The Paradox of India’s North–South Divide: Lessons from the States and Regions 9789351501411


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Table of contents :
Cover
Contents
Preface
1 - Introduction
2 - Studies of Regional Disparities: A Review
3 - Has the South Performed Better than the North?
4 - What Explains the North–South Divide?
5 - Southern Region versus Northern Region
6 - Conclusions and Policy Implications
Appendices
Bibliography
Index
About the Authors
Recommend Papers

The Paradox of India’s North–South Divide: Lessons from the States and Regions
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SAGE was founded in 1965 by Sara Miller McCune to support the dissemination of usable knowledge by publishing innovative and high-quality research and teaching content. Today, we publish more than 750 journals, including those of more than 300 learned societies, more than 800 new books per year, and a growing range of library products including archives, data, case studies, reports, conference highlights, and video. SAGE remains majority-owned by our founder, and after Sara’s lifetime will become owned by a charitable trust that secures our continued independence. Los Angeles | London | Washington DC | New Delhi | Singapore | Boston

The Paradox of India’s North–South Divide

The Paradox of India’s North–South Divide

Lessons from the States and Regions

Samuel Paul Kala Seetharam Sridhar

Copyright © Samuel Paul and Kala Seetharam Sridhar, 2015 All rights reserved. No part of this book may be reproduced or utilized in any form or by any means, electronic or mechanical, including photocopying, recording or by any information storage or retrieval system, without permission in writing from the publisher. First published in 2015 by SAGE Publications India Pvt Ltd B1/I-1 Mohan Cooperative Industrial Area Mathura Road, New Delhi 110 044, India www.sagepub.in SAGE Publications Inc 2455 Teller Road Thousand Oaks, California 91320, USA SAGE Publications Ltd 1 Oliver’s Yard, 55 City Road London EC1Y 1SP, United Kingdom SAGE Publications Asia-Pacific Pte Ltd 3 Church Street #10-04 Samsung Hub Singapore 049483 Published by Vivek Mehra for SAGE Publications India Pvt Ltd, typeset in 10/13 Times by RECTO Graphics, Delhi, and printed at Chaman Enterprises, New Delhi Library of Congress Cataloging-in-Publication Data Available

ISBN: 978-93-515-0141-1 (HB) The SAGE Team: N. Unni Nair, Isha Sachdeva, Megha Dabral, Anju Saxena and Rajinder Kaur

To the people of India’s states who deserve better

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Contents List of Figures List of Tables Preface

ix xi xiii

1. Introduction

1

2. Studies of Regional Disparities: A Review

9

3. Has the South Performed Better than the North?

23

4. What Explains the North–South Divide?

35

5. Southern Region versus Northern Region

88

6. Conclusions and Policy Implications

117

Appendices Bibliography Index About the Authors

131 228 232 236

List of Figures 3.1 3.2 3.3 3.4 3.5

Per Capita NSDP—TN and UP—in Constant 1993–94 Prices Poverty Ratios for TN and UP Share of Agriculture in NSDP—TN and UP Share of Industry in NSDP—TN and UP Share of Services in NSDP—TN and UP

26 27 29 29 30

4.1 4.2

Literacy Rate—TN and UP Percentage of Technical Graduates, 1961 and Technical Enrolment, 2004—TN and UP IMR for TN and UP Trends in Life Expectancy—TN and UP Young Working Age Population (15–29 years) as a Percentage of Children below 15 Years Percentage of Urban Population—TN and UP Installed Generating Capacity (per million population)— TN and UP Road Length in TN and UP Tele-density—TN and UP Agricultural Output—TN and UP Per Capita Developmental Expenditure—TN and UP Police Firing Incidents per Million Population—TN and UP Proportion of Civil Police to Total Police—TN and UP Percentage of Court Cases Pending Investigation at the End of Year

37

4.3 4.4 4.5 4.6 4.7 4.8 4.9 4.10 4.11 4.12 4.13 4.14

5.1 5.2

Per Capita NSDP—Southern and Northern States, 1960–2005, 1993–94 Constant Prices Total Poverty Rates—Southern and Northern States, 1973–2003

40 41 42 44 46 48 49 50 52 53 65 66 67

89 89

x 5.3 5.4 5.5 5.6 5.7 5.8 5.9 5.10 5.11 5.12 5.13 5.14 5.15 5.16 5.17 5.18 5.19 5.20

THE PARADOX OF INDIA’S NORTH–SOUTH DIVIDE

Literacy Rate—Southern States, 1951–2011 Literacy Rate—Northern States, 1951–2011 Proportion of Graduates—Southern States, 1971–2001 Proportion of Graduates—Northern States, 1971–2001 IMR of Population—Southern States, 1971–97 IMR of Population—Northern States, 1971–97 Trends in Life Expectancy—Southern States Trends in Life Expectancy—Northern States Installed Generating Capacity per Million Population— Southern States, 1960–2004 Installed Generating Capacity per Million Population— Northern States, 1960–2004 Trends in Urbanization—Southern States, 1971–2011 Trends in Urbanization—Northern States, 1971–2011 Agricultural Output—North and South Per Capita Developmental Spending—Southern States, 1980–2003 Per Capita Developmental Spending—Northern States, 1980–2003 Police Firing Incidents per Million Population— Southern States Police Firing Incidents per Million Population— Northern States Proportion of Pending Court Cases—Southern and Northern States

91 91 93 93 94 94 95 96 97 97 98 99 100 101 102 103 104 104

List of Tables 4.1 Average Tenure of CMs (number of days)—TN and UP 4.2 Initial Values and Compound Growth Rates for Selected Indicators—TN and UP 5.1 Average Tenure of CMs (number of days)—Northern and Southern States 5.2 Access to Basic Services and Amenities, 2001 and 2011—North and South 5.3 Households with Assets—Northern and Southern States 5.4 Access to Sanitation in Slums—North and South 5.5 Access to Bathing Facilities in Slums—North and South 5.6 Access to Drinking Water—Slums of Northern and Southern States 5.7 Access to Electricity and Lighting, Slum Households— Northern and Southern States 5.8 Assets, Slum Households—North and South

63 81

103 108 109 111 112 113 114 115

Preface

I

nternational comparisons of countries and their performance are commonplace today, with both print and digital media highlighting how countries differ in terms of income, poverty, health, education, corruption and even sexual mores. There is a global movement that promotes and disseminates ‘best practices’ drawn from the experience of a wide range of countries. Multilateral agencies and global research and consulting agencies are a major source of such information, and they invest significant resources for data collection and research on these themes. Governments, industry and other players in the knowledge economy are avid users of such information, and hopefully learn a great deal from it. The same cannot, however, be said about comparisons of regions within countries, even though there might be something to learn from them too. One reason is that data of a comparable nature is not collected or analysed by governments at the sub-national level, except for the most basic information needed for current planning and allocation of resources. Elites in developing nations may sometimes assume that there is little to learn from their different regions. Scholars too may not have paid much attention as funds and interest in such research tend to be limited. It is not surprising, therefore, that in India, in-depth regional comparisons and related studies are few and far between. Statistical and econometric comparisons do exist, but they seldom probe the underlying factors. Occasionally, newspapers publish anecdotal findings that compare regions, based on someone’s astute observation or other ad hoc evidence. In fact, our curiosity to check on the facts of the case was triggered by some press reports and short articles in news magazines highlighting India’s North–South differences. A preliminary search, however, convinced us that the reported North–South divide might be a timely subject for systematic research, and an exercise from which some lessons could be learnt. This study took a fairly long period, over four years, for us to complete, largely because of our several other commitments, and the

xiv

THE PARADOX OF INDIA’S NORTH–SOUTH DIVIDE

difficulties we encountered in gathering all the relevant data and conducting in-depth interviews and discussions in Uttar Pradesh (UP), Delhi and Chennai. Indeed, the study may have been delayed even further but for the timely assistance and support of a number of colleagues and friends at Public Affairs Centre, the Institute for Social and Economic Change (ISEC) and elsewhere. We thank Dr Venugopala Reddy, Professor M. Vivekananda, Sayali Borole and Sukanya Bhaumik for help with the data, analysis and comments, V.S. Prasad for his data collection from the Central Electricity Authority, Dr Ajay Kumar Singh, then DGP and IG, Government of Karnataka, for his insights, Srikant Patibandla for helping with National Crime Records Bureau (NCRB) data collection, Sriram Trikootam for his help with data collection and preliminary analysis and the Institute of South Asian Studies, National University of Singapore for the funds which we used for data collection. We gratefully acknowledge assistance from Dr Gopakumar Thampi and Mr R. Suresh for the support they extended for the study. Comments from G. Thimmaiah, V.M. Rao, S.L. Rao and K.C. Zachariah, and Vinod Vyasulu are gratefully acknowledged. Comments from the seminar participants when this paper was presented are appreciated. We thank the officials and scholars with whom the authors met in Chennai and Lucknow during their visits, for their insights. We thank Dr A.K. Singh, Director, Giri Institute of Development Studies, Lucknow, Dr Santosh Mehrotra, Director General, Institute of Applied Manpower Research, New Delhi, and Dr Sudha Pai of Jawaharlal Nehru University, New Delhi, whom the authors met, for their insights. We are grateful to Professor Irudaya Rajan at the Centre for Development Studies, Trivandrum, for sending us data on life expectancy for the Indian states. Thanks are due to Kanthi Prakash at Public Affairs Centre and Shanthi at ISEC for their help with data for the states and formatting. We thank the anonymous reviewer for his/her comments, which has added value to the book. We thank Mr N. Unni Nair, Commissioning Editor, SAGE Publications, for patiently taking this project from the time he met us to the book’s fruition, and are grateful to Ms Isha Sachdeva, Production Editor, SAGE Publications, for her patient checking of the manuscript and letting us know of editorial queries, which has improved the book’s quality. Any errors remain ours. Samuel Paul Kala Seetharam Sridhar Bangalore, August 2014

1

Introduction

I

n recent years, several observers of the Indian scene have noted that the country’s southern states have performed distinctly better than their northern counterparts. They have presented a range of evidence to show that the South has done better than the North in significant respects that matter to the people. Some authors have highlighted economic indicators to show that the South is ahead of the North in terms of development. Thus, the South’s current per capita income is shown to be higher than that of the North’s. South’s human development indicators are claimed to be better than that of the North’s. Others have mentioned less tangible aspects of social life or the functioning of government as areas in which the South is superior. There are two problems with generalizations based on a comparison of such current indicators. They do not shed light on whether this state of affairs has always existed, or it is a phenomenon of recent vintage. Nor do they tell us anything about the factors that may have caused the South to perform better on these counts. To find answers to the question of the divergence in performance between regions, one needs to undertake systematic investigations, including a study of past trends, and generate credible evidence that might explain the phenomenon. A search of the literature does not show that such systematic studies have been attempted so far.1

A number of studies have attempted to document and explain the patterns of economic growth in Indian states. There is a large body of literature on convergence/divergence between states. There is another strand of literature which examines the sources and timing of the shift in Indian output growth since 1

2

THE PARADOX OF INDIA’S NORTH–SOUTH DIVIDE

One reason why this subject is of much research interest is because, if true, it goes counter to the economic doctrine that over time the constituent parts of a country tend to converge in terms of development. The argument is that resources would flow to the less developed areas of the country, setting in motion the process of narrowing the gap between regions over time. Even if private resources fail to flow as predicted, public resources would flow and facilitate a move towards convergence. The reference here is largely to the convergence of economic outcomes, such as the production of goods and services, employment and standards of living. No one has claimed so far that convergence might also occur on the public governance or cultural fronts. A plausible reason is that these are more complex outcomes that are difficult to measure, and are seldom dealt with by economic analysts. A second reason why the subject is of interest is because just three decades ago, the popular perception about the North and South was exactly opposite of what we hear today. Appleby’s report on India’s public administration in 1953 and 1957 identified UP (and Bihar) as the best governed states in the 1950s.2 In the first three decades since Independence, a significant number of people from the South went to the northern and western Indian cities in search of jobs. In many lower-level jobs, in both the private and public sector, large numbers of southerners could be found in cities such as Mumbai, Kolkata and Delhi. There was no such migration from the North to the South. For many observers, it was a clear signal that the South had limited employment opportunities, and that its people had lower standards of living, forcing them to go out of their region to improve their lot. In fact, northerners used to look down upon the migrants and consider them backward in many respects. Casual observations of this kind have led observers to conclude that the the 1980s. This literature addresses a variety of questions such as: When did the shift in growth occur? Was the shift uniform across states? What were the factors causing the shift? Based on a review of this literature, we find that none of the studies have explored issues such as the North–South divide that is the subject of this book. A detailed literature survey is in Chapter 2. 2 This was pointed out by Dr A.K. Singh, Director of Giri Institute of Development Studies, Lucknow. Paul Appleby, Dean of the Maxwell School of Public Affairs, Syracuse University was invited by Prime Minister Nehru in the early 1950s to advise the Government of India on public administration.

Introduction

3

transformation of the South in development terms is a more recent phenomenon. Again, there is hardly any assessment available as yet of the precise dimensions of this transformation, if indeed it has occurred. But if it turns out to be true and credible evidence of the underlying factors can be assembled, it will certainly be of public interest and improve our understanding of how economic and social development works. Third, it is important for both the state and civil society to learn from their past development experience and the experience of other countries so that public policies and actions can be improved and fine-tuned. In this context, it is instructive to reflect on the report that India’s rank in the UN Human Development Index (HDI) of 2013 (135 out of a total of 187 countries) has not changed from the previous period. During 2008–13, China’s HDI has moved up by 10 ranks. Sri Lanka has moved up by five ranks, and Bangladesh and Nepal, whose per capita incomes are much lower than India’s, have moved up by two and four ranks, respectively. India’s record is poorer than the records of all these countries. Among Brazil, Russia, India, China and South Africa (BRICS) nations too, India is at the bottom with respect to HDI. It is clear that faster economic growth by itself does not cause human development indicators to rise, though China has managed to achieve a higher rank in human development alongside faster economic growth. If other countries have done better than India to improve their HDI rankings, there is something for us to learn from their experience. There is a limitation here that their sociopolitical contexts, administrative systems and cultural traditions are different from ours, and hence adapting lessons from their experience may not be easy for our governments and citizens. On the other hand, learning from the experience of different regions in our country is far easier. As Jean Dreze and Amartya Sen (1997) have observed, Given the extremely heterogeneous character of the Indian economy and society, India’s achievements and failures cannot be understood in composite terms, and it is essential to examine the experiences in disaggregated form…. The internal diversities in India offer a great opportunity to learn from each other.

We believe that the regional comparisons in this book and the search for factors underlying their divergence could potentially yield useful lessons for the governments and people of India’s lagging states.

4

THE PARADOX OF INDIA’S NORTH–SOUTH DIVIDE

As noted above, the debate on the subject of North–South divergence has so far been based on journalistic accounts. Economists, on the other hand, have paid more attention to the broader problem of convergence of income and the reasons for income variations across all the states of India. To explain interstate variations, they have used cross-section analyses of data based on which they have identified a plausible set of key variables. There is a consensus that the expected convergence trend is yet to happen in a significant way in the country (see Datt and Ravallion 1998, for instance). Mehrotra (2006) is among the few authors who examine this divergence and explore their causes.3 He argues that social mobilization is the cornerstone of Tamil Nadu’s success with broad based advance in the well-being of its population (a majority of whom are Dalits and other backward castes [OBCs]) which did not happen to the same degree in UP. This study suggests that there is a role for the central government to play to ensure empowerment of the lower castes in the northern states, until such social mobilization happens suo moto. The most noteworthy work on regional development experiences that has relevance to the present study is the comparison of the performance of UP, Kerala and West Bengal in the book edited by Jean Dreze and Amartya Sen (1997) that, to some extent, comments on the differences between these states. This work is examined in greater detail in Chapter 2. Varshney (2012) is another recent study which examines the divergence between the North and South Indian states. This author explores 3 Ramachandra Guha (n.d.) examines the North–South divergence phenomenon from a historical perspective. After offering some evidence in support of the divergence, he goes on to argue that the South had certain historical advantages that may have aided its better performance. He highlights the proximity to the sea coast that all the southern states enjoy and the trade links that have existed for centuries between them and many foreign countries. Indeed, these are important enabling conditions, and have had a significant influence on the course of South’s history. However, these factors have remained constant through history, and cannot explain the paradox that the South was considered economically backward or at least not ahead of the North only three decades ago. The causal factors underlying the North–South divergence phenomenon, if true, must therefore, be sought in other developments.

Introduction

5

social order and entrepreneurialism in India, focusing on some economic contrasts between the North and South. It examines the depth of southern transformation and identifies the mechanisms of transformation by concentrating on a particular southern caste, the Nadars. It also considers the commercial implications of an emerging political revolution in a northern state, bringing a Dalit party to power, but focuses only on social order and entrepreneurship. Pai (2002) is an example of an in-depth state study, which examines the emergence, ideology and programmes, mobilizational strategies, electoral progress and political significance of the Bahujan Samaj Party against the backdrop of a strong wave of Dalit assertion in UP. Based upon extensive fieldwork in western UP, government reports and interviews with Dalit leaders, this study, while highlighting the BSP’s considerable achievements, explores the reasons for the party’s failure to harness the forces of Dalit assertion in UP. It should be noted that this study does not compare UP with other states. The purpose of this volume is to examine this phenomenon in depth, and seek answers to several questions that it raises. Is the claim of South’s better performance credible, and backed by robust evidence? In precisely what respect has the South performed better than the North? Was the South always on a higher performance plateau, or is its better performance the result of a recent turnaround in the South? Is there a set of credible factors that can explain this phenomenon? What lessons and policy implications can we glean from this experience about how development comes about? Before proceeding to answer these questions, it is necessary to clarify a few definitional matters. Foremost among these is the definition of the North and the South. In this study, the South refers to the four major states of Tamil Nadu (TN), Karnataka, Andhra Pradesh (AP) and Kerala. The North consists of UP, Bihar, Madhya Pradesh (MP) and Rajasthan (also originally known as BIMARU states).4 These represent the North in our study mainly because they are the largest states and account for

Appendix 1 at the end of the book contains a brief profile, followed by a fact sheet, for each of these selected northern and southern states. 4

6

THE PARADOX OF INDIA’S NORTH–SOUTH DIVIDE

the bulk of the northern population.5 In the course of the study, we propose to examine the reasons behind the paradox of the divide between the northern and southern states as defined above and for the reasons described earlier. A second issue concerns the nature of the historical evidence used in this study. Since we are concerned here with the trends and patterns of developments over long periods, it is essential that we gather as much past data as possible. However, data availability is a problem in that the time series for some of the variables that we need for analysis simply do not exist. The states included in the study have also undergone changes due to reorganization since Independence, making comparisons over time difficult. We have no option but to live with these data limitations, much like a detective who has to solve the crime, using whatever bits of evidence he or she can gather, despite their partial nature. A final comment is a word of caution. Our reference to the better performance of the South should not be taken to mean that its development outcomes are of the highest order. No writer has made such a claim in the literature. The reference here is only to the relative positions of the regions involved in terms of development. It is not an invitation to the southern states to be complacent, and assume that they have reached a high level of development. India still remains a developing country, and even our better performing states are yet to reach a ‘middle-income country status’.6 The present study is different from most other studies that have examined the performance of regions within countries. The review of the literature on this subject summarized in Chapter 2 shows how most 5 The exclusion of other northern states such as Punjab and Haryana does not pose a problem as they are examples of the few northern states, along with Delhi (a city state) that have performed well in economic terms. In fact, Punjab, Haryana and the western states of Gujarat and Maharashtra have been high on the economic performance scale ever since Independence. But the debate has never focused on their performance vis-à-vis the four large northern states mentioned above. 6 The recent power crisis in TN is an example. There are recent reports that the current power crisis in the state was because of the failure of the distribution of power from the low-demand southern parts of TN to the high-demand northern part.

Introduction

7

researchers were primarily concerned with proving whether convergence or divergence between regions had occurred. This study too examines this issue, but goes further to investigate in depth the underlying factors that led to the eventual outcome. The identification of policy options, their sequencing and the strategies for their implementation will become easier and clearer only when we have a good understanding of the interventions and developments that may have contributed to the outcome. The framework for analysing these factors is presented in a later chapter. It is for this reason that this study draws upon the insights and methods of several social sciences. Economics, sociology, political science, history and management are the primary fields of study that we have drawn upon to derive a better understanding of the factors that might explain regional convergence or divergence in the Indian context. Complex phenomena of this nature invariably entail a constellation of factors that no single discipline fully comprehends. An econometric analysis of such phenomena, for example, may shed some light on certain underlying factors, but a fuller understanding of their role and significance may require a careful probing of certain political and historical factors and interventions that mere numbers may not reveal. In some contexts, social changes that occurred in a country may have prepared the ground for convergence. Policy prescriptions that fail to take those into account may not work in other regional or country contexts. A case in point is the launch of a ‘literacy campaign’ in UP, based on the Kerala model some decades ago. While the campaign had worked well in Kerala, it’s replication in UP was reported to have been a failure, by and large, possibly because the enabling socio-political environment that had evolved in Kerala over many years did not exist in UP. A more comprehensive and multidisciplinary perspective, therefore, is essential for understanding, analysing and interpreting such phenomena, and for deriving policy prescriptions based on the study findings. This study also presents a number of lessons that policy makers can use. Admittedly, there is a limitation here as these lessons are drawn from a single study and that too based on selected regions of the country. Hopefully, more such studies will be undertaken by other researchers, adding to this pool of knowledge, and generating new insights and perspectives on how the complex phenomenon of development works in specific country and regional contexts.

8

THE PARADOX OF INDIA’S NORTH–SOUTH DIVIDE

The core of this book consists of analyses at both the state and regional levels. To begin with, we undertook a detailed longitudinal analysis of the performance of one selected southern state (TN) and one northern state (UP). A similar analysis is then attempted of the southern and northern regions. As a backdrop to these analyses, in Chapter 2 we present a survey of the international and Indian literature on regional disparities and convergence. Chapter 3 presents the indicators we examined to understand the comparative performance of the two states—TN and UP. Chapter 4 attempts to offer explanation of factors which underlie the North–South divide, taking the case of TN and UP. Chapter 5 contains an analysis of the regions, and Chapter 6 concludes by summarizing the findings and the lessons to be learned from the study.

2

Studies of Regional Disparities: A Review

A

number of studies have attempted to document and explain the patterns of economic growth in Indian states. There is a large literature on convergence/divergence between states. There is another strand of literature which examines the sources and timing of the shift in Indian output growth since the 1980s. This literature addresses a variety of questions such as: When did the shift in growth occur? Was the shift uniform across states? What were the factors causing the shift? Based on a review of this literature, we find that none of studies have explored issues such as the North–South divide that is the subject of this book. This chapter is organized as follows. First we review studies whose interest is to examine when a break appeared in the growth rate of Indian states without worrying about why and how the break occurred. We then present the literature which summarizes disparities among states and the timing of a shift, and the stream which makes an attempt to explain the interstate growth differentials. We then present studies of convergence, taking the Indian and international evidence. When did a break appear in the growth rate of Indian states? Kurian (2000), taking a holistic view of development, drew attention to interstate disparities by presenting recent data for states on demographic characteristics, social characteristics, magnitude and structure of state domestic product (SDP), poverty ratio, developmental and nondevelopmental revenue expenditures, indicators of physical infrastructure development and of financial infrastructure. The paper found that

10

THE PARADOX OF INDIA’S NORTH–SOUTH DIVIDE

a sharp dichotomy between the forward and backward groups of states had emerged.1 This paper does not explain the causes of the observed dichotomy. It is also only cross-sectional, and clubs together all states with high per capita income and others with low per capita income, without making a distinction as to when these changes occurred. Virmani (2006) finds that the growth rate of manufacturing in Indian states accelerated after 1980–81, and this contributed to the acceleration in the growth of gross domestic product (GDP) from 1981 to 1982. The most important innovation of this paper is the use of a rainfall index to remove the confounding effect of large droughts. In contrast to Virmani (2006), Balakrishnan and Parameswaran (2007) find that the break in the growth rate of GDP occurred in 1978–79 —with the 1978–79 take-off in growth occurring prior to the positive break in manufacturing (1982–83). However, this paper does not look at the sub-national level. Thus, the interest of all these studies appears to be to examine when a break appeared in the growth rate of Indian states without worrying about why and how the break occurred. What explains interstate growth differentials? In all fairness, in addition to the literature which summarizes disparities among states and the timing of a shift, there is also a stream which makes an attempt to explain the interstate growth differentials. The most noteworthy work on this aspect of regional disparities between UP and South Indian states such as Kerala is the book edited by Jean Dreze and Amartya Sen (1997), which compares the development experiences of three states and explains differences between them. Tracing the causal antecedents of economic and social backwardness in UP, Dreze and Gazdar (1997) point to the disastrous functioning of public services in rural areas of that state, the persistence of widespread illiteracy and the suppression of women in society. In the same volume, a paper by Ramachandran focuses on how Kerala has been able to eliminate basic deprivations at an early stage of economic development. He relates these social achievements to historical conditions and public Kurian’s (2000) forward group consists of AP, Gujarat, Haryana, Karnataka, Kerala, Maharashtra, Punjab and TN. The backward group comprises of Assam, Bihar, MP, Orissa, Rajasthan, UP and West Bengal. 1

Studies of Regional Disparities

11

action. These state level studies, however, do not result in systematic comparisons between the states, with a focus on their performance and the underlying factors. This study not only presents more recent evidence, but goes further and uses a larger number of variables to understand the differences between UP and TN in the first phase, and then between the two regions as a whole in the second phase. Panel data regressions by Shand and Bhide (2000) examine variations in the size, income and structural characteristics of Indian states. It analyses total and per capita net state domestic product (NSDP) for the period 1970–71 to 1995–96. Sectoral analysis showed that reform in agriculture will yield the most benefit as growth in this sector is positively and significantly related to the overall growth. Infrastructure and human development were found to be other important determinants. Rao et al. (1999) analysed the determinants of growth of per capita SDP with data for the 14 major states. The coefficient on the initial income variable was significantly positive in the regressions for longer periods 1965–94, 1970–94 and 1975–94. The variable indicating private investment was found to be the most important determinant of growth. Next in importance was the literacy variable. Ravallion and Datt (2002), studying how the sectoral composition of economic growth and initial conditions interacted to influence how much growth reduced consumption poverty, found that the non-farm growth process was more pro-poor in states with initially higher literacy, higher farm productivity, higher rural living standards (relative to that in urban areas), lower landlessness and lower infant mortality. Ghate and Wright (2008) find that the ratio of Indian to US per capita output over the past 45 years has displayed a distinctive ‘V’-shaped pattern.2 They carry out preliminary investigations of correlates of the ‘V-factor’, using a new panel data set for Indian states from 1960 to 2005. Ghate and Wright (2008) observe that: • V states were on average more urbanized and more literate; Their approach in using the USA as a benchmark may be debatable, but given the USA is the head of the technological frontier, and the standard neoclassical model would predict that growth rates converge to the country on the technology frontier, their choice is somewhat understandable. 2

12

THE PARADOX OF INDIA’S NORTH–SOUTH DIVIDE

• They were somewhat more industrialized and somewhat less dependent on agriculture; • They spent somewhat less on development spending (revenue expenditure) than non-V states. Rodrik and Subramanian (2005) argue—in a similar vein to Virmani (2006)—that the improvement in India’s economic performance was driven by policy changes. In particular, Rodrik and Subramanian argue that the trigger for India’s upward break in growth—which they pin down to around 1980—occurred because of an ‘attitudinal shift’ on the part of the national government in 1980 in favour of businesses. While largely cross-national, this is one paper which takes into account the importance of non-economic factors in growth which need to be noted. Datt and Ravallion (1998) study the causes of rural poverty in a developing rural economy and ask the question as to why some Indian states have done better than others at reducing rural poverty. They model the evolution of various poverty measures using pooled state-level data for the period 1957–91. Differences in trend rates of rural poverty reduction were attributed to differing growth rates of farm yield per acre and differing initial conditions; states starting with better infrastructure and human resources saw significantly higher long-term rates of poverty reduction. Deviations from trend were attributed to inflation (which hurt the poor in the short term) and shocks to farm and non-farm output. This paper, while being quite insightful, unfortunately does not cover institutional factors such as the existence of the minimum support price to farmers and their impact on reducing rural poverty. Basu (2004) provides empirical evidence, from a study of 16 major Indian states for the period 1980–2001, that under the economic reform process, better institutional mechanism could actually help economies to grow faster with a higher level of economic well-being. This paper estimates the economic well-being index (by aggregating 15 socioeconomic variables, i.e., education, infrastructure, technological progress, income and so on) and an index of good governance (by aggregating 13 variables indicating rule of law, government functioning, public services, press freedom and the like) by multivariate statistical measures. Panel regression showed that governance measures and economic policy variables are crucial to explain the differential level of development

Studies of Regional Disparities

13

performance across states in India during the last two decades. It is worthy of note that this is one of the few papers to take into account the impact of governance and institutional factors on differential economic performance of the states. However, it is not clear how variables such as press freedom, for instance, would be different across the states. While the differential rate of growth among Indian states has been extensively probed in the literature, as is clear from above, no one has looked at what explains the differential growth records of the northern and southern Indian states, using historical data. Very few studies have gone beyond the standard economic variables to take into account noneconomic factors such as political stability and law and order, for example, which impinge upon economic activities and investment decisions. Mehrotra (2006) highlights the fact that for nearly two decades, UP had a movement to mobilize the Dalits and OBCs of that state. However, UP’s lower castes had, before the mobilization began, and still have, the worst social indicators in the state and in the country. He explains how earlier in the last century, TN also experienced a mobilization of the Dalits and backwards, but managed to transform the social indicators in health, nutrition, fertility and education after independence. Thus, while UP’s mobilizers of the Dalits have focused exclusively on capturing power, the gains to the lowest castes have been entirely of a symbolic nature, unlike in TN. Pai (2002), while being focused only on one state, UP, and entailing no comparisons with other states, based upon extensive fieldwork in western UP, government reports and interviews with Dalit leaders, highlights the BSP’s considerable achievements, and explores the reasons for the party’s failure to harness the forces of Dalit assertion in UP. Varshney (2012) explores social order and entrepreneurialism in India, focusing on some economic contrasts between the North and South. It examines the depth of southern transformation and identifies the mechanisms of transformation by concentrating on a particular southern caste, the Nadars. It also considers the commercial implications of an emerging political revolution in a northern state, bringing a Dalit party to power. Furthermore, it comments on Alexis de Tocqueville’s argument that the settlements on either side of the Ohio River, Kentucky and Ohio were identical in all respects except for slavery, and that slavery influenced their landscapes of economic dynamism and listlessness.

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Finally, he analyses lower castes in the North; the divergence between northern and southern India in terms of state-level economic growth rates and growth rates in per capita income; growth rates in both rural and urban enterprises; and growth rates of enterprises owned by OBCs.

Has Convergence Occurred? International and Indian Evidence While regional disparities in income and other measures of economic growth have been a recurring theme of research in recent decades, as is clear from the above, a basic question is whether regions within a country tend to converge or diverge in terms of per capita income over the course of development. Convergence implies a reduction in regional disparities while divergence refers to a widening of regional disparities measured in terms of per capita income. Though economic theory does not provide unambiguous predictions about convergence or divergence in per capita income levels across regions or countries, it does highlight several mechanisms that could potentially create either convergence or divergence. Since the 1980s, a rich literature has emerged that identifies the conditions under which countries and regions would tend to converge or diverge in terms of income over time (Abramovitz 1986, Baumol 1986). Economic growth models that predict convergence assert that lagging regions have an inherent advantage. They assume that there will be decreasing returns to scale in capital. As the stock of capital grows in the richer regions, their output will grow less than proportionately to the capital stock. The growth of the richer regions will slow down as the marginal productivity of capital declines and the incentive to save and invest gets weakened. Other things being equal, capital will then flow relatively more to the lagging or poorer regions where the marginal productivity of capital will be much higher. Convergence of per capita income between regions will come about as this process continues over time. Growth models that predict divergence, on the other hand, assume that increasing returns to scale in capital will obtain. The outcome here is a widening of income disparities, with the richer regions getting

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richer, without any limit. The basic assumptions about the properties of production technology and technical progress are, thus, at the root of differing predictions offered by the economic growth models. The assumption that decreasing returns to scale will obtain in technology as its stock increases may seem plausible. The catch-up effect with minimal investment that benefits poorer countries and regions may also work in the same direction. Yet, if the additional cost of innovation falls with increased scientific research or expanded production, the productivity of technological investment may tend to exhibit increasing returns to scale. This, in turn, could lead to a divergence in income. A third mechanism that could generate convergence is structural change in the economy (de la Fuente 2000). As countries and regions develop, a reallocation of resources across sectors may take place. Poorer countries are dominated by agriculture where output per worker tends to be low compared to other sectors. As capital and labour move increasingly to industry or services, and the farm sector declines, average productivity tends to increase in the economy. Such structural shifts are more likely to occur in poorer countries and regions, and they can be a major contributory factor to convergence over time. In brief, decreasing returns, technological diffusion and structural change can all act as mechanisms for convergence. How these mechanisms operate and interact in practice, can only be understood through careful empirical cases of country and regional experiences. A survey of these studies, however, shows that the optimistic predictions of convergence were, on the whole, unwarranted (de la Fuente 2000). As a result, scholars have turned to a search for other explanatory factors. The ‘endogenous growth’ theorists have focused on the possibility of non-decreasing returns to scale in capital (Romer 1986, 1990). Others have generated models in which the rate of technical progress is determined endogenously and differ permanently across countries (Lucas 1990). Econometric methods have been used to estimate sigma-convergence and beta-convergence, two popular measures of convergence that many scholars have examined in recent years.

Empirical Studies A detailed analysis of the regional disparities in Spain shows that convergence has occurred for a part of the period under study, but at a very slow rate. For the period 1955–91, the regression analysis shows that

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in a typical region, only 1.5% of the income differential with respect to the regional average was eliminated each year (de la Fuente 2000). The level of inequality seems to have stabilized after the second half of the 1970s, an indication that the regional income distribution may be nearing its steady state. The results are not very different when the experiences of other European countries, the USA and Japan are analysed. A study of Organization for Economic Cooperation and Development (OECD) countries and their regions shows a rate of convergence of about 2% per year for the study period. A major conclusion of these studies is that convergence is a truly long-term process, one to be measured in decades, not in years. And when it occurs, convergence moves at a very slow pace, a feature shared by many countries. In a study of 31 regions from three Central and East European countries (Poland, Hungary and Czechoslovakia), the authors found no evidence of regional convergence of incomes (Herz and Vogel 2003). There was some reduction of disparities between countries, but not within. The poorer regions within countries did not grow faster than the richer regions. One explanation is that migration has hurt the poorer regions more. The agglomeration effect benefited the richer regions through technology diffusion and capital inflows. Other factors such as lack of human capital and weak public institutions may also explain the tendency towards divergence in the lagging regions. Similar findings have been reported by studies of the regional disparities in Turkey. Analyses of Turkey’s lagging and prosperous regions have shown no evidence of convergence over time. Other studies too have confirmed polarization rather than convergence of regions during the period 1980–97 (Gezici and Hewings 2004). Only one study has shown a tendency towards convergence in some regions that benefited from the investment incentives and other support provided through the country’s five-year plans (Yildirim 2004). Examining the issue of international and intertemporal convergence of the standards of living of the global North and the South at the aggregative average and the disaggregative country levels over the period 1950–92, Sarkar (1999) finds significant evidence of widening gap in the standards of living of the two groups of countries. With only countries such as Korea, Taiwan and Thailand experiencing a converging trend,

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this paper finds that there is some evidence of convergence between less rich North and richer North and between poorer South and less poor South. In a comparative study of regional disparities in China and India, Yanrui Wu (2006) found that over the period 1980–2001, there was compelling evidence of income divergence in both countries. China and India had experienced fast economic growth during 1980–2005, with the former growing at 9.7% and the latter at 5.9% per year. The urbanized regions had the highest per capita incomes in both countries. But the gap between the rich and poor was much wider in China than in India. The ratio of per capita income of China’s richest region over its poorest was 13.1 while the same ratio was 8.1 in India. In both countries, the richer regions grew much faster than their poorer counterparts. Economic policies in China favoured and reinforced the fast growth of these regions while in India, historical and geographical factors may have held back the lagging regions. Wu analysed the trends in regional disparities in China and India after separating the highly urbanized regions from the rest. It showed that while the disparities between regions remained stable in the 1980s in India, divergence between the two sets of regions began to appear in the late 1980s and early 1990s when a wave of liberalization reforms was launched. In China, there was some evidence of modest convergence in the early 1980s, but in the years that followed, divergence became the rule as was the case in India. The Wu study has also explored the sources of regional disparity in India and China. In his regression model, the dependent variable was the real gross regional product per capita. The independent variables in the model were infrastructure, human capital, urbanization, industrialization and a control variable. For China, the control variable used was the value of exports as a share of the gross regional product. Investment in transport by each state was included as a control variable for India. All these are variables frequently used in growth analyses. Together, these variables explained a large proportion of the variation in regional income per capita. Of the variables, infrastructure and urbanization turned out to be the main sources of regional disparity in both countries. Surprisingly, none of the other variables was found to be statistically significant.

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Démurger (2001) provides empirical evidence on the links between infrastructure investment and economic growth in China. Using panel data from a sample of 24 Chinese provinces (excluding municipalities) during 1985–98, the estimation of a growth model showed that besides differences in terms of reforms and openness, geographical location and infrastructure endowment did account significantly for observed differences in growth performance across provinces. The results indicated that transport facilities were a key differentiating factor explaining the growth gap, and pointed to the role of telecommunication in reducing the burden of isolation. Though China shows evidence of a more severe regional income divergence than India over a period of two decades of fast economic growth, the study notes that the gap is widening more in India than in China. The new slogan of a ‘harmonious society’ (e.g., see Wu et al. 2013) is China’s response to the divergence problem. Taking the case of India, Marjit and Mitra (1996) present a preliminary study on the Indian states. It is observed that the states have been ‘diverging’ rather than converging in terms of their per capita income. Ghosh et al. (1998) confirm that Indian states have been diverging over the period of the last 35 years. Dasgupta et al. (2000), based on their statistical analysis of data for the period 1960–61 to 1995–96, find a clear tendency for Indian states to diverge in per capita SDP, but to converge in shares of different sectors in the SDP. While Jha (2000) finds the coefficient of variation of the rural head count ratio to be rising over time, indicating greater dispersion in rural poverty across states, there is (conditional) convergence (in terms of levels) in inequality and poverty measures across states. The analysis of Nagaraj et al. (2000) used panel data for 17 states for the years 1960–94. The growth regression included, apart from lagged per capita SDP, the share of agriculture, the relative price of agricultural and manufactured goods, several infrastructure indicators and fixed effects for states as explanatory variables. Evidence for conditional convergence was found. The results of the study suggested that focusing investment efforts on physical infrastructure (electricity, irrigation and railways) and social infrastructure (human development) would raise the overall effectiveness of public investment and raise growth.

