The Future of Companies in the Face of a New Reality: Impact and Development in Latin America 981162612X, 9789811626128

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Griselda Dávila-Aragón Salvador Rivas-Aceves   Editors

The Future of Companies in the Face of a New Reality Impact and Development in Latin America

The Future of Companies in the Face of a New Reality

Griselda Dávila-Aragón Salvador Rivas-Aceves

•

Editors

The Future of Companies in the Face of a New Reality Impact and Development in Latin America

123

Editors Griselda Dávila-Aragón School of Economics and Business Administration Panamerican University Mexico City, Mexico

Salvador Rivas-Aceves School of Economics and Business Administration Panamerican University Mexico City, Mexico

ISBN 978-981-16-2612-8 ISBN 978-981-16-2613-5 https://doi.org/10.1007/978-981-16-2613-5

(eBook)

© The Editor(s) (if applicable) and The Author(s), under exclusive license to Springer Nature Singapore Pte Ltd. 2021 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

Foreword

Research cannot be an occupation that disregards reality. According to the methodology of each discipline, scientific inquiry seeks to produce knowledge, gain a deeper comprehension of the world’s complexity, and provide solutions to the profound challenges faced by humankind and our world. The pandemic we endured in 2020, and that will continue through 2021, provides an excellent example. The crisis that we are confronting has challenged and changed us, in terms of our common forms of organization and the ways we relate to nature, and of course, with one another. Academic work cannot turn away from this predicament and should serve as one of the keys to navigating and overcoming these difficult times. Contributions from the field of medicine are an obvious application for this kind of crisis, but there are also high expectations of the roles of engineering, law, political science, humanities, and of course, the economics and business sciences. New means and locations of work and professional functioning, the implementation of, and adaptation to, new technologies in organizations, and macroeconomic perspectives on the effects of the pandemic are just some of the issues that demand an interdisciplinary response, TC \l 2 \n with a significant contribution from specialists in economics, management, and finance. For this reason, we welcome the publication of this book, which addresses many of the different challenges triggered by the pandemic from various perspectives of analyses. To illustrate the valuable contribution of this volume to the ongoing scientific and academic discussion on the subject, I will briefly present its content. The first chapter, “The impact of SARS-CoV-2 on the economic activity of México in 2020,” presents a quantification of the economic effect of COVID-19 on the economic activity of the country. Nuñez and Mata separate the impact of a recessionary trend that has been identified in recent years, demonstrating that COVID-19 served to aggravate the economic situation that has been developing since 2019. The consequences of the crisis in México, they deduce, are negative growth rates in the primary, secondary, and tertiary sectors of the economy; a high degree of future uncertainty, higher rates of poverty in various regions of the country, and massive unemployment in both the formal and informal sectors of the nation. The authors conclude that México must rebuild its social and productive v

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Foreword

foundations with a comprehensive vision and strategy that includes all citizens, as the weight of the crisis is monumental in particular for the informal labor market. The chapter “Survival Likelihood of Micro and Small Businesses Facing a Catastrophe” proposes a measurement methodology to quantify the probability of the survival of micro and small enterprises facing a catastrophic event such as the pandemic, assessing whether the development of a business continuity plan can offer a unique alternative for preparing management teams and preventing companies from bankruptcy. Davila, Rivas, and Ramírez argue that for businesses with high face-to-face customer interaction, a business continuity plan might provide a useful tool in addition to developing the experience and readiness of a crisis management team. The pandemic has modified traditional economic paradigms in which physical interaction with customers was the standard. A management team with crisis experience and a comprehensive preparedness plan presents an element with a positive impact in dealing with immediate catastrophic events. The authors conclude by revealing results that the availability and nature of liquidity sources do not exert a significant impact on the probability of business survival. Chapter 3 explores how COVID-19 accelerated the garment and financial investment industries’ adoption of Environmental, Social, and Corporate Governance (ESG) standards. López, Rojas, and González indicate that expectations have developed that once the COVID-19 crisis is over there will be a grand quantity of capital inflows toward ESG investment in emerging markets, which will drive business transformation. This is particularly true for businesses that have made progress in incorporating ESG factors such as the utilities and financial sector. The benefits are clear: (1) lower risk and long-term stability, (2) positive portfolio returns and indices demonstrating the efficacy of ESG criteria, (3) the positive externalities associated with success in the application of ESG criteria, and (4) the brand reputation and gain of market share facilitated by responding to the demands of a new generation of consumers. The chapter “contagion adverse degree, income inequality, and economic growth” calls attention to the ways in which COVID-19 has fomented more than a health crisis worldwide. Rivas-Aceves notes that the effects of the contagion adverse degree resulting from the pandemic caused an intertemporal marginal substitution rate of households as well as industry production processes to be changed. Furthermore, short and long-run economic growth rates have also been affected by the contagion adverse degree of households, human capital growth rate, and hence on their distribution dynamics. Poor households will allocate less time to leisure and rises in output or decreases in salary will occur due to the contagion adverse degree, which will lead to increased inequality. Such inequality decreases when human capital rises. Physical capital can generate small and positive changes to inequality. Financial capital also has a positive impact on inequality, and inequality also decreases when total multifactorial productivity increases. Finally, macroeconomic equilibrium depends on counterintuitive approaches as a result of the contagion adverse degree.

Foreword

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Chapter 5 identifies the impact of the COVID-19 contingency on economic activity in the microfinance sector of Argentina, Colombia, Ecuador, México, and Peru. Through a fuzzy autoregressive neural network with a pentagonal membership function, and correlational analysis allows the identification of levels of impact of the contingency and inferences on the effects in the microfinance industry. Castro, Medina, and Cabrera show that the agricultural sector will be the most affected by the current crisis, followed by tertiary activities and industry. These effects were observed and analyzed in the five economies, providing empirical evidence of the countercyclical condition of popular savings and credit industries, identifying that this economic sector can expect increases in profitability, and decreases in credit and liquidity risks. The Mexican savings and credit sector was the only exception in these results. The authors conclude that, in the face of public health and economic crises, financial institutions have a critical role in facilitating the positive reconstruction of the economic and social environment of Latin American families. The chapter “Balancing Work, Family, and Personal Life in the Mexican Context” explores the future of work for the “COVID-19 Generation.” The expectations of recent generations have pushed professional culture in the direction of work–life balance, which have been reinforced by the changes brought about by the COVID-19 pandemic. Responding to this shift, and inspired by the challenges of our “new normal,” this chapter presents research results from a survey conducted in México with respondents from generations Y and Z. The survey results provide important insight into how these generations perceive work–life balance, as well as the expectations of young Mexicans between the ages of 18 and 30 regarding family and work life. The chapter “Medical tourism in México. Analysis of the economic and technological model in the COVID-19 pandemic era” analyzes data obtained from various sources to determine the behaviors and preferences related to medical tourism. Dávila and Arrioja seek to identify the main factors in predicting consumption habits and facilitating various options for socially responsible medical tourism through the use of advanced analytical and artificial intelligence tools. Such tools will aid in the identification of the most attractive alternatives to benefit consumers in an adverse environment as the world is facing now due to the global COVID-19 pandemic, which represents a significant challenge for most industries, but also generates new opportunities with significant benefits for those who are equipped to take advantage of them. The chapter “Small Coffee Companies and the Impact of Geographical Indications as Productive Innovation in México in the New Reality” analyzes the Protected Designation of Origin (PDO) as a factor of innovation in the Coffee Pluma geographical region in Oaxaca, México. Velázquez and Pérez consider the PDO as a vital tool for solving the problem of the actual crisis in the supply chain as creating a new context of business and markets post-COVID-19. The authors evaluate the benefits that the coffee sector is capable of obtaining and generating through the development of this sectorial and territorial tool, citing geographical identity as an option that will improve production through the acquisition of

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exclusive rights to produce coffee within the region for the achievement of sustainable development while faced with a new reality. Chapter 9 explores the implications of engaging in corporate social responsibility to inform crisis management and maintain stakeholder satisfaction. López-Fernández discusses a conceptual model to describe how effective and socially responsible crisis management enables firms to transition “from survival mode to survivability.” The author emphasizes that creative approaches to crisis management are needed to face the current challenges and lead organizations and stakeholders to “survivability.” Finally, chapter 10, “Artificial Intelligence & Blockchain: The path to generate value for companies after the COVID-19 pandemic,” explores the adoption of new technologies to avoid bankruptcy. Technological developments of the Fourth Industrial Revolution, such as blockchain and artificial intelligence, have been gradually adopted by all companies that focus on ESG criteria. Newly emerging models will facilitate companies’ competition for market share (using AI algorithms to extract value from data) and improve forecasts and strategies to create value for customers with more personalized products and services. According to the authors, DLT blockchain technology will enhance product traceability and the development of new business models based on trust and decentralization. There is no doubt that this book offers a complete panorama of many of the different challenges faced by organizations at the current moment, as well as potential approaches for responding to the crisis. Unquestionably, this work is encouraging in terms of the possible solutions that academia can provide both for understanding and unraveling the current situation and developing different strategies to overcome it. The effects of the present crisis will resonate for years to come, so the reception and discussion of this volume among scholars will be interesting and fruitful for all of us. Ross Alberto Vice Chancellor for Research Universidad Panamericana, Ciudad de México, México email: [email protected]

Contents

The Impact of SARS-CoV-2 on Economic Activity of Mexico in 2020 . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . José Antonio Núñez Mora and Leovardo Mata Mata Survival Likelihood of Micro and Small Businesses Facing a Catastrophe . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . Griselda Dávila-Aragón, Salvador Rivas-Aceves, and Héctor X. Ramírez-Pérez How Covid-19 Has Accelerated the Garment and Financial Investment Industries’ Adoption of Environmental, Social and Corporate Governance (ESG) Standards . . . . . . . . . . . . . . . . . . . . Pablo López Sarabia, Silvia Rojas Padilla, and Ricardo González Díaz

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17

37

Contagion Adverse Degree, Income Inequality and Economic Growth . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . Salvador Rivas-Aceves

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Forecasting the Effects of the COVID-19 Crisis on Economic Growth and the Microfinance Sector in Latin America: An Approach with Fuzzy Neural Networks . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . Judith J. Castro Pérez, José E. Medina Reyes, and Agustín I. Cabrera Llanos

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Balancing Work, Family, and Personal Life in the Mexican Context: The Future of Work for the “COVID-19 Generation” . . . . . . . . . . . . . . 109 Germán Scalzo, Antonia Terán-Bustamante, and Antonieta Martínez-Velasco Medical Tourism in Mexico. Analysis of the Economic and Technological Model in the COVID-19 Pandemic Era . . . . . . . . . . . . . . 131 Griselda Dávila-Aragón and Edmundo Arrioja-Castrejón

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Contents

Small Coffee Companies and the Impact of Geographical Indications as Productive Innovation in Mexico in the New Reality . . . . 149 Marisol Velázquez Salazar and Pablo Pérez Akaki Corporate Social Responsibility Informing Crisis Management for Stakeholder Satisfaction: From Survival Mode to Survivability in a Pandemic . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 169 Andrée Marie López-Fernández Artificial Intelligence & Blockchain: The Path to Generate Value for Companies After the COVID-19 Pandemic . . . . . . . . . . . . . . . . . . . . . . 185 Michael Shane Reilly Marulanda and Pablo López Sarabia

The Impact of SARS-CoV-2 on Economic Activity of Mexico in 2020 José Antonio Núñez Mora and Leovardo Mata Mata

Abstract In this document, the economic effect of Covid-19 on the economic activity of Mexico in 2020 is quantified, separating the impact of the trend that has been observed in recent years. The procedure used is an ARIMA model with an intervention that incorporates the NIG probability distribution, where it is found that the explained variance of the seasonally adjusted time series of the IGAE due to Covid-19 is 44.20% and due to the administration on duty 18.89%. These two estimates quantify that Covid-19 came to aggravate the recessionary economic situation that existed since 2019. Keywords Covid-19 · SARS-CoV-2 · ARIMA · Recession · NIG JEL Classification I15 · E32

1 Introduction SARS-CoV-2 in the world. At the end of last year, several cases of pneumonia due to a new type of coronavirus, called SARS-CoV-2 by the International Committee on Virus Taxonomy, were presented in Wuhan (China). The disease that causes it has become popular as “Covid-19”. In this regard, this virus belongs to the Coronaviridae family, where there are four genera: alpha, beta, delta, and gamma, of which it is known so far that alpha and beta-type coronaviruses infect humans, causing diseases ranging from the common cold to more serious conditions (Palacios et al. 2020). At the close of July 31, 2020, according to the World Health Organization (WHO), 678, 775 deaths attributable to Covid-19 have been reported worldwide, making this condition a global public health emergency (CSSE 2020). Figure 1 shows the ten J. A. Núñez Mora (B) EGADE Business School, Tecnológico de Monterrey, Ciudad de México, México e-mail: [email protected] L. Mata Mata Universidad Anáhuac México, Ciudad de México, México © The Author(s), under exclusive license to Springer Nature Singapore Pte Ltd. 2021 G. Dávila-Aragón and S. Rivas-Aceves (eds.), The Future of Companies in the Face of a New Reality, https://doi.org/10.1007/978-981-16-2613-5_1

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J. A. Núñez Mora and L. Mata Mata

Fig. 1 Deaths due to Covid-19 in the world Source: own elaboration with data from Statista (2020)

countries most affected as of July 31, 2020, where it can be seen that Mexico occupies the third position. Now, the origin of this virus is not conclusive, but due to its close similarity to bat coronaviruses, it is likely that these are the primary reservoir of the virus, since it is 96% identical at the genome level to a bat coronavirus (Palacios et al. 2020). In terms of transmission, one person can infect approximately two to four people, which means that the infection can spread rapidly and widely among the population.

Fig. 2 Average annual variation of the IGAE 2010–2020 Source: own elaboration with data from INEGI (2020)

The Impact of SARS-CoV-2 on Economic Activity of Mexico in 2020 Table 1 Regional perspectives

Region

3

Projected economic growth in 2020

East Asia and the Pacific

0.50%

Europe and Central Asia

−4.70%

Latin America and the Caribbean

−7.20%

Middle East and North Africa

−4.20%

Southern asia

−2.70%

Sub-Saharan Africa

−2.80%

Source: own elaboration with data from World Bank (2020)

The new coronavirus can infect people of all ages, although older people and those with pre-existing medical conditions (diabetes, heart disease, among others) seem to be more vulnerable to becoming seriously ill with the virus, thus reporting a higher mortality rate than 8% in people over 70 years old. However, from an economic point of view, the coronavirus pandemic and the measures taken to contain it have caused a drastic contraction in the world economy. According to the World Bank, it would be the worst recession since World War II. This year a contraction of 5.2% of world GDP is expected (World Bank 2020). Overall, the economic impact of Covid-19 could affect the world economy at three key points: declining production, disruptions in the supply chain, and distortions in financial markets (Deloitte 2020; CEPAL 2020). In the next section, we will continue with the description of the economic impact of Covid-19, but focusing on México (Table 1). Economic Impact of Covid-19 in Mexico In the second half of 2020, one of the deepest falls in the Mexican economy occurred. The stoppage of a large part of economic activity in the face of the Covid-19 pandemic aggravated the recession that Mexico suffered since 2019. According to INEGI (2020), preliminary data in the second quarter of 2020, the Gross Domestic Product (GDP) of Mexico, presented an annualized fall of 18.9%. The most significant effect occurred in secondary activities, where an annualized contraction of 26.0% was recorded, while tertiary activities fell 15.6% and primary activities, 0.7%. Undoubtedly, the stoppage of activities during April and May in 2020 due to the health contingency primarily influenced the fall in GDP. However, an important variable that has contributed to the country’s current situation is the economic slowdown that had been observed since last year (CIEN 2020). In fact, according to the Global Indicator of Economic Activity data, during May 2020, the Mexican economy presented an annualized variation of negative economic activity of 21.6% (IDIC 2020b). The problem is not only that, because when studying the performance by sector from November 2019 to May 2020, tertiary activities register annual decreases in

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J. A. Núñez Mora and L. Mata Mata

Table 2 Steep falls of the IGAE (May 2020) Heading

Variation (%)

Temporary accommodation and food and beverage preparation services

−72.09%

Building

−35.89%

Manufacturing industries

−35.61%

Wholesale trade

−32.38%

Retail trade

−33.77%

Recreational, cultural, sporting and recreational services

−29.86%

Source: own elaboration with data from INEGI (2020)

five of seven months. Besides, the secondary sector presents fifteen months with negative figures. Only the set of primary activities achieved positive growth rates (CIEN 2020). In this regard, the entry into force of the new trade agreement of Mexico, United States, and Canada (T-MEC) on July 1 constitutes a hope for the current Government, which has pronounced itself for a mild and transitory economic crisis (IDIC 2020a). Table 2 shows the central falls in the elements of the IGAE. It can be seen that the most affected sector is accommodation services (−72.09%), followed by the construction sector (−35.89%). These figures show the Mexican economy’s reality since a decrease in economic activity of this nature had not been experienced since the 1994 recession. The effects on the labor market have also been dramatic since, in the first half of 2020, just over 921,500 workers were discharged from the Mexican Institute of Social Security (IMSS). These data are alarming since it approximates the total number of people who lost their formal employment in Mexico. Specifically, the most affected subsectors are professional services, manufacturing, commerce, and construction (INEGI 2020). Furthermore, considering both the formal and informal sectors of the economy, Mexico’s outlook is worrying. According to figures from the National Council for the Evaluation of Social Development Policy (CONEVAL 2020), the number of people working in small establishments fell by 2.9 million, while people working in micro-business decreased by 6 million first half of 2020. Undoubtedly, this scenario is reflected in poverty levels, since the number of workers whose labor income does not allow them to access a food basket rose to 54.9% in May 2020 (Esquivel 2020). In contrast, exports improved, since they registered a negative annual growth rate during June 2020 (−12.8%), but an increase is observed concerning May 2020. The most substantial advance was seen in the sector automotive, where a monthly rise of more than 500% happened. On the other hand, imports also showed a recovery, since, in the comparative May 2020 against June 2020, an advance of more than 20% was achieved (INEGI 2020). In this regard, the primary economic recovery strategy of the Government lies in the T-MEC. However, in an uncertain international business environment, the proposed policy may not be sufficient. When the annual variation of the IGAE (seasonally adjusted data) is reviewed, a downward trend is observed in recent years and an abrupt drop in 2020. This effect,

The Impact of SARS-CoV-2 on Economic Activity of Mexico in 2020

5

naturally, is related to the Covid-19 pandemic, but also with the previous temporal inertia, hence the objective of this work is to separate the two effects (see Fig. 2). In the next section, the methodology used to analyze the effect of Covid-19 on the economic activity of Mexico in 2020 will be discussed.

2 ARIMA Models with Intervention The analysis focuses on the study of the time series of the Global Indicator of Economic Activity (IGAE) in Mexico to measure the effect of Covid-19 on the development of the economic activity and separate its influence from events previous, particularly the period that comprises the Government in turn, called 4 T. This objective is carried out by an Integrated Autoregressive Moving Average (ARIMA) model will be used with intervention (Ferruz et al. 2011). The general expression of an ARIMA (p, d, q) model is given by: d yt =

p  j=1

φ j d yt− j +

p  j=0

θ j d εt− j +

k 

  β j f Xt j .

(1)

j=1

where d yt expresses that d differences have been applied to the originalseries  yt . This model can include intervention analysis using a transfer function f X t j that incorporates exogenous variables X t j . In this way, it is possible to measure the effects of an event in the behavior of the time series and evaluate its impact (Morettin and Toloi 1989). Formally, an ARIMA model (Tsay 2015) without intervention can be expressed as   φ p (L) 1 − L s (1 − L)d yt = θq (L)εt .

(2)

where L symbolizes the lag operator, the polynomial φ p (L) represents the autoregressive component, θq (L) the moving average component, and the factor (1 − L s )(1 − L)d corresponds to the operators of simple differentiation of order d and seasonal differentiation s. The modeling process is based on determining the appropriate values d and s. Then an identification process is performed to find the optimal values of p and q using simple and partial correlograms. Exogenous intervention variables are added to this specification to estimate Eq. (1), and the best fit model is chosen using the information criteria of Akaike, Schwarz, and Hanna-Quinn (Enders 2014). It is important to comment that the choice of parameters d and s is related to the concept of stationarity of Yt . Besides, a time series Yt is said to be stationary if the population mean is constant over time, the population variance is constant over time, and if the covariance between any two observations is zero, that is, they do not correlate throughout the periods.

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In this regard, the concept of integration is related. A time series is said to be integrated of order d and written I (d) if the time series can be converted into a stationary series through d differences of the form d yt = d−1 yt − d−1 yt−1 .

(3)

where d is an integer greater than or equal to zero (Hamilton 1994), also, it is important to note that in economics and finance, time series are usually integrated of order zero or one. The time series called I (0) present finite and time-independent variance, have limited memory, tend to fluctuate around their mean value, which may or may not include a deterministic trend, and present autocorrelations that decrease rapidly as the number of lags increases. On the other hand, the time series I (1), which shows a variance that depends on time, is not constant and tends to infinity as time progresses. Furthermore, any random shock permanently affects the stochastic process, and the time series fluctuates widely over time. Also, autocorrelations tend to unity, in absolute value, for almost any order of the lag. The usual hypothesis test to verify the stationarity condition of a time series is the augmented Dickey-Fuller test (DFA), whose null hypothesis is that there is a unit root (non-stationarity). Rejection of the null hypothesis under different specifications (Pindyck and Rubinfeld 2001; Hamilton 1994), throws evidence on the nature of the random variable of interest yt . In general, the regression model estimated to perform the DFA test follows the specification yt−1 = μ + γ yt−1 + δ1 yt−1 + . . . + δ p−1 yt− p+1 + εt

(4)

where the null hypothesis is translated concretely in verifying the significance of the γ coefficient (Dickey and Fuller 1979), in other words, H0 : γ = 0. Finally, it should be noted that the reason for using the ARIMA method with intervention is because it allows estimating the future behavior of a variable from the data observed in the past. These models do not need to be derived from a specific economic theory, since they are estimated with lags of the same variable (Kumar et al. 2011). In the simplest form, exogenous variables are dummy variables that indicate some event in a time interval. Similarly, the intervention effect provides a movement in the time series, which is linked to a temporary or permanent structural change. This change can be verified using different hypothesis tests, such as Zivot-Andrews, Chow, and Andrews-Ploberger (Bai and Perron 2003). In this document, the key events are the pandemic due to Covid-19 and the trend observed in the economy from July 2018. The following section will present the estimates and interpret the results found in the Indicator of Global Economic Activity (IGAE) for the period 1993–2020.

The Impact of SARS-CoV-2 on Economic Activity of Mexico in 2020

7

3 Estimates and Results The data used for this work has been obtained from INEGI (2020). The set of information covers the period from January 2010 to May 2020. This time interval covers Mexico’s economic activity in the last ten years, monthly with the Global Indicator of Economic Activity (IGAE), where the year Base is 2013. This period has been chosen to analyze the variation of the IGAE in the period after the international financial crisis of the years 2008–2009 and which allows to completely encompass the last two governments that have directed Mexico’s economic destiny. The IGAE allows knowing and monitoring the monthly evolution of the real sector of the economy. This indicator uses the same conceptual scheme, the methodology, the classification of economic activities, and the sources of information that calculate the Gross Domestic Product (INEGI 2020). In this document, the seasonally adjusted figures (Census X-12) of the IGAE are considered to calculate the annual variation: rt = ln(I G AE t ) − ln(I G AE t−12 ).

(5)

However, there are various hypothesis tests to verify if a time series is stationary, such as the increased Dickey-Fuller test (DFA), Phillips-Perron (PP), and Kwiatkowski— Phillips—Schmidt—Shin (KPSS). Specifically, the DFA and PP tests have as a null hypothesis the existence of unit root in the data set. Hence, the rejection of H0 throws evidence on the stationarity of the random variable of interest. In contrast, the KPSS test has as a null hypothesis the existence of stationarity, so it is sought not to reject H0 to find the same conclusion as with DFA and PP (Brooks 2010). In this document, the DFA, PP, and KPSS tests were performed for the time series of the annual variation of the IGAE. It can be seen in Table 3 that the base hypothesis of DFA and PP is rejected and that the null hypothesis for the KPSS test is not rejected. These tests indicate that the time series rt satisfies the stationarity condition. When carrying out a brief descriptive analysis of the time series rt , the bias is found to be negative, the standard deviation is greater than the average annual variation, and the kurtosis is higher than three, which suggests the absence of normality. Furthermore, it can be seen that kurtosis is high, which accounts for the leptokurtic behavior of the annual variation of the IGAE, which reflects that atypical falls have a higher probability of occurrence than would be observed with the normal distribution (see Table 4). Table 5 presents the ARIMA model (1, 1, 1) with the time series rt , where the Normal Inverse Gaussian distribution (NIG) has been used for the maximum likelihood process that involves the random disturbance εt of the Eq. (1). The normal Table 3 Unit root tests

Variable

DFA

PP

KPSS

IGAE annual variation

−2.38 (0.017)

−5.71 (0.000)

0.16 (0.131)

Source: own elaboration with data from INEGI (2020)

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Table 4 Descriptive statistics

Indicator

Statistic

Mean

0.0152

Median

0.0230

Maximum

0.0729

Minimum

−0.2160

Std. Dev.

0.0339

Skewness

−2.5597

Kurtosis

13.6589

Test

Statistic

Jarque Bera

908.83 (0.000)

Anderson–Darling

7.054 (0.000)

Kolmogorov

0.154 (0.000)

Note: p value is reported in parentheses Source: own elaboration with data from INEGI (2020)

Table 5 ARIMA model with intervention under the NIG distribution Variable

Coefficient

Std. Error

t-Statistic

p-value

COVID

−0.0103

0.0045

−2.2692

0.0249

4T

−0.0015

0.0008

−2.0263

0.0447

AR (1)

0.9562

0.0318

30.0407

0.0000

MA (1)

−0.6082

0.0866

−7.0259

0.0000

R-squared

0.350864

Akaike info criterion

−4.562544

Schwarz criterion

−4.653808

Hannan-Quinn criterion

−4.758994

Source: own elaboration with data from INEGI (2020)

distribution is not used, since the null hypothesis in Jarque–Bera (JB), Anderson– Darling (AD) and Kolmogorov–Smirnov (KS) is rejected for the time series of the annual variation of the IGAE. In this regard, an explanation of the NIG probability distribution is presented in Annex 1. Likewise, Annex 2 shows the parameters estimated by maximum likelihood and the hypothesis tests that support the goodness of fit of the NIG. The coefficients estimated in Table 5 correspond to a model without intercept with an autoregressive term AR (1) and a moving average term MA (1), where two dummy variables COVID and 4 T have been included to capture the effect of the Covid-19 pandemic and the inertia of the current administration in Mexico.  1 if it corresponds to the months after February 2020 C OV I D = 0 another case  1 if it corresponds to the months after June 2018 4T = 0 another case

The Impact of SARS-CoV-2 on Economic Activity of Mexico in 2020

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Fig. 3 Projections of the IGAE seasonally adjusted time series Source: own elaboration with data from INEGI (2020)

The time series of the annual variation rt can be projected to calculate the trajectory of the seasonally adjusted series of the IGAE (yt ) using the model in Table 5, controlling for the COVID and 4 T effects. In this way, it is possible to have a rough estimate of the impact that the current administration and the Covid-19 have had on economic activity. The explained variation of the time series yt in 2020 due to the Covid-19 is 44.20% and due to the administration on duty 18.89%. Figure 3 illustrates the projected IGAE seasonally adjusted time series. These estimations give quantitative evidence that one of the deepest falls in the Mexican economy occurred in 2020. The Covid-19 pandemic aggravated the recession that Mexico had suffered since 2019 (CIEN 2020).

4 Conclusions and Discussion The economic effect of Covid-19 globally is a new phenomenon in this century. This year a contraction of 5.2% of world GDP is expected (World Bank 2020) and the economic impact covers at least a declining production, disruptions in the supply chain, and distortions in financial markets. In Mexico, the observed effects are negative growth rates in the primary, secondary, and tertiary sectors of the economy, a high degree of uncertainty about the future, greater poverty in different regions of the country, and unemployment, both formal and informal sectors of the country.

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In this sense, this document shows the changes in the effect of Covid-19 on the economic activity of Mexico, considering the downward trend that the country has shown since July 2018. For this, in the section three, the description of the ARIMA model with the intervention was briefly presented. Also, the NIG probability distribution was presented to calibrate the Eq. (1) correctly. In this regard, additional elements were described in annexes 1 and 2 that made possible the estimates on the seasonally adjusted time series of the IGAE. Section four presents the results found on the annual variation of the IGAE seasonally adjusted time series. Specifically, it was found that the explained variance due to Covid-19 is 44.20% and due to the administration on duty 18.89%. These two estimates quantify that Covid-19 came to aggravate the recessive economic situation that had been in place since 2019 and statistically confirm that the economic context of Mexico was already in terrible shape before Covid-19. As a result, most of the economic cycle of the components of the IGAE exhibits a downward trend that must be reversed and for which an economic and industrial policy strategy with vision is required. The central point is to propose the strategies and mechanisms of economic policy that will allow facing and reversing the negative tendencies that Mexico’s productive activities suffer. However, there is no plan; it is only expected that the entry into force of the T-MEC will attenuate the fall of the Mexican economy. Although the United States has its problems with a contraction of 32.9% in the second quarter of 2020 (El País 2020). Mexico must rebuild its social and productive fabric with a comprehensive vision and strategy since the weight of the crisis is monumental for a mainly informal labor market. Furthermore, it should be noted that 94% of economic units are microenterprises, and 50% of economic growth; it depends on bigger companies. In other words, business composition is complex. Only an increase in investment will reverse the drop in growth and potential capacity of the IGAE. The mechanism to change it is found in investment and financing aimed at strategic sectors that can drive growth, employment, and productive chains. In this regard, it is appropriate to specify that this scenario is only the beginning since the United States’ recession and elections create uncertainty in decisionmaking. In other words, the proper economic policy measures for Mexico will not be clear. Besides, there is the possibility of a second closure of activities towards the end of the year 2020 due to Covid-19. The limitations of this research are that the separation of the Covid-19 effect, in relation to the trend observed by the administration in turn, was carried out under a univariate model, where other relevant endogenous variables or other exogenous variables are not considered, thus as possible control variables. The presented model is reasonable and is strengthened by the goodness of fit that the NIG distribution achieves since it allows to estimate in the best possible way the coefficients and, therefore, the effects captured by the dummies in the ARIMA specification with intervention. As future research lines, it is expected to expand the model to the multivariate case to analyze the decline in Mexico’s economic activity from a broader perspective. For

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this, it is desired to have more information in the following months and estimate an extended model in exogenous and endogenous variables. Annex 1 Generalized Hyperbolic Distribution This section briefly reviews the concepts that define the generalized hyperbolic distribution, and that allows us to robustly estimate the ARIMA model with the intervention of Eq. (1). The probability density function of a generalized hyperbolic random variable (GH) is defined as:   kλ−1/2 (x − μ)2 + δ 2 , α 2 f X (x; λ, α, β, δ, μ) = √   exp[β(x − μ)]. 2π kλ δ 2 , α 2 − β 2

(A.1)

where is δ ≥ 0 and λ, μ, β ∈ R with β ∈ [−α, α] (Paolella 2007). Intuitively, the parameters can be interpreted as follows: a) b) c)

d) e)

α is a parameter that determines the tails’ weight in the distribution, that is, the weight of the unlikely events. β is the parameter of bias. If it acquires a null value, then the distribution is symmetric around the expected value. μ is a location parameter and is related to the expected value of the random variable. In the particular case that it is true that β = 0, then μ is the mathematical expectation. δ is a parameter of “sharpness” and determines the magnitude of kurtosis, and it also controls the shape of the distribution around the mode. λ is a parameter of form that influences the dispersion of the random variable, around the mean and in the tails of the distribution. If the parameters χ := δ 2 and ψ := α 2 − β 2 are defined, then the expected value

is: E[X ] = μ + β

kλ+1 (χ , ψ) kλ (χ , ψ)

(A.2)

and the variance:  2 kλ+1 (χ , ψ) 2 kλ (χ , ψ)kλ+2 (χ , ψ) − kλ+1 (χ , ψ) V [X ] = +β kλ (χ , ψ) [kλ (χ , ψ)]2

(A.3)

where kλ (·) is the third order Bessel function, given by:  ∞  1 kλ (χ , ψ) = ∫ x λ−1 ex p − χ x −1 + ψ x d x 2 0

(A.4)

and that gives the GH function great flexibility in adjusting financial returns Corlu et al. (2016). Table A.1 presents the different generalized density functions, which are obtained under different ranges of the parameters, λ, μ, β and α. In the literature,

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the generalized hyperbolic distribution is parameterized in several ways. The most famous is that of Prause (1999), which has been described in this section. In this regard, it only remains to mention that the parameterization, although it helps to have a more intuitive meaning, has the drawback of a more sophisticated calibration (Alayón 2015). In this sense, various hypothesis tests are used to assess the goodness of fit of the random variable εt in Eq. (1) to the NIG probability distribution. In this work, the Anderson–Darling and Kolmogorov–Smirnov tests are used to verify the goodness of fit of the generalized hyperbolic probability distribution. The null hypothesis of the Kolmogorov–Smirnov (KS) test states that the given probability distribution is correct and uses the left-tail test statistic (Alonso and Chaves 2013) provided by ´ Dn+ = m ax{F n (x) − F(x)}.

(A.5)

Although the test can be carried out with the left-tail test statistic given by Dn− = m ax{F(x) ´ − Fn (x)}.

(A.6)

The empirical distribution according to the sample Fn (x) is defined as Fn (x) =

n 1 I (xi ) n i=1

(A.7)

where I (xi ) is an indicator function that takes the value of one when yi ≤ x and zero otherwise, and F(x) is the distribution according to the null hypothesis. In this document, F(x) is the generalized hyperbolic probability distribution (GH), and the larger the p-value, the higher the evidence in favor of a reasonable adjustment of the GH function to equity returns, as has been theoretically assumed. In contrast, the Anderson–Darling (AD) statistic calculates a weighted average of the squared differences:

2 ˆ . A2 = F(x) − F(x)

(A.8)

where the weights indicate the discrepancies in the tails of the theoretical and empirical distribution. As with the KS test, the null hypothesis assumes that the cumulative probability distribution F(x) consists of the function GH. The larger the p-value, the higher the evidence for the null hypothesis. Both tests can also be used to verify whether the residuals follow a normal probability distribution (Wooldridge 2015). Additionally, it should be noted that these hypothesis tests have been chosen because

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Table A.1 Special cases of the GH distribution Probability distribution function

Parameter range

Variance-gamma

λ>0

α>0

β ∈ (−α, α)

δ=0

Asymmetric Laplace

λ=1

α>0

β ∈ (−α, α)

δ=0

μ∈R μ∈R

Laplace

λ=1

α>0

β=0

δ=0

μ∈R

Hyperbolic

λ=1

α>0

β ∈ (−α, α)

δ>0

μ∈R

Hyperbolic asimetric t

λ0

μ∈R

t-student

λ0

μ∈R

Cauchy

λ = −1/2

α=0

β=0

δ>0

μ∈R

Normal Inverse Gaussian λ = −1/2

α>0

β ∈ (−α, α)

δ>0

μ∈R

α→∞

β → β0

δ α

μ∈R

Normal

λ∈R

→ σ2

Source: own elaboration based on Paolella (2007)

they are robust to the large or small sample size of the data and because AD considers the “weight” of the tails of the empirical probability distribution (Prause 1999). Annex 2 Table A.2 shows the estimated coefficients for the parameters that determine the Gaussian Inverse Normal probability distribution (NIG) obtained using maximum likelihood, based on the density function of Eq. (4). Similarly, Table A.3 shows the Kolmogorov–Smirnov and Anderson–Darling hypothesis test on the random disturbance of Eq. (1), denoted by εt . It can be seen that the null hypothesis is not rejected, at the significance level of 10%, which indicates that at least 90% confidence the NIG probability density function results in a reasonable fit to the random variable εt found in the specification of Eq. (1). Table A.2 Adjusted NIG probability distribution Parameters Variable

λ

α

β

δ

μ

Random disturbance εt

−1/2

52.6393

−37.6375

0.0198

0.0354

Source: own elaboration with data from INEGI (2020)

Table A.3 Tests of goodness of fit Test Variable

Kolmogorov–Smirnov

Anderson–Darling

Random disturbance εt

0.0276 (0.1087)

0.0312 (0.1103)

Source: own elaboration with data from INEGI (2020)

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References Alayón JL (2015) Distribución hiperbólica generalizada: una aplicación en la selección de portafolios y en cuantificación de medidas de riesgo de mercado. Revista De Economía Del Rosario 18(2):249–308 Alonso J, Chaves J (2013) Valor en riesgo: evaluación del desempeño de diferentes metodologías para 5 países latinoamericanos. Estudios Gerenciales 29(126):37–48 Bai J, Perron P (2003) Critical values for multiple structural change tests. Economet J 6(1):72–78 Brooks C (2010) Introductory econometrics for finance, 2nd edn. ICMA Centre, University Press CEPAL (2020) Enfrentar los efectos cada vez mayores del COVID-19 para una reactivación con igualdad: nuevas proyecciones. Informe Especial COVID-19. https://repositorio.cepal.org/bitstr eam/handle/11362/45782/1/S2000471_es.pdf CIEN (2020) ¿La peor crisis de la historia? Tecnológico de Monterrey. Reporte Perspectivas Económicas, vol 2, no 299 CONEVAL (2020) La política social en el contexto de la pandemia por el virus SARSCoV-2 (COVID-19) en México. https://www.coneval.org.mx/Evaluacion/IEPSM/Documents/ Efectos_COVID-19.pdf Corlu C, Meterelliyoz M, Tiniç M (2016) Empirical distributions of daily equity index returns: a comparison. Expert Syst Appl Int J 54(3):170–192 CSSE (2020) COVID-19 Dashboard by the Center for Systems Science and Engineering at Johns Hopkins University. https://gisanddata.maps.arcgis.com/apps/opsdashboard/index.html#/bda759 4740fd40299423467b48e9ecf6 Deloitte (2020) The economic impact of COVID-19 (novel coronavirus). Deloitte Insights. https:// www2.deloitte.com/us/en/insights/economy/covid-19/economic-impact-covid-19.html Dickey D, Fuller W (1979) Distribution of the estimators for autoregressive time series with a unit root. J Am Stat Assoc 74(366a):427–431 El País (2020) El coronavirus asesta a la economía de Estados Unidos el mayor golpe de su historia reciente. https://elpais.com/economia/2020-07-30/la-economia-de-estados-unidossufre-su-peor-caida-desde-que-existen-registros.html Enders W (2014) Applied econometric time series. Wiley, Hoboken Esquivel G (2020) Los impactos económicos de la pandemia en México. Documento de Trabajo. Banco de México Ferruz L, Sanjuán I, Knebel D (2011) Análisis de intervención de las series temporales patrimonio y flujo neto de dinero de los Fondos de Inversión Socialmente Responsables (FISR) de Brasil. Contabilidad y Negocios 6(12):26–35 Hamilton J (1994) Time series analysis. Princeton University Press, Princeton IDIC (2020a) IGAE, de una caída anunciada a la Reconstrucción de México. Instituto para el Desarrollo Industrial y el Crecimiento Económico. La Voz de la Industria, vol 8, no 225 IDIC (2020b) IGAE, crónica de una caída anunciada: ¿Qué Hacer? El momento del Estado Desarrollador Industrial. Instituto para el Desarrollo Industrial y el Crecimiento Económico. La Voz de la Industria, vol 8, no 221 INEGI (2020) Indicadores Macroeconómicos Nacionales. https://www.inegi.org.mx/temas/ Kumar A, Meerschaert MM, Vellaisamy P (2011) Fractional normal inverse Gaussian diffusion. Statist Probab Lett 81(1):146–152 Morettin P, Toloi C (1989) Modelos de Função de Transferência. In: Conferencia por la 3ª Escola e séries Temporais Econometria, Rio de Janeiro, July 2004 Paolella MS (2007) Intermediate probability: a computational approach. Wiley , Hoboken Palacios M, Santos E, Velázquez MA, León M (2021) COVID-19, una emergencia de salud pública mundial. Revista Clínica Española 221(1):55–61 Pindyck RS, Rubinfeld DL (2001) Econometrics. McGraw-Hill, New York Prause K (1999) The generalized hyperbolic model: Estimation, financial derivatives and risk measures. Verlag nicht ermittelbar, UK

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Statista (2020) Número de personas fallecidas a causa del coronavirus en el mundo a fecha de 31 de julio de 2020, por país. https://es.statista.com/estadisticas/1095779/numero-de-muertes-cau sadas-por-el-coronavirus-de-wuhan-por-pais/ Tsay RS (2015) Analysis of financial time series, 2nd edn. Wiley , Hoboken Wooldridge J (2015) Introductory econometrics: A modern approach Nelson education World Bank (2020) COVID-19 to Plunge Global Economy into Worst Recession since World War II. https://www.worldbank.org/en/news/press-release/2020/06/08/covid-19-to-plunge-glo bal-economy-into-worst-recession-since-world-war-ii

Survival Likelihood of Micro and Small Businesses Facing a Catastrophe Griselda Dávila-Aragón, Salvador Rivas-Aceves, and Héctor X. Ramírez-Pérez

Abstract This chapter proposes a measurement methodology throughout a Bayesian Network to quantify the survival probability of micro and small enterprises (MSEs) facing a catastrophic event, and to assess if a Business Continuity Plan (BCP) is a unique alternative to prevent companies from bankruptcy. Empirical evidence for a developing country shows the majority of companies are MSEs and without enough knowledge about a BCP; therefore, the likelihood of businesses’ survival will depend on BCP and several other elements that should be taken into account for owners when making decisions towards negative effects of catastrophic events. Results showed that for MSEs businesses with high face-to-face customer interaction, a BCP might be useful as well as the experience in crisis of the management team, but not as the only variable. Keywords Business Continuity Plan · Catastrophic events · Micro and small enterprises JEL Classification C11 · L21 · L25

1 Introduction The present situation of COVID-19, which created a pandemic, has urged companies to modify their operations. At a global scale, because of the important measures implemented by governments in order to control health and the expansion of the new virus among population, many businesses have gone bankruptcy. At the level G. Dávila-Aragón · S. Rivas-Aceves (B) · H. X. Ramírez-Pérez Escuela de Ciencias y Económicas y Empresariales, Universidad Panamericana, Ciudad de México, México e-mail: [email protected] G. Dávila-Aragón e-mail: [email protected] H. X. Ramírez-Pérez e-mail: [email protected] © The Author(s), under exclusive license to Springer Nature Singapore Pte Ltd. 2021 G. Dávila-Aragón and S. Rivas-Aceves (eds.), The Future of Companies in the Face of a New Reality, https://doi.org/10.1007/978-981-16-2613-5_2

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of the MSEs, at the time of this research, there are not final calculations about the damage yet, but under similar circumstances of what big companies are suffering, the economic damage might be one of a big impact. The motivation to conduct this research is to analyze the survival probability of MSEs during a catastrophic event and the need of a BCP to keep the business running, especially for those types of companies that require the physical contact with customers. Because of a catastrophe, businesses can face a wide range of consequences. From a decrease in sales to a total suspension of operations. In the case of a total suspension of activities, such as the one the world is experiencing with COVID-19, a Business Continuity Plan (BCP) might represent an alternative to prevent the end of the business. A BCP provides the procedures to increase the likelihood to keep running the main services or key areas for businesses, in the case of an interruption of the normal activities caused by a natural, human, or a catastrophic disaster. A BCP might represent the difference between survival and bankruptcy. A business suspension might create customers dissatisfaction, sanctions, losing brand reputation, layoffs, and bankruptcy. It seems that having a BCP has become a vital necessity and requires to be executed as soon as the suspension of activities start, and to keep running it as long as the catastrophic event lasts. The objective of a BCP is to decrease the economic negative impacts and the possibility to return to normal activities as fast as possible, which would increase the survival probability. Organization must seek ways to remain commercially operational under even the most exceptional circumstances (Lam 2002). Unless business have a plan prepared before a catastrophe, a disaster would lead to closing operations and the longer the firm is shut down, the more likely the firm will never reopen the business (Cerullo and Cerullo 2004). Having a good business continuity plan is like having insurance: you hope you do not have to use it, but you reap the rewards when you do. Can your organization afford not to have one? (Lam 2002). However, the challenge is not only having a BCP, which might represent a huge step, but also to design it accurately. According to a survey conducted by Ernst & Young, after the 2001 terrorist attacks in the US, only 53% of the firms surveyed had a BCP, and many of them were deficient and outdated, as they did not consider many major risks of business systems interruptions (Cerullo and Cerullo 2004). A BCP is a holistic management process that identifies potential impact that threaten an organization and provides a framework for building resilience and capability for an effective response that safeguards the interests of its key stakeholders, reputation, brand and value creating activities (Boehmer et al. 2009; Niemimaa et al. 2019). For businesses facing disasters, the chances for survival without a plan are very low (Clark 2010). This chapter aims to analyze if the BCP is useful for MSEs companies. Therefore, we consider Mexico because there are 98.2% of companies of this size. The organization of this chapter is as follows: it presents literature review regarding the size of companies, the origins of BCP, its importance, characteristics, design, and pitfalls. The methodology of the study is explained which consists in a Bayesian Network methodology. Then, the researchers conduct a Bayesian Network from a

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base situation and testing different scenarios. Conclusions are analyzed and finally some recommendations.

