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Essays in Development Economics Essays in Development Economics

Manini Ojha Southern Methodist University, mojha@smu.edu

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EMPIRICAL ESSAYS IN

DEVELOPMENT ECONOMICS

Approved by:

Dr. Daniel L. Millimet

Professor of Economics

Dr. Thomas B. Fomby

Professor of Economics

Dr. Elira Kuka

Assistant Professor of Economics

Dr. Anil Kumar

Senior Economist, Dallas Fed

EMPIRICAL ESSAYS IN

DEVELOPMENT ECONOMICS

A Dissertation Presented to the Graduate Faculty of the

Dedman College

Southern Methodist University

in

Partial Fulfillment of the Requirements

for the degree of

Doctor of Philosophy

with a

Major in Economics

by

Manini Ojha

B.A., Economics, University of Delhi, IndiaM.A., Economics, Jawaharlal Nehru University, New Delhi, India

M.A., Economics, Southern Methodist University, Dallas, TX

May 19, 2018

Copyright (2018)

Manini Ojha

All Rights Reserved

ACKNOWLEDGMENTS

First and foremost, I would like to thank my advisor, Dr. Daniel Millimet. Dr. Millimet,

it has been an honor to be your Ph.D. student. This work would not have been accomplished

without your wisdom, encouragement and constant guidance. Your direction not only helped

conceptualize my ideas but also influenced me as a writer. I sincerely thank you for allowing

me to grow as a researcher.

I am also grateful to Dr. Elira Kuka for being a constant source of motivation. Dr. Kuka,

your outlook, invaluable suggestions and research greatly influenced my work. I thank Dr.

Thomas Fomby and Dr. Anil Kumar, the other members of my dissertation committee, for

offering their valuable feedback regarding my work.

I extend my heartfelt thanks to Dr. Omer Ozak for his support throughout my doctoral

journey. Dr. Ozak, your accessibility as a professor was an immense source of comfort. I

cherish all my interactions with you through the years and greatly value our friendship.

I thank Dr. Santanu Roy for his guidance, support and valuable inputs, both on a profes-

sional and a personal front, throughout my career as a graduate student. I am also grateful

to Dr. Klaus Desmet, Dr. Danila Serra, Dr. Tim Salmon, and Dr. James Lake for their

advice and suggestions. Further it would be remiss of me if I did not thank Margaux Mont-

gomery and Stephanie Hall, who work tirelessly to ensure that our lives in the department

be as comfortable as possible.

I owe a special thanks to my colleagues, friends and co-authors, Andres Giraldo and

Priyanka Chakraborty. Andres and Priyanka, I have learnt a great deal from you and am

deeply grateful to you for being a part of my intellectual as well as emotional journey. I

also thank my classmates and colleagues Punarjit Roychowdhury, Erik Hille and Hao Li. I

couldn’t have hoped for a better set of colleagues.

To my friends in Dallas, you became family. Six years back, I came here alone and today,

I leave rich with friendships and memories that will last a life-time. Ankita and Akshay,

iv

thank you for accepting me with arms wide open and introducing me to the most natural

and easy friendships I have ever known. You been a home away from home. Manali, Madhu,

Aarushi, Amod, Sajid, Rohan and the whole crew, thank you for believing in me, walking

beside me and for all the love. This journey would not have been the same without each one

of your presence in my life. I cherish your friendship.

A special thanks to my family. Words cannot express how grateful I am to my parents,

Neeraja and Rajani Ranjan Rashmi, for their unconditional love, for letting me follow my

path, for encouraging me to be better and for all the sacrifices they made on my behalf.

Ma and Papa, I owe everything to you and more. I also thank my beloved brother, Pratyay

Ojha, for being my confidant, my sounding board, and for always cheering me up.

Finally, none of this would have been possible without the love and support of my hus-

band, Apurv. Apurv, I am forever grateful to you for being so patient with me, for pulling

me up every time I would was down, for believing in me even when I didn’t, for steering me

in the right direction and for the constant reminders about the world around me. Your work

ethic, passion for what you believe in, and pragmatism has always been an inspiration. Your

dogged confidence in us and our long, long-distance marriage has been a pillar of strength

through this journey. With you, life is a series of remarkable events. Thank you for being

my sankat-mochan.

v

Ojha, Manini B.A., Economics, University of Delhi, IndiaM.A., Economics, Jawaharlal Nehru University, New Delhi, India M.A., Economics, Southern Methodist University, Dallas, TX

Empirical Essays in

Development Economics

Advisor: Dr. Daniel L. Millimet

Doctor of Philosophy degree conferred May 19, 2018

Dissertation completed April 20, 2018

This dissertation consists of three empirical essays in development economics. In the first

essay, I examine the impact of a health insurance scheme called the Rashtriya Swasthya Bima

Yojana (RSBY), launched in 2008 in India, on schooling decisions and gender differences in

education. At the outset, it is not entirely obvious as to whether health insurance would

benefit education or have a detrimental impact. Healthier children could either mean greater

future economic returns from schooling or greater value as child labour. More specifically, the

questions I seek to answer are twofold: (1) Does access to a health insurance scheme designed

for the poor have an impact on school expenditure decisions of households? (2) Does it affect

school enrollment of boys and girls within the household? Employing difference-in-differences

and triple differences approaches, I find that access to RSBY is beneficial for child education

as school expenditure increases by 20 to 28 percent after the treatment. Additionally, I

find RSBY to be relatively more advantageous to girls as it reduces the existing gender gap

in school enrollment by 1/3rds. From a policy perspective, it is interesting to see that a

health insurance scheme has unintended positive consequences not only on household school

expenditure but also on parental responses within household in terms of enrollments of boys

versus girls. Such responses should ideally be considered when designing policies to remedy

any disadvantages among children, since parents can eliminate these effects by aiming at

equitable child human capital formation within the family.

vi

In the second essay, I study the impact of India’s Mahatma Gandhi National Rural

Employment Guarantee Act (MG-NREGA) on the pattern of household consumption be-

haviour. NREGA, passed in 2005, created the world’s largest public works programme under

a statutory framework, legally guaranteeing hundred days of employment. Guaranteeing such

employment opportunities can directly affect intra-household decisions through a change in

total resources but also allocation of resources. Using the phase wise roll-out of NREGA to

districts and employing a difference-in-differences approach, I find a shift in discretionary

spending towards ‘wiser’ consumption choices like school expenditure and durable goods,

away from ‘wasteful’ expenditure like entertainment. These effects are broadly suggestive of

an increase in female bargaining power since men and women are seen to have systemati-

cally different consumption preferences and spending patterns. I also find the shifts in con-

sumption patterns to be amplified in regions with higher share of women employed through

NREGA; in states that guarantee employment at higher minimum wages; and in rice growing

regions of India, where females are traditionally more intensively involved in production.

This dissertation also delves into the relationship between human capital formation and

socio-economic conditions in developing countries. To this effect, in the third essay, I evaluate

the impact of quality of education on violence and crime, using data from Colombia, a country

with a long standing history of violence and conflict. Over the long run, successful efforts to

improve school quality would imply an extraordinary rate of return, and may be a tool for

social mobility and development. I exploit geographic and time variation at the municipality

level and use an Instrumental Variable approach to identify this effect. The instruments are

based on transfer of funds from the central government to municipalities for investments

in education. I find that better education quality, measured by student test scores on a

mandatory school-exit examination, has a significant and negative impact on the intensity

of crime. A 1 standard deviation increase in test scores leads to a decline of 6.2 standard

deviations in property crimes. These effects are perhaps indicative of an ‘opportunity cost

effect’ of education. I also find that better education quality reduces violent crimes as well

as presence of illegal armed groups suggesting a ‘pacifying effect’ of education.

vii

TABLE OF CONTENTS

LIST OF FIGURES . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . xii

LIST OF TABLES . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . xiii

CHAPTER

1. GENDER GAP IN SCHOOLING: IS THERE A ROLE FOR HEALTH IN-

SURANCE? . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 1

1.1. Introduction . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 1

1.2. Literature . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 5

1.3. Background on RSBY . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 7

1.4. Empirics . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 8

1.4.1. Data . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 8

1.4.2. School expenditure - Estimation and identification . . . . . . . . . . . . . . . . 11

1.4.3. School enrollment - estimation and identification . . . . . . . . . . . . . . . . . . 14

1.5. Results . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 15

1.5.1. School expenditure as a budget share . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 15

1.5.2. School enrollment . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 17

1.6. Robustness Checks . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 18

1.6.1. School expenditure - other estimation issues . . . . . . . . . . . . . . . . . . . . . . . 18

1.6.1.1. School expenditure in levels . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 19

1.6.1.2. School expenditure - fractional logit estimation . . . . . . . . . . 21

1.6.1.3. School expenditure - panel analysis . . . . . . . . . . . . . . . . . . . . . . 22

1.6.2. School enrollment - other estimation issues. . . . . . . . . . . . . . . . . . . . . . . . . 23

1.6.2.1. School enrollment - probit with correlated random ef-fects . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 23

1.6.2.2. School enrollment - instrumental variable approach . . . . . . . 24

viii

1.7. Sensitivity Analysis . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 25

1.7.1. Variation in income distribution . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 25

1.7.2. Variation in treatment intensity . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 26

1.7.3. Variation in programme take-up by district . . . . . . . . . . . . . . . . . . . . . . . 29

1.7.4. Sub sample analysis . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 30

1.8. Conclusion . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 31

2. INTRA-HOUSEHOLD CONSUMPTION DECISIONS: EVIDENCE FROM

NREGA . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 38

2.1. Introduction . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 38

2.2. Background on NREGA . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 40

2.3. Literature review . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 41

2.4. Data . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 43

2.5. Empirics . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 44

2.6. Results . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 48

2.7. Sensitivity analysis . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 50

2.7.1. Women employment in NREGA jobs . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 51

2.7.2. State minimum wages . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 52

2.7.3. Crop regions . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 53

2.8. Robustness checks . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 55

2.8.1. Fractional logit estimation with correlated random effects . . . . . . . . . . 55

2.8.1.1. Baseline model . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 55

2.8.1.2. Heterogeneous effects . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 56

2.8.2. Consumption in levels . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 56

2.8.2.1. Baseline model . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 56

2.8.2.2. Heterogeneous effects . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 58

2.9. Conclusion . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 58

ix

3. THE EFFECT OF QUALITY OF EDUCATION ON CRIME: EVIDENCE

FROM COLOMBIA1 . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 66

3.1. Introduction . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 66

3.2. Literature . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 70

3.3. Background . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 72

3.4. Data and Identification . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 72

3.4.1. Data . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 72

3.4.2. Selection Issues . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 75

3.4.3. Estimation . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 76

3.4.4. Identification Issues . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 77

3.4.5. Institutional Framework . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 79

3.5. Results . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 80

3.5.1. Crime Rate . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 80

3.5.2. Property Crimes . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 81

3.5.3. Violent Crimes . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 82

3.5.4. Conflict . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 82

3.6. Transmission Channel . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 83

3.7. Robustness . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 83

3.7.1. Sub-Sample Analysis . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 83

3.7.2. Other Government Transfers . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 84

3.7.3. Other Measures of Education Quality . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 85

3.8. Conclusion . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 86

APPENDIX

A. GENDER GAP IN SCHOOLING: IS THERE A ROLE FOR HEALTH IN-

SURANCE? . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 93

1With Andres Giraldo, Southern Methodist University and Pontificia Universidad Javeriana

x

B. INTRA-HOUSEHOLD CONSUMPTION DECISIONS: EVIDENCE FROM

NREGA . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 107

C. THE EFFECT OF QUALITY OF EDUCATION ON CRIME: EVIDENCE

FROM COLOMBIA. . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 118

BIBLIOGRAPHY . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 144

xi

LIST OF FIGURES

Figure Page

1.1 Pre-trends at district level . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 35

A.1 RSBY Coverage . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 94

B.1 Districts map of India . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 108

C.1 Crime Rate 2007 . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 118

C.2 Education Quality 2007 . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 119

C.3 Crime Rate 2013 . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 120

C.4 Education Quality 2013 . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 121

xii

LIST OF TABLES

Table Page

1.1 Summary statistic - Household level . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 33

1.2 Summary statistics - Individual level . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 34

1.3 Impact of RSBY on household school expenditure . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 36

1.4 Impact of RSBY on child school enrollment . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 37

2.1 Impact of NREGA on expenditure shares - DID . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 60

2.2 Impact of NREGA on expenditure shares - DDD . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 61

2.3 Impact of NREGA on probability that household is female headed . . . . . . . . . . . . 62

2.4 Heterogeneous Impacts of NREGA on Expenditure Shares: Female Shareof NREGA Employment . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 63

2.5 Heterogeneous Impacts of NREGA on Expenditure Shares: State StipulatedMinimum Wages . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 64

2.6 Heterogeneous Impacts of NREGA on Expenditure Shares: Crop Regions . . . . . 65

3.1 Crime and Education Quality . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 88

3.2 Crime and Education Quality . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 89

3.3 Crime and Education Quality . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 90

3.4 Presence and Quality of Education . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 91

3.5 Lights and Education Quality . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 92

A.1 Robustness: Impact of RSBY on household school expenditure - Instrumen-tal variable approach . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 95

A.2 Robustness: Impact of RSBY on household school expenditure - Fractionallogit estimation . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 96

A.3 Robustness: Impact of RSBY on household school expenditure - Panel analysis 97

xiii

A.4 Robustness: Impact of RSBY on child school enrollment - Probit with cor-related random effects . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 98

A.5 Robustness: Impact of RSBY on child school enrollment - Instrumentalvariable approach . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 99

A.6 Sensitivity analysis: Impact of RSBY on household school expenditure andchild school enrollment - Variation in income categories . . . . . . . . . . . . . . . . . . . . 100

A.7 Sensitivity analysis: Impact of RSBY on household school expenditure -Variation by intensity of treatment . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 101

A.8 Sensitivity analysis: Impact of RSBY on child school enrollment - Variationby intensity of treatment . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 102

A.9 Sensitivity analysis: Impact of RSBY on household school expenditure -Variation in take-up by district . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 103

A.10 Sensitivity analysis: Impact of RSBY on child school enrollment - Variationin age groups . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 104

A.11 Sensitivity analysis: Impact of RSBY on household school expenditure andchild school enrollment - Rural vs urban . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 105

A.12 Sensitivity analysis: Impact of RSBY on household school expenditure andchild school enrollment - Variation by castes . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 106

B.1 Summary statistics . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 109

B.2 Impact of NREGA on Expenditure Shares - Fractional Logit Model withCorrelated Random Effects Approach . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 110

B.3 Heterogeneous Impacts of NREGA on Expenditure Shares: Female Shareof NREGA Employment - Fractional Logit Model with Correlated Ran-dom Effects Approach . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 111

B.4 Heterogeneous Impacts of NREGA on Expenditure Shares: State StipulatedMinimum Wages - Fractional Logit Model with Correlated RandomEffects Approach . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 112

B.5 Heterogeneous Impacts of NREGA on Expenditure Shares: Crop Regions -Fractional Logit Model with Correlated Random Effects Approach . . . . . . . . . 113

B.6 Impact of NREGA on Expenditure in Levels . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 114

B.7 Heterogeneous Impacts of NREGA on Expenditure in Levels: Female Shareof NREGA Employment . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 115

xiv

B.8 Heterogeneous Impacts of NREGA on Expenditure in Levels: State Stipu-lated Minimum Wages . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 116

B.9 Hetergeneous Impacts of NREGA on Expenditure in Levels: Crop Regions . . . . 117

C.1 Summary statistics . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 122

C.2 Crime and Education Quality (Without Bogota) . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 126

C.3 Disaggregated Crime and Education Quality (Without Bogota) . . . . . . . . . . . . . . . 127

C.4 Crime and Education Quality (Without State Capitals) . . . . . . . . . . . . . . . . . . . . . . 127

C.5 Disaggregated Crime and Education Quality (Without State Capitals) . . . . . . . . 128

C.6 Violence and Education Quality (With Population <200,000 Inhabitants) . . . . . 128

C.7 Disaggregated Crime and Education Quality (With Population < 200, 000Inhabitants) . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 129

C.8 Crime and Education Quality (Rural Areas) . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 129

C.9 Disaggregated Crime and Education Quality (Rural Areas) . . . . . . . . . . . . . . . . . . . 130

C.10 Crime and Education Quality (Urban Areas) . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 130

C.11 Disaggregated Crime and Education Quality (Urban Areas) . . . . . . . . . . . . . . . . . . 131

C.12 Crime and Education Quality (Total Transfers as Instruments) . . . . . . . . . . . . . . . 131

C.13 Disaggregated Crime and Education Quality (Total Transfers as Instru-ments) . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 132

C.14 Crime and Education Quality (Total Transfers as an Additional Regressor) . . . 133

C.15 Disaggregated Crime and Education Quality (Total Transfers as an Addi-tional Regressor) . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 134

C.16 Crime and Education Quality (Total Transfers instead of Total Expendi-tures) . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 135

C.17 Disaggregated Crime and Education Quality (Total Transfers instead ofTotal Expenditures) . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 136

C.18 Crime and Education Quality (Total Transfers Instrumented) . . . . . . . . . . . . . . . . 137

C.19 Disaggregated Crime and Education Quality (Total Transfers Instrumented) . . 138

C.20 Crime and Education Quality (Cognitive Areas) . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 138

xv

C.21 Disaggregated Crime and Education Quality (Cognitive Areas) . . . . . . . . . . . . . . . 139

C.22 Crime and Education Quality (Social Areas) . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 140

C.23 Disaggregated Crime and Education Quality (Social Areas) . . . . . . . . . . . . . . . . . . . 141

C.24 Crime and Education Quality (Total Score) . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 142

C.25 Disaggregated Crime and Education Quality (Total Score) . . . . . . . . . . . . . . . . . . . 143

xvi

To Casper, my forever-magnificent companion.

Chapter 1

GENDER GAP IN SCHOOLING: IS THERE A ROLE FOR HEALTH INSURANCE?

1.1. Introduction

Concerns about adequate healthcare and access to health insurance have witnessed pro-

found growth over the past few decades amongst policymakers worldwide. The WHO states

that 400 million people in the world have no access to essential health services and 6 per-

cent of people in developing countries are pushed further into extreme poverty due to health

spending (WHO [2015]). Health shocks can be particularly devastating for the poor in de-

veloping countries owing to a lack of affordable insurance (Hamoudi et al. [1999], Wagstaff

et al. [2009], Wagstaff et al. [2009]).1 Absence of a formal pervasive public insurance system

means large out-of-pocket expenditure is the main source of healthcare. As such, the burden

of health shocks may be greater if its consequences are transferred to human capital of future

generations in families unable to access formal insurance markets (Currie and Moretti [2007];

Bhalotra and Rawlings [2011]; Flores et al. [2008], Morduch [1999], Sun and Yao [2010]).

Child human capital formation can potentially be affected through the following chan-

nels. First, if children are considered as substitutes for adult labour in a family with an

ailing parent, they are compelled to be withdrawn from school and sent to work to smooth

consumption (Fabre and Pallage [2015]).2 Second, if the case is of an ailing child, they

are withdrawn from school as their survival and health status assume more importance in

such situations. Third, health shocks reduce a household’s ability to afford the upfront cost

of schooling. In the absence of safety nets coupled with poverty, households thus resort

1Most private healthcare deliveries have low penetration due to lack of awareness and affordability. As aresult, the government often fills this void in the market.

2They may even be asked to look after the sick parent reducing the time they can devote to school (Brattiand Mendola [2014]).

1

to financing healthcare expenditure through other costly measures like reduction in school

expenditure or delaying their children’s enrollments. However, it is not entirely obvious

whether access to health insurance would have a positive or a negative impact on child edu-

cation. On the one hand, the above mechanisms imply that insurance could protect children

from being pushed into labour and reduce school dropouts in households affected by health

shocks. On the other, better child health as a result of insurance could even mean more child

labour for such families. The effect is therefore ambiguous and speaks to the importance of

addressing it empirically.

Moreover, the impact of health insurance on education may not be gender-neutral. That

there exists a problem of gender gap in education in developing countries, is well known.3

Researchers cite several reasons for this gap, like differential economic returns to education,

parental preferences or biases, concerns over old-age support, and family’s economic condi-

tions, of which, health spending is a key determinant. Given this context, it is noteworthy to

examine whether a health insurance system designed for the poor impacts schooling decisions

and gender differences in education.

Gender specific roles within households invariably result in different time opportunity

cost of schooling for boys and girls. Health insurance in such a scenairo has the potential

to impact not only the time opportunity cost of schooling but also the monetary costs of

schooling. On the one hand, if resources are reallocated from schooling to cover health

expenses, then households may reallocate first from the girls’ expenditures if preferences

and/or returns to education favour boys.45 On the other hand, improved health due to

health insurance may increase the returns for child labour more for boys than girls and thus

3Girls tend to receive less schooling than boys (Burgess and Zhuang [2000], Schultz [2002], Colcloughet al. [2000], Alderman et al. [1996], Alderman and King [1998]).

4Girls invariably become the first victim of a health shock to the family without insurance (Garg andMorduch [1998]). In addition, resource constraints can exacerbate patterns of preferences within householdsas income changes (Hill and King [1995], Alderman and Gertler [1997]).

5Basic education in developing countries is public but school attendance still requires out-of-pocket ex-penditures, sometimes large enough to keep children out of school. Although direct fee is unlikely to differby gender, costs such as those of reaching school, learning materials, and uniforms may influence schoolingdecisions of girls more than boys.

2

reduce educational investments for boys relative to girls.6 Again, the direction of impact is

ambiguous and merits more empirical work. As such, access to a health insurance system

for the poor could perhaps ease some resource constraints, thus resulting in a change in the

gender gap in enrollments.

This paper focuses on India’s cashless, paperless and portable health insurance scheme

started in 2008, called the Rashtriya Swasthya Bima Yojana (RSBY) to investigate these

issues.7 RSBY was implemented with the aim to protect the poor, across rural and urban

areas, from financial liabilities and increase their access to quality healthcare. Given that

28 percent of India’s population is below poverty line (BPL), health care expenditure is

one of the most important reasons for indebtedness. Alarmingly, less than 15 percent of

the 1.1 billion population are covered by health coverage. Moreover, over 78 percent of all

medical expenditure in India is private financing most of which is out-of-pocket expenditure

and is amongst the highest in the world (Swarup and Jain [2011]).8 Initially targeted at

below poverty line households, RSBY has since expanded to cover other unorganized workers

and marginalized sections who enroll into the scheme. The beneficiaries of the scheme are

provided with a bio-metric smart card that can be used to receive health services from

hospitals empanelled under the scheme without any out-of-pocket expenditure subject to

certain conditions. RSBY therefore assumes importance as a policy measure to not only

decrease the vulnerability of credit-constrained households, but to also potentially protect

their children from adverse shocks.

While there exists literature on the impact of health insurance on health expenditure

and health related outcomes in India, most papers focus on smaller insurance schemes con-

6Sons are valued more as they are considered labour assets and support during old age. Daughtershowever, usually leave the natal family post marriage (Sen and Sengupta [1983], Bardhan [1985]; Rosenzweigand Schultz [1982], Duraisamy [1992], Garg and Morduch [1998], Kingdon [2005], Almond et al. [2010],Haddad et al. [1984]).

7In recent times, many developing countries have subsidized health insurance for the rural and informalsector workers and their families (Wagstaff et al. [2009]). China adopted a new health insurance systemfor the rural population called the New Cooperative Medical Scheme. On similar lines, Vietnam, Taiwan,Indonesia, and Philippines are also striving to achieve universal health coverage.

8External aid to the health sector accounts for a negligible 2 percent of the total health expenditure.

3

centrated in some states. Few recent empirical papers investigate the impact of RSBY on

financial burden, health services and expenditures (Azam [2018], Karan et al. [2017], Ravi

and Bergkvist [2015], Karan et al. [2014], Johnson and Krishnaswamy [2012]). However, thus

far, no evidence exists for the spillover effects of health coverage, in general, and RSBY, in

particular, on education. This is the first paper, to my knowledge, to investigate the role of

a public health insurance scheme in India in determining school expenditure and enrollment

decisions.

Using nationally representative longitudinal survey, my empirical analysis employs two

different identification strategies. First, I estimate the effect of RSBY on both school expen-

diture and enrollment using a difference-in-differences strategy. Second, I employ a triple

differences model which exploits the fact that rich households are significantly less likely to

be affected by the program (due to the initial focus on BPL households). Using nationally

representative household level data, I investigate the treatment impact of RSBY on house-

hold school expenditure. In addition, using nationally representative individual level data,

I quantify a similar treatment effect of RSBY on school enrollment and the existing gender

gap. I compare households in districts that are exposed to RSBY by the second wave of the

survey, to those that were not exposed to the scheme in the sample period in order to obtain

the intent-to treat (ITT) impact of the programme.

The findings are interesting and ought to serve as a guide to future research and policy

discussions. A key result is that access to health insurance is beneficial for child human

capital formation, as school expenditure increases at the household level after the treatment.

The estimates found imply an increase in the budget share of school expenditure of 0.5 to

0.7 percentage points. This effect is statistically and economically significant given that

school expenditure accounted for 2.5 percent of the budget share for such households prior

to RSBY. This amounts to an increase of 20 to 28 percent in its budget share after the

treatment. Given that health insurance reduces uncertainty about occupational hazards,

availability and access to RSBY mitigates costly choices a household may otherwise resort

to, like reducing school expenditure. These results are robust to several alternative modeling

4

choices.

Finding positive impacts on household school expenditure, the paper goes further to

quantify the effects of RBSY on school enrollments of children within households. I find

a clear reduction in the gender gap in school enrollment after implementation of RSBY.

Absent the programme, school enrollment of boys is about 6 percentage points more than

girls. I find that the probability of enrollment is 0.8 percentage points higher for boys and

2.7 percentage points higher for girls, after the programme went into effect. Thus, the

gap in enrollment reduces by one-third. Triple differences approach confirms this result for

relatively less well-off households.

Rest of the paper proceeds as follows. Section 1.2 presents a review on related literature.

Section 1.3 provides the background and programme details of RSBY. Section 1.4 is divided

into sub-sections: 1.4.1 describes the data, followed by the estimation and identification

strategy for the analysis of school expenditure and school enrollment in subsections 1.4.2

and 1.4.3 respectively. Section 1.5 discusses the baseline results followed by robustness of the

baseline models in Section 1.6. Section 1.7 presents sensitivity analysis of school expenditure

and school enrollment. The paper ends with the conclusion in Section 1.8.

1.2. Literature

This paper contributes broadly to two bodies of literature. First, it contributes to the

vast literature on the impact of public health insurance schemes. Effect of health coverage

on uptake of treatment, out-of-pocket expenditures, in-patient and out-patient services in

developing countries have been examined in Acharya et al. [2012], Wagstaff et al. [2009].

Currie and Gruber [1996], Chen and Jin [2012], Liu and Zhao [2014] study its impact on

other health-related outcomes like health care disparity, health statuses of new born children,

mothers and the elderly.

In the Indian context, the impact of health insurance, particularly RSBY, on various

outcomes are studied in Azam [2018], Karan et al. [2017], Raza et al. [2016], Devadasan

et al. [2013], Das and Leino [2011], Palacios et al. [2011], Johnson and Krishnaswamy [2012],

5

Rajasekhar et al. [2011], Virk and Atun [2015], Ravi and Bergkvist [2015]. Using panel

data from the India Human Development Survey (IHDS), Azam [2018] utilizes a difference-

in-differences with propensity score matching approach to estimate the average treatment

impact (ATT) of RSBY on the beneficiary households. The paper uses both the house-

hold and the individual level data from the IHDS to investigate the impact on utilization

of health services for short term and long term morbidity, total out-of-pocket expenditures,

per capita in-patient and out-patient expenditures. Both Karan et al. [2017] and Johnson

and Krishnaswamy [2012] use difference-in-differences with matching at household level to

evaluate the ITT impact of RSBY using cross-section data from the national sample sur-

vey (NSS). Karan et al. [2017] find marginal decline in in-patient, out-patient out-of-pocket

expenditures and budget share of out-of-pocket expenditure. Johnson and Krishnaswamy

[2012] find that the scheme has led to a small decrease in out-patient and total medical

expenditure of target households and some limited evidence of increased hospital utilization

rates. On similar lines, Ravi and Bergkvist [2015] also use data from NSS and implement

difference-in-differences across insurance districts versus uncovered districts to study the ITT

impact of publicly provided health insurance schemes in India on the likelihood of impover-

ishment, catastrophic health expenditure, and the poverty gap index. Nandi et al. [2013] use

district-wise official data on enrollment, and correlate those with district characteristics to

find the determinants of participation in RSBY. Fewer studies have focused on the impact

of health insurance on non health related outcomes. Among these papers, most have looked

at the impact on household choices associated with health shocks (Kochar [1995], Liu [2016],

Mohanan [2013]).

Second, the paper adds to the strand of literature on gender gaps in treatment of children

in south Asia. According to some papers, boys are favored over girls in terms of intra-

household allocation of resources and nutrients as found through indices like weight for age,

mortality rates, and breastfeeding (Barcellos et al. [2014], Behrman [1988], Bardhan [1985],

Sen and Sengupta [1983], Rosenzweig and Schultz [1982]). Other papers suggest household

income, parental education and supply side factors like quantity and quality of schools are

6

explanations for low educational achievements and gender gaps in such countries (Behrman

and Knowles [1999], Duraisamy [1992], Kambhampati and Pal [2001], Pal [2004], Dreze and

Kingdon [2001]). More specifically, in the context of India, evidence of gender differences in

child schooling exists for some states but very few studies are able to explain such differences

(Pal [2004], Glick et al. [2016]).

1.3. Background on RSBY

Rashtriya Swasthya Bima Yojana (RSBY) or the national health insurance scheme was

launched by the government of India as a cashless, paperless and portable health insurance

scheme in 2008. The scheme was initially designed to target below poverty line population

(BPL) both in rural and urban India but was later expanded to also cover unorganized

workers such as construction workers, domestic help, street vendors, rickshaw pullers etc.