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Asking the question as to whether regional disparity widened in the post-reform period, Bhattacharya and Sakthivel (2004) analyse growth rates of aggregate and sectoral domestic product of major states in the pre- and post-reform decades. Their results indicate that while the growth rate of GDP improved only marginally in the post-reform decade, regional disparity in SDP widened much more drastically. They find that industrial states are now growing much faster than backward states, and there is no evidence of convergence of growth rates among states. Ghosh (2006) evaluates relative performance of 15 major Indian states on human development, and examines the two-way nexus between economic growth and human development. The estimates of cross-sectional growth regressions provide strong evidence of regional convergence in human development despite considerable divergence in real per capita income, indicating that the poor states that failed to catch up with the rich ones in terms of per capita income, managed to catch up in terms of human development. In a panel data study for 16 Indian states for the period from 1978–79 to 2002–03, Nayyar (2008) found that (a) the states were not converging to identical levels of per capita income in the steady state; (b) once factors that affect steady-state levels of income are controlled for, the poor states grew faster on average than the rich ones; and (c) there was an increase in the dispersion of per capita incomes across states over time. This is indicative of Indian states converging to increasingly divergent steady states, which was attributed to increasing interstate disparities in levels of private and public investment and an insignificant equalizing impact of centre–state government transfers. Raju (2012) is the first study which finds that convergence is markedly evident for all states (non-special category and special category considered together) and non-special category states, while convergence among the special category states is slightly weak. Political scientists too have examined regional income disparities, but from a different perspective. The classic study of the regional disparities in Italy by Robert Putnam (1993) is the most widely known among such studies. As a political scientist, Putnam studied for two decades, along with his Italian colleagues, the process of decentralization that was launched in Italy in the 1970s. The study covered 20 regional governments in both northern and southern Italy. The regional institutions

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of governance that were given greater autonomy were same in both regions. In the early part of the 19th century, the income levels of both the North and South were more or less comparable. But the quality of governance in Northern Italy was generally viewed by many as superior to that of Southern Italy with its feudal past. By 1990, Putnam found that the income levels of the northern regions were substantially higher than those of the southern regions. His search for an answer to this paradox led him to the discovery that the North differed from the South in an important respect, namely, the role played by ‘social capital’ in the northern communities, and its relative absence in the South. He argued that the northern regional governments and communities were able to make better use of the greater autonomy provided by the decentralization reform to generate more economic activities and wealth compared to their southern counterparts that had a much weaker endowment of social capital. History is at the root of this problem. The southern region was dominated by hierarchical institutions and styles of functioning while the North’s city states had a tradition of nurturing associational life. Putnam defined social capital as a mix of societal traits such as trust, reciprocity and density of associational networks among citizens. Reciprocity and trust, when they go beyond the confines of the family, can create an enabling environment for collective action to take place on a larger scale, and reduce the scope for problems such as free riding and tragedies of the commons that tend to stifle collective action. Associational life is seen as critical to the sustenance of mutual trust and reciprocity. When people are members of various voluntary groups such as sports clubs, mutual aid societies, music groups and other ‘horizontal’ civic organizations, opportunities for reinforcing traits such as trust and reciprocity increase manifold. It is this ‘civic mindedness’ that eventually reduces the transaction costs of doing business and the enforcement of contracts. In contrast, ‘vertical’ associations are marked by hierarchical relationships that thrive on dependence, distrust and exploitation. The vibrancy of associational life, according to Putnam, offers a strong foundation for collective action, information sharing, cooperation and mutual support among members of local communities and regions. Historical evidence shows that these civic traditions were much stronger for centuries in the regions of northern Italy than in the South. Hence Putnam concludes that social capital played a critical role in facilitating

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the faster income growth of northern Italy compared to the South after the decentralization reform was implemented. In brief, income divergence occurred in the regions of Italy over a period of two decades, despite the fact that the scope of decentralization reform and the institutions of governance that were given autonomy were same in both the North and the South. What differentiated the two regions was the stronger role played by social capital in the northern regions, enabling the latter to enhance their quality of governance, promote more productive economic activities and increase their wealth and income on a sustained basis. Putnam’s case study of Italy highlights the causal links between social capital, institutional performance and economic prosperity. The lesson from Italy is that social capital can act as a catalyst for better institutional performance. Better performing and responsive public institutions in turn tend to stimulate, expand and sustain productive economic activities. Viewed thus, social capital functions much like physical or human capital in its role to enhance the level and quality of development of a country. The studies reviewed above show that outcomes in terms of convergence and divergence are influenced a great deal by contextual factors such as the initial conditions of regions, the stage of development of the country and the pace of economic growth. Theory does not offer any unambiguous guidance as to the interaction effects of such factors on the outcomes in terms of convergence or divergence. What is striking is that the evidence of convergence seems to exist far more in developed countries with a longer history of growth than in developing countries. It raises the question whether convergence can be expected to manifest itself only after very long periods of economic growth. Over shorter periods, whether periods of slow growth or recession tend to lead to convergence and boom periods lead to divergence is also a question that deserves to be explored more fully. The review of the literature on this subject summarized above shows how most researchers were primarily concerned with examining when a break appeared in the growth rate of Indian states without worrying about why and how the break occurred. The international studies are preoccupied with proving whether convergence or divergence between

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regions had occurred. The present study too examines this issue, but goes further to investigate in depth the underlying factors that led to the eventual outcome. As noted earlier, this study draws upon the insights and methods of several social sciences—economics, sociology, political science, history and management—to derive a better understanding of the factors that might explain regional convergence or divergence in the Indian context. This study takes the view that complex phenomena such as growth, break and convergence invariably depend on a variety of factors that no single discipline is in a position to fully explain. Another problem with existing studies is that they heavily depend on econometric analysis of such phenomena. We do agree that econometrics, which we also adopt for a very small part of the study here, may shed some light on certain underlying factors. But a fuller understanding of growth and convergence may require a careful probing of certain political and historical factors and interventions that mere stochastic relationships, as in regressions, may not reveal. A more comprehensive and multidisciplinary perspective, therefore, is essential for understanding, analysing and interpreting such phenomena, and for deriving policy prescriptions based on the study findings. One other problem with the existing literature as they pertain to Indian regions is that most of them use cross-sectional data for a single point in time. For various reasons, we think that is a very partial approach, since history is critical for understanding of the present. Ignorance of history—i.e., absent or defective collective memory—does deprive us of the best available guide for public action, especially in encounters with fundamental issues such as growth and convergence. This study overcomes these limitations in the existing literature on convergence by taking a holistic and multidisciplinary approach to the debate. It also lends itself to a framework that has been developed, which could be applied and tested in other regions of the country and world. We look at historical factors and time series data for many factors which we consider crucial in determining growth. Further, we go beyond statistics, econometrics and casual empiricism by supplementing the quantitative data with extensive and in-depth interviews. In the next chapter, we present evidence to examine whether the southern Indian states have performed better than the northern Indian states, taking the case of TN and UP first.

3

Has the South Performed Better than the North?

I

n the first phase of our study, we undertook a detailed historical analysis of the performance of one selected southern state and one northern state. It not only made the initial exploration more manageable, but also helped us to test our hypotheses and to experiment with different types of data. The states selected for this exercise were TN in the South and UP in the North. This selection was influenced by the fact that both were metropolitan regions of two large presidencies (Madras and United Provinces, respectively) of the British colonial era, and partly because it was easier to track and understand specific developments and policy changes at the level of an individual state rather than at the level of a region consisting of several states. Both of these states would have had common administrative systems, traditions and policies inherited from the British colonial past. Performance variations arising out of differences on this count were, therefore, likely to be minimal. Similarly, in explaining changes in performance with reference to certain policies or other actions taken in the past, the relevant accounts, data and insights are more likely to be applicable at the state level. Once an analytical framework was designed, tested and fine-tuned based on the data from TN and UP, the same framework was applied to the southern and northern regions as defined in the study. The purpose

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of the second phase was to see whether the same pattern holds good when the relevant states are aggregated and a regional comparison is attempted. If the findings are significantly different, it is clear that generalizations could not be made on a regional basis. At best, the patterns may be similar for some states of the North and South, but not for all. On the other hand, if the patterns and findings reinforce those of the TN–UP comparison, it would lend credibility to our approach and the analytical framework of the present study. How did we define performance for the TN–UP comparison? We have briefly referred above to the claims of some authors that the South is ahead of the North on several counts. Since there was no detailed discussion of analytical frameworks or data in prior work on the subject, it is essential that we begin with a discussion of the framework, criteria and data that we propose to use in order to compare the performance of the selected states. First of all, we recognize that there are many dimensions of development that are pertinent to a comparison of the performance of our states and regions. There are, for example, economic, social, political and cultural dimensions of development, all of which could be relevant to determining how well a state has performed. While all of them need to be identified and measured if we wish to be comprehensive in our approach, it has to be said that not all of these dimensions lend themselves to precise definition and measurement.1 Our framework, therefore, will consider only those dimensions that can be defined, identified and measured in a manner that can make comparisons fair and credible. Second, comparisons will be difficult when the required data do not exist or is incomplete. Hence some dimensions may have to be left out not because of any difficulty in identifying or measuring them, but simply because of a lack of adequate data. For instance, criteria for judging social progress or political development may exist, but the indicators to be used may be partial or the required data may not exist. And the problem may be rendered more difficult as historical data may be incomplete. 1 Cultural features are a case in point. Reasonable people may well disagree on the criteria for measuring cultural progress. Accomplishments in art forms, for example, are often unique and difficult to measure and compare.

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These limitations have forced us to focus exclusively on the economic dimensions of performance for which credible indicators are available, measurement is feasible and historical data exist. It is important to add that the economic indicators used may in turn reflect social or political factors and developments as we shall demonstrate below. Hence our inability to directly measure non-economic phenomena need not imply a complete lack of attention to such factors in the present study.2 Based on this approach, we examined the economic performance of TN and UP over the past nearly four decades, using two widely accepted criteria for economic evaluation, viz., measures of per capita income and poverty. While we recognize that well-being is a broader concept, in the interests of measurability, we use per capita income as a measure of the economic well-being and standard of living of the people.3 The proportion of population below the poverty line tells us, at least in part, how fairly income is distributed within a given society.4 It is also a measure of the extent of the socio-economic deprivation that exists in society. Taken together, these two measures provide us a succinct and balanced assessment of the economic progress of the two states. The availability of official data on these two performance outcomes for a long period enables us to see how the development of the two states has evolved over time. It can tell us, for example, whether the performance gap between the two states has narrowed or widened over time. A comparison of the rates of growth of the NSDP of the two states from 1960–61 to 2004–5 yielded a surprising result.5 Over most of this long period, the growth rates of the two states did not differ much at all. Until the 1980s, growth was slow in both states. Growth rates increased subsequently, but at about the same pace (105% over a 15-year period). UP, with its larger size and population, had a much

Towards the end of this chapter, we do examine the status of other development outcomes alongside the primary economic measures. 3 The time series used for analysis of per capita income is from 1960–61 to 2004–5. Appendix 2 contains the data sources we have accessed. 4 The time series used for analysing poverty is from 1973–74 to 2004–5. 5 Appendix 3 presents detailed data for TN, UP and selected states of the study that underlie several figures and tables in the book. 2

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larger NSDP (`2,469,980 million compared to `1,773,350 million for TN [at 1999–2000 prices] in 2006–7). A superficial reading of this finding may lead one to conclude that there was no real difference between the two states as far as overall economic growth performance is concerned. A comparison of NSDP, however, does not take into account the changes in the population growth rates that have taken place in the two states. Changes in the size of the labour force and its productivity are determinants of NSDP. Per capita NSDP is the performance measure that takes into account these factors. We present the historical trends in per capita NSDP in UP and TN in Figure 3.1, which shows that in 1960–61, TN had a per capita income of `5,053 while UP had a per capita income of `3,338. TN was ahead on this score by 51%. In the early 1980s, this gap had narrowed to 39%. By 2005–6, however, the gap between the two states in terms of per capita income had widened significantly to 128%. Figure 3.1 also tells us that the widening of the gap began after the mid-1980s, and became more pronounced since 1992–93. Based on this analysis, we conclude that judged by per capita income, TN was always ahead of UP by a modest margin, but that TN had moved far ahead of UP by 2005 (50% versus 128%). The economic gap between the two states has, thus, widened significantly in recent years. The divergence began in 1987–88 and accelerated from 1992 to 1993. Figure 3.1 Per Capita NSDP—TN and UP—in Constant 1993–94 Prices

Sources: EPW Research Foundation; authors’ computations.

1995–96

1990–91

1985–86 UP

2000–2001

TN

1980–81

1975–76

1970–71

1965–66

1960–61

16,000 14,000 12,000 10,000 8,000 6,000 4,000 2,000 0

Has the South Performed Better than the North?

27

Figure 3.2 Poverty Ratios for TN and UP 60.00 50.00 40.00 30.00 20.00 10.00

Average weighted poverty, TN Average weighted poverty, UP

0.00 1970 1975 1980 1985 1990 1995 2000 2005 2010 Sources: Planning Commission; authors’ computations.

The poverty ratios depicted in Figure 3.26 shed light on another equally important facet of economic performance. A comparison of the two states on this score shows that during the 1970s and until about 1985, TN was actually at about the same level as or perhaps worse than UP as far as the extent of poverty is concerned. In fact, Datt and Ravallion (1998) report nearly 70% rural poverty for TN in 1960 compared with only about 48% rural poverty rate for UP. This suggests that economic deprivation and inequality were much higher in TN earlier on, but that it made a surge in terms of reducing them rapidly at some later point. According to the Government of India’s Economic Survey for 2012–13, the poverty rates 6 It should be noted that the poverty rates depicted in Figure 3.2 are weighted total poverty rates—based on rural and urban poverty rates that were obtained from the Planning Commission. We are aware of the problems with the reliability and methodology of official data and the ongoing debate on poverty ratios. However, for lack of a better data source, we use these readily available estimates. The rural and urban poverty rates were weighted with rural and urban populations for the states and respective years to arrive at the total poverty estimate for each year and state. In fairness to UP, for 2004, the poverty (both rural and urban) data for Uttaranchal has been accounted for, in the interests of comparison with earlier years when UP was undivided.

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for both TN and UP for 2009–10 (based on the Tendulkar committee) were at 39.4% (rural poverty) and 31.7% (urban poverty) for UP and only 21.2% and 12.8% for TN (rural and urban poverty, respectively). When we look at poverty rates (rural and urban) state-wise for UP and TN, for 2004–5 based on the Tendulkar committee, the gap still is quite glaring at 37.5% (rural poverty) and 19.7% (urban poverty) for TN and 42.7% (rural poverty) and 34.1% (urban poverty) for UP. The combination of a higher level of income and more widespread poverty that is found in TN compared to that in UP signals a more unequal distribution of income in the former early on. But by the 1990s, a significant change in this combination seems to have occurred in TN. By 2005, not only did TN’s per capita income exceed that of UP by a much wider margin than before, but its poverty ratio had also declined well below that of UP. In other words, TN’s rising per capita income has been accompanied by a significant reduction in the extent of poverty in the state. We can now state our finding from this analysis of two important performance outcomes, namely, per capita income and poverty, with respect to TN and UP over the period from 1960–61 to 2004–5. The two states were not far apart at the beginning of this period. In fact, TN was worse off in respect of poverty than UP though its per capita income level was somewhat higher than that of UP. A baseline report on poverty and social monitoring in UP by the State Planning Institute there found that while the poverty rate was 16.7% in 1973–74, it had increased to 18.9% in 1993–94. By 2005, TN had stolen a march over UP on both counts. Its per capita income was higher by 128%, as discussed earlier, and its poverty ratio had fallen by 59% compared to the corresponding outcomes (reduction of 42%) for UP. It is this dramatic improvement in terms of economic performance that has put the spotlight on emerging North–South divide in India. It is a paradox that we could not have predicted this outcome based solely on the track record of the two states over the period 1960–85. It is instructive to examine which sector/s led the surge in per capita NSDP that we observe in TN. Figures 3.3–3.5 show the trend in the composition of NSDP by sector (respectively agriculture, industry and services) in two states. While as far as the share of agriculture goes,

29

Has the South Performed Better than the North? Figure 3.3 Share of Agriculture in NSDP—TN and UP 70.0 60.0 50.0 40.0 30.0 20.0 10.0 0.0

2005–6

2002–3

1999– 2000

1996–97

1993–94

1990–91

1987–88

1984–85

1981–82

1978–79

1975–76

1972–73

1969–70

1966–67

1963–64

1960–61

TN UP

Sources: EPW Research Foundation; authors’ computations. Figure 3.4 Share of Industry in NSDP—TN and UP 40.0 35.0 30.0 25.0 20.0 15.0 10.0

Sources: EPW Research Foundation; authors’ computations.

2000–2001

1996–97

1992–93

1988–89

1984–85

1980–81

1976–77

1972–73

1968–69

1964–65

1960–61

0.0

2004–5

TN UP

5.0

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Figure 3.5 Share of Services in NSDP—TN and UP 70.0 60.0 50.0 40.0 30.0 20.0

2000–2001

1996–97

1992–93

1988–89

1984–85

1980–81

1976–77

1972–73

1968–69

1964–65

1960–61

0.0

2004–5

TN UP

10.0

Sources: EPW Research Foundation; authors’ computations.

UP has always been ahead of TN, in the share of industry, TN scores well over UP for all the years except last couple of years.7 It did seem that during the last few years, the share of the industrial sector in UP has caught up with that in TN and surpassed it. It should, however, be remembered that the industrial base of TN has always been a dominant one—with examples of automobiles (Chennai) and textiles (Tiruppur). The trajectory of the service sector in the two states is an interesting one. Until 1980–81, the two states were more or less identical as far as the service sector share is concerned. However, post 1981, the service sector in UP declined in its share in NSDP when compared with that in TN where there was a constant increase in the share of the service sector in its NSDP (Figure 3.5). So there are grounds to believe that the service sector led the surge in per capita incomes in TN. This is consistent with the national growth story. It is possible that growth in information technology services explains a part of this surge. This might refer more to the surge of the service sector rather than a decline of industry in TN. 7

Has the South Performed Better than the North?

31

Did a similar transformation occur over time in other states? An analysis of the per capita income levels and poverty ratios of other high performers such as Punjab, Haryana, Gujarat and Maharashtra shows that they were always much better off than UP. For instance, in 1980–81, their per capita incomes were respectively `8,442, `7,506, `6,455 and `7,102 compared with only `3,825 for UP. Not only were their income levels higher, but their poverty ratios were much lower than that of UP. That these high performing states continued to do better in 2005 is not newsworthy. But TN’s surge forward is indeed news because it was not in the same league as these high performing states some 40 years ago. A deeper probe into this phenomenon is clearly in order. The big question is what accounts for this dramatic transformation. Experts have offered a wide range of hypotheses to explain the phenomenon of the economic transformation of countries. Their early models focused primarily on the role of capital as the proximate factor that led to economic growth. The post-World War II experience with the reconstruction of Europe via the Marshall Plan convinced many observers that the injection of capital would lead to the revival and acceleration of economic growth. Some experts even specified the rate of investment necessary for countries to reach the ‘take-off’ stage of development (Rostow 1960).8 But the experience of many developing countries that followed this approach pretty soon demonstrated that investment of capital does not automatically lead to the desired rate of a country’s economic growth. In fact, a number of empirical studies showed that investment of capital explains only a part of the growth differentials between countries. The unexplained ‘residual’ was often attributed to ‘technical progress’, a mix of factors that included technology and other influences that tended to enhance productivity, but were difficult to untangle and measure. Romer (1986) presents a model of endogenous technical change in which the accumulation of knowledge by forward-looking, profit-maximizing agents drives long-run growth. Knowledge is an intangible capital input in production with increasing marginal productivity. New knowledge is a product of research technology exhibiting diminishing returns. Further, the production of new knowledge by one firm generates a positive 8

Walt Rostow, the MIT economic historian, wrote an entire book on this theme.

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THE PARADOX OF INDIA’S NORTH–SOUTH DIVIDE

externality for other firms as knowledge cannot be kept secret. The creator can capture only a part of its benefits; a part is available to others without compensation to the creator. Using these assumptions, Romer demonstrates that growth rates can be increasing over time. He presents long-run evidence in support of his model of growth. In more recent years, measures of human resource development (HRD), the stock of different types of infrastructure, have been brought in as explanatory factors, thus giving greater specificity to the unexplained ‘residual’. Human resources such as skilled labour are complementary inputs that work with physical capital to produce goods and services. Infrastructure such as power and roads are essential factors without which capital will not be attracted to countries. Even if capital is invested, infrastructure gaps tend to reduce the productivity of capital. There is also a growing realization that non-economic factors such as the quality of public governance that obtains in a country have an influence on economic growth, although studies that have incorporated such variables are very few. It is government that generates public goods and creates an enabling environment for the productive use of both capital and labour. If a country’s government provides greater political stability, investors are likely to consider its policies to be more stable and predictable, and hence to respond more positively to the country as an investment destination. Similarly, if law and order, and dispute resolution are better in a country, it tends to create a more enabling environment for economic activities. If a government is seen to be more efficient in the creation of public goods such as infrastructure, the chances are that the incentives to invest in the country will be stronger. In brief, there is a greater awareness today that economic growth and progress of countries depend on both economic and non-economic factors that provide the triggers and an enabling environment for the growth process to be sustained. The incorporation of all these factors into explanatory models of growth, however, has not been easy. And this limitation applies not only to models explaining macroeconomic growth at the national level but also at the sub-national level. In general, explanatory studies have remained partial in their scope mainly because trend in the literature has been to use variables that are easier to identify and measure, and to ignore factors that are qualitative and difficult to quantify. Thus, much

Has the South Performed Better than the North?

33

attention may be given to familiar factors such as the market system’s stability, but not to the stability and functioning of the governance system. In the context of our study, this point assumes a special significance. In a federal system, policies affecting the functioning of the market, monetary and trade policies will be more or less common to all states. The factors that set apart one state from another will be governance and related features that are state specific. Skilled labour and technology can be imported. Substitutes can be found to make up for infrastructure gaps. Power shortage can be relieved through the use of generators or of a national grid. Railways may make up when roads fail. But political regime change is not an option for investors! If political instability or law and order problems are more severe in one state, all that economic actors can do is to plan to invest or operate in another state that has better governance. They have no option in the short run but to live with the quality of governance available in the state once they decide to invest there. Viewed thus, governance systems and practices are the least mobile of the factors considered here. While our understanding of the factors that contribute to or cause changes in economic growth has improved somewhat, the same cannot be said about the progress being made in specifying and measuring these factors. Comprehensive measures of the factors are not easy to craft. HRD and infrastructure, for example, have different components and facets. Literacy, higher education and institutional quality are pertinent to HRD. But data pertaining to all of them may not exist or may be difficult to combine in order to get a comprehensive measure. Governance tends to be ignored mainly because it has multiple dimensions, the quantification of which is extremely difficult. Analysts, therefore, end up using indicators and partial measures of the basic variables involved. Often they have no option but to make use of only factors for which the required data is available. There is a hierarchy of factors that impact economic performance. We propose to divide these factors into two categories: proximate and foundational. Proximate factors include those that are believed to have a close and immediate influence on the outcome, namely, economic performance. Literacy, health, public spending and infrastructure are examples of proximate factors. Foundational factors are broader factors that create an enabling environment for improved performance. The multiple

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THE PARADOX OF INDIA’S NORTH–SOUTH DIVIDE

dimensions of governance, such as the rule of law, are foundational in nature. The long-run movement of proximate factors can be impacted by foundational factors. A country or state with oil wealth or foreign aid, for example, may expand its infrastructure significantly. Its ability to effectively use the infrastructure, however, may still be determined by the quality of the state’s governance. On the demand side, pressure from citizens for better governance might also influence the state’s ability to govern effectively. In the short run, proximate factors could act as important determinants of performance, but their influence could be diminished in the long run if foundational factors fail to reinforce them. It is for this reason that most investors tend to assess both sets of factors in the context of their long-term planning and strategic decisions. In the present study, we propose to take into account both proximate and foundational factors (which are elaborated further) pertinent to longterm performance. Despite the numerous comparative studies of inter-country and interstate growth performance, mainly through cross-section analysis, we still have no satisfactory theory or model that fully explains these phenomena. A major limitation of such models is that they focus on a snapshot of these phenomena for regions or countries at a single point in time, ignoring influences that impact economic development over a long period of time.9 Part of the problem lies in the difficulty in analysing and quantifying the historical, institutional and contextual factors that interact with the proximate variables that economists often use in their econometric analysis and in specifying the lead–lag relationships that may exist among these factors. We believe, therefore, that it is more productive to explore in some detail the likely factors and their underlying processes that may have influenced the performance outcomes over time, rather than to work with incomplete models that fail to capture the complexity of the phenomena involved. The next chapter explores the reasons for the North–South divide, taking again the case of TN and UP, first. Basu (2009) points out that the role of social norms, institutions and culture has been ignored by economists in understanding economic development. He says that ‘...markets, trade and incentives are critically important for an economy to prosper but so are social norms, institutions and the state’ (pp. 43–44). 9

4

What Explains the North–South Divide?

A

s noted above, a study of the growth performance of TN and UP over the past four decades shows that significant divergence between them in terms of per capita income and poverty incidence occurred only in the recent past. A perusal of Figure 3.1 points to 1987–88 as the period when a noticeable upward shift in per capita income took place. A review of Figures 3.3–3.5 shows that the beginning of the 1980s was about the time when the service sector in TN surged compared with that in UP. It is during the same decade that poverty incidence of TN fell below that of UP (Figure 3.2). If we can identify the factors underlying this shift that began in 1987–88, we are likely to find an explanation of the phenomenon. But first, we need to establish the facts of the case. The primary focus of our analysis is on the changes in trends in per capita income, both because it is a widely accepted summary measure of economic performance and because we have a longer and more reliable time series for this variable.1 These trends may have changed over time for two reasons. First, the initial state of the contributory or causal factors may have been at different levels for the two states. Second, changes over time in these factors may have occurred at different rates in the two states.

1

Poverty reduction has also moved in the same direction during this period.

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THE PARADOX OF INDIA’S NORTH–SOUTH DIVIDE

We start by listing the categories of factors which may have contributed to the observed income divergence between the two states: 1. 2. 3. 4. 5. 6.

Human resource capabilities Urbanization Basic infrastructure, which enables economic growth to occur Efficiency of resource utilization Governance including political stability, and law and order The demand factor arising out of social mobilization.

The first four factors highlighted above are proximate factors, while the last two are foundational in nature. Some overlap between proximate factors is unavoidable. There are multiple indicators of human resource capabilities. We take into account measures of human resource capabilities which indicate education/skills and health. In education, we study the literacy rate, proportion of graduates and trained manpower. In health, we study infant mortality rate (IMR), life expectancy and the total fertility rate (TFR).

Literacy Rate Literacy rate can be expected to positively affect economic growth and per capita income in the states primarily because it is treated as a proxy for the knowledge and skills of the population. Our assumption is that a higher literacy rate makes people better informed, prepares the ground for higher skills, the ability to deal with technology, and enhances their efficiency at work.2 These capabilities enable them to generate more output and income. Figure 4.1 compares the literacy rate between TN and UP over a reasonably long period of time, during 1961–2011. Phenomena like the literacy rate are stable over short periods of time. Figure 4.1 shows that Literacy rate might be a crude measure of the population’s ability to read and write. A more precise measure to this may have been to substitute literacy with primary-level education. We found that trends in this variable also follow a similar trend as we find with the literacy rate. 2

37

What Explains the North–South Divide? Figure 4.1 Literacy Rate—TN and UP 90 80 70 60 50 40 30 20 10 0

TN UP 1960–61

1970–71

1980–81

1990–91 2000–2001 2010–11

Sources: Census of India; authors’ computations and analyses.

TN’s literacy rate has always been at a higher level when compared with that of UP. We reviewed the trends in the female literacy rate for the two states and the results are identical. TN started off with a much higher female literacy rate (21.27%) in 1961 increasing to 80% in 2011, when compared with that for UP (only 8.43% in 1961) which increased to 70% in 2011. We surmise that the steady growth of literacy rate and the female literacy rate may have been an important factor that enabled TN’s economic growth to accelerate in later years. Appendix 4 contains a description of the roots of education in TN and the history of elementary education in that state. Of course, there is no doubt that UP’s growth rate has also improved since 1998 and we expect this to contribute to its convergence.

Graduates The proportion of graduates (defined as those with university and postgraduate, engineering, medicine, agriculture, veterinary and dairy, technology, teaching and other degrees), as a proportion of population above 15 years of age, is expected to affect economic growth and per capita income positively. The proportion of graduates reflects the percentage

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THE PARADOX OF INDIA’S NORTH–SOUTH DIVIDE

of population that has attained a certain threshold level of education which equips them with certain skills used in specific kinds of economic activity. Hence, an increase in the proportion of graduates (including those with technical skills) enables them to contribute to increased output and income and, hence, become an engine of economic growth. When we examined the proportion of graduates for TN and UP during 1971–2001, we found no specific advantage that TN had over UP. In fact, UP had a higher proportion of graduates at the beginning and end of the period. This shows that TN was not always superior to UP with respect to all the factors indicating human capabilities. However, despite this, we observe rising per capita incomes in TN especially (as shown by Figure 3.1). This is the phenomenon we try to explain with the help of other factors further.3

Technical Education Productive sectors such as industry, mining and agriculture require specialized manpower that meets their needs. We made an attempt to examine the status of this talent pool, over and above general graduates, in the A major reform that was adopted in TN during the early and mid-1980s was in secondary education, whereby the 11+1+3 system (11 years of schooling plus a year of pre-university, followed by three years of university) was replaced with the 10+2+3 system. On 1 July 1978, the government of TN, de-linked the oneyear pre-university course from the control of the universities and introduced the 10+2+3 pattern of education, replacing the 11+1+3 pattern followed earlier. Select ‘high schools’ were converted into ‘higher secondary’ schools, offering the Higher Secondary Certificate (HSC) course that continues to be known as ‘+2’ to this day. This is based on the national pattern of 10+2+3 years with 12 years of schooling. The new 10+2+3 system had the advantage that adolescent students would be in schools longer than was the case earlier and not have to be dislocated from their schools (which is needed). Further, the teachers who were surplus as a result of the abolition of 1 year of pre-university, were sent to do higher studies for which additional seats were created in higher educational institutions. This then bred the advantage that teachers became post-graduates. This system also encouraged teachers to move to rural areas where a mid-tier system of schooling and human capital was created. 3

What Explains the North–South Divide?

39

two states. The proportion of technical labour force can be considered as an indicator for the supply of trained manpower that has the potential to increase output and incomes. We examined the proportion of those enrolled in technical courses such as B.E./B.Sc. (engineering/B.Arch.), medicine, dentistry, nursing, pharmacy, ayurvedic and unani, B.Ed. and B.T. as a proportion of population in the relevant age group (above 15 years) to examine if TN had an edge in this regard compared with UP. It can be considered a measure for the output of technical manpower in the state. We had, for some recent years, data on the proportion of technical student enrolment for TN and UP. We find that though in terms of the proportion of graduates TN does not have an edge over UP, in terms of technical enrolment, TN is well ahead of UP for the recent years for which we had this data. This certainly lends credence to the fact that technical manpower has increased significantly in TN mainly because the state has encouraged the setting up of engineering and other technical colleges in a big way in recent years. It would have been instructive to have this data for earlier time periods covering the 1980s, but it was not available. Given the data limitations, we examined the proportion of technical graduates in disciplines such as engineering, medicine, agriculture, veterinary and dairy, technology, teaching and other degrees at the beginning of the period, 1961, and the proportion of students enrolled at the end of our period, 2004, in each of our two states.4 Figure 4.2 presents this comparison for TN and UP. It shows that while TN was marginally better in terms of technical graduates in 1961, the percentage of TN’s students enrolled in technical courses (as a proportion of population above 15 years of age), however small, were well above their counterparts in UP, in 2004. Technical graduates, due to their specific technical skills, must have contributed to TN’s edge in economic growth. We conclude that TN’s superiority over UP in several education indicators has played a significant role in the higher growth rate achieved by the former.

The assumption we make in this comparison is that the proportion of graduates to those enrolled will remain about the same. 4

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THE PARADOX OF INDIA’S NORTH–SOUTH DIVIDE

Figure 4.2 Percentage of Technical Graduates,* 1961 and Technical Enrolment, 2004—TN and UP 2004, TN, 0.21

1961, TN, 0.08 1961, UP, 0.04

2004, UP, 0.04

1961

2004

Sources: Census of India; authors’ computations and analyses. Note: *The percentage is a proportion of population greater than 15 years of age.

Infant Mortality Rate Next we review the relative (health-related) human capabilities of the population in TN and UP. Good health and population control can enhance the productivity of the people. We examine a selected indicator of human capabilities—the IMR. While there are multiple indicators of health, the reason why we choose IMR is because it can indicate the low level of health care services, morbidity, ignorance of good health practices, poor maternal health as well as poor family health overall. Time and again, empirical studies have brought out the finding that hospitalization is one of the most important reasons for indebtedness and abject poverty, especially so in rural areas (see George 2009). Hence, we assume that states which have lower IMRs are healthier. A healthy population is capable of producing more output and income. However, Ashraf et al. (2008) find that the effects of health improvements on income per capita are substantially lower than those quoted by policy makers and may not emerge at all for three decades or more after the initial improvement in health. Figure 4.3 summarizes the three years’ moving average IMR of the population in TN and UP from 1971–73 to 1993–95. It shows that TN’s

What Explains the North–South Divide?

41

Figure 4.3 IMR for TN and UP Infant mortality rate—three years moving average 200 150 100 50 0 1971–73

1976–78

1981–83

Infant mortality rate TN

1986–88

1991–93

Infant mortality rate UP

Sources: Census of India; authors’ computations and analyses.

IMR has always been lower than that of UP, although the disparity in this factor has been slowly declining since the late 1980s/early 1990s. This implies that TN had one more precondition ready for its economic growth to take off, having a healthy population enjoying lower infant death rates, conducive to the promotion of economic growth.

Life Expectancy Life expectancy is another key indicator of human resource capabilities, which, it should be recalled, is used in the computation of the HDI. Life expectancy is the expected (in the statistical sense) number of years of life remaining at birth. Factors that are associated with determination of life expectancy include economic status, education, environment, climate and health care. We note that higher economic growth is caused by the same factors which contribute to higher life expectancy. When we reviewed the trends in life expectancy at birth for TN and UP (Figure 4.4), we found that TN was always ahead of UP, lending credence to the fact that TN was prepared in terms of its human capabilities for economic growth to take off.

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THE PARADOX OF INDIA’S NORTH–SOUTH DIVIDE

Figure 4.4 Trends in Life Expectancy—TN and UP 80 70 60 50 40 30 20

TN UP

10 0 1971

1981

1991

2001

2011

Source: Registrar General of India.

Total Fertility Rate It is instructive to examine the TFR for TN and UP to examine if TN’s fertility rate has been lower than that of UP. Why is it that a lower TFR is good for economic growth? It is because of its effect on the age distribution giving rise to the ‘demographic dividend’. As fertility declines, the proportion of children in the population declines and the proportion of population in young working age groups increases. As a result, the child dependency decreases and the number of young workers increases. Both these trends are highly conducive to the economic growth as it results in a decrease in consumption and increase in production. China is reaping the demographic dividend right now. Kerala and TN have just begun to reap this dividend. It will be some years before UP could realize this dividend. But it will, in another 20 years. We suggest that the better performance of TN in economic growth is partly due to its earlier start-up in demographic transition. TN had a very

What Explains the North–South Divide?

43

effective family planning programme for quite some years. Its fertility rate started declining very much earlier than in UP, being much lower than that in UP. We examined the fertility rates of the population for TN and UP over time during 1971–97. These data indeed lend credibility to the fact that TN’s fertility rate and natural growth rate of population were both always lower than those of UP during this entire period, which testifies to the successful adoption of family planning methods by TN. While lower population growth implies less human resources to produce output, if TN’s per capita income grew rapidly despite the slowdown in its growth rate of population, then it must have been the case that the TN population’s productivity was higher, possibly reflecting the impact of its rising literacy and associated skills. A major factor which helps to explain TN’s economic ascendancy in the late 1980s/early 1990s is its investment in human resources, primarily in health and education, in the post-1960s.5 We noted that no major investments were made in the state after 1960s on dams or irrigation for example. The investment in human capital was dominant and in two relevant sectors—health and education. In health, the various programmes focused on a family welfare programme, rather than merely on family planning.6 There was a steep rise in sterilizations, and the birth rate was controlled during 1980s. Hence the TFR declined, even well below that of Kerala. The other part of the investment in human capital was in health-cumeducation during the 1980s. A big part of this story was the mid-day meal scheme initiated by the then Chief Minister (CM) M.G. Ramachandran’s regime. The focus in this programme was not just feeding, but on nutritional improvement. There was a regular practice for doctors visiting government pre-schools to monitor the nutrition intake amongst children to ensure that there was the right combination of carbohydrates, proteins and vegetables. The local population was involved in the programme whenever it was possible, and their suggestions were included to make

5 Based on discussions with Dr P. Umanath, a current civil servant, and Mr P.V. Rajaraman and Mr R. Poornalingam, former civil servants. 6 This is based on discussions with Mr P.V. Rajaraman.

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THE PARADOX OF INDIA’S NORTH–SOUTH DIVIDE

Figure 4.5 Young Working Age Population (15–29 years) as a Percentage of Children below 15 Years 120

105.9 92.4

100 80 60

77.8 69.0

67.3

60.1

54.6

61.7

56.9

60.6

40 20 0

UP TN 1961

1971

1981

1991

2001

Sources: Census of India; authors’ computations and analyses.

the programme locally relevant. This model was subsequently replicated across the country. There was also a programme for girl children in the 1980s and 1990s which included the provision of a grant if the girl child completed 10th standard. The Public Distribution System (PDS) in states such as TN is also universal, not targeted as it is in other states. This may have contributed to the nutritional advancement of the poorer segments of the population. As a result of the population policy and its efficient implementation, the proportion of children in TN is much lower than that in UP. The proportion of population in the 15–29 age groups in TN is much higher than that in UP. We made some calculations for TN and UP for the young working age population (15–29 years) as percentage of children below 15 years. The results are summarized in Figure 4.4.7 Figure 4.5 shows 7

We are grateful to K.C. Zachariah for this figure.

What Explains the North–South Divide?

45

that the burden of the child population is far less in TN than in UP. This is a clear reflection of the total fertility decline in TN when compared to that in UP. Mehrotra (2006), after analysing the data from two National Family Health Surveys (1992 and 1999), addresses the reasons why UP’s social indicators, including the health and education status of the lower castes, are much worse than in TN. He points to the fact that the state government of TN has shown a remarkable initiative in its health policies. He states that ‘...the state (TN) is better prepared than most others in implementing many components of the reproductive health programme that India launched in October 1997’. Everything said and done, we need to remember that demographic dividend is a passing phase that is beneficial only for a while. While TN is enjoying it now, UP may experience the dividend later.

Urbanization There is a lengthy literature that attempts to explain the relationship between urbanization and economic growth. The reason why we expect urbanization to positively affect per capita incomes is that there are agglomeration, scale economies, easier access to new technologies and increased productivity which accrue to firms in cities. Thus, while urbanization in general has a positive impact on economic growth, we do observe that urbanization has now been occurring more rapidly in countries that have relatively lower levels of per capita income. Cohen (2004) attributes this to the reason that urban change is now more closely related to changes in the global economy than ever before. Hence, the causation between urbanization and per capita income is not a simple one-way relationship. Higher per capita income also promotes higher urbanization because of the population’s desire to enjoy higher standards of living and better quality of public services. While the above discussion highlights that there is a more complex relationship between urbanization and per capita income, we reviewed the urbanization levels of TN and UP over a long period of time to understand the trends. Figure 4.6 summarizes the trends in urbanization for

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THE PARADOX OF INDIA’S NORTH–SOUTH DIVIDE

Figure 4.6 Percentage of Urban Population—TN and UP 60 50 40 30 20 TN UP

10 0 1971

1981

1991

2001

2011

Sources: Census of India; authors’ computations and analyses.

TN and UP during 1961–2011. The finding from this figure comes as no surprise since we observe that TN has always been ahead of UP as far as urbanization is concerned. Moreover, since 1991, TN’s urbanization has taken off at a rate greater than that of UP’s, with a marked upward shift occurring only in the 1990s. According to census 2011, TN is one of the most urbanized states in the country at 48%, with UP’s urbanization at 22%. TN’s climatic conditions and natural resources are partly responsible for the greater degree of urbanization we observe in the state.8 TN has been a rain-starved state with limited scope for irrigation. Hence, most of the state’s economy has been non-agrarian which is one criterion based on which the Census of India defines urban areas.9 Further, in TN, This is based on discussions with a current civil servant, Dr P. Umanath. It may be recalled that the Census of India defines urban settlements as those having the following characteristics. 1. a population of 5,000 or more; 2. a minimum density of 1,000 people per square mile or 400 persons per square kilometre; 3. at least 75% of work force outside agriculture. 8 9

What Explains the North–South Divide?

47

trade became the driver of growth leading to centres of trade turning into cities. The colonial government laid the road and railway network. Based on the census definition, UP was only 22% urban as of census 2011. This might be related to the fact that UP’s lands are very fertile, and are highly suited for productive agriculture. Based on our discussions, we surmise that while a majority of towns in TN would be designated on the basis of the non-agricultural employment criterion, a majority of towns in UP would be designated on the basis of the population criterion. TN’s early network of railways also contributed to the development of towns. This strongly implies that its higher level of urbanization, to begin with, must have been a contributing factor to TN’s higher per capita incomes, which in turn led to increased urbanization.

Infrastructure Good infrastructure is necessary not only for increasing the quality of life, but also for increasing productivity and output. Among the critical infrastructure investments that matter for all sectors are electricity, roads and telecommunications. We review measures of critical infrastructure such as installed capacity (for electricity), road length and tele-density for the states to compare their contribution to economic growth. All sectors depend upon electricity. Installed generating capacity represents the potential to provide energy to the various sectors of the state. Roads represent connectivity to markets and mobility to take up employment. Similarly telecommunication infrastructure is essential for connectivity, reducing transaction costs and increasing organizational efficiencies. The literature conclusively shows that tele-density has a positive impact on growth. A number of researchers have hypothesized that telecommunication infrastructure lowers both the fixed costs of acquiring information and the variable costs of participating in markets (Norton 1992). They point out that as such infrastructure improves, transaction costs decline and output increases for firms in various sectors of the economy. Sridhar and Sridhar (2007) found positive impacts of mobile and landline phones on national output, when controlled for the effects of capital and labour.

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THE PARADOX OF INDIA’S NORTH–SOUTH DIVIDE

Figure 4.7 Installed Generating Capacity (per million population)— TN and UP 120.00 100.00 80.00 60.00 40.00 20.00 0.00 1960

1967

1974

1981

1988

1995

2002

IC per million population, TN IC per million population, UP Sources: Central Electricity Authority; authors’ computations and analyses. Note: IC: Installed capacity.

Figure 4.7 compares the installed capacity (for generating electricity) per million population for TN and UP for a long period of time (1960 to 2004) to examine their preparedness for economic growth to take off.10 We find TN’s installed capacity started at a much higher level in the 1960s than that of UP’s. The gap widened a great deal since the 1980s. Beginning from the late 1980s onwards, TN’s installed capacity generation took off while UP’s declined. There are reasons to believe that there was a boom in wind energy which explains this growth in installed capacity. Next, as discussed earlier, we compared road length in UP and TN to assess the enabling factors for economic growth to take off. Figure 4.8 compares road length in TN vis-à-vis that in UP for the period from 1970–71 to 2001–2 (it is a moving average). The road length per thousand population has always been higher in TN than in UP, which testifies to the market and employment opportunities available to residents For the years 2001–4, we have taken into account the data for Uttaranchal to make the data for undivided UP comparable to that during the pre-2000 period. 10

What Explains the North–South Divide?