2 Literature Review Criteria to define whether a company is micro, small, or medium is different in every country. Classification is based on the number of employees, sales, and assets, among other variables. The U.S. Small Business Administration (SBA) agency defines small business as “… one that is independently owned and operated, is organized for profit, and is not dominant in its field” (Greene 2011, p. 34). Unlike other countries, in Mexico the smallest classification is the micro business. In Mexico, the Ministry of Economy defines a micro company as one having up to 10 employees and with annual sales up to 4 million pesos (approximately $181,000 USD), and a small company having between 11 to 30 employees and annual sales up to 10 million pesos (approximately $450,000 USD) (México Emprende 2012). The MSEs represent 98.2% of the total companies, generate 60.6% of employment, and produce 29% of the GDP in the country (ENAPROCE 2018). Because of the number of micro and small enterprises, the percentage of people employed, and the level of national income, MSEs are the economic backbone of the economy (Calice 2016). In order to boost the economic and social development of a country, it is important to analyze and understand these types of companies. According to “Encuesta Nacional sobre Productividad y Competitividad de las Micro, Pequeñas y Medianas Empresas” (ENAPROCE 2018), MSEs’ economic sectors are commerce (56.5%), services (32.4%), and manufacturing (11.1%). Among all MSEs in Mexico, 88.9% of the MSEs have activities that depend on physical and direct interaction with customers. At the beginning of the second half of 2020, the “Confederación de Cámaras Nacionales de Comercio, Servicios y Turismo” (CONCANACO) published a study that analyzes the impact of the pandemic on affiliated companies during the first half on the same year. The study relies on a sample of 3,532 companies; which 46% correspond to commerce sector, 40% services and 14% to tourism. Regarding size, 66% are micro, 23% small, 8% medium and 3% are big. Because of the pandemic, 80% of all companies were with contracted operations, 2% were closed and only 8% had normal operations. Sales decreased 62% in average; employment was reduced in 32% in average; taxes payments were not accomplished by 77% of the companies. Regarding sales and unemployment, negative impacts were profound in 97% of micro companies, 81% of small companies, 74% of medium and 59% of big ones. In order to confront the economic crisis generated by the pandemic, around 57% of the sample declared to prefer a private credit and 73% to have access to both public and private special conditions credits. This study turned to be one of the motivators for this research as well as the basis for the empirical analysis.

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Business Continuity Plan Concept Even though the first research on BCPs started back in 1970 (Niemimaa et al. 2019), the studies of business continuity expanded in the 90s because of corporative globalization and the companies’ need to keep running the business in the event of a catastrophe obstructing regular operations. A BCP provides the procedures to increase the likelihood to keep running the main services or key areas of companies in the case of an interruption of the normal activities caused by a natural, human or a catastrophic disaster (Hojat et al. 2019). Disaster is defined as “a serious disruption of the functioning of a community or a society at any scale due to hazardous events interacting with conditions of exposure, vulnerability and capacity, leading to one or more of the following: human, material, economic and environmental losses and impacts” (UNGA 2017). Disaster risk is understood as “the potential loss of life, injury, or destroyed or damaged assets which could occur to a system, society or a community in a specific period of time, determined probabilistically as a function of hazard, exposure, vulnerability and capacity” (United Nations 2016, p. 1). Earthquakes, floods, electric storms, hurricanes, and fires can originate a natural catastrophe. Terrorism, strikes, and employees’ dissatisfaction are the origin of human catastrophe. Other interruptions caused by human error are utility disruptions (power outages) and malicious threats from outsiders (Cerullo and Cerullo 2004). In a study presented by Cerullo and Cerullo (2004) mentioned that 43% of companies hit by severe crisis never reopened and that an additional 29% of firms failed in the next two years. Furthermore, a statistic cited after Hurricane Andrew in 1992 that hit the US, stated that 80% of firms lacking a BCP closed operations two years after the storm (Cerullo and Cerullo 2004). Moreover, in 2016, 39% of US companies lack a basic crisis plan and 56% did not conducted drills to be prepared in the case of a disaster (Fischer et al. 2019). A BCP represents an alternative to prepare the company in case of a catastrophe, it is a document that outlines how a business will continue operating during an unplanned disruption in service. It outlines contingencies for business processes, assets, human resources, and business partners. The plan should identify the key business processes and define policies, plans, and procedures to keep relationship with customers (Nickolett and Schmidt 2008). Top managers in a company should be in charge of the development of the BCP (Fischer et al. 2019). A BCP is one of four basic aspects of a contingency planning besides business impact analysis, incident response plan, and disaster recovery plan. All of them have the aim to maintain a state of readiness for any situation at any time, especially to save the organization form going out-of-business (Clark 2010). The Association of Contingency Planners (ACP) describes contingency planning as: “Business continuity planning integrates knowledge from related disciplines such as information technology, emergency response, and crisis communications to create a strategy that ensures a business will remain resilient in the face of adversity” (Fischer et al. 2019, p. 250). A BCP is different to a Disaster Contingency Recovery Plan (DCRP), which many firms design. Although a DCRP is vital to recover from a disaster, this plan follows

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a reactive approach. A BCP’s approach is risk management: reduce or eliminate the impact from a catastrophe (natural or human), before of the event and seeks to reduce the time to restore conditions to a state of “business as usual” (Cerullo and Cerullo 2004; Clark 2010). There are different types of BCPs according to different catastrophic events, but the underlying idea is that a BCP keeps the company alive while it is recovering by the help of a disaster recovery plan (DRP) (Boehmer et al. 2009). In general, the term ‘business continuity’ broadly refers to a company’s socio-technical ability to withstand and restore from intra- and extra-organizational contingencies (Niemimaa et al. 2019).

3 BCP Design and Pitfalls To assure the correct design of the BCP, it is important to know and to understand all businesses’ processes and define deadlines, priorities, economic and human resources that will be required in the case of a catastrophic event (Thejendra 2020). According to Lam (2002), the BCP cycle is generic enough to have practical implications for all kinds of businesses. Key areas covered in a good BCP include contact points, roles and responsibilities, risk levels, continuity and recovery service levels, business continuity reviews, business continuity processes, incident reporting and documentation, testing, and training (Lam 2002). Cerullo and Cerullo (2004) stated that a BCP process should address three independent objectives: (1) identify major risks of business interruption, (2) develop a plan to mitigate or reduce the impact of the identified risk, and (3) train employees and test the plan to ensure that it is effective (Cerullo and Cerullo 2004). Although a BCP is generic and it is possible to implement in different types of corporations, there are some common pitfalls in the planning process of a BCP such as incomplete, uncommunicated, and out of date, among others. The most common pitfalls presented by Lam (2002) are in the following table: In a study conducted with companies that created a BCP, more than 40% of the respondents mentioned they had not carried out a business impact analysis. Furthermore, 21% of the respondents had not tasted their BCP, and less than 50% of the firms did not establish recovery timelines with the business, which could led to a wide expectation gap between what the business needs and what the plan provides for (Cerullo and Cerullo 2004). One of the advantages from a BCP is that the organization will be able to analyze and understand the effects and economic consequences from business’ interruption (Clark 2010). The longer the time of the catastrophic event that causes the suspension of business activities, the higher the probability of a company to face economic damages that might accelerate the dead of the business. According to Battisti and Deakins (2012), after a catastrophic event it is highly probable that MSEs implement measures to prevent the company for futures disasters (Battisti and Deakins 2012). Regarding testing the BCP, the challenge is a major one because incidents do not occur often, therefore there is not enough statistical information to use in order to

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Table 1 Common pitfalls in the business continuity planning process Pitfalls plans can be…

Description

Incomplete

The BCP process is not complete. Outputs such as the business continuity plan and policy either do not exist or exist in incomplete form

Inadequate

The plan and strategies cannot deal with the level of risk that the organization deems acceptable

Impractical

The plan is not practical or achievable within the organization’s constraints (manpower, time, and budget, for example)

Overkill

The plan is overly elaborate or costly with respect to the overall level of business risk that the organization is willing to take

Uncommunicated

The business continuity team has not communicated the plan to all the right people. Staff—both management and technical—remains unaware of business continuity issues

Lacking a defined process

Business continuity processes remain ill defined. Staffers are unsure of how to react in a failure scenario, or they discover too late that their existing processes fall short

Untested

The organization has not tested its plan or has not tested I thoroughly enough to provide a high level of confidence in its soundness

Uncoordinated

The business continuity effort lacks organization and coordination. The organization has either not established a business continuity team, or the team lacks individuals who can effectively drive the effort to completion

Out of date

The plan has not been reviewed or revised in light of changes in the organization, its business, or technology

Lacking in recovery thinking

The organization does not adequately address how it intends to recover to a fully operational state after executing its business continuity plans

Source Lam (2002)

analyze its utility. This is the main reason a Bayesian Network methodology was used for this chapter. Therefore, there is little proven knowledge about the proper functioning of a BCP: “in order to understand that a BCP will operate well in case of a disaster, a priori analysis techniques are required. Today’s best practices do not provide solutions for this” (Boehmer et al. 2009, p. 1) (Table 1).

4 Methodology In classic probability, it is assumed that the statistics of the sample belong to a certain population with a specific distribution, defined by a set of parameters, with a fixed value. The task for the statistics is to estimate the parameters as best as possible based on the available data, and when possible, to perform experiments several

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times, obtaining a sample large enough to assign values to these parameters. Thomas Bayes (1702–1761) investigated the problem of determining the likelihood of causes through effects.     P x|θ P(θ )   P θ |x = . P x

(1)

Observations x were taken from the population sample with a probability distribution defined by the parameter θ . After his studies, Bayesian statisticians allowed parameters to take random variables, where statements about the characteristics of a population are always dependent on empirical observations, data, and any other available knowledge to the statistic, prior to the initiation of observations (a priori). The Bayesian approach allows statisticians to supplement the information obtained from sampling with prior information obtained from an expert (Aczel and Sounderpandian 2009). The mathematical link between the probabilities associated with the observed data and the probabilities associated with the information obtained from the experts is achieved through the Bayes theorem. Researchers have used the Bayes theorem for many years in numerous applications; it is a rational way to review beliefs at the light of observed evidence. Once relevant a priori information is obtained regarding possible causes and conditional probabilities associated with each effect, the model is called the Bayesian network (BN). A BN is a targeted acyclic graph, where nodes represent variables of interest and edges represent causal or influence links among variables. Associated with each node, it is a node probability table, statistical distribution, or parameterized function (Cardozo-Ojeda and Arguello-Fuentes 2011). In the case of a node probability table, the relationship is governed by a set of conditional probability values that model the uncertain relationship among the node and its parent nodes along with any uncertainty present in that relationship. Initially excessive calculations based on probability theory made their use unworkable; however, the use of conditional independence in graph theory and development in efficient algorithms for the propagation of evidence through graphical structures have made this field more computationally feasible. This type of analysis is possible because of the use of statistical and engineering modeling (Cowell et al. 2007). As said by Beltran et al. (2014) the probabilistic framework from what a BN is defined allows valid inferences for events that are unknown (Beltrán et al. 2014). A Bayesian Network is a mathematical structure where cause and effect relations are analyzed by assigning probabilities and ponderations to every variable characterized in the network (Chan et al. 2018; Madsen and Kjærulff 2013). The causal relationship between the variables gives the nodes its name, so the node x1 is called “father” and the node x2 “child” as shown in Fig. 1. The causal relationship that exists between x1 and x2 means that the joint distribution can be expressed as the product of probability x1 and conditional probability of x2 by P(x1 )P(x2 |x1 ). Graphical models are part of a network of nodes that connect variables in some kind of relationship forming an acyclic-directed graph.

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Fig. 1 Father and child node Source own elaboration

Bayesian networks contain causality relationships and therefore their nodes are connected by directed edges. They are part of a subset of graphical models known as Acyclic Directed Graph (ADG). ADG are built with relationships like those in Fig. 1 as their basic block. These blocks are arranged in such a way that they are acyclic, that is, they move along the edges in the directions involved, it is impossible to return to a previous node. So node x1 is father node of x2 . For each variable xn+1 with fathers x1 , x2 , . . . xn exist an associated probability defined by P(xn+1 |x1 , x2 , . . . xn ). If xn+1 does not have fathers, the independent probability is P(xn+1 ). If X = {x1 , x2 , . . . xn } is a random variable, its joint distribution function is defined by P(X ) = P(x1 , x2 , . . . xn ). The function P(X ) grows exponentially with the number of variables. Bayesian Networks provide a compact representation of P(X ) factoring the joint distribution into a local conditional distribution  for each variable given its “parents”. In this sense,P(X ) = P(x1 , x2 , . . . xn ) = (xi | p(xi )), Fig. 2. Where the join probability is set by: P(X ) = P(x1 , x2 , x3 , x4 , x5 ) = P(x1 )P(x2 |x1 )P(x3 |x2 )P(x4 |x2 )P(x5 |x3 , x4 )P(x6 |x5 )

Factoring the joint distribution into a conditional distribution for the node given x5 its “parents” is verified in the following expression:   P x5 |x4 , x3 , x2, x1 = P(x5 |x3 , x4 ).

(2)

By maintaining the assumption of independence in the construction of the Bayesian Network, the number of conditional probabilities to be evaluated is considerably

Fig. 2 Sample of Bayesian network Source own elaboration

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reduced. A BN is used for inference, calculating conditional probabilities, given the information available so far for each node. The factorization of the joint distribution comes from the conditional independence property inherent in the structure of the ADG. However, even this property can make very complex manipulations of ADG, especially if the nodes represent variables with many states. One factorization that simplifies calculations is the structure called “Junction Tree”. This structure performs localized modular calculations that are executed using a “message pass” algorithm. These “Derivation Trees” are non-directional graphs and consist of a collection of graphics also called belief universes; there are node groups in which each node in the group is connected to other node in the group (Cowell et al. 1999). Expressed in this way, the evidence can be incorporated locally into each node with a number of smaller calculations. For the updated nodes, all nodes in the tree disperse the information. Therefore, there is no need to use the joint probabilities of the entire chart; it is done locally on each node.

5 Scenario Analysis According to Bayesian Networks methodology, probabilities for events that are associated to every element affecting on the probability for a company to survive a catastrophic phenomenon, must be calculated by considering the available information as well as experts’ considerations regarding the frequency of incidence of each element. The Bayesian network was created by the analysis of the following elements: the extend for a catastrophic event, the existence of a BCP, the socio-economic and political (SEP) environment, the costumer face-to-face interaction, the liquidity sources, the employees’ training for implementing a BCP, the experience in dealing crisis of the management team, and if operations are carried out on normal basis or if they are contracted. In order to understand how these elements can affect the survival probability, an individual analysis is needed. Extend of a catastrophic event: when the 2019–2020 pandemic was first known, several analyst and experts considered it would last 2 to 3 weeks at most. Nevertheless, at the time of this research, it has been 8 months since the pandemic started and there is not a defined date for the pandemic to be over. When a catastrophic event lasts a short period, like an earthquake, a tsunami, hurricane, or even a disease like influenza, it is easier for a company to foreseen economic problems and to solve them, since they could be not as expensive as in the case for a long catastrophic event. The longer the event endures, the higher the consequences. How often a catastrophic event is extended? Around the globe, for the last four massive catastrophic events, only the COVID-19 was long. Business Continuity Plan: usually companies design a BCP in order to resist events like labor strikes, losses or scarcity in inputs, losses in digital information, among others. When a BCP is defined, the company increases the probability of success in solving the problem. COVID-19 has affected most companies due to two

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key factors: not only the extended period, but also the type of economic activities a company does. Not having a BCP might mean to not be prepared to face any type of event and being able to resist it. Socio-economic and political environment: for companies not prepared for these types of catastrophic events, it might be easier to solve problems if there were other economic agents in which they can rely on. During the pandemic, there have been around the globe well-designed economic plans involving most sectors, private, public and banks, which have been implemented for facing the pandemic and the economic recession simultaneously. When all sectors are committed to support each other during this type of catastrophic events, the probability of surviving could be higher. However, there are cases, like in Mexico, that private and public sectors stand separately, thus affecting in a negative way the survival probability of a company. The design of macroeconomic plans typically implicates social, economic and political assessments. Costumer face-to-face interaction: this seems like a complex problem for companies in a long pandemic. Since the transmission of the COVID-19 disease occurs because of physical interaction among humans, any economic activity that depends on it will face several problems. Regardless the extension for the catastrophic event, having a strong face-to-face interaction would affect the economic behavior of a company. Liquidity sources: having access to financial resources could be a main factor to survive, especially if the company is facing contracted operations. Normally, there are three core sources for financing: savings, suppliers, and financial institutions. When facing economic problems, companies often appeal to these sources in order to continue economic activities. The type and amount of resources are not the same across these three financial resources, therefore the impact on survival probability is not equal. A company will use the resource that is going to be the less expensive or even all of them if needed. Employees trained for a BCP implementation: the type of a BCP needed to survive a catastrophic event such as a pandemic, would require involving most areas, if not all, within a company. Consequently, the implementation would hardly depend on only one area. For the BCP to be successful, training of employees will be necessary, especially if the economic activities are transformed to less face-to-face interaction. Crisis management: if a manager is well experienced when considering a crisis, he or she will tend to make better decisions at the precise moment. Up to this stage, knowing the SEP environment, having a BCP, getting access to any type of financial sources, and having employees trained, could simplify the scenario, information or the difficulty for taking a decision. Therefore, a manager that interpret wrong the available information, who does not know the BCP, or who would not be able to acquire financial resources, that manager would face a lower survival probability. Regular operations: the contraction in economic activities, in most countries, was based on the definition for an activity to be essential or not. For instance, food and medicine production and supply, emergency services, security, and communications were the sectors declared as essential. The rest had to contract substantially or even closed their activities, thus affecting the economic performance of companies.

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Lastly, is evident that the interaction for all these elements could occur and hence affect the survival probability of a company. For instance, imagine a company which face-to-face interaction with clients is high, having none BCP designed and therefore not trained employees for it, a lack of skills of dealing with a crisis from the management team, with restrictions to financial sources, that suddenly needs to contract or close economic activities because a catastrophic event such as a pandemic. Under this scenario, it would be hard for this company to survive. Of course, there are different combinations within these interactions affecting the survival probabilities, which will be analyzed. The base scenario considered for this research can be seen in Fig. 3. To define this scenario, the researchers reviewed information from ENAPROCE and CONCANACO, and consulted MSEs experts. Based on the study conducted by CONCANACO (2020) through a sample of 3,532 Mexican companies, elements Catastrophic Event, SEP Environment, Liquidity Sources and Regular Operations were defined in terms of probabilistic measures (CONCANACO 2020). Results of the base scenario showed 41% of survival probability in the case the following situations to happen: catastrophic event to be extended (25%), no definition of BCP, unstable SEP environment (30%), employees not trained for a BCP, null experience in Crisis Management (70%), liquidity sources relies on suppliers (53%), high need for customer face-to-face interaction (48%), and risk of regular operations to be suspended (55%). Therefore, the probability of not surviving is 59%. With the previous information, which considered companies from all sizes, combined with the information from MSE experts, the researchers could build the hierarchy and interactions of the nodes and construct the MSE Bayesian network. Under this scenario, an owner of a company at risk during a catastrophic event could consider that probabilities are even, and so to get the feeling that allocating resources to change any element in order to survive might be useless. An alternative to outbreak that perception is by analyzing the gap between both probabilities, in the sense that the higher the gap, the better to make decisions to improve surviving probability. Base scenario’s gap is 18%. Consequently, the greater the gap, the better

Fig. 3 Base scenario. Source own elaboration

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the scenario to increase the probability to survive. The worst scenario would be the opposite. The base scenario allows the researchers to analyze the surviving probability of a company when a particular element of the network is extrapolated. The first analyzed element is for a defined BCP probability of 100%, while remaining constant the rest of the elements. In this situation, the probability to survive an extended catastrophic event increase 6% (Fig. 4). If a BCP is not defined, then the same probability decreases 2% (Fig. 5). When analyzing scenario in Fig. 4, it seems evident that having a BCP defined increases the probabilities to survive since the gap resulted is 6%; hence, the element BCP turns out to be useful to survive. On the other hand, if a BCP is not defined, the gap is 22%, which confirms the importance of this variable for the survival of the company. This information highlights the importance of conducting a scenario analysis since it allows comparing different circumstances that could have an impact

Fig. 4 Defined BCP scenario Source own elaboration

Fig. 5 Not defined BCP scenario. Source own elaboration

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on the outcome. Furthermore, it provides valuable information about the change in the surviving probabilities if we improve a specific element. Considering the duration for the catastrophic event, results show that when the situation lasts for a longer period, survival probability decreases 5%; but if the length is brief, then probability increases 2% (Figs. 6 and 7). In particular, the gap between probabilities under this scenario is 32%, which is an indicator for business owners that in case of facing this scenario, the negative impact might be harder, especially if BCP is not defined to avoid problems. If the catastrophic event is brief, the gap decreases to 14%. During the scenario analysis, general results show there are elements that do not have a significant impact. The information regarding these elements are summarized at the end of this section. Consequently, the most important impacts on surviving probability are the ones analyzed. For example, having a stable SEP environment does not generate a significant change in the probability to survive; nevertheless, an unstable SEP environment does as it decreases the probability to 28%, which means

Fig. 6 Extended catastrophic event scenario. Source own elaboration

Fig. 7 Brief catastrophic event scenario. Source own elaboration

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Fig. 8 Unstable SEP environment scenario. Source own elaboration

that the probability of not surviving increased 13%. The gap for this scenario is 44% (Fig. 8). The face-to-face customer interaction was a main feature in the design of this Bayesian Network, due to social distancing as a key instrument to control the pandemic. Figure 9 shows that the probability for a company to bankrupt is 76% if face-to-face customer interaction is high; the probability decreases to 54% if faceto-face customer interaction is medium, and the probability continue falling to 33% when face-to-face customer interaction is low. Regarding a high face-to-face interaction scenario, the gap between probabilities is 52%. When face-to-face interaction is low, the gap in favor to surviving is 34% (Fig. 10). Now, a BCP definition could rely on the process of how to transform economic activities to decrease the need of face-to-face interaction, therefore spillovers because both elements will appear affecting in a positive way surviving probability. When analyzing liquidity sources, the probability remains constant for a company to survive, in relation with the base scenario, when liquidity sources are suppliers while facing a catastrophic event. To leverage on suppliers is not a significant element

Fig. 9 High face-to-face customer interaction scenario. Source own elaboration

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Fig. 10 Low face-to-face customer interaction scenario. Source own elaboration

for enhancing the probability to survive; one reason to explain this situation might be that a company cannot withdraw enough financial resources from suppliers. Conditions remain the same when liquidity source is a financial institution. However, the probability to survive decreases 20% when the company relies only on savings and the gap is 38% (Fig. 11). This result can be interpreted as even having own resources for a company, is not enough to face a catastrophic event. When the people responsible for decision-making have not experience for crisis managing, the probability for a company to bankrupt increases to 62% and the gap between probabilities is 24% (Fig. 12). At the time of this research, during the first 3 to 5 months of the COVID-19, most countries imposed a temporary close of activities to control de rate of contagious. The experts considered that element to be an important one within the Bayesian Network and turned out that when regular operations were analyzed, results were consistent. If the economic activities were suspended, the probability to survive is 23% with a gap of 54%. This probability rises up to 48% if activities are contracted, with a gap of 10%; finally, the probability to survive is 68% when there are normal operations, with a gap of 36% (Figs. 13, 14 and 15). The elements with no significant results are as follow: (a) medium face-to-face customer interaction decreases the probability of not surviving 5% and the gap under

Fig. 11 Savings as main liquidity source scenario Source own elaboration

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Fig. 12 Lack of experience in crisis management scenario. Source own elaboration

Fig. 13 Regular operations to be suspended scenario. Source own elaboration

Fig. 14 Regular operations to be contracted scenario. Source own elaboration

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Fig. 15 Regular operations as usual scenario. Source own elaboration

this scenario is 8%. (b) When employees are trained for a BCP, probability of not surviving decreases in 4%, whit a gap of 10%. (c) If employees are trained, probability of surviving decreases 1%. Consequently, allocating resources to trained employees is not significant to face any type of catastrophic event. On the other hand, if a manager is experienced in crisis, probability of not surviving decreases 7% with a gap of 4% between them. Finally, elements such as having a stable SEP and considering suppliers or financial institutions as main liquidity source, do not generate a change in probabilities, therefore their impact is null. For the last two elements, it can be considered that if any liquidity source does not ensure the survival of a company, then is better no decide to close than continue operating since implementing a BCP could be expensive. The elements with no significant results are as follow: (a) medium face-to-face customer interaction decreases the probability of not surviving 5% and the gap under this scenario is 8%. (b) When employees are trained for a BCP, probability of not surviving decreases in 4%, whit a gap of 10%. (c) If employees are trained, probability of surviving decreases 1%. Consequently, allocating resources to trained employees is not significant to face any type of catastrophic event. On the other hand, if a manager is experienced in crisis, probability of not surviving decreases 7% with a gap of 4% between them. Finally, elements such as having a stable SEP and considering suppliers or financial institutions as main liquidity source, do not generate a change in probabilities, therefore their impact is null. For the last two elements, it can be considered that if any liquidity source does not ensure the survival of a company, then is better no decide to close than continue operating since implementing a BCP could be expensive.

6 Conclusions Across the analysis performed, results show several elements that affect the survival probability of MSEs. Having a BCP turns out to be an important element. However, it is not the one with the highest impact, but it might mitigate the risk to disappear. In the

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base scenario, the survival probability is 41%. Nevertheless, if the only variable that changes in a given situation were the definition of the BCP, the survival probability increases to 47%. At this point, a business owner might think that the effort to design and implement a BCP would not be useful. For the same given situation, but withouth a BCP, the probability to survive drops to 39%. Cerullo and Cerullo’s (2004) study stated that without a BCP 80% of firms closed operations. Results from this research found that lacking a BCP would not be as determinant as the only variable. The gap between surviving versus bankrupt goes from 6 points in a defined BCP to 22 points when not defined. Therefore, it might be wise to have it. Because, as stated by Lam (2002), having a BCP is like having an insurace that you might never want to use, but in the case of need, a business owner will be better prepared for a catastrophic event. Even though, this findings are different to the ones in Clark (2010) who suggested that chances for survival without a plan are vey low, the resarchers recognize the statement on Clark (2010) that mentioned the advantages from a BCP is that the organization will be albe to analyze and understand the effects and economic consequences from business’ interruption. The results show that the variable with the greatest impact for the survival rate is when business operations are suspended or when business operations run normal. In a pandemic, when operations are suspended, there is only 23% of chances to survive; when operations run normal, there is 68% of probability to survive. These results might be evident because when the business is not working, the likelihood to disappear is high; the opposite would result in the best scenario to survive. This finding is consistent with Cerullo and Cerullo (2004) who stated that the longer the firm is shut down, the more likely the firm will never reopen the business. The researchers did not focus on this variable as the main one, because the decision to suspend businesses operations would come from the government or local authorities and in that case, a business owner has no control over the decision. An additional goal of this research is to provide suggestions to MSEs business owners to increase the probability to survive in the case of a catastrophe. This variable is beyond the business owner control. According to results of this research, the type of face-to-face interaction represents the most important element to survive in which a business owner has a decision to make. The actual pandemic is modifying the traditional economic paradigm in which the physical interaction with costumers was the normal way. Nowadays companies that are able to transform their economic activities not relying essentially on a face-toface interaction, have better chances to survive. This is in line with Lam (2002) who mentioned that an organization must seek ways to remain commercially operational under even the most exceptional circumstances. On the one hand, when a small business relies in a high need of face-to-face, it has only 24% of probability to survive. On the other, a company that is able to transform its economic activities to avoid physical interaction with costumers improves substantially the surviving probability. When the need of face-to-face contact is low, survival probability increases 67%, the highest probability in all considered scenarios. As stated before, nowadays companies that are able to transform their

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economic activities not relying essentially on a face-to-face interaction, have better chances to survive. Consequently, a defined BCP that could help to change economic activities in order to face a catastrophic event represents an opportunity. This definition could rely on the process of how to transform economic activities to decrease the need of face-to-face interaction, or how to carry it out without risk of contagion. Another element with a positive impact in dealing with a catastrophic event is having a management team with crisis experience. This team might react faster toward an unstable socio-economic and political environment, implement more effectively the BCP according to the type of catastrophic event the company is facing, think quicker ways to transform business activities to continue with operations as normal as possible and have a better understanding to identify what are the better liquidity sources if needed, among others. According to Cerullo and Cerullo (2004), a management team with a proper BCP should identify major risk of business interruption in advance, develop a plan to mitigate or reduce the impact of those risks, and train employees to ensure the plan is effective. A lack of crisis management in the management team would lead to a 62% of bankruptcy probability. Finally, results showed that the type of liquidity sources do not have an important impact on the survival probability. Whether a business owner gets resources from financial institutions, suppliers, or savings, these elements should not be the focus when facing a catastrophe. Overall, a business owner with the capability to transform customers’ interactions and depending less on face-to-face relations, with a defined BCP, and a management team with experience in dealing with crisis, will have better chances to survive, whether a pandemic last longer or if socio-economic and political environment is unstable. Only having a BCP might not be enough to survive in a catastrophe. Government might think of the elements that would help companies to survive, especially for countries that highly depend on MSEs, such as Mexico in which over 98% of companies are part of this classification. This is in line with Calice (2016) who assured that MSEs are the economic backbone of a country. Future studies might: 1) focus its attention to measure the time a micro business owner, who has relied on empirical experience to grow the business, would need to create a BCP, the investment that will require, and the type of testing and training in order to prove the effectiveness of the plan, and; 2) consider if, after a pandemic, those businesses who could transform their customer interaction in a low face-to-face requirement, they should keep with these new dynamics or if it is worthy to return to a high face-to-face interaction.

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Boehmer W, Brandt C, Groote JF (2009) Evaluation of a business continuity plan using process algebra. In: IEEE Toronto International Conference Science and Technology for Humanity, Toronto, pp 1–33. IEEE Thejendra BS (2020) Disaster Recovery and Business Continuity: A Quick Guide for Small Organizations and Busy Executives. IT Governance Publishing. http://www.jstor.org/stable/j.ctt 5hh4d4 Calice P (2016) World Bank. Assessing Implementation of the Principles for Public Credit Guarantees for SMEs. A Global Survey, July 2016: http://documents.worldbank.org/curated/en/730 551469021300941/pdf/WPS7753.pdf Cardozo-Ojeda E, Arguello-Fuentes H (2011) Aprendizaje Estructural de Redes Bayesianas: Un Enfoque Basado en Puntaje y Búsqueda. Ciencia e Ingeniería Neogranadina 21:29–50 Cerullo V, Cerullo MJ (2004) Business continuity planning: a comprehensive approach. Inf Syst Manag 21:70–78 Chan AP, Wong FK, Hon CK, Choi TN (2018) A Bayesian network for reducing acccident rates of electrical and mechanical (E&M) work. Int J Environ Res Public Health 15:2496 Clark P (2010) Contingency planning and strategies. In: Information security curriculum development, pp 131–140 CONCANACO (2020) Confederación de Cámaras Nacionales de Comercio, Servicio y Turismo. Impacto del COVID-19 en los Sectores Comercio, Servicios y Turismo: https://www.concanaco. com.mx/category/comunicados/. Accessed 31 Aug 2020 Cowell RG, Dawid AP, Verrall RJ, Yoon YK (2007) Modeling operational risk with Bayesian networks. J Risk Insur 74(4):795–827 Cowell RG, Dawid P, Lauritzen SL, Spiegelhalter DJ (1999) Probabilistic networks and expert systems. Springer-Verlag, New York ENAPROCE (2018) Instituto Nacional de Estadística y Geografía. Encuesta Nacional sobre Productividad y Competitividad de las Micro, Pequeñas y Medianas Empresas: https://www.inegi.org. mx/programas/enaproce/2018/ Fischer RJ, Halibozek EP, Walters DC (2019) Contingency planning emergency response and safety. In: Introduction to security, pp 249–268 Greene S (2011) Catalyzing Investment for Domestic Impact: The Impact Investing Initiative of the U.S. Small Business Administration. Innovations: Technology, Governance, Globalization, pp 27–34 Hojat RS, Ali ST, Navid S (2019) Developing a novel quantitative framework for business continuity planning. Int J Prod Res 57(3):779–800. https://doi.org/10.1080/00207543.2018.1483586 Lam W (2002) Ensuring business continuity. IT Prof 4(3):19–25 Madsen AL, Kjærulff UB (2013) Bayesian networks and influence diagrams: a guide to construction and analysis. Springer, New York México Emprende (2012) Secretaría de Economía. Microempresas: http://www.2006-2012.eco nomia.gob.mx/mexico-emprende/empresas/microempresario#:~:text=Las%20microempresas% 20son%20todos%20aquellos,ciento%20del%20Producto%20Interno%20Bruto Nickolett C, Schmidt J (2008) Business Continuity Planning Description and Framework, Comprehensive Solutions, EEUU, pp 1–10 Niemimaa M, Järveläinen J, Heikkilä M, Heikkilä J (2019) Business continuity of business models: evaluating the resilience of business. Int J Inf Manag 49:208–216 UNGA (2017) United Nations General Assembly. Report of the Open-Ended Intergovernmental Expert Working Group on Indicators and Terminology Relating to Disaster Risk Reduction: https://www.unisdr.org/archive/51767 United Nations (2016) Sustainable Development: Disaster Risk Reduction: http://www.preventio nweb.net/files/50683_oiewgreportenglish.pdf

How Covid-19 Has Accelerated the Garment and Financial Investment Industries’ Adoption of Environmental, Social and Corporate Governance (ESG) Standards Pablo López Sarabia, Silvia Rojas Padilla, and Ricardo González Díaz Abstract The transition of the apparel industry to an ESG business model will have a cascading effect in the whole supply chain (upstream, midstream and downstream) and will generate greater value for all stakeholders. The ESG investments are registering exponential growth in equity and fixed income markets due to the following benefits: 1) lower risk and long-term stability, 2) the returns of portfolios and indices with ESG criteria are not inferior to traditional investment, 3) the positive externalities associated to ESG criteria and 4) the reputation and gain of market share by responding to the demands of a new generation of consumers willing to pay a surcharge for sustainable goods and services that consider ESG criteria. Currently there are expectations that once the Covid-19 crisis is over there will be a grand quantity of capital inflows toward ESG investments on emerging markets, a situation that will drive the transformation of companies; particularly those that have made progress in incorporating ESG factors like the utilities and financial sector. Keywords Apparel industry · ESG investment · COVID-19 pandemic · ESG metrics JEL Classification G11 · G30 · L20 · L67 · M14 · O16 · Q50

1 Introduction Environmental, Social and Corporate Governance in “The New Normal” World. Prior to 2020, the world was already driven by a vigorous debate about environmental, social and governance practices. The growing exposure of young activists, such as Greta Thunberg, have drawn the spotlight to the new generations who demand stronger measures on climate change and a shift to socially responsible lifestyles. The P. López Sarabia (B) · R. González Díaz Tecnológico de Monterrey, Campus Santa Fe, Monterrey, México e-mail: [email protected] S. Rojas Padilla Instituto Cervantes, Sydney, Australia © The Author(s), under exclusive license to Springer Nature Singapore Pte Ltd. 2021 G. Dávila-Aragón and S. Rivas-Aceves (eds.), The Future of Companies in the Face of a New Reality, https://doi.org/10.1007/978-981-16-2613-5_3

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meaning of sustainable development has broadened from climate change mitigation to a holistic attitude towards major relevant matters such as fair trade, inclusion and respect for diversity, gender equality, support for local businesses and protection of the individuals. Even more importantly, the transition to a green and sharing economy has exposed the need for every social sphere, from governments to households, to work together to achieve tangible and long-lasting results. In a world where economic dynamics are founded on the principle of “adapt or die” and where a constant “clash of contradictions” function as catalyst for technological, geopolitical and social transformation, it is only natural to expect that companies undergo an equally continuous development. However, despite an evolutionary mindset that accepts short and medium-term disturbances, nothing could have prepared the world for the unprecedented challenges that Covid-19 would involve. This global health emergency occurred in an already shaken ground where elements with a trajectory of their own created a titanic joint effect with irreversible consequences. Brexit is the most recent example of a growing nationalism originated from unequal opportunities amongst regions, a rising distrust of multinational, financial and defence organisations and a generalized discontent with governmental policies. The fragmentation of one of the most relevant economic blocks in the world, has established uncertain factors for investment and financial markets since 2016, but also for individuals and companies not only in the United Kingdom but all through Europe, since they will have to prepare for timely changes within a yet unclear regulatory ground, see Yueh (2013). Amidst a global pandemic, US trade tensions with China and the European Union remain, as well as the uncertain impacts of China’s national security laws on Hong Kong’s autonomy (O’Grady and Berger 2020). The unresolved issue over the South China Sea dispute could potentially affect the economic dynamics of the region and generate distortions in the global supply chain in the manufacturing industry (for example, the textile and apparel sector), as well as in the financial sector (the City of London in Europe and Hong Kong in Asia). This adds up to the pre-existent transparency concerns about China’s operations in financial markets, exchange rate strategies, patents and protection of property and intellectual rights, among others. The particularly intense industrial activity all through the twentieth century has left a tangible mark in global temperature rise causing warming oceans, shrinking ice sheet, glacial retreat, and decreased snow cover, sea level rise, declining artic sea ice, extreme events and ocean acidification, amongst others. By the end of the 80 s, global warming was acknowledged as a real phenomenon with visible consequences, such as the hottest summer on record on 1988, droughts and wildfires, which, by that time, were already anticipated to become even more extreme and severe. Last year, Australia’s “Black season” of bushfires accounted for 306 million tonnes of CO2 emissions as of January 2020, over half Australia’s annual carbon emissions. The ecological impacts of these prolonged and severe bushfires include over 18 million hectares burned, the death of more than one billion animals and fear of extinction of some already endangered species due to the loss of habitat and food sources. The intense smoke and pollution stemming from the fires derived in the

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worst air quality index of any major city in the world, see Lee (2019). However, before the fires, Australia had experienced a lengthy period of drought, hindering farming and agricultural activities and causing a drop in local supplies and job losses. These events are part of a longer list of cases of extreme weather conditions caused by global warming such as the 11 billion tons of melted ice in one day in Greenland or the state of emergency declared in Venice in November 2019 due to a record flood in the city. The UN’s World Meteorological Organization reported in 2019 that the concentration of climate-heating greenhouse gases hit a record high, showing that action on the climate emergency is not having a real effect in the atmosphere despite the commitments under the Paris agreement, as Carrington (2018) states. The multifaceted impacts of climate change and the urgent call for action is driving change initiatives in business models, demands for a clear ecological regulation by governments and international institutions, as well as a generalized conscious about mitigation of individual carbon print. Alongside with Brexit, movements such as the Yellow Vests in 2018, the Chilean, Ecuadorian and Lebanese protests the year after, as well as the presidential crisis in Venezuela, Haiti, Algeria and Sudan, showcase the rise of social demands for governmental accountability. Changes in fiscal policies that are perceived as a burden for the middle-class and a way to protect the ruling class met the inflammatory reactions of the public towards corruption, unemployment rates and the stagnant state of the economies. Halfway through 2020, anti-racism rallies and protests against police brutality have taken place in most of the major cities around the world. The killing of George Floyd in the US rekindled the conversation about national and local matters regarding injustice and discrimination, framed in a Covid-19 environment that has exposed the dormant racism and xenophobia that permeates society all around the world. Underrepresentation of minorities and a shift in national and international attitudes towards race, class, gender and sexual orientation have given birth to a wave of protests as never seen before, where demographic diversity demands widespread public sympathy and to ensure the inclusion of diverse communities. The momentum generated by these protests seems to be a turning point in history with potentially irreversible consequences which have already triggered an increasing disclosure of diversity information and rebranding of products of major businesses such as the NFL, PepsiCo, Colgate and Nestlé, see McEvoy (2020). “Stop Hate for Profit” is the name of the campaign spearheaded by hundreds of companies with a particular purpose: to pause or withdraw advertising on social networks, especially on Facebook. Due to the company’s alleged lack of commitment against the control of toxic information and speech that incites or promotes hatred. Furthermore, the Covid-19 pandemic has shone a spotlight on worker safety, customer well-being, relevance and market solidity of companies which, combined with all the aforementioned events, has emphasized the “S” in the ESG movement. As disruptive as the Covid-19 pandemic has been, it has also played an accelerating role regarding long-term developments such as digitalization and remote work, ecommerce and robotization. Nonetheless, the use of robots and drones for pandemic

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control and the now generalized use of platforms such as “Microsoft Teams” or Zoom, take place in a global economy with an alarmingly high unemployment rate and an increased reliance on gig economy (Schlogl 2020). One of the main concerns about life post Covid-19 pandemic is the exclusion of disadvantaged groups from the digital economy due to lack of accessibility, skills or equipment. Social distancing politics will have an impact on low-skilled jobs that require face-to-face interactions and will most probably incentivize automation; while companies will keep relying on temporary contracts to face unforeseen changes in the market. The urge to migrate from face-to-face to online education has also revealed the dramatic magnitude of inequality all around the world in terms of accessibility, support and resource availability; while the transition to telework has exposed the lack of a clear legal frame addressing matters such as the new operational costs transferred to employees, as well as the separation of personal and professional time. All of the aforementioned events, accentuated by Covid-19, provoked tensions and severe contractions in financial markets, disruption and increased risk in supply chains, dramatic spikes in unemployment, mental health issues and domestic violence rates. Furthermore, they exposed important dysfunctions of the current systems, and have served as a reminder of pending issues; but have also offered a real opportunity for positive and permanent change. In a fast-paced changing world driven by UN’s 2030 Sustainable Development goals, as well as a generational shift towards a green and sharing economy, companies have identified that, in order to prevail, they need to have a strong and relevant purpose. They have also acknowledged the enormous advantages of high-speed innovation and the value of resilience over efficiency. Governments face important challenges regarding an increasingly enfeeble labour market and possible reshaping of basic income schemes within an environment that demands a strong sense of leadership from them. People have more actively questioned “the order of things” but many have also welcomed the positive impacts on the environment and a powerful sense of community. Nonetheless, it has also become apparent that xenophobia and racism permeate society and that more serious debate should be taking place to address them. What all of these elements have in common is the shift towards a long-term rationale where stability, sustainability and well-being lead the way. The impact of the stochastic shock that Covid-19 pandemic represents has been felt in a micro and macroeconomic level. The shock in both supply and demand, the reliance on long-term public financial support packages, as well as the high volatility of financial markets have irreversibly set the grounds for a global recession; but, at the same time, they have had a particularly accelerating effect on the transformation of business model operations. Investors remain focused on their pursue for profit but nowadays the allocation of resources is driven by the identification of financially stable and successful companies that proactively address adaptability, transparency and social responsibility. Companies transform if they have incentives to do so. The benefits of complying with ESG criteria go beyond ensuring the long-term sustainability and relevance of a company in the market. Investment has been following very closely the stability

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of the enterprises and assessing future risks from an ESG perspective. For companies, adhering to these criteria, is now highly linked to the possibility of accessing financing. After Covid-19 disruption, investment will most probably stimulate traditional enterprises to be part of the migration towards ESG practices. The intensive interrelation of companies through the supply chain will have a cascading effect since the evolution of a company’s practices may incentivize, or sometimes put pressure on, others to change in order to hold on to their piece of the market. The performance of companies compliant to ESG standards has not been proven to be better or worse to those who are not. Nonetheless, their operational differences have shown a positive impact on their value concerning less volatility and, therefore, an appealing long-term stability for investors our statistical models confirm these literature findings stated at Principles for Responsible Investment (2016). Also, transparency and legal privacy obligations have become a valuable asset in the market, not only to understand the management of a company, its values and priorities; but, more importantly, to ensure the protection of all stakeholders’ interests. The ESG focus has changed the operation and strategy of enterprises, impacting supply chains and the way companies insert and ensure their place in the market. Moreover, ESG criteria adopted by investors and governments will keep on driving the migration towards a sustainable model and will reward those agents that adapt faster. According to Orsagh (2019), relevance and profitability will depend not only in quantitative figures but in qualitative aspects regarding adaptability, resilience and relevance; nonetheless, further efforts towards institutionalizing these changes are required in order to ensure the permanence of change. In 2015, every United Nations Member State adopted the 2030 Agenda for Sustainable Development, setting a new standard in international policy and materializing the commitment of all members to adopt immediate actions in order to fulfill 17 ambitious goals for the planet, people and prosperity. The matters of these objectives broadly include poverty, hunger, sustainability and equality, amongst others. Goal 13 is a call for urgent action to combat climate change and its impacts through appropriate financial flows, a new technology structure and an enhanced capacity building framework (UN Sustainable Development Goals 2020). Despite the controversy and polarization concerning global warming and climate change effects, scientific consensus has been largely achieved and supported by major international publications and agreements, discussing the dangerous shifts in weather patterns that compromise food production and expose communities to high-risk catastrophes. Concerns regarding climate change can be traced back to 1988, when the Intergovernmental Panel on Climate Change was established, after the severe drought and heat waves in the US and fires in the Amazon rainforest displayed the consequences of greenhouse effect. The creation of the United Nations Framework Convention on Climate Change (UNFCCC) in 1992 is also a benchmark in the matter. Currently, 197 countries have ratified the convention, which in addition to the Kyoto Protocol, the Paris Agreement and the aforementioned 2030 Agenda, are some of the main drivers of global warming and climate change mitigation plan of action.