RSBY aims to protect the poor from financial risk arising from out-of-pocket expenditures

on hospitalizations and to improve the access to quality healthcare. Unlike most central

government schemes, implementation of RSBY did not follow a top driven approach. The

government marketed the scheme and rolled it out in districts based on factors such as need

for the scheme, ease of implementation and acceptance from local governments. By October

2013, approximately 36 million families out of a target of approximately 65 million were

enrolled in the scheme. As of 2013, the scheme was implemented in 512 districts out of 640

districts in 29 states across India (Government of India [2013b]).

Beneficiaries of the scheme are entitled to hospitalization coverage of up to INR 30,000

(approximately $460) for a family of five and transportation costs up to INR 1,000 (approxi-

mately $16). The scheme is jointly funded by the central and state governments with 75% of

premium from the center and 25% from the state.9 State governments set up state agencies

to prepare a list of identified households.10 Awareness campaigns are conducted through the

9In case of Jammu & Kashmir and North-eastern States, 90% of premium is from the central governmentand 10% from the state.

10These are referred to as the state ‘nodal’ agencies by the government.

7

Gram Panchayat and enrollment camps set up across districts.11 Insurance companies, se-

lected through a competitive bidding process by the government, are responsible for reaching

out to the beneficiaries for enrollments. Once a hospital is empanelled, a nationally-unique

hospital ID number is generated so that transactions can be tracked at each hospital.

Beneficiaries pay a small amount of INR 30 (approximately $5) as registration fee which

is aggregated at the state level and is used to take care of the administrative cost of the

scheme. Households that choose to enroll into the scheme receive a bio-metric card with

a national unique ID. Upon receiving the card, the beneficiary can visit any empanelled

hospitals across the country to get cashless treatment. Insurance companies are paid a fixed

price per household enrolled and must settle all claims with the hospitals directly based

on rates fixed by the central government. While all pre-existing diseases are covered, the

scheme does not cover out-patient procedures. There is no age limit on the enrollment of

beneficiaries.

1.4. Empirics

1.4.1. Data

I utilize two waves of the India Human Development Survey (IHDS), collected in 2004-

05 and 2011-12 for the analysis.12 IHDS is a nationally representative multi-topic survey

of approximately 40,000 households across 1503 villages and 971 urban neighbourhoods of

India. The surveys are collected from January to March.13

11The date and location of the enrollment camp are publicized in advance. Some mobile enrollment stationsare also established at local centers like public schools at each village at least once a year. These stationsare equipped by the insurer with the hardware to collect bio-metric information and photographs of themembers of the household covered.

12IHDS I refers to the time period 2004-05 and IHDS II to 2011-12.

13IHDS is collected by the National Council of Applied Research and Training (NCAER), New Delhiand University of Maryland. The waves are publicly available to be downloaded from the Inter-UniversityConsortium for Political and Social Research (ICPSR). IHDS-I surveyed 41,554 households and IHDS-II42,152 households.

8

IHDS-II is mostly re-interviews of households interviewed for IHDS-I. I merge the two

survey waves for my analysis both at household as well as individual level.14 The household

sample is restricted to include households with children and where the age of head lies

between 18 to 90 years. After dropping these observations, my sample consists of 29,381

households in the first survey wave and 25,226 in the second. The individual level sample is

restricted to children in the age group 5 to 18 years. The sample consists of 48,571 children

in the first survey wave and 41,576 in the second. I consider the individual level data as

repeated cross-section since it is difficult to track the same child over a period of 7 years

between 2005 and 2012. Some children may have finished school while new children are

enrolled. For consistency purposes, I also consider the household sample as a repeated cross-

section.15 I merge both the household and the individual samples separately with data on

implementation of RSBY at district-level. Information about the roll-out of health insurance

scheme is taken from the official ministry website.16 The final sample consists of 393 districts

across India. No districts were treated in the first wave and 53 districts were not treated as

of the second wave. In addition, I use three rounds of Household Consumption Expenditure

Surveys from the National Sample Survey (NSS) of India for the years 2004-05, 2005-06 and

2006-07 for district level average monthly consumption expenditure prior to implementation

of RSBY.

I consider budget share of school expenditure out of total monthly household expenditure

as my outcome variable for the baseline analysis at household level (see Eqns. 1.1 and 1.2).

Child specific school enrollment within each household is my outcome variable for the analysis

at individual level (see Eqn. 1.3 and 1.4). Standard errors are clustered at district level in

all estimations.17

14The states of Andhra Pradesh, Karnataka, Tamil Nadu have been dropped from my sample as thesestates already have state-funded health insurance schemes in place.

15I redo my analysis at household level treating the household data as a true household level panel datafor robustness purposes (refer to section 1.6.1.3).

16List of districts and phases of implementation can be found at http://www.rsby.gov.in/.

17This is true except when I estimate the treatment effect considering my sample as a panel data withhousehold fixed effects. I cluster the standard errors at household level in this case.

9

The set of controls for household level analysis (refer to Eqns. 1.1 and 1.2) includes

household size, age of the head of the household, age squared, educational characteristics of

male and female members of the household, number of years a family has stayed in one place,

indicators for caste (Brahmins, Scheduled Tribes, Scheduled Castes, and Other Backward

Class), indicators for religion (Hindu, Muslim, Sikh, Buddhist, Jain, other religion), dummy

for urban areas, whether head of the household can converse in English, gender dummy for

the head of the household, number of married male and females in the households, dummies

indicating number of years of marriage, whether the household has a bank account and a

credit card. Control variables for the individual level analysis (refer to Eqn. 1.3 and 1.4)

include household size, age of the child, age squared, mother and father’s education charac-

teristics, indicators for caste, religion dummies, dummy for urban areas, school facilities and

scholarships offered. In addition to this I also include an indicator for the relatively poorer

households, that takes value 1 if the household belongs to the bottom 70 percent of income

distribution in my sample and 0 otherwise (refer to Eqn. 1.2 and 1.4 in 1.4.2 and 1.4.3).

The summary statistics for my control and treatment districts in the two time periods are

presented in Tables 1.1 and 1.2.

Note that in all my models, I include household size as a regressor which is likely endoge-

nous. Excluding household size as a control while analyzing school expenditures and gender

differences in education implies that boys and girls live in families with similar characteris-

tics, in terms of both observables and unobservables. However, this assumption is likely to

bias the estimates if families have a preference for sons and follow male-biased stopping rules

of childbearing (Barcellos et al. [2014]). If fertility decisions are driven by a desire to have

a certain number of boys, then girls end up in larger families on average. To address this

concern, I instrument household size by by gender of the first born child in the family under

the assumption of no sex-selective abortion.18 Although this assumption is not without crit-

icism, in such cases, gender of the first child is likely to be a good predictor of the number

18Ban on sex-selective abortion was enacted in India in 1971 and later amended in 2004 making prenatalsex-screening and sex-selective abortion punishable by law (United Nations [2017a])

10

of children in the household or family size and excludable from the second stage (Barcellos

et al. [2014],Clark [2000],Clarke [2017]).

1.4.2. School expenditure - Estimation and identification

I use a difference-in-differences (DID) strategy to compare households in districts that

are exposed to health insurance by the second wave of the survey to those that were never

exposed to the scheme. All households in 2004-05 and some households in 2011-12 that

are never exposed to RSBY form my control group and the households in districts exposed

to RSBY in 2011-12 form my treatment group. A simple comparison of households from

districts that received the scheme to those that did not would likely lead to biased estimates.

I include district fixed effects to address the concern of any time invariant district level

characteristics that may be correlated with the treatment. Time fixed effects control for the

time-varying characteristics that impact all districts equally.

Identification relies on changes in household school expenditure at the district level after

the phase-wise implementation of RSBY in 2008. I do not identify which households directly

participated in the programme in my estimation. I use all the households in a treated

district and estimate the effect of access to the programme. This is the intent-to-treat

(ITT) effect of RSBY on school expenditure. Although IHDS data identifies the households

that participate in RSBY, I chose to estimate the ITT impact instead of the treatment-on-

the-treated (TOT) impact for two reasons. First, ITT is a more policy-relevant impact at

the district level when the idea of a government scheme is to provide the option of having

it available. Second, TOT would bring forth more complicated econometric problems, for

instance, extra selection issues leading to an added level of endogeneity.19 Moreover, if the

households participating in RSBY are not properly identified, we worry about measurment

error in the participation variable. At the same time, with better outreach and awareness

campaigns, take-up of the scheme can improve. However, how beneficial it is, is a separate

19Not only does the district have to have the programme, but the households in the districts have to decideto participate in it.

11

question, some of which has been addressed in Azam [2018].

I use the following DID specification to compare the households in districts over the two

time periods, 2004-05 and 2011-12, before and after RSBY was rolled out:

yhdt = β0 + β1Tt + βDDRSBYdt + γXhdt + µd + εhdt (1.1)

where yhdt is the budget share of school expenditure in household h in district d at time t. Tt

takes the value 1 for 2011-12 and 0 for 2004-05. RSBYdt a treatment indicator which takes

the value 1 if district d is exposed to RSBY in time t and and 0 otherwise.20 Xhdt is the

set of household level controls and µd depicts district fixed effects. The disturbance term

εhdt summarizes the influence of all other unobserved variables that vary across households,

districts, and time. The baseline Eqn. 1.1 is estimated using an Instrumental Variable

approach (IV).21 The parameter of interest is βDD which provides the differential impact of

RSBY on household’s expenditure on school after its introduction. β1 identifies the effect of

any systematic changes that affected households in all districts between 2004-05 and 2011-12.

A primary concern with the identification strategy in a DID approach is that the districts

may be trending differently prior to RSBY. Using three rounds of the Household Consump-

tion Expenditure Survey of the NSS, I provide evidence in Figure 1.1 that there are no

pre-existing differential trends between the control and the treated districts over 2004-05 to

2006-07. To further alleviate such concerns, I estimate a triple differences model where I

refine the definition of my control and treatment groups. I include the indicator variable

LowInch for poorer households as described in 3.4.1. Households in the top 30 percent are

now the controls for such differential trends in the districts. The assumption here is that

the richer households are perhaps less affected by RSBY. This is reasonable since richer

households are less likely to be resource constrained and in a position to insure themselves

20Note that RSBYdt varies with both district and time and is equivalent to the usual treat × post thatone finds in difference-in-differences analyses.

21Gender of the first born child in the family is used as an instrument for household size.

12

against unexpected shocks or have access to private health insurance.22

As such, the triple differences (DDD) estimator is more convincing as it looks at changes

among poorer households in treated versus the control districts and nets out any differential

change in wealthy households across treated versus control districts. The main identification

assumption in such triple differences model is no longer that changes in treatment households

should be uncorrelated with district level trends, but that these changes should be uncorre-

lated with district level trends that affect the rich and the poor differently. The assumption

in this model is indeed weaker. This methodology helps take care of two potential confound-

ing elements that are of concern in a DID model. One, the changes in school expenditure of

the poorer households in the treated districts is not a result of changes in school expenditure

of such households across all districts, nor is it a result of changes in school expenditure of

all households in the treatment districts (possibly due to other unobservables that affects all

households).

The second specification I estimate is the following triple differences model:

yhdt = β0 + β1Tt + β2LowInch + β3RSBYdt + β4Tt ∗ LowInch + βDDDRSBYdt ∗ LowInch

+ µd ∗ LowInch + γXhdt + µd + εhdt (1.2)

where RSBYdt is the treatment dummy that varies with district and time. The new coeffi-

cient of interest is βDDD which is the difference-in-difference-in-differences estimator. βDDD

captures the variation in school expenditure in poorer households (relative to the rich) in

treated districts (relative to control districts) after implementation of RSBY. Similar to the

DID model, the baseline triple differences in Eqn. 1.2 is also estimated using an IV approach.

Other set of controls are same as the baseline model. District fixed effects, time fixed effects,

time by income fixed effects and district by income fixed effects are included. Standard errors

22It must be noted that the initial target population intended by the scheme was the bottom 30 percent ofincome distribution. However, the scheme was later extended to several unorganized workers over the years(Government of India [2013b]). At the outset, it is necessary to caution that the top 30 percent may notform a clean control. I test the robustness of my triple difference results by altering the income distributioncategories for my control and treatment groups. These are discussed in the later sections.

13

are clustered at district by household income level.23

1.4.3. School enrollment - estimation and identification

Ideally, investigating within-household expenditure patterns on boys versus girls would

help quantify the exact gender differences in parental investment. However, studies that

have attempted to examine gender bias in schooling through household expenditure data

have met with little success. Expenditure on individual members of a household is typically

not observed in survey data which makes it impossible to directly observe gender biases in

allocation of expenditure. Most papers therefore, resort to indirectly detecting differential

treatment within households by examining changes in household expenditure with changes in

gender composition. Reliability of this methodology, however, has been called into question

because it generally fails to detect a gender bias (Deaton [1997]). Even in countries with

known gender bias, researchers thus far find mixed evidence of significant effects of the child’s

gender on the composition of household spending (Bhalotra and Attfield [1998]). Similar lack

of convincing expenditure data at the child level makes it impossible for me to quantify the

treatment impact on gender differences in parental investments in educational expenditure.

Instead, I use data at individual level on school enrollments to get at the treatment effect

on gender differences in boys’ and girls’ enrollments within households.

I estimate the following linear probability model (LPM) to estimate the treatment effect

yihdt = α0 + α1Tt + α2RSBYdt + α3RSBYdt ∗ Boyi + γXihdt + µd + εihdt (1.3)

where yihdt is an indicator variable which takes value 1 if the child i in household h in district

d is enrolled in school in time period t. Boyi takes value 1 if the child is a boy and 0 if a girl.

District and time fixed effects are included in the model and standard errors are clustered at

district level. α1 identifies the effect of any systematic changes that affect the child between

23For comparison purposes, I also estimate both the DID and DDD models for the numerator and denomi-nator of the budget share separately, that is, logarithm of school expenditure in levels and logarithm of totalconsumption expenditure in levels for the household. This is discussed in the robustness section 1.6.1.1.

14

the two time periods. α2 depicts school enrollment of a girl as a result of the treatment.

α2 + α3 identifies the school enrollment of a boy post the treatment. The coefficient of

interest is α3 which gives the change in the gender gap in school enrollment due to RSBY. I

also control for the gender dummy of the child, the coefficient of which identifies the school

enrollment of boys versus girls absent the treatment. All other relevant controls are included

as described in the section 1.4.1.

Similar to the school expenditure triple differences analysis, I also estimate an equiva-

lent model for school enrollment. Incorporating the new treatment and control groups, the

specification looks as follows:

yihdt = α0 + α1Tt + α2LowInch + α3RSBYdt + α4Tt ∗ LowInch + α5RSBYdt ∗ LowInch

+ α6RSBYdt ∗ LowInch ∗Boyi + µd ∗ LowInch + γXihdt + µd + εihdt (1.4)

where α5 depicts the effect of RSBY on enrollment of girls and α5 + α6 depicts the effect of

RSBY on enrollment of boys. Change in the gender gap in school enrollments as a result

of RSBY for poorer households in the treated districts is thus given by α6. It captures

the variation in boys’ and girls’ school enrollments within such households in the treatment

districts, nets out the change in the average enrollments in the control districts and then

nets out the change in the average enrollments in richer households in the treatment district.

As before, the model includes all controls, all relevant double interaction terms as well as

district and time fixed effects.

1.5. Results

1.5.1. School expenditure as a budget share

I present the baseline school expenditure results in Table 1.3. Panel A presents the results

for the DID specification 1.1. Column (1) shows that RSBY increases the budget share on

school expenditure by 0.5 percentage points and the effect is statistically significant at p¡0.01

15

significance level. Access to health insurance has positive spillover effect on school expendi-

ture decisions of households. Panel B presents the results for the triple differences estimation

of Eqn. 1.2. From column (4), notice that the triple differences analysis gives a treatment

effect of the order of 0.7 percentage points on the budget share of school expenditure for the

poor households relative to the rich in treatment district relative to control.24

Summary statistics in table 1.1. shows that the average share of school expenditure out

of total expenditure for such households in 2004-05 is about 2.5 percent. Both the DID

and DDD effects are therefore economically significant and imply that the budget share of

school expenditure increases by 20 to 28 percent after RSBY. To the extent that access

to public health insurance helps reduce household’s financial burden, RSBY benefits child

human capital formation through an increase in expenditure on school. As such, RSBY

perhaps helps eliminate costly smoothing mechanisms that households may resort to, in

absence of such an insurance coverage, like cutting down on school expenditure or delaying

their children’s enrollments.25

Note that, several diagnostic tests have been performed to assess the efficiency and re-

liability of the instrument. The endogeneity test reports test statistics that are robust to

various violations of conditional homoskedasticity. I reject exogeneity of household size.26

As far as underidentification is concerned, I report chi-squared p-values for the test where

rejection of the null implies full rank and identification [Baum et al., 2007b]. This test tells

us whether the excluded instrument is correlated with the endogenous regressor. The p-value

based on Kleibergen-Paap rk LM statistic allows me to clearly reject the null that the instru-

ment is uncorrelated with the endogenous regressor and that the model is underidentified.

From the weak identification test, rejection of the null represents absence of weak-instrument

24Columns (2), (3), (5) and (6) present the impact of RSBY on the logarithm of school expenditure inlevels and logarithm of total consumption expenditure in levels for DID and DDD models. These results arediscussed in detail in section 1.6.1.1.

25Selling assets, exhausting savings, non-institutional borrowings and reducing consumption below criticallevels are other examples of such costly measures (Morduch [1999],Sauerborn et al. [1996], Edmonds [2006]).

26Under conditional homoskedasticity, this endogeneity test is numerically equal to a Hausman test statis-tic.

16

problem. Since the specification has clustered standard errors at district level, the reported

test statistic is based on the Kleibergen–Paap rk statistic which indicates absence of weak

instrument problem, given that it is above 10 in the baseline specification of DID (column

(1)).27

1.5.2. School enrollment

Given that RSBY has an impact on budget share of school expenditure at the household

level, it is noteworthy to examine its impact on gender gap in school enrollments within

households. I present the results for the baseline school enrollment analysis in Table 1.4.

Panel A provides the DID results estimated using a linear probability model for specification

1.3. Column (1) presents the impact on enrollments without a gender differential whereas

column (2) presents the impact when I introduce a gender differential. In this case, notice

that absent the health coverage, a gender gap in school enrollment exists. More boys are

enrolled in school. In fact, enrollment of boys is about 6 percentage points higher than that

of girls. Average enrollment is 78.4 percent for boys and 72.4 percent for girls prior to the

treatment. Difference in parental expected future returns from their children’s schooling or

parental preferences could be possible explanations for this, as found in extant literature.

If parents expect higher returns from boys than girls, it limits the amount of equality a

household can afford. Column (2) shows that I find the treatment to have a larger impact

on girls. The probability of enrollment is 2.7 percentage points higher for girls after imple-

mentation of RSBY as compared to 0.8 percentage points higher for boys. The reduction in

the gender gap as a result of the treatment is by 1.9 percentage points and is statistically

significant at p¡0.01 significance level. The triple differences results for specification 1.4 are

presented in panel B. Column (4) shows a reduction (albeit smaller in comparison to DID)

in the gender gap in enrollment by 0.9 percentage points and is statistically significant at

p¡0.05 significance level. This suggests that benefits of the health insurance scheme accrues

27The instrument becomes slightly weaker in the baseline of triple differences model owing to perhapsmore number of controls and lower correlation.

17

more to girls insofar as school enrollment is concerned.

Gender specific roles in domestic chores and differential time opportunity cost of boys’

and girls’ schooling explains these results to some extent. As suggested, differential pat-

terns of preferences within the household are exacerbated with changes in household income

(Alderman and Gertler [1997]). Given that girls spend less time in school and more hours

working to substitute for mothers’ domestic duties, the greater impact on girls could per-

haps be a result of RSBY reducing the degree of impact of a shock to mother’s health on

daughters.28 One could perhaps also say that larger treatment effect on enrollment of girls is

because the demand for girls’ human capital is more income and price elastic than demand

for boys’. Moreover, although basic education in India is tuition-free, school attendance still

entails cost of reaching school, learning materials, uniforms that are large enough out-of-

pocket expenditures to keeps more girls out of school. Access to a cashless health insurance

system perhaps eases some resource constraints in the households leading to a reduction in

the gender gap in enrollments post the treatment.

1.6. Robustness Checks

There may be other potential concerns related with my baseline estimations. This section

discusses the additional analyses I conduct to explore the robustness of my results to different

modeling choices for both school expenditure as well as school enrollments. I start with a

discussion of school expenditure models and then proceed to school enrollments.

1.6.1. School expenditure - other estimation issues

Taking the budget share of household school expenditure as my outcome variable would

ideally require me to estimate a fractional response model.29 However, given that I am

controlling for a large number of districts, a fractional response model with fixed effects

28With women receiving less healthcare, a shock to the mother’s health would have a larger impact on thegirls required to take up on mother’s chores (Alam [2015], Hazarika and Sarangi [2008], Katz [1995], Skoufias[1993])

29The budget share is a fraction and is bounded between 0 and 1.

18

becomes infeasible. I therefore, compare the baseline IV results with those from two main

alternative estimation approaches.

1.6.1.1. School expenditure in levels

My treatment effect could possibly be understated when I consider budget share of house-

hold school expenditure as the dependent variable. A direct positive income effect of RSBY

could perhaps be translated to an increase in total household consumption expenditure itself

given that health insurance relieves household’s resource constraints. If total consumption

expenditure of households rises, this would mean a lower effect on the budget share of school

expenditure. Therefore, I first estimate a model where the outcome variable is the loga-

rithm of household’s school expenditure per month in levels excluding total consumption

expenditure from the specification. I also estimate the treatment effect on logarithm of total

consumption expenditure in levels. This helps me tease out the treatment effect on both

household school expenditure and total consumption expenditure separately.

Note that in IHDS survey, some households report zero expenditure on goods. My depen-

dent variable is in logarithms which implies that value of the corresponding outcome variable

will be undefined if I include such households. One way to avoid this problem is simply to

drop these households and run regressions based on the trimmed sample. However, this may

result in sample selection bias. Rather, a more sophisticated way to circumvent this problem

and include these households is to apply the inverse hyperbolic sine transformation of con-

sumption expenditures (Burbidge et al. [1988]). The inverse hyperbolic sine transformation

requires transformation of the variable in question, say, z as log(z2 +√z2 + 1) which unlike

log z, is defined even for z = 0.30 As such, in this paper I use the inverse hyperbolic sine

transformation to deal with households reporting zero consumption expenditure.

The results for these is presented in Table 1.3. Columns (2) and (5) provide the DID

and DDD effects on log of school expenditure in levels. I find that RSBY increases school

30According to Burbidge et al. [1988], except for very small values of z, the transformation is approximatelyequal to log(2zi) or log(2) + log(zi), and so it can be interpreted in exactly the same way as a standardlogarithmic dependent variable.

19

expenditure by 30.2 to 42.2 percent approximately. The effect is found to be greater in the

DDD model for the poorer households in treated districts. Column (3) provides the DID

effect on log of total consumption expenditure. An increase by 7.7 percent is seen from

column (3). I find a positive impact on log of total consumption expenditure in the DDD

model as well but the effect is not statistically significant (column (6)).

Second, I estimate the levels model while controlling for total consumption expenditure

as a regressor. This takes care of any income effect of the scheme as it holds the budget

constraint constant for the household. However, there may be a possible endogeneity concern

for total consumption expenditure here. I instrument total monthly household consumption

expenditure by assets possessed by the household at the time of the survey to circumvent

this problem. This serves as valid instrument because assets held at the time of the survey

do not directly impact the monthly expenditure on school but are a good predictor of total

household income or consumption. Monthly expenditures on commodities are usually out of

current earned income rather than out of assets or wealth.31

Panel A and B, Table A.1. present these results. Columns (1) and (3) repeat my

baseline results as in table 1.3. Columns (2) and (4) present the results where I include total

consumption expenditure and instrument it with total household assets. In this specification,

I have two endogenous regressors and two instruments. From column (2), the treatment effect

shows an 8 percent increase in the level of school expenditure and is statistically significant

while holding the budget constraint of the household constant. The triple differences model

also shows a higher and statistically significant impact on the level of school expenditure

of almost 18.7 percent for the poor households in the treated districts (see column (4)).

Here, the total treatment impact from the triple differences model is 8.2 percent which

is approximately equivalent to the difference-in-differences result. As before, diagnostic

tests have been performed to assess the efficiency and reliability of the instruments. The

instruments fair broadly well on these specification tests.

31Although, land could affect school expenditure to some extent since land requires work and missing workwould factor into opportunity cost of expenditure related to school.

20

1.6.1.2. School expenditure - fractional logit estimation

Here, I return to budget share as my outcome but estimate a fractional response model

with correlated random effects to account for district level characteristics since a fixed effects

fractional response model is not feasible. I estimate specification 1.1 via a fractional logit

model with correlated random effects (CRE). The advantage of using CRE fractional logit

is that it places some structure on the nature of correlation between the unobserved effects

and the covariates (Lake and Millimet [2016]). Formally, the structural model in the CRE

fractional logit is given by

E(yhdt|Xhdt, µd) = Φ(Xhdtβ + µd) (1.5)

whereXhdt includes the full set of covariates in specifications 1.1 and 1.2 and Φ is the standard

normal cumulative density function. The Mundlak [1978] version of the CRE probit model

further assumes

µd|Xhdt ∼ N(δ0 + Xhδ1, σ2µ) (1.6)

where Xh is the average of Xhdt for each district and σ2µ is the variance of µd. Under 1.5 and

1.6, we get

E(yhdt|Xhdt, µd) = Φ[(δ0 +Xhdtβ + Xhδ1).(1 + σ2µ)−1/2]

= Φ[δµ0 +Xhdtβµ + Xhδ

µ1 ] (1.7)

To capture the district fixed effects in 1.7, means of all controls at district level across time

are included as additional controls in the DID model. Standard errors are clustered at the

district level and time fixed effects are included. I include the means of all controls at district

by household income level as the correlated random effects for my triple differences model.

Here, the standard errors are clustered at district by household income level.

Following Wooldridge et al. [2011], Wooldridge [2015], Baum et al. [2013], Papke and

Wooldridge [2008], I use a two step control function approach to deal with the continuous

21

endogenous regressor, household size included in my model. In the control function approach,

I first estimate household size as a function of my instrument, which is, gender of the first

child in the household. This gives me residuals similar to the first stage of a 2SLS approach.

I then use the residuals from this model as an additional regressor in the main model which

is estimated as a CRE-fractional logit model.

I present the results in Table A.2. Panel A provides the DID results and panel B, the

triple differences results. Columns (1) and (3) repeat my baseline results as in table 1.3.

Columns (2) and (4) presents the results using IV results for the CRE-fractional logit model.

Since column (3) is the CRE fractional logit specification, I cannot interpret the coefficients

and thus calculate the marginal effect of the treatment. I find a small positive marginal

effect of RSBY but it is not statistically different from zero.

The CRE fractional logit specification of triple differences model in column (4) shows

a small but statistically significant difference in the marginal effects of RSBY for the poor

and the rich households in the treated districts after RSBY. There is no effect on the rich

households. The magnitude of this difference is of 0.2 percentage point which implies a

difference of 8 percent in the budget shares for the poor and the rich in treated districts.

1.6.1.3. School expenditure - panel analysis

As an additional robustness check of my baseline school expenditure model, I estimate

the treatment effect by considering the data as a panel since IHDS II are re-interviews

of most of IHDS-I households. The results are overall robust to this change. Table A.3.

presents the results. Panel A provides the difference-in-differences results and panel B, the

triple differences. Columns (1) and (3) repeat the baseline DID and DDD results as in Table

1.3. Column (2) shows the effect of RSBY using IV approach with household fixed effects

for the panel data. The standard errors are clustered at household level. RSBY increases

budget share of school expenditure by 0.3 percentage points as suggested by the DID model.

This implies a 12 percent increase in the budget share of school expenditure given that the

mean budget share was 2.5 percent from Table 1.1. From Column (4), the triple differences

22

estimator shows that RSBY leads to an increase of 0.4 percentage points in the budget share

of school expenditure for the poorer households in the treated districts. This is equivalent

to a 16 percent increase in the budget share of school spending for the poorer households.

1.6.2. School enrollment - other estimation issues

1.6.2.1. School enrollment - probit with correlated random effects

A first potential concern with my school enrollment analysis is that the dependent variable

is a binary outcome and should ideally be estimated as a non-linear model such as a probit

or a logit. Linear probability models are likely to give a biased and inconsistent estimate

(Horrace and Oaxaca [2006]). Probit or logit models however use a proper functional form

where the probability depends on x through the index xβ

Pr(yi = 1|xi) = F(xiβ)

where the functional form F(· ) maps into a response probability F : R −→[0, 1] for which we

consider CDFs as they map numbers from the entire real number line on to the unit interval.

Given that the difference between a probit or a logit is small in practice, I use a probit model.

However, as before, I have a fixed effects baseline model where I control for a large number

of districts making a simple probit estimation infeasible. Thus, I compare estimates from

the baseline fixed effects linear probability model with two alternative estimation approaches

to analyze the robustness of my results. First, a linear probability model with correlated

random effects. Second, an IV probit model with correlated random effects for which, a

variant of 1.7 would look like

Pr(yihdt = 1|Xihdt, µd) = Φ[(δ0 +Xihdtβ+Xiδ1).(1+σ2µ)−1/2] = Φ[δµ0 +Xihdtβ

µ+Xiδµ1 ] (1.8)

Again, to capture the district fixed effects, means of all controls at district level across time

are included as additional controls in the estimation. All standard errors are clustered at

23

the district level and time fixed effects are included.

The results are presented in Table A.4. Panel A presents the difference-in-differences

results and panel B, the triple differences results. One can compare the baseline results

presented in columns (1) and (4) with those from a linear probability model with correlated

random effects presented in columns (2) and (5) as well as CRE-IV probit model presented

in columns (3) and (6). Notice that from both the linear probability models with fixed

effects and correlated random effects, the DID approach show that, absent the treatment,

the probability of enrollment of a boy is approximately 6 percentage points higher than a girl.

