. The fiscal stimulus in per capita terms shows up India in an even worse comparative light, by virtue both of its much larger population and far greater levels of poverty and vulnerability. Just as an example, assuming that only one-half of India’s 1360 million population will need special assistance, the funds available from the fiscal package per needy person works out to just Rs. 2,500, or around USD 40!) As it happens, Plan 1 is probably of the order of only 0.6% of GDP. While observers such as Jean Drèze correctly laud the government for increasing in-kind allocations via the PDS, Drèze (2020) writes: Indeed, the budget has been padded. For instance, by including Rs.16,000 crore of precommitted [PM-Kisan] expenditure, and Rs.5,600 crore for [NREGA] wage increases that had already been notified by the rural development ministry on March 23. The release of excess foodgrain stocks is billed at so-called economic cost, when in fact, their opportunity cost is much lower. (This is an old accounting anomaly for which the FM is not responsible.) And the funds being sought from construction workers’ welfare funds don’t really belong to the Union government. If we focus on novel relief measures funded by the Centre, the budget is likely to be closer to Rs.1 lakh crore than Rs.1.7 lakh crore.

It might sound somewhat curmudgeonly of Drèze to castigate the government for including NREGA wage increases that had been announced on March 23, but a little postscript might convince the reader that this is actually quite charitable of him. The prescribed NREGA wages in most states are actually higher than the new announced minimum, and besides, would NREGA function under a lockdown at all? April 2020 has seen a year-over-year decline of 86% in NREGA employment: in that month, an estimated 3.4 million households were provided work as opposed to 17m in April of 2019 Agarwal (2020). This is a catastrophic decline which the Government is reportedly taking recent steps to reverse (with proper social distancing guidelines for public employment programs). A leading feature of Plan 1 is its transfer of Rs.500 per month to female-owned bank accounts under the JDY list. There is serious concern that the JDY list could be the wrong one to use, because it is not even clear how clustered these accounts are across families. It is hard to get a measure of such clustering, but it is certainly the case that many are unaware of having an account under JDY. Rohini Pande, Simone Schaner, and Charity Troyer Moore (2020) observes that:

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D. Ray and S. Subramanian A nationally representative survey from 2018, the Financial Inclusion Insights Survey, asked respondents whether they have a bank account and, if yes, whether it is a JDY account. Roughly 80% of female respondents stated they have a bank account, but only 21% said they have a JDY account. What drives the gap between government and survey numbers? Likely some combination of dormancy, account duplication in the system and the lack of knowledge among women about the type of account they hold.

The authors estimate that some 125–175 m poor women do not have access to JDY accounts, and so not, a fortiori, to the transfers.11 They also note that one in four poor women live more than 5 km away from their nearest banking point. Their implication is that Indian relief cannot rely on cash, or not on cash alone. We reserve judgment on this implication, but certainly, there is room to do more (at least temporarily) on the non-cash front. Indeed, there is enough foodgrain stock to do this for several months. Dipa Sinha (2020) examines the hike via PDS of 5 kg grain/ 1 kg pulses, and correctly observes that there is a clear scope for more aggressive in-kind transfers. If universal coverage is permitted under PDS (and 20% self-select out, leaving 80%), that coverage can comfortably persist at 10kg of grain per person for six months, which would work out to about 65 million tons, at a time when the stocks available with the Food Corporation of India already run to 75 million tons, with the prospect of an additional 30–35 million tons arriving from post-harvest procurement. But such expansion, when considered in particular States and Territories, appears to present unique bureaucratic challenges, as Sinha notes in her article.12 Meanwhile, the Indian Government appears to have settled on more creative ways of handling their food surplus. According to Agarwal (2020) in The Wire on April 21st, “[t]he Ministry of Petroleum and Natural Gas has announced that the ‘surplus rice’ available with the Food Corporation of India will be ‘allowed to be converted to ethanol for utilisation in making alcohol-based hand-sanitizers’ …The ministry has so far not clarified what it means by ‘surplus quantities of rice’, at what price it intends to buy the rice from FCI and when.” Returning now to cash transfers and the modalities of such transfer: there is certainly room to transfer more cash, much more if the Center’s capacity were to be viewed as the only constraint. On the modalities, if JDY is believed to be excessively 11

They argue that by the United Nations poverty criterion of USD 2.50 per day, roughly 325m women are under the line, giving them the lower bound of 125m even if all 200m JDY account holders are under the line as well. The upper bound comes from their estimate that no more than 75% of the JDY holders are below the poverty line. 12 In the same article, Sinha writes: “In Delhi for instance, a non-ration cardholder is required to enroll herself online first by entering their phone number and getting an OTP. They are required to then upload their Aadhar as well as a family photo. Once that is successfully done, they will receive an SMS which has a link to the e-coupon. Beneficiaries are expected to have smartphones through which they open the e-coupons when they go to collect their rations …Therefore, the assumption is that every person not only has access to a smartphone but also the technical capability to fill in online forms. …Moreover, after a day of launching, the website was down for over three days because of the overload. There was also a fake website claiming to be issuing ration cards …Each state seems to be coming up with some such system of identifying or verifying those who should be included, even whilst they are claiming to have made the PDS universal.”.

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clustered or urban, there are other lists at hand, each favored by different observers. Jean Drèze and Reetika Khera’s list of preference is the NREGA compilation, and they underscore Pande et al.’s reservations on the JDY list: The reason is that the job cards [NREGA] list is a transparent, recursive household list with village and gram panchayat identifiers, while the list of JDY accounts is an opaque list of individual bank accounts. Cash-in-hand may seem like the antithesis of [JDY and mobile banking], but this option may become important in the near future if the banking system comes under further stress. There are precedents of effective use of the cash-in-hand method, notably in Odisha for pension payments, and in various states for NREGA wage payments. Several states (including Andhra Pradesh, Odisha and Tamil Nadu) have already resorted to cash-in-hand for relief payments during the lockdown.

We agree that the reservations regarding JDY are well-founded, though there is little doubt that it represents the swiftest route available to the Central Government—never mind that from the point of view of the recipient it may be more problematic. The NREGA list has an estimated 140m job card holders on it; the PDS has 230 million cards that pertain to 800m households. The former is entirely rural, which certainly does not eliminate it from consideration, as one could combine it with other urban measures. But it seems odd to not regard the PDS list, at least at a first cut, as an obvious alternative. The Ashoka University group of economists mention the PDS, and Parikshit Ghosh (2020) uses this list to generate more explicit calculations for a quasi-UBI (quasi, given that the PDS covers 60% of all households): A little back-of-the-envelope calculation shows that a Rs. 1,000 monthly grant per member to each PDS household (Rs. 5,000 for a family of five), continued for 6 months, should cost about 3% of GDP, or Rs. 5.7 trillion (scale it up if you wish). For a crisis of the century, spending this much to secure the basic needs of our fellow citizens seems like a no-brainer …Financing this additional expenditure through borrowing should not pose a problem. Private investment has been weak for a long time and in the current climate of uncertainty, it is bound to suffer another blow. It is a safe bet that new government debt is not going to create a lot of crowding-out, and money in people’s pockets will also act as a much-needed fiscal stimulus.”

It appears that the PDS accounts are linked to well-defined bank accounts quite well in some States, and badly or perhaps not at all in others. Our own investigations (admittedly, at short notice), have not turned up anything definitive on the subject. While it should be easy to use such accounts for direct deposit where available, there is a question about whether cash can be taken efficiently and safely via the PDS when bank accounts are not available. We leave this as an open question that deserves immediate and urgent attention. Whether or not cash is sent through the NREGA or the PDS list, the NREGA list can and should also be used for what it was originally intended for. As already noted, there has been a dramatic decline in employment via this channel, overwhelmingly caused by the lockdown. While such zeal at the start of an unprecedented crisis is understandable, it runs clearly counter to the relief package of the Government, which explicitly factors in higher wages under NREGA as part of the payout. The Government cannot have it both ways. Indeed, as Reetika Khera (2020) has argued,

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NREGA could expand its work guarantees well beyond 100 days per annum, and she has called for 20 days per month during the crisis. As MNREGA is implemented in rural areas only, it has to be suitably complemented in cities in order not to prolong the human tragedy of the first week of the lockdown. Thousands of poor migrant workers have been frustrated by the shut down of transport facilities and closed state borders in their effort to return to their village homes. Many have been forced to undertake foot journeys of several hundreds of kilometers, with limited cash to hand, and little access to food, water, or shelter. Trekking laborers have been rounded up and locked up in crowded enclosures. In one shocking incident, a contingent of migrants was showered with corrosive disinfectant. Significant reports of deaths are beginning to come in, including the deaths of children. There is little doubt that these will multiply as the coming difficult days go by. In summary, the relief measures proposed under Plan 1 are quite inadequate. The Government has allocated just a little under 1% of GDP to the plan. Taking into account the fact that some provisions of Plan 1 are already in the Union Budget, while others (such as NREGA wages) rely on some dubious accounting, the effective assistance is significantly smaller than the promised 1%. This inadequacy is clearly mirrored in the numbers. Consider, for instance, the proposed allowance of Rs 500/month/family: the average monthly per person consumption expenditure is itself about two-and-a-half times this amount; and, indeed, even the average per person expenditure of the poorest 20% of the population exceeds the allowance Subramanian (2019). Far more is needed in terms of cash assistance, and significantly more can be done in terms of in-kind assistance. We’ve discussed these in detail, but that discussion omitted additional items not even mentioned in Plan 1. For instance, there are serious shortages even in the ability to equip health workers with protective personal equipment (PPE) such as surgical gloves, masks, and bodysuits. In feedback sought on the availability of health infrastructure from 266 District Collectors and other senior Indian Administrative Service officers, 47% disagreed or disagreed seriously about the availability of sufficient PPE, while this figure was of the order of 59% for Intensive Care Unit beds, and 72% for ventilators Vishnoi (2020). An aspect of sensitivity to priorities in planning is starkly reflected in the fact that despite a WHO notification on global shortages of PPE on February 27th, “the Indian government waited till 19 March to issue a notification prohibiting the export of domestically manufactured PPEs and the raw material for the same” Krishnan (2020). Plan 1 comes up short in addressing the severe resource scarcities faced by the State governments, which need, in turn, to decentralize further to local administrations who understand the nuances of ground realities. State repositories of funds cannot take advantage of “corporate social responsibility” spending: such spending seems only to be allowed for contributions to Central coffers. Specifically, a public charitable trust was created on March 27th, with the Prime Minister as ex-officio Chairman and the Finance, Home, and Defence Ministers as ex-officio Trustees, bearing the name of the “Prime Minister’s Citizen Assistance and Relief in Emergency Situations Fund” (PM CARES Fund). Donations to this Fund are meant to be

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employed for financing expenses incurred in dealing with the covid-19 epidemic, and are eligible for tax exemption. Corporates contributing to the fund will have their contributions counted as part of their mandatory Corporate Social Responsibility (CSR) obligations, including the option of setting off any donations above the CSR limit against future CSR liabilities. The States cannot do any of this. We have already noted that there are arrears due to the States in the matter of revenues from GST, and these were now heightened by a huge slowdown in business and hence in the generation of sales tax revenue, though some reversal appears to have occurred with the relaxation of economic activity in the third phase of the lockdown. The Center has already impounded the beneficial effects of an oil price drop by hiking the oil tariff. The States have nowhere to turn, and are increasingly dependent on the Center—or on hastily placed tax hikes on alcohol retail outlets; see, e.g., Balram and Malviya (2020)—for desperately needed funds. None of this was visibly addressed in Plan 1. One would be forgiven for imagining that in a situation of such apparent constraint, external sources of funding would be welcome. And indeed, the International Monetary Fund has mooted the issue of liquidity in the form of Special Drawing Rights (SDRs) to the tune of 500 billion US dollars—only for the proposal to be vetoed (unsurprisingly) by the US and (surprisingly) by India, for the ostensible reason, in the latter case, of guarding against the possibility of some countries (presumably Pakistan) employing the funds for “extraneous purposes” (supposedly terrorist activity). Commenting on India’s response, Jayati Ghosh (2020) writes: “Whatever may be the reason, this is a strategy that is not just fraught with risks but also illogical. Globally, it is a costly denial of a chance for the world economy to revive after this extraordinary shutdown. Internationally, it shows that India is willing to abandon the interest of the rest of the developing world, with little advantage to itself.” The covid crisis comes on top of an already enfeebled economy, weakened by demand contraction and a generalized slowdown, with unprecedented levels of unemployment coupled with zero employment growth. There are technological and market forces behind these trends for which no individual Government can be held entirely responsible. But Indian Government policy, with the ill-advised demonetization experiment of 2016, or the hastily introduced GST (though in principle, such harmonization may have something to commend it), has not helped either. If fiscal prudence is holding the government back now, then such a hasty retreat to excessive caution seems to be utterly misplaced in view of the crisis of lives and livelihoods that the country is currently facing.13 Simply put, the government should ramp up its spending. There is room to do it. For instance, Ayushi Bajaj and Gaurav Datt (2020) propose one way of monetizing government debt whereby the Reserve Bank of India (RBI) buys up government securities, credits the government’s account with the purchase value of the securities, and then writes off the debt, so that the initial expansion of government debt and in the RBI’s balances are subsequently reversed. Generous public spending is even 13

See also, in this general context, Agarwal and Srivas (2020), Sainath (2020), and The Wire Wire Staff (2020).

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more urgently indicated if there are options available for resource mobilization which will not necessarily stretch the fiscal deficit to extravagant limits. One such option has been pointed out by Gurbachan Singh (2020), who observes that India’s foreign exchange reserves are of the order of Rs. 35 tr—larger than might be dictated by considerations of abundant prudence. Plan 1 represented only 5% of these reserves, and Singh suggests that lowering their level by another 10% should not be seriously problematic, especially with the option of implementing a regime of Pigovian taxes in the event of sudden and large capital flows. This would immediately add a fiscal stimulus of another 2% of GDP. Invitations for discretionary donations based on philanthropic impulse are another source of funding. The Prime Minister’s National Relief Fund (PMNRF), which was established in 1948, accepts contributions which are deployed for (among other purposes) providing immediate relief to families affected by natural calamities of various kinds. We’ve already mentioned the creation of PM CARES, a fund parallel to the PMNRF. That act was questioned by opposition political parties, and its constitutional legality was the subject of a Public Interest Litigation case, which was however dismissed by a bench of the Supreme Court.14 Subject to the Centre– State asymmetries that we have already highlighted, we would welcome voluntary contributions. But these, by definition, are discretionary donations, as opposed to taxes—which are mandatory, have the force of law behind them, are an expression of public entitlement to private income and wealth, and have to be accounted for with transparency. Taxation is therefore a source of funding which, one might have thought, immediately suggests itself. Indeed, this is the subject of a very interesting recommendation in the European context by Camille Landais, Emmanuel Saez, and Gabriel Zucman (2020), who write: European governments have reacted swiftly to the COVID crisis and are now discussing ways to mutualize the cost of the epidemic. This column proposes the creation of a progressive, time-limited, European-wide progressive wealth tax assessed on the net worth of the top 1% richest individuals. If fighting COVID-19 requires issuing 10 points of EU GDP in Eurobonds (or a rescue fund worth 10 points of EU GDP), a progressive wealth tax would be enough to repay all this extra debt after ten years.

Following this, one of us Subramanian (2020) has suggested, employing the Hurun India Rich List data on the net worth of 953 wealthiest Indian entities with a net worth in excess of Rs. 1,000 crore each, that a wealth tax of 4% on just these entities should be levied. These families together account for less than 0.0004% of all households in India, but a 4% tax on their wealth would yield a revenue which is 1% of GDP, a remarkable fact. This prescription would, of course, require legislation for the enactment of a wealth tax, which was formally abolished in the Union Budget of 2016–17. Of related interest are the suggestions made by a group of Indian Revenue Service (IRS) officers in a report entitled “Fiscal Options & Response to Covid-19 Epidemic” (or FORCE), which was released to the media on the Twitter handle and website of 14

The accounts of the PM CARES Fund will be examined by private auditors, and not by the office of the Comptroller and Auditor General of India.

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the IRS Association. Among other recommendations made in the FORCE report are that the marginal income tax rate on incomes exceeding Rs. 10m per year should be raised from 30 to 40%, that a one-time “Covid Relief Cess” of 4% should be levied on taxable incomes exceeding Rs. 1 m per year; and that a wealth tax should be re-introduced on the “super rich,” defined as those with a net worth of Rs.50 m or more Jayaswal (2021). The government’s response to this report and its recommendations has been—to put it neutrally—extraordinary. According to a report filed in Scroll.in, The Central Board of Direct Taxes [on April 27th] issued chargesheets against three senior Indian Revenue Service officials, involved in preparing and publicising a report on increasing income tax …The three officials, at the rank of the principal commissioner, have been suspended [and] have been given 15 days to file a written reply in their defence and are supposed to convey whether they want to be heard in person …Scroll Staff (2020)

No comment is necessary, or (in our case) possible.

6.5 The Indian Government’s Relief Plan 2 On May 12, Prime Minister Modi announced that a new relief plan would be unveiled by the Finance Minister, Nirmala Sitharaman, over the following days. Plan 2 envisages an expenditure of around Rs. 20 trillion (or around USD 270 billion), more than 10 times the amount in Plan 1, and amounting to around 10% of Indian GDP. The exact break-up of this amount, or the precise nature of the accounting involved, is not completely clear at the time of writing. The components of Plan 2 were specified by the Finance Minister in five tranches over five days following the Prime Minister’s March 12 announcement. These can be summarized as follows, in line with the break-up provided by The Mint; see Dasgupta and Kumar (2020): Tranche 1: Tranche 2: Tranche 3: Tranches 4 &5: Total:

Rs. 5.95 tr Rs. 3.10 tr Rs. 1.50 tr Rs. 0.48 tr Rs. 11.03 tr

Where does the remainder of the Rs. 20 tr package come from? It turns out that included in Plan 2 package are the Plan 1 provision of Rs. 1.7 tr, revenue losses from certain tax concessions made after March 22nd amounting to Rs. 0.078 tr, and a sum of Rs. 0.15 tr for the health sector which had been earlier announced by the Prime Minister. But most significantly, the package also includes huge prior infusions of liquidity by the Reserve Bank of India, totaling Rs. 8.02 tr. These additional elements aggregate to Rs. 9.95 tr which, when added to the 11.03 tr from the earlier-mentioned 5-tranche announcement, yields Rs. 20.98 tr, the ostensible size of Plan 2. From what we can gather, the following are some salient components of the package, a list we have put together from various sources:

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1. Collateral-free loans to Micro, Small, and Medium-Scale Enterprises (MSMEs) up to Rs 3 tr and an equity infusion of Rs. 0.5 tr, with an additional Rs. 0.20 tr in loans to stressed MSMEs; 2. Rs. 0.9 tr for power distribution companies, Rs. 0.3 tr of “special liquidity” for non-banking financial institutions (NBFCs) and Micro-Finance Institutions (MFIs) and a Rs 0.45 tr. partial credit guarantee scheme for NBFCs, housing finance companies (HFCs), and MFIs with low credit rating. 3. Liquidity relief through reduction in Tax Deducted at Source (TDS) and Tax Collected at Source, valued at Rs. 0.5 tr, and “expedited” income tax refunds of Rs 0.18 tr. 4. Concessional credit of Rs 2 tr for 25m farmers, fishermen and animal husbandry farmers under PM-Kisan. 5. Additional emergency working capital of Rs. 0.3 tr for farmers through the National Bank for Agriculture and Rural Development. 6. Extension of the Credit Linked Subsidy Scheme for the housing sector and middle income group, amounting to Rs. 0.7 tr; 7. A special credit facility for street vendors, amounting to Rs. 0.05 tr; 8. Employees’ Provident Fund support for businesses and workers, amounting to Rs. 0.025 tr; 9. Promotion of Affordable Rental Housing Complexes for migrant workers and the urban poor, in a scheme whose physical contours and fiscal provision are as yet not quite clear; 10. Foodgrains for non-ration card holders (5 kg of wheat/rice per person and 1 kg chickpea per family) for 2 months, intended to cover 80 million migrants, amounting to Rs. 0.035 tr; and allowance for the use of ration cards anywhere in the country, with financial implications which are not yet clear; 11. An additional Rs 0.4 tr to be allocated to NREGA employment, over and above the earlier budget estimate of Rs 0.61 tr for fiscal 2021. The money for migrants will be provided to State governments, and District Collectors and Municipal Commissioners will have access to this money. Other features of Plan 2 include an extension of the due date for tax returns, to November 30, relief from regulatory penalties for real estate companies for up to six months, an emphasis on “buying domestic”—the banning of global tenders for government procurement contracts up to Rs. 0.002 tr, and an expansion of the definition of MSME by raising the thresholds for inclusion in these categories.15 If we add all the amounts above, we obtain a total of Rs. 9.55 tr, which accounts for nearly 87% of the 5-tranche announcement of Rs. 11.03 tr. This suggests that while the list above is conceivably not exhaustive (or is incompletely informed by particulars of the financial implications of all of its components), it cannot be far from being so. 15

For instance, the new threshold for microenterprise is investment up to Rs 1 cr and turnover under Rs 5 cr. The definition earlier was on investment criteria of up to Rs 10 lakh for services and Rs 25 lakh for manufacturing. For more detail, see https://economictimes.indiatimes.com/smallbiz/sme-sector/finance-minister-announces-revised-msme-definitions-no-different-betweenmanufacturing-and-service-enterprises/articleshow/75717694.cms.

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Given that trillions are not part of our everyday mental accounting, the following comparisons might help. First, the entire relief Plan 1 package, even with its faulty accounting, is half of what the Government is planning to give to MSMEs as loans. Second, we can quickly calculate what would be needed to provide half of India’s households with a minimum of Rs. 5000 each, which is a common estimate of minimum monthly needs. Using the figure of approximately 280 m households (247m is the number from the 2011 Census), this means that Rs. 0.7 tr would be needed per month.16 The resulting three-month total of around Rs 2.1 tr is, again, worth comparing with some of the items above. Third, lest it be thought that migrant laborers have not been given due attention, it turns out that they have been taken care of from a source outside of this new package: PM CARES has showered a grand total of Rs. 0.01 tr on them, which is half the amount that it will spend on ventilators; and though this is not a part of the new package, it is instructive to note that the allocation of Rs. 0.01 tr to migrant workers from the PM CARES Fund is a vanishingly small 0.05% of Rs. 20 tr. On the other hand, the new allocation of Rs 0.4 tr to NREGA could go part of the way toward helping returning migrants. Finally, the fiscal component of this package is small and somewhat unclear. We wish to be very clear that we understand the need for both monetary and fiscal stimuli, as we have both supply- and demand-side problems on our hands. But supply-side alleviation serves at best as a temporary palliative, while the demand constraint persists. One could counter with the view that a fiscal stimulus on its own has little value either, as long as the supply bottle necks continue. But it needs to be understood that the fundamental supply bottleneck comes today from the induced shortage of labor created by the covid lockdown, and the attendant chain reactions that have sprung from this shortage. This is a supply-side problem that—to the extent permissible by the pandemic—is either under our control, or not remediable through monetary easing. In any case, and at the very least, we would hope for some balance across the fiscal and monetary realms. At a stretch, we could count under “fiscal stimulus” all of the Plan 1 allocation of Rs. 1.7 tr, reduced, let us say, to Rs.1.3 tr to account for elements of double-counting alluded to earlier, the provisions for tax relief (Rs. 0.78 tr), the PM’s announcement for the health sector (Rs. 0.15 tr), the provident fund payments of Rs. 0.025 tr, the Rs. 0.035 tr provision for foodgrain to migrant workers without ration cards, and the entire new expenditure of Rs. 0.4 tr on NREGA, to arrive a bit south of Rs. 2 tr, which is around 1% of India’s GDP. (In fact, Barclay’s estimate of the actual fiscal impact of the package, at Rs. 1.5 tr, is even more pessimistic, and only 0.75% of GDP; see Business Today Staff (2020) and Sharma (2020). The rest of the items appear to be loans and liquidity injections from the Reserve Bank and from nationalized banks, or reductions in provident fund contributions and taxes deducted or collected at source. For a country already so flush with liquidity that banks are very reluctant to on-lend 16

This roughly aligns with P. Chidambaram’s estimate of Rs. 0.65 tr; see https://www. financialexpress.com/india-news/chidambaram-urges-congress-cms-to-demand-transfer-ofcash-to-poor-families-during-meeting-with-pm-modi/1925437/. Though we do not know how he arrived at this number, his final estimate is sensible.

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(see the discussion in Sect. 6.3 on the macroeconomic aspects of the crisis), this sort of strategy appears to be somewhat unthinking, to put it mildly. Alternatively, it is a strategy conceived with only some economic agents—principally business enterprises and tax assessees—in mind. This is a very gently reproving evaluation when compared with one journalistic assessment Jha (2020), which suggests that “Modi’s ‘Stimulus Package’ is a Gigantic Confidence Trick Played on the People of India.”

7 Other Outcomes 7.1 Introduction In what follows, we look briefly at some of the accompaniments to, and outcomes of, the lockdown. An important feature of the lockdown relates to the procedural aspects of its implementation—or more accurately, in the present context, its enforcement. We then consider three direct fallouts of the lockdown: supply chain disruptions in agriculture, the shut down’s impact on migrant labor, and its consequence for other morbidities. Three deeply unsettling accompaniments to the lockdown have been the phenomena of communalization of the pandemic, caste discrimination, and domestic violence. These issues are also briefly reviewed.

7.2 Enforcement of the Lockdown The Blavatnik School of Government of the University of Oxford has created an “Oxford COVID-19 Government Response Tracker (OxCGRT),” which collates government policy responses to the pandemic, scores the stringency of such measures, and creates a “stringency Index” from these. The OxCGRT website is careful to point out that “this index simply records the number and strictness of government policies, and should not be interpreted as ‘scoring’ the appropriateness or effectiveness of a country’s response. A higher position in the Stringency Index does not necessarily mean that a country’s response is ‘better’ than others lower on the index.” According to OxCGRT data, over the period April 1–May 1, India is among the highest ranking countries in terms of its calculated stringency level. Regrettably, India’s high stringency score seems to be most noticeably reflected in police excesses against “lockdown offenders.” Several anecdotal cases (with visual back-up) are available of police beating up “transgressing” citizens with lathis, getting them to frog-March, forcing them to perform push-ups and sit-ups, and harassing street vendors. Distressing video-recordings of such acts are available on the Scroll. The situation appears to have turned bad enough for The Editors Guild of India to lodge a protest against police assaults on working journalists, and for the Kerala High

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Court to take suo motu cognizance of alleged police excesses and seek a response from the Centre to its contention that Amidst the din of the pandemic that engulfs us all, our laws cannot remain silent…They must continue to operate so as to protect the rights of our citizens. It is well established in our jurisprudence that the fundamental right to life and personal liberty, under Article 21 of our Constitution, cannot be suspended even during an emergency. As the sentinel on the qui vive, this court must be alert to the cries of the citizenry, alleging violation of their Constitutional rights Scroll Staff (2020).

The lockdown period has also seen the arrests of human rights activists and student leaders, with appeals to the country’s law courts proving to be of little avail; see The Wire (2020) and Yamunan and Daniyal (2020). That the lockdown may have been implemented not wisely but too well is suggested by the content of the next three sub-sections.

7.3 Supply Chain Disruptions in Agriculture and Urban Food Markets One must expect that an inevitable concomitant of a thoroughgoing lockout must be serious breaks in backward and forward linkages of supply. As mentioned before, Sudha Narayanan (2020) provided an early diagnosis of supply chain failures in agriculture and suggested how these might be dealt with. The extent of the disruption is described in a very instructive appraisal by Vikas Rawal and Ankur Verma (2020). The authors note that it was only on the third day of the lockdown, on March 27th, that the government clarified that agricultural production and market activities were outside the purview of the lockdown. By this time, it would appear that substantial damage had already been done to the supply chains in agriculture, as the following account, based on the work of Rawal and Verma, testifies. An analysis of post-winter harvest (rabi) market arrivals in 1,331 designated mandis (market trading points) over the period March 15–April 14 for the years 2017, 2018, 2019, and 2020 reveals that there were serious shortfalls in these arrivals in 2020 for seven key food commodities, comprising foodgrains (wheat, chickpea, and mustard) and vegetables (potato, tomato, onion, and cauliflower). The quantity of wheat sold over the first lockdown period of 21 days (March 25–April 14) in 2020 was found to be only 6% of the quantity sold over the same period in 2019; the corresponding figures for chickpea and mustard are 6% and 4%, respectively. The performance of perishable commodities was somewhat better: even so, the declines in arrivals for onions, potatoes, tomatoes, and cauliflower between 2019 and 2020 were of the order of 70%, 59%, 26%, and 11%, respectively. Despite the (delayed) exemption of agricultural operations from the purview of the lockdown, supply disruptions could not be prevented because of the impact of the lockdown elsewhere, as Rawal and Verma note. Specifically, because of restrictions on mobility, labor from neighboring villages and from migrants who typically

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returned to their villages in the post-harvest season was unavailable in sufficient quantity for the crucial activities of loading and unloading. Additionally, restraints on vehicular mobility severely compromised the safe transportation of food commodities. In the event, aggregate demand shortfalls were matched also by aggregate supply shortfalls. The case of urban food markets has been analyzed in an instructive contribution by Sudha Narayanan and Shree Saha (2020). A face-to-face survey of 50 food retailers at 21 locations in 14 cities across India suggests that a set of common problems faced by them during the lockdown included irregular supply, difficulty in access to transportation, increased transportation costs, labor shortages for loading and unloading activities, significant hikes in traders’ prices, declining demand, and harassment of street vendors by the police. The resulting supply shortages have been reflected in the rising wholesale and retail prices of food. Over a 28-day period of the initial lockdown and its extension (March 24-April 21), Narayanan and Saha have analyzed daily price data for 22 food commodities—rice, wheat, wheat flour, edible oils, pulses, milk, potato, onion, tomato, sugar, jaggery, iodized salt, and loose tea leaves—across 114 Centers in India. The data collected at the Centers by the respective State Civil Supplies Departments are published by the government’s Ministry of Consumer Affairs, Food, and Public Affairs. Using the data described above, the authors construct a Food Price Index, both Wholesale and Retail, for the 22 commodities mentioned. Without entering into detail, we simply note here that over the “pre-lockdown” period of February 22– March 23, both the Wholesale and Retail Price Indices constructed by the authors display a declining trend, and then a perceptibly increasing trend over the “postlockdown” period of March 24–April 21. This is despite the decline in aggregate demand already apparent in the pre-pandemic downward spiral of the economy and the further post-pandemic stress on demand. The demand side of the market should have predicted a decline in food prices. The short-term spike in prices suggests that supply disruptions have more than compensated for demand declines, leading presumably to substantial rationing in quantities transacted in the market.

7.4 The Predicament of Migrant Laborers India has a large mass of internal migrant laborers, estimated at at least 120 million persons and considerably larger than the populations of the largest European countries.17 Most of these migrants are employed as low-paid casual workers in the informal sector. Considering annual migration flows, Clément Imbert Imbert (2020) estimates—employing Census and National Sample Survey data—that there may be up to 22 million persons who have migrated over the last year in the country, around half of which are inter-state migrants. These numbers only represent flows of 17 “Labor and Migration in India,” Aajevika Bureau, 2014, http://www.aajeevika.org/labor-andmigration.php.

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migrants, and there could be additional numbers from the stock of existing migrants who chose to return home when the lockdown is lifted. Certainly, one would expect the seasonal inter-state migrants to return in large numbers. Estimating the overall flow is problematic, but it can easily run to several million. While permanent migrants generally come from all over India, seasonal migrants have more concentrated origins (see Imbert (2020), Fig. 2), coming from poorer states such as Bihar, Chhattisgarh, Jharkhand, Madhya Pradesh, and Odisha. Their destinations are the megacities, where large-scale work (principally in construction) is available. We have already remarked on the large recent spike of cases in the covid epidemic in some of these states; see Table 2 and the discussion in Sect. 5. The incentive for migrants to return is obvious. When the lockdown was announced with just four hours’ notice on March 24th, migrant laborers knew they were facing the immediate prospect of being laid off from work, of not being paid their wage dues, and of facing a life without jobs, cash, food, and shelter. The Centre for Monitoring Indian Economy (CMIE) reports that unemployment was at 23.5% during April 2020, well up from 6–9% in the 12-month period before, with every indication that unemployment would continue to climb into May (Vyas (2020)). Figure 3 shows how the employment drop in April divided itself into various labor categories—small traders and laborers have been hit the hardest, with a drop of 91m in employment in a single month. (As a reference, total employment was already down to 396 m at the end of March Vyas (2020)) It is important to note that migrant laborers from other states cannot avail themselves of PDS rations even if they possess ration cards made out in their names, if the cards relate to their state of origin and not their destination state. (Agarwal (2020)). Most of these people would therefore not fall within the ambit of the relief provisions envisaged in the Covid-19 relief package announced by the government on March

Fig. 3 Employment changes for different occupational groups in the month of April 2020. Source Centre for Monitoring Indian Economy, https://www.cmie.com/kommon/bin/sr.php? kall=warticle&dt=2020-05-05%2008:22:21&msec=776

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27th. However, the second package announced by the government expands the PDS to cover these situations. At the time of writing, we are not in a position to fully evaluate the implementation of this very recent measure. With all inter-state bus and rail transport stalled by the lockdown, migrants in their desperation took to walking back to their homes, often hundreds of kilometers away from their present migrant destinations. A number of journalistic reports have described the suffering of these trekking migrants. Here is a small sample of the accounts available in these articles. An April 12th report by Jay Kishan Sharma (2020) indicates that over 1,000 stranded migrant laborers from Odisha spilled out on to the streets in Surat (Gujarat) on March 30th, and over 90 of them were arrested by the police for torching vegetable carts and vehicles in protest over the non-availability of transport to take them to their homes. When the second phase of the lockdown was announced on April 14th, some 3,000 migrant workers poured out on to the streets of Mumbai, seeking transport at the Bandra bus and railway stations, where they were caned in a charge by the police: there are estimated to be some 6 lakh migrants interned in make-shift shelters in several districts of Maharashtra; see Sukanya Shantha (2020). An earlier report filed by Shantha (2020) on April 7 notes that 120 migrant laborers from Rajasthan who had walked for 6 days from Bengaluru, were detained and beaten up by the police in Valsad District of Gujarat on March 31st, packed into container trucks, and sent off to Palghar District in Maharashtra: the Gujarat police are supposed to have initiated an inquiry into the event. On March 28th, thousands of migrant laborers who had trekked their way to Delhi en route to Uttar Pradesh thronged the Anand Vihar bus station in East Delhi in the expectation of being ferried over the Delhi-U.P. border, but the buses available were no match for the size of the exodus, and police resorted to blocking and breaking up the crowds Press Trust of india (2020). Other reports (see, e.g., Scroll Staff (2020) and Wire Staff (2020)) indicate that at least 22 persons belonging to migrant laborer families may, by that time, have died on their long marches home, for reasons of hunger, exhaustion or road accidents. The condition of stranded workers in different parts of the country is reflected painfully in the statistics that have been compiled by the Stranded Workers Action Network (SWAN) team (2020), on the basis of their conversations with subsets of 16,863 stranded workers who have reached out to the team from Maharashtra, Karnataka, Delhi, Haryana, Punjab, and Tamil Nadu. The period covered is the duration of the lockdown till April 26th: (a) Remuneration. The average daily wage of the workers was Rs. 380, and the median wage was Rs. 365. Only 6% received their full wages during the lockdown, while 78% were not paid at all. (b) Cash on hand. More than 97% of the workers received no cash aid from the government; 64% had Rs.100 or less; 74% Rs. 200 or less; and 78% Rs. 300 or less;

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(c) Hunger. 82% of the workers received no rations from the government, and 68% received no cooked food; 50% had food rations for less than a day, and 72% for less than 2 days. It was only on May 3rd that the government announced the operation of special Shramik trains for persons stranded during the lockdown; ANI (2020). According to government guidelines, the trains would be operated according to the requirements of state administrations. An issue that has created considerable consternation among commentators is the levying of train fare upon passengers, leading to one opposition party offering to meet the travel expenses of migrant workers; see Naqshbandi and Dutta (2020). Indeed, the plight of India’s migrant laborers has been the subject of a Public Interest Litigation filed by two activists, Harsh Mander and Anjali Bharadwaj, seeking immediate payment of wages to the laborers. Apparently, the petitioners’ concerns regarding the rights of those they were representing were not persuasive. After the final hearing on the case, the Supreme Court closed it by effectively leaving it to the discretion of the executive to act as it saw fit in the matter: “We call upon the respondent—Union of India—to look into such material and take such steps as it finds fit to resolve the issues raised in the petition;” see Mander (2020). Notwithstanding this, the predicament of migrant workers after the lockdown has been widely regarded as a serious human rights issue, on which there has been a fair amount of reporting and commentary in the international press as well; see, for instance, Slater and Masih (2020), or Ellis-Petersen and Chaurasia (2020).

7.5 Labor Laws Even as migrant laborers started returning to their homes in the special Shramik trains alluded to earlier, the state of Karnataka suddenly suspended the operation of these trains, apparently in response to the demands of the building-and-real-estate-lobby that migrant workers ought to be retained within the state to facilitate the revival of construction activity. (The suspension was revoked after a public outcry against the move; see Kulkarni (2020). Similarly, employers’ associations such as the Confederation of Indian Industries, the Federation of Indian Chambers of Commerce and Industry and the Associated Chambers of Commerce and Industry of India (ASSOCHAM) have begun clamoring for various dilutions of the law relating to labor (Press Trust of India (2020)). State governments have not been slow to respond to these demands: already, Rajasthan, Punjab, Himachal Pradesh, and Gujarat have decided to increase the length of the working day from 8 h to 12 h, and of the working week from 48 h to 72 h; in Madhya Pradesh, provisions relating to working conditions and to the appointment of a labor welfare officer have been relaxed; and in Uttar Pradesh, all but a few basic labor laws will stand suspended. These measures are expected to be in place for finite periods of time, from 2–3 months to 2–3 years; see Jainani and Ray (2020). These moves represent an upending of an entire global

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history of the struggle for workers’ rights, and are a stark example of our thesis of a political economy in which the interests of those that are neither noticeable nor significant in the scheme of things are traded for the interests of those who are visible and consequential.

7.6 Other Morbidities One further serious consequential impact of a covid-related lockdown is its impact on other morbidities, a matter of considerable concern to those public health experts who have had an eye on the global South. So, for instance, Vikram Patel (2020) points out that 1,000 people die of respiratory tract infections every day, a problem that could be rendered worse by contagion arising from people being confined to homes which are often tiny and insanitary habitations. A lockdown also imposes restrictions on mobility, makes transport costly, and curtails access to curative facilities, even as it contracts the availability of health staff and physicians in hospitals. Thus, chronic ailments like diabetes, cancer, and heart disease are liable to be edged out of the ambit of routine care. And mortality from suicide and trauma from domestic abuse must be expected to increase. A further major casualty is the routine activity of immunization. Tuberculosis is, or should be, a source of great worry, on which Madhukar Pai writes (2020): “The global Covid-19 response will likely result in diversion of healthcare workforce and resources away from routine TB services, or reduction in the number of health workers because of illness and self-isolation.”

7.7 Coronavirus, Religion, Caste, and Gender One of the most distressing features of the epidemic, reflecting poorly on both the state and sections of society, is the way in which religion, caste, and gender have been implicated in it. There are at least three events preceding the epidemic which have had a role to play in communalizing it. The first is the remarkable and peaceful sit-in protest conducted mainly by Muslim women in the East Delhi neighborhood of Shaheen Bagh against the government’s initiatives on the controversial Citizenship (Amendment) Act, the National Register of Citizens, and the National Population Register. The second is a highly communally charged election campaign launched by the BJP in the month leading up to the Delhi Assembly election of February 8th (which the BJP lost). The third is the violent communal violence unleashed toward the end of February in Delhi, which claimed 53 lives of which more than two-thirds were Muslim. These events set the stage for communalizing the coronavirus epidemic that unfolded around the same time. A salient episode in this connection concerns a religious gathering of the Tablighi Jamaat sect in the Nizamuddin area of Delhi. Many of the participants in the gathering

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were identified as covid-infected, and they were traced and quarantined. Through fake news and pliant media cooperation, it has since been put out that the Tablighi incident is part of a deliberate Islamic conspiracy to infect the Indian population with the coronavirus. For two detailed accounts (among others), see Jain (2020) and Daniyal (2020). Apart from this sort of calumniation of an entire community, there have also been instances of discrimination against patients based on religious grounds. In Meerut (Uttar Pradesh), a cancer hospital made the entry of Muslim patients into the hospital contingent on their producing evidence of having been tested and returned negative for Covid (see Pandey (2020)). In Ahmedabad (Gujarat), the Civil Hospital resorted to segregating Muslim patients from Hindu patients in separate wards, in response—according to the hospital—to a State Government decision to this effect (though this was subsequently denied by the Government’s Health Minister); see Ghosh and Dabhi (2020). A second episode has to do with the mob lynching on April 16th of three people (of whom two were Hindu monks) in Maharashtra’s Palghar district. Despite the arrest of people who were declared by the State Government to belong to the same community as the victims, a campaign of innuendo has been launched implicating members of the minority community and specific leaders of opposition political parties in the crime; see Patel (2020). In major national catastrophes, Dalits are often among the most badly affected groups. The covid-19 pandemic has proved to be no exception. A survey conducted in Tamil Nadu by the research collective Vaanavil has found that Scheduled Caste persons working as construction workers, loaders, and sanitary workers have not been paid wages on MNREGA work programs, and have not been beneficiaries of State and Central Government cash relief payments to which they are entitled.; see Tiwari (2020). Matters do not appear to be much different in one of India’s premier universities. A report by Bose (2020) states: “While the rest of the capital practices social distancing and stays indoors, sanitation workers, who clean the 18 hostels and messes of the Jawaharlal Nehru University, claim that they have not been paid three months’ wages. Despite being made to come to work amid the national lockdown, the workers have neither been provided with any protective gear such as masks or gloves nor do they have the luxury to opt for work from home or leave without pay.” Women are made to carry a disproportionate burden of the negative effects of an epidemic-induced lockdown. Ashwini Deshpande (2020) observes that women are more vulnerable to job-losses than men; that those women who are required to “work from home” during the lockdown, when domestic helps are also bound by it, will likely carry the double-burden of enhanced domestic work; and that women are also exposed to the risk of domestic and intimate party violence during a period of enforced joint cohabitation. Nalini Gulati (2020) reports that according to the National Commission for Women there has been a sudden spurt of complaints—in the form of 123 emails—of domestic abuse in the lockdown period between March 23rd and April 10th, and that similar worrying trends have also been registered by women’s commissions and state governments such as those of Kerala and Punjab.

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8 India’s Lockdown: Visibility, Invisibility, and Its Implications This brings to an end our interim assessment of India’s lockdown experience. What does the assessment add up to? To address this question, it is relevant now to return to Sects. 1, 3, and 4 of this essay (“Prologue,” “The Philosophy of Lockdown,” and “Lives versus Lives: The Visible and the Invisible”). We use these to revisit the conceptual core of a proposal for a relatively relaxed lockdown that was earlier advanced by Ray, Subramanian, and Vandewalle; see (2020) and (2020). In these articles, we pointed to certain features of the Indian economy—its occupational structure, and the prevalence of poverty, inequality, and informality—which could cause a prolonged and indefinite lockdown (sans safety nets) to wreak the harshest consequences on the well-being of the poor laboring classes. Our understanding of the covid-19 epidemic was that it is a relatively “low fatality-high contagion” disease whose most adverse impact is on the old-age (60+) population, which accounts for a relatively small proportion in developing societies like India, unlike in economically advanced countries such as those in Europe and North America. This understanding led us to the prescription that for India, unlike, say, for the United States, the strategy might have to be one of a relatively “relaxed” version of a lockdown which (a) allows for individuals of working age, but no older than 45, to work; (b) provides for special public (State-sponsored) care for the aged population while also leaving such care to be provided by the forces of private incentives and motivations at the individual household level; (c) ensures, crucially and indispensably, the organization of the most extensive antibody testing as such testing becomes widely available, and as antibody stocks in the population build up; (d) slowly brings older people into the workforce as the disease becomes less pervasive, and (e) guarantees compensating welfare measures from the State to enable the particularly vulnerable populations (the poor, and casual and informal-sector workers) to tide over the immediate and secular damage done by a lockdown to their lives and livelihoods. Furthermore, it goes without saying that easy-to-implement measures such as mask-wearing, physical distancing, and the avoidance of crowded gatherings should continue in full force. This is only a brief summary, intended to serve the purpose of a swift recall: the full version of our argument is available in our previously-mentioned article. Our proposal isn’t rocket science, and we do not stand alone in our advocacy. David Katz, a physician, in an early communication Katz (2020), writes: I am deeply concerned that the social, economic and public health consequences of this near total meltdown of normal life—schools and businesses closed, gatherings banned—will be long lasting and calamitous, possibly graver than the direct toll of the virus itself …The unemployment, impoverishment and despair likely to result will be public health scourges of the first order.

In seeking to quantify the benefits and costs of country-wide lockdowns in advanced and developing economies, Zachary Barnett-Howell and Ahmed Mushfiq Mobarak

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(2020) are skeptical about the virtues of comprehensive lockdowns in poorer countries: “Given the deeper concerns about the risks that economic shutdowns pose on the most vulnerable members of low-income societies…, it remains unclear whether the value of mitigation and suppression policies in poor countries outweighs the uncertain economic costs.”18

In a similar vein, Joelle M. Abi-Rached and Ishac Diwan (2020) write: In a recent opinion piece, the economists Debraj Ray and Sreenivasan Subramanian have broken the taboo by noting that India cannot afford to let people lose their livelihood and simply die of famine …The main risk of imitating the rich and imposing harsh but unworkable lockdowns is that the effort would crowd out scarce public capacity that can be better put to use elsewhere where it can make a difference.

Jayaprakash Muliyil, also a physician and one of India’s most distinguished epidemiologists, has this to say: Suppression is not going to work in India in my opinion …In India, suppression would mean hurting each other, exploitation, giving power to wrong kinds of people. That is not my response to a public health emergency …This is going to be a long haul. You have to be sensible about it …[I]f you apply drastic measures, people will rebel if they don’t have food and milk for their children—and healthcare for other ailments Muliyil (2020).

It is easy enough to provide several more references in line with the approach we advocate.19 But why, then, the forceful emphasis on a draconian lockdown? We claim that it is precisely because covid-19 poses a visible threat that the elites of India all know about, and are vulnerable to. In catering to these fears, the Indian Government also obtains international recognition as being on the frontlines against the pandemic. Moreover, for the elites, a lockdown is often not much more than a loss of cleaners and maids. But there is a price to be paid, which are the horrors and privations that the poor and truly vulnerable of India must endure as their livelihoods fail, often succumbing to these with their invisible lives. That invisibility is only in part due to poverty. In greater part, it is because non-covid deaths are diffuse; they are classified under a multitude of headings, and so lack the capacity to attract focal attention. We must value these lives, invisible though they may be, and however slavishly our covid-awareness and social distancing and curve-flattening might mimic the 18

See also, in this connection, Loazya (2020). Here are just three more examples. Alex Broadbent and Benjamin Smart (2020) write: “We are putting in place measures that will lead to malnutrition and starvation for millions of people, and for these horrors, children and especially infants are the most at risk. And very many of those infants are born, and will die, in Africa.” Vikram Patel, a doctor and public health specialist, expresses a similar point of view Patel (2020): “When one balances the vast uncertainties about Covid-19 when the lockdown was imposed …with the absolute certainty that such a lockdown would massively disrupt the lives and well-being of most of our population, it is hard to conclude that such a preemptive strike was justified.” And Julian Jamison (2020) observes: “Policymakers everywhere may be tempted to focus on the immediate fatalities from covid-19 while eliding the equally real but more remote mortality from malnutrition, psychological distress, extreme poverty, and sociopolitical unrest that lockdowns and economic disruption can cause. Yet the dangers of the latter are far sharper in the developing world …”.

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concerns of the West, by implementing a comprehensive system of transfers and safety nets that protect our vulnerable populations. Sadly, our assessment of India’s response so far does not give us any hope that such a system is in the works. We maintained initially, and we continue to do so, that a “relaxed” lockdown is not our option of first choice: that first option has always been a tight lockdown, but one accompanied by humane and imaginative welfare compensation for the citizenry adversely impacted by the provisions of a binding lockdown. For instance, we do not agree with the motley crowd of libertarians, mask-spurners, and business interests in the United States, who are asking for an immediate end to the lockdown on the grounds that economic activity and individual freedoms come above all else. The argument for developing countries is different. It, therefore, strikes us being a bit otiose to suggest, as some commentators have done, that the emphasis ought to be on “lives and lives,” as opposed to “lives versus lives,” or that one needs to attend to both the problems of checking the pandemic and the distress caused by a lockdown. Who would disagree? Certainly not we. But we also believe we need to proceed beyond a position which can become reductively platitudinous when it is unaccompanied by concrete and specific suggestions for safety nets. Indeed, as this review has shown, there is attention to such suggestions (on a scale and with a wealth of knowledge that is truly admirable), but it can all come to nothing if there is both inability and unwillingness on the part of the policy establishment to implement such safety measures. Given the short-run inelasticities of supply (especially in the matter of health infrastructure) and genuine feasibility constraints, it would be sensible to view the problem from a “lives versus lives perspective” even in the context of a capable and completely well-intentioned State. But when the latter presumption is misplaced, as our review strongly appears to suggest, the perspective we speak of a fortiori gains even more credibility. We hope it is clear, therefore, that our position is not one of exonerating laxity in State action, nor of offering the notion of a relatively “relaxed lockdown” as a substitute for competent State intervention.20 In summary, this essay’s interim assessment of post-lockdown developments in India suggests that we were not mistaken in bringing a “lives versus lives” perspective to bear on the problem of how to view the covid-19 pandemic. This perspective only gains in persuasiveness when the focus of the executive apparatus is on visibility21 and optics,22 on being seen to be acting in the interests of the articulate and 20

For a useful account of what the state can do with, and must spend on, the public health system in order to make a relatively relaxed lockdown possible, see Muralidharan Muralidharan (2020). 21 The notion of “visibility” applies also in other contexts. For example, Amartya Sen has noted in many of his writings on famine that starvation deaths in a time of famine attract a great deal more attention than do regular, “orderly” deaths due to undernutrition. Similarly, sporadic state action on regulating child labor in “hazardous occupations” such as fireworks attracts more immediate attention than would a long-term, systematic engagement with the phenomenon of “school-lessness” and child work in family activities. 22 Witness the extravagant spectacle, on May 3rd, of Indian Navy and Indian Air Force (IAF) helicopters showering rose petals on hospitals treating covid patients, and IAF planes conducting flypasts ostensibly to convey gratitude to the health staff battling the epidemic, even as migrant

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influential sections of society, and when this orientation is aided by the timorousness or complicity of the institutions of democracy. What we have put together is no more than a collation of the hard and conscientious work done by several commentators—not least by journalists, reporters, and opinionwriters—on the course of the coronavirus epidemic over the last few weeks. That it has been possible at all to write this interim report is due precisely to such sterling effort invested by so many people, to whom a great debt of gratitude is owed, and will continue to be owed as they continue to track the events which will unfold in the weeks and months to follow. It is obvious that our record has no merit of originality, but it is hoped that the effort of assembling widely scattered material on a number of issues into one cognate account will be of some little use in enabling all concerned actors to pause, reflect, and take stock. Does this record enable us to claim rigorous vindication for the thesis we laid out in favor of a “relaxed” lockdown? Certainly not. The period under consideration— roughly from March 25th to May 15th—is far too short to come up with any definitive conclusions. For mainly this reason, but also because of the nature and reliability of the data at hand, we do not believe it is currently possible to compare epidemicrelated lives that might have been saved by the lockdown with lives that might have been compromised by the lockdown due to loss of incomes, loss of employment, hunger, disruptions to the economy, and other (non-covid-related) morbidities and mortalities. This is a matter on which only time can pronounce. Meanwhile, however, we are not lacking in the signals sent out by tendencies. And the dominant tendency to which our assessment points is one of very substantial suffering caused by a lockdown which has been accompanied by serious shortfalls in state intervention aimed at mitigating the negative effects of the lockdown. Our assessment will probably seem to many to be quite critical of the State’s intervention in dealing with the epidemic. We do not apologize for this, because our attempt has been to present an account which is not consciously infected with prejudice or adversarial intent toward the government or other social actors. That is, we’ve tried to be honest. And we’ve attempted to present an account that is not willfully inaccurate, biased or misleading, and is in the interests also of India’s citizenry, in which cause, we cannot afford to prioritize the interests of the State machinery. This explicit statement of our goals is intended to serve as an explanation, which we believe is owed to those we are critical of, and which we hope will enable a sympathetic understanding of our motivation and our task.

laborers have to buy their train tickets to eventually get back home, and health workers have to struggle without adequate personal protective gear.

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press (Apr. 17, 2020). https://indianexpress.com/article/opinion/coronavirus-indialockdowneconomy-amartya-sen-raghuram-rajan-abhijit-banerjee-6364521/. Somdeep Sen. “Modi’s Government has Botched its Response to India’s Pandemic”. Jacobin (Apr. 15, 2020). https://www.jacobinmag.com/2020/04/india-narendramodi-pandemic-coronavirusbjp?. Rajeswari Sengupta and Harsh Vardhan. “Policymaking at a Time of High Risk- Aversion”. Ideas for India (Apr. 6, 2020). https://www.ideasforindia.in/topics/moneyfinance/policymaking-at-atime-of-high-risk-aversion.html. Sukanya Shantha. “Gujarat Police to Probe Allegation That Migrant Workers Were Forced Into Container Trucks”. The Wire (Apr. 7, 2020). https://thewire.in/government/gujarat-police-inquirymigrant-workers-container-trucks. Sukanya Shantha. “’Let Us Go Home’: No Sign of Relief in PM’s Speech, Migrant Workers Take to Mumbai Streets”. The Wire (Apr. 14, 2020). https://thewire.in/labour/mumbai-bandra-migrantcovid-19. Jay Kishan Sharma. “Migrant Workers’ Stir Turns Violent”. The Hindustan Times (Apr. 12, 2020). https://www.hindustantimes.com/india-news/migrant-workers-stir-turnsviolent/storyWIwF3Ye1YxVtlaT8U8jKiN.html. Samrat Sharma. “Modi’s Rs 21 lakh cr Special Economic Package Actually Costs the Govt Only This Much”. Financial Express (May 18, 2020). https://www.financialexpress.com/economy/ narendra-modis-rs-21-lakh-cr-special-economic-package-actually-costs-the-govt-only-thismuch-nirmala-sitharaman-relief-package/1962288/. Gurbachan Singh. “Covid-19: Reserves to the Rescue”. Ideas for India (Apr. 11, 2020). https:// www.ideasforindia.in/topics/macroeconomics/covid-19-reserves-to-the-rescue.htm. Dipa Sinha. “Food for All During Lockdown: State Governments Must Universalise PDS”. TheWire (Apr. 20, 2020). https://thewire.in/rights/covid-19-lockdown-foodsupply-pds. Joanna Slater and Niha Masih. “In India, theWorld’s Biggest Lockdown has Forced Migrants toWalk Hundreds of Miles Home”. The Washington Post (Mar. 28, 2020). https://www.washingtonpost. com/world/asia_pacific/india-coronavirus-lockdown-migrantworkers/2020/03/27/a62df1666f7d-11ea-a156-0048b62cdb51_story.html. Business Today Staff. “’Actual Fiscal Impact’ Mere Rs 1.5 lakh cr in Rs 21 lakh cr Economic Stimulus: Barclays”. Business Today (May 18, 2020). https://www.businesstoday.in/current/ economy-politics/govt-stimulus-measures-cause-actual-fiscal-impact-rs-15-lakh-cr-barclays/ story/404099.html. Scroll Staff. “Covid-19: At Least 22 Migrants Die While Trying to Get Home During Lockdown”. Scroll.in (Mar. 22, 2020). https://scroll.in/latest/957570/covid-19-lockdownman-collapses-dieshalfway-while-walking-home-300-km-away-from-delhi. Scroll Staff. “Covid-19 Lockdown: Kerala HC Seeks Centre’s Reply on Police Excesses, Says ’Laws Can’t Stay Silent”’. Scroll.in (Mar. 30, 2020). https://scroll.in/latest/957728/covid-19lockdown-kerala-hc-seeks-centres-reply-on-police-excesses-says-lawscant-stay-silent. Scroll Staff. “Three IRS Officials Suspended for Causing Panic with Report on Increasing Taxes”. The Hindustan Times (Apr. 27, 2020). https://scroll.in/latest/960407/three-irs-officialssuspended-for-causing-panic-with-report-on-increasing-taxes. Scroll Staff. “What? No New Task? Modi’s Lockdown Extension Speech Sparks Scathing Humour on Twitter”. Scroll,in (Mar. 4, 2020). https://scroll.in/article/959129/what-no-new-task-modislockdown-extension-speech-sparks-scathing-humour-ontwitter. Wire Staff. “22 MigrantWorkers, Kin Have Died Trying to Return Home Since the Lockdown Started”. The Wire (Apr. 30, 2020). https://thewire.in/rights/coronavirusnational-lockdownmigrant-workers-dead. Wire Staff. “Express Gyan: Why the Centre’s Lockdown Relief Plan for the Poor is not Enough”. The Wire (Mar. 26, 2020). https://thewire.in/economy/coronaviruslockdown-india-relief-packagepoor.

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Wire Staff. “International Network of Scholars Condemns Arrests of Teltumbde, Navlakha”. TheWire (Apr. 21, 2020). https://thewire.in/rights/anand-teltumbde-gautamnavlakha-arrestscholars-at-risk-network-statement. Wire Staff. “Modi’s Silence on Essential Supplies During 21 Day National Lockdown Sets Off Panic”. The Wire (Mar. 4, 2020). http://science.thewire.in/health/coronavirus-narendra-modinational-lockdown/. S. Subramanian. “Doing the Maths: Why India Should Introduce a Covid Wealth Tax on the Ultra Rich”. Scroll (Apr. 16, 2020). https://scroll.in/article/959314/doingthe-maths-why-indiashould-introduce-a-covid-wealth-tax-on-the-ultra-rich. S. Subramanian. “Letting the Data Speak: Consumer Spending, Rural Distress, Urban Slow-Down, and Overall Stagnation”. Arena Papers of The Hindu Centre for Politics and Public Policy (2019). https://www.thehinducentre.com/the-arena/currentissues/article30265409.ece. SWAN Team. “32 Days and Counting: COVID-19 Lockdown, Migrant Workers, and the Inadequacy ofWelfare Measures in India”. StrandedWorkers Action Network (May 1, 2020). https:// covid19socialsecurity.files.wordpress.com/2020/05/32-days-andcounting_swan.pdf. Teena Thacker. “Centre Decides toWithdraw Faulty Covid-19 Antibody Test Kits, Cancels Import Orders From China”. The Economic Times (Apr. 20, 2020). https://economictimes.indiatimes. com/industry/healthcare/biotech/pharmaceuticals/centre-decidesto-withdraw-faulty-covid-19antibody-test-kits-cancels-import-orders-from-china/articleshow/75406955.cms?from=mdr. Karan Thapar. “Interview: India Could Be Next Coronavirus Hotspot,Worst Case up to 60% Could Be Infected”. The Wire (Mar. 19, 2020). https://science.thewire.in/health/interview-india-couldbe-next-coronavirus-hotspot-worst-case-up-to-60-could-be-infected/. Sadhika Tiwari. “Amid COVID-19, Scheduled Caste Workers Face Discrimination”. IndiaSpend (May 3, 2020). https://www.indiaspend.com/amid-covid-19-scheduledcaste-workersface-discrimination/2020. Hari Vasudevan. “Food is a Necessity. So is Making It Available”. NewsClick (Apr. 22, 2020). https://www.newsclick.in/Food-Necessity-Making-Available. Anubhuti Vishnoi. “IAS Officers Point to Inadequate Health Infrastructure”. The Economic Times (Apr. 3, 2020). https://economictimes.indiatimes.com/news/politics-andnation/iasofficers-point-to-inadequate-health-infrastructure/articleshow/74959132.cms. Mahesh Vyas. The Jobs Bloodbath of April 2020. The Centre for Monitoring Indian Economy, 2020. Sruthisagar Yamunan and Shoaib Daniyal. “As Delhi Police Crack Down on Student Leaders, Courts Cite Lockdown to Justify Lack of Scrutiny”. Scroll.in (Apr. 29, 2020). https://scroll.in/article/960591/as-delhi-police-crack-down-on-studentleaders-courtcites-lockdown-to-justify-lack-of-scrutiny.

The COVID-19 Shock and the Indian Economy—A Cross-Country Comparative Analysis Maitreesh Ghatak and Ramya Raghavan

Abstract To understand the economic and public health impact of the COVID-19 pandemic on India, we take a comparative cross-country perspective. As all countries were exposed to this shock and were negatively affected, a relative comparison allows us to better assess India’s performance in these dimensions. We carry out three sets of exercises. First, we discuss the economic impact of COVID-19 using key macroeconomic indicators—gross domestic product and employment. Second, we review the fiscal policy response of the government and use household and individual-level surveys to understand the heterogenous impacts of the pandemic on households in the country. Finally, we compare public health indicators across countries. The analysis suggests that India’s record with respect to other countries, including countries that are comparable in economic status, puts it in the lower tail of the distribution—both in terms of economic and public health indicators. The fallout on the poorer sections and informal sector was particularly severe and policies to mitigate this were inadequate. We conclude that going forward, improving the public health infrastructure and the social safety net must be the public policy priority for the government. Keywords COVID-19 · Indian economy · Economic contraction · Unemployment · Fiscal response · Public health

1 Introduction After a lull of six months, when the effect of the pandemic in India was seemingly on a slow burn, the second wave of infections and deaths have been rising ominously since mid-February 2021, crossing the previous peak in September (Menon, 2021). The news on the economic front looks grim as well. It is not surprising that the pandemic of the century and the steps taken to deal with it would take an economic M. Ghatak (B) · R. Raghavan London School of Economics, London, UK e-mail: [email protected] R. Raghavan e-mail: [email protected] © The Author(s), under exclusive license to Springer Nature Singapore Pte Ltd. 2022 M. Dutta et al. (eds.), The Impact of COVID-19 on India and the Global Order, https://doi.org/10.1007/978-981-16-8472-2_3

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toll. Indeed, all countries and not just India have suffered in terms of public health and the economy. But because the pandemic is a common shock, one can also compare different countries in terms of what the economic and public health impact has been and to what degree this reflects their relative effectiveness in handling the crisis. In this paper, we evaluate the relative status of India, compared to other countries in both the economic and public health areas. An important question in this context is, are there trade-offs between the economic and public health outcomes from the crisis? Did countries adopting stringent lockdown measures suffer more economically but achieve better public health outcomes? Or, does the evidence suggest that some countries coped better on both dimensions, while others fared worse?1

2 Methodology for Country Selection For ease of understanding and clarity, rather than looking at a comprehensive list, we select a list of 27 countries (excluding India) comprising of advanced and emerging market and developing economies (Table 1). To make the cross-country comparisons meaningful, we first classify the countries as “developed” and “peer” according to two measures—GDP per capita in current prices (US dollar) and GDP per capita in constant prices (based on purchasing power parity; 2017 international dollar)— relative to India. The classification is based on the ratio of the respective country’s GDP per capita to India’s GDP per capita. The ratios range from 0.6 to 32.3 for GDP per capita in current prices and 0.8 to 15.0 for GDP per capita in constant prices based on purchasing power parity. Developed countries are classified as those with a ratio greater than 10 for GDP per capita in current prices and 5 for GDP per capita in constant prices. We conduct a robustness check of the classification metric using the 194 countries countries in the IMF World Economic Outlook Database. Using the classification metric based on GDP per capita in current prices, India’s GDP growth rate in 2020 ranks 115 out of 150 peer countries and 36 out of 45 developed countries. Similarly, using the classification metric based on GDP per capita in constant prices, India’s GDP growth rate ranks 112 out of 148 peer countries and 39 out of 47 developed countries. Both measures put India in a similar rank bracket and also yield the same classification (presented in Table 1). Overall, India’s GDP growth rate in 2020 ranks 150 out of 194 countries.

1

See Fernandez-Villaverde and Jones (2020) for an international perspective on COVID-19 and macroeconomic outcomes.

The Covid-19 Shock and the Indian Economy … Table 1 Classification of countries for comparison against India

65

Developed countries

Peer countries

Australia Canada France Germany Italy Japan Korea New Zealand Singapore Spain Taiwan United Kingdom United States

Bangladesh Brazil China Indonesia Malaysia Mexico Pakistan Philippines Russia South Africa Sri Lanka Thailand Turkey Vietnam

3 Macroeconomic Indicators We begin our economic analysis by looking at two key indicators that provide diagnostics on the state of the economy—the growth rate of GDP (constant prices) and unemployment. We summarize the analysis of economic indicators in Tables 2 and 3. In Table 2 we present both the GDP growth rate (at constant prices 2020) and the unemployment rate for India and peer countries, as well as India’s rank in this group of countries. In Table 3 we present similar figures, but for India and a selected group of developed countries. Since COVID-19 was a common shock facing both developing and developed countries, a comparative picture helps us put the performance of any country of interest (in our case, India) in perspective.

3.1 Gross Domestic Product The preliminary estimate of GDP presented at the end of February, according to national statistics, shows that national income in real terms contracted by 8% between 2019 and 2020. In per capita terms, the drop was by 9%. In the post-independence period, India’s national income has declined only four times before 2020–1958, 1966, 1973, and 1980. The largest drop was in 1980 (5.2%) when the economy was in turmoil due to a countrywide drought and the doubling of global oil prices due to the revolution in Iran. This implies that 2020/21 is the worst year in terms of economic contraction in India’s history, and much worse than the overall contraction in the world (Fig. 1). In Tables 2 and 3, we compare India’s growth to peer and developed countries, respectively. India ranks 13 out of 15 peer countries and 10 out of 14 developed countries in terms of GDP growth rate in 2020. The average rate of decline in GDP

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Table 2 Economic data—India versus peer countries Country

Bangladesh

GDP Growth rate in 2020 (%)

Rank in terms of GDP growth rate (Out of 15)

Unemployment 2020 (percent of total labor force) (%)

Rank in terms of unemployment rate (out of 15)

3.8

1

5.3

9

Brazil

−4.1

9

13.2

2

China

2.3

3

3.8

13

India

−8.0

13

7.1

5

Indonesia

−2.1

6

7.1

6

Malaysia

−5.6

10

4.5

10

Mexico

−8.2

14

4.4

12

Pakistan

−0.4

5

4.5

11

Philippines

−9.5

15

10.4

4

Russia

−3.1

7

5.8

8

South Africa

−7.0

12

29.2

1

Sri Lanka

−3.6

8

5.8

7

Thailand

−6.1

11

2.0

15

Turkey

1.8

4

13.1

3

Vietnam

2.9

2

3.3

14

World

−3.3

6.5

Emerging market and developing economies

−2.2

n/a

Source International Monetary Fund, World Economic Outlook Database, April 2021. Unemployment estimates for the World, India, and Bangladesh obtained from World Bank

for the world was 3.3%, for advanced economies 4.7%, while in developing and emerging economies it was 2.2%. However, on a positive note, for the next two years (2021–2022), the estimates show some light of hope. While all economies are expected to recover to some degree, the rate of recovery in India will be relatively higher. The IMF estimates the GDP of India to grow at 12.5% in 2021 and 6.9% in 2022, resulting in a compound growth rate of 3.6% between 2020 and 2021 and 10.8% between 2020 and 2022. We note two caveats here. First, the greater is the negative impact of the shock, the more the ground there is to recover—the more you fall behind, the greater the rate of improvement required just to regain your earlier position. India’s growth rate before the onslaught was relatively higher than other countries. Therefore, even after recovering in absolute terms, to achieve the same relative position will not be easy and would require an even higher rate of growth. Second, as the COVID-19 situation in India is currently evolving, the growth rates for 2021 would likely be adjusted downward by the IMF. The magnitude of the current situation on the GDP growth

The Covid-19 Shock and the Indian Economy …

67

Table 3 Economic data summary—India versus developed countries Country

GDP Growth in 2020 (%)

Rank in terms of GDP growth rate (Out of 15)

Unemployment 2020 (Percent of total labor force) (%)

Rank in terms of unemployment rate (Out of 15)

Australia

−2.4

3

6.5

7

Canada

−5.4

9

9.6

2

France

−8.2

11

8.2

4

Germany

−4.9

7

4.2

10

India

−8.0

10

7.1

6

Italy

−8.9

12

9.1

3

Japan

−4.8

6

2.8

14

Korea

−1.0

2

3.9

11

New Zealand

−3.0

4

4.6

8

Singapore

−5.4

8

3.1

13

Spain

−11.0

14

15.5

1

Taiwan

3.1

1

3.9

12

United Kingdom

−9.9

13

4.5

9

United States

−3.5

5

8.1

5

World

−3.3

6.5

Advanced economies

−4.7

6.6

Source International Monetary Fund, World Economic Outlook Database, April 2021. Unemployment estimates for the World, India, and Bangladesh obtained from World Bank

remains to be ascertained with certainty as the upcoming months unfold—depending on the intensification of lockdowns and acceleration of vaccine administration.

3.2 Is the Drop in the GDP Per Capita Representative? National income is just one measure of a country’s economic health and there are many well-known problems of using it. Some of these problems are universal— for example, in the presence of large inequalities in the distribution of income and wealth, the national income nor its growth rate is very informative about the economic conditions of the poor. Therefore, an average contraction rate of 8% does not mean that everyone’s income has fallen by 8%. Rather, evidence suggests that the income and wealth of those who are richer has actually increased and those who are poor has deteriorated. A study by Pew Research Center (Kochhar, 2021) suggests that the

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Fig. 1 Economic Contraction in India and the World due to the COVID-19 Pandemic

number of poor in India is estimated to have more than doubled post-pandemic and the number of people in the middle class have been cut by a third. Therefore, it is difficult to say exactly how much has national income decreased and what the economic impact has been on the poor. In the case of underdeveloped countries like India, the problem is worse, as the unorganized sector in the Indian economy accounts for about half of the national income. About 70% of India’s total workforce is engaged in the informal sector and they tend to be poorer. The contribution of the unorganized sector to national income is estimated largely based on guesswork. Fortunately, several surveys have been conducted since the onset of the pandemic and the announcement of the lockdown. The general picture that emerges from these surveys is that the income of these poorer sections has decreased much more than the rate of contraction of national income. One such study by researchers at the Azim Premji University (Center for Sustainable Employment, 2020) on the effect of the crisis on self-employed, casual, and regular wage workers across 12 states of India between mid-April to mid-May 2020 reported a 64% drop in earnings, which is more than two and a half-time the reported decrease in quarterly GDP of the same period. A second round of the survey conducted between October—December 2020 found that about one-fifth of the workforce who were employed before the lockdown, continued to remain unemployed. While earnings have recovered for those back to work, the situation remains dire for those who are not. Women and urban workers have been hit hard, a sign of rising inequality. Additionally, only one-third are consuming the same amount of the food as they had, pre-lockdown.

The Covid-19 Shock and the Indian Economy …

69

Another study, the Round 4 of the Delhi NCR Coronavirus Telephone Survey (National Council of Applied Economic Research, 2021) conducted between December 2020 and January 2021 found that about 80% of the households suffered economic hardship such as decreased salary or daily wages, job loss, difficulty in finding work, business closure, or decline in business income. Small business owners and salaried workers have been hit the hardest. Estupinan and Sharma (2020) note that while formal worker’s wages have been cut by 3.6%, informal workers experienced a decline in wages by 22.6%. Therefore, whatever the limitations of using the GDP as a measure of economic activity might be, there are two immediate conclusions one can draw from the reported GDP figures. First, the negative economic impact of the crisis has been large for India whether we look at its own past record or the contemporary experience of other countries. Second, the drop in GDP is in line with other macroeconomic indicators, but very likely under-estimates the impact on the informal economy, where a vast majority of the population is employed, implying that the crisis has hit the poor harder. Given the large fraction of the population that is poor, the drop in GDP does not fully capture how badly the crisis has affected people’s lives.

3.3 Unemployment Rate India is estimated to have witnessed an unemployment rate of 7.1% in 2020. In Tables 2 and 3, we compare India’s unemployment rate to peer and developed countries, respectively. India ranks 5 out of 15 peer countries and 6 out of 14 developed countries in terms of the unemployment rate. While several peer and developed countries have experienced greater unemployment rates as compared to India, it should be noted that India’s informal sector is typically not well accounted for in these measurements. According to CMIE estimates, the unemployment rate peaked to above 23% in the months of April and May 2020, nearly three times what it was in the months of January and February 2020, and came down to 10% in June 2020. We note that unemployment rates were more muted within the reference group economies, including both developed and peer countries. To what extent this reflects the impact on the shock itself and to what degree differential labor market policy responses to mitigate it, is difficult to tell with the data we have at present but certainly deserves further examination.