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Environmental and economic sustainability go hand in hand considering that the negative impacts of production on the already scarce natural resources jeopardize future economic viability. Environmental investment, driven by these principles, allocate financial resources based on the information made available by companies about their pollution, resource waste or conservation, and animal treatment practices, for example. Nonetheless, environmental investment is just a part of sustainable investing, which also considers other aspects such as well-being and welfare. Sustainable investment aims to stimulate development, not only from an environmental and economical viewpoint but, by providing communities with affordable services (housing, medical support, education, security), and contributing to equality, diversity, social participation of individuals, democracy and good corporate governance. The massive shock caused by Covid-19 exposed the vulnerability and complexity of the connections that hold society together, costing millions of lives and jobs worldwide but, also, raising questions about the system of beliefs and understanding about how society functions. The colossal loss of stock market value, unemployment, closure of businesses and other economic impacts of the Covid-19 add to the dramatic social cost of the pandemic regarding social cohesion, mental and physical health, domestic violence and racism. Furthermore, this health crisis exposed the importance of sanitation, hygiene and access to water services, the unaffordability of wasting water or any natural resource for that matter, and an urgent transition to an equally sustainable consumption and production lifestyle, see Carroll (2020). From an economical perspective, Joseph Schumpeter’s term “creative destruction” illustrates how Covid-19 has functioned as an accelerating factor for the dismantling of long-standing practices in order to make way for innovation. The pandemic provoked a shift on design, development, sourcing and manufacture, while the disruption of trade exposed the need for relocating manufacture and have a better control over risk and resilience of suppliers. Since the Covid-19 pandemic, the UN has addressed the current opportunity for a systemic shift to a more sustainable economy and the importance of rebuilding it, not only from a green economy perspective but also, from a socially responsible focus. All these issues reinforce the current relevance of the ESG movement and the impacts that the ESG mentality is having on both financial and non-financial sectors.

2 The Textile and Apparel Industry Transform to Face Consumers Demanding an ESG Perspective Not so long ago, ethics and fashion seemed like two very different concepts. Nowadays, with the rise of green and share economies, as well as a sustainable mindset in younger consumers, brands have had to seriously reconsider their manufacturing processes, impact in the community and the environment since the human, animal

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and natural resource exploitation of the apparel industry started affecting its position in the market and has forced it to change. The apparel industry is known as one of the most polluting businesses in the world, just after the oil industry. Sustain your Style (2020) shows that some of the negative impacts of garment-related activities on the environment include: • Water contamination through the release of untreated toxic wastewaters and of microfibers in every wash cycle, as well as the use of fertilizers for cotton production, • An excessive consumption of water (20,000 L for 1 kg of cotton, for example), • An alarmingly increasing textile waste conformed by mostly non-biodegradable components • Heavy use of chemicals which have been linked to diseases and death among farmers, • Ocean water pollution and soil degradation, and • Emissions of greenhouse gases due to the energy (mainly coal) used during the production process, manufacturing and transportation of millions of garments. The carbon footprint of the apparel and footwear industries represent 8% of the global total, more than international airline flights and maritime shipping trips combined (Cerullo 2018). The climate impact of the industry has escalated very quickly, particularly driven by the rise of “fast fashion”, low price and disposable garments. In pursuit of profits, major fashion brands locate their factories in countries that provide them with geographical, legal and social advantages. Nonetheless, cheap production combined with lax enforcement of regulations in certain countries has caused companies to turn a blind eye to questionable practices within their supply chains, not only from an environmental viewpoint but from a social and corporate governance one. Child labor, low wages, health and safety risks, forced and trafficked labor and breaches of international terms of trade are some of the main social concerns about the future of the industry; as well as the reluctancy of some apparel and footwear companies to implement a transparency pledge.

3 Key Elements for a Transition to an ESG Industry Environmental Incentives. Today, the greatest concerns about a highly globalized fashion industry refer to its sustainability practices. Consumer’s expectations have changed and influenced the evolution of apparel industry towards an ethical and socially responsible future. The availability and immediacy of information has motivated customers to dig deeper into reliable information before purchasing any goods or services in the markets. Also, the greater interest in understanding the practices of the whole supply chain, not only from consumers but from investors, has motivated companies to share honest and transparent information in order to ensure their access to financing and their place in the market.

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Nowadays, individuals wish to be associated with companies who display a strong set of values towards sustainability and fair practices. Therefore, they are willing to pay more for products with environmental and ethical labelling that support local, green and fair practices, as well as consider their health and well-being. Animal rights in the clothing industry have also been subject to debate since the use of animal products such as wool, fur, skins and leather have been associated to cruel practices and the endangerment of many species. The challenge for the textile and apparel industry will not be easy, as progress on environmental metrics is different among countries. For example, main apparel producers like China and Mexico show a relevant lag, while major apparel consuming countries such as the US, Germany, Japan, and Canada have greater gains. The costs of labor, energy, water and credit access will complicate the transition for producers, since the ESG process implies assuming higher costs in the short term. Consumers will have the final word as they will have to pay more for garments that meet ESG criteria. The increase in unemployment associated with the pandemic and its impacts on poverty, inequality and personal income could generate a divergence in the transformation of the apparel industry to the new normal, particularly in advanced and emerging economies, generating an eventual environmental arbitrage (see Fig. 1, 2 and 3). Social Challenges. The Rana Plaza building collapse in Bangladesh in 2013 rekindled the debate about garment industry structure and practices; but even more about corporate social responsibility. The direct reasons for the collapse were that the building, being constructed with substandard material in a filled-in pond, added 3 Environmental Performance Index

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extra floors above the original permit in order to adapt from commercial to industrial use. Around 1,100 people died and 2,500 were injured. More than half of the victims were women along with children who were in the nursery facilities within. The building manufactured for brands such as Prada, Versace, Gucci, Mango and Walmart where workers earned 38 euros a month, see BBC News (2013), Dhaka Tribune (2017) and Marriot (2013). The clothing industry is one with the lowest payments, longest working hours, poorest working conditions but also one with the highest child labor. The lack of

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regulations in certain underdeveloped countries, derive in the employment of anyone capable of working in a factory. The children that work here are deprived of their right to education and are subjected to abusive treatment which results in mental and physical detriment in their well-being. Furthermore, labor issues in the fashion supply chain now also involve forced and trafficked labor, see Suhrawardi (2019). According to award-winning documentary “Invisible Hands”, 50% of trafficked victims (including children) are sold into forced labor. Currently, race and culture, also play an increasingly relevant role in fashion. Back in 2006, Tommy Hilfiger was involved in one of the most controversial fashionlinked rumors in recent times. Hilfiger allegedly expressed his aversion to the idea of minorities wearing his clothes. The designer and the brand emphatically denied the allegations. A similar case with Michael Kors became viral when he was pointed as the responsible of a racist remark, which ended up being dismissed as it was part of a satirical website. Cultural appropriation is also one of fashion’s biggest issues nowadays. Recently, cultural minister of Mexico called out Carolina Herrera for using embroidery techniques and patterns specific to certain Mexican indigenous communities on her 2020 collection. Other brands such as Chanel using Koranic verses on its clothes, as well as Prada’s products strongly resembling sambo dolls reinforce the urgency to address the matter (ABC News 2019). In 2018, luxury brand, Dolce and Gabbana, faced an international scandal, which ended up with the cancelation of its Shanghai show, after offensive screenshots, allegedly from Stefano Gabbana, were published on social media. In those conversations, Gabbana, is pointed out as the responsible of offensive comments and mocking Chinese stereotypes. 1/3 of D&G sales are made by Chinese consumers. The Italian fashion house apologized and argued that their accounts had been hacked (ABC News 2018). The immediate consequences of this issue exposed how social media and independent websites are pioneer platforms that allow anyone to call out offenders and bring issues into public awareness. Technology and Corporate Governance Transparency. Fashion industry has found in technology one of its most important allies. The shift to e-commerce and the boost of social media have allowed brands to strengthen their reach and influence. Since 2014, mobile digital retail has grown 340%. Currently, video and image-based commerce on social media have revolutionized the industry, accelerating digital transactions and connecting with younger shoppers, which, according to Bloomberg, could potentially quadruple social commerce to over $84 billion by 2024. The ads shown in platforms, such as Facebook, are based on the information provided by the user’s online activity, as well as personal information such as age and location. The tracking of activity allows these social media to create categories of profiles that advertisers can use to target personalized ads, as Malone (2017) stands for. Nonetheless, the collection of personal data occurs not only online but also offline through mailing lists, public record information, loyalty cards, among others. Consumer, aware or not, provides with information that makes these systems work, offering products and services that seem more relevant and specific to the

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individual. This has raised questions about the protection of personal information, privacy policies and terms and conditions of companies. Additional to the concerns about legal privacy obligations and disclosure of work conditions, the apparel industry faces another challenge linked to governance transparency: false labelling. Australian sportswear brand Rip Curl publicly apologized last year when it was discovered that some of its gear, was labelled “Made in China” but, in fact, had been made in one of North Korea’s garment factories. Rip Curl blamed a rogue supplier for outsourcing to “an unauthorized subcontractor”. According with Wong and Wen (2017), if companies fail to set clear conditions for its suppliers regarding subcontracting, this may translate into irreversible reputational damage and exorbitant losses. “The New Normal” for the Apparel Industry. The transition of companies to more sustainable practices is already happening in an economy where demand is now driven by increasingly sustainable habits. It is natural to think that business models will now incorporate a more responsible use of water and energy, as well as recycling and reusing of materials. Some brands, such as Patagonia, are incentivizing the return of used items by giving store credit to the customer. To prevent further generation and accumulation of waste, fashion will most probably transition to become a service rather than a good framed in a world where new generations of consumers prioritize affordability and sustainability, accessing rather than owning. Large fashion brands can play a significant role in the economy, making use of their purchasing power to put pressure on suppliers to clean up their methods. The disruption caused by Covid-19 has had an exorbitant economic impact which has exposed the urgency of important public debates such as clean energy and more stringent environmental policies. The reduced industrial activity has had a positive impact on the environment, for example, with the first decrease in global emissions since the 2008 global financial crisis. Regarding social matters, the pandemic exposed the importance of inclusion and protection of individuals. Life post-Covid will bring a set of challenges, for example, regarding unemployment. Nonetheless, a new longterm mentality will drive decision-making in both public and private sectors and will create interesting opportunities, incorporating ESG standards for many industries. Mexico has made significant progress regarding the use of recycled materials, which represents 60% of the cost structure of a garment. For example, plastic waste (32% is recycled) is used as a raw material for clothing and footwear. The new USMCA trade agreement has incorporated circular economy indicators that will surely motivate a transformation of the industry with an export vocation. Recycling generates a new value chain that joins the traditional textile clothing industry chain, having a positive impact on the economy, society and the environment (see Fig. 4).

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Fig. 4 ESG transition of the apparel industry Source Own estimates, Chan (2020) and Panorama (2006)

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4 The ESG Investment Revolution Will Transform Financial Markets Investments with a focus on environment, social and governance (ESG) have grown exponentially in recent years among the main financial markets in the world. Particularly the investment in ESG ETFs have grown around 798% cumulatively in the last 5 years. The largest number of ETFs being concentrated in the Equity market (88%) is driving a revolution in ESG performance metrics and motivating companies to transform their organizational culture. The change is not only being generated in securities issuers, but also in financial intermediaries (see Fig. 5 and 6). This growth may be accountable for many reasons, one of them being the new social consciousness for a sustainable economy and the protection of minorities’ rights. Furthermore, various authors have agreed on the fact that the Covid-19 outbreak will induce a more rapid grow on sustainable investments. In fact, a poll conducted by J.P. Morgan Research (2020) showed that about 55% of investors think that the Covid-19 crisis will have a positive impact on ESG investing for the next three years. These concerns have been in the mind of investors and consumers for over two decades, and now, with a more globalized world, and a more extreme situation (specifically in terms of climate change and global warming,1 and the pandemic) society is more preoccupied than ever with Environment and Social criteria; even more since The Great Confinement, which has shown to consumers, firms, investors and governments, that changes with major impacts are, indeed, possible. This is why, there is a 150000

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the first time in the Global Risk Report (2020) published by the World Economic Forum, the long-term risks that are most likely to happen are all environmental ones.

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crescent demand of the public for the firms to have more transparent procedures, and to be held accountable (Governance) for not respecting these new trends, either with bad propaganda, consumer boycotting, financing difficulties, or even with litigation. ESG investments will grow rapidly, observing from the already exponential increase on ETFs. According to a study of BlackRock, in 2019 ETFs and Index Mutual Funds amounted for around $220 billions of dollars, and are projected to reach $550 billions in 2024, and $1.2 trillions of dollars over the next decade (Pradhan 2020). This demonstrates the capacity of doubling the capital invested catalogued as ESG, which, given the current circumstances, could possibly mean an even bigger increase. It is important to note that the top 4 Equity ESG ETFs have $ 5.8 billions of assets under management (see Fig. 7). The augmenting numbers of ESG assets has being driven mainly by the demand for these financial instruments from investors. In several studies performed by Morningstar (2018) and The Responsible Investing Association (2019), it has been demonstrated that, nowadays, there is an overall preference of the public for ESG investing. These reports are compiled by Tucker and Jones (2020) where they show that in the US, the demand for ESG investing is bigger than expected by financial advisors, and that not only Millennials and women are interested in these themes, but also Generation X, men and even, although at a lower rate, Baby Boomers. The greater supply of ESG investments is explained by the commitment that companies have made to change their organizational culture to one compatible with ESG factors. On August 2019, over 180 CEOs of the most important enterprises in the US, members of the Business Roundtable, signed the new Statement on the Purpose of a Corporation. This document states that the objective of a firm has changed

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nowadays to protect interests of all stakeholders, from stockholders to employees, and even the community where the firm has operations. In summary, businesses are nowadays expected to adopt Corporate Social Responsibility (CSR) and ESG criteria, see Business Roundtable (2019). Along the same line, governments may also be the ones that incentivize the incorporation of ESG factors in investment projects (Hallegatte and Hammer 2020). For example, the historic low oil prices caused the retirement of subsidies to oil related products; this can be the time to redirect that capital on other energy projects such as renewable energies, sustainable buildings or electric public transportation, and the opportunity to incentivize a green recovery. For example, the European Union approved the biggest green stimulus in history with $572 billions, see Lewis and Birt. (2020). The ESG’s research has been on the radar for many years, with CSR or Responsible Investment documents dating back to the 1970’s. Currently, research on ESG factors and their impact on investment strategies has gained popularity among investors. Not surprisingly, there are more than 2,000 academic studies (Bernow 2020) that account for the relationship between financial performance and ESG criteria. One topic that has been highly researched lately, is the metrics that should be used to evaluate the impact (or to measure the performance) of these criteria. Having many different metrics and methods for ESG Indexes complicates the process of comparing firms and deciding which one to integrate to a portfolio. This is why institutions as the World Economic Forum (WEF) are working, along academics and consulting firms, to develop standardized metrics and reporting disclosures to assess this problematic, see WEF (2020), Toward Common Metrics and Consistent Reporting of Sustainable Value Creation. When investing on sustainable or ESG funds one of the main concerns for investors is the long-time belief that when investing responsibly you have to sacrifice financial performance, and a study of Riedl and Smeets (2017) showed that, indeed, investors expect to earn lower returns on ESG funds. Yet, investors are still willing to incur on

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“sub performance” in order to continue with a responsible investment (Tucker and Jones 2020). The first question that comes around is: If ESG investing returns lower gains than a conventional investment? There have been many academic papers and articles that attempt to investigate this relation between ESG criteria and financial performance, from rentability to even the cost of equity and capital. In most of the cases authors find a positive correlation among ESG criteria and financial performance (Bernow 2020). Studies such as Edmans (2012), Albuquerque et al. (2018), Orlando et al. (2007) and López Sarabia (2006)2 have found a positive correlation among these two variables. Either measured throughout Tobin’s Q, see Orlando et al. (2007) and Albuquerque et al. (2018); or with stock returns, see López Sarabia (2006) and Edmans (2012). El Ghoul et al. (2014) and El Ghoul et al. (2011) have found consistently that a firm that is considered ESG may have lower costs of capital and debt. For example, Goss and Roberts (2011) found that firms that have high risks regarding ESG issues pay an extra risk prime on bank loans than those that have less threats. As well, firms that have high concerns for ESG issues do receive lower rates for bank loans. In other words, firms that have more risks, (as ESG issues may end up in litigation expenses, consumer boycotting, or financial underperformance3 ) end up having a higher cost of debt; and firms that take into account this kind of risks, hence are less exposed to them, have a lower cost of debt. In summary, different studies have found a positive correlation between ESG criteria and financial performance, arguing in favor of an inexistent sacrifice of returns (although there are articles that find the contrary, see Menz (2010) and Friede et al. (2015).

5 Have ESG Investing a Better Performance Than Other Assets? The performance of ESG investments is key to promoting the generalized adoption of environmental, social and governance criteria in companies. In our statistical analysis we selected a global sample of 592 public companies that comply with 6 ESG indicators and that cover 10 relevant economic sectors (see Fig. 8). The empirical results show that companies in the financial sector had the best performance in inclusive culture and pro-woman brand, while the utilities sector performed better in public disclosure and equal pay. The industrial sector showed the worst performance in 5 of 6 ESG indicators. Not surprisingly, the public disclosure indicator is one of the top-scoring indicators for almost all companies, reflecting the efforts of the government and financial regulators. In the opposite direction is the equal pay indicator (see Fig. 9). 2 The

study of López Sarabia (2006) is specific for two firms in Mexico, this makes it difficult to have external validation. However, it is still a good example on how authors have measured financial performance and addressed the quantification of ESG criteria. 3 In the case of Lopez Sarabia (2006) TV Azteca suffered a massive market value loss when the public found out it was being investigated by the SEC.

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Public disclosure

Female Leadership

Equal Pay

Sex Harrament Policy

Inclusive Culture

53 Pro-Woman Brand

Average (Avg.) 82.6 79.6 82.9 88.5 77.1

StdDev (Std.) 12.5 18.9 18.5 11.9 21.3

Avg.

Std.

Avg.

Std.

Avg.

Std.

Avg.

Std.

Avg.

Std.

38.9 48.8 39.8 46.7 44.2

12.6 16.1 14.6 12.7 15.6

32.8 37.1 39.6 45.0 33.4

30.2 32.9 31.8 34.1 32.3

50.3 48.2 52.9 54.6 46.8

23.2 21.2 27.6 22.3 23.6

54.3 44.0 40.9 60.5 32.2

21.6 24.5 26.1 20.6 21.6

48.6 44.9 31.9 51.3 35.0

27.9 24.7 20.0 23.9 24.7

Industrials

72.1

20.5

38.3

16.1

30.5

30.3

45.0

23.2

23.7

19.2

40.3

26.1

Materials

81.9

21.0

42.9

14.9

45.2

34.5

59.0

26.0

37.1

22.9

39.7

23.2

Communications Consumer Energy Financials Health Care

Real Estate

88.9

10.5

39.1

14.8

47.2

32.9

53.6

26.3

35.8

23.7

27.4

19.5

Technology Utilities

82.6 90.1

14.4 11.5

42.3 44.1

13.6 12.6

41.0 50.1

30.9 33.7

59.4 56.3

26.8 22.0

52.0 46.1

25.9 23.3

46.0 39.2

24.7 19.3

Total score

84.6

15.5

44.5

14.0

41.9

33.1

53.9

23.7

50.8

24.9

45.9

24.7

Fig. 8 ESG factor scores by economic sector* *Note Our sample includes 592 companies from 45 countries covering 10 sectors. The countries with the highest representation in the sample are: USA (65%), United Kingdom (7%), Japan (5.4%), Canada (5%), Switzerland (3.38%) and Mexico (1.86%). Source Own estimates with data from Refinitiv, Bloomberg BI and MSCI (2020)

100 84.6 80

60

53.9 44.5

40

45.9 33.1 23.7

20

50.8

41.9

15.5

24.9

24.7

14.0

0 Public disclosure

Female Leadership

Equal Pay

Sex Harrament Policy

Average (colors)

Inclusive Culture

Pro-Woman Brand

StdDev

Fig. 9 ESG factor scores (includes all our sample). Average and StdDev. Note green color: better performance, blue color: performance improvement, yellow color: low score and red color: urgent intervention. Source Own estimates with data form Refiintiv and Bloomberg BI

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The empirical results show that companies in the financial sector had the best performance in inclusive culture and pro-woman brand, while the utilities sector performed better in public disclosure and equal pay. The industrial sector showed the worst performance in 5 of 6 ESG indicators. Not surprisingly, the public disclosure indicator is one of the top-scoring indicators for almost all companies, reflecting the efforts of the government and financial regulators. In the opposite direction is the equal pay indicator (see Fig. 9). Our statistical analysis also included 6 ESG equity indices classified by Advanced Economies, Emerging Markets, Advanced Economies and Emerging Markets, SP500 companies, LATAM companies and Mexican companies. The estimated yield at annual and daily rate was contrasted against its benchmarks where ESG companies do not predominate. The results show that there is no significant difference in the average return at daily and annual rates, although the averages of the ESG indices are never lower than investments that are not predominantly ESG (see Fig. 10, 11 and 12). Regarding the variance of annual and daily returns, it is striking that the ESG indices of Mexico and Latam increased, although they did not have a significant difference with their benchmarks, a situation that could reflect the asymmetries to comply with ESG criteria. Also, we estimated GARCH (1, 1) models for the daily returns of the Mexican ESG index and its benchmark. The statistical results show Composition of companies

Mean

EA_EM_INDEX

Advanced Economy and Emerging Markets

5.10

ESG_EA_EM_INDEX

ESG Advanced Economy and Emerging Markets

MSCI_EA_INDEX

Advanced Economy

Index

Median Max

Min

StdDev

Skewness

Kurtosis

10.20

0.10

2.01

3.61

25.06 -14.06

5.35

5.54

24.73 -13.33

9.50

0.07

2.15

5.49

4.50

25.19 -12.73

9.56

0.08

2.09

ESG_MSCI_EA_INDEX ESG Advanced Economy

7.90

6.70

28.84 -10.63

9.74

0.13

2.13

MSCI_EM

Emerging Markets

2.34

-1.37

37.98 -25.24

16.78

0.26

1.91

ESG_MSCI_EM

ESG Emerging Markets

4.02

1.35

41.72 -22.25

16.39

0.32

2.08

MSCI_LATAM

Latin America Economies

0.70

3.51

44.28 -42.59

21.34

-0.25

2.42

ESG_MSCI_LATAM

ESG Latin America Economies

3.96

9.51

55.06 -39.96

23.72

-0.07

2.32

SP500

Index of the 500 largest U.S. 8.75

10.08

28.88 -8.81

8.48

-0.02

2.40

ESG_SP500

Securities meeting sustainability criteria (ESG)

8.97

10.59

30.59 -8.62

8.39

0.01

2.56

IPYC_MEXICO

Mexican IPC Index

-1.36

-0.39

10.82 -20.16

8.35

-0.56

2.22

ESG_MSCI_MEXICO

Mexican companies with ESG criteria

1.42

2.42

15.34 -16.06

8.59

-0.42

1.96

Fig. 10 Performance equity index: descriptive statistics overall vs ESG (Oct-2015 to Jul— 2020%YoY) Source Own estimates with data from Bloomberg

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Fig. 11 Performance ESG equity index (Oct—2015 to Jul—2020%YoY) Source Own estimates with data from Bloomberg Index EA_EM_INDEX

Composition of companies

Mean

Advanced Economy and Emerging Markets

Median Max

Min

StdDev

Skewness

Kurtosis

0.03

0.04

8.39

-9.51

0.94

-1.28

25.53

ESG Advanced Economy and ESG_EA_EM_INDEX Emerging Markets

0.03

0.05

8.52

-9.38

0.93

-1.22

26.24

MSCI_EA_INDEX

0.03

0.05

8.77

-9.92

0.97

-1.21

26.41

ESG_MSCI_EA_INDEX ESG Advanced Economy

0.04

0.06

8.76

-9.89

0.96

-1.18

26.19

MSCI_EM

Emerging Markets

0.01

0.05

5.73

-6.71

1.01

-0.65

8.71

ESG_MSCI_EM

ESG Emerging Markets

0.02

0.05

5.16

-6.98

1.02

-0.67

8.48

MSCI_LATAM

Latin America Economies

-0.01

0.00

12.09 -14.94

1.76

-0.87

14.21

ESG_MSCI_LATAM

ESG Latin America Economies

0.00

0.02

11.58 -14.86

1.84

-0.71

12.10

SP500

Index of the 500 largest U.S.

0.00

0.00

4.86

-6.42

0.98

-0.51

8.23

ESG_SP500

Securities meeting sustainability criteria (ESG)

0.01

0.00

4.33

-6.65

0.99

-0.48

8.20

IPYC_MEXICO

Mexican IPC Index

0.04

0.03

9.38 -11.98

1.15

-0.67

24.28

ESG_MSCI_MEXICO

Mexican companies with ESG criteria

0.04

0.04

9.58 -11.99

1.15

-0.61

24.39

Advanced Economy

Fig. 12 Performance equity index: descriptive statistics overall vs ESG (Oct.2014 to Jul-2020, % daily change) Source Own estimates with data from Bloomberg

56

P. López Sarabia et al.

Dependent Variable: ESG_MSCI_MEXICO Method: ML ARCH - Normal distribution

Dependent Variable: IPYC_MEXICO Method: ML ARCH - Normal distribution

Sample: 1 1507

Sample: 1 1507

Included observations: 1507

Included observations: 1507

GARCH = C(2) + C(3)*RESID(-1)^2 + C(4)*GARCH(-1) GARCH = C(2) + C(3)*RESID(-1)^2 + C(4)*GARCH(-1) Variable

Coefficient Std.Err z-Statistic Prob Variable

C

0.023

0.020

1.130 0.259 C

C

0.043

0.008

5.133

RESID(-1)^2

0.122

0.013

GARCH(-1)

0.831

0.018

Prob

0.020

0.733

0.463

0C

0.036

0.007

4.917

0

9.361

0RESID(-1)^2

0.118

0.013

9.095

0

45.278

0GARCH(-1)

0.842

0.017

48.129

0

Variance Equation

0.953

R-squared

z-Statistic

0.015

Variance Equation

Sum coeff

Coefficient Std.Err

Sum coeff

0.960

-0.0003 Mean dependent v 0.006 R-squared

-0.0004 Mean dependent var -0.005

Fig. 13 GARCH (1, 1) model using daily returns (Oct-2014 to Jul-2020) Source Own estimates with data from Bloomberg Categorization by COVID19 60 40 20 0 -20 -40

COVID19=0

ESG_MSCI_MEXICO

ESG_SP500

IPYC_MEXICO

MSCI_LATAM

ESG_MSCI_LATAM SP500

MSCI_EM

ESG_MSCI_EM

MSCI_EA_INDEX

ESG_MSCI_EA_INDEX

ESG_EA_EM_INDEX

IPYC_MEXICO

ESG_MSCI_MEXICO EA_EM_INDEX

SP500

ESG_SP500

MSCI_LATAM

ESG_MSCI_LATAM

ESG_MSCI_EM

MSCI_EA_INDEX

ESG_MSCI_EA_INDEX MSCI_EM

EA_EM_INDEX

ESG_EA_EM_INDEX

-60

COVID19=1

Fig. 14 Performance ewquity index: overal vs ESG (Oct-2015 to Jul-2020, %YoY) Note COVID19 = 0 period before to pandemic, COVID = 1 pandemic period Source Own estimates with data from Bloomberg

How Covid-19 Has Accelerated the Garment and Financial Investment Industries’ …

57

that the Mexican ESG index has a lower volatility in relation to its benchmark (see Fig. 13). It is important to note that the box-plots for our 6 ESG Equity Indices and their benchmarks registered a greater dispersion during the COVID-19 pandemic in relation to the previous period, but without this difference being statistically significant (see Fig. 14 and 15). Finally, we designed 3 portfolios with a sample of Mexican companies that meet ESG criteria according to the S & P/BMV Total Mexico index (Bolsa Mexicana de Valores 2020a, 2020b and Lara 2020). The first portfolio includes companies with betas greater than 1, known as aggressive, composed by: ALFA, Cemex, Banorte, Bimbo and Industrias Peñoles. The second portfolio called defensive includes companies with betas less than 1, composed out of: Qualitas, Prologis, Arca, Ienova and Corporativo Vesta. The last portfolio was a combination of both (called neutral) including: Cemex, Banorte, Prologis and Ienova. All portfolios are compared against the performance of the S&P/BMV IPC, the benchmark for the Mexican stock exchange. The sample uses information from July the 17th 2015 to July 31, 2020. Categorization by COVID19 15 10 5 0 -5 -10 -15

EA_EM_INDEX ESG_EA_EM_INDEX IPYC_MEXICO ESG_MSCI_MEXICO SP500 ESG_SP500 MSCI_EA_INDEX ESG_MSCI_EA_INDEX MSCI_EM ESG_MSCI_EM MSCI_LATAM ESG_MSCI_LATAM EA_EM_INDEX ESG_EA_EM_INDEX IPYC_MEXICO ESG_MSCI_MEXICO SP500 ESG_SP500 MSCI_EA_INDEX ESG_MSCI_EA_INDEX MSCI_EM ESG_MSCI_EM MSCI_LATAM ESG_MSCI_LATAM

-20

COVID19=0

COVID19=1

Fig. 15 Performance equity index: overall vs ESG (Oct-2014 to Jul-2020, % daily change) Note COVID19 = 0 period before to pandemia, COVID = 1 pandemia period Source Own estimates with data from Bloomberg

58

P. López Sarabia et al.

Minimum variance Aggressive

Period

Return

Std. Dev.

Beta

CV

Bechmark

Pre-COVID19

(0.0753)

0.0117

1.1231

15.4861

(0.0058)

Defensive

From July 2015

0.0763

0.0083

0.5885

10.8708

(0.0058)

Neutral

to January 2020

0.0707

0.0111

0.7402

15.7385

(0.0058)

Aggressive

COVID19

0.5735

0.0257

1.0273

4.4821

(0.3010)

Defensive

March to

0.0460

0.0173

0.5639

37.6431

(0.3010)

Neutral

July 2020

0.0460

0.0276

1.0317

59.9579

(0.3010)

Aggressive

From July 2015

0.0460

0.0249

0.8961

54.1432

(0.0388)

Defensive

to

0.0541

0.0097

0.6178

17.8892

(0.0388)

Neutral

July 2020

0.0460

0.0125

0.8568

27.2396

(0.0388)

Fig. 16 Portfolios with ESG Mexican companies—performance measure Note CV = coefficient of variation. Red color indicates the best portfolio by type of risk. The only ESG portfolio that lost against the benchmark was the aggressive Pre-COVID19. The agressive portfolio is composed by: Alfa, Cemex, GFNORTE, Bimbo and Peñoles. The defensive portfolio is composed by: Qualitas, Prologis, Arca Continental, Ienova and Vesta. The neutral porfolio is composed by: Cemex, GFNORTE, Prologis and Ienova. Source Own estimates with data from Bloomberg. Benchmark is the IPC-BMV Index return

Moreover, the portfolios were estimated under three different scenarios. One for the whole series, other without the pandemic outbreak of 2020 and the last for the main period of COVID-19 crisis (from March to July 2020). The results were consistent with what expected, ESG portfolios outperformed in the majority of the cases the benchmark (except, the aggressive portfolio in the Pre-COVID19 period, which could be explained by a long period of bull market and an accommodative monetary policy of the main central banks) and presented a lower volatility (see Fig. 16).

6 Conclusions, Challenges and Opportunities of an ESG Global Transition The year 2020 has become a milestone in history and a turning point regarding worldwide dynamics and connections. The COVID-19 pandemic acted as an accelerating factor of transformations that were already set-in motion. Sustainability will play a key role in decision-making the companies in order to ensure its economy viability in the future. For example, the textile and apparel industry are making significant changes in its operation to incorporate ESG factors that respond to the demands of new consumers.

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We must recognize asymmetries in the re-opening of economies at a global level, which could deepen the damage to the clothing industry and its ability to drive the adoption of ESG criteria. The breakdown of global supply chains could lead garment manufacturers to seek new suppliers whose quality standards are not compatible with the environment and labor rights, simply seeking to reduce costs. On the demand side, we could expect a drop in the consumption of luxury brand clothing and a lower willingness to pay a higher price for clothing that is compatible with ESG factors. Even more so, if we consider that the highest unemployment rate associated with the pandemic is focus among young people under 30 years of age. ESG portfolios, overall, are defensive, or at least tend to be. One of the reasons for a firm to take into account ESG criteria is that it contributes to risk control and mitigation, which makes these firms’ assets (shares and debt) less risky, hence, they are more likely to have an expected lower return that the one of the market; in other words, they are defensive. Although in the case of Mexico,4 the limited number of public companies makes the non-ESG indices and the ESG indices have a similar composition or weighting of companies, making the performance statistically similar. Although in a bull market scenario, the benchmark could perform better due to risk appetite. The fact that ESG indices or portfolios do not show a significant difference in performance is not necessarily a disadvantage, it is a focus problem. In other words, the correct reading is that a company that cares about ESG factors will not underperform compared to companies that are less sensitive to these factors, but will have the advantage that, by reducing risk in a broad sense, its performance will be considered relatively better. It is important to acknowledge that the results of our ESG portfolios are not as robust, since the portfolios were formed with few companies, which may have somewhat skewed their performance towards riskier investments (not completely eliminated, diversifiable risk of portfolios). That is, adding more firms to the portfolios could change their performance, but not the conclusion, since the more assets are incorporated, part of the total risk is reduced (but the systematic measured by beta is maintained, the criterion used to form the portfolios-aggressive, defensive and neutral-) and with-it profitability. Another element to consider is that the selection of companies was based on the availability of information and the length of time the company has held an ESG classification. One more element to take into account in the estimation of the portfolios is that only those with minimal variance were considered, leaving behind the portfolios that could have performed better by assuming more risk, opening a future line of research to see the effect of ESG portfolios in periods of high volatility or financial stress, such as that generated by COVID-19 (a more representative sample than the one used by us). We find great heterogeneity in the ESG performance indicators by industry and country, so it is essential that the agenda focuses on the formation of a harmonized system of indicators that are transparent and comparable between companies, 4 And

in other emerging markets such as Latin America.

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industry and countries. It is also essential that future research focus on the correct weighting of each component that makes up the general ESG indices, since today there is a mark of difference. Future lines of research for the garment industry should focus on: i) if an international regulation promoted within the World Trade Organization could accelerate the adoption of ESG criteria by the entire industry on a global scale, ii) the empirical evidence that allows us to see that social responsibility and good reputation are rewarded by consumers with greater purchases, and iii) the measurement of the impact that the adoption of ESG criteria has in the industry, considering the traditional variables (costs, sales, operating expenses, etc.), but also the positive and negative externalities associated with the protection of the environment, social responsibility and corporate governance, in a scheme of real options. Finally, when the crisis generated by the pandemic is resolved, the future research agenda should consider the empirical evidence of the real impact that Covid-19 had on the accelerated adoption of ESG criteria in the industry and the support given to these companies by the financial sector, beyond the momentum generated by the 2030 agenda of the United Nations sustainable development goals.

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Orsagh M (2019) Integrating ESG Standards: Qualitative and Quantitative Approaches. https:// business-ethics.com/2019/04/01/integrating-esg-standards-into-investment-analysis-qualitativeand-quantitative-approaches/. Accessed 14 July 2020 Panorama (2006) Climate chaos: Bush’s climate of fear. http://news.bbc.co.uk/2/hi/programmes/ panorama/5005994.stm. Accessed 21 July 2020 Pradhan T (2020) ESG Investment Insights. Bloomberg [From ESG Investment Insights: Getting What You Really Want From ESG Using Optimization Webinar] Principles for Responsible Investment (2016) Linking ESG ratings to returns and volatility. https:// www.unpri.org/listed-equity/linking-esg-ratings-to-returns-and-volatility/164.article. Accessed 14 July 2020 Riedl AM, Smeets P (2017) Why Do Investors Hold Socially Responsible Mutual Funds? J Finance Forthcoming. https://ssrn.com/abstract=2318987. https://doi.org/10.2139/ssrn.2318987. Accessed 21 July 2020 Schlogl L (2020) Pandemic’s Box: How Corona Changes the Future of Work around the Globe. https://www.gpidnetwork.org/2020/04/20/pandemics-box-how-corona-changes-thefuture-of-work-around-the-globe/. Accessed 17 July 2020 Suhrawardi R (2019) The Big Issues Facing Fashion in 2019. https://www.forbes.com/sites/rebecc asuhrawardi/2019/01/16/the-big-issues-facing-fashion-in-2019/#14cb297b23a9. Accessed 27 July 2020 Sustain your Style (2020) Fashion’s environmental impact. https://www.sustainyourstyle.org/oldenvironmental-impacts#scroll-to-top. Accessed 20 July 2020 The Global Risks Report (2020). World Economic Forum website: https://www.weforum.org/rep orts/the-global-risks-report-2020. Accessed 11 July 2020 The Responsible Investing Association (2019) 2019 RIA Investor Opinion Survey, pp 3–4. www. riacanada.ca/research/2019-ria-investor-opinion-survey/. Accessed 13 July 2020 Tucker J, Jones S (2020) Environmental, social, and governance investing: investor demand, the great wealth transfer, and strategies for ESG investing. J Financ Serv Prof 74(3):56–75 UN Sustainable Development Goals (2020) Goal 13: Climate Action. https://www.un.org/sustainab ledevelopment/climate-change/. Accessed 22 July 2020 WEF (World Economic Forum 2020) Toward Common Metrics and Consistent Reporting of Sustainable Value Creation Wong S, Wen P (2017) North Korea factories humming with ‘Made in China’ clothes, traders say. https://www.reuters.com/article/us-northkorea-labour-china-insight-idUSKBN1A T00Q. Accessed 20 July 2020 Yueh L (2013) The Economic Impact of Brexit and What It Means For Financial Stability, 28 November 2013. https://www.forbes.com/sites/lbsbusinessstrategyreview/2018/11/28/the-eco nomic-impact-of-brexit-and-what-it-means-for-financial-stability/#252d44b6383e. Accessed 15 July 2020

Contagion Adverse Degree, Income Inequality and Economic Growth Salvador Rivas-Aceves

Abstract By introducing the effects of the pandemic into an endogenous economic growth model, with a financial system, among human, physical and financial capitals, diminishing returns, constant scale effects and heterogenic agents, the impact by the contagion adverse degree in households is modelled. Results are: a) contagion adverse degree affects intertemporal marginal substitution rate of households and production process for industry; b) short and long run economic growth rate are also affected by the contagion adverse degree of households; c) human capital growth rate and distribution dynamics relies on contagion adverse degree as well; d) in the absent of a financial system, poor households will allocate less time to leisure if they want to consume more or increase human capital or both when the contagion adverse degree is low, and viceversa; e) physical and human capital ratio of the economy relies only in one sector when there is none financial system. Consequently, economic growth rate is lower since only one sector performs production activities while having a contagion adverse degree low; f) rises in output or decreases in salary due to the contagion adverse degree lead to increases in inequality; g) inequality decreases when human capital goes up; h) physical capital generates small and positive changes in inequality; i) financial capital causes positive impacts on inequality; j) inequality decreases if total multifactorial productivity increases; k) macroeconomic equilibrium depends in negative ways because of contagion adverse degree. Keywords Income inequality · Financial market · Economic growth and macroeconomic equilibrium

1 Introduction By the end of 2019, the coronavirus outbreak started in China with a high rate in the speed of contagions. At the beginning, in the first two months of 2020 daily new cases were below 5,000 but constant; on March 11 and because of the 118,000 verified S. Rivas-Aceves (B) Escuela de Ciencias Económicas y Empresariales, Universidad Panamericana, Ciudad de México, México e-mail: [email protected] © The Author(s), under exclusive license to Springer Nature Singapore Pte Ltd. 2021 G. Dávila-Aragón and S. Rivas-Aceves (eds.), The Future of Companies in the Face of a New Reality, https://doi.org/10.1007/978-981-16-2613-5_4

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cases the World Health Organization declared COVID19 a pandemic (WHO 2020). Since then, daily new cases have grown exponentially as Figure 1 shows: Due to the pandemic declaration, most countries decided to suspend none sciential economic activities as a control instrument. Asian economies were the first to take that decision, followed by European and American economies as the spread of the disease was increasing. The suspension for economic activities caused several affectations, production decreased substantially as well as investment. Around the globe, private sector was forced to apply the social distancing like individuals. Naturally, the rhythm of economic growth rate went down drastically, this rate was already with negative tendency because economic recession worldwide, but the pandemic did work as detonating element for a generalized economic crisis. By analyzing G7 economies regarding economic growth rate, is possible to understand the economic consequences. As Fig. 2 shows, economic growth rates before the pandemic were declining but closed to zero, mainly during the last two quarterlies of 2019; but in 2020 they collapsed down to −12% for most G7 economies. Social distancing also affected the rhythm of life on households. Because shopping for food and medicines were defined as the only essential activities in most economies, other activities were partially affected or even suspended; consequently, consumption also went down. Figure 3 presents pretty much the same dynamics as economic growth rates for G7 economies, during 2019 consumption growth rates were stable but after that they also decreased in 12% approximately. Nowadays, Japan is the only G7 economy that is showing a recovery in both economic and consumption growth rates, but still remain negative. When production and investment activities are reduced, the use of production factors similarly is

Fig. 1 Daily new confirmed cases of COVID19 per region Source: World Health Organization, daily COVID-19 situation report

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Fig. 2 Economic growth rate of G7 economies Source: Own elaboration based on Federal Reserve Economic Data

Fig. 3 Consumption growth rates for G7 economies Source: Own elaboration based on Federal Reserve Economic Data

abridged. Thus, could generate less costs for industry but also generates a decreasing in sales. The latter exasperates its behavior when consumption is also falling. Therefore, the natural outlet channel for entrepreneurs is employment. Some owners decided to partially cut salaries in order to avoid staff layoffs; but when the scenario was inevitable, mass layoffs or even bankruptcy appeared and then unemployment rate grew. Figure 4 shows how unemployment rates suddenly increased at the end of the first quarterly of 2020 in all G7 economies except Japan. Because of the worldwide economic performance but specially the decrease on employment, the World Bank published a global extreme poverty projection. Figure 5 shows that extreme poverty had a decreasing tendency in the last 5 years at least, by the end of 2019 there were 610 million of people in extreme poverty. Because of the

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Fig. 4 Unemployment rates for G7 economies Source: Own elaboration based on Federal Reserve Economic Data

pandemic, projection shows that by 2021 people in extreme poverty will rise to 720 million. The advances won since 2015 are going to be lost due to the pandemic. Most regions are going to be affected worldwide regarding extreme poverty, in particular Africa, Asia and Latin America. Projections of the World Bank estimate increases not only in extreme poverty, but in poverty within families that earn less than $5.5 dollars, see Fig. 6. With economic activities going down as well as consumption and unemployment rising, households are losing income and hence having lower quality of life. Nevertheless, poverty is the final consequence in this

Fig. 5 The Impact of COVID-19 on Global Extreme Poverty Source: World Bank, Lakner et al (2020), PovcalNet, Global Economic Prospects, Extreme poverty is measured as the number of people living on less than $1.90 per day

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Fig. 6 The regional distribution of the COVID-19-induced poor Source: World Bank Group (2020), Lakner et al. (2020), PovcalNet, Global Economic Prospects

historical generalized crisis, before there is an inequality process that set the basis for poverty. Income inequality is an extreme disparity of income distributions with a high concentration of income usually in the hands of a small percentage of a population. This disparity appears when labor in a broader sense either earn less or any salary. If the disparity increases, people in the below side is getting poor in relation with people in the upper side. Therefore, poverty in not measure only on the level of salary an individual earns, but also how far is that salary away from others. For instance, if all salaries go down then poverty remain constant. Income inequality has been a very important issue within economic theory, particularly the relation with economic growth, and it is easy to identify a possible relation between both. Most researches that deals with income inequality and economic growth causal effect, are based on the idea addressing by Kuznets (1955); he argued that the economic growth rate and income inequality connection can be described as an inverted-U shape. Nonetheless, several other studies have discovered diverse types of associations based on the theoretical economy setting. However, we know for sure that the relation between economic growth and inequality is dynamic. In that sense, the Ramsey (1928) model have become a standard framework for analyzing macrodynamics and growth theory, in where all agents are treated as identical basically because there are same endowments and preferences across them. Based on how pandemic is affecting all types of households, it is evident that inequality and economic growth studies must feature heterogeneity in agents as Piketty (2013), Stiglitz (2012), Piketty and Saez (2014), Piketty and Zucman (2015), Krusell and Smith (2015), Carroll and Young (2018) papers stand for. The Ramsey model can incorporate heterogeneity if preferences remain homothetic according to

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Gorman (1953), Eisenberg (1961), Becker (1980), Chatterjee (1994), Krusell and Smith (1998), Caselli and Ventura (2000), and Mailar and Mailar (2001). The relation between inequality and economic growth has also been conducted from the empirical point of view, for instance see studies of Anand and Kanbur (1993), Alesina and Rodrik (1994), Persson and Tabellini (1994) and Perotti (1996) with outcomes that evidence a negative connection. On the other hand, Li and Zou (1998), Forbes (2000), Barro (2000), Sorger (2000), Ehrlich and Kim (2007) and Turnovsky (2002, 2015) discovered positive relationships. The objective of the present research is to analyze the relationships between inequality and economic activities, by introducing the effects of the pandemic into an endogenous economic growth model, with a financial system, among human, physical and financial capitals, diminishing returns, constant scale effects and heterogenic agents. The impact for a pandemic is modelled by a parameter of contagion adverse degree in households. This parameter measures how concern an individual is for getting the disease, therefore the higher the concern is the smaller of the parameter will be since there is no motivation to perform any economic activity. On the other hand, heterogeneity comes from capital endowments in agents which also allows to introduce financial system into the economy. Heterogeneity allows to analyze the impact in different types of households. First, an economy without financial system is examined and then the financial system is introduced. Macroeconomic equilibrium, distributional dynamics and long-run shocks due to the interaction between a pandemic, economic growth, inequality and financial system are categorized. Core results are: a) contagion adverse degree affects intertemporal marginal substitution rate of households and production process for industry; b) short and long run economic growth rate are also affected by the contagion adverse degree of households; c) human capital growth rate and distribution dynamics relies on contagion adverse degree as well; d) in the absent of a financial system, poor households will allocate less time to leisure if they want to consume more or increase human capital or both when the contagion adverse degree is low, and viceversa; e) physical and human capital ratio of the economy relies only in one sector when there is none financial system. Consequently, economic growth rate is lower since only one sector performs production activities while having a contagion adverse degree low; f) rises in output or decreases in salary due to the contagion adverse degree lead to increases in inequality; g) inequality decreases when human capital goes up; h) physical capital generates small and positive changes in inequality; i) financial capital causes positive impacts on inequality; j) inequality decreases if total multifactorial productivity increases; k) macroeconomic equilibrium depends in negative ways because of contagion adverse degree.