49

Figure 4.8 Road Length in TN and UP All roads length/thousand population ('000 metres)

1970–71 1971–72 1972–73 1973–74 1974–75 1975–76 1976–77 1977–78 1978–79 1979–80 1980–81 1981–82 1982–83 1983–84 1984–85 1985–86 1986–87 1987–88 1988–89 1989–90 1990–91 1991–92 1992–93 1993–94 1994–95 1995–96 1996–97 1997–98 1998–99 1999–2000 2000–2001 2001–2

4 3.5 3 2.5 2 1.5 1 0.5 0

TN

UP

Sources: Centre for Monitoring Indian Economy (CMIE); authors’ computations and analyses. Note: The decline in road length in TN from 1991–92 onwards is due to reclassification of roads.

of the state. Investors would have been attracted more to TN than to UP on this score alone. This lends credence to the fact that TN had many preconditions necessary for economic growth, most of which were weaker in UP. Figure 4.9 compares the telephone penetration (consisting of both landline and mobile phones) for TN and UP for the period for which the data were readily available, 1999–2004.11 Even for this relatively recent period, TN’s telephone penetration is higher than that of UP. It is possible that historically also (if we had had the data) UP scored over TN as far as telephone penetration is concerned (quite unlikely). Even if this were to be the case, more recently, it is evident from Figure 4.9 that TN has taken over. Thus, in terms of all the infrastructure indicators, UP was considerably behind TN for a long period. We found that the telephone data for UP did not change post-2000 even after the creation of Uttaranchal because it continued to operate as UP-West circle, while the rest of UP operated as UP-East. 11

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THE PARADOX OF INDIA’S NORTH–SOUTH DIVIDE

Figure 4.9 Tele-density—TN and UP 16.00 14.00 12.00 10.00 8.00 6.00 4.00 2.00 0.00 1999

2000

2001

Tele-density, TN

2002

2003

2004

Tele-density, UP

Sources: Telecom Regulatory Authority of India; authors’ computations and analyses.

Resource Utilization and Efficiency of Resource Utilization Investment is a necessary but not sufficient condition for growth to take place. The efficiency with which investments are utilized also matter for growth. Two important sectors that use vast public resources are agriculture and social sectors such as education and health. Though agriculture deals with multiple crops, we focus here on food crops and their yield as a measure of efficiency. We also examined development expenditure in the two states. We examined several measures of resource utilization which show to what extent the states have been able to efficiently use their resources. One measure we choose is the agricultural output of the two states. Agricultural output demonstrates the utilization of land, water and plant resources of the states by the private sector (farmers). The assumption is that the higher the utilization of these resources, the greater the impacts

What Explains the North–South Divide?

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on economic growth, output and income. Land is a scarce resource. Its utilization and productivity in the rural sector, where the majority of the population lives, reflect efficiency, income and employment generation for the poor. Growth of per capita agricultural production for the rural population is a good indicator of both these outcomes. Agriculture and allied activities take place in rural areas and the rural population is the primary beneficiary of the incomes and employment generated through these activities. Datt and Ravallion (1998) attribute rural poverty reduction among Indian states to differing growth rates of farm yield per acre. Figure 4.10 presents the differences in the per capita output (NSDP) between TN and UP over a reasonably long period of time, 1961–2001. Figure 4.10 shows that TN’s agricultural production per capita (rural) was 23% higher than that of UP in 1960–61. In general, Indian agriculture before the Green Revolution (1951–65) was characterized by low productivity, when compared with the post-Green Revolution period (1966 onwards), which has been further divided into early adoption period (1968–88) and 1989–2006 (Chand et al. 2011). TN is a state which is especially rain-starved; hence, it is plausible to believe that in the absence of modern irrigation methods and high-yielding varieties of seeds, which was made possible by the Green Revolution, TN’s agricultural output was declining during 1960–80, after which it started picking up. By 2000–2001, TN’s per capita agricultural production had increased by 44% over UP’s per capita agricultural production. Several factors may have contributed to this outcome. Increased productivity through better practices, diversification of crops and better supply of agricultural inputs may all have helped augment production in TN. Since we are looking at a per capita measure, it is important to note that the slower population growth in TN compared to UP also played a role in this outcome. In relative terms, UP was unable to exploit these factors to the same extent as TN. It is an example of how more efficient use of scarce resources seems to have contributed to higher outputs and incomes in the rural sector of a southern state. The second area of resource utilization we choose is developmental spending of the states. The assumption is that whenever states allocate and use resources for development, they create assets which in turn increase their output and incomes. The foremost among these measures we summarize is the per capita developmental spending of states.

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Figure 4.10 Agricultural Output—TN and UP 7,000 6,000 5,000 4,000 3,000 2,000 1,000 0

1960–61

1970–71

1980–81

1990–91

2000–2001

Agriculture NSDP (per capita) UP Agriculture NSDP (per capita) TN Sources: EPW Research Foundation for agriculture NSDP; Census of India, various years, for rural population. Note: Agricultural output refers to per capita NSDP in 1999–2000 constant prices, and includes agriculture and allied sectors for the two states. The original agriculture NSDP which were in various series (for instance, the 1960–61 NSDP were in 1960–61 prices, the 1970–71 data were in 1970–71 prices and so forth). All these data in different series were converted into 1999–2000 constant prices, for agriculture and allied sectors, by using a linking factor, as recommended by the EPW Research Foundation, for purposes of comparison over time.

Developmental spending refers to public investment made in the creation of durable assets such as roads, bridges, higher installed capacity for generating electricity, expenditure resulting in outcomes such as higher enrolment in education, better IMRs and lower birth or death rates. We examine per capita developmental expenditure in the two states to review if there are disparities across TN and UP. Figure 4.11 summarizes these differences over time, during 1980–2003. Figure 4.11 conclusively

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Figure 4.11 Per Capita Developmental Expenditure—TN and UP 3,000 2,500 2,000 1,500 1,000 500 2000–2001

1998–99

1994–95

1996–97 UP

2002–3 (RE)

TN

1992–93

1990–91

1988–89

1986–87

1984–85

1982–83

1980–81

0

Sources: CMIE; authors’ computations and analyses. Note: RE: Revised estimates.

shows that while the gap between per capita developmental expenditure of TN and UP was within a narrow band until 1990, after 1990, TN’s per capita developmental expenditure grew by leaps and bounds, while UP’s per capita developmental expenditure stagnated or even declined (post-1999).12 While this may have been rather the result of rising per capita incomes, the data are testimony to the fact that TN attempted to utilize its resources through its higher developmental spending to create higher levels of output and income than UP did.

Efficiency of Resource Utilization The efficiency with which resources are utilized has impacts on economic growth. If resources are used in a manner which maximizes the useful goods and services derived from those resources, then we may The data for UP include those for Uttaranchal post-2000 to make the pre-2000 and post-2000 UP data comparable. 12

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expect greater economic growth to occur. The ‘doing more with less’ slogan indicates the focus on more outputs with fewer inputs (fewer resources). While we focus on outputs with fewer resources, we are unable to examine other resource utilization impacts on equity-related aspects such as the well-being of the poor, due to data limitations. In order to examine the efficiency of resource utilization, we examined expenditure on various sectors (such as roads) which are inputs and that on respective outcomes such as the change in road length. We understand that outcomes manifest themselves only with a lag after the initial expenditure/investment has been made. In the case of roads, we used the 1980–85 period for examining expenditure with a five-year lag, and 1985–90 for observing the outcome, i.e., road length, since there is a time lag for the spending to produce tangible outcomes. We found that TN spent a total of `92,483 during 1980–85 for creating every additional kilometre of road during 1985–90, whereas UP spent 3.5 times more than that of TN, `328,788 over 1980–85 to create an additional kilometre of road during 1985–90.13 Therefore, in the case of roads, given their relative record of spending, TN fares better than UP in efficiency. This shows that for a given budget, TN was able to build more kilometres of road than UP. We took another example from the social sector (primary education) to demonstrate the efficiency of resource utilization in the case of two states. Two surveys done by the Public Report on Basic Education in India (PROBE) team in the Hindi-speaking states (in 1996 and 2006) showed that despite the fact that schooling infrastructure had expanded rapidly, classroom activity levels had not improved during the decade. For instance, there was an impressive increase in the number of primary schools between 1996 and 2006, with one out of every four government schools being set up during this decade. Further, the proportion of schools in UP with at least two pukka rooms went up from 26% in 1996 to 84% in 2006. Next, in 1996, free uniforms and textbooks were We recognize that the mix of roads (e.g., rural roads, national and state highways), land terrain and other factors could make a difference to the cost of roads per km. Nevertheless, these factors may account for only a part of the cost differential noted above. 13

What Explains the North–South Divide?

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provided, respectively, only in 10% and less than half of schools, which increased to more than half of the schools and nearly 99% of schools in 2006. Let us relate the inputs discussed above to outcomes. In rural North India, in 1996, about half of the time, there was no teaching going on in primary schools. However, despite all the increases in resources and inputs during 1996–2006 reported above, a resurvey conducted in 2006 found that nothing had changed with respect to educational outcomes— half of the government schools still had no teaching activity when the investigators arrived. Dreze and Gazdar (1997) confirm several aspects of the long-term neglect in UP as it relates to education. They point out that …it is easy to cite many examples of continued indifference. One of the most telling symptoms in this respect is the sustained decline of real per capita public expenditure on education in recent years—by almost 20% between 1991–2 and 1993–4. The number of primary school teachers per capita has also steadily gone down in recent years … further aggravating the spiraling decline of teacher–pupil ratio in the eighties. Similarly the state government has taken little interest in the Total Literacy Campaign, even after the considerable potential of that campaign had been well demonstrated in several other states. The under utilization of large grants earmarked for the promotion of elementary education (received from international agencies as well as the central government) is yet another symptomatic indication of the low priority given to basic education by the state government. Here again, official neglect provoked little challenge from opposition parties, interest groups, the media or the general public. (pp. 88–89)

T.S.R. Subramanian (2004, p. 229), in his account as Chief Secretary, UP, records the following which throws light on the state of primary education in the state: In 1991, as I was once driving in the mountains in the course of an official visit, I stopped the car on an impulse, to visit a village which was situated on a ridge well above the highway, involving a steep climb on foot for about two kilometers. As I started walking up, the local block development officer who was accompanying me frantically tried to stop me, giving me various excuses. He finally talked about a hear-condition and I was a bit intrigued about why this functionary was so unwilling. Anyway, leaving him behind,

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THE PARADOX OF INDIA’S NORTH–SOUTH DIVIDE I went ahead, accompanied by my orderly. We found a medium sized village with perhaps two hundred inhabitants. A primary school was located there: Only one teacher was present. After some questioning, I managed to find out that the school had a regular strength of five teachers. However, by mutual arrangement among the teachers, only one would be present on any day; each of the five would take turns of fifteen days at a time, to attend to their teaching duties and take the rest of the time out to attend to other matters. I also discovered to my dismay that the village had not seen an inspection visit by a single block level officer, even though there were some fifteen officers attached to each block. The block level officers conveniently visited only the roadside villages. The village I visited was not particularly inaccessible. Yet, such was the apathy to the villagers’ needs. In a microcosm, we can, at one stroke, understand the failure of development to take hold in the hills. Nearing the end of my visit to the village, I was quite dispirited.

While we did not have data on classroom activity for the southern states, we found that the Annual Status of Education Reports (ASER) of Pratham tracks the status of selected educational indicators for all states in the country. We found in 2006, for instance, that the percentage of children out of school in TN was 4.9 in the age group 7–16, 3.6 in the age group 11–14 and 15.8 in the age group 15–16 (both boys and girls), compared with 8.9, 8.9 and 22.6, respectively, for UP during that year. Similarly, the proportion of children not going to any government, private school, balwadi or anganwadi was 57.7% in UP for children in the age group 3, whereas it was only 13.1 for TN. We found a similar trend for children not going anywhere in the four-, five- and six-year age groups in TN vis-à-vis UP. This is despite the fact that TN’s proportion of spending on elementary education during the period 1994–95/2009–10 was a meagre 1.67% of the total spending on education. We found evidence from TN’s Human Development Report (HDR) that TN had several important historical developments in the field of elementary education including those from the colonial era, and that its surge in primary education was not an overnight development. For instance, the earliest developments in the field of education in TN were brought on by the advent of the Christian missionaries as early as the beginning of the 18th century. Some interesting highlights on the status of girls’ education in TN reported by TN’s HDR revealed that the proportion of boys to girls in elementary schools changed from 4:1 in

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1911–12 to 3:1 in 1926–27. The need to open more girls’ schools so as to ensure access for girls was, thus, recognized. Appendix 4 contains a detailed description of initiatives on the history of elementary education in TN, which are excerpts from its HDR. We were unable to find any such material on the history of elementary or primary education in UP, which itself testifies to the fact that there were probably no important developments in this area in UP which were worth documenting. Thus, we find that lower efficiency in the deployment/utilization of resources, along with other factors, also may have been a key factor in the poorer economic performance of UP when compared with that of TN. The proximate factors discussed above helped TN to prepare the ground for attracting many new industries. A major factor influencing the location of industry in TN was the setting up of industrial estates in the 1950s which was an initiative introduced by R. Venkitaraman who was industries Minister in the state at the time. Land was freely available and acquired, and infrastructure developed for industry. Land acquisition and compensation were well managed in TN. Further, a factor influencing the growth of small and medium enterprises was the availability of cheap loans from Small Industries Development Bank of India and the TN Industrial and Investment Corporation. Further the utilization of schemes such as the Textile Modernization Scheme, when compared with its allocation, has been very high in TN, compared with that in other states. Based on discussions with industry representatives, we conclude that a relatively peaceful law and order environment, the availability of technical manpower, the presence of a good work culture (fewer strikes and lockouts), the promise of good infrastructure and the proximity to ports (Chennai, Ennore and Tuticorin) were major factors influencing the choice of industry to locate there. While a large part of the textile cluster in Tiruppur developed because of the natural availability of cotton in that area, the state government took a number of initiatives to develop infrastructure conducive to location of industry. It should be noted that all budget speeches made by TN’s finance ministers through the 1990s focused on all-weather, motorable roads which provided connectivity from the hinterland to the urban markets, which was a major factor influencing the choice of location for an industry in the state.

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As an outcome of the expansion of industry, there was increasing demand for electricity in the state, with the result that despite continually increasing installed capacities (as our evidence shows), there was a shortage of power. One official, with whom we met, quoted the fact that in Coimbatore district alone, the power consumption was nearly 1,000 MW in a recent year, whereas the total consumption of power in the entire state of Bihar was 1,000 MW!14 While corruption continues to be a problem even in TN, public services and infrastructure are delivered much more efficiently (as we have documented in the case of roads and primary education) than in other states.

Quality of Governance It is widely believed that the quality of public governance contributes a great deal to the economic and social progress and development of a country. Governance refers to the functioning of governments and public institutions that impact economic activities and the lives of citizens. When the processes of public decision-making and implementation of policies are carried out with credibility, transparency and accountability, governance is considered good. Given its complex nature and scope, however, it is far more difficult to define and measure governance than all the other factors discussed above. Per capita income is a summary measure of the economic performance of a society. Literacy rates can be used to measure some aspects of human capabilities. But there is no summary measure that reflects the multiple dimensions of governance. Nor is it easy to obtain the necessary data to quantify and measure the relevant dimensions of governance. This is a major reason why most explanatory models of growth and development ignore governance or merely pay lip service to its importance. Nevertheless, it is not difficult to see that governance can impact both the supply and demand sides of development. Credible and stable policy regimes, efficient and equitable allocation and utilization of scarce resources, and reliable law and order systems are factors that strengthen 14

This was based on discussions with Dr P. Umanath.

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the supply side of development. Basic services that citizens and entrepreneurs require are likely to be more efficiently provided under these conditions. Infrastructure will be better built and maintained when these conditions are met. But a good governance regime has a positive impact on the demand side too. Prospective investors, domestic and foreign, are more likely to invest or expand in a state with better governance that is likely to be more stable and reliable. In this sense, governance is valued not merely because public functions will be better delivered, but also because it instils longer-term confidence in prospective investors to make durable commitments. Reputation and public image of the host matter to them. Governance, thus, impacts the demand side of development through its influence on the psychology of investors. As noted above, there are limits to the extent of information we can put together on the quality of governance. Time series data on governance is especially difficult to obtain. After a careful assessment of the core elements that constitute governance and the feasibility of obtaining the necessary information on them, we have narrowed down our choice to four indicators of the quality of governance—political stability (which we measure by the tenure of CMs), law and order (as reflected in police firings and the proportion of civil to total police) and functioning of the judiciary (reflected in pending cases), though they are by no means comprehensive.

Political Stability: The Tenure of Chief Ministers Political stability is central to any system of governance. Frequent changes in government are known to create uncertainty about policies and key public decisions in the minds of economic actors, thus, adversely affecting economic performance and social progress. When policy making, implementation of projects and related actions become unpredictable, resources are unlikely to flow into such states or to be utilized efficiently. Though it is difficult to measure all aspects of stability, it is reasonable to assume that the tenure of a CM at the state level acts as a proxy for political stability. The stability in the sense of direction and style of functioning he or she brings provides the setting in which

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key economic actors will take long-term decisions. The longer a CM’s term, the greater are the chances that stability and continuity of policies and follow-up actions will prevail, and greater the probability that the continuity of the officials in charge of key departments and programmes or projects will exist. A new CM will most likely change his ministers and officials, thus creating further instability. Viewed thus, the tenure of the CM can confirm whether a key precondition for proper governance is in place. The above is not to say that tenure is a sufficient condition for good governance. It is an enabling condition that permits those in authority to craft and implement the right policies and programmes. A full and stable five-year term, for example, will permit a CM to plan and monitor his or her policies and their implementation without being distracted by political uncertainties and challenges. She/he will be able to take corrective actions and pursue the goals and outcomes promised by the government. Needless to say, a long tenure can also be misused or end up with poor outcomes. If a longer term did not result in positive economic outcomes, it could well be that the CM’s policies and actions were flawed. More detailed probes into what happened in such cases will need to be carried out before a firm conclusion can be drawn.

Law and Order: Police Firings and Proportion of Civil to Total Police It is well known that basic law and order conditions are essential for both economic and social progress. Despite improvements in law and order, if the public image of a place is that it is disorderly, it can negatively impact investment decisions and retention of a skilled workforce. Though there are multiple measures of law and order, we have selected a more visible indicator, namely police firings per million population, because it reflects at once the key aspects of the peace that prevails in a state. It signals the intensity and extent of inter-group conflicts in a society, the inability of the regime to bring them under control or a combination of both. Because firings are widely reported, and add to uncertainty and fear in the minds of people, they can adversely impact the smooth functioning of a society and its economic enterprises. Being more visible

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to the public eye, police firings are also less likely to be misreported or manipulated in official records. The above cannot be said for other types of crime like murders, domestic violence or property-related crimes where suppression of facts is more likely to happen. There is also data available with the NCRB regarding crimes such as murders, suicides, property-related crimes which can lead to insecurity in the minds of the citizens and investors regarding the nature of governance. There could be some problems with the way in which these crime data are reported. For instance, only when the first information report (FIR) is filed that instances of murders, thefts are recorded. But there is evidence that it is difficult to register an FIR. In events when no FIR is filed, those crime data go unrecorded. However, police firing incidents are reported in a standardized manner at the state level; hence, we place more faith in this as a measure of law and order. A second law and order indicator we choose relates to the proportion of civil to total police (consisting of civil and armed police). The proportion of civil to total police force is an indication of the peaceful conditions prevalent in a state.15 Only when disturbed conditions exist in a state, the para-military forces are called from the centre. Hence, the greater the proportion of civil to total police force, the greater the extent of peace, law and order prevailing in the state. A lower proportion of civil police to total police force reflects the prevalence of worsening law and order conditions in the state.

Functioning of the Judiciary: Pending Cases in Court A fair and efficient justice system is a key determinant of the quality of public governance in a state. A rise in pending cases may reflect growing numbers of disputes in society or the judiciary’s failure to deal with them efficiently. Potential investors will view this as a negative factor. Barriers to dispute settlement and the resolution of legal problems can slow down growth and development simply because they add to the costs of engaging in economic and social activities, and reduce public confidence in 15

We thank Dr Ajay Kumar Singh for these insights.

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the larger legal governance system. Once faith in key public institutions is shaken, it is difficult to attract investors and other development actors who need to allocate, augment and manage resources in the state. These indicators capture three essential ingredients of governance: political stability, law and order, and the dispensation of justice. As noted above, there are other dimensions of governance that also deserve to be considered. Indeed, a comprehensive assessment of governance will call for a review of the functioning of all public institutions. But precise measurement and quantification of their attributes are by no means easy. Nor is it essential as our three indicators constitute the foundation that enables other institutions, both public and private, to function. Our surmise is that many other aspects of governance will be indirectly captured by the measures discussed above. One such dimension is corruption, a phenomenon that has received much attention in the literature on growth and governance. The argument is that corruption can adversely affect the quality of governance and the pace of economic growth as it adds to the transaction costs of doing business and weakens the rule of law. There are multiple manifestations of corruption that make it difficult to measure and quantify its extent and impact. Corruption can take the form of monetary bribes, improper use of public power, nepotism and other non-monetary forms. The nature of corruption is such that reliable evidence on its prevalence is nearly impossible to get. It is possible, however, that the influence of corruption is reflected in the other indicators of governance that we have discussed above. We know, for example, that when political stability declines, the scope and opportunities for corruption tend to increase. When law and order break down, citizens may be forced to resort to corruption to solve their problems and to obtain essential public services. Poor or disorderly governance and corruption may, thus, go together, one reinforcing the other. In the present study, we have not attempted to incorporate corruption as a separate governance factor for these diverse reasons. We now turn to a comparison of TN and UP with respect to the governance indicators. Table 4.1 summarizes the average tenure of CMs of the two states, which we choose as a measure of political stability. The average tenure of CMs in TN and UP was not too different in the first period displayed in the table (late 1940s to mid-1960s). In fact, UP was slightly ahead

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Table 4.1 Average Tenure of CMs (number of days)—TN and UP Period

TN

UP

1949–50 to 1967–68

1,692

1,748

1967–68 to 1984–85

1,393

297

1984–85 to 2008–9

1,058

390

Sources: http://www.elections.in/tamil-nadu/ (accessed 1 January 2015); http:// www.elections.in/uttar-pradesh/ (accessed 1 January 2015); authors’ computations.

with an average tenure of 1,748 days for the CM, in contrast to TN’s average tenure of 1,692 days. Thereafter, however, the average tenure declined in both states. But the decline was much steeper in UP, causing a noticeable fall in terms of political stability. Between 1967 and 1985, UP saw 18 CMs and periods of President’s Rule. In contrast, CMs changed only three times in TN. Since 1986, there has been an increase in the average tenure of CMs in UP, while a decline is seen in TN. However, a clear divergence has persisted all these years between the two states, with the average tenure in UP being substantially below that of TN even during the latter period. Consequent to the changes in the leadership at the top, 420 out of the 500 Indian Administrative Services (IAS) officers in UP are also reported to have been transferred annually since 1992 (see Howes et al. 2003). Agriculture department secretaries were changed nearly six times in a year in the state.16 In fact, in a personal account of his tenure as Chief Secretary, UP, T.S.R. Subramanian states: …In the 1990s in UP, the average tenure of a collector in a district was nine months. I was to see, as Cabinet Secretary, that the average tenure of a collector all over India was about thirteen months. … In circumstances that are not conducive for the pride of satisfaction in work, how can one expect meaningful results? No wonder we have reached the state in which we are in today. … In the current state of affairs, few officers have the thought of contributing anything. An officer merely wishes to extract as

16

These observations are based on our discussions with officials in UP.

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THE PARADOX OF INDIA’S NORTH–SOUTH DIVIDE much as he can for himself and his family in every successive assignment… (Subramanian 2004, pp. 281–84)

Judged by the trends in the average tenure of CMs, the political stability in TN was significantly greater than in UP, except in the immediate period after Independence. Attention to policy making, control over administration and public expenditure, and public services would have suffered far more in UP than in TN as a result of the frequent changes in the CM’s post. TN’s achievement in terms of family planning is a case in point. Greater political stability with the consequent political support to this programme was key to its successful implementation. Such an enabling environment did not exist in UP where frequent changes in CMs meant less attention to the programme and its implementation. While the term of the bureaucracy is a function of the tenure of the CM which was progressively getting shorter in UP after the 1960s, this also reflected the fact that bureaucrats were mere instruments of the administration and were not allowed to function with certainty in the state. Our discussions with several officials in TN and UP revealed several other ways in which political stability helps to improve the quality of governance. TN’s governance has been dominated by parties which won a clear majority in elections. While coalition governments entail significant transaction costs, political parties which win a majority are better able to deliver services to citizens, and take more speedy policy decisions. Further, with regional parties in control, TN was often at the receiving end of the Centre with very few investments in the public sector. This, however, has not acted as a deterrent to the state’s entrepreneurial attitude as the state made its own efforts to attract private investment, with a highly pro-business environment. The result of this effort has been to get automobile manufacturing to the state with firms such as Ford, Hyundai, Mahindra, Ashok Leyland and the TVS group (which was home-grown in TN). Thus, the compulsion to attract private investment motivated the state to improve infrastructure and efficiently deliver services. S. Mahendra Dev (2013) in India Development Report 2012–13 contrasts governance in UP with that in TN: (The) Madras Presidency was relatively more developed in the colonial times as well: post-independence, successive state administrations have

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built upon this advantage. Policies such as the roll-back of power tariff increase, free power for agriculture, a sustained positive discrimination policy, and a targeted Public Distribution System (PDS) initiated the growth process in the state.

With respect to the second indicator, police firings per million population, there is a marked difference between the two states, though our data cover a much shorter period than the CM’s tenure. As compared to TN, police firings per million population reveal a mixed trend in UP for some periods (see Figure 4.12). Furthermore, in TN, there is a substantial decline in this indicator over time, while in UP police firing incidence is on the rise. Some explanation is in order with respect to the law and order conditions in UP and what led to the worsening law and order there in the 1990s. Our discussions in UP indicated that the ruling parties dominated by upper castes did not develop the backward classes such as the Scheduled Caste (SC) and Muslim population of the state which constituted 20% each, due to which the 1990s saw the revolt of the subalterns in the state which was characterized by law and order problems in Figure 4.12 Police Firing Incidents per Million Population—TN and UP 1.60 1.40 1.20 1.00 0.80 0.60 0.40

TN

2004

2003

2002

2001

2000

1999

1998

1997

1996

1995

1994

1993

1992

1991

0.00

1988

0.20

UP

Sources: National Crime Records Bureau; authors’ computations and analyses.

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Figure 4.13 Proportion of Civil Police to Total Police—TN and UP 100.00 90.00 80.00 70.00 60.00 50.00 40.00 30.00 20.00 10.00 0.00 1972

1977

TN, % civil to total police

1982

1987

UP, % civil to total police

Sources: National Crime Records Bureau; authors’ computations and analyses.

response to which police firing was used. Because of this, our choice of police firing as a measure of law and order is well placed. Figure 4.13 summarizes our second indicator of law and order, proportion over time of civil to total police (consisting of civil and armed police) in TN and UP. It shows that TN’s civil police strength has been historically much higher than that of UP (where it is around 80% and dropping to 60% in 1983), hovering around 90% of its total police force, testifying to the relatively more stable law and order conditions over there. The third governance indicator which we reviewed across the two states reinforces a similar trend which we find in the case of the other two indicators. The percentage of pending cases in the courts in UP is much higher than that in TN. Figure 4.14 shows that the initial condition is worse in UP and the deterioration continues over the period for which data are available. In TN, there is a marginal reduction in the percentage of pending cases (–0.29%) while the same has increased in UP by 0.75% per year. In other words, the functioning of the judiciary has worsened in UP while it has improved somewhat in TN.

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TN

2005–6

2004–5

2003–4

2002–3

2001–2

2000–2001

1998–99

1999–2000

1997–98

1996–97

1995–96

1994–95

1993–94

1992–93

1991–92

90.0 80.0 70.0 60.0 50.0 40.0 30.0 20.0 10.0 0.0

1990–91

Figure 4.14 Percentage of Court Cases Pending Investigation at the End of Year

UP

Sources: National Crime Records Bureau; authors’ computations and analyses.

The foregoing analysis confirms what many observers have intuitively surmised; UP’s governance track record remains well below that of TN. Our evidence further shows that at least in respect of three indicators, UP has continued to deteriorate over the period under review. The lack of progress noted in other factors in UP (discussed above) too could be attributed at least in part to the state’s governance record. Poor governance can adversely impact the mobilization and utilization of resources for education, health, infrastructure and other public goods, and result in their suboptimal outcomes, slowing down development in the process. Of the indicators discussed above, political stability is of critical importance. It is not implied here that the CM’s tenure fully ensures stability. What s/he achieves during a stable tenure is what truly matters. Here, the evidence from TN is impressive. The focus of the CMs on development, their attention to the bureaucracy’s delivery of basic services and their proactive efforts to attract investments to the state reflect the quality of leadership and ‘political will’, an essential foundation of good governance.17 This achievement, as we shall see, was aided by the ‘demand factor’ discussed further. This is not to say that there was no corruption or abuse of power in TN. But the political leadership did deliver despite these problems. 17

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In contrast, in UP, according to several observers, the quality of political leadership is poor, characterized by ‘identity politics’ where real commitment to the state’s public is missing and there are pressures in a coalition government. A major contributor to TN’s growth was the prevalence of systems and procedures. The bureaucracy and administration are required to deliver on various issues. Various institutions and systems are in place. For instance, Karunanidhi as CM took special interest in reviewing sales tax collection efficiency which incentivized tax officials to follow up on the administrative procedures to collect taxes. There are systems in place in the Government of TN, and the coordination between the relevant departments is much better here than in other states. Another example of TN’s efficiency with respect to procedures and processes is as follows. If there is a project (such as a hospital) which requires the coordination of the public works, planning and finance departments, there is a single file on which everyone (from the above departments) comments, with the result that the time taken for approvals is quite short. Further, the planning and finance departments, respectively, responsible for allocation and utilization decisions, were housed in a single department for a long time, which was quite conducive to fast decision-making. It is instructive here to mention the example of the prevalence of the auto industry cluster in TN. As one would note from the existence of Ashok Leyland, Hyundai, TVS Motors, the automobile industry occupies a pride of place in the industrial map of TN. What explains the automotive industry cluster in TN? It would be useful to note that having started in 1840, Simpsons pioneered India’s automobile industry rail coaches, motor cars, diesel engines and steam passenger buses. In 1948, Ashok Leyland was started for assembly of Austin cars. The Integral Coach Factory (ICF) was established in October 1955. In the 1960s, the TVS Group established a number of auto component manufacturing plants. This marked the first wave of the automotive revolution in TN. However, this momentum was lost in subsequent years. In the post-liberalization era since July 1991, with the dismantling of the licensing system, the Indian states had the autonomy to take various initiatives to accelerate and foster the pace of industrialization. In a speech delivered in April 2012 at a Daimler facility, the TN

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Chief Minister herself took cognizance of the prevailing competitive environment wherein TN was one of the earliest to seize the initiative and announce its Industrial Policy 1992 that became the cornerstone which laid the foundation for the growth of manufacturing in the state. During 1991–96, during her first tenure as CM, the government triggered the second wave of the automobile revolution in TN by attracting Ford and Hyundai. These major automobile projects had an agglomerative effect, attracting a large number of auto ancillary industries. As testimony to this, during 2001–6, TN attracted BMW. These automobile projects created a ‘brand equity’ for Chennai as the most attractive destination for automobile industrial projects. The abundant availability of skilled manpower in automobile engineering and port logistics, and the availability of reliable infrastructure, above all, a favourable investment climate and pro-active government support are the primary reasons for making TN the home of automobile manufacturing. All these examples from TN point to the important role the government and bureaucrats need to play in ensuring the seamless orchestration of government machinery. In government, functions and tasks are always split up and differentiated. But putting them back together and interpreting their outcomes is often ignored. TN’s leaders recognized and tackled this problem successfully. In contrast, in UP, any administrative machinery that came in the way of accomplishing the ulterior objectives of those in power was often ruthlessly crushed with the result that the bureaucracy in that state was incapacitated frequently due to the lack of risk taking of the state government. As an example, T.S.R. Subramanian (2004) in his account as Chief Secretary, UP, records the following (p. 15): Sometimes I wonder whether the planning department itself had little to do or was it that the commissioners responsible had little to do? One day I accompanied Virendra Dayal, who was officiating as collector of Moradabad at that time, to Bareilly to attend the divisional planning committee meeting. All the district collectors, district planning officers and the local officers dealing with development matters participated in this meeting. We left for Bareilly at 8.30 AM to attend the meeting scheduled at 10. The drive normally took about one and a quarter hours, but at Clutterbuckgunj, on the outskirts of Bareilly, the jeep had a flat and we were delayed by some

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THE PARADOX OF INDIA’S NORTH–SOUTH DIVIDE twenty minutes. We arrived at the meeting hall a few minutes late. It looked as if the meeting had not started. The head table was empty and the twenty or so participants were outside, laughing and chatting, seated on chairs in the main hall. The then officiating commissioner was I D N Sahi who was appropriately nicknamed ‘I do nothing’ Sahi. After about ten minutes, Dayal asked, ‘When is the meeting starting?’ There was laughter all around. We gathered that the commissioner had opened the meeting and stated that he had no comments to make and he hoped that everything was going satisfactorily, everyone immediately agreed that everything was going as well as possible. Thereupon, the commissioner concluded the meeting, since there was no further business to transact. So much for planning and development!

While Paul Appleby’s report on India’s public administration in 1953 and 1957 identified UP (and Bihar) as the best governed states in the 1950s, this deteriorated in the late 1960s and early 1970s. In 1970s, there developed a tilt which was characterized by student unrest and violence. Further, most of India’s Prime Ministers are from UP with the result that the Centre did not want to identify themselves with the state and ignored its developmental needs. The national parties did not encourage local leadership which made for the absence of dynamism. This is consistent with the view that leaders who are representatives of national parties are accountable to their superiors in Delhi. According to observers and scholars who have studied UP, coupled with the above-mentioned phenomenon, a nexus between leaders, bureaucrats and industrial houses was exploiting the system for personal gains. For instance, many a time, CMs were elected on the basis of caste, but that did not result in the development of those castes.18

The Demand Factor Most of the policies, programmes and services provided by governments can be characterized as ‘supply-side’ interventions. Governments, for example, invest heavily in infrastructure of all kinds. They establish schools and appoint teachers. They set up health centres and hospitals to 18 This observation is based on discussions with scholars in Lucknow and Delhi who have worked on UP for a long time.

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provide health care to the people. They provide police stations to receive complaints concerning law and order. The government’s anti-poverty programmes are meant to deliver a variety of welfare benefits to the poor. A major part of governance, thus, consists of supply-side activities that are expected to satisfy the needs of citizens. It is assumed that people want these investments, activities and services and will utilize them once they are supplied. These developments benefited greatly from the policies and infrastructural support at the political and bureaucratic levels. The expansion of urbanization in the state was in no small measure due to these factors. We have here an example of ‘social capital’ acting as an instrument for spreading the benefits of development to different parts of the state and different segments of the population. Effective governance, however, calls for more than supply-side activities by governments. When large numbers of people are unaware of government policies and programmes, they are unlikely to respond to them and take advantage of the benefits being offered to them through these interventions. When there are failures of service delivery or in the administration of justice, people are unlikely to demand corrective action unless they know their rights and there are organizations and instruments to assist them in their struggles. When the demand side is weak, the chances of misallocation and diversion of funds and benefits away from their intended target groups will also be high. Supply-side interventions, therefore, need to be complemented by pressure from the demand side for public governance to be effective and responsive. We hypothesize that the differential performance of states and regions is determined also by the pervasiveness and strength of the demand factor as explained above. There are three ways in which pressure may be exerted from the demand side to improve governance. First, when the public is more aware of their rights and entitlements, the demand-side pressure will be stronger. Well-informed citizens are more likely to demand what is due to them and fight the abuses and failures of governance. Second, when there are organizations that motivate the public and their communities to demand better governance, their capacity and determination to seek what is due to them and to monitor supply-side interventions will be enhanced.

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Third, organizational support will facilitate both networking and collective action to tackle abuses of public power and to demand greater responsiveness from governments. Organized sectors such as industry and trade are fine examples of how this process works. Unorganized citizens and marginalized communities, on the other hand, are unable to exert demand-side pressure precisely because of the absence or weakness of the three factors mentioned above. In recent years, we have witnessed many examples of how these factors have converged to create citizen pressure for improved governance. The use of new technologies such as the internet, mobiles and social media has significantly aided this process. In an earlier era, this role was played by large-scale social movements and their dedicated leaders who worked tirelessly for many decades to strengthen the demand side of governance, and to give ‘voice’ to the public who are at the margins of society. As noted above, both the demand factor and governance are longterm influences that impact economic performance. A historical perspective on their evolution is essential to understand their influence on development outcomes. We now turn to an analysis of the role played by the demand factor through social mobilization in the economic performance of the two states under review. The demand factor in TN played a key role in the spread of education, awareness of rights and motivation to take advantage of the opportunities offered by the states and its new policies. There is historical evidence to support the thesis that education in TN had benefited from the helping hand of the British colonial government in the 19th century. TN led the country in the reservation policy in education that others emulated in later periods. More importantly, the social movements that dominated TN politics and public discourse in the early part of the 20th century created a much greater awareness among the lower castes that constituted the majority of the population about their rights and the need for collective action to claim their entitlements. Scholars who have documented social movements across India have pointed out that similar movements did not occur in UP or other northern states. In both regions, there were movements that protested caste abuses and Brahminical dominance. But the distinguishing feature of the TN

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social movements was their focus on gaining access to education and economic opportunities such as jobs in government. These movements not only created greater awareness among the backward classes about the need for collective struggles to achieve their ends, but also increased their sense of solidarity and mutual trust among the members, and helped them create vast new networks to mobilize resources and launch collective political and social action to achieve common ends. It was, thus, that large numbers of schools, colleges and, in recent years, engineering colleges were set up by caste- and community-supported leaders and groups. A similar trend has been noted in the industry sector of TN where, again, impressive numbers of small and medium enterprises have been set up by entrepreneurs, who took advantage of their caste and community networks. The governments in power facilitated this process, resulting in a groundswell of private sector development. Among the political leaders who promoted this process in TN were K. Kamaraj, R. Venkataraman, Annadorai and C. Subramaniam. Developments of this kind do not seem to have occurred in UP. The importance of these historical factors, especially social movements, in laying the foundation for strengthening both the demand and supply sides of development in TN cannot be overemphasized. The historical sequence in which these developments occurred in TN is also quite revealing. In the 19th century, the British colonial government set in motion two major policy initiatives that significantly contributed to the development of business and the emergence of social movements by the intermediate and lower castes in the Madras Presidency. Development of roads and bridges and improved law and order conditions in the southern districts were a policy initiative that facilitated the expansion of trade and transport, and the creation of a cluster of new towns. In the north-eastern part of the Presidency, public investment in massive irrigation dams enabled farmers to increase agricultural output and productivity. The second policy initiative of the Madras Presidency pertained to education, partly aided by the proactive efforts of the Church and its missionaries in the South. The expansion of the school system in both urban and rural areas enabled the children of all castes to benefit from this policy. The princely states of Travancore and Mysore in the South also encouraged the education of the masses. Until then, education and

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government jobs were largely the preserve of Brahmins. At the end of the century, over two-thirds of graduates of Madras University were Brahmins. As literacy and the knowledge of English spread, the lower castes and their leaders became more aware of the need to demand a greater share for them in both education and government. The social movements that emerged were responsible for the colonial government’s ‘affirmative action’ policy of job reservation for non-Brahmins. The dominance of the Brahmins in education and government jobs was, thus, broken. The social movements in the South were not limited to concerns about jobs and education.19 Caste- and community-based associations in the early 20th century played a major role in promoting their social welfare and economic progress (Damodaran 2008). Their trade infrastructure, financed through a communal tax, provided the protection they needed to carry on commerce. These fora enabled members to pool their resources and skills for starting and financing enterprises, educational ventures and for lobbying with governments for the resolution of their problems. Some of them started their own banks. The Nadars in the southern districts, Gounders and Naidus in the western districts (Coimbatore and Tiruppur) are among the lower caste groups that in later years became successful entrepreneurs in major industries. These developments spawned a new generation of entrepreneurs from the lower castes in TN. The New York Times (NYT 2010) quotes the story of an entrepreneur Chezi K. Ganesan who splits his time between San Jose and Chennai, running his $6 million a year computer chipmaking company. The NYT story relates how his family has come a long way. His grandfather was not allowed to enter Hindu temples, or even to stand too close to upper caste people. Nonetheless, Nadars created business associations to provide entrepreneurs with credit they could not get from banks. They started charities to pay for education for poor children.

Social movements emerged about the same time in Maharashtra and Gujarat (Bombay Presidency), with similar results. In these western states, cooperatives led by intermediate caste groups such as Patidars in Gujarat and Marattas in Maharashtra laid the foundation for the economic progress of the region through the development of dairy, sugar and cotton enterprises on a large scale (Damodaran 2008). 19

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They built their own temples and marriage halls to avoid upper caste discrimination. The NYT article highlights that the Nadars’ spectacular rise from despised manual labourers who made a mildly alcoholic palm wine to business leaders in one of India’s most prosperous states offers significant clues to India’s caste conundrum and how it has impeded economic progress in many parts of the country. The article points out that ...the breakdown of caste hierarchy has broken the traditional links between caste and profession, and released enormous entrepreneurial energies in the south. This breakdown … goes a long way in explaining ... ‘why the south has taken such a lead over the north in the last three decades’.20

The NYT quotes this entrepreneur who said that ‘Caste has no impact on life today’, in an interview at one of the Chennai’s exclusive social clubs, the kind of place where a generation ago someone of his caste would not have been welcome. ‘It is no longer a barrier’. A distinguishing feature of the early entrepreneurs was their foresight in setting up educational institutions, including engineering and vocational schools that laid the foundation for the surge of technical education in many towns in TN. It is remarkable that higher castes like Brahmins and Chettiars who dominated trade and money lending during this period were not able to block this transformation. In contrast, the demand side of governance remained weak in UP until very recent times. According to a scholar (Pai 2002, pp. 30–31) who has studied these social movements, ‘More importantly, in contrast to the Bombay and Madras Presidencies, the United Provinces21 underwent no social reform movements which could have shaken the rigid caste The NYT article quotes that caste is so now crucial to Indian politics that caste-based parties have demanded that caste be included in India’s census, and the government, bowing to pressure, has now started collecting data on caste for the first time since independence. They hope that by showing their large numbers, caste-based parties can force government to set aside more jobs for their communities. 21 During the colonial period, the present state of UP was called the United Provinces. This was because it was an administrative construct, created by uniting the earlier provinces of Agra and Oudh. 20

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hierarchy, introduced egalitarian values and created a climate favourable to the emergence of a Dalit movement’. While the caste-based movement in TN was bottom-up and was socially sensitive, the caste movement in UP was top-down. Successive UP state governments dominated by the upper castes (read the Congress) focused only on the fertile western part of the state dominated by upper castes such as the Jats and Brahmins. Moreover, the national parties did not encourage local leadership. Overall development of the state needs the development of all the parts. The neglect of the lower castes in UP, thus, led to a revolt of the subalterns (consisting of the OBCs and Muslims) in 1990s. While the system was unjust, for various reasons, the revolt did not happen earlier in UP. There was no revolt of the lower castes which happened in TN due to the enlightened policies of the leaders such as Annadorai and others who encouraged reservation for them in educational institutions and with employers. TN set aside 69% of government jobs and seats in higher education for downtrodden castes, which helped rapidly move lower caste people into the mainstream. The North did put in place affirmative action policies, but because education was widely embraced in the South, southern people from lower castes were better able to take advantage of these opportunities than northerners. We find evidence of the control of the caste system in UP by Dreze and Gazdar (1997, pp. 94–95), where they state as follows: In pre-independence Uttar Pradesh, the institutional basis of local governance largely derived from the network of social and economic relations associated with zamindari and jajmani. The powerful zamindars dealt with higher levels of political authority, and sometimes also played a role in matters of collective interest at the village level. The jajmani system defined patron–client relations pertaining not only to private transactions but also to some public goods and services. While the services of non-agricultural castes such as carpenters, smiths, barbers … were privately consumed, some castes were responsible for services of a more public nature such as sweeping, sanitation, drainage and street maintenance. Even schooling was largely organized on the basis of traditional caste obligations (in this case involving Brahmin teachers) in many villages. This system of customary obligations was, of course, highly unequal and extremely unjust.