3.4 Summary of Macroeconomic Indicators and Fiscal Response In Table 4, we summarize India’s performance against the world and a “reference group” of countries based on classifications developed by IMF and the World Bank.

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Table 4 Summary of key macroeconomic indicators India

Reference group (%)

World (%)

GDP at constant prices 2019 (% change)

4.0

3.6

2.8

GDP at constant prices 2020 (% change)

−7.3

−2.2

−3.3

Unemployment rate 2019 (% of total labor force)

5.3

5.5

5.4

Unemployment rate 2020 (% of total labor force)

7.1

6.4

6.5

Above-the-line additional health sector fiscal measures in response to COVID-19 (% of GDP)

0.4

0.9

1.2

Above-the-line additional non-health sector fiscal measures in response to COVID-19 (% of GDP)

3.0

2.8

7.8

Note The “Reference group” refers to the closest peer group statistic under which India falls. The reference group for GDP per capita is the Emerging Market and Developing Economies (EMDEs) classification by the IMF. The reference group for the unemployment rate is the Low- and MiddleIncome Countries (LMICs) classification by the World Bank. The reference group for the fiscal measures is the Emerging Market and Developing Economies (EMDEs) classification by the IMF Source Data on gross domestic product, constant prices (percentage change) is obtained from the World Economic Outlook Database April 2021, International Monetary Fund. India’s GDP contraction is 8% according to the IMF and 7.3% from recent national estimates. Unemployment rates (for youth, adults: 15 + ) are ILO modeled estimates as of November 2021 and are obtained from ILOSTAT, International Labour Organization and World Bank. Fiscal measures are obtained from Fiscal Monitor Database of Country Fiscal Measures in Response to the COVID-19 Pandemic as of April 2021, International Monetary Fund

While the economies of all the countries have been hit hard, India ranks higher in terms of the intensity of the impact, compared to the rest of the world as well as a reference group of countries that are comparable in terms of economic status. The fact that India’s growth rate in 2019 was among the highest makes the drop due to COVID-19 in 2020 even more noticeable. Despite the scale of the pandemic, additional budgetary allocation to various social safety measures has been relatively low in India compared with other countries. While India seems comparable to the reference group in non-health sector measures, the additional health sector fiscal measures are less than half those in the reference group. More worryingly, the Indian government’s announced allocation in the 2021 budget for such measures does not show an increase, once inflation is taken into account.

4 Public Health Indicators Researchers at the University of Oxford (Hale et al., 2021) have come up with a measure for assessing the stringency in the restrictions that the governments have imposed to contain the spread of the COVID-19 crisis, referred to as the Stringency Index. At the early stages of the crisis, India imposed stringent measures compared to other countries, though these have been relaxed somewhat since. That there is a direct relationship between these constraints and negative impact on the economy is

The Covid-19 Shock and the Indian Economy …

71

not hard to guess. The question is despite the economic loss, have these restrictions been effective in terms of mitigating the public health crisis? At the outset, we acknowledge several caveats regarding our discussion on public health. First, that the pandemic is still evolving globally and in India and therefore, we can only report what is known at this point with the caveat that a fuller understanding of the impact of it would take a longer time horizon. Second, we have focused mainly on the economic aspect of the pandemic, however, the pandemic’s economic impact will inevitably depend on the length of the pandemic—which is not known with certainty at this point. Third, we provide only a broad overview of the public health situation and refer the interested reader to the work of epidemiologists (some of which we cite) who have been studying the public health data carefully. Finally, public health experts have highlighted the gross underreporting of data and hence, the quality and accuracy of the available health data should be taken with the appropriate degree of caution. Turning to the statistics, we first look at data on the total confirmed cases and deaths per million—as the most basic indicators of the public health catastrophe caused by COVID-19.2 For the first six months after the crisis began, the spread of the disease and the resulting mortality rates were alarming for India. At that time, India’s record was not looking particularly good relative to all countries as well as its peer group. Given the stringency of its reaction, this was a particularly disappointing outcome and shows that these measures were not very effective. In Table 5, we first analyze the total confirmed cases and deaths per million due to COVID-19 as of April 25, 2021. India records a lower number of total confirmed cases and deaths per million compared to some high-income countries such as Canada, France, Germany, Italy, Spain, United Kingdom, and United States. Some highincome countries such as Australia, Japan, Korea, New Zealand, Singapore, and Taiwan have managed to respond to the pandemic more effectively and thereby record a lower number of total confirmed cases and total confirmed deaths per million due to COVID-19 in comparison to India. India has also had a greater success in total vaccination doses administered per 100 people in the population compared to countries such as Australia, Japan, Korea, New Zealand, and Taiwan. However, we recognize that the countries mentioned above have recorded lower confirmed cases and deaths per million compared to India. Several developed countries such as Canada, France, Germany, Italy, Singapore, Spain, United Kingdom, and United States are vaccinating their population at a faster rate. Moving to India’s peer group in Table 6, we witness a more mixed performance. While India records a lower number total confirmed cases and deaths per million due to COVID-19 when compared to Brazil, Mexico, Russia, South Africa, and Turkey, a significant number of countries such as Bangladesh, China, Indonesia, Malaysia, 2

The numbers reported here are as of the end of April 2021 and do not capture the full impact of the ongoing second wave in India at the time of writing this article. Also, serious issues of underreporting have come up (see Banaji, 2021) and therefore, the health-related statistics have to be interpreted with appropriate caution.

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Table 5 Key health indicators and policy measure—India versus developed countries Location

Australia

Total confirmed cases per million people (as of April 25, 2021)

Total confirmed deaths per million people (as of April 25, 2021)

Total vaccination Stringency index doses (as of April 25, administered per 2021) 100 people in the population (as of April 25, 2021)

1163.9

35.7

7.6

75.5

Canada

31,450.0

634.6

31.9

75.5

France

81,574.6

1511.7

28.9

78.7

Germany

39,466.9

974.8

30.5

75.0

India

12,545.7

141.4

10.1

69.9

Italy

65,540.1

1972.1

29.5

80.6

Japan

4495.9

78.6

2.2

47.7

Korea

2328.6

35.4

4.6

58.3

539.4

5.4

4.6

22.2

New Zealand Singapore

10,427.8

5.1

37.8

50.9

Spain

74,187.4

1659.5

31.5

67.6

46.2

0.5

0.2

25.0

United Kingdom 65,115.7

1880.8

68.7

63.9

United States

1728.9

68.4

56.9

Taiwan

96,909.1

Source Max Roser, Hannah Ritchie, Esteban Ortiz-Ospina, and Joe Hasell (2020). Coronavirus Pandemic (COVID-19), Our World in Data, University of Oxford. Retrieved from: https://ourwor ldindata.org/coronavirus

Pakistan, Philippines, Sri Lanka, Thailand, and Vietnam have outdone India. That countries within India’s peer group have performed better in health and economic outcomes, coupled with less stringent policy measures, provide grounds questioning India’s initial policy response to the pandemic. India has also administered more vaccination doses per 100 people in the population compared to countries such as Bangladesh, Indonesia, Malaysia, Pakistan, Philippines, South Africa, Sri Lanka, Thailand, and Vietnam. However, we recognize again that most countries mentioned above have recorded lower confirmed cases and deaths per million compared to India. India has administered fewer doses per 100 people compared to countries such as Brazil, China, Mexico, Russia, and Turkey. Public health experts continue to highlight that the official reports on COVID-19 cases and deaths understate the magnitude of the pandemic in India. Gamio and Glanz (2021) report estimates of the underreporting based on three potential scenarios, that we summarize in Table 7. The estimates are developed in consultation with public health experts and analysis of official reports and nationwide antibody tests. The real number of infections are estimated to be 15 (conservative scenario) to 26 (worse scenario) times higher, while the infection fatality rate is estimated to be between 0.15% (conservative scenario) and 0.60% (worse scenario).

The Covid-19 Shock and the Indian Economy …

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Table 6 Key health indicators and policy measure—India versus peer countries Location

Bangladesh Brazil China

Total confirmed cases per million people (as of April 25, 2021)

Total confirmed deaths per million people (as of April 25, 2021)

Total vaccination Stringency index doses administered (as of April 25, per 100 people in 2021) the population (as of April 25, 2021)

4525.6

67.1

5.0

83.3

67,467.2

1838.5

17.9

68.1

71.1

3.4

15.6

78.2

12,545.7

141.4

10.1

69.9

Indonesia

6000.2

163.0

6.8

69.0

Malaysia

12,140.6

44.4

4.0

61.6

Mexico

18,059.0

1667.1

12.7

44.4

India

Pakistan

3623.7

77.8

1.0

58.8

Philippines

9103.1

153.2

1.6

68.1

32,265.4

729.3

12.4

36.6

South Africa 26,563.9

Russia

913.0

0.5

48.2

Sri Lanka

4734.4

30.0

4.3

47.7

Thailand

794.6

2.0

1.7

56.5

54,897.1

454.8

25.1

83.3

29.2

0.4

0.2

22.2

Turkey Vietnam

Source Max Roser, Hannah Ritchie, Esteban Ortiz-Ospina, and Joe Hasell (2020). Coronavirus Pandemic (COVID-19), Our World in Data, University of Oxford. Retrieved from: https://ourwor ldindata.org/coronavirus

In the conservative scenario, the total confirmed cases per million are about 13 times larger than in the rest of the world and the total confirmed deaths per million are about 85% of that in the rest of the world. The worst-case scenario, however, puts India far behind the rest of the world. We note an important caveat here: while the focus of this article is on India, underreporting of COVID-19 cases and deaths is prevalent globally (see Institute for Health Metrics and Evaluation, University of Washington, 2021). However, it should also be noted that as much as the contraction in national income understates the economic impact of this crisis on the average person, a similar factor is at play in the case of public health as well. The focus on the health indicators such as total cases and total deaths masks the impact on those chronically ill and their inability to obtain necessary medication and lifesaving treatment—which is likely due to the impact of the pandemic and the disruption of normal life due to government restrictions. Reports suggest that mortality in India has substantially increased and pattern of excess deaths observed in the CMIE household survey data cannot be attributed to COVID-19 (Rukmini, 2021).

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M. Ghatak and R. Raghavan

Table 7 Summary of Key Health Indicators as of May 24, 2021 Official counts

Estimates of India inclusive of Underreporting

India

Rest of world

Conservative scenario Worse scenario

Total confirmed cases of COVID-19

26,948,874

140,375,091

404,233,110

700,670,724

Total confirmed cases of COVID-19 per million people

19,528

21,883

292,922

507,731

Total deaths attributed to COVID-19

307,231

3,278,511

606,350

Total deaths attributed to COVID-19 per million people

223

511

439

4,204,024 3,046

Total number of 194,165,711 1,510,218,685 – COVID-19 vaccination doses administered

–

Total number of 14 COVID-19 vaccination doses administered per 100 people in the total population

–

24

–

Source Official counts for health indicators and population data are obtained from: Max Roser, Hannah Ritchie, Esteban Ortiz-Ospina, and Joe Hasell (2020), Coronavirus Pandemic (COVID-19), Our World in Data, University of Oxford, Accessed on June 15, 2021. Estimates of underreporting are based on the scenarios developed by Gamio and Glanz (2021) who obtain the numbers in consultation with public health experts and through analysis of official reports and nationwide antibody tests Note The real number of infections are estimated to be 15 (conservative scenario) to 26 (worse scenario) times higher than the official count, while the infection fatality rate is estimated to be between 0.15% to 0.60% of the estimated infections. We note here that while we focus on India, underreporting of COVID-19 cases and deaths is prevalent globally.

5 Conclusion There is no doubt that the pandemic is unprecedented and that it will take time for the whole world to recover from it, in terms of public health as well as the economy. However, faced with a common shock, different countries have reacted with different degrees of responsiveness and effectiveness. India’s record with respect to other countries, including countries that are comparable in economic status, puts it in the lower tail of the distribution both in terms of various economic and public health indicators. The fallout on the poorer sections and informal sector was particularly severe and policies to mitigate this were grossly inadequate. Countries that have prioritized investment in public health infrastructure and measures appear to have also experienced a lower economic impact. There are

The Covid-19 Shock and the Indian Economy …

75

lessons to be learnt from this crisis all over the world, but improving the public health infrastructure and the social safety net in India seems to be of the highest priority.

Appendix 1: Country-Level Comparison of Economic Indicators In this appendix, we provide a detailed, country-level comparison of the economic indicators.

Gross Domestic Products In Table 8, we compare the growth rate of India’s GDP against that of developed countries. While several developed countries such as France, Italy, Spain, and United Table 8 GDP at constant prices (Percentage change)—India versus developed countries Location

2019 (%)

2020 (%)

2021 (%)

2022 (%)

Australia

1.9

−2.4

4.5

Canada

1.9

-5.4

5.0

France

1.5

−8.2

5.8

Germany

0.6

−4.9

3.6

3.4

India

4.0

−8.0

12.5

6.9

3.6

10.8

Italy

0.3

−8.9

4.2

3.6

−5.1

−1.7

Japan

0.3

−4.8

3.3

2.5

−1.7

0.7

Korea

2.0

−1.0

3.6

2.8

2.6

5.5

New Zealand

2.4

−3.0

4.0

3.2

0.9

4.2

Singapore

1.3

−5.4

5.2

3.2

−0.5

2.7

Spain

2.0

−11.0

6.4

4.7

−5.3

−0.8

Taiwan

3.0

3.1

4.7

3.0

8.0

11.3

United Kingdom

1.4

−9.9

5.3

5.1

−5.1

−0.3

United States

2.2

−3.5

6.4

3.5

2.7

6.3

World

2.8

−3.3

6.0

4.4

2.6

7.1

Advanced economies

1.6

−4.7

5.1

3.6

0.2

3.8

2.8

Compound (2020–2021) (%)

Compound (2020–2022) (%)

2.0

4.8

4.7

−0.6

4.0

4.2

−2.9

1.2

−1.5

1.9

Source International Monetary Fund, World Economic Outlook Database, April 2021

76

M. Ghatak and R. Raghavan

Table 9 GDP at constant prices (Percentage change)—India versus peer countries Location

2019 (%)

2020 (%)

2021 (%)

2022 (%)

Compound (2020–2021)

Compound (2020–2022)

Bangladesh

8.2

3.8

5.0

7.5

9.0

Brazil

1.4

−4.1

3.7

2.6

−0.6

17.2 2.0

China

5.8

2.3

8.4

5.6

10.9

17.1

India

4.0

−8.0

12.5

6.9

3.6

10.8

Indonesia

5.0

−2.1

4.3

5.8

2.1

8.1

Malaysia

4.3

−5.6

6.5

6.0

0.5

6.6 −0.8

−0.1

−8.2

5.0

3.0

−3.6

Pakistan

1.9

−0.4

1.5

4.0

1.1

5.1

Philippines

6.0

−9.5

6.9

6.5

−3.3

3.0

Mexico

Russia

2.0

−3.1

3.8

3.8

0.6

4.4

South Africa

0.2

−7.0

3.1

2.0

−4.1

−2.2

Sri Lanka

2.3

−3.6

4.0

4.1

0.3

4.4

Thailand

2.3

−6.1

2.6

5.6

−3.7

1.8

Turkey

0.9

1.8

6.0

3.5

7.9

11.7

Vietnam

7.0

2.9

6.5

7.2

9.6

17.5

World

2.8

−3.3

6.0

4.4

2.6

7.1

Emerging market and developing economies

3.6

−2.2

6.7

5.0

4.3

9.5

Source International Monetary Fund, World Economic Outlook Database, April 2021

Kingdom have faced greater declines than India in 2020, India performs worse than most countries, including Australia, Canada, Germany, Japan, Korea, New Zealand, Singapore, Taiwan, and United States. Table 10 Unemployment (Percent of total labor force)—India versus developed countries

Location

2019 (%)

2020 (%)

Australia

5.2

6.5

Canada

5.7

9.6

France

8.5

8.2

Germany

3.2

4.2

India

5.3

7.1

Italy

9.9

9.1

Japan

2.4

2.8

Korea

3.8

3.9 (continued)

The Covid-19 Shock and the Indian Economy … Table 10 (continued)

77

Location

2019 (%)

2020 (%)

New Zealand

4.1

4.6

Singapore

2.3

3.1

14.1

15.5

Spain Taiwan

3.7

3.9

United Kingdom

3.8

4.5

United States

3.7

8.1

World

5.4

6.5

Advanced economies

4.8

6.6

Source International Monetary Fund, World Economic Outlook Database, April 2021, Unemployment estimates for the World and India obtained fromWorld Bank

In Table 9, we present the comparison between the growth rate of India’s GDP against that of peer countries. Philippines and Mexico are the only peer countries to witness a larger income decline rate in India. While it is evident that most peer countries have suffered large economic losses due to COVID−19, in relative terms, India has suffered more. Table 11 Unemployment (Percent of total labor force)—India versus peer countries Location

2019 (%)

2020 (%)

Bangladesh

4.2

5.3

Brazil

11.9

13.2

China

3.6

3.8

India

5.3

7.1

Indonesia

5.3

7.1

Malaysia

3.3

4.5

Mexico

3.5

4.4

Pakistan

4.1

4.5

Philippines

5.1

10.4

Russia

4.6

5.8

South Africa

28.7

29.2

Sri Lanka

4.8

5.8

Thailand

1.0

2.0

Turkey

13.7

13.1

Vietnam

2.2

3.3

World

5.4

6.5

Emerging market and developing economies

n/a

n/a

Source International Monetary Fund, World Economic Outlook Database, April 2021, Unemployment estimates for the World, India, and Bangladesh obtained from World Bank

78 Table 12 Above-the-line Fiscal Policy Measures (Percent of GDP) in Response to COVID-19 Pandemic as of March 17, 2021—India versus Developed Countries

M. Ghatak and R. Raghavan Location

Health sector

Non-health sector

Total

Australia

0.8

15.3

16.1

Canada

2.4

12.3

14.6

France

0.8

6.8

7.6

Germany

1.2

9.8

11.0

India

0.4

3.0

3.3

Italy

0.6

7.9

8.5

Japan

1.8

14.1

15.9

Korea

0.5

4.0

4.5

New Zealand

1.2

18.1

19.3

Singapore

0.2

15.9

16.0

Spain

1.3

6.3

7.6

Taiwan

n/a

n/a

n/a

United Kingdom

7.5

8.7

16.2

United States

3.3

22.2

25.5

World

1.2

7.8

9.2

Source International Monetary Fund, Fiscal Monitor Database of Country Fiscal Measures in Response to the COVID-19 Pandemic, April 2021 Table 13 Above-the-line Fiscal Policy Measures (Percent of GDP) in Response to COVID-19 Pandemic as of March 17, 2021—India versus Peer Countries

Location

Health sector

Non-health sector

Total

Bangladesh

0.1

1.3

1.4

Brazil

1.2

7.6

8.8

China

0.1

4.7

4.8

India

0.4

3.0

3.3

Indonesia

1.8

2.7

4.5

Malaysia

0.1

4.3

4.5

Mexico

0.4

0.2

0.7

Pakistan

0.4

1.6

2.0

Philippines

0.4

2.3

2.7

Russia

0.7

3.6

4.3

South Africa

0.8

5.1

5.9

Sri Lanka

0.1

0.4

0.5

Thailand

n/a

n/a

8.2

Turkey

0.3

1.5

1.9

Vietnam

0.0

1.4

1.4

World

1.2

7.8

9.2

Source International Monetary Fund, Fiscal Monitor Database of Country Fiscal Measures in Response to the COVID-19 Pandemic, April 2021

The Covid-19 Shock and the Indian Economy …

79

Unemployment In Tables 10 and 11, we present the unemployment rate of India against developed and peer countries, respectively. Several countries have experienced greater unemployment rates as compared to India. However, it should be noted that India’s informal sector is typically not well accounted for in these measurements. According to CMIE estimates, the unemployment rate peaked to above 23% in the months of April and May 2020, nearly three times what it was in the months of January and February 2020, and came down to 10% in June 2020. Fiscal Policy Measures In Tables 12 and Table 13, we summarize the above-the-line fiscal policy measures (as a percentage of GDP) undertaken by the governments in response to the COVID19 pandemic since January 2020 for developed and peer countries, respectively (International Monetary Fund, 2021).

References Atlantic Council. (2021). How much money is the G20 spending? Retrieved from: https://www.atl anticcouncil.org/blogs/econographics/how-much-money-is-the-g20-spending/ Banaji, M. (2021). Estimating Covid-19 Fatalities in India. The India Forum. Retrieved from: https:// www.theindiaforum.in/article/estimating-covid-19-fatalities-india Centre for Sustainable Employment. 2020. COVID19 Livelihoods Phone Survey. Azim Premji University. Retrieved from: https://cse.azimpremjiuniversity.edu.in/cse-surveys/covid19-liveli hoods-phone-survey/ Estupinan, X., & Sharma, M. (2020). Job and Wage Losses in Informal Sector due to the COVID-19 Lockdown Measures in India. Working Paper. Retrieved from: https://doi.org/10.2139/ssrn.368 0379 Gamio, L. & Glanz, J. (2021). Just How Big Could India’s True Covid Toll Be? New York Times. Retrieved from: https://www.nytimes.com/interactive/2021/05/25/world/asia/india-covid-deathestimates.html Hale, T., Angrist, N., Goldszmidt, R., Kira, B., Petherick, A., Phillips, T., Webster, S., CameronBlake, E., Hallas, L., Majumdar, S., & Tatlow, H. 2021. A global panel database of pandemic policies (Oxford COVID-19 Government Response Tracker). Nature Human Behaviour, 5(4), 529–538. Retrieved from: https://doi.org/10.1038/s41562-021-01079-8 International Monetary Fund. (2021). Fiscal Monitor Database of Country Fiscal Measures in Response to the COVID-19 Pandemic. Retrieved from: https://www.imf.org/en/Topics/imf-andcovid19/Fiscal-Policies-Database-in-Response-to-COVID-19 Kochhar, R. (2021). In the Pandemic, India’s Middle class shrinks and poverty spreads while china sees smaller changes. Pew Research Center. Retrieved from: https://www.pewresearch.org/ fact-tank/2021/03/18/in-the-pandemic-indias-middle-class-shrinks-and-poverty-spreads-whilechina-sees-smaller-changes/. Menon, G. (2021). Covid-19 and Indian Exceptionalism. The India Forum. Retrieved from: https:// www.theindiaforum.in/article/covid-19-and-indian-exceptionalism. National Council of Applied Economic Research. (2021). ‘Round 4: Delhi NCR Coronavirus Telephone Survey’. NCAER. Retrieved from: https://www.ncaer.org/event_details.php?EID=310. Rukmini, S. (2021). Can New Mortality Data Explain India’s Low COVID Death Numbers? IndiaSpend. Retrieved from https://www.indiaspend.com/covid-19/mortality-data-kerala-mumbaitoo-soon-to-say-india-covid19-less-deadly-second-wave-737270

Data as Guide to Policy: Bills of Mortality of 17th Century and COVID-19 of 21st Century Anirban Banerjee, Manisha Chakrabarty, and Subhankar Mukherjee

Abstract The Bills of Mortality, started in 1592 in London, is possibly the first effort toward data collection related to epidemiological reasons. John Graunt, in his book “Natural and Political Observations Made Upon the Bills of Mortality” presented one of the first systematic analysis of this data, particularly during the time of Plague epidemic. In the present article, we draw a parallel to the usefulness of such historical data collection and analysis in light of the ongoing COVID-19 pandemic. Specifically, we investigate whether decline in testing can be one plausible reason behind drop in reported daily cases from September 2020 in India. We carry out this study for four states in the country, namely Kerala, Maharashtra, Uttar Pradesh, and West Bengal, employing a VAR model. We find that there is heterogeneity among the states in terms of the direction of causality between number of cases and number of tests. We emphasize the importance of conducting systematic quantitative research in formulating informed policies, especially during a time of crisis such as the COVID-19 pandemic. Keywords Covid-19 · VAR · Granger causality

Authors’ names are in alphabetical order A. Banerjee Indian Institute of Management Ahmedabad, Ahmedabad, India e-mail: [email protected] M. Chakrabarty (B) Indian Institute of Management Calcutta, Calcutta, India e-mail: [email protected] S. Mukherjee Department of Industrial and Management Engineering, Indian Institute of Technology Kanpur, Kanpur, India e-mail: [email protected] © The Author(s), under exclusive license to Springer Nature Singapore Pte Ltd. 2022 M. Dutta et al. (eds.), The Impact of COVID-19 on India and the Global Order, https://doi.org/10.1007/978-981-16-8472-2_4

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1 Introduction In this article, we analyze the curious case of rise and fall of COVID-19 cases in India. The first case of Coronavirus infection in the country was reported on January 30, 2020. Since the month of March, the country witnessed a very high growth rate in number of COVID cases; in the first week of March, the basic reproduction rate (R0 ) reached as high as 3.2 (Marimuthu et al., 2021). In response, to contain the spread of the disease, the Government of India announced a nation-wide lockdown for three weeks, starting 25th March, and later extended it for another seven weeks in three more phases. This measure did bring down the average infection spread rate to about 1.29. The number of infected cases continued to rise though, even if at a slower pace, and with heterogenous trends across states and UTs. Given poor public health infrastructure and high population density in India, it was conjectured that the country will see a very high number of infections in a short period of time. Further, it was estimated that 60 to 70% of the population will need to be infected by the virus before herd immunity sets in and transmission of the virus subsides.1 However, interestingly, India started witnessing a decline in COVID-19 cases, starting from September 2020, even when the country was nowhere near reaching herd immunity.2 All databases tracking the number of COVID cases (and also other indicators, such as ICU utilization rate) showed a steady decline in active cases from this time. What could be the underlying reasons behind this reversal of the trend? Some explanations have been provided in various reports.3 Comparatively younger average age of the population may have been a reason. India’s tropical climate (which is not too cold in most of the regions in the country) may be another reason. Higher compliance in wearing masks and continuous public awareness campaign via messages through mobile phone may be further responsible (Banerjee et al., 2020) in reversing the trend. We must note that the drop in infection rate cannot be ascribed to vaccination drive, since the decline started months before vaccination efforts started in the country. Moreover, what makes this decline even more intriguing is the drop in cases around the festival season in the country—a time when it was suspected that the spread of the virus would occur at a higher rate. There can be another reason behind the drop in COVID-19 cases. The number of infections reported is solely based on the number of tests conducted to identify the positive cases. Therefore, if there is a drop in number of testing conducted, the reported number of cases will be lower, even though the actual number of cases remains unchanged, or even growing. In this article, we investigate if this may have been a reason behind the noticed drop. While the efficacy of the testing kits has been 1

https://www.indiatoday.in/diu/story/how-long-would-india-take-to-develop-herd-immunity-cov id19-coronavirus-1711562-2020-08-15). 2 https://science.sciencemag.org/content/370/6516/513. 3 for example, see. https://www.npr.org/sections/goatsandsoda/2021/02/01/962821038/the-mystery-of-indias-plu mmeting-covid-19-cases

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83

discussed for plausible downward trend,4 to the best of our knowledge, no systematic study has been performed so far to investigate the causal relationship between testing rate and reported cases, if any. We conduct our study for four states in the country: Kerala, Maharashtra, Uttar Pradesh, and West Bengal. The motivation behind selecting these four states comes from the Vasudevan et al. (2020) study that builds a score based on transparency of data reporting on COVID-19 among Indian States and UTs. We select the states that are representative of the whole distribution of the scores. Kerala is among the better performing states, while Uttar Pradesh is the worst-performing state, according to this score. Maharashtra and West Bengal are situated around the middle of the distribution. We use a bivariate VAR involving the daily data on number of cases and number of tests conducted to carry out our analysis. Using Granger causality, we find mixed results across the set of states. While for Kerala, the number of tests keep increasing or remain constant even when the number of cases decline, for Uttar Pradesh and Maharashtra, we find evidence of lower reporting of cases due to a reduction in testing. For West Bengal, the results indicate a feedback effect, i.e., both tests and cases impact each other. We situate our article against the backdrop of the long tradition of formulating data driven policy in the wake of infectious diseases. John Graunt, a tradesman by profession5 from London, was a pioneer in advocating data-driven policies to contain the spread of contagious diseases. In sixteenth and seventeenth century England, he meticulously analyzed data collected under the Bills of Mortality to comment on the pattern of occurrence of the Plague epidemic in the city of London. We attempt a similar analysis, while employing modern econometric methods, and using larger dataset on the COVID-19 pandemic. The rest of the paper is organized as follows. In Sect. 2, we briefly highlight John Graunt’s contribution to data analysis. Section 3 contains description of data sources. Next, in Sects. 4 and 5 we describe the methodology adopted, and the results obtained, respectively. Finally, Sect. 6 concludes.

2 John Graunt’s Contribution in Data Analysis John Graunt’s contribution in demographic statistics and epidemiology is immense. His published work “Natural and Political Observations on the Bills of Mortality” using the data on christenings and burials, causes of death for all parishioners in the Church of England, recorded in the Bills of mortality, may be considered as the first statistical analysis of demographic data. Bills of mortality was started in the end of 4

https://www.business-standard.com/article/current-affairs/questionable-testing-may-be-the-rea son-india-s-covid-19-numbers-are-down-120112000319_1.html. 5 In fact, John Graunt is referred to as one of the pioneers of the disciplines of Demography and Epidemiology.

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sixteenth century to collect data on weekly basis with information on burials and, specifically, on deaths due to plague. The other information such as christenings, causes of death other than plague were first recorded in 1604. From this year, in addition to the weekly figures, consolidated figure for the years were also made available. Production of the bills went into decline from 1819, with the last surviving weekly bill dating from 1858.

2.1 Description of Graunt’s Observations In about 1660 John Graunt analyzed the complete collection of the Bills collected thus far. He was possibly the first person to pay attention to these accounts. In 1662, he published a book based on his analysis, titled, “Natural and Political Observations Made Upon the Bills of Mortality”. In Graunt (1662), Graunt himself mentioned in the preface: Having been born, and bred in the City of London, and having always observed, that most of them who constantly took in the weekly Bills of Mortality, made little other use of them…Now, I thought that the Wisdom of our City had certainly designed the laudable practice of taking, and distributing these Accompts, for other greater uses.

It was quite evident, that Graunt was aware of the importance of insightful interpretations of the data, even approximately 350 years ago. when I had reduced into Tables so as to have a view of the whole together, in order to the more ready comparing of one Year, Season, Parish, or other Division of the City, with another, in respect of all the Burials, and Christnings, and of all the Diseases, and Casualties happening in each of them respectively. (Graunt, 1662)

As mentioned by Sutherland (1963), such critical appraisal of the data and approach of analysis by Graunt are fundamental to any scientific enquiry. Graunt’s tabulation of the annual data on christening and burial was used as a basis of estimation of population size. His statements regarding the causes of death including the grand casualty years of plague still serves as the main source of information on mortality in the seventeenth century. Graunt’s understanding of the data and his way of drawing conclusions could be described as a method of descriptive statistics. He was also regarded as the first one to give an idea of life table: Whereas we have found, that of 100 quick Conceptions about 36 of them die before they be six years old, and that perhaps but one surviveth 76, we, having seven Decads between six and 76, we sought six mean proportional numbers between 64, the remainder, living at six years, and the one, which survives 76, and finde, that the numbers following are practically near enough to the truth. (Graunt, 1662)

According to Sutherland (1963), these observations could be very well explained by arithmetic approach of diminishing differences in the distribution of deaths

Data as Guide to Policy: Bills of Mortality of 17th Century …

85

across ages. This was commendable given the fact that in the Bills of mortality no information was recorded on age distribution of deaths and size of living population. Graunt’s observations on high mortality from the weekly data due to plague in the years of 1592, 1603, 1625 and 1636 throw lights on seasonality, comorbidity, and time dependence. Use of these concepts are critical to modern day data analysis, particularly in the context of epidemiology research. He, for example, wrote It may be further observed, that the time of the Plagues continuance at the height was of several durations, for Anno 1592 it continued from the first week in July to the second of September, without increasing or decreasing above 100 in 1600; whereas in 1603 it remain’d but three weeks at the state, decreasing near 1 /4 the next week after the height; Anno 1625 it remain’d not three weeks at a stay, increasing 1 /16 part the next week before the height, and decreasing as much the next week after. Anno 1636 it stood five weeks without increasing or decreasing above 1 /16 part afore-mentioned. (Graunt, 1676).

and The last thing I shall observe is, that in all the four great years of mortality above-mentioned, I do not find that any week the Plague increased to the double of the precedent week above five times. (Graunt, 1676)

These observations can be described broadly as an early attempt for a time series analysis using modern statistical concepts. These four years (1592, 1603, 1625 and 1636) are very peculiar in terms of plague epidemic as shown in Fig. 1. Graunt’s observations on plague death using weekly data is also evident in graphical reproduction of the Bills of Mortality data, as in Fig. 2. Specifically, his conclusion that

Fig. 1 Yearly data on total deaths and deaths due to plague

A. Banerjee et al. 1

86

.8 .6 .4 .2

Dec-1666

Dec-1636 June-1666

Dec-1630 June-1636

Dec-1625 June-1630

Dec-1603 June-1625

Dec-1592 Jun-1603

Jun-1592

0

Proportion of Plague Death

Weekly Data from June to December, Various years

Month and Year

Fig. 2 Weekly data on proportion of plague death

1603 witnessed the highest as well as a sustained surge in plague deaths is evident in this figure.6 Further, Graunt himself categorized the contributions from his observations in the context of policymaking: The Observations, which I happened to make (for I designed them not) upon the Bills of Mortality, have fallen out to be both Political, and Natural, some concerning Trade, and Government, others concerning the Air, Countries, Seasons, Fruitfulness, Health, Diseases, Longevity, and the proportions between the Sex, and Ages of Mankinde. (Graunt, 1662)

Hence, we can conclude that John Graunt was the first person to use a scientific approach of handling data and making observations which could be linked to many modern statistical concepts of analyzing data. His work on Bills of Mortality reminds us that epidemiology largely depends on the availability of rich data and extensive record which is relevant in today’s context also.

6

Graunt writes, in Chapter IV of his book, “We must therefore conclude the Year 1603 to have been the greatest Plague-Year of this age” (Graunt, 1662).

Data as Guide to Policy: Bills of Mortality of 17th Century …

87

2.2 Examining Bills of Mortality Using Today’s Statistical Techniques In today’s time we can infer much more information from the data collected in the Bills of Mortality. In this paper, we have attempted to do the following statistical analysis using the annual table used by Graunt and try to provide numerical representation of his observations. In order to substantiate Graunt’s observation on time evolution of plague and to identify seasonal patterns of its occurrence, if any, we estimate the following regression equation: Pr op PlagueDeath t = α +

December 

βi Month Dummyi + εt

(1)

i=J uly

We consider the seven months starting from June till December, since this is the common period available across the years in Graunt’s tabulated data (Graunt, 1676). The table below (Table 1) shows that the proportion of plague death reaches Table 1 Test for seasonality for bills of mortality data

Dependent variable: Proportion of plague death Variables

Coefficients

July_Dummy

0.156*

August_Dummy

0.296***

(0.0830) (0.0830) September_Dummy

0.338***

October_Dummy

0.337***

November_Dummy

0.167**

December_Dummy

−0.0487

(0.0813) (0.0830) (0.0836) (0.0888) Constant

0.318***

Observations

156

R-squared

0.263

(0.0692)

Notes Standard errors in parentheses. *** p < 0.01, ** p < 0.05, * p < 0.1 Authors’ calculation based on weekly data from Table 24 of Graunt (1676)

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its peak during the months of September and October. This reconfirms Graunt’s observations regarding cyclicality of plague deaths. The importance of such data analysis resonates in Graunt’s own writing: Moreover, finding some Truths, and not commonly believed Opinions, to arise from my Meditations upon these neglected Papers, I proceeded farther, to consider what benefit the knowledge of the same would bring to the World; that I might not engage myself in idle, and useless Speculations (Graunt, 1662)

We attempt to follow the footsteps of such analysis in the wake of the present pandemic. Specifically, we exploit the interrelationships between the daily incidence and number of tests conducted to examine usefulness of data in policy measures.

3 Data As mentioned earlier, the primary objective of this article is to investigate if the drop in numbers of daily new cases in India is in anyway influenced by the reduction in testing. We obtain daily number of cases as well as testing data from the covid19india.org website. The website is an open-source crowdfunding initiative that sources data from various state government bulletins and official handles,7 and stores data on various aspects of spread of the COVID-19 for all states and UTs of India. Our focus is on four states in the country, namely Kerala, Maharashtra, Uttar Pradesh, and West Bengal. The choice of these states is influenced by the quality of COVID-19 data reported by the different states, based on Vasudevan et al. (2020). The article ranks almost all Indian states and UTs based on their COVID-19 Data Reporting Standard, and assign a score for them (CDRS). We select the states to cover the entire spectrum of this score. Kerala (2nd) is one of the best performing states, while Uttar Pradesh (29th) ranks as the worst. Maharashtra (8th) and West Bengal (9th) lie in the middle of the distribution of the score. To address our research objective, we consider the data series post peak periods for all the states in terms of daily number of cases, till February 15, 2021. Table 2 provides summary statistics regarding the state-wise observations of COVID-19 daily testing and infected cases for these states for the said period. The table also shows the date on which each of the four states reached its respective peak. We must note that for our empirical analysis, we perform log transformation for all these variables.8

7

https://www.covid19india.org/about The figures for these two variables post peak date for each state have been presented in the Appendix (Figures A1 to A4).

8

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89

Table 2 Summary statistics Kerala

Maharashtra Uttar Pradesh West Bengal

10-Oct-20

11-Sep-20

11-Sep-20

22-Oct-20

Mean 55,474.24

65,724.48

143,074.40

35,900.75

Peak Date Daily No. of COVID-19 Tests

(SD)

(12,196.82) (18,496.62)

(20,831.17)

(8,953.16)

6,876.46

1,963.41

2,047.88

(1,464.33)

(5,775.19)

(1,636.03)

(1496.39)

129

158

158

117

Daily No. of COVID-19 Cases Mean 5,728.05 (SD) No. of Obs

4 Methodology We use VAR methodology to find out the bidirectional relationship between log of cases (y1t ) and log of testing (y2t ). We estimate the parameters of the VAR model through the following equation. 

y1t y2t





      L  l ∅10 ∅11 ∅l12 y1,t−l ε = + + 1t ∅20 ∅l21 ∅l22 y2,t−l ε2t

(2)

l=1

where yit represents the level variable, or the growth variable, based on stationarity of the underlying series.9 The choice of the estimation method for the system characterized by Eq. (2) depends on the order of integration of the variables. If the system is cointegrated CI(d, b) system, for which linear combinations of I(d) variables are I(0), there are four different representations of this system (Watson, 1994), one of which is the VECM (Vector Error Correction Model). However, where the variables are individually I(0), they can be estimated using the basic VAR model (Lutkepohl, 2004). We proceed with testing for stationarity of the two variables of interest, for each state. If both the series turn out to be stationary, we directly employ a bivariate VAR model using the level variables. In case only one of the two variables is found to be stationary, we employ the VAR model, but using the growth rate of the variables. Finally, if both variables are found to be non-stationary, we test for cointegration using Johansen’s Trace Test (Johansen, 1988). If the variables are not cointegrated, we again proceed to estimate a VAR model using growth rate of the variables. Otherwise, we would implement a VECM model for estimation. As discussed previously, our main motive is to investigate causal relation between the number of new cases and number of tests conducted, if any. We are particularly interested in testing the following hypotheses, separately for each state: H1: In this hypothesis, we test if daily testing Granger causes daily cases. For our empirical model, specified in Eq. (2), the null hypothesis is that there is no 9

We employ ADF unit root test to find out stationarity property of our variables (Dickey and Fuller, 1979; Fuller, 1976).

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Table 3 Test statistics for ADF and Johansen’s Trace Test

State

ADF test statistic

Johansen’s Trace Test Statistic

Cases

Test

Rank 0

Kerala

−2.6739*

−3.9582***

Maharashtra

−1.895

−2.666

Uttar Pradesh −0.415 West Bengal

−2.211

17.78ˆ

−3.796** −1.957

17.64ˆ

Note *** p < 0.01, ** p < 0.05, * p < 0.1. We employ appropriate ADF-τ test (trend or drift) based on visual inspection of the series (see Appendix for Figures). Lag order in the ADF tests were chosen based on AIC criteria implies that this is the rank, selected by Johansen’s trace test procedure

relation between these two variables, H01 : ∅l12 = 0. Rejection of the null hypothesis (H11 : ∅l12 = 0) implies that number of daily tests Granger causes number of new cases. H2: This hypothesis tests the causality from opposite direction. Null hypothesis can be written as H02 : ∅l21 = 0. The alternate hypothesis, (H12 : ∅l21 = 0) in this case, implies that number of cases acts as a leading indicator for number of tests.

5 Results We start with a test for stationarity for the two variables of interest for each state separately. Our analysis suggests that both the cases and tests are stationary in level for the state of Kerala. Therefore, we use a VAR model using the level variable for this state. For Uttar Pradesh, while the number of tests is stationary, the number of cases turned out to be non-stationary. Therefore, we employ a VAR model for this state as well. However, in this case, we use the growth rate of the two variables. For the other two states, i.e., Maharashtra and West Bengal, both cases as well as tests are non-stationary (see Table 3). Therefore, we employ Johansen Cointegration test for these two states. Table 3 shows that the eigenvalue test statistic rejects the hypothesis of cointegration for both the states. Hence, we estimate a VAR model using the growth rate of the variables for these two states. In the rest of this section, we elaborate on the results for each state separately. We start with the state of Kerala. The ACF plot for both cases as well as tests suggests very strong correlation for every seventh lag.10 On further investigation, we found a negative shock on every Monday. We, therefore, include an additional exogenous dummy variable to isolate the effect of this shock in our VAR model. Our results based on the estimation of this VAR model (as shown in Eq. 2) suggest that the 10

Not reported in paper. Results available on request.

Data as Guide to Policy: Bills of Mortality of 17th Century …

91

relationship is unidirectional. Specifically, the number of daily cases Granger causes number of daily tests, but not vice versa. Column 1 in Table 4 shows only the first and sixth lags of number of cases significantly affect number of tests. Given that we consider the declining phase of cases, this result implies that a reduction in cases leads to a decrease in tests with a lag of six days. We also notice a slight upward movement of tests for this state. This fact is supported by the negative coefficient for the first lag of the VAR model. This implies that even when cases are decreasing, tests continue to increase. This result can also be verified in Figure A1 in the appendix. In summary, for Kerala, we are not able to reject the null hypothesis for H1 (i.e., ∅l12 = 0), but we are able to reject the null for H2 (i.e., ∅l21 = 0). Next, we turn our attention to the state of Maharashtra. For this state, both the series for daily cases and tests are non-stationary. We, therefore, conduct Johansen’s cointegration test. As shown in Table 3, the Trace statistic suggests acceptance of zero rank (at 5% level) implying no cointegration between these two variables. Hence, we use the growth rate of the variables and estimate the VAR model shown in Eq. 2. The results are shown in column 2 of Table 4. In this case, unlike Kerala, growth in tests Granger causes growth in cases reported in the state, but not the other way round. In other words, for this state, we are able to reject the null hypothesis for H1 (i.e., ∅l12 = 0), but we are not able to reject the null for H2 (i.e., ∅l21 = 0). Our analysis indicates that the growth in tests affects the growth in cases in lags 1 through 4. Since we are examining the declining phase of the cases, this result implies that the drop in cases is mainly driven by the drop in tests. Therefore, for Maharashtra, our main conclusion is that the reduction in cases may have been driven by a reduction in testing. We must also note that we witness a weekly pattern moving in the same direction, for both cases as well as tests data series for the state, as evidenced from the coefficients of the 7th lag of the estimated VAR model. The third state in our list is Uttar Pradesh. For this state, based on ADF test, we find that the time series for cases is non-stationary. However, the same for tests series is stationary. Therefore, we employ a VAR model for our estimation, considering growth rate of both variables. Like Maharashtra, for Uttar Pradesh also, number of tests Granger causes number of cases, and not the other way round. We find significant negative association between the number of tests variable and its own lags, which indicate an oscillating nature of the tests series. Further, we see a negative relationship between cases and tests beyond lag four, which implies that an increase in tests four days back leads to a decrease in cases. This results in a reduction in testing due to plausible policy complacency. Reduction in testing further decreases the number of cases with immediate effect. This is evident from the positive coefficients (of ∅l12 ) in lags 1 and 2. The last state considered in our analysis is the state of West Bengal. In this case, we find that both tests as well as cases are non-stationary. Further, Johansen’s test suggests no cointegration. We therefore use growth rate of these variables for our analysis and estimate the VAR model. Results from Granger causality indicate a feedback effect, i.e., both tests and cases impact each other (∅l12 = 0 and ∅l21 = 0). Specifically, given that we are considering only the declining phase of cases, a fall in growth rate of cases leads to a fall in growth rate of tests in lags 1, 2, and 4.

Equation for Variable 1

Variable 1 Variable 2

Table 4 Results from VAR model

Lag

L1

Variable2

L7

L6

L5

L4

L3

L2

Variable 1 L1

orders#

(−5.47) −0.39*** (−3.20)

(−5.00) −0.59***

(0.25)

0.43*** (4.56)

(−0.72)

(1.01)

(−0.43) −0.08

(−0.66) 0.12

(4.27) −0.05

(1.96)

0.31**

(−0.31)

−0.03

(0.84)

0.11

(−0.96)

−0.15

(3.17)

0.41***

(1.02)

(−2.62)

(−0.22)

(1.73)

0.22*

(−0.88)

−0.12

(−0.87)

−0.11

(−0.78)

−0.10

(continued)

diff(ln(Tests))

diff(ln(Cases))

0.13

−0.41***

−0.02

(−1.11)

(4) West Bengal

−0.10

(−4.21)

(−0.48)

0.46***

(−4.41) −0.64***

(1.31) −0.05

−0.13

−0.61***

−0.58***

0.03 0.15

(−10.20)

−0.88***

diff(ln(Tests))

diff(ln(Cases))

(−9.69)

ln(Tests) −0.82***

diff(ln(Tests))

ln(Cases)

(3) UttarPradesh

(3.39)

diff(ln(Cases))

Kerala

0.36***

(2) Maharashtra

(1)

92 A. Banerjee et al.

Equation for Variable2

Variable 1 Variable 2

Table 4 (continued)

L1

Variable1

Const

Exogeneous (Monday Dummy)

L7

L6

L5

L4

L3

L2 (1.66)

−0.14 (−1.27)

0.03 (0.47)

(−2.24)

(−2.69)

(2.21) −0.25**

−0.05***

2.99**

(−8.38)

−0.51***

(2.40)

(−0.72)

(−1.94) (0.97)

−0.09

−0.23* 0.27**

(0.80)

0.12

0.11

(0.10)

(1.97)

(1.22) 0.01

0.26**

0.14

(2.94)

0.18*

ln(Tests)

(0.10)

diff(ln(Tests))

ln(Cases)

0.36***

diff(ln(Cases))

Kerala

0.01

(2) Maharashtra

(1)

(3)

(4)

(−0.19)

−0.01

(−4.71)

−0.08***

(2.36)

0.25**

(−2.01)

(continued)

−0.02**

(0.87)

0.13

(−2.65)

−0.47***

(−2.18)

−0.41**

−0.35** (−2.17)

(−2.27)

(−3.13)

(−1.09) −0.41**

−0.53***

−0.21

(−0.76)

−0.14

diff(ln(Tests))

diff(ln(Cases))

West Bengal

(0.50)

0.09

(2.16)

0.35**

diff(ln(Tests))

diff(ln(Cases))

UttarPradesh

Data as Guide to Policy: Bills of Mortality of 17th Century … 93

Variable 1 Variable 2

Table 4 (continued)

−0.15 (−1.74)

−0.23 (−2.20)**

−0.05 (−0.39)

L3

(−2.53) −0.08 (−6.68) (−0.94)

−0.35 (−3.69)***

L2

0.07 (0.62)

0.41 (3.73)***

−0.21**

(2.70)

(0.99)

(−0.11)

−0.01

(−1.27)

−0.08

−0.36 (−4.51)***

0.28***

0.11

(1.04)

(−0.07)

(−0.43) (2.94)

−0.01

−0.05 0.14

(−0.20)

(−1.40)

0.33***

(0.23) −0.03

(0.11) −0.16

(continued)

−0.54 (−3.29)***

−0.79 (−4.99)***

−0.87***

(0.98)

0.11

(0.15)

0.02

(0.89)

0.10

(3.28)

0.35***

(−0.15)

(−1.32)

0.25** −0.02

−0.05

diff(ln(Tests))

diff(ln(Cases))

−0.08

(−0.82)

diff(ln(Tests))

diff(ln(Cases))

0.03

(0.71)

−0.09 0.01

0.07

ln(Tests)

(4) West Bengal

(2.27)

diff(ln(Tests))

ln(Cases)

(3) UttarPradesh

(−0.86)

diff(ln(Cases))

Kerala

L1

Variable 2

L7

L6

L5

L4

L3

L2

(2) Maharashtra

(1)

94 A. Banerjee et al.

24.20*** 9.13

Variable 1 Granger causes Variable 2 (χ2 )

Variable 2 Granger causes Variable 1 (χ2 )

35.83***

9.25

18.27

104.01

0.00 −0.21

25.74***

3.07

5.38

195.56

−0.01 −0.87

diff(ln(Cases))

Notes: Variables 1 & 2 for different states have been chosen based on their stationarity property. t−statistics in parentheses. *** p < 0.01, ** p < 0.05, * p < 0.1. #VAR Orders have been selected based on AIC information criteria and also the white-noise property of the error term.

242.45

4.02 (2.88)***

Const

5.24

(−0.45) (−7.12)***

Exogeneous (Monday Dummy)

LM Test for error autocorrelations

0.06 (0.47)

L7

0.23 (2.44)**

−0.38 (−3.45)***

−0.07 (−0.57)

L6

−0.21 (−2.45)**

−0.21 (−1.86)*

0.06 (0.50)

L5

−0.26 (−2.94)***

−0.27 (−2.45)**

0.24 (1.95)*

diff(ln(Tests))

ln(Tests)

L4

diff(ln(Tests))

diff(ln(Cases))

ln(Cases)

(3)

Kerala

UttarPradesh

(2) Maharashtra

(1)

Log likelihood

Variable 1 Variable 2

Table 4 (continued) (4)

19.86***

22.95***

5.24

296.31

0.00 −0.11

0.22 (1.65)*

−0.56 (−3.63)***

−0.67 (−4.13)***

−0.86 (−5.62)***

diff(ln(Tests))

diff(ln(Cases))

West Bengal

Data as Guide to Policy: Bills of Mortality of 17th Century … 95

96

A. Banerjee et al.

However, due to lower number of testing, the information about spread is reduced, possibly leading to the case number rising once again, as we observe in the negative significant coefficients in lags 4 through 6. This rise once again possibly leads to an increase in testing, which again is followed by a reduction in cases. Even though this policy does not indicate a fall in number of COVID-19 cases due to fall in number of tests conducted, this policy is at best a reactive policy, rather than a proactive policy.

6 Conclusions The primary aim of the article was to assess the usefulness of data in guiding policy for containment of an epidemic like COVID-19, in the context of India. In this endeavor, we have drawn a parallel with data analysis performed by John Graunt in seventeenth century England. We specifically investigate whether the decline in number of cases of COVID-19 infection reported on a daily basis in the country were due to a preceding decline in testing or not. Huge population as well as population density in India, its high rate of poverty and lack of public health infrastructure makes this question relevant. We employ a VAR model to assess the causal direction between testing and cases in four large states in the country: Kerala, Maharashtra, Uttar Pradesh, and West Bengal. As stated earlier, we selected these states based on the CDRS (COVID-19 Data Reporting Score). We see heterogeneity in policy decisions across the sampled states. For Maharashtra and Uttar Pradesh, we notice that the decline in reported cases may well be due to a reduction in testing. However, we do not find such compelling evidence for the other two states. For Kerala, we see the number of cases is affecting the number of tests in the opposite direction, and not the other way round. This is possibly due to a continued emphasis on testing even during the declining phase of cases in the state. Finally, for West Bengal, we notice bidirectional causality between cases and test, i.e., cases affecting tests and tests affecting cases. The analysis demonstrates that collecting proper data and analyzing it can guide to better policy making for containing the spread of the COVID-19 disease. Modernday data collection at higher frequency, unlike what Graunt had access to, makes such policymaking easier. However, lack of availability of data is still a persistent problem, especially in developing countries. For example, lack of availability of overall mortality data constrained us from performing analysis similar to what we could execute using the tests and cases data. Collecting and reporting such data, like what was available in the Bills of Mortality four hundred years ago, can be more informative in designing appropriate policies even in current times.

Appendix See Figs. A1, A2, A3, A4.

Data as Guide to Policy: Bills of Mortality of 17th Century … log(Daily New COVID-19 Tests in Kerala)

10

8

10.5

8.5

y3

y1

11

9

9.5

11.5

log(Daily New COVID-19 Cases in Kerala)

97

10/1/2020

11/1/2020

12/1/2020

1/1/2021

2/1/2021

10/1/2020

11/1/2020

12/1/2020

Date

1/1/2021

2/1/2021

Date

Fig. A1 Plot of daily new COVID-19 cases and daily COVID-19 tests in the state of Kerala log(Daily New COVID-19 Tests in Maharashtra)

11.5

y3

6

10.5

7

11

8

y1

9

12

10

12.5

log(Daily New COVID-19 Cases in Maharashtra)

9/1/2020

10/1/2020

11/1/2020

12/1/2020

1/1/2021

9/1/2020

2/1/2021

10/1/2020

11/1/2020

12/1/2020

1/1/2021

2/1/2021

Date

Date

Fig. A2 Plot of daily new COVID-19 cases and daily COVID-19 tests in the state of Maharashtra

log(Daily New COVID-19 Tests in Uttar Pradesh)

4

11.2

5

11.4

6

11.6

y3

y1

7

11.8

8

12

9

12.2

log(Daily New COVID-19 Cases in Uttar Pradesh)

9/1/2020

10/1/2020

11/1/2020

12/1/2020

Date

1/1/2021

2/1/2021

9/1/2020

10/1/2020

11/1/2020

12/1/2020

1/1/2021

2/1/2021

Date

Fig. A3 Plot of daily new COVID-19 cases and daily COVID-19 tests in the state of Uttar Pradesh

98

A. Banerjee et al. log(Daily New COVID-19 Tests in West Bengal)

y3

10.2

7 5

9.8

10

6

y1

10.4

8

10.6

9

10.8

log(Daily New COVID-19 Cases in West Bengal)

11/1/2020

12/1/2020

1/1/2021

Date

2/1/2021

3/1/2021

11/1/2020

12/1/2020

1/1/2021

2/1/2021

3/1/2021

Date

Fig. A4 Plot of daily new COVID-19 cases and daily COVID-19 tests in the state of West Bengal

References Banerjee, A., Alsan, M., Breza, E., Chandrasekhar, A. G., Chowdhury, A., Duflo, E., GoldsmithPinkham P. & Olken, B. A. (2020). Messages on covid-19 prevention in india increased symptoms reporting and adherence to preventive behaviors among 25 million recipients with similar effects on non-recipient members of their communities (No. w27496). National Bureau of Economic Research. Dickey, D. A., & Fuller, W. A. (1979). Distribution of the Estimators for Autoregressive Time Series with a Unit Root. Journal of the American Statistical Association, 74, 427–431. Fuller, W. A. (1976). Introduction to Statistical Time Series. New York: John Wiley and Sons. ISBN 0-471-28715-6. Graunt J. (1662). Natural and Political Observations Made Upon the Bills of Mortality. Graunt J. (1676).Natural and Political Observations Made Upon the Bills of Mortality. Johansen, S. (1988). Statistical Analysis of Cointegration Vectors. Journal of Economic Dynamics and Control, 12(2–3), 231–254. Lutkepohl, H. (2004). Vector autoregressive and vector error correction models, In: Lutkepohl, H., Kratzig, M. (eds.) Applied Time Series Econometrics, Cambridge: Cambridge University Press Marimuthu, S., Joy, M., Malavika, B., Nadaraj, A., Asirvatham, E. S., & Jeyaseelan, L. (2021). Modelling of reproduction number for COVID-19 in India and high incidence states. Clinical Epidemiology and Global Health, 9, 57–61. Sutherland, I. (1963). John Graunt: A tercentenary tribute. Journal of the Royal Statistical Society: Series A (general), 126(4), 537–556. Vasudevan, V., Gnanasekaran, A., Sankar, V., Vasudevan, S. A., & Zou, J. (2020). Disparity in the quality of COVID-19 data reporting across India. medRxiv. Watson, M. W. (1994). Vector autoregressions and cointegration. Handbook of Econometrics, 4, 2843–2915.

Creation of Vulnerabilities

Regional Disparity, Migration and Pandemic: Issues of Labour Market Integration and Future of Cities Amitabh Kundu and Yogesh Kumar

Abstract The paper overviews the trend and pattern of migration to understand the implications of concentration of migrants in specific urban centres in accelerating the spread of any epidemic or pandemic. The paper analyses the impact of COVID pandemic on the society and economy in different stages in India and plight of the migrants who struggled to cope with the situation paying heavy costs in terms of loss of human life, of property, along with intense physical and mental agony. It also highlights the challenges the migrants and urban poor faced in accessing basic amenities and benefits of the government schemes, bringing out their inadequacies. An attempt has also been made to assess the sufferings of the returnee migrants through primary data, generated by civil society organisations in different states in India with regard to loss of employment, housing and access to basic amenities and other entitlements during the periods of lockdown. The primary data also helps to assess their access to health and education at local level and to understand the attitude of the villagers toward them in the context of their inclusion in rural society. Sudden increase in workforce with different skill-sets in rural areas with the arrival of the returnees has been analysed in relation with the absorption capacity of the village economy. The distress faced by the migrants at their native places, which pushed them back to their work sites in urban and industrial centres, is probed in with scattered evidence from different regions in central India. The factors behind their sluggish and hesitant return to cities also help to understand the structural rigidities and institutional challenges, that the interstate migrants are facing currently. The paper makes a case for making the cities more welcoming toward the migrants and puts forward a perspective of inclusive growth, wherein decent working and living environment, along with fair compensation for the workers, can be guaranteed. A. Kundu (B) World Resources Institute, WRI India, LGF AADI 2, Balbir Saxena Marg, Hauz Khas, New Delhi, India e-mail: [email protected] Y. Kumar Samarthan-Centre for Development Support, 36, Green Avenue, Chuna Bhatti, Bhopal, Madhya Pradesh, India e-mail: [email protected] © The Author(s), under exclusive license to Springer Nature Singapore Pte Ltd. 2022 M. Dutta et al. (eds.), The Impact of COVID-19 on India and the Global Order, https://doi.org/10.1007/978-981-16-8472-2_5

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Keywords Migration · Labour market · COVID-19 · Urbanisation · India

1 Introduction Concerns have been raised with regard to the low level of population mobility in India affecting the process of balanced and sustainable development across regions. The percentage of internal migrants in the population has been very low historically, attributed generally to factors like social and linguistic diversity, low levels of skill development and poor access to information among prospective migrants, costs associated with shifting, etc. Researchers analysing the national level data sources such as Population Census and National Sample Surveys (NSS) have generally expressed concern over this, as also a declining trend in migration. Low share of women in rural–urban and inter-state migration; their low and falling work participation rate have also been posited as an issue requiring policy intervention. Declining share of vulnerable sections of population, such as Muslims and SC/ST, in rural urban migration stream, suggesting that they are not able to benefit by availing economic opportunities in urban areas through migration, is also an important empirical issue. Given the slower growth of agriculture in the national economy and high pressure on land and water resources, the urgency of taking workers out of agriculture to manufacturing and service activities is well recognised by policy planners. The regional variation in population growth due to natural factors too has opened up the possibility of massive north–south migration. However, the COVID-19 pandemic, impacting differently across rural and urban areas and regions, has devastated the economy, distorted the labour market, and changed the trend and pattern of migration. It has raised questions regarding the thesis of labour market integration in the country. With a large number of migrants returning back home during the pandemic, there is greater pressure on the agriculture sector and rural employment opportunities to absorb the workforce that was earning primarily from non-rural activities. A segment of these returnees have decided to stay back in villages making their small piece of land productive and economically viable. As a result, the possibilities of shifting workforce from inferior to superior crops, from low productive to high productive activities within the agrarian system, through production diversification are new policy priorities. Furthermore, social tensions relating to interstate migrants that were coming up in many of the in-migrating states like Kerala, Tamil Nadu, and Gujarat are being reassessed in the context of health and safety. It would be important to explore if it is indeed possible to bring down the need for migration through such diversification measures leading to productive engagement of the workers that are being rendered surplus in traditional farming activities, within the regional economy. Such shifts will have significant implications in reducing the impacts of COVID-19 and similar pandemics in future. Finally, new concerns pertaining to COVID-19 safe housing and workplace environment and adequate compensations for decent living for the migrants are gaining currency.

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Information available from 2001 Census appears to indicate that the decline in the overall migration rate had been arrested. The 2011 Census, however, showed a significant acceleration in the rate of migration, particularly for the women, though one has yet to analyse the factors responsible for this departure from the past trend with empirical rigour. Also, one would like to know if the poor and socially disadvantaged population can partake in the process and benefit from increased mobility. Lockdown in COVID-19 and large scale movement of migrants have further brought the issue on migration at the centre stage of development dynamics in the country. Given the above macro context, the present paper begins by overviewing the trend and pattern of migration at state level since the 1990s, focusing on those coming from other countries and various Indian states and analysing their socio-economic correlates. This helps to understand the implications of concentration of migrants in specific urban centres in accelerating the spread of any epidemic or pandemic. This is done in the second section which follows the present Introductory section. The third section discusses how the country got impacted by the COVID-19 pandemic in different stages and in different periods, and how the migrants struggled to cope with the situation and many among them managed to travel back to their home-towns. Scattered evidences from official records on movement of trains, buses, and other vehicles and state level registers concerning the facilities provided to the returnee migrants at the advent of the pandemics have been put together to present a macro picture on the loss of human life and property, along with physical and mental agony. The next section analyses the measures launched by the government to deal with the emergent health situation and economic crisis, resulting from full and partial lock-downs and their inadequacies. It also highlights the problems the migrants and urban poor had in accessing basic amenities and the benefits of the programmatic interventions by the centre and the state at the outbreak of the pandemic. The fifth section discusses the sufferings of the migrants who returned back to their villages due to their loss of employment, housing and access to basic amenities, and how they coped with their local situations—in particular the lockdown restrictions, village sentiments, access to health, employment and basic amenities. The absorption capacities of the rural economy with new set of migrant workforce has also been explored here. The sixth section analyses the distress faced by the migrants when they moved back to their work sites in urban and industrial centres. The factors behind their sluggish and hesitant return are also analysed here. The last section summarises the major conclusions while putting forward a perspective for inclusive growth of cities, wherein decent working and living environment along with fair compensation for the workers can be guaranteed.

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2 Trends and Pattern of Migration: Implications in COVID Scenario Data from Population Census indicate a falling trend of migration until 1991, as noted above. The percentage of both male and female migrants (persons whose place of enumeration is different from their last usual place of residence where they stayed continuously for at least six months) in the population can be noted to have gone down by 3.4 and 3.6 points respectively during 1981–1991. The subsequent decades, however, saw a marginal increase in the share of migrants. The 2011 data indicate that the rate of migration has gone up significantly for both, the increase in case of females being particularly significant (Table 1). The post 1990 increase in migration is confirmed by the NSS as well, particularly in urban areas (Table 2). The number of migrants by place of last residence in the country was 314.5 million as per 2001 Census. The figure has gone up to 453.6 million in 2011, showing an addition of 139 million. This is against the figure of 82 million migrants only added during 1991– 2001. This implies that the decadal growth rate of migration has gone up from 35.5 percent during 1991–1901 to 44.2 percent in 2001–2011. The number coming out of the Census data would, thus, indicate that an average of about 14 million migrated every year during 2001–2011 against the figure of 8 million in the previous decade. This increasing trend and volume indicate the magnitude of risk of transmission of COVID-19 and of having a number of mutated variants. Traditionally interstate migrants have been only a small percentage of the total migrants in India. As per the 2001 Census data, the interstate migrants constitute only 13 percent of the total migrants in the country, the figure being 25 percent among the migrants in urban areas. The National Sample Survey for the year 2007–2008 shows that the interstate migrants were 11.5 percent of the total migrants, up marginally from 10.3 percent in 1999–2000. At the aggregative level, these migrants constituted 4.19% of the total population in 2011 Census, the figure going up over the years from 3.26% in 1991 and 3.85 in 2001. The trend of returning migrants after the lockdown is a departure from the pattern of inter-state migration in the country. The movement is from the big industrial cities like Mumbai, Surat, Delhi, etc. Understandably, the states at a low level of economic and industrial development recorded high rate of out migration over the past few decades, their net migration rate being negative. Sikkim, along with a few other states in North-East, shows high rate of net outmigration that can be attributed to sociopolitical factors in the region, besides its economic backwardness. The large states showing high rates of outmigration are Bihar, Uttar Pradesh, Himachal Pradesh, and Kerala, followed by Tripura, Rajasthan, and surprisingly Punjab and Tamil Nadu. The developed states that record in-migration are Maharashtra, Gujarat, Karnataka, Haryana, and Goa along with Madhya Pradesh (MP). Punjab and Tamil Nadu, on the one hand, and MP, on the other, emerge as interesting cases. Punjab is an outmigrating state as the people there could avail opportunities that have been coming up in different parts of the country. The same explanation holds for the outmigration character of Tamil Nadu besides the caste politics that led to exodus of upper

28.9

38.8

Urban

35.3

12.6

18.2

42.8

45.9

45.2 32.3

26.1

27.7 27.6

10.2

14.8

Men

37.5

43.0

41.6

Women

Source Computed from various decadal census tables on migration Note 1. The Migration figures for 1981 exclude Assam and the 1991 figures exclude J&K

31.2

Person

Rural

1991

Women

Person

Men

1981

Total

Place of residence

Table 1 Percentage of migrants in the population as per decennial censuses 2001

36.4

28.3

30.6

Person

32.9

11.5

17.5

Men

40.3

46.1

44.6

Women

2011

48.4

32.5

37.5

Person

42.6

13.5

22.6

Men

54.6

52.6

53.2

Women

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Table 2 Percentage of migrants in different NSS rounds Round (year)

Rural Men

Urban Women

Men

Women

64 (2007–2008)

5.4 (6.5)

47.7 (68.6)

25.9 (31.4)

45.6 (57.9)

55 (July 1999–June 2000)

6.9 (9.0)

42.6 (64.5)

25.7 (32.0)

41.8 (55.4)

49 (January–June 1993)

6.5

40.1

23.9

38.2

43 (July 1987–June 1988)

7.4

39.8

26.8

39.6

38 (January–December 1983)

7.2

35.1

27.0

36.6

Note The figures in brackets are for population in the 15–59 age group Source Various NSS reports Note Percentages are based on all migrants including those from outside the country

caste people. MP until recently was in-migrating as it did not have even semiskilled manpower to sustain its process of industrial growth. This, however, has declined in the recent decades understandably since the local population, with education and skill development, have started getting absorbed in the emerging sectors. Correspondingly, the outmigration rate in Tamil Nadu declined over time while Punjab turned into an in-migrating states in 2001. It is clear that inter-state migration is an emerging reality. However, information on district to district inmigration and outmigration are unfortunately not available for designing location specific interventions for organising movement of migrants and creating alternate job opportunities. While the state governments, both in the receiving and destination states, are responding in an ad-hoc manner, there is evidence of no or very little preparedness to manage the influx of migrants. There are about 85 million long term migrants and about 55 million short term migrants (Persons who moved to a place for a period of at least three months but less than a year) moving from rural areas to urban locations for work. The shortterm circular migrants move to the cities to mitigate their economic distress through wage employment. They are the most vulnerable persons in the cities since they are often not covered under any social welfare schemes at their native place or at the destination, due to their periodic mobility. Short term migrants were more vulnerable at the beginning of the lockdown in April, 2020, and their movement on the roads was visible. Most of these migrants were daily wage earners with meagre savings to survive with their families. They, therefore, were the first one to leave the cities in spite of all odds. The numbers swelled toward the middle of May when many among the long duration migrants had lost their jobs and started returning back as they had no salaries to pay their house rent or buy provisions for their subsistence survival in their villages or small towns. It is interesting to look at the pattern of interstate an international migrants and that of incidence of Corona cases. The six urbanized states of India namely Maharashtra, Gujarat, Delhi, Tamil Nadu, Karnataka, and West Bengal, account for over 60% interstate migrants, recorded 70% of the total confirmed cases. These were at the top even when we consider the daily cases during the first two months of the

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second phase of the pandemic. Besides their population size, these states having large megalopolises, industrial hubs, wholesale markets, etc. would explain the pattern. In each of these states, 25 to 40% of urban population reside in their capital agglomerations. A significant part of these cities was declared as hot spot zones, necessitating a containment strategy. Social distancing has no meaning for the poor as 40 to 50 percent of the households in these mega cities live in one room units, against the national urban figure of 30% only. People in slums in large cities also have to use common drinking water, sanitation facilities, and other public utilities. Furthermore, urban West Bengal along with Tamil Nadu have percentages of households not having drinking water and toilets within premises—10% points higher than the national urban average. Bangalore along with Mumbai corporations record about 5% of households with at least one couple living without an exclusive room against the national figure of 2% only. Importantly, these primate cities claim 30 to 40% of the total urban population in their states. Along with their peripheries, these account for much of the concentration of the deprived population living in vulnerable conditions as well as of the short duration migrants. Such basic facilities and concentration of population have serious implications for the spread of the virus. The largest slum in the world known as Dharavi slums in Mumbai had the outbreak of COVID in early days of spread of the virus in spite of the lockdown. If figures for the cities of Mumbai, Bangalore, Chennai, Ahmedabad, etc. along with their peripheries are taken out, the state level figures would not be very alarming till the second month of the second wave of the pandemic. These nonetheless became high because of the spread of the virus from these cities to the interconnected areas in the state. In the first wave of the COVID spread, the states like Assam, Bihar, Jharkhand, Uttar Pradesh, and Madhya Pradesh recorded low rates of positive cases and deaths, compared to the national average as they contain not many globally linked cities. The picture, however, changes significantly, when we consider confirmed cases and deaths per thousand population. High figures are reported by states and UTs of Goa, Delhi, Maharashtra, Chandigarh, Karnataka, and Puducherry where a few of the global cities of India are located.