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2 Economic Fundamentals Consider an economy where physical and human capital are accumulated, there is no international trade and uncertainty, with heterogeneous households and total population to be constant across time, so N = 1. Since the economy is closed, production is intended either to invest or to consume according to Eq. (1); for simplicity in notation sub index t is not considered: y = k + c.

(1)

where y, k and c measure production, physical capital and consumption respectively. Taxation is not considered in this economy due to the absence of the government. Heterogeneous Households Households are modelled to be heterogeneity as assuming that they have different physical capital endowments; therefore, two types of households are defined: rich (R) and poor (P). Consequently, total population is N = n R + n P . Rich households hold the enough physical capital needed to produce besides a share of capital as a surplus (φk), due to economic activities in previous periods or because it was inherited, etc. Poor households do not retain enough capital to produce, accordingly capital satisfies: K = kR + kP,

K0 > 0

(2)

k R = k + φk, 0 < φ < 1

(3)

k P < k.

(4)

As usual households seek to maximize utility based on the consuming of a perishable good, however now their decisions are going to consider not only the anxiety for current consumption, but for the contagion adverse degree measure by ω as well. They are also going to maximize on leisure l, at any time t, in the presence of decreasing marginal yields. Subsequently, utility can be measure as follows: ∞ U = [ln c + θ ln l]ωe−ρt dt, i

(5)

0

for i = R, P, with ρ as the subjective discount rate and θ the share of utility due to leisure. If poor households want to increase their economic activities, they need to allocate less time to leisure in order to consume more or to produce. At the same time, either consumption or leisure can be inhibited due to an increase in ω, since 0 ≤ ω ≤ 1. The higher the contagion adverse degree is, the less willingness

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for consuming households will express because in order to consume they need to interact with other people, which means to put their self at risk, under this scenario the higher the contagion adverse degree is, the lower ω will be since any economic activity is being inhibited. Industry Production for the perishable commodity is carried out by industry and it is done by using physical capital, k, and human capital, h = 1 − l in the presence of constant returns to scale in both factors. All firms face identical competitive production conditions but different reaction to the contagion adverse degree that is affecting human capital. Nevertheless, all satisfy the following given technology: y = αk β ωh 1−β ,

(6)

where α stands for multi-factorial marginal productivity and β is a technological parameter. From a macroeconomic point of view the consumer sector owns all productions means. Equation (6) shows that the level of production is affected by the contagion adverse degree in the sense that producers can decide whether to contract, suspend or maintain economic activities in the presence of high risk in labor health. Human Capital Dynamics Initial allocations of human capital are the same for all households, H0 > 0, and is accumulated as the following dynamics fulfill: h˙ = ωγ h.

(7)

In Eq. (7) γ symbolizes the growth rate of human capital. The higher the amount of physical capital is allocated, the higher γ will be. Stock of human capital in the economy therefore is H = h R + h P . Not to invest in production activities is the opportunity cost of increasing human capital. Increasing human capital is also being affected by ω, because contagion risk is also present in academic or research activities since they depend on physical interaction with people. Financial System Rich households can allocate their capital surplus to loans or spend it in education in order to increase human capital. These options create a financial system in this economy so idle resources are reassigned, thus allowing households to ask for a credit so production process or human capital accumulation can be performed. This configuration in the financial follows Rivas-Aceves and Amato (2017) while adding human capital decisions. Let δ be the cost of a credit and an additional income to rich households. Assume credits are the only financial instrument households can access to, therefore: φk = k(1 + δ − γ ).

(8)

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Equation (8) shows possible allocations for capital surplus since it is assumed there is no savings in the economy, hence entire physical capital must be engaged in economic activities. Poor households lack the enough capital to produce, but they can use the capital they possess to increase human capital this means they can decide not to ask for credits. Other option is getting additional capital within credit market to produce or to increase human capital or both: k P + k = −δk − ωγ k.

(9)

The previous equation indicates that there is always a cost for poor households if they want to accumulate any type of capital. Financial activities are modelled to be not affected by the contagion adverse degree since they can be conducted by virtual means. Investment Decisions Investment, I , can be considered as the allocation of resources to increase either human capital or physical capital by households, so: I = k + h.

(10)

Returns of investment are marginal productivity of physical capital and salary according to the following market clearing conditions: ∂ yi = αβk β−1 ωh 1−β = r, ∂k i

(11)

∂ yi = α(1 − β)k β ωh −β = w. ∂h i

(12)

Salary depends directly on the level of human capital: the more human capital an individual possesses, the higher salary is. Therefore, total salaries in the economy are:  W i = h i wi . (13) Consequently, the cost of accumulating human capital defined in Eqs. (8) and (9) will be compensated by salaries of future periods.

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3 Macroeconomic Equilibrium Consequently, at this economy, physical capital accumulation relies on production, salary, credit market, consumption conditions and contagion adverse degree. Because heterogeneity in households, two capital accumulation dynamics can be identified: k˙ R = αk β ωh 1−β + W R + k(1 + δ − ωγ ) − c R , k˙ P =



αk β ωh 1−β + W P − k(1 + δ + ωγ ) − c P , k P = k k P < kt W P − kωγ − c P .

(14)

(15)

Equation (15) shows a separable scenario in which poor households are able to take productions decisions if they can access to credit market, while working and increasing human capital; or the contrary when no credit is accessible. Hence, this model allows to analyze macroeconomic equilibrium in the presence of a pandemic as well as the impact on income inequality. Equilibrium is obtained when households maximize (1) subject to (14)–(15). So, macroeconomic equilibrium depends on both types of households as follows: Scenario A: Macroeconomic equilibrium with credit market lim λke−ρt = 0,

(16)

  ci = ωθ αk β (1 − β)ωl −β + wi , li

(17)

ki βwi = , i h (1 − β)r

(18)

t→∞

α − δ − ωγ = ρ −

λ˙ , λ

(19)

ψ R = α + 1 + δ − ωγ − ρ,

(20)

ψ P = α − 1 − δ − ωγ − ρ,

(21)

=

ψR + ψP = α − ωγ − ρ, 2

(22)

where Eq. (17) is the intertemporal marginal substitution rate of households if all of them produce, (18) shows the physical and human capital ratio, while (19) stands for the Keynes-Ramsey consumption rule, Eqs. (20)–(22) are the economic growth rates of both sectors and the total economy. Within this equilibrium, contagion adverse degree affects the intertemporal rate between leisure and consumption, physical and human capital ratio and economic growth rate.

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Scenario B: Macroeconomic equilibrium without credit market lim λke−ρt = 0,

(23)

  cR = ωθ αk β (1 − β)ωl −β + w R , lR

(24)

cP = ωθ w P , lP

(25)

kR βw R = , hR (1 − β)r

(26)

t→∞

α−

=

λ˙ δ − ωγ =ρ− 2 λ

(27)

ψ R = α − ωγ − ρ,

(28)

ψ P = −ωγ − ρ,

(29)

α ψR + ψP = − ωγ − ρ. 2 2

(30)

When there is no financial system, poor households will allocate less time to leisure if they want to consume more or increase human capital or both, as Eq. (25) shows. In fact, their intertemporal marginal substitution rate depends only on salary besides the contagion adverse degree; while the corresponding to rich households on total income: benefits plus salary and contagion adverse degree as well, as Eq. (24) displays. Physical and human capital ratio of the economy relies only in one sector. Economic growth rate in this case is lower since only one sector performs production activities and is also affected by the contagion adverse degree.

4 Distributional Dynamics For analyzing the economy dynamics is necessary to withdraw the stationary growth path, under scenarios A and B, for output, physical capital and human capital. When combining (1), (6), (19) and (22) as well as (1), (6) (27) and (30) we get the following conditions for scenario A and B respectively: k˙ c˙ y˙ = = = φ, y k c

(31)

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φ=

c y − + α − ωγ − ρ, k k

(32)

φ=

y c α − + − ωγ − ρ. k k 2

(33)

Therefore, at the steady state the gap between output-capital and consumption-capital ratios in both scenarios is: y c − = ωγ + ρ − α, k k

(32 )

c α y − = ωγ + ρ − . k k 2

(33 )

Steady state is completed when the long-run net return to private capital that equals the rate of time preference is obtained, thus by combining (11) with (19) and (11) with (27) respectively, we get:   ω k 1−β = ρ + δ + ωγ , β h   ω k 1−β δ + ωγ . =ρ+ β h 2

(34)

(35)

In this economy, Income is defined as: y i = r k + wω(1 − l).

(36)

Equation (36) shows that salary could be affected by contagion adverse degree. Consequently, by combining (7), (32) and (33) into (36), income dynamics under both scenarios A and B respectively is: y˙ = r (ωγ + ρ − α) + wωγ , y y˙ α

= r ωγ + ρ − + wωγ . y 2

(37) (38)

Finally, the share of output earned by physical capital, εk , and the share of output earned by human capital,ε h , in this economy are: ωβ Fk K = ε = Y k k



yh β−1 α

 β1

,

  1 Fh H (1 − β)ω yk −β 1−β = . ε = Y h α h

(39)

(40)

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According to income dynamics, it can grow mainly due to an increase in the time preference and the rate of growth of human capital but decrease due to a high contagion adverse degree on households; see equations (37) and (38).

5 Long-Run Shocks To demonstrate the long-run dynamics in this economy shocks on the intertemporal marginal substitution rate of households, physical and human capital ratio, growth rate of output, returns of investment and on the share of output earned by both physical and human capital are characterized. First, long-run adjustments in the intertemporal marginal substitution rate of households are: 

∂c l = ωθ βαk β−1 − αk β + βαk β , ∂β  ∂c l = θ k β (1 − β)l β , ∂α P ∂c l P = ωθ. ∂w

(41)

(42)

(43)

The rate of substitution between consumption and leisure for households is affected by the contagion adverse degree, in the sense that a high degree will affect in a negative way; on the other hand, could increase if there is a positive adjustment on the multi-factorial marginal productivity, technological innovation and salary. Therefore, individuals are more likely to consume more under these conditions and in order to do so they need to devote less time to leisure. Consequently, either they find a job or increase their human capital. Regarding physical-human capital ratio, shocks are:  ∂k h w−r = , (44) ∂β [(1 − β)r ]2  ∂k h β = , (45) ∂w (1 − β)r  ∂k h wβ = . (46) ∂r (β − 1)r 2 Physical-human capital ratio remain unaffected by the contagion adverse degree, which is consistent since the amount of production factors is a producer’s decision. Nevertheless, this ratio would increase if the returns of investment or innovation also increase, thus leading to leaps in the multi-factorial productivity and in the

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rate of growth of human capital. When considering shocks on the growth rate of output, results show that there is a positive effect on it due to increases in returns of investment or in the growth rate of human capital, as Eqs. (47)–(50) show.  ∂ y˙ y = ωγ + ρ − α, ∂r  ∂ y˙ y = ωγ , ∂w  ∂ y˙ y = w, ˙ ∂h h P ∂ y˙ y P α = ωγ + ρ − . ∂r 2

(47)

(48)

(49)

(50)

If output increases, consumption will also increase. Therefore, households allocate less time to leisure and if they want to consume more, work will rise. At the same time, Eqs. (47), (48) and (50) show that the rhythm of growth for output depends on the contagion adverse degree in an adverse way. Shocks on the returns of investment due to adjustments in multifactorial productivity are also positive and they are also impacted by the contagion adverse degree. ∂r = βk β−1 ωh 1−β , ∂α

(51)

∂w = (1 − β)k β ωh −β . ∂α

(52)

Subsequently, market-clearing conditions in the long-run remain. Finally, shocks on the share of output earned by both types of capital are as follow: 

∂εk = ∂y

1 − β/ β − 1/ βh β ω y



∂ε h = ∂y

1 kα /β

β β ω y /1 − β k /β − 1 1 hα /1 − β

 ,

(53)

.

(54)



Equations (53) and (54) show that when output is rising the share earned by companies and educated workers also increase. The more human capital exists within the

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economy the highest the share will be earned by it. Along all the shocks that were analyzed in this section results proved that inequality could be reduced if household increase their level of human capital; however, human capital relies on the contagion adverse degree in a negative way.

6 Conclusions COVID-19 has driven not only health crisis worldwide, but also economic problems as social distancing has been used as a control tool for the spread of the disease. Ever since economic activities: production, consumption and investment has been affected by partial or complete suspension. Because of the health risk people around the globe is trying to avoid crowded places or doing nonessential activities. In order to analyze the effect of the pandemic on economic growth and income inequality, a contagion adverse degree was introduced into an endogenous economic growth model, with a financial system, among human, physical and financial capitals, diminishing returns, constant scale effects and heterogenic agents. Results confirm that intertemporal marginal substitution rate of households and production process within industry is affected by the contagion adverse degree of households. When individuals identify a high risk of contagion, then they restrained their economic activities. Consumption and production will go down each time the contagion adverse degree is high; therefore, short and long run economic growth rates are also negatively affected by. Social distancing is also affecting academic activities, then in this research human capital growth rate experience negative impacts due to the contagion adverse degree. In the absent of a financial system, poor households will allocate less time to leisure if they want to consume more or increase human capital or both when the contagion adverse degree is low; but they will allocate more time to leisure if they want to consume less or decrease human capital or both when the contagion adverse degree is high. In this financial system absent scenario, physical and human capital ratio relies only in one sector usually the higher income one. Consequently, economic growth rate is lower since only one sector performs production activities while having a contagion adverse degree low. If financial system is present, both type of households will perform economic activities while considering the contagion adverse degree. At any case, rises in output when normal activities are carried out or decreases in salary due to the contagion adverse degree, will lead to increases in inequality and in the long run to poverty. The inequality can be decreased when human capital goes up, on the other hand physical capital generates small and positive changes in inequality as well as financial capital. Nevertheless, inequality decreases if total multifactorial productivity increases. According to this research, macroeconomic equilibrium depends in negative ways because of contagion adverse degree, thus distribution dynamics in the long run also relies on contagion adverse degree negatively.

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References Alesina A, Rodrik D (1994) Distributive politics and economic growth. Q J Econ 109:465–490 Anand S, Kanbur R (1993) The Kuznets process and the inequality-development relationship. J Dev Econ 40:25–52 Barro R (2000) Inequality and growth in a panel countries. J Econ Growth 5:5–32 Becker R (1980) On the long-run steady state in a simple dynamic model of equilibrium with heterogeneous households. Q J Econ 95:375–382 Carroll D, Young E (2018) Neoclassical Inequality. J Macroecon 57:83–109 Caselli F, Ventura J (2000) A representative consumer theory of distribution. Am Econ Rev 90:909– 926 Chatterjee S (1994) Transitional dynamics and the distribution of wealth in a neoclassical growth model. J Public Econ 54:97–119 Ehrlich I, Kim J (2007) The evolution of income and fertility inequalities over the course of economic development: a human capital perspective. J Hum Capital 1:137–174 Eisenberg B (1961) Aggregation of Utility Function . Manag Sci 7:337–350 Federal Reserve Economic Data, Economic Research Division, August 12 2020. https://fred.stloui sfed.org Forbes K (2000) A reassessment of the relationship between inequality and growth . Am Econ Rev 90:869–887 Gorman W (1953) Community preferences fields . Econometrica 21:63–80 Krusell P, Smith A (1998) Income and wealth heterogeneity in the macroeconomy . J Polit Eocn 106:867–896 Krusell P, Smith A (2015) Is Piketty’s second law of capitalism fundamental? . J Polit Econ 123:725– 748 Kuznets S (1955) Economic growth and income inequality . Am Econ Rev 45:1–28 Lakner C, Mahler DG, Negre M, Prydz EB (2020) How Much Does Reducing Inequality Matter for Global Poverty? Development Research Group. Word Bank Group, pp 1–32 Li H, Zou H (1998) Income inequality is not harmful to growth: theory and evidence . Rev Dev Econ 2:318–334 Maliar L, Maliar S (2001) Heterogeneity in capital and skills in a neoclassical stochastic growth model . J Econ Dyn Control 38:635–654 Perotti R (1996) Growth, income distribution and democracy: what the data say . J Econ Growth 1:149–187 Persson T, Tabellini G (1994) Is inequality harmful for growth? . Am Econ Rev 84:600–621 Piketty T (2013) Capital in the twenty-first century. Harvard University Press, Cambridge Piketty T, Saez E (2014) Inequality in the long-run. Science 344(6186):838–843 Piketty T, Zucman G (2015) Wealth and inheritance in the logn-run. In: Elsevier handbook of income distribution, vol 2. Elservier Ramsey F (1928) A mathematical theory of savings . Econ J 38:543–559 Rivas-Aceves S, Amato C (2017) Government Financial Regulation and Growth. Investigación Económica, Universidad Nacional Autónoma de México, vol 76, no 299, pp 51–86 Sorger G (2000) Income and wealth distribution in a simple model of growth . Econ Theory 16:23–42 Stiglitz J (2012) The price of inequality. W.W Norton, New York Turnovsky S (2002) Intertemporal and itratemporal substitution and the speed of convergence in the neoclassical growth model. J Econ Dyn Control 26:1765–1785 Turnosvky S (2015) Economic growth and inequality: the role of public investment . J Econ Dyn Control 61:204–221 World Bank Group (2020) Pandemic, Recession: The Global Economy in Crisis, Wrold Bank, June, pp 1–238 World Health Organization (2020) Daily Situation Report, August 13, pp 1–16. https://www.who. int/emergencies/diseases/novel-coronavirus-2019/situation-reports/

Forecasting the Effects of the COVID-19 Crisis on Economic Growth and the Microfinance Sector in Latin America: An Approach with Fuzzy Neural Networks Judith J. Castro Pérez, José E. Medina Reyes, and Agustín I. Cabrera Llanos Abstract The objective of this research is to identify the impact of the COVID19 contingency on economic activity and the microfinance sector in Argentina, Colombia, Ecuador, Mexico, and Peru. Through a fuzzy autoregressive neural network with a pentagonal membership function, and correlational analysis, which allows the identification of levels of impact of the contingency and inferences of the effects in the microfinance industry. The results showed that the agricultural sector will be the most affected by the current crisis, followed by the tertiary activities and the industry, these effects were observed in the five economies analyzed; besides, we found empirical evidence of the countercyclical condition of the Popular Savings and Credit Entities, for which we identified that in this economic sector we expect increases in profitability, a decrease of credit and liquidity risks, being the Mexican savings and credit sector the exception in these results. In conclusion, today more than ever, financial institutions play a relevant role in achieving the best possible reconstruction of the economic and social environment of Latin American families. Keywords Forecast · Economic crisis · Microfinance · Fuzzy logic · Neural networks JEL Classification C45 · F37 · G2 · N16

1 Introduction The global economic landscape is currently experiencing a great deal of uncertainty. Since January 30, 2020, the World Health Organization (WHO) declared COVID19 a health emergency of international concern, and by March 11, 2020, it was characterized as a pandemic, due to its spread through several countries around the J. J. Castro Pérez (B) · J. E. Medina Reyes Instituto Politécnico Nacional, Mexico City, Mexico A. I. Cabrera Llanos Unidad Profesional Interdisciplinaria de Biotecnología, Instituto Politécnico Nacional, Mexico City, Mexico © The Author(s), under exclusive license to Springer Nature Singapore Pte Ltd. 2021 G. Dávila-Aragón and S. Rivas-Aceves (eds.), The Future of Companies in the Face of a New Reality, https://doi.org/10.1007/978-981-16-2613-5_5

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world, affecting a large number of people. The first case of coronavirus in Latin America was presented on February 26. Given this precedent, some countries in the region announced health measures to prevent the spread of the virus, mainly compulsory quarantine, generating a widespread impact in the interruption of value channels, closure of formal and informal markets, reduction of income due to the suspension of border areas and restriction of the free movement of people. These elements together have generated negative effects on the world economy. The severity of the financial impact will vary according to the characteristics of each economic sector; and will depend, to a large extent, on the action strategies adopted by the economic agents of each country. The objective of this chapter is to analyze the impact of the economic crisis, caused by the COVID-19 contingency, in five Latin American countries, specifically Argentina, Colombia, Ecuador, Mexico, and Peru; through an econometric analysis that visualizes the variations that have occurred in the different economies, at a national level and also at a sectoral scale, that is to say, in the sector of primary, secondary and tertiary activities, to identify the form and degree of affectation present in the microfinance institutions. In the second section, the theoretical review of economic crises in the microfinance sector is carried out. Also, the methodology of applied neural network models is analyzed, in which the estimation process and its contributions to the forecast of economic variables are described. From a macroeconomic environment, in the third section, the analysis of economic growth is developed, through the projection of scenarios, according to the effects on the behavior of variables such as the Gross Domestic Product (GDP), and the indicators of the primary, secondary, and tertiary economic activities. From the application of the methodology of fuzzy autoregressive neuronal networks, to evaluate the economic environment of each country and generate a forecast of the economic alterations caused by the pandemic. The fourth section presents the diagnosis of the impact of the current contingency on the microfinance market, specifically the impact on the profitability of assets, credit risk, and liquidity. In such a way, the results provide anticipated information that supports the generation of solution alternatives and improvement in decision making, on the part of the interested economic agents and the financial authorities. Finally, the conclusions of the chapter are discussed, according to the analysis of the data and information of the study carried out. The main contribution of this research is the identification of the impact of the COVID-19 contingency on the economic activity and the microfinance sector in Argentina, Colombia, Ecuador, Mexico, and Peru, using a fuzzy autoregressive neural network with a pentagonal membership function and correlational analysis, thus identifying the impact levels of the contingency in the microfinance industry. The results showed that the agricultural sector will be the most affected by the current crisis, followed by the tertiary activities and industry, these effects are predicted in the five economies analyzed; we also found empirical evidence of the counter-cyclical condition of the Popular Savings and Credit Industry, therefore in this economic sector is expected to increase profitability, decrease credit risks and liquidity, being the Mexican savings and credit sector the exception in these results.

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Therefore, today more than ever, financial institutions have a relevant role to achieve the best possible reconstruction of the economic and social environment of Latin American families.

2 Theoretical-Methodological Review: Effects of the Economic Crisis on the Microfinance Sector The study of economic crises, their causes, and repercussions on the production and distribution of income in the countries are one of the most analyzed topics in economic theory. The theory of crisis and economic cycles develops an explanation of how the various economic interactions occur, which lead to environments of expansion, deceleration, contraction, and recovery, identifying the causes in the market structure, political environments, conditions of economic competition, etc. Therefore, analyzing the effects of the fall in production on the family environment is important to establish which actions are priorities to solve the impact of economic crises (Villarreal and Bielma 2017). Economic and financial crises are caused by the unavailability of liquidity and capital to finance production, as well as the drop in consumption. This causes economic activity to enter a period of imbalance, where supply and demand lead to a drop in income generation. The importance of Microfinance Institutions in times of crisis is fundamental to identify the depth of the economic crisis (Jarrow 2014; Schnabel and Seckinger 2019 and Lee and Lin 2018). Therefore, understanding the effects of the economic crisis on the financial sector in advance allows policymakers to project the impact of economic crises and develop strategies for economic recovery. In this context, the economic situation brought about by the COVID-19 pandemic has caused great uncertainty among economic agents, as well as significant drops in economic activity. McKibbin (2020), studies the macroeconomic effects of the COVID-19 pandemic on different economies by making impact scenarios of the pandemic, specifically the effects on production, employment, stock market, consumption, and demand. The results indicate that, with the further spread of the virus, the negative effects on macroeconomic indicators will be greater. The probability of each of the scenarios depends on the level of economic, social, and human costs that the current contingency situation generates. On the other hand, the effects on microfinance institutions of economic crises vary according to the market conditions faced by Microfinance Institutions. According to Llanto and Badiola (2009), because credit Microfinance Institutions have a specific process structure, the impact of the crisis can lead to different outcomes. In other words, there is the possibility of severe impacts on the institutions, increased credit, liquidity, and operational risks; or they may also have benefited from adverse economic conditions, through an increase in the loan portfolio, greater capture of savings, and growth in the institution’s business. Therefore, it must be understood that

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market and operating conditions in the microfinance sector behave in counter-cyclical terms in the economy. Vogelgesang (2003) studies the behavior of microfinance in times of crisis. The research found that, due to the growth in the demand for microcredit, confidence in the microfinance sector and the proximity of the institutions to the clients increased; loan repayment, microcredit application, and savings collection increased in times of crisis. McGuire and Conroy (1998) found that in economies with a high concentration of poverty in the population, the effects of economic crises on the microfinance industry are smaller compared to countries with higher per capita incomes. These are caused by the business environment of the institutions, specifically, the credits are destined for economic activities that compete in the local market. However, in the current economic crisis, this effect is expected to be different because most economic activities stopped their activities. Wagner and Winkler (2012) examine the vulnerability of microfinance companies to economic crisis environments. They provide empirical evidence that there is a greater effect on the banking sector than on the microfinance sector brought about by economic downturns. However, the concentration of loans in economic sectors that are affected by the drop in production means that the impact of the economic crisis on the microfinance industry is more delayed; however, the impact is the same as in other industries in the financial market. Thus, the microfinance sector is not counter-cyclical as other researchers argue. Therefore, the impact that the microfinance industry will have because of the COVID-19 contingency is important to determine. In this regard, the implementation of the fuzzy theory is proposed to measure the degrees of uncertainty in the economy and thus forecast the impact of the crisis on production. And later, using historical correlation analysis between the microfinance sector and the economic activity, to identify the historical relations of the time series, to project the expected effects on the institutions of the sector analyzed.

3 Sugeno-Type Autoregressive Neural Network with Pentagonal Membership Function This section shows the theoretical structure of the Fuzzy Autoregressive Neural Network. In the first one, the neural network with pentagonal membership function and its capacity to generate predictions of the volatility of financial and economic variables, the methodology is built using the model of (Medina-Reyes et al. 2020), but adopting a new membership function. The Fuzzy Triangular NARNET is a firstorder fuzzy logic model of the Sugeno type, and their If–Then rules in the input layer are determined as follows:

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R1 R2 R3 R4 R5

: if : if : if : if : if

yt−i is yt−i is yt−i is yt−i is yt−i is

A1 then A2 then A3 then A4 then A5 then

f1 f2 f3 f4 f5

= w11 yt−1 + w12 yt−2 + · · · + w1n yt−n , = w21 yt−1 + w22 yt−2 + · · · + w2n yt−n , = w31 yt−1 + w32 yt−2 + · · · + w3n yt−n , = w41 yt−1 + w42 yt−2 + · · · + w4n yt−n , = w51 yt−1 + w52 yt−2 + · · · + w5n yt−n ,

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(1)

where R represents the If–Then rules of the fuzzy time series, A is the fuzzy triangular subset for each function f , which denotes each perceptron with its respective synaptic weight w. Figure 1 shows the membership function associated with each If–Then rule R corresponding to a fuzzy subset A. In this case, the fuzzy subsets are the volatility levels, and the If–Then rules are the fuzzy learning functions of the first layer neural network. The If–Then rules on the hidden layer are: R1 R2 R3 R4 R5

: if : if : if : if : if

yt−i is yt−i is yt−i is yt−i is yt−i is

A1 then A2 then A3 then A4 then A5 then

fl1 fl2 fl3 fl4 fl5

= wk1 f 1 + wk2 f 1 , = wm1 f 2 + wm2 f 2 , = wm1 f 3 + wm2 f 3 , = wm1 f 4 + wm2 f 4 , = wm1 f 5 + wm2 f 5 ,

(2)

where A is the fuzzy triangular subset of each f fuzzy perceptron with their respective weights w for the hidden layer of the neural network. The advantage of this method for the traditional Autoregressive Neural Network and other econometric methods. Is that the proposed model learns from volatility in three ways: high volatility caused by good news; high volatility generated by bad news; and in low volatility. For example, in environments of economic growth it is established that the membership function models the behavior of the asset according to the growth environment; in moments of economic stability, the methodology projects from the perceptron that it locates few variations in the behavior of the time series; and finally in moments of negative

Fig. 1 Triangular Membership function. Source: Own elaboration in MatLab

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Fig. 2 Model structure hybrid fuzzy nonlinear autoregressive neural network. Source: Extracted from Medina-Reyes (2019)

effects on the economic activity the neural network learns from the history of the time series providing degrees of negative impacts, to visualize in detail the methodology see (Medina-Reyes et al. 2020) and (Medina-Reyes 2019). In (1) and (2) five environments are shown, the first one refers to R1 the economy is in a positive moment, that is, production is rising, there are high consumption and the great demand for employment; R2 presents a period of economic growth, but with low rates; R3 is the common behavior of the economic activity, in other words, this equation models the average comportment; R4 represents the times of crisis of an economy, but in environments of moderate fall; and finally, R5 which models environments of high economic severity, that is, it tries to forecast the effects of strong economic crises. Therefore, the fuzzy autoregressive neural network provides degrees of uncertainty, which allows us to generate projections of future economic environments considering expectations. So, the first assumption is that the projections were made assuming that the economic activity will have a strong negative effect on the behavior of the economic activity (Fig. 2): Assumption 1. The behavior of the time series of economic activity is modeled by R5 , during the period of the pandemic. And the remaining months of 2020 will be analyzed with R4 , that is, all the fuzzy subsets will be taken as A4 and A5 . Assumption one allows the effects of economic crises to be modeled under two degrees of impact, the most severe during the phases of the pandemic, and the moderate impact in the months following 2020. This allows us to predict the effects that the COVID-19 contingency may have on economic activity. To complement the analysis, we also assume that the past correlations between economic activity and the variables of the microfinance industry are the same as in the future. Assumption 2. The correlation between economic activity Yt and the microfinance sector M F t , in period t will be equal in t + 1.

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ρYt ; M Ft = ρYt+1 ; M Ft+1

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(3)

Thus, identifying in assumption one, the effects of the current economic crisis and with assumption two, the relationship between economic activity and the microfinance sector. One can conclude that the expected effect of the COVID-19 pandemic will be the correlation (3) multiplied by the forecasts of the impact of the crisis on economic activity. E(M Ft+1 ) = ρYt+1 , M Ft+1 *Yt+1

(4)

4 Forecast of the Macroeconomic Environment in Latin American Countries Derived from COVID-19 The information considered as the GDP, of each economy, has a quarterly periodicity; in the case of Mexico and Ecuador, the period of study includes the first quarter of 2000 to the last quarter of 2019; while, for Argentina, Colombia, and Peru, the period of study begins in the first quarter of 2004, 2005 and 2007, respectively, ending in the last quarter of 2019. In all economies, the projection was made for the four quarters of 2020. The information corresponding to the economic activity in the primary, secondary, and tertiary sector, for each economy, has monthly periodicity; in the case of Argentina and Peru, the period of study includes from January 2004 to February 2020; while, for Mexico, Ecuador and Colombia, the period of study begins, respectively, in January of the year 2000, 2003 and 2005, ending in March, January and November 2020, correspondingly. In the economies of Argentina, Colombia, and Peru the forecast was made in the time interval between March and December 2020; for Ecuador between February and December 2020; finally, for Mexico between April and December 2020. According to the nature of the information, we know that the economic and financial time series keep, as the main characteristic, high volatility in their behavior, because, although it is possible to observe the seasonal, cyclical, trend, and stochastic components, the latter is the one that predominates. Therefore, it is assumed that the series at levels, both on average and variance, does not present the condition of stationary, so it is necessary to work with its return rate. The indicators of national economic activity, in each of the economies included in the study, represent the total amount of production of goods and services carried out within the country. Therefore, this is considered the best indicator to show the historical behavior of the economic operation produced in a determined nation, and to observe the fluctuations that this variable maintains, to forecast its trajectory in the future.

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Figure 3 shows the comparison, for the GDP indicator, of the last two years, i.e., 2018 and 2019, in each of the economies analyzed. It is possible to see that in 2018 the country that grew the most was Mexico, with 8.46%, for the immediately previous year, while Argentina decreased by 2.46%. In 2019 Colombia was the country that grew the most, with 3.26% over the previous year, and again Argentina decreased by 2.23%, this year Mexico also decreased by 0.55%. For 2020 the projections show that the five economies will decrease, with Argentina and Mexico being the most affected by the effects of the COVID-19 pandemic, decreasing by 12.95% and 10.75%, respectively. In the case of Colombia, Ecuador, and Peru, there will be an economic contraction of 4.18%, 3.93%, and 4.8%, respectively. On the other hand, agricultural production in the countries under study, see Fig. 4, shows that in 2019 Argentina showed great growth compared to the previous year when it was decreasing from -8.13% to 13.72%. In the case of the other economies, primary activities were growing in 2018, and by 2019 they continued to grow except for Ecuador, which decreased its agricultural activity by 2.8%. The predictions for 2020, refer that negative movements are expected for the five economies, there will be a decrease in the agricultural production of Colombia, Ecuador, and Peru, of 4.71%, 5.65%, and 7%, respectively. Being again Argentina the most affected countries by the impact of the contingency, then, will decrease by 30.67%. In Mexican case, the forecasted period shows an increase of 3.82%. These results reveal that there will be effects within the production and consumption of basic products, in addition to generating a decrease in the income of families whose main source of work belongs to this sector. The forecasts for the secondary activities sector, see Fig. 5, show a general decline in the five countries studied, although it is less than in the agricultural sector, mainly because the economies have been declining since 2019, except for Colombia, which grew by 0.49%. Argentina shows a downward trend since 2018, when it fell by

Fig. 3 Annual % variation in GDP. Source: Own elaboration with information of the Statistics Institute and Central Bank of each economy

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Fig. 4 Variation % annual production of primary activities. Source: Own elaboration with information of the Statistics Institute and Central Bank of each economy

Fig. 5 Annual % variation in the production of secondary activities. Source: Own elaboration with information of the Statistics Institute and Central Bank of each economy

4.95%, for 2020 is predicted a decrease of 24.86%, is the country most affected, within the industrial sector, by the contingency of COVID-19. Thus, negative effects are expected within the production and consumption of manufacturing goods, which is not surprising due to the close relationship between primary and secondary activities, since the lack of supplies will cause shortages in raw materials for making and producing industrial goods, in addition to causing a decrease in the income of families whose main source of work belongs to the industrial sector. Tertiary economic activity sees Fig. 6, for 2018 presented growth in four of the five economies analyzed, it was Argentina that decreased by 4.34%; in 2019 it was Argentina and Ecuador that presented a contraction in the service sector, of 7.9% and 7.4%, respectively. The forecasts made for 2020 show that Colombia will be the most

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Fig. 6 Variation % of the production of tertiary activities. Source: Own elaboration with information of the Statistics Institute and Central Bank of each economy

Table 1 Growth prospects for Latin America 2020 Country

GDP

Primary sector

Secondary sector

Tertiary sector

Argentina

−12.95%

−30.67%

−24.86%

−23.71%

Colombia

−4.18%

−4.71%

−10.80%

−34.46%

Ecuador

−3.93%

−5.65%

−1.61%

−2.77%

Mexico

−10.75%

3.82%

−3.18%

−22.59%

−4.80%

−7.06%

−5.71%

−2.61%

Peru

Source: Own elaboration with information of the Statistics Institutes and Central Bank of each economy

affected economy in its tertiary sector since it will decrease by 34.4%, a result that is considered even more worrying because the two previous years Colombia maintained a growing trend. Argentina, on the other hand, will continue the downward trend it had maintained since 2018, registering a decrease of 23.7% in its forecast. Finally, Mexico will also present a significant contraction in its tertiary activity, with a value of 22.59%. In this respect, the restrictive measures taken by the countries generate great uncertainty in the services sector, although this contingency has demonstrated the importance of the telecommunications industry and has led to an increase in demand from those companies whose business is in the service of online consumption and packaging, there is also the financial sector, which has been among the most affected by the negative implications on the economy of many families. The use of technologies is a proactive response in the implementation of tactics to mitigate the negative effects, however, there are limitations in the economies studied that result in relevant effects within this sector.

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Table 1 shows the results of the projections made for the five economies in 2020. Argentina will present the greatest decrease in GDP, and, in the industrial and agricultural sectors, decreasing by 12.95%, 30.67%, and 24.86%, respectively. Colombia will be the country that presents the greatest negative impact on the tertiary sector, decreasing its activity by 34.46%, compared to the previous year. In the case of Mexico, will be the second most affected economy, by the environment of uncertainty caused by the contingency of the COVID-19, because the GDP will decrease by 10.75%, also, the primary sector and services will have a decrease of 23.22% and 22.59%, respectively. Forecasts from Argentina The behavior of some macroeconomic variables of the Argentine economy, such as the GDP, the manufacturing industry, and the trade, have maintained a decreasing trend in the last three annual periods (see Table 2). This is observed through the percentage variations of each year for the immediately previous one. The result translates into decreases in consumption and production. Figure 7 shows the behavior of the variations in all the economic activity of Argentina, they present a pronounced fall from February to May 2020, registering small variations in the next months and with a tendency in negative values. This is due to the impact of the restrictions on economic activity, implemented under the health contingency status by COVID-19, and its subsequent effects on economic reactivation. Figure 8 shows the monthly behavior of the variations in the primary activities in the period from January 2019 to December 2020. The primary sector presented an important growth in the months from February to May 2019, in this last one presenting a historical maximum value of 38%; from this point the tendency to the fall in the agricultural activity begins, registering a historical maximum decrease of 109%, later, a recovery in the behavior of this variable is observed. In the same way, Fig. 9 reflects the variations in the manufacturing industry and its behavior, which had presented a recovery trend at the beginning of the period, until reaching its maximum historical increase of 1.12%, in December 2019; later, the variable begins to decrease until reaching a maximum historical decrease of 43.17%, which is expected in October 2020; from there and until the end of the period it will have a slight recovery. On the other hand, the variations in the tertiary activities sector Table 2 Annual variation economic activities Argentina Year

GDP

Primary sector

Secondary sector

Tertiary sector

2015

2.63%

4.10%

0.65%

3.27%

2016

−2.02%

−2.22%

−5.70%

−3.26%

2017

2.62%

2.44%

2.34%

1.97%

2018

−2.46%

−8.13%

−4.95%

−4.34%

2019

−2.23%

13.72%

−6.49%

−7.94%

2020

−12.95%

−30.67%

−24.86%

−23.71%

Source: Own elaboration with information of Instituto Nacional de Estadística y Censos (2020)

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Fig. 7 Variation % per month in Argentinean activities (2019–2020). Source: Own elaboration with information of Instituto Nacional de Estadística y Censos (2020)

Fig. 8 Variation % per month in Argentine primary activities (2019–2020). Source: Own elaboration with information of Instituto Nacional de Estadística y Censos (2020)

in Argentina, see Fig. 10, maintained fluctuations oriented towards a recovery, during the whole year 2019, it was even in the last month of this year when it registered its historical maximum of 0.84%; then, an exponential fall is observed from February until May 2020, point in which it presents a historical minimum value of −43%; later, the forecast points out an upward trend. One explanation for this situation is that it is expected that the restrictive measures, which mainly affect consumption, will decrease in the second half of the year, which will provide an incentive for

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Fig. 9 Variation % per month in Argentine secondary activities (2019–2020). Source: Own elaboration with information of Instituto Nacional de Estadística y Censos (2020)

Fig. 10 Variation % per month in Argentine tertiary activities (2019–2020). Source: Own elaboration with information of Instituto Nacional de Estadística y Censos (2020)

wholesale and retail trade. However, the sector will not return to the same levels recorded at the beginning of the study period. Forecasts from Colombia Table 3 shows that, since 2018, the Colombian economy has experienced an expansion within the total economic activity, and particularly in the primary and tertiary economic sector, with 2019 being the year with the highest growth in the last five years. Differently, the manufacturing sector presented decreasing variations from 2017 to 2019, continuing with this trend in the 2020 forecast. Projections made for 2020 show decreases in the three economic sectors and the value of the Colombian economy in general for the immediately preceding year; thus, the GDP shall decrease in 4.18%, the primary sector in 4.71%, the industrial sector in 10.8%, and the tertiary sector in 34.46%. The above results show that the service sector will be the most

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Table 3 Annual variation economic activities Colombia Year

GDP

Primary activities indicator

Secondary activities indicator

Tertiary activities indicator

2015

2.94%

1.04%

3.52%

3.42%

2016

2.06%

−0.08%

3.39%

2.18%

2017

1.34%

0.84%

−1.92%

2.51%

2018

2.47%

0.49%

0.84%

3.28%

2019

3.26%

1.98%

0.49%

4.13%

2020

−4.18%

−4.71%

−10.80%

−34.46%

Source: Own elaboration with information of Sistema Estadístico Nacional - Colombia (2020)

affected by the current crisis caused by COVID-19, followed by the manufacturing sector, although to a lesser extent. The behavior of the variations in the economic activity of Colombia, see Fig. 11, shows stable fluctuations throughout 2019; being in February 2020, the month in which it presents a maximum historical increase of 4.7%, as of that moment it begins to decrease until reaching its minimum historical variation of −25%, which is foreseen in October 2020. Subsequently, stability in the growth of economic activity is expected for the coming months. Figure 12 shows the monthly behavior of the variations in the primary activities of Colombia, in the period from January 2019 to December 2020. The primary sector maintained a growing trend between April 2019 and April 2020, when it registered a maximum historical increase of 9.2%. Then, a downward trend in agricultural activity begins, registering a maximum historical decrease of 14.08%, in August 2020, subsequently, a slight recovery in the behavior of this variable is observed. In the same way, Fig. 13 reflects the variations in the Colombian manufacturing sector and its behavior, which maintained a relatively constant trend at the beginning of the period, until reaching a maximum historical increase of 3%, in February 2020; subsequently, the variable begins to decrease until reaching a maximum historical

Fig. 11 Variation % Indicator of Monitoring of the Colombian Economy (2019–2020). Source: Own elaboration with information of Sistema Estadístico Nacional - Colombia (2020)

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15% 10% 5% 0% -5% -10% -15% -20% Ene2019 Feb2019 Mar2019 Abr2019 May2019 Jun2019 Jul2019 Ago2019 Sep2019 Oct2019 Nov2019 Dic2019 Ene2020 Feb2020 Mar2020 Abr2020 May2020 Jun2020 Jul2020 Ago2020 Sep2020 Oct2020 Nov2020 Dic2020

Month % Var anual

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Time

Fig. 12 Variation % primary activity indicator Colombia (2019–2020). Source: Own elaboration with information of Sistema Estadístico Nacional - Colombia (2020)

Fig. 13 Variation % indicator of secondary activities Colombia (2019–2020). Source: Own elaboration with information of Sistema Estadístico Nacional - Colombia (2020)

10% 0% -10% -20% -30% -40% -50% -60% -70%

Ene2019 Feb2019 Mar2019 Abr2019 May2019 Jun2019 Jul2019 Ago2019 Sep2019 Oct2019 Nov2019 Dic2019 Ene2020 Feb2020 Mar2020 Abr2020 May2020 Jun2020 Jul2020 Ago2020 Sep2020 Oct2020 Nov2020 Dic2020

Month % Var anual

decrease of 28.22%, which is expected in October 2020; from there and until the end of the period recovery is observed. On the other hand, variations in the tertiary activities sector in Colombia, see Fig. 14, maintained stable and positive fluctuations throughout 2019, even presenting a maximum historical increase of 5.4%, in July 2019; afterward, a slight decrease is observed until February in 2020, month in which an abrupt fall in the tertiary activity was registered, reaching a maximum historical decrease of 67.98%, in October 2020.