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When compared with what happened in TN, the struggle for social transformation took place in UP much later. As Pai (2002, p. 8) points out, It was only in 1980s after considerable acceleration of the process of democratization, leading to formation of movements and parties espousing the interests and demands of regional, sectional and oppressed groups, that the BSP was formed in 1984. This coincided … with a slight structural shift in the economy from agriculture towards industry for the first time since independence—a process which provided Dalits the economic potential to assert against upper-caste domination. Under the leadership of the BSP, the Dalit movement in UP entered a new phase of separation and hostility towards mainstream parties and the upper-caste Hindu community. … A number of significant socio-economic changes within the Dalit community by the 1980s made this possible. There were considerable improvements in the conditions of Dalits. The terrible poverty and absolute dependence on landowners and old patron–client relations disappeared in areas such as the eastern UP plains. … Urbanization increased non-land employment opportunities in brick kilns, construction activities, and rickshaw-pulling in the cities. No longer prepared to suffer indignities, the Dalits gave up unclean traditional jobs, such as carrying and skinning dead animals and scavenging. The catalyst for change everywhere has been education...

While the caste-based reservation has been in existence in UP since independence, due to the lack of adequately qualified personnel, many positions in the government and educational institutions remained unfilled. One outcome of poor governance in UP was, therefore, that funds for developmental purposes remained unutilized (the case of the Integrated Child Development Scheme [ICDS] was cited in our discussions with officials). In UP and other northern states, a social churning in the nature of what happened in TN, and the consequent creation of institutional networks, which supported education and entrepreneurship, thus, did not happen. The Bania and Vaishya groups in UP had a stranglehold over trade, industry and money lending. The lower castes were unable to overcome the barriers to entry erected by these higher castes. The incentives for the farming classes to demand change were also weaker. Farmers who prospered from agriculture were content and used their surpluses for

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conspicuous consumption rather than invest them in trade and industry. It is no surprise, then, that a new and broad based entrepreneurial class did not emerge in the North. On the other hand, in TN, the social movements led by the intermediate and lower castes and affirmative action by the state mutually reinforced each other. Starting from caste-based associations, a wide variety of non-governmental organizations sprung up in TN in the 20th century, expanding the scope for networking, exchange of information and serving the diverse needs of society. Their influence on political movements also increased over time. Apart from educational institutions, cooperatives, healthcare institutions, social welfare organizations, civic groups and professional organizations of all kinds emerged in TN in recent decades, making it a leader in the voluntary sector. As education, especially technical education, became more accessible to all, new entrepreneurs appeared on the scene, creating employment and income opportunities for larger numbers of people, notably after Independence. The India Human Development Report (IHDR) (2011) states that Tamil Nadu … has taken strong measures to ensure the effectiveness of the public health system and its health policies. The Dravidian movement, which began in Tamil Nadu, aimed at providing opportunities to all, irrespective of the caste. With the dual objective of educating all and eradicating superstition, the movement proved to be one of the biggest achievements of the state government. This was one of the main reasons for higher enrolment rates for SC and OBC children in the state. Thus, the real explanation for the better than average health, education, and nutritional status of the populace lies in the social movements and technical interventions initiated by the Government of Tamil Nadu. The Dravidian movement in the state provided socio-political and cultural space for even the deprived sections, making the process of development more inclusive. (Mehrotra 2006)

Sudha Pai (2002, p. 7) states additional evidence of the above and explains why UP experienced no lower-caste movement: Both industrialization and land reform were expected to release forces of social and economic change. The latter, though limited in scope and poorly implemented, together with the Green Revolution, enabled the first wave of backward class horizontal mobilization. It took place in the North Indian

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countryside, under leaders such as Charan Singh, against upper-caste socioeconomic and political domination. This was seen in the formation of a backwards-led government in 1967 in which, for the first time, a number of backward caste ministers were appointed. The RPI-led Dalit movement in the 1960s was the first attempt in the post-independence period, to grapple with the issues outlined before, but it proved to be short-lived. This was because its leaders were divided over the ideology and strategy of the Party, particularly, its relationship with the Congress, which was able to absorb it by the late 1960s. Moreover, the existing power structure in UP, in the rural areas in the first two decades after independence, did not allow any space for the RPI to become a strong force and it remained a marginal party. In the districts, the Congress established ‘vote-banks’ or ‘caste-coalitions’ by which the upper-caste leaders could, on the basis of patron–client relationships, mobilize the lower castes. Moreover the Dalit movement in UP, unlike in western and southern India, was taking place in a very rigid and conservative society, which had experienced no strong anti-caste movement in the colonial period. Even today, the Dalits encounter a rigid caste system in the urban areas and open hostility regarding practice of untouchability in some parts in the rural areas. As a result, in the 1970s, except during the Emergency, the Dalits, attracted by Mrs. Gandhi’s ‘Garibi Hatao’ (remove poverty) and Twenty-Point Programme, supported the Congress Party. Thus, UP experienced no lower-caste movement.

Mehrotra (2006) points out the lessons from TN for UP: The lesson of the social transformation in Tamil Nadu is that there are technical interventions needed to transform the health, nutrition and education of the poor.… Those interventions are the responsibility of the state government, since health and education are state subjects (although they are also on the concurrent list in the Indian Constitution). The state governments are the ones that account for nearly 90 per cent of total government expenditure on health and education. However … those technical interventions are unlikely to happen without a social mobilisation—a la Tamil Nadu and Kerala.

It is reasonable to conclude from the foregoing discussion that the social movements that occurred in TN strengthened the demand side of governance in significant ways. They increased the awareness of large segments of the population, especially those of the lower castes

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concerning their rights, and motivated them to exert pressure on the government to reform existing practices and systems and initiate programmes for their development and welfare. They provided the organizational platforms for the people to network with each other, take collective action to promote their common interests and open the door to new vistas such as entrepreneurship. Summarizing, in Table 4.2, we present the initial levels of per capita income in 1960–61 and the rates of growth of per capita income for TN and UP for the two periods, 1960–61 to 1987–88 (defined as the I period), and 1987–88 and 2004–5 (defined as the II period). Alongside this, we give the initial levels and the growth rates for a number of factors that could have influenced this growth pattern and the shift observed from 1987 to 1988. Some points are worth noting from Table 4.2. It shows that it is not the case that TN was always doing better than UP. In fact, UP was better than TN in some respects to begin with (e.g., proportion of graduates and police firing), and that TN’s surge is recent (e.g., per capita NSDP, urbanization, electricity installed capacity, police firing and the percentage of cases pending investigation). Our hypothesis is that both the initial conditions and the rates of change in the selected factors could have led to the divergence observed between the two states. The foregoing analysis sheds useful light on the role that different factors play in the process of economic growth. We see how the proximate factors of higher levels of human resource capabilities, infrastructure, urbanization, and the quantum and efficiency of public investment are associated with the faster growth of per capita income in TN than in UP. But we also see that the foundational factors, namely the quality of governance and the demand factor (working through social mobilization), were also stronger in TN when compared to UP. While it is difficult to attribute the contribution of each of these factors to the differential performance of the two states, we suspect that the foundational factors did create an enabling environment for the proximate factors to perform better in TN than in UP. Foundational factors contribute directly through efficient production, delivery and utilization of public goods that enhance productivity and incomes. They also contribute indirectly by sustaining proximate factors in the long run.



15.5 0.75

Electricity installed capacity per 1,000 population*

Proportion of graduates|| 1971 4,773

26.7

% of urban population*

Per capita agricultural NSDP*

263

Per capita development expenditure (in `) 1980–81

3,887

0.85

5.4

12.8

179

20.9

22

17 31.4 §

UP 3,338

TN 5,053

Literacy rate‡

Natural growth rate of population (1971–73)

Per capita NSDP* (in `)

Indicator

Initial values (1960–61 unless otherwise specified)

–0.02

5.66

3.26

0.9

11.25

2.63

–0.78

0.98

From 1960–61 to 1987–88 (I period)

TN

0.04

3.1

2.61

2.23

10.52

1.58

–1.65

4.52

From 1987–88 to 2004–5 (II period)

Table 4.2 Initial Values and Compound Growth Rates for Selected Indicators—TN and UP

0.00

8.25

6.55

1.71

11.37

2.42

0.61

1.0

UP

0.01

3.61

–2.39

0.61

8.65

3.29

–0.76

1.75

From 1987–88 to 2004–5 (II period)

(Table 4.2 Contd.)

From 1960–61 to 1987–88 (I period)

73 (II period)

60.4 (II period)

% cases pending investigation in courts at the end of year# (1990–91)

NA

NA

From 1960–61 to 1987–88 (I period)

TN

–0.29

–11.34

From 1987–88 to 2004–5 (II period)

NA

NA

From 1960–61 to 1987–88 (I period)

UP

0.75

2.29

From 1987–88 to 2004–5 (II period)

Sources: EPW Research Foundation; Census of India; CMIE; Central Electricity Authority; National Crime Records Bureau; authors’ computations. Notes: *refers to the periods from 1960–61 to 1987–88 and from 1987–88 to 2004–5. † refers to the periods from 1970–71 to 1987–88 and from 1987–88 to 2004–5. ‡ refers to the periods from 1960–61 to 1987–88 and from 1987–88 to 2000–2001; for agricultural output, the period I refers to from 1960–61 to 1980–81 and the period II refers to from 1980–81 to 2000–2001. § refers to the periods from 1980–81 to 1987–88 and from 1987–88 to 2004–5. || refers to the period from 1970–71 to 1987–88 and from 1987–88 to 2000–2001. # refers to the period from 1987–88 to 2005–6.

0.44 (II period)

UP

0.5 (II period)

TN

Initial values (1960–61 unless otherwise specified)

Police firing incidences for one million population# (1990–91)

Indicator

(Table 4.2 Contd.)

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Interpretation of the Evidence Let us now see whether the evidence on different factors presented above sheds any light on the divergent paths of per capita income growth observed in UP and TN. The puzzle to be explained is why TN’s per capita income began to shift upwards markedly compared to that of UP since the mid 1980s. Till this period, per capita incomes were growing in two states at about the same slow rate. We present below our interpretation of the factors that may have led to this divergence. Differences in the initial conditions with respect to different factors in two states could at least in part account for the divergence. Divergent growth rates of factors over the period we have studied could be another explanation. Furthermore, residual factors that we have not taken into account in the study also could have contributed to the outcome. With respect to most of the factors, TN had higher initial levels than UP. Indicators such as literacy, IMR (reflecting health status), urbanization, food crop yields per acre, electricity and roads showed that TN’s initial conditions were better in human capabilities, urbanization, infrastructure and resource efficiency, though the degree of superiority varied between the factors. But initial conditions in UP in terms of the stock of all graduates and political stability as measured by CM’s average tenure were about the same or even slightly better than those in TN. With respect to per capita development spending, TN’s initial condition was only slightly better than UP’s. We conclude that while TN had an edge with regard to the initial conditions of several factors that we have highlighted, it did not have an initial advantage in all of them. The rates of growth of these factors over the period of study were similar in both states for most of the factors. The main exceptions were electricity, development spending and the CM’s average tenure. The first two accelerated by the mid-1980s in TN while the last indicator declined markedly in UP since 1967. With regard to the stock of graduates, a component that reflects technical manpower (engineers) grew much faster in TN than in UP since the late 1980s. There were, thus, some notable differences in the growth rates of the factors the implications of which need to be considered. A closer look at these two factors is revealing. Technical manpower signals a critical resource that modern industries and the service sector

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need. Electricity is an essential sector for most economic activities, especially manufacturing. Both of them have long gestation periods of five years or more before the output comes on stream. A perusal of the data clearly shows that TN had encouraged investment in these two critical sectors much before per capita income had begun to climb. Organizing the supply of these factors well ahead may have played a key role in the transformation that TN experienced from the mid-1980s. It is reported that TN had a total of over 540 engineering colleges in 2008 compared to 11 colleges in the 1970s. UP, on the other hand, has less than half this number though it had a head start in this arena in the 19th century. Better governance, strategic thinking, a proactive industrial policy or a combination of these factors may well have contributed to what TN managed to achieve in this arena. 1. A surprising finding is that despite the edge that TN had in terms of the initial conditions, the growth rate of per capita income in both the states remained sluggish during a major period of our study. Between 1961 and 1985, per capita income grew at mere 1% per year in TN and UP. It tells us that an edge in terms of initial conditions need not automatically result in faster growth for a country or a state. They may have been necessary conditions, but do not fully explain what triggered the take-off of the TN economy. At best, we can conclude that the potential for economic growth existed more in TN than in UP for the reasons set out above, but that the potential was not exploited for some reason. 2. For an explanation of the puzzle, we need to turn to the policy shifts that occurred in the Indian economy since the mid-1980s. It was during Rajiv Gandhi’s regime that the first steps towards decontrol and liberalization occurred in India. Delicensing of industries and more liberal policies towards foreign investment were adopted during this period. In 1991, Prime Minister Narasimha Rao and Finance Minister Manmohan Singh further opened up the Indian economy and created favourable conditions for private sector investment, both domestic and foreign. It also happened to be the period when the winds of liberalization were blowing across the globe, facilitating capital and technology flows into developing countries. Needless to add, the policy

What Explains the North–South Divide?

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shift was national, with all the states free to take advantage of the opportunities it offered. 3. The marked upward shift in per capita income and the subsequent reduction in poverty that TN experienced since the mid-1980s can be attributed to the flow of substantial investments into the state. Though investment data is not available for the entire period of our study, we find that during 2000–2006, TN attracted foreign investment proposals worth `85 billion while UP received a mere `150 million. That year, TN had the third place in the country in foreign direct investment, after Delhi, Haryana and Maharashtra. Per capita development spending also moved up much faster in TN than in UP, though the uptrend began only after TN’s per capita income growth had accelerated. The poorer performance of UP on both counts could well have been due to the weaknesses in the foundational factors and the resultant inability to stem its relative decline in terms of political stability and other law and order indicators. In summary, we have demonstrated that though for a long period, the per capita income levels of TN and UP were not far apart, a marked upward shift in per capita income and a reduction in poverty levels occurred in TN relative to UP since the mid-1980s. We have offered an explanation of the underlying factors behind this striking divergence between the two states. We have concluded that the upward shift in per capita income and downward trend in poverty reduction that occurred in TN relative to UP could be explained only in part by the advantage the former had in terms of human capabilities, infrastructure and internal resources. These were reinforced by TN’s better showing in terms of political stability and law and order, a reflection of its relatively better governance than in UP. Similarly, TN had undergone a remarkable period of social transformation decades before that in UP. It significantly strengthened the ability and willingness of large sections of the population to demand better governance from the state. Surprisingly, despite these advantages, for nearly 25 years, TN’s growth record was no better than that of UP, mainly because the national policy regime was restrictive and limited the scope for potential investors to take advantage of the differential conditions prevailing in our

86

THE PARADOX OF INDIA’S NORTH–SOUTH DIVIDE

states. The potential for faster growth these preconditions created were exploited more fully and effectively only when major policy shifts occurred at the national and global levels, facilitating the massive flow of investment resources into the state. The foregoing analysis sheds useful light on the role that different factors play in the process of economic growth. We see how the proximate factors of higher levels of human resource capabilities, infrastructure, urbanization, and the quantum and efficiency of public investment are associated with the faster growth of per capita income in TN than in UP. For nearly 25 years (1961–85), TN was way ahead of UP with respect to literacy, infant mortality, urbanization, installed power generating capacity and roads.22 In terms of per capita development expenditure and technical education, the gap between the two states was much smaller. These proximate factors contributed to the per capita income surge in TN in the period after 1985. They provided the inputs and other resources that prepared the ground for TN to make the leap, leaving UP behind. Our analysis also shows that the foundational factors, namely the quality of governance and the demand factor (working through social mobilization), were stronger in TN when compared to UP. During the period 1961–85, TN’s record in terms of political stability (the CM’s tenure), ratio of civil police to the total police force and the pendency of court cases was well above that of UP. Police firings, another law and order indicator, however, shows a mixed trend, with UP’s record getting worse in later years. On the demand side, we have already offered a wide range of evidence to show that TN had a head start in terms of social movements that helped vast segments of the population to demand their rights and access the services offered by government. Social and political mobilization of this scale and scope has occurred in UP only in more recent decades. These two foundational factors have worked in an interactive fashion in TN. Leaders in government responded positively to the signals from the grassroots (social movements) and strengthened the proximate factors mentioned above. The population at large was better prepared through In China too, progress in terms of education and health had occurred well before economic performance surged in a big way. 22

What Explains the North–South Divide?

87

social mobilization to take advantage of the public goods and services thus provided. While it is difficult to attribute the contribution of each of these factors to the differential performance of the two states, we suspect that the foundational factors did create an enabling environment for the proximate factors to perform better in TN than in UP. Foundational factors contributed directly through the efficient production, delivery and utilization of public goods that enhance the productivity and incomes of people. They also contributed indirectly by sustaining the proximate factors for long periods. Two other comments are in order. First, though the focus of our analysis has been on explaining the income divergence between TN and UP, it should be noted that TN’s lead in terms of human resource capabilities, urbanization, infrastructure and investment has been maintained intact even in the recent years. Income growth has, therefore, not been at the cost of these other important development outcomes. In fact, rising incomes seem to have permitted TN to generate the resources and capacity to sustain these outcomes. Second, our analysis also offers some hopeful signs of the trends in UP. In terms of both literacy and infant mortality, the gap between UP and TN has narrowed in the years since 1985. There is a similar positive move in recent years on the political and social mobilization front too. As the proximate and foundational factors in UP turn increasingly positive and converge, we can expect the economic performance of UP to catch up with TN. Rising above the details of the individual states which have been studied extensively in this chapter, the next chapter focuses on the divide between the northern and southern regions.

5

Southern Region versus Northern Region

W

e now make an attempt to extend the two-state analysis to the two regions and ask whether India’s southern region is ahead of the North recently since the 1990s. In this, we particularly note that in the 1960s, the southern region was behind the North in terms of the incidence of rural poverty. To answer the question of the recent surge of the South, we have aggregated the performance data of the states in the two regions for purposes of comparison. It should, however, be noted that we could not make the analysis of the regions exactly identical to that of the individual states, due to the lack of availability of all data. The last section of this chapter examines access to basic services and assets in the North and the South, making use of data from the census 2001 and 2011. In the case of census 2011, we had the additional advantage that it also presents data on access to basic services and assets in the slums of the Indian states. If, in the course of our analysis, we find that the resulting pattern is similar to what our comparison of TN and UP showed, we may conclude that the southern region has indeed pulled ahead, leaving the northern region behind. Figures 5.1 and 5.2 show how the two regions have performed with respect to per capita income and poverty reduction, respectively, over a 40- and 30-year period (respectively, from 1960–61 to 2004–5 and from 1973–74 to 2004–5). A perusal of the figures affirms the pattern of change that we have already seen in our comparison of TN and UP. The two regions differed only by 39% in terms of per capita income

89

Southern Region versus Northern Region Figure 5.1 Per Capita NSDP—Southern and Northern States, 1960–2005, 1993–94 Constant Prices

2000–2001

1995–96

1990–91

1985–86

1980–81

1975–76

1970–71

1965–66

1960–61

14,000 12,000 10,000 8,000 6,000 4,000 2,000 0

Average PC (weighted) NSDP, South Average PC (weighted) NSDP, North Source: EPW Research Foundation. Note: PC: Per capita. Figure 5.2 Total Poverty Rates—Southern and Northern States, 1973–2003 70.00 60.00 50.00 40.00 30.00 20.00 10.00 0.00 1973

1978

1983

1988

1993

1998

Weighted total poverty rate, South Weighted total poverty rate, North Sources: Planning Commission; Census of India; authors’ computations.

2003

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THE PARADOX OF INDIA’S NORTH–SOUTH DIVIDE

in 1960–61 while the gap had widened to 101% by 2004–5. The southern per capita income rose faster since 1992–93 compared to that of the North. But prior to this period, the annual per capita income growth rates of the two regions were low and similar (average growth rate of 1.78% for the South and 2.20% for the northern states during 1960–91). The economic surge of the South is, thus, a recent phenomenon. Similarly, on the poverty front, some of the northern states were better off compared to their southern counterparts. In fact, in 1960–61, the average rural poverty of the four northern states was only 55% when compared with the average rural poverty levels in the southern states (66%) (based on the data from Datt and Ravallion 1998). But by 2004–5, the southern states’ poverty levels had fallen well below those of the northern states. Judged by the criteria of per capita income growth and poverty reduction, the North–South divide in India is a significant phenomenon that has emerged in the past decade and a half. Are the factors associated with the North–South divide similar to what we found in our analysis of TN and UP? Our hypothesis is that the same factors may have been at work here too, though the specific historical factors and sequences may not have been the same. We turn to an analysis of these factors in the following section.

Human Capabilities, Skills and Awareness Similar to our analysis in the TN–UP section, we choose the literacy rate and the proportion of graduates in the southern and northern states as indicators of education. We choose the IMR of population and life expectancy as our measures of health. The reasons why we may expect the literacy rate to affect the economic growth have been explained in the earlier section. Figures 5.3 and 5.4 summarize the literacy rate historically since 1951, respectively, for the southern and northern states. While the literacy rate in the northern states increased from only 10% (most of the states) in 1951 to a little above 60% in 2001 (MP), it increased from 40% in 1951 (Kerala) to nearly 90% (Kerala). Apart from Kerala, which is an outlier with respect to the literacy rate, the other southern states also made a leap forward from only 13.2% literacy in

Southern Region versus Northern Region

91

Figure 5.3 Literacy Rate—Southern States, 1951–2011 100.00 80.00 60.00 40.00 20.00

AP Karnataka Kerala TN

0.00 1951 1961 1971 1981 1991 2001 2011 Sources: Census of India; authors’ computations. Figure 5.4 Literacy Rate—Northern States, 1951–2011 80.00 70.00 60.00 50.00 40.00 30.00 20.00

Bihar MP Rajasthan UP

10.00 0.00 1951 1961 1971 1981 1991 2001 2011 Sources: Census of India; authors’ computations.

1951 to nearly 61% literacy in 2001. Overall, the weighted average literacy rate in the southern states increased from 23.5% in 1951 to nearly 70% in 2001, recording a threefold increase. In the northern states, the weighted literacy rate increased from only 10.4% in 1951 to 59% in 2001, registering a fivefold increase. Since this is consistent with the trends in TN and UP, we surmise that the literacy rate must have been one of the preconditions necessary for economic growth to have taken off in the southern states as a whole, similar to that in TN.

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THE PARADOX OF INDIA’S NORTH–SOUTH DIVIDE

Next, we review trends in the proportion of graduates in the South and the northern states. We have discussed the expected impacts of the proportion of graduates on per capita incomes and their rationale in the context of TN–UP analysis, and hence no repetition is required. It is sufficient to note that the proportion of graduates indicates those in the population with a threshold level of education with specific set of skills, required for certain firms or industries. Figures 5.5 and 5.6 summarize the trends in the proportion of graduates in the South and northern states, respectively. While, individually, TN does not have a distinct edge over UP in terms of the proportion of graduates, other southern states seem to be a little ahead of the northern states on this account. In fact, TN is the laggard among the southern states as far as the proportion of graduates is concerned. In fact, both Karnataka and Kerala have more than 6% of their population above 15 years, as graduates, while AP has 5.5% and TN has only 4.8% graduates as of 2001. If we take the weighted proportion of graduates in all southern states, it increased from 0.83% in 1971 to 5.5% in 2001, registering seven times growth.1 As far as the northern states are concerned (Figure 5.6), the maximum proportion of graduates are in MP at 5.2% followed by UP at 5.1%; Rajasthan and Bihar are laggards at 4.3% and 4.4% of graduates, respectively, as of 2001. The weighted average proportion of graduates in all the northern states increased from 0.83% in 1971 (the starting point for both the regions in terms of the proportion of graduates was the same) to only 4.9% in 2001, registering a sixfold increase. Given that there is not much difference in the growth of the proportion of graduates in the two groups of states, it is plausible that an explanation of the southern growth story lies elsewhere—in the presence of a larger labour force with technical skills in the southern region when compared with those in the northern states. In fact, the current evidence is that intake into engineering colleges in the four southern states

The data on graduates for 2001 had been obtained from the Census of India 2001, based on which the proportion of graduates had been computed as a proportion of those above 15 years in 2001. At the time this research was completed, data on the number of graduates (which is part of the C series) had not been released by census 2011. 1

Southern Region versus Northern Region

93

Figure 5.5 Proportion of Graduates—Southern States, 1971–2001 7.00% 6.00% 5.00% 4.00% 3.00% 2.00% 1.00% 0.00% 1971 AP

1981

1991

Karnataka

Kerala

2001 TN

Sources: Census of India; authors’ computations. Figure 5.6 Proportion of Graduates—Northern States, 1971–2001 6.00% 5.00% 4.00% 3.00% 2.00% 1.00% 0.00% 1971 Bihar

1981 MP

1991 Rajasthan

2001 UP

Sources: Census of India; authors’ computations.

accounts for nearly 53% of all intake into engineering colleges in the country while the North has a mere 16% (see Banerji and Muley 2007). Vyas (2014) highlights that the southern states have focused on labourintensive investments, particularly information technology, which has benefited their cities.

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THE PARADOX OF INDIA’S NORTH–SOUTH DIVIDE

Infant Mortality Rate of Population The IMR is chosen as a measure of the health of the population for reasons discussed in the earlier chapter. Just as we did in the case of the TN–UP analysis, we compared the IMR of population across the southern and northern states. Figures 5.7 and 5.8 summarize this, respectively, for the southern and northern states. The weighted average IMR Figure 5.7 IMR of Population—Southern States, 1971–97

71 72 73 74 75 76 77 78 79 80 81 82 83 84 85 86 87 88 89 90 91 92 93 94 95 96 97

140 120 100 80 60 40 20 0 AP

Karnataka

Kerala

TN

Sources: Census of India; authors’ computations. Figure 5.8 IMR of Population—Northern States, 1971–97 250 200 150 100

0

71 72 73 74 75 76 77 78 79 80 81 82 83 84 85 86 87 88 89 90 91 92 93 94 95 96 97

50

Bihar

MP

Rajasthan

Sources: Census of India; authors’ computations.

UP

Southern Region versus Northern Region

95

of population in the southern states declined from 98.2 in 1971 to 50.5 in 1997, registering a fall of 47.6 percentage points. In the northern states, on the other hand, the weighted average IMR fell from 156.7 in 1971 to 83.4 in 1997, recording a reduction of nearly 73.4 percentage points, much higher than that in the southern states. Hence, the southern states did not have an advantage in this factor compared with the northern states, and hence differences in this across the two regions may not have been a factor influencing economic growth in the two regions. Next, we reviewed trends in life expectancy for the southern and northern states. As explained earlier, life expectancy can signal some preconditions such as good health care, environment and other factors which are necessary for economic growth to take off and vice-versa. Figures 5.9 and 5.10 summarize trends in life expectancy, respectively, for the southern and northern states. The figures show that the southern states were always ahead of the northern states as far as life expectancy is concerned (1971–2011). We find that over 1971–2011, on average, life expectancy at birth in the southern states was 63 years, whereas in the northern states, over the same period, it was only 56 years on average. This lends support that better human capabilities in the southern states Figure 5.9 Trends in Life Expectancy—Southern States 80 70 60 50 40 AP Karnataka Kerala TN

30 20 10 0 1971

1981

1991

2001

2011

Source: Sample registration system of the Registrar General of India.

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THE PARADOX OF INDIA’S NORTH–SOUTH DIVIDE

Figure 5.10 Trends in Life Expectancy—Northern States 80 70 60 50 40 Bihar MP Rajasthan UP

30 20 10 0 1971

1981

1991

2001

2011

Source: Sample registration system of the Registrar General of India.

over a long period of time may have contributed to economic growth taking off there.

Installed Generating Capacity As discussed earlier, installed generating capacity of electricity is a critical input for industries and services. We found in the case of the TN– UP analysis that TN had much higher installed capacity in the 1960s than that of UP. Further, beginning from the late 1980s onwards, TN’s installed capacity generation took off while UP’s declined. We reviewed the installed generating capacity of the southern states versus the northern states to examine if the TN–UP story holds good. Figures 5.11 and 5.12, respectively, summarize the trends in the installed generating capacity of electricity in the two regions for a reasonably long period of time, 1960–2004. The figures confirm what we learned in the case of the individual states. The weighted installed capacity per million population had always been higher in the southern states beginning with 10.23 (000 KW) in

97

Southern Region versus Northern Region Figure 5.11 Installed Generating Capacity per Million Population— Southern States, 1960–2004 120.0 100.0 80.0 60.0 40.0 20.0 2002

2004 2004

2000

1998

1996

1994

2002

AP Kerala

1992

1990

1988

1986

1984

1981

1979

1974

1971

1668

1966

1960

0.0

Karnataka TN

Sources: Central Electricity Authority; authors’ computations.

Figure 5.12 Installed Generating Capacity per Million Population— Northern States, 1960–2004

Bihar Rajasthan

MP UP

Sources: Central Electricity Authority; authors’ computations.

2000

1998

1996

1994

1992

1990

1988

1986

1984

1981

1979

1974

1971

1668

1966

1960

80.0 70.0 60.0 50.0 40.0 30.0 20.0 10.0 0.0

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THE PARADOX OF INDIA’S NORTH–SOUTH DIVIDE

1960, compared with only 6.35 (000 KW) for the northern states. The installed generating capacity per million population for Kerala was much lower than for other southern states. With this caveat, while there was a continuous surge in the weighted (with population) installed capacity of the southern states, there was in fact a decline in the northern states in the late 1990s (1996 to be precise) and since then has been diverging. This shows that the southern states were prepared to take the plunge when the economic reforms of 1991 took place, whereas the northern states simply did not have the prerequisites in place for economic growth to occur.

Urbanization Finally, we examine another important indicator of disparities between the two regions—urbanization. The findings here are consistent with what we find in the two-state analysis. Figures 5.13 and 5.14 present the urbanization rates for the South and northern states separately. Not only is the average proportion of urban population higher in the southern states, to begin with, compared with their northern counterparts, but their rate of urbanization has also proceeded at the same rate, with the result that the northern states are only half as urbanized as their southern counterparts. For instance, the average proportion of urban population in the southern states was 31% over 1971–2011 compared to only 19% Figure 5.13 Trends in Urbanization—Southern States, 1971–2011 60.0 50.0 40.0 30.0

AP Karnataka Kerala TN

20.0 10.0 0.0 1971

1981

1991

2001

Sources: Census of India; authors’ computations.

2011

Southern Region versus Northern Region

99

Figure 5.14 Trends in Urbanization—Northern States, 1971–2011 30.0 25.0 20.0 15.0

Bihar MP Rajasthan UP

10.0 5.0 0.0 1971

1981

1991

2001

2011

Sources: Census of India; authors’ computations.

for the northern states. The southern states’ urbanization was on average 42% in 2011 when compared with only 22% for the northern states. So it does appear that the northern states failed to benefit from the benefits of agglomeration and urbanization economies with the passage of time, compared with the South.

Agricultural Output: An Aid to Inclusive Growth Growing urbanization and expansion of industries in the southern states have reduced the relative importance of agriculture in their SDP. However, it is significant that despite this trend, their agricultural sector has performed better than that in the northern states. Figure 5.15 shows that during the period from 1960–61 to 1990–91, the per capita agricultural production of the southern and northern states differed only by 16% (with the exception of 1980–81, when there was a smaller gap of 14%). By 2000–2001, however, the southern states had left the North far behind, widening the gap between them by 81%. The same factors that were highlighted in the TN–UP comparison may have played a major role in the other southern states too. This outcome certainly testifies to the greater efficiency in the use of scarce resources

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THE PARADOX OF INDIA’S NORTH–SOUTH DIVIDE

Figure 5.15 Agricultural Output—North and South

Agriculture NSDP (per capita) North, Weighted Average

2000–2001

1990–91

1980–81

1970–71

Agriculture NSDP (per capita) South, Weighted Average 1960–61

8,000 7,000 6,000 5,000 4,000 3,000 2,000 1,000 0

Sources: Census of India various years for rural population; EPW Research Foundation for data on agriculture NSDP. Note: See Figure 4.10, a similar procedure is used to compare data from various years for all states. Here, weighted averages are used for the states, with rural population of the respective states being the weights.

such as land and water in the South. Needless to say, there is much more to be done to improve efficiency in the northern states. It also tells us that the rural population need not be left behind in terms of incomes and employment even when urbanization and industrialization are gathering momentum.

Public Investment Next we examine trends in public investment and look at per capita developmental expenditure in the southern region versus that in the northern states. Per capita developmental expenditure, as discussed earlier, could be important as it results in the creation of productive assets. The hypothesis is that the southern states spend more on developmental expenditure.

Southern Region versus Northern Region

101

2000–2001

1998–99

1996–97

1992–93

1990–91

1994–95

Karnataka TN

2002–3 (RE)

AP Kerala

1988–89

1986–87

1984–85

1982–83

3,500 3,000 2,500 2,000 1,500 500 0

1980–81

Figure 5.16 Per Capita Developmental Spending—Southern States, 1980–2003

Sources: Centre for Monitoring Indian Economy; authors’ computations. Note: RE: Revised estimates.

We present the per capita developmental expenditure of the southern and northern states during 1980–2004 in Figures 5.16 and 5.17. Figure 5.16 shows that the per capita developmental expenditure of all the southern states very closely clustered during the entire period we examine. Interestingly, Figure 5.17 shows a similar trend for the northern states, with the exception of MP (which started at a much higher level than the other northern states) during the period we examine. When we examine the average weighted (weighted with population) per capita developmental expenditure, the southern states have experienced a much steeper increase starting from only `208 in 1980 and increasing to `2,812 in 2003–4, compared with the northern states which started with a higher `474 but increased only to `1,623 per capita in 2003–4. Thus, what we notice is an upward shift in investment spending after the increase in incomes. This could be a result of increasing revenues and must not have been a causal factor for increasing incomes.2 2 We do not have the detailed data on developmental outcomes in the eight states for us to make a comparison of relative efficiencies of spending.

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THE PARADOX OF INDIA’S NORTH–SOUTH DIVIDE

Figure 5.17 Per Capita Developmental Spending—Northern States, 1980–2003 2,500 2,000 1,500 1,000 500 2000–2001

1998–99

1996–97

1994–95

1992–93

Bihar + Jharkhand Rajasthan

2002–3 (RE)

UP + Uttarakhand MP + Chhattisgarh

1990–91

1988–89

1986–87

1984–85

1982–83

1980–81

0

Sources: Centre for Monitoring Indian Economy; authors’ computations. Note: RE: Revised estimates.

Governance A comparison of the North and South in terms of three indicators of governance, average tenure of CMs, police firing cases per million population and the proportion of pending cases in the state judiciary is summarized, respectively, in Table 5.1, and Figures 5.18 and 5.19. The southern region has performed distinctly better than the northern region on all the dimensions (except police firing incidents for which the evidence is mixed). We find that the police firing incidents in the South are dominated by AP (1987–2002), which was characterized by frequent naxalite disturbances during which there was a sharp increase in the number of police firing incidents. It should be noted that AP which is high on this score (law and order problems) is lowest on the per capita income front among the southern states (implied in Figure 5.1). By and large, police firing incidents in the southern states have always been at a lower level than the North (Figures 5.18 and 5.19). On average, the proportion of pending cases in courts in the northern states has also been higher than that in the South (Figure 5.20).

Southern Region versus Northern Region

103

Table 5.1 Average Tenure of CMs (number of days)—Northern and Southern States Average number of days (weighted), North

Year

Average number of days (weighted), South

1960–61

831

29

1,268

25

1980–81

717

30

449

42

2000–2001

914

31

663

41

Number of CMs, South

Number of CMs, North

Sources: http://www.elections.in/; authors’ computations and analyses. Note: Average number of days includes the tenure of president’s rule. Figure 5.18 Police Firing Incidents per Million Population—Southern States

1968–69 1969–70 1970–71 1971–72 1987–88 1988–89 1989–90 1990–91 1991–92 1992–93 1993–94 1994–95 1995–96 1996–97 1997–98 1998–99 1999–2000 2000–2001 2001–2 2002–3 2003–4 2004–5

9.0 8.0 7.0 6.0 5.0 4.0 3.0 2.0 1.0 0.0

AP

Karnataka

Kerala

TN

Sources: National Crime Records Bureau; authors’ computations.

Overall, these findings are similar to what we learned from the UP– TN comparison. It is significant that the North started with a better record in terms of CMs’ tenure than the South, but experienced a clear decline in later periods. As noted earlier, we have captured only some dimensions of governance through these indicators.

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THE PARADOX OF INDIA’S NORTH–SOUTH DIVIDE

Figure 5.19 Police Firing Incidents per Million Population—Northern States

1968–69 1969–70 1970–71 1971–72 1987–88 1988–89 1989–90 1990–91 1991–92 1992–93 1993–94 1994–95 1995–96 1996–97 1997–98 1998–99 1999–2000 2000–2001 2001–2 2002–3 2003–4 2004–5

4.0 3.5 3.0 2.5 2.0 1.5 1.0 0.5 0.0

Bihar Rajasthan

MP UP

Sources: National Crime Records Bureau; authors’ computations.

Figure 5.20 Proportion of Pending Court Cases—Southern and Northern States

Average % of court pending cases (weighted), South Average % of court pending cases (weighted), North Sources: National Crime Records Bureau; authors’ computations.

2004–5

2003–4

2002–3

2001–2

2000–2001

1999–2000

1998–99

1997–98

1996–97

1995–96

1994–95

1993–94

1992–93

1991–92

1990–91

90.0 80.0 70.0 60.0 50.0 40.0 30.0 20.0 10.0 0.0

Southern Region versus Northern Region

105

Our findings here do not imply that governance was of the highest order in any of the states under review. In fact, allegations and evidence of corruption, abuse of power and injustice have existed in both regions. But in a relative sense, based on these indicators, we have concluded that governance was better in the South than in the North during the period under review though the enabling conditions may have been better in the North at the outset. A more detailed assessment might have shed much more light on the quality of governance in the two regions. Further analysis of what was done during a CMs’ tenure, for example, would have given us insights into how policies and implementation might have differed between the states involved. The role of social movements as a precursor to the growth of education and the spread of entrepreneurship is borne out at the level of regions too (see Damodaran 2008). Like TN, Kerala also had seen strong social movements early in the 20th century that promoted greater awareness and interest in education among lower castes that had not received such opportunities in the past. Andhra Pradesh and Karnataka that were part of the erstwhile Madras Presidency had also witnessed a similar awakening and networking among their lower caste groups. The ‘social capital’ created through this process in the region strengthened the demand side of governance and laid the foundation for more widespread education through institutions established by communities and caste groups. The explosion of technical education in the South in the 1990s could also be traced to this phenomenon. There was hardly any comparable development of educational institutions through non-governmental initiatives in the northern states. Mehrotra (2006) says as follows: Until these social mobilisations happen in the northern states that are lagging behind—the so-called BIMARU (Bihar, Madhya Pradesh, Rajasthan, UP) states—there is a role for the central government to trigger actions at the state level to ensure some empowerment of the lower castes in these states. With the exception of Madhya Pradesh (which has been far more successful at effective social service delivery compared to the other three Hindi-belt BIMARU states within the last decade), there is growing evidence that these state governments have been unwilling to devolve transfer functions and finance on basic health and education to the panchayati raj institutions.

106

THE PARADOX OF INDIA’S NORTH–SOUTH DIVIDE

It is only if the central government turns the screw on these state governments might they be more proactive and make decentralization work, consistent with the spirit of the 1993 constitutional amendment mandating the creation of local government institutions. The central government could make fiscal transfers to state governments conditional upon functions and finance being devolved in the health and education sectors to the panchayats. Transferring these functions and finance will help make functionaries (teachers, doctors, auxiliary nurse midwives and nurses) at least partly responsive and accountable to their clients they are meant to serve, rather than to a superior official in a line ministry. Such an institutional mechanism of accountability to local clients will help to empower the poor and the lowest castes—who are, as we have seen … excluded from access to basic services...