3 Impact of the Pandemic on Migrant’s Socio-Economic Conditions, Their Return to Home Towns COVID-19 made its appearance in India in early January 2020 and since then has hit the country in several waves, their impact being differentiated in space. It is a matter of some satisfaction that, despite the rise in the number of cases infected and of deaths during March April 2021 being much sharper and more spatially dispersed, the infections per million people are low by global average and that about 50% of the districts and 70% of the Talukas (sub-division of district) have recorded less than 3 percent of their population testing positive. Also, the number of talukas and villages that can be classified as highly deprived on socio-economic grounds are far

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higher than those reporting sizable positive cases and the overlap between these two sets is not very high. This nonetheless should not give a false sense of complacency about the pandemic, since deprivation is not contagious but corona is devastatingly so. The second wave that became ominous by the end of March, 2021, reached to the small towns and villages that had limited health infrastructure, professional and administrative capacity to handle the situation. Apprehending massive wave hitting the whole country, Indian Prime Minister announced one of the most stringent lock-downs in the world in March, 2020. Sideeffects of the lockdown were problematic as the resultant slowdown of production and consumption had disastrous consequences for the economy. It had been already on the slow lane—the lockdown put more than a metaphorical emergency brakes on, causing down slides in different sectors, particularly the manufacturing. It was difficult to ensure the supply of essential goods and services and maintain livelihood sources for the poor, unless inter-district and interstate mobility was maintained to a certain extent. The essential products were crossing the district and state borders with occasional disruptions due to lack of preparedness at collection/transhipment points as also the formalities mandated at state borders. The hassles in such movements were due to shortage of labour and security protocols. It is pertinent to mention that horticulture output was estimated at 313 million tons and wheat production at 109 million tons in the Kharif season in 2020, higher than that of the previous year. Disruption in procurement chain resulted in a fall of procurement prices in the mandis (grain markets) of several districts. The problem was severe in case of horticulture produce, resulting in price rise in consuming centres. The state nonetheless continued to make efforts to bring the rural economy back on track through procurement of Ravi crop and sowing of the Kharif and facilitating the movement of commodities across district and state borders. Thankfully, the pandemic in the first stage did not spread rapidly in large cities. During March 1 and June 15, 2020, the daily increase was about 7%, much below the global rate. Besides virological reasons that are debatable, this can be attributed to the class composition of the population and separation and segmentation of residential, commercial, and recreational space of the rich and poor. The passengers coming from another global city within or outside the country is less likely to take a metro or bus in India in comparison to the countries in Europe or America. However, once the virus was passed on from the globally linked class to the common people and the informal sector, it was spreading very rapidly because of the physical conditions of living in these cities. Lockdown for them meant increase in proximity as they had to remain confined to their slums, 24 hour of the day and night. Understandably, virus spread rapidly in Delhi, Mumbai, Pune, Nagpur, Bangalore, Chennai, Hyderabad Thiruvananthapuram, etc. Furthermore, it spread to other towns and rural areas within the state and outside as these cities have a high percentage of interstate and intra state migrants. Understandably, as a result of the lock-down, a large number of interstate migrants were rendered jobless who were concentrated in a few large cities in the in-migrating states such as Maharashtra, Gujarat, Delhi, Kerala, Tamil Nadu, and Karnataka. This resulted in an exodus that no one was ready for. Unfortunately the government could

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Table 3 Occupation wise employment for migrants at the destination Designation state with highest migrant

Highest employer

Second highest employer preferred

Third highest employer

Gujarat (n-111)

Wage Labour (26%)

Construction (25%)

Manufacturing (24%)

Delhi (n = 878)

Wage Labour (30%)

Manufacturing (22%)

Self Employed (18%)

Maharashtra (n = 566)

Wage Labour (30%)

Manufacturing (26%)

Constructions (17%)

Source RCRC Survey

put up no reliable figures of the interstate migrants who left the cities to go back to their villages, but the estimated number easily exceed 20 million. Field level data generated by a civil society coalition—Rural COVID Response Coalition (RCRC)— with a membership of more than 65 organisations across the country (See Rapid Rural Community Response (RCRC), n.d.) are useful in this context. The RCRC generated three rounds of data from about 11,000 rural households from 11 states of India, namely AP, Assam, Gujarat, Jharkhand, Bihar, MP, Odisha, UP, Rajasthan, Chhattisgarh (CG), and Meghalaya in three rounds. The first round of data was collected in May, 2020, second in July, 2020, and third in Dec–Jan, 2021. The data covered various issues of returnee migrants as well as of the rural economy. The RCRC data are useful in understanding the nature of jobs the returnee migrants had before they went back to their hometown. The survey reveals, as shown in the table given below, that more than a quarter or little less than one-third of the migrants were casual informal sector wage labourers in petty production and services, followed by manufacturing sector. Even within the manufacturing sector, people were mostly contractual workers with weak social security status. Construction sector engaged quarter of the interstate migrants in Gujarat and 17% in Maharashtra. In Delhi, 30% migrants were in wage employment, followed by manufacturing and self-employment, accounting for 22% and 18% of the migrant workforce (Table 3). The Samarthan’s Survey in CG in May 2020 suggested that most of the migrants in metropolitan cities and large towns are absorbed in informal sector or employed as contractual/casual staff. The distress migrants from relatively less developed states, particularly moving out from remote rural villages have very limited skill sets to be absorbed in the formal economic activities. There was concentration of employment in construction sector. Of the 3266 migrant workers surveyed, 38% were unskilled workers and 16% were semi-skilled masons. Only 13% were trained masons. Samarthan conducted another telephonic survey in January 2021 with 10,047 migrants of 10 districts in Chhattisgarh, who were in the official list of the Government as returnees at the time of the lockdown. The survey was conducted to know their current status of work at source and destination, skill sets, entitlement realisation, and their current challenges after 8–9 months of lockdown period. The data further confirmed that most of these returnees from urban areas were unskilled, low paid, and seasonal migrants. Due to casual nature of engagement and daily wage living, survival in destination cities/states was difficult during lockdown without social protection

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measures from the Government or the employees. Mass scale movement of the migrants after the announcement of lockdown, therefore, became inevitable as their financial capacity to sustain in urban areas was very weak. Given the impossibility of implementing social distancing norms for reducing the spread of the infection, increase in the number of cases in these cities is understandable since forty percent of their unorganised workforce had lost or were at the threat of losing livelihood who were trying to find sustenance through alternate businesses or struggling to access state provided benefits. Any restriction on the slum dwellers to move out of their homes or localities can only increase social proximity and density of interaction, people standing in queues or sitting in large groups being an inevitable outcome. Understandably, these have led to an alarming rise of infection in slums and low income areas in the mega cities that have become the major hot spots. The states with cities that are linked to the global system and have a large percentage of international and interstate migrants were, thus, the centres where not only the Pandemic started but continued to record a large number of cases in the second wave of the Pandemic during March–April 2021, due to the living environment in their large cities, mediated, partially, by climatic and behavioural factors. Thousands of migrant workers in these centres walked home on foot. With a looming economic crash, the migrants, packing their belongings as much as they could, took the long roads toward their homes. About half of these migrants worked in the construction sector as plumbers, masons, electricians, etc. Many worked as street vendors, domestic help and in informal transport and professional agencies. They left behind a vacuum for essential services in the cities. Certain facilities were provided to them by the central and state governments, but these turned out to be highly inadequate. For majority of these migrants, however, COVID-19 was a city affliction and an escape to the village home a sanctuary and, therefore, were willing to avail any mode of transport including walking to return back. Apart from creating an initial wave of homeward rush of panicked migrant workers across multiple state borders, the lockdown measures also exposed the limitations of India’s law and order enforcement apparatus in differentiating between policing for curfew and ensuring compliance of safety protocols. A large segment of the migrants had to walk their way back to their villages, with their families, travelling hundreds of kilometres (some more than a thousand kilometres) on foot as the stringent and sudden lockdown saw the suspension of all public transport. This had serious implications. It was difficult to follow any social distancing protocol in these large travelling groups. The travelling migrants were stopped, beaten, and even put in custody, by the police, at state and city borders. Many migrants lost their lives in road accidents. In two months of the lockdown, 198 migrants lost their lives in road accidents (Save Lives Foundation, 2020) while many others died due to exhaustion and hunger. There was a complete unpreparedness of the Government machinery to deal with the situation of exodus. In other words, the unprecedented movement of the migrants was beyond the capacities of the administrative machinery to cope when the fear of spread of COVID was the highest. The survey findings of Jan Sahas, a CSO working with migrants, highlighted the miseries of the migrants and their families, at their

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destination cities (Sahas, 2020). Most of the migrants were with their wives and small children, requiring various measures of support. After a few days of witnessing the difficulties faced by the travelling migrants, many support groups, several civil society organizations, and community groups at different locations stepped in to provide buses and arrange vehicular transport for them. The migrants were provided various services on the highways to reduce their miseries and meet their basic needs viz. water, food, medicines, footwear, sanitary pads, etc. For small children and lactating mothers, special arrangements for milk, fruits, biscuits, juice, etc. were also organised. In certain places, arrangement for their rest, mobile charging stations, and safe toilets for women were created to sensitively deal with their problems. Some of their initiatives were in collaboration with the Government, local business community, social and religious groups, etc. Civil society, however, had limited resources and outreach, and consequently, the facilities were available only in a few places and these measures were highly inadequate to meet the enormous challenge of the problem. Undeniably, the number of infections in rural areas increased since several returnees carried infections or got infected due to their unsafe travel. The rural infection rate, nonetheless, did not go up alarmingly until the second wave hit the country. The question was whether the risk of spreading infection by keeping them in ghettos and congested buildings and camps in an unplanned manner, resulting in heavy crowding, was less than that of spreading in the villages. Added to that is the risk of agricultural produce not being harvested/marketed properly, owing to inadequacy of labour, resulting in loss of value to the household and the rural economy. Government designed interventions without having an answer to these questions.

4 Impact of the Interventions by State to Provide Health, Sustenance, and Transportation for the Migrants The Union Home Ministry, a month after the announcement of the lock-down issued an advisory requiring the migrant workers or all workers to register with the local authorities to “find out their suitability for various kinds of work” that would enable certain priority industries to start operation in non-hotspot areas from the 20th April 2020 (Ministry of Home Affairs, GoI). This also proposed to ban inter-state mobility of migrant workers. This dampened the demand for provisioning of return journey for migrants and interestingly, the government in Karnataka in early May 2020 withdrew its request to Railways to run special trains for the migrants. All these reflect a dilemma of the governments whether to facilitate or restrict the return of migrants to their native places. As a consequence, public transportation was not available to most of the migrants. Many among them managed some goods carrier vans or trucks or any possible means. A large number of them walked on foot. A survey of 500 migrants in MP by a CSO—Vikas Samvad revealed that more than 75% migrants had only Rs. 500 or less with them when they arrived in villages. Jan Sahas study

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also revealed that 31% were in debts from the moneylenders, contractors, or banks in the range of a couple of thousands to Rs. fifty thousand Sahas, 2020). On 26 March, 2020, Government of India rolled out a Rs.1.7 trillion relief package—PM Garib Kalyan Yojana (PMGKY)—amounting to about 1% of gross domestic product, marking a massive intervention to limit the economic damage caused by the pandemic, particularly, the loss of livelihood of millions of poor due to nationwide lockdown (Misra, 2020). The Government further announced an additional 5 kg rice/wheat per person for 80 crores poor families, registered under the National Food Security Act for the period May and June 2020 initially, allocating INR 26,000 crore subsidy. This support got extended till the end of November with an additional estimated outlay of 90,000 crores (Sharma, 2021). Under PM Garib Kalyan Yojana, 29.1 crore households were eligible to receive money through direct benefit transfer. This included about 8.7 crore cultivators who received the first instalment of Rs 2,000 under PM Kisan. In June 2019, the landholders owning more than 2 hectares of land were also included under the scheme, which was criticised as wrong targeting. Additionally, 20.40 crore women holding Jan Dhan account were given Rs 500 per month till June. Here too, no income criteria was specified. An additional benefit of Rs 1000 during the period of April–June, 2020 was announced for about 3 crore elderly, widows and disabled beneficiaries to meet the exigencies during the pandemic (Hussain, 2020). On 13 May 2020, the Finance Minister announced 15 relief measures under the mega Rs 20 lakh crore package. Out of these, six were aimed at bringing the lockdown-hit Micro, Small and Medium Enterprises (MSME) sector back to life that engaged a large number of migrant workers. These included interest rate incentives for borrowing from the banks. Investment less than Rs 1 crore and turnover under Rs 5 crore were the criteria for identifying the micro-units, while small businesses were those that had investment of less than Rs 10 crore and turnover under Rs 50 crore. Medium enterprises were defined on the basis of investment under Rs 20 crore and turnover of less than Rs 100 crore. The relaxation in the criteria for identifying these units along with stimulus package was expected to accelerate economic growth while creating employment opportunities for the poor in the industrial and service sector. Various state governments launched programmes of targeted health care, provisioning of grants, additional pension, and cooked/dry food for the vulnerable population, as per their requirements, for varying period during the pandemic. They responded to the crisis based on the state specific demands and priorities. There was additional resource allocation over the national Pradhan Mantri Grameen Kalyan Yojana (PMGKY) in different states utilizing the state funds on state specific projects. Odisha, for example, announced advance payment of scholarship to students. Chhattisgarh reduced electricity bills to half and deferred the payments, MP announced advance payment of Rs. 2000 per family to specific tribal groups. West Bengal announced a special scheme Sneher Paras for financial assistance of Rs 1000 to the migrant workers of West Bengal stranded in different parts of the country. Appendix 1 provides details of various measures launched by the state Governments to deal with the crisis.

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Hunger Watch, a loose collection of social groups and movements, underetook a periodic study on the status of hunger, food access, and livelihood security among various disadvantaged populations in the wake of the country-wide lockdown. Their first report, based on interviews with 3,994 households across 11 states conducted in October 2020, was released on May 6, 2021 which compared their present with pre-lockdown parameters. The report revealed that on an average, urban respondents reported a 15 percentage point worse condition than their rural counterparts across all important parameters. It also mentions that the income levels of over half of the urban respondents were reduced by a quarter or more. The corresponding figure was a little over one-third for the rural respondents. About 54% urban respondents had to borrow money for food, the figure being 38% less for rural respondents. While 45% rural respondents had to skip a meal in October 2020; for urban respondents, the figure was over 66% (Kapil, 2021).

5 Problems of Absorption of the Returnee Migrants in the Rural Economy: Grassroot Experience The database prepared by RCRC and Samarthan as discussed above in Sect. 3 provides useful insights into the process of absorption of the returnees. These surveys were conducted to know their employment status at source and destination, skill sets, entitlement realisation, and the challenges faced after 8–9 months of lockdown period. The analysis in the present section is largely based on information obtained from primary sources to understand the plight of the poor return migrants. The first challenge of the migrants was to reach home. Each state had made some arrangements in rural areas before the migrants arrived in their villages. For example in MP, CG and many other less developed states, quarantine centres were set up in high migration districts, at district or block head quarter for the migrants showing Covid 19 symptoms. The rest of the migrants were advised home quarantine for a few days. In CG, the quarantine centres were developed even at Panchayat level or for clusters of Gram Panchayats. The Samarthan (2020) survey of 109 quarantine centres in four districts of CG with 1095 registetred migrants (618 men, 331 women, and 146 children) revealed that half of the quarantine centres did not have separate rooms for women and no separate toilet for women in most of the cases. Furthermore, the food supplied was ‘not nutritious’ and there was no provision for the children accompanying the migrants, requiring different type of snacks, cooked, or packaged. With the civil society monitoring and support, the Government of CG was receptive to suggestions that led to significant improvement in the conditions of the quarantine centres. The quality of facilities and services improved in most of the centres as reflected in the ‘before’ and ‘after’ situation in the Figure below. One can argue that engagement of civil society in managing and monitoring the functioning of the quarantine and health facilities can significantly improve the outcome and hence needs to be promoted. However, cleaning of the toilets, bathrooms, and rooms could

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Improvements in Quarantine Centres (% of 635 Centres) Satisfactory condition 100.00 90.00 80.00 70.00 60.00 50.00 40.00 30.00 20.00 10.00 0.00

98.11

94.65

57.48

64.57

99.21

57.48

98.90

96.85

97.32

97.64

65.98 54.80

45.51 22.83

Drinking Water

Water for bathing & cloth Washing

Food & Ration

98.43

Beds

Cleaning of Bathroom Quarantine Cleaning centre After Before

97.48

62.83

26.61

Toilet Sanitary kit Sanitary Pad Cleaning (soap, for women nirma)

Fig. 1 Status of improvements in community quarantine centres through community monitoring in CG. Source Participatory monitoring report of Samarthan on quarantine centres, Samarthan, 2020

not improve due to absence of workforce willing to undertake the responsibility. In rural areas, due to socio-cultural taboos as well as lack of personal or public toilets over centuries, such kind of occupations have not evolved (Fig. 1). The second challenge for the migrants was entry and inclusion in the political economy of the rural areas. The migrants, once back to their respective hometowns, were treated with bias and fear. Some of them were hosed down with disinfectants, soap solutions, and in one case, bleach. With no infrastructure or monitoring mechanism for isolation or quarantine in the villages and small towns, many migrants faced backlash from their own communities, while some were subjected to class bias, where the isolation centres were open only for ‘upper caste members’. Samarthan’s survey provides insights into the socio-economic profile of the responding migrants and reasons for social discrimination. Of the total migrants interviewed, 32% belonged to the ST, 27% SC, and 36% belonged to the OBC category. Only 5% were from the general caste category. The data indicated that most of the migrants who responded were distress migrants. Only 14% of landless families and 3% of the tribal families were allotted land under the Forest Rights Act (FRA). The socio-economic profile suggested that most of the respondents were from the disadvantaged social categories where the distress migration rate is high due to landlessness, unproductive small landholding as well as low employment opportunities in rural areas. The discrimination against the ’COVID spreader migrants got accentuated with their caste and class identities in the social-cultural beliefs and practices. Yet, there were home at last, under a roof they could afford and without fear of dying of hunger, as the country and the economy came to a halt. The third challenge was in the migrants accessing the benefits of the government schemes and services. The Samarthan Survey in CG reveals that, 33.7% of the returnee migrant worker’s HH did not have Mahatma Gandhi National Employment Guarantee Scheme (MGNREGS) job cards, 50.3% did not have access to Ujjawala gas connection, 29.6% did not have ration cards, 23.5% did not have government’s

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health insurance card, and 49.3% did not have PM Jan Dhan Yojana (PMJDY) bank account for receiving the direct benefit transfers, announced by the Government of India (Samarthan, 2020). Eligibility under many of the social sector scheme is dependent on multiple identity cards required by different schemes. Furthermore, only 30.7%, 15.4%, and 15.9% of the respondents possessed caste certificate, birth certificates, and labour cards. MGNREGS ‘job cards’, one of the crucial gateways to demand for work in the rural areas for cash income, were with 54.7% migrants only. A special job card was announced for the migrants who lost their work at destination. Understandably, due to lack of sufficient documents with the legitimate people to get benefits, many schemes remained underutilized or misused by undeserving people. The situation looked better in the survey of migrants conducted by Samarthan during January–February, 2021 in CG. One would observe that about 99.3% of the returnees had Aadhaar card, 88.8% had ration cards, and 86.4% had a bank accounts. Understandably, many of the migrants have got their basic certification done during their stay in villages. Also, there were drives from the state Government for registration of these migrants after the lockdown period was over. The basic identity cards and facilities improved enabling them to receive additional subsidized ration announced by the Government as well as to avail of Rs. 500 transferred in Jan Dhan accounts of the poor households (Samarthan, 2021). The most critical card for the migrant labour, however, is the ‘labour card’ which was with very few migrants. The labour card is prepared by the Labour Department of the state governments to access various social security schemes designed for the labours viz. accidental cover, death compensation, maternity benefits, scholarships for children, etc. (Fig. 2). The fourth challenge of the migrants was to find employment in rural areas. The returnee migrants had acquired certain skills at their destinations and hence were looking for skill based work opportunities. The state governments, however, also were not prepared to receive a large number of migrants possessing specific skills. Consequently, the returnees had to seek employment under the MGNREGS or similar employment schemes providing manual work. A segment of the migrants, however, got in to cultivate their small piece of land. In the RCRC survey in May, 2020, 31% households had reported a returnee migrant in their family, among whom about 35% 100.0 80.0 60.0 40.0 20.0 0.0

Adhar card Ration card

Caste certificate

Address proof

Male

Birth MNREGS job certificate card

Female

Total

Fig. 2 Migrants Possessing various certificates to claim entitlements

Bank account

Labour registration

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100.00 50.00 0.00

46.01

53.99 5.43

Source

Yes

No

Destination

Fig. 3 Employment status of migrants at source and Destination

had found work on their own field while a similar number of returnees were engaged in MGNREGS. However, 17.6% had reported no employment. Furthermore, 79% who were engaged in cultivation had no funds for sowing Kharif crop. The fifth and the most critical challenge was to stay employed for livelihood. Unfortunately, 41% of the migrants remained unemployed on their return. Most of these respondents were from Bihar and UP. Only 7% migrants during the survey reported to be working under the MGNREGS. It is a matter of serious concern that in one of the poorest state, Bihar, only 2% migrants found engagement under the MGNREGS. Understandably, as many as 69% migrants wanted to return back to their destinations in the second round of the RCRC survey, conducted in July 2020. Similar pattern was observed from the data of the Samarthan survey conducted in CG over phone during Jan-Feb 2021. It revealed that among those who stayed back in their villages, about 55% reported no work. However, only 5.4% reported no work at destination. Understandably, those who had confirmed employment commitment moved back to the destination after the peak in the first phase of the pandemic, in September 2020 (Fig. 3)

6 Post Lockdown Economic Hardships of Migrants and Their Return to Destinations The desire to return from the villages to urban centres by the migrants got reduced from 69% in July 2020 to 59% in December 2020 due to the slow recovery of economic activities, continued fear of COVID infection and high cost of living to ensure hygiene and social distancing, as reflected in the RCRC surveys. The gravity of the situation is reflected in the fact that there has been an increase of 75% in the number of people who earned less than Rs 2500 per month over this period in absolute terms. About 70% of the respondents reported to have their income reduced compared to that before the outbreak of the pandemic. Furthermore, 30% of the migrants recorded ‘not working currently’ in December 2021 in comparison to the 41% figure in July, 2020. Out of those who reported working in December, 2021, 40% were casual labourers compared 27% reporting casual work status in July, 2020. There was a significant drop in casual work in the peak period of the pandemic. A constant proportion 19% reported working on their own farm reflecting that those who had a piece of land continued their cultivation for their survival. One would infer

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Construction labour 48%

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Agriculture Private work job 2% 5%

Domestic work 1% Mason 5%

Bricks manufacturing 33%

Technical Small work Others business 3% 2% 1%

Fig. 4 Work profile and concertation of migrants in different occupations at destination sites

that a small piece of land in rural areas is an asset for risk absorption and mitigation of the impact of any crisis. However, there is a felt need for state support to subsidise their agricultural inputs for making it a viable economic activity (Fig. 4). Due to low skill level and high concentration of migrant workers in construction sector, their return back to urban business centres was constrained by sluggish growth in demand. As high as 59% responded that they had stayed back in their villages at the time of Samarthan’s survey in January 2021. Of those who returned, 56% joined back in brick kilns and another 29% joined as construction sector. It shows that those who were most distressed economically had to return back in their odd jobs without any change in their vulnerabilities. The brick kiln and construction work are known as being highly exploitated as most people work here like bonded labourers, especially in the states of CG, MP, UP, and Rajasthan. They take advance wage payments in case of medical or other emergencies from brick kiln owners or construction managers who exploit them by charging high interest rates to sustain their services below minimum wages for a long period. Moreover, women and children in their families experience other forms of exploitation. Of those who returned, only 6% were skilled masons, who were in demand in urban areas to complete unfinished construction projects. These labours were in some sort of negotiating position to ask for appropriate amenities and wages. A painful fact revealed in the Samarthan survey January, 2021 in CG was that among the returnee migrants, 52% returned as couples (husband and wife), 15% left alone, and 18% went back with their friends. Not many were able to take their children with them. Of the 640 respondent who reported that they had left behind their children back home informed that 62% had left them with their grandparents and 6% had left the younger children in care of their elder siblings. Over 20% of the migrants reported that they left their children alone without any supervision. This is alarming and a matter of serious policy concern. This reflects the distress situation of the migrants who were pushed by poor economic conditions and bleak work

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Alone 22%

Others/ Relatives 10% Grand Parents 58%

Fig. 5 Migrants reporting on care of children post their return to destination sites

opportunities at the village level to return back to work to the destination centres for survival (Fig. 5).

7 Key Conclusions and Recommendations Several lessons are learnt from the experiences of the pandemic management in India. These have significant implications for the present policies and practices and provide a perspective for the future strategy of intervention. Strategic interventions at the outbreak of the pandemic were seriously constrained by nonavailability of data on interstate and intrastate migration. Unfortunately, the Census data, cross tabulating district to district migration, were not available for the year 2011 and the sample size in NSS was not large enough to generate robust estimates at district kevel. These could have been used to plan transport, livelihood, and provisioning of sustenance much better, to target the distressed and vulnerable migrant population. The Ministry of Labour launching an all India Survey to ascertain the trend and pattern of migration, their changing movement pattern and socioeconomic conditions, focusing on the pandemic period is a welcome initiative which will partially remedy the situation. The analysis of the spatial spread of the pandemic in relation to the trends and pattern of migration reveals that the incidence of infection is closely linked with the size of international migrants, inter-state migrants, concentration of population in slums, housing facilities, access of basic services to the poor and middle class, etc. International migrants in India landed in most of the metropolitan cities and hired private transport facilities that were driven by the people mostly living in the low income colonies. The pandemics cross class-boundaries based on the patterns of public–private transportation, housing pattern, etc. In order to deal with the pandemics in future, it will be pertinent to work on the spatial planning to design

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mixed housing, public services and settlement pattern in such a manner that the need for long distance movement within thr cities is minimised and the contagious infections can be contained at zonal level at the start of the spread. A large number of the migrants concentrate in the industrial areas that are in close proximity of the large cities that need to be planned better. It has been realised that the migrants during the pandemic could not stay in their work places in the urban areas due to lack of social security and lack of support to sustain for a few months without wages or work. The package of support to the small and medium industries in form of soft loan was announced much later. The migrant workers had left the cities by that time. Moreover, a large number of migrants having worked as informal sector worker had no choice but to return back home due to their inability to hold back without any daily earning. There is a need for better spatial, social, economic and cultural planning so that at the time of pandemic, migrant workers are able to sustain themselves and are not forced to flee back to their home towns. The city planning has been unable to address the needs of the migrants that come in large numbers in metropolitan and other large cities. It is important to orient the city planners and officials to bring concerns of the migrants in the centre of the long term planning viz. infrastructure development, basic services provisions, housing for the migrants, worksite facilities, etc. Unless the planning frameworks are transformed to include pandemic prone areas and liveability of the migrants, the outcry at the time of crisis will continue. There is need for continuous review of the allocation of available physical resources and including program funds and expertise, possibility of participatory planning, and monitoring of plans. The rural and urban local governments as well as various departments continue to undertake planning exercises independently, without recognising close rural–urban linkages. This is more relevant in case of small towns and district headquarters that are the service centres of the rural hinterlands. The economic base of small towns is very weak in India. These have limited capacity to provide employment to the rural surplus labour. As a result, there is large scale migration from rural areas to metropolitan cities. It is pertinent that policy is designed for comprehensive planning of the district taking towns and villages together. It will require technical expertise of professionals from the field of spatial/ regional planning, economists, finance experts, etc. There is a need for ensuring convergence of resources of various programs and implememting them in a spatially coordinated manner. Social sector allocations in urban areas are as much important as in rural areas. The returnee migrants were looking for grater work opportunities in MGNREGS, at least those who were manual construction sector workers. There is a need for substantial enhancement of allocations of the schemes like MGNREGS as well as ensure monitoring of benefits of the social sector schemes form the lens of the migrants. It is realised that the migrants with small piece of land retuned back home with a purpose to engage as cultivators to ensure food security for the family from their agriculture produce. They did not have enough capital to invest in much neglected, rain-fed, and low fertility land. The Government programs need to enhance allocation of funds to provide subsidised agriculture inputs and ensure purchase of surplus

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production at a fair price. With promotion of sustainable agriculture along with horticulture and animal husbandry, distress migration can be contained to some extent. Focus of MGNREGS to build assets on the farm lands of small and marginal farmers will transform rural economy to reduce distress migration. Migrants need to be considered as a separate constituency of workforce in India as an economic, social, and political entity. It was found that the migrants were running around for information to access public transport and other supportive services. Post lockdown also, E reservation in trains was compulsory before taking journey. A large number of migrants do not have smart phone and are unlettered to use web portals. Similarly, a large number of migrants were fighting with contractors for receiving their pending payments that were due for the period of their work pre-lockdown. There is no legal guidance available with them. Out of distress and indebtedness, many of the migrants once again are moving into exploitative workplaces (brickkilns) and landing in nearly bonded labour situation. There is a need to initiate a large scale national flagship program to establish Migrant Support Centres in large cities and block headquarters to serve various needs of the migrants at source and destination. Much of the constraints were also realised due to inter-state nature of migrants and differentiated services and benefits of various social sector schemes. There is a political announcement to implement one nation- one card system using the Aadhaar card so that various benefits of the migrants at source can be easily transferred at destination or vice-versa. The Union and the state Governments have to build consensus and update portals of various programs to record transfer of benefits. The current portals need to be updated or redesigned for ensuring portability of benefits. At least some of the benefits of the Union Government schemes viz. subsidised ration, social security pensions, health insurance, scholarships, etc. should be taken up on priority for portability. An important policy implication of the thesis of integrated labour market and acceleration in labour migration is to promote concentration of economic activities and organize shift of labour force from less developed regions and depressed rural areas to these agglomerations. The strategy envisages reduction of poverty by withdrawing labour from overburdened agriculture, promotion of sectoral diversification and boosting demand in backward regions through remittances, etc. sent by the migrants. A critical overview of the macro level statistics reveals that the proposition of increased labour mobility is neither robust nor provides a consistent narrative for poverty reduction. The medical emergencies, witnessed currently, reminiscent of the eighteenth century Europe, further raises question mark to the claims of equitable and inclusive development through spatially unbalanced development or of ‘dispersing only a few large agglomerations’ in the country. Many of the developed states in India have gone for capital intensive industrialisation and therefore have not attracted or have been able to resist large scale in migration from outside the state in recent years. Although a few of their large cities are drawing migrants from across the states, it is unlikely to continue for a long time due to assertion of regionalism and more importantly, emerging threats of pandemics. Several

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states have passed legislations, ordinances, etc. reserving jobs for local population, both in public and private sectors. Emergence of new urban centres and industrial expansion in rural peripheries and along major transport corridors, therefore, hold the key for future urbanisation. The possibility of spatial mobility of labour within and across the districts within states, shifting them from low productive to high productive regions, from inferior to superior crops, production diversification, etc., need to be explored at ground level. This indeed can result in more productive engagement of the workers who have been rendered surplus in traditional farming and lead to a more balanced urban structure in the country. Programmatic interventions to integrate the regional economy through a system of small and medium towns and to build symbiotic relationship between the core and periphery of cities need to be pursued within the perspectives of goal 11 of SDG to which India stands committed.

Appendix: Announcement of Various Relief Packages for the Poor by the State Governments in Response to the First Wave of COVID-19 1

Odisha

• 3 months advance scholarships to Students—Rs. 2250 to boys and Rs. 2400 to Girls • Rs 225 crore sanctioned to fight Covid-19 for Sample collection, quarantine centres, healthcare facilities and temporary accommodations • A 3000 bed super speciality hospital established at SCB MCH Cuttack • Advance payment of old age pension for 4 months to the 48.38 lakh beneficiaries (Apr-July’20) • Rs. 1500 each to the 22 lakh registered construction workers • 94 lakh beneficiaries given ration for 3 months in advance and • Rs. 1000 each to the 9.4 million beneficiaries under FSCW department (Food & civil Supplies dept.) • Rs. 3000 each to 65,000 street vendors in urban areas- targeting • Cooked meal for 2.43 lakh poor, homeless and needy persons at 4563 g Panchayats • Cooked meal for 18,445 poor, homeless and needy persons at 10 municipalities • THR??? home delivery to households having 3–6 years children • Provisioning of 5 kg Rice and 1 kg Dal per head per month to the beneficiaries under Pradhan Mantri Gareeb Kalyan Anna Yojna (continued)

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(continued) 2

Chhattisgarh

• Provision of home delivery take-home dry rations from 125 to 750 g for children between the age of 3 to 6 • Primary and middle schools to provide dry ration to children: 4 kg rice and 0.8 kg pulses for middle school students, 6 kg rice and 1.2 kg pulses for primary children • Rice for two months to be provided in April to 56.55 lakh ration card holders. Antyodaya & Annapurna ration card holders to get sugar & salt for two months in April • Electricity meter reading / billing to be stopped till March 31st. Half rate on electricity rates for two months under the “Half Rate Scheme” • MNREGA works resumed and proper arrangements for hand washing (soap and water) made available at worksites • Provision of 2 quintal rice and 25–50 kg dal at each Gram Panchayats for the needy and poor family and direction for usage of 14th FFC fund for supporting the needy families • Need based support/cash transfer of Rs. 1000 to 5000 to the needy persons and migrant labourers in special cases through district officials • PRD directed to use miscellaneous funds for procurement and distribution of food free of cost to needy families • Home delivery of ready to eat food to 24.38 lakh beneficiaries (women, pregnant women and children) by Anganwadi workers, while maintaining social distancing norms • Doorstep deliveries of Mid Day Meal dry rations (Rice and Dal) to 29 lakh children for 40 days done on the 3rd and 4th April, 2020 • Entitled persons not having a ration card claim 5 Kilos of Rice from the gram panchayat heads by providing personal/contact details • Revised rate under MNREGA—Rs190 per day for unskilled labourers • Under the Public Distribution System- Antyodaya, Priority, Differently-abled, Single Destitute and Annapurna categories of ration card holders to be given rice free of cost (instead of Rs. 1 per kg) in the months from April-June 2020 • Under the Pradhan Mantri Garib Kalyan Anna Yojana, Antodaya ration card holders to be provided additional 5 kg ration per head for three months and Priority ration card holders to be given additional 3 kg per head for three months • Identification of Migrant workers and provisioning for allotting work according to their skills within the state • Pregnant women to get additional of 450 g of ready to eat meals per week in place of hot cooked meals. Also, they would receive a packet weighing 900 gms per week (75 gms per day) • CM Suposhan Abhiyan dated 31/03/2020: Anaemic and Malnourished Children aged 6 months to 6 years and Anaemic women aged 15–49 yrs are to be provided with hot meals under the CM Suposhan Abhiyan. Also, they will get dry ration packets consisting of rice, wheat, dal and other locally available nutritious foods items for the period of 21 days starting from 25th March, 2020 • Ordered for utilization of untied fund of the Urban Local bodies for relief measures • The Minimum Suppoty Price of Mahua increased from Rs. 17 to Rs. 30 rupees • 650 crore wages for the tribals engaged in Tendu leaf collection • Employment of 13 thousand labours for 7 crore tree plantations, 16 thousand labourers under Gothan and stop dam construction, 10,000 persons for individual works, and 6700 persons under prevention of forest/wild fire • Construction of 4133 bore wells for drinking water • Creation of online education platform—“Padhai Tunhar Dwar” for children to study at home during the lockdown period (continued)

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(continued) 3

Madhya Pradesh • Free ration for one month for all Below the Poverty Line (BPL) households • 5 kg free ration to poor with no eligibility slips under National Food Security Act (NFSA) 4 kg wheat & 1 kg rice and 32 lakh population will be benefitted from this • Delivering food and ration to the 2 lakh rural and 2.5 lakh urban needy population daily • 15 lakh farmers to receive Fasal Bima (Crop Insurance) as the government paid 2200 crore premium • 2 months advance payment of all social security pensions. Furthermore 600 Rs. to be given to those with Social Security Pensions • |1000/- onetime payment to registered workers • 2 month advance payment to the accounts of Sahria, Baiga and Bhariya families at the rate of Rs. 2000 per families • 65.91 lakh students received 156 crore rupees in their accounts against Mid Day Meal (MDM) scheme. Rs. 155 to be transferred into the account of primary school students and Rs. 232 to be transferred into the account of secondary school students. (but in field dry ration distributed to the students) • Rs. 30,000 from the funds released by the Finance Commission to be used for the respective Panchayat’s Office Expenses can be used for the food/ shelter of needy individuals • Door-to-door delivery facilitated under the provision of food (wheat and rice) to people in isolation, homeless persons, and people left destitute because of the lockdown free of cost • 6 months-3 years’ children and pregnant/lactating mothers to receive 3 weeks THR at a time • Provision of free ration to the 32 lakh beneficiaries of Samagra Samajik Suraksha who are not currently availing PDS

4

Uttar Pradesh

• State Government. to give Rs. 1000/- per month to needy persons by the District Magistrate (DM) in the 58,906 GPs of UP • Rs. 1000 each will be transferred to the 20.37 lakh labourers registered construction workers (total 203 crore) • 2 months advance pension release to the 83.83 lakh beneficiaries of social security schemes • Rs. 1000 each to the 15 lakh (estimated) labourers/ workers at street vendors • Students, workers, labourers (chhatra, shramik or karmachari) won’t be asked for rent for one month by their respective landlords. If this is violated, action will be taken under sec. 51 of the Disaster Management Rules • Once in a 15 days Take Home Ration (THR) provided at the houses of Angan Wari Centre (AWC) beneficiaries (children, pregnant and lactating women) • One month free ration to the 1.65 crore needy families (in April) registered under Antoday Yojna, MGNREGA and labour dept

5

Jharkhand

• Full wage payment for lockdown period for registered daily wage workers • Advance distribution of 2 months ration for April and May • 6.9 Lakh families with pending applications for ration cards will also be provided 10 kilos of rice at Re1/kilo of rice procured from local market. Identification of families left to the discretion of district and block officials • Community kitchens (Dal bhaat kendras), with a serving capacity of at least 200 persons each, to be opened in all districts of Jharkhand until May 2020 • Community kitchens (Dal bhaat) kendras to be opened at all thanas in the districts to serve migrants, homeless, and disabled persons until May 2020. A budget of about 3crores has been approved for the same • Dry ration comprising of 2kgs chuda (flatenned rice), 0.5 kg jaggery, 0.5 kg chana to be delivered to places where access to community kitchens is restricted. 5000 of these emergency relief packets shall be distributed in Ranchi and 2000 in the remaining 23 districts of the state (continued)

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(continued) 6

Delhi

• Temporary ration coupon to the needy by a simple method of registering themselves on mobile phones with help of just a mobile number and Aadhaar card number and avail themselves of ration coupons. Any person who has a coupon can collect ration from the nearest government school during lockdown • 7.5 kg free ration to 72 lakh beneficiaries attached to ration scheme for one month • Workers, including contract/outsourced/temporary to be treated as “on duty” and paid in full • Cash support of |5000 to all daily wage labourers and construction workers • Doubled pension for widows, differently-abled and elderly for March • Children in conflict with the law to provide interim relief to the children during the present lockdown at the time of their release on bail/parole/ discharge unless denied. Cash of Rs 1500/- to be paid by the Juvenile Justice (JJ) Fund and dry ration including rice- 5 kg, dal- 2 kg, sugar-1 kg • 5 kg additional food grains shall be provided per person per month for the next 3 months (April-June) under the PMGKY over and above the NFSA entitlements to all beneficiaries covered under Targetted Public Distribution System (TPDS). This 5 kg includes 4 kg wheat and 1 kg rice, and shall be available at all fair price shops • The allocation of 1 kg pulses per household under PMGKY is made free on household count basis for AAY, PR-S(Old BPL) and Priority category identified under NFSA • Distribution of Food Grains to Non-PDS Beneficiaries in need of food during lockdown. Each beneficiary shall be provided with 4 kg of wheat and 1 kg of rice ‘free of cost’.

7

West Bengal

• The government has decided to introduce a new scheme, named, SNEHER PARAS, for financial assistance to the migrant workers who are residents of West Bengal and are stranded in different parts of the country. Rs 1000 will also be provided to these workers under this scheme • 7.8 crore poor in different categories of ration card holders to get free rations from 1st April to 30th September • RKSY-2 beneficiaries to get rice at |13/kg and wheat at |9/kg

8

Tamil Nadu

• Cash benefits distribution under various State and Central Government schemes to women account holders, farmers, old age and widow pensioners • Construction workers and auto rickshaw drivers who are members of the welfare board would get |1000/-, 15 kg of rice, 1 kg of dal and 1 kg of cooking oil • For platform vendors who have registered with the government, an additional |1000/- will be given • Those who are working under Mahatma Gandhi Rural Employment Scheme will get a two-day special salary • All the ration card-holders would get |1000/- and free rice, dal, cooking oil and sugar • Doorstep delivery of food for the elderly (continued)

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(continued) 9

Kerala

10 Andhra Pradesh

• Local Self Governments to start community kitchens at panchayat and municipality ward level (CM stated that nobody would go hungry in the state) • Volunteers in protective gear will reach food and medicine if needed, to homes of poor, aged and chronically ill • Free provisions to all ration cardholders in the state. BPL cardholders would get an additional 35 kgs of rice free next month. Other cardholders including those above the poverty line (APL) and those holding non priority cards would get up to 15 kg of rice free • Government would give free provision kits to those under home quarantine irrespective of their income status. The bags would comprise of rice, wheat, sugar, salt, edible oil, pulses and spices required for a week or month. The state has tasked the elected LSG representatives to ensure that subsidised rations reach the targeted homes • Advance payment of pensions for the month of April • Distribution of MGNREGA wages to be completed by March 31st • One-month extension for the payment of water and electricity bills, and plans to launch restaurants which will provide meals at |20/• Local Self Government bodies to pay honorariums to cooks in community kitchens operated by Kudumbasree, at a rate of Rs. 400/- for preparation of lunch only and Rs.650/- for preparation of meals for the entire day • Rs. 2,000 crore will be distributed as village employment assurance programme for April and May • |1330 Crores (given to all ration card holding families through a onetime support of |1000/-) • Free rice and 1 kg dal for April on FOC basis • Ordered All Government and Private establishments to pay full salaries for the period of lockdown to both permanent and contractual workers • All BPL households will get ration along with a kg of dal thrice—on March 29th, April 15th & April 29th • Pension will be door delivered as usual to all the eligible beneficiaries • Financial assistance of Rs 1000 will also be provided to pensioners families on 4th of April, with the help of village volunteers • Rs 1300 crores to be released toward scale of assistance to all BPL families in the State. Amounts will be distributed based on Rice Cards data available with Civil Supplies Department

Source Compilation of data from various news and announcements available on websites, by Samarthan in November, 2020

References Indian Express. (2020). 12-year-old walks 100 km, dies just short of Bijapur home. Indian Express. Jan, S. (2020). Voices of Invisible Citizens. Retrieved May 2021, from https://ruralindiaonline.org/ en/library/resource/voices-of-the-invisible-citizens/. Ministry of Home Affairs, GoI. (n.d.). Order dated 15 April, 2020. https://www.mha.gov.in/ sites/default/files/MHA%20order%20dt%2015.04.2020%2C%20with%20Revised%20Consoli dated%20Guidelines_compressed%20%283%29.pdf. Rapid Rural Community Response (RCRC). (n.d.). Retrieved May 2021, from https://www.rcrc.in. Sreevatsan, A. (11 June 2020). Mystery Shrouds Migrant Numbers. Delhi: Mint. Save Lives Foundation. (2020). Road crash deaths due to covid lockdown tracker. Save lives foundation.

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Samarthan. (2020). Status of Migrants in Chattisgarh in May 2020 . Raipur, CG: Samarthan (Unpublished). Samarthan. (2021). Current Challenges of Migrants Post Lockdown in Chhattisgarh. Raipur, India: Samarthan ( Unpublished). Samarthan. (2020). Demonstration of participatory management of quarantine institutions for prevention of COVID-19 through building capacity of PRI’s, monitoring committees & behaviour change in 8 districts of Chhattisgarh. Raipur, India: Samarthan-Center for Development Support. The Hindu. (2020). Coronavirus lockdown | Railways to run ‘Shramik Special’ trains to move migrant workers, other stranded persons. The Hindu. The Print. (2020). 97 migrants died on-board Shramik special trains, government tells Rajya Sabha. The Print.

How Can We Facilitate Psychological Recovery Following the COVID-19 Pandemic? Soumitra S. Datta, Arnab Mukherjee, and Raka Maitra

Abstract The impact of the pandemic caused by the SARS-CoV-2 has been profoundly felt in several areas including health, education, economy, and geopolitics at community, regional, national, and international levels. However, while this bigger picture is important, the emotional consequence of the pandemic on individuals has been overwhelming as well. This chapter will be broadly looking at three aspects of the pandemic. The first part will synthesize the available literature and reflect on the various direct and indirect factors that contributed to stress during and following the pandemic. Along with other factors, loss of jobs and livelihood are likely to have contributed to the hopelessness and despondence. Other than commenting on the general effects, we will also cover certain high-risk groups as healthcare workers. The second part will focus on the societal response to the pandemic and evaluate the various behaviors using the framework of game theory. The third and final part of the chapter will cover the factors that are likely to facilitate psychological recovery that may be adopted by society, organizations, and individuals to adapt to the new normal of the post COVID world. Funding for mental health services, often not being a priority area of the health budget, may get further curtailed in the face of mounting health-related expenses. Psychological recovery needs to be planned urgently. We as authors have provided helpful schematic diagrams to illustrate the complex concepts so that they are accessible to non-specialists and policymakers. Keywords COVID19 · Psychological · Emotional · Health · Impact · Recovery S. S. Datta (B) · A. Mukherjee Department of Palliative Care & Psycho-oncology, Tata Medical Center, 14 Major Arterial Road, New Town, Kolkata 700160, India S. S. Datta MRC Clinical Trials Unit, Institute of Clinical Trials and Methodology, University College London, 90 High Holborn, London WC1V 6LJ, UK R. Maitra Department of Psychosis Studies, Institute of Psychiatry, Psychology and Neuroscience, King’s College London, 16 De Crespigny Park, London SE5 8AF, UK Department of Child and Adolescent Mental Health, The Tavistock and Portman NHS Foundation Trust, 120 Belsize Lane, London NW3 5BA, UK © The Author(s), under exclusive license to Springer Nature Singapore Pte Ltd. 2022 M. Dutta et al. (eds.), The Impact of COVID-19 on India and the Global Order, https://doi.org/10.1007/978-981-16-8472-2_6

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1 Introduction The COVID-19 pandemic affected all parts of the globe, and the impact has been felt beyond the conventional spheres of health and disease. No other global crisis in the recent past has affected so many aspects of the life of so many people around the world. This chapter will focus specifically on the psychological impact of the SARS-CoV-2 pandemic and make a case for why psychological recovery needs to be planned urgently. Alongside this objective, the chapter will attempt to examine the behavioral determinants of the pandemic associated with it getting out of control and the also ways human behaviors contributed to the recovery. We as authors believe that the pandemic impacted the fundamental value system of modern life, often taken for granted. It affected our perception of freedom of movement, right to free speech, right to health, and dignity at death. All these tenets of modern civilization were shaken up in the past 2 years. Over the past year, we have seen considerable variation in the stage of the pandemic and regional response to it between various international regions. India is currently at number one for the number of daily new cases of COVID-19 (Fig. 1) and in the second place for the number of deaths per day per million of the population after Brazil (Fig. 2) (Roser et al., 2021).

Fig. 1 Daily new COVID-19 cases per million of population

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Fig. 2 Daily deaths due to COVID-19 per million of population

2 Psychological Effects of the Pandemic on People Who Were Not Directly Infected by the Virus It is reasonable to say that, even if not infected by the virus, the pandemic affected almost everyone. The immediate impact of the lockdown was restricted social activities, travel, face-to-face educational activities, and restricted workplaces. This was followed by limited access to essential items and financial setbacks due to reduced productivity that had a direct impact on many people. As the pandemic unfolded and pressure on health services increased, it became apparent that almost all parts of the world had inadequate health infrastructure to handle the sudden deluge of affected patients. In a globalized world, where real-time news is at everyone’s fingertips, fear preceded the infection. This unfolding of the crises impacted emotionally all those who were yet to develop the infection. Early reports from Wuhan residents suggested that many people at the outset of the pandemic perceived it as the end of the world (Lima et al., 2020; Rodríguez-Rey et al., 2020). People from China reported feeling lonely, bored, and angry with an increase in stress, anxiety, and depression secondary to confinement due to COVID (Brooks et al., 2020; Guo et al., 2020; Huang & Zhao, 2020; Lima et al., 2020; Luo et al., 2019; Rodríguez-Rey et al., 2020 Jun; Serafini et al., 2020; Wang et al., 2020). Prevalence of each condition varied across geographical locations and culture but all over the world people responded being on the edge and worried for their future. The risk factors for mental health problems during the pandemic were urban habitat,

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younger age, female gender, single, or not cohabiting with a partner, lower educational status, employed in professions like healthcare industry or a local enterprise having a significant public interface, adverse economic conditions, premorbid physical and mental health conditions, and increased exposure to misinformation (Gao et al., 2020; Hossain et al., 2020; nCoV, 2021; Rodríguez-Rey et al., 2020).

3 Psychological Impact on COVID Patients People who tested positive for COVID-19 had significant stress, anxiety, and depressive symptoms. They were worried about the uncertainty they were faced with. Some were afraid of falling very sick and even of dying and others were worried about passing on the infection to others who were close to them. People were worried if they will be able to get adequate medical care. Misinformation increased their stress (Gao et al., 2020; nCoV, 2021). One study reported prevalence of generalized anxiety symptoms among patients was 35.1%, whereas 20.1% of patients had depressive symptoms and 18.2% of patients had impaired sleep (Covid, 2020). Another mixed-method study showed people testing positive for COVID-19 were more likely to suffer from anxiety, depression, and intrusive symptoms of PTSD (Guo et al., 2020). Steroid-induced mania and psychosis were also seen in patients who developed SARS. Delirium, hypoxic encephalopathy, and encephalitis were also reported in patients with COVID-19 (Rogers et al., 2020). A systematic review on patients with acute infection also suggested that delirium is common amongst those infected with COVID-19, along with depressed mood, anxiety, insomnia, impaired memory, and reduced concentration (Sahoo et al., 2020). When asked about their subjective experience, 54–72% of patients reported feelings of disbelief, shock, and sadness, and close to one-fifth of them felt they had fear of imminent death (Sahoo et al., 2020). For most days during the hospital stay, 70–96% of people experienced feeling disconnected, worried, hopeless, and demoralized. Patients were uncomfortable with doctors wearing personal protective equipment, almost one-fourth of them perceived as if they were surrounded by aliens or robots. Though three forth of them expressed they received extra care from the treating team during their COVID-related hospital stay, many of them rated their ICU experience as painful. One-fifth of those needing admission felt that the stay in the COVID ward was the worst experience of their life. Around two-thirds of patients reported that talking to friends and family members over the phone and their faith and spiritual beliefs had helped them to cope during the hospital stay (Bukhman et al., 2020). A proportion of COVID survivors continued to have milder symptoms over a longer period. They were also concerned about their job and finance.

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4 Psychological Impact on People with Known Psychiatric Morbidities Mental health conditions are common in the general population. People with psychiatric diagnoses may face social disadvantages and economic hardships even in nonpandemic times, and the relationship between poverty and mental illness is bidirectional (Rains et al., 2020). Needless to say, people with pre-existing mental health issues are more vulnerable during the COVID-19 pandemic. It was hypothesized that the inequalities and adversities faced by people with mental health conditions could be accentuated due to inherent asymmetries in needs and opportunities of those with a psychiatric diagnosis during and following the pandemic (Hao et al., 2020). They are more likely to face social, financial, employment, and housing-related challenges. They are also at increased risk of getting infected, and widespread measures for infection control may further stifle health care delivery for them. They may not have access to their usual support and recourses at the community level due to increasing social isolation. Hao et al. reported a significantly higher score for depression, stress, PTSD-like symptoms, and insomnia among psychiatric patients in comparison to healthy controls (Berthelot et al., 2020). Studies that had a prepost design comparing individuals before and following the COVID-19 pandemic, showed an increase in psychiatric symptoms after the onset of the pandemic (Beatriz Lara et al., 2020; Neelam et al., 2021). Neelam et al. in their meta-analysis concluded there was a significant increase in the rate of symptoms in people with pre-existing mental illness (Vardavas & Nikitara, 2020). People with pre-existing substance use disorders are at increased risk of infection due to multiple causes ranging from smoking as an independent risk factor to various personality factors. COVID-19 related confinement, boredom, the anxiety of losing the job, disruption in treatment, and medication access further increased craving in these individuals. Studies have shown a significant increase in substance use during the pandemic (Alexander et al., 2020, 2021; Dubey et al., 2020). The use of substances along with many other diverse causes has been shown to reduce behavioral control and increase domestic violence during the pandemic (AFP, 2020; Kang et al., 2019; Radhakrishnan et al., 2020; Vijayalakshmi & Dev, 2020).

5 Impact on Healthcare Workers It is important to discuss the psychological impact of the pandemic on healthcare workers as they are crucial in delivering medical care during the pandemic. While developing any large-scale pandemic response, it is important to keep this aspect of workforce management in mind. Healthcare workers are also involved in implementing pandemic management measures as vaccination drives. Since the declaration for COVID-19 as a pandemic in March 2020, there have been large-scale emergency management measures including strict social isolation

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in many parts of the world. The uncertainty of life and livelihood along with selfisolation has significantly influenced the well-being of the general population and healthcare workers were no exception. On top of the universal stressors, healthcare workers met with overwhelming new challenges, leading to significant stress, anxiety, and depressive symptoms (Xiao et al., 2019). The medical fraternity is one of the most high-risk groups for psychological morbidities and suicide (Angres et al., 2003; Knaak et al., 2017). Previous research has shown that even in normal times, there are multiple barriers to seeking mental health support and treatment among healthcare workers, including lack of time and stigma (Gold, 2020; Liu et al., 2020). A situation so unprecedented like that of the current pandemic increased the likelihood of worsening of the pre-existing psychological morbidities and also for new-onset psychiatric diagnosis in this high-risk group. A survey across multiple centers in China that included 1563 medical staff showed a relatively higher prevalence of psychological morbidity. Around half of the participants qualified for depression or anxiety, more than one-third had insomnia, and more than three-fourth had stress-related symptoms (Hu et al., 2020). A study among 2014 frontline nurses in Wuhan, China, showed close to two-thirds of them had emotional exhaustion and 42.3% showed depersonalization. Though 91.2% of nurses reported fear, almost all of them (97%) were willing to offer their professional services and 60% had a feeling of accomplishment. The mental health issues in them were also seen to be negatively correlated with their willingness to do frontline work, resilience, and social support (Pappa et al., 2020). A review with more than thirty-three thousand healthcare workers revealed concluded that one in every five participants had either anxiety or depression. Close to one in every three healthcare workers complained of insomnia. Female participants had more affective symptoms than their male counterparts. Nursing staff showed a similar trend as doctors (Lai et al., 2020). Lai J and colleagues also suggested women and people with intermediate professional positions were associated with higher anxious-depressive symptoms (Spoorthy et al., 2020). Cai and colleagues tried to evaluate the association of symptoms with age. Medical staff in the age group of 31–40 years were more anxious about transmitting the infection to their families. Staff aged more than 50 years were more stressed with the patient’s death (Chatterjee et al., 2020 May 1; Spoorthy et al., 2020). Studies from the Indian subcontinent also showed a similar trajectory. A study by Chatterjee et al. showed that around one-third of doctors have anxiety or depression (Mohindra et al., 2020). 21.1% of doctors were ostracized by society for working in a hospital and perceived as having the potential to spread the infection. Long duty hours were identified as a risk factor for adverse mental health outcomes in health professionals. Another study from India looked at the worries among frontline healthcare workers. The risk of transmitting infection and putting family and colleagues at risk, the chance of getting isolated/quarantined, issues due to nationwide lockdown were the prevailing concerns (Ellepola & Rajapakse, 2020). Reports from Sri Lanka (Anwar et al., 2020), Bangladesh (Sandesh et al., 2020; Shammi et al., 2020), and Pakistan (Gulati & Kelly, 2020; Rana et al., 2020) raised similar concerns. There were reports of several healthcare workers from the subcontinent

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attempting or dying by suicide secondary to COVID-related stress (Kolkata, 2020; Liu et al., 2020). Multiple factors potentially increased the psychological stress in healthcare staff during the pandemic.

5.1 Job-Related Stresses Even before COVID-19 was declared a pandemic, dealing with progressing numbers of infected individuals and having to face unexpected deaths caused psychological stress, anxiety, and depression among healthcare workers (Xiang et al., 2020). Perceived risk of COVID infection, longer working hours, physical fatigue, loneliness, shortage of PPE was found to be risk factors for poor psychological health among healthcare workers (Xiao et al., 2019). There were reports of healthcare professionals who attempted or died by suicide (Kolkata, 2020). Being the frontline warriors during the time of the pandemic, staff had to often work away from home in settings that they did not any prior experience. They often felt they lacked the skills needed to do the job. There had been a shortage of personal protective equipment in most parts of the world which increased the stress and trauma (Chen et al., 2020 Apr; Liu, et al., 2020). The moral dilemma of triaging patients in resource-poor settings, to decide on whom to treat and whom not to prioritize, increased work-related stress (Xiang et al., 2020). The stigma and discrimination faced by healthcare workers, in some countries while working in COVID wards, led to the feeling of being disowned by the same society for whom the frontline healthcare workers were striving so hard. There were instances of a reduction in salary of healthcare workers and at some extremes of not being paid at all for the work they did. This further increased the uncertainty and feelings of hopelessness (Kisely et al., 2020). A lower household income was found to be adversely affecting mental health outcomes during the pandemic (Wallace et al., 2020). Facing unexpected deaths and followed by complicated grief reactions were important issues for not only patients but also healthcare workers providing care at the frontline (Kisely et al., 2020).

5.2 Social Stressors Healthcare workers exposed to COVID patients were worried about transmitting the infection to their near ones at home. There was an increased concern related to the uncertainty of one’s own life, resulting in financial and social insecurity in case the staff succumbs to the infection. There was also psychological trauma and grief related due to the death of colleagues. The threat of job losses increased the anxiety as well (Abused & Attacked, 2020). Many hospitals in developing countries had reduced the salaries for doctors due to a decrease in revenue generated across specialties due to COVID-related lockdown and fear (Kisely et al., 2020). There were many localities or

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gated communities that did not allow the health professionals to stay on the premises, leading to an increased difficulty for healthcare staff (Cai et al., 2020;. Pandemic has fueled an increase in substance use and intimate partner violence (Kang et al., 2019; Radhakrishnan et al., 2020; Vijayalakshmi & Dev, 2020) in many parts of the world during the lockdown and health-care staff also experienced such issues at home. At times social support from the larger community was often perceived to be lacking, due to fear of infection (Earnshaw et al., 2020; Hou et al., 2019). Younger healthcare staff having dependent children at home, when faced with the double jeopardy of increased domestic responsibilities due to infection in a close family member had a worse mental health outcome (Liu et al., 2020; Wallace et al., 2020).

6 Societal Responses to the Pandemic 6.1 Stigma Related to COVID Any new infective illness is known to be associated with stigma, and theoretically, a stigmatized person is perceived to have attributes that reduce their value and degrade them in society. Literature from developed and developing nations showed stigma was common across the world during the COVID pandemic (Daalen et al., 2021; Gopichandran & Subramaniam, 2021). However, stigma often worked differently in different parts of the world. In the USA, perceived stigma reduced the acceptability of COVID testing in the initial phase of the pandemic. In many LMICs stigmas led to insensitive behavior, rudeness, and neglect (Daalen et al., 2021; Gopichandran & Subramaniam, 2021). Stigma made vulnerable people reluctant to seek health care, causes trust deficit in health agencies, caused disproportionate allocation of resources, increases neglect, poverty, and susceptibility to the disease (Snider & Flaherty, 2020). Internalized stigma may lower self-esteem and experience of stigma may lead to significant psychological stress and depression. Interaction of stigma of COVID infection and that of mental illnesses with other social constructs like gender, race, ethnicity, employment status, and poverty can have a wider impact (Bhanot et al., 2021; Foucault, 2017; Pickersgill, 2020 Aug; Snider & Flaherty, 2020; Solomon et al., 2021).

6.2 Usage of Certain ‘Words’ Leading to Heightened Emotional Arousal Words are important in describing a disease, during communications with patients and their family members, during inter-professional communications, while documenting prognosis, and even during planning medical care for patients at their end of life. As much as words used sensitively may be comforting to us, at other times

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they may evoke extreme anxiety. The value and emotional impact of using certain words in verbal and written communication have not always received its due importance in medicine. It has been suggested that ‘words’ confer ‘power’ to those who deliver healthcare services over the service users (Corrigan et al., 2001). The COVID19 pandemic led to the usage of many strong words, which often unintentionally contributed to the collective psychological distress. Let us elaborate on this with a few specific examples.

6.2.1

Social Distance

Although unintentional, the usage of the word ‘social distance’, originally meant to convey a sense of physical separation needed between two individuals to prevent transmission of COVID19. This may have had some unfortunate consequences. In social psychology, ‘social distance’ refers to prejudice between members of two social groups. In other words, social distance is the ‘remoteness’ that members of one group feel toward the members of another group. Previous research has shown that increased social distance may be associated with increased prejudice and taking an authoritarian attitude toward the weaker group (76]. A better terminology would have been ‘physical distance’. This would have taken out the emotive element of maintaining the barrier between two persons.

6.2.2

Personal Protective Equipment

The words ‘personal protective equipment’, abbreviated to PPE, is a constant reminder of the need for protection often undermining the fact that protection is for both patients and clinicians. The usage of the words like ‘personal’ and ‘protection’ often evoked a primal fear of vulnerability amongst healthcare workers. This, coupled with the lack or absence of availability of PPEs, contributed to the stress in many parts of the world.

6.2.3

Other Words

The power of words cannot be undermined and the glossary of terms (Arora & Grey, 2020) used in tackling the pandemic was at one stage more threatening than the pandemic itself. The list is endless: asymptomatic carrier, case fatality rate, clinical trial, community spread, confirmed positive case, contact tracing, containment, epidemic, flattening of the curve, herd immunity, surveillance, incubation period, novel coronavirus, national emergency, patient zero, physical distancing, screening, self-isolation, trustworthiness and academic fraud, ventilator, and vaccine. Many of these ‘words’, reminiscent of medieval rhetoric on war and death, are associated with fear of an unknown and dangerous enemy. This has certainly contributed to the level of stress perceived by the people under the pandemic.

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7 Why Behavioral Change Matter in COVID-19? There were reports of profound behavior changes with negative consequences since the onset of the pandemic in 2020 (Coulthard et al., 2021). The authors summarized the impact of the pandemic on sleeping patterns and suggested that there has been a change in total sleep duration, and the quality of sleep is poorer for many people. Selfreports of eating behavior during the pandemic have shown increased consumption of home-cooked food, engaging in comfort eating, and higher intake of high-energy food items (Ornell et al., 2020). There is a bidirectional association between substance misuse and COVID-19. On the one hand, substance misuse may increase the risk of COVID-19 infections, and on the other hand, quarantine and lockdown may increase substance misuse (Weston et al., 2020). Along with the above behaviors, another group of important behaviors are to do with the ability to conform to the advocated behaviors to reduce the spread of the pandemic and support each other. Some of the proposed solutions to tackle the pandemic are as simple as remembering to wear a mask, avoid social contact and maintain a physical distance from others. However, it has been a challenge to implement this simple behavior change for the entire human population. Unless there is a sustained behavior change for a large proportion of the population, the pandemic may continue to worsen. Technologically advanced solutions are likely to be effective only if simple high-risk behaviors related to the spread of the SARS-CoV-2 virus are controlled at the population level.

7.1 Applying Game Theory to the COVID-19 Pandemic A review (Yong & Choy, 2021) applied behavior change theories in the context of infectious disease outbreaks in understanding the patterns of decontamination behavior, medication adherence, maintenance of social distance, and conforming to hand hygiene practices. Evolutionary game theory has been used to predict noncompliance to safety guidelines during the pandemic of COVID-19 (Laerhoven & Ostrom, 2007). The ‘tragedy of commons’ is a situation where several independent people share a common resource and people act in self-interest and maximize their gain. People act in a self-centered way and contrary to the best interest of the whole group. Although this observation has made long ago, game theory has used this concept (Cole & Kocherlakota, 2021). During the COVID-19 pandemic during the initial period, people resorted to hoarding face masks and other essential items for themselves at the cost of others not getting them. People did not think that not sharing a scarce resource as a face mask will be counterproductive in stopping the spread of the virus. Several factors as economic pressures may have caused people not to prioritize safety and act collectively in a responsible way. In the later stages of the

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pandemic, once vaccines were available, many people perceived the personal risk of vaccination more than the collective benefit of the community on being vaccinated. ‘Hidden actions’ have been discussed in game theory. All behaviors of people are not observable, and they may behave differently outside the gaze of the public eye (Arrow, 1970). People often did not follow government norms, quarantine advice, and at times did not report early symptoms of the disease. Many people continued to disregard safety behaviors when away from the public eye. Arrow’s Impossibility Theorem (Saijo, 2021) postulates that when all group members have a say in the actions themselves, they are likely to have a wide range of protective and risk-taking behaviors. Some of these choices may be suboptimal. Individuals may reject a choice of a safety behavior such as staying at home that may be good for others but less enjoyable for one’s self. Game theory also talks about Hawk-Dove games (Vega-Redondo, 2003) that could be applied to behavioral interactions. The coexistence of aggressive and submissive members in a community may play out differently depending on the nature of the interactions. If the aggressive individual (hawk) is interacting with a submissive individual (dove), the latter may surrender the reward to the aggressive person. When two submissive individuals interact, they share the resource equally. When two aggressive members meet, they may fight and as a result, each of them ends up getting lesser rewards than for instance when two submissive individuals co-exist. In the pandemic scenario, when one stays home, he misses out on the fun but reduces the chance of infection. In a hypothetical game of two people, one possibility could be that both players stay at home and gets the benefit of reduced infection risk but miss out on the fun of being outside. One alternative scenario is one player stays home and another person goes out. The third alternative is both the players go out and thus put both of them at risk of infections. Nash equilibrium predicts if one stays at home, the opponent will go out. Repeated interactions (Rubinstein, 1979) predict that games that are repeated between known players may lead to a certain degree of cooperation that benefits both players. Nations with relative affluence, fewer population pressures on resources, and a narrower wealth divide acted in collusion early on. This resulted in individuals adopting a cooperative stance regarding norms of acceptable behavior. With time, for the countries with worse second waves, the self-interest of individuals may have led to cooperative behavior due to fear of ostracization by others in the same social group. This is explained in the game theory paradigm of repeated Interactions with ‘no end game in sight’ (Brooks et al., 2020). A mathematical treatment of the game theory is beyond the scope of this chapter. We have attempted to present a few simple illustrations to demonstrate the application of game theory to explain the behavioral patterns of people during the pandemic (Figs. 3 and 4).

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Fig. 3 Individual behavior difference as predicted by game theory

Fig. 4 Population behavior as explained by game theory

8 The Way Forward Toward Planning a Psychological Recovery 8.1 Facilitating Psychological Recovery for the General Population A high-impact systematic review, published early on during the pandemic, summarized the measures that could be implemented to improve the psychological wellbeing during a period of quarantine (Barrantes, 2021). Based on available evidence

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Fig. 5 Stringency index of countries around the globe

experts suggested that a shorter duration of quarantine following exposure to infection is associated with less psychological impact to the individual. Having adequate good quality up to date information about the pandemic and guidance on healthrelated queries were expected to reduce psychological stresses. Additionally, it was stressed that everyone under quarantine should be supported by others in the community in getting provisions and supplies and be in touch with friends and neighbors remotely via telephone and other methods of communications to reduce the sense of isolation. Public health officials were encouraged to maintain clear lines of communications with those under quarantine. Those who had persistent and more serious psychological and emotional symptoms were encouraged to access formal mental health support remotely (Figs. 5 and 6).

8.2 Facilitating Psychological Recovery of Individuals Following COVID-19 Infection It has been advocated that universal mental health screening is coupled with physical health monitoring of all COVID-19 infected individuals. Psychological support may be provided by mobilizing lay counsellors, good samaritans, and volunteers. Where needed specialist mental health inputs may be organized via use of telephone helplines and other methods. A proportion of people may require formal

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Fig. 6 Vaccination of the proportion of the population

psychological interventions in combination with psychotropic medications. As the SARS-CoV-2 is a neurotropic virus, there can be a considerable overlap of neurological symptoms of fatigue and impaired cognitions with psychiatric syndromes in the period following COVID-19 infection (Connolly et al., 2021). Using technology to deliver mental health interventions remotely requires a careful attention to the details such as the complexity of the intervention apposite to the population, degree of readiness of the potential users of the technology, and local regulatory issues around health and social care (Ruzek et al., 2007). There may be additional economic considerations to be taken into account before investing in the technology.

8.3 Facilitating Psychological Recovery for Healthcare Workers When the pandemic was unfolding, healthcare authorities appreciated the need to address stressors for healthcare workers to help them cope better. Chen and colleagues in their study showed that direct interaction with staff to understand their felt needs, adequate provisions for food and rest area, provision for leisure and training, and access to a counsellor helped in improving the mental health of staff. These methods were found to have better outcomes than an earlier plan of group activities for

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the reduction of stress (TThe New Indian Express, 2020). The need to pragmatically address the psycho-social stressors for a worker was found to be of immense value. Measures for adequate rest, easing childcare and caregiver concerns, provisions like ‘hazard pay’ to reduce financial constraints can reduce adverse mental health outcomes (Liu, Yang, et al., 2020). Lack of PPE was perceived as a betrayal by authority who are responsible for the safety of the dependents and compounded the trauma (Liu, Yang, et al., 2020). Clear communication regarding the adequacy of PPE and adequate rest along with practical and psychological support were found to improve psychological health (Wallace et al., 2020). ‘Psychological first aid’ has been considered to mitigate the emotional trauma of healthcare workers currently exposed to due to the pandemic. Non-compulsory, culturally sensitive psychological intervention, focusing more on comfort, safety, and social support, for acute management of trauma has been tried (Greenberg, 2020; Liu et al., 2020). Like in any trauma, long term psychological outcomes of healthcare workers following the pandemic would depend on the duration of exposure to traumatic events and experience of the trauma itself (Datta et al., 2020). Availability of social support at that point is an important determinant of the long-term outcome. The role of healthcare managers is thus very important in managing the work pressure as well as supporting the staff adequately during these difficult times. The literature suggests that several factors can improve staff well-being of healthcare workers exposed to traumatic stressors, such as early identification of psychological issues, appropriate management of the problems faced, and graded reintegration into routine work. Adequate acknowledgment of the challenging work they perform can improve their resilience. Absenteeism from work should be interpreted as an indicator that the person probably needs psychological help. Greater attention should be paid to the vulnerable groups, to people who are working at a level above their experience and beyond their comfort zone, and to those who have experienced bereavement during the pandemic. Proactive inquiry about the mental health of staff by their managers can positively influence and increase the help-seeking by vulnerable staff. The transition to “normal” following the trauma should be a monitored one, allowing the staff to decide the time for joining back work. Helping staff to discuss the psycho-social aspect of their work and how they coped using a meaningful narrative can lead to better psychological health (Datta et al., 2020).

8.4 Facilitating Organizational Recovery Institutions should be encouraged to develop a plan for the restoration of the psychological health of its employees. Many institutions developed special facilities to support its staff under these unprecedented conditions. Once ‘work from home’ became the norm, it became essential to address the unique psychological challenges associated with this. Returning to work following COVID-19 infections needed staff to be emotionally supported. There were some short-term measures to address the

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immediate anxieties of the staff (Mullard, 2020). Over the longer term, in the face of economic hardships, it is essential that psychological recovery is planned side by side of physical health monitoring of employees. Bereavement due to the death of a long-term colleague is also an issue that needs to be looked into.

8.5 Facilitating Psychologically Nuanced Response of a Nation to the Pandemic Each nation has tried to contain the pandemic in its way. Many countries used special pandemic-related legislation to legitimate a centralized response. Countries can be assessed for the stringency of the response based on an index (Fig. 3; Roser et al., 2021). Despite the stringency of the official government response, most countries had to also rely on the cooperation of their citizens to the national disaster plans. The stringency did not predict the control of the pandemic around the globe. The uptake of vaccination depends on the availability and acceptance of vaccines in the country. Nature reported in Nov 2020 (Greenberg et al., 2010) that a handful of affluent nations that together is home to only 13% of the total global population had pre-ordered more than half of the world’s vaccines against COVID-19 with Canada leading the team with more than 8 doses ordered per citizen of the country. This panic reaction of some of the affluent nations often went against the interest of the populous low- and middle-income countries. Despite being one of the global leaders in vaccine production, India could vaccinate less than 5% of its population till early May 2021 (Fig. 4; Roser et al., 2021). In the non-pandemic times, the yearly incremental economic burden posed by adults with depression alone in the US was 326.2 billion dollars as per 2020 values and the workplace costs accounted for the largest portion of the costs (Retracted coronavirus, 2020). It is evident from the synthesis above that mental health morbidity has increased significantly during the pandemic. While budgeting and planning the medium- and long-term pandemic management strategies, it is imperative that the policymakers systematically think through the ways to facilitate the psychological recovery of the population. Unfortunately, there is a risk of further budgetary cuts for mental health services worldwide in the face of economic hardships in the next few years. If this happens, the psychological recovery will be delayed and surely affect the productivity and overall happiness of the population.

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9 Ethical Challenges of Managing the Pandemic Policies, guided by international agencies like World Health Organization, funded and implemented by national governments, needed to be translated into action by regional and local health authorities. Unfortunately, as the pandemic unfolded, there were multiple versions of ‘truth’ that quickly changed over time, often contradicting the earlier beliefs based on later findings. To make matters worse, more than 100 research papers related to COVID-19 had to be formally retracted (Koh et al., 2021). There were accusations of research fraud and allegations that private enterprises influenced the public narrative for their gain often done clandestinely or without properly disclosing the conflicts of interests. This led to enormous erosion of trust of healthcare workers and the public toward international and national agencies. An effective response during the pandemic should include good communication between the government, healthcare workers, scientists, media, and public without sensationalizing the facts (Raus et al., 2021). This requires adherence to certain ethical standards by all the stakeholders. Health policymakers may face ethical challenges while setting priority for testing for COVID-19 in the face of an inadequate number of testing kits. Using various tracing techniques of potential contacts may pose serious ethical dilemmas to governments (Binagwaho et al., 2021). Those requiring hospitalization may use a very scarce health resource when numbers of infected people are on the rise. Managing access to COVID-19 vaccines equitably and ethically is not easy (Syrous et al., 2021). Even physician-related variability in decision-making at the end-of-life during the pandemic gave rise to psychological stress to the patient, family members, and healthcare workers (Garbarino et al., 2021). Making complex clinical and ethical decisions as delineated above may pose to be emotionally stressful. There were reports from Italy of significantly increased suicide rates of police officers who were responsible for enforcing social restrictions during the period of the pandemic (Syrous et al., 2021).