After the treatment, I find a larger effect on probability of girls’ enrollment. A reduction in

the gender gap in enrollment of 1.8 percentage points is seen from column (2). Since column

(3) presents the probit model results, I cannot simply interpret the coefficients. Looking at

the marginal effects of RSBY, I find consistent results. Notice that marginal effect of the

treatment on the probability that enrollment of a girl is statistically significantly higher than

that of a boy. The DID estimation shows that reduction in gender gap as a result of access

to health insurance is of 3.2 percentage points and statistically significant.

The triple differences results confirm a similar story. Results from both a LPM with

fixed effects and LPM with CRE are quantitatively similar. The impact of RSBY on the

probability of enrollment of girls is higher. The reduction in enrollment gender gap of 0.9

percentage points is seen in both specifications. I also find a reduction in the gender gap in

enrollment of 1.4 percentage points from the CRE-IV probit as seen by the marginal effects

of RSBY on a boy and a girl in column (6), however, the effects are not precisely estimated.

1.6.2.2. School enrollment - instrumental variable approach

The second concern is related to the instrument used for household size in my school

enrollment analysis. I compare how my results change from the baseline LPM model where

I instrument household size with gender of the first born in the family, with two alternative

specifications. First, I include household size in the specification but do not use an instrument

for it. Second, I exclude household size from the specification.

24

In Table A.5., I present the baseline LPM results in columns (1) and (4) and compare

with LPM specifications where household size is included but not instrumented for (columns

(2) and (5)) as well specifications where I omit household size as a regressor (columns (3) and

(6)). Panel A presents the difference-in-differences results and panel B presents the triple

differences results. Strikingly, the DID results from all three columns (1), (2) and (3) are

qualitatively and quantitatively similar as well as statistically significant. I find a reduction

in the gender gap in enrollment as a result of RSBY, of 1.8 to 1.9 percentage points, in all

three estimation choices. The triple differences results also confirm a statistically significant

reduction in gender gap in enrollment of 0.8 to 0.9 percentage points as a result of RSBY from

all three specifications (columns (4), (5) and (6)). These alternative specification choices thus

support validity of my baseline results.

1.7. Sensitivity Analysis

This section first discusses the sensitivity of my baseline results to variations in income

distribution introduced in my models in 1.7.1. Second, I explore the heterogeneous effects of

the treatment by intensity in section 1.7.2. Third, I exploit the variation in take-up of RSBY

by district to estimate the heterogeneous treatment effect in 1.7.3. Lastly, I explore whether

my baseline effects are different across sub samples varied by age groups for enrollment; by

areas and by castes for both expenditure and enrollment in 1.7.4.

1.7.1. Variation in income distribution

I introduce variation in the income distribution categories used to define treatment and

control groups in the triple differences model. To maintain symmetry with my baseline triple

differences, I first restrict the sample to households in the top 30 percent and bottom 30

percent of income distribution. For this, I redefine LowInch in Eqn 1.2 such that the top

30 percent households form controls for my new treatment group, which is, the bottom 30

percent. Observations in the middle 40 percent are dropped. Second, I drop observations

from the bottom 30 percent and re-define LowInch such that the top 30 percent are now

25

controls for households in the middle 40 percent of the sample. Since RSBY was expanded

to cover other unorganized and domestic workers, the expectation for this second variation

in treatment group is that the effect is perhaps positive, but smaller.

I present these results in Table A.6. Panel I provides the results for school expenditure

analysis. Panel A repeats the baseline DID results. Panel B presents the results for the two

variations in my triple differences model. Column (2) shows that RSBY has a treatment

effect of 0.5 percentage points increase in the budget share of school expenditure for the

households that belong to the bottom 30 percent in the treated districts. This was the initial

target group of the scheme. Average budget share of school expenditure for this target group

in 2004-05 is approximately 1.8 percent. A treatment effect showing 0.5 percentage point

increase thus implies approximately 27 percent rise in their budget share of school spending.

Contrary to the expectation, the triple differences estimator in column (3) shows a zero effect

on the households in the middle 40 percent of the sample. This could perhaps be a result of

difference in the take-up of the program as the sample for this specification changes.

An equivalent school enrollment analysis is presented in panel II. RSBY leads to small

reduction in the gender gap in enrollment of 0.2 percentage points for the bottom 30 percent

in the treated districts. However, I do not find any reduction in the gender gap for the

middle 40 percent households.

1.7.2. Variation in treatment intensity

Here, I exploit the variation in treatment intensity to estimate the heterogeneous effect

of RSBY on household school expenditure as well as school enrollment. I define a three

dummy variables based on the duration a household has been exposed to RSBY. Intensity1d

takes value 1 if RSBY has been in effect in the district for one year by the second wave

of the IHDS survey; Intensity2d takes value 1 if RSBY has been in effect for two years;

and Intensity3d takes value 1 if it has been effect for three years.32 One may expect the

effect of RSBY to vary with time since implementation. To explore this, I use the following

32By the second wave of the survey, the scheme was active for three years.

26

difference-in-difference and triple differences models for school expenditure:

yhdt = β0+β1Tt+β2RSBYdt+3∑j=1

βj3Intensityjd+

3∑j=1

βj4RSBYdt∗Intensityjd+γXhdt+µd+εhdt

(1.9)

yhdt = β0 + β1Tt + β2LowInch +3∑j=1

βj3Intensityjd + β4RSBYdt + β5Tt ∗ LowInch

+ β6RSBYdt ∗ LowInch +3∑j=1

βj7RSBYdt ∗ LowInch ∗ Intensityjd

+ µd ∗ LowInch + γXhdt + µd + εhdt (1.10)

The parameter of interest varies with time t and district d, where the total impact of RSBY

is given by β2 +∑3

j=1 βj4Intensity

jd depending upon the duration of exposure to RSBY in

Eqn. 1.9. The heterogeneous effect of RSBY is captured by βj4. Similarly the parameter

that captures the heterogeneous effect of RSBY in the triple differences Eqn. 1.10 is βj7 and

the total effect of RSBY for the poor households is given by β4 + β6 +∑3

j=1 βj7Intensity

jd.

Similarly, I estimate the following DID and DDD models for school enrollment:

yihdt = α0 + α1Tt + α2Boyihdt + α3RSBYdt +3∑j=1

αj4Intensityjd + α5RSBYdt ∗Boyihdt

+3∑j=1

αj6RSBYdt ∗ Intensityjd +

3∑j=1

αj7RSBYdt ∗ Intensityjd ∗Boyihdt + γXhdt + µd + εihdt

(1.11)

27

yihdt = α0+α1Tt+α2Boyihdt+α3LowInch+α4RSBYdt+3∑j=1

αj5Intensityjd+α6RSBYdt∗LowInch

+ α7RSBYdt ∗ LowInch ∗Boyihdt +3∑j=1

αj8RSBYdt ∗ LowInch ∗ Intensityjd

+3∑j=1

αj9RSBYdt ∗ LowInch ∗ Intensityjd ∗Boyihdt + α10Tt ∗ LowInch

+ µd ∗ LowInch + γXihdt + µd + εihdt (1.12)

In Eqn. 1.11, α3 +∑3

j=1 αj6Intensity

jd provides the heterogeneous effect of the treatment

on enrollment of girls by intensity of treatment duration whereas (α3 + α5) +∑3

j=1(αj6 +

αj7)Intensityjd captures the heterogeneous effect on enrollment of boys by intensity. The

change in the gender gap due to RSBY is captured by α5 +∑3

j=1 αj7Intensity

jd. Similarly,

the change in the gender gap in enrollment due to RSBY for the poorer households in Eqn.

1.12 is given by α7 +∑3

j=1 αj9Intensity

jd.

Results for the heterogeneous effects of RSBY by intensity of treatment are provided in

Tables A.7. and A.8. Panel A and B provide the DID and DDD results respectively. Panel

I and II provide the results for school expenditure and school enrollment respectively.

Table A.7. shows a treatment impact of 0.1 percentage point increase in budget share of

school expenditure for households in districts that are exposed to the scheme for one year;

0.2 percentage point increase for households in districts exposed to RSBY for two years and

0.3 percentage point increase for households in distrcits exposed to RSBY for three years

respectively. This points to a weighted average equal to my baseline result found in column

(1) Table 1.3. The DDD results do not show a statistically significant effect in this model,

but the effects point to a similar story.

Column (1) in Table A.8. shows that the reduction in gender gap for the individuals in

districts that have been exposed to RSBY for one year is by 1.6 percentage points but is

not statistically significant. This effect is of the order of 6.2 percentage and 3.2 percentage

points in districts that have had RSBY for two and three years respectively by 2011-12

and are both statistically significant. The DDD analysis confirms this pattern. Panel B of

28

Table A.8. shows a reduction in the gender gap in enrollments for boys and girls in poor

households in districts exposed to RSBY for one year is by 2.6 percentage points. This result

is a statistically significant and the gender gap consequently increases for such households

that have had longer access to the scheme.

1.7.3. Variation in programme take-up by district

Third, I exploit district variation in the take-up of the programme to estimate the effect

of RSBY on household school expenditure. Administrative reports suggests that health

insurance take-up reached approximately 50 percent by 2013. Considering this, one would

expect the treatment effect to be double if full take-up could be achieved. To explore this, I

use the following difference-in-difference model

(1.13)yhdt = β0 + β1Tt + β2RSBYdt + β3DistrictTakeupd + β4RSBYdt ∗DistrictTakeupd+ γXhdt + µd + εhdt

The coefficient of interest is β2 + β4. Data for district-wise enrollment into the scheme is

taken from the official RSBY website. RSBY enrollment data is available for districts from

15 states out of 29 is either because some districts have not been exposed to the scheme or

simply because of unavailability of data.

Table A.9. presents the results. Panel A, column (1) shows the simple difference-in-

differences treatment results without differential take-up for the districts with available data.

I find an RSBY leads to 0.7 percentage point increase in the household’s budget share of

school expenditure for the districts enrolled into the scheme. This is equivalent to a 28

percent increase in their budget share of school spending given that the school expenditure

comprised approximately 2.6 percent of the total household expenditure for such households

before RSBY. Column (2) in panel A provides the differential treatment of RSBY effect by

take-up. If the treatment effect is extrapolated to a 100 percent take-up, I find household’s

budget share of school expenditure increases by 0.9 percentage points which is equivalent

to almost 35 percent increase in the budget share of school spending for such the treated

households. This is an economically large effect and is of interest since my treatment is

29

the availability of the scheme and not household participation. However, a word of caution

is warranted here that this is only suggestive evidence of the treatment effect since it is

based on incomplete data and enrollment into the scheme is endogenous. In addition, my

instrument for households size does not pass the specification tests in this model. The triple

difference analysis does not show any statistically significant results in this case.

1.7.4. Sub sample analysis

I re-estimate my baseline school expenditure and school enrollment models in Eqns. 1.1,

1.2, 1.3 and 1.4 to find the treatment impact by changing the samples.

First, I estimate three sub-sample regressions for school enrollment analysis by varying

age groups. Table A.10., panels A and B present the difference-in-differences and triple

differences results. I restrict the sample to children in the age group 5-9 years, 10-14 years

and 15-17 years. I find consistent results with the baseline for the sub-sample of 5-9 years

and 10-14 years. For both the age groups, the gender gap in enrollment reduces by 1.9

percentage points as a result of access to health insurance. I do not find any impact of

RSBY for the sub sample 15-17 years. This could possibly be explained by lower marginal

benefit of keeping older children in school than that of younger children.

Second, I conduct sub sample regressions of both school expenditure and enrollment

models for rural and urban areas separately. Table A.11. presents the results. From Panel

I, I find RSBY to have a larger treatment effect on household’s budget share of school

expenditure in urban areas than rural from the DID model. The treatment effect is found

to be 0.7 percentage points in the urban areas compared to 0.4 percentage points rise in

the rural areas. Both effects are statistically significant. However, from the triple differences

model, I do not find a statistically significant impact of RSBY in the urban areas. In contrast,

RSBY has a treatment effect of 0.8 percentage points increase in the budget share of school

expenditure for the poor households in the treated districts of rural areas. For the school

enrollment model (see panel II), RSBY reduces the gender gap in enrollment by 1.5 to 2.4

percentage points in the rural areas, perhaps owing to the low levels of girls’ enrollments to

30

begin with. No such impact is found in the urban areas from either the DID or the DDD

models. This speaks to the effectiveness of having access to such a health insurance in rural

parts.

Table A.12. presents the sub sample results for both expenditure and enrollment models

estimated by caste categories. Panel I suggests that the treatment has a positive effect on

school expenditure for the general category and other backward castes. This can be seen from

the DID models. The DDD model also suggests a positive impact on the poor households

in the treated districts belonging to other caste categories, apart from those in belonging to

general castes. Panel II suggests that RSBY reduces the gender gap in enrollment to a large

extent for the other backward castes (OBC) category. The magnitude of reduction in the

gender gap for OBCs is economically large. This is confirmed by both the DID and DDD

models. The triple differences model also suggests reduction in the gender gap of enrollments

for the scheduled tribes and other castes. However, these results are not confirmed in the

DID models.

1.8. Conclusion

Gender gap in schooling remains a concern for most policy makers and educationists. The

UN Millennium Development Goals first enunciated in 2000, emphasized reducing gender

gap in school that disadvantage girls (Grant and Behrman [2010], Nations [2015]). Such

differences in education could potentially lead to further gender inequalities in income, work

and social status. Given that women are significant contributors in the labour force in

most developing countries, gender gaps can act as constraints on economic growth. In fact,

investment in female education is widely regarded as essential by policymakers owing to the

positive externalities associated with it, such as, better child health, household welfare, and

lower population growth (Song et al. [2006], Alderman and King [1998]). Appropriate policy

responses to reduce the gender gap thus require an understanding of its determinants. Little

evidence exists on the impact of health insurance on school expenditure, in general, and on

this gender gap, in particular, in India and my paper attempts to analyze this unexplored

31

determinant.

Understanding the impact of shocks on education decisions of vulnerable households

and the channels of this impact could help in designing safety nets and other policies to

insulate investments in education from health shocks (Glick et al. [2016]). An undesired

consequence of negative health shocks may be taking children out of school either to protect

their health or to send them to work for additional income. These strategies can have

undesired consequences in the long term for human capital accumulation of future generations

and labor market opportunities.

From a policy perspective, it is not only interesting to see if a health insurance scheme has

an unintended role to play on school expenditure decisions of households but also on parental

response within household in terms of enrollments of boys versus girls. At the outset, it is not

entirely obvious as to whether health insurance would benefit children’s education or have

a detrimental impact. Healthier children could either mean greater future economic returns

from schooling or greater value as child labour. Such responses need to be considered when

designing policies to remedy any disadvantages among children, since parents can eliminate

these effects by aiming at equitable child human capital formation within the family.

Although RSBY was implemented with the intention of reducing financial burden for

the poor, I find that it has unintended positive consequences for children. First, I find that

household expenditure on school increases as a result of access to RSBY. Second, I find that

access to a health insurance systems provides additional resources to parents, in a society

that depends largely on sons for support during old age, to not exclude their daughters from

education opportunities. Robustness checks and sensitivity analyses support the validity

of my results. This is evidence that health insurance protects the poor and also helps such

households keep their children in school in the face of health shocks. This unintended benefit

could help push households out of the vicious cycle of poor health in childhood leading to

lesser education and hence lower incomes and health in adulthood. In addition, there is also

a long-term positive effect of health insurance coverage on economic development, this effect

being reinforced through the positive impact on school enrollments of girls.

32

Tab

le1.

1.Sum

mar

yst

atis

tic

-H

ouse

hol

dle

vel

Vari

ab

les

Mean

SD

Mean

SD

Mean

SD

Mean

SD

Sch

oo

l ex

pen

dit

ure

59.5

5129.6

7137.0

8272.5

7104.1

3218.8

4162.6

0288.1

9

To

tal co

nsu

mp

tio

n e

xp

end

iture

3527.2

13315.3

07719.3

37977.0

54022.4

83917.9

77631.8

97318.7

1

Age

45.5

113.1

346.2

713.2

046.8

213.3

347.0

813.6

0

Ho

use

ho

ld s

ize

6.3

82.9

55.5

62.1

46.7

13.0

95.8

52.3

1

Urb

an (

1 =

yes

)0.3

00.4

60.3

10.4

60.2

70.4

40.2

90.4

5

Oth

er B

ackw

ard

Cas

tes

(1 =

yes

)0.4

20.4

90.2

10.4

10.4

00.4

90.2

10.4

1

Sch

edule

d C

aste

(1 =

yes

)0.2

10.4

10.4

30.4

90.2

20.4

20.4

10.4

9

Sch

edule

d T

rib

e (1

= y

es)

0.1

00.3

00.1

90.4

00.0

80.2

70.2

40.4

2

Oth

er c

aste

s (1

= y

es)

0.2

40.4

30.1

40.3

40.2

40.4

30.0

90.2

9

Musl

im (

1 =

yes

)0.0

90.2

80.0

90.2

80.1

40.3

50.1

60.3

6

Ch

rist

ian

(1 =

yes

)0.0

20.1

20.0

30.1

70.0

30.1

60.0

20.1

4

Sik

h o

r B

ud

dh

ist

(1 =

yes

)0.0

30.1

60.0

20.1

40.0

40.1

90.0

30.1

8

Oth

er r

elig

ion

(1 =

yes

)0.0

10.0

90.0

00.0

60.0

10.1

10.0

10.0

8

HH

Hea

d -

lit

erat

e (1

= y

es)

0.6

40.4

80.7

00.4

60.6

30.4

80.6

60.4

7

HH

Hea

d -

kn

ow

s en

glis

h (

1 =

yes

)0.1

50.3

50.1

60.3

60.1

80.3

80.1

60.3

7

HH

Hea

d -

ever

att

end

ed s

cho

ol (1

= y

es)

0.6

50.4

80.6

50.4

80.6

30.4

80.6

10.4

9

Mal

e w

ith

pri

mar

y ed

uca

tio

n (

1 =

yes

)0.1

50.3

50.1

50.3

50.1

40.3

50.1

50.3

6

Mal

e w

ith

sec

on

dar

y ed

uca

tio

n (

1 =

yes

)0.2

80.4

50.3

80.4

90.2

60.4

40.3

70.4

8

Mal

e w

ith

sen

ior

sec.

ed

uca

tio

n (

1 =

yes

)0.0

60.2

40.1

40.3

50.0

60.2

40.1

20.3

3

Mal

e w

ith

co

llege

ed

uca

tio

n (

1 =

yes

)0.0

40.2

00.1

30.3

30.0

50.2

20.1

10.3

1

Fem

ale

wit

h p

rim

ary

educa

tio

n (

1 =

yes

)0.1

70.3

70.1

60.3

60.1

60.3

70.1

50.3

6

Fem

ale

wit

h s

eco

nd

ary

educa

tio

n (

1 =

yes

)0.3

70.4

80.3

50.4

80.3

80.4

80.3

00.4

6

Fem

ale

wit

h s

enio

r se

c. e

duca

tio

n (

1 =

yes

)0.1

20.3

30.1

00.3

00.1

00.3

00.0

90.2

9

Fem

ale

wit

h c

olle

ge e

duca

tio

n (

1 =

yes

)0.0

90.2

90.0

70.2

60.1

00.3

00.0

80.2

6

Gen

der

of

the

hea

d (

1 =

mal

e)0.9

40.2

30.9

10.2

90.9

20.2

80.8

70.3

4

# o

f m

arri

ed m

ales

1.4

50.8

61.3

00.7

01.4

50.8

81.2

60.7

3

# o

f m

arri

ed f

emal

es1.4

80.8

61.3

40.7

11.5

30.9

01.3

80.7

3

Pro

po

rtio

n o

f ch

ildre

n0.3

90.1

50.3

70.1

40.3

90.1

50.3

80.1

6

HH

has

a b

ank a

cco

un

t (1

=ye

s)0.3

40.4

80.3

40.4

70.3

60.4

80.3

40.4

7

HH

has

a K

isan

cre

dit

car

d (

1=

yes)

0.0

40.2

00.0

60.2

40.0

50.2

10.0

50.2

3

HH

has

a c

red

it c

ard

(1=

yes)

0.0

10.1

10.0

20.1

50.0

10.1

20.0

30.1

6

No

tes:

Sam

ple

isre

stic

ted

toh

ouse

ho

lds

wh

ere

age

of

the

hea

do

fth

eh

ouse

ho

ldis

bet

wee

n18

to90

year

s.T

he

tab

lesh

ow

sth

esu

mm

ary

stat

isti

csin

the

con

tro

ld

istr

icts

and

trea

tmen

td

istr

icts

in2004-0

5an

d2011-1

2fo

rh

ouse

ho

ldle

vel

.D

um

my

var

iab

les

con

tain

ing

info

rmat

ion

abo

ut

educa

tio

nle

vel

s,

dem

ogr

aph

y,b

ank

info

rmat

ion

,ca

ste

and

relig

ion

of

the

ho

use

ho

ldar

ein

clud

ed.M

usl

imta

kes

val

ue

1if

the

ho

use

ho

ldis

Musl

im,0

oth

erw

ise.

Ch

rist

ian

=1

ifth

eh

ouse

ho

ldis

Ch

rist

ian

,0

oth

erw

ise.

Sik

h=

1if

the

ho

use

ho

ldis

Sik

h,

0o

ther

wis

e.O

ther

relig

ion

=1

ifth

eth

eh

ouse

ho

ldfa

llsun

der

any

of

the

oth

er

cate

gori

eslik

eJa

inis

m,

Bud

dh

ism

,Z

oro

astr

ian

ism

,an

do

ther

s,0

oth

erw

ise.

ST

=1

ifth

eh

ouse

ho

ldis

sch

edule

dtr

ibe,

0o

ther

wis

e.SC

=1

ifth

eh

ouse

ho

ld

is s

ched

ule

d c

aste

, 0 o

ther

wis

e.

OB

C =

1 if

the

ho

use

ho

ld b

elo

ngs

to

oth

er b

ackw

ard

cas

tes,

0 o

ther

wis

e. O

ther

rel

igio

n =

1 if

the

ho

use

ho

ld b

elo

ngs

to

oth

er

oth

er c

aste

s o

r ge

ner

al c

ateg

ory

, 0 o

ther

wis

e.

Tre

ate

d D

istr

icts

Co

ntr

ol

Dis

tric

ts

2004-0

52011

-12

2004-0

52011

-12

33

Tab

le1.

2.Sum

mar

yst

atis

tics

-In

div

idual

leve

l

Vari

ab

les

Mean

SD

Mean

S

DM

ean

SD

Mean

SD

En

rolle

d (

1 =

yes

)0.7

95

0.4

04

0.8

86

0.3

17

0.7

58

0.4

29

0.8

65

0.3

42

Gen

der

(1 =

bo

y)

0.5

22

0.5

00

0.5

35

0.4

99

0.5

25

0.4

99

0.5

25

0.4

99

Ho

use

ho

ld s

ize

6.9

19

3.2

13

7.1

53

3.2

19

7.1

00

3.1

49

7.3

77

3.3

47

Sch

ola

rsh

ip f

rom

sch

oo

l (1

= y

es)

0.0

74

0.2

61

0.3

65

0.4

81

0.0

89

0.2

85

0.3

50

0.4

77

Age

10.9

68

3.6

83

11.4

69

3.6

09

10.8

76

3.7

12

11.3

21

3.6

34

Ben

efit

s fr

om

sch

oo

l (1

= y

es)

0.4

87

0.5

00

0.5

47

0.4

98

0.3

44

0.4

75

0.4

34

0.4

96

Urb

an (

1 =

yes

)0.2

74

0.4

46

0.2

51

0.4

34

0.2

68

0.4

43

0.2

87

0.4

52

OB

C (

1 =

yes

)0.3

55

0.4

79

0.2

57

0.4

37

0.3

84

0.4

86

0.2

28

0.4

20

SC

(1 =

yes

)0.1

70

0.3

76

0.3

74

0.4

84

0.2

34

0.4

23

0.4

11

0.4

92

ST

(1 =

yes

)0.1

59

0.3

66

0.1

62

0.3

69

0.0

54

0.2

26

0.2

44

0.4

29

Oth

er c

aste

s (1

= y

es)

0.2

72

0.4

45

0.1

62

0.3

69

0.2

68

0.4

43

0.0

62

0.2

41

Musl

im (

1 =

yes

)0.1

16

0.3

20

0.1

02

0.3

03

0.1

76

0.3

80

0.1

81

0.3

85

Ch

rist

ian

(1 =

yes

)0.0

38

0.1

90

0.0

28

0.1

64

0.0

17

0.1

31

0.0

16

0.1

25

Sik

h &

Bud

dh

ist

(1 =

yes

)0.0

33

0.1

77

0.0

26

0.1

59

0.0

43

0.2

03

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er r

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ion

(1 =

yes

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reate

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icts

2004-0

42011

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5

34

Figure 1.1. Pre-trends at district level

35

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le1.

3.Im

pac

tof

RSB

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hou

sehol

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hool

exp

endit

ure

(1)

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oo

l ex

pd

.

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are

(2)

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g S

ch

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l ex

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.

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(3)

Lo

g T

ota

l co

nsu

mp

tio

n

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pd

. L

evels

(4)

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oo

l ex

pd

.

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dg

et

Sh

are

(5)

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g S

ch

oo

l ex

pd

.

Levels

(6)

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g T

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l co

nsu

mp

tio

n

ex

pd

. L

evels

RSB

Y*P

ost

0.0

05**

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0.0

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20

-0.0

15

(0.0

01)

(0.1

27)

(0.0

14)

(0.0

02)

(0.2

57)

(0.0

74)

Lo

w I

nco

me

(=1 f

or

bo

tto

m 7

0%

)-0

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(0.0

24)

(0.4

99)

(0.0

84)

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w I

nco

me

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98

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88)

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der

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tp

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k W

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do

gen

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er C

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ple

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wh

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age

of

the

hea

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bet

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ach

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l(1)

and

(4):

dep

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bud

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shar

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ld's

sch

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/to

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sum

pti

on

exp

end

iture

).C

ol.

(2)

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inver

se

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atio

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vel

s.C

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(3)

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(6):

dep

end

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leis

the

inver

seh

yper

bo

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ne

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of

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ure

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vel

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dd

itio

nal

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ls

incl

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1if

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dis

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tw

asex

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sed

toR

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ther

wis

e,d

um

my

for

Lo

wIn

com

e=

1if

HH

do

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ot

bel

on

gto

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30%

and

0o

ther

wis

e(f

or

DD

D),

HH

size

(in

stru

men

ted

by

gen

der

of

the

firs

t

child

),h

igh

est

educa

tio

nd

egre

eso

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ale

and

fem

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ber

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dic

ato

rsfo

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ligio

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fH

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ato

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of

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um

my

for

urb

anar

eas,

num

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of

mar

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men

inth

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um

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of

mar

ried

wo

men

inth

eH

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pro

po

rtio

no

fch

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n,

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dad

ult

s,in

dic

ato

rfo

rif

HO

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mar

ried

,d

um

my

for

ifth

eH

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asa

ban

kac

coun

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um

my

for

ifth

eH

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edit

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,d

istr

ict

fixed

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cts,

tim

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xed

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y in

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e fi

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(fo

r D

DD

), t

ime

by

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me

fixed

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r D

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). S

tan

dar

d e

rro

rs r

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tan

dar

d e

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36

Tab

le1.

4.Im

pac

tof

RSB

Yon

child

school

enro

llm

ent

(1)

(2)

(3)

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En

roll

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t w

ith

gen

der

dif

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al

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men

t

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roll

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t w

ith

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der

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fere

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al

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ost

0.0

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-0.0

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(0.0

05)

(0.0

06)

(0.0

17)

(0.0

17)

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y0.0

53**

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* 0

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*0.0

55**

*

(0.0

04)

(0.0

04)

(0.0

04)

(0.0

04)

Lo

w I

nco

me

(=1 f

or

bo

tto

m 7

0%

)-3

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-3.5

76

(9.6

08)

(9.5

77)

RSB

Y*P

ost

*Bo

y -0

.019**

*

(0.0

05)

RSB

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ost

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w I

nco

me

0.0

42**

0.0

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(0.0

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(0.0

20)

RSB

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w I

nco

me*

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y-0

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(0.0

01)

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der

iden

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on

tes

tp

=0.0

00

p=

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p=

0.0

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k-i

den

tifi

cati

on

tes

t

Kle

igb

erge

n P

aap

rk W

ald

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tati

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43.4

34

44.0

22

42.2

42

42.4

6

En

do

gen

eity

tes

tp

=0.5

09

p =

0.5

70

p=

0.4

01

p=

0.4

14

Oth

er C

on

tro

ls

YY

YY

Dis

tric

t F

ixed

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ects

YY

YY

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e F

ixed

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ects

YY

YY

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tric

t*In

com

e F

ixed

Eff

ects

YY

Tim

e*In

com

e F

ixed

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ects

YY

N83221

83221

83221

83221

Pan

el

A.

DID

Pan

el

B.

DD

D

*p

<0.1

0,

**p

<0.0

5,

***

p<

0.0

1.

Th

esa

mp

leis

rest

rict

edto

child

ren

abo

ve

the

age

of

5an

db

elo

wth

eag

eo

f18.

Pan

elA

and

Bp

rovid

eth

eD

IDan

dD

DD

resu

lts

resp

ecti

vel

y.E

stim

atio

nis

usi

ng

aL

PM

.D

epen

den

tvar

iab

leis

sch

oo

len

rollm

ent

of

ach

ildin

ah

ouse

ho

ld.