According to the IHDR (2011), the poorer states, namely, Bihar, Jharkhand, MP, Orissa, Rajasthan, UP, Chhattisgarh and West Bengal, account for 56% of the SC and 55% of the Scheduled Tribe (ST) population of the entire country. In relation to this, it states the following: There is a two-way relationship here; poorer states are so because there are large proportions of the excluded social groups (who are generally poorer) living there; conversely, in the poorer states the different development programmes do not reach the targeted population—especially the economically and socially deprived sections.

We also performed econometric regressions to understand the dependence of the state per capita NSDP, urban and rural poverty rates on various explanatory factors which have been described above. Appendix 5 contains the results of these regressions. The next section contains the summary of services and assets to residents and the urban poor of the selected states, making use of data from census 2001 and 2011.

Access to Basic Services and Assets In this section, we focus on access to basic services and assets in the northern and southern states to understand whether the latter are better than the former. We ask two questions which we attempt to answer:

Southern Region versus Northern Region

107

Does a higher growth rate translate into a better quality of life for the public? Does a higher growth rate translate into a better quality of life for the weaker sections who do not often share the fruits of national prosperity? We focus on access to amenities and assets at two levels: state-level and at the level of the urban poor in the states, making use of data from census 2001 and 2011 for the former, and from census 2011 slums for the latter. To be consistent with our other analyses where we reviewed historical data on each of the variables, it would have been useful to check where the two groups of states were at the beginning of the 1950s and 1960s as they relate to amenities and assets, and how they compare currently. However, we found that data on amenities (at least the indicators we use in this study) were collected by the census only from 1991 onwards, and while data on assets were available for earlier years, they were not in electronic copy, and certainly not compatible with the digital data we had for census 2001 and 2011. Hence, we restrict ourselves only to 2001 and 2011 data here. Both private goods (which we call as assets) and public services can be expected to enhance one’s quality of life and general well-being. But there is nothing automatic about these outcomes. While the purchase of private assets is determined by income, public services reflect the government’s ability and willingness to deliver them. To answer the first question, we examine access to amenities and basic services at the state level, since we have the benefit there of comparing the census 2001 and 2011 data. We focus on the following basic services: drinking water, electricity, sanitation (latrine), bathing and lighting facility. Table 5.2 summarizes these data for the northern and southern states for 2001 and 2011. Table 5.2 shows that there was deterioration over 2001–11 with respect to access to a latrine facility within the premises in the northern states, as there was an increase in the percentage of households who did not have access to latrine within their premises. Quite in contrast, in the southern states, over the same decadal period, there was a definite 44% reduction in the percentage of households who did not have latrine in their premises. Further, as we expect, a higher percentage of households in the southern states had their waste water outlet connected to a closed drain, when compared to their northern

27.74

% households with main source as tap water

Sources: Census of India 2011 data on slums; authors’ computations.

41.91

24.81

% households having bathing facility within the premises with bathroom 57.15

57.18

% households with waste water outlet not connected to any drain

% households with main source of lighting as kerosene

84.35

35.09

% households with waste water outlet connected to open drain

% households with main source of lighting as electricity

53.33

7.73

% households with waste water outlet connected to closed drain

38.34

72.32

78.69

64.68

15.35

73.20

71.92

% households not having latrine facility within the premises

2011

2001

Basic service/amenity

38.21

26.54

87.76

160.72

47.52

51.98

98.60

1.78

% change (2001–11)

Northern states

Table 5.2 Access to Basic Services and Amenities, 2001 and 2011—North and South

47.48

25.77

73.54

50.17

58.09

27.94

13.96

52.59

2001

61.26

6.66

92.64

75.84

46.43

29.01

24.56

29.43

2011

29.02

–74.17

25.98

51.16

–20.08

3.8

75.93

–44.02

% change (2001–11)

Southern states

Southern Region versus Northern Region

109

counterparts. Naturally, households who had their waste water connected to an open drain were less in the South than in the North, both in 2001 and 2011. As we would expect, the percentage of households having a bathing facility, another basic service, within their premises is much higher in the southern states than in the northern states, both as of 2001 and 2011. In fact, there was more than a 100% increase in the percentage of households with a bathing facility in the northern states, but even with this increase, as of census 2011, only 65% of households in the northern states had a bathing facility within their premises, whereas more than three-fourths of households in the southern states had a bathing facility in-house (at least within their premises). When we examine electricity as a source of lighting (refer to Table 5.2), we find that only 79% of households in the northern states used electricity, while more than 92% of households in the southern states used electricity as their main source of lighting. Conversely, the percentage of households using kerosene as their source of lighting was small in the southern states, being only one-fourth as of 2011, whereas this was more than 50% in the northern states, even as of 2011. Table 5.3 summarizes the possession of private goods (assets) by households in the northern and southern states. It shows that the percentage of households with assets such as a radio/transistor, television and Table 5.3 Households with Assets—Northern and Southern States Northern states

Southern states

Asset

2001

2011

% change, 2001–11

2001

2011

% change, 2001–11

% households with radio/ transistor

33.11

16.12

–51.32

42.64

20.98

–50.79

% households with television

25.33

39.58

56.28

36.69

70.65

92.58

% households with landline only

5.88

2.50

–57.50

12.91

7.08

–45.17

Sources: Census of India 2011 data on slums; authors’ computations.

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THE PARADOX OF INDIA’S NORTH–SOUTH DIVIDE

landline phone is unambiguously higher in the southern states than in the northern states. Thus, we find that a higher growth rate in the southern states has also translated into a better quality of life for them, both in terms of basic amenities and possession of assets, whereas a lower growth rate has prevented the residents of northern states from enjoying a better quality of living. Does a higher growth rate translate into a better quality of life for the weaker sections who do not often share the fruits of national prosperity? If economic growth is highly skewed and only a small minority benefits from it, the average person’s ability to improve his or her quality of life will be limited. If the government is not efficient and the governance is poor, public amenities may not keep pace with incomes or may not be of the quality that satisfies the public. To test whether the poor have actually improved their quality of life, we have drawn the latest data on urban slums from the census of 2011. We have the basic data to answer the question: Is the quality of life of the urban poor in the southern states distinctly better than that of their northern counterparts, measured in terms of private assets and public services? Here we did not have comparable data from 2001. First, we examine access to sanitation and latrine facilities, which is a basic service impacting human dignity, child and women safety, personal health and hygiene. It also has negative environmental consequences. Lack of access to such a basic service can be the outcome or the cause of low per capita income. Table 5.4 summarizes the state of sanitation in the northern and southern states. As we expect, the proportion of households with access to a latrine within their premises is much higher in the southern states on average (being 75%), when compared with that in the North, where only 66% of households have a latrine in their premises, on average. While having a latrine in one’s own premises is determined by the household’s per capita income, literacy and education, and cultural habits, the existence of public latrines is a basic service which is to be provided by the state. As Table 5.4 shows, the proportion of slum households who have access to a public latrine is relatively higher in the southern states (being 9%) when compared to the 5% of slum households on average which have access to a public latrine in the northern states.

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111

Table 5.4 Access to Sanitation in Slums—North and South % households % households % households with access % households not having having to public who defecate latrine within latrine within latrine in the open the premises the premises Northern states average

65.55

34.45

4.63

29.82

Minimum

48.67

8.30

2.15

6.06

Maximum

91.70

51.33

9.68

42.49

SD

15.58

15.58

2.60

13.84

Southern states average

74.97

25.03

8.49

16.54

Minimum

61.01

6.79

2.87

3.34

Maximum

93.21

38.99

15.92

24.97

SD

15.47

15.47

6.39

9.85

Sources: Census 2011; authors’ computations.

The obvious outcome is that a higher proportion of households (30%) on average in the northern states defecate in the open, when compared with that in the southern states where on average, 17% of slum households defecate in the open. The standard deviation for those who defecate in the open in the southern states is also much lower than it is in the case of the northern states, showing that the data are fairly robust across each of the states. Open drainage also causes major sanitation problems and diseases to residents. On average, we find that the southern states had 38.28% of their slum households having open drainage, whereas their counterparts in the North had 55.24% such households. Further, the proportion of households with their waste water connected to an open drain is much less (38%) in the slums of southern states than they are in the northern states (where 55% of slum households’ waste water outlet was connected to an open drain). Table 5.5 compares another basic facility, the existence of bathing facilities in the slums of the North and the South, based on data from census 2011. Unfortunately, such data were not reported in census 2001;

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Table 5.5 Access to Bathing Facilities in Slums—North and South % households having bathing facility within the premises

% households having bathing facility within % households the premises with only with no enclosure (no roof) bathroom

Northern states average

53.90

18.70

27.40

Minimum

29.89

9.04

11.16

Maximum

79.79

26.86

43.25

SD

17.34

5.37

13.78

Southern states average

77.78

9.75

12.47

Minimum

64.84

6.19

8.79

Maximum

84.01

13.31

21.84

SD

9

3

6

Sources: Census of India 2011 data on slums; authors’ computations.

hence, it is not possible to compare these services over time for the slums in the two sets of states. Having access to a bathing facility has implications for basic sanitation and hygiene, personal health and women’s safety. On average, more than three-fourths of slum households in the southern states have a bathing facility within their premises, whereas only over half of northern households have this basic facility within their premises. Further, more than one-fourth of slum households in the northern states have no bathroom where they can bathe at all; this is only 12% in the case of the southern slum households. Next, we examine the availability of a drinking water source in the slums of northern and southern states. Table 5.6 summarizes the summary statistics for the slum households in the northern and southern states, as far as the drinking water source is concerned. The statistics could not be more telling here as well—on average, more than two-thirds of the southern states have access to tap water from a treated source, such as the city’s water supply network, whereas less than half of the slum households in the northern states have access to a treated source.

Southern Region versus Northern Region

113

Table 5.6 Access to Drinking Water—Slums of Northern and Southern States % households with tap water from treated source

% households with tap water from untreated source

Southern states average

66

10

Minimum

53

5

Maximum

78

16

SD

10

6

Northern states average

44

9

Minimum

12

4

Maximum

76

18

SD

23

5

Sources: Census of India 2011 data on slums; authors’ computations.

Next we examined sources of lighting households use, including electricity and other sources such as kerosene, solar and other oils. Table 5.7 summarizes this disparity across the northern and the southern states. There is not much of a disparity in access to electricity across the slums of northern and southern states, although it is much higher in the slums of southern states, as we expect. However, what we find different is that four times as many slum households in the northern states use kerosene as a source of lighting, compared with their counterparts in the southern states, only 5% of whom use kerosene. It should be noted that kerosene is subsidized to keep its price low for the poorer households. We finally examined the private goods of the slum households in the north and the South. We studied assets such as radio/transistor, TV, computers and laptops with and without internet, landline and mobile phones, vehicles such as bicycles, scooter, car, jeep, van and possession of none of these assets. Table 5.8 summarizes the differences in the possession of these assets across the slum households of the northern and southern states. What we find in Table 5.8 is as we expect. The slum households in the southern states, on average, have better possession

114

THE PARADOX OF INDIA’S NORTH–SOUTH DIVIDE

Table 5.7 Access to Electricity and Lighting, Slum Households—Northern and Southern States Electricity Kerosene (%) (%)

Solar (%)

Other oil (%)

Any other (%)

No lighting (%)

Northern states Average

82.2

16.6

0.2

0.2

0.4

0.5

Minimum

82.2

16.6

0.2

0.2

0.4

0.5

Maximum

55.1

5.0

0.1

0.1

0.1

0.3

SD

93.8

43.5

0.3

0.4

0.8

0.8

Average

94.7

4.7

0.1

0.1

0.1

0.3

Minimum

92.3

2.7

0.1

0.1

0.0

0.1

Maximum

96.6

6.9

0.3

0.1

0.1

0.5

2.1

2.0

0.1

0.0

0.0

0.2

Southern states

SD

Sources: Census of India 2011 data on slums; authors’ computations.

of common assets such as radio/transistor, TV, computers and laptops, landline and mobile phones, and motor vehicles such as cars, jeeps and vans. Conversely, a greater proportion of the slum households of the northern states, where we have found incomes are lower, have bicycles and none of the above-mentioned assets. We, thus, find that on all indicators of basic services, the slum households in the northern states are much worse than their counterparts in the southern states. Our analysis of the data from census 2011 shows that on average, not only possession of assets improves with higher income, but also access to basic public services improves with higher incomes as a result of the rapid economic growth in the southern states. The data show that the significant increase in per capita income in the southern states enabled a larger proportion of their urban poor to acquire certain private assets than their counterparts in the North with lower incomes. Further, access to basic public services, such as water supply, electricity, better sanitation and sewerage, is also much better in the slums of the southern than in the northern states. More efficient

Southern Region versus Northern Region

115

Table 5.8 Assets, Slum Households—North and South Asset

North average (%)

South average (%)

Radio/transistor

16.44

19.37

Television

59.63

77.17

Computer/laptop with Internet

2.67

4.33

Computer/laptop without Internet

7.23

8.20

Landline only Mobile only Both

3.63

5.76

59.41

62.32

3.99

8.43

Bicycle

50.52

33.27

Scooter/motorcycle/moped

26.06

25.35

Car/jeep/van

4.25

5.07

Households with TV, computer/ laptop, telephone/mobile phone and scooter/car

5.06

6.28

14.02

8.43

None of the assets specified

Sources: Census of India 2011 data on slums; authors’ computations.

public service delivery can make growth more inclusive and create a momentum for more equitable governance reforms. We conclude from the evidence above that in the urban areas of India’s states with higher economic growth, the lower income population too has benefited from rising incomes. This, in turn, has enabled them to acquire goods and assets that improve quality of life. Furthermore, better governance in these states seems to have enhanced the reach and spread of public services to the urban poor. We do not imply that nothing more needs to be done in the southern states to enhance the quality of life of the poor. For instance, even in the southern states, the proportion of slum households who have access to a public latrine is only 9%. Hence, there is much more to be done even in the South, but the way forward is clear. The lagging states can learn much from their experience. A notable lesson is that a combination of rising incomes and good governance is the pre-requisite for improving the quality of life of the poor.

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THE PARADOX OF INDIA’S NORTH–SOUTH DIVIDE

We conclude that the rising per capita incomes have led to an improvement of the quality of life of the citizens in the southern states. The access to assets and amenities available to southern citizens is decidedly better than that of their northern counterparts. This holds true also for the low-income people living in urban slums in the South. This is not to say that the poor have benefited to the same extent as rest of the population. But the data show that the quality of life of the poor in the South, judged by assets and amenities, is better than that of their counterparts in the North. No data was available, however, to make a similar comparison of the assets and amenities of the rural poor in the two regions. The next and the final chapter summarizes and concludes our primary observations regarding the paradox of India’s North–South divide, discussing the case of TN and UP, drawing lessons for the lagging states.

6

Conclusions and Policy Implications

T

his study has examined whether the economic divide between India’s North and South is a real phenomenon or a product of media hype. After establishing that the North–South divide does indeed exist, and that it is a fairly recent phenomenon, we investigated the underlying factors that may have led to this outcome. The factors examined by us included both proximate factors and foundational factors. The study applied this framework first to analyse the historical experience of TN and UP and later extended it to probe the historical experience of the two regions. We summarize below the main conclusions, policy implications and lessons from the study for the lagging states. 1. As in the case of TN and UP, the economic divide between India’s North and South is also a relatively recent phenomenon. The economic performance of the South began to surge ahead by the late 1980s when the Government of India had launched a modest regime of liberalization. Three out of four northern states had also shown an acceleration of their growth rates during this period, but their pace of growth was not fast enough to catch up with the South. In the 1990s, the gap between the North and South widened even more with the result that the South’s per capita income was more than double that of the North by 2005. The incidence of poverty in the South had also declined at a faster rate than that of the North during this period.

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THE PARADOX OF INDIA’S NORTH–SOUTH DIVIDE

2. Rising per capita incomes have led to an improvement of the quality of life of the citizens in the southern states. The access to assets and amenities available to the southern citizens is decidedly better than that of their northern counterparts. This holds true also for the low-income people living in urban slums in the South. This is not to say that the poor have benefited to the same extent as rest of the population. But the data show that the quality of life of the poor in the South, judged by assets and amenities, is better than that of their counterparts in the North. No data was available, however, to make a similar comparison of the assets and amenities of the rural poor in the two regions. 3. The evidence from the two regions reinforces our earlier conclusion about the convergence of both proximate and foundational factors in explaining the North–South divide. The two regions differed significantly with respect to literacy, urbanization, infant mortality of population (health status) and infrastructure, especially, electricity, and the gap widened in favour of the South over the study period. But the gap in terms of proportion of graduates in the population was negligible. Further analysis, however, shows that despite this seeming similarity, engineering education had surged ahead in the South, leaving the North way behind. It reflects a strategic move by the southern states in response to certain policy shifts that the North had failed to exploit. Among the proximate factors, public investment does not seem to explain the North–South gap in economic performance. In fact the northern states had a head start in this regard as per capita development spending was higher in the North than in the South in the early decades after Independence, but the latter overtook the North in the 1990s. A closer probe reveals that the increase in public investment in the South was financed by the rising revenues of its state governments. 4. Does size of the state matter? On some basic parameters such as population and geographical area, we found that the southern states are also smaller with an average 2011 population of 503 million, when compared with an average of 733 million population of the northern states whose land area in 2011 (being 179,138 sq km

Conclusions and Policy Implications

119

on average) was higher than that of the southern states (whose average land area in 2011 was 127,151 sq km). We find population density of the southern states to be higher, being 707 persons per sq km, when compared to a population density of only 487 per sq km in the northern states (see Table A.60). Thus, the southern states are smaller, denser and presumably more homogenous and, therefore, possibly easier to govern. However, based on our research here, we are unable to definitively conclude that the northern states should be split up to enable better governance. 5. The foundational (governance-related) factors also showed the South to be way ahead of the North. We conclude that the proximate and foundational factors highlighted in our TN–UP comparison may have played a dominant and joint role in explaining the differential in the long-term growth performance of the two regions too. Explanations that consider just one or two of these factors may provide only a partial understanding of what lies behind the development outcomes observed at the national and regional levels. 6. Though the per capita development spending in the northern states was higher than that in the South in the early decades, it did not translate into higher growth rates of literacy, health status or infrastructure (proximate factors) in the North. Our surmise is that this phenomenon signals the lower resource efficiency achieved by the North relative to the South. The increased resources deployed in the North did not result in a correspondingly larger volume of public goods as was shown in the UP–TN comparison. This outcome may have resulted also from the weaknesses in the foundational factors. A government that suffers from greater political and administrative instability, and law and order problems is less likely to make optimal and efficient use of its resources. When the demand side is weak as a result of the ignorance of large segments of the population, then again governance will remain inefficient and unresponsive. Diversion of resources, delays and corruption are likely to increase under these conditions. The net result will be slower progress in terms of the proximate factors. 7. Our analysis of the two regions also shows that their growth rates remained similar, but low, for a very long period of over two

120

THE PARADOX OF INDIA’S NORTH–SOUTH DIVIDE

decades. A similar pattern was evident in our study of TN and UP too. Our earlier finding that even when the preconditions in terms of the factors mentioned above are present, a region might not perform well in the context of a restrictive policy regime is, thus, reaffirmed. In fact, the South’s growth rate was lower than that of the North for over two decades despite its better preparedness in terms of preconditions. The final outcomes, thus, depend not only on the factors that strengthen the supply side, but also on factors that create incentives to invest, take risks and expand economic activities. It was only after the move towards liberalization began in the late 1980s that the investors turned positive. A case in point is the expansion of engineering education in the South from the early 1990s that resulted in its remarkable dominance in technical manpower. The South, as of 2006, accounted for 53% of the student intake of engineering colleges in India while the North had a share of only 16% (Banerjee and Muley 2007). The decision of numerous entrepreneurs to enter this field reflects the joint influence of the proximate and foundational factors along with a liberalized policy regime that permitted such investments. The same policy was available in the North, but no such large-scale investments took place in the northern states. Is it reasonable to speculate that their relatively poorer record in terms of governance may have acted as a barrier in this regard? 8. The demand factor too followed a similar pattern between the northern and southern regions. Social movements that energized the lower castes and strengthened their ability to demand better governance from the state were at work in the southern states way ahead of their northern counterparts. The role of social movements in strengthening the demand factor and as a precursor to the growth of education and the spread of entrepreneurship is borne out at the level of regions (see Damodaran 2008). Like TN, Kerala also had seen strong social movements early in the 20th century that promoted greater awareness and interest in education among the lower castes that had not received such opportunities in the past. Andhra Pradesh and Karnataka that were part of the erstwhile Madras Presidency had also witnessed a similar awakening and networking among their lower-caste groups.

Conclusions and Policy Implications

121

The ‘social capital’ created through this process in the region may have laid the foundation for more widespread education through institutions established by communities and caste groups. The explosion of technical education in the South in the 1990s could also be traced to this phenomenon. There was hardly any comparable development of educational institutions through nongovernmental initiatives in the northern states. 9. It is difficult to say whether the same set of proximate and foundational factors are adequate to explain the differential performance of all countries and regions. Specific country contexts may reveal the role of yet other factors that we may have ignored here. It is also possible that the weakness in one factor may be compensated by the strength of another. As noted in an earlier section, the absence of educated manpower could be offset through the import of trained personnel from other places. It is possible for the state to intervene and achieve certain outcomes when the private sector is not developed enough to play this role. The most difficult factors to import or substitute will be in the area of governance. A cursory look at other better performing states in India confirms that they are closer to the southern states than to the North in respect of governance. Rajasthan, the best performing state among the northern states, also has relatively better governance indicators than the other three. 10. This study does not offer a standard recipe for achieving development outcomes or a formula to plan for or sequence the preconditions for growth. This is because the historical legacies and endowments available in different country and regional contexts tend to vary a great deal. These in turn will determine how and when the preconditions for economic growth will be created. But even if the preconditions are created, restrictive policy regimes can result in a failure to utilize the full potential of the preconditions. In the present case, it is the liberalization policies and the global opportunities that became available in the late 1980s and 1990s that enabled the South to surge ahead at a faster pace than the North. But the responses of different states with the right preconditions need not be the same. In TN, the proximate and foundational factors facilitated the inward flow of resources

122

THE PARADOX OF INDIA’S NORTH–SOUTH DIVIDE

for investment in manufacturing and services. As a result, both domestic and foreign investments expanded at a fast pace in TN. In Kerala that had similar educational endowments, a major response was for the workforce to go abroad in large numbers (migration) as local policies did not create a proper environment for investment. Kerala’s per capita income also rose significantly despite the different path it adopted. In both cases, people of the two states were able to respond to the new opportunities, raise their income levels and achieve a fair measure of poverty reduction. It was preconditions such as education, improved health status and an enabling environment in terms of governance (in relative terms) that enabled them to craft their own responses and strategies to take advantage of the unfolding economic opportunities.

Some Lessons for the Lagging States There are several reasons why there is a need for caution while making generalizations from a comparative study of a few states or regions in a country. The level and pace of the development of states and regions are influenced by unique historical factors, their initial conditions and endowments, and even serendipitous events that no one could have predicted or engineered. Nevertheless, after allowing for these factors, there may be some lessons to be learnt from comparative studies of the kind we have presented in this book that could be of some value to the policy makers and leaders, especially of our lagging states and regions. Such studies may point to certain areas for intervention by leaders, experts and communities that are within their control to initiate and manage. There is no presumption here that lagging states can or should replicate the policies and actions that better performing states have designed and implemented. But it is quite possible that through a careful diagnosis of their contexts, leaders of lagging states may find ways to adapt policy interventions or even discover altogether new interventions that fit their socio-political contexts. It is in this spirit that we present below a few lessons from this study for policy makers and political leaders.

Conclusions and Policy Implications

123

A Strong Focus on the Efficient Utilization of Resources, and not Merely on Their Mobilization and Deployment, Is Essential for Development From the time of Independence, India’s economic planners have emphasized the importance of saving and investment in speeding up economic development. Public investment was stepped up in both the regions covered by this study. Our analysis shows that per capita public investment increased over time, for example, in both UP and TN. An interesting finding, however, is that though TN and UP were not far apart in the earlier decades in terms of per capita public investment, the efficiency with which TN used the investment resulted in higher outputs/incomes than in UP. There could be several explanatory factors behind this differential outcome. The Government of TN was better organized and structured to deliver greater efficiency. As we have documented, the Government of TN had better administrative systems and practices in place, which, possibly, could be traced back even to the colonial era. Most importantly, it had political leaders who held the officials accountable for their performance. The key lesson here is that the priority that governments give to the mobilization and investment of resources should be matched by equal attention to the efficiency with which resources are utilized and the outcomes they produce. Such changes, however, are unlikely to occur overnight. They call for learning by doing, constant fine-tuning of public systems and cooperative behaviour among multiple stakeholders. They point to the importance of the time dimension in development. It is here that the southern states had a head start. What makes the story interesting is that this strong focus of efficiency was sustained over years, despite parallel efforts by the state government and social movements to give the lower castes greater opportunities for jobs in public institutions. The ongoing struggles for these reforms and the conflicts between castes would have likely created law and order problems in the state (although our evidence on police firing and other law and order indicators do not show problems in TN, Kerala and Karnataka, with AP being an exception). The normal prediction under these conditions would have been that efficiency and productivity in government would suffer and that the focus on implementation would wither away. But that does not seem to have happened in TN.

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THE PARADOX OF INDIA’S NORTH–SOUTH DIVIDE

The factors that enabled the government of TN to sustain its efficiencyoriented implementation culture, including the role played by their political parties and their leaders, are worth exploring further, for the benefit of the leaders of our lagging states.

Creation of an Enabling Environment for Economic Activities and Job Creation Is Essential for Development A second lesson pertains to the creation of a more enabling environment for economic activities in TN and other southern states.1 Though the Indian Constitution has endowed the Central Government with important powers and a dominant role in the allocation of resources, state governments do have substantial powers in important areas for policy making and implementation. TN’s track record in terms of infrastructure building, education and delivery of other essential services bear testimony to this fact. The manner in which law and order problems were handled in the southern states also seems to have contributed to the creation of a more enabling environment in states like TN. A case in point is the way caste-related and other social movements were dealt with at the political level in TN and Kerala. A positive response to such struggles through reforms by political leaders may well have diffused difficult law and order situations. In the northern states, inter-caste conflicts and Hindu–Muslim riots possibly derailed the development agenda and the prospects of creating an enabling environment for economic activities. Chief Ministers such as Kamaraj, Annadurai, Karunanidhi and Marudhur Gopalan Ramachandran (MGR) in TN, most of whom had fairly long tenures, also enabled them to give their state a fair degree of policy continuity and public confidence that is essential for investments and related economic activities to flourish.

There Is No Substitute for Building People’s Capabilities except through Education A third lesson that the South offers stems from education. Most governments view education as a top-down process that ends in the delivery of Kerala has lagged in this regard compared to other southern states. But it has nurtured important service industries. 1

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125

an essential service. The uniqueness of the educational progress in the South, however, points to the way in which a bottom-up process complemented and often pushed the state to do more for education. A variety of social movements and community initiatives played a key role in this process. In more recent decades, the private sector too played a similar role in the field of technical education. What is noteworthy here is the demand-creating role that these movements played. Public accountability becomes stronger when the people at large, especially those at the bottom of the ladder, demand education. An important by-product of education is the increased awareness of rights and entitlements that it generates among the people. This, in turn, broadened and deepened the demand factor, extending it beyond education to other services and needs of the people. Social movements clearly played a major role in inculcating these capabilities, especially in the weaker sections of society. Replicating these historic developments in lagging states is by no means easy. Social movements and their leaders may not arise in all contexts and times. But other options could be tried out instead. A good example of this comes from Kerala where literacy and education had made tremendous progress in the Travancore–Kochi part of the state originally ruled by local kings. Social movements, churches and other non-governmental organizations had played a major part in achieving this, along with the princely state governments. After the state was reorganized, Malabar, an educationally backward region, of the erstwhile Madras presidency, was added to Kerala. Within a couple of decades, the literacy and education levels in the Malabar region caught up with the rest of Kerala, largely because of the proactive policies and programmes pursued by the Government of Kerala. The creation of demand and greater public accountability in the delivery of education and the creation of skills could, thus, come through different sources, the government, the private sector or collective actions by the weaker sections, depending on the contextual factors. A factor which is concomitant with education and skills is the use of new technologies which are available to empower citizens with knowledge of their rights, duties and entitlements. Education can also be extended and improved through the use of new technologies. Social media have become a powerful force for networking and collective action that can strengthen the demand side of governance. This is something

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that the northern states necessarily can adopt, to enable a bottom-up approach to development.

When the Factors Discussed above Converge, the Ability of Governments, Enterprises and People to Respond to Emerging Opportunities, Both Local and Global, Is Enhanced When the quality of public governance improves, it leads to more efficient delivery of services and infrastructure, a positive environment for economic activities, fairly stable law and order, and citizens get better informed and educated. This is when a state is able to take advantage of the emerging opportunities for economic development. The southern states offer several examples of how this convergence tends to enable citizens, enterprises and the state to take advantage of new opportunities for development. It is this mix of capability and motivation that invites positive responses from prospective investors. When such convergence is absent in a state, it will be difficult for anyone to effectively respond to new economic opportunities. Karnataka, whose capital, Bangalore, has emerged as the silicon valley of India (although scholars such as Anna Saxenian have cautioned against comparing India’s Bangalore to California’s silicon valley) as a result of the presence of certain favourable factors, shows how this process works. Bangalore was known for its educational and research institutions that created a critical mass of technical and scientific manpower that was essential for the IT industry. It attracted several entrepreneurs also because of an enabling environment of reasonable political stability, peaceful labour relations, physical infrastructure and connectivity that were better than in other states. These were by no means world-class, but entrepreneurs pitched in as the government was supportive in many respects. Of these, the presence of a pool of technical manpower certainly was a dominant factor. It is the convergence of these factors that enabled the budding IT enterprises in Bangalore to take advantage of the global market opportunities that arose in the 1990s. Their success attracted even more firms to set up shop in Bangalore, and created a spiral of growth and a booming job market for thousands of skilled young people.

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In the small town of Tiruppur in TN, there was a concentration of small units that engaged in the production of hosiery items even during the pre-Independence period. As India began to liberalize in the late 1980s, entrepreneurs in Tiruppur took advantage of the new opportunities that had removed certain trade restrictions for the export of garments and other items to western countries. This quick response was possible because Tiruppur had the necessary infrastructure and could extend it with industry and government support as market demand for their products increased. Tiruppur also benefited from the availability of a skilled workforce in that region of TN, and collective action by an association of entrepreneurs that was far sighted and proactive. A more recent example of how a state can take advantage of new economic opportunities comes from Chennai, the capital of TN. Chennai is India’s leading centre today for the automotive industry. Ford, Hyundai, BMW, Mahindra and Mahindra, and other large companies have made large investments in TN, because of its political stability, reasonably good infrastructure, large pool of skilled manpower and a supportive government interested in facilitating investment and production. Automotive units had existed in Chennai for some decades, but their operations were on a modest scale. It was the liberalization that began a quarter century ago that made outside entrepreneurs look for suitable locations for investment. Chennai became their choice because of the convergence of factors mentioned earlier. As we discussed earlier, abundant availability of skilled manpower in automobile engineering, port logistics; availability of reliable infrastructure; above all, a favourable investment climate and proactive government support are, thus, the primary reasons for making TN the home of automobile manufacturing.

Southern States Shed Some Light on What It Takes to Move Towards ‘Inclusive Growth’ As the evidence in earlier chapters show, India’s southern states have moved ahead of their northern counterparts not only in terms of economic growth and per capita income, but also in respect of poverty reduction. They have performed better also in terms of human development indicators such as literacy and health. The census 2011 show that the delivery of essential services such as public distribution of food (PDS),

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drinking water supply and sanitation that matter especially for the poor has a wider reach and efficiency in the South than in the North.2 Even more significant is the finding that rural productivity in the South has increased at a faster rate than in the North, and has created more income and employment opportunities for the relatively unskilled and poorer section of the population. All these are indicative of a more inclusive growth process at work in the South than in the North. Inclusive growth is achieved when prosperity is widely shared in society, not merely through handouts given to the poor, but primarily through sustainable job opportunities and productivity enhancement for the vast majority of the people. Income and wealth inequalities continue to be a serious problem in the South, and much more remains to be done to improve the condition of the poor. What is noteworthy, however, is that their development journey has already built into it the need to be more inclusive. The key factors behind the South’s economic progress that we have discussed in this book, namely education, social movements and the quality of public governance, also seem to have facilitated the South’s move towards a more inclusive growth process and outcome. By extending the access to education to all, the southern state governments and non-governmental organizations ensured that the capabilities and general awareness of the masses, including low-caste citizens, are strengthened and improved. Social movements, led by a set of highly committed and talented persons, prepared the ground for this to happen. Their impact was not limited solely to the spread of education, but also to the creation of capabilities in the low-income population to share information and network for collective action to demand their basic rights and entitlements from the state. It is this process that eventually opened up economic opportunities for jobs and income generation for low-caste and low-income groups even in the rural sector. It also paved the way for the spread of entrepreneurship among these people, leading to more widely shared prosperity. The key role that governments played in achieving a more inclusive growth in the South offers another lesson for the lagging states. The We have analysed this data set in Chapter 5, section on access to services and assets. 2

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greater responsiveness of southern governments to peoples’ demand for their rights and entitlements, their relative success in service delivery and their greater attention to implementation in general are reflected in various social indicators analysed in this book. The quality of political leadership was key to this achievement as discussed in earlier chapters. This is not to say that these governments were without flaws or that corruption and injustice did not exist in the southern states. But, for the most part, they were able to create a setting in which prosperity, job opportunities and basic services were available to a much larger proportion of the population than was the case in the North. All this is not to claim that the southern states followed a deliberate strategy for inclusive growth. But the cumulative impact of different strands of policy, actions and state–society interactions that happened in these states over many decades seem to have moved them in that direction. There are some lessons here for the lagging states. Our analysis has highlighted the importance of historical factors in the development of countries and regions. This is not to say that ‘history is destiny’. We do not believe that historical advantages or disadvantages are the sole determinant of the future. There are many examples of countries that have overcome the burden of history through the imaginative use of new opportunities and smart ways of exploiting their strengths. Some have taken the export route, while others have adopted innovative strategies for the development of infrastructure, human resources and new industries that raised their income levels. Yet others have borrowed ideas from external sources and built on the external assistance available to them. We believe this has implications for states like UP, especially with regard to the demand factor and governance where much more needs to be done.3 Many things have changed over the last 50 years. Today, new technologies are available to empower citizens with knowledge of their rights, duties and entitlements. Social media have become In the past few years, some of the northern states, such as Bihar, have stepped up their growth rates, though it is too early to assess how inclusive and sustainable their growth is. 3

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a powerful force for networking and collective action that can strengthen the demand side of governance. Education can be extended and improved through the use of new technologies. More resources are available for development today than some decades ago. Best practices in governance can be borrowed and adapted from other states and countries. Once there is political will, there are many such avenues that our lagging states can pursue to shake off the burden of history and move forward.

Appendices

Appendix 1: State Profiles For purposes of informing the reader who may not be aware or may want to know more details about the states we have studied, in this appendix we summarize the profiles of the selected states covering general information about their demographics, education, economy and budget. This descriptive information is followed by a fact sheet for every state.

Rajasthan Rajasthan, the largest state of India is located in the north-western part of the subcontinent. It is bounded to the north and north-east by the states of Punjab and Haryana, to the east and south-east by the states of UP and MP, to the south-west by the state of Gujarat, and to the west and north-west by Pakistan. The southern part of the state is about 225 km from the Gulf of Kutch and about 400 km from the Arabian Sea. Jaipur is the capital city and lies in the east-central part of the state. The Thar Desert in the state is the most densely populated desert in the world, with a population density of 83 people per sq km. Demographics Rajasthan has a population of 68,621,012 as per 2011 census. The population growth over the last 10 years has been around 21.44%. The sex ratio is 926 per 1,000 males. While the state was 25% urban as of census 2011, the largest cities of the state are Jaipur, Jodhpur and Kota. The state has 33 districts and 25 parliamentary constituencies. Education The literacy rate in Rajasthan has increased significantly in the recent years. From an average of 38.55% (54.99% male and 20.44% female)

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in 1991, the state’s literacy rate increased to 67.06% (80.51% male and 52.66% female) in 2011. Economy The economy of the state is primarily agricultural and pastoral. Wheat, barley, pulses, sugarcane and oilseeds are the main food crops, while cotton and tobacco are the state’s cash crops. A major portion of edible oils in the country is produced by the state, which is also the second largest producer of oil seeds. The state is the biggest producer of wool and opium in the country. Crops are irrigated using water from wells and tanks. The north-western region of the state receives ample water from the Indira Gandhi Canal. Mineral-based, agriculture-based and textile industries dominate the scenario in the state which is the second largest producer of polyester fibre and cement in the country. Several prominent chemical and engineering companies are located in Kota, in the southern part of the state. The state is also known for its marble quarries, copper, zinc mines and salt deposits in Sambhar Lake. The state has shown rapid growth in the industries and services sectors over the last 10 years. These sectors have grown at an average growth rate of 6.8% and 8%, respectively, at constant prices. Sixty-two per cent of the state GDP comes from just 12 districts, the top five districts being: • • • • •

Jaipur—15.30% Alwar—5.95% Jodhpur—5.77% Ajmer—5.04% Bhilwara—4.69%

Budget Highlights Budget size (2012–13): A surplus budget with `770,720 million (total receipts), and `766,750 million for its total expenditure. Development Expenditure • Water, sanitation, housing and urban development: `61,960 million • Rural development: `48,970 million

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Receipts and Expenditure Within taxes, sales tax is the major driver, accounting for nearly 39% of tax revenues. State share of central taxes is another important tax revenue source (11.5% in 2012–13). The other major increase in tax revenue is expected through stamps and registration duties which are expected to increase by 18% in 2013–14. In 2013–14, the Rajasthan government is expected to spend around 80% in the form of revenue expenditure. Fifty-two per cent is on nonplan expenditure, which is expenditure not covered by the plans and includes items such as interest payments on government debt, expenditure on police and even maintenance of existing government establishments such as schools and hospitals. Plan expenditure follows non-plan expenditure with contribution of 30%. Plan expenditure covers expenditure on schemes and projects covered by the five-year plans. The biggest proportion of spending by the Rajasthan government is on social services (38% of total expenditure). This includes spending on education, health and family welfare. The other major expenditure item is economic services. This includes spending on areas like road, transport and industries and is expected to increase by 13% in 2013–14. Sources 1. http://www.rajasthan.gov.in/Pages/Rajasthan-StatePortal.aspx State Government website 2. http://www.ibef.org/, i.e., Indian Brand Equity Foundation (an initiative of the Ministry of Commerce and Industry, Government of India) 3. National skill development corporation 4. http://mla.prsindia.org/sites/default/files/policy_guide/Rajasthan%20 Budget%202013-14.pdf

Rajasthan Fact Sheet Established

1 November 1956

Capital

Jaipur

Largest city

Jaipur

Districts

33 total

Total area

342,239 km2 (132,139 sq mi)

Area rank

First

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Population (2011) Total

68,621,012

Rank

Eighth

Density

201/km2 (520/sq mi)

Sex ratio

926

HDI rank

21st (2005)

Literacy

68% (20th)

Official language(s)

Hindi

Website

rajasthan.gov.in

Uttar Pradesh Garlanded by the Ganga and Yamuna, the two iconic rivers of Indian mythology, UP is surrounded by Bihar in the east, MP in the south, Rajasthan, Delhi, Himachal Pradesh and Haryana in the west and Uttaranchal in the north. Nepal touches the northern borders of UP and assumes strategic importance for Indian defence. The area of 236,286 sq km makes UP the fourth largest state of India. Demographics Uttar Pradesh has a population of 199,581,477 as per the 2011 census. The sex ratio of the state is 908 per 1,000 males. In the last 10 years, the state’s population has grown at a compounded annual growth rate of 1.86%, as compared to 1.6% for the all-India growth rate. The state is 22% urban and 78% of the state’s population is rural, as of census 2011. About 16.6% of the country’s villages are located in the state. Education The literacy rate in the state has seen an upward trend. In 2001, literacy rate stood at 56.27% of which males and females were 67.30% and 43% literate, respectively. The overall literacy rate increased to 67.68% as of census 2011, with male literacy at 77.28% and female literacy at 51.36%. Economy The distinguishing feature of UP’s economy is its regional imbalances. The western UP is agriculturally prosperous. It is relatively

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135

industrialized and has seen greater degree of urbanization. At the other end is Bundelkhand. Low agricultural growth, less number of industrial units and lesser gross value of industrial products mark out this region as the least developed in the state. The state is one of the top tourist destinations in India with almost 25% of all Indian foreign tourists travelling there. Taj Mahal, one of the seven wonders of world is in Agra, UP. The state economy is predominantly service based. This is followed by secondary sector and primary sector. The top 10 districts contribute to about a third of GDP of the state (out of which five are listed below), while the top 35 (half) of the districts contribute to about 72% of the GDP of the state. • • • • •

Lucknow Kanpur Nagar Gautambudh Nagar Ghaziabad Agra

However, 34 of the 75 districts of the state are classified amongst the (250) most backward districts in the country. Budget Analysis Budget size (2013–14): Deficit budget with `2,159,198.2 million as total receipts, and `2,212,011.9 million as its total expenditure. In the 2013–14 budget for the state, education was the priority for the allocation of funds, followed by social welfare schemes, health and for developing infrastructure in the state. The share of tax revenue receipts was 65.14%. Expenditure includes 75.5% under revenue expenditure and approximately 24.5% under capital expenditure. Sources 1. http://upgov.nic.in 2. http://www.nsdcindia.org/pdf/up-sg-presentation.pdf, National skill development Corporation. 3. http://information.up.nic.in/other_info_view.aspx?id=82 UP budget at a glance and highlights.