10 Conclusion The COVID-19 pandemic was closely followed by an upsurge of mental health diagnoses and crises in the general population, high-risk groups, and also healthcare workers. Scare resources, poverty, unaddressed grief, and psychological trauma coupled with the anxiety-driven reaction of the global community led to a significant degree of mistrust. Both individuals and organizations need to address the problem urgently. We must plan adequately to facilitate the psychological recovery of humankind. Acknowledgements The authors also acknowledge the source for Figs. 1, 2, 5, and 6 as the website www.OurWorldInData.org. The authors acknowledge the inputs from Rajendrani Mukherjee for co-producing the illustrations in Figs. 3 and 4.

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Brooks, S. K., Webster, R. K., Smith, L. E., Woodland, L., Wessely, S., Greenberg, N., et al. (2020 Mar). The psychological impact of quarantine and how to reduce it: Rapid review of the evidence. The Lancet., 395(10227), 912–920. Barrantes FJ. The unfolding palette of COVID-19 multisystemic syndrome and its neurological manifestations. Brain Behav Immun - Health. 2021 Jul;14:100251. Connolly, S. L., Kuhn, E., Possemato, K., & Torous, J. (2021 May 7). Digital Clinics and Mobile Technology Implementation for Mental Health Care. Current Psychiatry Reports, 23(7), 38. Ruzek, J. I., Brymer, M. J., Jacobs, A. K., Layne, C. M., Vernberg, E. M., & Watson, P. J. (2007). Psychological First Aid. Journal of Mental Health Counseling, 29(1), 17–49. Greenberg, N. (2020). Mental health of health-care workers in the COVID-19 era. Nature Reviews. Nephrology, 19, 1–2. Datta SS, Mukherjee A, Ghose S, Bhattacharya S, Gyawali B. Addressing the Mental Health Challenges of Cancer Care Workers in LMICs During the Time of the COVID-19 Pandemic. JCO Glob Oncol [Internet]. 2020 Oct 5 [cited 2021 May 9];6. Available from: https://www.ncbi. nlm.nih.gov/pmc/articles/PMC7605381/ Mullard A. How COVID vaccines are being divvied up around the world. Nature [Internet]. 2020 Nov 30 [cited 2021 May 9]; Available from: https://www.nature.com/articles/d41586-020-033 70-6 Greenberg PE, Fournier A-A, Sisitsky T, Simes M, Berman R, Koenigsberg SH, et al. The Economic Burden of Adults with Major Depressive Disorder in the United States (2010 and 2018). PharmacoEconomics. 2021 May 5; Retracted coronavirus (COVID-19) papers [Internet]. Retraction Watch. 2020 [cited 2021 May 9]. Available from: https://retractionwatch.com/retracted-coronavirus-covid-19-papers/ Koh, K. A., Raviola, G., & Stoddard, F. J. (2021 May 1). Psychiatry and Crisis Communication During COVID-19: A View From the Trenches. Psychiatric Services (washington, d. c.), 72(5), 615–615. Raus K, Mortier E, Eeckloo K. Ethical issues in managing the COVID-19 pandemic. Bioethics. 2021 May 5; Binagwaho A, Mathewos K, Davis S. Time for the ethical management of COVID-19 vaccines. Lancet Glob Health. 2021 May 4. Nordenskjöld Syrous A, Malmgren J, Odenstedt Hergès H, Olausson S, Kock-Redfors M, Ågård A, et al. Reasons for physician-related variability in end-of-life decision-making in intensive care. Acta Anaesthesiol Scand. 2021 May 8; Garbarino, S., Fornaro, M., Messina, R., Pompili, M., & Ciprani, F. (2021). Suicide mortality data from the Italian police during the COVID-19 pandemic. Annals of General Psychiatry, 20(1), 28.

How Susceptible is the Black and Ethnic Minority (BAME)? An Analysis of COVID-19 Mortality Pattern in England Anindita Chakrabarti, Kausik Chaudhuri, and Jose Martin Lima

Abstract UK’s Office for National Statistics has recognized a greater risk of COVID-19 disease burden for minority ethnic groups but underlying socio-economic reasons are yet to be quantitatively established. Our analysis drawing data from Middle Layer Super Output Area (MSOA) in England aims at analyzing the association between COVID-19 mortality and disaggregated ethnicity profile; establishing the impact of demographic profile and economic characteristics on COVID19 deaths; and assessing the role played by co-mortality in COVID-19 deaths at the MSOA level. We also quantify the extent of the neighborhood effect of COVID-19 mortality to assess the impact of social distancing measures. Our regression analysis reveals heterogeneity within the Black and Ethnic minority (BAME) community: Areas with a higher proportion of individuals with “Black-Caribbean”, “BlackAfrican”, “Indian”, and “Chinese” recorded a significantly higher ratio of death from COVID-19 to total death. Areas with a higher unemployment rate, higher proportion of individuals dying from cancer and circulatory diseases demonstrated higher COVID-19 deaths. The spatial analysis also showed a positive significant spill-over effect of COVID-19 deaths across MSOAs in England: Areas adjacent to neighborhoods reporting higher BAME population showed a large indirect effect possibly because social networks can be racially or economically differentiated, exacerbating the spread of harmful behavior during epidemics. Keywords COVID-19 · Health Inequality · BAME · Spatial clusters

1 Introduction The emergence of a new respiratory illness, caused by the severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2), popularly known as COVID-19, came to light when the first hospitalization was recorded in China on December 12, 2019 (Wu et al., 2020, Zhou et al., 2020). Epidemiologists attributed the disease to a A. Chakrabarti (B) · K. Chaudhuri · J. M. Lima Economics Department, Leeds University Business School, Leeds LS2 9JT, United Kingdom e-mail: [email protected] © The Author(s), under exclusive license to Springer Nature Singapore Pte Ltd. 2022 M. Dutta et al. (eds.), The Impact of COVID-19 on India and the Global Order, https://doi.org/10.1007/978-981-16-8472-2_7

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local seafood market in Wuhan, Hubei province. The disease-causing virus has since spread rapidly to other regions in the world leading the World Health Organization to declare it as a Public Health Emergency of International Concern on January 30, 2020.1 Europe was considered as a second hotspot for COVID-19 after the situation improved in China, with Italy, France, Germany, and Spain recording a doubling on their diagnosed cases every 2–4 days as of March 13, 2020 (Thamina et al., 2020, p. 4). The outbreak of the disease in the UK, first recorded on January 29, 2020 (EmburyDennis 2020), started taking alarming proportions propelling the government to initiate unprecedented lockdown measures in late March 2020. Subsequently (as of May 7, 2020), the UK has emerged as one of the top ten most affected countries with a very high observed case fatality ratio (John Hopkins University & Medicine 2020). Along with the spread of the dreaded respiratory illness, another growing cause for concern was the disproportionate impact on individuals from an ethnic minority background. The issue was first highlighted by the study conducted by Cook et al., (2020), which showed that until April 12, 2020, 63 percent of the 106 healthcare workers who had died from COVID-19 in the UK were from the Black, Asian, and minority ethnic (BAME) background. This also drove the UK government to initiate a further investigation on the risk faced by this group (Rimmer, 2020). The higher odds of contracting the disease for the community as a whole was confirmed when the initial pattern revealed Black and ethnic minority, and those from South-Asian origin appeared to have a higher likelihood of contracting COVID-19 compared to White ethnicity (Office for National Statistics, 2020a). A similar disproportionate prevalence of the disease for specific minority groups has also been recorded from Norway (Cookson and Milne, 2020) and the US (Centre for Disease Control and Prevention, 2020). For example, in the US there was an over-representation of the Black in overall hospitalized cases (Garg et al., 2020) and counties with the majority of the Black population had a significantly higher rate of COVID-19 cases and higher rates of death (Khunti, 2020). Higher risk of certain segments of the population to a pandemic of this nature posits major challenges to government, and channels through which such population groups become particularly susceptible to the disease raise concern. Are wealthier people healthier? This question has been grappled by health economists using both a theoretical framework (Wildman, 2003) and an empirical analysis based on broad health status indicators such as life-expectancy and mortality (Bartley, 2012; Marmot, 2007; Newton et al., 2015) or self-assessment measures of general health status (Shouls et al., 1996; Doorslaer & Koolman, 2004) drawn from the UK and EU nations. Although income inequality did emerge as an important factor to explain health disparities, other socio-economic dimensions such as nonlabor force participation, low levels of education, age, marital status, and nature of employment also emerged as important contributing factors. 1

World health Organisation (2020), Rolling updates on coronavirus disease (COVID-19), accessed on the 8th of May 2020, available at: https://www.who.int/emergencies/diseases/novel-coronavirus2019/events-as-they-happen.

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Apart from such individual characteristics, population health in the UK is also likely to be impacted by a strong neighborhood effect: “areas where high levels of illness tend to be clustered and that this is not fully explained by the individual characteristics of the people of whom the district population is composed” (Shouls et al., 1996, p. 373). For example, as pointed out by Shouls et al. (1996), a north–south mortality variation is seen in the UK within any social class such that northern areas depict consistently poorer mortality indicators. Newton et al. (2015) also emphasized on the importance of studying geographical variation in disease burden that may not be always explained by individual parameters. Can the disproportionate disease burden of COVID-19 on minority ethnic groups in England be attributed to disparity in some of the key socio-economic factors and spatial clustering discussed above? Note, although Office for National Statistics (2020a) has recognized the greater risk of the disease for this population sub-group, the underlying socio-economic reasons are yet to be quantitatively established. The report has only suggested the following characteristics as plausible factors: poor housing conditions, deprived neighborhood, low income, and employment status. Our analysis is the first step toward meeting this gap in the literature and establishes a direct link between COVID-19 mortality and socio-economic deprivation and ethnicity features. The primary focus of this study is the following: Firstly, to analyze the role of ethnicity in explaining COVID-19 deaths. Ethnicity was captured at disaggregated levels for Asian (separated as Indian, Pakistani, Bangladeshi, Chinese, and Other Asian), Black (separated as African, Caribbean, and Other Black), Mixed, and Other ethnic group with the White population being the excluded category. Secondly, to assess importance of socio-economic factors such as employment and education status in explaining COVID-19 mortality. Thirdly, to assess the significance of other (ill)health characteristics like cancer and circulatory diseases in explaining COVID19 death across the local area. Finally, to quantify the extent of the neighborhood effect of the COVID-19 disease burden that helps us to assess the impact of social distancing measures introduced by the government. We document that even after controlling for demography, socio-economic factors, and presence of higher fatality from other debilitating diseases, COVID-19 death is significantly higher in the local areas with a higher percentage of residents who are from Indian, Chinese, Black African, and Black Caribbean origin. Spatial regression result portrays a significant neighborhood effect: areas adjacent to neighborhoods reporting a higher proportion of people with certain ethnicity also showed a large indirect effect in spite of strict social distancing measures.

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2 Empirical Results and Analysis 2.1 OLS Regression Model and Data Source An OLS model was estimated to see the impact of ethnicity at disaggregated levels, other socio-economic factors, and presence of co-mortality in explaining the percentage of deaths due to COVID-19 across 6,726 Middle Layer Super Output Areas (MSOA) in England from April to July 2020 using the regression Eq. 1, which is defined as follows: Covid Pr opi = α +

10  i=1

θi X i + +

7 

γ j Z j + β1 H H si zei + β2 Pop Densit yi

j=1

+β3 Older Alonei + β4 GC S E i + β5 U nemploymenti + β6 DeathCanceri +β7 DeathCir culator y Diseasei + u i (1) where X i (i = 1 to 10) includes the percentage of population in ten different race/ethnic backgrounds, Z j (j = 1–7) denotes the proportion of population in seven different age categories, HH size is the average household size, Pop Density is the population density in each MSOA, Older Alone accounts for percentage of old people living on their own in each MSOA, GCSE and Unemployment control the educational and employment status of each local area, and Death Cancer and Death Circulatory Disease control for the presence of other debilitating health conditions in the MSOA. For further details on how each of the variables in Eq. 1 is defined, please look at Table 1. The percentage of death from COVID-19 for all persons at Middle Layer Super Output Areas (MSOA) in England is calculated using the data from the UK COVID-19 Dashboard: https://coronavirus.data.gov.uk/. Deaths from COVID-19 for all persons at MSOA in England is calculated as the total number of people who had a positive test result for COVID-19 and died within 28 days of the first positive test, reported on or up to the date of death.2 Deaths are allocated to the deceased’s usual area of residence. The ethnicity data used is from https:// www.nomisweb.co.uk/census/2011/ks201ew. The age profile of male and female residents, household size (HH size), and population density (Pop Density) data is from http://www.ons.gov.uk/ons/rel/census/2011-census/population-and-househ old-estimates-for-england-and-wales/index.html.3 Data on the percentage of pupils achieving 5 GCSE grades of A star to C including English and Maths, at the end of the academic year (GCSE), percentage of the working age population who are claiming out of work benefit (Unemployment), number of people aged 65 and over living alone, 2 3

Depending on availability. Tables PP05, PP06, and Table PHP01, respectively.

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155

Table 1 Descriptive statistics Variables

Acronyms

Observation

Mean

Standard deviation

Minimum

Maximum

Percentage of deaths due to COVID-19

Covid Prop

6,726

19.725

10.988

0.000

67.568

Percentage of mixed ethnicity

Mixed

6,726

2.169

1.768

0.154

11.230

Percentage of Indian

Indian

6,726

2.436

5.444

0.000

77.248

Percentage of Pakistani

Pakistani

6,726

1.917

5.922

0.000

77.575

Percentage of Bangladeshi

Bangladeshi

6,726

0.767

3.106

0.000

71.602

Percentage of Chinese

Chinese

6,726

0.645

0.917

0.000

12.889

Percentage of Other Asian

Other Asian

6,726

1.433

2.242

0.000

23.966

Percentage of Black African Black African

6,726

1.720

3.476

0.000

38.064

Percentage of Black Caribbean

Black Caribbean

6,726

1.054

2.241

0.000

22.669

Percentage of Other Black Ethnicity

Other Black

6,726

0.490

1.064

0.000

12.890

Percentage of Other Ethnicity

Other Ethnicity

6,726

0.939

1.581

0.000

21.626

Proportion in 25–34 age group

Pop 25–34

6,726

0.132

0.053

0.038

0.431

Proportion in 35–44 age group

Pop 35–44

6,726

0.140

0.019

0.040

0.234

Proportion in 45–54 age group

Pop 45–54

6,726

0.139

0.021

0.027

0.200

Proportion in 55–64 age group

Pop 55–64

6,726

0.119

0.030

0.022

0.206

Proportion in 65–74 age group

Pop 65–74

6,726

0.088

0.030

0.012

0.240

(continued)

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Table 1 (continued) Variables

Acronyms

Observation

Proportion in 75–84 age group

Pop 75–84

6,726

0.056

0.021

0.005

0.178

Proportion in Pop > 84 > 84age group

6,726

0.023

0.011

0.001

0.110

Household Size

HH Size

6,726

2.376

0.234

1.600

4.100

Population Density

Pop Density

6,726

32.799

34.363

0.100

247.200

Percentage of aged 65 and over living alone (out of total aged 65 and over)

Older Alone

6,726

32.271

6.826

14.400

65.500

Percentage of GCSE pupils achieving 5 GCSE grades of A star to C including English and Maths, at the end of the academic year

6,726

57.541

13.065

13.100

100.000

Percentage of the working age population who are claiming out of work benefit

6,726

1.878

1.466

0.100

11.600

Deaths from Death Cancer all cancer, all ages, standardized mortality ratio

6,726

102.069

20.440

48.400

201.100

Deaths from Death circulatory Circulatory disease, all Disease ages, standardized mortality ratio

6,726

102.715

25.764

38.200

254.100

Unemployment

Mean

Standard deviation

Minimum

Maximum

How Susceptible is the Black and Ethnic Minority (BAME)? …

157

as a percentage of the total number of people aged 65 and over, as reported in the 2011 Census (Older Alone), deaths from all cancer, all ages, standardized mortality ratio (Death Cancer), and deaths from circulatory disease, all ages, standardized mortality ratio (Death Circulatory Disease) are from Public Health England.

2.2 OLS Regression Results The OLS regression results are reported in Table 2. In agreement with Office for National Statistics (2020a), MSOA with higher ethnic sub-categories like Indian, Chinese, Black African, and the Black Caribbean has suffered significantly higher COVID-19 deaths (see Indian, Chinese, Black African, and Black Caribbean in Table 2). No such significant impact was observed for Bangladeshi and Pakistani minorities. Even after controlling for ethnicity and age composition, employment status matters: MSOA reporting a higher proportion of people claiming unemployment benefits are also the ones showing higher percentage of deaths due to COVID-19 (see Unemployment in Table 2). A similar positive impact is also seen for HH Size, Pop Density, Pop 75–84, Death Cancer, and Death Circulatory Disease. Our empirical finding suggests that socio-economic challenges posited by no jobs, family members residing in overcrowded houses (possibly because of cohabiting with extended family as is true for Asian culture), higher population density, presence of aged population and fatality from cancer and circulatory illness is positively associated with percentage of death due to COVID-19. Note, contrary to expectation, a unit increase in the percentage of pupils attaining GCSE and population belonging to relatively younger age category (25–34, 35–44) also increased the COVID-19 mortality.4 This could be explained as adolescents and young adults develop mild or no COVID-19 symptoms compared to other age groups, therefore spreading the disease to other individuals (World Health Organisation, 2020; Sebastiani and Palù, 2020). As shown, ethnic minorities are predisposed to COVID-19 mortality. This motivates a need for “urgent exploration through robust analysis of routinely collected prospective data on Covid-19, ….including place of residence, area clustering, sociodemographic factors, ….to determine if the observed signal between ethnicity and Covid-19 outcomes is real or an artefact” (Khunti, 2020, p. 2). Hence, we conduct a spatial impact analysis in the following sub-section.

4

In Table 2, we have also looked at an alternative specification (see Model 2) where we have controlled for Female to Male gender ratio across the different age categories along with the other explanatory variables in Eq. 1 as a robustness check. This does not change any of the results reported above.

158 Table 2 OLS regression results

A. Chakrabarti et al. Variable Mixed Indian

Model 1 0.167

0.181

(0.156)

(0.159)

0.137*** (0.033)

Pakistani

−0.020 (0.030)

Bangladeshi Chinese

0.080

0.605** 0.280** 0.211** 0.567*** (0.139)

Other Black

−0.268 (0.333)

Other Ethnicity Pop 25–34 Pop 35–44

0.539***

Pop 65–74 Pop 75–84

Pop Density

0.176* (0.077) 0.628*** (0.141) −0.151 (0.330) 0.679*** 32.171***

(7.271)

(7.409)

55.187***

40.220***

60.496***

(11.824) 55.053*** (13.360)

−2.849

8.950

(12.630)

(12.970)

4.500

16.947

(15.330)

(15.596)

64.859*** 17.066 (22.773)

HH Size

(0.096)

29.577***

(18.351) Pop > 84

0.276**

(0.123)

(12.944) Pop 55–64

0.661** (0.228)

(0.123)

(11.252) Pop 45–54

(0.032) (0.049)

(0.074) Black Caribbean

−0.017

(0.047)

(0.095) Black African

0.127*** (0.034)

0.065

(0.224) Other Asian

Model 2

8.887***

70.333*** (19.788) −39.616 (24.415) 8.939***

(1.484)

(1.570)

0.013*

0.012 (continued)

How Susceptible is the Black and Ethnic Minority (BAME)? … Table 2 (continued)

Variable

159 Model 1 (0.007)

Older Alone

0.056 (0.037)

GCSE

0.081*** (0.013)

Unemployment

0.475*** (0.136)

Death Cancer

0.033*** (0.009)

Death Circulatory Disease

0.021** (0.007)

Gender ratio 25–34

Model 2 (0.007) −0.001 (0.039) 0.080*** (0.013) 0.368** (0.135) 0.029** (0.009) 0.016* (0.007) 4.558*** (1.251)

Gender ratio 35–44

−1.978

Gender ratio 45–54

−3.467 (1.805)

Gender ratio 55–64

−2.336 (1.739)

Gender ratio 65–74

3.406* (1.451)

Gender ratio 74–84

2.983*** (0.870)

Gender ratio >84

1.524*** (0.269)

(1.663)

Constant R-squared N

−41.618*** (7.019) 0.230 6,726

−45.532*** (7.024) 0.238 6,725

Note Standard errors within brackets. ***, **, and * represent statistical significance at 1%, 5%, and 10% levels, respectively

2.3 Spatial Regression Results Figure 1 displays the COVID-19 death as a percentage of total deaths in 6,726 MSOA in England from April to July 2020. The color scheme depicted as red, black, yellow, white, and green shows the highest to lowest percentage of COVID-19 deaths. The map indicates clusters of regions with high COVID-19 fatality—primarily in London and surrounding areas, the Midlands, the North-western metropolitan areas of Manchester and Liverpool, and the North-east area of Newcastle and Durham. The

160

A. Chakrabarti et al.

Fig. 1 Percentage of COVID-19 deaths to all deaths in England

estimated residuals from the OLS regression in Fig. 2 indicate that despite controlling for disaggregated ethnicity, demographic characteristics, socio-economic indicators, and fatality from cancer and circulatory disease, the percentage of deaths of COVID19 is more clustered than expected in parts of London and its surroundings, in the Midlands, the North-West and North-East regions.5 Given this, we perform a spatial regression analysis using explanatory variables (listed in Table 1) and allow for spatial dependence in the percentage of COVID-19 deaths to total deaths using the 5

The presence of spatial effects in the estimated residual from OLS is being confirmed using Moran’s I-statistics.

How Susceptible is the Black and Ethnic Minority (BAME)? …

161

Fig. 2 Estimated from OLS regression

following equation6 :

6

We use contiguity matrix where bordering MSOA can influence one another, but not others. The pseudo-R2 remains almost the same irrespective of whether we use the contiguity matrix or the inverse-distance matrix.

162

Covid Pr opi = α + ϕW Covidpr opi +

A. Chakrabarti et al. 10  i=1

θi X i + +

7 

γj Z j

j=1

+β1 H H si zei + β2 Pop Densit yi + β3 Older Alonei + β4 GC S E i +β5 U nemploymenti + β6 DeathCanceri + β7 DeathCir culator y Diseasei + ei (2) where W denotes a spatial contiguity matrix, and ei the i.i.d. disturbance (Drukker et al., 2013). The spatial regression results based on disaggregated ethnicity and other explanatory variables are reported in Table 3. The estimated coefficient on the spatial lag of percentage of COVID-19 deaths (φ) is 0.182 and 0.184 for Model 3 and Model 4,7 respectively, indicating a significant positive correlation between the percentage of COVID-19 deaths in one MSOA and that in a neighboring MSOA.8 Table 4 shows both the direct and indirect marginal effects for each of the explanatory variables estimated using Model 3. The direct effect is the effect of the change within the MSOA whereas the indirect effect is the spill-over/neighborhood effect across MSOA that shares a border. The spatial regression results corroborate the OLS results. Positive and significant direct impact on the percentage of deaths from COVID-19 was found for the following: Indian (0.117), Chinese (0.518), Black African (0.177), Black Caribbean (0.512), Pop 75–84 (64.978), HH size (7.589), Pop Density (0.025), GCSE (0.066), Unemployment (0.429), Death Cancer (0.029), and Death Circulatory Disease (0.020). Such significant direct impact was also recorded for the population belonging to the younger age group. Importantly, the presence of spatial spill-over is validated by the positive significant indirect effect: Indian (0.019), Black African (0.029), Chinese (0.085), Black Caribbean (0.084), Pop 75– 84 (10.654), HH size (1.244), Pop Density (0.004), GCSE (0.011), Unemployment (0.070), Death Cancer (0.005), and Death Circulatory Disease (0.003). What possibly can explain the presence of certain ethnic minority communities exerting a positive significant spill-over effect on the percentage of COVID-19 deaths? Social networks can be racially differentiated and can exacerbate the spread of behavior that may be harmful or good during such epidemics (Bavel et al., 2020). A racially differentiated social network means that an individual from a minority ethnic background is more likely to increase the risk of infection/mortality for someone from his/her own background. Also, “racial and ethnic minority communities, in particular, have both historical and contemporary experiences of discrimination, leading to distrust” (Bavel et al., 2020, p. 4). Hence, individuals from certain community groups may be more resistant toward adopting personal safety measures suggested 7

Model 4 controls for Female to Male gender ratio across the different age categories, similar to Model 2. 8 In Table 3, we have also looked at an alternative specification (see Model 4) where we have controlled for Female to Male gender ratio across the different age categories along with the other explanatory variables in Eq. 2 as a robustness check. This does not change any of the results reported above.

How Susceptible is the Black and Ethnic Minority (BAME)? … Table 3 Results from spatial regression model

163

Variable

Model 3

Model 4

Mixed

−0.028

−0.013

(0.154)

(0.157)

0.116***

0.109**

Indian

(0.033)

(0.034)

Pakistani

−0.023

−0.019

(0.030)

(0.031)

Bangladeshi

0.025

0.040

(0.047)

(0.050)

0.516*

0.571*

Chinese

(0.221)

(0.225)

Other Asian

0.257**

0.251**

(0.095)

(0.096)

Black African

0.176*

0.147*

(0.073)

(0.075)

0.510***

0.552***

Black Caribbean

(0.137)

(0.139)

Other Black

−0.278

−0.161

(0.332)

(0.329)

Other Ethnicity

0.415***

0.538***

(0.121)

(0.122)

20.920**

22.507**

Pop 25–34

(7.363)

(7.524)

Pop 35–44

54.411***

41.058***

(11.069)

(11.633)

Pop 45–54

40.874**

36.831**

(13.040)

(13.383)

−2.768

7.112

Pop 55–64

(12.398)

(12.723)

Pop 65–74

−6.500

4.777

(15.055)

(15.305)

Pop 75–84

64.735***

68.957***

(17.914)

(19.326)

31.360

−22.331

Pop > 84

(22.219)

(23.810)

HH Size

7.561***

7.477***

(1.469)

(1.549)

Pop Density

0.025***

0.024*** (continued)

164 Table 3 (continued)

A. Chakrabarti et al. Variable

Model 3 (0.007)

(0.007)

Older Alone

0.048

−0.006

(0.036)

(0.038)

GCSE

0.065***

0.064***

(0.013)

(0.013)

0.427**

0.332*

Unemployment

Model 4

(0.132)

(0.132)

Death Cancer

0.029***

0.026**

(0.009)

(0.009)

Death Circulatory Disease

0.020**

0.016*

(0.007)

(0.007)

Gender ratio 25–34

3.808**

Gender ratio 35–44

−2.213

Gender ratio 45–54

−2.619

(1.226) (1.625) (1.770) Gender ratio 55–64

−1.366

Gender ratio 65–74

3.180*

Gender ratio 75–84

2.901***

(1.709) (1.424) (0.856) Gender ratio 84
84

31.477

HH Size

−3.234 −7.594

***

10.654

***

7.589

***

1.244

***

8.833

***

Pop Density

0.025

***

0.004

***

0.029

***

Older Alone

0.049

GCSE

0.066

***

0.011

***

0.077

***

Unemployment

0.429

***

0.070

***

0.499

***

5.161

75.632

***

36.639

0.008

0.057

Death Cancer

0.029

***

0.005

***

0.034

***

Death Circulatory Disease

0.020

***

0.003

***

0.024

***

Note ***, **, and * represent statistical significance at 1%, 5%, and 10% levels, respectively

by political institutions and furthermore convey the negative signal to others in their own community. A similar explanation may account for the indirect spill-over effect across MSOAs based on employment status.

3 Conclusion As the world struggles to cope with the COVID-19 outbreak with drastic social and economic adjustments due to the stringent social distancing norms to reduce the spread of the pandemic, a message seemed to emanate from the media and the political front-runners: the virus does not discriminate! However, our empirical study paints a contrasting picture. The virus and the death thereof disproportionately affect

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the local areas with an over-representation of individuals who are Indian, Chinese, Black African, and Black Caribbean ethnicities. Moreover, the most susceptible are the areas with residents in the vulnerable age group (75–84), with high population density or areas that already demonstrate relatively higher mortality from cancer and circulatory illnesses. Surprisingly, MSOA with a higher percentage of pupils achieving 5 GCSE also reported higher COVID-19 deaths. Contrary to popular notions, a unit increase in the younger adult population resulted in higher COVID-19 deaths. This could be explained as adolescents and young adults develop mild or no COVID-19 symptoms compared to other age groups, therefore spreading the disease to other individuals. The spatial regression results suggest a strong neighborhood effect in the disease burden. Areas adjacent to neighborhoods reporting higher Indian, Chinese, Black African, and Black Caribbean ethnicities also showed a large indirect effect in spite of strict social distancing measures. This questions the practice seen in the existing literature and government messages to club the ethnic minority as one homogeneous entity (BAME) because our empirical result suggests that there are substantial differences within the minority in terms of their COVID-19 fatalities even after we control for other socio-economic and demographic aspects. Apart from the inherent genetic disposition (for minority groups) and inequality in access to resources and employment opportunities, higher prevalence of mortality in certain segments of the population is possibly because social networks can be racially or economically differentiated and can exacerbate the spread of behavior that may be harmful or good during such epidemics. An empirical analysis of how such differences in behavioral norms and choices across communities have impacted their risk of contracting the deadly virus is left for future investigation.

Reference:s Bartley, M. (2012). Explaining health inequality: Evidence from the UK. Social Science & Medicine, 74, 658–660. Bavel, J. J. V., Baicker, K., Boggio, P. S., et al. (2020). Using social and behavioural science to support COVID-19 pandemic response. Nature Human Behaviour, 4, 460–471. https://doi.org/ 10.1038/s41562-020-0884-z Cook TE, Kursumovic E, Lennane S et al. (2020) Exclusive: deaths of NHS staff from covid-19 analysed. Health Service Journal, April 22 2020. https://www.hsj.co.uk/exclusive-deaths-of-nhsstaff-fromcovid-19-analysed/7027471.article Cookson C, Milne R (2020) Nations look into why coronavirus hits ethnic minorities so hard. Financial Times, April 29 2020, accessed on 9th May, 2020. https://www.ft.com/ Doorslaer, E. V., & Koolman, X. (2004). Explaining the differences in income-related health inequalities across European countries. Health Economics, 13, 609–628. Drukker, D. M., Prucha, I. R., Raciborski, R., et al. (2013). Maximum likelihood and generalized spatial two-stage least-squares estimators for a spatial-autoregressive model with spatialautoregressive disturbances. Stata Journal, 13, 221–241. Embury-Denis T (2020) Coronavirus: A timeline of how Britain went from ‘low risk’ to an unprecedented national shutdown. Independent, March 21 2020, accessed on 8th

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May, 2020. https://www.independent.co.uk/news/uk/home-news/coronavirus-uk-timeline-dea ths-cases-covid-19-nhs-social-distancing-a9416331.html Garg S, Kim L, Whitaker M et al. (2020) Hospitalization Rates and Characteristics of Patients Hospitalized with Laboratory-Confirmed Coronavirus Disease 2019 — COVID-NET, 14 States, March 1–30, 2020. MMWR Morb Mortal Wkly Rep. 69:458–464. DOI: http://dx.doi.org/https:// doi.org/10.15585/mmwr.mm6915e3 John Hopkins University & Medicine (2020) Mortality in the most affected countries, Corona Virus Resource centre. Accessed on 8th of May, 2020. https://coronavirus.jhu.edu/data/mortality. Khunti, K. (2020). Is ethnicity linked to incidence or outcomes of covid-19? British Medical Journal, 2020, 369. https://doi.org/10.1136/bmj.m1548 Marmot, M. (2007). Achieving health equity: From root causes to fair outcomes. Lancet, 370, 1153–1163. Newton, J. N., Briggs, A. D. M., Murray, C. J. L., et al. (2015). Changes in health in England, with analysis by English regions and areas of deprivation, 1990–2013: A systematic analysis for the Global Burden of Disease Study 2013. Lancet, 386, 2257–2274. Office for National Statistics (2020a) Coronavirus (COVID-19) related deaths by ethnic group, England and Wales: 2 March 2020 to 10 April 2020. Accessed on 8th May, 2020. https://www. ons.gov.uk/ Office for National Statistics (2020b), Deaths involving COVID-19 by local area and socioeconomic deprivation: deaths occurring between 1 March and 17 April 2020. Accessed on 8th May, 2020. https://www.ons.gov.uk/ Rimmer A (2020) Covid-19: Disproportionate impact on ethnic minority healthcare workers will be explored by government. British Medical Journal, 2020:369. : https://doi.org/10.1136/bmj. m1621 Sebastiani G and Palù G (2020) COVID-19 and school activities in Italy. Viruses 2020, 12(11):1339. https://doi.org/10.3390/v12111339 Should, S., Congdon, P., & Curtis, S. (1996). Modelling inequality in reported long term illness in the UK: Combining individual and area characteristics. Journal of Epidemiology and Community Health, 50, 366–376. Thamina, A., Uddin, N., Das, J., et al. (2020). Evolution of severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2) as coronavirus disease 2019 (COVID-19) pandemic: A global health emergency. Science of the Total Environment, 730, 1–19. https://doi.org/10.1016/j.scitot env.2020.138996 Wildman, J. (2003). Modelling health, income and income inequality: The impact of income inequality on health and health inequality. Journal of Health Economics, 22, 521–538. World Health Organisation (2020) Coronavirus disease (COVID-19): Adolescents and youth. Newsroom, 4 May 2020. Accessed on 4th April, 2021. https://www.who.int/news-room/q-a-det ail/coronavirus-disease-covid-19-adolescents-and-youth#:~:text=As%20with%20adults%2C% 20pre%2Dexisting,intensive%20care%20admission%20in%20children. Wu, F., Zhao, S., Yu, B., et al. (2020). A new coronavirus associated with human respiratory disease in China. Nature, 579, 265–269. https://doi.org/10.1038/s41586-020-2008-3 Zhou, P., Yang, X., Wang, X., et al. (2020). A pneumonia outbreak associated with a new coronavirus of probable bat origin. Nature, 579, 270–273. https://doi.org/10.1038/s41586-020-2012-7

The Shock that Shook the World

Effects from Pandemic and Stagnation Seppo Honkapohja and Kaushik Mitra

Abstract We discuss the impact of shocks from a sudden pandemic in a recessionary economy. The framework is one of imperfect knowledge with a standard New Keynesian model. Global aspects of dynamics with possible interest-rate lower bound are discussed. Keywords Stagnation · Expectations · Productivity shocks · Adaptive learning · New Keynesian model JEL classification: E62 · E63 · E52 · D84 · E71

1 Introduction The global financial crisis of 2008–09 dealt a major blow to most market economies. It displaced many economies from their trend growth path and even at present many countries have not been able to get back to their pre-2008 trend levels. This period of stagnation can be viewed as an episode of very slow recovery or ongoing stagnation, see the comments on the literature below. The COVID pandemic that struck in 2020 can be seen as generating further macroeconomic shocks that led to worsening macroeconomic developments for countries still recovering from the 2008–09 crisis. We begin by looking at basic macro data from 2005 to present. This period covers the aftermath of the 2008–2009 financial crisis and also the very beginning of the crisis from the COVID pandemic. Figure 1 illustrates the post-2008 development of GDP per capita for the United States of America and the Euro area. It is seen that the 2008–2009 financial crisis resulted in sharp recession in both economies. For the US, the decrease from 2007Q4 to 2009Q2 was about 6.0%. For the euro area, the drop in GDP per capita from S. Honkapohja Aalto University School of Business, Helsinki, Finland K. Mitra (B) University of Birmingham, Birmingham, England, UK e-mail: [email protected] © The Author(s), under exclusive license to Springer Nature Singapore Pte Ltd. 2022 M. Dutta et al. (eds.), The Impact of COVID-19 on India and the Global Order, https://doi.org/10.1007/978-981-16-8472-2_8

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Fig. 1 Real GDP per capita, US and Euro area

2008Q1 to 2009Q2 was 5.5%. The US and Euro area economies started to recover quickly after the rapid decline of GDP in 2008, but neither has succeeded in returning to its pre-2008 trend. The data for the beginning of 2020 shows a new decline in GDP per capita due to the COVID pandemic. The decline appears to be even bigger than 2008 recession. Unfortunately, it is not possible at present to have a complete picture as the pandemic is ongoing. Figure 2 shows the monthly development of consumer prices in the US and Euro area since 2005. The data describes core inflation, i.e., the relatively volatile food and energy prices are excluded from the indices. The data is seasonally and work-day adjusted. Looking at the period 2008 and after, it is seen that during the financial crisis US price index shows a couple of short intervals with basically constant prices, but otherwise the US index of core prices has risen at relatively constant rate. In the Euro area, the core price index has continuously increased but its rate of change decreased in the 2009–2010 time interval and more systematically since 2013. The beginning of 2020 marks possible additional variability for the US and Euro area core consumer prices. The data does not show actual deflation even if the risk of deflation has been discussed. The development of interest rates has been remarkable as a very rapid decline of policy interest rates to near-zero level occurred in the 2008–2009 crisis, see Fig. 3. Both the US Federal Reserve (US Fed) and the European Central Bank (ECB) lowered their policy rate to approximately zero (and later even slightly below zero by Euro area). Policy rates have, on the whole, remained at remarkably low levels since the financial crisis. US Fed lowered its policy rate very quickly to 0.25 percent (on

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Fig. 2 US and Euro area monthly consumer prices, excluding food and energy in 2005–2020

Fig. 3 Policy interest rates of the US Federal Reserve and the ECB from 2005 to present

average). It then began to increase very gradually the rate in 2016 and by 2019 the rate reached 2.5 percent as an attempt to get to normal levels of the policy rate. This process was abruptly reversed when an onset of the COVID crisis became inevitable. The ECB lowered its policy rate very quickly to one percent, but its subsequent decisions became gradualist until 2015, when the ECB policy rate was reduced to zero and then in 2016 even to slightly negative level. The ECB did not have an

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opportunity to attempt a move to more normal policies before the start of the COVID crisis. The extensive period of very low levels of policy interest rates is unusual as it can be argued that it is not possible to lower these rates much below zero.1 For convenience, we refer to the approximately zero interest rates as the ZLB (zero lower bound). The remarkable macroeconomic phenomena since 2008–2009 have led to different ways of thinking about the period since 2008–2009 and alternative approaches have been introduced to study the post-crisis macroeconomic developments. One possibility is to see the 2008–2009 crisis as a persistent major shock to economic fundamentals that have displaced the economies from their normal time path for a significant period of time. In this approach, it is thought that the economy will return to normality once the shock has subsided. In the literature, a persistent exogenous shock to the discount rate of households or, more plausibly, emergence of a creditspread is seen as the fundamental reason for very low interest rates and sluggish GDP development. These shocks have been emphasized, for example, by Eggertsson and Woodford (2003), Christiano et al. (2011), Corsetti et al. (2010), and Woodford (2011). While this approach has been fruitful in suggesting suitable monetary and fiscal policy responses to such shocks, it has several somewhat unattractive features. It relies heavily on the persistence of a shock that evaporates according to an exogenous process, and recession ends as soon as the exogenous negative shock ends. Furthermore, this approach does not do justice to an independent role for expectations. A different view is to think of the post-2008 period as a new macroeconomic regime, ongoing (or “secular”) stagnation.2 The stagnation regime was effectively initiated by negative shocks that created uncertainties and pessimism to economic expectations. In standard New Keynesian macroeconomic models, stagnation can be a possible macroeconomic regime that arises from a second self-fulfilling steady state due to the ZLB. This approach, developed by Benhabib et al. (2001) and first used by Reifschneider and Williams (2000), emphasizes the existence of multiple rational expectations equilibria (REE) when the interest-rate rule is subject to the ZLB. Bullard (2010) suggests the possibility of the second equilibrium using data for the US and Japan. The second approach has a major weakness in that it fails to associate times when the ZLB binds with periods of recession and possibly stagnation. The source of the problem lies in the very minor long-run trade-off between output and inflation in the NK model: the level of aggregate output at the unintended low inflation steady state is only slightly below that of the intended steady state. However, real-world evidence clearly points to the contrary, e.g., real GDP per capita for the US, Japan, and the

1

A sufficiently negative rate would lead to hoarding of cash, which would have stability issues for the financial system. 2 For different arguments and explanations about long-lasting stagnation see, for example, Summers (2013), Teulings and Baldwin (2014), Eggertsson et al. (2019) and Benigno and Fornaro (2018).

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euro area since 2001 has been at depressed levels along with the prevalence of the ZLB. This is inconsistent with the view of two steady states in the second approach. In this paper, we employ the stagnation approach and use a nonlinear New Keynesian model with imperfect knowledge and learning to study consequences of the COVID shocks to stagnation dynamics.3 As already noted, the onset of the COVID pandemic is seen as adding new negative shocks to the aftermath of shocks from the global financial crisis. We consider two types of negative shocks: (i) persistent shock of pessimism to expectations of aggregate output and (ii) persistent negative shock to total factor productivity. Dynamics of more severe stagnation arising from the COVID shocks are then described and possibilities for macroeconomic policies to improve outcomes are considered. Our analysis is very much a first approach, as a one-sector model is used and dynamics of a health epidemic are not integrated to macroeconomic dynamics.

2 Imperfect Knowledge and Learning This paper belongs to the line of research that emphasizes imperfect knowledge and learning behavior in the formation of expectations about the future. This is in contrast to the hypothesis of rational expectations (RE) which has been the main paradigm for modeling expectation formation in macroeconomics over the past several decades. RE is usually formulated as complete knowledge of the systematic aspects of the economy including the underlying model structure and parameters. This assumption is sometimes restrictive since, in reality, economic and policy decisions are made under incomplete knowledge about the underlying structure and parameters. Naturally this has led to an increase in interest in situations where agents are endowed with less knowledge than is presumed by RE. For instance, economic agents may try to improve their knowledge of the economy by using recursive least squares over time. This approach to adaptive learning behavior has been incorporated into macroeconomic theory since the 1990s (see, e.g., Evans and Honkapohja (2001)). Despite limited knowledge on the part of agents, the economy can still converge to a rational expectations equilibrium (REE) in the long run, provided an appropriate stability condition is satisfied. Furthermore, the typical situation analyzed in economic models is one in which no future change in structure (surprise or anticipated) is contemplated by economic actors (and/or policymakers). However, in practice, changes in economic structure (e.g., policy changes) do take place. Again the standard way to analyze such policy changes in economic models continues to be the assumption of RE. The benchmark assumption of RE is very strong and arguably unrealistic when analyzing the effect of structural or policy changes. Economic agents need to have complete knowledge 3

The RE viewpoint to multiple equilibria has some difficulties, which can be avoided by modeling expectations as arising from adaptive learning behavior, see the discussion in Evans et al. (2020). The approach was developed by Evans et al. (2008), Benhabib et al. (2014).

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of the underlying economic structure, both before and after the policy change. They must also fully and rationally incorporate this knowledge in their decision-making, and do so under the assumption that other agents are equally knowledgeable and equally rational. The COVID pandemic may be thought of as a supply shock (say a negative shock to productivity as in Section 5.2 below) which changes the structure of the economy. Assuming RE after these sudden, large unpredictable changes to the economy is especially problematic. Adaptive learning instead becomes a much more plausible approach to analyze these situations. The essence of the adaptive learning approach is that agents are assumed not to understand the general equilibrium considerations that govern the evolution of the central endogenous variables (e.g., labor and factor prices). Again the assumption of imperfect knowledge is particularly plausible in the face of sudden, large changes in the economy. When learning agents are assumed to forecast the key variables statistically using any number of a variety of reduced form estimation techniques. In the learning literature, the benchmark is to assume recursive least-squares updating in which agents use a “perceived law of motion” (an econometric specification) that corresponds in functional form (but not parameter values) to the rational expectations equilibrium of interest. However, the learning approach is flexible and alternative assumptions include agents using underparametrized or pure time-series models. In the context of infinite-horizon agents solving dynamic optimization problems, our approach can be viewed as a version of the “anticipated utility” approach formulated by Kreps (1998) and discussed in Sargent (1999) and Cogley and Sargent (2008). In this approach, at every date, given their future forecasts agents use the solution of a dynamic optimization problem, which is based on an estimated forecasting model, to compute their decisions for that date. This process is repeated in the next period, i.e., agents resolve their dynamic optimization problem to make their decisions in the following period by updating their future forecasts and forecasting model. As recommended by Kreps, in the anticipated utility approach, while agents do update their forecasts over time, they do not take into account the fact that their forecasting model will change in later periods. This is the sense in which this approach is boundedly rational: the uncertainty in the parameters of the estimated forecasting model would be taken into account in a full Bayesian approach. The difficulty with the Bayesian approach is that this cannot be typically implemented in macroeconomic settings since it is too complicated. Consequently, the anticipated utility approach becomes quite attractive and is an appealing way to implement bounded rationality. Moreover, the approach often provides an excellent approximation of Bayesian decisions, as noted by Cogley and Sargent (2008).

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3 The Model 3.1 Household-Producers The model is a standard New Keynesian macroeconomic model in which nominal price stickiness arises from adjustment costs in price setting as suggested by Rotemberg (1982). We use the Rotemberg formulation rather than the Calvo (1983) model of price stickiness because it enables us to study global dynamics in the nonlinear system. The analysis is based on the model in Evans et al. (2020) which should be consulted for the formal details. Here we only outline the structure of the model. There is a continuum of household-producers, who produce differentiated goods and are maximize present value of period utilities over an infinite horizon. Utility in each period depends (i) positively on the aggregate of private consumption and a weighted value of government consumption per capita, (ii) positively on beginning-of-period real balances, (iii) negatively on labor supply, and (iv) negatively on price adjustment costs. Utility functions are identical across agents. The flow budget constraint (in real terms) states that resources are spent on consumption, end-of-period real balances, end-of-period bond holding, and payment of lump-sum tax. The recourses come from initial money balances, interest, and principal from bond holdings and revenue from production activity. Households treat government spending per capita as exogenous. The household decision problem is also subject to the usual “no Ponzi game” (NPG) condition. Household expectations over the entire future are in general subjective and may not be rational in accordance with anticipated utility maximization. Formally, subjective expectations are expectations of nonlinear functions of future random variables with an unknown distribution. A specific form of bounded rationality is assumed, so agents use point expectations, i.e., agents treat the expectation of a nonlinear function of random variables as equal to the value of the nonlinear function at the point expectations. The quality of this approximation depends, of course, on the severity of nonlinearities and the size of the shock variances. Production function yt,i = At h αt,i for each good variety i depends on labor input h t,i by the household-producer and a random aggregate productivity variable At . Production functions including the realization of the value of At are taken to be identical across agents. Output is differentiated and firms operate under monopolistic competition. Household-firms face a downward-sloping demand curve with random constant elasticity of substitution νt between any two goods.

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3.2 Government For simplicity, the government is assumed to follow a balanced budget policy, so that lump-sum taxes are used to pay for government spending.4 The discount factor is given by the product of one-period real (gross) interest rates which in turn is the ratio of nominal (gross) interest rate divided by (gross) inflation. Monetary policy is specified as a forward-looking interest-rate rule taking an exponential form e e , yt+1 ) Rt = R(πt+1

= 1 + (R ∗ − 1)



e  B R ∗ /(R ∗ −1) πt+1 π∗



e φ y yt+1 y∗

(1) ,

(2)

e e where Rt denotes the gross nominal interest rate in period t and πt+1 , yt+1 denote 5 inflation and output expectations in period t + 1. B > 1 and φ y ≥ 0 are policy parameters and R ∗ = β −1 π ∗ is the policy interest rate at the target steady state π ∗ . y ∗ is the level of output associated with π ∗ . As R ∗ ≥ 1 this interest-rate rule satisfies the zero lower bound for net interest rates. Market clearing in aggregate and for each goods variety hold in the usual way. It is assumed the government can require that its demand is always met, so we have yt,i ≥ gt (i) for all i and yt ≥ gt . Government purchases are distributed equally to the households. As government guarantees a subsistence level of consumption to households, agents are required to pay their taxes and hence must work to produce at least the amounts that the government purchases.

3.2.1

Pricing Decisions P

t, j ) is assumed to be asymmetric in the The price adjustment cost function ( Pt−1, j inflation factor Pt, j /Pt−1, j for variety j. Here Pt, j is the price of goods variety j in period t. Decisions of consumption, production, and pricing are identical for all agents and goods varieties in the representative agent economy with identical agents and expectations. The optimal pricing, i.e., inflation is given by



 (πt )πt = ζt +

∞ 

e β s ζt+s , where

s=1

e ζt+s

4

νe = t+s α



e yt+s Aet+s

(1+ε)/α

(3)

 e  e e e − νt+s − 1 yt+s × (yt+s − (1 − ξ )gt+s )−1 ,

Similar analysis can be done with more general formulation of government budget constraint and policy. For example, see Benhabib et al. (2014). 5 Forecast of a variable is denoted using the superscript e.

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provided a transversality condition holds. β is the subjective discount factor and 0 < ξ < 1 is a parameter in the agents’ utility function. We will treat (3) as the pricing decision rule. e by agent i as producer generally requires forecasts of paths In (3) the forecast ζt+s,i for the exogenous variables νt+s , At+s , the fiscal policy variable gt+s , aggregate , market demand yt,i for variety i, and the term in output yt+s , relative price PPt+s,i t+s ct+s,i + ξ gt+s that arises from marginal utility. (3) is a conditional decision rule as Pt+s,i includes a future decision variable of the firm i. Agents are assumed to use this Pt+s . conditional decision rule supplemented by forecasts of the future relative prices PPt+s,i t+s e e 6 As a simplification, it is assumed that Pt+s,i = Pt+s for all i. Agents are assumed to use adaptive learning based on observed Pt,i /Pt to forecast their expected future relative price. Thus, agents’ future pricing decisions Pt+s,i will not in general be consistent with what would be their optimal choices under current expectations.7 Similarly, if heterogeneous agents were allowed for, each agent would need to forecast its own output (demand) yt+s,i as well as aggregate output yt+s . In the representative agent case, these are identical and agents have learned this relationship. e e − (1 − ξ )gt+s )−1 in (3) can then be conveniently The marginal utility term (yt+s forecasted using the market clearing condition. For fiscal policy, we focus on the case in which the path of future government spending is credibly announced and is therefore known.

3.2.2

Consumption and Temporary Equilibrium

For the representative agent model, the consumption function can be shown to be     ξβ + ct = max 0, (1 − β) yt − gt 1 + 1−β

∞   e −1  e  e (1 − β) Dt,t+s yt+s − gt+s (1 − ξ ) .

(4)

s=1

e Letting rt+ j

denote the forecasted gross real interest rate,

e Dt,t+s

=

s 

e rt+ j is the point

j=1

expectation of the real discount factor for s periods into the future. For simplicity, e e and gt+s denote expected agents are assumed to know the interest-rate rule. yt+s aggregate output (income) and government purchases per capita, respectively. We collect the expectation variables, which are taken as given in the time t equie } and productivity shocks {Aet+s }, output librium: {νt+s e exogenous markup shocks e e }, as well as the implied yt+s , government spending {gt+s }, and inflation {πt+s e is forecasted aggregate price level for period t + s. Pt+s 7 Note that the agent’s forecasts of future aggregate variables will in general be revised over time as new data become available. 6

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e discount factors Dt,t+s . Temporary equilibrium consumption, output, interest rates, and inflation are determined from (3), (4), interest-rate rule, and market clearing for period t. Following the adaptive learning literature, our approach views aggregate dynamics as a sequence of temporary equilibria. At each point t, exogenous random variables are realized, economic agents form expectations of relevant future variables, and their optimal decision rules are formed conditional on those expectations. Market clearing determines the time t temporary equilibrium levels of all variables including aggregate output and inflation. In the subsequent period t + 1, new values of exogenous variables are realized, expectations are revised, and a new temporary equilibrium is generated. Adaptive learning specifies how the forecast rules that are used to form expectations conditional on the information available are revised over time.

4 Dynamics of the Economy We now consider how expectations are revised over time and the aggregate dynamics under adaptive learning. The key bounded-rationality assumption is that forecasts of future variables are made using the adaptive learning approach. Agents in the model are assumed to forecast like econometricians, regressing variables to be forecasted on observed explanatory variables, and updating the forecast rule coefficients as new data become available. Updating of the coefficients is done using the recursive leastsquares learning to expectation formation as developed in Bray and Savin (1986), Marcet and Sargent (1989), and Evans and Honkapohja (2001). Because the model is nonlinear and stochastic, it is illuminating to begin with the nonstochastic case in which adaptive learning rules are particularly simple. The nonstochastic version of the model provides initial formal results and also intuition to the global picture of the dynamics of the economy. If the random shocks are small, the nonstochastic version gives an approximation for the mean dynamics of the model. The shocks are fixed to be constants νt = ν > 1 and At = A > 0 and also e = g. ¯ government spending and its forecasts are fixed and constant gt+s = gt+s In the nonstochastic case, agents’ forecasting model reflects a steady state and agents’ beliefs are thus about the long-run averages. Introducing the notation e e = yte , and πt+s = πte for all s > 0 yt+s

for expectations in period t over all future periods, adaptive learning rules for the nonstochastic case take the simple form e e + ω(yt−1 − yt−1 ) and yte = yt−1

(5)

e e πte = πt−1 + ω(πt−1 − πt−1 ),

(6)

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where 0 < ω < 1 is the learning “gain” parameter. Adaptive learning usually focuses on cases with ω small and examines local stability of steady states for all sufficiently small ω > 0. Adaptive-learning rules of the form (5)–(6) are often called “steadystate learning” since agents are estimating a mean rather than a more complex timeseries model. Different assumptions about the computation of the mean have been used in the literature. One possibility is the simple arithmetic mean (so that all data receive an equal weight, namely, traditional “least-squares learning”) while another is to allow for different weights on the data. It is assumed here that agents use exponential discounting of past data, an assumption commonly used in the learning literature when agents are concerned that structural changes may be occurring. Thus, agents use a weighted average of observed inflation rates to estimate their mean, which they use to forecast future inflation rates. To elaborate further, the parameter ω in (5)–(6) measures the extent to which past data is discounted. Under this algorithm, the relative weight on data j periods earlier is (1 − ω) j , i.e., past data is discounted at rate 1 − ω. The optimal choice of ω is not straightforward and is most naturally addressed in a stochastic framework, since it involves a trade-off of “filtering” and “tracking.” Lower values of ω more effectively filter out random noise, while higher values of ω are better at tracking structural change. Because the optimal choice of ω in general, and in the current context, is not straightforward, ω is treated as a given parameter.8 To continue, in a perfect-foresight steady state yt = y e = y and πt = πte = π, thus we have (1 − β) (π )π =

ν (y/A)(1+ε)/α − (ν − 1) y × (y − (1 − ξ )g) ¯ −1 . α

(7)

The Fisher equation with the interest-rate rule R(π, y)/π = β −1 is the remaining steady-state equation. As is well known, there is a targeted steady state with π = π ∗ and the level of output y ∗ determined from (7) with π = π ∗ . It is also well known that, due to ZLB, requiring y > g¯ and R(π, y)/π = β −1 with R  (π, y) < β −1 results in a second steady state (π L , y L ) with π L and y L determined from equations R(π, y)/π = β −1 and (7). Under the adopted model calibration 1 > π L > β with ¯ then there π L ≈ β.9 Finally, if output is constrained to the lower bound y = g = g, exists a third, stagnation steady state, with inflation π S (actually deflation) at this ¯ steady state determined from (7) with yS = g. We now turn to stability of the three steady states π ∗ , π L , and π S under adaptive learning.10 Stability and instability properties of the three steady states are: (i) The targeted steady state at (π ∗ , y ∗ ) is locally stable under steady-state learning,

See Evans and Honkapohja (2001), Chap. 14, for a discussion of the choice of ω in stochastic models with structural change. 9 If the ZLB were binding at π = π , so that R = 1, then π = β and there would be deflation. L L 10 Loosely speaking, a stable steady state may be viewed as an asymptotic outcome of the use of recursive least squares and related learning rules by private agents. 8

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Fig. 4 Global E-stability dynamics with curve SS giving the boundary of

provided the policy parameter φ y is not too large. (ii) The steady state (π L , y L ) is not locally stable under steady-state learning if φ y is not too large. (iii) The steady state (π S , yS ) is locally stable under steady-state learning, provided φ y is not too large.11 Because we have fully specified the temporary equilibrium of the nonlinear system, the analysis can be extended to look at the global system under learning. Figure 4 shows a schematic phase diagram for the system which illustrates the target steady state (π ∗ , y ∗ ), the liquidity trap steady state (π L , y L ), and the stagnation steady state ¯ 12 The two steady states (π ∗ , y ∗ ) and (π L , y L ) have been widely discussed in (π S , g). the literature.13 As noted above, (π ∗ , y ∗ ) is locally stable under the learning dynamics, while (π L , y L ) is locally unstable. (π S , g) is locally stable under learning. At ¯ output y = g¯ is at the minimal level, with households receiving only g¯ as (π S , g) Condition that φ y not be too large is standard and known to be necessary, with forward-looking interest-rate rules, in order to avoid indeterminacy of the targeted steady state. 12 Figure 5 shows a numerical illustration of the phase diagram and the domain of attraction of (π ∗ , y ∗ ). The diagram is limited to an area that includes steady states (π ∗ , y ∗ ) and (π L , y L ). 13 See, e.g., Benhabib et al. (2014) and the references therein. 11

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subsistence consumption (private consumption is zero). This steady state also involves rapid deflation. For the targeted steady state (π ∗ , y ∗ ) it is possible to construct a set , called the domain of attraction in the expectations space of (πte , yte ). consists of all points (πte , yte ) ∈ such that the economy with (πte , yte ) as the initial conditions will converge under learning to the targeted steady state (π ∗ , y ∗ ). Figure 4 illustrates so that the curve SS is its outer boundary. Note that there is divergence to the stagnation steady state from all points outside , except for the one-dimensional curve SS which is the global stable manifold of (π L , y L ). Thus, there is a real possibility that after significant shocks, in the form of an adverse shift in expectations (π e , y e ), the economy moves into and becomes stuck in a region leading to stagnation under unchanged policy with government spending remaining constant and the central bank adhering to its interest-rate rule and ZLB. Figure 4 illustrates some challenges in the design of fiscal and monetary policy.14 It is evident that if the economy is within the stagnation region, then sufficiently aggressive policy needs to be taken so that dynamics are transferred to inside the domain. Clearly the size of the required policy change will depend on the initial position (π e , y e ) following the shock and thus choosing the magnitude of the policy can be delicate. It is important to note that the preceding analysis is only approximate if there are small random shocks At and υt . With shocks, steady states become stochastic and the concept of convergence or divergence is in general probabilistic. One can numerically construct a domain of attraction, for example, by requiring convergence with probability one. (Such a domain would be inside .) Then convergence to the target steady state occurs only with positive probability if the economy moves slightly across the boundary of a stochastic domain of attraction.15

5 Effects of Shocks from Pandemic The data in Section 1 clearly indicates that the COVID pandemic has resulted in major shocks to the economy and the episode is likely to continue for some time. Our stylized model, summarized in Sections 2–4, is now used to uncover some central implications of a pandemic on an economy recovering from stagnation.16 The model is very stylized and hence does not account for many of special aspects of a recession caused by the pandemic. Nevertheless the analysis provides some basic lessons about consequences of pandemic shocks in an economy in stagnation.

14

For more details, see Evans et al. (2020). Probability of convergence goes to zero if one moves further outside the boundary. For example, see Evans et al. (2020). 16 As already noted, the model we use is presented in detail in Evans et al. (2020). 15

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As a starting point, it is assumed that the economy is in the middle of a recovery from stagnation.17 Formally, according to the model the economy is inside the domain of attraction but away from the target steady state, i.e., in Figure 4 the current state is point above the curve SS but located south-west of the target steady state. One shock at the onset of a pandemic is that economic expectations about future output and inflation abruptly shift in negative direction and there is also increased uncertainty about the future development of the economy. Such shocks are also part of a usual recession. The model is designed for analysis of the effects of a negative shift to output expectations, i.e., a downward shift in the perceived demand curve faced by the producers. Importantly, a pandemic also influences the supply side of the economy. Production activities face new difficulties of various types, including the health of workers and even lockdown of the sector. The supply-side effects probably differ between sectors with especially the service sector being badly hit. However, contractionary development of the “directly affected” sector negatively influences also the “unaffected” sector, so the economy overall is hit by the pandemic.18 Our model is a one-good economy so the effects are proxied by a negative shock to total productivity At . We thus consider two effects of the pandemic: (i) There is increased pessimism. In the model, the pessimistic shock is formally represented by a negative shock to output expectations y0e that describe current forecasts of the economy about the medium to long term. (ii) Production technology receives a negative shock and the economy becomes less efficient. This is formally represented by a negative shock to total factor productivity At , t = 0, ... We discuss the consequences of (i) and (ii) separately.

5.1 Shock to Output Expectations The effects of a negative expectations shock about future aggregate demand are fairly familiar, see Evans et al. (2020) for details in the current model. We summarize the details briefly here. Assume that the economy is recovering from stagnation and in terms of Figure 4 is inside the domain of attraction and moving on a convergent path toward the target steady state. Then a pessimistic shock, i.e., negative shock to expectations of aggregate demand y0e hits the economy. (For simplicity, assume that inflation expectations π0e remain unchanged.) 19

We set π0e = 1.000375, i.e., expected inflation of 1.5 percent in annual terms, and y0e = 0.9985, i.e., 2.3 percent below y ∗ in terms of 2-year equivalents computed in the cited paper. 18 The transmission between sectors is emphasized in Blanchard (2020) and Guerrieri et al. (2020). 19 Formally, in the model y e shifts to 0.9975, which is about one percentage points decline in terms 0 of the equivalents mentioned in the preceding footnote. From time t = 1 onward expectations start 17

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Note that the direction of the shock is qualitatively in line with the data in Figs. 1 and 2. If the shock is small, the economy remains inside and continues on a convergent path to the target steady state. In contrast, when the shock is sufficiently big, it displaces the economy outside and the economy begins to move toward stagnation if fiscal policy remains unchanged. Correcting this divergence requires expansionary fiscal policy (increased government spending) which needs to be sufficiently large.20

5.2 Shock to Productivity Assume that the state of the economy before the shock is the same as in the preceding section, i.e., it is in the middle of an ongoing recovery from stagnation and is moving toward the target steady state. Then a supply-side shock hits the economy, as in the current period total factor productivity At declines permanently (or for many periods). The effects of this shock are more complex than a shock to expectations, because a shift in aggregate productivity changes the structure of the economy. The shock thus moves the target steady state. Variations in At directly imply shifts in the Phillips (or aggregate supply) curve, see the variable Aet+s in (3). There will also be a shift in the monetary policy rule (1) if the interest-rate rule depends on the output gap. Looking at Figure 4, a negative productivity shock shifts the domain of attraction downward and also changes the shape of somewhat. This takes place as, quite naturally, lower productivity decreases the steady-state level of output y ∗ .21 The shape of usually changes because of the nonlinearities. Figure 5 illustrates numerically the movement of the domain of attraction after a shift in A0 .22 The dynamics after the shock depend on the magnitude of the negative productivity shock. If the magnitude of the permanent shock is small, then the economy continues to converge toward the new steady state with a lower value for output. However, after a larger productivity shock, the economy may start to move away from the post-shock target steady state.23 The shifting structure of the economy from the change in productivity can create surprising outcomes. It can happen that after a negative productivity shock convergence back to (post-shock) target steady state from an initial condition (πˆ 0e , yˆ0e ) ≈ (π ∗ , y ∗ ) before shock is more fragile than from different initial condition (π˜ 0e , y˜0e ) that is below (π ∗ , y ∗ ) in both components. The productivity shock to adjust according to learning. One could assume that shock to y0e affects expectations for more periods. 20 In the model, there are also cases where the probability of convergence to target steady state lies between zero and one. For example, see Evans et al. (2020). 21 Steady-state inflation π ∗ remains unchanged as it is part of the monetary policy framework. 22 The shift in A is from 1.113 to 1.11. The parameter values in the model are otherwise those 0 used in Evans et al. (2020). 23 The pre-shock value is A = 1.113. For a permanent shock and given starting point (π ∗ , y ∗ ) at the steady state, there is convergence for A = 1.112 and divergence for A = 1.111.

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Fig. 5 Effect of productivity decline on the phase diagram

causes a shift of the target steady state and the domain of attraction shifts downward as illustrated in Figure 5. After the shift, it is possible that (π˜ 0e , y˜0e ) is better situated than (πˆ 0e , yˆ0e ) for convergence to the post-shock steady state. Another surprising result is that a negative productivity shock can sometimes stabilize an unstable situation for the economy. To see this, assume that the initial pre-shock condition (π0e , y0e ) is slightly below the (pre-shock) middle steady state, i.e., π0e < π L , y0e < y L (so if no shock occurs the economy is beginning to descend to stagnation) and then a negative productivity shock occurs. One possibility is that the economy continues to converge toward stagnation steady state. However, depending on the magnitude of the shock, it is also possible that the economy begins to converge toward the post-shock target steady state. This is the case if (π0e , y0e ) is inside postshock domain of attraction . The analysis just presented can be extended to consider the more realistic case of a negative COVID productivity shock that has finite duration. In this case, the

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general flavor of the results remains similar to results with a permanent shock but there are some modifications. 24

6 Concluding Remarks We have studied the impacts of demand and supply shocks from the appearance of a pandemic on an economy in stagnation or recovering from stagnation. In the model, a pandemic is thought to result in (i) negative demand shocks to aggregate output and inflation and (ii) negative supply shocks to productivity in the economy. Such shocks usually add to stagnation pressures. As regards policy, usual fiscal policy in the form of increased government spending on consumption goods is the right kind of response to a demand shock due to increased pessimism in the form of decline in output expectations. In contrast, a negative productivity shock is a supply-side phenomenon and would require different response that offsets the decline in aggregate productivity due to the pandemic. One natural possibility would be increased public investment that raises productivity in the economy and thereby offsets the shock to At . This case was not studied as our simple model excludes private and public investment. It should be emphasized that our analysis is very much a first approach. In particular, it is assumed that the economic impact of an epidemic comes about only through exogenous (permanent or persistent) shocks. Dynamic interactions of the pandemic with the economy, e.g., on the labor force through health, are excluded from the model.25

References Benhabib, J., Evans, G. W., & Honkapohja, S. (2014). Liquidity Traps and Expectation Dynamics: Fiscal Stimulus or Fiscal Austerity? Journal of Economic Dynamics and Control, 45, 220–238. Benhabib, J., Schmitt-Grohe, S., & Uribe, M. (2001). The Perils of Taylor Rules. Journal of Economic Theory, 96, 40–69. Benigno, G., & Fornaro, L. (2018). Stagnation Traps. Review of Economic Studies, 85, 1425–1470. Blanchard, O. J. (2020): “The COVID Economic Crisis, chapter for Macroeconomics 8th Ed.,” manuscript. Bray, M., & Savin, N. (1986). Rational Expectations Equilibria, Learning, and Model Specification. Econometrica, 54, 1129–1160. Bullard, J. (2010). “Seven Faces of The Peril,” Federal Reserve Bank of St. Louis Review, 92, 339–352. Calvo, G. A. (1983). Staggered Pricing in a Utility-Maximizing Framework. Journal of Monetary Economics, 12, 383–398. 24

For instance, a negative shock to A0 with finite duration may result in convergence toward stagnation even though a permanent shock of the same magnitude would deliver convergence to (post-shock) target steady state. Simulations are available on request. 25 See, e.g., Eichenbaum et al. (2020) for a model with these effects.

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Christiano, L., Eichenbaum, M., & Rebelo, S. (2011). When is the Government Spending Multiplier Large? Journal of Political Economy, 119, 78–121. Cogley, T., & Sargent, T. J. (2008). Anticipated Utility and Rational Expectations as Approximations of Bayesian Decision Making. International Economic Review, 49, 185–221. Corsetti, G., Kuester, K., Meier, A., & Muller, G. J. (2010). Debt Consolidation and Fiscal Stabilization of Deep Recessions. American Economic Review: Papers and Proceedings, 100, 41–45. Eggertsson, G. B., Mehrotra, N. R., & Robbins, J. A. (2019). A Model of Secular Stagnation: Theory and Quantitative Evaluation. American Economic Journal: Macroeconomics, 11, 1–48. Eggertsson, G. B., & Woodford, M. (2003). The Zero Bound on Interest Rates and Optimal Monetary Policy. Brookings Papers on Economic Activity, 1, 139–233. Eichenbaum, M., S. Rebelo, and M. Trabandt (2020): “The Macroeconomics of Epidemics,” mimeo. Evans, G. W., Guse, E., & Honkapohja, S. (2008). Liquidity Traps, Learning and Stagnation. European Economic Review, 52, 1438–1463. Evans, G. W., & Honkapohja, S. (2001). Learning and Expectations in Macroeconomics. Princeton, New Jersey: Princeton University Press. Evans, G. W., S. Honkapohja, and K. Mitra (2020): “Expectations, Stagnation and Fiscal Policy: a Nonlinear Analysis,” Discussion paper 15171, CEPR. Guerrieri, V., G. Lorenzoni, L. Straub, and I. Werning (2020): “Macroeconomic Implications of Covid-19: Can Negative Supply Shocks Cause Demand Shortages?,” Nber working paper 26918. Jacobs, D., Kalai, E., & Kamien, M. (Eds.). (1998). Frontiers of Research in Economic Theory. Cambridge: Cambridge University Press. Kreps, D. M. (1998): “Anticipated Utility and Dynamic Choice,” in Kalai, and Kamien (1998), pp. 242–274. Marcet, A., & Sargent, T. J. (1989). Convergence of Least-Squares Learning Mechanisms in SelfReferential Linear Stochastic Models. Journal of Economic Theory, 48, 337–368. Reifschneider, D., & Williams, J. C. (2000). Three Lessons for Monetary Policy in a Low-Inflation Era. Journal of Money, Credit and Banking, 32, 936–966. Rotemberg, J. J. (1982). Sticky Prices in the United States. Journal of Political Economy, 90, 1187–1211. Sargent, T. J. (1999). The Conquest of American Inflation. Princeton NJ: Princeton University Press. Summers, L. (2013): “Why stagnation might prove to be the new normal,” Financial Times. Teulings, C., & Baldwin, R. (Eds.). (2014). Secular Stagnation: Facts. Causes and Cures: CEPR Press, London. Woodford, M. (2011). Simple Analytics of the Government Expenditure Multiplier. American Economic Journal: Macroeconomics, 3, 1–35.

Festival of Death: Global Stock Markets During the Pandemic Partha Ray and Parthapratim Pal

Abstract A stylized fact during the COVID-19 pandemic has been a total disconnect between the movements of indicators in the Main Street and in the Wall Street. Illustratively, while the number of deaths in the United States went up consistently and the GDP growth nosedived to negative territory, stock indices like the Dow-Jones industrial average (DJIA) index or the Nasdaq composite index started moving up and scaled new heights. This disconnect between the real and the financial sectors is noticeable in many of the developed stock markets and some of the major emerging markets (like China or India). This chapter looks at this disconnect and the complete apathy of the stock markets toward the health and humanitarian crisis unfolding itself during 2020. The chapter argues that the clue to understanding this phenomenon lies in the active pursuit of quantitative easing and other aggregative monetary and fiscal stimulus measures by the authorities of the developed countries. The chapter hypothesizes that possibly a sizeable part of the economic stimulus packages found their way to the stock markets. This raised a question about the fairness of the system, the design of the stimulus packages and raised apprehensions about a further rise of wealth inequality across the world. Keywords Pandemic · Stock market · Monetary stimulus · Fiscal stimulus · Inequality · COVID-19

Supplementary Information The online version contains supplementary material available at (https://doi.org/10.1007/978-981-16-8472-2_9). P. Ray (B) National Institute of Bank Management, NIBM Post Office Rd, Kondhwa, Pune, Maharashtra, India e-mail: [email protected] P. Pal Economics Group, Indian Institute of Management Calcutta, Joka, D H Road, Kolkata, India e-mail: [email protected] © The Author(s), under exclusive license to Springer Nature Singapore Pte Ltd. 2022 M. Dutta et al. (eds.), The Impact of COVID-19 on India and the Global Order, https://doi.org/10.1007/978-981-16-8472-2_9

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1 Introduction The relationship between the Wall Street and the Main Street is possibly one of the most vexed questions of macro-financial economics. The caricatured two extreme views are often expressed as “Efficient Market Hypothesis” versus “irrational exuberance”. The issue came to shaper spotlight after the financial crisis cum global recession of 2008–2009, whereby the greed of the Wall Street and the alphabet soup of various structured derivative products like CDS or ABCDs are perceived to have led to the financial crisis and the subsequent global recession. The backlash against Wall Street greed and corruption is best captured in the Occupy Wall Street movement of 2011.1 The relationship between the Main Street and the Wall Street emerged as another enigma during 2020, the year of the COVID-19 Pandemic. A telling stylized fact during the current pandemic is a total disconnect between the movements of indicators in the Main Street and those in the Wall Street. Illustratively, while the number of deaths in the United States went up consistently since February 2020 and GDP growth nosedived to negative territory, the Dow-Jones industrial average (DJIA) index or the Nasdaq composite index started moving up since the end of March 2020. To put it somewhat crudely, as the rolling 3-day average of daily confirmed number of COVID-19 cases increased from 21,651 on April 1, 2020, to 70,157 on July 23, 2020, the DJIA index rose from 20,944 to 26,652. There was also a sharp drop in the CBOE Volatility Index (VIX), a measure of expected price fluctuations in the S&P 500 Index options over the next 30 days, often termed as the “fear index”. This disconnect is noticeable in many of the developed stock markets, as well as some of the major emerging markets (like China or India). This chapter looks at these stylized facts across select developed and emerging economies’ equity markets and argues that the clue to understanding this disconnect primarily lies in the active pursuit of quantitative easing and other aggregative monetary stimuli measures by the central banks of the developed countries. This chapter is expected to shed some light on the possibility of formation of a bubble in equity markets across the globe. The rest of the chapter is organized as follows. In order to under the different dimensions of this disconnect, Sect. 2 delves into various indicators of the pandemic, trends in GDP growth as well as stock price movements during 2020. Section 3 presents various facets of the ultra-expansionary monetary policies of developed country central banks. While implications of this disconnect is looked into in Sect. 4. Section 5 concludes the chapter.