Ad

dit

ion

alco

ntr

ols

incl

ud

eR

SB

Y=

1if

the

dis

tric

tw

asex

po

sed

toR

SB

Y&

0o

ther

wis

e,d

um

my

for

Lo

w

Inco

me

=1

ifH

Hd

oes

no

tb

elo

ng

toto

p30%

and

0o

ther

wis

e(f

or

DD

D),

age

nd

erd

um

my

=1

for

ab

oy

and

0fo

ra

girl

,R

SB

Y,

HH

size

,p

aren

tal

educa

tio

nch

arac

teri

stic

s,in

dic

ato

rsfo

r

relig

ion

of

HH

,in

dic

ato

rsfo

rca

ste

of

HH

,d

um

my

for

urb

anar

eas,

sch

oo

lfa

cilit

ies

and

sch

ola

rsh

ips

off

ered

,d

istr

ict

FE

,ti

me

FE

,d

istr

ict

by

inco

me

fixed

effe

cts

(fo

rD

DD

),ti

me

by

inco

me

fixed

eff

ects

(fo

r D

DD

). H

H s

ize

is in

stru

men

ted

by

the

gen

der

of

the

firs

t ch

ild. Sta

nd

ard

err

ors

rep

ort

ed a

re c

lust

ered

sta

nd

ard

err

ors

.

37

Chapter 2

INTRA-HOUSEHOLD CONSUMPTION DECISIONS: EVIDENCE FROM NREGA

2.1. Introduction

Public works programmes are a popular tool used to address the issues of poverty and

unemployment in developing countries. The Mahatma Gandhi National Rural Employment

Guarantee Act (MG-NREGA) passed in 2005 in India created the world’s largest public

works programme under a statutory framework. The programme legally guarantees hundred

days of unskilled manual work to participants with the intention to alleviate rural poverty.1

Guaranteeing such employment opportunities can directly affect intra-household decisions

through a change in total resources and the allocation of these resources. In this paper,

I examine the impact of NREGA on the pattern of household consumption expenditure.

Looking at changes in consumption patterns within households also gives us some insights

into the possible effects of NREGA on bargaining power since men and women are seen to

have systematically different consumption preferences and spending patterns (Kanbur and

Haddad [1994], Quisumbing et al. [2000], Doepke and Tertilt [2016]).

NREGA represents a compelling policy change for several reasons. First, its annual cost

is close to 1% of India’s GDP, generates around 2.35 billion person-days of employment

and currently benefits more than 50 million households of rural India Ministry of Rural

Development [2016]. A primary contribution of the paper is thus to speak to the welfare

effects of such a large scale public works programme. Any conclusions drawn on the basis of

this pervasive scheme will therefore be of broad interest. Second, since NREGA was rolled

out in a phase-wise manner starting with the most backward districts in 2006, eventually

1The programme was initially called National Rural Employment Guarantee Act (NREGA) but later waschanged to MG-NREGA in 2009. I use NREGA to refer to this programme throughout the paper.

38

covering the entire country by mid 2008, the variation provides an opportunity to evaluate

the impact of this programme. I use two rounds of cross-sectional data from the National

Sample Survey (NSS) that span final implementation of the programme. The data allows

for a comparison of households in the districts before and after the programme to those

in districts that have the programme in both the survey waves. Lastly, it mandates that

one-third beneficiaries be women providing an impetus to female autonomy.

Considerable literature exists on the impact of NREGA on labour market outcomes, agri-

cultural wages, consumption, time-use and impact on children (Bose [2017], Imbert and Papp

[2015], Ravi and Engler [2015], Deininger and Liu [2013], Diiro et al. [2014]). In contrast, this

paper remains unique because it not only evaluates the impact on household consumption

expenditure behaviour but also sheds light on traditionally overlooked outcomes, particularly

on channels through which bargaining power of women may be affected in households. In

general, most papers evaluating the impact of income shocks to households find that a boost

to income increases expenditure on all commodities that households spend on. However,

my analysis shows a change in the pattern of spending depicting a shift in discretionary ex-

penditure towards some commodities more than others. This could be suggestive of greater

involvement of women in household decisions given their preferences for welfare improving

commodities.

A key result found in the paper is a shift in discretionary spending towards school expen-

diture as a result of NREGA. To the extent that women are the primary caregivers in the

family and are concerned with their children’s well-being (Diiro et al. [2014], Jacoby [1995],

Glick [2002]), this suggests a transformative shift in pattern of resource allocation towards

goods women care more about. At the same time, a stark decline in the budget share of

entertainment is seen implying that the pattern has changed to what can be considered

‘wiser’ consumption choices. These shifts are accompanied by increase in expenditure share

of durable goods. This result could potentially be driven by more resources being allocated

to commodities that substitute women’s chores in the households given that they spend more

time at NREGA work sites.

39

The paper goes further to see if the effects of the programme are magnified in situations

where one would expect them to be stronger. For instance, greater share of women employed

through NREGA should lead to a greater impact on allocation towards goods women pre-

fer. The paper finds that the pattern of consumption is similar to the baseline results but

exhibit larger effects where women-to-total employment ratio is higher. Moreover, guaran-

teed employment should induce larger impacts where higher minimum wages are provided

as part of the programme. Analyzing the heterogeneous effects due to variations in state

stipulated minimum wages, the paper finds the magnitude of impact to be greater where

participants’ wages are subject to higher minimum wages. Another source of variation in

the programme effect may arise due to differences in the degree of women’s involvement in

agricultural processes employed for crop production. Considering this heterogeneity, it is

found that households belonging to wheat and rice growing regions are affected differentially

given differences in the status of women prior to the treatment. Lastly, the programme is

found to marginally increase the probability of female headed households for the sample

consisting of at least one male and female adult.

Rest of the paper proceeds as follows. Section 2.2 provides the background and pro-

gramme details of NREGA. Section 2.3 presents a review on related literature. Section 2.4

describes the data followed by the empirical strategy in Section 2.5. Section 2.6 discusses

the baseline results followed by sensitivity analysis in Section 2.7. The paper ends with

robustness checks presented in Section 2.8 followed by the conclusion in Section 2.9.

2.2. Background on NREGA

The Mahatma Gandhi National Rural Employment Guarantee Act, 2005 is aimed at

enhancing the livelihood of households in rural areas. In February 2006, the programme was

introduced to 200 backward districts as the first phase of its implementation. The second

phase was rolled out in April 2007 and extended to additional 130 districts. By April 2008,

284 more districts were covered exposing entire rural India to the programme. NREGA

provides at least 100 days of guaranteed wage employment every financial year to house-

40

holds where adult members volunteer to undertake unskilled manual work. This is the first

incidence of a legally binding commitment made by the government to provide employment.

In a short span of operation, NREGA has had a substantial impact in generating rural em-

ployment affecting approximately 50 million households. A minimum statutory requirement

of the policy is to have 33 percent women participation. Current statistics suggest that the

actual participation is about 52 percent. This is particularly striking, given that women

make up less than 30% of the total labor force (Ministry of Rural Development [2013]).

To obtain work, adult members of a household apply for a job card at the local Gram

Panchayat. 2 3 After due verification, the registered household is issued a job card within

15 days. The card is valid for at least five years after which it can be renewed. Once

the household obtains the job card, members can apply for a job at any time and are

assigned work within 15 days, failing which they are eligible for unemployment compensation.

Projects sanctioned under NREGA are employment projects decided by the intermediate

administrative body between Gram Panchayat and the district. These projects pertain to

water conservation, irrigation, land development, construction of roads and ponds, building

of canals, afforestation, leveling of fields, fisheries, rural sanitation and government relief

works. Workers are paid either a piece rate or a daily wage subject to a minimum specified

by the state and governed by a national minimum (Ministry of Rural Development [2013]).

2.3. Literature review

This paper contributes to two strands of literature on NREGA. One pertains to the

evaluation of NREGA as a welfare programme. The impact of NREGA has been studied on

labour market outcomes like participation in public works, private employment, wages and

welfare outcomes (Bose [2017], Imbert and Papp [2015], Deininger and Liu [2013]). Imbert

and Papp [2015] estimate the effect on private employment and wages and find that public

sector low skilled manual work crowds out private sector work (similar to Zimmermann

2A household in this analysis is defined as the set of individuals who cook around one common stove.

3The lowest governing body at the village level.

41

[2014]) and increases private sector wages. Azam [2012] finds a positive impact on labor

force participation which is driven by significant female participation. Similarly, Diiro et al.

[2014] show that presence of work opportunities in the villages increases average wages

of casual workers, reduces gender wage gap and increases the probability of female labor

market participation. Ravi and Engler [2015] measure the welfare impact of NREGA and

find significant impacts on rural poverty alleviation, increasing food security, and probability

of saving. Bose [2017] finds an increase in consumption for the marginalized caste group and

that in general consumption patterns to have shifted to higher caloric food.

The second strand of literature pertains to NREGA effects on outcomes impacting chil-

dren. Afridi et al. [2016] specifically find greater participation of mothers relative to fathers

is associated with children spending more time spent in school and girls benefiting more from

an increase in mother’s participation. Islam and Sivasankaran [2014] on the other hand find

that time spent on education for younger children increases but time spent working outside

the household for older children increases post NREGA. Li and Sehkri [2013] also find such

unintentional perverse effects in terms of increase in child labour.

Despite the benefits of the programme, some papers advocate a roll-back owing to its

high costs and corruption Niehaus and Sukhtankar [2012]. Therefore, if NREGA does in

fact alter consumption patterns, another benefit of the paper would be a contribution to an

accurate cost and benefit analysis of the programme.

In addition, this paper is also an effort to contribute to the literature on unitary mod-

els of households versus bargaining models. There is considerable evidence refuting models

assuming common preferences Becker [1974] in favor of models where intra-household bar-

gaining takes place (McElroy and Horney [1981], Manser and Brown [1980], Heath and Tan

[2014], Lundberg and Pollak [1996], Chiappori [1988, 1992]). Extant literature finds that

final consumption allocations are made on the basis of weights attached to the preferences

of household members towards goods they especially care about. Such difference in con-

sumption preferences between men and women is well documented across many settings

(Lundberg and Pollak [1996], Anderson and Baland [2002], Basu [2006]). Mencher [1988],

42

Riley [1997], Desai and Jain [1994] suggest that the a woman’s preferences are visible in

household decisions depending by her actual contribution to household budget. On simi-

lar lines, Anderson and Eswaran [2009] find that any contribution to an income generating

activity potentially increases female autonomy. NREGA as an income generating and em-

ployment guarantee policy should therefore alter consumption patterns and have some effects

on female bargaining power within households.

2.4. Data

The 64th and 68throunds of repeated cross-section data from the employment and un-

employment survey of the National Sample Survey Organization (NSSO) are used. The

two waves pertain to 2007-08 and 2011-12. The survey is conducted from July to June to

capture the full agricultural cycle and is stratified by urban and rural areas.4 Information

on roll-out of NREGA to districts across India is taken from the official NREGA website.5

Employment and women participation statistics at district level, data on consumer price

index and state-wise minimum wages for NREGA workers as per the Minimum Wage Act,

1948 and NREGA Act, 2005, for the relevant years are taken from the Ministry of Labour

and Employment, Government of India.6 Information on rice and wheat producing districts

is obtained from the Ministry of Agriculture and Farmers Welfare, Government of India.

Urban areas from the survey sample have been dropped since NREGA is only applicable

to the households in rural areas. All districts of India are included except those from the state

of Jammu and Kashmir which is ridden with persistent internal conflict and has missing data

problem. Districts of Mumbai, New Delhi, Ladakh, Andaman & Nicobar islands and some

other districts for which there is no information are also excluded. The sample is restricted

to include only households with at least one adult male and female member to circumvent

any issue related to absence of a male in the household due to migration, ill-health or death.

4NSS Survey is stratified by urban and rural areas of each district and is further divided into four sub-rounds each lasting three months.

5List of districts and phases can be found at http://nrega.nic.in/MNREGA Dist.pdf.

6Provided as per the central Government notification for the relevant years upon request.

43

A basket of fourteen commodities - cereals and cereal products; pulses and pulses prod-

ucts; edible oil; fuel and light; meat, fish, milk and milk products; intoxicants and tobacco;

entertainment; vegetables and fruits; spices, salt and condiments; personal items, toiletry

and other miscellaneous products; school expenditure; durable goods; medical expenditure;

and clothing, bedding and footwear - are considered as my outcome variables. Cases for

which consumption expenditure has many zero values are dropped.7 NSS data uses a thirty

day time frame for some commodities while for some a three hundred and sixty five day time

frame. All expenditures are converted to the monthly time frame before estimation. The

dependent variables are in the form of budget shares spent on fourteen separate commodity

categories out of the total monthly spending by a household in a district at a particular point

in time. The sample is further restricted to include only households with children for the

model where my outcome variable is budget share of school expenditure. Standard errors are

clustered at district levels in all estimations. The set of controls include household size, age

of the head of the household, age squared, number of children, number of literate males and

females, number of males and females with primary, middle, higher and technical education,

and indicators for caste and religion (scheduled tribe, scheduled caste, other backward class,

Hindu, Muslim, Christian, Sikh, and other religion).

2.5. Empirics

The following difference-in-differences specification is used to compare phase 1 and 2

districts to phase 3 districts before and after NREGA is rolled out in its third phase:

yidt = β0 + β1Tt + βDIDNREGAdt + γXidt + µd + εidt (2.1)

where yidt is the log of the budget share for a particular commodity for household i in

district d at time t, Tt takes the value 1 for 2011-12 and 0 for 2007-08, and the treatment

NREGAdt takes the value 1 if the household belongs to district d where NREGA has been

7Around 200 observations are dropped from approximately 90,000 observations.

44

implemented at time t. Xidt is the set of controls; and µd depicts district fixed effects.

The disturbance term εidt summarizes the influence of all other unobserved variables that

vary across households, districts, and time. The baseline model is estimated via OLS with

fixed effects. Taking budget share of each commodity would ideally require me to estimate a

fractional response model. However, given that I am controlling for 576 districts, a fractional

response model with fixed effects becomes infeasible.

While a fixed effects fractional response model is not feasible, I compare the OLS re-

sults with those from two alternative estimation approaches. First, I estimate a correlated

random effects fractional logit model (section 2.8.1). Second, I estimate the model using

an instrumental variable approach where my outcome variable is the logarithm of consump-

tion per month for each commodity category. Log of total consumption per month is then

added as an explanatory variable in this model to hold the household budget constraint

constant. Note that NREGA could affect consumption decisions by altering the household

budget constraint or by affecting bargaining power through guaranteed employment. Given

this, controlling for total consumption isolates the bargaining power effect of the programme.

Land possessed by the household at the time of the survey is used as an instrument for total

consumption in this specification since total consumption is likely endogenous. The details

and results of these models are discussed in robustness checks (section 2.8.2).

My coefficient of interest is βDID which the differential impact of NREGA introduced in

phase 3 districts on the budget share of expenditure on relevant commodity for household i

in district d.8 β1 identifies the effect of any systematic changes that affected households in

all districts between 2007-08 and 2011-12.

My empirical strategy exploits the phased roll out of NREGA to different districts and

compares households in districts that received the programme earlier to districts that received

it later. Households in NREGA’s early implementation districts are my control group and

late implementation districts are my treatment group. The phased roll-out of NREGA means

8Percentage change in the budget shares due to NREGA is given by 100.{exp(β2) − 1} (see Halvorsenand Palmquist [1980], Thornton and Innes [1989] for further discussion on interpreting dummy variables insemi-logarithmic regressions).

45

that some districts remained uncovered in 2007-08. Identification therefore relies on changes

in household consumption behaviour at the district level when NREGA is introduced in

its third phase. Phase 3 of the programme comprised of the largest part of the roll-out of

NREGA covering 284 districts of India making it pertinent to examine. NSS data does not

identify which households participated in the programme. Thus, I use all the households

in a district and estimate the effect of access to the program which is the intent to treat

(ITT) effect on consumption patterns. The empirical strategy employed in this paper is

closest to the strategy used by Bose [2017] and Imbert and Papp [2015]. A word of caution

warranted here is that roll-out of the programme was not randomly determined. Phase 1

districts are the more ‘backward’ districts. Simple comparison of households from districts

that received the programme earlier to those from districts that were covered later is thus

biased. To address the concern of any time invariant district level characteristics that may

be correlated with the treatment, I include district fixed effects. Time fixed effects control

for the time-varying characteristics that impact all districts equally.

A primary concern with this identification strategy is that the districts that received the

programme in different phases may be trending differently prior to NREGA. Ideally, two

rounds of survey waves prior to the programme would aid in analyzing the pre-trends. How-

ever, extensive missing consumption data in the 61st round of employment-unemployment

survey of NSS restricts my analysis of pre-trends in consumption. Survey rounds prior to the

61st round do not conduct the consumption survey as part of the employment-unemployment

survey. Although nothing can be said about the trends in consumption outcomes for the

control and treatment districts, other outcomes analyzed in several papers show that the

districts that received NREGA in different phases are not trending differentially.9 To alle-

viate this concern further, I estimate a difference-in-difference-in-difference (DDD) model.

9Using data from 1999-00, Imbert and Papp [2015] show no differential increase in public employment inearly districts relative to the late districts prior to NREGA. Similarly, Li and Sehkri [2013] conclude thatgrowth in school enrollment in districts that received the programme in different phases is similar in thepre-treatment periods. Azam [2012] conducts a falsification test using data from 1999-00 and 2004-05 tosuggest that overall labor force participation as well as male and female labor force participation in treatmentand comparison districts were moving in tandem absent the program.

46

I introduce a dummy variable sector which takes value 1 if the household belongs to a ru-

ral sector and 0 if urban and modify Eqn. 2.1 to include a triple interaction term given

sector ∗ NREGAdt. The DDD estimate calculates the changes in average consumption in

the treatment districts in rural sector while netting out the change in average consumption

in the control districts in rural sector and the change in average consumption in the treated

districts in the urban sector. This methodology helps take care of two potential confounds

and ensures that the changes in average consumption in the treated districts in the rural

sector is not a result of changes in consumption for all districts in the rural areas, nor is it

a result of changes in consumption for all households in the treated districts.

A secondary concern with my strategy would be if NREGA changes the sample through

rural to rural migration. However, migration from early implementation districts to late

implementation districts is unlikely since rural to rural migration in India is limited. Only

about 0.4 percent of adult population report having migrated to different rural districts for

employment Imbert and Papp [2015]. Additionally, households are required to show proof

of residence in the village to obtain job cards that will permit them to work under NREGA

which eliminates the concern of rural to rural migration to gain work udnder the scheme.

Another potential shortcoming of the baseline model is that it masks meaningful het-

erogeneous effects the programme may have across different households. I go beyond the

baseline to consider if the programme effects are amplified in situations where one would

expect them to be stronger to address this concern. First, I analyze whether households

with higher female employment share in NREGA lead to greater changes in consumption

patterns. Second, whether guaranteed employment leads to greater bargaining power effects

in areas where higher minimum wage are provided as part of NREGA. Third, I estimate

if different agricultural processes used for rice and wheat production in the country induce

differential treatment effects conditional on the prevailing status of women in such crop ar-

eas. Various interactions to control for these heterogeneous treatment effects are used in my

model specifications, the details of which are discussed in section 2.7.

47

I also estimate the following model to capture the importance of bargaining power as

a mechanism to explain the shifts in the pattern of consumption spending as a result of

NREGA.

DfemheadHHidt = β0 + β1τt + βDIDNREGAd ∗ τt + γXidt + µd + εidt (2.2)

where DfemheadHH takes the value 1 if the household i is headed by a woman in period

t in district d and 0 otherwise.10 Note that female headed households will not simply pick

the lack of males in the household since my sample includes households with at least one

male and female adult. The marginal effect of access to the programme on the expected

probability of whether the households is female headed is given by the parameter βDID.11

2.6. Results

Table 2.1. provides results for my baseline analysis where the outcome variable is the

log of the budget share for each commodity group. Statistically significant increase of ap-

proximately 2.7 percent in the budget share of school expenditure, 2.2 percent in the budget

share of durable goods and 0.5 percent in clothing, bedding and footwear are found. At

the same time, there is a fall in the budget shares of entertainment; spices, salt and other

condiments; meat and milk products, personal commodities and fuel and light. Share of

expenditure on spices and condiments reduces by about 1.3 percent, fuel and light by 0.8

percent, milk products and poultry by 0.5 percent and personal commodities by 0.5 percent.

Share of spending on entertainment shows a larger decline of 2.3 percent.12

10The model is estimated using a linear probability model (LPM). Merits of LPM over Probit/Logit modelsin cases of Limited Dependent Variable (LDV) Models are debatable. However, there are some advantagesof LPM despite its shortcomings as MLE estimates are inconsistent in many cases. Additionally, given thatI have fixed effects where I control for 576 districts, a probit specification becomes infeasible.

11This may be an imperfect indication of bargaining power as a self-reported ‘female-headed household’in the survey may still be a male-headed family. But for purposes of policy and programme implementation,the term female headed household is a practical proxy for a whole range of family structures in which womenare the primary providers Buvinic and Gupta [1997].

12Note that systematic missing data problem could potentially bias the estimate for entertainment as thenumber of observations is much lower. The coefficient should be interpreted with caution. Also note that

48

With guaranteed employment increasing women’s contribution to household income,

there seems to be a shift towards expenditure on commodities women tend to care more

about such as investment in children’s education, durable goods and other households items

like bedding and clothes. Moreover, higher school expenditure suggests a causal effect on

children’s education of mother’s relative control over household resources.13 A rise in the

share of school expenditure and a fall in the share of entertainment expenditure makes a

compelling story for greater female bargaining power as a consequence of NREGA because

household welfare-improving commodities are valued higher by women Hoddinott and Had-

dad [1995].

A plausible explanation for an increase in the budget share of durables could be that

it reflects purchases designed to replace female chores in the household since women are

now actively part of the labour force. This seems consistent with anecdotal references in

Mann and Pande [2012] indicating that women exercise independence in spending NREGA

wages suggesting that greater decision-making power. The decline in the budget share of

fuel and light is however somewhat surprising.14 There could be two reasons for this. With

majority of rural population dependent on agriculture, access to fuel relies heavily on common

property resources. NREGA under its environment-conserving initiative emphasizes natural

resource regeneration and promotes green economy through creation of sustainable rural

assets to reduce reliability on such resources Mann and Pande [2012]. Moreover, more

women engaged in NREGA through the day could potentially imply that lesser household

resources are allocated to the use of fuel and light.

Decline in the budget share of milk products, egg, fish, and meat could be attributed

to NREGA providing impetus to create infrastructure that promotes livestock farming such

the total number of observations for each commodity changes due to missing data.

13Exposure to awareness programmes at NREGA work-sites may have contributed to parent’s motivationto invest in school expenditure. This could perhaps be a mechanism in which NREGA works regardless ofwhether the participants are male or female. Thus, we cannot rule out that such programmes could changepreference of males rather than change bargaining power of women.

14Effect of NREGA on household total consumption per month increases but the budget share of fueland light declines. However, when estimating the treatment effect on the level of consumption expenditure(2.8.2) on fuel and light, I find the expenditure to decline while holding total consumption fixed.

49

as poultry, cattle ownership, and small fisheries Ministry of Rural Development [2013]. Ra-

jasthan state governments under the initiative promotes individuals from low socio-economic

strata to develop their own agricultural land under a sub-scheme called ‘Apana Khet, Apana

Kam’.15 Similarly, the Madhya Pradesh government designed schemes that help job card

holders build assets like small land, poultry, fisheries, and farm ponds Ministry of Rural De-

velopment [2013]. Goods like salt, spices and condiments are typically considered essential

goods for rural households and additional income invariably leads to decline in relative ex-

penditure on these items.16

Table 2.2. provides the results for the difference-in-difference-in-difference analysis. No-

tice that the results are largely similar to the results found in Table 2.1. which provides

further evidence that this estimation controls for the potential counfounding elements that

may arise from the trends in average consumption in control and treated districts. Note that

the zero share cases for consumption cannot be dropped from this sample as they are large

in number and would lead to sample selection bias. 17 The sample now consists of urban as

well as rural areas, the total number of observations being approximately 195,000. A more

sophisticated way to circumvent this problem and include these households in the analysis

is to apply the inverse hyperbolic sine transformation to the variable. Inverse hyperbolic

sine transformation requires simply to transform the variable, say, z as log(z2 +√z2 + 1)

which unlike log z, is defined even for z = 0. I use the inverse hyperbolic sine transfor-

mation to deal with households reporting zero consumption expenditure (Burbidge et al.

[1988],Friedline et al. [2015]).

Table 2.3., specification (1) shows the marginal effect of NREGA on the probability that

a household is female headed. It increases marginally by 0.3 percent and is statistically

significant at 10 percent lending some support to a bargaining power effect of NREGA on

women.

15Translates to ‘my land- my labour’.

16An effect similar to fuel and light is found in the case of spices and condiments as well.

17Unlike the baseline which consisted roughly of only 200 observations out of approximately 90,000.

50

2.7. Sensitivity analysis

2.7.1. Women employment in NREGA jobs

If household consumption behaviour is in fact suggestive of higher female involvement,

these effects should be larger where higher share of women are employed by NREGA. I

interact the programme with variation in share of women employed at district level in the

two time periods.18 I calculate this heterogeneous effect at two levels of women employment

share - 25 percent and 75 percent - with the idea that districts with higher share of women

employed by NREGA would exhibit these effects more prominently.

(2.3)yidt = β0 + β1Tt + β20NREGAdt + β21NREGAdt ∗ ShareOfWomenEmployeddt+ β22ShareOfWomenEmployeddt + γXidt + µd + εidt

DFemheadHHidt = β0 +β1T +β20NREGAdt+β21NREGAdt∗ShareOfWomenEmployeddt+ β22ShareOfWomenEmployeddt + γXidt + µd + εidt

(2.4)

The parameter of interest varies with time t, and district d where the total impact of

NREGA is given by β20 + β21ShareOfWomenEmployeddt.

Results in Table 2.4. confirm that households with greater share of women employed by

NREGA shift expenditure towards commodities like school, medical, durables and house-

holds items that may maximize general household welfare. Where the share of women-to-

total employed by NREGA is 75 percent, the budget share of school expenditure rises to 1.2

percent and medical expenditure to 1.6 percent as compared to when women-to-total em-

ployment share is at 25 percent. Similar to the baseline, a statistically significant rise of 1.7

percent and 1 percent is found on durables and clothing, bedding and footwear respectively.19

Table 2.3., specification (2) shows the probability that a household is female headed

increases with NREGA where a higher share of women are employed by the programme but

the impact is not precisely estimated.

18Share of women-to-total NREGA employment is calculated from total person-days of employment gen-erated by NREGA and women participation rates.

19However, note that the direction of impact does not increase for entertainment and intoxicants forhouseholds with higher share of women employed through NREGA as expected.

51

2.7.2. State minimum wages

Given the wide variation in the state stipulated minimum wages provided under NREGA

across different states, I assert that if the baseline effects are due to bargaining power, the

effects should be amplified when NREGA employment pays more. To get at this, I exploit

variation in minimum wages to see if the NREGA effects are larger in areas where higher

minimum wages are provided.

I use the following specifications introducing an interaction between the treatment and

the state stipulated median - standardized minimum wage for district d in time period t.20

(2.5)yidt = β0 + β1Tt + β20NREGAdt + β21NREGAdt ∗minW dt + β22minWdt

+ γXidt + µd + εidt

(2.6)DFemheadHHidt = β0 + β1T + β20NREGAdt + β21NREGAdt ∗minWdt

+ β22minWdt + γXidt + µd + εidt

The parameter of interest varies with time and district where the total impact of NREGA

is given by β20 + β21minWdt. Table 2.5. provides the results.

As before, a noticeable increase of 4.2 percent in the share of school expenditure for

households with higher minimum wages is estimated making a case for women having greater

say in the household decisions as they work for higher minimum wages. A rise of 2.1 percent

in this share is found for households with lower minimum wages as well but the magnitude

of impact is lower than the impact evaluated at the maximum limit. The difference between

households that receive higher and lower minimum wages is statistically significant. This

supports the assertion that if NREGA provides higher bargaining power to women, this

bargaining power must be higher where higher minimum wages are provided.

NREGA is also found to increase the budget shares of durables, and clothing, bedding

and footwear for higher minimum wage households. Impact evaluated at the maximum limit

shows a statistically significant rise of 1.5 and 2.1 percent in their budget shares respectively

20I create a standardized measure of minimum wages across states by dividing the minimum wages foreach state by the median wage for the year in consideration.

52

whereas these impacts evaluated at the minimum limit do not show statistically significant

results. To the extent that women who work for higher minimum wages may care more about

durable commodities that help substituting their chores, as well as clothing and household

items, NREGA employments seems to show a significant shift towards these items. Results

also suggest that these households are substituting wheat and wheat products with more

nutritious foods like vegetables and fruits. Statistically significant increase in their monthly

budget share of vegetables and fruits of approximately 1.6 percent and decline of 1.3 percent

in the budget share of wheat products are found.

Households with lower minimum wages depict a statistically significant decline in the

budget shares of entertainment and personal commodities post the treatment. These im-

pacts when evaluated at the maximum limit also show a decline however the results are not

precisely estimated.

Table 2.3., specification (3) shows that the probability a household is headed by a female

increases with NREGA employment at higher minimum wages but the impact is not precisely

estimated.

2.7.3. Crop regions

Literature suggests that women have a comparative advantage in rice production rela-

tive to wheat farming (Flueckiger [1996], Bardhan 1974).21 As a result, absent NREGA,

bargaining power for women ought to be higher in rice regions. Since the ‘baseline’ level

of bargaining power is different in rice regions compared to wheat, the effect of NREGA

may differ across the two regions. However, it is not clear a priori where the effect should

be larger. The effect may be larger in wheat regions because women’s bargaining power is

initially lower. On the other hand, the effect may be larger in rice regions because the ad-

ditional bargaining power conferred onto women from NREGA may ‘tip-the-scales’ in favor

21“Transplantation of paddy is an exclusively female job in paddy [rice] areas; besides, female labour playsa very important role in weeding, harvesting and threshing of paddy. By contrast, in dry cultivation andeven in wheat cultivation, under irrigation, the work involves more muscle power and less tedious, oftenback-breaking, but delicate, operations...” [Bardhan, 1974, p. 1304]

53

of women within these households. Thus, while the effects are likely to be heterogeneous

across regions, the direction is an empirical question.