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Uttar Pradesh Fact Sheet Country

India

Capital

Lucknow

Districts

75

Total area

243,286 km2 (93,933 sq mi)

Area rank

Fifth

Population (2011) Total

199,581,477

Rank

First

Density

820/km2 (2,100/sq mi)

Sex ratio

908

HDI rank

32nd (2005)

Literacy

69.72% 79.24% (male) 59.26% (female)

Official language(s)

Hindi English Urdu

Website

upgov.nic.in

Bihar Bihar is located in the eastern part of the country. It is an entirely landlocked state, although the outlet to the sea through the port of Kolkata is not far away. Bihar lies mid-way between humid West Bengal in the east and the sub-humid UP in the west, which provides it with a transitional position in respect of climate, economy and culture. It is bounded by Nepal in the north and by Jharkhand in the south. The Bihar plain is divided into two unequal halves by the river Ganga which flows through the middle from west to east. Demographics The Bihar census 2011 revealed that the state has the third highest population in the country and the growth rate is 25% which exceeds the national average of about 17%. Total population of the state as per the 2011 census is 104,099,452 of which males and females are 54,278,157 and 49,821,295, respectively.

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137

The sex ratio is 918, i.e., for each 1,000 males, which is below the national average of 940 as per census 2011. Nearly 89% of the state lives in villages. Education The literacy rate of the state has seen an upward trend and is 61.80% as per the 2011 population census, with male literacy at 71.20% and female literacy at 46.40%. Economy Bihar is one of the strongest agricultural states, with very high productivity. The percentage of population employed in agricultural production in Bihar is estimated to be 74%, which is much higher than the national average. It is the largest producer of vegetables and the second largest producer of fruits in the country. The state has a large base of cost-effective industrial labour. As of today, agriculture accounts for 35%, industry 9% and service 55% of the economy of the state. In terms of income the districts of Patna, Munger and Begusarai in Bihar were the three best districts out of a total of 38 districts in the state, recording the highest per capita gross district domestic product. Budget Analysis Budget size (2012–13): Deficit budget with `773,840 million as its total receipts during the year, and `786,870 million as its total expenditure. Development Expenditure • Water, sanitation, housing and urban development: `30,770 million • Rural development: `61,750 million In Bihar, central taxes contribute the most to the state exchequer. In 2012–13, these taxes were estimated to contribute 49% of the total revenue receipts of the state. Public debt, comprising of internal debt and loans from the central government, continued to remain the main source of capital receipts. In 2012–13, 58% of total expenditure was estimated to be spent on non-plan items. Approximately 93% of this expenditure was spent on

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THE PARADOX OF INDIA’S NORTH–SOUTH DIVIDE

the revenue account, which includes items such as interest payments on government debt, expenditure on police and maintenance of existing government establishments such as schools and hospitals. In 2012–13, the public debt of the state was estimated to be 22.64% of the state GDP. Corporation taxes contribute the highest to tax revenues of the state, followed by other income taxes. Collections from state excise tax were estimated to increase by 54% in 2012–13. The highest increase in expenditure allocation was seen in the case of education, sports, arts and culture, and agriculture and allied services. Sources 1. http://gov.bih.nic.in/ 2. http://www.ibef.org/states/Bihar.aspx 3. http://mla.prsindia.org/sites/default/files/policy_guide/state_budget%20_ bihar_2012_13.pdf

Bihar Fact Sheet Established

1912 as Bihar and Orissa Province 1936 as Bihar

Capital

Patna

Largest cities

Patna, Gaya, Bhagalpur, Muzaffarpur, Purnea

Districts

38 total

Total area

94,163 km2 (36,357 sq mi)

Area rank

12th

Population (2011) Total

104,099,452

Rank

Third

Density

1,102/km2 (2,850/sq mi)

Sex ratio

916

HDI rank

21st (2011)

Literacy

61.80% (28th) 71.20 % (male) 46.40% (female)

Official language(s)

Hindi, Urdu

Website

gov.bih.nic.in

Appendices

139

Madhya Pradesh The State of MP is centrally located and is often called the heart of the country. It is a part of peninsular plateau of India lying in north central part, whose boundary can be classified in the north by the plains of Ganga–Yamuna, in the west by the Aravali, east by the Chhattisgarh plain and in the south by the Tapti valley and the plateau of Maharashtra. Over 30% of the state’s total area is enveloped by the forest. Demographics The total population of MP as per the 2011 census was 72,626,809 of which male and female were 37,612,306 and 35,014,503, respectively. The total population growth during the decade 2001–11 was 20.35%. The sex ratio in the state is 931, that is, for each 1,000 males, which is below the national average of 940 as per census 2011. Only 28% of the state’s population lives in urban areas. Education The literacy rate in the state has seen an upward trend and is 69.32% as per the 2011 population census. Of that, male literacy stands at 78.73% while female literacy is at 54.49%. Economy Madhya Pradesh, India’s second largest state, which occupies 9.38% of the country’s area, is also the second richest in terms of its mineral resources. Primarily, it has an agricultural and pastoral economy. Industrial development is primarily concentrated in the more advanced districts like Indore, Bhopal, Gwalior and Jabalpur. Budget Analysis Budget size (2013–14): Deficit budget for the year with `797,283.3 million as total receipts, and `919,468.6 million as its total expenditure. Development Expenditure: `195,715.9 million The distribution of the components of revenue receipts is 41.93% from state’s own tax revenue, 29.76% as share in central taxes followed by states’ own non-tax revenue and central grants. 40.90% is plan expenditure out of the total expenditure estimated for 2013–14. Plan expenditure covers expenditure on schemes and projects covered by the

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THE PARADOX OF INDIA’S NORTH–SOUTH DIVIDE

five-year plans. In its budget for 2013–14, the fiscal deficit was estimated at 2.98% of gross state domestic product (GSDP), revenue surplus estimated at 1.27% of GSDP and the total net debt of the state had come down to 14.30% of GSDP. Sources 1. http://www.mp.gov.in 2. http://www.census2011.co.in/census/state/madhya+pradesh.html Census 2011 3. http://planningcommission.nic.in/plans/finres/fr_2013_14/fr_mp2013_14. pdf

Madhya Pradesh Fact Sheet Established

1 November 1956

Capital

Bhopal

Largest city

Indore

Districts

51

Total area

308,252 km2 (119,017 sq mi)

Area rank

Second

Population (2011) Total

72,626,809

Rank

Sixth

Density

236/km2 (610/sq mi)

Sex ratio

931

HDI rank

26th (2005)

Literacy

69.32% (2011)

Official language(s)

Hindi

Website

mp.gov.in

Uttarakhand The scenic and beautiful state of Uttarakhand formerly known as Uttaranchal was carved out of the state of UP in 2000 and became the 27th state in the country. Uttarakhand is also a major tourist destination and the concept of religious tourism has grown here in recent times. Dehradun is the provisional capital of the state and is a beautiful city in itself.

Appendices

141

Located at the foothills of the Himalayan mountain ranges, it is largely a hilly state, having international boundaries with Tibet in the north and Nepal in the east. On its north-west lies Himachal Pradesh, while on the south is UP. It is rich in natural resources, especially water and forests with many glaciers, rivers, dense forests and snow-clad mountain peaks. Char-dhams, the four most sacred and revered Hindu temples of Badrinath, Kedarnath, Gangotri and Yamunotri are nestled in the mighty mountains. Demographics The total population of Uttarakhand as per the 2011 census is 10,086,292 of which males and females are 5,137,773 and 4,948,519, respectively. The total population growth during the decade 2001–11 was 18.81%. In terms of population, the state accounted for 0.83% of the country’s population in 2011. The density of Uttarakhand is 189 per sq km which is lower than the national average of 382 per sq km. The sex ratio of the state is 963, that is, for every 1,000 males, which is above the national average of 940 as per census 2011. Nearly 31% of the state’s population is urban. Education The literacy rate in the state has seen an upward trend and was 78.82% as per the 2011 population census, with male literacy at 87.40% and female literacy at 67.06%. Economy The state is one of the fastest growing with state’s economy having shown a healthy growth path during the recent years. The tertiary sector contributes the maximum to GSDP followed by the secondary and primary sectors. As one would expect, the share of primary sector has come down but secondary sector has shown significant growth. Budget Analysis Budget size (2013–14): Deficit budget with `210,430.5 million (total receipts) and `219,317.7 million (total expenditure). The estimated budget of the state for 2013–14 had 58.07% share of tax revenues (the state’s own tax revenues and central share of taxes) in

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THE PARADOX OF INDIA’S NORTH–SOUTH DIVIDE

total revenue receipts and 41.92% as non-tax revenue (including nontax revenue and central grants in aid). Borrowing and other liabilities were the major component of capital receipts followed by recovery from loans. Similarly, non-plan expenditure had 65.61% share in total expenditure followed by plan expenditure. Sources 1. http://www.census2011.co.in/census/state/uttarakhand.html 2. http://rbidocs.rbi.org.in/rdocs/Speeches/PDFs/SGBPU310812.pdf 3. http://budget.uk.gov.in/pages/display/97-budget-(2012-13)

Uttarakhand Fact Sheet Established

9 November 2000

Capital

Dehradun (Provisional capital)

Largest city

Dehradun

Districts

13

Total area

53,484 km2 (20,650 sq mi)

Area rank

18th

Population (2011) Total

10,086,292

Rank

19th

Density

189/km2 (490/sq mi)

Sex ratio

963

HDI rank

Seventh (2011)

Literacy

79.63% 88.33% (males) 70.70% (females)

Languages spoken

Hindi Garhwali Kumauni Jaunsari

Official language(s)

Hindi Sanskrit

Website

uk.gov.in

Appendices

143

Jharkhand The state of Jharkhand was carved out of the state of Bihar in 2000. While Ranchi is the capital of Jharkhand, its largest city is Jamshedpur. The state has had many challenges in terms of crime and social issues and the governments here have found it difficult to come to terms with the same. The state is located in the eastern part of the country and has MP and West Bengal as neighbours apart from Bihar and Orissa. The languages spoken in the state include Hindi. In total, the state comprises 24 districts. Demographics The total population of Jharkhand as per the 2011 census is 32,988,134 of which males and females constitute 16,930,315 and 16,057,819, respectively. The population of Jharkhand formed 2.72% of the country’s population in 2011. The density of the state is 414 per sq km which is higher than the national average of 382 per sq km. The state’s sex ratio is 948 for every 1,000 males, which is above the national average of 940 as per census 2011. More than three-fourths of the state’s population lived in villages as of census 2011. Education The literacy rate of the state was 66.41% as per the 2011 population census, with male literacy at 76.84 % and female literacy at 52.04%. Economy The state is one of the richest mineral zones in the world. The state boasts of 40% and 30% of India’s mineral and coal reserves, respectively. Jharkhand is the only state in the country to produce coking coal, uranium and pyrite. Considering the sectoral composition in 2010–11 of GSDP, the tertiary sector contributes to 47.2% followed by the secondary sector (28%) and the primary sector (24.8%). Budget Analysis Budget size (2013–14): A balanced budget with `395,489 million (total receipts) and `395,489 million (total expenditure).

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THE PARADOX OF INDIA’S NORTH–SOUTH DIVIDE

Development Expenditure: `82,935.4 million In terms of its revenues for 2013–14, the state’s major contributor was its own taxes at 25.67% followed by grants at 25.10% and with the central taxes share being 23.65%. The state’s expenditure is primarily on education with 15.91% of total followed by general services, legislature and other services (10.99%), social security (9.73%) and rural development (8.96%). These are the major components of expenditure. However, non-plan expenditure is more than plan expenditure. Non-plan expenditure is expenditure not covered by the plans and includes items such as interest payments on government debt, expenditure on police and maintenance of existing government establishments such as schools and hospitals. Sources 1. 2. 3. 4.

http://www.jharkhand.gov.in/ (finance department/budget at glance for budget) http://nsdcindia.org/pdf/jharkhand-presentation.pdf http://finmin.nic.in/fmspeech/fmspeech_JHkhandBudget20132014.pdf http://planningcommission.nic.in/plans/finres/fr_2013_14/ jharkhandbrief1314.pdf (for development expenditure).

Jharkhand Fact Sheet Established

15 November 2000

Capital

Ranchi

Largest city

Jamshedpur

Districts

24

Total area

79,714 km2 (30,778 sq mi)

Area rank

15th

Population (2011) Total

32,988,134

Rank

13th

Density

414/km2 (1,070/sq mi)

Sex ratio

940

HDI rank

24th (2005)

Literacy

67.6% (25th)

Main languages

Hindi, Maithili, Santhali, Bengali, Urdu

Website

http://www.jharkhand.gov.in/

Appendices

145

Chhattisgarh Chhattisgarh is in the central part of India and was carved out of MP on 1 November 2000. The regions of south-east MP were included in this new state which accounts for 15% of the steel production in the country. The state borders Maharashtra, AP, Orissa and UP. It also shares its boundary with the state of Jharkhand which was also formed on the same day as Chhattisgarh. The state has tropical climate and Chhattisgarhi is the main language spoken in the state. The city of Raipur is the most prominent city in the state and has a popular domestic airport. The Chhattisgarh census 2011 throws light on the most important statistics about the state. Demographics The total population of the state as per the 2011 census is 25,545,198 of which males and females constituted 12,832,895 and 12,712,303, respectively. The population of the state accounted for 2.11% of the country’s population in 2011. The population density of the state was 189 per sq km, lower than the national average of 382 per sq km. The sex ratio of the state was 991 for every 1,000 males, higher than the national average of 940 as per census 2011. In this predominantly tribal state, only 23% of population lived in cities, as of census 2011. Education The literacy rate in the state was 70.28% as per 2011 population census, with male literacy at 80.27% and female literacy at 59.58%. Economy The state’s economy mainly comprises of agriculture, electrical power and steel production. The economy of the state has boomed rapidly in the recent years with a growth rate of about 11.49% in GDP in 2010. The high growth rate is due to the growth in agricultural and industrial production. Budget Analysis Budget size (2014–15): Deficit budget with `546,610 million (total receipts) and `547,100 million (total expenditure). Development expenditure: `449,150 million In its estimated budget for 2014–15, growth of 15% in tax revenue and 6% in non-tax revenue is expected. Grants-in-aid have the highest

146

THE PARADOX OF INDIA’S NORTH–SOUTH DIVIDE

contribution of 27% followed by sales tax and share in central tax with 18%. The other components contribute less than 10%. In the state’s estimated budget for the year 2014–15, education is kept on priority with 20% expenditure, followed by agriculture and allied activities, and administrative services with 19% and 11%, respectively. Sources 1. http://www.census2011.co.in/census/state/chhattisgarh.html 2. http://cgstate.gov.in 3. http://dprcg.gov.in/news/budget-2014-15-press-note-english

Chhattisgarh Fact Sheet Established

1 November 2000

Capital

Raipur

Largest city

Raipur

Districts

27 (9 districts new)

Total area

135,194.5 km2 (52,198.9 sq mi)

Area rank

10th

Sex ratio

991

Population (2011) Total

25,545,198

Rank

16th

Density

190/km2 (490/sq mi)

HDI rank

23rd (2005)

Literacy

71.04% (23rd)

Official language(s)

Hindi, Chhattisgarhi

Website

cgstate.gov.in

Andhra Pradesh Andhra Pradesh is one of the more highly populated states mainly due to the development and its location along the sea coast. The census over the years has seen the state grow steadily in terms of population. Located in the southern region of the country, the state shares its borders with states like TN, Orissa and Karnataka. Its capital is the vibrant city of Hyderabad that has seen the rule of the Nizams for a very long time.

Appendices

147

This has been one of the oldest states in the country and finds mention in many ancient works of literature. Demographics The total population of the state as per the 2011 census is 84,580,777 of which males and females constituted 42,442,146 and 42,138,631, respectively. The growth rate of the state’s population is 11% which is below the national average. The state has a population density of 308 which is below the national average and, thus, the population is spread well over the entire area of the state. The population contributed 6.99% of the country’s population in 2011. The sex ratio of the state was 993 for every 1,000 males, higher than the national average of 940 as per census 2011. One-third of the state’s population lived in urban areas, as of census 2011. Education Literacy rate in AP has seen an upward trend and is 67.02% as per the 2011 population census. Male literacy is at 74.88% while female literacy is at 58.68%. Economy The state’s service sector has emerged as the greatest contributor to its GSDP over the years. The agriculture sector, although still employing the largest share of workforce, contributes minimum to the GSDP. Growth has been mainly driven by the services sector. The top five districts of the state in terms of GDP are Hyderabad, Rangareddy, Vishakhapatnam, East Godavari and Krishna. Budget Analysis Budget size (2013–14): Surplus budget with total receipts of `1,617,318.2 million, and `1,613,486.8 million as the total expenditure. Development Expenditure: `859,604.4 million In its budget for 2013–14, the state’s revenue receipts had the highest contribution followed by public debt, loan recoveries and public account. In revenue receipts, state taxes and duties contributed to 57% followed by the share of central taxes (19%), other non-tax revenue (17%) and interest receipts (7%). Revenue expenditure’s major component was development expenditure with 67%, other expenditure (13%), debt

148

THE PARADOX OF INDIA’S NORTH–SOUTH DIVIDE

services (12%), administrative services at 6%, tax collection charges and surplus 1% each. The state’s annual plan for 2013–14 focused on social services (43%), irrigation (26%), and agriculture and allied activities (17%) as three major sectors for budget allocation. Sources 1. http://www.census2011.co.in/census/state/andhra+pradesh.html 2. http://nsdcindia.org/pdf/ap-sg-presentation.pdf 3. http://mla.prsindia.org/sites/default/files/policy_guide/State%20Budget%20 -%20Andhra%202012-13%20v4.pdf 4. http://www.apfinance.gov.in/ 5. http://www.apfinance.gov.in/uploads/budget-2013-14-books/ap-budget-inbrief-2013-14-v-6.pdf

Andhra Pradesh Fact Sheet Established

1 November 1956

Capital

Hyderabad

Largest city

Hyderabad

Districts

23 total

Total area

275,045 km2 (106,195 sq mi)

Area rank

Fourth

Population (2011) Total

84,580,777

Rank

Fifth

Density

308/km2 (800/sq mi)

Sex ratio

993

HDI rank

15th (2011)

Literacy

67.77% (2011)

Official state language

Telugu, Urdu

Website

ap.gov.in

Karnataka Formerly known as the State of Mysore, the state of Karnataka is located in the southern part of India and has the states of TN, Maharashtra, AP and Kerala as neighbours. The capital of the state is the city of Bangalore

Appendices

149

which has evolved as one of the most vibrant cities in the country. Bangalore has grown into a hub for many major companies mainly IT companies. Many youngsters have flocked to Bangalore in recent times in search of employment. The state of Karnataka was formed in 1956 as the State of Mysore and was renamed as Karnataka in 1973. Demographics The total population of the state as per the 2011 census is 61,095,297 of which males and females are 30,966,657 and 30,128,640, respectively. The population of Karnataka accounted for 5.05% of India’s population in 2011. The decadal population growth during 2001–11 was 15.60%. The population density in the state is 319 per sq km which is lower than the national average of 382 per sq km. The sex ratio in Karnataka is 973 for every 1,000 males, which is above the national average of 940 as per census 2011. Nearly 39% of the state’s population lived in urban areas as of census 2011. Education Literacy rate in Karnataka has seen an upward trend and was 75.36% as per the 2011 population census, with male literacy at 82.47% and female literacy at 66.01%. Economy Karnataka has natural advantages that serve it well for diversified agricultural production. The state has been credited by the Planning Commission for presenting a separate agriculture budget. It is the largest producer of coarse cereals, coffee and raw silk among all states. The income generated from horticulture constitutes over 40% of income generated from agriculture and it is about 17% of the state’s GDP. Even though agriculture and allied sectors have only a 17% share in state’s GSDP, they continue to provide employment to about 55% of the total workforce. In floriculture, Karnataka occupies the second position in the country in terms of production. The contribution of primary, secondary and tertiary sectors to Karnataka’s GSDP in 2010–11 was 16.06%, 28.48% and 55.35%, respectively. The sectoral composition of Karnataka’s GSDP is comparable with that of the country’s GDP.

150

THE PARADOX OF INDIA’S NORTH–SOUTH DIVIDE

Five districts—Bangalore Urban, Belgaum, Dakshin Kannada, Bellary and Mysore—contribute to over 52% of the state’s income. Budget Analysis Budget size (2013–14): Surplus budget with `1,617,318.2 million (total receipts) and `1,613,486.8 million (total expenditure) Development Expenditure: `859,604.4 million As disclosed in the state’s budget for 2014–15, of its sources of income, 51% is from state tax revenue. Other sources are by borrowings (18%), grants from central government (15%), share of central taxes, state non-tax revenue and public account. The state’s commercial taxes make a big contribution to its tax revenue, accounting for 60% followed by state excise at 21%, and stamps and registration at 11%. Similarly, the state’s main component of expenditure is other general services, which constitute 21% of total expenditure. Agriculture and irrigation account for 20% followed by education (15%), other economic services (14%), debt, social welfare, other social services, health and water supply being the least with 2%. Sources 1. http://www.cii.in/States.aspx?enc=5WcSaM0yzEfRFlFUNeIvYGPI9xL9Hy pQH+qrcmYEmb6v8pxsbRyLw5qzJ69DQkUEkUXAoOXztHp4gwEleMK +MQ 2. http://www.census2011.co.in/census/state/karnataka.html 3. http://finance.kar.nic.in/bud2014/bseng14.pdf

Karnataka Fact Sheet Established

1 November 1956

Capital

Bangalore

Largest city

Bangalore

Districts

30

Total area

191,791 km2 (74,051 sq mi)

Area rank

Eighth

Population (2011) Total

61,095,297

Rank

Ninth

Density

319/km2 (830/sq mi)

Appendices Sex ratio

973

Official language(s)

Kannada

Literacy

75.60%

HDI rank

12th (2011)

Website

karunadu.gov.in

151

Kerala Kerala lies along the coastline, to the extreme south-west of the Indian peninsula, flanked by the Arabian Sea on the west and the mountains of the Western Ghats on the east. This land of Parasurama stretches north– south along a coastline of 580 km with a varying width of 35–120 km. Cascading delicately down the hills to the coasts covered by verdant coconut groves, the topography and physical characteristics change distinctly from east to west. The nature of the terrain and its physical features divide an east–west cross section of the state into three distinct regions—hills and valleys, midland and plains, and the coastal region. Demographics The total population of the state as per the 2011 census is 33,406,061 of which males and females constituted 16,027,412 and 17,378,649, respectively. The population of Kerala accounted for 2.76% of the country’s population in 2011. The population density in the state was 860 per sq km, higher than the national average of 382 per sq km. The sex ratio in the state was 1,084 for every 1,000 males, which is well above the national average of 940 as per census 2011. This state with advanced human development indices had 48% of its population living in urban areas as per census 2011. Education The high literacy rate in Kerala has seen a continuously upward trend and was 94% as per the 2011 population census, with male literacy at 96.11% and 100% female literacy. Economy The sectoral distribution of the state’s income shows high contribution by the tertiary sector followed by the secondary and primary sectors.

152

THE PARADOX OF INDIA’S NORTH–SOUTH DIVIDE

The contribution from the primary and tertiary sectors is decreasing but that from the secondary sector is showing an increase. The push factor for the growth of the secondary sector is primarily because of the growth in the construction sector which is showing an increase with increasing urbanization. Budget Analysis Budget size (2013–14): This was a surplus budget, with `705,181.3 million as total receipts, and ` 7,007,634 million as the total expenditure. Development Expenditure: `352,343.7 million (58.40%) The state’s budget for 2013–14 had contribution of 64.59% of public account1 in total receipts, followed by tax revenue (20.73%), public debt (9.70%) and non-tax revenue with 9.70%. In expenditure, 64.51% belongs to public account followed by development expenditure (revenue and capital) at 22.06% and non-development expenditure at 11.11%. The highest budget outlays are 30.83% to social and community services, 22.75% to local government plan programmes and 14.97% to transport and communications. Sources 1. http://www.spb.kerala.gov.in/images/pdf/er13/Chapter1/chapter01.html 2. http://www.census2011.co.in/census/state/kerala.html 3. http://keralagovernment-homepage.blogspot.in/2013/03/budget-2013-14. html

Kerala Fact Sheet Established

1 November 1956

Capital

Thiruvananthapuram

The public account deals with transactions on public money relating to deposits, advances, remittances and suspense. It is meant to record transactions relating to debt (other than the debts included in the consolidated fund). In respect of the transactions of the public account the state government functions as banker and incurs liability to repay the money received. Money lying in the public account does not belong to the state government as they have to be paid back at some time or other. The public account consists of small savings, reserve funds, deposits and advances, suspense and miscellaneous remittances, and cash balances. 1

Appendices

153

Other major cities

Kochi, Kozhikode, Kollam and Thrissur.

Districts

14

Total area

38,863 km2 (15,005 sq mi)

Area rank

21st

Population (2011) Total

33,406,061

Rank

12th

Density

860/km2 (2,200/sq mi)

Sex ratio

1,084

HDI rank

1st (2011)

Literacy

95.5%

Official language(s)

Malayalam, English

Website

kerala.gov.in

Tamil Nadu Tamil Nadu is most known for its monumental ancient Hindu temples and classical form of dance Bharatanatyam. TN lies in the southernmost part of the Indian Peninsula and is bordered by the union territory of Puducherry and the states of Kerala, Karnataka and AP. It is bounded by the Eastern Ghats on the north, by the Nilgiris, the Annamalai Hills and Kerala on the west, by the Bay of Bengal in the east, by the Gulf of Mannar and the Palk Strait on the southeast, and by the Indian Ocean on the south. Chennai is the capital of TN and lies on the eastern coastline of India. Demographics The total population of the state as per the 2011 census was 72,147,030 of which males and females constituted 36,137,975 and 36,009,055, respectively. The state contributed to 5.96% of the country’s population in 2011. The density of the state is 555 per sq km, much higher than the national average 382 per sq km. The sex ratio in TN is 996 per 1,000 male, which is above the national average of 940 as per census 2011. Similar to Kerala, this state had 48% of its population living in its urban areas as per census 2011.

154

THE PARADOX OF INDIA’S NORTH–SOUTH DIVIDE

Education The literacy rate in the state has seen an upward trend and is 80.09% as per the 2011 population census, with male literacy at 86.77% and female literacy at 73.14%. Economy The state is a leading producer of agricultural products in India. The agriculture in the state is heavily dependent on river water and monsoon rains. TN is also creating favourable climate for industrial growth. The global economic slowdown had its impact on the state economy. Chennai is the second largest software exporter in India. India’s largest IT Park is housed in Chennai. The sectoral composition of GDP in TN had the highest contribution of the tertiary sector followed by secondary and primary sectors. The contribution of the top four districts (Chennai, Kancheepuram, Thiruvallur and Coimbatore) to the state’s GDP is 42.8%. Budget Analysis Budget size (2014–15): This was a deficit budget, with `1,638,836.8 million as total receipts, and `1,759,753.6 million as total expenditure. The important components of revenue receipts for the state’s budget for 2014–15 are its own tax revenues at 9.75%. The state’s own tax revenues include commercial taxes, state excise, stamp duty and registration, and tax from motor vehicles. Further the revenue receipts include nontax revenues, grants from union government and shares in central taxes. Revenue expenditure caters to salaries, subsidies and grants, expenditure in non-wage operations, and maintenance and interest payments. Capital expenditure is growing substantially. As much as 37% of the state’s budget allocation is towards the social sector. Sources 1. http://www.nsdcindia.org/pdf/tn-sg-presentation.pdf 2. http://www.census2011.co.in/census/state/tamil+nadu.html

Tamil Nadu Fact Sheet Established

26 January 1950

Capital

Chennai

Largest city

Chennai

Appendices Districts

32

Total area

130,058 km2 (50,216 sq mi)

Area rank

11th

155

Population (2011) Total

72,147,030

Rank

Seventh

HDI rank

Sixth (2011)

Literacy

80.3% (2011 census)

Official language(s)

Tamil

Website

tn.gov.in

Appendix 2: Data Sources Data sources for education/health and urbanization indicators are the Census of India. Historical data on the IMR are obtained from the publication, Sample Registration System: Statistical Report 2006, published by the Census of India. Data on life expectancy were obtained from the Sample Registration System, and provided to us by Prof. Irudaya Rajan of the Centre for Development Studies, Trivandrum. NSDP data are from the Central Statistical Organization (or the Economic and Political Weekly Research Foundation [EPW-RF]). Poverty data are from the Planning Commission. Law and order indicators such as the number of police firing incidents, proportion of pending cases in the court and the proportion of civil to total police force are all from the National Crime Record Bureau. Infrastructure measures such as installed capacity of electricity are from the Central Electricity Authority, Ministry of Power, Government of India. Data on telephone penetration are from the Department of Telecommunications (DoT). Data on total and developmental expenditures by sector (education, sports and culture, energy, roads and bridges, public health and medical facilities) are from the EPW Research Foundation. Data on road length are from the CMIE. Literacy rates and proportion graduate for all states by year are from the Census of India. Literacy and urbanization rates for 2011 for all states are based on provisional totals released by census 2011. Data on the proportion of technical degree holders are from the Ministry of Human

156

THE PARADOX OF INDIA’S NORTH–SOUTH DIVIDE

Resources Development’s publication, Selected Educational Statistics. Annual time series data on the population in various states are from the EPW-RF. Data on urbanization are from the Census of India.

Appendix 3: Detailed Data Tables This appendix contains the detailed data tables which underlie the figures and computations found in the text. Table A.1 Per Capita NSDP—TN and UP—in Constant 1993–94 Prices Per capita NSDP constant prices 1993–94 series (in `) TN

UP

1960–61

5,053

3,338

1961–62

4,917

3,331

1962–63

4,947

3,220

1963–64

4,932

3,088

1964–65

5,008

3,493

1965–66

4,796

3,316

1966–67

4,857

2,933

1967–68

4,978

3,242

1968–69

4,978

3,169

1969–70

5,068

3,419

1970–71

5,281

3,581

1971–72

5,445

3,316

1972–73

5,354

3,441

1973–74

5,481

3,213

1974–75

4,672

3,287

1975–76

5,436

3,493

1976–77

5,509

3,515

1977–78

5,945

3,729 (Table A.1 Contd.)

Appendices

157

(Table A.1 Contd.) Per capita NSDP constant prices 1993–94 series (in `) TN

UP

1978–79

6,081

3,788

1979–80

6,072

3,183

1980–81

5,305

3,825

1981–82

5,808

3,822

1982–83

5,406

4,025

1983–84

5,602

4,085

1984–85

6,227

4,054

1985–86

6,368

4,117

1986–87

6,214

4,198

1987–88

6,506

4,290

1988–89

7,038

4,743

1989–90

7,415

4,769

1990–91

7,922

4,946

1991–92

8,038

4,871

1992–93

8,367

4,836

1993–94

8,955

4,869

1994–95

9,932

5,035

1995–96

10,147

5,111

1996–97

10,451

5,586

1997–98

11,260

5,438

1998–99

11,592

5,445

1999–2000

12,167

5,693

2000–2001

12,995

5,575

2001–2

12,484

5,603

2002–3

12,696

5,830

2003–4

12,976

5,975

2004–5

13,999

6,138

Sources: EPW Research Foundation; authors’ computations.

158

THE PARADOX OF INDIA’S NORTH–SOUTH DIVIDE

Table A.2 Poverty Ratios for TN and UP Average weighted poverty, TN

Average weighted poverty, UP

1973

54.9

57.1

1977

54.8

49.0

1983

51.6

47.1

1987

43.4

41.5

1993

35.2

40.9

1999

21.2

31.2

2004

22.5

33.1

Sources: Planning Commission; authors’ computations. Table A.3 Share of Agriculture in NSDP—TN and UP TN

UP

1960–61

46.2

58.2

1961–62

43.9

56.7

1962–63

42.9

54.6

1963–64

41.7

52.5

1964–65

40.8

55.2

1965–66

37.9

52.7

1966–67

38.0

49.5

1967–68

36.1

54.1

1968–69

34.9

52.9

1969–70

35.1

53.5

1970–71

35.7

53.1

1971–72

36.3

50.5

1972–73

35.1

50.9

1973–74

36.9

49.3

1974–75

30.2

50.8

1975–76

34.6

51.3

1976–77

31.7

50.2 (Table A.3 Contd.)

Appendices (Table A.3 Contd.) TN

UP

1977–78

33.6

50.5

1978–79

31.6

48.8

1979–80

26.8

41.5

1980–81

25.6

47.8

1981–82

29.1

47.7

1982–83

23.6

45.9

1983–84

26.9

45.9

1984–85

28.2

44.6

1985–86

24.9

43.4

1986–87

25.6

42.6

1987–88

24.3

41.4

1988–89

22.8

40.3

1989–90

24.0

38.2

1990–91

23.0

38.7

1991–92

24.9

39.7

1992–93

24.2

38.3

1993–94

24.3

38.9

1994–95

24.2

38.1

1995–96

20.2

37.4

1996–97

19.1

37.0

1997–98

19.1

35.2

1998–99

20.1

36.1

1999–2000

17.8

36.9

2000–2001

17.7

36.2

2001–2

17.5

36.0

2002–3

13.6

34.8

2003–4

12.4

34.4

2004–5

13.3

32.7

2005–6

13.6

31.2

2006–7

14.1

30.6

2007–8

13.0

31.6

Sources: EPW Research Foundation; authors’ computations.

159

160

THE PARADOX OF INDIA’S NORTH–SOUTH DIVIDE

Table A.4 Share of Industry in NSDP—TN and UP TN

UP

1960–61

19.4

9.0

1961–62

20.8

10.0

1962–63

21.5

11.2

1963–64

22.2

12.3

1964–65

23.0

11.8

1965–66

25.5

13.0

1966–67

25.2

13.0

1967–68

25.7

11.1

1968–69

26.3

11.3

1969–70

26.7

11.9

1970–71

26.7

12.7

1971–72

26.1

13.0

1972–73

26.4

13.2

1973–74

24.4

13.8

1974–75

28.5

12.7

1975–76

26.7

12.9

1976–77

29.2

13.5

1977–78

27.7

14.2

1978–79

28.9

15.0

1979–80

34.1

17.6

1980–81

32.5

16.0

1981–82

30.5

15.8

1982–83

33.3

17.5

1983–84

31.1

17.5

1984–85

31.5

17.8

1985–86

30.4

18.6

1986–87

29.1

19.0

1987–88

28.4

19.6

1988–89

33.0

20.7

1989–90

29.2

20.3 (Table A.4 Contd.)

Appendices

161

(Table A.4 Contd.) TN

UP

1990–91

32.2

20.6

1991–92

28.6

20.0

1992–93

29.2

19.8

1993–94

29.2

19.5

1994–95

29.7

20.9

1995–96

31.5

21.2

1996–97

29.9

22.0

1997–98

27.9

21.7

1998–99

26.0

20.0

1999–2000

27.3

19.6

2000–2001

27.6

19.6

2001–2

24.8

19.0

2002–3

26.8

19.9

2003–4

27.3

20.2

2004–5

27.3

21.4

2005–6

27.1

23.0

2006–7

26.6

23.7

2007–8

26.4

32.8

Sources: EPW Research Foundation; authors’ computations. Table A.5 Share of Services in NSDP—TN and UP TN

UP

1960–61

34.4

32.8

1961–62

35.3

33.2

1962–63

35.6

34.2

1963–64

36.1

35.2

1964–65

36.2

32.9

1965–66

36.6

34.4

1966–67

36.9

37.5

1967–68

38.2

34.8 (Table A.5 Contd.)

162

THE PARADOX OF INDIA’S NORTH–SOUTH DIVIDE

(Table A.5 Contd.) TN

UP

1968–69

38.8

35.8

1969–70

38.2

34.6

1970–71

37.7

34.3

1971–72

37.6

36.5

1972–73

38.5

35.9

1973–74

38.7

36.9

1974–75

41.3

36.5

1975–76

38.7

35.8

1976–77

39.2

36.3

1977–78

38.8

35.4

1978–79

39.5

36.2

1979–80

39.1

40.8

1980–81

41.9

36.2

1981–82

40.4

36.5

1982–83

43.1

36.6

1983–84

41.9

36.6

1984–85

40.3

37.6

1985–86

44.7

38.0

1986–87

45.4

38.5

1987–88

47.3

39.0

1988–89

44.2

39.0

1989–90

46.8

41.5

1990–91

44.8

40.7

1991–92

46.5

40.3

1992–93

46.6

42.0

1993–94

46.5

41.6

1994–95

46.1

41.1

1995–96

48.3

41.4

1996–97

51.0

41.0

1997–98

53.1

43.1

1998–99

53.9

43.9 (Table A.5 Contd.)

Appendices

163

(Table A.5 Contd.) TN

UP

1999–2000

54.8

43.5

2000–2001

54.7

44.1

2001–2

57.6

44.9

2002–3

59.6

45.3

2003–4

60.3

45.4

2004–5

59.4

45.9

2005–6

59.3

45.9

2006–7

59.3

45.7

2007–8

60.6

35.6

Sources: EPW Research Foundation; authors’ computations. Table A.6 Literacy Rate—TN and UP Literacy rate TN

UP

1960–61

31.4

20.87

1970–71

39.5

23.99

1980–81

54.4

32.65

1990–91

62.7

40.71

2000–2001

73.5

57.36

2010–11

80.3

69.70

Sources: Census of India; authors’ computations and analyses. Table A.7 Percentage of Technical Graduates,* 1961 and Technical Enrolment, 2004—TN and UP Proportion of engineering graduates UP

TN

1961

0.04

0.08

2004

0.04

0.21

Sources: Census of India; authors’ computations and analyses. Note: *The percentage is a proportion of population greater than 15 years of age.

164

THE PARADOX OF INDIA’S NORTH–SOUTH DIVIDE

Table A.8 IMR for TN and UP TN

UP

1971–73

114

182

1972–74

112

183

1973–75

109

182

1974–76

109

183

1975–77

108

181

1976–78

106

174

1977–79

103

169

1978–80

99

166

1979–81

94

157

1980–82

89

152

1981–83

87

151

1982–84

83

152

1983–85

82

151

1984–86

80

143

1985–87

79

134

1986–88

77

128

1987–89

73

123

1988–90

67

114

1989–91

61

105

1990–92

58

98

1991–93

57

96

1992–94

58

93

1993–95

56

89

Sources: Census of India; authors’ computations and analyses. Table A.9 Trends in Life Expectancy—TN and UP 1971

1981

1991

2001

2011

TN

51.2

54.2

61.8

65.5

69

UP

42.7

47.6

53.4

57.9

62.3

Source: Registrar General of India.

Appendices

165

Table A.10 Young Working Age Population (15–29 years) as a Percentage of Children below 15 Years TN

UP

1961

69.00

60.1

1971

67.30

54.6

1981

77.80

56.9

1991

92.40

61.7

2001

105.90

60.6

Sources: Census of India; authors’ computations and analyses. Table A.11 Percentage of Urban Population—TN and UP TN

UP

1971

30.26

14.02

1981

32.95

17.95

1991

34.15

19.68

2001

44.04

20.78

2011

48.45

22.28

Sources: Census of India; authors’ computations and analyses. Table A.12 Installed Generating Capacity (per million population)— TN and UP IC per million population, TN

IC per million population, UP

1960

15.5

5.4

1965

27.4

11.6

1966

36.3

11.4

1967

38.1

14.4

1968

37.5

16.0

1970

33.5

15.4

1971

52.6

16.3 (Table A.12 Contd.)

166

THE PARADOX OF INDIA’S NORTH–SOUTH DIVIDE

(Table A.12 Contd.) IC per million population, TN

IC per million population, UP

1973

38.2

16.7

1974

37.5

19.4

1975

39.4

21.5

1979

48.9

31.2

1980

48.3

32.9

1981

52.0

33.4

1982

51.1

32.6

1984

48.8

34.4

1985

48.4

35.3

1986

52.7

36.2

1987

61.3

37.8

1988

67.4

41.0

1989

70.8

40.9

1990

73.6

40.1

1991

76.7

36.8

1992

76.0

35.5

1993

74.9

38.3

1994

81.2

41.0

1995

85.9

40.3

1996

84.7

39.5

1997

83.9

39.6

1998

86.5

38.8

1999

86.3

35.0

2000

92.6

34.1

2001

95.2

31.7

2002

99.6

31.1

2003

100.2

30.5

2004

100.3

29.9

Sources: Central Electricity Authority; authors’ computations and analyses. Note: IC: Installed capacity.

Appendices

167

Table A.13 Road Length in TN and UP All roads length/ thousand population (‘000 metres) TN

All roads length/ thousand population (‘000 metres) UP

1970–71

2.1

0.7

1971–72

2.2

1.0

1972–73

2.3

1.5

1973–74

2.4

1.5

1974–75

2.4

1.5

1975–76

2.5

1.1

1976–77

2.5

1.5

1977–78

2.5

1.5

1978–79

2.4

1.5

1979–80

2.4

1.8

1980–81

2.5

1.3

1981–82

2.7

1.4

1982–83

2.9

1.3

1983–84

2.8

1.4

1984–85

2.9

1.4

1985–86

3.0

1.4

1986–87

3.0

1.4

1987–88

3.1

1.4

1988–89

3.6

1.4

1989–90

3.6

1.4

1990–91

3.5

1.5

1991–92

3.5

1.4

1992–93

2.4

1.3

1993–94

2.4

1.4

1994–95

2.4

1.3

1995–96

2.4

1.6

1996–97

2.4

1.7 (Table A.13 Contd.)