Time

Fig. 14 Variation % tertiary activities indicator Colombia (2019–2020). Source: Own elaboration with information of Sistema Estadístico Nacional - Colombia (2020)

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Table 4 Annual variation economic activities Ecuador Year

Primary activities

Secondary activities

Tertiary activities

2015

GDP 0.11%

−3.89%

−4.95%

−8.96%

2016

−1.23%

−2.34%

0.79%

7.52%

2017

2.34%

3.33%

4.76%

3.53%

2018

1.28%

3.07%

−0.36%

5.52%

2019

0.06%

−2.80%

−3.82%

−7.43%

2020

−3.93%

−5.65%

−1.61%

−2.77%

Source: Own preparation with information from Banco Central del Ecuador (2020)

Then, we infer that the restrictive measures, which mainly affect consumption, would cause negative impacts in the second and third quarter of 2020.

Ene2019 Feb2019 Mar2019 Abr2019 May2019 Jun2019 Jul2019 Ago2019 Sep2019 Oct2019 Nov2019 Dic2019 Ene2020 Feb2020 Mar2020 Abr2020 May2020 Jun2020 Jul2020 Ago2020 Sep2020 Oct2020 Nov2020 Dic2020

Month % Var anual

Forecasts from Ecuador In general, activity in the Ecuadorian economy showed a trend with positive variations during the period 2017 to 2019, while the forecast for 2020 suggests that there will be a 3.93% decrease in GDP (see Table 4). While primary, secondary, and tertiary activities will show decreases of 5.65%, 1.6%, and 2.77%, respectively, by 2020. The main result is that the agricultural sector will have the greatest negative impact, due to all the implications of the COVID-19 contingency. The behavior of the variations in Ecuador’s economic activity, see Fig. 15, shows great fluctuations during the entire period of study; being in September 2019, the month in which it presents a minimum historical variation of −16.49%; and in January 2020, the month in which it presents a maximum historical increase of 12.9%. Subsequently, a downward trend is projected in the coming months, from August until the end of the period, you can see a slight recovery in the Ecuadorian economy. Figure 16 shows the monthly behavior of the variations in the primary activities of Ecuador, in the period from January 2019 to December 2020. The primary sector maintained an upward trend between March 2019 and January 2020, the month in

Time Fig. 15 Variation % general indicator Ecuador (2019–2020). Source: Own preparation with information from Banco Central del Ecuador (2020)

95

15% 10% 5% 0% -5% -10% -15% -20%

Ene2019 Feb2019 Mar2019 Abr2019 May2019 Jun2019 Jul2019 Ago2019 Sep2019 Oct2019 Nov2019 Dic2019 Ene2020 Feb2020 Mar2020 Abr2020 May2020 Jun2020 Jul2020 Ago2020 Sep2020 Oct2020 Nov2020 Dic2020

Month % Var anual

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Time Fig. 16 Variation % agriculture, livestock, hunting and forestry Ecuador (2019–2020). Source: Own preparation with information from Banco Central del Ecuador (2020) 12% 10% 8% 6% 4% 2% 0% -2% -4% -6% -8% -10%

Fig. 17 Variation % manufacturing industry Ecuador (2019–2020). Source: Own preparation with information from Banco Central del Ecuador (2020)

which it registered a maximum historical increase of 8%. Then, a downward trend in agricultural activity begins, registering a historical minimum decrease of 19.2%, in July 2020, subsequently, a recovery in the behavior of this variable is observed. In the same way, Fig. 17 reflects the variations in the Ecuadorian industrial sector and its behavior, which maintained significant fluctuations throughout the study, until reaching a maximum historical increase of 10.12%, in January 2020; subsequently, the variable begins to decrease until reaching a minimum historical decrease of 9.36%, which was presented in July 2020; from there and until the end of the period recovery of the variable is observed. On the other hand, the variations in the tertiary activities sector in Ecuador, see Fig. 18, maintained a decreasing trend from February 2019 to September of the same year, month in which it presented a historical maximum decrease of 30.28%; later it presented a great recovery for November 2019, presenting a historical maximum increase of 11.97%; as of this month, a decreasing trend is observed, expecting a decrease of up to 14.26% in November 2020. Considering this result as the greatest

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Fig. 18 Variation % wholesale and retail trade Ecuador (2019–2020). Source: Own preparation with information from Banco Central del Ecuador (2020)

impact, due to the restrictive measures imposed in Ecuador by the contingency of the COVID-19, within the tertiary sector. Forecasts from Mexico The Mexican economy was not exempt from suffering all those impacts stimulated by the restrictions implemented in the economic activity, under the sanitary contingency state by the COVID-19. It is possible to observe this in the annual variation of economic activities, see Table 5. It is possible to observe this in the annual variation of economic activities, see Table 5. Thus, it is shown that the total of economic activities in Mexico will decrease significantly, both in 2019 and in the forecast made for 2020, with a decrease of 0.55% and 10.75% respectively. In the predictions made for 2020, the primary sector shows a decrease of 23.22%; the activity in the manufacturing industry decreases by 3.18%; and the tertiary sector decreases by 22.59%. These results show that the primary activity sector will be the most affected by the COVID-19 contingency, followed by the tertiary sector. The behavior of the variations in the economic activity of Mexico, see Fig. 19, decreases to a historical maximum level of 13.55%, in May 2020, subsequently, a slight recovery is observed in the forecast made, expecting in the last month of 2020 a decrease of 10.8%. Table 5 Annual variation economic activities Mexico Year

GDP

Primary activities

Secondary activities

Tertiary activities

2015

12.97%

1.42%

1.24%

4.17%

2016

11.46%

3.29%

0.39%

3.86%

2017

8.43%

3.08%

−0.24%

2.99%

2018

8.46%

2.44%

0.48%

2.80%

2019

−0.55%

2.62%

−1.78%

0.53%

2020

−10.75%

3.82%

−3.18%

−22.59%

Source: Own elaboration of data from Instituto Nacional de Estadística y Geografía (2020)

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5% 3% 1% -1% -3% -5% -7% -9% -11% -13% -15%

Fig. 19 Variation % global activity indicator of Mexico economy (2019–2020). Source: Own elaboration of data from Instituto Nacional de Estadística y Geografía (2020)

This situation reflects the fact that there could be shortages in the local production of agricultural and mining goods and services, which means a possible food shortage, and given the drop in supply, it would be logical to expect a rise in the prices of these products. Satisfactorily, the sector of secondary economic activities remains with the least level of negative variations, see Fig. 21, this means that, although the industrial sector in Mexico has suffered lags due to the different restrictions imposed by the COVID-19 contingency, the productive activity will grow within the sector. The forecast shows the most significant fall in April 2020, going from a value of 1.74% to a value of −8.54% for June 2020, the latter being its maximum historical decline in the period of study, from there is great recovery for the industrial sector, closing the projected period with a growth of 3.5% (Fig. 20).

Fig. 20 Variation % primary activity indicator Mexico (2019–2020).1 Source: Own elaboration of data from Instituto Nacional de Estadística y Geografía (2020) 1 In-sample

forecast period was shifted to January 2000 to November 2020 in Mexico for primary activities, and the out-of-sample forecasting period is December 2020.

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Fig. 21 Variation % Indicator of secondary activities Mexico (2019–2020). Source: Own elaboration of data from Instituto Nacional de Estadística y Geografía (2020)

Fig. 22 Variation % tertiary activities indicator Mexico (2019–2020). Source: Own elaboration of data from Instituto Nacional de Estadística y Geografía (2020)

The main cause of the above is globalization since it is a highly constituted sector through global value chains so that the current nature of industrial production processes makes it possible to maintain a constant level of stock. On the other hand, the tertiary activities sector also decreases significantly, see Fig. 22, presenting a maximum historical decrease of 36.87%, in June 2020, estimating a recovery for the end of the projected period. The service sector was one of the first to comply with the measures imposed by the health authorities since many of these are not considered essential or of primary need, so it was expected that economic activity in this sector would decrease considerably. Forecasts from Peru Peru Gross Domestic Product has maintained a growing trend in recent years, see Table 6, which can be seen in the agricultural sector and trade. This growth oscillates around 3% on average in the national economy and the case of the sectors mentioned, presents a greater variability from one year to the next, with 2018 standing out as the year of greatest economic growth in the last five years. On the other hand, the manufacturing sector shows negative growth in three of the last five years, being 2019 the year in which it registers its highest decrease, with 1.47%.

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Table 6 Annual variation economic activities Peru Year

GDP

Primary activities

Secondary activities

Tertiary activities

2015

3.18%

2.09%

−1.44%

3.87%

2016

3.89%

2.41%

−1.29%

1.85%

2017

2.48%

2.71%

0.04%

1.01%

2018

3.87%

9.43%

5.94%

2.66%

2019

2.14%

3.19%

−1.47%

2.98%

2020

−4.80%

−7.06%

−5.71%

−2.61%

Source: Own elaboration with information from Banco Central de Reserva del Perú (2020)

Although the growth of Peru’s economy maintained positive variations, the forecast for 2020 indicates changes in the behavior of aggregate economic activities; a decrease of 4.8% is projected for the GDP, for the primary, secondary and tertiary activities sector, there are decreases of 7%, 5.7%, and 2.61%, respectively. Being the agricultural sector of Peru the one that will have the greatest negative impact due to the contingency of the COVID-19. The economic environment projected for Peru in 2020 shows a negative trend and a significant economic contraction, caused by the conditions of the current economic crisis. Confinement measures, mobility restrictions, and closure of non-essential economic activity will have strong effects on income generation and production for Peruvian families; it is expected that the rural area will be the most affected, followed by industry and thirdly by local commerce. Figure 23 shows the monthly behavior of the variations in the primary activities of Peru, in the period from January 2019 to December 2020. The primary sector presented a maximum historical increase of 10% in December 2019, while it registered a maximum historical decrease of 7.19% in September 2020; as of this moment, a recovery in the growth of Peruvian agricultural activity is projected for the next months and until the end of the period. In the same way, Fig. 24 reflects the variations in the Peruvian manufacturing sector and its behavior, which maintained great fluctuations throughout the period, 12% 10% 8% 6% 4% 2% 0% -2% -4% -6% -8% -10%

Fig. 23 Variation % agriculture, livestock, forestry, and fishing Peru (2019–2020). Source: Own elaboration with information from Banco Central de Reserva del Perú (2020)

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Fig. 24 Variation % manufacturing industry Peru (2019–2020). Source: Own elaboration with information from Banco Central de Reserva del Perú (2020)

until reaching a maximum historic increase of 8.3%, in July 2019; later, the variable begins to decrease, presenting two important falls, until reaching a maximum historic decrease of 13.03%, which is expected in August 2020; from there and until the end of the period recovery is observed. On the other hand, the variations in the tertiary activities sector in Peru, see Fig. 26, maintained fluctuations oriented towards a recovery, during the whole year 2019, it was even in the last month of this year in which it registered a historical maximum growth of 3.65%; afterward, a great fall is observed as of January and until August 2020, month in which it presents a historical minimum value of −8.3%; as of this moment the forecast indicates an upward trend. One explanation for this situation is that it is expected that the restrictive measures, which mainly affect consumption, will decrease in the second half of the year, which will provide an incentive for wholesale and retail trade. However, the sector will not return to the same levels recorded at the beginning of the study period (Fig. 25).

Fig. 25 Variation % Wholesale and Retail Trade Peru (2019–2020). Source: Own elaboration with information from Banco Central de Reserva del Perú (2020)

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Fig. 26 Expected impact on the return on assets of Microfinance Institutions by 2020. Source: Own elaboration with information of the Statistics Institutes and Central Bank of each economy

5 Expected Impact on the Microfinance Industry Caused by the COVID-19 Economic Crisis The forecast of the impact on the sector was made with a historical analysis of the evolution of economic activities and the different variables studied in the financial sector, the period of study corresponds to January 2015 to December 2019. In this section, assumption two, presented in the methodological section, is applied. Specifically, the indicator of return on total assets, the non-performing loan ratio, and the liquidity coefficient were used, as well as the average for the microfinance sector or the cooperative sector, depending on the case of each economy (CNVB 2020; ASOMICROFINANZAS 2020; Superintendencia de Banca, Seguros y AFP de la República del Perú 2020; Banco Central de la República Argentina 2020; Super Intendencia de Bancos 2020). The Business’ Profitability Microfinance Institutions in selected Latin American countries are facing a period of strong change due to the new normality. There are multiple effects on this sector, such as branch closures, a drop in savings, a drop in loan placement, a lack of staff mobility, and an increase in operating expenses, just to mention a few. Given this situation, it is important to analyze the impact in terms of profitability and risks. Table 7 shows the proportion of the impact of economic activity on the profitability of assets, measuring the effect it will have on the savings and credit sector, in aggregate form in each economy. In this aspect, there are various effects on Microfinance Institutions, the positive values indicating a favorable impact on the profitability of assets. This event provides important information regarding the counter-cyclical position of Microfinance Institutions, with the trend of economic activity. The negative impact indicates that the Microfinance Institutions in the sector are being adversely affected by the current situation, with a negative return on assets expected by 2020. The economies that are predicted to have losses concerning each economic activity are Colombia and Mexico; with the Mexican microfinance sector suffering the largest drop. Likewise, the decreases in production in the primary and tertiary sectors are the most significant impacts on Microfinance Institutions in terms

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Table 7 Impact forecast on the profitability of Microfinance Institutions’ assets in 2020 Country

GDP

Secondary sector

Tertiary sector

Argentina

11.81%

Primary sector 4.55%

17.64%

17.65%

Colombia

−1.09%

−0.09%

−1.40%

3.27%

Ecuador

3.25%

4.49%

1.45%

1.73%

México

−1.95%

−4.44%

−0.12%

−5.87%

3.59%

1.14%

0.57%

1.31%

Perú

Source: Own elaboration with information on the financial market regulatory institutes and the Central Bank of each economy.

of negative variations. On the other hand, Argentina is the country where the greatest benefits are expected from the current situation, followed by Ecuador and Peru. The economic activity that provides a positive impact on the greater proportion is different in each country. For example, the secondary and tertiary sectors are expected to have positive impacts on the profitability of the economies of Argentina and Colombia. Figure 26 shows the impact on the profitability of the assets of the microfinance sector, brought about by the decline in production in the economy at large, primary, secondary, and tertiary activities. The vertical axis indicates the expected impact, and the horizontal axis presents the five economies studied. The microfinance sector in Argentina is projected to be the most benefited by the current crisis environment, in Ecuador and Peru the same results are expected. The effects of the pandemic on the secondary sector in Colombia will be positive for the profitability of the assets of the Colombian microfinance sector. These results strongly indicate the counter-cyclical structure of the microfinance industry in Argentina, Colombia, Ecuador, and Peru. In the specific case of Mexico, it is expected that microfinance institutions will have a drop in the profitability of their assets, in other words, the current environment is expected to negatively affect the Mexican savings and loan sector. Credit Risk This section generally discusses the expected aggregate impacts of the economic crisis on the credit risk and liquidity risk of microfinance institutions, as measured by the non-performing loan ratio and the liquidity ratio. For credit risk or probability that the financial institution’s clients will not comply with their loan repayment obligations, Table 8 shows positive values that are interpreted as increases in probability and default, while negative values indicate decreases in credit risk. In general, an increase in the probability of default in Microfinance Institutions is estimated, derived from the total economic activity, except for Peru which shows that the current financial market conditions favor an environment in which the savings and credit sector is the most reliable for companies. This implies that economic agents prefer to have a better reputation with institutions in the microfinance sector since it is perceived that this type of Microfinance Institutions can provide the necessary sources of financing to reactivate economic activities in each sector. The countries

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Table 8 Forecast of impact on the credit risk of Microfinance Institutions in 2020 Country

GDP

Primary sector

Secondary sector

Tertiary sector

Argentina

5.62%

15.36%

−14.88%

9.22%

Colombia

N/D

N/D

N/D

N/D

Ecuador

0.52%

0.29%

0.07%

0.73%

Mexico

3.86%

12.45%

−0.79%

8.59%

Peru

−2.69%

0.18%

0.13%

−1.53%

Source: Own elaboration with information of the Statistics Institutes and Central Bank of each economy

that show a higher probability of defaulting on loans by 2020 are Argentina and Mexico (Table 8). The effect of primary activities on this risk is observed with greater problems, in the economies studied the probability of default is expected to increase due to the economic downturn in this sector. The secondary activities show a counter-cyclical behavior because the credit risk is projected to be low due to the growing demand of credits in the sector, as a source of financing in moments of economic activity fall. On the other hand, the effect of the drop in tertiary activities generates an increase in the risk of credit default, except in Peru, where the tertiary sector indicates a positive relationship, that is, having a good reputation with the microfinance industry allows financing service activities, to reactivate production. The level of impact of economic activities that increase or decrease the credit risk of Microfinance Institutions is shown in Fig. 27. There, it is possible to observe that, in general terms, the COVID-19 contingency increases the probability of default in the savings and loan sector. Also, the information provided by these results allows for the identification of conditions for decision making in the institutions. For example, loan

Fig. 27 Impact on credit risk for microfinance institutions in 2020. Source: Own elaboration with information of the Statistics Institutes and Central Bank of each economy

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Table 9 Forecasted Impact on liquidity risk for microfinance institutions in 2020 Country

GDP

Primary sector

Secondary sector

Tertiary sector

Argentina

N/D

N/D

N/D

N/D

Colombia

N/D

N/D

N/D

N/D

Ecuador

1.70%

2.27%

0.40%

1.30%

México

2.63%

−0.64%

0.22%

6.65%

Perú

−3.89%

−0.62%

−0.63%

−1.35%

Source: Own elaboration with information of the Statistics Institutes and Central Bank of each economy

placement plans for the current year should be established based on risk management by economic activity, and thus, establish plans based on macroeconomic indicators. Liquidity Risk The liquidity risk is lower with the credit risk and, in most of the economies analyzed, it has been observed that the current contingency establishes fewer problems in terms of liquidity. The financial entities of the savings and credit sector are benefited by their proximity to the economic agents, thus generating greater certainty for the savings of the economic agents. The increase in the liquidity risk is observed in two countries, mainly Mexico and Ecuador, caused by the total economic activity; the sector that generates greater adverse effects in terms of liquidity is the primary activity, but the secondary sector is the activity that affects a greater number of economies (Table 9). Figure 28 shows the effect of the COVID-19 economic crisis on financial institutions, in terms of liquidity risk. A disparity can be observed between the increase and decrease in risk. This is a consequence of the fact that an increase in liquidity risk is understood as the fact that the capture and sources of financing are not sufficient to resolve the activities and processes of the institution. Conversely, when there is an improvement in liquidity risk, it is necessary to define how the extra cash that the institution has will be placed and invested, proposing an economic cycle analysis, as the current macroeconomic environment is showing. Finally, the risks faced by financial institutions in the savings and credit sector point to various effects (increase and decrease) with the market condition or structure of each economy. Therefore, financial institutions must generate and establish strategies to overcome the environment of the current economic crisis and thus obtain the best benefits in terms of business.

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Fig. 28 Expected impact on liquidity risk in microfinance for 2020 Source: Own elaboration with information of the Statistics Institutes and Central Bank of each economy.

6 Conclusions The main results of this study are that the agricultural sector will be the most affected by the restrictions imposed by the health contingency corresponding to COVID19 in three of the five economies, Argentina, Peru, and Ecuador. In the case of Colombia and Mexico, the tertiary sector was the most affected. In particular, the most significant decline in the projected industrial sector is in Argentina, followed by Colombia, and in third place, Peru; Ecuador and Mexico are the countries where the manufacturing sector was least affected. In general, the country in which the greatest contraction in economic activity was projected was Argentina, followed by Mexico, then Peru, then Colombia, and finally Ecuador, this being the country in which the least negative impact on its economy was estimated. Likewise, there is an estimated increase in credit and liquidity risks within the financial institutions of the countries analyzed; however, there are some cases where the risk is decreased. In this sense, it is recommended that an analysis of the vulnerabilities of the portfolio of partners or clients of each financial institution be performed, for economic activities and forecasted effects. Today more than ever, financial institutions play a relevant role in achieving the best possible reconstruction of the economic and social environment of Latin American families. The financing of primary economic activities such as agriculture, manufacturing, and services, will be fundamental to encourage economic growth and the improvement of conditions caused by the current contingency. The main contribution of this research is the identification of the impact of the COVID-19 contingency on the economic activity and the microfinance sector within Argentina, Colombia, Ecuador, Mexico, and Peru, using a fuzzy auto-regressive neural network with a pentagonal membership function and correlational analysis, thus identifying the impact levels of the crisis in the microfinance industry.

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As a second contribution, we highlight the proposal of a new fuzzy autoregressive neural network, characterized by a pentagonal membership function. This new methodology makes to identify the behavior of the Historical Economic Time Series in different contexts of the economic cycle, in such a way to predict the evolution of the economic variables according to the expected economic conditions. This characteristic of the model’s non-linearity allows us to study the effects of the COVID19 crisis on economic activity and, in combination with correlational analysis, the corresponding one in the microfinance sector. This is a new advance in the study of econometric models to evaluate and diagnose the impact of economic crises on productive activity. Therefore, it is possible to model in advance the effects of the uncertainty of these to provide information to economic agents for decision making.

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Balancing Work, Family, and Personal Life in the Mexican Context: The Future of Work for the “COVID-19 Generation” Germán Scalzo, Antonia Terán-Bustamante, and Antonieta Martínez-Velasco

Abstract Intergenerational talent management—i.e., attracting and retaining employees across generations and with different motivations—is one of companies’ greatest challenges. The expectations that recent generations bring with them have pushed culture in the direction of work-family balance, which is now seen as a key tool for human resources departments in charge of creating support mechanisms to attract and retain the next generation of workers. This trend has been reinforced by the changes brought about in light of the COVID-19 pandemic. Responding to this shift, and inspired by the challenges that our “new normal” posits, this chapter presents research results from a survey conducted in Mexico with respondents from generations Y and Z. The survey results offer important insight into how these generations perceive work-life balance, as well as the expectations that young Mexicans between the ages of 18 and 30 hold in terms of family and work. Keywords Generations Y and Z · Human resource management · Mexico · Work-family balance JEL Classification M12 · Z13

G. Scalzo (B) · A. Terán-Bustamante Escuela de Ciencias Económicas y Empresariales, Universidad Panamericana, Ciudad de México, México e-mail: [email protected] A. Terán-Bustamante e-mail: [email protected] A. Martínez-Velasco Facultad de Ingeniería, Universidad Panamericana, Ciudad de México, México e-mail: [email protected] © The Author(s), under exclusive license to Springer Nature Singapore Pte Ltd. 2021 G. Dávila-Aragón and S. Rivas-Aceves (eds.), The Future of Companies in the Face of a New Reality, https://doi.org/10.1007/978-981-16-2613-5_6

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1 Introduction1 Significant debate around the future of work has emerged in large part based on the complex challenges that dizzying technological change has presented in recent years. The COVID-19 pandemic, for its part, has brought about once in a generation changes to the work environment, thereby reviving discussion about the future of different generations working together in the same workplace, together with questions of what each generation will demand from companies in the short-term, once the pandemic has passed (Foroohar 2020). The youngest generations—especially digital natives— took to remote work like “a duck to water” (Raišiené et al. 2020). While, as we will see in this chapter, work-life balance trends have been on the rise in recent years, the coronavirus pandemic has accelerated this process, breaking down many barriers, especially cultural ones. As a result, many organizations are already beginning to work on the various approaches that they need to retain not only the generations currently in the office, but also the ones to come, which is already being called “the COVID-19 Generation” (Rudolph and Zacker 2020; Twenge 2020) or “Generation Crown” (Frometa 2020). In this context, talent management has become an imperative for human resources departments charged with creating support mechanisms aimed at retaining multiple generations with diverse expectations and motivations (Costanza et al. 2012; Díaz et al. 2017). Along with the different types of diversity inherent to the labor market— including gender, race, socioeconomic status, etc. (Barak 2013; Shen et al. 2009)— companies today face the increasingly problematic challenge of intergenerational management since a total of four generations are currently engaged in the labor market (Davis 2014; Las Heras and Destefano 2011; Kelan et al. 2009). Furthermore, companies are tasked with attracting and retaining young employees for the health and sustainability of business, and this task has been neither simple nor straightforward. As the workforce has evolved, so have these employees’ expectations, which have prompted organizations to try to improve people’s lives equitably and ethically (Deloitte 2020). Generations Y and Z’s motivations and expectations have contributed to the creation of a culture consistent with work-family balance, as expressed in several decades of theoretical development (Grzywacz and Carlson 2007; Sturges and Guest 2004) and evidenced in a wide range of organizational policies (Cegarra-Leiva et al. 2012; Haar and Spell 2004). Indeed, companies all around the world are currently focusing on combining family life with the demands of the work world (Crawford et al. 2019; Powell et al. 2019). The integration of family and work aims to improve people’s quality of life by facilitating compatibility among life’s diverse spheres, including commitments to loved ones, work, and free time. This balance should be understood as harmonious engagement in the different roles that each person exercises both in the family and 1 The

research in this chapter was funded by a grant (UP-CI-2017-FING-01) from the UP 2019 Research Development Fund. The authors wish to thank the CONFyE center (IAE, Argentina) and especially its director, Dr. Patricia Debeljuh, for her collaboration. They also thank Dr. María Elena Ordoñez y Revuelta for her technical support.

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in the work environment throughout their life span (Debeljuh and Ordóñez 2019). And this is no small matter because the family is of strategic importance for society; in addition to connecting people to the social structures in which they participate, it generates a more humane way of life (Bosch et al. 2016). With this context in mind, we aim to specifically study the difficulties of intergenerational talent management and focus specifically on generation Y and Z in Mexico. Prior research in Mexico on this topic is limited. We will present results from a survey of young people between 18 and 30 years old in Mexico that corresponds to 24.2% of the economically active population (Merca2.0 2014). Also, discuss the results with implications for how the youngest working generations perceive work-life balance and their expectations of family and work life, thus hoping to bolster companies’ efforts to attract and retain that talent bracket in Mexico. Work-Life Balance and Generational Divides: Framework Work-life balance is a multifaceted construction supported by several decades of theory (Grzywacz and Carlson 2007; Allen et al. 2013; Kelly et al. 2008). Evidence from the literature points to a positive relationship between the enrichment of work and adequately balanced family life (Grzywacz and Marks 2000; Thompson and Prottas 2005; van Steenbergen et al. 2007; Voydanoff 2004) for achieving the firm’s goals. Motivating factors and feedback (Hackman and Oldham 1976), autonomy associated with time management (Voydanoff 2004), task variety (Barnett et al. 1992), flexibility, and freedom are all believed (Boudreau et al. 2015), among other factors, to contribute to the enrichment of work and family. The phenomenon of an intergenerational workforce has been the object of recent study; however, consensus still lacks regarding its conception and behavioral features. Of course, it is a complex matter since each generation has differences and particularities; based on the time they were born, people face diverse cultural, political, and social contexts, and they conceive of relationships with their employers and peers differently. In addition to the above, the generation gap is getting bigger, with age gaps that exceed 20 years in some cases. This age difference impacts aspects like communication, the use of technology, motivation, recruitment, and incentives, among other things. As a consequence, managing multiple generations has become both a great challenge and an opportunity for organizations. Before describing the characteristics and traits associated with the different generations currently in the workforce, we must first define what is understood by generation (Díaz et al. 2017). For Ogg and Bonvalet (2006) a generational group is an age group that shares a set of training experiences throughout its history that distinguish it from its predecessors. Four generations currently coexist in the workforce as follows: (1) Baby Boomers born between 1951 and 1964, (2) Generation X born between 1965 and 1980, (3) Generation Y born between 1981 and 2000 (Ogg and Bonvalet 2006), and (4) Generation Z born from 2000 to 2014 (see Table 1). As noted, this phenomenon leads to a greater diversity of expectations, experiences, and motivations, and therefore to greater challenges for companies to manage.

112 Table 1 Classification of the various generations (according to year of birth)

G. Scalzo et al. Generation

Birth year range

Traditionalist

Before 1946

Baby Boomer (BB)

1947–1964

Generation X

1965–1980

Generation Y (Millenials)

1981–1999

Generation Z

2000–2014

Generation Alfa

2015–Actual

“COVID-19 Generation”

–

Source: Authors’ elaboration

The most representative characteristics of these four generations are presented below: Baby Boomers Baby boomers were born after World War II; their upbringing was marked by demographic explosion and the postwar period. They were largely raised by young mothers, mostly traditional and conservative housewives (Díaz-Sarmiento et al. 2017). At work, they are traditionalist, rigid and structured, but they also have great vision and knowledge. They are committed to their work and motivated by financial reward. They consider work as a way of being and existing, seeing it as stable, long-term, addictive, and a place to register achievements. They believe in work, as well as the status it lends them and the trajectory associated with it (Forbes 2015). They stand out for their security and independence. They are mostly members of large families and do not spend much time on leisure and recreation, however, they value spending time with family and following traditions; likewise, they consider people’s education to be important. They are active, concerned about their health and interested in the digital world (Merca2 2014). Generation X This generation grew up without computers and witnessed the transformation from analog to digital technology during adulthood. They were the first part of the workforce to become familiar with computers. They are considered a “sandwich” generation: at work, they are usually under baby boomers and, lead Generation Y employees; at home, they take care of their parents and their millennial children (Universum 2017). Current generation X professionals are characterized as mature and well prepared; they take on responsibility and make up much of the current job market. Work occupies an essential place in their self-definition, and they value recognition and feedback from bosses, as well as their relationships with colleagues and peers (Díaz-Sarmiento et al. 2017). They are tired of the vision and mission statements written by their predecessors (Zemke et al. 2013) and rather focus on highlighting their motivations and projections in their work. Seeing work only as work, they seek a balance between their personal and work life (Marshall 2004).

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Generation Y This generation, also called millennials, was the first generation born in the world of technology. Millennials shape and, at the same time, are shaped by the world of work; in other words, they are redefining the employer-employee relationship. Children of parents whose jobs became less secure from the 1970s through the 1990s entered the labor market during an almost unprecedented global recession that resulted in high youth unemployment rates, rapid changes in business cycles, and increased demand for new skills. They are highly competent in digital knowledge, quickly learning new IT tools and devices. They are mindful of social relationships, easily accepting cultural differences and really enjoying a fast-paced life (Krishnan et al. 2012; Bencsik et al. 2016). They are flexible and do not like to plan for long periods, enjoying today and often questioning traditional values. Their experiences determine their decisions and actions (Bittner et al. 2013; Bencsik et al. 2016). For the most part, this generation is already present in the job market; they have college degrees and work together with baby boomers, generation X and, to a lesser extent, generation Z. If they feel stifled, they will quickly change jobs. They are characterized by multitasking, i.e., by a multifaceted and distributed attention span (Bencsik et al. 2016). For them, the concepts of success, career, and money are a top priority because they have learned what it takes to advance in the consumer society (Tari 2010). Their communication occurs mainly in virtual space and their online presence is endless. They are motivated to push, advance, and achieve success; work is a priority for them, while the family remains in the background. Having free time and relaxation is inevitably important to them and they have very wide and diverse desires. Ultimately, it is important for them to work where they want and do what they enjoy. Generation Z Generation Z was born in a fully technological world; as a result, the digital world is highly present for them with technological devices wholly integrated into their lives and an omnipresent online environment (Bencsik et al. 2016), which is why they are sometimes known as the Facebook generation, digital natives or sometimes the iGeneration (Tari 2010). They have grown up in an uncertain and complex environment that defines their point of view on work, education, and the world. Compared to previous generations, certain forms of socialization are very difficult for them; they are practical, quite intelligent and they like to take the initiative. They are more impatient and more agile than their predecessors and continually seek new challenges and impulses. They are not afraid of continuous change and have a lot of information at hand; in effect, they try to solve everything on the Internet (Tari 2011; Bencsik et al. 2016). They have different expectations of the workplace and are more ambitious and enterprising than millennials. They learn quickly and are often self-taught, which makes them much more irreverent than their colleagues. Their technical and linguistic knowledge is quite developed, making them an excellent workforce. Generation Alpha Generation Alpha is made up of purely digital natives in their first years of schooling and their education represents a great challenge. Alphas are completely familiar with

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everything happening on social networks; they were born with a cell phone, a tablet or a computer in hand and they are used to a constant flow of information and content. This generation is educated in the framework of respect for human rights and nondiscrimination, and shy away from stereotypes, something that manifests itself in androgynous fashion trends. They have more tools to access information, wanting everything instantaneously and to be convinced according to their particular tastes and interests. Artificial intelligence is part of their lives (Quezada and Gómez 2019).

2 Balancing Work, Family and Personal Life Essentially, work-life balance reflects the equilibrium between work, family, and personal commitments. According to Kalliath and Brough (2008), it is possible to supplement work-related obligations and interests with family and personal responsibilities and interests. Debeljuh and Ordoñez (2019) and Bosch and Hernández (2020) affirm that this balance must be understood in terms of co-responsibility, that is, in terms of shared responsibility between men and women, as well as between companies and society (Bosch et al. 2014, 2016). The family is the primary sphere of development for any human being, constituting the basis for identity construction, self-esteem development, and learning basic social norms of coexistence. As the nucleus of society, the family is a fundamental institution for the education and promotion of essential human values that are transmitted from generation to generation (CONAPO 2013). Therefore, the task of safeguarding this basic social unit makes achieving a balance between it and work necessary. Scholars highlight the fact that the work and family spheres have significant influence on one another (Ford et al. 2007; Gatrell et al. 2013; Jain and Nair 2013; Shockley and Singla 2011; Vyas and Shrivastava 2017). Hence, most of the literature regarding this relationship has centered on two perspectives: (1) the conflict that arises between these spheres (Greenhaus and Beutell 1985; Frone et al. 1992) and (2) the satisfaction and enrichment achieved in them (Sieber 1974; Crouter 1984; Greenhaus and Powell 2006). The relationship between work and family began to clash starting with increased inclusion of women in the workplace, which gave rise to a large number of couples in which both members worked, marking a clear contrast with the prevailing model in Western industrialized and urban societies until the 1950s and 1960s. In this way, with a new model emerging in society, research began to focus on studying these couples to see how they met new expectations of keeping up with job requirements without neglecting household and family tasks (Davidson and Cooper 1992; Kossek et al. 2011). Although the relationship between work and family was first approached as in conflict, in the 1990s, a more enriching perspective emerged with the suggestion that the roles in one domain improve or make easier role performance in the other domain and vice versa (Grzywacz and Marks 2000).

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This mutually enriching relationship has had an impact on companies’ ability to generate a humane approach at all levels, empowering people to work productively with technology, among different generations and surrounded by diversity and creating lasting value for both the company and for society in general. Organizations have proven that, by helping their members integrate their family and work lives, they can enhance people’s strengths, obtain greater results and drive higher performance (Deloitte 2020). Indeed, work, family, and personal life should enrich one another; they are fundamental, inalienable, and complementary dimensions for both men and women (Debeljuh and Reyes 2016).

3 Work-life Balance and Generations Y and Z in the Mexican Context Mexico’s current family dynamics are very different from prior generations given that Mexican women have entered the labor market at higher rates. Although female participation in the market economy is the lowest of any OECD country (2015), by 2017, it increased to 38% of the economically active population, although with high rates of gender inequality within what has traditionally been a male-dominated culture (Kelan et al. 2009). According to INEGI (2017), the fertility rate has decreased from 4.84 children per woman in the 1980s to 2.2 in 2016. Not only are fewer children born, but also an increasing number tends to live without both parents. Only 56.2% of households are two-parent and 17.5% are single parent, of which 27% are femaleheaded. In this context, children are being left in the care of someone else in the home, older siblings are left in charge of younger ones, or they are growing up in daycare centers. This combination of factors makes work-life balance in Mexico a matter of utmost importance for the current and rising workforce. Yet, according to Morales (2020), work-life balance already represented a significant challenge for the Mexican workforce before the coronavirus crisis, which has exposed its gaping deficiencies. Among the associated problems, three especially stand out. First, a high workload—in Mexico, almost 29% of employees work long hours, one of the highest rates in the OECD, where the average is 11% (OECD 2015). Second, a gender gap when it comes to care tasks both at home and work. At home, Mexican women spend 4 more hours per day than men engaged in unpaid care of others and the home. Finally, low levels of quality free time. According to the OECD Better Life Index, Mexico has one of the lowest levels of work-life balance of 40 countries analyzed, occupying position 39 in the ranking, with a rating of 1.1 on a scale of 0 to 10 (OECD 2015). In what follows, we discuss the methodology and results of a survey that measures work-life balance perceptions and expectations among generation Y and Z Mexican students and professionals. As indicated above, understanding them is vital for current and future talent management in Mexico.

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3.1 Methods This research is quantitative and cross-sectional. Data was collected using a semistructured2 online survey to measure research questions. The survey focuses on a representative sample of young workers in Mexico between the ages of 18 and 30. It was disseminated between July 2019 and January 2020 through a wide range of local contacts, both from academia and the business world, and through the use of social media. The survey, hosted on the SurveyMonkey platform, consisted of 30 questions. It took respondents an average of 15 min to complete. Some questions use a 5-point Linkert scale ranging from “strongly disagree” to “strongly agree.” The survey was first tested on a non-probabilistic sample in Mexico to ensure questions were well understood and that the survey was user friendly. Statistical and machine learning techniques are used to carry out the analysis. To find the variables that best represent the data set, the Chi-square (p) analysis was applied. To carry out the analysis, a p-value is used. That is, the significance of Chisquare (p) which is a more exact measure than the Chi value itself, and therefore this data is better used to check whether the result is significant or not. Two Chi-square (p) analyzes are carried out, one with sociodemographic data and the other with the family and work environment.

3.2 Model and Variables Three constructs were used as follows: I. II. III.

Family environment: Questions 1 to 8 and 26 Work environment: Questions 9 to 15 and 27 to 30 Socioeconomic and demographic: Questions 16 to 25

The variables used in the study were as follows (see also Table 2): 1. 2. 3. 4. 5. 6. 7. 8. 9. 2 The

Family as a model Type of work parents engage in Perceived difficulty of parents’ role balance at home Distribution of housework and childcare among parents Most valued aspects of life Intention to have children Motivations for working Professional development Criteria for choosing a job

survey was developed by specialists from the CONFYE center. It has been implemented in Argentina since 2009, and has been updated to capture new realities and for application outside of Argentina. See: https://www.iae.edu.ar/es/ConocimientoEImpacto/Centros/Confye/Paginas/invest igacion.aspx#generaciones.

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Table 2 Work-life balance Construct

Variables

1. Family environment

(1) To measure the respondent’s 1, 2, 3, 4, 5, 6, 7, 8, 26 family as model we used the measures developed by Debeljuh and Destéfano (2015) (2) To measure the type of work respondents’ parents are engaged in, we used the measures developed by Conlon (2002) (3) To ask respondents about their perception, we used the question developed by Conlon (2002) (4) To measure this perception, the alternatives proposed by Conlon (2002) were used

Questions

2. Work environment

(5) To measure these, we used the 9, 10, 11, 12, 13, 14, 15, items proposed by Kleinbeck 27, 28, 29, 30 and Schmidt (1990) (6) To measure the intention of having children, we used the alternatives proposed by Hernández Ruiz (2008) (7) To measure motivations for working, we used the measures proposed by Grant (2008) (8) To measure professional development, or the place where respondents would like to work, alternatives proposed by Hernández (2008) were used (9) To measure the criteria for choosing a job, the measurements proposed by Debeljuh and Destéfano (2015) were used (10) These measures were based on the items proposed by Debeljuh and Destéfano (2015), as well as by Gilbert et al. (1991) (11) These measures were built based on the items proposed by Thompson et al. (1999) (12) To measure the intention to leave the company, one item from among those proposed by O’Reilly et al. (1991), as well as by Debeljuh and Destéfano (2015), was used (continued)

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Table 2 (continued) Construct

Variables

Questions

3. Socioeconomic and demographic questions

(13) Control variables included age, gender, nationality, number of siblings, current marital status, highest level of educational attainment, current job status, type of job (contractual/self-employed), and length of time in current position

16, 17, 18, 19, 20, 21, 22, 23, 24, 25

Source: Authors’ elaboration

10. 11. 12. 13.

Perspectives on work-life balance Family-friendly culture Intention to leave current company Sociodemographic control variables

3.3 Machine Learning Analysis To better understand the most relevant factors and correlations when it comes to worklife balance, machine learning analysis techniques were employed after normalizing the variables. To carry out the analysis, the p-value was used because Chi-squared (p) is a more accurate measure of significance than the Chi value itself. In this case the hypotheses are: Ho: Variables are independent of each other. H1: Variables are not independent of each other. • If p < 0.05 the result is significant, that is, it rejects the null hypothesis of independence. We, therefore, conclude that the variables in question are dependent, i.e., there is a relationship between them. There is less than a 5% probability that the null hypothesis is true in our sample. • If p > 0.05 the result is not significant, that is, we accept the null hypothesis of independence and therefore we conclude that the variables in questions are independent and have no relationship. This means that there is more than a 5% probability that the null hypothesis is true in our sample, which we consider sufficient to accept. The value 0.05 was established in accordance with a 95% confidence level. In this way, dependency between the variables indicates whether there is a relationship between them. The first analysis was carried out with all the variables; the most relevant dependent variable, as seen in the variable 1 column below, is age, followed by gender (Table A1). For the second analysis, and upon analyzing the different variables, the evidence shows that there is a significant relationship between family

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and work environments. The most relevant variables are related to respondents’ vision of work-life balance in which they expect employers to respect work within an assigned schedule, flexibility, and their personal and family life, in addition to fomenting a good work environment (Table A2). To find the variables that best represent the dataset, Principal Component Analysis (PCA) was applied. The PCA is used to identify a small number of uncorrelated variables, called “core components”, from a large set of data. The goal of core component analysis is to explain the maximum amount of variance with the fewest major components that are easier to interpret and analyze than the total initial variables. The PCA was performed, after normalization of the variables, for the personal, family, and work-life expectations data set. The next step was to generate clusters based on the Machine Learning clustering technique, by grouping individuals together according to their similarities. We identified three clusters (C1, C2, C3) and the features that stand out per cluster are Family, Marriage/Couple, Schooling, Friends, Work, Free time, Religion, and Volunteering (Table A3).

4 Results and Discussion Sociodemographic Variables 388 people, mostly from Mexico City and the State of Mexico (91.2% of the sample), responded to the survey. 43.3% of respondents were men and 56.7% were women. 37.3% were part of generation Z, 45% were Millennials, and 7.7% were from Generation X. 71.1% of respondents were studying for a university degree. Of the total surveyed, only 15.1% were married or with a stable partner and only 4% of respondents had children. The rest of the participants were single (with or without a significant other). These data points were chosen because they most impacted perceptions of work-life balance (see Table 3). Family Context Results point to the fact that family serves as an important model. And indeed, the home is where one learns the difference between selfishness and cooperation, where one discovers what a good person is and grows in confidence, where one learns to relate to people and get one’s point of view across while respecting those of others, all of which facilitates the ability to work with people in the future (Bosch et al. 2016; Kim and De las Heras 2012). Thus, the ability to successfully combine work, family commitments, and personal life is important for the well-being of all family members; however, according to the OECD (2020), finding the right balance between work and daily life is a challenge that all workers face, especially parents. One’s upbringing and satisfaction with it influence future decisions regarding work, family, and personal life balance, including what one perceives in the home in terms of parents’ work and household chore and care management. This is evidenced in the survey data, where more than 50% of the respondents said that they sought support and advice from their parents when faced with a situation characterized by

120 Table 3 Respondent profiles

G. Scalzo et al. Gender Feminine

220

56.7%

Masculine

168

43.3%

Generations Generation X

30

7.7%

Generation Y- Millennial

175

45%

Generation Z

145

37.3%

Married

17

4.4%

Divorced

1

0.3%

Coupled (long-term relationship)

42

10.8%

Single (with a significant other)

114

29.4%

Single (without a significant other)

214

55.1%

Marital status

Family – Children With children

16

4%

Without children

372

96%

Currently in college

276

71.1%

College complete

61

15.7%

Postgraduate

49

12.7%

High school

2

0.5%

Educational level

Employment status Employed

275

71%

Not employed

113

29%

Housing situation Live with parents

261

67%

Live apart from parents

124

33%

Total

388

100%

Source: Authors’ elaboration

conflict. The results show that these younger generations find highly relevant and value the family first, followed by study, and then friends/work. Regarding their parents’ work situations during their childhood and adolescence, most of the respondents’ fathers were employed full time, while only 42% of their mothers were. 49% of respondents stated that their mothers took on the majority of household responsibilities, 23% stated that their parents shared the responsibility equitably, and only 2% claimed that their fathers assumed full responsibility. Regarding the level of difficulty that parents had in combining their work and family roles when they were children, both male and female respondents perceived that both their fathers and mothers found it difficult to balance work and family. This is also

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reflected in the fact that families were reported to have lower levels of labor division at home between parents. The respondents indicate that they learned how to solve problems from their parents, who support their decision-making outside of the home. In addition, faced with conflict, they seek the support and advice of their parents, however, when projecting into the future, 30% of those surveyed reject the model their parents provided them with. They see themselves as part of dual-income families in the future, are interested in supporting their partner’s professional development, and are willing to make their careers more flexible to care for children or dependent elderly adults. Both women and men seek to take an active role in their own homes and care about having a social life apart from work. Work Environment Only 15% of respondents state that they have flexibility in their job, the remaining 44% work full time, and 41% work part-time. In terms of motivations for working, women ranked “doing good for others through work” (transcendent motivation) as their main motivation, unlike men who ranked “I enjoy working” (extrinsic motivation) first and “earning money” (extrinsic motivation) second. In the case of women, intrinsic motivation comes after transcendent motivation and before extrinsic motivation. Analyzing the responses of those who currently work, 49% of respondents state that they are very satisfied (they do not plan to change jobs), 35% are undecided and 16% openly state that they want to change jobs. Work-Related Expectations and Balance The aspects that these young people value most in their personal lives are reflected in their job expectations; they seek to work at and appreciate organizations/companies with the following characteristics: • Support employees’ quality of life • Have policies geared toward work-family balance with corresponding leadership and culture • Large companies or can freelance/have their own personal project • Offer flexible work environments (with home office and other balance-related policies) • Respect hours after work and weekends as personal/family time Regarding leadership management (analyzing responses from those who currently work), we observed the following: • 49% of these young people believe that their companies’ organizational culture respects their generations’ work and lifestyles. • 65% of female respondents, and 59% of male respondents, believe that their leaders are willing to listen to their work and/or personal issues, conveying closeness, support, and giving continuous feedback. • Both male and female respondents coincided in their belief that male leaders are more proactive in noticing improvement, communicating it, and making decisions.