To estimate these heterogeneous impacts of NREGA, I estimate the following models:

yidt = β0 + β1T + β20NREGAdt + β21NREGAdt ∗DRiced + β23NREGAdt ∗DBothd+ β24DRiced + β25DBothd + γXidt

+ µd + εidt(2.7)

(2.8)DFemheadHHidt = β0 + β1T + β20NREGAdt + β21NREGAdt ∗DRiced

+ β23NREGAdt ∗DBothd + β24DRiced+ β25DBothd + γXidt + µd + εidt

DRice takes the value 1 for districts that belong to rice producing states and 0 otherwise.

DBoth takes the value 1 if the districts belong to both rice and wheat producing states and

0 otherwise. Wheat growing districts are given by when DRice = 0 and DBoth = 0.22 All

the other variables remain the same as my baseline. The parameter of interest now varies

with crop districts consequently the impact of NREGA differs with crop districts considered.

Table 1.6. provides the results.

At the outset, notice that although the patterns alter slightly for the two regions, most

of the impacts are found to be higher for rice regions. Women in rice regions presumably

have greater say in household decisions absent the programme. Introduction of NREGA thus

boosts their position further flipping the balance of power to some extent. Whereas, absent

the programme, women have much lower decision making power in wheat regions. NREGA

alone is therefore insufficient to alter bargaining power fundamentally.

Similar to the baseline, a statistically significant increase in the budget share of school

expenditure is seen as a result of NREGA in both rice and wheat growing regions but the

impact is larger in rice regions. Other effects found in the rice regions are decline in the

budget shares of entertainment and condiments. Rice regions also show a decline in budget

shares of meat and milk and personal commodities but an increase in clothing, footwear and

22Regions that produce neither rice or wheat are excluded because nothing can be said about the statusof women in regions that grow other crops. Note that this causes the total numer of observations to be lowerthan the baseline, women employment share as well as the minimum wages models.

54

household items. This is in line with the evidence provided earlier that NREGA helps create

own infrastructure that promotes livestock farming reducing their reliance on purchase of

such commodities from the market. Shift from spending on personal commodities towards

goods that may increase the overall household utility suggests a change in the pattern more

in line with preferences of women.

Exposure to NREGA in wheat regions depicts a shift from the budget shares of cereals

and cereal products, fuel and electricity towards durables. Similar results were noticed in the

baseline model suggesting more resources being spent on durables which substitute women’s

chores in the house.

The marginal impact of NREGA on expected probability that a household is female

headed is found to rise for the rice regions but the impact is not statistically significant

(Table 2.3., specification (4)). No such impact is seen for the wheat regions.

2.8. Robustness checks

2.8.1. Fractional logit estimation with correlated random effects

2.8.1.1. Baseline model

Given that my outcome variables are in the form of monthly budget shares spent on each

commodity, a fractional logit model is more suitable for estimation. However, fractional

logit is infeasible with fixed effects. I therefore estimate my model via a correlated random

effects (CRE) fractional logit model. The advantage of using CRE fractional logit is that it

places some structure on the nature of correlation between the unobserved effects and the

covariates. To capture the district fixed effects, means of all controls at district level across

time are included as additional controls in the estimation. All standard errors are clustered

at the district level.23 The point estimates from Appendix table B.2. suggest that the results

23As additional robustness checks, I estimate the baseline via OLS without fixed effects and compare theresults with a fractional logit without fixed effects. The results are generally similar and available uponrequest.

55

are robust. The estimations show similar results in terms of statistical significance and the

magnitude of impact as the baseline.

2.8.1.2. Heterogeneous effects

I follow the same procedure and re-estimate a CRE fractional logit model to examine

the heterogeneous impacts of NREGA (Appendix Table B.3., B.4., and B.5.). For all three

models capturing the heterogeneous impacts of NREGA, the marginal effects of NREGA are

found to be broadly robust to their baseline results.

One surprising result is that the marginal effect of NREGA evaluated at the maximum

of the stipulated minimum wages has a negative impact on school expenditure. However,

this effect is imprecisely estimated. For the crop regions, marginal effect of the treatment in

rice regions are found to be higher than wheat. The pattern of spending shifts in the wheat

areas as well but NREGA seems to be insufficient to change the balance of power in these

households.

2.8.2. Consumption in levels

2.8.2.1. Baseline model

I alter my estimation by changing the outcome variable to the log of monthly consump-

tion of each commodity. As a control for this model, I include the log of total monthly

consumption of the household since the outcomes are no longer in form of budget shares.

However, total consumption is likely endogenous since it is the sum of consumption expendi-

tures on each commodity. Using an instrumental variable approach therefore, I instrument

total monthly consumption by land possessed by the household at the date of the survey to

circumvent this problem. This serves as a valid instrument because land possessed makes

up the assets held at the time of the survey and does not directly impact the monthly ex-

penditure on each commodity. Theory suggests that monthly expenditures on commodities

56

are out of current earned income rather than out of household assets or wealth.24

Table B.6. provides the results. Several diagnostic tests have been performed to assess

the efficiency and reliability of the model. The endogeneity test reports test statistics that

are robust to various violations of conditional homoskedasticity. I reject exogeneity of log

of total consumption for most specifications.25 As far as underidentification is concerned,

I report chi-squared p-values for the test where rejection of the null implies full rank and

identification Baum and Schaffer [2007]. This test tells us whether the excluded instrument

is correlated with the endogenous regressor. In all the specifications, the p-value based on

Kleibergen-Paap rk LM statistic allows me to clearly reject the null that the instrument is

uncorrelated with the endogenous regressor and that the model is underidentified.

I also report the Cragg-Donald (1993) Wald F statistic. Rejection of the null here rep-

resents absence of weak-instrument problem. The F-statistics are well above 10 across all

estimations indicating that none of the specifications suffer from weak instrument problem.

Since all the specifications have clustered standard errors at district level, the reported test

statistic is based on the Kleibergen–Paap rk statistic which also indicates absence of weak

instrument problem.

Point estimates show that the results found for this model are broadly consistent with the

baseline results. There is a 20 percent increase in expenditure on school and approximately

18.7 percent rise in expenditure on durables. Household expenditure on spices and condi-

ments has reduced by about 10 percent and on fuel and light by 4.8 percent. Expenditure on

entertainment shows a large decline of 18 percent. The pattern of spending is thus consistent

with commodities that women prefer and suggests a bargaining power effect.

24Although, land could affect school expenditure to some extent since land requires work and missing workwould factor into opportunity cost of expenditure related to school. Moreover, it cannot be disregarded thatland possessed could also possibly be correlated with commodities like meat, poultry as well as milk whichrequire land for production.

25Under conditional homoskedasticity, this endogeneity test is numerically equal to a Hausman test statis-tic.

57

2.8.2.2. Heterogeneous effects

Results are found to be robust and the patterns of spending similar to the baseline when

I estimate the impact of NREGA with variation in the share of women employed using IV

approach (Table B.7.). All specifications perform well on the diagnostic tests. Similarly,

results robust to the baseline are found for the model with state stipulated minimum wages

(Table B.8.). The programme effects for different crop regions are also found to yield results

that are similar to the baseline crop regions model (Table B.9.).

2.9. Conclusion

This paper evaluates the world’s largest public works programme, NREGA, with an at-

tempt to marry the literature on welfare programmes with the literature on intra-household

resource allocation decisions. Such welfare programmes, despite their long standing history,

have been subject to constant debate regarding their requirement and efficacy. However, the

enormous scope of NREGA ensured a highest ever allocation of INR 480 billion in the finan-

cial budget for 2017-18 by the government India Union Budget [2017]. More importantly,

NREGA generated approximately 2.35 billion total person days of employment in 2015-16

of which approximately 55 per cent were by women Ministry of Rural Development [2016].

Given this background, it is imperative to evaluate the impact of the programme.

The paper addresses how the consumption patterns in rural households change as a

result of NREGA and if these effects are suggestive of higher bargaining power for women. I

provide empirical evidence that an employment guarantee programme such as this leads to

an apparent shift in the pattern of household consumption behaviour towards goods mostly

preferred by women, consistent with a bargaining power effect of the programme.

I estimate the causal impact of the phase wise roll out of NREGA on the pattern of

monthly household consumption expenditure using two rounds of nationally representative

survey data. Households belonging to phase 3 are richer and more developed districts in

general but to my knowledge, any causal impacts of phase 3 of the programme on pattern

of consumption expenditure has not been studied. NREGA having any sort of impact on

58

backward districts, those covered in phases 1 and 2, seems like an expected conclusion, but

any evidence of bargaining power shifts through changes in consumption patterns found for

the rich districts speaks to the effectiveness of the programme even in the richer areas.

One of the key policy relevant impacts found is that NREGA increases the household

monthly budget share of school expenditure by approximately 2.7 percent. This has impor-

tant policy implications for developing countries considering employment schemes. I find

that in general, expenditure on durables and clothing, bedding and footwear increase while

the expenditure on entertainment decline. The results potentially imply that households in

the more developed rural districts are now switching to purchases that substitute women’s

chores. Importantly, the effects documented are stronger where one would expect, lending

further credence to the interpretation that NREGA is atleast partially affecting consumption

patterns via changes in female bargaining power. Specifically, the effects are larger in areas

with greater share of female participants, a higher minimum wage and specializing in rice

production.

59

Tab

le2.

1.Im

pac

tof

NR

EG

Aon

exp

endit

ure

shar

es-

DID

Vari

ab

les

Cere

als

P

uls

es

Ed

ible

Oil

Fu

el

& L

igh

tIn

tox

ican

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nte

rtain

men

t

Veg

&

Fru

its

Co

nd

imen

ts

Meat

&

Mil

k

Med

ical

Ex

pd

Sch

oo

l

Ex

pd

Pers

on

al

Clo

thin

g &

bed

din

g

Du

rab

le

Go

od

s

NR

EG

A-0

.001

-0.0

02

-0.0

04

-0.0

08**

*-0

.003

-0.0

23**

*0.0

01

-0.0

13**

*-0

.005**

0.0

01

0.0

27**

*-0

.005*

0.0

05*

0.0

22**

*

(0.0

02)

(0.0

03)

(0.0

02)

(0.0

02)

(0.0

04)

(0.0

04)

-0.0

03

-0.0

03

(0.0

03)

(0.0

07)

(0.0

06)

(0.0

03)

(0.0

03)

(0.0

06)

Oth

er C

on

tro

ls

Yes

Yes

Yes

Yes

Yes

Yes

Yes

Yes

Yes

Yes

Yes

Yes

Yes

Yes

Dis

tric

t F

ixed

Eff

ects

Yes

Yes

Yes

Yes

Yes

Yes

Yes

Yes

Yes

Yes

Yes

Yes

Yes

Yes

N80234

79427

79610

79738

57931

37019

80157

80248

78466

63887

52018

80159

80082

79628

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tes:

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<0.1

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***

p<

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clude

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ere

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sch

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pden

dit

ure

.

60

Tab

le2.

2.Im

pac

tof

NR

EG

Aon

exp

endit

ure

shar

es-

DD

D

Vari

ab

les

Cere

als

P

uls

es

Ed

ible

Oil

Fu

el

&

Lig

ht

Into

xic

an

tsE

nte

rtain

men

t

Veg

&

Fru

its

Co

nd

imen

ts

Meat

&

Mil

k

Med

ical

Ex

pd

Sch

oo

l

Ex

pd

Pers

on

al

Clo

thin

g &

bed

din

g

Du

rab

le

Go

od

s

NR

EG

A0.0

01

-0.0

05

-0.0

07**

*-0

.011**

*0.0

62**

*-0

.111**

*0.0

02

-0.0

18**

*0.0

11**

*-0

.004

0.0

70**

*-0

.010**

*0.0

08**

*0.0

16**

*

(0.0

02)

(0.0

04)

(0.0

02)

(0.0

02)

(0.0

10)

(0.0

13)

(0.0

02)

(0.0

03)

(0.0

03)

(0.0

10)

(0.0

07)

(0.0

03)

(0.0

02)

(0.0

05)

Sec

tor

0.0

15**

*0.0

06**

*0.0

04**

*-0

.010**

*-0

.015**

*-0

.025**

*-0

.001

0.0

00

0.0

03*

-0.0

11**

*-0

.048**

*-0

.007**

*0.0

11**

*-0

.001

(0.0

01)

(0.0

02)

(0.0

01)

(0.0

01)

(0.0

04)

(0.0

06)

(0.0

01)

(0.0

01)

(0.0

01)

(0.0

04)

(0.0

04)

(0.0

02)

(0.0

01)

(0.0

02)

NR

EG

A*S

ecto

r-0

.003**

-0.0

02

00.0

04**

-0

.067**

*0.0

51**

*-0

.002

0.0

04**

-0.0

11**

*0.0

03

-0.0

49**

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03

-0.0

06**

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01

(0.0

01)

(0.0

02)

(0.0

01)

(0.0

01)

(0.0

05)

(0.0

08)

(0.0

01)

(0.0

02)

(0.0

02)

(0.0

05)

(0.0

05)

(0.0

02)

(0.0

01)

(0.0

03)

Imp

act

of

NR

EG

A o

n r

ura

l

sect

or

-0.0

02

-0.0

07**

-0.0

07**

-0.0

07**

-0.0

05

-0.0

60**

*0.0

00

-0.0

13**

*0.0

01

-0.0

02

0.0

20**

-0.0

07**

0.0

03

0.0

16**

(0.0

02)

(0.0

03)

(0.0

02)

(0.0

02)

(0.0

10)

(0.0

12)

(0.0

02)

(0.0

03)

(0.0

03)

(0.0

10)

(0.0

09)

(0.0

02)

(0.0

02)

(0.0

05)

Oth

er C

on

tro

ls

Yes

Yes

Yes

Yes

Yes

Yes

Yes

Yes

Yes

Yes

Yes

Yes

Yes

Yes

Dis

tric

t F

ixed

Eff

ects

Yes

Yes

Yes

Yes

Yes

Yes

Yes

Yes

Yes

Yes

Yes

Yes

Yes

Yes

N182532

181699

181956

182056

127809

152429

182400

182556

180667

156793

164254

182304

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61

Tab

le2.

3.Im

pac

tof

NR

EG

Aon

pro

bab

ilit

yth

athou

sehol

dis

fem

ale

hea

ded

Variables

(1)

(2)

(3)

(4)

NR

EG

A0.0

03*

-0.0

55

-0.0

30

-0.0

20

(0.0

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(0.0

94)

(0.0

25)

(0.0

13)

NR

EG

A*f

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are

of

NR

EG

A

emp

loym

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0.0

60

(0.0

18)

NR

EG

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0.0

32

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26)

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EG

A*R

ice

0.0

52**

(0.0

23)

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-0.0

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H0: F

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are

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NR

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A

emp

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ent

= 2

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32

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86

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159.4

0 p

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0p

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.118

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*Bo

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N80279

78471

80279

38164

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ind

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for

area

sp

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cro

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s sa

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62

Tab

le2.

4.H

eter

ogen

eous

Impac

tsof

NR

EG

Aon

Exp

endit

ure

Shar

es:

Fem

ale

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eof

NR

EG

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mplo

ym

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ab

les

Cere

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uls

es

Ed

ible

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&

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Into

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an

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t

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its

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nd

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ts

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k

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pd

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pd

Pers

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Clo

thin

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din

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rab

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Go

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s

NR

EG

A0.0

39**

* 0

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0.0

14**

*0.0

06**

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66**

* -

0.0

39**

* 0

.010**

*-0

.026**

* -

0.0

37**

* -

0.0

45**

*0.0

00*

-0.0

21**

*0.0

03

-0.0

05

(0.0

03)

(0.0

04)

(0.0

03)

(0.0

03)

(0.0

06)

(0.0

06)

(0.0

03)

(0.0

03)

(0.0

05)

(0.0

07)

(0.0

09)

(0.0

03)

(0.0

03)

(0.0

06)

NR

EG

A*f

emal

e sh

are

of

NR

EG

A e

mp

loym

ent

-0.0

57**

*-0

.017**

*-0

.031**

*-0

.034**

*0.1

19**

*0.0

29**

*-0

.016**

*0.0

18**

*0.0

17**

*0.0

81**

*0.0

16*

0.0

25**

*0.0

10**

0.0

29**

*

(0.0

05)

(0.0

06)

(0.0

04)

(0.0

04)

(0.0

09)

(0.0

06)

(0.0

04)

(0.0

05)

(0.0

07)

(0.0

10)

(0.0

10)

(0.0

05)

(0.0

05)

(0.0

06)

Fem

ale

shar

e o

f N

RE

GA

emp

loym

ent

= 2

5%

0.0

25**

*0.0

03

0.0

06**

-0.0

02

-0.0

36**

*-0

.031**

*0.0

06**

-0.0

21**

*-0

.033**

*-0

.024**

*0.0

04**

-0.0

15**

*0.0

05**

0.0

02

(0.0

03)

(0.0

04)

(0.0

03)

(0.0

02)

(0.0

05)

(0.0

05)

(0.0

03)

(0.0

03)

(0.0

04)

(0.0

06)

(0.0

08)

(0.0

03)

(0.0

02)

(0.0

05)

Fem

ale

shar

e o

f N

RE

GA

emp

loym

ent

= 7

5%

-0.0

04

-0.0

06

-0.0

10**

*-0

.019**

*0.0

24**

*-0

.017**

*-0

.002**

*-0

.013**

*-0

.024**

*0.0

16**

0.0

12*

-0.0

02

0.0

10**

*0.0

17**

*

(0.0

04)

(0.0

04)

(0.0

03)

(0.0

03)

(0.0

06)

(0.0

05)

(0.0

03)

(0.0

03)

(0.0

05)

(0.0

07)

(0.0

10)

(0.0

03)

(0.0

03)

(0.0

06)

Oth

er C

on

tro

ls

Yes

Yes

Yes

Yes

Yes

Yes

Yes

Yes

Yes

Yes

Yes

Yes

Yes

Yes

Dis

tric

t F

ixed

Eff

ects

Yes

Yes

Yes

Yes

Yes

Yes

Yes

Yes

Yes

Yes

Yes

Yes

Yes

Yes

N78436

77787

77919

77971

56617

36164

78356

78448

76716

62531

65037

78366

78287

77885

Marg

inal

Eff

ects

of

NR

EG

A

No

tes:

*p

<0.1

0,

**p

<0.0

5,

***

p<

0.0

1.

Est

imat

ion

isvia

OL

Sap

pro

ach

.T

he

sam

ple

isre

stri

cted

toin

clude

ho

use

ho

lds

wit

hat

leas

to

ne

adult

fem

ale

and

mal

em

emb

er.

Dep

enden

tvar

iab

les

are

inth

efo

rmo

fb

udge

tsh

ares

spen

to

n14

sep

arat

eco

mm

odit

y

cate

gori

eso

ut

of

the

tota

lm

on

thly

spen

din

gb

ya

ho

use

ho

ldin

ad

istr

ict

ata

par

ticu

lar

po

int

inti

me.

Addit

ion

alco

ntr

ols

incl

uded

inea

chsp

ecif

icat

ion

-dis

tric

tfi

xed

effe

cts,

wo

men

toto

tal

emp

loym

ent

rati

oin

NR

EG

Ajo

bs,

ho

use

ho

ldsi

ze,

age

of

the

hea

do

fth

e

ho

use

ho

ld,

age

squar

ed,

num

ber

of

child

ren

,n

um

ber

of

liter

ate

mal

ean

dfe

mal

em

emb

ers,

num

ber

of

mal

ean

dfe

mal

em

emb

ers

wit

hp

rim

ary,

mid

dle

,h

igh

eran

dte

chn

ical

educa

tio

n,

Sch

edule

dT

rib

e(S

T),

Sch

edule

dC

aste

(SC

),O

ther

Bac

kw

ard

Cla

ss(O

BC

),

Hin

du,

Isla

m,

Ch

rist

ian

ity,

Sik

his

m,

and

oth

erre

ligio

n.

Sta

ndar

der

rors

are

clust

ered

atdis

tric

tle

vel

and

rep

ort

edin

par

enth

esis

.Sam

ple

isre

stri

cted

toh

ouse

ho

lds

wit

hat

leas

t1

mal

ean

dfe

mal

ead

ult

wh

oh

ave

sch

oo

lgo

ing

child

ren

for

the

mo

del

wh

ere

outc

om

eis

sch

oo

l ex

pden

dit

ure

.

63

Tab

le2.

5.H

eter

ogen

eous

Impac

tsof

NR

EG

Aon

Exp

endit

ure

Shar

es:

Sta

teSti

pula

ted

Min

imum

Wag

es

Vari

ab

les

Cere

als

P

uls

es

Ed

ible

Oil

Fu

el

&

Lig

ht

Into

xic

an

tsE

nte

rtain

men

t

Veg

&

Fru

its

Co

nd

imen

ts

Meat

&

Mil

k

Med

ical

Ex

pd

Sch

oo

l

Ex

pd

Pers

on

al

Clo

thin

g &

bed

din

g

Du

rab

le

Go

od

s

NR

EG

A0.0

20**

-0.0

24**

-0.0

27**

*-0

.021**

-0.0

08

-0.0

56**

*-0

.026**

-0

.042**

*0.0

00

0.0

12

0.0

09

-0.0

07

-0.0

24**

0.0

28

(0.0

09)

(0.0

11)

(0.0

09)

(0.0

09)

(0.0

15)

(0.0

15)

(0.0

10)

(0.0

10)

(0.0

10)

(0.0

25)

(0.0

24)

(0.0

11)

(0.0

10)

(0.0

20)

NR

EG

A*m

inW

-0.0

21**

0.0

22**

0.0

23**

*0.0

13

0.0

05

0.0

33**

0.0

26**

0.0

30**

*-0

.005

-0.0

10.0

19

0.0

01

0.0

28**

*-0

.008

(0.0

08)

(0.0

10)

(0.0

09)

(0.0

09)

(0.0

15)

(0.0

13)

(0.0

10)

(0.0

10)

(0.0

09)

(0.0

24)

(0.0

23)

(0.0

10)

(0.0

10)

(0.0

19)

Min

imum

Wag

e =

Rs.

82.5

0

per

day

0.0

03

-0.0

06

-0.0

07**

*-0

.010

-0.0

04

-0.0

28**

*-0

.004

-0.0

17**

*-0

.005

0.0

04

0.0

21**

-0.0

06*

-0.0

01

0.0

21

(0.0

03)

(0.0

04)

(0.0

03)

(0.0

03)

(0.0

05)

(0.0

06)

(0.0

03)

(0.0

03)

(0.0

03)

(0.0

08)

(0.0

10)

(0.0

03)

(0.0

03)

(0.0

07)

Min

imum

Wag

e =

Rs.

159.4

0

per

day

-

0.0

13**

0.0

11*

0.0

11*

-0.0

10**

*-0

.001

-0.0

03

0.0

16**

0.0

06

-0.0

09

-0.0

03

0.0

42**

-0.0

04

0.0

21**

*0.0

15**

*

(0.0

05)

(0.0

06)

(0.0

06)

(0.0

06)

(0.0

10)

(0.0

08)

(0.0

07)

(0.0

07)

(0.0

06)

(0.0

15)

(0.0

18)

(0.0

06)

(0.0

07)

(0.0

12)

Oth

er C

on

tro

ls

Yes

Yes

Yes

Yes

Yes

Yes

Yes

Yes

Yes

Yes

Yes

Yes

Yes

Yes

Dis

tric

t F

ixed

Eff

ects

Yes

Yes

Yes

Yes

Yes

Yes

Yes

Yes

Yes

Yes

Yes

Yes

Yes

Yes

N80234

79427

79610

79738

57931

37019

80157

80248

78466

63887

52018

80159

80082

79628

No

tes:

*p

<0.1

0,

**p

<0.0

5,

***

p<

0.0

1.

Est

imat

ion

isvia

OL

Sap

pro

ach

.T

he

sam

ple

isre

stri

cted

toin

clude

ho

use

ho

lds

wit

hat

leas

to

ne

adult

fem

ale

and

mal

em

emb

er.

Dep

enden

tvar

iab

les

are

inth

efo

rmo

fb

ud

get

shar

essp

ent

on

14

sep

arat

eco

mm

odit

yca

tego

ries

out

of

the

tota

lm

on

thly

spen

din

gb

ya

ho

use

ho

ldin

adis

tric

tat

ap

arti

cula

rp

oin

tin

tim

e.A

ddit

ion

alco

ntr

ols

incl

uded

inea

chsp

ecif

icat

ion

-dis

tric

tfi

xed

effe

cts,

min

imum

wag

es,

ho

use

ho

ldsi

ze,

age

of

the

hea

do

fth

eh

ouse

ho

ld,

age

squar

ed,

num

ber

of

child

ren

,n

um

ber

of

liter

ate

mal

ean

dfe

mal

em

emb

ers,

num

ber

of

mal

ean

dfe

mal

em

emb

ers

wit

hp

rim

ary,

mid

dle

,h

igh

eran

dte

chn

ical

educa

tio

n,Sch

edule

dT

rib

e(S

T),

Sch

edule

dC

aste

(SC

),O

ther

Bac

kw

ard

Cla

ss(O

BC

),H

indu,Is

lam

,C

hri

stia

nit

y,Sik

his

m,an

do

ther

relig

ion

.Sta

nd

ard

erro

rs

are

clust

ered

at

dis

tric

t le

vel

an

d r

epo

rted

in

par

enth

esis

. Sam

ple

is

rest

rict

ed t

o h

ouse

ho

lds

wit

h a

tlea

st 1

mal

e an

d f

emal

e ad

ult

wh

o h

ave

sch

oo

l go

ing

child

ren

fo

r th

e m

odel

wh

ere

outc

om

e is

sch

oo

l ex

pd

end

iture

.

Marg

inal

Eff

ects

of

NR

EG

A

64

Tab

le2.

6.H

eter

ogen

eous

Impac

tsof

NR

EG

Aon

Exp

endit

ure

Shar

es:

Cro

pR

egio

ns

Vari

ab

les

Cere

als

P

uls

es

Ed

ible

Oil

Fu

el

&

Lig

ht

Into

xic

an

tsE

nte

rtain

men

t

Veg

&

Fru

its

Co

nd

imen

ts

Meat

&

Mil

k

Med

ical

Ex

pd

Sch

oo

l

Ex

pd

Pers

on

al

Clo

thin

g &

bed

din

g

Du

rab

le

Go

od

s

NR

EG

A-0

.013**

*-0

.001

-0.0

03

-0.0

13**

*-0

.005

-0.0

12

-0.0

01

0.0

01

0.0

01

0.0

07

0.0

35**

*-0

.007

0.0

00

0.0

31**

(0.0

05)

(0.0

05)

(0.0

04)

(0.0

04)

(0.0

07)

(0.0

07)

-0.0

05

-0.0

05

(0.0

04)

(0.0

15)

(0.0

12)

(0.0

06)

(0.0

08)

(0.0

14)

NR

EG

A*R

ice

0.0

20**

*-0

.006

-0.0

04

0.0

11*

-0.0

02

-0.0

11

-0.0

08

-0.0

37**

*-0

.018**

*-0

.022

0.0

04

-0.0

07

0.0

12

-0.0

18

(0.0

06)

(0.0

08)

(0.0

05)

(0.0

05)

(0.0

10)

(0.0

09)

(0.0

06)

(0.0

07)

(0.0

06)

(0.0

18)

(0.0

14)

(0.0

07)

(0.0

09)

(0.0

15)

NR

EG

A*B

oth

0.0

00

-0.0

07

0.0

10**

0.0

02

0.0

38**

*-0

.017

0.0

11*

0.0

01

0.0

02

0.0

44

-0.0

27*

-0.0

03

0.0

09

-0.0

2

(0.0

09)

(0.0

08)

(0.0

05)

(0.0

05)

(0.0

13)

(0.0

23)

(0.0

07)

(0.0

10)

(0.0

07)

(0.0

29)

(0.0

16)

(0.0

10)

(0.0

10)

(0.0

21)

Wh

eat

Reg

ion

s-0

.013**

*-0

.001

-0.0

03

-0.0

13**

*-0

.005

-0.0

12

-0.0

01

0.0

01

0.0

01

0.0

07

0.0

35**

*-0

.007

0.0

00

0.0

31**

(0.0

05)

(0.0

05)

(0.0

04)

(0.0

04)

(0.0

07)

(0.0

07)

-0.0

05

-0.0

05

(0.0

04)

(0.0

15)

(0.0

12)

(0.0

06)

(0.0

08)

(0.0

14)

Ric

e R

egio

ns

0.0

07

-0.0

07

-0.0

07

-0.0

02

-0.0

07

-0.0

23**

-0.0

08*

-0.0

37**

* -

0.0

17**

-0.0

15

0.0

39**

* -

0.0

14**

0.0

12**

0.0

13

(0.0

05)

(0.0

07)

(0.0

05)

(0.0

05)

(0.0

08)

(0.0

09)

(0.0

05)

(0.0

07)

(0.0

06)

(0.0

15)

(0.011)

(0.0

05)

(0.0

06)

(0.0

10)

Reg

ion

s p

roduci

ng

bo

th-0

.013

-0.0

08

0.0

07*

-0.0

11**

0.0

34**

-0.0

29

0.0

10*

0.0

02

0.0

03

0.0

51*

0.0

08

-0.0

10

0.0

09

0.0

11

(0.0

05)

(0.0

06)

(0.0

05)

(0.0

04)

(0.0

10)

(0.0

25)

(0.0

06)

(0.0

10)

(0.0

07)

(0.0

20)

(0.0

14)

(0.0

08)

(0.0

08)

(0.0

18)

Oth

er C

on

tro

ls

Yes

Yes

Yes

Yes

Yes

Yes

Yes

Yes

Yes

Yes

Yes

Yes

Yes

Yes

Dis

tric

t F

ixed

Eff

ects

Yes

Yes

Yes

Yes

Yes

Yes

Yes

Yes

Yes

Yes

Yes

Yes

Yes

Yes

N38141

37536

37722

37866

28802

16616

38103

38146

37211

30407

25213

38112

38081

37814

No

tes:

*p

<0.1

0,

**p

<0.0

5,

***

p<

0.0

1.