168

THE PARADOX OF INDIA’S NORTH–SOUTH DIVIDE

(Table A.13 Contd.) All roads length/ thousand population (‘000 metres) TN

All roads length/ thousand population (‘000 metres) UP

1997–98

2.4

1.8

1998–99

2.5

1.8

1999–2000

2.6

1.5

2000–2001

2.6

1.5

2001–2

2.7

1.5

Sources: Centre for Monitoring Indian Economy (CMIE); authors’ computations and analyses. Table A.14 Tele-density—TN and UP TN

UP

1998–99

4.6

1.4

1999–2000

6.0

1.8

2000–2001

7.9

2.3

2001–2

8.1

2.5

2002–3

11.0

3.1

2003–4

14.9

4.2

2004–5

18.4

6.0

Sources: Telecom Regulatory Authority of India; authors’ computations and analyses. Table A.15 Per Capita Developmental Expenditure—TN and UP Per capita developmental expenditure TN

UP

1980–81

262.83

178.83

1981–82

298.89

194.57

1982–83

338.53

218.50

1983–84

388.42

248.89 (Table A.15 Contd.)

Appendices

169

(Table A.15 Contd.) Per capita developmental expenditure TN

UP

1984–85

422.97

302.16

1985–86

452.98

318.21

1986–87

492.26

347.15

1987–88

577.00

356.64

1988–89

618.74

412.67

1989–90

749.74

468.66

1990–91

853.12

602.64

1991–92

1,320.70

596.68

1992–93

1,256.52

710.61

1993–94

1,234.18

661.50

1994–95

1,394.51

791.60

1995–96

1,450.94

742.44

1996–97

1,771.02

879.96

1997–98

1,877.63

959.18

1998–99

2,018.17

1,103.57

1999–2000

2,124.00

1,153.79

2000–2001

2,296.42

1,147.08

2001–2

2,394.27

1,344.07

2002–3

2,821.12

1,245.64

Sources: CMIE; authors’ computations and analyses. Table A.16 Police Firing Incidents per Million Population—TN and UP Police firing incidences/ million population TN

UP

1988

1.2

0.64

1990–91

0.50

0.44

1991–92

1.19

1.14

1992–93

1.11

1.24

1993–94

0.50

0.72 (Table A.16 Contd.)

170

THE PARADOX OF INDIA’S NORTH–SOUTH DIVIDE

(Table A.16 Contd.) Police firing incidences/ million population TN

UP

1994–95

0.19

0.24

1995–96

0.22

0.21

1996–97

0.50

0.16

1997–98

0.43

0.28

1998–99

0.15

0.19

1999–2000

0.39

1.55

2000–2001

0.52

1.40

2001–2

0.08

0.64

2002–3

0.17

0.14

2003–4

0.19

1.13

2004–5

0.14

1.40

Sources: National Crime Records Bureau; authors’ computations and analyses. Table A.17 Proportion of Civil Police to Total Police—TN and UP TN, % civil to total police

UP, % civil to total police

1972

89.30

75.1

1974

89.80

69.8

1975

90.30

71.3

1976

90.00

78.4

1977

89.90

78.6

1978

90.30

78.5

1979

90.70

80.4

1982

88.30

79.8

1983

88.37

60.1

1984

88.11

77.6

1986

87.29

76.1

1988

93.60

78.6

Sources: National Crime Records Bureau; authors’ computations and analyses.

Appendices

171

Table A.18 Percentage of Court Cases Pending Investigation at the End of Year % cases pending investigation at the end of year by court TN

UP

1990–91

60.4

73.0

1991–92

58.2

73.2

1992–93

60.3

75.3

1993–94

60.8

76.3

1994–95

59.0

77.0

1995–96

61.4

79.6

1996–97

57.2

78.9

1997–98

61.2

82.1

1998–99

56.8

79.5

1999–2000

58.6

81.6

2000–2001

61.3

80.8

2001–2

57.3

79.6

2002–3

60.4

82.5

2003–4

56.9

81.9

2004–5

56.5

81.6

2005–6

57.5

81.6

Sources: National Crime Records Bureau; authors’ computations and analyses. Table A.19 Per Capita NSDP—Southern and Northern States, 1960–2005, 1993–94 Constant Prices Average PC NSDP (weighted), South

Average PC NSDP (weighted), North

1960–61

4,302

3,095

1961–62

4,364

3,120

1962–63

4,384

3,012

1963–64

4,438

2,956 (Table A.19 Contd.)

172

THE PARADOX OF INDIA’S NORTH–SOUTH DIVIDE

(Table A.19 Contd.) Average PC NSDP (weighted), South

Average PC NSDP (weighted), North

1964–65

4,518

3,198

1965–66

4,210

2,910

1966–67

4,365

2,677

1967–68

4,473

3,079

1968–69

4,454

2,869

1969–70

4,569

3,043

1970–71

4,803

3,321

1971–72

4,888

3,157

1972–73

4,649

3,117

1973–74

4,988

3,054

1974–75

4,715

3,020

1975–76

5,002

3,264

1976–77

4,770

3,239

1977–78

5,183

3,436

1978–79

5,341

3,440

1979–80

5,304

2,927

1980–81

5,054

4,014

1981–82

5,430

4,073

1982–83

5,316

4,162

1983–84

5,417

4,395

1984–85

5,614

4,310

1985–86

5,661

4,378

1986–87

5,591

4,442

1987–88

5,941

4,445

1988–89

6,528

5,015

1989–90

6,864

4,948

1990–91

7,110

5,332

1991–92

7,398

5,011

1992–93

7,454

5,073 (Table A.19 Contd.)

Appendices

173

(Table A.19 Contd.) Average PC NSDP (weighted), South

Average PC NSDP (weighted), North

1993–94

8,031

4,947

1994–95

8,554

5,221

1995–96

8,838

5,182

1996–97

9,259

5,683

1997–98

9,491

5,709

1998–99

10,255

5,815

1999–2000

10,693

6,016

2000–2001

11,443

5,965

2001–2

11,657

6,083

2002–3

11,805

6,149

2003–4

12,386

6,618

2004–5

13,298

6,606

Source: EPW Research Foundation. Note: PC: Per capita.

Table A.20 Total Poverty Rates—Southern and Northern States, 1973–2003 Average weighted total poverty, South

Average weighted total poverty, North

1973

49

58

1977

43

53

1983

35

50

1987

31

43

1993

25

42

1999

15

32

2004

17

34

Sources: Planning Commission; Census of India; authors’ computations.

174

THE PARADOX OF INDIA’S NORTH–SOUTH DIVIDE

Table A.21 Literacy Rate—Southern States, 1951–2011 1951

1961

1971

1981

1991

2001

2011

AP

13.2

24.6

24.6

29.9

44.1

61.1

67.7

Karnataka

19.3

29.8

31.5

38.5

56.0

67.0

75.6

Kerala

40.7

55.1

60.4

70.4

89.8

90.9

93.9

TN

20.8

36.4

39.5

46.8

62.7

73.5

80.3

Sources: Census of India; authors’ computations. Table A.22 Literacy Rate—Northern States, 1951–2011

Bihar MP Rajasthan UP

1951

1961

1971

1981

1991

2001

2011

12.2

21.8

19.9

26.2

38.5

47.53

63.82

9.8

20.5

22.1

27.9

44.2

64.11

70.6

8.9

18.1

19.1

24.4

38.6

61.03

67.06

10.8

20.7

21.7

27.2

41.6

57.36

69.72

Sources: Census of India; authors’ computations. Table A.23 Proportion of Graduates—Southern States, 1971–2001 AP (%)

Karnataka (%)

Kerala (%)

TN (%)

1971

0.71

0.90

1.11

0.75

1981

1.74

2.28

2.12

1.95

1991

3.44

3.86

3.89

3.78

2001

5.46

6.19

6.13

4.81

Sources: Census of India; authors’ computations. Table A.24 Proportion of Graduates—Northern States, 1971–2001 Bihar (%)

MP (%)

Rajasthan (%)

UP (%)

1971

0.78

0.93

0.74

0.85

1981

1.40

1.98

1.86

2.33

1991

3.24

3.42

3.20

3.60

2001

4.38

5.21

4.33

5.06

Sources: Census of India; authors’ computations.

175

Appendices Table A.25 IMR of Population—Southern States, 1971–97 AP

Karnataka

Kerala

TN

1971

106

95

58

113

1972

116

95

63

121

1973

105

90

54

108

1974

111

86

54

106

1975

123

80

54

112

1976

122

89

56

110

1977

125

83

47

103

1978

117

82

42

105

1979

106

83

43

100

1980

92

71

40

93

1981

86

69

37

91

1982

79

65

30

83

1983

77

71

33

87

1984

78

74

29

78

1985

83

69

31

81

1986

82

73

27

80

1987

79

75

28

76

1988

83

74

28

74

1989

81

80

21

68

1990

70

70

17

59

1991

73

77

16

57

1992

71

73

17

58

1993

64

67

13

56

1994

65

67

16

59

1995

67

62

15

54

1996

65

53

14

53

1997

63

53

12

53

Sources: Census of India; authors’ computations.

176

THE PARADOX OF INDIA’S NORTH–SOUTH DIVIDE

Table A.26 IMR of Population—Northern States, 1971–97 Bihar

MP

Rajasthan

UP

1971

135

1972

156

123

202

1973

145

137

176

1974

137

133

172

1975

151

155

198

1976

138

142

178

1977

148

142

168

1978

143

140

177

1979

143

109

162

1980

167

142

105

159

1981

118

142

108

150

1982

112

134

97

147

1983

99

125

109

155

1984

95

121

122

155

1985

106

122

108

142

1986

101

118

107

132

1987

101

120

102

127

1988

97

121

103

124

1989

91

117

96

118

1990

75

111

84

99

1991

69

117

79

97

1992

73

104

90

98

1993

70

106

82

94

1994

67

98

84

88

1995

73

99

86

86

1996

71

97

85

85

1997

71

94

85

85

Sources: Census of India; authors’ computations.

177

Appendices Table A.27 Trends in Life Expectancy—Southern States 1971

1981

1991

2001

2011

AP

50.3

55.9

61.9

65.7

68.9

Karnataka

53.4

60

63.8

68.2

71.1

Kerala

62.0

68.5

70.3

73.2

75

TN

51.2

54.2

61.8

65.5

69

Source: Sample registration system of the Registrar General of India. Table A.28 Trends in Life Expectancy—Northern States 1971

1981 49

59.1

65.2

68.7

51.5

48.2

57.1

61.7

65.2

Bihar MP

1991

2001

2011

Rajasthan

46.9

49.6

59.6

65

68.1

UP

42.7

47.6

53.4

57.9

62.3

Source: Sample registration system of the Registrar General of India. Table A.29 Installed Generating Capacity per Million Population— Southern States, 1960–2004 IC/million population, AP

IC/million population, Karnataka

IC/million population, Kerala

IC/million population, TN

1960

7.6

8.2

8.2

15.5

1965

7.6

17.5

10.5

27.4

1966

10.5

16.8

18.8

36.3

1967

15.8

22.9

27.0

38.1

1968

15.1

28.8

27.2

37.5

1970

14.1

30.3

25.9

33.5

1971

13.8

32.6

25.4

52.6

1973

14.6

31.2

27.8

38.2 (Table A.29 Contd.)

178

THE PARADOX OF INDIA’S NORTH–SOUTH DIVIDE

(Table A.29 Contd.) IC/million population, AP

IC/million population, Karnataka

IC/million population, Kerala

IC/million population, TN

1974

19.0

30.4

27.3

37.5

1975

20.7

32.5

32.4

39.4

1979

37.1

37.3

40.7

48.9

1980

42.2

40.0

39.9

48.3

1981

41.3

46.2

39.4

52.0

1982

48.3

48.7

38.9

51.1

1984

54.5

55.3

37.8

48.8

1985

56.9

61.8

48.3

48.4

1986

59.5

60.6

53.7

52.7

1987

58.5

59.5

52.5

61.3

1988

57.3

58.4

52.0

67.4

1989

63.0

60.1

51.5

70.8

1990

62.7

66.5

50.9

73.6

1991

62.8

65.8

50.3

76.7

1992

61.8

66.1

49.6

76.0

1993

67.9

67.1

49.7

74.9

1994

73.6

70.3

49.5

81.2

1995

72.6

69.0

49.1

85.9

1996

78.5

67.9

51.0

84.7

1997

83.5

68.0

57.2

83.9

1998

82.7

77.3

58.2

86.5

1999

82.2

83.6

67.7

86.3

2000

88.2

84.5

69.9

92.6

2001

94.0

99.9

70.0

95.2

2002

97.5

95.2

69.1

99.6

2003

98.4

94.6

68.4

100.2

2004

97.2

99.7

67.7

100.3

Sources: Central Electricity Authority; authors’ computations. Note: IC: Installed capacity.

179

Appendices Table A.30 Installed Generating Capacity per Million Population— Northern States, 1960–2004 IC/million population, Bihar

IC/million population, MP

IC/million population, Rajasthan

IC/million population, UP

1960

7.6

8.4

3.6

5.4

1965

13.3

8.6

11.2

11.6

1966

13.8

9.8

13.0

11.4

1967

14.5

12.9

16.7

14.4

1968

17.8

18.1

21.0

16.0

1970

8.9

17.7

21.3

15.4

1971

10.5

17.2

20.7

16.3

1973

10.2

17.6

21.1

16.7

1974

10.0

17.1

20.1

19.4

1975

9.8

19.3

19.8

21.5

1979

13.2

30.1

25.0

31.2

1980

13.6

31.6

24.0

32.9

1981

13.3

30.8

23.3

33.4

1982

14.5

34.0

28.6

32.6

1984

18.2

47.3

31.2

34.4

1985

20.6

50.8

31.7

35.3

1986

20.2

50.5

31.0

36.2

1987

19.2

50.0

29.7

37.8

1988

18.8

49.5

35.1

41.0

1989

18.5

48.3

40.3

40.9

1990

18.1

50.4

39.5

40.1

1991

17.6

50.5

38.9

36.8

1992

17.3

51.7

38.0

35.5

1993

22.7

73.4

41.4

38.3

1994

25.2

73.3

40.6

41.0

1995

24.6

71.7

40.3

40.3 (Table A.30 Contd.)

180

THE PARADOX OF INDIA’S NORTH–SOUTH DIVIDE

(Table A.30 Contd.) IC/million population, Bihar

IC/million population, MP

IC/million population, Rajasthan

IC/million population, UP

1996

26.9

70.1

39.3

39.5

1997

26.3

68.7

38.3

39.6

1998

25.7

70.9

42.1

38.8

1999

25.0

73.8

45.6

35.0

2000

25.8

72.6

44.5

34.1

2001

20.0

53.5

52.1

31.7

2002

19.7

53.4

52.4

31.1

2003

19.3

52.5

58.7

30.5

2004

19.0

58.3

29.9

Sources: Central Electricity Authority; authors’ computations. Note: IC: Installed capacity.

5,196.236 5,062.733 5,871.82 7,301.231

1970–71

1980–81

1990–91

2000–2001

2,740.072

3,265.309

3,015.995

3,275.067

3,586.128

Bihar

7,847.661

4,874.981

4,617.629

4,929.645

4,096.346

Karnataka

5,601.234

5,085.104

4,186.164

4,750.671

4,891.045

Kerala

3,359.352

4,929.565

4,381.888

4,785.726

5,436.494

MP

5,073.48

6,199.664

4,053.367

5,726.545

3,986.035

Rajasthan

6,406.931

4,340.11

3,175.65

4,155.785

4,772.96

TN

Sources: EPW Research Foundation for agriculture NSDP; Census of India, various years, for rural population.

4,988.718

1960–61

AP

Table A.31 NSDP Per Capita Agriculture and Allied Sectors—North and South

4,434.082

4,256.263

3,913.446

3,862.385

3,886.57

UP

182

THE PARADOX OF INDIA’S NORTH–SOUTH DIVIDE

Table A.32 Trends in Urbanization—Southern States, 1971–2011 1971

1981

1991

2001

2011

AP

19.31

23.32

Karnataka

24.31

28.89

26.89

27.3

33.49

30.92

33.99

38.57

Kerala

16.24

18.74

26.39

25.96

47.72

TN

30.26

32.95

34.15

44.04

48.45

Sources: Census of India; authors’ computations. Table A.33 Trends in Urbanization—Northern States, 1971–2011 1971

1981

1991

2001

2011

Bihar

10.00

12.47

10.4

10.46

11.30

MP

16.29

20.29

25.27

26.46

27.63

Rajasthan

17.63

21.05

22.88

23.39

24.89

UP

14.02

17.95

19.68

20.78

22.28

Sources: Census of India; authors’ computations. Table A.34 Agricultural Output—North and South Northern states Agriculture NSDP (per capita) Bihar

MP

Rajasthan

UP

1960–61

3,586

5,436

3,986

3,887

1970–71

3,275

4,786

5,727

3,862

1980–81

3,016

4,382

4,053

3,913

1990–91

3,265

4,930

6,200

4,256

2000–2001

2,740

3,359

5,073

4,434

Southern states Agriculture NSDP (per capita) AP

Karnataka

Kerala

TN

1960–61

4,989

4,096

4,891

4,773

1970–71

5,196

4,930

4,751

4,156 (Table A.34 Contd.)

183

Appendices (Table A.34 Contd.) AP

Karnataka

Kerala

TN

1980–81

5,063

4,618

4,186

3,176

1990–91

5,872

4,875

5,085

4,340

2000–2001

7,301

7,848

5,601

6,407

Sources: Census of India various years for rural population; EPW Research Foundation for data on agriculture NSDP. Table A.35 Per Capita Developmental Spending—Southern States, 1980–2003 Per capita developmental expenditure AP

Karnataka

Kerala

TN

1980–81

208

209

246

189

1981–82

236

228

266

235

1982–83

241

270

263

269

1983–84

327

303

343

316

1984–85

382

366

359

348

1985–86

411

409

458

368

1986–87

482

453

475

408

1987–88

478

495

468

498

1988–89

574

535

534

539

1989–90

591

610

588

666

1990–91

667

748

708

772

1991–92

741

922

766

1,252

1992–93

848

983

856

1,154

1993–94

980

1,133

983

1,159

1994–95

1,164

1,234

1,144

1251

1995–96

1,303

1,412

1,301

1,330

1996–97

1,412

1,611

1,513

1,598

1997–98

1,460

1,627

1,850

1,803

1998–99

1,685

1,896

2,002

1,944

1999–2000

1,751

2,164

2,262

2,028

2000–2001

2,254

2,369

2,178

2,231

2001–2

2,392

2,818

2,038

2,170 (Table A.35 Contd.)

184

THE PARADOX OF INDIA’S NORTH–SOUTH DIVIDE

(Table A.35 Contd.) Per capita developmental expenditure AP

Karnataka

Kerala

TN

2002–3 (RE)

2,505

2,791

2,478

2,567

2003–4 (BE)

2,977

2,991

2,887

2,420

Sources: Centre for Monitoring Indian Economy; authors’ computations. Notes: RE: Revised estimates; BE: Budget estimates. Table A.36 Per Capita Developmental Spending—Northern States, 1980–2003 UP + Uttarakhand

Bihar + Jharkhand

MP + Chhattisgarh

1980–81

152

128

1,802

194

1981–82

162

161

1,812

239

1982–83

182

178

1,706

260

1983–84

207

179

1,715

278

1984–85

248

203

1,875

289

1985–86

272

256

1,641

337

1986–87

309

274

1,355

408

1987–88

334

293

1,372

562

1988–89

380

329

1,058

529

1989–90

431

358

907

504

1990–91

528

432

909

645

1991–92

485

470

749

867

1992–93

607

512

671

885

1993–94

580

730

800

997

1994–95

625

710

656

1,101

1995–96

673

752

634

1,335

1996–97

778

711

578

1,380

1997–98

856

711

615

1,527

1998–99

1,002

864

442

1,646

1999–2000

1,056

1,401

395

1,662

2000–2001

1,085

1,128

444

1,755

2001–2

1,139

1,108

724

1,839

Rajasthan

(Table A.36 Contd.)

Appendices

185

(Table A.36 Contd.) UP + Uttarakhand

Bihar + Jharkhand

MP + Chhattisgarh

Rajasthan

2002–3 (RE)

1,344

1,314

1,019

2028

2003–4 (BE)

1,822

1,239

1,209

2,348

Sources: Centre for Monitoring Indian Economy; authors’ computations. Notes: RE: Revised estimates; BE: Budget estimates. Table A.37 Police Firing Incidents per Million Population—Southern States AP

Karnataka

Kerala

TN

1968–69

4.6

0.1

0.3

0.1

1969–70

1.4

0.3

0.2

0.3

1970–71

0.8

0.3

0.1

0.2

1971–72

1.3

0.3

0.2

0.4

1987–88

2.9

0.8

0.2

0.2

1988–89

2.4

0.8

0.4

0.4

1989–90

2.9

0.9

0.5

0.6

1990–91

4.8

0.6

0.9

0.5

1991–92

3.8

1.9

1.2

1992–93

2.9

0.5

0.2

1.1

1993–94

2.3

1.2

0.3

0.5

1994–95

3.9

0.6

0.1

0.2

1995–96

2.7

0.4

0.1

0.2

1996–97

3.2

0.8

0.1

0.5

1997–98

3.2

0.2

0.1

0.4

1998–99

3.1

0.2

0.4

0.1

1999–2000

2.8

0.3

0.0

0.4

2000–2001

2.4

0.6

0.4

0.5

2001–2

1.3

0.2

0.1

0.1

2002–3

2.8

0.2

0.3

0.2

2003–4

1.0

0.1

0.1

0.2

2004–5

1.1

0.2

0.1

0.1

Sources: National Crime Records Bureau; authors’ computations.

186

THE PARADOX OF INDIA’S NORTH–SOUTH DIVIDE

Table A.38 Police Firing Incidents per Million Population—Northern States Bihar

MP

Rajasthan

UP

1968–69

0.2

1.4

1.7

0.9

1969–70

0.2

3.5

1.0

0.2

1970–71

0.1

3.0

1.1

0.7

1971–72

0.1

1.4

0.2

2.1

1987–88

0.9

0.4

0.5

0.7

1988–89

1.4

2.6

0.8

0.6

1989–90

2.1

2.6

1.0

0.5

1990–91

1.7

0.4

0.7

0.4

1991–92

2.1

3.4

0.8

1.1

1992–93

1.7

0.6

0.4

1.2

1993–94

1.1

0.8

0.2

0.7

1994–95

0.8

0.8

0.1

0.2

1995–96

0.8

0.6

0.3

0.2

1996–97

1.0

0.6

0.3

0.2

1997–98

1.6

1.2

0.3

0.3

1998–99

1.1

1.4

0.4

0.2

1999–2000

1.0

1.0

0.7

1.5

2000–2001

0.6

0.6

0.5

1.4

2001–2

0.8

1.0

0.5

0.6

2002–3

0.5

0.4

0.2

0.1

2003–4

0.2

0.4

0.2

1.1

2004–5

0.2

0.6

0.4

1.4

Sources: National Crime Records Bureau; authors’ computations. Table A.39 Proportion of Pending Court Cases—Southern and Northern States Average percentage of court pending cases (weighted), South

Average percentage of court pending cases (weighted), North

1990–91

66.2

80.0

1991–92

66.0

78.7 (Table A.39 Contd.)

Appendices

187

(Table A.39 Contd.) Average percentage of court pending cases (weighted), South

Average percentage of court pending cases (weighted), North

1992–93

68.8

79.5

1993–94

67.4

80.5

1994–95

69.5

80.1

1995–96

65.8

80.4

1996–97

66.1

79.7

1997–98

66.5

81.2

1998–99

65.4

80.5

1999–2000

66.4

82.3

2000–2001

66.9

81.8

2001–2

66.2

81.8

2002–3

68.2

82.7

2003–4

66.7

82.8

2004–5

66.3

82.7

Table A.40 Banking Services, Radio and Television—Slum Households Total number of households availing Radio/transistor banking services (%) (%)

Television (%)

India

53.18

18.73

69.56

Rajasthan

48.52

18.26

63.23

UP

56.07

20.33

61.41

Bihar

48.56

22.82

38.12

MP

50.32

14.58

63.51

Uttarakhand

70.83

9.42

75.39

Jharkhand

59.13

16.99

53.80

Chhattisgarh

47.54

12.67

61.94

Northern states average

54.42

16.44

59.63

Minimum

47.54

9.42

38.12

Maximum

70.83

22.82

75.39 (Table A.40 Contd.)

188

THE PARADOX OF INDIA’S NORTH–SOUTH DIVIDE

(Table A.40 Contd.) Total number of households availing Radio/transistor banking services (%) (%)

Television (%)

SD

8.44

4.59

11.41

AP

47.10

12.33

73.78

Karnataka

38.39

16.11

67.72

Kerala

63.55

27.07

82.72

TN

45.97

21.98

84.46

Southern states average

48.76

19.37

77.17

Minimum

38.39

12.33

67.72

Maximum

63.55

27.07

84.46

SD

10.59

6.49

7.85

Sources: Census of India; authors’ computations and analyses. Table A.41 Computer/Laptop, Landline and Mobile—Slum Households Computer/ Computer/ laptop laptop without with Internet Internet (%) (%)

Landline only (%)

Mobile only (%)

Both landline and mobile (%)

India

3.32

7.08

4.43

63.46

4.78

Rajasthan

1.82

5.56

3.30

65.55

4.55

UP

3.21

7.90

4.91

63.58

3.30

Bihar

2.16

7.69

3.19

53.89

2.81

MP

1.75

5.96

3.42

57.45

3.63

Uttarakhand

4.94

10.12

4.72

68.40

7.92

Jharkhand

2.73

7.32

3.16

57.86

2.67

Chhattisgarh

2.09

6.10

2.72

49.19

3.05

Northern states average

2.67

7.23

3.63

59.41

3.99

Minimum

1.75

5.56

2.72

49.19

2.67

(Table A.41 Contd.)

189

Appendices (Table A.41 Contd.) Computer/ Computer/ laptop laptop without with Internet Internet (%) (%)

Landline only (%)

Maximum

4.94

10.12

4.91

SD

1.13

1.56

AP

2.81

7.13

Mobile only (%)

Both landline and mobile (%)

68.40

7.92

0.84

6.80

1.84

4.37

66.46

3.56

Karnataka

1.98

7.73

5.07

60.07

3.27

Kerala

8.88

10.95

8.12

56.86

21.87

TN

3.67

7.01

5.47

65.90

5.01

Southern states average

4.33

8.20

5.76

62.32

8.43

Minimum

1.98

7.01

4.37

56.86

3.27

Maximum

8.88

10.95

8.12

66.46

21.87

SD

3.10

1.86

1.64

4.65

9.00

Sources: Census of India; authors’ computations and analyses. Table A.42 Bicycle, Scooter/Motorcycle/Moped and Car/Jeep/Van— Slum Households

India

Bicycle (%)

Scooter/motorcycle/ moped (%)

Car/jeep/van (%)

40.16

22.01

3.56

Rajasthan

43.20

30.37

3.54

UP

56.33

26.59

4.65

Bihar

44.54

13.38

2.63

MP

48.63

26.51

3.03

Uttarakhand

43.15

34.35

8.00

Jharkhand

53.81

24.92

4.38

Chhattisgarh

63.97

26.30

3.54

Northern states average

50.52

26.06

4.25 (Table A.42 Contd.)

190

THE PARADOX OF INDIA’S NORTH–SOUTH DIVIDE

(Table A.42 Contd.) Bicycle (%)

Scooter/motorcycle/ moped (%)

Car/jeep/van (%)

Minimum

43.15

13.38

2.63

Maximum

63.97

34.35

8.00

SD

7.88

6.45

1.80

AP

34.76

24.78

2.50

Karnataka

28.14

19.19

2.60

Kerala

27.29

28.96

12.07

TN

42.88

28.47

3.09

Southern states average

33.27

25.35

5.07

Minimum

27.29

19.19

2.50

Maximum

42.88

28.96

12.07

7.23

4.51

4.68

SD

Sources: Census of India; authors’ computations and analyses. Table A.43 Public Amenities—Slum Households Households with TV, computer/laptop, telephone/mobile phone and scooter/car (%) India

4.61

None of the assets specified (%) 10.70

Rajasthan

3.75

12.16

UP

5.15

10.76

Bihar

2.71

23.94

MP

3.91

14.47

Uttarakhand

9.65

7.09

Jharkhand

5.29

15.73

Chhattisgarh

4.96

14.01

Northern states average

5.06

14.02

Minimum

2.71

7.09 (Table A.43 Contd.)

Appendices

191

(Table A.43 Contd.) Households with TV, computer/laptop, telephone/mobile phone and scooter/car (%)

None of the assets specified (%)

Maximum

9.65

23.94

SD

2.23

5.22

AP

4.55

9.92

Karnataka

2.96

13.35

12.09

4.71

TN

5.52

5.75

Southern states average

6.28

8.43

Minimum

2.96

4.71

Maximum

12.09

13.35

4.01

3.97

Kerala

SD

Sources: Census of India; authors’ computations and analyses. Table A.44 Latrine Facility—Slum Households Number of households not Number of having latrine households facility within having latrine the premises facility within (%) the premises (%)

Public latrine (%)

Open (%)

India

66.01

33.99

15.09

18.90

Rajasthan

71.60

28.40

2.15

26.26

UP

77.48

22.52

3.76

18.76

Bihar

53.84

46.16

3.67

42.49

MP

62.86

37.14

5.50

31.65

Uttarakhand

91.70

8.30

2.24

6.06

Jharkhand

52.69

47.31

5.43

41.88

Chhattisgarh

48.67

51.33

9.68

41.65

(Table A.44 Contd.)

192

THE PARADOX OF INDIA’S NORTH–SOUTH DIVIDE

(Table A.44 Contd.) Number of households not Number of having latrine households facility within having latrine the premises facility within (%) the premises (%)

Public latrine (%)

Open (%)

Northern states average

65.55

34.45

4.63

29.82

Minimum

48.67

8.30

2.15

6.06

Maximum

91.70

51.33

9.68

42.49

SD

15.58

15.58

2.60

13.84

AP

82.35

17.65

2.87

14.78

Karnataka

63.30

36.70

11.72

24.97

Kerala

93.21

6.79

3.45

3.34

TN

61.01

38.99

15.92

23.08

Southern states average

74.97

25.03

8.49

16.54

Minimum

61.01

6.79

2.87

3.34

Maximum

93.21

38.99

15.92

24.97

SD

15.47

15.47

6.39

9.85

Sources: Census of India; authors’ computations and analyses.

1.72

MP

2.33 1.98 1.25

Chhattisgarh

Northern states average

Minimum

1.25

3.35

Bihar

2.56

1.63

UP

Jharkhand

1.52

Rajasthan

Uttarakhand

1.50

India

Number of households having bathing facility within the premises with bathroom (%)

3.72

6.02

5.49

5.62

11.06

4.52

3.72

5.31

5.56

6.91

Number of households having bathing facility within the premises with only enclosure (no roof) (%)

Table A.45 Bathing Facility—Slum Households

2.31

4.73

2.57

2.32

8.96

5.10

2.31

5.06

6.20

5.28

No Bathroom (%)

3.35

5.48

9.84

6.92

3.35

4.81

5.20

4.65

3.76

5.28

Waste water outlet connected to closed drainage (%)

1.41

1.93

1.76

2.41

1.55

1.75

2.53

1.41

1.77

2.26

Waste water outlet connected to opened drainage (%)

(Table A.45 Contd.)

2.27

6.71

3.02

2.27

17.25

4.52

2.42

12.92

5.97

5.33

Waste water outlet connected to no drainage (%)

1.22 1.24 1.19 1.54 1.30 1.19 1.54 0.16

AP

Karnataka

Kerala

TN

Southern states average

Minimum

Maximum

SD

3.72

16.17

7.51

11.08

7.51

16.17

9.40

11.26

2.38

11.06

Number of households having bathing facility within the premises with only enclosure (no roof) (%)

3.11

11.38

4.58

9.19

4.58

10.20

11.38

10.59

2.47

8.96

No Bathroom (%)

Sources: Census of India; authors’ computations and analyses.

3.35 0.73

SD

Number of households having bathing facility within the premises with bathroom (%)

Maximum

(Table A.45 Contd.)

0.40

3.15

2.31

2.68

2.38

3.15

2.87

2.31

2.23

9.84

Waste water outlet connected to closed drainage (%)

0.70

3.45

2.04

2.75

3.45

3.26

2.04

2.26

0.42

2.53

Waste water outlet connected to opened drainage (%)

2.48

8.04

2.66

5.09

3.46

2.66

6.21

8.04

5.87

17.25

Waste water outlet connected to no drainage (%)

Electricity (%) Kerosene (%) India 0.905 0.082 Rajasthan 0.891 0.092 UP 0.780 0.200 Bihar 0.551 0.435 MP 0.898 0.093 Uttarakhand 0.938 0.050 Jharkhand 0.772 0.219 Chhattisgarh 0.922 0.071 Northern states average 0.832 0.155 Minimum 0.55 0.05 Maximum 0.94 0.43 SD 0.14 0.14 AP 0.966 0.027 Karnataka 0.923 0.069 Kerala 0.964 0.033 TN 0.934 0.059 Southern states average 0.947 0.047 Minimum 0.923 0.027 Maximum 0.966 0.069 SD 0.021 0.020 Sources: Census of India; authors’ computations and analyses.

Table A.46 Main Source of Lighting—Slum Households Solar (%) 0.003 0.001 0.003 0.002 0.001 0.002 0.002 0.001 0.002 0.00 0.00 0.00 0.003 0.001 0.001 0.001 0.001 0.001 0.003 0.001

Other oil (%) 0.002 0.002 0.003 0.004 0.002 0.001 0.003 0.002 0.002 0.00 0.00 0.00 0.001 0.001 0.001 0.001 0.001 0.001 0.001 0.000

Any other (%) 0.002 0.005 0.008 0.004 0.002 0.005 0.002 0.001 0.004 0.00 0.01 0.00 0.001 0.001 0.000 0.001 0.001 0.000 0.001 0.000

No lighting (%) 0.005 0.008 0.007 0.004 0.003 0.005 0.003 0.003 0.005 0.00 0.01 0.00 0.002 0.005 0.001 0.005 0.003 0.001 0.005 0.002

0.484 0.682 0.216 0.426 0.439 0.117

Jharkhand total

Chhattisgarh total

Northern states average

Minimum

Bihar total

Uttarakhand total

0.117

UP total

MP total

0.760 0.386

Rajasthan total

0.653

India total

0.039

0.086

0.183

0.058

0.055

0.125

0.039

0.068

0.070

0.087

Tap water Tap water from from un-treated treated source (%) source (%)

0.001

0.010

0.010

0.032

0.001

0.012

0.008

0.004

0.005

0.008

Covered well (%)

Table A.47 Drinking Water Source—Slum Households

0.001

0.045

0.049

0.181

0.001

0.047

0.027

0.004

0.006

0.023

0.075

0.319

0.216

0.409

0.228

0.169

0.713

0.420

0.075

0.127

0.022

0.079

0.106

0.084

0.022

0.131

0.074

0.102

0.032

0.076

Tube well/ borehole Uncovered Hand (%) well (%) pump (%)

0.000

0.001

0.001

0.001

0.000

0.001

0.001

0.000

0.000

0.002

Spring (%)

0.000

0.002

0.001

0.006

0.000

0.002

0.001

0.000

0.001

0.002

River/ canal (%)

0.001

0.005

0.003

0.002

0.001

0.010

0.002

0.002

0.014

0.004

Tank/ pond/lake (%)

0.006

0.016

0.006

0.011

0.009

0.020

0.019

0.013

0.035

0.020

Other sources (%)

0.101

SD

0.055

0.162

0.047

0.102

0.135

0.047

0.162

0.062

0.051

0.183

0.067

0.141

0.004

0.040

0.008

0.141

0.007

0.004

0.010

0.032

0.114

0.248

0.019

0.077

0.019

0.248

0.021

0.020

0.063

0.181

Sources: Census of India; authors’ computations and analyses.

0.532 0.778

Maximum

0.664

Southern states average

Minimum

0.532

0.675

Karnataka total 0.671

0.778

AP total

TN total

0.231

SD

Kerala total

0.760

Maximum

0.032

0.078

0.007

0.041

0.078

0.007

0.021

0.056

0.214

0.713

0.027

0.083

0.016

0.051

0.055

0.016

0.083

0.052

0.040

0.131

0.001

0.002

0.000

0.001

0.002

0.000

0.002

0.001

0.000

0.001

0.002

0.004

0.000

0.002

0.002

0.000

0.004

0.000

0.002

0.006

0.001

0.003

0.001

0.001

0.001

0.001

0.003

0.001

0.005

0.014

0.010

0.028

0.007

0.021

0.028

0.007

0.023

0.026

0.010

0.035

198

THE PARADOX OF INDIA’S NORTH–SOUTH DIVIDE

Table A.48 Public Amenities in Bihar—General Households Bihar 2001 (%)

2011 (%)

Total number of households availing banking services

21

44

182

23

Households with radio/ transistor

28

26

26

–2

Households with television

9

15

117

5

Households with landline only

2

2

36

0

Number of households having latrine facility within the premises

19

23

63

4

Number of households not having latrine facility within the premises

81

77

29

–4

4

7

103

2

Households with waste water outlet connected to open drain

34

36

44

2

Households with waste water outlet not connected to any drain

62

58

26

–4

Number of households having bathing facility within the premises with bathroom

10

37

427

28

Households with main source of lighting as electricity

10

16

116

6

Households with main source of lighting as kerosene

89

82

25

–7

Households with main source of lighting as solar

0

1

169

0

Households with main source of lighting as other oil

0

0

635

0

Households with waste water outlet connected to closed drain

% change (2001–11)

% point change

(Table A.48 Contd.)

199

Appendices (Table A.48 Contd.) Bihar 2001 (%)

2011 (%)

Households with other source of lighting

0

0

603

0

Households with no lighting

0

0

164

0

Main source as tap water

4

4

60

1

78

87

51

9

Main source as hand pump Main source as tube well

% change (2001–11)

% point change

5

3

–19

–2

13

4

–53

–8

Main source as tank/pond and lake

0

0

629

0

Main source as river/canal

0

0

–3

0

Main source as well

Main source as spring

0

0

–50

0

Other sources

0

1

299

1

Sources: Census of India; authors’ computations and analyses. Table A.49 Public Amenities in MP—General Households MP 2001 (%)

2011 (%)

% change (2001–2011)

% point change

Total number of households availing banking services

28

47

129

19

Households with radio/ transistor

21

15

–5

–6

Households with television

30

32

49

3

Households with landline only

6

2

–47

–4

Number of households having latrine facility within the premises

24

29

65

5

Number of households not having latrine facility within the premises

76

71

28

–5

(Table A.49 Contd.)

200

THE PARADOX OF INDIA’S NORTH–SOUTH DIVIDE

(Table A.49 Contd.) MP 2001 (%)

2011 (%)

% change (2001–2011)

% point change

Households with waste water outlet connected to closed drain

8

10

74

2

Households with waste water outlet connected to open drain

26

30

56

4

Households with waste water outlet not connected to any drain

66

60

25

–6

Number of households having bathing facility within the premises with bathroom

24

47

167

23

Households with main source of lighting as electricity

70

67

31

–3

Households with main source of lighting as kerosene

30

32

49

3

Households with main source of lighting as solar

0

0

165

0

Households with main source of lighting as other oil

0

0

258

0

Households with other source of lighting

0

0

66

0

Households with no lighting

0

0

69

0

Main source as tap water

25

23

27

–2

Main source as hand pump

39

47

65

8

Main source as tube well

4

8

164

4

29

20

–6

–9

Main source as tank/pond and lake

0

0

69

0

Main source as river/canal

1

1

–24

–1

Main source as well

Main source as spring

1

0

–20

0

Other sources

0

1

94

0

Sources: Census of India; authors’ computations and analyses.

Appendices

201

Table A.50 Public Amenities in Rajasthan—General Households Rajasthan % change 2001 (%) 2011 (%) (2001–11)

% point change

Total number of households availing banking services

29

68

217

39

Households with radio/ transistor

34

16

–37

–18

Households with television

28

38

80

9

Households with landline only

8

2

–58

–6

Number of households having latrine facility within the premises

29

35

62

6

Number of households not having latrine facility within the premises

71

65

23

–6

8

11

82

3

Households with waste water outlet connected to open drain

29

31

45

2

Households with waste water outlet not connected to any drain

63

58

24

–5

Number of households having bathing facility within the premises with bathroom

32

56

131

23

Households with main source of lighting as electricity

55

67

65

12

Households with main source of lighting as kerosene

44

31

–6

–13

Households with main source of lighting as solar

0

1

148

0

Households with main source of lighting as other oil

0

0

110

0

Households with waste water outlet connected to closed drain

(Table A.50 Contd.)

202

THE PARADOX OF INDIA’S NORTH–SOUTH DIVIDE

(Table A.50 Contd.) Rajasthan % change 2001 (%) 2011 (%) (2001–11)

% point change

Households with other source of lighting

0

0

200

0

Households with no lighting

1

1

123

0

Main source as tap water

35

41

55

5

Main source as hand pump

26

25

29

–1

Main source as tube well

7

12

151

6

24

11

–39

–13

Main source as tank/pond and lake

5

6

56

1

Main source as river/canal

1

1

–8

0

Main source as well

Main source as spring

0

0

–20

0

Other sources

1

4

301

3

Sources: Census of India; authors’ computations and analyses. Table A.51 Public Amenities in UP—General Households UP % change 2001 (%) 2011 (%) (2001–11)

% point change

Total number of households availing banking services

44

68

–25

24

Households with radio/ transistor

40

16

–80

–23

Households with television

25

38

–27

13

Households with landline only

6

2

–78

–3

Number of households having latrine facility within the premises

31

93

45

62

Number of households not having latrine facility within the premises

69

21

–85

–47

(Table A.51 Contd.)

203

Appendices (Table A.51 Contd.) UP % change 2001 (%) 2011 (%) (2001–11)

% point change

Households with waste water outlet connected to closed drain

10

34

73

24

Households with waste water outlet connected to open drain

61

147

18

86

Households with waste water outlet not connected to any drain

30

81

34

52

Number of households having bathing facility within the premises with bathroom

29

144

146

116

Households with main source of lighting as electricity

32

96

48

64

Households with main source of lighting as kerosene

67

162

17

95

Households with main source of lighting as solar

0

1

69

1

Households with main source of lighting as other oil

0

1

282

1

Households with other source of lighting

0

1

314

1

Households with no lighting

0

1

99

0

Main source as tap water

24

71

47

48

Main source as hand pump

63

170

31

107

Main source as tube well

1

8

439

7

12

10

–56

–1

Main source as tank/pond and lake

0

0

206

0

Main source as river/canal

0

0

36

0

Main source as well

Main source as spring

0

0

–62

0

Other sources

0

2

123

1

Sources: Census of India; authors’ computations and analyses.