1

The movement is described in its web-site as, “Occupy Wall Street is a leaderless resistance movement with people of many colours, genders and political persuasions. The one thing we all have in common is that we are the 99% that will no longer tolerate the greed and corruption of the 1%. We are using the revolutionary Arab Spring tactic to achieve our ends and encourage the use of nonviolence to maximize the safety of all participants” (available at http://occupywallst.org/).

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Table 1 Top ten countries in number of COVID-19 cases (till March 2021) No

Country

Total cases

Total deaths

Population in million

Number of cases per million population

Number of deaths per million population

Deaths as a percent of cases (%)

1

USA

28,879,927

2

India

11,285,561

523,986

330.91

87,274.16

1,583.47

1.81

158,189

1,383.20

8,159.00

114.36

3

Brazil

11,122,429

1.40

268,370

212.56

52,325.38

1,262.54

2.41

4

Russia

5

UK

4,351,553

90,275

145.89

29,828.45

618.81

2.07

4,229,002

124,797

67.88

62,298.45

1,838.41

6

2.95

France

3,865,011

88,613

65.27

59,212.95

1,357.57

2.29

7

Spain

3,164,983

71,727

46.71

67,763.76

1,535.71

2.27

8

Italy

3,101,093

100,479

60.47

51,279.50

1,661.52

3.24

9

Turkey

2,807,387

29,160

84.36

33,278.01

345.65

1.04

10

Germany

2,518,591

72,489

83.75

30,072.57

865.54

2.88

Source WHO COVID-19 Dashboard (available at https://covid19.who.int)

2 The Rise and Fall of COVID-19 Cases The first officially recorded case of COVID-19 was traced back to Wuhan on December 1, 2019. While there are suspicions that the virus may have been in circulation even before that, the Chinese government formally notified about a viral outbreak on January 3, 2020.2 In the next one year, COVID-19 changed the world in an unprecedented manner. Starting from January 2020, COVID-19 spread rapidly across the globe and by the end of 2020, it officially infected about 84 million people worldwide and resulted in more than 1.8 million deaths.3 Almost no region of the world was spared, though the virulence and fatality rate of COVID-19 has been uneven across the world. Table 1 shows the top ten countries with the maximum number of COVID-19 cases to illustrate the point. As the Table shows, the U.S. has the largest number of COVID-19 cases in the world. India, Brazil, and some European countries are also among the other top ten most affected countries. The number of cases per million population (among the top ten countries mentioned in Table 1) varies from 8,159 in India to more than 87,274 in the U.S. In terms of COVID-19 infections, globally, Montenegro and the Czech Republic top the list with 128,665 and 126,179 COVID cases per million population. The U.S. is also the most adversely affected country in terms of the total number of deaths. If we normalize the number of deaths in terms of per million population, it can be seen that there is a significant variation among the countries. Countries 2

This timeline is available at: https://www.who.int/emergencies/diseases/novel-coronavirus-2019/ interactive-timeline. 3 The number of global infections and deaths are 117 million and 2.6 million, respectively, on March 8, 2021.

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from Europe are at the top of this list with more than 2,000 COVID-related deaths recorded per million in the Czech Republic and Slovenia; outside Europe, the U.S. has the highest death per million population (1,583 deaths/million). To understand the policy response of the governments regarding the crisis, it is important to look at the chronology of the pandemic and the WHO advisories. The starting point of the WHO timeline on the COVID-19 virus is December 31, 2019. That day, WHO picks up a report of “pneumonia of unknown cause“ in the Wuhan province of China. After tracking the spread of the virus for around 3 months, on March 11, 2020, WHO declared COVID-19 to be a “pandemic”. WHO was emphatic about the situation. It says: “We have never before seen a pandemic sparked by a coronavirus. This is the first pandemic caused by a coronavirus…And we have never before seen a pandemic that can be controlled, at the same time”. It then goes on to add “If countries detect, test, treat, isolate, trace, and mobilise their people in the response, those with a handful of cases can prevent those cases becoming clusters, and those clusters becoming community transmission”. It also advised that all countries must strike a balance “between protecting health, minimising economic and social disruption, and respecting human rights”.4 This essentially posed a challenge of life versus livelihood for policymakers across the world. Given this tradeoff, different countries responded differently to tackle the COVID crisis. “Our World in Data” website has published a “government stringency index” to track various government’s responses toward the COVID crisis.5 This stringency index is available on a daily basis. This index shows a wide variation in the restrictiveness of the policies used by the countries. The daily stringency index for a select group of countries is shown in Fig. 1. It shows that some country like India imposed the maximum level of stringency (100) very early and gradually lowered it to a moderate level (69) by the end of 2020. On the other hand, the U.S. never imposed very stringent measures and kept it at a moderate level (71.8–74.5) for 2020. A stark contrast is New Zealand, where strict government restrictions were imposed thrice, but these were brought down to low levels within a short time span. The European countries faced a second wave of pandemic, and the stringency index shows an upward trend since November 2020 (Fig. 1). From these data, two things can be safely asserted. First, that the world has not seen such a massive and coordinated shutdown of economic activities ever. And secondly, country responses were not homogenous and given the tradeoff between life and livelihood, and different countries chose to adopt different paths and tried to manage the pandemic in different ways. 4

https://www.who.int/director-general/speeches/detail/who-director-general-s-opening-remarksat-the-media-briefing-on-covid-19---11-march-2020. 5 This is a composite measure based on nine response indicators including school closures, workplace closures, and travel bans, rescaled to a value from 0 to 100 (100 = strictest). If policies vary at the subnational level, the index is shown as the response level of the strictest sub-region. The full description of the methodology used is available here: Microsoft Word—Calculation and presentation of the Stringency Index 28 Apr 2020.docx (ox.ac.uk).

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Fig. 1 COVID Stringency index for select countries. Source https://ourworldindata.org/grapher/ covid-stringency-index

Despite the reasonably widespread lockdown, global COVID-19 cases kept increasing throughout 2020. And only by the middle of January 2021, the daily increase in COVID cases started showing a consistent declining trend. The 7-day Moving Average of daily deaths started showing a downward trend from early February 2021 (Fig. 2a, b). The U.S. has not done very well in its fight with COVID19. Daily numbers suggest that during the early part of 2021 (till the first week of March 2021), on average, around 25% of global COVID cases and 20% of global COVID deaths are recorded in the U.S. Though the third wave of COVID is presently

Fig. 2 Number of worldwide COVID cases and deaths (daily) and its 7-day moving average. Source Our World in Data Covid Dataset (available at https://github.com/owid/covid-19-data/tree/master/ public/data)

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spreading across Europe and is forcing new restrictions, as vaccines are becoming available worldwide, it can be hoped that the worst of COVID-19 may be behind us.

3 The Economic Impact of COVID-19 What started as a medical and health crisis soon transformed itself to be an economic and humanitarian crisis. The “Great Lockdown” resulted in the sharpest GDP contraction the world has seen in the last 75 years. The shocks came from both the demand and supply sides. It started with widespread lockdown measures that imposed severe curbs on the entire “nonessential” economy and created supply restrictions. These supply-side restrictions include production lockdowns, movement restrictions, and closure of ports and other physical infrastructures. As these restrictions led to the shutdown of markets and closure of production, income and jobs were lost, and this led to demand-side problems. The demand side contraction has been further driven by a combination of factors, including shrinkage in economic activities, reduction in purchasing power, increased restrictions on movements of goods and people, and generally higher uncertainties in short to medium terms. The combined impact of these demand and supply sides issues have led to a severe and widespread economic downturn across the world. With new variants of the virus still spreading and causing new economic restrictions, it is possible that the COVID-19 crisis will eventually emerge as the worst economic contraction recorded in human history. To look at the brighter side, as vaccination drive picks up, the supply side constraints should ease, and the world is likely to limp back to normal. The IMF, in its World Economic Outlook of January 2021 (IMF, 2021) is expecting a robust global recovery in 2022. However, if the virus continues to mutate and spread, and there are renewed restrictions in some part of the world, there can be long-term supply-side problems or even permanent disruptions of some supply chains. Given these downside risks, the recovery may become uneven and slower. Figure 3 shows the real GDP growth rates of the world and major country groups for the period 2000–2020. It shows that the contraction of GDP due to the COVID-19 crisis differs from the recession caused by the Global Financial Crisis (GFC) of 2008– 09 in two very fundamental ways. First, for every country group, the contraction in GDP is much higher in 2020 as compared to 2009. And secondly, the contraction is much more uniform across country groups. In 2009, while the “Advanced Countries” and Latin American countries faced an economic contraction, the other country groups faced a slowdown but managed to avoid a negative growth rate. In contrast, in 2020, every country group faced sharp economic contractions, and the decline is much sharper across all country groups (Table 2). Also, in terms of per capita income, a similar trend can be seen. The economic impact of the virus and the lockdown have spared no region, and except for “Emerging and Developing Europe”, every country group has suffered a higher decline in per capita income due to the COVID-19 crisis.

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Fig. 3 Real GDP Growth across major Country Groups (%). Source World Economic Outlook Database, October 2020, IMF (IMF, 2020) Table 2 GDP growth Per Capita GDP growth across country groups: 2009 & 2020 GDP Growth (%)

Per Capita GDP Growth (%) (at PPP, 2017 International dollar)

2009

2009

2020

2020

World

−0.1

−4.4

n.a

n.a

Advanced economies

−3.3

−5.8

−3.9

−6.2

Major advanced economies (G7)

−3.6

−5.9

−4.2

−6.2

European Union

−4.2

−7.6

−4.5

−7.8

Emerging market and developing economies

2.8

−3.3

0.8

−4.7

Emerging and developing Asia

7.6

−1.7

6.4

−2.7

Emerging and developing Europe

−5.7

−4.6

−6.2

−4.7

Latin America and the Caribbean

−2.0

−8.1

−3.4

−9.1

Middle East and Central Asia

1.1

−4.1

−1.2

−6.4

Sub-Saharan Africa

3.8

−3.0

1.0

−5.6

Source World Economic Outlook Database, October 2020, IMF (IMF, 2020)

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A possible enduring impact on the economy will possibly come from growing sectoral disparities. The pandemic affected specific sectors more than the others. Travel, hospitality, food, and offline retail sectors are among the worst affected sectors. On the other hand, digital services, online retail, and technology companies have benefitted from the pandemic. Stiglitz (2020) argues that the advent and possible lingering of the COVID virus will lead to growing inequality as a few activities, certain goods, and services, and some production processes will be viewed as riskier and costlier. In these subsectors, there will be increased use of automation and consequently, there is likely to be employment losses. This shift may exacerbate the trend of rising inequality which was already in place. Stiglitz concludes that unless the right policies are in place, the pandemic itself is likely to increase disparities and leave long-lasting scars. In this context, it is essential to highlight that Piketty (2014) has argued that when r (asset returns) is greater than g (real income growth) it leads to growing asset inequality. Piketty has termed this scenario “the central contradiction of capitalist economics”. During the COVID pandemic, among the deepest and most widespread economic contraction experienced ever, the major stock markets of the world rose sharply, and many major stock indices broke all time high records. There are good reasons to believe that some of the policies the major central banks adopted to boost the real economy ended up fueling this massive stock market boom. While most of the countries were suffering from loss of live and livelihood, the financial markets did not mirror this despondency.

4 The Stock Markets During the Pandemic Amid all the doom and gloom of the real economy, stock markets across the world rose sharply. The contrast was especially stark in the United States. As discussed before, WHO declared the COVID to be a pandemic on March 11, 2020. The first death was recorded in the U.S. on February 29, 2020. If we look at the behavior of Nasdaq and S&P 500, both indices declined initially and by March 23, 2020, had reached a low point of 6860.7 and 2237.4, respectively. But since then, the U.S. stock markets experienced a rapid rate of growth and by the end of 2020, Nasdaq more than doubled to reach a value of 14,095.5 on December 12, 2020. The S&P 500 also grew by more than 75% and reached 3935 on the same day. While subsequently, Nasdaq declined marginally, the S&P reached a peak of 3969 on March 15, 2021. Due to a coincidence of investor sentiment or pure chance, Indian stock markets reached their low point of 2020 on the same day as the U.S. On March 23, 2020, both the NSE 50 and the BSE Sensex reached their low of 7610 and 25,981, respectively. And just like the U.S., both the indices rose rapidly in 2020. These Indian indices peaked around the middle of February 2021. NSE 50 and BSE Sensex reached their lifetime peaks of 15,315 and 52,154 on February 15, 2021. As it can be understood from these numbers, both these stock indices registered more than 100% gain in less than a year.

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Fig. 4 Movements in equity markets (Index, 2017 = 100). Notes (1) Euro Stoxx Index: It is Europe’s leading blue-chip index for the Eurozone covering 50 stocks from 9 Eurozone countries, viz., Belgium, Finland, France, Germany, Ireland, Italy, Luxembourg, the Netherlands, and Spain. (2) S&P 500 Index: It is a market-capitalization-weighted index of the 500 largest U.S. publicly traded companies. (3) MSCI Emerging Markets Index: It captures large- and mid-cap representation across 26 Emerging Markets (E.M.) countries, viz., Argentina, Brazil, Chile, China, Colombia, the Czech Republic, Egypt, Greece, Hungary, India, Indonesia, Korea, Malaysia, Mexico, Pakistan, Peru, Philippines, Poland, Qatar, Russia, Saudi Arabia, South Africa, Taiwan, Thailand, Turkey, and the United Arab Emirates. (4) TOPIX: The Tokyo Stock Price Index (TOPIX) is a capitalizationweighted index of all companies listed on the First Section of the Tokyo Stock Exchange. Source World Economic Outlook, IMF, October 2020.

As mentioned before, the exuberance in the stock market has turned out to be a worldwide phenomenon whereby stock indices around the world had experienced a spurt since April 2020 (Fig. 4). This contrast between the euphoria of the equity markets and the despair of the real economy becomes even more disturbing if we look at the co-movement of the stock indices and the deaths due to the COVID virus. In the following figure, we have plotted the S&P 500 on the primary axis and the number of deaths in U.S. due to the COVID virus in the secondary axis. The correlation between the two lines compelled us to describe the situation as the “festival of death” (Fig. 5).

5 Is Economic Stimulus the Key to the Stock Market Boom? We argue that a key to understanding the disconnect between the Wall Street and the Main Street is the substantial amount of economic stimuli across the world, particularly in the advanced countries as a result of the massive quantitative easing. The basic tenet of the thesis is as follows: in order to counter the economic effects of the pandemic, advanced countries central banks injected a huge amount of liquidity.

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Fig. 5 USA S&P 500 and Number of Deaths in USA. Source S&P 500 data from Wall Street Journal database, U.S. number of deaths from the OWID COVID database mentioned in Fig. 2a, b

A large chunk of this liquidity ended up on the Wall Street, which in turn has given a fillip to the exuberance to the stock prices. In fact, the more has been the death toll, the more aggressive have been the central banks, and after the initial downfall till end March 2020, stock prices have zoomed northward exuberantly (perhaps also irrationally).

5.1 Monetary Stimuli Across the Advanced Countries A digression on the advanced countries’ monetary policy in recent times is in order here. In its fight against the global financial crisis culminating into global recession, policy rates of the central banks of the major developed countries reached almost zero lower bounds. While in the U.S., effective Federal Funds rate touched near zero by 2008, the ECB followed this course within a year. In fact, the interest rate on ECB deposit facility after remaining at 0% for a prolonged period of time (during July 11, 2012–June 10, 2014) entered the negative territory in June 2014 and continued to be there since then. The U.S. Fed, on the other hand, has started increasing policy rates since June 2015. With the onslaught of the pandemic the U.S. Fed has started reducing policy rates. The ECB has reduced its deposit facility rate to (-) 0.5% per annum in September 2019 and maintained that rate till date (Fig. 6). Admittedly, monetary policy is less effective in the negative interest rate territory can be less effective than lowering them in positive territory; after all, deposit rates remain stuck at zero, and bank profits are squeezed (Ulate, 2021). Low policy rates, thus, made the

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Fig. 6 Monetary Policy Rates of the U.S. Fed and the ECB (2000–2020) (% per annum). Source Websites of U.S. Fed and ECB

conventional weapons of monetary policy toothless. Consequently, both the U.S. Fed and the ECB, have resorted to unconventional monetary policy actions, comprising aggressive quantitative easing as well as forward guidance. In particular, to tackle the onslaught of the pandemic-induced recession, after lowering the Federal funds rate by 150 bps in March 2021 to the range of 0–25 bps the U.S. Fed has undertaken various non-conventional policy actions. First, it started purchasing Treasury and agency securities in substantial amounts. Second, the overnight and term repos were extended. Third, the cost of discount window lending were lowered. Fourth, the U.S. Fed reduced the existing cost of swap lines with major central banks and extended the maturity of foreign exchange operations, broadened U.S. dollar swap lines to more central banks, and offered temporary repo facility for foreign and international monetary authorities. Fifth, the U.S. Federal banking supervisors encouraged depository institutions to use their capital and liquidity buffers to lend and indicated that COVID-19-related loan modifications would not be classified as troubled debt restructurings. These apart, the U.S. Fed introduced the following facilities to support the flow of credit: (a) (b) (c)

(d)

Commercial Paper Funding Facility to facilitate the issuance of commercial paper by companies and municipal issuers; Primary Dealer Credit Facility to provide financing to the Fed’s 24 primary dealers; Money Market Mutual Fund Liquidity Facility (MMLF) to provide loans to depository institutions to purchase assets from prime money market funds (covering highly rated asset-backed commercial paper and municipal debt); Primary Market Corporate Credit Facility to purchase new bonds and loans from companies;

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(e)

Secondary Market Corporate Credit Facility to provide liquidity for outstanding corporate bonds; Term Asset-Backed Securities Loan Facility to enable the issuance of assetbacked securities backed by student loans, auto loans, credit card loans, loans guaranteed by the Small Business Administration, and certain other assets; Paycheck Protection Program Liquidity Facility to provide liquidity to financial institutions that originate loans under the Small Business Administration’s Paycheck Protection Program (PPP), which provides a direct incentive to small businesses to keep their workers on the payroll; Main Street Lending Program to purchase new or expanded loans to small and mid-sized businesses; and Municipal Liquidity Facility to purchases short term notes directly from state and eligible local governments.

(f)

(g)

(h) (i)

Thus, apart from the traditional government securities, assets of governmentsponsored enterprises like Fannie Mae and Freddie Mac and individual corporate papers, including junk bonds, have entered the Fed balance sheet in a big way. On the other side of the Atlantic, apart from undertaking additional asset purchases of e120 billion until end-2020 under the existing program, the ECB introduced various other measures to augment liquidity in the Euro area. First, temporary additional auctions of the full-allotment, fixed rate temporary liquidity facility at the deposit facility rate were provided. Second, existing targeted longer term refinancing operations (TLTRO-III) were extended with interest rates that can go as low as 50 bp below the average deposit facility.6 Third, the ECB introduced a new liquidity facility, PELTRO (Pandemic Emergency Longer-Term Refinancing Operations), which consists of a series of non-targeted Pandemic Emergency Longer-Term Refinancing Operations carried out with an interest rate that is 25 bp below the average MRO rate prevailing over the life of the operation. The PELTROs commenced their operation in May 2021. Fourth, in March 2020, the ECB introduced an additional e750 billion asset purchase program of private and public sector securities (under the Pandemic Emergency Purchase Program, PEPP), initially through end-2020. Fifth, further measures were included an expanded range of eligible assets under the corporate sector purchase program (CSPP), and relaxation of collateral standards for Eurosystem refinancing operations. sixth, the ECB also announced a broad package of collateral easing measures for Eurosytem credit operations in early April. Finally, the European Commission proposed on July 24 a Capital Markets Recovery Package with targeted adjustments to capital market rules. These were aimed at encouraging greater investments in the economy and increasing banks’ capacity to finance the recovery. The end product of all these new facilities or extension of old facilities has been an injection of massive liquidity and expansion of the central banks’ balance sheets. Illustratively, during the year 2020, the balance sheet size of central banks of euro Area went up by slightly more than euro 2.35 trillion; the expansion in the U.S. Fed balance sheet during 2020 was USD 3.19 trillion (Fig. 7). Such huge monetary 6

Existing facilities are MROs, LTROs, TLTROs.

Festival of Death: Global Stock Markets … (a) Assets of the U.S. Fed (Billions of USD) 8,000

201 (b) Assets for the Euro Area Central Banks (Billions of Euros)

7,000 6,000 5,000 4,000 3,000 2,000 1,000

1-Dec-03 1-Dec-04 1-Dec-05 1-Dec-06 1-Dec-07 1-Dec-08 1-Dec-09 1-Dec-10 1-Dec-11 1-Dec-12 1-Dec-13 1-Dec-14 1-Dec-15 1-Dec-16 1-Dec-17 1-Dec-18 1-Dec-19 1-Dec-20

0

Fig. 7 Assets of the Central Banks in the U.S. and the Euro Area: 2003–2020. Note a The numbers are weekly and less Eliminations from Consolidation. These are not seasonally adjusted. Source St Louis Fed (available at https://fred.stlouisfed.org/series/ WALCLhttps://fred.stlouisfed.org/series/WALCL). b The numbers are weekly and are not seasonally adjusted. Source European Central Bank, Central Bank Assets for Euro Area, retrieved from FRED; available at https://fred.stlouisfed.org/series/ECBASSETSW

stimulus and massive expansion of U.S. Fed balance sheet gave a fillip to the U.S. stock market. It is instructive to turn to Sunder (2020), who commented: The COVID-19 pandemic also induced an economic panic in March 2020. The FRS responded to the panic by again expanding its balance sheet assets by 66%.... not surprisingly, the stock market soon recovered most of its March–April 2020 losses during May–June 2020. …. These interventions to induce a price recovery have created a not-so-unreasonable expectation that any future precipitous stock price declines may also elicit a similar response from the central bank, especially during an election year under heavy political pressure. If rising stock prices are considered normal by investors and policymakers, and the latter tend to intervene to engineer a recovery from sharp declines, it does not seem unreasonable to believe (as some do) that the stock market will always go up. This expectation of policy has earned its own moniker of “central bank put”.

Furthermore, in an experimental study Hirota et al. (2020) showed that based on their laboratory data, “in markets with speculating investors, the high level of money supply enlarges positive price bubbles from fundamentals, whereas the low level of money supply causes negative price bubbles”.

5.2 Fiscal Stimuli Apart from the monetary stimulus, there are parallel episodes of fiscal stimulus and credit guarantees across the world. The huge rise in fiscal deficit all over the world

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Fig. 8 Expansions in Fiscal Deficit during 2020. (As % of Country/Group/World GDP). Legends: A.E.s: Advanced Economies; EMDEs: Emerging Market and Middle-Income Economies; MENA: Middle East & North Africa; LIDC: Low-Income Developing Countries. Source Fiscal Monitor, IMF, October 2020.

bear testimony to the massive quantum of fiscal stimuli. As a proxy to fiscal stimulus, Fig. 8 plots the extent of increases in general government net borrowing across select economic groups and countries.7 A look at it confirms the massive expansion of the government deficit across the countries/country-groups. Also, apart from the extent of the impact of the pandemic, fiscal expansions have to do with the fiscal space and economic muscle power of the country/country-groups under consideration. Illustratively, in the advanced countries and in oil-producing economies, the extent of expansion of fiscal deficit is far greater than the same in the emerging market & developing economies (EMDEs) or in low-income developing countries (LIDCs).

5.3 Size of the Total Economic Stimulus In fact, it is well known that the sizes of the total economic stimuli (i.e., monetary and fiscal taken together) in the advanced economies during the current pandemic 2020 far exceeded the stimuli injected during the global financial crisis of 2008– 2009 (Table 3). In fact, governments seemed to have resorted to forms the stimulus packages: guarantees, loans, value transfers to companies and individuals, deferrals, and equity investments (Cassim et al. 2020).

7

It shows the difference in “net borrowing of the General Government” in 2020 over 2019.

Festival of Death: Global Stock Markets … Table 3 Size of economic stimulus: Then and Now (As % of Country-specific GDP)

203

Country

During the 2008 Global Financial Crisis

During the 2020 COVID-19 Pandemic

1. Germany

3.5

33.0

2. Japan

2.2

21.0

3. France

1.4

14.6

4. UK

1.5

14.5

5. US

4.9

12.1

6. Canada

2.8

11.8

Source Cassim et al. (2020)

5.4 How Did the Economic Stimuli End Up in Wall Street? Faced with a once-a-century type crisis like the current COVID-19 pandemic, it is absolutely appropriate to undertake a sizable economic stimulus, the amount of which would vary depending on (a) the extent of the downturn of the business cycle and (b) fiscal space. This is part of the standard stabilization policy prescription. But how could then the stimulus money ended up in Wall Street? It is here that one is reminded of the Keynesian possibility of the paradox of thrift. The standard textbook version of Keynesian commodity market equilibrium shows that an autonomous negative shock in the savings function leads to a fall in equilibrium income.8 Keynes elaborated his arguments as follows: An act of individual saving means - so to speak - a decision not to have dinner to-day. But it does not necessitate a decision to have dinner or to buy a pair of boots a week hence or a year hence or to consume any specified thing at any specified date. Thus it depresses the business of preparing today’s dinner without stimulating the business of making ready for some future act of consumption. It is not a substitution of future consumption-demand for present consumption-demand, - it is a net diminution of such demand” (Keynes, 1938; p. 210; emphasis in original).

Did the stimulus money end up in increased savings and not increased consumption? Since monetary stimulus is primarily for financial institutions, it is quite likely that in the absence of a substantial demand for loans from the Main Street, a large of such stimulus ended up in the Wall Street banks and from there to equity markets. In fact, it has been reported in the popular press that banks in the U.S. have tended to keep much of the cash in the form of excess reserves instead of lending it out into the economy (Schulze, 2020). Insofar as the impact of fiscal stimulus is concerned, Coibion et al. (2020) have recently done an interesting survey in the U.S. They based their survey on individuals participating in the Nielsen Homescan panel of 80,000–90,000 individuals, whose purchases are tracked on a daily basis. Using a large-scale survey of U.S. households, they studied the impact of the Coronavirus Aid, Relief, and Economic Security Act (popularly known as the CARES Act), encompassing a $2.2 trillion economic 8

See Ahiakpor (1995) for a critique of paradox of thrift.

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Table 4 Distribution for the use of stimulus payment (qualitative) in the U.S. (per cent) Those who received a cheque (actual spending)

Those who expect to receive a cheque (planned spending)

Those who did not receive and do not expect to receive a cheque (hypothetical spending)

Mostly to increase spending

15.04

12.97

14.17

Mostly to increase savings

32.80

38.38

45.76

Mostly to pay off debt

52.17

48.65

40.07

Number of observations

9,966

336

1,491

Source Coibion et al. (2020)

stimulus bill passed on March 27, 2020. They found that only 15% of recipients receiving the transfer spent (or planned to spend) most of their transfer payment, “with the large majority of respondents saying instead that they either mostly saved it (33%) or used it to pay down debt (52%)” (Coibion et al., 2020) (Table 4).

6 Implications of the Phenomenon of “Festival of Death” What are the implications of this irrational exuberance in the Wall Street? While writing on live history could be difficult, at the risk of rash generalization, we point out two significant implications of this phenomenon.

6.1 Will It Lead to Increased Inequality? It is by now well established that globalization has given enough fillip to global inequality. Piketty (2014) noted specifically, “Since the 1970s, income inequality has increased significantly in the rich countries, especially the United States, where the concentration of income in the first decade of the twenty-first century regained— indeed, slightly exceeded—the level attained in the second decade of the previous century”. Defining wealth as “the value of a household’s financial assets plus real assets (principally housing), minus their debts”, a recent Credit Suisse Global Wealth Report revealed, “The world’s richest 1 per cent, those with more than $1 million, own 44 per cent of the world’s wealth”.9 The data also indicated that adults with less than $10,000 in wealth makeup 56.6% of the world’s population but hold less than 9

https://inequality.org/facts/global-inequality/.

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2% of global wealth. Individuals owning over $100,000 in assets make up less than 11% of the global population but own 82.8% of global wealth. What has been the implication of the pandemic on global inequality? An Oxfam report indicated that from March 2018 to the end of 2020, global billionaire’ wealth increased by $3.9 trillion (Oxfam, 2021). By contrast, data from the ILO has revealed that with the great lockdown around global workers’ combined earnings fell by $3.7 trillion. Thus, the pandemic has exacerbated economic disparities across the globe. Does the phenomenon of “festival of death” have any implication for global inequality? It is in this context that we refer to Piketty (2014)’s famous proposition that the growth in wealth inequality is determined by the difference between the returns to wealth (r) and growth in national income (g).10 Piketty (2014) refers to r > g as ”the central contradiction of capitalist economics” (p. 398); it can be summarized as follows: The primary reason for the hyper concentration of wealth in traditional agrarian societies and to a large extent in all societies prior to World War I ….is that these were low- growth societies in which the rate of return on capital was markedly and durably higher than the rate of growth. This fundamental force for divergence…functions as follows. Consider a world of low growth, on the order of, say, 0.5–1 per cent a year, which was the case everywhere before the eighteenth and nineteenth centuries. The rate of return on capital, which is generally on the order of 4 or 5 per cent a year, is therefore much higher than the growth rate. Concretely, this means that wealth accumulated in the past is recapitalised much more quickly than the economy grows, even when there is no income from labour. For example, if g = 1% and r = 5%, saving one-fifth of the income from capital (while consuming the other four- fifths) is enough to ensure that capital inherited from the previous generation grows at the same rate as the economy. If one saves more, because one’s fortune is large enough to live well while consuming somewhat less of one’s annual rent, then one’s fortune will increase more rapidly than the economy, and in equality of wealth will tend to increase even if one contributes no income from labour (Picketty, 2014; p. 351)

The trend in the last one year shows that returns from stock markets have been almost uniformly exorbitant across the major global financial markets. This, is in stark contrast with the GDP growth rates achieved by these countries, which have been negative for almost all countries in 2020. If we go by Piketty’s formulation of (r > g) as “the central contradiction of capitalist economics”, it appears that during COVID, the contradiction exacerbated itself manifold (Table 5). Table 5 shows that the (r-g)-gap has widened dramatically during the COVID pandemic. If Piketty is right about his formulation that in periods when r > g, it leads to growing wealth inequality, then considering the trends in r-g world over and the concentration of loss of job among the low-wage earners, pandemic seems to have given a fillip to the already increasing wealth inequality.11

10

See Madsen (2017) for a recent discussion on r-g. Using weekly administrative payroll data from the largest U.S. payroll processing company, Cajner et al. (2020) has noted for the US, “Employment losses have been concentrated disproportionately among lower wage workers; as of late June employment for workers in the lowest wage quintile was still 20 percent lower relative to mid-February levels”.

11

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Table 5 Stock market returns and real GDP growth Country

Stock index

52-week Stock Returns as on 20 March 2021 (%) (r)

GDP Growth in 2020 (g)

r-g

1. Australia

All Ordinaries

43.37

−2.90

46.27

2. China

Shenzhen Composite

28.77

2.30

26.47

3. Germany

DaX

63.75

−5.40

69.15

4. India

S&P BSE Sensex

66.66

−8.00

74.66

S&P CNX Nifty

68.59

−8.00

76.59

5. Indonesia

JSX Index

51.52

−1.90

53.42

6. Japan

Nikkei 225

79.98

−5.10

85.08

7. Malaysia

FTSE Bursa Malaysia KLCI

24.78

−5.80

30.58

8. Philippines

PSEi Index

34.68

−9.60

44.28

9. S. Korea

KOSPI

94.08

−1.10

95.18

10. Thailand

SET

38.74

−6.60

45.34

11. UK

FTSE

29.24

−10.00

39.24

12. US

DJIA

70.17

−3.40

73.57

NASDAQ

92.10

−3.40

95.50

S&P 500

69.77

−3.40

73.17

Source Wall Street Journal for stock indices, IMF WEO January 2021 for country growth numbers

The COVID-19 crisis and the subsequent recovery may also be exacerbating the existing global inequality in business. Before the pandemic struck, international business was already being dominated by a few “superstar” firms. It has been observed. This increase in profits of large firms has been a major driver of global functional inequality, associated with declines in the global labour income share during the last two decades. Market concentration increases as industries become progressively dominated by “superstar” firms with high profits and low shares of labour in firm value added, and as the importance of superstar firms increases, the aggregate labour share tends to fall (UNCTAD, 2018, p. 57)

The increased dominance of big firms including the digital behemoths (including the so called FAANGM, viz., Facebook, Amazon, Apple, Netflix, Google-Alphabet, and Microsoft) during the COVID-19 crisis will only increase their market power and possibly lead to even more concentration of power and wealth. Moreover, as the world is now looking at a possible uneven K-shaped recovery, there is likely to be growing disparity with “stacked inequity on one side and stacked privilege on the other.”12 12

This phrase is attributed to Peter Atwater (see, “How a ‘K-Shaped’ Recovery Is Widening U.S. Inequality?” Catarina Saraiva, Bloomberg, December 10, 2020).

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6.2 Has Finance Become Independent of Growth? Another apprehension that comes to our mind in the context of this phenomenon of “festival of death” is: Has the financial market in general and the stock market, in particular, become independent of growth? There is an extensive literature on the relationship between finance and growth, arguing completely different directions of causality between these two important economic variables. The centrality of finance in growth is perhaps best captured in the following assentation of Miller (1998), who said, “The idea that financial markets contribute to economic growth is a proposition too obvious for serious discussion”. There is a large literature on finance and growth arguing that a developed financial system tends to enhance the mobilisation of savings, to identify high return projects, thereby diversifying risks and facilitating transactions (Ross, 2005). On the contrary, economists like Joan Robinson argued, “where enterprises go, finance follows” (Robinson, 1952). But both these two views are about the direction of causality between finance and growth. But, if the evidence presented in this paper has any validity, then it is perhaps a pointer to the fact that the Wall Street and the Main Street has been running parallel in recent times. But why is it so? Why has financial activity been so heartless that the huge loss of human lives does not matter for it anymore? These questions seem to be bothering global think tanks and policymakers alike. In the standard textbook version of “circular flow model”, finance is typically seen under two loops, viz. (a) a direct channel from household to firms (via the equity or debt market); or (b) an indirect channel from households to firms via financial intermediaries (via the credit market). In this scheme, for finance to have any role, it has to land up in the hands of firms producing goods and services. But what happens if there is enormous churning of finance within the financial intermediaries. Much of the massive derivatives market may in fact qualify under such a scheme. Under such a situation, what Rajan and Zinjales (2004) call as “utopia for finance”, finance may be independent of the real sector. What then explains the stock market euphoria during the pandemic? In the context of the stock market exuberance of 2020, McKinsey (2020) has put forward three hypotheses. First, the stock market could take a long-run perspective and may not take into account the temporary loss of human life. Second, such overall exuberance may camouflage the micro/sectoral stories. For example, within the retail sector, while departmental stores suffered, smaller grocery stores seemed to have done well. Third, the market value of listed U.S. companies may not reflect employment or GDP levels in the real economy. Illustratively, many of sectors like of construction and professional services firms, services like gymnasiums, hairdressers, hospitals, restaurants, and other service businesses that make significant contribution to GDP are not listed. Thus, It is possible that the overall “stock market can do relatively well even when employment and GDP are severely depressed” (McKinsey, 2020). But this view, at best, provides a partial explanation to this phenomenon of the “festival of Death”. It is here that we turn to the literature of the sociology of finance,

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in which the financial system is seen much beyond the traditional circular flow of the economy. This stance of literature is best summarized in the following passage of Cettina (2013): A financial market is an intermediary arrangement involving hardware, software, and human components whose performance and outcomes are not controlled by any of the firms involved. Traders, not bankers, are the key operators in these arrangements; agency shifts from the firm to individual traders when it comes to market-making and managing. Traders, whose social role is absent in producer markets and traditional bank lending, represent and embody the nature of financial markets .....In the ......OTC market, traders are market-makers; they take their own positions in the market in trying to profit from price differences, while also offering trades to other market participants. Traders thereby provide liquidity to the market and sustain it - if necessary against their own position and interest. In other words, traders act as custodians of the market - they fulfill bridging and liquefying functions when gaps arise and activities seem to gel, and they may also try to revive markets when they break down. Although banks limit traders’ losses and the volume of instruments they can trade, traders are not constrained by any of the banks’ views on the development of price movements but instead develop their own stance on the instrument they trade. Indeed, as participants confirm, it is quite common for the trading book and banks’ proprietary position to be at odds with one another (Cettina, 2013).

In our view, it is this combination of the traders and the screens that can make the movements in stock prices completely oblivious to the pandemic-related mortality and morbidity.

7 Concluding Observations The paper presents some stylized facts of the recent stock market exuberance across the world. It tries to show that while the current pandemic and related loss of human lives and jobs are getting reflected in a global recession with a contraction of GDP perhaps comparable to or exceeding the Great Depression of the 1940’s, the equity market seems to be quite oblivious to it. This is entirely in contrast to the experience of the Great Depression. It appears as if the stock market had been celebrating some sort of festival of death! In a partial explanation of this paradox of “festival of death”, we look at the huge monetary stimuli of central banks across the world and the fiscal stimuli of the governments. We argue that a substantial chunk of such stimuli seems to have ended up in the stock market, and in the midst of such deluge of liquidity, stock markets seem to have experienced an irrational exuberance, notwithstanding the increasing spread of the pandemic and its mortality. Various questions about the festival of death, however, remain unanswered. Does it mean there is a bubble in stock market that is going to burst sooner rather than later? Does it mean, authorities across the world need to far more careful about designing the stimulus package? Should the stimulus packages need to be targeted better? While some of these are in the nature of speculating about the future, others constitute future research agenda.

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References Ahiakpor, J. C. W. (1995). A Paradox of Thrift or Keynes’s Misrepresentation of Saving in the Classical Theory of Growth? Southern Economic Journal, 62(1), 16–33. Cajner, Tomaz , Leland D. Crane, Ryan A Decker, John Grigsby, Adrian Hamins-Puertolas, Erik Hurst, Christopher Kurz, and Ahu Yildirmaz (2020): “The U.S. Labor Market During the Beginning of the Pandemic Recession”, University of Chicago, Baker-Friedman Institute Working Paper No. 2020–58, available at https://bfi.uchicago.edu/wp-content/uploads/HurstBFI_WP_202058_ Revision.pdf Cassim, Ziyad., Borko Handjiski, Jörg Schubert, and Yassir Zouaoui (2020): The $10 Trillion Rescue, available at https://www.mckinsey.com/industries/public-and-social-sector/our-insights/ the-10-trillion-dollar-rescue-how-governments-can-deliver-impact Cettina, K K (2013): What is a financial market?: Global markets as micro-institutional and post traditional social forms, Chapter 6, in Karin Knorr Cetina and Alex Preda (eds.): Oxford Handbook of the Sociology of Finance, Oxford: Oxford University Press. Coibion, Olivier., Yuriy Gorodnichenko, and Michael Weber (2020): How Did U.S. Consumers Use Their Stimulus Payments?, NBER Working Paper 27693, available at https://www.nber.org/sys tem/files/working_papers/w27693/w27693.pdf Hirota S, Huber J, Stoeckl T, Sunder S (2020): Speculation and price indeterminacy in financial markets: an experimental study, Cowles Foundation discussion paper 2134R. Journal of Economic Behaviour and Organization, (forthcoming), available at https://cowles.yale.edu/sites/ default/fles/fles/pub/d21/d2134-r.pdf IMF (2021): World Economic Outlook Update: Policy Support and Vaccines Expected to Lift Activity, International Monetary Fund, January 2021, available at: World Economic Outlook Update, January 2021: Policy Support and Vaccines Expected to Lift Activity (imf.org) IMF (2020): World Economic Outlook: A Long and Difficult Ascent, International Monetary Fund, October 2020, available at: World Economic Outlook, October 2020: A Long and Difficult Ascent (imf.org) Keynes, J. M. (1974). (1936): The General Theory of Employment, Interest and Money (Paperbound). Macmillan. Levine, R. (2005): Finance and growth: theory and evidence, in Aghion, Philippe and Steven Durlauf (eds.), Handbook of Economic Growth, Amsterdam: North Holland, 1: 865–934. Madsen, Jakob B (2017): Is Inequality Increasing in r - g?: Piketty’s Principle of Capitalist Economics and the Dynamics of Inequality in Britain, 1210–2013, Australian National University Crawford School of Public Policy Working Paper 63/2017, available at https://ssrn.com/abs tract=3059334 McKinsey (2020): Wall Street versus Main Street: Why the disconnect?, available at https://www. mckinsey.com/business-functions/strategy-and-corporate-finance/our-insights/wall-street-ver sus-main-street-why-the-disconnect Miller, M. H. (1998). Financial markets and economic growth. Journal of Applied Corporate Finance, 11, 8–14. Oxfam (2021): The Inequality Virus, Oxfam Briefing Paper, available at https://oxfamilibrary.ope nrepository.com/bitstream/handle/10546/621149/bp-the-inequality-virus-250121-en.pdf Piketty, T., & (Translated by Arthur Goldhammer),. (2014). Capital in the Twenty-First Century. Harvard University Press. Rajan, R. G., & Zingales, L. (2004). Saving Capitalism from the Capitalists: Unleashing the Power of Financial Markets to Create Wealth and Spread Opportunity. Princeton University Press. Robinson, J. (1952). The Rate of Interest and Other Essays. MacMillan. Schulze, Elizabeth (2020): Here’s why economists don’t expect trillions of dollars in economic stimulus to create inflation, July 23, 2020, available in https://www.cnbc.com/2020/07/23/whytrillions-of-dollars-in-economic-stimulus-may-not-create-inflation.html

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Stiglitz, Joseph (2020): Conquering the Great Divide, Finance and Development, Fall 2020 issue, International Monetary Fund, available at: https://www.imf.org/external/pubs/ft/fandd/2020/09/ COVID19-and-global-inequality-joseph-stiglitz.htm Sunder, S. (2020). How did the U.S. stock market recover from the Covid-19 contagion? Mind and Society, Available at. https://doi.org/10.1007/s11299-020-00238-0 Ulate, M. (2021). Going Negative at the Zero Lower Bound: The Effects of Negative Nominal Interest Rates. American Economic Review, 111(1), 1–40. UNCTAD (2018): Trade and Development Report: Power, Platform and the Free Trade Delusion, Geneva: UNCTAD, United Nations.

COVID-19 and The Corporate Sector: Winners, Losers, and What Lies Ahead Sudipto Dasgupta, Jayati Sarkar, Subrata Sarkar, and Jiali Yan

Abstract There are confusing signals about how corporate sectors around the world have fared in the pandemic. While the pandemic has created opportunities for some firms or sectors, most others have struggled. However, the overall stock market has surged. In some of the more commonly followed stock indices, only the larger firms are represented. Thus, it is possible that the gains are limited to the larger firms. We take a close look at the U.S. and Indian corporate sectors and conclude that while in both markets, some of the very largest firms have recorded substantial gains, the smallest (lowest quartile based on market capitalization) listed firms have also recorded significant gains through the 2020 calendar year. We find that sales growth performance of listed firms in both markets tells a very different story. Firms in the largest size-quartile have performed the best both in 2019 and 2020 in both markets, and those in the smallest size-quartile have performed the worst. We conjecture that the impact on sales and income performance of the small and medium enterprises (SMEs) is likely to be at least as severe as that of the smallest size-quartile of listed firms. We consider several challenges that confront the corporate sector in India, and argue that the role that the large firms play would be a key determinant of the outcome. Keywords COVID-19 · Pandemic · Corporate sector · Stock markets · Indian corporate sector · Big firms S. Dasgupta (B) Department of Finance, ABFER, CEPR and ECGI, Chinese University of Hong Kong, Hong Kong, China e-mail: [email protected] J. Sarkar · S. Sarkar Indira Gandhi Institute of Development Research, Mumbai, India e-mail: [email protected] S. Sarkar e-mail: [email protected] J. Yan University of Liverpool, Liverpool, United Kingdom e-mail: [email protected] © The Author(s), under exclusive license to Springer Nature Singapore Pte Ltd. 2022 M. Dutta et al. (eds.), The Impact of COVID-19 on India and the Global Order, https://doi.org/10.1007/978-981-16-8472-2_10

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1 Introduction One of the most remarkable manifestations of the COVID-19 pandemic that emerged in December 2019, has been its uneven impact on different groups in society. For example, the exposure to the virus has been widely different for different ethnic groups (notably, in the U.S., where the Hispanic and Black workers were infected at much higher rates), age groups (older people experienced much higher mortality than younger people), and occupational groups (“essential”, “frontline”, or “key” workers as opposed to people who could work from home; migrant or out-of-town temporary workers versus local workers). The economic costs have also been unevenly distributed—women suffered job losses at higher rates than men, blue-collar workers have been more affected than white-collar workers. The uneven economic impact of the pandemic was evident quite early in the U.S., in the so-called dichotomy between “Wall Street” and “Main Street”. The stock markets worldwide had been soaring before the onset of the pandemic. As COVID19 became a pandemic, stock markets crashed in the month of March, 2020. The S&P 500 Index in the U.S., having reached a peak in February of 2020, fell 34% by March 23. In India, the Nifty 50 fell 23% in March. However, in the next two quarters, as businesses closed, people lost jobs, and, even in a country like the U.S., people were seen queuing up in front of food banks, the stock market resumed its upward surge. The S&P 500 reached a new high in August, 2020, and the Nifty 50 recovered most of the March loss in the next 4 months, even as real economic activity shrank and job losses mounted in both countries.1,2 The last 2 months of the year saw major surges in the stock markets. For the U.S., the surge has continued, and new highs were recorded, with the stock market responding positively to massive 1

The behavior of the U.S. stock market during the pandemic and the “Wall Street” versus “Main Street” divide has attracted much attention and remains an unresolved issue. One argument is that the stock market has performed well because of the Federal Reserve’s commitment to continuing with a low-interest regime to revive the economy. When the discount rate is low, discounted future profits are relatively more important. In this scenario, stock prices are less affected by immediate profit shortfalls. A related argument is that when asset prices affect aggregate demand with a lag, the central bank optimally induces asset prices to “overshoot” if there is an output gap (Caballero and Simsek (2020). Price dynamics during the COVID-19 period seems to be reasonably well explained by variations in risk-aversion and expectations of future dividends (Gormsen and Koijen (2020). 2 In India, there has been little systematic analysis of the phenomenon of rising stock market despite sharp economic slowdown during the pandemic. However, a variety of explanations can be found in the business press ranging from stimulus packages in economies around the world benefiting emerging markets like India, the fiscal and monetary stimulus provided by the Government of India, increased participation by younger retail investors in the stock market, influx of liquidity in the stock market, and that the stock market rally mirroring future growth prospects. Further, some commentators were of the view that the market was indeed being driven by fundamentals with selective stocks which were expected to benefit from the pandemic going up and those which are not, going down. See for instance, https://theprint.in/economy/why-indian-stock-markets-have-hitall-time-high-despite-covid-lockdown-record-slowdown/540678/; https://indianexpress.com/art icle/explained/an-expert-explains-making-sense-of-the-sensex-6509574/; https://www.outlookin dia.com/website/story/opinion-why-are-stock-markets-booming-when-economic-chips-are-down/ 358511.

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economic stimulus and success on the vaccine front. In India, however, the story of the first 3 months of 2021 (as we write this chapter) is very different. The stock market seems to have lost its momentum, and an apathetic vaccination rollout and relaxation of restrictions has led to a new wave of infections that threaten to derail economic recovery. Even in the second quarter of 2020, economists were debating the “shape” of the recovery (i.e., whether it would be a “V”-, “W”-, or “U”-shaped recovery). By the end of the year, it had become apparent that a “K”-shaped recovery, where different sections of the economy recover at divergent rates, was a more appropriate characterization of the likely recovery process. In other words, the recovery is likely to create a further wedge between winners and losers—some groups being even better off than they were before the pandemic, while other groups continuing to struggle and becoming even more marginalized. Both survey evidence and latest employment and establishment-level data for the U.S. suggest that the economic impact there has been K-shaped. A poll conducted by the Associated Press and NORC Center for Public Affairs Research in the last week of February 2021 finds that 44% of those surveyed had suffered income loss during the pandemic that was still having an effect on their finances, 25% had been laid off at some point, 30% had been scheduled for fewer hours of work, 20% had taken unpaid time off, 23% had wages or salaries reduced, and 14% had to quit a job to take care of family members. Ten percent said they could not make a housing payment and 25% said they could not make one or more payments in the previous month. The impact was particularly hard on Hispanics, Blacks, and young people below 30 (40% of the latter group reported lower income in the previous month than a year ago). In contrast, 16% of those surveyed said their household income was higher than before the pandemic. These were households where members were holding office jobs and were able to work from home, and their incomes were boosted by government support programs implemented earlier in the year. Not only that, some of the same households have saved more because of less eating out, less travel, and fewer expenditures on entertainment, and are financially better off.3 Using the latest microdata from two well-known data sources, Dalton et al. (2021) find that (a) establishments paying the lowest average wages, and (b) the lowest wage workers, had the steepest decline in employment and were still the furthest from recovery as of the end of 2020. They find that the experience of low-wage workers is not entirely because they are concentrated in certain low-wage sectors—even within sectors, the lowest wage workers suffered the most significant declines in employment. In contrast to the K-shaped recovery pattern in the U.S., in the case of India, there are two contrasting views. One is the contention that the recovery path in India 3

An Economist article estimates that in 21 rich countries, households saved $6 trillion during the pandemic compared to $3 trillion they would have saved if the pandemic had not happened. This “excess saving” is tenth of the annual consumer spending in these countries. (See “The world’s consumers are sitting on piles of cash. Will they spend it?”, The Economist, March 13, 2021). Housing prices are also rapidly rising in the U.S. as cash-rich home buyers anticipate working more from home and compete aggressively in the housing market for more spacious accommodation. Low interest rate expectations are also behind this surge in housing demand.

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is V-shaped. As a case in point, the Economic Survey by the Ministry of Finance of the Government of India (GOI, 2021), in its report in January 2021, concluded that the body of relevant evidence supported a V-shaped recovery, of a strong and sustained economic recovery from a sharp decline following COVID. According to the Survey, while in the first quarter of 2020, GDP contracted by 23.9%, there was only a 7.5% decline in the second quarter with the recovery showing up in terms of all key economic indicators. This was attributed to the stringent lockdown that was imposed nationwide and also at state levels, with a strong correlation of recovery rates of states with the stringency of the lockdown measures. Similar conclusions have also been arrived at by the Reserve Bank of India in its report on Financial Stability as well as by the National Statistical Organisation, as also industry bodies like the Associated Chambers of Commerce. Second is the contrary view (for instance, Lahoti et al., 2021; UNESCAP, 2021), of India being on a K-shaped recovery path as in the U.S., based on evidence of rising income inequalities on account of COVID following disproportionate job losses in the lower income group, adverse impact on the earnings of small businesses, and a sharp rise in profits of listed companies.4 In this chapter, we will focus on the impact of the pandemic on the corporate sector. We will argue that for both the U.S. and Indian corporate sectors, the economic impact of the pandemic has been uneven and significantly skewed in favor of the larger firms. However, this is an outcome that is belied by the performance of stock prices. As we will see, while a few of the largest firms in both economies have done very well in both markets, the stock market gains are not confined to the larger firms as a group. Contrary to the common perception, the smaller listed firms, as a group, have shown even more stellar stock price performance than the larger firms in both markets in 2020. However, the dichotomy between stock price (or stock returns) performance and sales (captured, for example, by sales growth, with implications for employment) becomes more apparent as we look at the smaller listed firms. In almost every sector of the economy, the two smallest size groups of firms have performed the worst in terms of sales growth. While there is limited data as yet for the small and medium enterprises or SMEs (almost none that we are aware of for India, but some for the U.S.), we conjecture that the real performance of the smallest publicly traded firms is more likely to capture the impact on the SMEs, which are by far more important for employment creation than the publicly traded firms.5 These findings may not be completely unexpected, but they have a rather serious implication, especially for India, for at least three reasons. First, bad as the year 2020 was for the Indian corporate sector, 2019 was worse. Therefore, the vulnerable segments of the sector were even more precariously placed at the end of 2020. Second, India’s vaccination drive is progressing extremely slowly, and at the same time, daily infection rates have reached their highest levels since the onset of the pandemic. The prospect of 4

According to reported estimates, the cumulative net profits of listed companies grew by 23% in the first half of 2020–2021 compared to an average growth of 12% over the earlier 5 years. See https:// www.thehindubusinessline.com/opinion/recovery-is-k-shaped-not-v-shaped/article33741322.ece. 5 According to the Office of the United States Trade Representative, 30 million SMEs account for nearly 2/3rd of private sector jobs (https://ustr.gov/trade-agreements/free-trade-agreements/transa tlantic-trade-and-investment-partnership-t-tip/t-tip-12).

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another widespread lockdown and a further slowing down of economic activity are looking very likely as this chapter is being written. Finally, the mechanisms for dealing with large-scale business failures are still quite weak in India compared to advanced countries, and this is likely to further slow down the recovery process.

2 Stock Market Performance of Big Firms Tables 1 and 2 list the top 50 publicly listed companies in the U.S. and India, and report the buy-and-hold returns for 2019 and 2020.6 The first noticeable observation is about the sectoral composition of the top 50 stocks. In both markets, Computer and Digital Processing Services (5 and 7 firms, respectively, for India and the U.S.), Commercial Banks (7 and 5 firms, respectively), and Chemical and Allied Products, including drugs (6 and 8 firms, respectively) are the most heavily represented sectors in the top 50. Thus, the sectoral compositions of the largest firms are quite similar in the two markets. In both markets, the Computer and Digital Processing Services are among the top performers among the largest firms in the pandemic year (all 5 of the firms from this sector are among the top 12 best performers in India; all 7 of the U.S. firms are in the top 20).7 Companies producing Chemical and Allied Products (including drugs/pharmaceuticals) also perform well in the two countries. Insurance companies and Commercial Banks are some of the worst performers among the top 50 in both countries. These findings are consistent with the opportunities and challenges created by the pandemic. The pandemic saw a surge in the demand for digital or online platforms and services8 ; demand for chemical products (including pharmaceuticals, cleaning agents, etc.) increased; however, with the slowdown of economic activity, the financial sector, including the major commercial banks, underperformed. Much of the discussion regarding winners and losers in the pandemic has been about the dominance (in terms of market capitalization and its growth) of the “Big 5” U.S. companies in that market: Apple, Microsoft, Amazon, Facebook, and Alphabet (parent company of Google).9 Apple’s market capitalization value was $740 billion (5180,000 crores in INR) at the end of 2018. As Table 1 shows, it increased threefold in market value over the next 2 years, and currently has a market capitalization value of $2.2 trillion. The pandemic did nothing to stop its growth. The increase in the market 6

The buy-and-hold return is the continuously compounded return over the year, inclusive of dividend payments. 7 Reliance Industries, India’s largest company in terms of market capitalization, is a conglomerate. While it is classified based on its primary three-digit SIC code as being in the Petroleum Refining sector, it has significant assets in the digital and telecommunications sector, the retail sector, biotechnology, and media, among others. 8 Amazon, though classified in the Retail sector, is essentially a “technology” company. Inclusion of Amazon and Apple would increase the weight of the “tech-focused” companies in the list of top U.S. performers. 9 See, for example, the recent book by Galloway (2020).

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Table 1 Buy-and-hold returns for the top 50 companies in the U.S. Company name

SIC Code

Industry description Returns (2020) (%)

Returns (2019) (%)

Apple Inc

3663

Communications Equipment

80.47

88.41

Microsoft Corp

7372

Computer and Data Processing Services

42.27

59.97

Amazon.Com Inc 5961

Nonstore Retailers

50.63

13.25

Facebook Inc

7370

Computer and Data Processing Services

25.89

47.21

JP Morgan Chase 6020 & Co

Commercial Banks

−5.98

45.67

Alphabet Inc

7370

Computer and Data Processing Services

23.86

25.84

Johnson & Johnson

2834

Drugs

10.71

19.82

Walmart Inc

5331

Variety Stores

20.22

25.71

Visa Inc

6099

Functions Closely Related to Banking

13.71

39.29

Bank of America 6020 Corp

Commercial Banks

−11.21

44.21

Procter & Gamble Co

2840

Soaps, Cleaners and Toilet Goods

16.12

39.68

Mastercard Inc

6099

Functions Closely Related to Banking

16.20

55.53

Exxon Mobil Corp

2911

Petroleum Refining

−35.21

4.56

At&T Inc

4812

Telephone Communication

−23.57

38.62

Unitedhealth Group Inc

6324

Medical Service & Health Insurance

14.13

21.13

Disney (Walt) Co 4888

Cable and Other Pay Television Services

14.84

32.14

Intel Corp

3674

Electronic Components and Accessories

−17.95

35.56

Verizon Communications Inc

4812

Telephone Communication

1.33

11.67

Berkshire Hathaway

9997

Nonclassifiable Establishments

−0.12

13.25

Home Depot Inc

5211

Lumber and Other Building Materials

24.28

35.28

Coca-Cola Co

2086

Beverages

5.92

20.54 (continued)

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Table 1 (continued) Company name

SIC Code

Industry description Returns (2020) (%)

Merck & Co

2834

Drugs

Chevron Corp

2911

Wells Fargo & Co

6020

Returns (2019) (%)

−5.20

25.30

Petroleum Refining

−24.84

13.40

Commercial Banks

−39.93

18.98

Pfizer Inc

2834

Drugs

5.03

−7.20

Comcast Corp

4841

Cable and Other Pay Television Services

15.13

38.33

Cisco Systems Inc

3576

Computer and Office Equipment

−5.15

13.83

Pepsico Inc

2080

Beverages

16.10

29.33

Boeing Co

3721

Aircraft and Parts

−32.38

8.48

Citigroup Inc

6199

Finance Services

−24.61

55.76

Oracle Corp

7370

Computer and Data Processing Services

20.41

19.17

Adobe Inc

7370

Computer and Data Processing Services

56.05

45.22

Abbott Laboratories

3845

Medical Instruments & Supplies

30.44

28.04

Bristol−Myers Squibb Co

2834

Drugs

1.19

31.18

Mcdonald’s Corp 5812

Eating and Drinking Places

7.26

14.42

Salesforce.Com Inc

7372

Computer and Data Processing Services

36.51

17.99

Nvidia Corp

3674

Electronic Components and Accessories

124.07

87.60

Amgen Inc

2836

Drugs

Netflix Inc

7841

Video Tape Rental

Philip Morris International

2111

Cigarettes

Abbvie Inc

2836

Thermo Fisher Scientific Inc

3826

Costco Wholesale Corp Raytheon Technologies Corp

3.67

35.66

49.59

19.01

5.23

37.56

Drugs

28.46

10.24

Measuring and Controlling Devices

41.33

55.12

5399

Miscellaneous General Merchandise Stores

25.32

45.22

3724

Aircraft and Parts

−18.91

44.06

(continued)

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S. Dasgupta et al.

Table 1 (continued) Company name

SIC Code

Industry description Returns (2020) (%)

Returns (2019) (%)

Paypal Holdings Inc

7374

Computer and Data Processing Services

131.31

25.27

Honeywell International Inc

9997

Nonclassifiable Establishments

20.94

40.63

Nike Inc −Cl B

3021

Rubber and Plastic Footwear

38.84

39.27

Lilly (Eli) & Co

2834

Drugs

29.39

15.89

Broadcom Inc

3674

Electronic Components and Accessories

41.04

37.64

Union Pacific Corp

4011

Railroads

15.95

38.26

This table reports the buy-and-hold returns for the top 50 public U.S. companies by market value (as of December 2019) for the entire corporate sector listed in NYSE, AMEX, and NASDAQ. We report the company name, the Standard Industrial Classification (SIC) code, the industry description at the three-digit SIC code level, and buy-and-hold returns in 2019 and 2020. Returns (2020) are the buy-and-hold returns of a firm from January 1 to December 31, 2020. Returns (2019) are the buy-and-hold returns of a firm from January 1 to December 31, 2019

capitalization value of the top 6 U.S. companies over the 2-year period is 15.80% of the market capitalization of the entire U.S. stock market as of the beginning of 2019. The numbers, if one focused only on the pandemic year, are even more lopsided. In the pandemic year, the increase in the market value of the top 6 was 77% of the increase in market capitalization value of the top 50 companies (compared to 63% over the 2-year period). The big became even bigger in the pandemic. Clearly, the very big firms in the U.S. market—most of them “tech” companies—have been major winners in an absolute sense. Their dominance in their respective sectors, as well as the entire economy, has increased. The case of the six largest Indian companies as of the end of 2019 (Reliance Industries, Tata Consultancy Services, HDFC Bank, Housing Development Finance Corporation, Hindustan Unilever, and ICICI Bank) is only slightly less striking, but that may be only because the banks performed poorly in 2020. The top 6 in India captured 53% of the increase in market capitalization value of the top 50 in 2020, and over the 2-year period starting in 2019, the corresponding number is also 53%. However, if one replaces ICICI Bank with Infosys,10 the corresponding percentages become 65 and 57%. The gain in market capitalization over the 2-year period for the top 6 as a percentage of the overall stock market capitalization at the beginning of 2019 is almost the same as the U.S., at 11%.11 10

Our classifications based on market capitalization is as of the end of 2019. As of April 9, 2021, Infosys has replaced ICICI Bank in the top 6. 11 One caveat that is worth keeping in mind is that the performance measured displayed in Tables 1 and 2 are based on buy-and-hold returns unadjusted for risk. Some would regard the return of the

COVID-19 and the Corporate Sector …

219

Table 2 Buy-and-Hold Returns for the Top 50 Companies in India Company name

SIC code Industry description Returns (2020) (%) Returns (2019) (%)

Reliance Industries Ltd

2911

Petroleum Refining

33.56

38.48

Tata Consultancy Services Ltd

7371

Computer and Data Processing Services

37.17

19.36

HDFC Bank Ltd

6029

Commercial Banks

12.65

22.01

Housing Development Finance Corpn. Ltd

6162

Mortgage Bankers & Brokers

5.50

25.08

Hindustan Unilever Ltd

2844

Soaps, Cleaners and Toilet Goods

27.22

7.78

ICICI Bank Ltd

6029

Commercial Banks

−1.84

51.53

Kotak Mahindra Bank Ltd

6029

Commercial Banks

19.81

34.59

Infosys Ltd

7371

Computer and Data Processing Services

74.85

14.80

State Bank Of India

6029

Commercial Banks

−17.04

13.03

ITC Ltd

2111

Cigarettes

−6.68

−13.84

Bajaj Finance Ltd

6141

Personal Credit Institutions

26.22

60.53

Bharti Airtel Ltd

4812

Telephone Communication

13.66

60.42

Maruti Suzuki India Ltd

3711

Motor Vehicles and Equipment

4.25

0.70

Axis Bank Ltd

6029

Commercial Banks

−17.15

21.74

Larsen & Toubro Ltd

1611

Highway and Street Construction

3.75

−8.22

Asian Paints Ltd

2851

Paints and Allied Products

54.18

32.28

Oil & Natural Gas 1311 Corpn. Ltd

Crude Petroleum and Natural Gas

−21.55

−10.46

HCL Technologies Ltd

7371

Computer and Data Processing Services

67.88

18.97

Bajaj Finserv Ltd

6311

Life Insurance

−4.48

45.81

Nestle India Ltd

2023

Dairy Products

25.81

37.36

Wipro Ltd

7371

Computer and Data Processing Services

57.08

0.61

Coal India Ltd

1221

Bituminous Coal and Lignite Mining

−25.28

−12.41 (continued)

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S. Dasgupta et al.

Table 2 (continued) Company name

SIC code Industry description Returns (2020) (%) Returns (2019) (%)

HDFC Life Insurance Co. Ltd

6311

Life Insurance

Indian Oil Corpn. Ltd

1311

NTPC Ltd Ultratech Cement Ltd

7.96

65.02

Crude Petroleum and Natural Gas

−23.81

−6.10

4911

Electric Services

−13.58

−1.70

3241

Cement, Hydraulic

32.73

1.83

Avenue Supermarts Ltd

5411

Grocery Stores

45.87

17.18

Bharat Petroleum Corpn. Ltd

2911

Petroleum Refining

−17.34

43.70

Titan Company Ltd

3911

Jewelry, Silverware, and Plated Ware

31.07

28.77

−40.46

−3.69

36.33

0.55

8.73

−0.03

Indusind Bank Ltd 6029

Commercial Banks

Sun Pharmaceutical Inds. Ltd

2833

Drugs

Power Grid Corpn. Of India Ltd

4911

Electric Services

SBI Life Insurance Co. Ltd

6311

Life Insurance

−6.06

63.86

Bajaj Auto Ltd

3751

Motorcycles, Bicycles, and Parts

13.03

22.18

Hindustan Zinc Ltd

1031

Lead and Zinc Ores

37.37

−24.98

Bandhan Bank Ltd

6029

Commercial Banks

−20.13

−7.79

Dabur India Ltd

2844

Soaps, Cleaners, and Toilet Goods

18.51

8.17

−19.05

−5.51

10.53

Adani Ports & 4491 Special Economic Zone Ltd

Water Transportation Services

Tech Mahindra Ltd

Computer and Data Processing Services

34.31

Shree Cement Ltd 3241

Cement, Hydraulic

21.28

19.27

Britannia Industries Ltd

2052

Bakery Products

22.24

−1.58

Pidilite Industries Ltd

2891

Miscellaneous Chemical Products

26.65

27.70

7371

(continued)

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Table 2 (continued) Company name

SIC code Industry description Returns (2020) (%) Returns (2019) (%)

Godrej Consumer Products Ltd

2841

Soaps, Cleaners, and Toilet Goods

9.76

−13.99

ICICI Prudential 6311 Life Insurance Co. Ltd

Life Insurance

3.32

50.59

JSW Steel Ltd

3312

Blast Furnace and Basic Steel Products

44.36

−10.26

Mahindra & Mahindra Ltd

3523

Farm and Garden Machinery

36.14

−32.23

ICICI Lombard 6331 General Insurance Co. Ltd

Fire, Marine & Casualty Insurance

8.85

61.28

Eicher Motors Ltd 3751

Motorcycles, Bicycles, and Parts

12.77

−0.85

Tata Motors Ltd

3711

Motor Vehicles and Equipment

−0.59

6.32

DLF Ltd

1531

Operative Builders

3.26

32.89

This table reports the buy-and-hold returns for the top 50 public traded companies of India by market capitalization (as of December 2019) for the entire corporate sector listed in the NSE. We report the company name, the Standard Industrial Classification (SIC) code, the industry description at the three-digit SIC code level, and buy-and-hold returns in 2019 and 2020. Returns (2020) are the buy-and-hold returns of a firm from January 1 to December 31, 2020. Returns (2019) are the buy-and-hold returns of a firm from January 1 to December 31, 2019

3 Sectoral Stock Market Performance Tables 3 and 4 provide the buy-and-hold returns for some key sectors, for the U.S. and India, respectively. Within each sector, firms are grouped into quartiles, based on their market capitalization value at the end of the previous year. Group 1 represents the largest firms, and group 4 the smallest. Focusing on 2019 first, for the U.S. stock market, 2019 was a stellar year, with the listed firms experiencing buy-and-hold returns in excess of 30% (Panel B of Table 3). Leading up to the last quarter of 2019, the economy had enjoyed 23 quarters of uninterrupted economic growth. The larger firms started to outperform the smaller firms in the later part of this period. As seen from Panel A, in 2019, in almost every sector, the largest size group outperformed the smallest size group. Panel B where the groups are aggregated across sectors confirms this pattern.

entire market as a compensation for a higher risk premium under a particular economic environment. In this setting, shareholders of a firm that has more exposure to market risk would require higher compensation for risk. Indeed, the risk-adjusted returns for many of the stocks in Tables 1 and 2 turn negative for both 2019 and 2020.

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Table 3 Sectoral Buy-and-Hold Returns by Size Group in the U.S. One-digit SIC

Industry

Size Group

Returns (2020) (%)

Returns No. (2019) Firms (%)

Panel A: Buy-and-Hold Returns for Each Industry-Size Group 1

Mining and Construction

1 (largest)

−5.65

9.93

1

Mining and Construction

2

−1.67

18.81

43

1

Mining and Construction

3

−12.30

−4.29

43

1

Mining and Construction

4 −8.14 (Smallest)

−5.38

43

whole sector

−5.46

10.27

172

43

2

Light Manufacturing

1 (largest)

6.79

20.97

161

2

Light Manufacturing

2

28.49

27.82

162

2

Light Manufacturing

3

30.19

18.35

161

2

Light Manufacturing

4 56.25 (Smallest)

25.24

162

whole sector

7.82

21.19

646

3

Heavy Manufacturing

1 (largest)

41.55

48.36

173

3

Heavy Manufacturing

2

29.95

36.33

174

3

Heavy Manufacturing

3

32.45

26.69

173

3

Heavy Manufacturing

4 54.63 (Smallest)

22.22

174

whole sector

40.80

47.32

694

4

Transportation & Public Utilities

1 (largest)

6.18

25.93

56

4

Transportation & Public Utilities

2

−2.09

24.77

56

4

Transportation & Public Utilities

3

2.58

8.76

56

4

Transportation & Public Utilities

4 6.49 (Smallest)

8.59

56

Whole sector

5.27

25.26

224

5

Retail and Wholesale Trade 1 (largest)

27.04

24.30

63

5

Retail and Wholesale Trade 2

16.26

26.96

63

5

Retail and Wholesale Trade 3

15.25

21.23

63

5

Retail and Wholesale Trade 4 82.65 (Smallest)

4.30

64

24.32

253

Whole sector

26.53

(continued)

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Table 3 (continued) One-digit SIC

Industry

Size Group

Returns (2020) (%)

Returns No. (2019) Firms (%)

6

Finance, Insurance and Real Estate

1 (largest)

0.50

33.54

222

6

Finance, Insurance and Real Estate

2

−2.32

24.07

223

6

Finance, Insurance and Real Estate

3

−4.90

21.23

222

6

Finance, Insurance and Real Estate

4 −8.26 (Smallest)

16.62

223

Whole sector

0.16

32.52

890

7

Services

1 (largest)

39.52

39.32

100

7

Services

2

34.97

26.61

100

7

Services

3

34.42

23.21

100

7

Services

4 51.69 (Smallest)

23.86

101

Whole sector

38.13

401

39.17

Panel B: Buy-and-Hold Returns for Each Size Group Size Group

Returns (2020)

Returns (2019)

1 (largest)

20.24

32.21

2

14.35

28.34

3

13.81

18.23

4 22.13a (Smallest)

14.98

Whole sector

31.68

19.76

a Note that the reason that the return for U.S. size group 4 (smallest firms) in 2020 is only 22.13%, less than half of the size group 4 returns in the manufacturing sector (56.25% and 54.63%) is due to the low returns of the smallest firms in the finance industry (-8.26%) in the U.S. Also, the industry component in India and the U.S. is slightly different: India has only 160 public firms in the finance sector traded in NSE (a majority of firms are in the manufacturing industry), while U.S. has about 900 firms in the finance sector. The return for size group 4 across the entire corporate sector in the U.S. in 2020 is 41.95% if we exclude the financial firms.) This table reports the value-weighted buy-and-hold returns, in percent per year, for the quartile group sorted on the basis of market capitalization in each corporate sector in the U.S. Panel A shows the buy-and-hold returns for each industry-size group in a year, we first sort the public U.S. firms in each sector into the quartile portfolios according to their market value at the end of the previous year, and then we calculate the buy-and-hold returns for each firm-year and finally, we average the buy-and-hold returns across each industry-size group using firm market capitalization as weight. Panel B shows the value-weighted buy-and-hold returns for each size group across the entire corporate sector in the U.S., and the last row reports the returns for the whole corporate sector in the U.S.