Est

imat

ion

isvia

OL

Sap

pro

ach

.T

he

sam

ple

isre

stri

cted

toin

clude

ho

use

ho

lds

wit

hat

leas

to

ne

adult

fem

ale

and

mal

em

emb

er.

Sam

ple

isfu

rth

erre

stri

cted

toin

clude

on

lyth

ose

regi

on

sth

atar

eri

cep

roduci

ng,

wh

eat

pro

duci

ng

and

tho

seth

atp

roduce

bo

thri

cean

dw

hea

t.D

Ric

e=1

for

rice

regi

on

s.If

DR

ice=

0,

then

DB

oth

isal

soeq

ual

toze

ro.

Dep

enden

tvar

iab

les

are

inth

efo

rmo

fb

udge

tsh

ares

spen

to

n14

sep

arat

eco

mm

odit

yca

tego

ries

out

of

the

tota

l

mo

nth

lysp

endin

gb

ya

ho

use

ho

ldin

adis

tric

tat

ap

arti

cula

rp

oin

tin

tim

e.A

ddit

ion

alco

ntr

ols

incl

ud

edin

each

spec

ific

atio

n-

dis

tric

tfi

xed

effe

cts,

dum

my

for

rice

regi

on

,dum

my

for

regi

on

sth

atp

roduce

bo

thri

cean

dw

hea

t,h

ouse

ho

ldsi

ze,ag

eo

fth

e

hea

do

fth

eh

ouse

ho

ld,

age

squar

ed,

num

ber

of

child

ren

,n

um

ber

of

liter

ate

mal

ean

dfe

mal

em

emb

ers,

num

ber

of

mal

ean

dfe

mal

em

emb

ers

wit

hp

rim

ary,

mid

dle

,h

igh

eran

dte

chn

ical

educa

tio

n,

Sch

edule

dT

rib

e(S

T),

Sch

edule

dC

aste

(SC

),O

ther

Bac

kw

ard

Cla

ss(O

BC

),H

indu,

Isla

m,

Ch

rist

ian

ity,

Sik

his

m,

and

oth

erre

ligio

n.

Sta

ndar

der

rors

are

clust

ered

atdis

tric

tle

vel

and

rep

ort

edin

par

enth

esis

.Sam

ple

isre

stri

cted

toin

clude

on

lyh

ouse

ho

lds

wit

hat

leas

t1

mal

ean

dfe

mal

ead

ult

mem

ber

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65

Chapter 3

THE EFFECT OF QUALITY OF EDUCATION ON CRIME: EVIDENCE FROM

COLOMBIA1

3.1. Introduction

The World Health Organization reports suggest that 500,000 people are murdered around

the world [World Health Organization, 2014]. Besides homicides, men and women are ex-

posed to violence in some form or the other, be at home, school, or on the streets, given its

prevalence worldwide. As per the WHO, violence is preventable and its impact may be re-

duced but the efforts made have not been enough to tackle it in an effective way. Krug et al.

[2002] asserts that this might be the result of an absence of sound decision-making, reduced

feasibility of policy options, or lack of determination. Besides its causes, since violence is

considered as a form of crime, the actions to address mainly involve investing in more police

and army.

In 1996, the World Health Organization (WHO) declared violence “...as a major and

growing public health problem across the world” Krug et al. [2002, pp. XIX]. Treating violence

as a public health problem instead may help in addressing the problem through investment

in other kinds of policy interventions, such as better education systems and socio-economic

conditions. As such, education policy may be a tool that countries use not only to contribute

to the development of human capital but to also reduce violence and its impacts.

Education may affect violence through different channels. First, education may increase

expectations of being absorbed in the labor market and of future returns, discouraging

engaging in criminal activities. This is what it is called the ‘opportunity cost effect’ of

education. Second, investments in education may generate environments that are less violent,

1With Andres Giraldo, Southern Methodist University and Pontificia Universidad Javeriana

66

as well as promote social and political stability. It is a way in which the government may

positively affect social development. In this sense, education may have what we refer to as a

‘pacifying effect’. Third, education may even be used as a means of indoctrination of ideas

in regions with a strong presence of politics or religion. Strong ideological differences on

account of political ideas could plausibly be fueled by education and lead to conflict between

parties as well as against the government machinery. We call this the ‘indoctrination effect’.

As a fourth channel, improvements in quality of education will likely impact enrollment and

years of education as well in a country over time, which in turn has a direct impact on

violence levels.2

The relationship between education and violence has garnered significant interest from

researchers and policy-makers over the last few decades. In general, a violent environment

is found to hurt economic development in the long run and affect human capital investment

decisions of households [Rodrıguez et al., 2009]. Determining optimal public and private

policies required to combat violence, specifically crime, are thus of utmost importance in

such environments [Becker, 1968]. Apart from greater expenditure on defense, police, and

an efficient judicial system, research suggests that these policies could also be extended to

include expenditure on areas that generate better socio-economic conditions. Improving

local educational systems is a primary way to achieve this goal [Lochner, 2004, 2010a,b].

Extant literature in this field focuses on the relationship between violence and quantity

of education measured by educational enrollment or attainment. Much less is known about

the impact of quality of education on violence. Recent debates however emphasize the

importance of looking at education quality rather than quantity as a reliable indicator of

economic impacts for a country. The number of years a student stays in school may not be

an adequate measure of a good education system or even student achievement. Measures of

individual cognitive skills that incorporate dimensions of test-score performance are found

to provide better indicators of economic outcomes [Hanushek, 2005, Hanushek et al., 2016,

2In this paper we do not explore neither the indoctrination effect nor the fourth channel. For discussionon school quality and school choice impacting educational attainment and in turn crime, see Lochner [2010b].

67

Hanushek et al., 2017].

In line with this, we assert that assessing the impact of education quality is essential for

researchers to understand the existence and persistence of violence and conflicts. Moreover,

from a policy perspective, investment in better quality education may be a tool of social

mobility and long run development for the country. When students learn more in school,

they become more skilled and effective participants in the country’s workforce. Over the

long run, successful efforts to improve school quality would thus imply an extraordinary rate

of return. Thus, quantity of education without quality may not matter.

This paper therefore attempts to analyze the causal impact of quality of education on

violence, specifically on different types of violent crimes and on presence of conflict. One

limitation of the literature that tries to evaluate the impact of education on crime and

conflict is the lack of an identification strategy that overcomes the traditional endogeneity

problem [Barakat and Urdal, 2009, Collier et al., 2004, Hegre et al., 2009, Melander, 2005,

Shayo, 2007]. Moreover, the existing papers are cross country analyses which increases the

probability of having omitted variable bias as the data and institutions across countries are

less comparable at aggregate levels. Our paper on the other hand, addresses the endogeneity

issues and exploits geographic and time variation at a disaggregated level to study this

relationship.

We examine the ‘opportunity cost’ and the ‘pacifying’ effects using Colombia as a case

study.3 Our empirical analysis is at the municipality level and spans a period of six years

from 2007-2013. We use results from a mandatory standardized examination for students at

the last level of high school as the measure of quality of education. Test scores as a measure

of quality are associated with selection issues as they are conditional on taking the exam.

Therefore, we correct for the self - selection problem in test scores to minimize measurement

error in our estimates.

3Although religion is an important aspect of Colombian society, given its hispanic roots, it is not con-sidered as a country in which religion may be used as a way of indoctrinating people. In fact, the religiouseducation may be a root of social cohesion and stability, though we do not explore this channel in the paper.

68

We follow an instrumental variable approach for our estimation since education quality is

endogenous. Quality of education is dependent on funds allocated by the central government

for education to each municipality. However, this allocation is likely endogenous as well given

that there are unobservables associated with the process of allocation of funds that could

be correlated with violence in municipalities. We construct two instruments to address this

endogeneity problem. The first instrument is a spatial instrument constructed by taking

spatially lagged transfers of funds from the center to the municipalities. More specifically,

it is based on central government transfers to neighboring municipalities for investment in

quality of education. The second instrument is based on a shift-share approach which exploits

variation in the size of the central budget, but is not a function of current allocation decisions.

We take the investment in education quality by the central government in municipalities in

the year 2001 as fixed and multiply that with yearly total central government budget for 2007

to 2012 to arrive at the investment figures.4 We use one period lag of both our instruments

for quality recognizing that investment in education may have a lag effect. Both instruments

are in per capita terms.

Our main findings show that quality of education has a significant and negative impact

on crime at an aggregate level, as well as on more disaggregated measures of crime such

as property crime and violent crimes. More specifically, these include crimes like car theft,

total kidnappings and non-political kidnappings. We categorize all types of thefts as crimes

on property (or property crimes) and the results point towards an opportunity cost effect of

education quality.

On the other hand, violent crimes include kidnappings and homicides. Our results are

perhaps suggestive of a pacifying effect of better education quality in this case. Finally,

we analyze the impact education quality has on the presence of illegal armed groups in

municipalities as additional outcome. Better quality of education in municipalities is found

to reduce the probability of presence of such groups This corroborates our results suggesting

4The year 2001 for investment allocation decision was considered due to data limitations. This is the onlyyear for which central government investment data was available before our period of analysis.

69

a pacifying effect of better education quality as it points to a general state of peace and

stability. The results are robust to sample restrictions like exclusion of state capitals or

municipalities with less than 200,000 population as well as urban areas.

The rest of the paper is organized as follows. Section 3.2 presents a review of related

literature on education and violence which is followed by a brief background on violence in

Colombia in section 3.3. Section 3.4 consists of five subsections. Subsection 3.4.1 describes

how we construct our data followed by subsection 3.4.2 describing selection issues and sub-

section 3.4.3 describing our estimation strategy. Subsection 3.4.4 gives a detailed account of

our identification strategy and subsection 3.4.5 provides the institutional framework for cen-

tral government allocation of funds in Colombia. Section 3.5 discusses the baseline results

followed by robustness checks in section 3.7. The paper ends with conclusion and policy

discussion in Section 3.8.

3.2. Literature

Considerable macro-level and cross-national studies exploring the correlation between the

levels of education and conflict find that countries with higher average levels of education

have a lower risk of experiencing conflict. Most of the evidence focuses on education levels

measured by some variant of secondary education enrollment or years of education. In

particular, it is found that young male population are more likely to increase the risk of

conflict in societies where secondary education is low, especially in low and middle income

countries. Increasing secondary male enrollment and average schooling of population thus

reduces risk of civil war and conflict [Collier et al., 2004, Melander, 2005, Shayo, 2007,

Barakat and Urdal, 2009, Hegre et al., 2009].

Single country papers studying the causal impacts of education levels on terrorism, re-

ligious and ethno-communal violence find ambiguous results. Urdal [2008] suggests that

literacy has no causal impact on armed conflict risk and a slightly positive effect on political

violence. Mancini [2005] finds that on average, inter-ethnic educational inequality is gen-

erally lower in peaceful districts for Indonesia. Krueger and Maleckova [2003] present that

70

terrorists have slightly better average education than the population in general in Gaza.

Other papers exploring the relation between quantity of education and violence for single

countries are Berrebi [2007], Humphreys and Weinstein [2008] and Oyefusi [2008] but these

papers report correlations. Buonanno and Leonida [2009] find a negative impact of education

on crime using a set of region fixed effect, year fixed effects and region-specific time trends

together with an extensive set of variables, trying to address the endogeneity problem the

relationship between education and crime intrinsically has.

Another strand of literature focuses on educational policies and violence. Brown [2011]

in his theoretical paper examines the ways in which education policies impact dynamics of

violent conflict. Moretti [2005] argues that the reductions in violence and property crime are

caused by increased schooling although education increases the returns to white collar crime

more than the returns to work. Lochner [2004] finds that arrest rates for white collar crimes

increase when education levels rise. Rodrıguez et al. [2009] explores in-prison behavior

in Argentina to asses the effect of educational programs on violence and finds that such

programs significantly reduce property damages in prison.

Lochner [2010b] in his review of empirical work recognizes that both school quality and

the type of school students attend are important for determining the impacts of quantity of

education on crime. However, there are no studies estimating a direct impact of school quality

on crime. Some causal papers investigate the impact of school choice on student outcomes

including delinquency and crime Cullen et al. [2006], Deming [2011], Guryan [2004]. These

point to the fact that school quality has an impact on enrollment and through this channel,

reduces crime.

Lastly, some papers investigate the reverse relationship, that is, the causal effect of vio-

lence on education and labor market outcomes. Rodrıguez and Sanchez [2012] estimate the

causal effect of armed conflict exposure on school drop-outs and labor decisions of Colom-

bian children and find that conflict affects children older than 11, inducing them to drop

out of school and enter the labor market too early. Barrera and Ibanez [2004] develop a

dynamic theoretical model on the relationship between violence and education investments.

71

They identify that violence affects utility of households directly, modifies consumption of

education, rates of return of education and thus changes investment in education.

3.3. Background

The relationship between education and violence is of special interest in Colombia since it

has suffered a long standing conflict. Following the assassination of the presidential candidate

in 1948, Colombia was engulfed in violent civil war known as La Violencia. Civil conflict

among the main political parties in rural areas eventually ended with a political agreement

known as Frente Nacional under which the two parties agreed to alternate power as a sign

of peace. Interpreted as a discriminatory policy by some factions of the liberal party, this

motivated the creation of two left wing guerrillas - FARC and ELN - that are still active

today. The 1970s marked the onset of the drug phenomena that resulted in acute violence

across the country and extended to the urban areas as well. According to the United Nations

Office on Drugs and Crime (UNODC), Colombia was one of the most violent countries in the

1990s measured by homicide rates. Although the homicide rates have decreased significantly,

it remains a country with severe levels of violence even today.

3.4. Data and Identification

3.4.1. Data

Our data for this analysis is taken from four different sources. First, we use municipality

level panel data constructed by the Studies Center of Economic Development (CEDE by its

acronym in Spanish). The panel contains information on 1122 municipalities and around

2000 variables from the last two decades. It consists of 5 sub-panels: general characteristics,

land and agriculture, fiscal policy, conflict and violence and education.5 Second, we use the

Colombian Institute for Evaluation of Education (acronym ICFES in Spanish) database for

test scores at individual level within the municipalities. Third, we use the census information

5The CEDE collects information from different public and private institutions and is publicly available.

72

from the National Administrative Department of Statistics (acronym DANE in Spanish) ad-

ministered by Minnesota Population Center, University of Minnesota, IPUMS International

[Minnesota Population Center, 2015]. The IPUMS sample contains information for approx-

imately 4 million individuals and the census was conducted between May 2005 to February

2006. Fourth, we use data from the National Planning Department (DNP) for information

on investment in educational quality. Our final constructed data is at municipality level and

spans the years 2007 to 2013.

Our main outcome variables are different forms of crime in a particular municipality at a

given point in time. These are homicides, kidnappings, and thefts. Theft is further divided

between theft on persons, car theft, commerce theft and household theft. Kidnapping is

segregated between total, political and non-political kidnappings. Homicides are defined as

the number of people killed. Kidnapping is defined as the abduction or illegal transportation

of a person, and political kidnapping is a kidnapping committed by an illegal armed group.6

For ease of understanding and analysis, we first generate a measure of intensity of crime

which is the sum of all crime rates. We then group our crime measure into two categories -

property crimes and violent crimes. We construct a measure of intensity of property crimes

which includes different theft rates. Similarly, we create a measure of intensity of violent

crimes which includes non-political, and political kidnappings, and homicides. We also use

all disaggregated rates of crime discussed above as our outcome variables. Crime rate is

total crime divided by total population times 100,000 inhabitants respectively for the entire

analysis.

Another outcome of interest in our paper is the presence of illegal armed groups in a

municipality at a given point in time. Presence of illegal armed groups is a dummy variable

which takes the value 1 if either FARC, ELN or both are present in the municipality. This

outcome is of special interest because they suggest the impact of education quality on violence

associated solely with conflict in Colombia.

6Political kidnapping is perpetrated by guerrillas and para-militaries and non-political kidnapping isperpetrated by common delinquencies, narco-traffickers and others.

73

Our main variable of interest is quality of education at municipality level for which we

consider student test scores at a standardized examination at their last level of high school.

ICFES provides individual standardized test scores for mathematics, language, social sci-

ences, philosophy, biology, chemistry, and physics. We construct a municipality level mea-

sure of test scores that accounts for selection into the examination. Our preferred measure

of quality is an average of the selection-corrected median scores in the subjects combined.

We also consider test scores in only mathematics and language to ascertain performance

in terms of cognitive ability, as well as social sciences and philosophy to examine perfor-

mance in the social area. These measures are an average of the selection-corrected median

scores in mathematics and language; and social sciences and philosophy, respectively. Ad-

ditional measures of quality are explored in this paper such as average z-score index of the

selection-corrected median in seven subjects, average individual total score, median score in

mathematics, median score in language, median score in social sciences, and median score

in philosophy, separately.

Our control variables include a linear time trend, demographic and economic municipality

level controls like total population, birth rate, infant mortality rate, a rurality index of

municipality as an indicator of inequality and development, and agricultural yield7; projected

population to attend primary and secondary school, as measures of quantity of education

or enrollment; and fiscal characteristics like per-capita municipality expenditures and tax

revenue as measures of economic growth. Table C.1 summarizes the variables used in our

analysis.8

7Agricultural yield is the ratio of agricultural cultivation to agricultural production for all crops at mu-nicipality level.

8General characteristics of municipality (notaries, banks, churches, health centers, clinics, tax collectionoffices, electricity coverage), historical characteristics (history of violence, Spanish occupation of municipality,presence of indigenous population, presence of land conflict, presence of illegal crops, armed groups) andgeographical characteristics (area of municipality in squared km., height of municipality in squared km.,linear distance to state capital in squared km) distribution of land and land owners in municipality are notincluded as we estimate a fixed effects model and these are time invariant characteristics of the municipalities.

74

For illustration purposes, Figures (C.1)-(C.4) shows the distribution of crime rate9 and

the average score in subjects across the country in 2007 and 2013. The correlation between

crime rate and the average score in subjects is 0.2359 in 2007 and 0.2360 in 2013. The initial

positive correlation apparent from the figure is intriguing and speaks to the importance of

analyzing the causal link between quality of education and violence in Colombia further.

3.4.2. Selection Issues

A potential issue with using test scores as a measure of education quality is that test

scores suffer from self-selection issues. Since the test scores are conditional on going to school

till grade 11 and taking the standardized exam, they do not represent the true quality of

education in the municipality and would lead to measurement error in our estimates. We

correct this self-selection issue by using data from the 2005 IPUMS Census and estimate the

drop out rates at municipality level to minimize the measurement error. All municipalities of

a state are not included in the IPUMS Census sample. IPUMS aggregates the municipalities

with population less than 20,000 into one category for every state. To arrive at the final

municipality level dropout rates, we make two assumptions. First, we assume that the

dropout rate for each municipality that falls under the aggregated category of IPUMS is

same. We believe this is a valid assumption since these are smaller municipalities and are

similar in population characteristics to each other. Second, municipality level drop out rates

do not change significantly across time.

To estimate the drop out rates, we use probability weights provided in the census data and

calculate the total population in each state in 2005 for the age category of 16-18 years. This

is the age group at which most students take the examination in high school in Colombia.10

We then calculate the population of 16-18 year olds who never attended school, were not

attending school in 2005 or had studied up to middle school but did not complete schooling

in 2005. This depicts the total number of dropouts for each municipality. Dividing the

9We measure crime rate as the sum of the individual crime rates included in the analysis.

10ICFES data shows that approximately 77% of the population that took this examination belonged tothis age category in 2007.

75

total dropouts by the total population in this age group for each municipality gives us the

weighted drop out rates for 2005.

Using individual level test scores from ICFES, we arrive at the median score at munic-

ipality level. Our aim is to impute the dropouts as those scoring below this median score.

We impute zeros for those students who belong to the dropout category and then take the

median score for each municipality since the zero is irrelevant as long as dropouts are below

the median. The assumption for this imputation is that those students who did not appear

for the exam or dropped out are considered to be students who would have scored below the

median. This brings us to the selection-corrected median test scores which is our measure

of education quality at the municipality level.11

3.4.3. Estimation

We estimate the following model to identify the causal impact of quality of education on

violence and crime measures

Ymt = β0 + β1EducationQualitymt + β2Xmt + µm + trendt + εmt (3.1)

where Ymt is first taken as the index of crimes in municipality m at time period t, which is

the sum of all individual crimes, then as the index of only property crimes, and finally as

the index of only violent crimes in municipality m at time period t. This is followed by a

disaggregated analysis where eight separate rates of crime are taken in municipality m at

time period t; EducationQuality is municipality level measure of test scores explained in the

previous section; Xmt are the set of covariates; µm are the municipality fixed effects; trendt

captures time trend of the outcome variable and εmt the mean zero error term in equation.

Education quality is instrumented by two instruments given the existing endogeneity issues.

The instruments are discussed in the next subsection. The parameter of interest is β1 giving

11Note that in the database, there are some missing values for the municipality of residence. We imputethe municipality of residence with the municipality where the students took the examination. For 2007 therewere 198, 2008: 207, 2009: 223, 2010: 535, 2011: 1171, 2012: 95 missing values in ICFES database.

76

us the causal impact of education quality on violence. We also estimate another model to

identify the causal impact of quality of education on presence of illegal armed groups

Presencemt = β0 + βp,1EducationQualitymt + β2Xmt + µm + trendt + εmt (3.2)

where the outcome variable Presencemt = 1 if any of the illegal armed groups (ELN or

FARC) is present in municipality m at time t. We also decompose this outcome variable

and estimate the model separately for presence of FARC and presence of ELN. Equation

3.2 is estimated by a correlated random effects (CRE) probit model. The advantage of the

CRE probit model is that it places some structure on the nature of the correlation between

unobserved effects and the covariates. In order to capture the municipality fixed effects,

we include the means of all the controls at the municipality level across time as additional

controls in the model. We use instruments here as well to deal with the endogeneity of

education quality thereby estimating a CRE IV-probit model.12

3.4.4. Identification Issues

With reverse causality present from violence to education, a simple Ordinary Least

Squares (OLS) estimation of our baseline model is not likely to yield unbiased or consis-

tent estimates of the impact of education quality on violence measures. Moreover, education

quality is likely endogenous even otherwise, since test scores are a noisy proxy of true ed-

ucation quality. We therefore employ an Instrumental Variable approach to find a causal

impact of education quality on violence. We use two instruments in our model.

Our first instrument is constructed from the data on central government transfers to

municipalities for investment in quality of education. Quality of education depends on central

government’s allocation of funds to municipalities. Transfer of funds for investment in quality

of education to every municipality is based on three criteria, which are, population projected

12We run a linear probability model for this as well and find a negative impact of education quality onlikelihood that the illegal armed group is present in the municipality but the estimates are not statisticallydifferent from zero and thus maybe imprecisely estimated.

77

to attend school in the municipality, population that attended school in the municipality

and a measure of equality between municipalities.13 Given this, transfers directly assigned

to a municipality is likely endogenous since there could be unobservables associated with

this process of allocation of funds that are correlated with violence in the municipality.

Thus, we do not use the central transfers directly to municipality m as this may impact

violence in municipality m directly and would violate the exclusion restriction required for a

valid instrument. Instead, our instrument is based on transfers allocated to the neighboring

municipalities. The central government has a fixed budget for education in a state and

distributes it to different municipalities within the state. Funds allocated to the neighboring

municipalities thus affect the funds allocated to municipality m which in turn would affect the

quality of education in m. We believe that such investments do not have a contemporaneous

correlation with test scores. Additionally, such investments have a gestation period and

take time to have an impact. Moreover, in construction of our instrument, we exclude the

neighboring municipalities that share a common border with m because government funds

to the neighbors may still impact violence in m due to easy mobility between municipalities

which share borders with m. To avoid such spillovers, we exclude the first ring of neighbors.

Our instrument is therefore, the average of the funds for investment in quality of education

allocated to the neighboring municipalities of m eliminating the first ring of neighbors in

time period t− 1.

The second instrument is also based on the central government investment for education

quality in municipality, m however it is constructed using a ‘shift-share’ formula. We take

the base year of 2001 for investment in education and calculate the share of government

funds allocated to each municipality in 2001, sm,2001.14 This is municipality specific and

time invariant. The shift-share of investment is calculated by multiplying the share sm,2001

by the total central government budget in years 2007 to 2013. We use one period lag of

the shift-share of investment as the instrument given the belief that education quality has a

13Details on the institutional framework is provided in the next section.

14The year 2001 is considered due to availability of data.

78

lagged impact on violence and crime. This is posited to be exogenous since the proportion of

funds are based on the year 2001 making it time invariant and unlikely to be correlated with

violence or crime today. It can be argued that violence in Colombia is persistent which could

invalidate the exogeneity restriction. However, during this decade, violence at an aggregate

level has been on a declining trend. Thus, the fixed share of government investment in

education quality in 2001 will not likely influence or be influenced by violence rates today.15

Since our instruments are predictors for educational quality, for which we use student test

scores, all our instruments are employed at per capita level.

3.4.5. Institutional Framework

The political constitution of 1991 required the central government in Colombia to pro-

vide resources to states, special districts and municipalities with the aim of encouraging

decentralization.16 Fraction of transfers to states and special districts were called Situado

Fiscal (SF) and the fraction to municipalities were called municipalities participation (PM

by its acronym in Spanish). The SF and PM resources were calculated as a fraction of the

current national revenue (ICN by its acronym in Spanish). Resources constituting the SF

were to be spent on education and health, whereas the PM on health, education, potable

water, physical education, recreation, sports and investment.

Post the 1999 crisis, the initial system of allocation was reformed, the SF was eliminated

and replaced by a General System of Participation (SGP by its acronym in Spanish). The

resources allocated were to be invested in education (58.5%), health (24.5%) and general

purposes (17%) in the states and municipalities. The criteria of transfers extended overtime

to include population that attended school; population projected to attend school; equality

15One concern that could arise here is that even though the trend of violence is declining, if there existsa positive serial correlation between the violence measures over time, then central government allocations in2001 may still be correlated with violence today. However, in our analysis, we cluster the standard errors atmunicipality level which takes care of the serial correlation in the idiosyncratic error term [Drukker, 2003,Wooldridge, 2010]. Moreover, we see no serial correlation between most of violence measures from 2007-2013except for the case of homicide rates and rate of household thefts.

16An excellent summary of the way the fiscal decentralization works in Colombia may be found in Bonetet al. [2014]. This section is mainly based on this document.

79

and administrative efficiency for health; relative poverty, rural and urban population, fiscal

and administrative efficiency for general purposes.

This system underwent further reform in 2007. The new law included investment in

education, health and general purposes as well as potable water and basic sanitation. Share

of resources to be allocated changed to 58.5% for education, 24.5% for health, 11.6% for

potable water and basic sanitation and 5.4% for general purposes.17

By 2012, the SGP represented 4% of the GDP, 30% of the ICN and 15.7% of the total

public expenditure [Bonet et al., 2014]. With respect to education, its share in ICN changed

from 23.17% in 2002 to 16.61% in 2012. This sector receives the biggest portion of the

national transfers. The reform in 2007 sought to include quantity and quality criteria in

education. The main goal was to increase coverage to 100% of territory and improve the

score on the standardized test that we is used in this paper.18

3.5. Results

3.5.1. Crime Rate

Our measure of education quality is the average of the selection-corrected median scores

in all subjects (see section 3.4.1). Table (3.5) shows the effects of test scores on the index

of crime rate. The first six columns present the OLS estimates of equation (3.1), where

column 6 presents the reduced form of the same equation but with the instruments instead

of our measure of education quality. The first column shows the simple correlation without

controls, fixed effects, and trend. As it is shown in Figures (C.1 - C.4), the correlation is

positive. However, when we include both fixed effects and trend, the effect becomes negative.

When we include the demographic and the economic controls, the impact remains negative

and significative. When the measures of quantity of education are included as well as the

variables that capture economic growth, the effect of test scores on crime rate is consistently

17The Congress and central government follow a strategy to control and monitor the way the resourcesare invested under this reform.

18We do not discuss whether the quality goal has been achieved.

80

negative and significative. This implies that quality is more important than quantity of

education. Finally, the reduced form in column 6 shows that the impact of the spatial

instrument is negative, as expected.

Column 7 shows the result of the instrumental variable estimation, which corrects the

identification issues. Our model fairs well on all specification tests. We report the p-value

of the Kleibergen Paap rk LM statistic which depicts the underidentification test. The

null here is that the model is underidentified and we are able to safely reject the null for

all six specifications implying that our instruments are relevant and correlated with the

endogenous regressor. The Kleibergen Paap F statistic is also reported which depicts the

weak-identification test. The F statistic is well above 10 across all specification suggesting

absence of weak-instrument problem. Since we use two instruments, we report the Hansen J

statistic for overidentification of our model. The null here is that the instruments are jointly

valid and we do not reject the null in our specifications (see Baum et al., 2007b).

The point estimate from Table (3.2) suggests that one standard deviation increase in

average median score leads to a decline of approximately 5.9 standard deviations in the

crime index.

3.5.2. Property Crimes

Tables (3.2) depicts the impact of test scores on the crime index described in subsection

3.5.1, the index of property crime, as well as the index of violent crime. Our models fair well

on all specification tests.

The point estimates from Table (3.2) suggests that one standard deviation increase in

average median test scores leads to a decline of approximately 6.2 standard deviations in the

index of economic crime. In accordance with this result, when we look at a disaggregated

analysis of the crime separately in Table (3.3), we find that that test scores leads to a

statistically significant decline in rate of theft on cars. An increase in average median test

scores in all subjects by one standard deviation results in a marginal decline of 6.4 standard

deviations in the rate of theft on cars. These results support the assertion that better quality

81

of education has an ‘opportunity cost effect’ on such property crimes. Better performance

in the school-exit examination encourages students for better potential opportunities in the

labor market increasing their opportunity cost of engaging in criminal behavior.