204

THE PARADOX OF INDIA’S NORTH–SOUTH DIVIDE

Table A.52 Public Amenities in Uttarakhand—General Households Uttarakhand 2001 (%)

2011 (%)

% change (2001–11)

% point change

Total number of households availing banking services

60

81

70

21

Households with radio/transistor

50

15

–63

–35

Households with television

43

62

82

19

Households with landline only

10

3

–60

–7

Number of households having latrine facility within the premises

45

66

83

21

Number of households not having latrine facility within the premises

55

34

–21

–21

Households with waste water outlet connected to closed drain

10

19

131

9

Households with waste water outlet connected to open drain

38

42

41

4

Households with waste water outlet not connected to any drain

52

39

–6

–13

Number of households having bathing facility within the premises with bathroom

39

69

125

31

Households with main source of lighting as electricity

60

87

82

27

Households with main source of lighting as kerosene

37

11

–63

–26

Households with main source of lighting as solar

2

1

–18

–1

Households with main source of lighting as other oil

0

0

201

0

Households with other source of lighting

0

0

43

0

Households with no lighting

0

0

34

0

(Table A.52 Contd.)

Appendices

205

(Table A.52 Contd.) Uttarakhand 2001 (%)

2011 (%)

Main source as tap water

66

68

Main source as hand pump

20 1

Main source as tube well

% change (2001–11)

% point change

30

2

22

40

2

2

145

1

Main source as well

1

1

15

0

Main source as tank/pond and lake

1

1

–25

0

Main source as river/canal

2

1

–40

–1

Main source as spring

2

1

–37

1

Other sources

7

4

–27

–3

Sources: Census of India; authors’ computations and analyses. Table A.53 Public Amenities in Jharkhand—General Households Jharkhand 2001 (%)

2011 (%)

% change (2001–11)

% point change

Total number of households availing banking services

30

81

10

51

Households with radio/transistor

26

15

–77

–12

Households with television

17

62

48

45

3

3

–61

0

Number of households having latrine facility within the premises

20

68

42

49

Number of households not having latrine facility within the premises

80

241

23

161

6

22

44

16

Households with waste water outlet connected to open drain

23

69

22

46

Households with waste water outlet not connected to any drain

70

218

27

148

Households with landline only

Households with waste water outlet connected to closed drain

(Table A.53 Contd.)

206

THE PARADOX OF INDIA’S NORTH–SOUTH DIVIDE

(Table A.53 Contd.) Jharkhand 2001 (%)

2011 (%)

Number of households having bathing facility within the premises with bathroom

15

79

114

64

Households with main source of lighting as electricity

24

142

139

117

Households with main source of lighting as kerosene

75

164

–10

89

Households with main source of lighting as solar

0

2

335

2

Households with main source of lighting as other oil

0

1

175

1

Households with other source of lighting

0

0

95

0

Households with no lighting

% change (2001–11)

% point change

0

0

33

0

Main source as tap water

13

40

30

27

Main source as hand pump

27

136

106

109

Main source as tube well

3

11

42

8

52

113

–10

61

Main source as tank/pond and lake

0

1

–41

0

Main source as river/canal

3

5

–25

2

Main source as well

Main source as spring

1

3

–24

1

Other sources

1

2

–15

1

Sources: Census of India; authors’ computations and analyses. Table A.54 Public Amenities in Chhattisgarh—General Households Chhattisgarh 2001 (%)

2011 (%)

% change % point (2001–11) change

Total number of households availing banking services

24

49

174

25

Households with radio/transistor

23

11

–36

–12

(Table A.54 Contd.)

Appendices

207

(Table A.54 Contd.) Chhattisgarh 2001 (%)

2011 (%)

22

31

97

10

4

2

–45

–2

Number of households having latrine facility within the premises

14

25

135

10

Number of households not having latrine facility within the premises

86

2

–96

–83

Households with waste water outlet connected to closed drain

4

5

62

1

Households with waste water outlet connected to open drain

17

19

53

2

Households with waste water outlet not connected to any drain

79

76

30

–3

Number of households having bathing facility within the premises with bathroom

12

20

121

8

Households with main source of lighting as electricity

53

75

92

22

Households with main source of lighting as kerosene

46

23

–32

–23

Households with main source of lighting as solar

0

1

632

1

Households with main source of lighting as other oil

0

0

182

0

Households with other source of lighting

0

0

–30

0

Households with television Households with landline only

Households with no lighting

% change % point (2001–11) change

0

0

5

0

Main source as tap water

15

21

81

5

Main source as hand pump

53

58

49

5

2

7

489

6 –13

Main source as tube well Main source as well

25

11

–37

Main source as tank/pond and lake

0

0

–22

0

Main source as river/canal

2

1

–35

–1

Main source as spring

1

1

–33

–1

Other sources

1

1

–44

–1

Sources: Census of India; authors’ computations and analyses.

208

THE PARADOX OF INDIA’S NORTH–SOUTH DIVIDE

Table A.55 Public Amenities in AP—General Households AP 2001 (%)

2011 (%)

Total number of households availing banking services

31

53

113

22

Households with radio/transistor

22

9

–46

–12

Households with television

31

59

133

27

9

4

–41

–5

Number of households having latrine facility within the premises

33

50

88

17

Number of households not having latrine facility within the premises

67

12

–77

–55

Households with waste water outlet connected to closed drain

14

22

97

8

Households with waste water outlet connected to open drain

38

35

16

–3

Households with waste water outlet not connected to any drain

48

43

11

–5

Number of households having bathing facility within the premises with bathroom

40

67

110

27

Households with main source of lighting as electricity

67

92

71

25

Households with main source of lighting as kerosene

32

7

–73

–25

Households with main source of lighting as solar

0

0

46

0

Households with main source of lighting as other oil

0

0

126

0

Households with other source of lighting

0

0

61

0

Households with no lighting

0

0

62

0

Households with landline only

% change % point (2001–11) change

Main source as tap water

48

70

81

22

Main source as hand pump

26

14

–34

–12

(Table A.55 Contd.)

Appendices

209

(Table A.55 Contd.) AP 2001 (%)

2011 (%)

6

7

45

1

16

6

–52

–10

Main source as tank/pond and lake

1

0

–66

–1

Main source as river/canal

1

0

–52

–1

Main source as tube well Main source as well

% change % point (2001–11) change

Main source as spring

0

0

44

0

Other sources

1

2

120

1

Sources: Census of India; authors’ computations and analyses. Table A.56 Public Amenities in TN—General Households TN

Total number of households availing banking services

2001 (%)

2011 (%)

23

53

% change % point (2001–11) change 200

30

Households with radio/transistor

44

23

–32

–21

Households with television

39

87

187

47

Households with landline only

11

6

–34

–6

Number of households having latrine facility within the premises

35

48

79

13

Number of households not having latrine facility within the premises

65

52

4

–13

Households with waste water outlet connected to closed drain

17

25

96

8

Households with waste water outlet connected to open drain

28

25

15

–3

Households with waste water outlet not connected to any drain

55

50

18

–5

Number of households having bathing facility within the premises with bathroom

40

64

110

24

(Table A.56 Contd.)

210

THE PARADOX OF INDIA’S NORTH–SOUTH DIVIDE

(Table A.56 Contd.) TN 2001 (%)

2011 (%)

Households with main source of lighting as electricity

78

93

56

15

Households with main source of lighting as kerosene

21

6

–63

–15

Households with main source of lighting as solar

0

0

–61

0

Households with main source of lighting as other oil

0

0

440

0

Households with other source of lighting

0

0

–15

0

Households with no lighting

0

0

51

0

Main source as tap water

63

80

66

17

Main source as hand pump

18

5

–66

–13

Main source as tube well

% change % point (2001–11) change

5

8

105

3

11

5

–38

–6

Main source as tank/pond and lake

1

0

–38

–1

Main source as river /canal

0

0

–17

0

Main source as well

Main source as spring

0

0

–35

0

Other sources

2

1

–6

–1

Sources: Census of India; authors’ computations and analyses. Table A.57 Public Amenities in Kerala—General Households Kerala

Total number of households availing banking services

2001 (%)

2011 (%)

51

74

% change % point (2001–11) change 70

23

Households with radio/transistor

59

30

–41

–30

Households with television

39

77

132

38

Households with landline only

19

12

–29

–7

(Table A.57 Contd.)

Appendices

211

(Table A.57 Contd.) Kerala 2001 (%)

2011 (%)

Number of households having latrine facility within the premises

84

95

33

11

Number of households not having latrine facility within the premises

16

5

–65

–11

Households with waste water outlet connected to closed drain

8

25

267

17

Households with waste water outlet connected to open drain

12

21

112

10

Households with waste water outlet not connected to any drain

80

54

–22

–27

Number of households having bathing facility within the premises with bathroom

62

86

62

24

Households with main source of lighting as electricity

70

94

57

24

Households with main source of lighting as kerosene

29

5

–79

–24

Households with main source of lighting as solar

1

0

–59

0

Households with main source of lighting as other oil

0

0

70

0

Households with other source of lighting

0

0

–9

0

Households with no lighting

% change % point (2001–11) change

0

0

32

0

20

29

68

9

Main source as hand pump

1

0

–47

–1

Main source as tube well

2

4

130

2

Main source as tap water

Main source as well

72

62

1

–10

Main source as tank/pond and lake

1

1

9

0

Main source as river/canal

1

0

–57

0

Main source as spring

2

1

–13

0

Other sources

2

2

57

1

Sources: Census of India; authors’ computations and analyses.

212

THE PARADOX OF INDIA’S NORTH–SOUTH DIVIDE

Table A.58 Public Amenities in Karnataka—General Households Karnataka 2001 (%)

2011 (%)

% change % point (2001–11) change

Total number of households availing banking services

40

61

97

21

Households with radio/transistor

46

22

–38

–24

Households with television

37

60

109

23

Households with landline only

13

7

–30

–6

Number of households having latrine facility within the premises

37

51

76

14

Number of households not having latrine facility within the premises

63

49

1

–14

Households with waste water outlet connected to closed drain

17

26

95

9

Households with waste water outlet connected to open drain

34

35

31

1

Households with waste water outlet not connected to any drain

49

39

4

–9

Number of households having bathing facility within the premises with bathroom

59

86

89

27

Households with main source of lighting as electricity

79

91

49

12

Households with main source of lighting as kerosene

21

9

–47

–12

Households with main source of lighting as solar

0

0

14

0

Households with main source of lighting as other oil

0

0

251

0

Households with other source of lighting

0

0

24

0

Households with no lighting

0

0

56

0

Main source as tap water

59

66

45

7

Main source as hand pump

17

6

–58

–12

(Table A.58 Contd.)

Appendices

213

(Table A.58 Contd.) Karnataka

Main source as tube well Main source as well Main source as tank/pond and lake

2001 (%)

2011 (%)

% change % point (2001–11) change

9

16

140

7

12

9

–7

–3

1

1

13

0

Main source as river/canal

1

1

–4

0

Main source as spring

0

0

39

0

Other sources

1

1

214

1

Sources: Census of India; authors’ computations and analyses.

25

Households with television 28 72 8 35 57

Number of households having latrine facility within the premises

Number of households not having latrine facility within the premises

Households with waste water outlet connected to closed drain

Households with waste water outlet connected to open drain

Households with waste water outlet not connected to any drain

6

33

Households with radio/transistor

Households with landline only

35

2001 (%)

Total number of households availing banking services

Table A.59 Comparison of Public Amenities

84

53

15

73

48

3

40

16

62

2011 (%)

48

52

99

2

72

–57

56

–51

77

% change (2001–11)

Northern states

27

18

8

1

20

–3

14

–17

27

% point change

58

28

14

53

47

13

37

43

36

2001 (%)

46

29

25

29

61

7

71

21

60

2011 (%)

–20

4

76

–44

29

–45

93

–51

66

–12

1

11

–23

14

–6

34

–22

24

% change % point (2001–11) change

Southern states

0

Households with no lighting

1 2

Main source as spring

Other sources

2

1

1

1

24

7

78

38

0

0

0

1

72

79

65

Sources: Census of India; authors’ computations and analyses.

1 1

Main source as river/canal

22

Main source as tank/pond and lake

Main source as well

3

0

Households with other source of lighting

Main source as tube well

0

Households with main source of lighting as other oil

28

1

Households with main source of lighting as solar

42

57

Households with main source of lighting as kerosene

Main source as hand pump

42

Households with main source of lighting as electricity

Main source as tap water

25

Number of households having bathing facility within the premises with bathroom

19

–5

0

0

13

113

84

38

74

194

266

88

27

88

161

0

0

0

0

3

4

36

11

0

0

0

0

15

37

40

1

1

1

1

28

5

16

47

0

0

0

0

26

74

50

2

1

0

1

21

9

6

61

0

0

0

0

7

93

76

32

–20

–45

–37

–26

61

–61

29

21

–9

123

–41

–74

26

51

0

0

0

0

–7

3

–9

14

0

0

0

0

–19

19

26

49,386,799

72,138,958 50,266,179

AP Telangana Karnataka Kerala TN Average

1

2

3

4

5

68,621,012 73,318,268

MP Chhattisgarh UP Uttarakhand Rajasthan Average

3

4

5

6

7 57,561,482

51,540,236

7,025,583

155,111,022

19,603,658

52,537,899

25,036,946

92,075,028

28,443,144

37,189,229

17,445,506

37,552,529

21,585,313

Rural population

Sources: Census of India; authors’ computations and analyses.

10,116,752

199,581,477

25,540,196

72,597,565

32,966,238

Jharkhand

2

103,804,637

Bihar

1

33,387,677

61,130,704

35,286,757

Population

Sl. No. States

Table A.60 Basic Differences—North and South

15,756,786

17,080,776

3,091,169

44,470,455

5,936,538

20,059,666

7,929,292

11,729,609

22,017,185

34,949,729

15,932,171

23,578,175

13,608,665

Urban population

179,138

342,239

53,483

240,928

135,191

308,245

79,714

94,163

127,151

130,058

38,863

191,791

114,840

160,205

Land area (sq km)

487

201

189

828

189

236

414

1,102

707

555

859

319

307

Population density (sq km)

Appendices

217

Appendix 4: Historical Roots of Education in TN Education in the Early Years of Madras Presidency Government enquiry into the state of education in Madras Presidency, initiated by Sir Thomas Munro in 1822, showed that there was approximately one school per thousand population and that the number of boys taught was one-fourth of the total school age population. It also showed that the instruction imparted in these indigenous institutions was of little practical value tending to burden the memory rather than to train the intellect. A board was, therefore, appointed to organize a system of public instruction, and an annual grant of `50,000 was sanctioned for the establishment of schools. In 1826, 14 collectorate and 81 taluk schools, with a central school at Madras, were opened. In 1836, this scheme was pronounced a failure and the schools were abolished as inefficient. In 1840, a University Board was constituted by Lord Ellenborough’s Government to organize and establish a central school and a few provincial schools. In 1841, the central school was converted into a high school; in 1853, a college department was added to it and later it developed into the Presidency College. In 1854, the Court of Directors issued its memorable dispatch regarding education. Thereupon the Department of Education, with the Directorate of Public Instruction and its inspecting staff was organized; the so-called Madras University was re-modelled and designated the Presidency College; a normal school was established; zilla or district schools were opened; and the grant-in-aid system was introduced. While there were 460 educational institutions in 1853 with 14,900 pupils, by 1904, this number had risen to 26,771 with 784,000 pupils (TN HDR, 2003, Box 1.1).

History of Elementary Education in Tamil Nadu The earliest developments in the field of education in the state were brought on by the advent of the Christian missionaries as early as the beginning of the 18th century. Though the English East India Company had started a school at Fort St George in 1673 for educating the children

218

THE PARADOX OF INDIA’S NORTH–SOUTH DIVIDE

of its own employees, it was the missionaries who were responsible for spreading education among the local population. The Report of the Elementary Education Survey of the Madras Presidency, 1925, gives us some interesting insights into the history and progress of elementary education in the state. The report points out that there were three agencies managing elementary schools in the province: 1. private bodies, mission and non-mission including private individuals and teacher managers; 2. local boards and municipal councils; and 3. government. Three distinct periods are also traced in the spread of elementary education in the province: 1. the early period up to 1910; 2. the middle period from 1911–20; and 3. the period from 1921 onwards. The earliest period is characterized by major changes in policy, both regarding the medium of instruction, agency to start and run elementary schools as well as the methodology of funding of aided institutions. Though early initiatives like Munro’s minute of 1820 made some headway in vernacular education, these were often cancelled by contradictory policies such as Macaulay’s directives on English as the medium of instruction. Progress was made after Wood’s despatch of 1854, which introduced the system of grant-in-aid for encouraging private participation in primary education. Spurred by the national movement under leaders like G.K. Gokhale, there was a marked shift in the educational policy of the government from 1910 onwards, marking the second period in educational development in the Madras Presidency. The Government of India agreed to subsidize the opening of elementary schools in every village with more than 500 inhabitants. In pursuance of this policy, a liberal recurring grant of `5 million was sanctioned out of Imperial subsidies which enabled the Provincial Government to subsidize district boards for the opening of such new schools.

Appendices

219

The third major breakthrough in the spread of education came with the Madras Elementary Education Act 1920. Under this act, local bodies were given the responsibility for elementary education and were also given powers to levy special cess to raise funds for education. The act also directed the local bodies to introduce compulsory primary education in selected areas based on their financial position. Some interesting highlights on the status of girls’ education in the state in a recent article reveal that the proportion of boys to girls in elementary schools changed from 4:1 in 1911–12 to 3:1 in 1926–27. A report published on ‘Development of Women’s Education’ (1929) revealed the various obstacles that stood in the way of girls’ education. Since the society at large and the backward communities in particular had not accepted coeducation as a system, there was a need to open more girls’ schools so as to ensure access for girls. But the limited funds for education were used up for the opening and development of boys’ schools for which there was much more public clamour and support. Private aided agencies also were not keen to open girls’ schools which would necessarily serve a more limited group of children. Further, the spread of girls’ education was severely hampered by the non-availability of trained women teachers, especially among Hindu and Muslim women. In March 1927, as against 39,000 male teachers in higher and lower elementary grade, there were only 6,000 women teachers, which was considered ‘satisfactory’ by the authorities at that time (TN HDR, 2003, Box 5.1).

Appendix 5: Econometric Regressions To verify the authenticity of the findings regarding the individual states in phase I of the study and the regions (North versus South) in phase II of the study, in the final phase, we extended the analysis to all states, by engaging in econometric regressions, which are essentially a crosssectional view of the study (although the data set contains data on a number of variables over time). This section summarizes the results from various regressions we performed to examine the dependence of the per capita income, urban poverty and rural poverty, as dependent on various factors including those indicating governance, and economic and human capabilities. A logical outcome of the exercise was also to understand

220

THE PARADOX OF INDIA’S NORTH–SOUTH DIVIDE

whether being in the South or in the North had an impact on its economic performance, measured in terms of per capita income or urban/rural poverty rates. We did several regressions, but we report only the three most important here—one each with the state per capita income, urban poverty and rural poverty as dependent variables, since they are important economic outcomes. The independent variables in all regressions are defined as follows: 1. CMs’ tenure: This is measured as the number of days in any given year, for which a state’s CM was in office. If the same CM continued in a state during a complete year, this variable for him/her would be 365. 2. Urbanization: This is just the proportion of urban area in a state during any given year. 3. Pending cases: This refers to the percentage of court cases pending investigation at the end of year for each state. 4. Police firing: This refers to the number of police firing incidences in each state for any given year. 5. Literacy rate: This refers to the effective literacy rate in a state during a year, which is defined as the number of literates as a percentage of those in ages above 7. 6. We defined the northern and southern state dummies in such a way that all states south of the Vindhyas (AP, Karnataka, TN, Kerala and Goa) were taken as southern states and all the remaining states were considered as the northern states. The data set is a panel, consisting of all Indian states from 1960–61 to 2008–9.

Regressions a. b.

Dependent variable: PCNSDP at constant (1999–2000) prices Predictors: (constant), Dummy North = 1, South = 0, police firing, CM tenure, pending cases, literacy rate, urbanization

221

Appendices Model Summary R

R-squared

Adjusted R-squared

Standard error of the estimate

0.71

0.51

0.49

4,183.07

Sources: The Census of India; http://www.elections.in; National Crime Records Bureau; authors’ computations and analyses. ANOVA Model

Sum of squares

df

1

Regression

4262096287.90

6

Residual

4147039686.18

237

Total

8409135974.08

243

Mean square

F

Sig.

710349381.32 40.60

0.00

17498057.75

Sources: The Census of India; http://www.elections.in; National Crime Records Bureau; authors’ computations and analyses. Estimation of State NSDP Unstandardized coefficients B Constant CM tenure

Standard error

t

–1,613.20

2,902.17

–0.56

–1.21

2.97

–0.41

Urbanization

284.25***

38.72

7.34

Pending cases

–29.43

29.56

–1.00

2.26

–2.27

Police firing Literacy rate Dummy North = 1, South = 0

–5.12** 190.98*** 1,201.78

29.77

6.42

887.98

1.35

Sources: The Census of India; http://www.elections.in; National Crime Records Bureau; authors’ computations and analyses.

The adjusted R-squared for this model is a reasonable 0.49, which implies that the model explains nearly half of the variations occurring in the state per capita income. The ANOVA table shows that the significance of the model is statistically important.

N

Sig. (2-tailed)

Pearson correlation

Urban poverty

Rural poverty

0.002 171

Sig. (2-tailed) N

–.232**

171

N Pearson correlation

0.038

Sig. (2-tailed)

1136

167

0

–.484**

167

0

–.627**

171

0

.541**

171

1

167

.158*

1135

–.627**

171

0.038

.158*

0

1

1135

0

–.117**

Rural poverty

0

–.117**

1322

1

Pearson correlation

Pearson PCNSDP correlation at constant (1999–2000) Sig. (2-tailed) prices N

Dummy North = 1, South = 0

Dummy North = 1, South =0

PCNSDP at Constant (1999– 2000) prices

171

1

171

0

.541**

167

0

–.484**

171

0.002

–.232**

Urban poverty

132

0

–.356**

132

0

–.524**

835

0

.697**

896

0

–.317**

Literate

75

0.969

0.005

75

0.208

0.147

402

0.958

0.003

402

0

.430**

Pending cases

75

0.086

0.2

75

0.799

–0.03

402

0.004

–.142**

402

0.627

0.024

Police firing

165

0.004

–.224**

165

0.013

–.194*

1070

0.033

.065*

1112

0.393

0.026

CM tenure

138

0.603

0.045

138

0

–.428**

727

0

.560**

746

0

–.444**

Urbanization

0 746

0 727

138

0

–.428**

165

0.013

–.194*

75

0.799

–0.03

75

0.208

0.147

132

0

–.524**

138

0.603

0.045

165

0.004

–.224**

75

0.086

0.2

75

0.969

0.005

132

0

–.356**

612

0

.531**

818

0.835

0.007

255

0.028

–.137*

255

0.102

–0.103

896

1

309

0.638

0.027

399

0.209

–0.063

402

0.764

–0.015

402

1

255

0.102

–0.103

309

0.009

.149**

399

0.484

–0.035

402

1

402

0.764

–0.015

255

0.028

–.137*

717

0.06

–0.07

1113

1

399

0.484

–0.035

399

0.209

–0.063

818

0.835

0.007

746

1

717

0.06

–0.07

309

0.009

.149**

309

0.638

0.027

612

0

.531**

Sources: The Census of India; http://www.elections.in; National Crime Records Bureau; authors’ computations and analyses. Notes: ** Correlation is significant at the 0.01 level (2-tailed). * Correlation is significant at the 0.05 level (2-tailed).

N

Sig. (2-tailed)

.560**

1070

–.444**

1112

N

0.033

0.393

.065*

402

0.004

Sig. (2-tailed)

402

N 0.026

0.627

Sig. (2-tailed)

–.142**

402

0.958

0.003

835

0

.697**

Pearson Correlation

0.024

402

0

.430**

896

0

–.317**

Pearson correlation

N

Sig. (2-tailed)

Pearson correlation

N

Sig. (2-tailed)

Pearson correlation

Urbanization Pearson correlation

CM tenure

Police firing

Pending cases

Literate

224

THE PARADOX OF INDIA’S NORTH–SOUTH DIVIDE

The regression of state per capita NSDP on governance, urbanization and human capability indicators shows that urbanization has a positive impact and leads to higher per capita incomes, due to the benefits from increased productivity and agglomeration economies. We note that urbanization implies the density of people, networks and the possibilities for sharing of knowledge that it enables and facilitates. Police firing is used as an indicator of the general law and order conditions in a state. Police firing has the expected negative impact on per capita income, implying that whenever there is an increase in the number of police firing incidents in a state signalling law and order problems, there is a decrease in the state per capita income, reflecting a poor business environment and investor apathy in creating jobs, output and income. The literacy rate has a positive and significant impact on the state per capita income as we expect, as literacy rate can be considered an indicator for improved human capabilities and skills, which result in increased output and income. Further, the literacy rate is the foundation on which further knowledge sharing, scientific, business and technical education take place. We recognize that these are indicators on which southern states had an edge; hence, the literacy rate has a positive impact on the per capita NSDP. However, the dummy for North versus South does not have an impact on the state per capita income as we would have expected. This could be due to the fact that most of the exogenous variables we have considered in the regressions are correlated with the North– South dummy (see the correlation matrix table). Further, this finding is explained by the fact that the dummy for ‘North’ is a mixed bag of states consisting of not only the BIMARU states such as Bihar, Jharkhand, UP, but also high-income states such as Gujarat, Maharashtra and agriculturally prosperous states such as Punjab and Haryana.

Dependent Variable: Urban Poverty Model Summary Model

R

R-squared

Adjusted R-squared

Standard error of the estimate

1

0.61

0.37

0.27

11.19

Sources: The Census of India; http://www.elections.in; National Crime Records Bureau; authors’ computations and analyses.

Appendices

225

ANOVA Sum of squares

Model 1

df

Mean square

Regression

2,710.34

6

451.72

Residual

4,635.11

37

125.27

Total

7,345.45

43

F

Sig.

3.61

0.01

Sources: The Census of India; http://www.elections.in; National Crime Records Bureau; authors’ computations and analyses.

Dependent Variable: Urban poverty Predictors: (Constant), Dummy North = 1, South = 0, CM tenure (number of days per year), police firing, pending cases, literacy rate, urbanization Coefficients Unstandardized coefficients B

Standard error

(Constant)

61.78***

19.49

CM tenure

Model 1

Standardized coefficients Beta

t 3.17

–0.03

0.02

–0.19

–1.33

Urbanization

0.34

0.24

0.27

1.45

Pending cases

–0.07

0.19

–0.06

–0.35

Police firing

0.05

0.03

0.22

1.60

Literacy rate

–0.51**

–0.44

–2.53

Dummy North = 1, South = 0

–8.72

–0.28

–1.54

.201 5.65

Sources: The Census of India; http://www.elections.in; National Crime Records Bureau; authors’ computations and analyses.

The dependent variable in this model, as we discussed earlier, is the urban poverty rate of the states. This is available only intermittently for the years for which the Planning Commission published it.2 Hence, the These years, both for urban and rural poverty, are: 1973–74, 1977–78, 1983–84, 1987–88, 1993–94, 1999–2000 and 2004–5. 2

226

THE PARADOX OF INDIA’S NORTH–SOUTH DIVIDE

sample size here is substantially lower than in the case where the state per capita NSDP is used as the dependent variable. Here, the literacy rate is the only variable which has a statistically significant and negative impact on urban poverty. This implies that the literacy rate is an indicator for various skills reflecting productivity which have the impact of increasing income and reducing the urban poverty rate. The constant is positive and significant indicating that there is bound to be some poverty in the urban areas, irrespective of the level of per capita income, literacy and other factors. Given the sample size is smaller, the model is a lot poorer in terms of explaining urban poverty, as may be seen in an adjusted R-squared of 0.27. In an overall sense, however, the model is statistically significant. The F statistic and its significance show this. Dependent Variable: Rural poverty Model Summary

Model

R

R-squared

Adjusted R-squared

1

0.67a

0.45

0.36

Standard error of the estimate 11.81

Sources: The Census of India; http://www.elections.in; National Crime Records Bureau; authors’ computations and analyses. Note: a. Predictors: (Constant), Dummy North = 1, South = 0, CM tenure, police firing, pending cases, literacy rate, urbanization ANOVA Model

Sum of squares

df

Mean square

F

Sig.

1

Regression

4,239.63

6

706.61

5.07

.001

Residual

5,157.48

37

139.39

Total

9,397.10

43

Sources: The Census of India; http://www.elections.in; National Crime Records Bureau; authors’ computations and analyses.

Appendices

227

Dependent variable: Rural poverty Coefficients Unstandardized coefficients Model 1

(Constant) CM tenure Urbanization

B

Std. Error

Standardized coefficients Beta

T

41.28**

20.56

0.00

0.02

0.01

0.10

0.25

–0.46

–2.67

–0.66***

2.01

Pending cases

0.16

0.20

0.12

0.81

Police firing

0.01

0.03

0.04

0.29

Literacy rate

–0.21

0.21

–0.16

–0.98

5.14

5.96

0.15

0.86

Dummy North = 1, South = 0

Sources: The Census of India; http://www.elections.in; National Crime Records Bureau; authors’ computations and analyses.

The dependent variable in this model, as we have discussed, is the rural poverty rate of the states. This, like the urban poverty rate, is available only intermittently for the years for which the Planning Commission published it (see the earlier footnote). Hence, as in the case of urban poverty, the sample size for rural poverty is substantially lower than in the case where the state per capita NSDP is used as the dependent variable. The most significant result here is that urbanization has a statistically significant and negative impact on rural poverty. This implies that whenever the poor migrate from rural to urban areas in search of livelihoods, the rural area’s poverty reduces. Thus, urbanization is viewed by many as a panacea for rural ills. As in the case of urban poverty regression, the constant is positive and significant indicating that there is bound to be some poverty in the rural areas, irrespective of the level of per capita income, literacy and other factors. Given the sample size is smaller, the model is a lot poorer in terms of explaining rural poverty, but better than the model explaining urban poverty, the adjusted R-squared in the case of the rural poverty model being 0.36. In an overall sense, however, the model is statistically significant as may be seen by the F statistic.

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Index

affirmative action, 74, 76, 78 agglomeration, 16, 45, 99, 224 agriculture, 11–12, 15, 18, 28–29, 37–39, 46, 50, 52, 63, 65, 77, 99–100, 132, 137–38, 145–50, 154, 158, 181–83 productive, 47 Indian, 51 Amartya Sen, 3–4, 10 Annual Status of Education Report, 56 armed police, 61, 66 art, 24, 138 assets, 51–52, 88, 100, 106–7, 109–10, 113–16, 118, 128, 190–91 automobiles, 30 Bahujan Samaj Party, 5 Bania, 76 bathing facility, 108–9, 112, 193–94, 198, 200–201, 203–4, 206–7, 208– 9, 211–12, 215 BIMARU, 5, 105, 224 boom, 21, 48 Brahmins, 74–76 bribes, 62 BRICS, 3 British, 23, 72–73 bureaucracy, 64, 67–69 Central Electricity Authority, 48, 82, 97, 155, 166, 178, 180 China, 3, 17–18, 42, 86 civil society, 3

collective action, 20, 72, 80, 125, 127– 28, 130 computers, 113–14 Congress, 76, 79 convergence, 1–2, 4, 7–9, 14–19, 21–22, 37, 118, 126–27 corruption, 58, 62, 67, 105, 119, 129 Dalits, 4, 13, 77, 79 decentralization, 19–21, 106 delicensing, 84 demographic dividend, 42, 45 developmental expenditure, 52–53, 100–101, 155, 168–69, 183–84 divergence, 1, 3–4, 7, 9, 14–19, 21–22, 26, 35–36, 63, 80, 83, 85, 87 drinking water, 107, 112–13, 128, 196 economic growth, 1, 3, 9, 11, 14–15, 17–19, 21, 26, 31, 33, 36–39, 41–42, 45, 47–49, 51, 53–54, 62, 80, 84, 86, 90–91, 95–96, 98, 110, 114–15, 121, 127 macro, 32 education, 12–13, 36, 38, 41, 43, 45, 50, 52, 54, 58, 67, 72, 79, 86, 90, 92, 106, 110, 122, 124, 126, 128, 130–31, 133–35, 137–39, 141, 143–47, 149–51, 154–56, 217 higher, 33, 76 primary-level, 36, 218 elementary, 37, 55, 56, 57, 218, 219

Index technical, 38, 75, 78, 86, 105, 121, 125, 224 secondary, 38 primary, 54, 55, 56, 57 basic education, 54, 55 girls’ education, 56, 219 engineering, 118, 120 vernacular, 218 women, 219 efficiency, 36, 50–51, 53–54, 57, 68, 80, 83, 86, 99–100, 119, 123–24, 128 electricity, 18, 47, 52, 58, 80, 81, 83, 84, 96, 107–9, 113–14, 118, 195, 198, 200–201, 203–4, 206–8, 210– 12, 215 employment, 2, 47–48, 51, 77–78, 100, 128, 149 endogenous growth, 15 engineering, 37, 39, 69, 73, 75, 84, 92–93, 118, 120, 127, 132, 163 entrepreneurialism, 5, 13 entrepreneurs, 59, 73–75, 77–78, 120, 126–28 Europe, 16, 31 family planning, 43, 64 fertility, 13, 36, 42–43, 45 FIR, 61 foundational, 33–34, 36, 80, 85–87, 117–21 Garibi Hatao, 79 Gounders, 74 governance, 13, 20, 21, 32, 33, 36, 58, 59, 61, 64, 66, 72, 75–77, 79–80, 86, 102–3, 105, 110, 120, 122, 125, 129, 130, 219, 224 public, 2, 32, 58, 61, 71, 126, 128 good, 12, 59, 60, 67, 115

233

legal, 62 Effective, 71 graduates, 36, 37–40, 74, 80–81, 83, 90, 92–93, 118, 163, 174 Green Revolution, 51, 78 health, 13, 33, 36, 41, 43, 45, 50, 67, 70–71, 78–79, 83, 86, 90, 94–95, 105–6, 110, 112, 118–19, 122, 127, 133, 135, 150, 155 maternal, 40 family, 40 public, 156 Hindu, 74, 77, 124, 141, 153, 219 history, 4, 7, 20–22, 37, 57, 129–30, 217–18 human capital, 16–17, 21, 38, 43 human development, 1, 3, 11, 18–19, 56, 78, 127, 151 hypotheses, 23, 31 Indian Administrative Service, 63 industrialization, 17, 68, 78, 100 inequality, 16, 18, 27 infant mortality rate, 36, 40–41, 94 infrastructure, 9, 11–12, 17, 32–34, 36, 47, 49, 54, 57–59, 64, 67, 69–70, 74, 80, 83, 85–87, 118–19, 124, 127, 129, 135, 155 physical, 18, 126 social, 18 innovation, 10, 15 installed generating capacity, 47–48, 96–98, 165, 177, 179 Internet, 72, 113, 115, 188–89 investment, 11, 13, 15–19, 31–32, 43, 47, 50, 52, 54, 60, 64, 67, 69, 71, 73, 80, 84–87, 93, 100–101, 118, 120, 122–24, 127

234

THE PARADOX OF INDIA’S NORTH–SOUTH DIVIDE

irrigation, 18, 43, 46, 51, 73, 148, 150 Italy, 19–21 Jats, 76 Jean Dreze, 3–4, 10 judiciary, 59, 61, 66, 102 kerosene, 108–9, 113–14, 195, 198, 200–201, 203–4, 206–8, 210–12, 215 landline phone, 47, 110 laptops, 113–14 latrine, 107–8, 110–11, 115, 191–92, 198–99, 201–2, 204–5, 207–9, 211–12, 214 law and order, 13, 32–33, 36, 57–62, 65–66, 71, 73, 85–86, 102, 119, 123–24, 126, 155, 224 liberalization, 17, 68, 84, 117, 120–21, 127 life expectancy, 36, 41–42, 90, 95–96, 155, 164, 177 lighting facility, 107 literacy, 7, 33, 37, 43, 55, 58, 74, 81, 83, 86–87, 90–91, 110, 118–19, 125, 127, 131–32, 134, 136–51, 153–55, 163, 174, 220–21, 224–26 Madras, 23, 64, 73–75, 105, 120, 125, 217–19 market, 33–34, 47–48, 57, 126–27 Marshall Plan, 31 medicine, 37, 39 mid-day meal, 43 migrants, 2 mobile phones, 49, 113–14 municipalities, 18 Muslim, 65, 76, 124, 219

Nadars, 5, 13, 74–75 Naidus, 74 National Crime Records Bureau, 65–67, 82, 103–4, 170–71, 185–86, 221, 223–27 naxalite, 102 nepotism, 62 New York Times, 74 non-governmental organizations, 78, 125, 128 NSDP, 11, 25–26, 28–30, 51–52, 80–81, 89, 100, 106, 155–58, 160– 61, 171–73, 181–83, 220–22, 224, 226–27 nutrition, 13, 43, 79 OBCs, 4, 13–14, 76 open drainage, 111 output, 1, 9, 11–12, 14–15, 36, 38–40, 43, 47, 50–53, 73, 82, 84, 99–100, 182, 224 panchayati raj, 105 pending cases, 59, 61, 66, 102, 104, 155, 186–87, 220–23, 225–27 per capita income, 1, 10, 14, 17–19, 25–26, 28, 31, 35–37, 43, 45, 58, 80, 83–86, 88, 90, 102, 110, 114, 117, 122, 127, 219–21, 224, 226–27 planning commission, 27, 89, 149, 155, 158, 173, 225, 227 police firing, 59–61, 65–66, 80, 82, 86, 102–4, 123, 155, 169–70, 185– 86, 220–27 poverty ratio, 9, 27–28, 31, 158 press freedom, 12–13 productivity, 11, 14–15, 26, 31–32, 40, 43, 45, 47, 51, 73, 80, 87, 123, 128, 137, 224, 226

Index proximate, 31, 33–34, 36, 57, 80, 86–87, 117–21, 135, 137 public administration, 2, 70, 232 public distribution system, 44, 65 public goods, 32, 67, 76, 80, 87, 119 Public Report on Basic Education, 54 railways, 18, 33, 47 recession, 21 reservation, 72, 74, 76–77 returns to scale, 14–15 roads, 32–33, 47, 49, 52, 54, 57–58, 73, 83, 86, 155, 167–68 rule of law, 12, 34, 62 sanitation, 76, 107, 110–12, 114, 128, 132, 137 Scheduled Caste, 65 Services, 2, 15, 28, 30, 53, 58–59, 64, 67, 70–71, 76, 86–88, 96, 106–8, 112, 114, 122, 124–29, 132, 152, 161, 214 public, 10, 12, 45, 58, 62, 64, 107, 110, 114–15 information technology, 32 health care, 40 Indian Administrative, 63 social, 133, 148 economic, 133, 150 allied, 138 general, 144, 150 administrative, 146, 148 debt, 147 banking, 187, 188, 198, 199, 201, 202, 204, 205, 206, 208, 209, 210, 212 skilled labour, 32–33 slums, 88, 107–16, 118

235

Small Industries Development Bank of India, 57 social media, 72, 125, 129 social mobilization, 4, 36, 72, 80, 86–87 social order, 5, 13 standards of living, 2, 16, 45 take-off, 10, 31, 84 teaching, 39, 55–56 technology, 11, 15–16, 30–31, 33, 36–37, 39, 84, 93 telecommunication, 18, 47, 155 tele-density, 47, 50, 168 television, 109, 115, 187–88, 198–99, 201–2, 204–5, 207–10, 212, 214 Tendulkar committee, 28 textiles, 30 Tiruppur, 30, 57, 74, 127 TN Industrial and Investment Corporation, 57 transaction costs, 20, 47, 62, 64 transfers, 19, 106 transformation, 3, 5, 13, 31, 75, 77, 79, 84–85 transport, 17–18, 73, 133, 152 trust, 20, 73 Twenty-Point Programme, 79 urbanization, 17, 36, 45–47, 71, 77, 80, 83, 86–87, 98–100, 118, 135, 152, 155–56, 182, 220–27 Vaishya, 76 wind energy, 48 women, 10, 110, 112, 219

About the Authors

Samuel Paul is the Former Director and Professor of Economics at the Indian Institute of Management, Ahmedabad, and the Founder of the Public Affairs Centre, Bangalore, a non-profit organization known for its promotion of public accountability and good governance. He has taught at the Kennedy School at Harvard University and at the Woodrow Wilson School at Princeton University. He also served several international organizations such as the World Bank in Washington, where he was an advisor, and the United Nations where he was a special advisor to the United Nations Commission on Transnational Corporations. He is a recipient of both national and international honours, including the Padma Shri (Government of India), Fred Riggs Award of the American Society of Public Administration, All India Management Association’s Nohria Award and the Gill Memorial Award of the World Bank. Kala Seetharam Sridhar is Professor and Head, Centre for Research in Urban Affairs, Institute for Social and Economic Change (ISEC), Bangalore. She has written several books on urban issues, and contributed to several journals and articles in various books. She is the recipient of several national awards such as the VKRV Rao award for her contributions to social sciences, and international awards by the Global Development Network (GDN) for her research. She was India country expert for ADB’s project on green urbanization in Asia, and is involved in ADB’s project on urbanization in India and China. She was senior advisor to GDN’s global project on urbanization, and member of the government of Karnataka’s expert committees on urban development.