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Table 4 Sectoral Buy-and-Hold Returns by Size Group in India One-digit SIC

Industry

Size Group Returns (2020)

Returns (2019)

No. Firms

Panel A: Buy-and-Hold Returns for Each Industry-Size Group 1

Mining and Construction

1 (largest)

−5.32

−6.74

29

1

Mining and Construction

2

5.30

−24.05

30

1

Mining and Construction

3

40.25

−41.09

30

1

Mining and Construction

4 (Smallest)

66.43

−53.26

30

Whole sector

−4.52

−8.08

119

2

Light Manufacturing

1 (largest)

28.74

11.47

104

2

Light Manufacturing

2

41.31

−12.94

105

2

Light Manufacturing

3

33.62

−24.41

105

2

Light Manufacturing

4 (Smallest)

80.76

−31.25

105

whole sector

29.28

9.81

419

3

Heavy Manufacturing

1 (largest)

19.73

−3.34

103

3

Heavy Manufacturing

2

33.84

−21.11

104

3

Heavy Manufacturing

3

34.54

−27.04

104

3

Heavy Manufacturing

4 (Smallest)

45.07

−32.52

104

Whole sector

20.76

−5.11

415

4

Transportation & Public Utilities

1 (largest)

13.03

4.39

23

4

Transportation & Public Utilities

2

26.75

−17.10

23

4

Transportation & Public Utilities

3

48.28

−39.05

23

4

Transportation & Public Utilities

4 (Smallest)

38.36

−32.14

24

Whole sector

14.54

0.87

93

5

Retail and Wholesale Trade

1 (largest)

31.17

12.81

10

5

Retail and Wholesale Trade

2

11.92

−18.01

10

5

Retail and Wholesale Trade

3

37.35

−31.27

10

5

Retail and Wholesale Trade

4 (Smallest)

37.58

−35.40

11 (continued)

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Table 4 (continued) One-digit SIC

Industry

Size Group Returns (2020)

Returns (2019)

No. Firms

Whole sector

30.29

9.76

41

6

Finance, Insurance and Real 1 (largest) Estate

0.07

19.87

39

6

Finance, Insurance and Real 2 Estate

−3.68

−5.20

39

6

Finance, Insurance and Real 3 Estate

12.44

−20.73

39

6

Finance, Insurance and Real 4 Estate (Smallest)

22.18

−18.00

40

Whole sector

−0.09

17.11

157

7

Services

1 (largest)

49.39

12.27

32

7

Services

2

67.68

−19.33

32

7

Services

3

85.97

−26.75

32

7

Services

4 (Smallest)

37.32

−24.90

32

Whole sector

48.83

11.12%

128

Size Group Returns (2020)

Returns (2019)

1 (largest)

18.59%

8.85%

2

18.49%

−13.79%

3

33.72%

−28.15%

4 (Smallest)

49.66%

−31.14%

Whole sector

18.75%

6.76%

This table reports the value-weighted buy-and-hold returns, in percent per year, for the quartile group sorted on the basis of market capitalization in each corporate sector in India. Panel A shows the buy-and-hold returns for each industry-size group in a year. We first sort the public firms of India in each sector into the quartile portfolios according to their market value at the end of the previous year. Then we calculate the buy-and-hold returns for each firm year, and finally, we average the buyand-hold returns across each industry-size group using firm market capitalization as weight. Panel B shows the value-weighted buy-and-hold returns for each size group across the entire corporate sector in India, and the last row reports the returns for the whole corporate sector in India

In the first quarter of 2020, U.S. GDP contracted 5% from the previous quarter, and by 30% in the following quarter. 2020Q3 saw a 30% rebound, and 2020Q4 saw a modest growth of 4%. For the year 2020 as a whole, the economy shrank 3.5%. In terms of stock market performance, we notice from panel A in Table 3 that the smallest size group outperformed the largest size group in the key sectors of Light

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and Heavy Manufacturing, Wholesale and Retail Trade, and Services. When these groups are aggregated across sectors, as seen from panel B, the difference between the largest and smallest size groups becomes marginal. However, as footnote 22 explains, this is because Finance, Insurance, and Real Estate industry has close to 900 firms, and the smallest group in this sector underperforms the other groups. Once this sector is dropped, the smallest size group in Panel B has a return of 42%. The overall U.S. stock market earned a return of almost 20%, which is remarkable for a year that saw heavy job losses and slowdown of economic activity. Much of this stock price performance was driven by government stimulus measures, partial resumption of economic activity and return to growth, and a rebound in the stock markets in the last 2 months of the year fueled by news about vaccines and the expectation of more government support under a new administration. So in the pandemic year, while the few really big firms further consolidated their dominance in the stock market, the smaller firms as a group outperformed the larger firms. Not only that, if we consider the performance of the smallest size group in the one-digit industries 2, 3, 5, and 7, with the exception of Apple and Nvidia in SIC industry 3, and Paypal in SIC industry 7), none of the firms in the list in Table 1 from the corresponding industry outperformed the smallest size groups in these industries. In other words, the “Wall Street” versus “Main Street” disconnect was not only a large firm phenomenon. It is beyond the scope of this paper to explain these stock return patterns. There is a well-known phenomenon in Finance called the “small firm effect”. Smallcapitalization stocks (commonly referred to as “small-caps”) have been known to outperform (in terms of returns) large-capitalization stocks over long time horizons. This is known as an asset pricing “anomaly”. One reason why this could be the case is that smaller firms (are expected to) grow faster, which is reflected in higher returns. However, the relative performance of small and large-caps around economic downturns is particularly interesting. Small firms do worse in the early stages of an economic downturn, perhaps reflecting a flight to quality. Small firms are more likely to go bankrupt and struggle to raise financing during downturns. They are also less diversified and more exposed to local economic conditions, which makes them riskier. However, as a recovery appears likely, small firms begin to outperform the larger firms. During the Global Financial Crisis, the lowest decile (by market capitalization) of U.S. listed firms did worse than the highest decile during 2007–2008 and 2008–2009; however, the smallest decile significantly outperformed the largest decile during 2009–2010. The smaller firms suffered the bigger losses in the first two quarters of 2020 as the pandemic set in, but were by far the bigger gainers when the outlook started to improve in the next two quarters, and especially toward the end of the year. Turning to India’s experience in Table 4, one thing is immediately clear from Panel B. The year 2019 was a much worse year, at least in terms of stock market performance, than the pandemic year. This was especially the case for the smaller firms. In 2019, only the largest firms experienced stock price gains, and the losses were especially large for the smallest size group. The Indian economy, since the end of 2017, has been experiencing unprecedented growth slowdown, possibly triggered by deep-rooted structural problems that have remained unaddressed, and a series of

COVID-19 and the Corporate Sector …

227

policy missteps. In 2019, private sector investment reached a 15-year low. Consumer spending significantly contracted, throwing millions out of a job. As Panel A of Table 4 shows, in every sector, except for the largest size group, all other size groups lost value, especially the smallest size group. In 2020, the overall stock market performance of all listed firms was considerably better (18.75% buy-and-hold return). However, now, the tables were turned on the larger (but not the largest!) firms—in every sector except Services,12 the smallest size group outperformed the largest size group (although the largest size group lost value only in the Mining and Construction sector). This phenomenon is similar to that of the U.S. in 2020, but even more robust across different sectors. The rebound of the smaller firms in terms of stock price performance (driven largely by the last 2 months of the year) may suggest that an economic recovery is on the way. As noted, small firms outperform large firms in the early stages of a recovery. After all, stock prices are supposed to be forward-looking, and the smaller firms are likely to be eschewed by investors unless the indications for recovery look firm. However, at least for India, this would be a hasty conclusion. First, the gains to the small firms in 2020 barely offset the losses in the prior year, which puts these firms at a significant disadvantage vis-a-vis the larger firms in the same sector, who made significant gains in both periods, with regard to their growth prospects, capital raising activities, or survival. Second, the pandemic may actually get worse in India in 2021, or at least, drag on much longer than in the U.S. After bringing new infection numbers down to low levels toward the end of 2020 and introducing a vaccination program, India seems to have been dropped the ball. Third, and perhaps most important, there seems to be a complete absence of policy or support programs directed toward the most vulnerable element of the corporate sector—the micro-, small-, and medium-size enterprises.

4 The “Real” Picture Next, we examine the growth of sales of publicly listed companies in the U.S. and India. While stock prices are forward-looking, they can also reflect sentiment. Optimism can be misplaced when a rare event like the pandemic is unfolding. Little is known, for example, about potential adverse reactions to the vaccines, the emergence of new variants of the virus, and so on. Another reason for examining sales is that since data on SMEs is not readily available, the impact of the pandemic on the smallest size group of listed firms should give us some idea of how the SMEs have been affected. In Tables 5 and 6, we report sales growth of U.S. and Indian firms, respectively. Since the fiscal year for Indian firms ends on March 31, annual sales figures for 2020 are not yet available. Because of this, we present quarterly sales data (available 12

Services Industry (Standard Industry Classification 7) includes Business Services, which in turn includes Computer and Data Processing Services.

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Table 5 Sectoral sales growth by size group in the U.S. One-digit SIC Industry

Size Group

2020 (%) 2019 (%) No. Firms

Panel A: Sales Growth for Each Industry-Size Group 1

Mining and Construction

1 (largest)

−4.24

3.99

1

Mining and Construction

2

−5.01

2.84

55

1

Mining and Construction

3

−5.67

−0.47

55

1

Mining and Construction

4 (Smallest) −36.09

−3.28

2

Light Manufacturing

1 (largest)

5.40

1.41

182

2

Light Manufacturing

2

3.16

0.00

182

2

Light Manufacturing

3

0.00

0.00

182

2

Light Manufacturing

4 (Smallest) 0.00

0.00

182

3

Heavy Manufacturing

1 (largest)

7.73

2.19

187

3

Heavy Manufacturing

2

0.77

2.80

187

3

Heavy Manufacturing

3

1.76

3.22

187

3

Heavy Manufacturing

4 (Smallest) −1.90

0.00

188

4

Transportation & Public Utilities

1 (largest)

−1.28

0.07

73

4

Transportation & Public Utilities

2

1.46

1.12

73

4

Transportation & Public Utilities

3

0.36

2.62

73

4

Transportation & Public Utilities

4 (Smallest) −2.33

−2.43

74

5

Retail and Wholesale Trade

1 (largest)

6.30

4.91

66

5

Retail and Wholesale Trade

2

−3.26

3.76

67

5

Retail and Wholesale Trade

3

−0.02

5

Retail and Wholesale Trade

4 (Smallest) 2.33

6

Finance, Insurance and Real 1 (largest) Estate

6

55

55

1.11

66

0.37

67

1.11

7.08

178

Finance, Insurance and Real 2 Estate

1.92

6.87

178

6

Finance, Insurance and Real 3 Estate

3.70

7.15

178

6

Finance, Insurance and Real 4 (Smallest) −0.58 Estate

5.47

178

7

Services

1 (largest)

8.91

9.96

111

7

Services

2

4.25

12.14

112

7

Services

3

3.60

7.10

112

7

Services

4 (Smallest) −6.23

0.19

112 (continued)

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Table 5 (continued) One-digit SIC Industry

Size Group

2020 (%) 2019 (%) No. Firms

Panel B: Sales Growth for Each Size Group Size group

Sale growth (2020) (%)

Sale growth (2019)

1 (largest)

4.44

3.92

2

1.46

4.72

3

0.00

3.46

4 (Smallest)

−0.10

0.00

Whole sector

0.41

3.19

This table reports the median sale growth, in percent per year, for the quartile group sorted on the basis of market capitalization in each corporate sector in the U.S. Panel A shows the sales growth for each industry-size group in a year, we first sort the public U.S. firms in each sector into the quartile portfolios according to their market value at the end of the previous year, and then we use the quarterly sales number to compute the percentage change in sales growth and next calculate cumulative sales growth across each firm-year and finally, we calculate the median sales growth for each industry-size group. Panel B shows the median sales growth ratio for each size group across the entire corporate sector in the U.S., and the last row reports the median sales growth for the whole corporate sector in the U.S.

for most listed U.S. firms and many listed Indian firms) for the fourth quarter of a calendar year. Firms are classified into size groups based on market capitalization value, as in Tables 3 and 4. The 2019 figures compare sales in the last quarter of 2019 to sales in the last quarter of 2018, and similarly for the 2020 figures. We only examine firms for which data is available for all three quarters. Since sales and earnings growth numbers can often have extreme values, the figures represent median sales growth for a given group. It is important to note that this approach has the disadvantage that sales lost (or gained) in the first three quarters of a year are not reflected in these sales or earnings growth figures. For example, if firms lost significant sales in the third quarter of the pandemic, but sales recovered quickly in the fourth quarter to the level of a year before, the balance sheets of the firms would be a lot weaker than one year before. This, though, would not be reflected in our numbers. However, the numbers we report have the advantage that they give a more accurate picture of the (recovery of) the firm’s economic activity. However, to get a clearer picture of the potential balance sheet impact of lost sales, in the first two columns of Table 7, we report annual sales growth for U.S. firms for 2019 and 2020, compared to the previous years, respectively. In column 3, we report sales growth for Indian firms for 2019 compared to the previous year. Note that for India, the sales growth numbers reported in Table 7 capture the impact of the last three quarters of 2019 and the first quarter of 2020. Table 5 shows that in 2019, while the larger firms in the U.S. had experienced robust sales growth over the year in almost every sector, sales for the smallest firms had stagnated when compared to the last quarter of a year earlier. In 2020, while the largest size group of firms had recorded significant sales growth in the last quarter relative to last quarter sales a year ago, the two lowest size groups had struggled, with

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Table 6 Sectoral sales growth by size group in India One-digit SIC

Industry

Size group

2020

2019

No. Firms

Panel A: Sales Growth for Each Industry-Size Group 1

Mining and Construction

1 (largest)

9.25%

−5.05%

26

1

Mining and Construction

2

−9.59%

−0.47%

26

1

Mining and Construction

3

12.23%

−3.27%

26

1

Mining and Construction

4 (Smallest)

−14.29%

−31.29%

27

2

Light Manufacturing

1 (largest)

7.52%

3.21%

109

2

Light Manufacturing

2

8.07%

−0.18%

109

2

Light Manufacturing

3

1.32%

0.76%

109

2

Light Manufacturing

4 (Smallest)

−0.53%

−7.61%

109

3

Heavy Manufacturing

1 (largest)

14.22%

−2.68%

102

3

Heavy Manufacturing

2

13.15%

−8.41%

102

3

Heavy Manufacturing

3

13.16%

−8.48%

102

3

Heavy Manufacturing

4 (Smallest)

4.05%

−15.68%

102

4

Transportation & Public Utilities

1 (largest)

2.87%

−2.20%

22

4

Transportation & Public Utilities

2

0.92%

1.55%

22

4

Transportation & Public Utilities

3

−2.87%

−4.49%

22

4

Transportation & Public Utilities

4 (Smallest)

−14.04%

−0.98%

23

5

Retail and Wholesale Trade

1 (largest)

−14.01%

−1.87%

10

5

Retail and Wholesale Trade

2

−19.97%

2.82%

10

5

Retail and Wholesale Trade

3

−1.85%

3.31%

10

5

Retail and Wholesale Trade

4 (Smallest)

7.75%

−5.83%

10

6

Finance, Insurance and Real Estate

1 (largest)

0.11%

11.95%

36

6

Finance, Insurance and Real Estate

2

−2.08%

8.51%

36

6

Finance, Insurance and Real Estate

3

−11.40%

2.80%

36

6

Finance, Insurance and Real Estate

4 (Smallest)

16.67%

−17.28%

37

7

Services

1 (largest)

−3.99%

7.64%

30

7

Services

2

2.83%

0.48%

30

7

Services

3

−10.95%

−0.78%

30

7

Services

4 (Smallest)

−21.04%

−4.66%

31 (continued)

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Table 6 (continued) One-digit SIC

Industry

Size group

2020

2019

No. Firms

Panel B: Sales Growth for Each Size Group Size group

Sales growth (2020)

Sales growth (2019)

1 (largest)

7.24%

3.00%

2

5.03%

−0.77%

3

1.54%

−2.38%

4 (Smallest)

−1.55%

−8.55%

Whole sector

3.95%

−1.41%

This table reports the median sale growth, in percent per year, for the quartile group sorted based on market capitalization in each corporate sector in India. Panel A shows the sales growth for each industry-size group in a year. We first sort the public firms of India in each sector into the quartile portfolios according to their market value at the end of the previous year. Then we use the quarterly sales number to compute the percentage change in sales growth and next calculate cumulative sales growth across each firm year and finally, we calculate the median sales growth for each industry-size group. Panel B shows the median sales growth ratio for each size group across the entire corporate sector in India, and the last row reports the median sales growth for the whole corporate sector in India

Table 7 Sales growth by size Group in U.S. and India based on annual figures Size group

U.S. sales growth 2019–2020 (%)

U.S. sales growth 2018–2019 (%)

Indian sales growth 2018–2019 (%)

1 (largest)

−0.95

4.29

−0.15

2

−1.99

5.46

−3.74

3

−1.01

6.91

−4.53

4 (Smallest)

−2.14

4.46

−9.35

Whole sector

−1.37

5.09

−3.56

As we write this chapter, the annual financial information (such as sales) for firms of India in 2020 is not available yet, and so we can only report the annual sales growth from FY 2018 to FY 2019 This table reports the median sale growth, in percent per year, for the quartile group sorted based on market capitalization in each corporate sector in U.S. and India. To calculate the sales growth from 2019 to 2020 (2018 to 2019), we first sort the public firms in each sector into the quartile portfolios according to their market value at the end of 2018 (2017). Then we use the annual sales number in 2019 and 2020 (2018 and 2019) to compute the percentage change in sales growth, and next we calculate the median sales growth for each size group: Sales Growth 2019–2020 (Sales Growth 2018–2019)

the smallest size group registering negative growth. Recall that the smallest size group registered an annual return of 42% in 2020 if we exclude the Finance, Insurance, and Real Estate sector. These firms may have been oversold earlier in the year, but is appears that the market is betting on a very rapid return to growth for a group of firms that had seen relatively little growth even before the pandemic. In first two columns of Table 7, we report the yearly sales growth of U.S. firms based on annual sales

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figures. The first column, titled “US Sales Growth 2019–2020”, compares annual sales in 2020 to annual sales in 2019. We find the even for the largest size group, the annual sales growth in the pandemic year is negative (-0.95%). Other size groups suffer more in 2020, with the smallest group’s sales growth rate dropped by -2.14%. This confirms that although firms’ sales recovered quickly at the end of 2020, the balance sheets look worse than one year before. In Table 6, we report the sales growth for Indian listed firms based on fourthquarter sales. 2019 was a very bad year for all but the largest size group. The smallest size group registered negative sales growth of -8.5%. While the largest size group recorded median sales growth of 3%, this was low for a country like India, where GDP growth has been typically above 5%. These sales growth numbers are consistent with the stock return numbers reported in Table 4, where we noted that all but the largest size group of firms recorded heavy losses in market value. Compared to 2019, sales growth performance was significantly better in 2020. However, the smallest size group slipped further, while the next highest size group appeared to have not fully recovered the losses of the previous year. The largest size group experienced robust sales growth, remarkable for a pandemic year. It appears that only the largest firms have the resources to continue with any degree of normality.13 Perhaps the most concerning is the performance of the smallest size group of firms. The remarkable 48% stock return in 2020 for this group of firms just about offsets the 31% loss a year earlier. This is probably good news, since the consequences of a pandemic year after a year like 2019 could have been much worse. However, sales have not recovered, and continue to decline. If the smaller publicly listed firms experience sales losses and struggle to recover, the implications for the SMEs are dire—especially if another round of lockdowns brings economic activity to a standstill.14

5 Challenges for the Indian Corporate Sector As the preceding discussion suggests, the pandemic presents many challenges for the Indian corporate sector, and these challenges are likely to be much more severe for the smaller listed firms and the SMEs. These challenges, in some instances, will be amplified by India’s institutional weaknesses, possible predatory responses from larger firms, and the interactions of the two. We discuss some of these now.

13

The third column in Table 7 shows that annual sales for Indian companies decreased for all size groups. The decline was drastic for the three lower size groups, but the largest size group was able to cope much better. 14 Available evidence from the U.S. indicates that the sales losses to SMEs have been substantial. Based on a survey of Small Business, Bloom et al., (2021) find evidence of significant sales losses that peaked in 2020Q2 at -29%. The respondents expected the losses to be persistent, and extending into mid-2021.

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5.1 Business Failures and Bankruptcy Pre-pandemic debt levels are likely to pose major problems for smaller firms. Smaller firms and SMEs depend more on bank loans than larger firms.15 Combined with the more severe impact due to loss in sales, as documented above, in the absence of debt rollover, many of these firms would face bankruptcy. Gourinchas et al. (2021) simulate alternative scenarios and estimate likely failure rates for SMEs in 13 countries— Belgium, the Czech Republic, Finland, France, Greece, Hungary, Italy, Poland, Portugal, Romania, Slovakia, Slovenia, and Spain. They find that in the absence of rollover of pre-pandemic debt, failure rates would be quite high (8.44 percentage points higher than in normal times), even among the healthier SMEs who would have survived 2020 in the absence of government support. Given that for Indian firms, 2019 was an even worse year, the cumulative impact (even ignoring the prospect of a further slowdown) could trigger a very large number of business failures.16 When a firm becomes insolvent, there are two outcomes—liquidation or reorganization. If the firm’s “going concern value” exceeds its value in liquidation, but it is unable to repay debt obligations, the firm can continue if the debt claims are restructured. The equity holders are typically wiped out, and the debt holders become the new owners. If, however, the liquidation value is higher than the continuation value, the firm is liquidated. In the U.S., both liquidation/exit and reorganization can happen outside of bankruptcy court (and often do). However, a firm can also place itself under the authority of a bankruptcy court, and liquidate part or all of its assets. In the U.S., this is known as Chap. 7 liquidation. Alternatively, it can file for possible reorganization (Chap. 11). If the courts deem that the liquidation value exceeds the going concern value, then the firm is liquidated; otherwise, it is reorganized. The bankruptcy process is usually heavily tilted against smaller firms. The U.S. evidence shows that firms that emerge from reorganization are typically large firms. The majority of firms that liquidate are small firms. This is because smaller firms have fewer options to restructure their business. The costs of the reorganization process are also disproportionately high for the smaller firms. Consequently, many small firms may be forced to inefficiently liquidate (i.e., liquidate even when the going concern value exceeds liquidation value), for example, when they run out of cash and/or cannot raise additional debt without risking default.

15

For evidence on U.S. non-listed firms based on 2013 Inland Revenue Services data, see Greenwood, Iverson, and Thesmar (2020). The authors also estimate the potential impact of a 30% decline in revenue, assuming no change in fixed costs and investment, on different size groups of firms. For the smallest businesses (less than $1 million in assets), such a shock would wipe out the entire equity in the business. 16 Ma (2020) estimates that more than 6% of bond issuers overseen by Moody’s at the start of 2020 would default in 2020, with additional defaults in the following years. A much higher percentage would have their ratings withdrawn.

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India enacted a new bankruptcy code (the Insolvency and Bankruptcy Code or IBC) in 2016. The Code imposes a mandatory upper limit (180 days with the possibility of a 90-day extension) for the process to be completed.17 A ten-member Insolvency and Bankruptcy Board of India is the main supervising body. Licensed professionals are in charge of the debtor’s assets and are supposed to manage the insolvency process. Bose, Filomeni, and Mallick (2021) cite the Economic Survey released by the Government of India and report that debt recovery has improved to 42.5% postIBC compared to 14.5% before. In terms of time required for resolution, the survey stated that post-IBC, it has taken 340 days on average, compared to 4.3 years earlier. In their study, Bose et al. (2021) find that post-IBC, financially distressed firms were able to access more short- and-long-term debt, and their cost of finance also decreased. The IBC emphasizes reorganization, and failing which, liquidation of a business as a going concern via an auction, rather than piecemeal liquidation. Indeed, most of the high-profile insolvencies have been resolved via an auction and a sale of the company as a going concern.18 However, there are several aspects of the Code that bias the process toward liquidation, especially at a time when many firms are expected to be financially distressed. Moreover, this bias is likely to affect the smaller firms more. First, liquidation is mandatory if a reorganization plan is not agreed to by the creditor committee by the deadline. This strict timeline makes liquidation more likely. Information asymmetry is likely to make it more difficult to find outside buyers for smaller firms, and piecemeal liquidation of assets is the likely outcome. Second, the Code requires that the creditor committee, comprising of all financial creditors, be in complete control of the resolution process, and the board of directors has no role (a resolution professional is in charge of the day-to-day operations). A resolution plan or liquidation decision has to be approved by 66% of the creditors (by value). Excluding board members from the resolution process reduces the likelihood that reorganization plans, which board members may be more qualified to evaluate or be in a better position to propose, may not be implemented. Third, most of the creditors of Indian firms are banks, which normally work in favor of a resolution, except that at a time when banks are saddled with bad loans, there is an inherent bias toward exit via liquidation. Last but not least, the sharp rise of expected insolvency cases is likely to severely stress the capacity of the new system—including the workload of the National Company Law Tribunals (NCLT) and the availability of qualified professionals.19 The onset of the pandemic led to the temporary suspension by the Government of the Corporate Insolvency Resolution Procedure (CIRP) under the IBC for a period not 17

For companies with an annual turnover of less than INR 10 million ($0.14 million), the limit is 90 days, with the possibility of a 45-day extension. 18 For a partial list of high-profile cases, see https://en.wikipedia.org/wiki/Insolvency_and_Bankru ptcy_Code,_2016. 19 The specter of an overcrowded bankruptcy court system has been raised in the U.S. as well. Greenwood, Iverson, and Thesmar (2020) estimate a 158% increase in caseloads. The U.S. currently has 349 bankruptcy judges, and between 15 and 50% additional temporary judges (e.g., recalling retired judges) may be needed (Iverson et al. (2020)).

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exceeding one year, starting from March 25, 2020. The objective of the suspension was to provide relief for corporate sector entities that would be adversely impacted by the pandemic defaults during this period. The suspension has been revoked since March 26, 2021, and it is expected that there would be a spike in fresh cases of default owing to the pandemic. This, along with the slowing down of the resolution process due to the suspension of CIRP earlier and the piling up of cases of default,20 is expected to put additional load on the NCLTs, calling for the need to introduce a slew of measures to increase the number of benches and fill up vacancies in existing benches. Else, both increased workload and the non-availability of qualified professionals would favor inefficient liquidations and associated job losses over more orderly resolutions of insolvency.

5.2 Debt Overhang and “Predation Risk” Firms with too much debt suffer from “debt overhang”, which prevents them from raising new financing to undertake needed capital spending. One consequence of this is loss of growth, and failure to assure employees, suppliers, and customers that the firm is a reliable employer or trading partner. This often leads to a loss of sales, and eventual bankruptcy. The pandemic, following a year that already caused severe stress on the balance sheets of companies, is likely to create another type of pressure to which many firms could be vulnerable. This is called predation, i.e., aggressive tactics from larger and better capitalized rival firms in the same industry to drive them out of business. For firms experiencing debt overhang, meeting such threats is difficult because this involves cutting prices, increasing advertising, accelerating the launch of new products, etc., which make it even more difficult to meet debt and interest payments. These firms could lose their competitive positions even when rivals do not directly follow predatory strategies—for example, well-capitalized rivals may take advantage of the debt overhang of their competitors by accelerating product development and innovation, which would put them in a much stronger competitive position down the road. Even when well-capitalized rivals do not directly poach valued employees, they would appear more attractive to the latter, and less well-capitalized firms may not be able to hold on to key workers.21 Clearly, the largest firms are in the best position to benefit from the overhanginduced predicament of their industry rivals. As we have seen, the large firms as a 20

As per recent data of March 2021, the National Company Law Tribunals which are in charge of overseeing the insolvency process across the country admitted 283 companies into insolvency post-lockdown during the pandemic. These defaults happened prior to March 25, 2020, when the suspension of CIRP went into effect. https://timesofindia.indiatimes.com/business/india-business/ over-280-companies-declared-bankrupt-amid-pandemic/articleshow/81647225.cms. 21 Bernstein, Townsend, and Xu (2020) use proprietary data on individuals’ online job searches and applications before and after COVID-19, and find that these searches/applications switch more toward larger firms relative to early stage ventures during the emergence of the crisis.

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group have continued to enjoy sales growth and gain in market value over the last 2 years. They are likely to enjoy “deep pockets” and be in a position to sacrifice profits today if that means eliminating competition and reaping higher profits in the future.

5.3 Dealing with a “Re-Allocation Shock” The COVID-19 pandemic has created sectoral winners and losers among industries. It has created new demands, with firms in these sectors adding to their workforce, while at the same time causing business closures and loss of jobs in other sectors. Barrero et al. (2020) estimate that early in the pandemic, for every ten jobs that were lost, three new jobs were created. Some of these effects are expected to be permanent. Barrero et al. (2020) estimate that 42% of layoffs in the first 2 months of the pandemic would become permanent job losses.22 We have noted the structural similarities of the Indian and the U.S. corporate sectors, one indication of which is that the same set of industries have a major presence in the list of top 50 firms (by market capitalization value) in Tables 1 and 2. So, some of the underlying structural shocks that are relevant for the U.S. and are expected to be persistent would also affect Indian firms in the same way. However, the major difference between the two economies is that the U.S. is in a far better position to manage the type of resource reallocation that is required to mitigate such shocks than India is. For example, while the insolvency process in India has undoubtedly improved post-IBC, it is worth remembering that even with the passage of the IBC, India remains ranked outside the top 100 out of 189 countries in insolvency resolution. India’s recovery rate post-IBC of slightly above 40% is half of that in the U.S. and the U.K. Even after the pandemic, U.S. banks are in a far better position than Indian banks. India’s labor laws, while protecting workers, may also slow down the process of reallocation of resources. However, the reallocation that may be the most challenging for Indian companies to deal with is the one that is happening outside India, but will no doubt affect Indian companies. Production networks and supply chains worldwide are being redefined in response to the pandemic. As the global economy rebounds, companies in major markets will scrutinize their supply-chain relationships very carefully. They will avoid debt-laden supply-chain partners, especially partners in countries where the pandemic is still not under control, and the public policy response is inadequate. India is in danger of been seen as such a country. With so many firms from all size groups depending on global demand to revive their fortunes, the costs to the economy may indeed be very long-lasting. 22

In his book, Galloway (2020) makes the insightful point that the pandemic simply accelerated changes that were already happening in the way people and businesses work, produce, buy, sell, and interact. Firms had already been making the necessary investments in these potentially efficiencyenhancing trends. The pandemic provided an impetus for adopting these practices that otherwise would have taken much longer to materialize. These practices are likely to last (as they are more efficient), and cause significant reallocation of economic activity.

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6 The “Good” and the “Bad” of the “Big” As we noted, in both India and the U.S., larger firms in general—and not the few very large firms—have shown greater resilience during the pandemic—especially if we look at sales performance, as opposed to stock price performance (where the evidence is more nuanced). This should not be surprising—the largest firms are often more diversified, have more established business models, and have better access to finance. That is not to say that some big firms have not struggled—the nature of the COVID19 crisis has challenged some existing business models that are slowly becoming obsolete, causing some well-known companies to declare bankruptcy.23 However, large companies in the U.S. that have prospered, like Amazon and Google, have also hired heavily during the pandemic, helping cushion some of its adverse impact on the labor market. These are the same companies that have been held responsible for causing much disruption by aggressively leveraging their business models, entering new areas, and forcing out the competition. Most of the big technology companies have unenviable records when it comes to anti-competitive practices. The large companies in India would have a major role to play if the economic crisis deepens. They can provide employment stability and even growth. The very large companies also have the clout to influence public policy for the greater good of society, from which they will also benefit. The crucial question is whether they can resist the predatory opportunities that will surely be available in the current environment. Given India’s weaker institutions, the consequences of such predatory actions could be devastating.

References Barrero, J. M., N. Bloom, & S. Davis, (2020). Covid-19 is also a Reallocation Shock, NBER Working Paper 27137. Bernstein, S., R. Townsend, & T. Xu, (2020). Flight to Safety: How Economic Downturns Affect Talent Flows to Startups, NBER Working Paper 27907. Bloom, N., R. Fletcher, & E. Yeh, (2021). The Impact of Covid-19 on US Firms, NBER Working Paper 28314. Bose, U., S. Filomeni, & S. Mallick, (2020). Does Bankruptcy Law Improve the Fate of Distressed Firms? The Role of Credit Channels, Journal of Corporate Finance, forthcoming. Caballero, R., & A. Simsek, (2020). Monetary Policy and Asset Price Overshooting: A Rationale for the Wall/Main Street Disconnect, NBER Working Paper 27712. Dalton, M., J. Groen, M. Loewenstein, D. Piccone, & A. Polivka, (2021). The K-shaped Recovery: Examining the Diverging Fortunes of Workers in the Recovery from the COVID-19 Pandemic Using Business and Household Survey Microdata, Covid Economics, Issue 71. Galloway, S. (2020). Post Corona: From Crisis to Opportunity, Bantam Press. GOI (2021). Economic Survey 2020–2021, Government of India, Ministry of Finance, Department of Economic Affairs Economic Division, New Delhi. 23

The list of U.S. bankruptcies in 2020 includes such well-known names as Hertz, JCPenny. Neiman Marcus, Frontier Communications, etc. See https://fortune.com/2020/12/21/retail-bankru ptcies-2020-department-stores-retailers-energy-hospitals/.

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Gormsen, N., & R. Koijen, (2020). Coronavirus: Impact on Stock Prices and Growth Expectations, NBER Working Paper 27387. Gourinchas, P., S. Kalemli-Ozcan, V. Penciakova, and N. Sander, (2021). Covid-19 and SMEs: A 2021 “Time Bomb”?, NBER Working Paper 28418. Greenwood, R., B. Iverson, & D. Thesmar, (2020). Sizing up Corporate Restructuring in the Covid Crisis, NBER Working Paper 28104. Iverson, B., J. Ellias, & M. Roe, (2020). Estimating the Need for Additional Bankruptcy Judges in Light of the COVID-19 Pandemic, Harvard Business Law Review Online Journal, Volume 11, forthcoming. Lahoti, R., M. Jha & A. Basole (2021). What 2020 did to India’s Inequality, Discussion Paper, Centre for Sustainable Employment, Azim Premji University, Bangalore. https://cse.azimpremj iuniversity.edu.in/what-2020-did-to-indias-inequality/ Ma, A., (2020). Forecasting Corporate Downgrades and Defaults, Unpublished Working Paper. Harvard University. UNESCAP (2021). Asia and the Pacific: SDG Progress Report 2021, United Nations Economic and Social Commission for Asia and Pacific, United Nations.

The Institutional Response

Populists, Pragmatists, and Pandemics: Explaining the Variance in Response of Democracies to Sars-COVID-19 Biju Paul Abraham

Abstract China’s success in controlling the SARS-COVID-19 pandemic within a few months of the outbreak has been attributed to its authoritarian one-party political system which seems uniquely suited to enforcing extremely strict quarantine measures, and an effective “test, track and trace” system. China’s success has been contrasted with the failure of many democratic countries to contain the pandemic, despite many of them seeking to implement the same measures that seem to have worked in China. However, the effectiveness of response of democratic countries show marked differences. There are some democracies that have been successful in combating the health impact, and the consequent economic effects, of the pandemic. Taiwan and New Zealand are notable examples. Pragmatic leaders in these countries appear to have provided inclusive leadership, “followed the science”, garnered public support, and dealt with the crisis effectively. Some others, like Brazil, India, and the U.S. have been far less effective. These countries have seen the largest number of infections and deaths worldwide. Many analysts have attributed this outcome to the nature of the leadership of these three countries—with “populist” leaders in Brazil, India, and the U.S. seeking to avoid or delay lockdown measures, or ease them early, worried as they are about short-term economic consequences of lockdown measures, and an erosion of popular support if they are prolonged. The paper considers the response of both democratic nations led by pragmatic leaders, and of those led by populists, and draws some conclusions regarding the reasons for variations in response of democratic governments to the pandemic. Keywords Brazil · Coronavirus · COVID-19 pandemic · Democracy · India · Lockdown · New Zealand · Populist Leaders · Public Health Policy · SARS-CoV-2 · Taiwan · United States The COVID-19 pandemic, which spread rapidly throughout the world from early 2020 has been an event unprecedented in modern history. The economic disruption has been accompanied by social disruption and since the pandemic is an ongoing B. P. Abraham (B) Public Policy & Management Group, Indian Institute of Management Calcutta, Kolkata, India e-mail: [email protected] © The Author(s), under exclusive license to Springer Nature Singapore Pte Ltd. 2022 M. Dutta et al. (eds.), The Impact of COVID-19 on India and the Global Order, https://doi.org/10.1007/978-981-16-8472-2_11

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global health event, it will be sometime before the full effects are known. However, the way in which different governments around the world have handled the epidemic has re-energized a debate that has always existed about the ability of democratic governments to deal effectively with emerging challenges, particularly national emergencies. China’s relative success in controlling the COVID-19 pandemic within a few months of the outbreak has been attributed to its authoritarian one-party political system which seems uniquely suited to enforcing stringent quarantine measures, and an effective “test, track and trace” system. Some have even seen China’s success as an epochal event in history that enables China to seek to reorganize the post-World War II international order and carve out a dominant role within it (Smith & Fallon, 2020). China’s success is contrasted with the failure of many democracies to contain the pandemic, despite many of them seeking to implement the same measures that seem to have worked in China. Notable examples are the United States’ and Brazil’s failure to implement national lockdowns and prevent an extremely high death toll, and India’s initial failure to contain the rapid spread of the virus, both within its cities and in rural areas, and the difficulties it faced in enforcing its national lockdown. However, not all democracies have failed in their response. Some democracies seem to have been successful in combating the health impact, and the consequent economic effects, of the pandemic much more effectively. The relative success of containment measures in countries like New Zealand, Taiwan are notable examples. Many analysts have attributed these variations in response of democratic countries to the nature of these countries’ leadership with “populist” leaders in the U.S., India, and Brazil seeking to delay lockdown measures, implement them poorly, or ease them early, worried as they are about short-term economic consequences of lockdown measures, and an erosion of popular support if they prolonged (Brubaker, 2020; Saad-Filho, 2020). By contrast, “pragmatic” ones in New Zealand and Taiwan have been seen as taking the right decisions early enough, that enabled them to deal with the pandemic effectively (Summers et al., 2020). The strength of lockdown measures that were implemented has also been linked to the nature of democratic governments, with “populist” leaders using the lockdowns as an excuse to crack down on domestic dissent. In contrast, it is argued, “pragmatic” governments such as those in New Zealand and Taiwan—which are more broad-based and inclusive—have been concerned with both the short-term health impact and long-term economic effects and have been much more successful in dealing with the pandemic with higher levels of public support. The paper considers the response of “populist” and “pragmatic” governments to the pandemic in two time periods in 2020, from January to March and from April to December, and asks—did the nature of the democratic government have an impact on their response to the pandemic? Were pragmatic leaders more efficient in handling the pandemic than populist ones? It considers only the issue of responses to the pandemic itself and does not go into the issue of vaccinations or vaccine preparedness of these countries. It is inevitable that as time progresses, and more data emerges, the evaluations of the response will change. However, an analysis of the response in the first 12

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months of the pandemic provides some insights into the difficulties that democratic governments face when they deal with the national impact of a global pandemic. It also illustrates some of the problems of classifying responses to public health emergencies based on regime types. This paper is divided into four sections. The first section considers the distinctions between different types of democracies and between different types of populist leaders. The way five countries, two traditional multi-party democracies and three led by populist leaders, responded to the pandemic in its initial days (January–March, 2020) is discussed in the second section. The third section analyses the different paths that the five countries took once the pandemic had taken hold (April–December, 2020). A concluding section offers some thoughts on the relationship between success in handling the pandemic, and the form that democracy in a country has taken, with a view to understanding the challenges that democracies face when they handle such epidemics.

1 Democracy, Populism, and COVID-19 While it is relatively easier to differentiate democratic countries from authoritarian ones, making a distinction between “real” democracies and those led by “populists” is a much more challenging exercise. The fact that most populist leaders are democratically elected in multi-party democratic systems makes the task of classifying them separately from traditional democracies is an even more difficult exercise. For this paper, the five countries chosen—Brazil, India, New Zealand, Taiwan, and the United States—are selected from the categorization of democracy in different countries provided by the Economist Intelligence Unit’s annual Democacy Index for 2020 (EIU, 2021). The report categorizes states into four categories—full democracies, flawed democracies, hybrid regimes, and authoritarian regimes. The categorization is based on five indicators—electoral process and pluralism, functioning of government, political participation, political culture, and civil liberties (EIU, 2021: 61–68). New Zealand and Taiwan are two of 23 countries classified as full democracies. Brazil, India, and the U.S. are three of 52 countries that are classified as flawed democracies. Table 1 gives the five countries’ rank in the Democracy Index and their scores across the five parameters. The rise of populist leaders has been a feature of many democratic countries around the world from the early 2000s. The rise of populist leaders has undermined political stability in many democracies. Democratic politics is very often been built on a foundation of elite consensus on the broad directions that a country should take, both in terms of mediating conflicting interests and for ensuring relative distributional equality. This consensus enables democratic political systems to reconcile conflicting interests within existing institutions thus ensuring the stability necessary for economic progress. (Higley, 2020). Populism has done much in the last two decades to undermine this consensus in many established democracies.

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Table 1 Democracy indicators Categorization Country Democracy Indicators Overall Rank Electoral Functioning Political Political Civil score process of participation culture liberties & Government pluralism Full democracies Flawed democracies

New 9.25 Zealand

4

10.00

8.93

8.89

8.75

9.71

Taiwan

8.94

11

10

9.64

7.22

8.13

9.71

United States

7.92

25

9.17

6.79

8.89

6.25

8.53

Brazil

6.92

49

9.58

5.36

6.11

5.63

7.94

India

6.61

53

8.67

7.14

6.67

5.00

5.59

Source Economist Intelligence Unit. Democracy Index 2020: In sickness and in health? (London: 2021). 8–13

While definitions of the terms “populism” and “populist leaders” are often imprecise, they are usually used to refer to movements led by democratically elected heads of government who present electoral contests as a fight between the “true people” and “corrupt” elites (Meyer, 2020). There are two features of such movements that set them apart from traditional political parties and movements. The first is that they successful draw support from voters who are disenchanted with traditional political parties, which these leaders portray as being led by those who are corrupt and incompetent. Second, they are almost always led by charismatic leaders who are able to convert voter disenchantment into electoral support (van der Brug & Mughan., 2007). Since these movements are often new, or these leaders rise in parties which have traditionally been at the fringes of the existing party system, they do not have a broad-based leadership or a deep leadership pool to choose from. Electoral politics revolves around a charismatic leader who present the elites as existential threats to the livelihood and well-being of the “true people” these leaders claim to represent. Populist leaders are also successful at selling narratives of victimization, with the masses presented as the “victims” of treacherous elites. These leaders successfully present themselves and their supporters as real patriots fighting to protect themselves and their country from enemies, both foreign and domestic, real and perceived (Al-Ghazi 2021). The rapid spread of COVID-19 from early 2020 seemed to play into the hands of populist leaders, always on the lookout for imported and domestic threats. The need to impose restrictions on large gatherings, and the need to track movement of people in real time to identify the possible routes by which the virus could spread, seemed to provide the necessary justification for banning protest demonstrations, tracking movement of people, and collecting personal data. The imminent danger to public health seemed the perfect excuse for brushing aside long-held notions of privacy as well as freedom of movement and association.

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While the pandemic might have provided the justification for restricting civil liberties, it also seemed possible that populist leaders might be constrained in terms of their willingness and ability to deal with the pandemic because of their deeply held beliefs. McKee et al. identify four features of populist leaders that could potentially make them unsuited to lead a response to a public-health emergency (McKee et al. 2020:512–514). First, since populist leaders often try to appeal to the sense of victimization that their supporters feel, it would be tempting to use the ever-present threat of a pandemic to create an artificial sense of crisis so as to reemphasize the need for strong leadership. Second, the populists’ mistrust of elites and the mainstream media could prevent government agencies from effectively communicating public health messages to the public. Third, some populist leaders tended to deny the real causes for the spread of the virus, believing that it was part of a conspiracy by domestic opponents or foreign enemies to weaken their position, and therefore could end up fighting their opponents rather than the virus. Fourth, the populists’ distrust of institutions, scientists, and experts (often identified as being part of the elites) would prevent them from following their advice on critical public health interventions. This argument seemed to have been borne out by the initial trends in the spread of COVID-19 with countries led by populist leaders such as the U.S., Brazil, and India showing a sharp increase in infections and deaths from COVID-19. However, it is important to recognize that the response of all countries led by populist leaders to the pandemic has not been uniform. Meyer classifies populists into three categories—cultural, socio-economic, and anti-establishment (Meyer, 2020). Cultural populists draw support from majoritarian cultures, which could be race- or religion-based, which are portrayed as different and superior to minority cultures. Socio-economic populists tend to identify with the workers, pitting them against exploitation by big business. Anti-establishment populists generally target incumbent regimes, identifying them with national elites and seek to lead popular revolts against them and capture power. There are variations in the response of populist leaders to COVID-19 with some who take the pandemic seriously and seek to use it as an excuse to crack down on dissent (illiberal responses) and others who seek to downplay the existence of a serious threat and try to get societies back to normal within a short period of time. Some populist leaders have also taken the threat of the pandemic seriously, but have been relatively more liberal in the sense that they have not used the pandemic as an excuse to prevent or crack-down on protests. Table 2 gives a list of populist leaders, populist types, and their responses to COVID-19.

2 The Initial Response: January–March 2020 Table 3 indicates the date of first cases in each of the five countries, and the dates on which full lockdowns were implemented in these countries. Of the five, only two, India and New Zealand, imposed full national lockdowns. Taiwan was confident that it would be able to cope with the outbreak without a lockdown. The presidents of the United States and Brazil believed that the virus was not as big a threat as was being

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Table 2 Current Populist Leaders and COVID-19 responses Country

Leader

Populist type

Belarus

Alexander Lukashenko

Anti-Establishment Downplay

Brazil

Jair Bolsonaro

Cultural

Mexico

Andreas Manuel Lopez Obrador Socio-Economic (AMLO)

Downplay

Nicaragua

Daniel Ortega

Socio-Economic

Downplay

United States

Donald Trump

Cultural

Downplay

Bulgaria

Boyko Borisov

Anti-Establishment Serious-Liberal

Czech Republic Andrej Babis

Response type Downplay

Anti-Establishment Serious-Liberal

Italy

Giuseppe Conte

Anti-Establishment Serious-Liberal

Venezuela

Nicolas Maduro

Socio-Economic

Serious-Liberal

Israel

Benjamin Netenyahu

Cultural

Serious-Intermediate

Serbia

Alexander Vucic

Cultural

Serious-Intermediate

Sri Lanka

Gotabaya Rajapaksa

Cultural

Serious-Intermediate

Hungary

Viktor Orban

Cultural

Serious-Illiberal

India

Narendra Modi

Cultural

Serious-Illiberal

Philippines

Rodrigo Duarte

Cultural

Serious-Illiberal

Poland

Mateuz Morawiecki

Cultural

Serious-Illiberal

Turkey

Recep Tayyip Erdogan

Cultural

Serious-Illiberal

Source Meyer (2020). Pandemic Populism: An Analysis of Populist Leaders’ Responses to COVID19 (Tony Blair Institute for Social Change: August), pp. 7–9 Table 3 Date of first case and date of national lockdown Categorization

Country

Days between first case and lockdown First case

Date of national lockdown

Days between first case and national lockdown 26

Full democracies

New Zealand

28 February 2020

26 March 2020

Taiwan

21 January 2020

None

Flawed democracies

United States

20 January 2020

None

Brazil

25 February 2020

None

India

27 January 2020

24 March 2020

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made out by epidemiologists and public health experts. They were also concerned about the economic impact of lockdowns on their core support base.

2.1 New Zealand and Taiwan New Zealand and Taiwan followed similar paths in terms of their initial response, with both countries deciding to go in for mitigation measures involving restrictions on travelers from affected countries. Taiwan’s response to the pandemic was led by its Centre for Disease Control (CDC) and Central Epidemic Command Centre (CECC). They followed the country’s pandemic plan which was prepared following the SARS epidemic of 2003 and the H1N1 novel influenza epidemic in 2009–10 (CDC, 2012). This plan had a built-in warning system for diseases emerging in China and it ensured that testing was carried out, initially for those traveling from Wuhan in China where the first cases of the disease emerged, and later for all those entering the country from abroad. Taiwan’s Pandemic Control Act of 2007 which enabled linking of travel histories of individuals to their National Health Insurance Cards alerted hospitals to possible cases in real time (Summers et al., 2020). New Zealand was unable to replicate the almost clinical approach that Taiwan had to responding to the pandemic since it was less prepared for the crisis and depended primarily on its influenza pandemic plan which was not suited for COVID-19. The country had less than 300 ventilators for its 25 million citizens and the government’s aim was to slow down the spread of the disease (Mazey & Richardson, 2020). It tried to prevent any infections from reaching its territory by banning all flights from China on February 3, soon after the WHO announced the first death outside China, and from Iran on February 28, the day the first case was reported in New Zealand. Not as well prepared for responding to pandemic as Taiwan was, the country then tried an elimination strategy once cases were reported in the country. (Baker et al., 2020). On March 21, Prime Minister Jacinda Arden announced a four-level alert system that she herself suggested to experts (Wilson, 2020). It began with a Preparation phase in Level 1 and ended with a Lockdown in Level 4. A lockdown level was considered essential to protect the indigenous Maori and Pacific communities in New Zealand because of the health inequalities that existed between them and the majority white population (Summers et al., 2020). While both Taiwan and New Zealand are considered countries that successfully responded to the initial disease outbreak their responses exhibit crucial differences. Taiwan established its CDC in 1990. New Zealand closed its equivalent facility, the New Zealand Communicable Disease Centre in 1992. Its functions were transferred to a network of national research institutes, and its services then contracted out to New Zealand’s Department of Health. Taiwan also put in place a plan for distributing masks to its entire population and required citizens to wear it in all confined public spaces even when there was no community transmission of the virus. New Zealand by contrast did not promote mask-wearing in the initial stages of the pandemic. New

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Zealand also lacked an effective national digital capability to identify travel histories of individuals and to identify potential contacts who could be infected (Summers et al., 2020). Unlike Taiwan, New Zealand could not avoid a national lockdown which eventually began on March 28.

2.2 India, Brazil, and the U.S. Of three “flawed democracies” in this study, India’s response was closest to that of Taiwan and New Zealand. Once the World Health Organization (WHO) declared COVID-19 a “Public Health Emergency of International Concern” on January 30, the country began screening of international passengers from China. When the WHO declared the outbreak as a pandemic on March 11, India banned all travel from China, Iran, Italy, South Korea, France, Spain, and Germany (Siddiqui et al., 2020). Travel restrictions were imposed from March 13, and all international travellers were required to undergo screening. On March 25, when the lockdown began there were only 533 confirmed cases, and 10 deaths in India (Jha & Jha, 2021). The Indian government initially seemed keen to generate a sense of national crisis that could be met only through collective resolve, with the government and the people fighting a common enemy. The government was helped in its efforts to portray the virus as a national emergency, brought in from abroad, by the outcome of an event organized by an Islamic group, known as the Tablighi Jamaat, from March 10 to 13 which was attended by foreign delegates. This was before the national health ministry issued restrictions on large gatherings. Members of the ruling Bharatiya Janata Party (BJP) sought to politicize this event and link it to a large rise in the number of cases around the country in late March (Prasad, 2020). When many of those who attended the gathering began to test positive for the virus after the lockdown began, over 2600 members of the movement were forcibly shifted into hospital quarantine (Radhakrsihnan, 2020). The Delhi police registered a formal complaint against the head of the mosque where the movement was headquartered in Delhi and the national Ministry of Home Affairs, which oversees internal security, banned foreign nationals who attended the event from participation in any of the group’s activities in India (Prasad, 2020). Brazil responded to initial reports of the outbreak of the disease by establishing an Emergency Operations Centre (COE) to coordinate its response and prepare the health infrastructure to cope with the health impact. Created by the Ministry of Health (MoH) on January 22, it comprised of three main institutions: Fiocruz (Oswald Cruz Foundation—Fundação Oswald Cruz), Anvisa (Brazilian Health Regulatory Agency—Agência Nacional de Vigilância Sanitária), and the Pan American Health Organization (PAHO) (Szylovec et al., 2021). The MoH began an influenza immunization campaign early, in the third week of March, rather than the second half of April to reduce the pressure on Brazilian hospitals that could increase from the rise in influenza cases that was usual for this time of the year. Children, the elderly, pregnant women, and health professionals were targeted as part of the campaign since they

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were identified as potentially the most vulnerable group. Brazil also tried to portray the virus as a foreign threat. Fearing entry of the virus from Venezuela, where the national health system had almost collapsed because of domestic political instability, Brazil closed its borders with that country on March 18. Faced with criticism that it was targeting only one country, Brazil extended the ban to other Latin American countries and to select Asian and European countries on March 19 (Szylovec et al., 2021). In the United States, a total of 14 cases of COVID-19 were detected in six states between January 21 and February 23. Of these, 12 were in those who arrived from China and two were infected at home by those who had contracted the virus in China. Community transmission, involving cases with no travel history to affected areas or family members who were infected, began to increase from late February. Significant increases in the number of cases began to happen in mid-March (Schuchat, 2020). Unlike other countries, the U.S. government underestimated the potential harm that the virus might cause. When the WHO began to give early warnings of an epidemic in January 2020, President Trump’s response was to downplay its significance terming it a disease “like the flu”, concerned as he was of the impact of a serious outbreak on the stock markets and his own election campaign (Altheide, 2020: 521). President Trump also seemed keen to portray the virus as a foreign threat that could be forestalled by closing borders. On January 31, 11 days after the first confirmed infection in China, President Trump imposed a ban on entry of foreign nationals who had a recent history of travel to China (Corkery & Karni., 2020). The U.S. reported its first death from COVID-19 on February 29, in Seattle, Washington State. It was only on March 15, that the U.S. CDC advised U.S. citizens not to gather in groups of 50 or more.

3 The Crisis Phase: April–December 2020 3.1 Taiwan While the initial response to the outbreak of the pandemic from January to March seem broadly similar in all five countries, Taiwan’s success in combating the disease in the second phase from April to December seems remarkable. By the end of June, Taiwan had only 446 reported cases and 7 deaths from COVID-19. (Fig. 1). By contrast, the U.S. had 125,000 deaths by that time, more than 1,200 times per capita than Taiwan (Lo 2020). Taiwan maintained its enviable record in combating the disease right up to the end of 2020. From the beginning of the pandemic, upto December 31, Taiwan recorded only 799 cases and the number of deaths remained constant at 7. Taiwan success is even more remarkable given the fact that it was particularly vulnerable to a pandemic emerging from China because of its close economic links with its giant neighbour. Taiwan depends on Chinese factories for final assembly of finished electronic products. 70% of Taiwan’s intermediate goods exports go to

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Fig. 1 Taiwan—Cumulative confirmed COVID-19 deaths and cases. Source https://ourworldi ndata.org/coronavirus/country/taiwan?country=~TWN

China. (Liu et al., 2020). Close business links also mean heavy travel between the two countries. Taiwan, despite its closeness to China, was successful in avoiding a lockdown while maintaining its economic links with the country. Taiwan’s COVID strategy stressed transparency and authority, with the country drawing on already existing social capital to confront the disease effectively (Huang, 2020: 665). In addition to the extensive infrastructure for battling pandemics that it had put in place before COVID-19, several other measures helped it in its efforts. These included continued tough border control measures which included exclusion of travellers from countries with significant outbreaks, extensive screening of the population, effective quarantining of both affected and potential cases, contract tracing, promotion of mask-wearing and highly effective public communication (Summers et al., 2020). Along with other East Asian countries that had earlier experience of the SARS epidemic—such as Hong Kong, South Korea and Singapore—Taiwan had also created emergency response facilities which were well funded, well-staffed and appropriately equipped, and could easily be rolled-out once the number of cases increased. These local facilities had significant autonomy in issuing local emergency guidelines, which ensured that local outbreaks could be easily controlled. Emergency manuals were created, and faster approval processes for developing test kits and clinical trials were approved, prior to the outbreak (An & Tang., 2020). The government promoted appropriate behaviour, such as mask-wearing and frequent handwashing, in public messaging, widely seen around the country in TV messages and public billboards. To prevent hoarding of facemasks, the CECC put in place

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a system of rationing them to ensure that all citizens had equal access. Residents were given these masks on alternate dates depending on whether the last two digits of their health cards were odd or even. Workers who could not personally collect face masks because they were working during the day could order masks online for home-delivery, thus ensuring equity of access to all residents. Social distancing rules were specific and not general in nature, with appropriate standards developed for restaurants, schools, offices, public transport, supermarkets, and institutions such as elder care facilities and prisons (Huang, 2020). While pandemic preparedness helped, tough enforcement of health guidelines undoubtedly played a role. In addition to creating the infrastructure, Taiwan also had prior legislation in place for restrictions on personal freedoms and heavy penalties for breaking quarantines and these helped Taiwan to avoid a lockdown. Tough government enforcement, especially of quarantine rules helped with compliance (An & Tang., 2020). A culture of deference to government directives also helped the Taiwanese government in its efforts. Taiwan has traditionally felt itself to be a country under siege, with China not ruling out reunification of Taiwan with the mainland by force, and this has fostered a sense of national unity against threats emanating from China. It also needs to be kept in mind that Taiwan is in the process of transitioning from military conscription to maintaining a volunteer army and it has a large civilian reserve force that can be called up at short notice in case of conflict. The defence forces and reserve forces have a civilian mobilization mission as well (Easton et al., 2017). This provided the volunteers needed to implement quarantine measures effectively at low cost. Despite Taiwan being a multi-party democracy a culture of following government directives without questioning did help Taiwan to overcome the crisis. In Taiwan the national government ensured coordination, scientific expertise and adequate resources. It also provided government agencies with the legal tools that allowed them to establish their authority and effectively control the outbreak. (Lo 2020). This when combined with largely voluntary compliance, often brought about by fear of punitive action, ensured that Taiwan did not suffer the large number of infections and deaths that afflicted many other democracies.

3.2 New Zealand Though New Zealand is often cited as the “gold standard” for effective democratic responses to the pandemic its handling of the pandemic does not seem have been as successful as it is often made out to be. Both in terms of cases per-capita and deaths per-capita it was far less successful than Taiwan. Total confirmed cases of COVID19 per million population up to August 31, was 278 in New Zealand vis-à-vis 20.7 in Taiwan. Total confirmed deaths due to COVID-19 per million population, up to August 31, was 4.4 in New Zealand vis-à-vis 0.3 in Taiwan (Summers et al., 2020: 3). This is despite New Zealand being a unitary state like Taiwan, and able to use the centralization that allowed it to ensure some national planning and co-ordination.

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Fig. 2 New Zealand—Cumulative confirmed COVID-19 cases. Source https://ourworldindata.org/ coronavirus/country/new-zealand?country=~NZL

(See Figs. 2 and 3) Both countries are also islands making border controls much easier to implement.1 New Zealand faced problems of coordination between different government agencies in terms of its response. It was simply not prepared for a crisis of this magnitude (Boin et al., 2020). It was ranked thirty-fifth out of 195 countries in the 2019 Global Health Security (GHS) Index with a relatively lower score of 54/100. New Zealand had problems with distribution of Personal Protective Equipment (PPE), nasal swabs and influenza vaccines. The contact tracing and isolation measures also did not work as efficiently as expected (Gorman & Horn, 2020). However, what made a critical difference was the leadership provided by the Prime Minister, Jacinda Ardern. Realizing the lack of preparedness, she relied heavily on public health and medical experts, both from within New Zealand and abroad. She also used her already high approval rating, generated in part by her empathetic response to a terrorist attack on two mosques in Christchurch in March 2019 to gain the attention of New Zealanders and successfully communicate public health messages to the public. (Mazey & Richardson, 2020). This ensured greater compliance with lockdown rules. However, unlike Taiwan, New Zealand could not prevent a national lockdown. On March 23, the prime minister announced that the whole country would go into 1

Taiwan and New Zealand found it easier to impose border controls because they were island territories. However, as the experience of the United Kingdom with Covid-19 demonstrates, that by itself does not reduce the threat posed by Covid-19, or make it much more easier to deal with.

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Fig. 3 New Zealand—Cumulative confirmed COVID-19 deaths. Source https://ourworldindata. org/coronavirus/country/new-zealand?country=~NZL

lockdown from March 26. The lockdown continued until May 13. Most offices and schools were shut, and only essential workers and businesses were allowed to function. A state of national emergency was declared the day before the lockdown came into effect, along with new laws passed by Parliament to deal with the pandemic (Summers et al., 2020). In addition to school and office closures, the lockdown also imposed restrictions on large gatherings. Scientific risk assessment, rapid testing and tracing, and preparation of hospitals for a surge in patients by procurement of ICU and ventilator facilities were also emphasized in planning efforts. (Panneer et al., 2020). New Zealand did not develop a national digital framework for contact tracing until May. Until a national strategy was adopted, different local authorities used different approaches to contact tracing. (Summers et al., 2020). Strong leadership, effective communication and voluntary compliance helped New Zealand reach a situation where it thought it had eliminated the virus by May. However, maintaining New Zealand’s virus-free status would have involved continued restrictions on all in-bound travel, or if that was allowed, a highly effective test, trace and quarantine system for incoming travellers (Baker et al., 2020). The country proved unable to ensure either. New Zealand’s inability to maintain its COVID-19 negative status after August has been largely attributed to confusion about the movement between different alert levels (Level 1 to 4) and its inability to enforce quarantinne guidelines effectively. While the strict lockdown and a ban on entry of all international travellers, including New Zealanders ensured elimination of the virus from New Zealand, pressure soon

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grew to allow citizens stranded abroad to return to the country and the government relented. After remaining in Level 4 for a little over a month New Zealand moved to Level 3 on then to Level 2 on May 14. On June 9, it entered Level 1 (Jamieson, 2020). Though in-bound passengers were supposed to be quarantined and regularly tested for 14 days, some were allowed to leave early on compassionate grounds. Eventually the New Zealand government had to bring in the armed forces to manage the quarantine process (Mazey & Richardson, 2020). However, a new outbreak occurred in Auckland in August. Though the source of the infections were never identified, lax enforcement of quarantine rules at a facility where those arriving from abroad were housed is thought to have been the likely cause (Summers et al., 2020).

3.3 India, Brazil and the U.S. 3.3.1

India

Once the lockdown began on March 25 India adopted a strategy of suppressing the virus by implementing the lockdown strictly. Protesters who blocked some roads in Delhi against new citizenship laws that denied citizenship to certain categories of migrants from other South Asian countries were forced to end their protests (Chung, 2020). This ended a stand-off between the government and protestors that began in December 2019 and showed no sign of ending until the pandemic began. Initially there was high trust among the public for government policy with the a one-day “public curfew” on March 22, being voluntarily observed across large parts of the country (Pandey & Saxena, 2022, 2020). Prime Minister Narendra Modi sought to keep up public support for government measures by encouraging collective action to show support for the fight against COVID-19. In a nationally televised address he asked citizens to clap for doctors, nurses and law-enforcement personnel on March 22, the day of the national curfew. Once the lockdown began, he once again urged citizens to light lamps and candles to show their support and solidarity on April 5, 2020. Wartime imagery was invoked with those involved in treating patients, enforcing restrictions and maintaining law and order during the lockdown being called “Covid Warriors”. The government also set up an eponymous website (https:// covidwarriors.gov.in) to provide information on those involved in the campaign and to enable volunteers to sign up. India was also the only democratic country that made a locally developed mobile COVID-tracking App, Aarogya Setu, compulsory (Klar & Lanzerath, 2020). The decision to make downloading of such the app for mandatory for accessing government services and inter-state travel was later reversed following intervention from the courts. Even as the lockdown progressed the number of confirmed COVID-19 cases and deaths in India rose rapidly indicating that it may have been ineffective or poorly implemented (Alanezi et al., 2020). Figures 4 and 5 indicate the cumulative number of conformed cases and cumulative number of deaths respectively in India from COVID-19.

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Fig. 4 India—Cumulative confirmed COVID-19 cases. Source https://ourworldindata.org/corona virus/country/india?country=~IND

Fig. 5 India—Cumulative confirmed COVID-19 deaths. Source https://ourworldindata.org/corona virus/country/india?country=~IND

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Two aspects of government strategy seem to have negatively impacted the effectiveness of the lockdown and led to increased infections and deaths. The first was the movement of migrant workers once the lockdown began. The imposition of the lockdown and the advice to all resident to remain in their respective locations were meant to prevent the further spread of the virus. However, the government seemed to have underestimated the ability of rural migrants, who had come to work in urban centres, to sustain themselves through the initial lockdown phase without gainful employment. The announcement of the lockdown, which was imposed from March 25, was made late in the evening on March 24, with less than four hours’ notice. This was probably intended to convey agility and decisiveness in the face of a national threat, and had almost the same element of surprise as the sudden demonetization of high-value currency notes in November, 2016. Migrant workers who tried to return to their villages because they had lost their jobs found that bus and train services had been suspended (Sengupta & Jha, 2020). Within a week of the lockdown starting, millions of migrant workers who were now unemployed began a mass migration back to their villages, often on foot because of lack of public transport (Jha & Jha, 2020). Though the government later announced help for migrant workers and began to run special train services for such workers from the end of April 2020, it proved to be too late. Case numbers had begun to increase sharply by then. One study indicated that based on case numbers, the states of Maharashtra, Gujarat, Delhi, Tamil Nadu, Rajasthan, Madhya Pradesh, Uttar Pradesh, Andhra Pradesh, Telangana, Punjab, and West Bengal faced a higher threat from the virus than other states. These were also the states which showed significant movement of migrants (Pandey & Saxena, 2020). A second factor that contributed to policy ineffectiveness was the overcentralisation of the response to the pandemic. In the initial phases the national government seemed keen to take the lead in the national response to the virus. India faced a shortage of PPE with domestic production volumes being low. Much of imported PPE kit turned out to be defective (Kakar & Nundy, 2020). In early April the national government imposed restrictions on import of PPEs by state and local governments, insisting that procurement would be done nationally. Shortages of protective clothing put medical staff at risk, with many of them testing positive for the virus (Shringare & Fernandez, 2020). However, as case numbers began to increase the imposition of stringent lockdown measures also became more difficult and “lockdown fatigue” began to set in. Gradually, the national government began to devolve many decisions, particularly with regard to relaxation of lockdown rules, to the states. The government seemed keen to let states take responsibility for the pandemic response, restricting itself to issuing guidelines and planning for future vaccination campaigns. India’s lockdown had four phases beginning on March 24 and ending on June 7. The fifth “unlock” phase beginning on June 8 allowed for a gradual opening up of the economy subject to maintaining hygiene and social distancing rules (Siddiqui et al., 2020). Despite the economic impact of the pandemic the government also passed three farm bills which liberalized rules relating to sale of farm produce which had the potential to negatively impact farm incomes (Ghosh, 2020). This led to protests by

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farmers all over the country, with the northern states particularly affected. Large groups of farmers camped outside the national capital in Delhi raising concerns about its potential impact on the spread of the virus. However, unlike the police action against the Tablighi Jamaat in March/April the government did not act to break up the protests despite concerns raised by the Indian Supreme Court while hearing a petition filed with it demanding action against protestors for ignoring social distancing norms (Rajagopal, 2021).

3.3.2

Brazil

Brazil is one country in Latin America that could have been expected to handle the epidemic more effectively than others. The Bolsa Familia programme, a conditional cash transfer programme initiated by President Luiz Inácio Lula da Silva in 2003 improved Brazil’s health indicators by making cash payments for poverty reduction dependent on children and expectant mothers undergoing regular health check-ups and vaccinations. The Cadastro Único Para Programas Sociais (Unified Registry for Social Programs), a digital database that supported the programme, was unique in the sense that it had registered and collected information of low-income Brazilian households, including those with no fixed residential addresses (Dowbor, 2020; Wong n.d.). The ability to reach out to affected populations and provide medical assistance that the database provided was a significant advantage that Brazil had, going into the pandemic. However, Brazil’s ability to handle the epidemic was severely compromised by the response of the Brazilian President Jair Bolsonaro to the outbreak of the virus. Concerned about the possible economic impact of the virus President Bolsanaro downplayed the seriousness of the crisis, calling the virus “a little flu”. He derided those who demanded lockdowns and requirements for face-masks and blamed China, and Brazilian state governors and mayors for the deaths and the economic effects of the virus (Abers et al., 2021) Disregarding public health advisories he asked his supporters to participate in demonstrations across Brazil on March 15, to protest against actions by the Brazilian Congress and Supreme Court and in support of the military (Prfrimer and Barbosa 2020). He went out of his residence to join protestors in Brazilia and shook hands and hugged many of them. Brazil’s response to the pandemic was led by its Ministry of Health (MoH) and by state and local governments, especially its municipalities. From March 17, municipalities across the country started to declare states of emergency and imposed restrictions on movement. Brazil’s Senate passed a presidential decree to impose a national emergency and provide resources to deal with the public health emergency. The MoH also expanded testing and initiated a Strategic Action plan to register and provide online training to healthcare workers around the country. The aim of the MoH was to reduce transmission by providing states with technical information, resource management and research support. (Szylovec et al., 2020).

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The attempt by the Brazilain MoH, under the federal health minister Luiz Henrique Mandetta to effectively combat the pandemic was undermined by differences between him and President Bolsonaro. Tensions arose as the president advised Brazilians to use chloroquine to protect themselves from the virus and also called for “vertical isolation”. This refers to measures which allow most citizens to resume normal daily routines, with social distancing restricted to more vulnerable sections of the populations such as the elderly, pregnant women and those with health conditions which made them more vulnerable to the disease (Edlaine et al., 2021). This contradicted the advice of the MoH for isolation for people in all groups who had come into contact with those affected by the virus. The president also criticized governors of Brazil’s states who imposed local restrictions on movement and called on them to prioritize the economy by removing restrictions on movement. Some states did follow the president’s advice and did not implement social distancing measures (Szylovec et al., 2020). Differences between the president and the health minister finally led to the minister resigning from his position on April 16. The new health minister Nelson Teich, a medical oncologist, also resigned within a month when the president overruled his advice and ordered the reclassification of gyms, beauty salons, and barbers as essential services and thus eligible to reopen (Philips, 2020). Restrictions on movement and quarantine requirements were relaxed soon afterwards leading to the inevitable increase in infections and deaths. Figures 6 and 7 indicate

Fig. 6 Brazil—Cumulative confirmed COVID-19 cases. Source https://ourworldindata.org/corona virus/country/brazil

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Fig. 7 Brazil—Cumulative confirmed COVID-19 deaths per million people. Source https://ourwor ldindata.org/coronavirus/country/brazil

the cumulative confirmed cases and cumulative confirmed deaths per million people from the pandemic in Brazil. Once cases in Brazil began to rise rapidly the Brazilian government shifted to a denial mode, changing the method of reporting pandemic information in early June. On June 6, the presentation of total number of cases and deaths was stopped, ostensibly to “facilitate the communication of data to the public” (Idrovo et al., 2021: 31–32). This was reversed on June 8 only after the Supreme Court intervened and asked the government to report accumulated figures for both infections and deaths. The relentless rise in cases has made Brazil one of the countries worst affected by the pandemic. The failure to control cases also probably led to the rise of a new more virulent strain of the virus that significantly raised infection levels, first in the Brazilian north-east and then in other parts of Brazil and in other countries. The Brazilian variant is of particular concern because of its apparent ability to infect those who have already recovered from a COVID-19 infection (Andreoni, 2021).