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5 Conclusion Mexico has a huge and continuously growing market, making understanding the workforce’s expectations essential for managing it more effectively. Family-oriented culture is very much a salient feature in Mexico, influencing expectations of a better balance between work and family life for young workers. This cultural and generational predisposition towards the ends of work is reflected in employment decisions. A family-oriented culture affects perceptions of work-family balance since expectations around care remain high, leading to high conflict in double-income households. Although young Mexicans, both men and women, have similar reasons for working, women still find it more difficult to achieve work-life balance and respond more favorably to balance-focused policies. Given the diversity among generations, Mexican companies should focus on generating changes in the workplace that allow for them to interact, making that diversity a competitive advantage. This is especially true since, due to increased average life span in Latin American populations, young workers (generations Y and Z) will soon have greater intergenerational and parental care obligations. As a future avenue for research, these new generations face two particularly great challenges posed by the informal economy and intergenerational solidarity. Mexico’s economically active population continues to operate with a large number of participants in the informal labor sector (Biles 2008). Added to problems of poverty and inequality, this has enormous implications for taxes, social security, retirement, access to credit, and stability. The informal labor sector values balance as much as the formal labor sector, and therefore efforts must be made to discourage informal labor agreements. Increased trust in the formal sector will have an impact on new generations’ obligations and tax burdens, and companies will have to start preparing for the demands that their current and future employees will face. Because of the pandemic, companies and workers all over the globe switched to remote work to contain the transmission of COVID-19, which has unexpectedly served as a massive and unplanned pilot program for new ways of working that support work-family balance. Remote virtual meetings have become the norm in 2020, and economic activity has increased on a variety of digital platforms. As restrictions are lifted, we still do not know how the “new normal” will shape the organization of work or what the future will look like, but we can be sure that the desire for work-life balance that the youngest generations of workers express, combined with the remote-work social experiment brought on by the pandemic, will lead the way.

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Appendix: Machine Learning Analysis Table A1 Dependence between variables: work-life balance factor Variable

Associated variable

Chi squared

p value

Association

1.

Age

Identify _a

22.17

0.036

Dependency between variables

2.

Age

Identify _d

37.4

0

Dependency between variables

3.

Age

Imp_life_b

30.45

0.002

Dependency between variables

4.

Age

Frequency _Leave _Work

197.04

0

Dependency between variables

5.

Age

Work_sector

215.7

0

Dependency between variables

6.

Age

Seniority_work

248.75

0

Dependency between variables

7.

Age

Homework_organization

45.97

0

Dependency between variables

8.

Age

S_D

22.01

0

Dependency between variables

9.

Age

couple _work

113.15

0

Dependency between variables

10.

Age

Civil_status

91.73

0

Dependency between variables

11.

Age

Level _studies

244.78

0

Dependency between variables

12.

Gender

Gender leader

10.56

0.005

Dependency between variables

13.

Gender

Work_motivation_agreement_7

11.96

0.018

Dependency between variables

14.

Gender

Work_motivation_agreement_4

13.11

0.011

Dependency between variables

15.

Gender

Work_motivation_agreement_2

10.61

0.031

Dependency between variables

16.

Gender

Work_motivation_agreement_1

10.37

0.035

Dependency between variables

Source: Authors’ elaboration.

Table A2 Dependence between family and work environment-related variables #

Variable

Associated variable

Chi squared

p value

Association

1.

Vision_wlk_5

World_Agreement_Work _2

139.01

0

Dependency between variables

2.

Vision_wlk_5

World_Agreement_Work _3

130.04

0

Dependency between variables

3.

Vision_wf_10

World_Agreement_Work _3

123.22

0

Dependency between variables

4.

Vision_wlk_5

World_Agreement_Work _1

111.57

0

Dependency between variables

5.

World_Agreement_Work _1

Vision_wlk _5

111.57

0

Dependency between variables

6.

Future work_1

W_criterion _1

107.85

0

Dependency between variables

7.

Imp_life_a

Vision_wlk_8

103.71

0

Dependency between variables

8.

Vision_wlk _10

Work_motivation _agreement _2

100.41

0

Dependency between variables

9.

World_Agreement_Work _5

Work_motivation _4

98.29

0

Dependency between variables (continued)

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Table A2 (continued) #

Variable

Associated variable

Chi squared

p value

Association

10.

Imp_life_a

Vision_wlk_7

90.01

0

Dependency between variables

11.

Iden_b

Imp_life_c

88.04

0

Dependency between variables

12.

Leadership management agreement _2

Work_motivation _4

80.24

0

Dependency between variables

13.

Identify_b

Imp_life_a

74.23

0

Dependency between variables

14.

Identify_c

Imp_life_a

74.23

0

Dependency between variables

15.

Imp_life_a

World_Agreement_Work _5

70.25

0

Dependency between variables

16.

Vision_wlk_10

Work_motivation _8

67.29

0

Dependency between variables

17.

Future work_1

Work_motivation _3

63.45

0

Dependency between variables

18.

Identify_b

Vision_wlk_8

60.25

0

Dependency between variables

19.

Identify_b

Imp_life_e

59.46

0

Dependency between variables

20.

Imp_life_a

Work_motivation _4

59.26

0

Dependency between variables

21.

Imp_life_a

Vision_wlk_2

56.84

0

Dependency between variables

22.

Iden_c

Vision_wlk_1

54.18

0

Dependency between variables

23.

Imp_life_a

Vision_wlk_1

53.46

0

Dependency between variables

24.

Imp_life_a

World_Agreement_Work_3

53.09

0

Dependency between variables

25.

Iden_c

Imp_life_c

50.49

0

Dependency between variables

26.

Iden_d

Vision_wlk_3

47.7

0

Dependency between variables

27.

Iden_b

Vision_wlk_1

46.27

0

Dependency between variables

28.

Iden_b

Imp_life_d

44.82

0

Dependency between variables

29.

Iden_d

Vision_wlk_6

43.74

0

Dependency between variables

Source: Authors’ elaboration.

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Table A3 Most important variables in clusters Variable

Importance

Family Cluster

Important

Very important

Not important

Neutral

Less important

C1

21

83

1

7

2

C2

18

128

C3

17

104

2 1

5

Marriage/couple Cluster

Important

Very important

Not important

Neutral

Less important

C1

29

54

4

24

3

C2

52

70

2

20

4

C3

36

71

3

14

3

Not important

Schooling Cluster

Important

Very important

C1

35

66

C2

45

98

C3

45

76

1

Neutral

Less important

12

1

3

1

5

1

Friends Cluster

Important

Very important

Not important

Neutral

Less important

C1

48

54

1

10

1

C2

56

80

11

1

C3

54

59

11

3

Cluster

Important

Very important

Neutral

Less important

C1

57

46

C2

66

69

C3

61

52

Work Not important

11 2

9

2

13

1

Free time Cluster

Important

Very important

C1

56

49

C2

62

69

C3

44

58

Not important

Neutral

Less important

8

1

1

13

3

1

22

2

Religion Cluster

Important

Very important

Not important

Neutral

Less important

C1

26

24

18

34

12

C2

36

25

13

46

28

C3

26

29

23

30

19

Solidarity activities/volunteering Cluster

Important

Very important

Not important

Neutral

Less important

C1

33

24

7

40

10

C2

53

23

4

52

16

C3

38

17

4

50

18

Source: Authors’ elaboration.

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Medical Tourism in Mexico. Analysis of the Economic and Technological Model in the COVID-19 Pandemic Era Griselda Dávila-Aragón and Edmundo Arrioja-Castrejón

Abstract In the last decades, organizations have generated and exploited the data product of their operational activity using technological tools to support executives in decision-making, seeking to incorporate economic and social benefits. A key factor today to increase the competitiveness of service providers is taking advantage of the exponential increase in Internet purchases that has been further enhanced by the COVID-19 pandemic. The use of social networks as a means of reference and knowledge of recommendations based on the experience of other users, as well as the use of mobile applications, have contributed to exponentially exploding e-commerce and making it increasingly profitable for companies. This document analyzes the data obtained from various sources, in order to determine the behaviors and preferences related to medical tourism. The study seeks to determine which are the main factors that allow predicting consumption habits and leading the various options for socially responsible medical tourism through the use of advanced analytical and artificial intelligence tools, in order to identify the most attractive alternatives to benefit consumers in an adverse environment like the one the world is facing because of the global pandemic caused by COVID-19, which represents a significant challenge for most industries, but also generates new opportunities with significant benefits for those who know how to take advantage of them. Keywords Medical tourism · Artificial intelligence · e-commerce · Technological model · Economic model · COVID-19 pandemic · Mexico JEL Classification D21 · D23 · D33

G. Dávila-Aragón (B) · E. Arrioja-Castrejón Escuela de Ciencias Y Económicas Y Empresariales, Universidad Panamericana, Ciudad de México, México e-mail: [email protected] E. Arrioja-Castrejón e-mail: [email protected] © The Author(s), under exclusive license to Springer Nature Singapore Pte Ltd. 2021 G. Dávila-Aragón and S. Rivas-Aceves (eds.), The Future of Companies in the Face of a New Reality, https://doi.org/10.1007/978-981-16-2613-5_7

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1 Introduction There is an area of opportunity for service providers who seek to know more about their clients by aligning their expectations to market preferences. COVD-19 has transformed the way that society is behaving to face a pandemic. This has promoted the use of technology to allow consumers to maintain social distancing and control the threat of contagion of this disease that worries all nations and economies. Several authors, such as Leon and Kanuk (2005), Zukin and Maguire (2004), and Toro (2019), agree that knowledge of customers and their consumption habits are the main factor that will enhance the way of doing business in the present and future. The increase in e-commerce services and the availability of sophisticated technology, as well as the expansion of using mobile devices and social networks, suppose a major challenge for medical and tourism service providers. Within the medical tourism industry that integrates both industries, the need to improve the user experience is perceived. Presenting an integrated and differentiated offer using cutting-edge technology that includes all the options that a user has according to their profile as a patient and traveler, and not just based on likes or search history. Tourism and medical services in Mexico represent the main source of direct jobs and social welfare in the country, so now more than ever it’s necessary to strengthen both industries in an uncertain environment where the fear of the contagion of COVID-19 prevails in the minds of consumers. Both industries are an important generator of indirect jobs through their respective supply chains. The extensive network of suppliers of raw materials and related service providers must be also considered because of the economic impact on the country. Social responsibility is a crucial factor to achieve by these service providers in a nation whose spirits have been affected by a common enemy that has ended the lives of many human beings in the last months. Currently, e-commerce sites, videoconferencing solutions, and social networks are an important source of customers for sectors that are closely related in this business model and that has grown at accelerated rates in recent years. It’s essential to enhance their use for formalizing this economic activity under an industrialized model that allows having a significant social impact. It’s important to analyze the various factors that improve the perception and user experience from the moment of selecting the provider of the required medical treatment until the reservation of complementary services such as accommodation, transfers, and meals to guarantee an integral and safe medical experience. The proposal is to provide the possible alternatives in a single e-commerce portal using artificial intelligence tools, like entertainment tourism recommendation applications such as TripAdvisor. Improving the user experience represents a great challenge for medical tourism service providers. The preferences and consumption habits of customers in this sector must be considered, for guaranteeing confidentiality, security and the patient’s budget, incorporating offers, promotions and integrated packages that are currently available. The use of artificial intelligence tools and routines are essential

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to contemplate other alternatives that the user has available according to their integrated profile as a patient and traveler, considering loyalty programs, timeshare plans, memberships, vacation clubs, insurance plans and agreements with other service providers. This research explores the habits, needs, and preferences of consumption of medical tourism services for a medical service provider, analyzing information of the countries that most demand these services from the databases of international organizations specialized in medical tourism, such as Patients Beyond Borders and Medical Tourism Association. Reviewing the hotel occupancy in Mexico in the last few years (Euromonitor 2020), is useful to determine how to improve the customer experience by formalizing the service offer using a digital platform that allows to integrate the various options available. The projections in the short and medium-term, according to the growing market demand allows to quantify the economic impact of this sector. The research is limited to the offer of medical tourism services in Mexico. This chapter contributes with a detailed analysis of the prevailing conditions in the medical tourism sector in Mexico and the world, highlighting the opportunities and challenges derived from the COVID-19 pandemic. It presents the theoretical framework related to the prevailing technologies that can be applied to facilitate contact between providers and users of medical services under this business model. The recommendation of medical services and the perception of consumers of medical tourism services about online services are undoubtedly crucial factors to promote this industry internationally. It’s essential to reinforce the concepts of trust and patient safety to ensure this industry grows for the following years. These variables, as well as market behavior, are analyzed in this document to determine the effect on consumption habits, and the relationship within the next dimensions: medical, technological, economic, and financial.

2 Theoretical Framework Reviewing the state of the art, it’s easy to determine which industries are the ones that exploit better the technological tools to maximize their benefits today. It’s possible to identify among them, the consumer and tourism industries, and in a lesser degree the offer of medical services, that are normally contracted by recommendation. Several authors, such as Juddoo (2015), Gandomi and Haider (2015), and Cotino Hueso (2019), agree on the importance of taking advantage of the data. They point out the exploitation that is given to them through analytics and comment on the importance of having not only systems, but also specialized teams to take advantage of companies’ information. Projects that integrate technology should focus more on information and less on technology (Marchand et al. 2013) since the objective of any system is to provide information or execute an action based on the data.

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There are several key elements to consider when handling large volumes of data: veracity, variability or complexity, and value. When these elements are correctly classified using artificial intelligence, many companies such as clinics, hospitals, laboratories and other service providers will be able to benefit more from this information to boost their businesses and integrate the value chain more efficiently. Many companies are investing huge amounts of money in information technology and data experts to extract valuable information from the large volumes of data available on the networks. Those companies and people that respond more efficiently and recognize that competing in the world of analytics to extract and interpret the preferences of their potential customers will provide them with great competitive advantages and will be the main beneficiaries of this technological revolution (Davenport and Patil 2012). This is especially important today in the economy of companies that must entrust a large part of their sales and focus their commercial strategy on a virtual scheme in the face of the challenge of a global pandemic. The information can come from social networks, images, the internet, sensors in devices, among other sources, so service providers must learn to ask the right questions and adopt evidence-based decision making. Organizations must identify patterns in very large data sets and translate them into useful business information (McAfee and Brynjolfsson 2012). The use of analytics is and will surely be a clear opportunity and advantage for companies that incorporate and take advantage of it. Today, many successful business models are being created through knowledge of mobile devices and the Internet of Things (IoT). In the medical industry, there are very important advances to diagnose certain diseases using state-of-the-art technology, which allows us to be much more precise in predicting diseases and in programming the necessary treatments. Several internet portals of prestigious universities that have emerged with advanced analytics using artificial intelligence routines. Nowadays, they have become a benchmark as a result of the discovery and monitoring of the COVID19 pandemic, such as the University of Singapore with his Scientific Research App and the Johns Hopkins University with his Coronavirus Tracking Dashboard. Other examples of this technological revolution are the use of increasingly sophisticated analytical and artificial intelligence tools and routines for processing data generated on the Internet, and on social networks (Facebook, Twitter, WhatsApp, LinkedIn, Pinterest, Instagram, TripAdvisor, among others). The increase in the number of variables and the availability of various types of data (structured and unstructured) make its abstraction, organization, and filtering increasingly complex for its correct use. We can clearly corroborate this by taking as a basis the sophistication and complexity of the models for predicting user habits and preferences, which have made tourism service providers such as Expedia, TripAdvisor, Hotels.com, and Trivago, very valuable brands globally for the online trade of tourist offers and all of them are positioned today among the most successful companies in this field, however, their solutions and service leave out an important growing sector that is medical tourism. All these companies, act as wholesalers and intermediaries of tourist services. Taking advantage of this wealth of information and transform it into market intelligence by

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using disruptive techniques, tools, and processes, allow them to fully exploit their capabilities to detect needs and focus the sale of tourism services around the world. Unfortunately, those companies focus on entertainment and relaxation tourism that has been significantly impacted by the COVID-19 pandemic and until now they don’t integrate the medical services that cause many travelers to move from their places of origin due to the high costs or the lack of specialized medical supply. This opens an important business opportunity with a social responsibility character in benefit for some countries such as Mexico to meet this increasingly growing market demand, and which has been boosted by the medical deficiencies that exist in many other countries and sectors of the world population. The use of predictive technology based on artificial intelligence models makes it easier for medical and hospital service providers to optimize the way they process large volumes of data that flow through networks and their customer service portals. Which leads to the continuous evolution of these companies to achieve a competitive advantage by replicating the functionality of the human brain to interpret, for example, these data through the use of neural networks (Romero Bataller 2019). This can be facilitated in a very important way with the most recent advances, that allow increasing the processing capacity of computers and intelligent devices. These devices born from Turing machines and evolve to reach the most recent discovery with the quantum processor launched by Google at the end of 2019. This new technology will surely eliminate many of the current processing barriers and will increase the possibility of analyzing a greater amount of data concurrently. The Institute for the Future in Palo Alto identified in 2011 the disruptive changes that will transform the way of life, which are: the increase of systems and intelligent machines and automation that daily removes human workers of mechanical and repetitive tasks; the exponential increase in sensors and processing capacity that make the world a measurable and programmable system; as well as increased global connectivity that allows interacting all kinds of devices and people (Valderrama 2019). As an example, we can appreciate with the events arising from the COVID19 pandemic that shows the society growing need to use technology to maintain the operation of millions of businesses, services, and keeping human contact to deal with a disease that encourages social distancing. By reviewing the articles about medical tourism and disruptive changes in the operating models of service providers, we can see an increase in the need for tools based on artificial intelligence to analyze the human behavior in areas of understanding, perception, problem solving, and decision making to be able to reproduce them with the help of a computer (Hardy 2001, pg. 12). Artificial intelligence is a concept that has been the object of study for several decades, but it has been enhanced especially in recent years by the large number of applications and the impact it’s having on modern society and the consequences of this technology on behavior and in people’s decision making (Dwivedi et al. 2019). The COVID-19 disease has caused the exponential use of innovative developments that use technological models based on artificial intelligence to facilitate users and companies to get in contact by offering goods and services that otherwise be extremely slow and difficult to achieve.

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Several articles consulted agree on the importance of taking advantage of data. This is illustrated in detail in the article Big data analytics in healthcare: promise and potential (Raghupathi and Raghupathi 2014), that shows the exploitation that is given to large volume of data through advanced analytics for the personal care sector. This sector is closely related to some treatments that are sell under the medical tourism model such as dental and breast implants, gastric bypass, liposuction, among others. In the article Data Mining Applications in Healthcare (Koh and Tan 2011), various sources of data are exposed, their constant interrelation and the applications available for their processing and use for personal care. As detailed in the article Beyond the hype: Big data concepts, methods, and analytics (Gandomi and Haider 2015) several key elements must be considered in handling large volumes of data: veracity, variability, or complexity, and value. To be sure that these elements are correctly classified through the use of technology, service providers such as hospitals, clinics, airlines, hotels, and restaurants that are related to medical tourism will be able to benefit more and more from this information to boost their business, offer goods, and services focused on potential customers with a much higher degree of acceptance. Related industries in medical tourism have benefited from constant contact with people through frequent use of the Internet. Advances in telecommunications, particularly mobile devices such as cell phones, electronic tablets, and smartwatches, have facilitated access to millions of sites on the Internet. And with the integration of solutions based on artificial intelligence, through personal interaction devices such as Amazon Alexa and Google Home, how people seek all kinds of services and recommendations, including health, is being revolutionized. There are organizations on the Internet that use the large volumes of data of their members to analyze them to obtain a commercial benefit, “this is evidenced by the burgeoning popularity of many online social networks such as Twitter, Facebook, and LinkedIn” (Aggarwal 2011, pg. 2). The best representative is Facebook, through its social network and mobile applications such as WhatsApp recently acquired, who sells many goods and services, without being an electronic commerce portal, this is the case of Mercado Libre, Linio or eBay. All these portals with the beginning of the pandemic have substantially increased their online sales due to the limitation caused by social distancing. Social networks and the internet have helped to obtain information immediately and, it has served as a filter to be able to segment the different types of consumers by their interests, geographical location, education, sex, and marital status (Toro 2019). This allows companies such as Amazon, Google, and Facebook to anticipate the needs and consumption habits of people to be able to bring goods and services according to the characteristics of everyone, which has favored them today. With the increasing access of mobile devices, the use of the Internet has intensified for many personal applications that give way to the development of programs capable of distinguishing locations and movements. This allows to locate the consumer and determining their mobility and consumption habits, making easier for companies to offer their services to the user in real-time when moving from one place to another as they approach the location of the service provider (Naveed 2013).

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Among the innumerable applications of artificial intelligence, there are recommendation systems that learn from user preferences to suggest potentially interesting elements. A clear example of this occurs in the tourism industry, specifically in the hotel industry, where group recommendation systems generate recommendations that seek to satisfy a group of users in addition to individual users. In the article Social group recommendation in the tourism domain (Christensen et al. 2016), show a social media-based approach to recommendation systems for the tourism industry, where a group profile is built by analyzing not only user preferences, but also social relationships between members of a group, this is particularly useful if we consider groups with common health problems such as heart bypass or corneal or heart valve transplants. Recommendation systems based on artificial intelligence tools represent user preferences. This became fundamental applications in electronic commerce and social networks, providing all kinds of recommendations that filter large volumes of data in a very efficient way. Allowing to direct the users towards the information and service providers that best satisfy their needs and preferences. The recommender systems have developed in parallel with the Internet. Initially, they were based on demographic filters, using the content that people enter on the sites they navigate on the internet. Currently, these systems are incorporating data obtained from social networks, as well as informational and commercial portals. In these sites, they use implicit, local, and personal information from devices through the Internet of Things (IoT), allowed by advanced analytics through artificial intelligence routines. The article Recommender systems survey (Bobadilla et al. 2013) provides a comprehensive description of recommender systems, as well as collaborative filtering algorithms and methods. A great diversity of techniques to make recommendations have been proposed over the last few years, including techniques based on knowledge, content, collaboratives, among others. Some methods have combined these techniques and generate hybrid recommendation systems. In the essay Hybrid Recommender Systems: Survey and Experiments (Burke 2002), the panorama of hybrid recommendation systems is examined and a hybrid system is introduced that combines recommendations based on knowledge and collaborative filters to suggest restaurants, which can be a good basis for the development and optimization of a hybrid system useful for medical tourism. There are important achievements in the tourism industry that has especially benefited from the constant technological advances that arise every day. In the article Building an expert travel agent as a software agent (Schiaffino and Amandi 2009), the development of an expert software agent, called Traveler, is shown to help users select their trip. This agent combines collaborative filtering with content-based recommendations and demographic information about clients to suggest tailored tourism packages. The recommendation systems based on analytics and advanced artificial intelligence routines to determine the behavior of customers in tourism constitute a phenomenon of increasing relevance due to the economic impact it represents for this industry. Among these, it’s worth highlighting the study of the role played by

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information technologies, as well as the so-called e-Tourism in the competitiveness of the company (Martínez et al. 2006). A comprehensive search has been carried out for e-Tourism smart recommendation systems that have been cited in magazines and conferences for more than a decade. In the research Intelligent tourism recommender systems: A survey (Borràs et al. 2014) a detailed and updated field survey is provided, considering the different types of interfaces, the diversity of recommendation algorithms, the functionalities offered by these systems, and the use of artificial intelligence techniques. These findings can be useful for developers of recommendation systems based on artificial intelligence tools, as well as for all companies and organizations that offer tourism services of all kinds, particularly in medical tourism, which is one of the sectors that have been sought. Stay at the forefront of technology to differentiate yourself from your competitors. It is necessary to contemplate linking e-commerce platforms with appropriate artificial intelligence tools. In the article Integration of Business Intelligence with e-commerce (Ferreira et al. 2019) they propose an architecture to combine them. This, however, is the subject of another study.

3 Methodology The methodology carried out in this chapter is descriptive, the variables involved in the object of study are analyzed, starting with the qualitative part, with a phenomenological perspective focused on “lived experiences” (Merrian and Tisdell 2016, p. 26) by specialists in medical tourism services interviewed. And was complemented with data collected from medical tourism organizations consulted to determine the categories to be obtained from several quantitative sources. With the increase in the amount of information available through several related sources such as interviews, surveys, social networks, and electronic commerce portals, which have increased the variety of data and the speed at which it’s produced. The necessary use of advanced analytics tool such as Microsoft PowerBi to be able to collect, order, store, and present data for a correct interpretation, correlation, and analysis to accept or refute the hypotheses derived from the objects of study and thus be able to conclude with the necessary support to guarantee its validity and reliability. Possibly within these activities, the most difficult to complete is data mining, since it involves three extremely complex steps: explorations, identification of patterns, and display of information (Mishra et al. 2016). That requires advanced analytical and artificial intelligence tools to obtain effective results. Among the data mining techniques proposed by these authors, regression, the dependency model, classification, clustering, and anomaly detection stand out.

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4 Financial Results and Analysis The results obtained from the first interviews confirm the importance of the recommendation of medical services and the perception of consumers of medical tourism services about online services, highlighting parameters or categories such as trust and patient safety. Several internet researches was carried out in databases of recognized international organizations specialized in medical tourism such as Patients Beyond Borders and Medical Tourism Association, where valuable data was obtained about main medical tourism treatments and the countries where they are most commercialized, the detail results are illustrated in Table 1. Based on the following data of the medical treatments that are programmed under the modality of medical tourism and of the main countries where they are marketed, an analysis was made using the Microsoft PowerBi and Microsoft Excel tools to relate and interpret them in a better way. Figure 1 shows a comparison of the average cost of each medical tourism treatment for each of the main countries that offer these services and you can see that Mexico plays a relevant role in the offer of all treatments because it has very competitive rates. Figure 2 shows a comparison of the average cost of each treatment marketed in America, you can observe that Mexico has very competitive rates against Costa Rica and Colombia; all of them were below the United States, which makes Mexico a very attractive medical tourism destination for the North American market. In Fig. 3, map of Mexico, you can see the relation of hotel offer for each state (Fig. 3), considering the total number of hotels available, it is evident that the hotel offer is mainly concentrated in the center of the country and the northern border in second place. Mexico City and the metropolitan area stand out, as well as in the state of Jalisco (Guadalajara and Puerto Vallarta), both destinations have become important centers of attention for medical tourism services in the country, in addition to the cities of Monterrey, Tijuana and Cancun, the first two due to their proximity to the United States. Consultations on the concepts of medical tourism and medical tourism in Mexico throughout the world have a seasonal behavior as can be seen in Fig. 4. There are more requests in Google databases for information in certain seasons over others, confirming that the demand for services aligns with the holiday periods. Almost 75% of foreign medical tourists in Mexico are border-type and come from the southern border states of the United States and Canada, which is justified given the proximity to these countries. The remaining 25% are tourists seeking medical services of European origin, mainly from the United Kingdom and Spain, as well as from Asian countries (among which India and Malaysia stand out) which register more searches than other nations about surgeries and treatments in Mexico. In 2018, after the United States, Mexico is the main destination for medical tourism with a 13% share of the global market. This is possible because Mexico has very competitive costs for medical services and the renowned prestige of its hospitals and doctors, which attracts a large population from the United States to cross the border due to the proximity and commercial relationship between both nations.

2500

4500

Breast implants

Rhinoplasty

3500

2400

ND

1600

Tummy tuck

Lasik (both eyes)

Cornea (per eye)

Cataract surgery (per eye)

4000

2900

Hysterectomy

2500

12,200

Gastric bypass

Liposuction

11,200

Gastric sleeve

Face lift

8500

7200

Knee replacement

Lap band

10,500

Hip resurfacing

14,500

8400

Hip replacement

1200

10,450

Heart valve replacement

Dental implant

14,800

Heart bypass

Spinal fusion

7100

Angioplasty

India 5000

8000

25,000 9500

20,100 9000

36,000 8000 7700

12,500

8000

28,500 14,400 13,500

7300

900

1700

9800

2400

5000

2800

4500

3800

3500

6900

1500

2800

1000

3500

2800

3500

2400

3000

3200

12,900 7000

11,500 7300

9450

800

1000

1400

3950

2900

4000

3700

ND

3800 2400

5000

4900

10,900 4200

2500

6800

4600

3800

14,500 6600

24,000 7500

20,000 7500

17,300 7000

1200

3000

ND

3450

3900

2500

3550

2200

3800

4200

9900

8400

8150

1500

2100

ND

1900

4500

3000

4900

3800

4500

4500

11,500

8900

6500

900

15,400

12,900

12,500

13,500

28,200

27,000

10,400

13,400

750

ND

1850

3550

1800

4000

2500

3900

2200

9750

9400

6700

925

6200

8200

9200

5500

3250

9000

3800

4650

2900

440

2200

8400

10,400

13,700

11,500

9200

2700

12,800

16,000

16,350

13,900

19,000 16,900

14,000 17,200

5300

ND

ND

1700

5000

2900

6000

3980

3800

10,400

10,900

9950

10,200

1350

16,900

17,500

19,500

21,000

39,900

26,000

17,700

Jordan Malaysia México Poland Singapore S. Korea

28,000 14,400 12,100

7500

Israel

15,700 10,300 33,500 10,000 6000

12,500 6600

13,200 9700

13,600 7200

30,000 9500

27,000 7900

13,800 5700

Treatment/Country Colombia Costa Rica

Table 1 Main countries and medical tourism treatments in the world

1800

3600

2310

5300

2500

3950

3300

3500

3650

16,800

9900

11,500

1720

9500

14,000

13,500

17,000

17,200

15,000

4200

28,200

ND

Vietnam

9250 8000

ND

2500 14,000

1600

7000

1700

4000

3000

6700

3100

4500

7000

3500

17,500

4000

8000

5500

11,000

6500

6400

15,400

13,800 25,000

12,900 16,500

8600

1100

(continued)

ND

ND

1720

3000

3000

4150

2100

4000

ND

ND

ND

ND

ND

16,800 110,000 6150

10,400 35,000

10,100 28,000

13,900 40,364

17,200 170,000 ND

13,900 123,000 ND

4800

Thailand Turkey USA

140 G. Dávila-Aragón and E. Arrioja-Castrejón

2500

ND

IVF treatment

Source Own Elaboration. Medical Tourism.com

5450

India

Treatment/Country Colombia Costa Rica

Table 1 (continued)

5000

4900

14,900

7900

5000

5500

6900

Jordan Malaysia México Poland Singapore S. Korea

Israel 4100

5200

12,400

Thailand Turkey USA ND

Vietnam

Medical Tourism in Mexico. Analysis of the Economic … 141

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Fig. 1 Comparison of the average cost of Medical Tourism for each country. Source Own elaboration

Fig. 2 Comparison of the average cost of Medical Tourism for each country. Source Own elaboration

However, there is also a small percentage of medical tourism that travels to the main cities such as Mexico City, Monterrey, and Guadalajara due to its growing avantgarde medical infrastructure, as well as the recent flourishing of medical services in the main tourist centers with a presence of foreigners such as Puerto Vallarta, Cancun, and Los Cabos. In 2019, the United States was the main consumer of medical tourism services in global terms, with a 66% share (Fig. 6). In economic and financial terms, according to data from Banco de México, GDP at the end of 2018 in round numbers was 1.2

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143

Fig. 3 Relation of hotel offer for each state. Source Own elaboration

Fig. 4 Consultations in Google: Medical Tourism. Source Own elaboration

trillion dollars, of which total income from tourism was approximately 105.2 billion dollars, which represent 8.6% of GDP according to data from the Ministry of Tourism for that same year. Of these revenues, 21% is derived from international tourism that is equivalent to 22.6 billion dollars, and the remaining 79% is the product of domestic tourism that is equivalent to 82.6 billion dollars (Fig. 5). Regarding the income from medical tourism, according to INEGI figures for the same 2018, these were around 7.1% of the total income from international tourism, which in figures represents 1.6 billion dollars of that year. This data only considers foreign medical tourism, so it should be added to national medical tourism. Figure 7

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Fig. 5 Medical tourism market share. Source Own elaboration

Fig. 6 Global demand of Medical Tourism services by country. Source Own elaboration

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Fig. 7 Medical Tourism growth by country. Source Own elaboration

shows the countries that have had the greatest movements in their annual growth rates in the last three years due to medical tourism. Mexico has maintained sustained growth except during the financial crisis of 2008, the H1N1 epidemic in 2009, and the entry of Donald Trump to the presidency of the United States in 2016 (Euromonitor 2020). The rest of the years between 2005 and 2020, service providers and consumers, have seen Mexico as a good alternative to treat certain conditions that can be scheduled for care in clinics and hospitals, mainly due to the enormous price differential and the good quality of medical care available.

5 Conclusions A common point of the database management experts is that these alone aren’t enough to be able to make decisions that can add value in organizations. Analytical and artificial intelligence tools are required to be able to harvest, mine and interpret data to deliver valuable information to make decisions and implement actions that may have a favorable impact on people, companies, and society. This is especially important in the adverse scenario such as the one experienced by the COVID-19 pandemic. It has been shown that in the tourism industry very important efforts and investments have been made, especially in the last decade, income for Mexico for this concept represented 8.8% of GDP in 2017 and it’s expected that in the next 20 years it will arrive at levels of 10% of GDP (Secretaria de Turismo 2018). The data available on the Internet must be organized to generate valuable information for the traveler through advanced analytical and artificial intelligence tools. The technology is available to convert this information into knowledge and improve the experience to the customers and providers. However, the medical tourism sector has been relegated in this type of investments because it has a more specialized market that depends to a large extent on the recommendation of medical services. Having analysis tools by consumption needs, as well as a recommendation system will be a

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key factor to enhance this sector, taking benefits of all the advantages that Mexico represents. One of the most complex challenges will be to apply data mining to extract relevant information from the various existing information sources and to model travel data and scheduled medical treatments of users of medical tourism services for analysis and interpretation. However, there are recommendations and resampling algorithms that can be applied to balance and improve the predictive performance of the data obtained from various sources such as DataTur, INEGI, Euromonitor, among others. The financial analysis of the prevailing conditions in the medical tourism sector in the world and the current market share shows a favorable scenario for Mexico related to other countries. Nowadays the opportunities and challenges derived from the COVID-19 pandemic represent an opportunity for the countries that take advantage of this market. This research presents several technologies available that can be used to improve the user experience of medical tourism services, this being the object of another study.

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Juddoo S (2015) Overview of data quality challenges in the context of Big Data. In: 2015 International Conference on Computing, Communication and Security (ICCCS), pp 1–9. https://doi. org/10.1109/CCCS.2015.7374131 Koh HC, Tan G (2011) Data mining applications in healthcare. J Healthc Inf Manag 19(2):64–72 Marchand DA, Peppard J (2013) Why IT fumbles analytics: tech projects should focus less on technology and more on information. Harvard Bus Rev 104–112 Martínez J, Majó J, Casadesús M (2006) El uso de las tecnologías de la información en el sector hotelero. In: Turitec 2006: VI Congreso Nacional Turismo y Tecnologías de La Información y Las Comunicaciones, pp 1–13. http://www.aecit.org/jornal/index.php/AECIT/article/view/118/109 McAfee A, Brynjolfsson E (2012) Big data: the management revolution. Harvard Bus Rev 90:60–68. https://doi.org/10.3823/5004 Medical Tourism Association (2019) Compare Prices Report. https://www.medicaltourism.com/ compare-prices Merrian S, Tisdell E (2016) Qualitative Research: a guide to design and implementation, 4th edn. Jossey-Bass, San Francisco Mishra BK, Hazra D, Tarannum K, Kumar M (2016) Business intelligence using data mining techniques and business analytics. In: Proceedings of the 5th International Conference on System Modeling and Advancement in Research Trends, SMART 2016, pp 84–89. https://doi.org/10. 1109/SYSMART.2016.7894496 Naveed M (2013) Online learning based contextual model for mobility prediction BT - evolving ambient intelligence. In: O’Grady MJ, Vahdat-Nejad H, Wolf K-H, Dragone M, Ye J, Röcker C, O’Hare G (eds) Springer International Publishing, Cham Raghupathi W, Raghupathi V (2014) Big data analytics in healthcare: promise and potential. Health Inf Sci Syst 2:1–10 Romero Bataller A (2019) La era de la Inteligencia Artificial. Nuevas herramientas para los creadores. (Universidad Politécnica de Valencia). https://riunet.upv.es/ bitstream/handle/10251/117729/Romero-LaeradelaIA.Nuevas herramientasparalos creadores.pdf?sequence=1&isAllowed=y Schiaffino S, Amandi A (2009) Building an expert travel agent as a software agent. Expert Syst Appl 36(2PART1):1291–1299. https://doi.org/10.1016/j.eswa.2007.11.032 Leon S, Kanuk L (2005) Comportamiento del consumidor - Leon G. Schiffman, Leslie Lazar Kanuk - Google Libros. Pearson Secretaria de Turismo (2018) EL GRAN MOTOR DE LA ECONOMÍA NACIONAL Valderrama B (2019) Transformación digital y organizaciones ágiles. Universidad Politécnica de Madrid, VI, pp 15–54 Zukin S, Maguire JS (2004) Consumers and consumption. Ann Rev Sociol 30(1):173–197. https:// doi.org/10.1146/annurev.soc.30.012703.110553

Small Coffee Companies and the Impact of Geographical Indications as Productive Innovation in Mexico in the New Reality Marisol Velázquez Salazar and Pablo Pérez Akaki

Abstract This paper analyzes the Protected Designation of Origin (PDO) as a factor of innovation in the Coffee Pluma geographical region in Oaxaca, Mexico, a vital tool to solve the problem of the actual crisis in the chain and for the new context of business and markets in the post-COVID 19 era due to the need for new marketing methods. Two case studies are presented under the Global Value Chain (GVC) methodology proposed by Gereffi et al. (1994, 2005, 2018) with a contribution from the conceptual framework of Geographical Indications (GI) used by Belletti et al. (2017) to analyze the PDO as an innovation. The first are small-size producers and the second are medium-size producers, both considered as small companies by the number of people employed. Even on a small scale, the coffee sector, through the appellation of origin, has the potential to generate economic benefits in the place of origin by promoting the development of two other economic sectors such as tourism and retail marketing. Likewise, it gives a comprehensive answer considering the business economic field and incorporating, as required by the current reality, other capitals such as social, cultural and environmental. The aim of this chapter is to evaluate the benefits that coffee sector, will obtain and generate through this sectorial and territorial development tool, considering that the G.I. emerges as an option to improve production by acquiring the exclusivity of producing coffee within that region to achieve sustainable development faced with the new reality. Keywords Coffee · Small producers · Global value chains · Protected designation of origin · New reality Post-COVID19 JEL Classification M21 · O34 · O35 · 054 · P13 · P17 · P18

M. Velázquez Salazar (B) Escuela de Ciencias y Económicas y Empresariales, Universidad Panamericana, Ciudad de México, Mexico e-mail: [email protected] P. Pérez Akaki Facultad de Responsabilidad Social, Universidad Anáhuac México, Naucalpan de Juárez, Mexico © The Author(s), under exclusive license to Springer Nature Singapore Pte Ltd. 2021 G. Dávila-Aragón and S. Rivas-Aceves (eds.), The Future of Companies in the Face of a New Reality, https://doi.org/10.1007/978-981-16-2613-5_8

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1 Introduction The new reality started in 2020 has seriously affected world economies and Mexico has been no exception. The closure of shops, the decrease in income from precarious working conditions, the contraction in local and external demand and the change in the provision of agri-food have forced economic activity to focus on responding to new requirements. It is necessary, then, to develop marketing strategies that reactivate the economy within a virtuous circle. According to Vandecandelaere et al. (2010) the virtuous circle begins with the identification of a product that has the potential to generate long-term sustainable development from the region in which it is located to beyond its borders. Specifically, it is proposed that the Geographical Indications (GI) are a strategy of marketing innovation that achieves the dynamics of this circular process (Vandecandelaere et al. 2010). The hypothesis of the virtuous circle approach based on the Protection of Denomination of Origin (which is a subset of GI) shows that from a product associated with a territory, economic growth can be achieved through sustainable development (Marescotti et al. 2020). This will be possible with the identification of the product and its behavior throughout the global value chain, the connection between economic agents and material resources in the place of origin and the link with other private agents, organizations, national and international public institutions. Applied studies show that this process has worked in other countries (Vandecandelaere et al. 2010; Belleti et al. 2017; Marescotti et al. 2020). The innovation of this strategy lies in the implementation of the Denomination of Origin and the economic reactivation, but it involves other areas too, without which it would no longer be integral and lose the links that sustain the virtuous circle. Social aspect, the characteristics of the society in which the product is produced, marketed and sold; the culture in which the production and consumption society is immersed; the environment and ecosystem that is preserved and promoted from productive practices, are the pillars not only sufficient but necessary to generate economic development in a new business reality that requires comprehensive innovation strategies. The agri-food production company that is analyzed under the GI as an innovation in the face of the new reality is coffee, an industry that accounts $30.9 billion in exports per year globally (OEC 2018) and involves more than 25 million people along the global value chain (ICO 2010, Nestlé 2010). Mexico has been characterized by being a producer of high-altitude coffee, which is directly associated with quality and is one of the main organic producers worldwide, generates an inlet of $351 million (FND 2020) and at the same time has the characteristics of a public good according to Belletti’s definition (2017). Given these conditions, this agri-food is able to generate productive spillover not only at the company but at the inter and intersectoral level by benefiting the other economic activities in addition to the primary; such is the case of industry, from the benefit of coffee which is industrial processing; trade, through the provision of inputs for

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cafes, restaurants and other similar companies and; tourism, as coffee is currently associated with guided ecological routes that revolve around the grain and are offered by tourist companies, hotels and cafes. At the company level, Mexico’s primary coffee production supplies roasters, cafes, restaurants, hotels, coffee bars, supermarkets and convenience stores nationwide and intermediate marketers internationally, mainly to the United States. Likewise, the coffee producing companies supply about 60% to the domestic market. Therefore, what happens on the primary market directly affects the other links in the global value chain. At the same time as generating added value for other companies, 15.4% of the final price stays in the production companies, while 84.6% is appropriate for processing and marketing companies, as well as carriers, packers and storage companies, the coffee sector has been affected not only by the pandemic but by the disarticulation that has existed between the different participants in the production chain, especially the relationship between production companies and marketing companies, as well as the lack of monitoring of public policies in the sector (Velázquez 2017). The virtuous circle of PDO may be carried out if such links are healed. In productive terms, pests and climate have seriously affected planting and harvesting, this, like the pandemic, would not have been so catastrophic if the virtuous circle had been fostered from a PDO coordinated by small entrepreneurs and reinforced by public institutions. Mexico has the same behavior as the other countries in which coffee is produced and marketed in terms of the production structure since most coffee companies are small-scale. Small producers are those who have less than five hectares and medium producers are those who own between more than five and less than fourty hectares. Both are considered small companies because they formally employ less than 10 workers. The problem found in the sector is the presence of a mixed crisis: on the demand side, there is a contraction in consumption resulting in continuous low prices (ICO 2020); on the supply side companies have had to look for new ways to promote and market the grain. PDO applied in a structured way and with the participation of producers could be a solution to the new reality. The objective of this work is to analyze the effects of the recent Protected Denomination of Origin Coffee Pluma in Oaxaca-Mexico (PDO Pluma henceforth) on coffee companies and the conditions under which it is possible to initiate the virtuous circle to achieve sustainable development in the region. The Global Value Chains (GVC) methodology allows the analysis of each of the factors mentioned. This framework developed by Gereffi et al. (1994) is followed to study small and medium producer´s chains in the Mexican coffee companies within the region named Pluma. Used a mixed quantitative and qualitative technique based on data obtained from official sources and in-depth interviews with key stakeholders who belong to the coffee production chain. The first section of this research presents the theoretical framework and the proposed methodology. To give the context in which the problem is situated, the

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second part describes the situation of coffee in Oaxaca state, analyzing the importance and the main characteristics of Pluma region. Third section studies the initiative of the designation of origin for Pluma Coffee published in 2003 and approved in 2020. Within these general facts, the next step is to present the results of the analysis of the coffee chains of small companies under the methodology of GVC to understand the relevance of the geographical indication for this product and the implications in the new reality. At the end, we discussed the conclusions and some alternatives for coffee organisations in the post CODIV-19 era.