3.5.3. Violent Crimes

Table (3.2) shows in column 3 the effects of test scores on the index of violent crimes and

columns 5-8 in Table (3.3) show disaggregated violent crimes like total kidnapping rates,

political, and non-political kidnapping rates, and homicide rates, respectively. As before,

our models do well on the specification tests and our instruments are valid and strong. If

the effect of education quality is found to be negative on these measures, one could assert a

‘pacifying effect’ of education in play.

Notice that the impact of test scores on the z-score index of violent crime suggests a

positive impact however the effects are not precisely estimated (column 3 Table 3.2). Upon

disaggregation, we find a statistically significant and negative impact of test scores on total

kidnapping rates as shown in column 5 Table (3.3). An increase in average median test scores

in all subjects by one standard deviation results in a decline of approximately 3.3 standard

deviations in total kidnapping rates and 4.6 standard deviations in non political kidnapping

rate.

3.5.4. Conflict

Results for the model 3.2 from Table (3.4) suggests that better quality of education

lowers the likelihood of presence of illegal armed groups in the municipalities. From the

marginal effects, notice that one standard deviation increase in test scores leads to a decline

in probability that FARC is present in the municipality by 1.1 percent, and either FARC

or ELN by approximately 1 percent. These marginal effects are found to be statistically

significant.19

19We find better education quality reduces the presence of coca crops but the marginal effect is not foundto be significant.

82

As a consequence of the above models estimated, we assert that the results are indicative

of a ‘pacifying effect’ since a decline in the likelihood of presence of illegal armed groups is

found.

3.6. Transmission Channel

The results presented above indicate that both the ‘opportunity cost’ and the ‘pacifying’

effects explain the impact of quality of education on crime. To confirm if the mechanism

behind the negative effect of education on property crime is the ‘opportunity cost’ effect,

we estimate a similar model represented in equation (3.1), but with an outcome variable

that signifies development. This is done to tease out the effect of quality of education from

quantity. Better educational attainment is found to be correlated with higher economic

growth or development. Recent literature has shown that one way to measure economic

growth is through satellite data on night lights. The advantage of using light intensity is

that the measure of economic activity can even cover areas that are typically difficult to

access. Additionally, lights data have high spatial resolution, and is able to access sub-

national levels as well [Henderson et al., 2012, Donaldson and Storeygard, 2016]. Table (3.5)

presents the results.

We find that quality of education remains a more important factor in explaining the

positive impact on development. In particular, the point estimate shows that one standard

deviation increase on the test scores increases the per capita mean of light intensity in approx-

imately 2 standard deviations. This result indicates that development deters involvement in

criminal activities.

3.7. Robustness

3.7.1. Sub-Sample Analysis

We carry out sub-sample analyses to check the robustness of our models (see Appendix).

First, we exclude Bogota from the full sample (Tables C.2 and C.3) as well as all capital

83

cities from the full sample (Tables C.4 and C.5) and the results are found to be broadly

similar to the baseline results in terms of the direction and magnitude of the impact. The

results are statistically significant and the models perform well on all diagnostic tests.

Second, we run a robustness check by restricting our sample to municipalities with pop-

ulation less than 200,000 to give some indication of how results change for smaller and more

rural areas (Tables C.6 and C.7). Results are robust and remain the same as the benchmark

model we estimate.

Lastly, we explore the rural-urban divide and choose municipalities with the proportion

of rural population greater than half of the total municipality population to evaluate the

effect for rural areas (Tables C.8 and C.9). We find statistically significant results similar

to our benchmark case, although at a disaggregated level only non political kidnappings

exhibits significant results. However, restricting our sample to include only urban areas with

the proportion of rural population less than half of the total, we find that the effects are

the opposite to those we found in rural areas. Specifically, the test scores affects negatively

the index of violent crime and the homicide rate. These results are statistically significant

(Tables C.10 and C.11), although the models do not perform well on the underidentification

test. This suggests that rural areas may be driving most of our baseline results.

3.7.2. Other Government Transfers

We run our baseline model using two different instruments for education quality - spatial

as well as shift-share - based on central government transfers to municipalities for other

purposes and not investment in education quality. These transfers to municipalities are

for purposes of education, health, food and general purposes (Tables C.12 and C.13). We

find our models to do well on the specification tests as the baseline. The coefficients for

the aggregated measures of crime maintain the expected sign but only property crime is

statistically significant. At disaggregated levels, the results remain the same but are not

precisely estimated. This suggests that the transfers from central government for other

purposes are good predictors of education quality but they do not have statistically significant

84

impact on our outcome variables. This perhaps implies that our baseline model does in fact

capture the impact of transfers from the central government for the purpose of improving

quality of education specifically on violence through test scores. The same effects are not

found through other kinds of transfers from the central government to the municipalities.

Further, we include these transfers to municipality for other purposes as mentioned above

as an additional regressor in our models (Tables C.14 and C.15). We then replace per capita

total expenditures by per capita total transfers (Tables C.16 and C.17). Finally, we instru-

ment this variable by constructing spatial and shift-share instruments based on central gov-

ernment transfers for other purposes to neighboring municipalities (Tables C.18 and C.19).

We instrument both education quality and central government transfer to municipalities for

other purposes. We estimate this model to study if our results are not merely capturing state

presence in terms of transfers of funds to municipalities. Our instruments become weak or

not valid in most of the specifications. In those specifications in which the instruments are

valid (Tables C.14, column 3; C.15 columns 2-8), the results remain the same as the baseline.

However, we find that the variable capturing transfers from the center to each municipality

for general purposes has no economic impact on crime and violence measures. The coefficient

associated with the regressor is of the order of zero. The effect of test scores change slightly

in terms of magnitude but the sign remains broadly robust to the baseline. This suggests

that we are perhaps capturing the effect of education quality and not just state presence in

general.

3.7.3. Other Measures of Education Quality

We carry out our analysis using other measures of education quality to compare if our

results change from the baseline. Other measures used are average of median selection-

corrected test scores in specific subjects like mathematics and language depicting cognitive

ability of students and philosophy and social sciences depicting social area; and the original

test scores in the exam provided by ICFES without correcting for self-selection (Tables C.20-

C.25).

85

We find our results to be robust when consider the aggregated measure of crime and the

measure of education quality used are the median scores in cognitive subjects, social areas,

and total score. The models perform well on the specification tests and instruments are valid

and relevant based on the underidentification, weak-identification as well as overidentification

tests. Our results are similar to baseline model. However, when we use disaggregated levels

of crime, the models perform well only when we use the average score in social areas, and

the results are similar to those found in the baseline. The signs of the coefficients are as

expected and the statistical significance remain robust.

3.8. Conclusion

This paper attempts to understand if the inherent assumptions about the trade-offs as-

sociated with education, work and involvement in violent or criminal activity do in fact exist

[Lochner, 2004]. Theoretically, education quality can have ambiguous impacts on crimes.

Better quality of education may have an opportunity cost effect that reduces incentives of

engaging in criminal activities due to higher future labor market returns; or a pacifying ef-

fect on crime as a result of more political and social stability. Better education quality may

even lead to organized violence or sometimes indoctrination of political ideas on account of

ideological differences fueled through education systems. In this paper, we evaluate the first

two hypotheses and using an Instrumental Variable approach, we gauge the causal impact

of education quality on violence and crime. Although the paper uses Colombia as a case,

the results found could be applied to wider range of countries with a history of violence.

Our measure of quality of education is the performance of students in a mandatory

standardized examination at the last level of high school. We correct for selection bias in

the test scores to minimize measurement error since test scores are conditional on taking

the exam. We estimate the municipality level drop out rates using the Census sample and

impute zeros as the grades for those students who neither finished nor were enrolled in high

school for this examination. We arrive at the selection bias corrected test scores and use the

standardized average median scores across subjects indicating education quality as a more

86

accurate measure of central tendencies. Crime outcomes are given by theft rates, kidnapping

rates, and homicide rates.

We instrument education quality by constructing spatial instruments based on central

government transfers of funds for improving quality of education to neighboring municipal-

ities of a municipality in consideration. We also use instruments based on the investment

by central government into education quality in every municipality in 2001 and construct

shift-share of investments in each municipality for the periods 2007-2013.

Our results suggest that education quality could have differential impacts on different

forms of crimes. Improvement in quality of education has a statistically significant and

negative impact on an aggregate measure of crime and property crimes one period later.

Furthermore, a disaggregated analysis of economic crime rates shows that the higher the

median scores in the exam, the lower the rates of theft on cars one period hence. This

is in line with an opportunity cost effect thus lowering the incentives of engaging in such

economic crimes. We also find that better education quality leads to a statistically significant

but marginal decline in total and non-political kidnappings. Besides we find better education

quality reduces the presence of illegal armed groups in municipalities suggesting a pacifying

effect.

Our results speak to the importance of designing educational policies that focus not only

on increasing the quantity of education in terms of higher enrollments, years of education

or construction of more educational establishments as suggested by previous works but also

on improving the quality of education with a focus on better facilities, teacher quality and

higher student performance.

87

Tab

le3.

1.C

rim

ean

dE

duca

tion

Qual

ity

OL

SR

edu

ced

Form

IV

(1)

(2)

(3)

(4)

(5)

(6)

(7)

Aver

age

Sco

rein

Su

bje

cts

0.2

1***

-0.3

0***

-0.1

7**

-0.1

5**

-0.1

6**

-5.8

5***

(0.0

2)

(0.0

7)

(0.0

7)

(0.0

7)

(0.0

6)

(2.0

6)

Tota

lP

op

ula

tion

(log)

-0.9

3*

-0.0

60.0

00.5

0-5

.78**

(0.5

3)

(0.7

9)

(0.8

0)

(0.8

4)

(2.4

5)

Bir

thR

ate

0.0

8***

0.0

9***

0.1

0***

0.1

0***

0.0

4(0

.03)

(0.0

3)

(0.0

3)

(0.0

3)

(0.0

5)

Infa

nt

Mort

ali

tyR

ate

-0.4

4***

-0.3

5***

-0.3

2***

-0.3

5***

0.1

3(0

.08)

(0.1

0)

(0.1

1)

(0.1

1)

(0.2

0)

Ru

rality

Ind

ex0.0

60.3

10.3

0-0

.23

2.4

2**

(0.4

0)

(0.4

1)

(0.4

1)

(0.5

5)

(1.1

7)

Agri

cult

ura

lY

ield

-0.0

2-0

.02

-0.0

2-0

.02

-0.0

6(0

.06)

(0.0

6)

(0.0

6)

(0.0

5)

(0.0

7)

Pro

ject

edP

op

ula

tion

toA

tten

dP

rim

ary

Sch

ool

(log)

-0.5

6-0

.50

-0.2

61.0

8(0

.41)

(0.4

1)

(0.4

9)

(0.6

9)

Pro

ject

edP

op

ula

tion

toA

tten

dSec

un

dary

Sch

ool

(log)

-0.3

5-0

.41

-0.9

1*

3.2

8**

(0.4

7)

(0.4

8)

(0.5

4)

(1.6

6)

Tota

lE

xp

end

itu

re0.0

8*

0.2

0***

0.2

3***

(0.0

5)

(0.0

6)

(0.0

6)

Tota

lT

ax

Rev

enu

e0.0

10.0

0-0

.01

(0.0

3)

(0.0

3)

(0.0

3)

L.P

erC

ap

ita

Aver

age

Inves

tmen

tin

Qu

ality

of

Nei

ghb

ors

-0.1

2***

(0.0

5)

L.P

erC

ap

ita

Sh

ift

Sh

are

of

Inves

tmen

ton

Qu

ality

-0.0

1(0

.01)

Mu

nic

ipality

FE

No

Yes

Yes

Yes

Yes

Yes

Yes

Tre

nd

No

Yes

Yes

Yes

Yes

Yes

Yes

Ad

just

ed-R

20.0

50.0

10.0

20.0

20.0

30.0

3-2

.72

Ob

serv

ati

on

s5508

5508

5460

5460

5452

4489

4486

Un

der

iden

tifi

cati

on

0.0

12

Wea

kId

enti

fica

tion

22.4

12

Over

iden

tifi

cati

on

0.5

03

Note

s:S

tan

dard

ized

coeffi

cien

tsfr

om

Ord

inary

Lea

stS

qu

are

s(O

LS

)an

dIn

stru

men

talV

ari

ab

le(I

V)

regre

ssio

ns.

Het

erosk

edas-

tici

tyro

bu

stst

an

dard

erro

res

tim

ate

scl

ust

ered

at

mu

nic

ipality

level

are

rep

ort

edin

pare

nth

eses

;***

den

ote

sst

ati

stic

al

sign

if-

ican

ceat

the

1%

level

,**

at

the

5%

level

,an

d*

at

the

10%

level

,all

for

two-s

ided

hyp

oth

esis

test

s.U

nd

erid

enti

fica

tion

Tes

tre

port

sth

ep

-valu

efo

rth

eK

leib

ergen

-Paap

(2006)

rkst

ati

stic

wit

hre

ject

ion

imp

lyin

gid

enti

fica

tion

;E

nd

ogen

eity

Tes

tre

port

sth

ep

-valu

ew

ith

nu

llb

ein

gvari

ab

leis

exogen

ou

s;F

-sta

tre

port

sth

eK

leib

ergen

-Paap

Fst

ati

stic

an

dC

ragg-D

on

ald

Wald

Fst

ati

stic

for

wea

kid

enti

fica

tion

;O

ver

iden

tifi

cati

on

test

rep

ort

sth

ep

-valu

efo

rth

eH

an

sen

Jst

ati

stic

wit

hth

enu

llb

ein

gth

at

the

inst

rum

ents

are

join

tly

vali

d.

88

Table 3.2. Crime and Education Quality

Crime Rate Economic Crime Violent Crime(1) (2) (3)

Average Score in Subjects -5.85*** -6.17*** 0.26(2.06) (2.24) (0.99)

Municipality FE Yes Yes Yes

Controls Yes Yes Yes

Trend Yes Yes Yes

Adjusted-R2 -2.72 -3.00 -0.20Observations 4486 4491 6134Underidentification 0.012 0.011 0.001Weak Identification 22.412 22.412 22.190Overidentification 0.503 0.614 0.804

Notes: Standardized coefficients from Instrumental Variable (IV) re-gression. Heteroskedasticity robust standard error estimates clus-tered at municipality level are reported in parentheses; *** denotesstatistical significance at the 1% level, ** at the 5% level, and * at the10% level, all for two-sided hypothesis tests. Underidentification Testreports the p-value for the Kleibergen-Paap (2006) rk statistic withrejection implying identification; F-stat reports the Kleibergen-PaapF statistic and Cragg-Donald Wald F statistic for weak identification;Overidentification test reports the p-value for the Hansen J statisticwith the null being that the instruments are jointly valid.

89

Tab

le3.

3.C

rim

ean

dE

duca

tion

Qual

ity

Car

Com

mer

ceH

ou

seh

old

Per

son

Kid

nap

.P

ol.

Kid

nap

.N

on

Pol.

Kid

nap

.H

om

id.

(1)

(2)

(3)

(4)

(5)

(6)

(7)

(8)

Ave

rage

Sco

rein

Su

bje

cts

-6.3

7***

0.6

90.0

1-3

.65

-3.3

2**

-0.2

7-4

.59**

0.7

2

(2.2

1)

(1.2

1)

(0.8

8)

(2.6

9)

(1.6

1)

(0.8

2)

(2.1

2)

(1.0

3)

Mu

nic

ipal

ity

FE

Yes

Yes

Yes

Yes

Yes

Yes

Yes

Yes

Con

trol

sY

esY

esY

esY

esY

esY

esY

esY

es

Tre

nd

Yes

Yes

Yes

Yes

Yes

Yes

Yes

Yes

Ad

just

ed-R

2-1

.77

-0.2

1-0

.19

-1.5

1-0

.52

-0.1

9-0

.83

-0.2

2

Ob

serv

atio

ns

4586

5962

6036

6130

6213

6213

6213

6134

Un

der

iden

tifi

cati

on0.

013

0.0

01

0.0

01

0.0

01

0.0

01

0.0

01

0.0

01

0.0

01

Wea

kId

enti

fica

tion

19.1

52

21.8

99

22.1

12

22.1

55

22.3

65

22.3

65

22.3

65

22.1

90

Ove

rid

enti

fica

tion

0.58

10.1

93

0.2

30

0.2

95

0.5

47

0.8

06

0.4

90

0.9

76

Note

s:S

tan

dard

ized

coeffi

cien

tsfr

om

Inst

rum

enta

lV

ari

ab

le(I

V)

regre

ssio

n.

Het

erosk

edast

icit

yro

bu

stst

an

dard

erro

res

tim

ate

scl

ust

ered

at

mu

nic

ipality

level

are

rep

ort

edin

pare

nth

eses

;***

den

ote

sst

ati

stic

al

sign

ifica

nce

at

the

1%

level

,**

at

the

5%

level

,an

d*

at

the

10%

level

,all

for

two-s

ided

hyp

oth

esis

test

s.U

nd

erid

enti

fica

tion

Tes

tre

port

sth

ep

-valu

efo

rth

eK

leib

ergen

-P

aap

(2006)

rkst

ati

stic

wit

hre

ject

ion

imp

lyin

gid

enti

fica

tion

;F

-sta

tre

port

sth

eK

leib

ergen

-Paap

Fst

ati

stic

an

dC

ragg-D

on

ald

Wald

Fst

ati

stic

for

wea

kid

enti

fica

tion

;O

ver

iden

tifi

cati

on

test

rep

ort

sth

ep

-valu

efo

rth

eH

an

sen

Jst

ati

stic

wit

hth

enu

llb

ein

gth

at

the

inst

rum

ents

are

join

tly

valid

.

90

Table 3.4. Presence and Quality of Education

FARC ELN Either

(1) (2) (3)

Average Score in Subjects -0.072** -0.002 -0.071**

(0.033) (0.047) (0.033)

Control Yes Yes Yes

Controls Mean Yes Yes Yes

N 6215 6215 6215

Note: Estimation is via Instrumental Variable approach. Dependent vari-ables are rates of different forms of violence per 100000 inhabitants. Con-trol variables include birth rate, death rate, infant mortality rate, years ofestablishment of municipality, rurality index, agricultural yield and fiscalcharacteristics. Clustered standard error estimates are reported in paren-theses; ∗∗∗ denotes statistical significance at the 1% level, ∗∗ at the 5%level, and ∗ at the 10% level, all for two-sided hypothesis tests.

91

Table 3.5. Lights and Education Quality

Mean of Lights

OLS Reduced Form IV

(1) (2) (3) (4) (5) (6) (7)

Average Score in Subjects 0.12*** 0.11*** 0.32*** 0.37*** 0.37*** 1.92*(0.02) (0.04) (0.04) (0.04) (0.04) (1.05)

Total Population (log) 0.21 3.07*** 3.07*** 3.20*** 5.66***(0.64) (0.69) (0.69) (1.02) (1.60)

Birth Rate -0.02 0.01 0.01 -0.01 0.03(0.02) (0.02) (0.02) (0.02) (0.03)

Infant Mortality Rate -0.65*** -0.32** -0.32** -0.21 -0.48**(0.12) (0.14) (0.14) (0.16) (0.19)

Rurality Index -1.73*** -0.91*** -0.90*** -1.54*** -2.46***(0.36) (0.33) (0.33) (0.56) (0.72)

Agricultural Yield 0.08* 0.07* 0.07* 0.07 0.07(0.04) (0.04) (0.04) (0.05) (0.04)

Projected Population to Attend Primary School (log) -1.40*** -1.39*** -1.69*** -2.41***(0.43) (0.43) (0.57) (0.64)

Projected Population to Attend Secundary School (log) -1.59*** -1.59*** -1.32** -3.01***(0.46) (0.46) (0.53) (1.06)

Per Capita Total Expenditure -0.01 -0.05 -0.01(0.02) (0.03) (0.03)

Per Capita Total Tax Revenue 0.01 0.00 -0.01(0.01) (0.02) (0.02)

L.Per Capita Average Investment in Quality of Neighbors 0.09**(0.05)

L.Per Capita Shift Share of Investment on Quality -0.01(0.01)

Municipality FE No Yes Yes Yes Yes Yes Yes

Trend No Yes Yes Yes Yes Yes Yes

Adjusted-R2 0.01 0.00 0.07 0.09 0.09 0.08 -0.42Observations 6654 6654 6555 6555 6529 5178 5176Underidentification 0.003Weak Identification 28.900Overidentification 0.261

Notes: Standardized coefficients from Ordinary Least Squares (OLS) and Instrumental Variable (IV) regressions. Heteroskedas-ticity robust standard error estimates clustered at municipality level are reported in parentheses; *** denotes statisticalsignificance at the 1% level, ** at the 5% level, and * at the 10% level, all for two-sided hypothesis tests. UnderidentificationTest reports the p-value for the Kleibergen-Paap (2006) rk statistic with rejection implying identification; Endogeneity Testreports the p-value with null being variable is exogenous; F-stat reports the Kleibergen-Paap F statistic and Cragg-DonaldWald F statistic for weak identification; Overidentification test reports the p-value for the Hansen J statistic with the nullbeing that the instruments are jointly valid.

92

Appendix A

GENDER GAP IN SCHOOLING: IS THERE A ROLE FOR HEALTH INSURANCE?

93

Figure A.1. RSBY Coverage

Source: www.rsby.gov.in

94

Tab

leA

.1.

Rob

ust

nes

s:Im

pac

tof

RSB

Yon

hou

sehol

dsc

hool

exp

endit

ure

-In

stru

men

tal

vari

able

appro

ach

(1)

(2)

(3)

(4)

Bu

dg

et

Sh

are

Lo

g S

ch

oo

l ex

pd

.

Levels

Bu

dg

et

Sh

are

Lo

g S

ch

oo

l ex

pd

.

Levels

RSB

Y*P

ost

0.0

05**

*0.0

80**

* -

0.0

03*

-0.1

05

(0.0

01)

(0.0

14)

(0.0

02)

(0.1

69)

Lo

w I

nco

me

(=1 f

or

bo

tto

m 7

0%

)-0

.047**

-0

.384**

(0.0

24)

(0.1

69)

RSB

Y*P

ost

*Lo

w I

nco

me

0.0

07**

*0.1

87**

*

(0.0

01)

(0.0

88)

Un

der

iden

tifi

cati

on

tes

tp

=0.0

00

p=

0.0

00

p=

0.0

01

p=

0.0

13

Wea

k-i

den

tifi

cati

on

tes

t

K

leig

ber

gen

Paa

p r

k W

ald

F s

tati

stic

11.6

46

17.1

43

6.5

80

12.3

79

En

do

gen

eity

tes

tp

=0.0

10

p=

0.0

31

p=

0.0

10

p=

0.0

38

Oth

er C

on

tro

lsY

YY

Y

To

tal C

on

sum

pti

on

Exp

end

iture

NY

NY

Dis

tric

t fi

xed

eff

ects

YY

YY

Tim

e fi

xed

eff

ects

YY

YY

Dis

tric

t*In

com

e fi

xed

eff

ects

YY

Tim

e*In

com

e fi

xed

eff

ects

YY

N47421

47421

47421

47421

Pan

el

B.

DD

DP

an

el

A.

DID

*p

<0.1

0,

**p

<0.0

5,

***

p<

0.0

1.

Th

esa

mp

leis

rest

rict

edto

HH

wit

hch

ildre

nan

dw

her

eag

eo

fth

eh

ead

isb

etw

een

18

to90

year

s.P

anel

Aan

dB

pro

vid

eth

eD

IDan

dD

DD

resu

lts

resp

ecti

vel

y.C

ol.

(1)

&(3

)re

pea

tth

eb

asel

ine

IVre

sult

.D

epen

den

tvar

iab

leis

HH

bud

get

shar

eo

fsc

ho

olex

pen

dit

ure

.(2

)&

(4)

are

esti

mat

edvia

IVap

pro

ach

.D

epen

den

tvar

iab

leis

the

inver

se

hyp

erb

olic

sin

etr

ansf

orm

atio

no

fH

Hex

pen

dit

ure

on

sch

oo

lin

level

s.T

ota

lco

nsu

mp

tio

nex

pen

dit

ure

isad

ded

asa

regr

esso

ran

din

stru

men

tuse

dfo

rit

isH

Has

sets

.A

dd

itio

nal

con

tro

lin

all

regr

essi

on

sar

eR

SB

Y=

1if

the

dis

tric

tw

asex

po

sed

toR

SB

Y&

0o

ther

wis

e,d

um

my

for

Lo

wIn

com

e=

1if

HH

do

esn

ot

bel

on

gto

top

30%

and

0o

ther

wis

e(f

or

DD

D),

HH

size

(in

stru

men

ted

by

gen

der

of

the

firs

tch

ild),

hig

hes

ted

uca

tio

nd

egre

eso

fm

ale

and

fem

ale

mem

ber

s,in

dic

ato

rsfo

rre

ligio

no

fH

H,in

dic

ato

rsfo

rca

ste

of

HH

,d

um

my

for

urb

anar

eas,

num

ber

of

mar

ried

men

in

the

HH

,n

um

ber

of

mar

ried

wo

men

inth

eH

H,

pro

po

rtio

no

fch

ildre

n,

teen

san

dad

ult

s,in

dic

ato

rfo

rif

HO

His

mar

ried

,d

um

my

for

ifth

eH

Hh

asa

ban

kac

coun

t,d

um

my

for

ifth

eH

Hh

asa

farm

ercr

edit

card

,d

istr

ict

fixed

effe

cts,

tim

efi

xed

effe

cts,

dis

tric

tb

yin

com

efi

xed

effe

cts

(fo

rD

DD

),ti

me

by

inco

me

fixed

effe

cts

(fo

rD

DD

).Sta

nd

ard

erro

rsre

po

rted

are

clust

ered

stan

dar

d

erro

rs.

95

Tab

leA

.2.

Rob

ust

nes

s:Im

pac

tof

RSB

Yon

hou

sehol

dsc

hool

exp

endit

ure

-F

ract

ional

logi

tes

tim

atio

n

(1)

(2)

(3)

(4)

IV w

ith

FE

CR

E F

racL

og

it

(co

ntr

ol

fun

cti

on

)IV

wit

h F

E

CR

E F

racL

og

it

(co

ntr

ol

fun

cti

on

)

RSB

Y*P

ost

0.0

05**

*0.0

29

-0.0

03*

0.0

08

(0.0

01)

(0.0

64)

(0.0

02)

(0.0

68)

Lo

w I

nco

me

(=1 f

or

bo

tto

m 7

0%

)-0

.047**

0.0

69

(0.0

24)

(0.0

91)

RSB

Y*P

ost

*Lo

w I

nco

me

0.0

07**

*0.0

67

(0.0

01)

(0.0

43)

Mar

gin

al E

ffec

t o

f R

SB

Y:

0.0

01

(0.0

01)

Ho

use

ho

lds

that

bel

on

g to

bo

tto

m 7

0%

0.0

02**

(0.0

01)

Ho

use

ho

lds

that

bel

on

g to

to

p 3

0%

0.0

00

(0.0

02)

Un

der

iden

tifi

cati

on

tes

tp

=0.0

00

p=

0.0

01

Wea

k-i

den

tifi

cati

on

tes

t

K

leig

ber

gen

Paa

p r

k W

ald

F s

tati

stic

11.6

46

6.5

80

En

do

gen

eity

tes

tp

=0.0

10

p=

0.0

10

Oth

er C

on

tro

lsY

YY

Y

Dis

tric

t fi

xed

eff

ects

YN

YN

Co

rrel

ated

ran

do

m e

ffec

tsN

YN

Y

Tim

e fi

xed

eff

ects

YY

YY

Tim

e*In

com

e fi

xed

eff

ects

YY

N47421

47421

47421

47421

*p

<0.1

0,

**p

<0.0

5,

***

p<

0.0

1.

Th

esa

mp

leis

rest

rict

edto

HH

wit

hch

ildre

nan

dw

her

eag

eo

fth

eh

ead

isb

etw

een

18

to90

year

s.P

anel

Aan

dB

pro

vid

eth

eD

IDan

dD

DD

resu

lts

resp

ecti

vel

y.C

ol.

(1)

and

(3)

rep

eat

the

bas

elin

eIV

wit

hfi

xed

effe

cts

resu

lts.

Co

l.(2

)an

d(4

)via

afr

acti

on

allo

git

mo

del

wit

hco

rrel

ated

ran

do

mef

fect

susi

ng

aco

ntr

ol

fun

ctio

nap

pro

ach

.

Dep

end

ent

var

iab

lein

all

spec

ific

atio

ns

ish

ouse

ho

ld's

bud

get

shar

eo

fsc

ho

ol

exp

end

iture

.A

dd

itio

nal

con

tro

lsin

all

spec

ific

atio

ns

incl

ud

e:

RSB

Y=

1if

the

dis

tric

tw

asex

po

sed

toR

SB

Y&

0

oth

erw

ise,

dum

my

for

Lo

wIn

com

e=

1if

HH

do

esn

ot

bel

on

gto

top

30%

and

0o

ther

wis

e(f

or

DD

D),

HH

size

(in

stru

men

ted

by

gen

der

of

the

firs

tch

ild),

hig

hes

ted

uca

tio

nd

egre

eso

fm

ale

and

fem

ale

mem

ber

s,in

dic

ato

rsfo

rre

ligio

no

fH

H,in

dic

ato

rsfo

rca

ste

of

HH

,d

um

my

for

urb

anar

eas,

num

ber

of

mar

ried

men

inth

eH

H,n

um

ber

of

mar

ried

wo

men

inth

eH

H,p

rop

ort

ion

of

child

ren

,te

ens

and

adult

s,in

dic

ato

rfo

rif

HO

His

mar

ried

,d

um

my

for

ifth

eH

Hh

asa

ban

kac

coun

t,d

um

my

for

ifth

eH

Hh

asa

farm

ercr

edit

card

,d

istr

ict

fixed

effe

cts,

tim

efi

xed

effe

cts,

dis

tric

t b

y in

com

e fi

xed

eff

ects

(fo

r D

DD

), t

ime

by

inco

me

fixed

eff

ects

(fo

r D

DD

). S

tan

dar

d e

rro

rs r

epo

rted

are

clu

ster

ed s

tan

dar

d e

rro

rs.