3.3.3

United States

The U.S. has, in more senses than one, come to symbolize the failure of democracies to deal effectively with the pandemic. Not only does it have the dubious distinction of being the country with the largest number of infections and deaths, it is also seen as a

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country where politicians at both the national and state levels failed comprehensively in their response. This is particularly surprising given the fact that the U.S. ranks first among 195 countries in the Global Health Security (GHS) Index maintained by John Hopkins University (http://www.ghsindex.org) and also spends the most per capita on health. Despite its global ranking and per capita spending on health, the U.S. went into the pandemic particularly unsuited to deal with a serious virus outbreak. In 2018, President Trump closed down the Global Health Security and Biodefense Unit that had been set up in 2015 by the Obama administration (An & Tang, 2020). This severely undermined its ability to anticipate, plan and prepare for a pandemic. This problem was compounded by the federal and state governments’ response when the virus began to spread from early April. Two aspects of the U.S. response stand out. The first was the Trump administration’s decision to transfer responsibility for dealing with the crisis to the states. Once the full extent of the virus-spread became clear President Trump announced that the states would take the primary role in handing this public health crisis. This was the first time that a sitting U.S. President sought to give away the power to coordinate the response to a national crisis to the states. This prevented national coordination of virus testing, contact tracing, purchase of PPE and policy relating to issues such as lockdowns and reopening of schools (Altman, 2020). The second was the constant undermining of expert advice and guidance to the public by the president using both daily press conferences and also social media. Of particular concern was President Trump’s undermining of messages contained in the U.S. CDC’s manual for public communication during a pandemic by promoting alternate advice (the advice on the prophylactic use of chloroquine being just one example) that often had no scientific backing (Schrager, 2020). Evidence that there was a shortage of PPE, and expert advice on the need for more testing that were provided by the Task Force that the president himself had set up were ignored (Solinas-Saunders, 2020). The response of the governors of the various states were also heavily influenced by partisan politics. States led by Democratic governors were quicker to implement orders restricting movement. The probability of states with Democratic governors implementing stay-at-home orders was estimated to be 50% more than that of Republican ones. In terms of responses, Democratic governors seemed more concerned about the health impact of the pandemic, while Republican ones were more concerned about the impact of restrictions on employment and their states’ economy (Baccini & Brodeur, 2021). The problem was compounded by lack of state-level coordination caused by the party heading county and municipal governments being different from party holding the governorship. Democratic governors found it difficult to implement stay-at-home orders when Republican county and municipal officials failed to implement them locally. Democratic county and municipal officials who tried to implement requirements of wearing face-masks were often prevented from doing so by orders of Republican governors (Kincaid & Leckrone, 2021). The lack of coordination in pandemic response between the federal, state and local governments has had terrible

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Fig. 8 US—Cumulative confirmed COVID-19 cases. Source https://ourworldindata.org/corona virus/country/united-states)

consequences for U.S. citizens. Figures 8 and 9 indicate the cumulative confirmed cases and cumulative confirmed deaths per million people from the pandemic in the U.S.

4 Conclusions The pandemic is an ongoing global health crisis and it is still too early to draw definite conclusions about the effectiveness of response of different governments. Newer strains might develop which spread faster and affect even those who have been vaccinated, thus reversing successes that some governments have had in dealing effectively with the crisis. However, an analysis of the first year of the pandemic allows us to draw more definite conclusions about whether authoritarian governments or democratic ones are more suited to handling such crises, and whether “pragmatic” or “populist” leaders better able to respond to such events. The first conclusion is that when countries have past experience of pandemics and have put in place surveillance systems and health infrastructure, including appropriately trained personnel, they have been able to respond faster to the crisis and ensure that the virus does not spread widely and paralyze economies and societies. It does

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Fig. 9 US—Cumulative confirmed COVID-19 deaths per million people. Source https://ourworldi ndata.org/coronavirus/country/united-states

not seem to matter whether such governments are authoritarian or democratic. Both can be effective as the experience of China and Taiwan show. The second conclusion is that when countries are not well prepared for the pandemic, pragmatic governments are better able to respond to the crisis than those led by populists. New Zealand was fortunate to be led during the crisis by a prime minister who already had high approval ratings, was inclusive in terms of decisionmaking and was willing to be guided by expert advice on handling the pandemic. Such leaders can elicit the willing public cooperation that is needed to ensure that stayat-home, masking, and social distancing guidelines are followed in the absence of coercive enforcement mechanisms that are lacking in most democracies. However, as the experience of Taiwan shows, even pragmatic governments need supporting legislation to impose punitive measures, and the capability and the will to enforce such measures, if government guidelines are not followed. Populist leaders by contrast are hindered in their ability to deal with such crisis on two counts. “Cultural populists” especially those of an “illiberal” bent often find it tempting to use such crisis to garner additional support by blaming domestic “enemies” for the crisis and coming down hard on domestic opponents, thus undermining national cohesion and support. “Socio-economic populists” fear the effects that pandemic lockdowns have on the livelihoods of their core support base to whom they have promised economic advancement. Such leaders are often keen to downplay the dangers of the virus. The consequences of downplaying the dangers of the

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virus are evident in the U.S. and Brazil and the impact of easing lockdowns too soon, without putting in place effective test-and-trace systems are evident in India. Populist leaders are also handicapped by their inability to get along with scientists and subject-experts since they have in the past derided them as part of the “treacherous elite”. This further constrains their ability to put develop appropriate responses sufficiently early. The fact that they also have a limited pool of capable second-rung leaders who can work together to coordinate the crisis response only compounds the problem still further.

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Corkery, M., & Karni, A. (2020). Trump administration restricts entry into U.S. From China, New York Times. January 31. https://www.nytimes.com/2020/01/31/business/china-travel-corona virus.html Dowbor, L. (2020). Inequality and politics in Brazil: Bolsa familia and beyond1. Economics and Business Review, 6(4), 7–26. https://doi.org/10.18559/ebr.2020.4.2. Easton, I., Stokes, M., Cooper, C. A., & Chan, A. (2017). Transformation of Taiwan’s defence force. Rand Corporation. EIU. (2021). Democracy Index 2020: In sickness and in health? Economist Intelligence Unit. de Moura Villela, E. F., López, R. V. M., Sato, A. P. S., de Oliveira, F. M., Waldman, E. A., Van den Bergh, R., Fodjo, J. N. S., & Colebunders, R. (2021). COVID-19 outbreak in Brazil: Adherence to national preventive measures and impact on people’s lives, an online survey. BMC Public Health, 21, 1–10. https://doi.org/10.1186/s12889-021-10222-z de Moura Villela, E. F., López, R. V. M., Sato, A. P. S., de Oliveira, F. M., Waldman, E. A., Van den Bergh, R., Fodjo, J. N. S., & Colebunders, R. (2021). COVID-19 outbreak in Brazil: Adherence to national preventive measures and impact on people’s lives, an online survey. BMC Public Health 21, 152 (2021). https://doi.org/10.1186/s12889-021-10222-z Ghosh, J. (2020). Farmers vs. the Indian State. Project Syndicate. December 11. https://www.projectsyndicate.org/commentary/india-farmer-protests-government-reaction-by-jayati-ghosh-2020-12 Gorman, D., & Horn, M. (2020). On New Zealand’s weak, strong and muddled management of a COVID-19 epidemic. Internal Medicine Journal, 50, 901–904. https://doi.org/10.1111/imj.14928 Higley, J. (2020). Elite trust and the populist threat to stable democracy. American Behavioral Scientist, 64(9), 1211–1218. https://doi.org/10.1177/0002764220941215 Huang, I. Y. (2020). Fighting COVID-19 through Government Initiatives and Collaborative Governance: The Taiwan Experience. Public Admin Rev, 80, 665–670. https://doi.org/10.1111/puar. 13239 Idrovo, A. J., Manrique-Hernández, E. F., & Fernández Niño, J. A. (2021). Report from Bolsonaro’s Brazil: The consequences of ignoring science. International Journal of Health Services, 51(1), 31–36. https://doi.org/10.1177/0020731420968446 Jamieson, T. (202)0. Go hard, go early”: Preliminary lessons from New Zealand’s response to COVID-19. American Review of Public Administration, 50(6/7), 598–605. https://doi.org/10. 1177/0275074020941721 Jha, A. K., & Jha, R. (2021). India’s response to COVID-19 crisis. The Indian Economic Journal. https://doi.org/10.1177/0019466220976685 Kakar, A., & Nundy, S. (2020). COVID-19 in India. Journal of the Royal Society of Medicine, 113(6), 232–233. https://doi.org/10.1177/0141076820927668 Kincaid, J., Leckrone, J. W. (2021). Partisan eractures in U.S. federalism’s COVID-19 policy responses. State and Local Government Review. https://doi.org/10.1177/0160323X20986842 Klar, R., & Lanzerath, D. (2020). The ethics of COVID-19 tracking apps—Challenges and voluntariness. Research Ethics, 16(3–4), 1–9. https://doi.org/10.1177/1747016120943622 Lewis, T. (2020). How the U.S. Pandemic response went wrong—and what went right—During a year of COVID. Scientific American, March 11. https://www.scientificamerican.com/article/ how-the-u-s-pandemic-response-went-wrong-and-what-went-right-during-a-year-of-covid/ Lo, M.-C.M.: How Taiwan’s precautionary approach contained COVID-19. Contexts, 19(4), 18–21. https://doi.org/10.1177/1536504220977929 Liu, Y., Lee, J. M., & Lee, C. (2020). The challenges and opportunities of a global health crisis: The management and business implications of COVID-19 from an Asian perspective. Asian Bus Manager, 19, 277–297. https://doi.org/10.1057/s41291-020-00119-x Mazey, S., & Richardson, J. (2020). Lesson-drawing from New Zealand and Covid-19: The need for anticipatory policy making. The Political Quarterly, 91, 561–570. https://doi.org/10.1111/1467923X.12893 McKee, M., Gugushvili, A., Koltai, J., & Stuckler, D. (2021). Are populist leaders creating the conditions for the spread of COVID-19?; comment on “A scoping review of populist radical right parties’ Influence on welfare policy and its implications for population health in Europe”.

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Patent Protection and Access to COVID-19 Medical Products in Developing Countries Sudip Chaudhuri

Abstract When new vaccines and other medical products for COVID-19 are developed, it is of critical importance to ensure that these are affordable and accessible to the people of all the countries. If the new medical products are patented, then the patentees will have the right to prevent others from entering the market for a prolonged period of time. The resultant monopoly markets may lead to high prices. Experience shows that the most effective way of ensuring lower prices is generic competition. Generic competition is possible when patent protection expires, or when patents can be prevented or denied or suspended. When product patents are granted, competition is possible when the patentees give voluntary licences or when generic companies get compulsory licences. Voluntary licensing has attracted huge attention in the context of the proposal of Costa Rica to create a voluntary pool mechanism for medical products and technologies. WHO and Costa Rica have followed it up through a Solidarity Call emphasizing the need for voluntary licensing on non-exclusive basis to Medicines Patent Pool (MPP). The call has been endorsed by some WHO member countries but not by the more influential developed countries (such as the US and Germany) and developing countries (such as India and China). The paper provides a critical review of the voluntary licensing mechanisms that have been used and argues that voluntary initiatives must be supplemented by using the flexibilities which the TRIPS agreement of WTO provide including compulsory licensing and using Article 73(b) of the TRIPS agreement to suspend patent rights for security reasons. Keywords Patent · Pandemic · Vaccines · Pharmaceuticals · TRIPS

The paper was written in late June 2021 and hence does not contain references to later events. S. Chaudhuri (B) Formerly, Indian Institute of Management Calcutta, Kolkata, India e-mail: [email protected] © The Author(s), under exclusive license to Springer Nature Singapore Pte Ltd. 2022 M. Dutta et al. (eds.), The Impact of COVID-19 on India and the Global Order, https://doi.org/10.1007/978-981-16-8472-2_12

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1 Introduction It was clear right from the beginning of the COVID-19 pandemic that measures such as lockdown may help to contain the pandemic to some extent, but it will be difficult to properly control it without effective vaccines and other medical products for preventive and curative purposes. Development of new medical products and ensuring its access is of critical importance to combat the pandemic. But if new medical products developed are patented, then the patentees will have the right to prevent others from entering the market (till the patents expire). This may result in supply shortages and high prices. Together with the need for developing new products, the problem of access under product patent protection also attracted wide attention. Usually it takes years of R&D to develop a vaccine. But in the case of COVID-19, this has been compressed into less than a year since the World Health Organization (WHO) declared the coronavirus disease to be a pandemic on 11 March, 2020. Several vaccines have been developed and are in use in different parts of the world including those developed by Pfizer-BioNTech, Moderna, AstraZeneca/Oxford, Johnson & Johnson, Gamaleya (Sputnik V), Sinovac, BBIBP-CorV, and Bharat Biotech (Covaxin). But the access has been grossly unequal. Relevant medical products for COVID19 include diagnostics (such as testing kits), equipment (such as ventilator valves, N95 masks), treatments (for example, medicines such as Remdesivir), and most importantly, vaccines. As South Africa’s Statement in the TRIPS Council meeting of the World Trade Organization (WTO) reveals, the US, the UK, and the EU accounted for about 50% of the vaccines administered globally as on 22 February, 2021. More than 130 countries did not receive even a single dose.1 The situation has improved since then. But huge inequalities remain. The share of people who received at least one dose of COVID-19 vaccine is 63.56% in the UK, 53.11% in the US and 47.76% in the EU. The corresponding figure for people in low-income countries is only 0.9% as on 21 June, 2021.2 As apprehended, various COVID-19 medical products are patent protected including some on the technologies used for the three widely used vaccines developed by Pfizer-BioNTech, Moderna, and AstraZeneca/Oxford (MSF, 2020a). These organizations control the production, supply, and pricing of these vaccines. And most people in low-income countries have been unable to access the vaccines as mentioned above. This is reminiscent of the situation during the HIV/AIDS pandemic when despite the development of antiretrovirals drugs (ARVs), people were dying in developing countries unable to access the patented ARVs. After Indian generic companies started supplying the ARVs, prices fell dramatically resulting in a remarkable improvement in access to ARVs. India could do so because these ARVs were not patented in India. The situation is different now. In line with WTO’s Agreement on Trade-Related Aspects of Intellectual Property Rights (TRIPS), India has reintroduced product patents in pharmaceuticals in 2005. India is manufacturing indigenously developed Covaxin and Covishield (under licence from AstraZeneca/Oxford

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with some restrictions as discussed below). But India or other developing countries such as China or Cuba cannot manufacture and sell Pfizer-BioNTech, Moderna, and other patented vaccines unless permitted to do so. Though it is mandatory for all WTO member countries to recognize product patents, TRIPS provides for exceptions and exemptions depending on circumstances. TRIPS permits generic production of patented products under certain conditions. India and South Africa have asked for a waiver for patent and other intellectual property (IP) for COVID-19 medical products. Can suspension of patent rights improve access to vaccines and other medical products? In the light of this proposal, the basic objective of this paper is to re-visit for COVID-19, the old debate on patent protection and access.

2 The Patent Waiver Proposal On 2 October, 2020, India and South Africa submitted a joint proposal to the TRIPS Council requesting a waiver so that WTO member countries are not required to implement, apply, and enforce patents (and other intellectual property—copyright, industrial designs and protection of undisclosed information) relating to prevention, containment or treatment for COVID-19.3 The proposal was for a temporary waiver “until widespread vaccination is in place globally, and the majority of the world’s population has developed immunity”. A revised proposal was submitted on 21 May, 2021 by India, South Africa and several other developing countries.4 It addresses two concerns that the October, 2020 proposal was too broad and did not specify a time limit. The May 2021 proposal clarifies that the temporary waiver is “…in relation to health products and technologies including diagnostics, therapeutics, vaccines, medical devices, personal protective equipment, their materials or components, and their methods and means of manufacture for the prevention, treatment or containment of COVID-19”. The May 2021 proposal also specifies that the waiver will be in force for at least three years from the date of the decision. While the objective of the waiver is to eliminate all IP barriers for the development, production, and supply of all COVID-19 medical products, in this paper we will primarily focus on patents. Article IX 3 and 4 of the Marrakesh Agreement Establishing WTO permits such a waiver in exceptional circumstances provided a justification is provided. And WTO did grant waivers in the past, for example, the waiver related to Paragraph 6 of the Doha Declaration. (This permitted countries to export generic medicines under compulsory licences to other countries which lacked the manufacturing capacity to produce the products themselves). The Ministerial Conference or the General Council decides whether to grant the waiver based on deliberations at and recommendations from the TRIPS Council (MSF, 2020b). Since October 2020 the TRIPS Council has deliberated on the waiver proposal in several meetings. The vast majority of more than two-thirds of the WTO members have supported the call for the waiver but a handful of developed countries—the EU, the UK, Japan, Canada, Switzerland, Norway, Australia, and initially the US

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opposed it and no decision could be taken.5 These countries opposing the waiver to eliminate patent barriers are the ones which have grabbed much of the vaccines supply. The high-income countries including the UK, the US, and the EU have secured vaccines which would cover 200% of their populations.6 But on 5 May, 2021 the US has expressed support to the waiver proposal and has agreed to participate in text-based negotiations. The US announcement however, mentions only vaccines and not the other COVOD-19 products. But the EU continues to oppose it and in fact in a Communication to the TRIPS Council on 7 June, 2021 has argued for use of compulsory licensing and other TRIPS flexibilities rather than waiving of IP. The Chair of the TRIPS Council has informed the countries that text-based negotiations will begin but has cautioned that “we should not think that substantial differences have evaporated”.7 The disagreements essentially revolve around four issues: whether suspension of patent rights will act as a disincentive for development of new medical products; whether patented products can be manufactured in the absence of manufacturing capacities; whether voluntary initiatives are better than a patent waiver; and whether a waiver is necessary in view of compulsory licensing and other measures which TRIPS permits. We will briefly review these issues in the next four sections. In the light of this review, we will provide some suggestions in the last section about what developing countries can do to make COVID-19 medical products affordable and accessible.

3 Patent Protection and Incentive for Innovation for COVID-19 A basic objection to suspending patent and other IP protection for COVID-19 products is that it “would jeopardize future medical innovation, making us more vulnerable to other diseases”, to quote the Director General of the International Federation of Pharmaceutical Manufacturers and Associations, which represents the MNCs.8 Development of new medicines is costly and risky. Without profits from selling patented products, MNCs such as Pfizer and AstraZeneca may not find it worthwhile to spend on R&D for new drugs. But the proposal is only for a temporary suspension of IP rights related to only COVID-19 medical products. It is not applicable to other products and hence the incentive for the MNCs to do R&D for other diseases remain intact. Moreover, the new vaccines which have been developed for COVID-19 are not the result of the efforts and investment by the MNCs alone. The fact that vaccines have been developed in such a short period of time is the result of huge public funding and global collaboration among research institutions, universities, and industry. Perhaps the apprehension is that if the COVID-19 patent waiver is granted, then similar demands will be made in future for other diseases as well and the incentive effect of patent protection will be blunted. The principal economic rationale for granting patents is indeed that it will stimulate R&D for innovation. This is the

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expected positive effect. But when patent rights prevent others from producing and marketing the product as in the case of COVID-19, fewer people benefit from it. This is the negative effect. The net benefits of the patent system to society have remained controversial over the years. What is recognized in patent laws around the world and also emphasized in the TRIPS agreement is that the protection of the rights of the patentees is not the sole concern of patent laws. It is important to balance the rights and the obligations. Patent rights can be constrained in broader public interest as acknowledged also by the WTO Secretariat in a Note issued on 15 October, 2020 after the India/South Africa proposal was submitted (WTO, 2020). In the extraordinary public health crisis created by the COVID-19 pandemic, ensuring access to affordable medical products should be accorded greater priority than patent rights.

4 Patent Protection and Manufacturing Capacity Another important argument in this context that has been advanced is that the main constraint is not IP but manufacturing capacity. Even if the waiver is granted, most developing countries will not be able to take advantage of it because they lack capacities and capabilities to manufacture the products. It is of course true that patent is not the only barrier. But the basic premise of the waiver proposal is that there are unused and underutilized capacities and that some firms in some developing countries at least can initiate steps to develop and manufacture if not all the patented products, at least some of them.9 This will permit a larger number of people to access vaccines and other products. But if new manufacturers cannot manufacture COVID-19 products protected by IP, then as the India Representative pointed out in the TRIPS Council meeting, commercial interest of the exiting IP holders will not suffer.10 Hence why make such a hue and cry about the negative impact of patent waiver on innovation? The monopoly markets can continue to provide the necessary incentives for MNCs. Actually, vested interests in developed countries have traditionally resisted attempts to dilute patent protection in developing countries citing manufacturing deficiencies. In the debate that preceded the abolition of product patent protection in pharmaceuticals in 1972 in India, doubts were expressed that India will not be able to take advantage of it. The Indian generic industry proved them wrong. To give an example from vaccines, an Indian firm Shantha Biotechnics was successful in developing complex recombinant Hepatitis B vaccines in the 1990s at a much lower cost despite the scepticism of MNCs such as Merck and GSK which dominated the market at that time (Krishtel & Malpani, 2021). The tendency to downplay the negative role of patent protection has continued during discussions on COVID-19 medical products. The fact that no other firm has manufactured the vaccine despite the announcement of Moderna that it will not exercise its patent rights, is used as an example that patent protection is not a barrier. But the mRNA technology used by Moderna for

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manufacturing its vaccine is protected by hundreds of patents including pending applications (Correa, 2021). And Moderna which uses these patents is reluctant to licence out the vaccine (Rowland, Rauhala, and Berger, 2021. Therefore, any of these patentees can file a suit alleging infringement against any non-patentee attempting to manufacture the vaccine. An environment where there are no patent barriers whatsoever and no threat of litigation is more conducive for experimentation and development of products. Elimination of patent barrier is necessary. But this is not sufficient. What played a significant role in India, for example, was also supportive industrial policies.11 It is also important to take proactive steps to enhance manufacturing capabilities and capacities. The focus of this paper is on issues related to patent protection.

5 Voluntary Initiatives Voluntary initiatives have been advocated not only by the MNCs but by others as well. It is contended that voluntary licensing offers a better solution than patent waiver or other mandatory measures such as compulsory licensing. The point is not voluntary initiatives are not necessary or not desirable. If voluntary licensing works, it is of course a better option. In the case of voluntary licensing, the patentees may provide technological assistance. If patent rights are suspended, it is likely that patentees will resist such attempts and will not want to share technologies. Particularly when generic companies may not have the necessary manufacturing capabilities, this can be a critical bottleneck. The question really is whether voluntary licensing is working and helping to realize the desired goal of making products accessible. If not, rather than depending only on voluntary measures, it is necessary to explore other options. This may involve not only non-voluntary measures such as compulsory licensing but also taking steps to enhance manufacturing capabilities and capacities to reduce the dependence on patentees. In this section we will try to analyze the experience with voluntary initiatives in recent years and draw lessons from it. COVID-19 Technology Access Pool: The initial response to facilitate universal access to COVID-19 medical products was to set up a voluntary patent pool. Within a fortnight of WHO declaring the disease to be a pandemic, Costa Rica requested WHO on 23 March, 2020 to create a voluntary pooling mechanism for “rights to technologies that are useful for the detection, prevention, control and treatment of the COVID-19 pandemic”. The idea was that patented products and other relevant technology for COVID-19 would be placed voluntarily in a pool and be available for licensing in every country to make these affordable and accessible.12 Despite some dissenting voices about the effectiveness of voluntary mechanisms without binding commitments, the Costa Rica proposal evoked huge interest and optimism. In early April, 2020, the WHO Director General responded by welcoming the idea and in late May, 2020 the COVID-19 Technology Access Pool (C-TAP) was launched. The WHO DG and the President of Costa Rica issued a Solidarity Call “to key stakeholders and the global community to voluntarily pool knowledge, intellectual

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property and data necessary for COVID-19”. The holders of IP were specifically called upon to provide voluntarily licences on non-exclusive and global basis.13 The Call has been endorsed by 40 other WHO members states including some developed countries such as Belgium, Norway and the Netherlands. This however does not include developed countries such as the US, the UK, France, and Germany and developing countries such as China and India. The MNCs, the patent holders have opposed the move and Pfizer, Moderna and AstraZeneca, and other vaccine manufacturers in developed countries have refused to join C-TAP.14 Thus, the voluntary pool has remained inactive and non-operational. And it remained so because patentees did not want to join it and WHO or others could not induce them or force them to do so. If the governments who have been funding the development of COVID-19 medical products had insisted that the pharmaceutical companies should mandatorily place the patents in a pool, then it could have worked. But none of the developed country government (or charitable donors) has announced or indicated that R&D funding will be conditional. If C-TAP had succeeded, it may not have been necessary to ask for a patent waiver. It is important to note that the patent waiver proposal was made in October, 2020 several months after the creation of C-TAP. Medicines Patent Pool: The example of MPP is often used to tout voluntary licensing as a solution to the COVID-19 medical products access problem. In fact, C-TAP was influenced by the MPP model. But what has been the experience with MPP? The initial focus of MPP was on ARVs. The number of people receiving antiretroviral treatment increased from 8 million in 2010 to 21.7 million in 2017.15 Surely MPP has contributed to improving access to ARVs. This has been a major achievement. But this has been possible because the MNCs already pursuing voluntary licences were keen to join MPP in their own interests.16 MPP extended its activity to include Hepatitis C and Tuberculosis medicines in 2015. But here, where the background and the context are different and where the MNCs are not that keen to join MPP, the success has been much less. In 2018 MPP decided to expand its activities to other life-saving medicines such as cancer and diabetes and in 2020 to COVID-19 medical products. But hardly any progress has been made in these sectors. Even in ARVs where the performance is better, MPP could not prevail upon the MNCs to agree to grant of voluntary licence to all the LMICs. The MNCs continued to impose restrictions on the countries eligible for licensing. In the countries not covered under licensing through MPP, prices remained high and unaffordable. So, even in products under MPP, some countries are required to use compulsory licence or to take other measures to make medicines more affordable. Thus, the experience of MPP shows that voluntary licensing cannot be considered to be a substitute for mandatory measures. Voluntary licensing in COVID-19 pandemic: Among the three major developers of COVID-19 vaccines, only AstraZeneca has signed licensing agreements with technology transfer for its product. The vaccine licensing initiative of AstraZeneca is

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commendable. In India, the vaccine is manufactured as Covishield by the Serum Institute of India. Covishield and the indigenously developed Covaxin have contributed significantly to the vaccination drive in India. But AstraZeneca/Oxford voluntary licences are not non-exclusive licences and not without conditions. In India, Serum Institute is not free to decide where to sell. It is prevented from supplying to more profitable markets in upper middle income and high-income countries (MSF, 2020a). There are also issues with pricing. Serum Institute of India is reported to be have sold the vaccine to South Africa and Uganda at 2–3 times the price of the vaccine in Europe.17 Licences have been given to only selected firms in selected countries—Argentina, Brazil, China, India, and Indonesia. Licences have not been offered to other firms in these countries or in other countries such as Cuba, Thailand, and Bangladesh which have vaccine manufacturing capabilities. Some firms in some countries, for example, in Bangladesh are keen to manufacture vaccines under licences but are unable to do in the absence of any positive response from the patentees (Gebrekidan and Apuzzo, 2021; Rowland et al., 2021). Neither Pfizer/BioNTech nor Moderna has taken any initiative to licence out their vaccines or to transfer technology, though as we have mentioned above Moderna has announced that it will not exercise its patent rights against any company manufacturing its vaccine. The main lesson that can be drawn from the experience with voluntary licensing will not surprise its critics—that it is voluntary and hence it depends on what the patentees decide to do. Some firms in some products have given voluntary licences. Others have not. Even those who have done so, have not offered voluntary licences without any conditions attached. Thus, voluntary licensing cannot be relied upon as a general solution in a public health crisis. In a pandemic such as COVID-19, the fundamental issue of access to medical products cannot be left to the discretion of the patentees. COVAX: Another initiative which has raised hopes that people in developing countries will be able to access vaccines is COVAX. It is the vaccine pillar of the Access to COVID-19 Tools (ACT) Accelerator—a global collaboration to accelerate development, production, and equitable access to COVID-19 tests, treatments, and vaccines. (The other pillars deal with diagnostics, treatment, and health system strengthening). It is coordinated by GAVI, the Vaccine Alliance, the Coalition for Epidemic Preparedness Innovations (CEPI), and the WHO and the target is to procure and distribute equitably between developed and developing countries two billion doses of vaccines by the end of 2021.18 COVAX is a very promising initiative. It is funded by donations from high-income countries and 92 lower income member countries are not required to pay for the vaccines supplied. Unlike C-TAP supported by only 41 countries, 190 countries have joined COVAX. This includes major developed countries who did not endorse C-TAP. But the performance has lagged far behind the targets. It has succeeded in procuring far less than the two billion doses planned. While funding is one reason, the bigger problem is the vaccine grab by developed countries. These countries have

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directly negotiated with vaccine manufacturers and procured or reserved for themselves vaccine supplies. COVAX does not forbid countries to directly purchase from manufacturers and the latter find it more convenient and profitable to directly deal with the former. This has resulted in huge inequality in vaccines access among countries as mentioned above. Developing countries who are unable to directly purchase from manufacturers have suffered the most. In contrast to the aim of free distribution of vaccines, many in the lower income countries have received not a single dose.19 More funding has been promised but larger funding will not eliminate the structural problem of inability of developing countries to compete against developed countries to get supplies from limited number of manufacturers.20 What is critical is to enlarge the manufacturing base. It is important to take steps to eliminate patents and other barriers. Larger production by a larger number manufacturers can actually make COVAX more effective. The dependence of MNCs and donors will also reduce. Even if COVAX functions properly as planned, it can at best distribute two billion doses of vaccines for one billion people by the end of 2021. This is far below the need of vaccinating 7.7 billion people in the world. Thus, COVAX funded through donations is insufficient to ensure equitable access to vaccines.21

6 Compulsory Licensing and Other Options within TRIPS Finally, we consider the argument that the patent waiver is not necessary because TRIPS provides WTO members with several options to intervene when necessary to realize the objectives of providing access to vaccines. While TRIPS has made it obligatory for WTO member countries to recognize product patent protection in all fields including pharmaceuticals, the protection of the rights of patentees is not the sole concern of TRIPS as we have mentioned above. Countries enjoy some flexibilities to fine tune the protection to ensure that social and economic goals are also taken into account. Article 7 of TRIPS on “Objectives” and Article 8 on “Principles” specifically speak of the mutual advantage of both producers and users of technological knowledge, stress the need for a balance of rights and obligations, and empower the member countries to take steps to protect public health and prevent abuse of patent (and other intellectual property) rights. To avoid any ambiguity, it was clarified and confirmed by the Doha Declaration on TRIPS and Public Health (2001) that the TRIPS flexibilities can in fact be interpreted and implemented to protect public health and in particular to promote access to medicines. Article 31 of TRIPS permits compulsory licensing and government use of a patent without the consent of the patentee under certain conditions on payment of royalties to the patentees. This is one of the most important flexibilities that countries can use in COVID-19.22 Another option which countries have in the COVID-19 crisis is to use the national security exception under Article 73 of TRIPS. Compulsory licensing: As different studies and reports have highlighted, in a product patent regime, a proper compulsory licensing system is of vital importance to

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deal with the negative implications of product patent protection. If generic companies are given licences to produce a patented drug, not only supplies will be enhanced. Competition among manufacturers would drive down prices, but the royalty paid to the innovators would continue to provide funds and the incentive for R&D. (WHO & WTO, 2002, p. 99) points out that compulsory licensing is one of the ways in which TRIPS attempts to strike a balance between promoting access to existing drugs and promoting R&D into new drugs. But compulsory licensing is not a substitute for waiving of patents. There are several structural and practical limitations of using compulsory licensing. To grant compulsory licences, certain conditions listed in Article 31 need to be satisfied. These include (i) that authorization of such use will have to be considered on its individual merits, (ii) that before permitting such use (except in such cases as situations of national emergencies, extreme urgency, public non-commercial use), the proposed user will have to make efforts over a reasonable period of time to get a voluntary licence on reasonable commercial terms, (iii) that the legal validity of the compulsory licensing decision and the remuneration will be subject to judicial or other independent review. These conditions need to be satisfied for each application for each product in each country. Hence, the patent waiver is a better option because the country-by-country and case-by-case consideration of compulsory licensing applications can be avoided. In a pandemic, the speed with which medical products are provided is critical. And the patent waiver is a faster and more convenient solution. For countries with no manufacturing capacities, compulsory licensing is practically unworkable. Article 31 as originally drafted imposed the condition that compulsory licensing can be used predominantly for supply in the domestic market. In other words, a compulsory licence could not be granted in countries with manufacturing capacities exclusively or mainly to export to countries with no manufacturing capacities. Thus, compulsory licensing provisions of TRIPS could not be used by a country with no manufacturing capacity to import drugs to take care of her health needs. Initially through a temporary waiver and later permanently incorporated in the TRIPS agreement through Article 31bis, this lacuna has been corrected and now compulsory licences can be granted for exports to countries which are essentially dependent on imports. But several onerous and time-consuming conditions have been imposed which make it almost impossible to use this flexibility. Not surprisingly only one such compulsory licence has been granted till now for exporting an antiretroviral drug from Canada to Rwanda. Conditions which need to be satisfied include the following: compulsory licences need to be granted both in the importing and exporting country. The importing country needs to inform the TRIPS Council specifying the names and quantities of product(s) needed; establish that it has insufficient or no manufacturing capacities. The exporting country also needs to inform the TRIPS Council the quantities being supplied to each destination and the distinguishing features of the product(s). Products are required to be clearly identified through specific labeling or marking and through special packaging and/or special coloring/shaping of the products.23

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In the case of the patent waiver, all these problems can be avoided and countries with manufacturing capacities can export and countries with no manufacturing capacities can import without much difficulty. The patent waiver is a better option and developing countries should continue to pursue it in the TRIPS Council. But simultaneously, it is important to use the existing compulsory licensing provisions. Even countries with manufacturing capacities have been finding it difficult to do so. 40 countries have issued 74 compulsory licences till April, 2021 and this may appear impressive.24 But what is striking is that among the major pharmaceutical manufacturing countries, Brazil and India have issued one licence each and China has only two licences. Again, out of the 74 compulsory licences executed, 58 are for HIV. Out of the remaining 16, 8 licences were issued in Ecuador and Thailand (4 each). And the remaining 8 licences were issued in only 6 countries—India, Italy (2 licences), Malaysia, Russia, and Taiwan. In the COVID-19 pandemic, only three licences have been issued in Hungary, Israel, and Russia. But in Israel, it is for importing antiretroviral medicine, lopinavir/ritonavir for experimental use for treating COVID-19 patients. And in Hungary and Russia, it is for the COVID-19 medicine, Remdesivir, which no longer is considered to be very effective. No compulsory licence has been issued for COVID-19 vaccines. (WTO, 2020, p. 9; MSF, 2021, p. 5). Thus, the TRIPS flexibility of compulsory licensing has remained practically unused even by countries which have manufacturing capacities except for HIV in some countries. A major reason is the undue political and economic pressure exerted by some developed countries on developing countries to forego the use of TRIPS flexibilities (UN High Level Panel on Access to Medicines, 2016, p. 25). In fact, just before the outbreak of the pandemic, the EU issued a report and in the middle of the pandemic, the US issued the Special 301 Report condemning a number of developing countries including India, Indonesia, and Turkey for their laws on compulsory licences.25 In the TRIPS Council meetings, developed countries opposing the patent waiver highlight the use of TRIPS flexibilities. In the formal intervention in the TRIPS Council meeting on 10 December, 2020, South Africa asked whether the EU and the US would henceforth stop being critical about use of compulsory licensing by developing countries.26 Developed countries are yet to make any such commitment. In the TRIPS Council meeting on 16 October, 2020, another problem with the use of compulsory licensing was raised. South Africa stated that some countries suffer from limitations with respect to their national laws and lack of practical and institutional capacity required to utilize TRIPS flexibilities during the pandemic.27 But for countries such as India the problem is not of lack of legal or institutional capacity. The problem is with the willingness to put in place an easy-to-use compulsory licensing system. Despite the conditions imposed by TRIPS, it is possible to introduce a simpler compulsory licensing system. But in India the process is excessively legalistic, costly, time consuming, and uncertain.28 The result is that despite having one of the strongest generic sectors in the world, only three applications have been made by generic firms in India and only one has been granted. Compulsory licensing pre-supposes that developing countries have the manufacturing capacity. To make full use of compulsory licensing what is required is not

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only simplification of procedures but also enhancing manufacturing capacities and capabilities. Security exception: Under Article 73 (Security Exceptions) of TRIPS, any WTO member is permitted to take any action “for the protection of its essential security interests” in an “emergency of international relations”. Carlos Correa has advocated the use of the Security Exception in the COVID-19 pandemic to suspend obligations relating to granting and enforcement of intellectual property rights.29 Though not very common, there is a history of countries using security exceptions to justify non-compliance of rules otherwise applicable. But using this exception has not been smooth and objections from other countries had to be adjudicated by the WTO Dispute Settlement Body. The security exception has never been used in a public health emergency. And the issue is whether the COVID-19 pandemic satisfies Article 73 conditions. Reviewing the legal rules and jurisprudence, Abbott (2020) has concluded that the COVID-19 pandemic does constitute an emergency in international relations and suspension of IPRs may be considered as necessary to protect the essential security interests. The advantage of using the security exception is that no further authorization is required from WTO. Despite the potential and the promise, no country has yet tried to invoke it.

7 Conclusions and Discussion The basic problem is that MNCs and developed countries have been dictating terms and developing countries have not been able to resist this and act without or against their consent. • Compulsory licensing is permitted by TRIPS. One of the reasons why it has remained practically unused is that developing countries have been subjected to undue political and economic pressure to forego the use of TRIPS flexibilities. • TRIPS was amended to permit compulsory licences for exports of products from countries with manufacturing capacities to those who lack it. But developed countries succeeded in putting several onerous conditions which have made it practically unworkable. • Developed countries have opposed and prevented in the last nine months any decision at the TRIPS Council on the patent waiver proposal. In a pandemic like COVID-19, time is critical. In the absence of a consensus, the decision can be taken by voting. Developing countries have the required two-thirds majority for the purpose. But they have not been able to take the proposal to the General Council for approval by voting. The situation has changed with the US deciding to support the waiver for vaccines. But it is not certain how long it will take to arrive at a decision and what form it will take. Developing countries should continue to pursue the proposal of patent waiver. But it is also important for them to take other steps which are possible. Even after a year

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of the pandemic, a large number of people in developing countries have received not even a single dose of vaccines. The pandemic has resulted in more than 3.8 million deaths with more than 179 million confirmed cases.30 Developing countries should pursue both compulsory licensing and security exception permitted under TRIPS. Simultaneously, the pandemic should be used as an opportunity to develop manufacturing capabilities and capabilities. Compulsory licensing: Two important reasons why the potentially powerful TRIPS flexibility of compulsory licensing has not been adequately used are political and economic pressure from developed countries and lack of preparedness on the part of developing countries. Compulsory licensing procedures can be simplified. Interestingly some developed countries—Canada and Germany have carried out amendments to Acts to facilitate grant of compulsory and government use licences in situations of public health emergencies such as COVID-19. Hungary issued a special legal order to take extraordinary measures and grant special public health emergency compulsory licensing.31 In the case of compulsory licensing in a national emergency or other circumstances of extreme urgency, TRIPS permits waiving of certain conditions such as prior negotiation for voluntary licences. The Doha Declaration affirms that WTO members have the right to determine what constitutes a national emergency or other circumstances of extreme urgency and have the freedom to determine the grounds for compulsory licences. There is no reason why developing countries cannot take special and urgent measures in the extraordinary situation arising out of COVID-19 pandemic. It is a question of political will and priority. Significantly enough while India is playing a leading role in the deliberations at WTO for IP waiver, India is not keen on using compulsory licensing at home.32 Security exception: Not as a substitute for compulsory licensing but simultaneously, the developing countries can pursue the security exception. The effect is similar to patent waiver—patent rights can be suspended. This is critical for those countries who lack adequate manufacturing capacities and find it very difficult to use compulsory licensing. For countries such as India which has incorporated the security exception in its patent law (Syed, 2020), it is a question of applying it. Others need to take legal and administrative steps to invoke the security exception. If the political will is there, it should not be difficult to amend the acts or to take other necessary steps in a pandemic. Unlike the patent waiver, using security exception does not require any further approval of TRIPS/General Council. The problem is that developed countries may oppose it and take the matter to the WTO dispute settlement body. But the developing countries will have the opportunity to defend their action. The onus will be on developed countries to argue for the rejection of security exception unlike in the case of patent waiver where the onus is on the developing countries to argue for its approval. Industrial and technology polices: The focus of this paper has been on issues related to patent protection. But developing countries also face manufacturing barriers. Where the necessary capabilities are absent, developing countries will not be able to manufacture the patented products even if patent barriers are eliminated.

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To ensure universal access to COVID-19 medical products, it is important not only to eliminate patent barriers but also to take proactive steps to enhance manufacturing capabilities and capacities. Since biologic products such as vaccines are more complex large molecule products, these are more difficult to manufacture compared to the traditional chemical-based drugs. Hence more focussed intervention is required. There are pockets of manufacturing and innovation competencies and success stories in different developing countries including in Bangladesh, Brazil, China, Cuba, India, Indonesia, South Africa, and Thailand. One major reason why developed countries are able to dictate terms is their control over advanced technology. Through appropriate national industrial and technology policies and collaboration among developing countries, the technology gap with developed countries can be reduced. This will make them less dependent on developed countries and make them better prepared to deal with COVID-19 like situations. Notes 1. 2. 3.

4.

5.

6.

7.

8.

9.

“South Africa’s intervention at the formal TRIPS Council meeting of 23 February 2021” accessed from https://www.keionline.org/35453. https://ourworldindata.org/covid-vaccinations, accessed on 24 June, 2021. “Waiver from certain provisions of the TRIPS agreement for the prevention, containment and treatment of covid-19: Communication from India and South Africa” 2 October, 2020 (IP/C/W/669), Council for Trade-Related Aspects of Intellectual property Rights ((https://docs.wto.org/dol2fe/Pages/SS/directdoc. aspx?filename=q:/IP/C/W669.pdf&Open=True). “Waiver from certain provisions of the trips agreement for the prevention, containment and treatment of covid-19: Revised Decision Text”, 21 May, 2021 ((IP/C/W/669/Rev.1), Council for Trade-Related Aspects of Intellectual property Rights. (https://docs.wto.org/dol2fe/Pages/SS/directdoc.aspx?filename=q:/IP/C/ W669R1.pdf&Open=True). “Strong support for TRIPS waiver amidst opposition by Big Pharma”, TWN Info Service on Health Issues, 12 March, 2021 (https://www.twn.my/title2/hea lth.info/2021/hi210306.htm). The Report of the Independent Panel for Pandemic Preparedness & Response on Covid-19: Make it the Last Pandemic, 2021 (https://theindependentpanel. org/wp-content/uploads/2021/05/COVID-19-Make-it-the-Last-Pandemic_ final.pdf), p. 41. “Developing countries remain upbeat on TRIPS waiver negotiations”, TWN Info Service on WTO and Trade Issues, 21 June 2021 (https://twn.my/ title2/wto.info/2021/ti210612.htm). Thomas Cueni, “The Risk in Suspending Vaccine Patent Rules”, New York Times, 10 December, 2020 (https://www.nytimes.com/2020/12/10/opinion/cor onavirus-vaccine-patents.html). For vaccines manufactured in different countries, see “KEI Notes on Vaccine Manufacturing Capacity” (https://www.keionline.org/covid-19-vaccine-man ufacturing-capacity), accessed 7 April, 2021.

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10.

11.

12.

13.

14. 15. 16. 17.

18. 19. 20. 21. 22.

23. 24.

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“Two-thirds of WTO members issue call for a TRIPS waiver”, TWN Info Service on WTO and Trade Issues, 25 February 2021 (https://www.twn. my/title2/wto.info/2021/ti210220.htm#:~:text=Geneva%2C%2024%20Feb% 20(D.,19%20pandemic%20by%20ram). But since the 1990s, the government has withdrawn from much of what it did earlier (Chaudhuri, 2021). As the Statement of 1 June, 2021 of the All India Peoples’ Science Network endorsed by 214 eminent scientists, academicians and doctors has pointed out, the huge public sector infrastructure to manufacture vaccines has been neglected. But in the situation arising out of Covid-19, it is important for the government to give priority to the revival of the public sector units so that the latter together with the private sector may contribute to the production of vaccines (https://www.indianpsu.com/news/1033/urgentlyexpand-public-private-sector-production-to-meet-vaccine-requirement). See the Letter from the President and the Minister of Health of Costa Rica to WHO Director General (https://www.keionline.org/wp-content/uploads/Pre sident-MoH-Costa-Rica-Dr-Tedros-WHO24March2020.pdf). See “Making the response to Covid-19 a public common good” (https:// www.who.int/initiatives/covid-19-technology-access-pool/solidarity-call-toaction). The Chief Executive of Pfizer reportedly called the concept “nonsense” - see, Rowland, Rauhala and Berger, 2021. See MPP, 2018 Annual Report: Expanding Access to Public Health. The discussion on MPP is based on Chaudhuri (2020), which provides a more detailed analysis on the background and limitations of MPP. See, E R Fletcher, “Patents ‘Not’ The Main Barrier To Equitable Vaccine Rollout – Two Leading Vaccine Scientists & Pharma Execs At WHO Event”, COVID-19 Science, 8 March, 2021 (https://healthpolicy-watch.news/ipnot-the-main-barrier-to-equitable-vaccine-rollout-say-two-leading-pharmaexecs-at-who-press-conference/). See https://www.gavi.org/vaccineswork/covax-explained. For an assessment of COVAX, see Ghosh, 2021; Usher, 2020 and de Menezes, 2021. See in this connection MSF (2020a). See, “South Africa’s Intervention at the formal TRIPS Council meeting of 10 December 2020”, accessed from https://www.keionline.org/34811. Among the other common TRIPS flexibilities are Exemptions from grant of patents in certain cases under Article 27(1) and Exceptions to exclusive rights in certain cases under Article 30. The three most common exceptions are Early working (Bolar exception), Parallel imports and Research and Experimental use. See “Annex and Appendix to the TRIPS Agreement” (https://www.wto.org/ english/docs_e/legal_e/31bis_trips_annex_e.htm). See the TRIPS Flexibilities Data Base maintained by Medicines Law and Policy (http://tripsflexibilities.medicineslawandpolicy.org/), accessed on 3 April, 2021.

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25.

See, “WTO TRIPS Council (October 2020): South Africa issues clarion call urging support for TRIPS waiver proposal”, (https://www.keionline.org/ 34235). Only one compulsory licence has been granted in India. But if India which is a major generic manufacturing country starts giving more compulsory licences, then it can have a major effect globally. The purpose of critical comments by developed countries seems to be to preclude such possibilities. South Africa’s Statement, cited in endnote 21. South Africa’s Statement, cited in endnote 25. For a critique of India’s compulsory licensing system, see Chaudhuri, “Industry Response” in Chaudhuri, Park and Gopakumar, 2010, pp. 29–30; 69–70. See the Open letter dated 4 April, 2020 from Carlos Correa to WHO, WIPO and WTO (https://www.southcentre.int/wp-content/uploads/2020/04/COVID-19Open-Letter-REV.pdf); webinar presentation by Carlos Correa, “The Covid19 Pandemic: Intellectual Property Management for Access to Diagnostics, Medicines and Vaccines”, 30 April, 2020 (https://us5.campaign-archive.com/? u=fa9cf38799136b5660f367ba6&id=c865cddeb9). “WHO Coronavirus (COVID-19) Dashboard” (https://covid19.who.int/) accessed 25 June, 2021. The special legal order has been terminated in Hungary but Canada and Germany have not rolled back the amendments (WTO, 2020, p. 9). NITI Aayog, the policy think tank of the Government of India has stated that: “Compulsory Licensing is not a very attractive option since it is not a ‘formula’ that matters, but active partnership, training of human resources, sourcing of raw materials and highest levels of bio-safety labs which is required. Tech transfer is the key and that remains in the hands of the company that has carried out R&D” (“Myths & Facts on India’s Vaccination Process”, 27 May, 2021—https://pib.gov.in/PressReleasePage.aspx?PRID=1722078). The argument is counter to the stand that India has taken at WTO regarding patent waiver. If technology transfer is the key and if compulsory licensing does not help then how does a patent waiver ensure it? (see Arul George Scaria, “NITI Aayog’s Position on Compulsory Licensing Fails Both NITI and NYAYA”, 29 May, 2021 (https://science.thewire.in/health/niti-aayog-position-compulsorylicensing-covid-19-vaccines-ip-waiver/).

26. 27. 28. 29.

30. 31. 32.

References Abbott, F. (2020). The TRIPS Agreement Article 73 Security Exceptions and the Covid-19 Pandemic. South Centre Research Paper 116, August https://www.southcentre.int/wp-content/ uploads/2020/08/RP-116.pdf. Correa, C. M. (2021). Expanding the Production of COVID-19 Vaccines to Reach Developing Countries: Lift the Barriers to Fight the Pandemic in the Global South. South Centre, Policy Brief No 92, April. https://www.southcentre.int/wp-content/uploads/2021/04/PB-92.pdf.

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Chaudhuri, S. (2020). Making Covid-19 Medical Products Affordable: Voluntary Patent Pool and TRIPS Flexibilities, South Centre, SouthViews, No. 200, 16 June (https://www.southcentre.int/ southviews-no-200-16-june-2020/. Chaudhuri, S., Park, C. and Gopakumar, K. M. (2010). Five Years into the Product Patent Regime: India’s Response, UNDP. http://content.undp.org/go/cms-service/download/public ation/?version=live&id=3089934. Chaudhuri, S. (2021). ““How did the ‘Pharmacy of the World’ become Dependent on China: Indian Pharmaceutical Industry before and after the 1990s,” (mimeo). de Menezes, H. Z. (2021). “The TRIPS Waiver Proposal: An Urgent Measure to Expand Access to the COVID-19 Vaccines”, South Centre Research Paper 129, March. https://www.southcentre. int/wp-content/uploads/2021/03/RP-129.pdf Gebrekidan, S. and Apuzzo, M. (2021). “Rich Countries Signed Away a Chance to Vaccinate the World Despite Warnings”, 21 March, New York Times. https://www.nytimes.com/2021/03/21/ world/vaccine-patents-us-eu.html Ghosh, J. (2021). “The Political Economy of Covid-19 Vaccines”, The India Forum, 5 March. https://www.theindiaforum.in/article/political-economy-covid-19-vaccines Krishtel, P and Malpani, R. (2021). Suspend Intellectual Property Rights for Covid-19 Vaccines: Waivers are Essential for Global Vaccine Equity. BMJ 2021;373:n1344, http://dx.doi.org/https:// doi.org/10.1136/bmj.n1344 MSF. (2020a). “WTO COVID-19 TRIPS Waiver Proposal: Myths, Realities and an Opportunity for Governments to Protect Access to Lifesaving Medical Tools in a Pandemic”. https://msfaccess.org/sites/default/files/2020-12/COVID_TechBrief_MSF_AC_IP_ TRIPSWaiverMythsRealities_ENG_Dec2020.pdf MSF. (2020b). “India And South Africa Proposal for WTO Waiver from Intellectual Property Protections for COVID-19-Related Medical Technologies”, Briefing Document, 8 October. https://msf access.org/sites/default/files/2020-10/COVID_Brief_ProposalWTOWaiver_ENG_2020.pdf MSF. (2021). Compulsory Licenses, the TRIPS Waiver and Access to Covid-19 Medical Technologies. Briefing Document, May. https://msfaccess.org/sites/default/files/2021-05/COVID_TechBr ief_MSF_AC_IP_CompulsoryLicensesTRIPSWaiver_ENG_21May2021_0.pdf Rowland, C., Rauhala, E., & Berger, M. (2021). Drug Companies Defend Vaccine Monopolies in Face of Global Outcry. The Washington Post, 21 March (https://www.washingtonpost.com/bus iness/2021/03/20/covid-vaccine-global-shortages/ Syed, S. (2020). COVID-19: India Should Invoke Section 157A?” SpicyIP, 27 July. https://spicyip. com/2020/07/covid-19-india-should-invoke-section-157a.html. UN High Level Panel on Access to Medicines. (2016). Report of the United Nations SecretaryGeneral’s High-Level Panel on Access to Medicines: Promoting Innovation and Access to Health Technologies. United Nations Development Programme. Usher, A. N. (2020). South Africa and India Push for COVID-19 Patents Bangladesh, The Lancet, Vol 396, 5 December (https://www.thelancet.com/action/showPdf?pii=S0140-6736%2820%293 2581-2. WTO. (2020). The TRIPS Agreement and Covid-19: Information Note, 15 October. https://www. wto.org/english/tratop_e/covid19_e/trips_report_e.pdf. WHO & WTO. (2002). WTO Agreements & Public health: A Joint Study by the WHO and the WTO Secretariat, Geneva, WTO. https://www.wto.org/english/res_e/booksp_e/who_wto_e.pdf.

Pandemic, Market Structure, and Institutions Anindya S. Chakrabarti and Chirantan Chatterjee

Abstract From a macroeconomic point of view, it is sometimes argued that business cycles have a cleansing effect on an economy, where good times lead to entry of inefficient firms and bad times lead to their exit. Thus, the wave of creative destruction leads to waxing and waning in the degree of market concentration, and resultant market power, social welfare, inequality, and labor market outcomes. In reality, however, the dynamics may be far more complex and influenced by underlying economic and financial institutions. In this article, we explore the industry dynamics (domestic and global) in the context of the COVID-19 pandemic in 2020 which represented both demand and supply shocks. We review the latest research which documents the firms’ reactions both in the intensive margin and extensive margin by exiting the market, stopping production temporarily and in some cases continuing productions at a low rate. We posit that the resulting effect on market structure, global trade, and labor market would be shaped by the presence of strong or weak institutions. We end the discussion by noting underlying trends in technology and institutions globally which can be fundamentally disruptive. Keywords Pandemic · Demand and supply shocks · Cleansing effect · Business cycles · Institutions

A. S. Chakrabarti (B) Economics Area, Indian Institute of Management, Ahmedabad, India e-mail: [email protected] C. Chatterjee Science Policy Research Unit, Business School, University of Sussex, Brighton, UK Hoover Institution, Stanford University, Stanford, USA © The Author(s), under exclusive license to Springer Nature Singapore Pte Ltd. 2022 M. Dutta et al. (eds.), The Impact of COVID-19 on India and the Global Order, https://doi.org/10.1007/978-981-16-8472-2_13

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1 Introduction The COVID-19 pandemic had paralyzed the global economy for the better part of 2020. Global trade collapsed, many industries were shut down, labor markets were disrupted and consequently, there was a significant loss in the global GDP. Such a major shock of the magnitude of the pandemic in the post World War II period is globally unheard of. The episode that comes closest in terms of negative economic impacts was the global financial crisis (2007–2008) which also spilled over to many countries and caused significant disturbances although nowhere close to the disruption due to the current pandemic. There has been a flurry of academic work related to the pandemic on virtually all possible fields of academic interest.1 In particular, economics and public policy has seen a tremendous surge in analysis related to the effects of the pandemic on the global and local economies in conjunction with possible policy-related steps that can counteract the negative effects. However, the complete impact is yet to be measured. Globally economies are trying to recover better in 2021, while paying attention to vulnerable populations and greener principles. But there have been many changes in the global order that might be close to irreversible. In other words, the global economy is showing signs of resilience to the pandemic shock. But many internal economic linkages are broken and probably will take significant amount of time to recover. In this article, we review some of the recent developments from a macroeconomic point of view. The goal is to take stock of the latest understanding of economic loss and evaluate them in context of the underlying evolution of the long run trends in labor markets and technology. From an economic view point, the pandemic presents a unique scenario where it constituted a demand side as well as supply side shock (Brinca et al., 2020; Del Rio-Chanona et al., 2020). This can be contrasted to the usual macroeconomic shocks which originate either solely from the demand or the supply side. The net effect of the economic collapse was sharp, large in magnitude, and economic agents on both sides of the markets were affected. This observation raises an important question regarding the nature of the labor markets and the role of institutions2 (Chong & Gradstein, 2007). In terms of public policy, the question can be cast in terms of what are the heterogeneous outcomes expected in the market and what is the role of the social planner here in shaping the outcomes? We can go one step further and ask, what is the role of the social planner in presence of such a major disruptive shock where the market does not exist or is not well functioning?

1

For example, Nature published an article on “How a torrent of COVID science changed research publishing” in December, 2020 (https://www.nature.com/articles/d41586-020-03564-y) and documented that around 4% of the world’s total research output was related to coronavirus in some ways. 2 Also see: http://pubdocs.worldbank.org/en/596451600877651127/Remaking-the-Post-CovidWorld-DAcemoglu-0923.pdf.

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In this context, it is useful to note that the effects of pandemic did not take place in isolation. Even prior to that, there were a number of prevailing trends in the world economy that was fundamentally disruptive and causing structural change in the global economy. The labor market was unstable, inequality was soaring, technology and artificial intelligence (AI) became a threat to job safety and market concentration was on the rise along with discussions on monopsony power facilitated by superstar firms (Autor et al., 2020). The “great moderation” of business cycles was over by the first decade in the 2000s. The period of low and stable inflation coupled with steady economic growth ended into the beginning of volatile output growth with soaring inequality. Given the evolution of the macroeconomic variables, it is worthwhile asking what happened to their microeconomic counterparts. Below, we review the global and local impacts of the pandemic with the background of long run economic trends in technology and labor markets.

2 Global Supply Chain: Resilience and Robustness We start by discussing what happened to markets, global and local. While the exact estimates are still not determined, the projected loss of gross domestic production for major economies is around 2–4%.3 However, the global production technology involves a complex web of economic linkages through trades in goods and services across countries. A direct implication of the pandemic is that the global supply chain is fractured. The reason is fairly intuitive. During times of great uncertainty in global coordination and policies, firms would be more inward-looking and would want lesser reliance on the global partners for input supply. Trade experts predict that this will lead to a rethinking in firms in terms of global exposure and looking for suppliers inside their own countries.4 In a sense, this leads to a reversal (or at least, tapering) of the trend of globalization. Bonadio et al. (2020) calls it renationalization of supply chains and Antras (2020) calls it as de-globalization. Here it is useful to introduce two terms, viz., resilience and robustness of supply chains. From a risk management point of view (see Miroudot, 2020), resilience can be defined as the propensity of a supply chain to return to normalcy after a period of time, after a certain shock. Robustness on the other hand can be defined as the ability to continue normal operations even in the face of disruptions. We propose another idea in the form of supply chain flexibility. The idea is as follows. When an external shock like COVID-19 disrupts economic activities, then the firms have to necessarily look for suppliers within their own countries in order to maintain the production level. Therefore, a resilient global supply chain almost necessarily requires adaptive input– output linkages across firms so that in presence of a shock the supply chain as a whole shows flexibility and reorients itself till the global economy recovers soon, however 3 4

https://www.statista.com/topics/6139/covid-19-impact-on-the-global-economy/. https://www.nytimes.com/2020/04/16/upshot/world-economy-restructuring-coronavirus.html.

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at the cost of a fragmented and “renationalized” supply chain. Therefore, the idea of robustness is not very suitable to understand the effects of a large scale disruption, what may be worthwhile to examine are the losses in firm productivity compared to a previous counterfactual of global supply chains in given the reorientation. The prior economic literature has actually noted this before. Boehm et al. (2019) had documented the effects of the 2011 earthquake in T¯ohoku in Japan on the international transmission of shocks via trade and multinational firms. They show that for Japanese affiliates in the US, imports and output had an almost one-to-one relationship indicating a very high degree of complementarity between domestic and imported inputs. An interesting point to note here is that in case of the T¯ohoku earthquake, the shock hit one side of the trading partners. For COVID-19 all trading partners were affected. More importantly, since the shock in case of the T¯ohoku earthquake was localized (only within Japan), the supply chain connections were not affected. Therefore, the impact of shock could be transmitted across the supply chain. This is a form of supply chain rigidity. However, for the COVID-19 pandemic, the global chain was fragmented a firms found local suppliers. Thus, the global supply chain showed flexibility allowing itself to recover sooner than in the counterfactual case where firms could not reorient themselves from the global markets to the local markets.5 We look deeper into the supply chain resilience and try to disentangle what are the factors that cause resilience (or the lack of it). Fundamentally, the idea is related to the dynamical response of the production network to an exogenous shock and its spillover across time. Carvalho et al. (2016) considered the same T¯ohoku earthquake (they referred to it as the Great East Japan Earthquake as is done in many other academic work as well) to capture the propagation and amplification of the shock on total output through the channel of input–output linkages. They show that due to the earthquake and its aftermath, Japan’s real GDP growth suffered by 0.47% in the next year. Therefore, the dynamic responses can be substantial. In case of COVID-19, the effect is compounded by the fact that along with a supply shock, it also constitutes a demand side shock. This creates a new channel for persistent negative effects. Because of a low aggregate demand, a classic Keynesian mechanism can be at play and lead to lower output which again leads to lower demand in equilibrium. While there are signs of recovery in the demand side, the effect is quite heterogeneous across sectors. From a macroeconomic point of view, all of this absent social planner provided interventions/stimulus could impact inflation adversely. Barrot et al. (2020) analyzed an interesting complementary channel for persistence in the negative shock. They consider the case of social distancing which reduces the quantity of labor and consequently, has an impact on output. In an input–output model calibrated to sectoral data from France, they estimate that six weeks of social distancing reduces GDP by more than 5%. Further analysis on the demand side 5

While data and analysis on domestic supply chains during COVID-19 are scant, there has some recent attempt to quantify the effects via online grocery stores in India. Mahajan and Tomar (2020) show that the effect was seen via fall in quantity and prices did not respond a lot. However, price stickiness exhibited substantial heterogeneity as the online store prices were stickier than the prices in local, physical commercial stores, at least for agricultural commodities.

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channel as well as employment channel (for example, via social distancing norms) would be interesting. Additionally, we note that as a corollary of the fragmentation in the supply chain, the same period also saw a global trade collapse. According to the report by UNCTAD (United Nations Conference on Trade and Development)6 global trade saw a drop of 19% in the second quarter of 2020 compared to the second quarter in 2019. Even in the next quarter, the global trade growth remained negative. This period can again be contrasted with the so-called Great Trade Collapse in 2008–09 due to the global financial crisis. During this time also, the total world GDP decreased by 1% and world trade decreased by 10% (see Baldwin, 2009). Some recent articles have described the pandemic-driven collapse as The Greater Trade Collapse of 2020.7 A major difference from the 2008–2009’s trade collapse is that the current event is much larger in scope, much more severe and much more synchronized. Due to the lockdowns, the consumers and producers were systematically cut off from the markets (with some exceptions to be discussed below). With the ease in lockdowns, consumer demand was revived at least to decent level, but the supply side did not. In particular, the labor market along with small scale firms suffered major economic losses.

3 Firm-Level Impacts: Inefficient Cleansing? Recessions are often conjectured to have cleansing effects (Barlevy, 2002; Lee & Mukoyama, 2015). A simplistic version of the story is that during booms inefficient firms are created, plants are built and demand for labor goes up reducing unemployment. During times of recessions, inefficient firms exit and plants are closed resulting in a lower demand for labor employments. This view is consistent with the theory of creative destructions, which implies that recessions are not necessarily bad. They allow redistribution of capital to more efficient firms.8 Lee and Mukoyama (2015) show that the actual dynamics is more nuanced than that. They study US manufacturing plants between 1972 and 1997 in terms of the birth rates (entry in the market) and death rates (exit from the market). They show that the birth rate is pro-cyclical whereas the death rate of firms has much lesser cyclicality. This is interesting as it indicates that the selection of firms at the entry margin is more useful indicator of business cycle effects than the selection of firms at the exit margin. The COVID-19 pandemic-induced shock does not quite follow the pattern described above. While it created a major recession globally with impacts on the intensive margin, the effects on the extensive margin can be more detrimental to an 6

https://unctad.org/system/files/official-document/ditcinf2020d4_en.pdf. https://voxeu.org/article/greater-trade-collapse-2020. 8 https://voxeu.org/article/cleansing-effects-recessions-new-evidence#:~:text=Entry%20and%20e xit%20of%20manufacturing%20plants%20over%20the%20business%20cycle&text=It%20is% 20commonly%20believed%20that,are%20steady%20over%20the%20cycle. 7

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economy. We note that during usual recessions, the selection of firms at the exit can be biased toward inefficient firms, which makes the mechanism of creative destruction economically useful. But during the pandemic, many firms would also have to exit due to demand shocks as well (over and above the supply side factors). Therefore, the selection of the firms at the exit margin may not be welfare improving or economically efficient. Part of the identification problem arises from differentiating permanent exit and temporary closure of business in such a short time scale. However, there are some recent attempts at quantifying the extensive margin. Miyakawa et al. (2021) estimate the exit rate to increase by around 20% in Japan (compared to the previous year). However, the official data release clearly lags by a large amount of time with respect to real time events. Thus the true picture of the firm-level dynamics cannot be fully pinned down immediately. Crane et al. (2020) utilized a number of alternative sources of data including payroll events and phone-tracking. They show that around 7.5% of firms exit annually in the US and the firm-exits show countercyclicality; and both of these properties are driven by small firms. They found tentative evidence that during the pandemic the exit rate was higher in the US. Gourinchas et al. (2020) estimated business failures among small and medium size enterprises in seventeen countries and they document an increase in failure rate of around 9%. The heterogeneous effects on the firms can be seen in the background of an underlying trend is concentration in the global markets for large firms (De Loecker et al., 2020).

4 Disruption in the Labor Market Disruption in the labor market has also been quite substantial. Coibion et al. (2020) present a preliminary estimate of the job losses in the US. By using Nielsen Homescan panel data, they estimated approximately 20 million lost jobs by the beginning of April, 2020. However, many of the people who lost jobs were not actively looking for new jobs. This peculiarity implies that the net increase in unemployment rate has a small magnitude (barely around 2%). They also documented a large fall of around 7% in labor force participation rate. Labor market data for other countries are not easily available. However, Kikuchi et al. (2020) show that in the Japanese labor market, female, contingent, and low skilled workers were hurt the most. Alfaro et al. (2021) estimate the employment at risk to be at least around 20% in Colombia which seems to be representative of other Latin American countries as well. Hensvik et al. (2020) show that new vacancy postings drop by 40% in Sweden immediately after the COVID-19 outbreak. At the same time, job seekers also reduced the search intensity by 15%. This is consistent with the findings of Coibion et al. (2020) that COVID-19 affected both sides of the labor market negatively. The negative effect also extends to the labor market for immigrant workers. Generally, immigrants are more likely to be employed (partly attributable to their more active participation in job search) than natives. Borjas and Cassidy (2020) show that immigrant men, especially the undocumented ones, were hit particularly hard by the pandemic. The

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rate of employment of immigrant men fell in April 2020 below the rate of employment of native men in the US and undocumented immigrant men lost jobs at a much higher rate than documented immigrant men.9 However, the problems in the labor market runs deeper. Global labor market was already suffering from multiple types of disruptions. Inequality does not show any sign of dampening and cross-country inequality trends have already been affected by deeper technological shifts (Acemoglu, 2003). Christopoulos and Mcadam (2017) show that the time series measures of inequality across countries remain extremely persistent even though redistributive measures have attempted to curb the inequality. Autor (2010) documents job polarization and relates it to skill premium. Employment growth has polarized the job market into high-skill, high-wage jobs and low-skill, low-wage jobs, contributing to the emergence of a widening gap. Another major disruption in the labor market is happening with the advent of artificial intelligence although the effects can be mixed. Just like computer, theoretically AI-driven technology can substitute labor and hence increase unemployment. But it can also create new jobs thereby reducing unemployment. Thus a priori it is unclear which way the employment rate would react to AI-driven technology. Acemoglu et al. (2020) categorize establishments (using online vacancy data) according to how much “AIexposure” they have. They document a substitution effect in the US where AI-exposed establishments are expanding hiring in AI-related jobs at the expense of non-AIrelated jobs. The aggregate effect on employment and wages is not discernible, at least as of now. However, since skills are not easily transferable between AI-related and non-AI-related jobs, even though the aggregate effect is small, a bias for particular skillset would emerge due to substitution of jobs.10 Given this major disruption in the labor markets, it was only a matter of time before policymakers had to take actions. For the developing countries, the debate took the form of lives and livelihood, which soon gave way to a debate on lives versus lives as in many cases loss of jobs was so severe that it would leave a deep and long-term impact on health, education, and the societal structures. Thus the question of future economic growth and who will participate in it, became central. Both the policymaking world and academia have started looking into the role of institutions as a corrective measure to lean against the wind, or at least, to correct the market failures to ensure participation of a larger group of people in the economic activities. This period also has seen a remarkable resurgence of the idea of the iron law of oligarchy proposed by Robert Michels that democracy has a tendency to lead to the growth of elite oligarchs. Given the current level of wealth concentration, labor market polarization, global trade collapse, and rise of superstar firms, one can conjecture that large scale institutional changes are imminent.

9

This is also consistent with the general decline in labor market prospects for less-educated men (Binder and Bound, 2019). 10 See a discussion on work-from-home: https://news.stanford.edu/2020/03/30/productivity-pit falls-working-home-age-covid-19/.

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5 Institutions and Pandemic Institutions shape economic growth (Acemoglu et al., 2005). In the aftermath of the pandemic, generally there was a loss of direction in terms what should be the institutional responses to the pandemic. Many short run measures were taken and many long run deep institutional changes also have been taking place. Broadly speaking, there are two view-points emerging around the roles and responsibilities of domestic and global institutions. One, in the form of polycentric institutions and two, in the form of the Great Man view of the world economic history.11 However, there seems to be little consensus on the identity and nature thereof. Partly, this can be attributed to a general loss of trust on the public institutions12 which was already under way even before the pandemic.13 The problem has compounded given the unprecedented nature of the pandemic.14 Short run responses of the governments have been often inadequate across the world with very heterogeneous impact on the economies.15 Fiscal policies and monetary policies were quickly deployed. However, the recovery seems to be slow. The theoretical understanding of the macroeconomic effects of pandemic is also not well informed although there have been some attempts (Faria-e-Castro, 2020, Barrot et al., 2020). However, the responses from the governments across the world went beyond standard fiscal policies and there has been a significant push toward health and societal outcomes including but not limited to the consideration of gender, ethnicity, immigration status, and race. But soon the debate has taken shape in the form of health-first vis-à-vis economy first approach (see, for example, Great Barrington Declaration16 and John Snow Memorandum17 ). A combination of them came in the form of Social and Economic Recovery Plan in 2020 from the United Nations.18 As Pritchett and Summers pointed out long ago, the relationship between health and wealth seems to be more important than ever both in the short and long run (Pritchett and Summers, 1993). At this point, one can only conjecture about the long run influences. While nationalization of the global supply chain was beneficial in the short run to survive through the pandemic, a long run shift toward that can be potentially damaging in terms of

11

This debate is not new. See for example, Jones and Olken (2005). https://www.telegraphindia.com/opinion/loss-of-public-trust-in-governments-across-us-europeand-india/cid/1792678. 13 https://www.pewresearch.org/fact-tank/2019/07/22/key-findings-about-americans-decliningtrust-in-government-and-each-other/. 14 See Stevenson and Wolfers (2011) for a discussion on trust in public institutions and how it relates to business cycles. 15 https://www.imf.org/en/Topics/imf-and-covid19/Policy-Responses-to-COVID-19. 16 https://gbdeclaration.org/. 17 https://www.johnsnowmemo.com/. 18 https://www.undp.org/content/undp/en/home/news-centre/news/2020/UN_sets_out_COVID_ social_and_economic_recovery_plan.html. 12

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gains from trade, both from an economic as well as political point of view.19 A widely recognized feature of the pandemic has been the sectoral heterogeneity in terms of negative impacts. Service-oriented sectors (especially the ones in which the work could be done from home) were not affected much. But workers working in other non-flexible jobs often lost their livelihoods in the extreme, and in many cases, faced temporary loss of pay at the minimum. All in all, the exacerbation of inequality is seen in many dimensions.20 In particular, pandemic has reversed or at least, significantly affected the long run fall in extreme poverty in the poor and developing countries. Finally, we also note that macroeconomic events also do have effects on institutions. Puga and Trefler (2014), for example, documented how traders in Venice brought in modern innovations around circa 1000 creating economic growth. But circa 1297, a major political reorientation in the form of oligarchization led to a societal shift from economic competition and growth to inequality and social stratification with corresponding loss in welfare both economically and socially. The question then becomes what type of new institutions the post pandemic world order will create? What role here will be of global solidarity and multilateralism? How much of that will be inequality reducing and labor market supporting? What role here will be of the UN Sustainable Development Goals and the digitalized world we have seen across sectors, be that in education, medicine, or health care? what is the role of the social planner In addition, what may happen to social cohesion and human rights, also life of people in conflict zones, once vaccines and their versions contest with the virus variants to bring some semblance of socio-economic sanity to the global economy, let’s say by late 2022? What role here will quality of leadership play along with science, innovation, and information in markets in enhancing the role of institutions? The verdict on all these questions are not out but it will guide the relationship between the pandemic and market power globally, calibrating its effects on global welfare, economic development, and inequality.