2 Theoretical and Methodological Framework for the Analysis of Global Value Chains for the Coffee Producers in Pluma Hidalgo, Oaxaca The Global Value Chains (GVC) framework analyzes the chains at the international level, allowing to observe the transformation of the product through the different steps along the chain, as well as the economic relations between agents inside and outside the chain. With this framework, researchers have demonstrated the low benefits captured by the agricultural companies by this commodity around the world, included the coffee (Talbot 1997; Fitter and Kaplinsky 2001; Ponte 2002; Daviron and Ponte 2005; Pérez-Akaki 2012; Velázquez 2016). The original approach based on the Gereffi et al. (1994; 2005) contribution includes four dimensions of study. The first is the input and output dimension and refers to products and services chained in a sequence of added value in economic activities; the second is spatiality, which means the economic geography, as well as spatial dispersion or concentration of production and distribution networks; then the institutional framework describes the rules of the game between organizations and the operation of the chain and the last one; governance, understood as domain of the chain, the terms under they are linked and the different kind of conventions presented within it. Figure 1 resumes the first three dimensions with the associated variables and indicators, specifically for the coffee chain enterprises in the studied region. Unlike previous dimensions, involving quantitative indicators obtained from official sources like International Coffee Organization (ICO), Food and Agriculture Organization (FAO) and United States Department of Agriculture (USDA), the governance dimension requires the evaluation of qualitative attributes, as shown in Fig. 2. This could be measured by the observation and interviews in the field with the entrepeneurs coffee producer. Governance can be understood as the power of the actors in the chains, the way they can influence in the whole chain. It can be evaluated by the type of capital, areas in which the firms compete, barriers to the entry, property of the firm and investment. Also, it captures the level of coordination and asymmetry through the kind of transactions between the producers and the firm. And involves the conventions which are the differentiating attributes of the product, rules by a third-party

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Input-output

Economic geography

Instuonal framework

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Variable Characteriscs of the export product

Indicators Coffee exports Coffee exports of green coffee, extracts of coffee and roasted coffee Final price Added value in consuming countries Income distribuon Price obtained by the producer along the chain Costs of transport Storage costs Parcipants in each link Structure of the Chain Small and medium producer chain Coffee producon Geoeconomic Socioeconomic characteriscs of producers (place and high of the land, structure of stratum of marginalizaon and human development index) producon Exports Consumpon

Naonal context Quality

Desnaon of Export Level of consumpon Typology of Consumer Changes and agreements of government and no government instuons related to the coffee chain Qualies of coffee (gradaons and defects of grain) Quality organizaons

Fig. 1 Dimensions, variables, and indicators to assess the behavior and evolution of the global coffee value chain in Pluma region Source Own elaboration based on Pérez Akaki (2012), Gereffi et al. (1994; 2005), Gibbon and Ponte (2008), Velázquez (2016)

Fig. 2 Governance indicators for the global value chain of coffee Source Own elaboration based on Pérez Akaki (2010), Gereffi et al. (1994, 2005), Ponte and Gibbon (2008), Velázquez (2016)

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agent. From these variables, chains can be classified as driven by the producer or the buyer.1 They could have a low or high coordination (Gereffi et al. 2005) and could be differentiated by conventions of quality, tradition, social welfare, environmental well-being, creativity, among others (Ponte and Gibbon 2005). Once the variables and indicators were organized, the data inside the region was analyzed. Data for the analysis was obtained by two ways, from secondary sources obtained from official statistics, Institute of Statistics and Geography of Mexico (INEGI in Spanish), the Council of Agriculture (SAGARPA in Spanish), United States Department of Agriculture (USDA) and from primary sources with a field work in Pluma Region developed between 2014 and 2019. Although during this year it has not been possible to go out into the field, various exchanges with entrepeneurs in the sector have been possible and show the effects that have already been mentioned. The final results showed congruence with the quantitative data of the primary sources. The two selected cases are located in the area which is integrated by the 302 of the 570 municipalities in Oaxaca, México and refer to small formal companies. The sample was formed by producers, marketers, and certifiers who located in the municipality of Pluma Hidalgo, the main center of trade in the region, that served as a point of connection with the national and international market. Research typology was deduced from a cross-sectional analysis between units, that is, comparison of the units of study in a single period. The variables and indicators were those proposed in Figs. 1 and 2. On the visits, it was observed that there was a group of agents, concentrated in the center of the town of Pluma Hidalgo, who controlled the dynamics of the chain. These were the medium producers organized in small businesses who have coffee shops and distribute not only their coffee but the coffee of their neighbors, that are organized into small entrepreneurial companies and are made up of small-scale producers. This relationship between small and medium producers is still in force in this new reality and strengthens the supply, since it maintains it somewhat stable. Even though the distribution points are located in downtown, the coffee farms are situated in the suburbs and even in other municipalities, covering the entire Pluma region. In fact, a small group of medium producers’ companies organizes the dynamics to market the grain.

1 The original categories are producer-driven and buyer-driven chains in the original Gereffi’s paper.

Later, a new classification was proposed, but in the literature, is not used for classifying the whole chain, but specific nodes. 2 Candelaria Loxicha, Pluma Hidalgo, San Agustín Loxicha, San Baltazar Loxicha, San Bartolomé Loxicha, San Francisco Ozolotepec, San Gabriel Mixtepec, San Juan Lachao, San Juan Ozolotepec, San Mateo Piñas, San Miguel del Puerto, San Miguel Panixtlahuaca, San Pablo Coatlán, San Pedro el Alto, San Pedro Pochutla, Villa de Tututepec de Melchor Ocampo, Santa Catarina Juquila, Santa Catarina Loxicha, Santa María Huatulco, Santa María Ozolotepec, Santa María Temaxcaltepec, Santiago Xanica, Santiago Yaitepec, Santos Reyes Nopala, Tataltepec de Valdés, Putla Villa de Guerrero, San Pedro Mixtepec, San Marcial Ozolotepec, San Sebastián Coatlán and San Jerónimo Coatlán.

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3 The Production of Coffee in Oaxaca, Mexico Oaxaca is one of the leading coffees producing states, where 104,879 producers are registered (CEPCO 2014), only behind Chiapas, which is the state with the higher number of producers; also, between 200,000 and 500,000 people are employed directly or indirectly in this value chain. By 2019, production reached more than 76 thousand tons of coffee generating about 8% of the value of the coffee production of the country and representing 19% of the total planted area (SAGARPA 2019). The average plot size is around 1.2 hectares per producer and the obtained yield is 0.68, lower than the national average of 1.2 tons per hectare (SAGARPA 2020). The total number of coffee-producing municipalities is 151 of 570 that conform Oaxaca (CEPCO 2014; SAGARPA 2019). The type of coffee produced historically in this region is Coffea Arabica Typica in its majority but also the varieties of Bourbon, Maragogipe, Mundo Novo, Marsellesa, Oro Azteca, Geisha, Java and Sarchimor. However, there has been a strong change of the varieties because of the coffee berry borer beetle (Hypothenemus hampei) and rust (Hemileia vastatrix). This has meant a transformation in the plant type for Oro Azteca variety, which is more resistant. One of the determinants for producing quality coffee is the altitude: while higher the altitude in the production area, the attributes of coffee will be more prominent. The medium altitude in the coffee municipalities amounts to 1,000 m above sea level, which means an average height that falls within the classification of high grown washed coffee. Map in Fig. 3 locates the region of Pluma in the south, with an average altitude of 907 m, but there are farms with more than 1,600 m above

Fig. 3 Average altitudes of coffee municipalities in Oaxaca, Marescotti et al. 2020 Source Own elaboration with data of AMECAFE 2010; CEPCO 2014; Velazquez 2018 and SAGARPA 2020

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sea level, giving the coffee from this region greater quality attributes. Also, this region has greater importance because several locations are part of protected areas for conservation of biodiversity. In 2019, the value of coffee production in Oaxaca amounted to almost 355 million pesos (18 million dollars) and half is generated by 15% of coffee municipalities, notably Pluma Hidalgo, which is a very small municipality and an important producer of coffee in Oaxaca. It is worth mentioning that these municipalities are now in the PDO (SIAP 2020). In the same period, there were 135,197 hectares of coffee, and 82% of this amount was harvested. Pluma region has reached 5,160 hectares, with a harvest of 51%. In terms of production, from the 76 thousand tons of coffee, 1.5 corresponds to Pluma (SAGARPA 2020). On the organic coffee chain, only three states of the Mexican Republic concentrated the 97% of production: Chiapas (82%), Oaxaca (11%) and Nayarit (4%). The production in 2016 from this type of coffee in Oaxaca was 5,135 tons, with a value of 23 million pesos (1.5 million dollars approximately) in 5,200 harvested hectares. In 2019, the organic production decrease to 3,547 tons, which generated 22 million pesos in 4,730 harvested areas (SAGARPA 2016; SIAP 2020). In the Pluma region, only Pluma Hidalgo municipality produced 808 tons contributing 23% at the state level and close to 4% at the national level, while the other municipalities of the proposed region produced no organic grain (SAGARPA 2016; SIAP 2020). To understand the proposal of the denomination of origin, it is necessary to define the product and the area where it has been found historically as well their characteristics and motivation of this geographical indication. The analysis starts from the evolution of the production in the protected region and its relationship to the territory.

4 History of Production in the Protected Region and Its Value Chain Analysis The proposal of PDO Pluma for the Coffea Arabica typica started with the application in 2003. Specifically, the application was published in the Official Journal of the Federation on August 27th and expressed the interest in protecting the green, roasted and ground coffee from this region. Historical support for the GI argued that Pluma Hidalgo municipality was founded in the second half of century XIX because of coffee production. In 1873, the first planting of 40 thousand coffea bean caused the need to legally establish the town, because of the need to have an authority and legal protection of the settlers, what finally drove to the official foundation of Pluma Hidalgo on December 1st of 1880. Coffee production expanded throughout the region and this was possible due to the appropriate agroecological conditions, which were manifested in important yields of each plant, even higher than those of other producing regions of greater tradition, as in Veracruz (Rojas 1964). In the 1930s, Pluma Hidalgo coffee already had worldwide recognition, comparable to other traditional regions such as Coatepec and Jalapa (Ukers 1935). Since

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then, Oaxaca has occupied the third place in importance in Mexico’s production. Rojas (1964) reported in that decade the reputation of the Pluma coffee as the main producing region of the state of Oaxaca (Rojas 1964). This is how Pluma’s coffee has built a reputation for quality and flavor over the years at an international level. The PDO was approved in February 2020, just a few days before the quarantine began in the country, by the Ministry of Economy through the Mexican Institute of Industrial Property. In other countries like Spain, France and Italy, PDO’s are a successfully innovation tool because is local and comes from the bottom to the top. It means that the product is protected not only by law, but for the owners. In PDO Pluma case, somebody approved it, but the entrepreneurial producers do not agree with it. Although one of the original approaches of GI is that they generate added value to chaining, in Mexico, it has not worked that way. GI can work as an innovation factor to enhance grain, but it must be accompanied by an environment that includes upgrading, polyculture, a coherent institutional framework and other economic activities such as tourism, gastronomy and culture. Currently, the area called Pluma involves thirty municipalities including Pluma Hidalgo. The latter remains the most important in terms of production and generation of value added per unit of production. Given the current situation, it is possible to resume the development strategy based on GI to redefine its impact on entrepreneurial companies.

5 Input-Output Dimension This dimension examines the characteristics of the marketed product, the flow that makes possible the coffee since the inputs in the farms, the process that transforms the grain along the chain up to the mug. It is also considered here the distribution of income throughout the chain and the structure of the chain in the different steps of transformation. In terms of product features, the value chain of this coffee is distinguished from the others within the state of Oaxaca by the distinctive flavor of the grain with excellent aroma and slight body. It is Coffea typica variety coffee grain characteristic of this region that is classified as subvariety Pluma Hidalgo, a grain that has gained a reputation over other origins along the history in Mexico and worldwide. Figure 4 shows the structure of coffee chain companies in Pluma Hidalgo. Almost all the coffee in this region is transformed by the wet process, what gives the coffee special characteristics that are valuable to traders and consumers. The coffee is marketed locally, in the tourist beaches of Huatulco and Puerto Escondido, as well as in major cities within the national territory. It is also exported to USA and Europe, but not in large volumes. Exports are possible when the producers are linked to an organization that collects coffee in several subregions and has the ability and knowledge to sell abroad, mainly to large USA retailers. Retail companies often buy coffee from various countries and then make coffee blends to get specific qualities. These blends make the original local flavors and characteristics of the coffee are lost to get greater volume.

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Fig. 4 The structure of the value chain of coffee for companies with small (upper) and medium size producers (lower), Oaxaca, Mexico: 2019 Source Own elaboration

It has been observed that there is a strong external demand for sanitized products which can result in unexpected results. Those companies which have the infrastructure and logistics will be able to benefit from it, but all the others will suffer because they will have to absorb the costs of traceability that they had not considered before the pandemic. Some will be able to afford it, but not most. The new reality has caused coffee entrepreneurs to seek new ways of trading the grain, such is the case of sales through social networks, producer support programs, as well as access to technological means. The structure of the chain is different for both types of producers since the medium scale producers are not exporters and can function as intermediaries or grain collectors, while small producers must sell grain to the different marketers, including local or international collectors, and/or national or international brands. Figure 5 shows the 10 principal municipalities of the PDO which generates more value per harvested

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Fig. 5 Average of Yield per Hectare and Generated Value per Harvested Area of Coffee in de PDO Companies Source Own elaboration with INEGI and Sagarpa statistics, 2019

area and have the highest yield per hectare. Pluma Hidalgo stands out as it is one of the smallest municipalities that generates the same value as other municipalities, even when its yield is lower. This is due, in part, to the fact that the coffee from that particular area is more appreciated for its origin and tradition, and for the same reason, this can be taken as an advantage in the new reality. The distribution of income throughout the chain is nearly equal for all small companies made op of small and medium-scaled producers, both get around 60% of the final price of the green coffee, but hardly cover production costs. The net profit is 50 cents of pesos (2 cents of dollars) per kilo of green coffee for coffee cultivators. In the case of participation in fair trade chains, in the fieldwork, producers declared they have a guaranteed fixed premium of 2.89 pesos (12 cents of dollars) per kilo. Medium-sized producers have shorter and direct linkages to final consumers since they can collect coffee and sell in greater volume, in addition to having a stable national market where they can obtain better prices when marketing the grain to the coffee shops directly. However, it is worth mentioning that this size of producers is not usual in Mexico, and they have some advantages over the small producers, like having several sources of funding for the coffee process because of the diversified production system, what allows them to better market the coffee. Then medium-size coffee producers are more resilient than smaller ones: if there is a drop in the price or a pest destroy the crop, they have an economically diversified system capable of supporting this problem and continue with the process. For small companies, small and medium producers have a non-economic factor to still producing it is a passion for everything that involves the process, is the origin, tradition and compromise with the coffee field that make this activity very special for

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them and the world. On the demand side, there are consumers interested in products with these characteristics. In the new reality there are several restrictions to trade, mainly sanitary requirements to deal with the new conditions of the world. That means that producers may warrant the optimal conditions of their agri-food products to avoid contamination but preserving quality. Unfortunately, new investments in the value chain are needed to deal with them and this will cause new ways of differentiation and exclusion from markets of those that cannot support the new demands of economic actors along the chain. Under this situation, some of the producers are trying to commercialize the coffee online, but they are only those who have the infrastructure and organization to invest in new ways of trade. These are the cases of medium-size producers that have a direct relationship with the final consumers, or those small-size producers organized in associations or cooperatives. Economic Geography Dimension This dimension encompasses the production and consumption spaces in which the evolution of the productive chain takes place. In terms of production, the physical volume and the monetary value generated by the region are analyzed. It also includes a socio-economic context that shows the social, cultural and economic conditions of coffee producers. In the past years, it was estimated that in the Pluma region there were 10,753 producers, however, with the visits made and according to the information collected it is estimated that this number decreased drastically, perhaps by 30%, which means that little more than 7,500 people have the capacity to generate coffee valued at approximately 63 million pesos (more than 3 million dollars) with 16,000 tons of coffee per year (INEGI 2007; AMECAFE 2010; SAGARPA 2020). In the new normality, it is estimated that there is the same number before the pandemic, because the producers stay and the younger people of the community that study or work in other places returned to their towns of origin during the quarantine and work in the field. Figure 6 shows the sown and harvested area, produced tons and production value per municipality pertaining to PDO Pluma. Once again, the more dynamic are Pluma Hidalgo and also Putla Villa de Guerrero; both generate 25% of the value of the PDO area (SAGARPA 2020). In socio-economic terms, these producers live in conditions of marginality in basic services such as electricity, drainage, and gas for cooking. Although the majority has at least one of these services, half of them do not have none. In terms of characteristics of living places, 80% have toilet, latrine or cesspit and around 60% has a place with concrete walls and cement floor. This is a pessimistic scenario of the producers in the new reality, because they are not enough capitalized for the new conditions of business. Regarding the educational level, half of the producers have some degree completed, while the other half did not finish the basic level. It should be mentioned that it has been observed that there is a significant effort on the part of producers to send their children to study, especially to the main cities, which has increased

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Fig. 6 Main characteristics of production in the PDO Region (percentage of total) Source Own elaboration with INEGI and Sagarpa statistics, 2019

the scholar level of current and future producers. This has brought about improvements in production processes, greater marketing reach and a genuine concern for the environment, leadered by young people returning to farms after studying in the city. Another characteristic to highlight is the presence of a high indigenous component within the coffee production units. In the Pluma area, at least one in three producers belongs or speaks an indigenous language and there are municipalities, such as San Pedro El Alto, where 98% of producers have this origin. As in other items, the pandemic caused by COVID19, highlights the lack in the conditions of people who live from the grain. This situation makes visible the problems in the sector, both, in production and in commercialization. Although small producer companies can be considered as the most disadvantaged social group in terms of housing, services, and education, the higher incomes of the medium-sized ones allow them to market their coffee outside the producing region in the main cities of the country. This means a forward integration in the chain what let them aspire to more benefits. The main destinations of consumption for Pluma’s coffee are the tourist sites nearby, in Huatulco and Puerto Escondido, the state capital, Oaxaca city, but also an important network of specialty coffee shops in the main cities of the country. There is a distinction between small and medium-sized producers in the way they trade the coffee. In the case of small producers, the grain is sold to medium-sized producers and the final consumption is at the local or regional level, but also, if the higher distributors are present, the coffee can be marketed at convenience stores,

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coffee shops or supermarkets. For medium-sized producers, the grain ends at local or regional coffee specialty coffee shops or is exported. During the new normality, the markets are closed because of the fall in the tourism activity all around the world and it is expected to recover in several years. Institutional Framework Dimension The main organization in the institutional framework at the country level is the SAGARPA (Ministry of Agriculture), a regulator of agricultural practices, that operates through the Mexican Association of the Coffee Production Chain (AMECAFE), a non-government association which represents the interests of all actors in the coffee chains. AMECAFE’s activities are advisory in agricultural practices, assistance on administrative process, training and sometimes promotion of coffee consumption through contests, workshops, and exhibitions. At the state level, the government of Oaxaca has been intermittent in the Pluma region. Before COVID19, the coffee policy was reduced to the donation of tools of work, new coffee plants when there are plagues like rust, fertilizers and economic incentives proportional to the production and the extension of hectares. Regularly these supports were received out of time when the sowing has finished. It should be mentioned that these donations are conditional on the invoiced sale of production, favors associations of producers and does not enter those considered as major producers. In the new reality, it is necessary to add that there is an important reduction of fiscal resources and several programs for assistance are reducing or closing, which makes more complex this scenario for the agricultural activities. The municipal government has more participation and interaction with producers as there are several initiatives that they launch themselves and can work together. This happens especially in the main coffee-producing municipalities of the region such as Pluma Hidalgo, Candelaria Loxicha, and San Pedro Pochutla, as well as Santa María Huatulco that benefits from tourist activity and where coffee can be an additional attraction. There are two examples of this cooperation between producers and municipalities. The first is the initiative that was launched in 2015 to make Pluma Hidalgo a Pueblo Mágico (Magic traditional town, as a touristic attraction), based on the coffee activity of the area, although this recognition is not yet achieved. The second is the set of coffee fairs that are held in the area. There are continuous activities and festivals, that show the importance of the crop in the region and the interest of the consumer to approach the primary producer. Likewise, it should be said that social networks have made it possible to disseminate these events on a larger scale, which has increased visits by tourists in this region. Producer associations have also operated intermittently in the region. They were created under a certain context and then suddenly disappear due to lack of interest or internal conflicts. Some of these organizations group several producers, but in fact are run by a few. Several times these organizations are led by a medium-sized producer who buys coffee from small producers to market it and in these terms, govern the commodity chain. The new aspect must be considered under the current scenario is the “new” coffee policy, the proposal of a new institution of coffee and the government supports for

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the sector. Along with the pandemic, a new government appears in Mexico, with a supposedly new proposals and a vision somewhat different from the previous ones. Within the 2019–2024 national development plan, the chapter on support for coffee is included as part of the activities in the agricultural sector, which is already an advantage. It establishes that there will be support regarding the renewal of coffee plantations, the use of better genetic materials, the implementation of sustainable production practices, value addition and product differentiation, as well as support on conservation issues and better use of soil and water, in addition to the conservation of biodiversity. The beneficiaries will be 50% of the coffee census that is estimated at 500 thousand producers (Mexican government, 2019). This proposal does not get to be a coffee policy, there are just some disaggregated support that does not solve the crisis in the sector. Even with the initiative to create a new coffee institution, which for now is on hold, it will not go further than what was achieved in previous periods. What is different in this case is that if resources fall in time for harvest and there is also a relationship of trust between the government and producers, this could generate a virtuous circle to take advantage not only of government support but also of the Denomination of Origin. Until today, building this relationship between formal and non-formal actors has been almost impossible. It will be necessary to see that one fulfills its function of administering and managing and another of using the resources properly to produce efficiently while respecting biodiversity. Governance Dimension Based on the Gereffi et al. (1994) methodology of global value chains analysis, three different typologies can be identified: governance as control, governance as coordination and governance as normalization. The first is the original proposal of Gereffi, the second was introduced several years later and is focused to specific nodes along the chain. The third one tries to complement the first one, strengthening the institutional dimension. This paper will focus on the first and third typologies because they both analyze the whole chains. In the governance as control approach, governance means what agent has the control or is powerful along the chain, that keeps it organized and operating in the production and trade of coffee. Two types of chains can be characterized with this approach, the producer-driven or buyer-driven. The small producers of coffee in Pluma belong to buyer-driven chains, while the medium-sized chains are producerdriven. In the first case, the producers have no interference in the price, must comply with the standards established by the intermediate buyers, either the cooperatives they belong to or by the external market. The second case is the opposite since the producers several times set their price, choose their buyers and demand that the coffee does not blend to preserve the origin and flavor of the grain. In the third typology, governance is related to the imposition of regulations, rules, conventions, and standards within the chain as a condition to belong or to remain in it, as a form to control it from a distance (Ponte and Gibbon 2008). Standards are generally evaluated by external agents, third party system, and they guarantee that the product meets certain specified quality, related to the demands of the consumer, although there is not always complete information. The chains of Pluma coffee have

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domestic conventions, i.e. confidence and trust are the main drivers in the relation between the participants of the chains, that were built historically; also, civic conventions are present, relating to the preservation of the environment and social traditions and; to a lesser extent, conventions of opinion, which value the product of agreement non-chain experts, as for example judges of specialty coffee. In the new reality, security standards in the agri-food chain have increased, increasing also administrative costs along the supply chain. Combined with lowering of demand because of confinement and reduction of private consumption because of unemployment.

6 The PDO as an Innovative Proposal in the New Reality for Small Coffee Producers in Pluma Hidalgo Even though the production companies in the area have lost interest in carrying out due to the delay of the process, the politicization of it and the contradictions that exist in the application presented, the PDO was accepted in February 2020. Mexican law establishes that when obtaining a GI, a change of the institutional framework is needed, because it requires the fulfillment of a series of conditions and requirements for the participants in the chain, to get the level of quality desired. This is owned by the Mexican State, administered by a Regulatory Council, as Indicates the Law of the Mexican Institute of Industrial Property (MIIP). However, since the application for the first GI Pluma Coffee, there is no common effort for the companies to standardize a level of quality that offers certainty to consumers of the grain and serve as a reference to producers. This can slow down the opportunity for innovation advantages for coffee producers. Also, there is a political conflict between the leading companies that comes from the first DO initiative. In order to take advantage of GI, communication between the economic agents involved must be fluid and cooperative, but this seems not to be the case. We found that to run successfully a GI there is a need for consensus among the actors in the chains in the path to follow with their product, so there is a need of a cooperative organization to guarantee that all those involved will be benefited and rules that incentive the inclusion of the whole actors in the chains, in the way of a sustainable process (Vandecandelaere et al. 2010; Belleti et al. 2017). Cooperative working has been a challenge in the region because the organizations last short and they are organized for specific purposes. Historical bad experiences of political use of the cooperatives made producers distrustful of the collective initiatives. Previous experiences GI in Mexico found that strong investments are necessary for making it function, but also an important private participation, keeping governments far enough to avoid politicization (Pérez-Akaki and Pérez Tapia 2012). Huge amounts of time and material resources are needed, as well as institutional support, to make it work, otherwise, the designation is not a solution to the fundamental problem in rural spaces, the improvement in the quality of life of farmers and the preservation of their culture and traditions.

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Climate change means that Pluma coffee is in danger of reducing its share in markets. A GI could help to preserve this variety because producers can have a quality standard for the grain and proudly preserve it in the future, including the ecosystems where the coffee is produced. GI can also be combined with other economic activities, like ecological, cultural and gastronomic tourism, increasing the resilience of the regions (Belletti et al. 2017). The exposed factors exhibit that the new reality challenges the main obstacles found in the main GI in Mexico, but in a new environment, more complex than the previous one. Even the problems described in the previous paragraphs, a GI for Pluma coffee could help to reach a new equilibrium favoring the producers in the coffee chains because it can give them control over it, what can be interpreted as a new form of governance based on domestic and civic conventions. A kind of governance where producers have a special place because the important activities, they do in the rural spaces in producing foods, ecological goods and services and preserving traditional knowledge. Also, the new reality can be a juncture to balance governance and to promote short chains, that let coffee producers improve in the driveness of the coffee chain. But this result needs lots of efforts and long investments in time and resources to capitalize.

7 Conclusions In conclusion, we can consider that there are conditions in the coffee sector that have remained the same during the pandemic and there are others that have deepened and made visible. The combination of both has had adverse effects on both production and marketing, and the PDO has not been enough to compensate for the damage caused in this new reality. The innovation that is sought with this tool from the valorization of coffee within a specific territory must be accompanied by a coherent, constant and stable institutional framework under a dynamic based on trust between the formal and non-formal actors of the production chain. The characteristics that remain stable are the number of companies, producers, the structure of the chain and the actors that participate in it; the qualifies and qualities of the coffee; the reputation based on the tradition of the region; the origin of production and the commitment of small companies to continue in the market. In addition to this, some advantages were found in the new reality, especially for those producers who have an infrastructure and logistics that allow them to make investments in new forms of marketing such as the online and home option. The first of them is that under a PDO the traceability of the product offered can be guaranteed, allowing the consumer to know which is the path that the grain has traveled to reach it and in what sanitary conditions, which responds to the current demand for agri-food products. Another point in favor is that the forward integration of primary producers continues along the chain, especially by medium-sized producers. Regarding the institutional framework, there is in the National Development Plan a set of supports on which the sector could be based to create broader networks of infrastructure, communication and marketing.

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However, there are aspects that have their origin in the decades prior to the pandemic and have now become more acute and visible. The main disadvantage is the socioeconomic conditions under which the producers live. Which is a contradiction since, being marketers of a quality product, they do not have guaranteed basic services or housing. Another flaw that drastically affects the behavior of the markets, both internal and external, since they are now closed, making it impossible to flow through the chain. Especially in the study region where tourism is the main activity and the main link between producers and final consumers. The demand from the demand has increased in terms of food safety standards, increasing costs that can hardly be absorbed by coffee companies. In addition to this, the PDO region has not yet reached a standardization of the product, which makes commercialization difficult, especially externally. Regarding the institutional framework, there are good intentions, but so far, they have not gone down to the field. Today, support has been given to coffee growers due to the pandemic, but the support is marginal and is only for half of the producers, which means survival, but not profit in production. The austerity policy of the new government has reduced the number of social programs and we will have to wait to see the effects of this support. The situation is complicated for the coffee sector and it will recover slowly and marginally unless a comprehensive and structured coffee policy is added, perhaps with the new coffee institute. But what can save the sector, as in past decades, is the organization of producers in associations or cooperatives that work collaboratively. In terms of the virtuous circle, the conditions of a new reality are an opportunity to resume the valuation strategy based on the PDO since the circumstances create new ways of commercializing. The entrepreneurial producer is closer to the final consumer through social networks. Consumers feel closer by establishing direct relationships with the producer and thus a long and sustainable cycle can be started. It will be necessary to link with other economic agents, with institutions and with other productive organizations. In addition, it will be necessary to build a legal institutional framework that supports the productive relations between different companies. In this way, the existing productive infrastructure and the PDO that has been approved would be used.

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Corporate Social Responsibility Informing Crisis Management for Stakeholder Satisfaction: From Survival Mode to Survivability in a Pandemic Andrée Marie López-Fernández Abstract The second quarter of 2020 is ending, and the covid-19 pandemic is ongoing. The intricacies of this crisis are unprecedented; yet, it would be challenging to find a time in history when the human race has not been confronted with crisis and catastrophic consequences. Most individuals and organizations, surviving a crisis, are infused with collateral effects of trauma, but survive. The question is why are organizations ceasing to exist, or unable to transition to survivability? It is plausible that a different approach to crisis management is needed to face the current challenges. The general objective of this study is to analyze the implications of engaging in corporate social responsibility to inform crisis management and maintain stakeholder satisfaction. A conceptual model is discussed to describe how effective socially responsible crisis management enables firms to transition from survival mode to survivability. Keywords Corporate social responsibility · Crisis management · Stakeholders · Survival mode · Survivability JEL Classification M14 · H12

1 Introduction Severe and traumatic events have pervaded the world since the dawn of time; there is not a region that has not endured disaster and catastrophe, the two most common effects of a crisis. Given their reality of devastation, most of such events are associated with natural disasters, such as: earthquakes, typhoons, volcanic eruptions, floods, hurricanes, tornados, tsunamis, amongst others. That being said other events such as wars, Chernobyl, Hiroshima and Nagasaki, various oil spills, Black Sunday, Bloody Sunday, Apartheid, Terrorism, the Holocaust, genocides, massacres, the Great Depression, multiple toxic chemical releases, as well as Pandemics, A. M. López-Fernández (B) Universidad Panamericana Escuela de Ciencias y Económicas y Empresariales, Ciudad de México, México e-mail: [email protected] © The Author(s), under exclusive license to Springer Nature Singapore Pte Ltd. 2021 G. Dávila-Aragón and S. Rivas-Aceves (eds.), The Future of Companies in the Face of a New Reality, https://doi.org/10.1007/978-981-16-2613-5_9

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amongst many others, have undoubtedly caused disaster, catastrophe, and trauma, both individual and collective. Human-made crises disproportionately surpass the effects of natural disasters; they bring exceedingly high costs, financially speaking as well as the damage to the environment, human beings, and future generations, which is inestimable (Pearson and Mitroff 1993). Whether disaster is natural, man-made, or hybrid (Mohamed Shaluf 2007) each, undoubtedly, causes various degrees of crisis to the environment, organizations, and humanity at large. Moreover, the lack of effective and timely crisis management can result in the escalation from potential disaster to catastrophe (Davies and Walters 1998). The second quarter of 2020 is ending and the Global Health Pandemic, due to covid-19, is going strong. This crisis is complex because, 1) there is still no vaccine and medical professionals are still unclear on the most effective treatment and, 2) it is happening worldwide, which means that nearly everyone is directly affected, and a lucky few will only be indirectly affected. Survivors of past traumatic events, who also deem the younger generations as “snowflakes”, may consider them to be less resilient in dealing with crises; if they believe they have insufficient abilities to tolerate frustration, and are easily offended, why would they think they have what it takes to survive a crisis. However, it really depends on who you ask. For instance, members of Gen Z in Mexico are far too familiar with catastrophe, as they have already survived a deadly earthquake and are currently in the process of surviving a Global Pandemic; perhaps they are more sensitive, but that does not decrease their resilience. This has been a determining factor in the survival of the human race; there has not been a single generation untouched by crisis and trauma, which is a clear indicator that there is no reason human beings, critical cases of infection notwithstanding, cannot survive the effects of covid-19. In the business realm, the crisis is happening to organizations and their stakeholders. The reality is that while organizational leaders are struggling to redesign in order to ensure that the “new normal” functions smoothly, stakeholders are enduring lurking effects that are not only affecting them financially, but also physically and emotionally. The general objective of this study is to analyze the implications of engaging in CSR to inform crisis management and maintain stakeholder satisfaction. The specific objectives include: 1) to understand the potential cognitive and emotional effects of the trauma, driven by the current crisis, that impact stakeholder wellbeing and, thus, productivity and performance; and, 2) to describe aspects that positively impact organizations and their stakeholders in the transition to survivability. The study contributes to the understanding of the effects of a crisis on stakeholders’ state of mind and, therefore, organizational performance and social and business growth of the firm; further, it contributes to previous literature on corporate social responsibility and crisis management. The chapter is sectioned as follows: section two includes a review of previous literature on the core concepts of the study; section three consist of a discussion of the dynamics of the current crisis on stakeholders, as well as a discussion of the conceptual model; and section four includes concluding remarks and directions for future research.

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2 Literature Review Stakeholders Organizations are successful when they steadily tend to the needs and wants of their stakeholders (Werther and Chandler 2011); in other words, creating and satisfying stakeholder value must be a priority in order to achieve desired outcomes (Freeman 2009). Stakeholders are any and all interested parties, both internal and external, that can directly or indirectly affect an organization’s actions, and are correspondingly affected by them (Werther and Chandler 2011; Oluwafemi and Oyatoye, 2012). They may be current and potential interested parties, including: collaborators, consumers, regulatory authorities, investors, shareholders, suppliers, the community, the environment, and the firm itself (Fernández and Rajagopal 2014), as it has stake in society and the environment. Firms have particular responsibilities that ought to be carried out to satisfy stakeholders and add to their value; the first is creating profit (Drucker 1984), and the second is to perform in a socially responsible manner, meaning, ethically ensuring financial, social, and environmental performance. Crisis Management There are many variables that interact in crisis management which, in most cases, are defined by an organization’s definitions of both crisis and crisis management. Further, Bundy et al. (2017) have stated that there are internal and external perspectives to the definition of crisis management. It is important to review the effects of its components (i.e. crisis and management) to understand the dynamics of crisis management. The term crisis does not have a universal definition as the latter tends to be developed on the basis of the particularities of each event; in other words, the concept’s definition will vary depending on the succinct aspects that trigger the crisis, be it economic, military, political, social, and/or environmental, amongst others. As such, there are numerous definitions differing amongst authors, areas of research, and industries, etcetera; for instance, Goel (2009) has posited that a crisis “may be a traumatic or stressful change in a person’s life, or an unstable and dangerous social situation, in political, social, economic, military affairs, or a large-scale environmental event, especially one involving an impending abrupt change.” DiTomasso et al. (2000) have argued that crisis “occurs when a person is confronted with a critical incident or stressful event that is perceived as overwhelming despite the use of traditional problem-solving and coping strategies.” And, Laws et al. (2007) have stated that a crisis is “synonymous with an event which disrupts the pre-existing state of affairs”. Therefore, there are numerous sources of crises and change seems to be at the center of them; meaning that, a crisis both alters the prevailing conditions at the moment of impact, as well as requires considerable adjustments to effectively address it. Organizational crisis tends to be associated with potential complications that can hinder or threaten a firm’s operation, productivity, performance, and profitability. For instance, Coombs (2007) describes crisis as an unforeseeable incident which can have negative effects on both stakeholders and the organization’s overall performance. Organizational crisis has been considered as an event that is burdened by

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ambiguity, threatens the firm’s activities, objectives and even its survival, as well as requires prompt decision-making (Pearson and Clair 1998; Kovoor-Misra 2020). Therefore, a crisis, regardless of its source and cause, disturbs the status quo of any given individual, organization, institution, and State; as expected, it increases risk substantially which, ultimately, involves a degree of uncertainty that undoubtedly informs decision making. As such, crisis calls for immediate attention and effective management to avoid further catastrophic effects. Management implies that a variety of internal and external resources are planned, organized, led, and controlled to drive operations, productivity and performance, given external forces such as political, economic, social and environmental. Therefore, it is natural that most crisis management definitions at least share a common aspect with the definition of management. For instance, according to Booth (2015), there are four aspects that influence crisis management, including environmental, institutional, behavioral and cultural. Fearn-Banks (2007) has posited that crisis management is part of an organization’s strategic planning. Tse et al. (2006) describe crisis management as a process which consists of four steps, including, “crisis identification, damage assessment, tactic formulation, and evaluation”. Cabric (2015) also views crisis management as a process concerned with tackling a situation threatening an organization. Gainey (2010) has stated that crisis management entails six actions which begin with prevention and preparation for the crisis, leading into response and management, followed by the organization’s recovery and learning processes. The latter suggests that crisis management does not begin when a crisis occurs, in fact, Legg and Sweeny (2012) argue that in order for the management in question to be effective, preparation and prevention are essential. Moreover, it has been suggested that crisis management should be executed by a team (Brislin 2014) in order to make more effective decisions. Strategic planning makes it possible to prepare for a crisis via the design and development of contingency and crises plans. Their success is determined by the volume of different scenarios considered, as well as accuracy of the action plans, and effective training for execution. Preparedness, by no means, is intended to suggest that organizational leaders should have the ability to foresee the future; rather, that they ought to strategically consider a variety of likely situations and events, given recent history, environmental conditions, and market trends. Once a crisis has occurred, its management requires organizational leaders and decision makers to weigh their decisions on the basis of potential chaos, risk, disaster, and catastrophe, for the entire organization as well as the environment and community in which they operate. The fact is that understanding how crisis works and having a definition for it is fundamental to properly prepare for it, manage it, and survive it. Consider the example of a trauma patient (i.e. a person that has suffered traumatic injuries) who enters the emergency room. The patient is immediately tended to by doctors who assess the trauma and conclude that surgery is needed urgently. The patient is taken to the ICU (Intensive Care unit), after a successful surgery that has saved her/his life. The next three to five days are critical because trauma is one of the leading causes of death globally (Aswani et al. 2018) and approximately twenty five percent of patients may develop multiple organ dysfunction syndrome (MODS) (Aswani

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et al. 2018). Sometimes the trauma is so severe that although the patient’s major injuries have been tended to, there is still a probability of MODS. For such matter, researchers have dedicated significant efforts towards understanding the causes of MODS, secondary to the original cause – trauma. This is the reality of any crisis; addressing the immediate visible effects is a no brainer, although not uncomplicated, tackling the collateral consequences are another issue altogether as, in most cases, they become visible much later. In a sense, they materialize like the ripple effect; meaning that, the effect of an initial disruption in a given area and/or object can cause incremental disturbances outwards. Failure to effectively manage crisis can result in stakeholder dissatisfaction which, in turn, negatively impacts business growth of the firm. This poses another issue, namely, the organizational approach to crisis management. Corporate Social Responsibility Since the late 1940s, the concept of socially responsible organizations and corporate social responsibility (CSR) has been discussed and debated amongst scholars and practitioners across industries and areas of research around the world. As such, it seems remarkable that, seventy years later, there is still a debate on whether engaging in CSR is worthwhile. What is more, it seems absurd that CSR’s validity, benefit, and need are still questioned in the current global climate. Organizations around the world are challenged by the achievement of the 2030 Agenda for Sustainable Development (UN 2020), an action plan aimed at the fulfillment of freedom and peace by enhancing the growth and development of the three Ps: people, planet and prosperity (UN 2015). The Sustainable Development Goals (SDGs) paint a picture of the global reality: climate emergency, extreme poverty, violence, lack of education, health and empowerment, inequality, hunger, unclean and unsafe water, indecent work conditions, irresponsible consumption, and lack of peace and justice (UN 2020). Crane et al. (2013) have found that there are six prominent aspects contained in most definitions of corporate social responsibility, including: “practices and values, beyond philanthropy, voluntary, managing externalities, multiple stakeholder orientation, and social and economic alignment”. Corporate social responsibility is a mindset and an approach to business dynamics for business and social growth and development. It is grounded in an organization’s corporate philosophy, which means that it describes an organization’s values, the manner in which it operates, sets strategic goals and objectives, designs and executes strategies and tactics, achieves competitive advantage and desired performance, as well as fulfills stakeholder added value. CSR is undoubtedly interrelated with ethics (Sen and Bhattacharya 2001); in fact, it is not possible to be engaged in CSR without acting ethically, in the same manner that financial, social, and environmental performance must coexist for an organization to be considered socially responsible. In other words, engagement in questionable and/or unethical practices is descriptive of a non-socially responsible firm. And, if it is meant to maximize an organization’s positive effects on society and

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minimize the negative (Nicolae and Sabina 2010), then, it is only fitting that current and potential stakeholders’ needs and wants are properly and successfully satisfied. Therefore, in reference to a crisis, a firm engaged in corporate social responsibility will make strategic decisions to manage it ethically, and in alignment with stakeholder satisfaction, social, environmental and financial performance to achieve business and social growth and development. Collaborators are one of firms’ most influential stakeholders because they drive their operations, productivity, and performance; and, their perception of the organization’s CSR engagement significantly influences their commitment (Brammer et al. 2007) to the firm. For such matters, it is mindboggling how poorly collaborators are treated in comparison to other stakeholders, both internal and external. That being said, it is noteworthy that all firms’ actions either directly or indirectly impact both current and potential stakeholders. Maintaining satisfactory relationships with stakeholders is elemental to successful organizational performance (Post et al. 2002), as their satisfaction, along with the achievement of competitive advantage, exerts positive influence on stakeholder value (Husted and Allen 2007). CSR is consistent with the organization’s interest in their actions’ impact on their stakeholders (Ismail 2009); it also has a positive effect on stakeholder behavior, and their perception of the firm’s reputation (Maden et al. 2012), because CSR engagement improves trust in the firm (Park et al. 2014). Stakeholder trust and credibility in the organization are elemental; trust entails the expectation that others’ actions will be positive (Bhattacharya et al. 1998), ethical, just, non-threatening (Carnevale 1992), kind, safe, etcetera. Credibility depends on the degree of trust amongst stakeholders and organizations (Stacks and Watson 2006). Engagement in questionable and/or unethical practices alters and breaks trust and credibility in the organization which, in turn, negatively impacts business growth and development. Crisis Dynamics In addition to all issues that permeate society and devastate the environment, organizational leaders have the added challenge of a Global Pandemic; to date, cases of Coronavirus (covid-19) have been confirmed in 216 areas, territories and countries, accounting for 16, 523, 815 cases, and 655, 112 deaths (WHO 2020). The Global Pandemic is just the latest crisis that organizational leaders have had to manage. The fact that the firm itself is a stakeholder elevates the complexity of crisis management; the firm is comprised of people who are, individually and collectively, experiencing potential trauma effects. These collaborators are those driving everyday operations, leading teams, as well as designing and executing strategic decisions, and they are all in the midst of an unprecedented global crisis. The internal and external variables that decision makers need to account for are vast and unparalleled; therefore, managing the current covid19 crisis will require more than strategic planning and prompt action-taking to ensure stakeholder satisfaction and, thus, survivability. What are stakeholders going through? What are stakeholders, as individuals, in all likelihood suffering during quarantine, isolation, and exposure to a potential lifethreatening virus? To be clear, no distinctions are made amongst stakeholders during

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a crisis, meaning that all interested parties are considered to have equal stake. As such, it is considered that everyone can potentially experiment a degree of the same effects; granted, those privileged with financial stability have one less concern that can cause high levels of stress and anxiety. Pérez-Fuentes et al. (2020) recently developed a study and found that the emotional and cognitive effects of the threat of covid-19 are a cause-effect relationship, in that the idea of the existence of covid-19 causes emotional and cognitive effects which, in turn, are a cause for a persisting sense of threat; negative emotional and cognitive effects include: sadness, depression, anxiety, anger, and hostility (Pérez-Fuentes et al. 2020). Yang and Ma (2020) carried out a study in China and found that, during the pandemic, there was a significant decrease, 74%, in participants’ emotional wellbeing. According to the Centers for Disease Control and Prevention (CDC) stress during this Global Pandemic can cause (CDC 2020): “Fear and worry (related to health -own and loved ones’-, financial situation, job, and/or loss of support services), changes in sleep and/or eating patterns, difficulty sleeping and/or concentrating, worsening of chronic health problems and mental health conditions, and increased use of tobacco, and/or alcohol and other substances”. According to Ahmad and Murad (2020), coronavirus related data and information retrieved from social media have a negative effect on mental health and are a cause for panic. And, Chao et al. (2020) found that even following the news, with varying influences between traditional and social media, of the Global Pandemic has negative effects including: “depression, anxiety, and stress”. Many may suggest that people should just steer away from social media; however, it is, for millions, or the only means of social contact; such contact and a sense of community are considered elemental to combat negative effects and even increased suicidal thoughts and feelings (CDC 2020). Moreover, domestic violence has also increased. According to the Mexican National Institute for Women (INMujeres), calls to 911 in Mexico related to violence against women increased 53% during the first quarter of 2020 (INMujeres 2020). The UN Department of Global Communications has stated that there has been an increase in reports between 25 and 40% in Singapore, Cyprus, Australia, New South Wales, France, and Argentina, and in the United Kingdom “calls, emails and website visits to Respect, the national domestic violence charity, have increased 97%, 185%, and 581% respectively” (UN Department of Global Communications 2020). Even after considering the above cognitive and emotional effects of the crisis, there is still a critical matter which must be addressed. Organizational leaders will have to take a moment to consider the reality of post-traumatic stress disorder (PTSD); it is not only plausible; it is a reality. According to the Mayo Clinic (2020), PTSD is “a mental health condition that’s triggered by a terrifying event - either experiencing it or witnessing it”; and, according to the World Health Organization (WHO), PTSD occurs with the “exposure to a stressful event or situation (either short or long lasting) of exceptionally threatening or catastrophic nature, which is likely to cause pervasive distress in almost anyone” (WHO 1993); these situations may include: “natural or man-made disaster, combat, serious accident, witnessing the violent death of others, or being the victim of torture, terrorism, rape, or other crime” (WHO 1992).

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And, the Hazelden Foundation (2008) has stated that “7 to 12 percent of people develop post-traumatic stress disorder (PTSD) at some point in their lives, with women more likely than men to develop it.” Although most trauma survivors pull through PTSD without professional intervention, others will require crisis intervention to recover (McNally et al. 2003). Finally, stakeholders may also be burdened with yet another collateral effect that must be considered – collective trauma. According to Hirschberger (2018), collective trauma is a psychological reaction that occurs in response to a catastrophic, traumatic, event that affects and even destroys a society. This trauma reflects the devastating effects of the crisis, a crisis of meaning, collective memory, and trans-generational collective (Hirschberger 2018). Collective memory prevails across generations (Hirschberger 2018) to those that have not experienced the trauma firsthand, like an inherited injury. Therefore, collective trauma can be descriptive of trauma from the same event in generations to come. As a result, the collective trauma of the current global pandemic must be considered as a factor shaping current and future stakeholders’ mindsets, approach to life and, therefore, work. Crisis Stages There are three critical stages that can occur when managing a crisis, each of which alters an organization’s response to a crisis. The first is ceasing, second is survival mode and the third is survivability; the last two are desired states given the very real possibility of succumbing to the trauma of the crisis and ceasing to exist ahead of time. Figure 1 includes the effects of the types of actions that a firm can take during a crisis on stakeholders and the stage of crisis. Firms that do not take action to face the internal and external challenges of a crisis, have as a result a dissatisfied stakeholder and may be in a ceasing-to-exist stage. Firms that practice crisis management to tend to the crisis’ immediate and visible effects, may account for mildly satisfied stakeholders, and enter into survival mode. Finally, firms that engage in CSR, practice socially responsible crisis management which yields positive results as stakeholders are satisfied; further, the firm is able to transition into survivability. In terms of survival psychology, the first-stage phenomenon is referred to as psychogenic death, whereby “a biological process takes place as in natural death, but it is triggered at a premature stage in the person’s life when they are under duress (Leach 2011).” Survival psychology has mostly focused on the reasons why people survive while others do not; however, this does not describe the entire panorama.

Fig. 1 Action effects on stakeholder satisfaction and crisis stages Source: Author’s elaboration

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Therefore, Leach (2011) posits that the better question is “why so many people die when there is no need”; in other words, if the variables are the same, then why is it that so many people do not survive? An analogous phenomenon occurs with firms. Unquestionably, the covid-19 pandemic has taken a toll on firms around the world. It seems every day there is news of another organization announcing that they will be shutting their doors. Multiple industries have been negatively impacted, including tourism, travel, leisure and entertainment, retail, restaurants, gyms and fitness, amongst others, ranging from small, medium, large, to multinational. Why have so many firms gone out of business while others have survived? A decrease in demand surely has played a significant part; however, if the variable is demand, then why have others survived? The organizations and stakeholders that survived the initial impact of the crisis went into survival mode. Survival mode (Chemtob et al. 1997) is a mechanism implemented in order to be able to function, continue, and persist, under threatening circumstances. When a person is in survival mode, they are not designing long term plans because they are not thinking that far into the future. Common effects of crises, which persist in survival mode, include anxiety, distress, change, trauma, stress, fear, risk, ambiguity, disruption, and urgency, amongst many others. Clearly, organizations worldwide are in distress. Strangely, it has become common to see firms delegate the responsibility of business survival to stakeholders, relying heavily on current consumers; firms’ posts on social media read “help us survive” and “help us get through these hard times”. The reality is that stakeholders, particularly consumers, while in survival mode, are not able to “help firms survive”. So many are being cautious about their spending because their income is uncertain, therefore, it becomes unfeasible to support so many businesses, even in their own communities. What can or has kept firms in stages one and two? Crisis management of the current Global Pandemic has shed light on dubious practices. Too many organizational leaders have made questionable and unethical decisions that have inevitably, and negatively, affected their stakeholders. For instance, decisions related to collaborators have included: furloughing (term most did not even include in their vocabulary before), firing (due to sickness and/or choice to self-quarantine), pay cuts, suspended services and benefits, layoffs, unpaid sick leave, amongst others. It is noteworthy that unemployment insurance is neither a global practice nor entails a living wage for most; further, places like Mexico City have currently reached their limit of worker aid (Gobierno de la Ciudad de México, 2020). Some collaborators, if lucky, have received organizational communication in dribs and drabs, whilst others remain unaware of the organization’s plans for the near future. This has left some collaborators uncertain of their job status, with increased anxiety, stress, fear, and distress, whilst others have lost their jobs, therefore have no income and even lost their homes. Others have defied regulatory authorities’ warnings and instructions, putting stakeholders, their families, and the community in significant risk, which also has further strained the health system. Entire supply chains have been disrupted; some organizations have abruptly ended supplier relationships, and others have stopped

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payment on debts, directly and negatively impacting their stakeholders. And, other organizations’ price gauging practices (including masks, hand sanitizer, food, pads, tampons, disinfecting wipes, and certified and uncertified covid-19 tests, etcetera) have been revealed, amongst other practices. These practices, which are certainly not socially responsible, have directly or indirectly affected consumers who may very likely take on roles as other stakeholders. Granted, many organizational leaders are still struggling to figure out the best strategy to meet current challenges and successfully remain in the marketplace; however, questionable and unethical practices, as well as doing without collaborators, are the wrong approach to attaining desired productivity and performance. Without question, there is no business growth and development without social growth and development; hence: P1: Failure to effectively manage crisis in a socially responsible manner can result in pervasive survival mode and potential stakeholder dissatisfaction, which, in turn, negatively impacts social and business growth and development.

3 Survivability According to Leach (1994), survival behavior consists of four major phases, these being: “pre-impact, impact, recovery, rescue and post-trauma”. Survivability is considered a functioning “new normal” which requires a socially responsible approach to crisis management of all phases experienced in survival behavior toward transitioning from survival mode. Figure 1 includes a conceptual model describing the effects of crisis management on stakeholders, the transition from survival mode to survivability and the latter’s effect on social and business growth and development. There are three steps that may enable organizations to transition to survivability: Step one – Preparedness. This step requires organizational leaders and decision makers to develop contingency and crisis plans, in relation to their existing strategic plan, for various scenarios of potential crisis based on historical events, social and political change, trends in the market, and environmental conditions. Step two – Assessment. This step requires the meticulous evaluation of the crisis’ impact; that is, 1) acknowledging it has occurred, 2) determining the scope of damage (i.e. affected stakeholders and operations), and 3) determining the impact of the damage (i.e. degree of disaster catastrophe). Step three – Redesign. This step requires challenging the organizations’ status quo and, with the information and data from steps one and two, organizational leaders must make strategic decisions to adapt to the changes in the environment. The key is to do so ethically, whilst sustaining financial, social, and environmental performance; in other words, in complete alignment with the organization’s CSR engagement. This may be achieved by considering the five basic survival skills (BSS), fire, shelter, signal, food/water, and first aid (Wilderness Awareness School 2020) and interpret them in terms of business dynamics.

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BSS Fire → Innovation. Not all innovations require costly investments; in fact, the best way to innovate processes, procedures, products and services, is to invest in collaborators. They have the human capital (know how, know what, know who) to strategically and effectively drive innovation needed to seize the opportunities created in a crisis. Including collaborators in the process of innovating may increase motivation and their engagement. Furthermore, this significantly lessens the need to reduce the payroll by firing, laying off, furloughing, amongst other practices. BSS Shelter → Infrastructure. Thoughtful evaluation of the organization’s foundation (i.e. corporate philosophy), structure, policies, standards, processes and procedures are required. Organizations’ infrastructure is not meant to be static, on the contrary, they ought to be ever evolving. Further, organizational leaders cannot afford to hold fast to the infrastructure’s current status; rather, they must be flexible as it is quite possible that certain aspects need to be altered in order to face the crisis’ challenges. For instance, many organizations will have to develop policies regarding the decisions to effectively manage the present crisis. BSS Signaling → Communication. Transparently communicating with current and potential stakeholders about the progress of crisis management should be an ongoing process. Information and data are critical for decision making, particularly during a crisis; as such, it is important to continuously provide updates; even if they are deemed as a small action, can reduce anxiety due to uncertainty. Furthermore, increased communication about a firm’s progress may enhance stakeholder engagement. BSS Food/Water → Resources. There is no way a firm can continue its operations or provide products and services without resources; as such, effective and satisfactory supplier relationships are elemental. The entire supply chain must work together to ensure that they are all effectively managing the crisis in a socially responsible manner; the latter suggests that there is a significant effort being made so nobody is “left behind”. Doing so will ensure that the needed resources are available to continue production and remain in the marketplace. BSS First aid → Stakeholder well-being. Risk significantly increases when individuals are forced to adapt to an unknown and potentially threatening environment; therefore, there is a very real possibility that organizational leaders will be challenged with stakeholders’ emotional and cognitive effects of trauma, including their own. These may vary from stress, anxiety, depression, to PTSD, as well as the consequences of collective trauma. Furthermore, understanding what stakeholders may be experiencing and helping them manage it is essential to even begin to consider redesigning for the “new normal”. Indicating to stakeholders that the organization is aware of their challenges and cares by providing assistance and/or contacting them with professionals that can effectively intervene, sends the message that trauma effects are a “we” issue and validates their state of mind. These practices have a direct and positive impact on stakeholders. For instance, they positively impact collaborator satisfaction which influence their loyalty, productivity and performance; the latter, in turn, positively impacts social and business growth and development; hence,

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Firm

Crisis

Business growth and development

Social growth and development

P1 P2

Crisis Management

CSR

Survival Mode

Survivability

Stakeholder satisfaction

Fig. 2 From survival mode to survivability for business and social growth and development Source: Author’s elaboration

P2: Effectively managing crisis in a socially responsible manner has a positive impact on stakeholder satisfaction, and enables the transition into survivability which, in turn, positively impacts social and business growth and development (Fig. 2).

4 Concluding Remarks Effective management means that a variety of internal and external resources are planned, organized, led, and controlled to drive operations, productivity and performance, given external forces such as political, economic, social and environmental. In order to do so, stakeholder satisfaction must be met, which suggests managing ethically – in a socially responsible manner. Further, extending CSR practices to crises’ effects on collaborators is crucial to a socially responsible risk management, as they impact collaborators’ commitment to the firm (Brammer et al. 2007), who are essential for the achievement of desired productivity and performance. Therefore, effective crisis management is successful when it includes the above and leads the organization and its stakeholders into survivability. The current Global Pandemic is ongoing and affecting millions around the world, both directly and indirectly, and with varying degrees of collateral effects (PérezFuentes et al. 2020; Yang and Ma 2020; CDC 2020). People are stressed, anxious, concerned, and scared. They fear the risk of infection for their families, communities, and themselves; they worry about their income, how they will support themselves and families, keep their homes; they are anxious about the “end” of the pandemic, when it will happen, if it will happen, and, in either case, what will the “new normal” look like. Some are struggling with severe emotional responses to the trauma, some are developing PTSD and, in all likelihood, we are all developing collective trauma. These people are organization’s current and potential stakeholders; expecting them to

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perform at their previous level of productivity, then, is not plausible without adequate and prompt attention. Their anguish is real, must be validated, and considered in organizational leaders’ decision making to collectively survive the effects of covid19. The greatest advantage has been taught by history; human beings are resilient. We survive. Future Research Directions Future research could analyze the effects of particular practices emerging from socially responsible crisis management on stakeholder engagement, and their perceived added value. And, a cross cultural study could be carried out in order to evaluate the impact of socially responsible crisis management at each stage of a crisis across industries; this study would also provide insights into different approaches to management and stakeholder receptiveness given their different cultures.

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Artificial Intelligence & Blockchain: The Path to Generate Value for Companies After the COVID-19 Pandemic Michael Shane Reilly Marulanda and Pablo López Sarabia

Abstract Technologies of the fourth industrial revolution, such as Blockchain and Artificial Intelligence, have gradually been adopted by all companies that are including ESG criteria. The financial sector has taken the lead in these two technologies, but the challenge generated by the COVID-19 pandemic forced many companies in other sectors to innovate rapidly in order to avoid bankruptcy. The new models in which companies will compete for market share (using the AI algorithms to extract value from data), will improve forecasts and strategies to create value for customers with more personalized products. Finally, DLT Blockchain technology will enhance product traceability and the development of new business models based on trust and decentralization. Keywords Artificial intelligence (AI) · Blockchain (DLT) · COVID-19 pandemic · Digital economy JEL Classification L24 · L60 · L86 · M13 · M15

1 Introduction The 4IR and COVID-19 accelerate the adoption of disruptive technologies in all economic sectors. The invention of the steam machine led to the first industrial revolution, instigating a vast amount of changes not only in the economy, but also in society as a whole. Today we face the fourth industrial revolution (4IR) characterized by extreme automation & hyper-connectivity, which has been forged by developments in hardware and software, as well as the creation and improvements of programming languages such as Python, Java, C, C++ amongst others. Its growth is coalescing thanks to an expansive array of disruptive technologies such as artificial intelligence (AI), Blockchain (BC) or the Internet-of-Things (IoT) (Tan and Shang-su 2017). M. S. Reilly Marulanda · P. López Sarabia (B) Tecnológico de Monterrey, Campus Santa Fe, Mexico City, México e-mail: [email protected] © The Author(s), under exclusive license to Springer Nature Singapore Pte Ltd. 2021 G. Dávila-Aragón and S. Rivas-Aceves (eds.), The Future of Companies in the Face of a New Reality, https://doi.org/10.1007/978-981-16-2613-5_10

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These new technologies are opening a pandora’s box of opportunities and solutions, which corporations and small businesses are implementing to solve different difficulties arising from the present business environment. The current business environment,which we will call the new reality, has put pressure on businesses through external and internal factors. External factors are challenges that the companies face which cannot be controlled, such as generational changes, pandemics and geopolitical changes. On the other hand, internal factors are challenges and opportunities which can be influenced by the company, such as the sharing economy, social demands with ESG criteria, technological innovation, amongst others. Consequently, our new reality has increased the need for technologies from the 4IR. Hence, prior to engaging in how the disruptive technologies will forge the business of the future, it is relevant to understand the current obstacles that companies will have to face, after the first outbreak of COVID-19 pandemic. The COVID-19 pandemic has exposed the shortcomings of health institutions around the world. The lack of a vaccine and treatment forced governments to confine the population in order to reduce the risk of contagion; but generating a historic drop in economic growth and high unemployment rates. COVID-19 was expressed in a supply and demand shock that affected most economic sectors, which had to innovate and adapt to the new environment to avoid bankruptcy. Governments around the world face the great test of creating economic recovery policies and programs focused on the recovery of family income and job creation, but with a cross-cutting impact that encourages business innovation and adoption of new technologies with environmental, social and governance (ESG) criteria. Surprisingly, the pandemic has not generated a coordinated global response, especially after the management failures of many countries. This situation could motivate citizens to punish their governments in the upcoming electoral processes. Transitions in political leadership come with transformations in policies, taxes amongst others, which could lead to beneficial or disadvantageous outcomes, not only locally but also internationally. Even though businesses tend to manage geopolitical changes well, unconventional events such as Brexit or the US-China Trade war, have created difficult scenarios for many businesses around the globe, such as vehicle manufacturers and technology companies. Companies such as Toyota and Nissan, who invested heavily in Britain, now face significant disruptions to their supply chains threatening their transactions, logistics and warehousing costs (Morelli 2020). In relation to technology companies established in Europe, some of their operations will be taxed by the governments of the region, a situation that could limit innovation, according to specialists in exponential technology. EU countries are forcing the 5G carriers of China to a greater transparency in the handling of information and restricting their participation in support infrastructure to provide internet of things (IoT). For its part, the US government announced sanctions against Huawei and operating restrictions to the Chinese video platform “TikTok”. Although businesses cannot predict these events and, furthermore, cannot manipulate the outcome, they could adopt AI and Blockchain technologies to mitigate risk. For example, companies could predict electoral outcomes

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through sentiment analysis and use DLT technology for the traceability of information on a good or service such as 5G communications. Companies can use the 4IR technologies to design different strategies that help mitigate any risk which could threaten the company’s stability. Generational changes, like political ones, present challenges and opportunities. COVID-19 undoubtedly transformed consumption channels and preferences of families, as the lockdown boosted online purchases and electronic payments. Many small and medium-sized companies had to migrate to e-commerce to avoid bankruptcy. Hence, transform their traditional business models to an online one supported by home delivery. This generated additional costs that, in the long term, could become an investment, since the online model generates valuable information that could be processed with AI and serve to design a marketing strategy tailored to its customers that generates greater value to the company, but also to all stakeholders. The current technological and data driven generations gain more utility, from online goods and services, such as streaming platforms or online shopping. On the contrary, older generations do not get as much utility from online goods and services and would rather go to the local market to buy groceries instead of receiving them through Amazon. Thus, it becomes crucial for businesses to understand the behavior of each generation and take advantage of the change in the consumption patterns of families associated with the COVID-19 crisis. In the past, industries such as the toy industry where seriously affected by generational changes. Millennials have shifted away from traditional toys towards digital games. Toy retailers such as Toys R Us have been struggling to adapt to these new changes. The company’s net income plummeted from 2013 until 2015. By 2017 the losses were too big to handle and the company filed for bankruptcy, closing most of its stores. Today Toys R Us is restructuring its business strategy to adapt to theses generational changes (Wolfe 2019). Hence, it is clear that the change in preferences in each generation is crucial knowledge for any business. Therefore, Artificial intelligence becomes relevant. Applications such as client clustering or client segmentation as well as other data analytics are necessary to understand each client and adapt the business model/strategy before it’s too late. Traditionally, the main purpose of any business is to maximize the wealth of the owners or stockholders” (Berk et al. 2019). Although, this purpose is still at the core of most corporations around the world, it is changing. On August 19, 2019, the Business Roundtable organization released a statement signed by 181 of the most important CEOs in the US in which they commit to act for the benefit of all people with interests in the company (shareholders, customers, employees, suppliers and communities) see BRT (2019). This vision is compatible with the requirements of consumers to incorporate environmental, social and corporate governance (ESG) criteria into production processes of the companies. Businesses under the ESG criteria take into account not only the return on investment (ROI) of the business model but other issues as equal pay, sexual harassment policy, inclusive culture, water recycling, fight against racism and gender equality inside of the Board of Directors.

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The ESG approach creates a more diverse environment, with stronger values which could lead to higher productivity, increasing the firm’s value. Hence, it is not surprising that socially responsible investing (SRI) has become a popular strategy amongst investment funds. In the UK, 53% of portfolio managers perceive a positive impact of SRI on financial performance (Duuren et al. 2016). Although, it may seem that SRI is going to lead the future of financial investing in the short term, the paper briefly suggests that ESG monitoring increases costs for the investment funds. However, we think that incorporating Blockchain technology could easily reduce the monitoring costs, making research into the ESG companies less expensive, while adding value to these corporations. The sharing economy is a socio-economic system, which is built around the sharing of human and physical resources (Matofska 2017). This new economic model has led to a wave of products and services which have disrupted the market and their specific industry such as Uber, Airbnb and Drivy. Thus, the sharing economy has pathed the way into less ownership of assets, which raises the need for a new business model, particularly in the automotive industry. To achieve such a system traceability of ownership of vehicles and other valuables that might take part in this socio-economic system is crucial, therefore blockchain technology becomes relevant. The technologies derived from the 4IR create a collection of opportunities and challenges for the business and provide a whole range of solutions for the new business models that will be developed in the future. These models will become more service-first in nature, meaning that supporting automation, smart devices, AI and the sharing economy will force industry stakeholders to reinvent themselves (Fitch Solutions 2020). Particularly, AI and DLT Blockchain are modifying the business model, operations, the supply chain, marketing and generation of value for any business regardless of the size.

2 DLT Technology-Blockchain is Generating New Business Models with High Value The surge of the global pandemic as a consequence of the outbreak of Covid-19 has made evident that internet has become a basic life necessity. Internet, as we know it, is usually referred to as the internet of information, as it allows us to send any piece of information from one part of the world to another in seconds. Applications such as Email, Zoom, WhatsApp amongst others, have helped businesses to keep operating, universities to keep teaching classes and performing essential activities from home. However, the pandemic has also made evident that the internet of information has its flaws. The internet of information makes it really hard to transfer an item which has value associated to it. This is due to the fact that it is susceptible to hacking, manipulation and malpractice. Hence, people would rather use an intermediary such as banks, or other application such as PayPal, which eliminate these risks at the expense of high fees. With online shopping increasing due to lockdown restrictions, the urge for the new era of internet has increased dramatically.

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The internet of value is the term usually associated with the new era of the internet. It refers to the new technology that allows sending any item with value associated to it and that can be digitalized. The technology that allows us to do this is Blockchain. The term “Blockchain” is usually associated with cryptocurrencies such as Bitcoin; but, in reality, Bitcoin is just one of the many applications of Blockchain see Nakamoto (2008). Hence, Blockchain technology could be defined as “a public permanent, append-only distributed ledger” (MIT Technology Review Editors 2018). In simple words, Blockchain is a mathematical algorithm used to store valuable data in a decentralized network which is impossible to fake or hack. Because of this, the Distributed Ledger Technology (DLT) is commonly called Blockchain. This technology enables many companies to have thousands of possible solutions within the business, leading to a reduction of costs as well as value creation. In highly competitive markets, businesses spend large amounts of resources to make processes more efficient to reduce marginal and fixed costs, in the interest of gaining market share and consequently higher profits. Adequate expense management in a business makes a difference between a profitable or non-profitable company. Common issues, such as communication inconsistencies, create a big burden on operational expenditure (OpEx). Managers often struggle to transmit information across the whole company, while workers also struggle to interact between different areas and divisions in the company. This inconsistency usually leads to an extra effort required to complete tasks. Hence, the firm is being less productive increasing relative OpEx costs. As blockchain is a decentralized processing network, meaning that the information is stored in thousands of synchronized devices, it could easily solve this issue. This could be achieved through a single organization blockchain, as it reduces communication inconsistencies by increasing the speed at which information can be transferred between divisions. Thus, reducing backlog and overall costs for the company as well as standardizing the information in the ledger across all divisions. Other factors which are not costs but represent a loss in potential revenue is shopping cart abandonment rates. With the growth of E-commerce especially during the pandemic, most companies have shifted their business model to include online retailing. Although this has led to increases in revenue for most, it has also represented a new risk for the firm. Consumer data, such as credit card numbers as well as other personal information is now vulnerable for malicious practices. Consumer confidence in a web page is one of the key determinants for a client to carry on his payment. By 2018 nearly 70 percent of online shoppers in the U.S abandoned their online shopping carts. This means that the client scrolled through the business’s web page, added something to their cart but then failed to proceed with the payment. Abandonment rates in online retailers’ stores can go up to 75.7% in food & drink and 74.1% in consumer electronics (Rice 2018). The use of the distributed ledger and a digital signature provided by blockchain technology, reduces the risk of fraud or theft, thus increasing consumer confidence and sales while reducing shopping cart abandonment. Applications of blockchain technology to reduce costs will lead to a vast array of ideas and opportunities not thought of yet. However, applying blockchain technology

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to every small activity could be counterproductive. Hence, it is important to remember that the need for this technology flourishes from the urge of transferring valuable information. This idea is essential due to the fact that there is no real benefit in applying this technology to information with no value, as applications from the internet of information such as Email are far cheaper and more practical. The second aspect which is needed to apply blockchain technology might sound obvious, but it is rather important. This is the fact that the asset has to be able to be digitalized. This means that you can’t send a car through blockchain, but you could send the ownership rights of the vehicle through it. The third aspect which would justify the use of blockchain is the existence of multiple players who are adversaries and thus have certain mistrust amongst them, leading to risks which generate the need for a third party. In this case adopting blockchain technology becomes relevant and by eliminating the intermediaries there is a cost reduction in transactions. The Blockchain technology also enables to add value to a business. This can be achieved due to the programmable flexibility, which enables new capabilities to be added to existing services and processes (Deloitte 2016). Programming flexibility in the blockchain has given light to diverse applications of the technology such as smart contracts. A smart contract is a digitally signed agreement between two parties which contains a set of rules written in code which executes itself on its own, without the need of an intermediary (Espinosa 2019). As smart contracts are transparent and autonomous it leads to products and services being executed in a faster and more trustworthy manner. For example, insurance companies have created flight insurance through blockchain smart contracts. This insurance executes itself as soon as the flight has been delayed or cancelled. This client friendly service eliminates the hassle the user has to go through to claim the insurance which reduces company costs of researching the event, as well as higher fees could be charged for a what could be considered a premium insurance product. Smart contracts are not the only way in which the blockchain technology could add value to a business. Consumer preferences have shifted towards buying products from companies which are transparent. In other words, consumers are demanding traceability, they want to know where the product was made, under what conditions and if it was subject of any child-labor, animal cruelty or the use of pesticides and fertilizers. In 2016 a study from Label Insight proved that 39% of consumers would be willing to change to a new brand if it offered full transparency (Audrey 2016). Through the distributive ledger of a public blockchain this can be achieved. Consumers may be able to track products all throughout the supply chain. This increase in transparency could lead to an aggregate value of the product as well as an increase in market share for the company. Blockchain technology is not a panacea for companies, as it is a developing technology that requires cultural and regulatory changes have to take place for a full adoption of the technology in most industries. The financial sector is one of the few industries in which the feasibility of the technology and the impact of the applications is high, around 30% of all identified applications have been associated with the financial sector (Hileman and Rauchs 2017). For this reason, multiple applications

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Fig. 1 Blockchain impact and feasibility by economic sector 2020. Source: Higginson and Carson (2018) and Fu et al. (2020)

in sectors such as automotive, financial services, health care and agriculture are viable and profitable as seen in Fig. 1. Blockchain in Financial Services. Many industries have started to adopt blockchain technologies, but the one industry which has been completely shaken is the financial sector. The introduction of blockchain in the financial sector was driven by the creation of many new players which are commonly referred to as Fintech companies. Fintech companies have put pressure on large financial institutions to innovate and change the traditional business model to be more inclusive and technological. Therefore, blockchain applications have expanded across many different sectors such as payments, capital markets, trade services, investment and wealth management, amongst others which can be observed in Fig. 2. It is estimated that the introduction or the distributed ledger technology could reduce the financial services infrastructure cost between US$15 billion and $20 billion per annum by 2022 (Gregorio 2017). Although many blockchains applications are being used throughout the industry, the use of smart contracts combined with the tokenization of the rights of an asset, have been the most influential. The tokenization of an item is basically the possibility to “tokenize”1 a share of a company, a U.S. Treasury Bond, a syndicated loan credit, 1 The

term tokenization refers to the method in which the rights of an asset are converted into a digital token.

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Fig. 2 Blockchain applications in the financial sector. Source: Hileman and Rauchs (2017) and Consensys (2020)

or other securities, and rapidly exchange the token as it was a crypto asset or any asset for that matter (Filippi and Wright 2018). For example, this practice is being tested in the Over the counter (OTC) derivatives market. Implementing blockchain into the OTC derivatives has a high feasibility and a moderate impact. Companies usually manage risk through financial instruments such as forwards. A forward “is a customized contract between two parties to buy or sell an asset at a specified price on a future date” (DHIR 2020). Forwards are usually

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regarded as OTC instruments as there is no third party involved to enforce contracts, consequently exposing companies to counterparty risk throughout the life of the derivative. As a result of the latter, it is more common for companies to enter into the futures market as it is more regulated and the risk of counterparty is eliminated, but at the expense of fees and longer processes. Through smart contracts derivatives can be enforced automatically under a blockchain network, they may also be programmed to stand by market rules, increasing the transparency of the contracts. So, by eliminating the need for third parties who usually have a long list of prerequisites to access the market, smart contracts that offer secure OTC derivatives may give smaller businesses access to these risk management instruments increasing the businesses wealth and ensuring stable cash flows. Blockchain in the Non-financial Sector. DLT Technology has also had an impact in non-financial industries such as the agricultural sector. The need for traceability systems in the agricultural supply chain has been growing as a result of consumer preferences. The use of pesticides, hormones and fertilizers has been associated with some health issues in humans, leading to an increase in the demand of organic products. Other issues such as child labor and the treatment of animals also have a great impact in the demand for products which are certified against these things. Although many organizations grant different services to ensure the authenticity of products, these certifications are usually costly and benefit only big suppliers. Hence, blockchain technology has seen a rise in its applications in specific areas as seen in Fig. 1. The use of these technologies benefit consumers as they can track and identify the origin of agricultural products easily. It also benefits the small companies as they will be able to charge a premium for these products increasing the revenue of the business without the need of third parties to certify. As observed in this section, blockchain technology has many applications in every industry. If applied correctly, it will help to solve many problems, as well as benefit the companies by reducing costs and also by creating value for the company. Although blockchain on its own is already a game changer, when combined with the applications of AI, it can turn a business 180 degrees. Artificial Intelligence (AI) Creating Value in the Prediction and Business Strategies of Companies. The outbreak of the pandemic did not only affect the surge in blockchain technology, as seen in the previous section, it also increased the need for fast decisions and quick thinking to save lives, thus creating a need for advanced analytics and AI based techniques (Rao and Butterfield 2020). Applications such as modeling human behavior to predict the compliance of the lockdown measures, as well as systematic modeling techniques are the key inputs for policy decisions during the pandemic in developed countries. Other applications such as robotic arms programmed to serve hot beverages at coffee shops in Silicon Valley have also been introduced to help contain the spread of the virus. Consequently, the fast adoption of AI technologies

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is predicted to generate a gross added value of US$ 814 billion to the UK in 2035, as well as US$ 8,305 billion to the United States in the same year (Accenture 2016). The term Artificial intelligence encompasses a vast array of technological advancements which all have a common denominator. This denominator is that AI technology is founded on the idea that the computer system can accomplish tasks which would normally require human intelligence. For example, speech recognition, text sentiment analysis, as well as image recognition. But contrary to the common belief, “the current wave of advances in artificial intelligence doesn’t actually bring us intelligence but instead a critical component of intelligence: prediction.” (Agrawal et al. 2018). Computers can be “taught” to predict via machine learning. Machine learning can be described as algorithms which enable the computer to learn from large amounts of data to make a prediction. The idea behind this technology may not be as straightforward. “A computer program is said to learn from experience E with respect to some task T and some performance measure P, if its performance on T, as measured by P, improves with experience E. Tom Mitchell, 1997” (Géron 2019). Although the algorithm may sound complicated, AI technology will lead to cost reductions in companies as well as added value. Expenditure management is a fundamental task in any business, the AI will help to reduces cost. Artificial intelligence has a vast amount of complex applications which could be implemented, but it also can be programmed to do the most simple and repetitive tasks. By automating these monotonous tasks, workers have more time to focus on other responsibilities which generate more value. Also, by programming these tasks it will reduce human error reducing the costs implied. For example, one of the most complex tasks in a business is finding the right people to hire. AI algorithms con easily scan a large pool of CV’s and identify different qualifications, characteristics or attributes which the employer is looking for. Other applications which lead to cost reduction is target advertising. Companies such as Google or Facebook, record large quantities of data from the websites we visit. Aspects such as: the things we like, the things we do not like, what things we purchase amongst many other things, are all being stored on a server. The data from each of us is then processed through a machine learning algorithm which clusters different groups of people which portray similar personalities, these are referred to as “lookalikes”. This practice makes it easier for marketing companies to direct advertising at certain groups, this reduces costs as only a specified cluster will be shown the advert, instead of using mass media, which will increase revenue resulting in a higher return on investment. AI solutions can also make the production process more efficient by enabling things like predictive and preventive maintenance and upgrades. This will result in lower downtime and less expense. Hence, increasing the revenue for the manufacturing company (Pineda 2019). AI has led to cost reduction applications by making a more efficient work environment, it has also led to applications in which there is value creation as we will see next.

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The value that artificial intelligence creates for the business is based on its accurate predictions. As predictions are the key aspect of any decision in a business, it is fundamental to have the least margin of error. For example, a portfolio manager measures his success on the performance of his portfolio. As portfolios are constructed based on predictive analysis such as fundamental or technical analysis, if his predictions are correct his decisions will therefore be precise, hence he will have a good performance in his portfolio. This idea also applies to the management of a business. A manager must decide how much to produce or how much raw material to buy, by predicting future sales based on previous activity. A manager must also forecast companies’ revenues based on previous performance (Géron 2019). If done correctly, the business manager will make better decisions and will adapt the business strategy accordingly increasing the revenues of the firm. As well as reducing cost, by identifying and clustering different clients into segments through AI, companies can also use this information to target a suitable service or product to each customer. This specialized service generates value to the firm as each product can be seen as unique; therefore, the firm could charge more for the value generated by modifying the service to your needs. The impact of AI adoption as a percent of industry revenue is very diverse. As we can observe in Fig. 3, economic sectors such as retail, government administration, automotive, financial services and telecommunications are all predicted to have a high impact as a percentage of their revenue. This is due to the effects of AI technologies in their cost structure as well as in revenue generation. Technologies such as Fraud and risk analysis are helping to close the gap of US$ 42 billion total fraud losses reported by companies in 2020 (PWC 2020). Hence, the main AI technologies in which companies are investing, as observed from Fig. 4, in fraud/risk analytics. Other technologies, such as IOT, open a new range of products which require internet to operate; therefore, it is clear why telecommunication companies are eager for this technology to advance. Given the latter, it is not surprising that industries such as financial services who face big losses due to credit card fraud and telecommunication companies, which see an increase in revenue as a result of these technologies, are the biggest investors in the technology, as seen in Fig. 5. Other industries, such as consumer electronics, are also investing large quantities as they see potential in new products, as well as online media which is interested in data processing to provide better services. AI in the Financial Sector. As with blockchain, the applications of Artificial intelligence in the financial sector are also huge. The estimated cost savings from introducing this technology into a bank would be around US$447 billion. For example, the front office would see a benefit of US$199B by implementing biometrics technologies for easier customer verification. Likewise, they could also use AI to get insightful information about their clients so they can personalize the banking experience of each customer (Digalaki 2019). Other areas of opportunity such as algorithmic trading, predictive modeling and risk management all make part of a vast vector of applications. For example, the use

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Fig. 3 Impact of AI technology as a percentage of the industries revenue. Note: The green bars represent a high impact in the industry as the percentage is above 2%, yellow bars represent a medium impact in the industry as the percentage lies between [1.5% - 2%), the red bars are industries in which AI technologies are predicted to have a low impact in revenues as the percentage is below 1.50%. Source: Bughin et al. (2018)

Fig. 4 Percentage of investment in different AI technologies as a percentage of total AI investments. Note: The green bars represent a high investment in the technology as the percentage is above 25%, yellow bars represent a medium investment in the technologies as the percentage lies between [10% - 25%), the red bars are technologies in which the investment is low, below 10% as applications are still not so profitable. Source: The Big Data Market. Naimat (2016a)

of unsupervised machine learning is widely being adopted to predict credit default in customers. The way the algorithm does this, is by taking all the client’s history of transactions and whenever it sees an anomaly it can raise flags suggesting a possible risk of default on a loan.

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AI in the Non-financial Sector. In the agricultural sector the use of AI image recognition algorithms is widely used to separate good products from bad ones in the production line. For example, in a banana farm the algorithm would be trained to recognize different types of bananas. It will be instructed to remove specific bananas as they have over ripened. This guarantees a higher quality product delivered to the final consumer. To this point we have discussed how blockchain an artificial intelligence create value to the business model, as well as how these applications also help reduce costs. Although all of this technology sounds promising, it is still really new in its applications and it will take a while before it is fully implemented. New Business Models in the Future. By 2018 humans were generating 2.5 quintillion bytes of data each day (Marr 2018). This number has exponentially increased with the introduction of gadgets under IoT. All of this data will have to be processed in a matter of seconds to serve customers better. Let’s set a futuristic example to understand how businesses will have to work in the future. You wake up in the morning with the help of a virtual assistant such as Alexa or Siri. This virtual assistant will inform you of the day, the weather and the news. You will then go to the kitchen where smart machines will have your coffee and breakfast ready. You will open your smart fridge and take out the last yogurt, through AI image recognition the fridge will know there are no yogurts left and will send this data automatically to your local supermarket so they can resupply, you wouldn’t even have to worry about payment as a secure blockchain smart contract will automatically generate the transaction. Then, you will get into your vehicle, which will be driven by an AI algorithm to go to a smart store. As soon as you walk into the smart store

Fig. 5 Investment in AI technologies by industry as a percentage of total investment Note: The green bars represent a high investment in AI technology as the percentage is above 6%, yellow bars represent a medium investment on the technologies as the percentage lies between [4% - 6%), the red bars are industries which are investing lower percentages as the value is below 4%. Source: The New Artificial Intelligence Market. Naimat (2016b)

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image recognition will know who you are and therefore your preferences, generating a set of offers and product recommendations. You will walk out of the store without having to go the cashier, as AI technology recorded all the items you picked up, and through a secure blockchain network the payment was executed. Once you are outside you get notified that you may be getting ill as the data processed through an algorithm sent by your smart watch has detected abnormalities in your vital signs, so you tell your virtual assistant to set a doctor’s appointment. Although this example sounds futuristic, that is where we are heading. Companies will have to reconstruct their business model towards data driven decisions, ESG supply chains and secure online transactions. Hence, market share will be gained based on these three things: 1)

2)

3)

The performance of the algorithms used to interact with clients and the deep understanding of client’s behavior and preferences are crucial. Thus, businesses will compete to process personal information in the most precise way so they can offer specialized products or services build according to needs, preferences and personality. ESG compliance will be crucial in the future. The use of blockchain to create more transparent supply chains will become a new baseline for companies. Businesses that do not comply will not survive for long. Secure payments and smart contracts will connect companies with client’s smart machines. Hence, transactions with IoT devices is secured for consumers and businesses.

Today we live in the era where our new reality is setting the necessary conditions for businesses to start shifting towards building their future business models. But we are not quite there yet, to achieve this, regulatory changes have to be accelerated and technological advances have to be adopted, or there will be no tomorrow for many businesses.

3 Conclusion In this chapter we discussed the idea that businesses are operating in a new reality catalyzed by the effects of the Covid-19 pandemic, which is being driven by aspects such as social pressure, geopolitical changes, generational changes, the sharing economy and, of course, the fourth industrial revolution as the main driver. We presented the idea that this new reality will forge the businesses of the future by putting pressure on companies to adapt if they seek to perdure in the long run. Technologies such as blockchain and artificial intelligence, have and will continue to change the business model and business strategies of firms in major industries. Blockchain will revolutionize the internet of value, leading to new applications in the financial sector, agricultural sector amongst many others, which will forge a secure and transparent environment. Likewise, artificial intelligence will boost strategic planning and customer interactions through data analytics, leading to improved managerial decisions and personalized products.

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Lastly, we presented our vision of the changes needed in a generic business to adapt to the business of the future. We discussed how data driven business models will perdure and only the companies with the best AI technologies combined with secure blockchain transactions will be able to outperform the market. Nevertheless, it is relevant to point out certain limitations that weren’t considered throughout the text. Without a doubt, DLT Blockchain technology is extremely valuable, but we must recognize the fact that it cannot solve all the problems of a company or industry. Blockchain’s success depends on a high-valuable asset that can be digitalized or the need to automate a process safely, in addition economic incentives will have to exist allowing stakeholders and economic agents that are adversaries to work together. Hence, reducing friction (costs) and solving trust issues. Also, DLT technology allows us to incorporate processes in real time and to verify the traceability of an asset, since on many occasions we care more about the origin of the asset than the reputation of the person who generated or distributes it (for example, we want to know that the manufacture of a diamond meets ESG criteria such as respect for human rights and use of legally obtained resources). Smart contracts determine the rules that relate to ecosystem participants, rather than to individual companies. Therefore, if incentives are not aligned amongst agents in an industry the creation of private blockchains would not be feasible and the value of the technology would not be beneficial. Another limitation lies around blockchain technology and its association with environmental damage and cybersecurity. Environmental concerns are understandable, but they may be oversized, as people associate blockchain technology with miners in cryptocurrencies such as Bitcoin. It is important to clarify that, although one of the best-known applications of DLT is cryptocurrencies, blockchain technology does not necessarily require mining, as there are ecosystems that are directly funded by participants who want to resolve these frictions in a business process or in the purchase and sale of an asset (cross-subsidies are generated to pay off the ledger cost). On the other hand, the technologies encryption process is considered robust given the state of current affairs, although it is clear that the development of quantum computing could make the technology vulnerable, requiring continuous monitoring and updating of security measures to maintain the integrity of the processes. It is important to recognize that blockchain technology tries to drive the creation of an ecosystem, which solves a problem generated by the interaction of different companies, rather than solving an isolated problem of a company. This implies that the companies involved with blockchain technology are flexible, adaptable and resilient to see the big picture behind the process, making the technology ideal for improving supply chain efficiency and value generation. Other limitations also arise as there are concerns if DLT technology can be auditable. It is important to recognize that public DLTs are more difficult to audit in relation to private ones, since the consortium that makes up the ecosystem defines who will have the role to audit and supervise. Another recurring concern about blockchain technology is that it is only applicable in large companies, a situation that leaves small and medium-sized enterprises out. This consideration is wrong, blockchain’s value comes from its decentralized and un-hackable transactions. Therefore, the technology

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seeks to connect and generate a network with reduced capital (since one can use the best tools available in the cloud). Thus, in blockchain technology the scarce resource is not capital, it is innovation and financially sustainable business models. Likewise, the COVID-19 pandemic has highlighted the benefits of AI’s efficient microeconomic scale forecasts, generating high-value custom strategies. However, we must acknowledge well-founded criticisms of the racial and gender bias of many AI algorithms, particularly in the in the provision of social credits or programs as well as in credit granting. This could lead to higher transaction costs such as lawsuits, boycotts, amongst others. Hence, although adopting this technology will be beneficial, business have to take precautions and adapt the organization, so the technology does not seek to replace a worker but on the contrary assist him to make better decisions. Other limitations with AI technology arise with a controversial topic which is data governance. The use of AI facial recognition and some biometrics to mine the information of users in social networks, raises concerns about data governance, i.e. who will be responsible for the care and treatment of the data. We believe that, future research agenda, has important challenges that should be explored: i) cybersecurity and robust encryption given the advancement of quantum computing, ii) blockchain design for environments where there is reduced friction and little incentives to work between agents to finance the ledger, iii) use of blockchain with assets which cannot be digitalized or that assets which have a low relative value, but high social impact, iv) development of ethics committees to audit and improve the bias of AI algorithms, and v) efficient global regulation for the governance of data and information extracted from the AI process, even more so when using biometrics or sensitive user information (possibility of compensation for the use of customer information and protection of the sale of information to third parties).

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