6 Summary In this article, we have discussed some macroeconomic patterns of the global production process, labor market, and institutions in the context of the COVID-19 pandemic. To summarize, we see (i) renationalization of the global supply chain with a collapse of global trade, (ii) gradual revival of the global economy, (iii) heterogeneous impact of the pandemic on the labor market which also resulted in sharp increase in extreme poverty in the developing world, (iv) a general lack of directions from the institutions. In our view, these economic impacts will probably bring in large scale institutional changes. The loss of trust in public institutions has eroded social capital globally, which may take more time to restore than the recovery from economic damages. The 19

https://www.worldpoliticsreview.com/articles/28699/nationalizing-supply-chains-is-the-wrongresponse-to-covid-19. 20 https://www.ft.com/content/cd075d91-fafa-47c8-a295-85bbd7a36b50.

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rise in economic uncertainty (Altig et al., 2020) along with social instability creates new challenges for public policies. Acknowledgments We would like to thank Sawan Rathi, Dhruv Goel and Ashish Kumar for giving us useful feedback on a preliminary draft of the chapter. We have benefitted from discussions with Shekhar Tomar and Kanika Mahajan.

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Chong, A., & Gradstein, M. (2007). Inequality and institutions. The Review of Economics and Statistics, 89(3), 454–465. Christopoulos, D., & McAdam, P. (2017). On the persistence of cross-country inequality measures. Journal of Money, Credit and Banking, 49(1), 255–266. Coibion, Olivier, Yuriy Gorodnichenko, and Michael Weber. Labor markets during the COVID-19 crisis: A preliminary view. No. w27017. National Bureau of Economic Research, 2020. Crane, Leland D., Ryan A. Decker, Aaron Flaaen, Adrian Hamins-Puertolas, and Christopher Kurz. “Business Exit During the COVID-19 Pandemic: Non-Traditional Measures in Historical Context.“ (2020). Del Rio-Chanona, R. Maria, Penny Mealy, Anton Pichler, Francois Lafond, and J. Doyne Farmer. “Supply and demand shocks in the COVID-19 pandemic: An industry and occupation perspective.“ Oxford Review of Economic Policy 36, no. Supplement_1 (2020): S94-S137. Faria-e-Castro, Miguel. “Fiscal policy during a pandemic.“ Journal of Economic Dynamics and Control (2021): 104088. Gourinchas, Pierre-Olivier, S.ebnem Kalemli-Özcan, Veronika Penciakova, and Nick Sander. Covid19 and SME failures. No. w27877. National Bureau of Economic Research, 2020. Hensvik, Lena, Thomas Le Barbanchon, and Roland Rathelot. “Job search during the COVID-19 crisis.“ Journal of Public Economics 194 (2021): 104349. Jones, B. F., & Olken, B. A. (2005). Do leaders matter? National leadership and growth since World War II. The Quarterly Journal of Economics, 120(3), 835–864. Kikuchi, Shinnosuke, Sagiri Kitao, and Minamo Mikoshiba. “Who suffers from the COVID-19 shocks? Labor market heterogeneity and welfare consequences in Japan.“ Journal of the Japanese and International Economies 59 (2021): 101117. De Loecker, Jan, Jan Eeckhout, and Gabriel Unger. “The rise of market power and the macroeconomic implications.“ The Quarterly Journal of Economics 135, no. 2 (2020): 561–644. Lee, Y., & Mukoyama, T. (2015). Entry and exit of manufacturing plants over the business cycle. European Economic Review, 77, 20–27. Mahajan, K., & Tomar, S. (2021). COVID-19 and Supply Chain Disruption: Evidence from Food Markets in India. American Journal of Agricultural Economics, 103(1), 35–52. Miroudot, S. (2020). Reshaping the policy debate on the implications of COVID-19 for global supply chains. Journal of International Business Policy, 3(4), 430–442. Miyakawa, Daisuke, Koki Oikawa, and Kozo Ueda. “Firm exit during the covid-19 pandemic: Evidence from japan.“ Journal of the Japanese and International Economies 59 (2021): 101118. Pritchett, Lant, and Lawrence H. Summers. Wealthier is healthier. Vol. 1150. World Bank Publications, 1993. Puga, D., & Trefler, D. (2014). International trade and institutional change: Medieval Venice’s response to globalization. The Quarterly Journal of Economics, 129(2), 753–821. Stevenson, B., & Wolfers, J. (2011). Trust in public institutions over the business cycle. American Economic Review, 101(3), 281–287.

Integration Without Coordination: Revisiting Globalization in the Light of the Pandemic Parikshit Ghosh and Vaibhav Ojha

Abstract Free trade has led to extreme specialization, e.g., most of the world’s supply of APIs for medicine come from China, critical software and microchips come from the US, etc. Therefore, a lockdown in one country can have serious effects on other economies by rupturing the global value chain. This would be bad enough; what makes it much worse is that the pandemic response across nations is not coordinated, and so lockdowns happen in a staggered way across nations. Trade liberalization without global governance has not only helped us reap all the benefits of comparative advantage but has also exposed the global economy to much greater risks. We explore this efficiency-risk trade-off and draw some normative conclusions. Keywords Global value chain · COVID-19 · Supply interruption · Price competition JEL Classification F1 · F6 · L1

1 Introduction The last three decades have witnessed an expansion of world trade, especially in intermediate inputs, and the proliferation of global value chains (GVCs). In an increasingly integrated and interdependent world economy, demand and supply shocks arising in one country due to disruptive events like a disease outbreak can lead to a domino effect and interrupt production everywhere else. COVID-19 surges have arisen asynchronously across the world and so have national policy responses like lockdowns and travel restrictions. Since a chain is only as strong as its weakest link, this staggered policy response has produced a sustained crisis of global production. We argue that private firms will tend to privilege cost reduction over supply risk, and will consequently outsource more than what is socially optimal. To address the Research support was provided by the Bill and Melinda Gates Foundation. P. Ghosh (B) · V. Ojha Department of Economics, Delhi School of Economics, Delhi, India e-mail: [email protected] © The Author(s), under exclusive license to Springer Nature Singapore Pte Ltd. 2022 M. Dutta et al. (eds.), The Impact of COVID-19 on India and the Global Order, https://doi.org/10.1007/978-981-16-8472-2_14

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problem in the future, we either need to create an institutional framework for international policy coordination or dial down on the inexorable march toward global economic integration.

2 Rise of Global Value Chains The proliferation of global value chains is one of the most remarkable developments in the arena of world trade and production in the last three decades. It is an increasingly tenuous exercise to label products with tags like “Made in India” or “Made in Germany”. Production of many manufactured goods has become globally fragmented. The final good is assembled in one country using a substantial share of intermediate inputs originating from firms in other countries, which in turn procure their materials from yet other countries of origin. This complex international network of production and supply has come to be known as global value chains (GVCs). In the terminology of Baldwin and Venables (2013), GVCs take one of two pure forms—spiders and snakes. Snakes refer to a pattern of trade where a firm in country A supplies an input to a firm in country B, which uses it to produce another input which it passes on to a firm in country C, and so on. Spiders refer to an arrangement where the final good is assembled in one central place using inputs arriving from several different sources, giving rise to a hub-spoke network. Technologically, snakes relate to tasks that must be done in a particular sequence, while spiders are pertinent to assembly operations that can be done in no particular order. Actual GVCs are often complex and combine elements of both snakes and spiders. To appreciate the increasingly important role of GVCs, let us first look at the expansion of world trade in recent years. Figure 1a plots world merchandise trade as a share of world GDP in the last three decades. From around 30% in the early 90s, there has been a dramatic increase in this measure, reaching 50% by 2008. Since 2011, however, there has been somewhat of a reversal, thanks to an incipient protectionism in some parts of the world and the US–China trade war. Nevertheless, the share of merchandise trade in world GDP stands at a very healthy 45% in 2019. Figure 1b decomposes this time trend by region. The export reliance of economies is especially pronounced in Europe and Central Asia, East Asia and the Pacific, and the Middle East and North Africa (largely due to oil exports in the latter). In these regions, exports account for nearly half of GDP or higher. In the early 90s, the figure was below 40% for all regions. Figure 1 shows the expanded role of trade in the world economy but does not distinguish between intermediate and final goods, the former being central to GVCs. Figure 2a plots foreign value added (FVA) as a share of value added of exports for the world as a whole, while Fig. 2b decomposes it region wise. The data span the last three decades and show an increase from 24% in 1990 to 30% in 2008, but has since declined to around 28%. The increase is particularly pronounced for Europe (30 to 37%) and North America (14 to 20%). Asian economies also experienced significant growth, with FVA going up from 20% in 1990 to 25% in 2011, though there has

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Fig. 1a Share of merchandise trade in world GDP, 1980–2019. Source World Development Indicators

Fig. 1b Share of merchandise trade in GDP by region, 1980–2019. Source World Development Indicators

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Fig. 2a Share of foreign value added in exports for the world economy, 1990–2018. Source UNCTAD-Eora database

Fig. 2b Share of foreign value added in exports by region, 1990–2018. Source UNCTAD-Eora database

been a marginal decline in the last decade. The contribution of imported inputs in the export sector is particularly high in the European Union. Figure 3a, 3b, 3c, further drive home the point by illustrating how intermediate goods constitute a large part of world trade. The time trend of this share is shown for North America, Europe, and the ASEAN countries. The proliferation of GVCs can be attributed to multiple factors—lower trade barriers, reduced shipping costs, improved communication technology, and economies of scale. Trade liberalization picked up pace significantly since the 1980s,

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Fig. 3a Ratio of intermediate goods to final goods in North America. Source UN Comtrade database

Fig. 3b Ratio of intermediate goods to final goods in ASEAN countries. Source UN Comtrade database

through bilateral, regional, and multilateral trade agreements. This is illustrated in Fig. 4a, 4b, which show the time trend in average tariffs for countries from different regions as well as different levels of per capita GDP. There is a steady downward trend, with average tariff rates converging toward zero across geography and income level. While much of the affluent world already had fairly liberal trade regimes in the beginning of the 1990s, the embrace of free trade is particularly noticeable in

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Fig. 3c Ratio of intermediate goods to final goods in the European Union. Source UN Comtrade database

Fig. 4a Weighted mean import tariffs by region, 1992–2017. Source World Development Indicators

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Fig. 4b Weighted mean import tariffs by income, 1993–2017. Source World Development Indicators

Asia and upper middle-income countries. Unsurprisingly, these countries also play a significant role in today’s GVCs. These figures present a clear picture that compared to 30 years ago, we have a much more integrated and interdependent world economy today. Global manufacturing is now characterized by diversification in terms of input sourcing—firms, instead of producing most parts in-house or procuring from withincountry suppliers, reach out across the globe to find the cheapest components. A related but distinct aspect of GVCs is the concentration of supply for each product. Many intermediate goods are supplied by only a handful of countries. One measure used in the literature is the concentration ratio (CR5), the market share of the top five supplying nations. For computer-related products, the top five account for 79% of the export market, while for phones, they contribute as much as 75% (Arriola et al. 2020). Figure 5 (reproduced from Arriola et al. 2020) provides a more comprehensive picture of market concentration on the demand and supply side. The graph depicts the distribution of concentration ratios for exports as well as imports for a range of different products. The distribution for exports is much more right skewed than that for imports, i.e., traded items tend to have numerous buyers but few sellers. The highly concentrated supply side allows GVCs to reap the economic benefits of comparative advantage, scale economies, and learning-by-doing. Using a counterfactual protectionist regime in a computable general equilibrium framework, Arriola et al. (2020) estimate that tariff barriers and import substitution would reduce world GDP by 5.5%. Nevertheless, these efficiency gains derived from free trade and emerging GVCs come at a cost. One undesirable consequence that has received much attention is the rising inequality observed across the world in the last three decades. For low-skilled labor in

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Fig. 5 Distribution of Concentration Ratios of Top Five Countries. Source Arriola et al. (2020)

rich countries, competition with cheap labor in developing nations in an era of capital mobility has, of course, led to wage stagnation. What is more surprising is the gains to capital and skilled labor even in poorer countries where comparative advantage favors unskilled labor. GVCs, especially in manufacturing, tend to employ capital and skill-intensive technologies, driving up the returns to these factors over time at the cost of low-skilled labor. These technologies of production were developed in advanced economies in response to their relative scarcity of unskilled labor, but poorer nations have adopted them in the interest of integration with global production. Using the world input–output database, Timmer et al. (2014) computed the gains/losses of various factors of production in 560 GVCs across the world during the period 1995–2008. Their findings are summarized in Table 1 and support the points made above. Along the same lines, Rodrik (2018) has argued that integration Table 1 Changes in factor shares in 560 GVCs, 1995–2008 Countries

Change in income share (in percentage points) of Capital (%)

High-skilled labor (%)

Medium-skilled labor (%)

Low-skilled labor (%)

High income

2.9

5.0

−3.9

−4.0

Other

3.2

1.7

1.4

−6.3

World

6.5

1.5

−4.2

−3.8

Source Timmer et al. (2014)

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with the arrival of GVCs presented a “double whammy” to poorer countries, first by reducing their comparative advantage in labor-intensive manufacturing, and second by reorienting domestic production toward capital-intensive methods stalling job creation. A second shortcoming of the reliance on GVCs is the loss of risk management in the search of efficiency. A chain, it is said, is only as strong as its weakest link. The fragmentation of production multiplies risk by creating too many independent links, only one of which needs to be broken to halt the entire production process, as in Kremer’s (1993) O-ring production function. A disruption of production in one country may, therefore, rupture several supply chains and have far-reaching ripple effects on other countries and products. In normal times, this systemic risk may remain hidden from sight but a black swan event like COVID-19 has brought the vulnerability to the fore.

3 The COVID-19 Shock COVID-19 is the biggest shock to the world economy since the Great Depression. It has brought some important sectors, such as the hotel and airline industry, to a near standstill, while others have been affected to varying degrees. This is both due to consumer flight and regulatory impositions like bans on travel and gatherings, social distancing rules, and lockdowns. In the quarter after the draconian lockdown of March 25, 2020, India’s GDP fell by nearly 25% year-on-year. While this lies at an extreme end for major economies, many others experienced double digit contraction over the same period. China’s lockdown in the early days of the pandemic seriously disrupted world trade and GVCs, since that country is a major supplier of many intermediate goods such as active pharmaceutical ingredients (APIs) for the drug industry. Although China opened up relatively quickly, the disease spread to Europe and North America, overwhelming healthcare systems and triggering stringent restrictions on mobility and economic activity in some of the major economies of the world. The effect on world trade since the beginning of the outbreak is shown in Fig. 6. It took nearly 1 year, till the end of 2020, for trade to recover to pre-pandemic levels. At the height of the disruption, in May 2020, it had shrunk nearly 17% relative to the baseline. This is in spite of the fact that mercantile shipping was usually exempt from lockdown restrictions. To some extent, the devastating economic repercussion of the outbreak is understandable. The situation often presented policymakers with a trade-off between the economy and public health. Politics in many countries has been caught in the crosscurrent of the conflicting desires to save jobs and businesses on the one hand and lives on the other. The pandemic has produced an acute distributive conflict in which white-collar workers with ample work-from-home opportunities have squared off against blue-collar workers whose jobs require on-site presence. The political

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Fig. 6 World trade index (January 2019 = 100). Source CPB world trade monitor

dogfight between the Republican Party under Donald Trump, who repeatedly downplayed the virus threat, and the much more cautious Democrats, who if anything stoked fear and anxiety, can be seen through this lens. Nevertheless, in our view, the trade-off between lives and livelihoods has been exaggerated. In the longer run, there is a complementarity between attempts to keep the disease down and the economy afloat. Suitably designed tax-transfer policies can achieve this, spreading the pain of contraction more evenly as well as incentivizing appropriate restraint in economic activity while caseloads are high (Ghosh, 2021). Failure to take enough redistributive measures has arguably increased both the economic damage and the disease burden. The world has had to bear an unnecessarily high volume of infections and deaths not only due to the failures of epidemiological and fiscal policy but also the complete absence of any international policy coordination, which is extremely critical in the face of an infectious disease. The progression of the virus across the globe has been extremely asynchronous, with different countries experiencing peaks and troughs at different times. This is illustrated in Fig. 7, which plots the time path of daily new cases in four of the ten largest economies of the world, each chosen from a different continent. Every country in the world has pursued a pandemic policy that is almost exclusively driven by national interest, using some form of a “hammer and dance” (Pueyo, 2020) as a function of the national case count. In light of the temporal pattern observed above, this means that at practically any point in time, some critical parts of the world economy have been under a hammer. Given the strong forward and backward linkages generated by GVCs, economic activity and world trade have experienced continuous headwinds since at least March 2020. It would have been better if the whole world,

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Fig. 7 Time path of new cases for four major economies (7-day moving average). Source Our World in Data

including countries with low case counts, could coordinate on a synchronous, wellimplemented lockdown at some point. Even countries which successfully suppressed a COVID wave (such as New Zealand) had to maintain economically costly border restrictions for a long time while the disease raged elsewhere. As a popular saying goes, nobody is safe until everybody is safe. A similar tragedy of the commons is unfolding due to vaccine nationalism. Rich countries, using financial muscle and policy foresight, have spent billions of dollars to pre-order vaccines. To hedge risk, they have pre-emptively booked millions of doses of multiple vaccines even before clinical trial results and regulatory approvals came about, and are consequently sitting on stockpiles that in some cases exceed what is needed to vaccinate the entire population. Poorer countries, beset with financial struggles and bad governance, have been pushed back far to the end of the queue. This extreme vaccine inequity is actually inimical to the long-term interest of the prosperous world, both from an economic and public health perspective. In a globalized world, it leaves vaccinated nations to huddle in their own corner, risking import of new cases and foregoing a good part of the synergies of international trade and commerce that they themselves took pains to create. Beginning in the second half of the twentieth century, there has been some limited success in forging international cooperation to solve global commons problems. This is reflected in the fields of climate change, trade liberalization, financial stability, and international security, through multilateral institutions like the Paris Accord, WTO, IMF, and the United Nations. There is one major difference when it comes to fighting a pandemic. The other problems are well anticipated and chronic, which has allowed us time to build up an institutional framework for cooperation. A pandemic of this

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scale has not been seen since the 1918 flu outbreak, catching the entire world offguard. The World Health Organization (WHO) is limited to mostly an advisory role, lacking the kind of deliberative, financial, and decisive powers that are the privilege of, say, the United Nations or the IMF. To make a virtue out of necessity, it is imperative to rethink the power and mandate of WHO, and develop international agreements to expand its scope and reform its governance. Instead of leaving healthcare resources to be captured by the highest bidder, there should be a commonly agreed mechanism for the distribution of vaccines and pharmaceuticals in a way that is sensible and equitable, with the apex body having some enforcement powers. There should at least be a platform where policymakers can talk to each other and coordinate their responses to a global crisis. Eventually, national sovereignty will trump full-fledged global governance in health care, but cooperation is not a binary—something is better than nothing.

4 The Economics of Outsourcing Realism requires us to admit that we cannot be too ambitious about international cooperation in healthcare policy. What else can be done? Since GVCs exacerbate the economic effects of health shocks by making local problems global, it is perhaps time to rethink their role. Is our house too infested with snakes and spiders? Will it be sacrilege to start a conversation about some modest tariff barriers or restrictions on capital flows? Are we going to see a new trend toward onshoring, some signs of which can arguably be glimpsed in the data from the last decade? This is obviously a broad question that requires a range of theoretical and empirical tools to be applied. We do not aspire to do a comprehensive analysis in this chapter. Instead, we focus on developing a specific theoretical argument why profit-seeking firms, especially in highly competitive industries, may take recourse to “too much” offshoring if such opportunities present themselves. Of course, to some extent, manufacturers will internalize the risk of supply disruptions hitting their foreign partners because that affects their own bottom lines too. What they will not take into account is the effect on consumers and workers. Firms which are forced to leave a lot of surplus to the latter groups, perhaps due to stiff product or labor market competition, will be especially tempted to offshore since they have little to lose. In a hypothetical world of certainty, a firm’s private benefit from cost reduction coincides with the social benefit since it can pocket the difference. Even, and especially, under conditions of perfect competition, the firm has no reason to pass on the benefits to consumers or workers in the form of lower prices or higher wages. Of course, cost reduction does not typically come free—the increase in operating profits or social surplus has to be weighed against the fixed cost of R&D or supply chain infrastructure that the firm must invest in. Nevertheless, since marginal profits are the same as marginal social surplus, the private decision is also the socially optimum one.

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Private and social optima start diverging the moment a strictly positive probability of a supply chain breach is introduced into the picture. Note that fragmentation of production multiplies risks by introducing independent probabilities of disruption at different sites. When General Motors manufactures all its automotive parts in-house, production may grind to a halt due to a labor strike, mechanical failure, power outage, or disease outbreak in Detroit. However, under the GVC system, political turmoil in Mexico City may hold back the chassis, winter disruptions in Ottawa may delay the arrival of the seats, and a blocked Suez Canal may keep the engine held up on its way from Munich, in addition to any trouble in Detroit. The company and the social planner must both weigh the cost reduction against this incremental risk. But the company risks losing only profits, while the benevolent planner risks losing consumers’ surplus and worker benefits in addition to profits, and will therefore require a bigger cost reduction to make it worthwhile to offshore tasks. The company’s risk tolerance will match that of the planner only in one of two extreme cases. In the first scenario, it enjoys a monopoly in the product market and a monopsony in factor markets (with the capability to practice first-degree price discrimination to boot!), leaving no other meaningful stakeholders to be harmed. In the other scenario, the company has fully insured consumers and workers against any loss from work disruption. None of these conditions are remotely likely to be satisfied in the real world. The last condition (rather, its absence), which may be termed limited liability, is analogous to the circumstances under which borrowers indulge in excessive risk-taking in Stiglitz and Weiss (1981) and are kept in check by credit rationing. The rush to offshore production and tap into GVCs to lower costs may also have an element of a race to the bottom. If a firm’s rivals have already sought out cheapest input sources globally, its own profits are squeezed out except in interludes when those rivals have been hit with delivery delays. These strategic complementarities lend a momentum to the fragmentation of production once it reaches a certain critical mass. The potentially huge impact of globalization on income distribution, especially in the prosperous nations, has been the subject of much discussion and policy debate of late. That is not our focus here. Fairly elementary economic logic suggests that the kind of business decisions which gave rise to GVCs may represent a market failure to some degree. There could be a case for regulation on efficiency grounds.

5 Conclusion The outbreak of COVID-19 has thrown the world economy into a kind of disarray not seen since the Great Depression. Just like the infection, the economic malady is hard to contain within national borders. Disruptions in one country quickly spread to others through global value chains that have emerged over the last two decades. We make two broad points. First, in this highly interconnected world, economic and viral damage could be reduced significantly if we could develop strong institutions

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for health policy coordination between nations, such as synchronizing lockdowns and ensuring vaccine equity. Second, there is some reason to believe that offshoring and fragmentation of production, driven by the profit motive, can be carried too far. Firms internalize the benefits of cost reduction but not the loss to consumers and workers from supply chain disruptions. Policy should correct this.

References Arriola, C., Sophie, G., Seung-Hee, K.„ Przemyslaw, K., Elena, R., Frank van Tongeren (2020). Efficiency and risks in global value chains in the context of COVID-19. OECD Economics Department Working Paper no. 1637. Baldwin, R., & Venables, A. J. (2013). Spiders and Snakes: Offshoring and Agglomeration in the Global Economy. Journal of International Economics, 90(2), 245–254. Ghosh, Parikshit (forthcoming): “Developing Economic Herd Immunity in the Face of A Pandemic.” In Rajib Bhattacharya, Ananya Ghosh Dastidar and Soumyen Sikdar (eds), Corona Pandemic, India and the World: Economic and Social Policy Perspectives, Routledge. Kremer, M. (1993). The O-ring theory of economic development. The Quarterly Journal of Economics, 108(3), 551–575. Pueyo, Thomas (2020): “Coronavirus: The Hammer and Dance.” Medium, March 20. https://tom aspueyo.medium.com/coronavirus-the-hammer-and-the-dance-be9337092b56 Rodrik, Dani (2018): “New Technologies, Global Value Chains, and Developing Economies.” Working Paper No. w25164. National Bureau of Economic Research. Stiglitz, J. E., & Weiss, A. (1981). Credit Rationing in Markets with Imperfect Information. The American Economic Review, 71(3), 393–410. Timmer, M. P., Erumban, A. A., Los, B., Stehrer, R., & De Vries, G. J. (2014). Slicing up Global Value Chains. Journal of Economic Perspectives, 28(2), 99–118.

Towards a Post COVID-19 World

The COVID-19 Pandemic and Happiness Amitava Krishna Dutt

Abstract The COVID-19 pandemic has affected almost all countries and people in the world. As of the end of May 30, 2021, the total number of people reported to be have been infected by the virus is almost 170 million, and the number of deaths from it exceeded 3.53 million worldwide. Estimates show huge losses in GDP, per capita income, and employment and increases in poverty and inequality around the world. However, these numbers do not begin to show the full extent of the effects of the pandemic on. Subjective well-being or happiness has, over the last several decades, become a popular measure of well-being. This paper reviews some evidence on what these measures reveal about the effects of the pandemic. The short answer, it argues, is not much, despite the considerable effort that has been given to collecting and analyzing this data. Despite that, some of the results found by the general literature on what has been called the science of happiness can provide some useful ideas about the likely effects of the pandemic. What is more, seeing this literature as a part of the broader study of happiness can not only make possible a better understanding of the limitations of interpreting this as a science but also, more importantly, the possibilities that events like pandemic may, yet need not open up possibilities for a happier and better world. Keywords COVID-19 · Pandemic · Happiness · Subjective well-being · Ethics · Inequality

1 Introduction The COVID-19 pandemic has affected every corner of the globe and, to a greater or lesser extent, every person in it. The media provides us with the numbers of people who have been affected by the virus, and the number who have died of it. The effects on the future health of those who have been infected and recovered are not well A. K. Dutt (B) Department of Political Science, University of Notre Dame, Notre Dame, USA e-mail: [email protected] FLACSO, Quito, Ecuador © The Author(s), under exclusive license to Springer Nature Singapore Pte Ltd. 2022 M. Dutta et al. (eds.), The Impact of COVID-19 on India and the Global Order, https://doi.org/10.1007/978-981-16-8472-2_15

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understood. While some have disputed the accuracy of these numbers, there is little doubt that the problems created by it are serious. Even those who claim that the pandemic is merely a hoax do not doubt that the world and its people have been dramatically affected by it, or by the responses of governments and people to it. In addition to illness and death, many people have lost their means of livelihood and experienced losses in income, and there is no doubt that people who have not been infected by the virus have also suffered, or even died, because of it. It is also clear to most reasonable people, and probably to the overwhelming majority of people, that these effects would have been even more dire had governments, non-government organizations, and many private people and groups not helped to provide health care the sick and make available food and other basic necessities to people, especially to those needing it the most. As of the time of this writing several vaccines have become available, only a fraction (although an increasing one) of the people on earth have actually been vaccinated, and people continue to become infected and die around the world, though in smaller numbers than before. Moreover, it is unclear whether the vaccines will effectively contain the spread of the pandemic and for how long, especially as mutations create variants of it, and there is also the possibility of newer, and perhaps more deadly, pandemics. It is therefore of great importance to assess the likely effects of this pandemic. The discussion of the effects of the pandemic, which includes the effects of the responses of people and governments to it, for example, due to lockdowns, has usually focused on the number of people infected and the number who have died because of it, and also been measured by losses in income, employment, and unemployment.1 Important as they are, they do not capture the whole range of effects of the event. No simple set of indicators or measures will be able to take into account these effects. The purpose of this paper is to draw some lessons from the rapidly growing literature on happiness studies, or what has been called by some scholars the “science” of happiness. It will do so by reviewing the contributions that this approach makes toward this goal, by questioning some aspects of the approach, and using these criticisms in a constructive way to better understand the effects of the pandemic. The rest of this paper proceeds as follows. Section 2 presents reviews of some information on the numbers of people infected and killed, and the estimates of income, unemployment, income and wealth inequality, and poverty. Section 3 provides a very brief review of the “science” of happiness and of happiness studies more generally. Section 4 provides a brief review of the effects of the pandemic as shown by the happiness numbers, provides some criticisms of the science of happiness, and makes some comments on likely effects and implications of the pandemic, using both the science of happiness and the broader approach to happiness studies, and conclusions are given in Sect. 5.

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2 Some Facts and Figures on the Effects of COVID-19 The cumulative total of COVID-19 cases (most of them recovered) and deaths reported by the World Health Organization as of May 30, 2021 in the world is almost 170 million and over 3.5 million. For the US, which leads the world in both these numbers, they are close to 33 million and over 588 thousand, while in second place in cases is India, with close to 28 million and almost 326 thousand, and in third place, in terms of numbers infected, is Brazil, with almost 16.4 million, while in number of deaths it is in second place, with over 459 thousand. For China, where the virus originated, 110,766 are infected and 4,945 dead. These country totals, of course, do not take the population of the countries into account. For the total number infected per 100,000, in world as a whole it is 2173; in the US, 9944; in India, 2021; and in Brazil, 7712, while for total deaths the corresponding numbers are 45, 178, 24, and 216. For many countries and the world as a whole, these numbers are almost certainly underestimated, since testing has been limited in many countries, and all COVID-related deaths have not been reported. The last is shown by comparing the recorded figures on deaths to the “excess deaths” calculated over the previous year’s death rates, although these may not provide good estimates. While in some countries, such as the United States, the United Kingdom, the cases and deaths have tapered off, in some others, in which the cases and deaths had tapered off earlier, there has been a resurgence, especially in India and Brazil, whether the recent cases and deaths have been higher than in their first wave, although for now their worst seems to be over. While more and more people are getting vaccinated, there seems to be stubborn opposition from many for whom vaccinations are available, and lower income countries, given their inadequacy of healthcare facilities and the global political economy of power imbalances, are far behind. The emergence of new variants of the virus, the speed at which people around the world can be fully vaccinated, the uncertainty about how long the immunity provided by vaccines will last so that boosters will be necessary, and the efficacy of different vaccines are also causes of concern. When the virus spread to countries around the world, most of their governments responded by resorting various kinds of non-pharmaceutical interventions, such as curfews, stay-at-home orders, quarantines, restricting the movement of people into or out of a specified city or region, closing down shops and production facilities (with the exception of those that are deemed essential, such as food, drugstores, and medical facilities), schools and universities, and transportation services (such as planes and trains), collectively called lockdowns in common parlance. Starting with the cordoning of areas in Hubei Province in China in January, where the pandemic originated, by April 2020 it is estimated that about half the world’s population was under some form of lockdown. However, different areas varied in the intensity of the measures, their timing, and whether or not they were mandatory. The US was slow to implement lockdown measures and arguably removed them prematurely, thereby allowing the infection to spread more than it would have, Sweden’s approach was to minimize restrictions which led to a larger number of cases than its neighbors which imposed lockdowns, China imposed stringent measures and quickly contained the

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virus, and India went into a very strict lockdown which kept the spread of the virus to a low level, although the later relaxation of restrictions and other factors led to a second wave. It is generally accepted that restrictions were effective in containing the spread of the disease. However, lockdowns also led to a fall in the level of economic activity. Production and employment fell, as did income and consumption, but savings increased in especially higher income countries because of the fall in consumer spending. It is difficult, if not impossible, to separate out the effects of the pandemic itself (due to illness, death, and uncertainty) and the lockdown restrictions. Governments have been criticized for not resorting to lockdowns quickly enough in countries such as the US, but also blamed for the excessively stringent and sudden imposition of lockdowns without sufficient consideration of how it would affect employment, especially among the poor, in India, and the subsequent easing which, as mentioned earlier, was followed by a rapid escalation of cases and deaths. The economic effects of the pandemic and consequent restrictions can be seen in terms of the effects on GDP and its growth rate. The simplest way to examine these effects is to compare what happened after the pandemic started and to what occurred earlier. For the year 2020 as a whole, GDP declined in almost all countries. According to estimated annual data collected by the IMF (2021), GDP fell by 3.5% in the world, by 4.9% in high-income countries, and by 2.4%. in low- and middle-income countries (which the IMF calls the emerging and developing countries). Among the larger economies, only China had a positive rate of growth (2.3% compared to 6.0% in 2019). Among the high-income countries, France (−9.0% vs. 1.4%), Italy (−9.2% vs. 0.3%), Spain (−12 vs. 2.0%), and the UK (−10% vs. 1.4%) had the steepest declines. Among low- and middle-income countries, the declines were high in Argentina (−10.4% vs. −2.1%), India (−8.0% vs. 4.2%), Mexico (−8.5 vs. − 0.1%), and the Philippines (−9.6% vs. 6.0%). Among other countries, the US grew at −3.4% compared to 2.2% in the previous year, Japan at -5.1% and 0.3%, and South Africa at −7.5% and 0.2%. These annual figures, however, conceal sharp variation within the year. Table 1 shows quarterly growth rates in four countries. China experienced a sharp decline in the first quarter, when it was hit hard by the disease and adopted drastic lockdowns, after which it experienced steady increase in the growth rate. India suffered a dramatic decline in the second quarter and continued decline in the third quarter, ending up with an overall decline. The major declines in the UK and US were experienced Table 1 Quarterly GDP growth rates in China, India, UK, and USA, 2020 Quarter 1

Quarter 2

Quarter 3

Quarter 4

Annual

−6.8

3.2

4.9

6.5

2.3

3.1

−24.4

−7.2

0.4

−8.0

United Kingdom

−2.9

−19.0

16.1

1.0

−9.9

United States

−5.0

−31.4

33.4

4.1

−3.5

China India

Sources National Bureau of Statistics, China; National Statistical Office, India; Office of National Statistics, UK; Bureau of Economic Analysis, USA

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in the second quarter, although they had already started in the first quarter. Both countries, especially the US, experienced a sharp rebound, making up for the loss, but the overall growth was negative. An alternative way to examine the growth effects of the pandemic is to compare the growth rates forecast for 2020 by the World Bank in January 2020, before the COVID-19 pandemic started, with the estimated actual growth rate for the year as the pandemic progressed. For global growth, the forecasted growth rate was 2.5%, while the actual estimated growth was -4.9%, that is, a loss of 6.8% (see World Bank, 2020 for further details). The reduction in growth rates led to increase in unemployment rates. For instance, in the US, according to the BLS, the unemployment rate increased by 11.2 percentage points, that is, from 3.5% in February to 14.7% in April 2020, which is the highest recorded since 1950, but started falling after that. Despite the extremely low reliability of unemployment figures for low-income countries,2 we may note that the unemployment rate in India increased from 5.27% in 2019 to 7.11% in 2020, the highest recorded since 1991, according to data collected by the ILO. Estimates by the Centre for Monitoring Indian Economy, a private research firm, tell a more detailed story. It increased from 7.22% in January 2020 to 8.75% in March and jumped to 23.52% in April, remained high at 21.73% in May, but fell to 10.18% and to 6.5% in January 2021. With the onset of the second wave, it increased from 6.5% in March 2021 to 7.97% in April, and further to 14.5% in May. These aggregate data on growth and unemployment do not show the income distributional effects of the pandemic and its effects on poverty. There is much evidence to reveal disproportionate effects on low-income groups and low-income ethnic minorities. For instance, the US unemployment rate was 14.2% in April 2020, for Whites, as compared to 16.7% for Blacks or African Americans, 18.9% for Hispanics and Latinos, and 14.5% for Asians. It started falling for Whites and Hispanics for April, but increased for Blacks and Asians for 1 month, after which it has fallen steadily. By October the White unemployment rate was 6.0%, as compared to 10.8% for Blacks, 8.8% for Hispanics, and 7.6% for Asians. Aaronson and Alba (2020) report that the increase in the unemployment was highest for young workers, increasing from February to April by 20.9 percentage points for those aged 16–19 and increased by the largest percentage points for Blacks and Hispanics among those aged 16–29. According to Parolin et al. (2020), the monthly US official poverty rate increased from 15 to 16.7% from February to September 2020, despite government measures such as payments under the Coronavirus Aid, Relief, and Economic Security (CARES) Act which blunted the increase in poverty temporarily, raising it again after the expiration of the payment in July. According to the same study, the increase in the poverty rate has been highest among Black and Hispanic people. The Pew Research Center used World Bank data to find that the number of poor in the world showed a significantly higher number of the poor in the world at the end of 2020, defined as those living on $2 a day in PPP-adjusted 2011 prices, as compared to the level projected for the year before the pandemic started (Kochhar, 2021). The number is 131 million more, with most of it occurring in South Asia, especially India, and Sub-Saharan Africa. South Asia is estimated to have had the

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largest increase in the number of the poor due to the pandemic, that is, 78 million, while Sub-Saharan Africa experienced a 40 million increase. The largest decline in the middle-income group, that is, those living at above $10 to up to $20 a day, was in South Asia, with the number down by 32 million compared to what was projected before the pandemic. There is a great deal of evidence to suggest that income inequality has worsened during the COVID-19 pandemic (Oxfam, 2021). The richest billionaires in the world have added significantly to their income and wealth, while conditions for low-income people in high-income countries and low-income ones have worsened, with increase in poverty rates. Available facts and figures do not capture the magnitude of these effect. One way to gauge the effects of the pandemic and lockdowns is to learn about the experiences of people whose voices are otherwise not heard. An opportunity to do this is provided through Mobile Vani, a network of voice-based community media platforms across India, which record the views of people from India’s rural heartlands and urban industrial worker colonies (Seth and Vishwanathan, 2020). Rumors about how the virus spreads and lockdowns led to a contraction in the demand for goods and shopkeepers, street vendors, and especially daily wage works who live hand-to-mouth using their daily income to buy food were severely affected. Food prices increased due to shortages in supply and hoarding. Numerous people stated that if the coronavirus did not kill them, their families would starve and many would die. A nationwide lockdown, suddenly announced, started from March 25, exacerbated the situation with further closings of shops, factories, and transportation services. The loss of jobs and the consequent failure to pay rents, lack of food and uncertainty about the future, police harassment because of the lockdown, and not least the desire to die at their family home led migrant workers who lived hundreds of miles away working in cities for low wages with chronic job insecurity head back to their homes in rural areas by whatever means they could, such as walking, bicycling, or private motor transportation for the lucky, since train and bus services were shut. Photographs of large groups of people heading back to their villages provided graphic testimony to the plight of the people, some—an unknown number—dying from hunger, exhaustion, and even accidents. Conditions in rural areas were sometimes not much better, because of the inability to walk to their fields, hire workers, to buy inputs and to sell food grains, and the quarantining of migrants and fear of them as carriers of disease. Had it not been for food distribution and other assistance—however inadequate it may have been—by the government, non-government organizations, and private citizens, not to speak of the lobbying efforts of slum leaders in some areas (see Auerbach & Tachil, 2021), death due to the pandemic and starvation would certainly be far more widespread than death caused directly by the pandemic. The closure of many health facilities added to the problem, both for people infected and for those with other health problems. Some groups of people among the deprived were affected worse than others. For instance, wage discrimination and greater incidence of job loss of women made their lives more precarious. These numbers and facts, of course, do not tell us all that we would like to know about the effects of COVID-19 and the policy responses to it. For instance,

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people who have lost family and friends to death due to the pandemic, people who are afraid of getting infected or dying, or people who were afraid of losing their jobs and who received cuts in income and are unable to pay for their normal, or even basic, needs are also affected in ways that are not necessarily captured by income and production figures, unemployment rates, and poverty rates as they are measured. Only some inkling of these problems can be gleaned from the more anecdotal evidence mentioned earlier. Moreover, people’s health is affected because of their inability or unwillingness (for fear of being infected) to get medical attention for reasons other than COVID due to lockdowns, adversely affecting not only their current health, but also their long-run health conditions. Furthermore, people are prevented from going outside their homes, leading their normal lives, and meeting their friends and family members who do not live with them. If young people are unable to go to schools and other educational institutions, their education may suffer and they are deprived of a major avenue for socialization. The long-term health consequences of getting infected and then recovering are not known, but could be significantly negative.

3 A Very Brief Guide to the “Science” and Study of Happiness We now turn to how the pandemic and measures to deal with it affected people in terms of measures of happiness, subjective well-being, and life satisfaction (which, despite some differences in wording and results, we will refer to from now on as happiness). Some of the issues discussed at the end of the previous section are, in fact, taken into account by these measures. Mainstream economists—who may be called neoclassical economists—do not conceptualize or measure how well people are doing in terms of production or income, but in terms of what they call utility, that is, how well-off people are in terms of their own reckoning. These empirical happiness measures are often taken as measuring this utility.3 Of course, the standard assumption also made by most mainstream economists is that utility depends positively on consumption and income, or their streams over time, although sometimes “leisure”, that is, time not spent of “working” is taken into account. Mainstream—and even heterodox— economists, therefore, often focus on production per capita and its rate of economic growth when evaluating how an economy is doing, despite all of the well-known problems with such measures (see Stiglitz et al., 2010). The most popular way to measure happiness in the recent literature is by surveying samples of people, asking them how “happy” or “satisfied” they are with their lives “these days”, all things considered, on—say—a five- or ten-point scale, with higher numbers denoting higher levels of happiness according to their own evaluation, for instance, for life satisfaction, the lowest being completely dissatisfied and the highest being completely satisfied. A variant of the point scale, called the Cantril scale, asks respondents to first imagine a ladder with steps 0 through 10, where 0 represents the worst possible life for the respondent, and 10 represents the best possible life, and

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then asks them where they personally stand on this scale. Some other studies ask respondents to choose words to rate their level of happiness, for instance, whether they are “very happy”, “pretty happy”, “not too happy”, or “don’t know”. These kinds of surveys are done once in a while, and usually not more than once a year. However, in 2020, to examine the effects of COVID-19, more studies have been conducted, by various organizations. In the vast literature on what Layard (2005) calls the new science of happiness that has developed over the last several decades (see also Layard, 2020). Layard (2005) provides his summary of the literature by pointing out that although peoples’ selfreported happiness can depend on their genes and their family upbringing, especially as children, the “big seven” factors, or dimensions of their life, that affect them are: family relationships (whether they are married, single, or separated), financial situation, work (job security, being unemployed, the unemployment rate), community (trust in others) and friends (number and frequency of interaction with them), health (evaluated subjectively), personal freedom (measured by their belief about the quality of government), and personal values (for instance, religiosity), holding the other factors constant.4 Here the first five factors are more important in terms of their effect on happiness and are in their order of importance, while the last two have a smaller effect. One major and important, although slightly controversial, finding of the happiness literature is that about the relationship between income and happiness. Although, as Layard mentions, one’s financial situation, measured by family income given per capita income for a country positively affects happiness, increases in income do not have a positive effect on happiness as average income increases over time in highincome countries such as the US and Japan. An important finding of this literature is that the relation between income and self-reported level of income is not very clear, and does not generally support the view that money (that is, income) buys happiness. Some studies suggest that despite rising levels of income over time in some countries, happiness levels do not have an upward trajectory. This was first noted by Easterlin (1974), who reported that despite a significant increase in income in the US over time, there was no upward trend in the level of happiness, but there is a positive relation between income and happiness in cross sections of people for the countries. Across countries, while there is an upward-rising relation between income and happiness, with high-income countries having on average higher levels of happiness, this relationship is flat beyond a certain level of income; at lower levels of income, some countries have low levels of happiness and some higher ones (see also Easterlin, 2001, Layard, 2005, and for a recent review, Dutt & Radcliff, 2019). Although some of the research questions the absence of the income–happiness relation above a certain level of income, most studies have confirmed it. The standard interpretation of these findings (Easterlin, 1974, 2001) is that when everyone’s income rises over time in high-income countries, people do not feel better off because it is relative income and status that make people happier in high-income countries and these do not change much when average income increases. Thus, for these countries, time series data does not show a positive relation between average income and happiness. However, since higher income people have a relatively higher

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income than the average at a point in time within a country, and lower income people have a relatively low level, the former are happier than the latter. It has also been argued that people feel happier when their income increases, but after some time elapses, they adjust to the higher level of income and do not feel happier than they did when their income is lower, and hence the cross-country result. These are issues to which we will return later. A few further interesting results of the happiness literature may also be noted (see Dutt & Radcliff, 2019, for more details and references), although some of them have already been mentioned in Layard’s list. Among economic factors happiness has consistently been found to depend negatively on unemployment, not only for a person who is unemployed, but also the unemployment rate in the country, controlling for the person’s income. Some studies have found that income inequality has a negative effect on happiness, perhaps because those with higher income attach less happiness to additional income than the lower income people but also because the relative deprivation of those with lower income reduces their happiness. But this finding is not true for all countries, including the US, perhaps because of the belief in the American dream and the false belief in high levels of social mobility. The kind of work people do also affects their happiness, more so for intrinsic factors like the degree of autonomy than extrinsic factors like income. People also seem to prefer greater security and thus prefer stronger government involvement in providing social protections. Social connections (of a non-market kind), which includes closer connections with family and friends, as well as generalized trust in others in society, and membership of groups and clubs, including labor unions and religious groups such as churches, promote happiness. Finally, there is much evidence to suggest that people are happier if they have closer connections with the natural environment, and if the natural environment improves (for instance, air quality as measured by the reduced presence of air pollutants) and by temperature and weather patterns (to measure the impact of climate change). Most of these results have been derived from quantitative measures such as those based on happiness surveys and with the use of econometric methods. They have sometimes been supplemented by the use of laboratory experiments and the use of neuroscientific methods. Perhaps for these reasons, some scholars, who study happiness issues, have used the term the “science” of happiness (see Layard, 2005). Another conscious or unconscious motivation may well be to elevate the scholarly status of this field of study, given the high esteem in which the “hard” and/or natural sciences are held, both by many in the general public and by scholars in both the “hard” and the “soft” social sciences and the humanities. Although much of the study of happiness uses quantified survey measures of the types described earlier, it should be recognized that there is no unique concept, let alone, measure of happiness, as there is of body temperature, height, or weight of people. Even psychologists recognize that there are different concepts of happiness. Nettles (2005), for instance, distinguishes between different levels of happiness examined by them: a first referring to (transient) emotions and feelings, a second referring to people’s cognitive judgments and memories about their feelings over a span of time, and a third that refer to whether one fulfills one’s true potential or

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flourishes. The second is what seems to be measured in most surveys, which ask about overall happiness, life satisfaction, or subjective well-being. The first has also been measured by many psychologists, by asking people rate their level of happiness or affect at relatively frequent intervals, say every 15 minutes, perhaps by pushing buttons in a device, or on a daily basis, where they are asked to remember what they felt on the previous day, but is less frequently measured because they need to be measured at frequent. The first two, however, can both be called scientific, not only because they can be measured, but also possibly on the ground that apparently do not invoke value judgments external to the person whose happiness is measured. One of the most respected psychologists working on happiness, Daniel Kahneman (1999), has called them objective and subjective, respectively, since the former relies less on judgments and memories, and involves real-time measures of feelings, which can be aggregated by any analyst to get the same result, while the latter involves cognitive subjective judgments about feelings and their importance. Initially, Kahneman (1999) took the objective measure as the “true” measure and examined biases in the subjective measure due to the failure to remember some things and attach too much importance to others. But in later work he (Kahneman, 2011) acknowledges that level-one happiness, which he prefers to call instantaneous rather than objective happiness, is not necessarily better than level-two happiness because the difference between them is not caused by forgetting alone, but also by what people judge to be more important in the sense that they really matter overall happiness. The third-level happiness comes in different versions, even in the psychology literature on happiness, including Csikszentmihalyi’s idea of flow, the state one experiences when fully engrossed in an activity with a resulting loss of one’s sense of place and time; Seligman’s notion of authentic happiness that involves positive feelings as well as positive activities that have no feeling component (which may be limited by a person’s temperament) but make life worth living, involving wisdom, justice, and authenticity; Ryff’s emphasis on personal growth, purpose in life, and self-directedness; and Maslow’s idea of self-actualization. Nettles concedes that level-three happiness refers to things of value, but argues that including them in the definition of happiness makes that definition incoherent, and involves moralizing and the use of normative judgments rather than only involving people’s own positive feelings or positive judgments about feelings, or what can be called “positive” psychology. This is to emphasize not only that it focuses on positive things rather than psychological illnesses or negatives that had been the earlier focus of psychology, but also because it is a value-free positive science that does not involve normative or ethical values, that is, judgments about what is right and wrong, or good and bad. Level-three happiness is much closer to earlier religious and philosophical ideas (see Dutt & Radcliff, 2019). For instance, according to the Upanishads, true happiness—sreyas—comes from self-realization, the true recognition that the atman and brahman, that is, the individual and universal souls, are one and the same, rather than from preyas—pleasures derived from the fulfilment of earthly desires—which is fleeting. In Greek philosophy—especially Aristotle’s—idea of the good life, or flourishing, the Greek word for which is eudaimonia is seen to be different from what is obtained from wealth, fame and power. In later Christianity and Western philosophy

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(on which, see McMahon, 2006), there exist many different notions of happiness, some of which involve ethical values, only one of which, Jeremy Bentham’s utilitarianism sometimes comes close to the first two levels of happiness by referring to pleasure and pain and using their difference to represent utility, without consistently distinguishing between the two levels. John Stuart Mill, who carried the mantle of utilitarianism after him, went further away, by explicitly recognizing higher and lower pleasures. While this is not the place to enter into problems with the fact-value dichotomy, it needs to be pointed out that a long tradition of thought on happiness in both Eastern and Western philosophy and religious traditions cannot just be swept aside, for a variety of reasons. First, it is not clear why the study of happiness should be seen as a “scientific” one in the sense that it is value free and does not involve ethical judgments, which is something that most earlier traditions were concerned about. Second, since happiness, as it is used by those who see it as a science, agree that it is subjective, and as such can be seen as being affected by their moral values, something that can be examined in a “positive” manner. We have already noted that even scientific notions of happiness depend on whether one is thinking of instantaneous happiness or about judgments about happiness over longer periods of time, that is, happiness levels one and two. Kahneman (1999) argues that happiness measures based on surveys attempting to measure level-two happiness do not measure level-one happiness if these happiness levels are aggregated for the period of time for which people are surveyed, because people do not remember exactly what they felt in the past, instant by instant and are, in fact, subject to many biases when they try to remember based on the heuristics they employ, such as large effects from people’s current moods and conditions (because of what is called the availability heuristic). Extending this logic, it is quite possible and, in fact likely, that a yet longer term and lasting notion of happiness as experienced by people themselves, as what is truly important, may well be related to level-three happiness. It may, in fact, be argued that the Hindu idea of moksha or self-realization is not just a normative idea that people should aspire toward, but also a positive one that people will aspire toward if they live their lives experimenting with truth (to borrow the subtitle of Gandhi’s autobiography) in their own way. In other words, people’s own ideas of what happiness means to them can change as they live their lives and get exposed to new situations and reflect on their actions and beliefs. In a sense, this refers to endogenous changes in one’s values about what they understand by happiness. However, this endogeneity needs to be contrasted with a different notion of endogeneity, that of the endogeneity of preferences, which assumes that people have a stable and clear understanding of what they mean by happiness, yet allow preferences to change, as discussed quite widely in the theoretical mainstream literature on endogenous preferences (see Bowles, 1998). This idea had, indeed, been recognized in the empirical literature on happiness studies and in critiques of it. Two related issues can be noted. First, in examining the effects of increase in income on utility or happiness, even if it is true that increase in income increase happiness with all other measured things constant, happiness need

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not increase with income if the increase in income results in an increase in something else which is not measured, but which reduces happiness. Easterlin (2001) has provided a unified framework that states that although an increase in income increases utility, this increase in income increases income aspirations, which in turn reduces happiness, thereby possibly leaving utility unchanged. It has been well documented that people report that the level of income they required for maintaining a minimum standard of living in their community increases with their income (see Layard, 2005: 42–43). Easterlin (2001) points out that aspirations can increase for a variety of reasons, including those that make people want further improvements after they get used to, or adapt to, a higher material lifestyle, or because they see others in their new peer groups who consume more, which makes them want to keep up with the Joneses. This last consideration implies that peoples’ happiness depends not just on their absolute income but also their income relative to that of others or of their own past income. It has been argued that the weight of relative income compared to absolute income increases with increase in absolute income since, after more basic needs are met, people are more likely to consumer more for reasons of emulation and status and stimulation which explains the paradoxical findings on the relation between income and happiness discussed earlier. Second, adaptation may also occur for people at low levels of income. The poor may get used to their material deprivation and have low aspirations and hence may not be subjectively unhappy. Sen (1999) uses this argument against the relying on subjective well-being or happiness to conceptualize or measure how well people are doing, at least for those who are materially deprived. Instead, he advocates the use of functionings (that is, whether people are able to achieve particular good results in terms of things—such as good health, adequate nutrition, and even adequate self-respect and dignity—that are considered through public deliberation within a community and, above all, with good reason, to be valuable) or capabilities (that is, whether people have the opportunity to achieve these functionings). The deprived, however, need not always adapt to their deprivation. Whether or not they will do so is likely to depend on at least three things. First, whether deprivation involves a given level or it is dynamic and leads to unpredictable changes. Second, on level of inequality of income and wealth between them and those who they are exposed to in their lives or even through the media. Third, on the social norms that shape peoples’ attitudes toward inequality. For the deprived, whether they are too “broken” to imagine that they deserving anything better, believing that higher income people are better than them due to their nobility or merit, rather than viewing inequality as unjust and unfair, resulting from exploitation and domination. For the relatively rich, it may involve blaming the poor for making wrong decisions or being lazy: why do they buy cellphones and not get a job? These last views may, but need not necessarily, reflect selfishness and lack of sympathy, but may spring from the failure to recognize that observers are trying to understand people who are in very different positions from themselves. This is not only the case of those who have not examined the issue carefully, but also from scholars who study people who come from a far lower socio-economic class from

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themselves and come to judgments about what they themselves would have done had they been in the position of those they study.

4 The Effects of COVID-19 on Happiness Several studies have provided subjective well-being or happiness data based on surveys following the onset of the COVID-19 pandemic. A widely reported annual event for happiness data and studies is the release of the World Happiness Report, published by the Sustainable Development Solutions Network (SDSN),5 which reports on life satisfaction data using the Cantril ladder and analyzes that data. Two reports have been released since the onset of the pandemic. Since 2020 report used data collected before the pandemic started, it is therefore not relevant for our purposes. But a shorter report was released in late July 2020, by the Imperial College London’s Institute of Global Health Innovation, in partnership with the SDSN and the World Happiness Report. It used global survey data collected at about biweekly intervals over 3 separate weeks between April 27 and June 14, during the pandemic, for 26 countries,6 each with representative samples of the population, approximately 1,000 individuals (ranging from 500 to 2,000) per survey per week. Among other things, the report found that among countries in the West, those with the lowest levels of life satisfaction, including Italy, Spain, the US, and the UK, have the highest death rates from COVID-19 in the group. The report for 2021, published in March, used Gallup World Poll data reported on both overall life satisfaction (in which people were asked to evaluate their current life as a whole) and daily emotions or positive and negative affect,7 surveying about 1,000 people for each of 95 countries, and calculating population-representative weighted national averages for each country.8 The ranking of countries in terms of life evaluation was virtually the same in the 2021 report as those in the earlier reports, with Finland winning the “happiest” country position in both. To evaluate the effects of COVID-19, the report compared the scores for 2020 to the average scores for 2017– 2019.9 For the 95 countries for which the relevant data is available, life evaluation showed a statistically insignificant increase, positive affect was unchanged, and for negative affect, worry and sadness showed a statistically significant increase for the sample of countries, while anger did not change (see Helliwell et al., 2021). The results did not, therefore, generally show the declines in life satisfaction that one would expect from the disruption caused by the pandemic. The co-editors of the report concluded while the pandemic changed emotions, especially negative ones, peoples’ long-term evaluation of their life did not change decisively. Jeffrey Sachs, one of the co-editors, stated that “when people take the long view, they’ve shown a lot of resilience in this past year”.10 Although it may feel good to suggest that people are resilient in the face of adversity, such a conclusion may well be unwarranted. First, as the report shows, the average overall effects (or non-effects) hide some statistically significant regional- and country-level changes. There was an increase in life evaluation in East Asia (including China and Japan), South Asia

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(including India and Bangladesh), and Africa (including Zambia and Nigeria); a decrease in Latin America and the Caribbean (including Brazil and Mexico); and there was no change in the US. There was an increase in positive affect in some Eastern European and South Asian (including India) countries and decrease in Latin America and the Caribbean and East Asia. Negative affect increased in Latin America and the Caribbean (including Brazil and Mexico), East Asia (including China and Thailand), Central and Eastern Europe, and in some Western European countries such as the Netherlands and Sweden, and actually fell in countries like Morocco and India (that is, there was less negative affect). Second, as the report concedes, some of the groups hardest hit by the pandemic, such as people in hospitals and prisons, and people who moved due to job loss are not included in the surveys and the effects on their lives were not accounted for, and some of the comparisons, because of the switch from in-person to phone surveys, such as those in China, where phone surveys typically involved people with higher income people, biasing the results. Third, in turbulent times such as during the pandemic, people’s perception of their overall life satisfaction is likely to be overly affected by their recent experiences, such as how they are personally affected, what news they are exposed to, how the government seems to deal with the problem, and what they think about the future, as compared to other less turbulent times when biases are more likely to cancel out across people. The positive and negative affects apparently intend to capture instantaneous happiness, as discussed earlier, were measured only once, rather than at regular intervals, and therefore also suffer from the turbulent times problem. Also for the world as a whole, according to IPSOS (2020), a market research company, while over the past decade, that is, in 2011 and 2020, the percentage of people who are happy dropped by 14 percentage points, and in 2020, 63% of adults across 27 countries reported that they were happy, it was nearly unchanged from the previous year—when it was 64%—despite the COVID-19 pandemic. However, there were changes compared to the previous year for some countries: it declined by eight points or more in Peru, Chile, Mexico, India, the US, Canada, and Spain, while it increased by more than eight points in China, Russia, Malaysia, and Argentina. This study is based on a survey of 19,516 people conducted between June 24 and August 7, 2020. The surveys were conducted on IPSOS’s Global Advisor online survey platform. While for most high-income countries, the samples were representative of their general adult population, for middle and lower income countries (including Brazil, China, India, Mexico, Russia, and South Africa), the samples are more urban, more educated, and more affluent than the general population. Again, these results are not very illuminating and difficult to interpret. Some other surveys are available for particular regions or countries of the world. For the US, the NORC (National Opinion Research Center) funded by the NSF (National Science Foundation) found that in a poll of 2,279 adults done between May 21 and 29, 2020, the percentage who said they were “very happy” was 14% compared with 31% in 2018, being the lowest percentage recorded since the poll started collecting data, in 1972. The same poll found that in 2020, 50% of the respondents felt isolated either sometimes or very often, compared to 28% on 2018. Furthermore, 45% felt they lacked companionship compared to 27% in 2018. These

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findings seem to make more sense. Some studies are available for the UK and for European countries (see Helliwell et al., 2021), which show statistically significant reductions in happiness levels. The India Happiness Report for 2020 provides some information on how the COVID-19 pandemic affected people (Pillania, 2020) using a sample of close to 17,000 people who were surveyed between March and July 2020. The worst affected by the pandemic in terms of happiness according to the report were Maharashtra, Haryana, and Delhi, while the least affected were Manipur, Andaman and Nicobar Islands, Gujarat, and Uttarakhand. The relation between infections and deaths and these changes is not high, although Maharashtra is at the top of all three lists. Since these effects were found from a direct question asking how the pandemic affected them, the results are not comparable to the other studies, which compare happiness levels before and after the pandemic, since this is the only year in which the report is available, such comparisons are not possible. Asking a direct question may result in people answering questions that reflect what they know, rather than how they feel. Some of the findings, especially those that find some negative effects of the pandemic on happiness can be explained by some of the results of the happiness literature discussed earlier. Since the COVID-19 pandemic and the associated lockdowns and other policy responses resulted in many people losing their jobs and experiencing reductions in (and in many cases complete loss of) income, worsening both their financial and work situations, it can all be expected to reduce happiness. Furthermore, a reduction of interaction with friends and family due to “social distancing” and the death of some family members and friends, and a possible loss of trust in others (with fears of being infected by them or with people having different views on the pandemic and government responses) can also have the same effect. Similarly, health concerns due to actually getting infected, the fear of getting infected, and being unable to get medical attention, can also reduce happiness. These factors, of course, do not affect everyone equally. First, those who live primarily on non-wage income, those who have large amounts of financial assets which they can draw on, or who can work from their homes (for instance, those who can do their work online) will not be as badly affected in terms of income, consumption, or job loss. However, some whose income did not fall, a reduction in consumption because of closed stores that sell consumer goods (although they can still be delivered to their homes), and lockdowns that affect service consumption can also reduce happiness. People who work in firms and other business which face sharp reduction in sales due to the loss of income or decline in consumption levels are more likely to lose their jobs, people who work in service sectors that cannot operate online lose their income, as well as those, especially in low-income countries, who work as casual laborers and are self-employed sellers are likely to be most adversely affected. People who are connected to the Internet can not only work in some jobs, but can also communicate with friends using online channels. People who are well-off may feel happier being with their families and because they do not have to commute to work and can work more autonomously. Some who face sharp reductions in income or lose their jobs, and who have to stay at home can become deeply unhappy and even become violent at home; in some countries, the incidence

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of domestic violence seems to have increased, although not necessarily reported cases, and all of this has obvious adverse effects on how happy people feel. Doctors and other healthcare workers can find their work more stressful and challenging, and therefore feel less happy, controlling for other characteristics. The same can be the case with others designated as essential workers who have to work under stressful and risky conditions. How people feel about their present circumstances is clearly worth examining and speculating on. A problem with many events such as the pandemic, which affect so many things in the world, however, is that these feelings are based on what mainstream economists call “imperfect” information both about what is happening now and their likely effects in the near and distant futures. Although it may never be useful to assume that there is a hypothetical situation in which there is perfect information if only to use as a benchmark to examine the effect of information imperfections, events like the pandemic reveal the utter futility of that way of thinking. Not only do people not know what is happening now (some people seem to believe that COVID19 is a hoax and a plot hatched by governments to control people), but we have little knowledge about how the pandemic will spread in future days or weeks, and what effect it will have in the more distant future. Some of the happiness research that shows global warming can make people happier because of milder temperature in colder places suggests that people are affected more by their current situations than by dangers that may lurk in the future! Rather than focusing only on how peoples’ happiness is affected now, it is also worth examining how happiness in future might change. A major event like the pandemic, which affects the lives of almost every person in the world, can be expected to change the way people think about the world and themselves. This is not only because they face a very different world, but also—for many people, but by no means all—because they get an opportunity to think about themselves and how they relate to the world. In terms of our discussion on happiness, they are not only likely to reevaluate what their happiness (given their existing, even if implicit, understanding of the terms) depends on, but also change their understanding of what happiness means to them. Of course, the difference between the two is a theoretical one, since what it ultimately boils down to is what they feel and think is important and how they behave. There is no necessary relation between the pandemic and its external effects, and how people’s thoughts, feelings, and behavior will change, and it is possible that different people may be affected differently. However, an examination of the possibilities may allow people to make choices that they consider to be better for them. In doing so, it should first be made clear that the following analysis applies only to those people who have the time and possibility of thinking about these matters, and not for those who, because they are generally involved in barely getting by or may do things which take their minds away from their plight, at least temporarily, have little time for such niceties. Scholars in the social sciences are often prone to believing some behavior to be plausible if they conform to their own experiences. However, it is hazardous to try to understand our own behavior and motivations,

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let alone those of others, and it is particularly foolhardy to try to understand those of people who are unable to meet their basic necessities. The applicability of one of the basic postulates of much of neoclassical consumer theory, diminishing marginal utility, which seems so plausible to people who think about the additional happiness they obtain out of an extra dose of comfort from a good or from income, is likely to miss how additional consumption affects a low-income person who is not obtaining comfort but reducing pain, that is, using a salve. For such a person, as Karelis (2010) suggests, a little amount of salve will not have much of an effect, but more and more of it can finally reduce the deprivation significantly, implying increasing marginal utility. This may imply that people may engage in activities and purchases that give large returns and enjoyment (like buying a cellphone or a TV), rather than engaging in activities that require large investments to bear fruit (like getting more education) or splurge sometimes and go hungry at others. What all this implies is that there needs to be a significant increase in the amount of resources, sometimes in the form of goods and services such as food, health care, and education rather than only in the form of money, to help the deprived to overcome their basic deprivations rather than expecting them to make marginal changes toward what scholars would consider to be wise choices. At the risk of over simplification, for those who have the opportunity to reflect on their actions and beliefs, two general possibilities may be distinguished, which can be called selfishness and selflessness. Although these words are used in various senses, and it has even been claimed that selflessness is actually selfishness, in rough terms by selfishness we mean exclusive concerns with one’s own needs, wants and interests, and the lack of concern for others, while by selflessness we mean greater concern for others over oneself. It is fair to say that there are elements of both in all people, but under particular conditions the influence of one of them can become stronger. The pandemic can move a person toward greater selfishness due to feelings of frustration, personal deprivation, and being constrained to do what one wants to. Alternatively, it can push people toward greater selflessness due to feelings of sympathy and compassion for others, the recognition of the suffering of people more disadvantaged than themselves, getting used to being constrained, and the feeling of oneness. A loss in income is less difficult to bear if many people suffer such losses, and even if there is an initial feeling of deprivation, people can adapt to it. It is also possible for people to be pushed in the selfish direction initially and then pushed in the selfless direction later. Most philosophical and religious traditions take the view that selflessness is virtuous while selfishness is not or at best can be morally justified by the claim that it leads to good consequences (as in the common interpretation of Adam Smith’s invisible hand), or in the specific and problematic way Ayn Rand does in terms of genuine selfishness involving rational self-interest for the purposes of living and being happy. There is evidence of people going in both directions, some who protest against their loss of freedom, and sometimes even committing violent acts, when asked to wear masks and are unconcerned by what happens to others, while some who engage in acts of kindness toward others, putting the needs of others before theirs.

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The discussion of these possibilities can be examined in three spheres, a person’s relation to one’s self, to other people, and to nature. It is more convenient to being with the second and third spheres. In terms of the relationship with others, several different but related things may be at work. First, the plight and misery of others, especially those who live in crowded slums and lose their jobs and income can lead to a greater appreciation for the injustices of poverty and inequality. Second, despite the fact that there is much evidence to suggest that the effects of the pandemic have not been the same for all people, there is a sense in which it has affected everyone to some extent, due to the loss of their dear ones, or the reminder of one’s mortality, which can well make people more aware of the problems they share with others. In some parts of the world, as in India, people from relatively high-income groups have fallen sick and died, and the fear of being afflicted by the pandemic that are greater for the more deprived can be shared by them. Third, social distancing can make them realize the importance of what they miss in their “normal” lives, that is, companionship and interaction with others and be selective about whom they meet, thereby improving the average quality of their interactions. Finally, people who can reflect on what happiness means to them, rather than those having to struggle with economic difficulties and the lack of dignity, may come to realize that they know very little about the happiness of the latter, and be induced to do more to support the expansion of social safety nets, large redistributions in income and resources, including public resources targeted to the poor and excluded. In terms of one’s relationship with nature, the pandemic reveals nature to be not something that is to be tamed and mastered, but feared and respected in its own right. The lockdowns and reduction in economic activity and travel led both to appreciating the beauty of nature and the natural environment, with clearer skies, and one’s closeness to far away objects (for instance, the Himalayas from the north Indian city of Jalandhar, Panjab), and in getting used to not buying and traveling around as much as before the pandemic, thereby reducing the strain on the environment. As the happiness literature suggests, this should increase happiness. Finally, regarding the relation of a person to ones’ self, turning attention to others and nature is likely to draw attention away from one’s own self, and happiness is seen more as happiness in general rather than happiness for oneself. This is not the same as the sense in which people are asked how happy they are in happiness surveys. People have been forced to spend less on consumption, sometimes because of losses in income, but also because they could not go out to spend on services or go to stores to buy goods and because uncertainty about the future led people to spend less to save more. Spending on what people consider necessary, such as food, medicine and other health supplies, and basic household goods have fallen less than on services, eating out, and entertainment that require people to leave their homes. Lockdowns and consequent disruptions in the supply of goods and services have also had their effect, although it appears that aggregate demand has been reduced more than aggregate supply. The loss of the pleasures from consumption and selfgratification and the disruption in their routines and habits, and declines in their consumption may have initially affected peoples’ feelings adversely, and some held

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their breath so that they could return to their normal pre-pandemic lives, it is possible that many others may discover that what they gave up are not important realizing that buying less does not lead to significant reductions in happiness. If the latter is the case, they may spend less on consumer goods, reducing their production, and travel less, thereby reducing the environmental damage they cause. Possible reductions in employment and income of lower-income groups due to the fall in aggregate demand could be offset with appropriate government policy made politically feasible by greater concern for others.

5 Conclusion The COVID-19 pandemic has had a profound effect on the world and its people. For many, if not most people, the experience has been unprecedentedly dramatic and devastating. Many people have died, many more have been infected by the deadly disease. Although most of those infected have recovered, some of them have gone through near-death experiences, suffered from serious health conditions, and it is not very clear how the infection will affect their future health. The pandemic and government and individual responses to it have also resulted in major economic, social, and political effects. Measures of the production of goods and services and income, and their growth rates have fallen, and unemployment rates, poverty rates, and income inequality by many measures have increased. Many people have lost their jobs, experienced reductions in income and consumption, lost their homes, and had to move long distances from where they lived. Many more have lost their loved ones, had to deal with their illness, and worried about falling sick and even dying, or losing their jobs and income. These and other effects cannot be captured by the income, growth, unemployment, and poverty data. The recognition of these problems led us to turn to happiness and subjective well-being surveys which have become popular in recent years, and to the so-called science of happiness. We saw that although these studies have not generally produced clear and particularly illuminating results, some showing that the massive changes in the world did not affect happiness around the world, they do suggest that there are some negative effects of the pandemic on happiness as indicated by these measures, although not as clear cut as one would have expected. The effects that are found are generally along the lines consistent with the suggestions of the research on happiness. However, the pandemic, as a major event that has affected almost all the people of the world, has arguably allowed many people to reassess their lives and rethink what happiness means to them, what they think about themselves, how they relate to other people in society, and how they relate to nature. These effects may be impossible to capture by standard ways of measuring happiness and are unlikely to make levels of happiness depend in a law-like way on peoples’ external environments and conditions of life. Although the likely effects on happiness are by no means foreseeable in any sense, a shock like the pandemic makes possible, and can even lead to, a major reassessment of the perspective a significant number of people take about what it

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means to be happy, and this can lead to greater happiness for many people. Of course, the opportunity to reassess their lives may not be available to all, especially those who are deprived through material, social, and political exclusion. However, if a sufficiently large number of people with the opportunity do so, they can change their own behavior and values, and push for government policy changes that can make the lives even of people who do not now have an opportunity to reflect on these issues happier. Notes 1.

2.

3.

4. 5.

6.

7.

8.

There has been very few attempts—to the best of my knowledge—to measure the effect of losses in human life in terms of the monetized statistical values of life based on the likely loss of future incomes. But see Musango et al (2020). It is some consolation that we have been spared more of this, given the serious ethical and analytical problems involved with this approach. Partly because of high levels of unemployment and the low level of government support, the estimates during a lockdown are even less reliable; see Mohanan and Kar (2020). Although this interpretation is not necessarily valid, since there are different meanings of utility used by neoclassical economists and different meanings of happiness are used when making statements about how happy they feel. In his more recent book, Layard (2020) finds no reason to change his basic views on the major determinants and dimensions of happiness. The report uses data from the Gallup World Poll and the Lloyd’s Register Foundation which provided access to the World Risk Poll. They were written by a group of independent scholars acting in their personal capacities and edited by John F. Helliwell, Richard Layard, Jeffrey Sachs, and Jan-Emmanuel De Neve. The countries are: Australia, Brazil, Canada, China, Denmark, Finland, France, Germany, Hong Kong, India, Indonesia, Italy, Malaysia, Mexico, Norway, Philippines, Saudi Arabia, Singapore, Spain, Sweden, Taiwan, Thailand, the UAE, UK, US, and Vietnam. For all countries, the samples are representative of their national populations, except for China, for which it is representative of its online population, and for India, of its urban online population. For positive emotions, respondents were asked whether they smiled or laughed a lot on the previous day and whether they experience enjoyment during a lot of the previous day; if they said yes, they were given a score of 1 and if no, 0. For negative emotions, respondents were asked whether they experienced specific emotions of worry and sadness. For each of these if they said yes, they were given 1 and for no, 0. For some countries for which in-person interviews were conducted in the earlier years, this was not possible for 2020 because of COVID-19, and were replaced by phone interviews. This could have changed the pool of respondents in various ways, some of which could not be adjusted by weighting techniques. Thus, the results are not strictly comparable.

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Almost all the surveys for 2020 were conducted after the pandemic was under way, David Keyton, “Happiness Report: World shows resilience in face of COVID19”, AP News March 19, 2021, https://apnews.com/article/2021world-happiness-report-covid-resilience-79b5b8d1a2367e69df05ae68b58 aa435.

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