Pan

el

B.

DD

D

Pan

el

A.

DID

96

Tab

leA

.3.

Rob

ust

nes

s:Im

pac

tof

RSB

Yon

hou

sehol

dsc

hool

exp

endit

ure

-P

anel

anal

ysi

s

(1)

(2)

(3)

(4)

Rep

eate

d

Cro

ss-S

ecti

on

Pan

el

Rep

eate

d

Cro

ss-S

ecti

on

Pan

el

RSB

Y*P

ost

0.0

05**

*0.0

03**

* -

0.0

03*

-0.0

01

(0.0

01)

(0.0

01)

(0.0

02)

(0.0

02)

Lo

w I

nco

me

(=1 f

or

bo

tto

m 7

0%

)-0

.047**

-

0.7

76*

(0.0

24)

(0.4

45)

RSB

Y*P

ost

*Lo

w I

nco

me

0.0

07**

* 0

.004**

(0.0

01)

(0.0

02)

Oth

er C

on

tro

lsY

YY

Y

Dis

tric

t F

ixed

Eff

ects

YN

YN

Ho

use

ho

ld F

ixed

Eff

ects

NY

NY

Tim

e F

ixed

Eff

ects

YY

YY

Dis

tric

t*In

com

e F

ixed

Eff

ects

YY

Tim

e*In

com

e F

ixed

Eff

ects

YY

N47421

45676

47421

45676

*p

<0.1

0,

**p

<0.0

5,

***

p<

0.0

1.

Th

esa

mp

leis

rest

rict

edto

HH

wit

hch

ildre

nan

dw

her

eag

eo

fth

eh

ead

isb

etw

een

18

to90

year

s.P

anel

Aan

dB

pro

vid

eth

eD

IDan

dD

DD

resu

lts

resp

ecti

vel

y.D

epen

den

tvar

iab

lein

all

spec

ific

atio

ns

isb

ud

get

shar

eo

fh

ouse

ho

ld's

sch

oo

lex

pen

dit

ure

.C

ol.

(1)

and

(3)

rep

eat

the

bas

elin

eIV

wit

hF

Ere

sult

s.D

ata

istr

eate

din

bas

elin

eas

a

rep

eate

dcr

oss

-sec

tio

n.C

ol(2

)&

(4)

are

esti

mat

edtr

eati

ng

dat

aas

ap

anel

dat

ausi

ng

IVw

ith

HH

FE

.A

dd

itio

nal

con

tro

lsin

clud

e:R

SB

Y=

1if

the

HH

inth

ed

istr

ict

was

exp

ose

dto

RSB

Y&

0

oth

erw

ise,

dum

my

for

Lo

wIn

com

e=

1if

HH

do

esn

ot

bel

on

gto

top

30%

and

0o

ther

wis

e(f

or

DD

D),

HH

size

(in

stru

men

ted

by

the

gen

der

of

the

firs

tch

ild),

hig

hes

ted

uca

tio

nd

egre

eso

f

mal

ean

dfe

mal

em

emb

ers,

ind

icat

ors

for

relig

ion

of

HH

,in

dic

ato

rsfo

rca

ste

of

HH

,d

um

my

for

urb

anar

eas,

num

ber

of

mar

ried

men

inth

eH

H,

num

ber

of

mar

ried

wo

men

inth

eH

H,

pro

po

rtio

no

fch

ildre

n,

teen

san

dad

ult

s,in

dic

ato

rfo

rif

HO

His

mar

ried

,d

um

my

for

ifth

eH

Hh

asa

ban

kac

coun

t,d

um

my

for

ifth

eH

Hh

asa

farm

ercr

edit

card

,d

istr

ict

fixed

effe

cts,

HH

fixed

eff

ects

an

d t

ime

fixed

eff

ects

. Sta

nd

ard

err

ors

rep

ort

ed a

re c

lust

ered

sta

nd

ard

err

ors

.

Pan

el

B.

DD

D

Pan

el

A.

DID

97

Tab

leA

.4.

Rob

ust

nes

s:Im

pac

tof

RSB

Yon

child

school

enro

llm

ent

-P

robit

wit

hco

rrel

ated

random

effec

ts

Pan

el

A.

DID

Pan

el

B.

DD

D

(1)

(2)

(3)

(4)

(5)

(6)

LP

M w

ith

FE

LP

M w

ith

CR

EC

RE

Pro

bit

L

PM

wit

h F

EL

PM

wit

h C

RE

CR

E P

rob

it

RSB

Y*P

ost

0.0

27**

*0.0

17**

*0.1

26**

-0

.023

-0.0

23

0.1

91**

*

(0.0

06)

(0.0

05)

(0.0

60)

(0.0

17)

(0.0

17)

(0.0

73)

Bo

y0.0

60**

*0.0

59**

*0.2

93**

*0.0

55**

*0.0

55**

*0.2

53**

*

(0.0

04)

(0.0

04)

(0.0

21)

(0.0

04)

(0.0

04)

(0.0

45)

RSB

Y*P

ost

*Bo

y -0

.019**

*-0

.018**

*-0

.032*

(0.0

05)

(0.0

05)

(0.0

14)

Lo

w I

nco

me

(=1 f

or

bo

tto

m 7

0%

)-0

.042

-0.0

43

-0.1

23

(0.0

23)

(0.0

23)

(0.1

87)

RSB

Y*P

ost

*Lo

w I

nco

me

0.0

46**

0.0

46**

-0

.151

(0.0

20)

(0.0

20)

(0.1

03)

RSB

Y*P

ost

*Lo

w I

nco

me*

Bo

y-0

.009**

*-0

.009**

*0.0

38

(0.0

01)

(0.0

01)

(0.0

75)

Marg

inal

Eff

ects

of

RS

BY

:

Bo

y 0.0

94*

0.0

26

(0.0

55)

(0.0

68)

Gir

l0.1

26**

0.0

40

(0.0

60)

(0.0

73)

Un

der

iden

tifi

cati

on

tes

tp

=0.0

00

p=

0.0

00

p=

0.0

00

p=

0.0

00

Wea

k-i

den

tifi

cati

on

tes

t

Kle

igb

erge

n P

aap

rk W

ald

F s

tati

stic

44.0

22

48.3

96

42.4

650.6

66

En

do

gen

eity

tes

tp

= 0

.570

p=

0.5

46

p=

0.4

14

p=

0.4

23

Oth

er C

on

tro

ls

YY

YY

YY

Dis

tric

t F

ixed

Eff

ects

YN

NY

NN

Co

rrel

ated

Ran

do

m E

ffec

tsN

YY

YY

Y

Tim

e F

ixed

Eff

ects

YY

YN

YY

Dis

tric

t*In

com

e F

ixed

Eff

ects

YY

Y

Tim

e*In

com

e F

ixed

Eff

ects

YY

Y

N83221

83221

83221

83221

83221

83221

*p

<0.1

0,

**p

<0.0

5,

***

p<

0.0

1.

Th

esa

mp

leis

rest

rict

edto

child

ren

abo

ve

the

age

of

5an

db

elo

wth

eag

eo

f18.

Pan

el.

Ap

rovid

esth

eD

IDre

sult

san

dP

anel

.B

pro

vid

esth

eD

DD

resu

lts.

Co

l.(1

)an

d(4

)ar

e

esti

mat

edvia

LP

Mw

ith

FE

.C

ol.

(2)

and

(5)

are

esti

mat

edvia

LP

Mw

ith

corr

elat

edra

nd

om

effe

cts.

Co

l.(3

)an

d(6

)ar

ees

tim

ated

via

IVp

rob

itm

od

elw

ith

corr

elat

edra

nd

om

effe

cts.

Dep

end

ent

var

iab

lein

all

spec

ific

atio

ns

issc

ho

olen

rollm

ent

of

ach

ildin

ah

ouse

ho

ldin

ad

istr

ict

ata

par

ticu

lar

po

int

inti

me.

Ad

dit

ion

alco

ntr

ols

incl

ud

e:ge

nd

erd

um

my

=1

for

ab

oy

and

0fo

ra

girl

,R

SB

Y=

1if

the

dis

tric

tw

asex

po

sed

toR

SB

Yan

d0

oth

erw

ise,

Lo

wIn

com

ed

um

my

=1

ifH

Hd

oes

no

tb

elo

ng

toto

p30%

and

0o

ther

wis

e(f

or

DD

D),

HH

size

,p

aren

taled

uca

tio

nch

arac

teri

stic

s,in

dic

ato

rsfo

rre

ligio

no

fH

H,in

dic

ato

rsfo

rca

ste

of

HH

,d

um

my

for

urb

anar

eas,

sch

oo

lfa

cilit

ies

and

sch

ola

rsh

ips

off

ered

,d

istr

ict

fixed

effe

cts,

tim

efi

xed

effe

cts,

dis

tric

tb

yin

com

efi

xed

effe

cts

(fo

rD

DD

)an

dti

me

by

inco

me

fixed

effe

cts

(fo

rD

DD

).H

Hsi

zeis

inst

rum

ente

d b

y th

e ge

nd

er o

f th

e fi

rst

child

. M

ean

s o

f al

l co

ntr

ols

at

dis

tric

t le

vel

hav

e b

een

in

clud

ed in

(2),

(3),

(5)

and

(6).

Sta

nd

ard

err

ors

rep

ort

ed a

re c

lust

ered

sta

nd

ard

err

ors

.

98

Tab

leA

.5.

Rob

ust

nes

s:Im

pac

tof

RSB

Yon

child

school

enro

llm

ent

-In

stru

men

tal

vari

able

appro

ach

Pan

el

A.

DID

Pan

el

B.

DD

D

(1)

(2)

(3)

(4)

(5)

(6)

LP

M w

ith

FE

, IV

LP

M w

ith

FE

LP

M w

ith

FE

LP

M w

ith

FE

, IV

LP

M w

ith

FE

LP

M w

ith

FE

RSB

Y*P

ost

0.0

27**

*0.0

28**

0.0

28**

-0

.023

-0.0

12

-0.0

16

(0.0

06)

(0.0

11)

(0.0

11)

(0.0

17)

(0.0

18)

(0.0

19)

Bo

y0.0

60**

*0.0

58**

*0.0

59**

*0.0

55**

*0.0

53**

*0.0

54**

*

(0.0

04)

(0.0

04)

(0.0

04)

(0.0

04)

(0.0

03)

(0.0

04)

RSB

Y*P

ost

*Bo

y -0

.019**

*-0

.018**

*-0

.018**

*

(0.0

05)

(0.0

06)

(0.0

06)

Lo

w I

nco

me

(=1 f

or

bo

tto

m 7

0%

)-0

.042

-0.0

59**

*-0

.052**

*

(0.0

23)

(0.0

06)

(0.0

06)

RSB

Y*P

ost

*Lo

w I

nco

me

0.0

46**

0.0

34

0.0

38*

(0.0

20)

(0.0

22)

(0.0

22)

RSB

Y*P

ost

*Lo

w I

nco

me*

Bo

y-0

.009**

*-0

.008**

*-0

.008**

*

(0.0

01)

(0.0

01)

(0.0

01)

Oth

er C

on

tro

ls

YY

YY

YY

Ho

use

ho

ld S

ize

YY

NY

YN

Dis

tric

t F

ixed

Eff

ects

YY

YY

YY

Tim

e F

ixed

Eff

ects

YY

YY

YY

Dis

tric

t*In

com

e F

ixed

Eff

ects

YY

Y

Tim

e*In

com

e F

ixed

Eff

ects

YY

Y

N83221

83221

83221

83221

83221

83221

*p

<0.1

0,**

p<

0.0

5,**

*p

<0.0

1.T

he

sam

ple

isre

stri

cted

toch

ildre

nab

ove

the

age

of

5an

db

elo

wth

eag

eo

f18.

Pan

el.A

pro

vid

esth

eD

IDre

sult

san

dP

anel

.B

pro

vid

esth

eD

DD

resu

lts.

(1)

and

(4)

are

esti

mat

edvia

LP

Mw

ith

FE

.(2

)an

d(5

)ar

ees

tiam

ted

via

LP

Mw

ith

FE

incl

ud

ing

HH

size

asa

regr

esso

rb

ut

no

tin

stru

men

tin

gfo

rit

.(3

)an

d(6

)ar

ees

tim

ated

via

LP

Mw

ith

FE

excl

ud

ing

HH

size

asa

regr

esso

r.D

epen

den

tvar

iab

leis

sch

oo

len

rollm

ent

of

ach

ildin

ah

ouse

ho

ldin

ad

istr

ict

ata

par

ticu

lar

po

int

inti

me.

Ad

dit

ion

alco

ntr

ols

incl

ud

edin

each

spec

ific

atio

n-

gen

der

dum

my

=1

for

ab

oy

and

0fo

ra

girl

,R

SB

Y=

1if

the

dis

tric

tw

asex

po

sed

toR

SB

Yan

d0

oth

erw

ise,

Lo

wIn

com

ed

um

my

=1

ifH

Hd

oes

no

tb

elo

ng

toto

p30%

and

0o

ther

wis

e(f

or

DD

D),

par

enta

led

uca

tio

nch

arac

teri

stic

s,in

dic

ato

rsfo

rre

ligio

no

fH

H,

ind

icat

ors

for

cast

eo

fH

H,d

um

my

for

urb

anar

eas,

sch

oo

lfa

cilit

ies

and

sch

ola

rsh

ips

off

ered

,d

istr

ict

fixed

effe

cts,

tim

efi

xed

effe

cts,

dis

tric

tb

yin

com

efi

xed

effe

cts,

tim

eb

yin

com

efi

xed

effe

cts.

HH

size

isin

clud

edas

are

gres

sor

and

inst

rum

ente

db

y

gen

der

of

the

firs

t ch

ild in

th

e fa

mily

. Sta

nd

ard

err

ors

rep

ort

ed a

re c

lust

ered

sta

nd

ard

err

ors

.

99

Table A.6. Sensitivity analysis: Impact of RSBY on household school expenditure and child

school enrollment - Variation in income categories

Panel A. DID

(1) (2) (3)

Baseline Top and Bottom 30% Mid 40 and Top 30%

Panel I. School Expenditure

RSBY*Post 0.005*** -0.001 -0.001

(0.001) (0.002) (0.002)

Low Income 0.001 0.010

(0.002) (0.019)

RSBY*Post*Low Income 0.005* 0.002

(0.003) (0.003)

Other Controls Y Y Y

District fixed effects Y Y Y

Time fixed effects Y Y Y

District*Income fixed effects Y Y

Time*Income fixed effects Y Y

N 47421 27592 32835

Panel II. School EnrollmentRSBY*Post 0.027*** -0.024** -0.040***

(0.006) (0.012) (0.008)

Boy 0.060*** 0.057*** 0.040***

(0.004) (0.006) (0.004)

Low Income 0.056 -0.086

(0.158) (0.092)

RSBY*Post*Boy -0.019***

(0.005)

RSBY*Post*Low Income 0.063* 0.049***

(0.033) (0.011)

RSBY*Post*Low Income*Boy -0.002* 0.001

(0.001) (0.006)

Underidentification test p=0.000 p=0.001 p=0.000

Weak-identification test

Kleigbergen Paap rk Wald F statistic 44.022 16.721 39.832

Endogeneity test p = 0.570 p=0.529 p = 0.947

Other Controls Y Y Y

District Fixed Effects Y Y Y

Time Fixed Effects Y Y Y

District*Income Fixed Effects Y Y

Time*Income Fixed Effects Y Y

N 83221 47876 57884

Panel B. DDD

* p<0.10, ** p<0.05, *** p<0.01. The sample is restricted to HH with children and where age of the head is between 18 to 90 years. Panel A and B provide the

DID and DDD results respectively. Col (1) repeats the baseline IV results. Col (2) provides DDD results where sample is restricted to top and bottom 30% of

income distribution. Middle 40% is dropped. Col. (3) provides the DDD results where sample is restricted to middle 40% and top 30% of income distribution.

Bottom 30% is dropped. Dependent variable in all specifications is budget share of household's school expenditure. Additional controls include : RSBY = 1 if the

district was exposed to RSBY & 0 otherwise, Low Income dummy =1 if HH belongs to bottom 30% and 0 if HH belongs to top 30% (for Col (2)), Low Income

dummy =1 if HH belongs to middle 40% and 0 if belongs to top 30% (for Col. (3)), HH size (instrumented by gender of the first child), highest education

degrees of male and female members, indicators for religion of HH, indicators for caste of HH, dummy for urban areas, number of married men in the HH,

number of married women in the HH, proportion of children, teens and adults, indicator for if HOH is married, dummy for if the HH has a bank account,

dummy for if the HH has a farmer credit card, district fixed effects, time fixed effects, districy by income fixed effects (for DDD), time by income fixed effects

(for DDD). Standard errors reported are clustered standard errors.

100

Table A.7. Sensitivity analysis: Impact of RSBY on household school expenditure - Variation

by intensity of treatment

Panel A. DID Panel B. DDD

(1) (2)

School Expenditure RSBY*Post 0.000 0.001

(0.001) (0.001)

RSBY*Post*Intensity1 0.001

(0.002)

RSBY*Post*Intensity2 0.003

(0.002)

RSBY*Post*Intensity3 0.003*

(0.001)

RSBY*Post*Low Income 0.002

(0.003)

RSBY*Post*Low Income*Intensity1 0.005

(0.004)

RSBY*Post*Low Income*Intensity2 0.005

(0.005)

RSBY*Post*Low Income*Intensity3 0.008

(0.012)

Underidentification test p=0.000 p=0.000

Weak-identification test

Kleigbergen Paap rk Wald F statistic 12.292 12.213

Endogeneity test p=0.012 p=0.008

Other Controls Y Y

District fixed effects Y Y

Time fixed effects Y Y

District*Income fixed effects Y

Time*Income fixed effects YN 37885 37885

p<0.10, ** p<0.05, *** p<0.01. The sample is restricted to HH where age of the head is between 18 to 90 years. Panel A and B provide the DID and

DDD results respectively on school expenditure. Dependent variable is the budget share of household's school expenditure. Additional controls include in

panel A: RSBY = 1 if the district was exposed to RSBY & 0 otherwise, Low Income dummy =1 if HH belongs to bottom 30% and 0 otherwise, discrete

indicator variable for intensity depending on duration of treatment, relevant two way and three way interaction with intensity, HH size (instrumented by

gender of the first child), highest education degrees of male and female members, indicators for religion of HH, indicators for caste of HH, dummy for

urban areas, number of married men in the HH, number of married women in the HH, proportion of children, teens and adults, indicator for if HOH is

married, dummy for if the HH has a bank account, dummy for if the HH has a farmer credit card, district fixed effects, time fixed effects, district by

income fixed effects (for DDD), time by income fixed effects (for DDD). Standard errors reported are clustered standard errors.

101

Table A.8. Sensitivity analysis: Impact of RSBY on child school enrollment - Variation by

intensity of treatment

Panel A. DID Panel B. DDD

(1) (2)

School EnrollmentBoy 0.060*** 0.056***

(0.004) (0.004)RSBY*Post 0.030*** -0.023

(0.006) (0.018)

RSBY*Post*Boy -0.013***

(0.005)

RSBY*Post*Intensity1 -0.027***

(0.010)

RSBY*Post*Intensity2 0.016

(0.012)

RSBY*Post*Intensity3 0.007

(0.025)

RSBY*Post*Intensity1*Boy -0.003

(0.010)

RSBY*Post*Intensity2*Boy -0.049***

(0.013)

RSBY*Post*Intensity3*Boy -0.097***

(0.028)

RSBY*Post*Low Income 0.050**

(0.021)RSBY*Post*Low Income*Boy 0.001

(0.006)

RSBY*Post*Low Income*Intensity1 -0.022*

(0.013)

RSBY*Post*Low Income*Intensity2 0.008

(0.014)

RSBY*Post*Low Income*Intensity3 -0.017

(0.030)

RSBY*Post*Low Income*Intensity1*Boy -0.027*

(0.014)

RSBY*Post*Low Income*Intensity2*Boy -0.052***

(0.017)

RSBY*Post*Low Income*Intensity3*Boy -0.091**

Underidentification test p=0.000 p=0.000

Weak-identification test

Kleigbergen Paap rk Wald F statistic 44.977 44.589

Endogeneity test p=0.592 p=0.473

Other Controls Y Y

District Fixed Effects Y Y

Time Fixed Effects Y Y

District*Income Fixed Effects Y

Time*Income Fixed Effects YN 83221 83221

p<0.10, ** p<0.05, *** p<0.01. The sample is restricted to HH with children. Panel A and B provide the DID and DDD results respectively on child

school enrollment. Dependent variable is school enrollment of a child in a household in a district at a particular point in time. Additional controls include:

RSBY = 1 if the district was exposed to RSBY & 0 otherwise, Low Income dummy =1 if HH belongs to bottom 30% and 0 otherwise, discrete indicator

variable for intensity depending on duration of treatment, relevant two way and three way interaction with intensity, HH size (instrumented by gender of

the first child), indicators for religion of HH, indicators for caste of HH, dummy for urban areas, parental education characteristics, school facilities and

scholarships offered, district fixed effects, time fixed effects, district by income fixed effects (for DDD), time by income fixed effects (for DDD).

Standard errors reported are clustered standard errors.

102

Tab

leA

.9.

Sen

siti

vit

yan

alysi

s:Im

pac

tof

RSB

Yon

hou

sehol

dsc

hool

exp

endit

ure

-V

aria

tion

inta

ke-u

pby

dis

tric

t

Pan

el

B(1

)(2

)(3

)

DID

DID

wit

h d

istr

ict

en

roll

men

tD

DD

wit

h d

istr

ict

en

roll

men

t

RSB

Y*P

ost

0.0

07

0.0

03

-0.0

98

(0.0

14)

(0.0

15)

(0.1

77)

RSB

Y*P

ost

*Dis

tric

tEn

rollm

ent

0.0

06*

(0.0

04)

RSB

Y*P

ost

*Lo

w I

nco

me

0.1

06

(0.2

02)

RSB

Y*P

ost

*Lo

w I

nco

me*

Dis

tric

tEn

rollm

ent

0.0

01

(0.0

10)

Un

der

iden

tifi

cati

on

tes

tp

=0.2

85

p=

0.1

43

p=

0.1

24

Wea

k-i

den

tifi

cati

on

tes

t

Kle

igb

erge

n P

aap

rk W

ald

F s

tati

stic

1.1

11

2.1

16

2.2

03

En

do

gen

eity

tes

tp

=0.3

22

p=

0.3

55

p=

0.3

13

Oth

er C

on

tro

lsY

Y

Dis

tric

t fi

xed

eff

ects

YY

Tim

e fi

xed

eff

ects

YY

Dis

tric

t*In

com

e fi

xed

eff

ects

Y

Tim

e*In

com

e fi

xed

eff

ects

YN

15265

15265

15265

Pan

el

A

p<

0.1

0,

**p

<0.0

5,

***

p<

0.0

1.

Th

esa

mp

leis

rest

rict

edto

HH

wit

hch

ildre

nan

dw

her

eag

eo

fth

eh

ead

isb

etw

een

18

to90

year

s.P

anel

Aan

dB

pro

vid

e

the

DID

and

DD

Dre

sult

sre

spec

tivel

y.D

epen

den

tvar

iab

leis

the

bud

get

shar

eo

fh

ouse

ho

ld's

sch

oo

lex

pen

dit

ure

.A

dd

itio

nal

con

tro

lsin

clud

e:R

SB

Y=

1

ifth

ed

istr

ict

was

exp

ose

dto

RSB

Y&

0o

ther

wis

e,D

istr

ict

enro

llmen

tra

te(=

enro

lled

targ

eted

ho

use

ho

lds/

tota

lel

igib

leh

ouse

ho

lds)

,L

ow

Inco

me

dum

my

=1

ifH

Hb

elo

ngs

tob

ott

om

30%

and

0o

ther

wis

e,H

Hsi

ze(i

nst

rum

ente

db

yge

nd

ero

fth

efi

rst

child

),h

igh

est

educa

tio

nd

egre

eso

fm

ale

and

fem

ale

mem

ber

s,in

dic

ato

rsfo

rre

ligio

no

fH

H,in

dic

ato

rsfo

rca

ste

of

HH

,d

um

my

for

urb

anar

eas,

num

ber

of

mar

ried

men

inth

eH

H,n

um

ber

of

mar

ried

wo

men

inth

eH

H,p

rop

ort

ion

of

child

ren

,te

ens

and

adult

s,in

dic

ato

rfo

rif

HO

His

mar

ried

,d

um

my

for

ifth

eH

Hh

asa

ban

kac

coun

t,d

um

my

for

ifth

eH

Hh

asa

farm

ercr

edit

card

,d

istr

ict

fixed

effe

cts,

tim

efi

xed

effe

cts,

dis

tric

tb

yin

com

efi

xed

effe

cts

(fo

rD

DD

),ti

me

by

inco

me

fixed

effe

cts

(fo

rD

DD

).Sta

nd

ard

erro

rs r

epo

rted

are

clu

ster

ed s

tan

dar

d e

rro

rs.

103

Tab

leA

.10.

Sen

siti

vit

yan

alysi

s:Im

pac

tof

RSB

Yon

child

school

enro

llm

ent

-V

aria

tion

inag

egr

oups

5-9

years

10-1

4 y

ears

15-1

7 y

ears

5-9

years

10-1

4 y

ears

15-1

7 y

ears

RSB

Y*P

ost

0.0

31**

*0.0

35**

*0.0

17

-0.0

22

-0.0

41**

*0.0

70**

*

(0.0

09)

(0.0

09)

(0.0

14)

(0.0

21)

(0.0

11)

(0.0

16)

Bo

y0.0

43**

*0.0

61**

*0.0

81**

*0.0

39**

*0.0

58**

*-0

.097**

*

(0.0

05)

(0.0

08)

(0.0

17)

(0.0

04)

(0.0

08)

(0.0

22)

RSB

Y*P

ost

*Bo

y -0

.019**

*-0

.019**

*-0

.012

(0.0

07)

(0.0

07)

(0.0

12)

Lo

w I

nco

me

(=1 f

or

bo

tto

m 7

0%

)0.0

44

-0.0

85

-0.1

54

(0.1

40)

(0.1

24)

(0.3

27)

RSB

Y*P

ost

*Lo

w I

nco

me

0.0

37

0.0

69**

*0.1

11**

*

(0.0

29)

(0.0

20)

(0.0

32)

RSB

Y*P

ost

*Lo

w I

nco

me*

Bo

y-0

.009**

*-0

.016*

0.0

19

(0.0

01)

(0.0

08)

(0.0

14)

Un

der

iden

tifi

cati

on

tes

tp

=0.0

00

p=

0.0

00

p=

0.0

00

p=

0.0

00

p=

0.0

00

p=

0.0

00

Wea

k-i

den

tifi

cati

on

tes

t

Kle

igb

erge

n P

aap

rk W

ald

F s

tati

stic

25.2

14

13.1

84

11.1

72

23.6

69

12.0

68

11.1

87

En

do

gen

eity

tes

tp

= 0

.464

p=

0.8

53

p =

0.2

93

p=

0.4

17

p=

0.8

24

p=

0.2

39

Oth

er C

on

tro

ls

YY

YY

YY

Dis

tric

t F

ixed

Eff

ects

YY

YY

YY

Tim

e F

ixed

Eff

ects

YY

YY

YY

Dis

tric

t*In

com

e F

ixed

eff

ects

YY

Y

Tim

e*In

com

e F

ixed

eff

ects

YY

Y

N29411

32824

18650

29411

32824

18650

Pan

el

A.

DID

Pan

el

B.

DD

D

*p

<0.1

0,

**p

<0.0

5,

***

p<

0.0

1.

Pan

elA

and

Bp

rovid

eth

eD

IDan

dD

DD

resu

lts

resp

ecti

vel

y.E

stim

atio

nis

usi

ng

LP

M.

Th

esa

mp

lein

(1)

isre

stri

cted

toch

ildre

nb

etw

een

the

ages

5to

9ye

ars;

in(2

)is

rest

rict

edto

child

ren

bet

wee

nth

eag

es10

to14

year

s;an

din

(3)

isre

stri

cted

toch

ildre

nin

the

ages

15

to17

year

s.D

epen

den

tvar

iab

leis

sch

oo

len

rollm

ent

of

ach

ildin

ah

ouse

ho

ldin

ad

istr

ict

ata

par

ticu

lar

po

int

inti

me.

Ad

dit

ion

al

con

tro

lsin

clud

edin

each

spec

ific

atio

n-

gen

der

dum

my

=1

for

ab

oy

and

0fo

ra

girl

,R

SB

Y=

1if

the

dis

tric

tw

asex

po

sed

toR

SB

Y,

trea

tmen

tin

tera

ctio

ns

wit

hge

nd

erd

um

my,

Lo

wIn

com

ed

um

my

=1

HH

do

esn

ot

bel

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104

Tab

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.11.

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105

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.12.

Sen

siti

vit

yan

alysi

s:Im

pac

tof

RSB

Yon

hou

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dsc

hool

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child

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efi

xed

effe

cts

(fo

rD

DD

),ti

me

by

inco

me

fixed

effe

cts

(fo

rD

DD

).H

Hsi

zeis

inst

rum

ente

db

yth

ege

nd

ero

fth

efi

rst

child

.Sta

nd

ard

erro

rs

rep

ort

ed a

re c

lust

ered

sta

nd

ard

err

ors

.

106

Appendix B

INTRA-HOUSEHOLD CONSUMPTION DECISIONS: EVIDENCE FROM NREGA

107

Figure B.1. Districts map of India

The map shows all rural districts of mainland India, colour-coded according to NREGA implementation phase. Phase 1 districts are

shown in yellow, phase 2 in orange and phase 3 in brown (Source: Berg et al., 2012)

108

Table B.1. Summary statistics

Time period 2007-08

Demographics N Mean SD Min Max N Mean SD Min Max

Age 42356 47.078 12.948 8 109 25766 48.806 13.382 15 99

Number of Adult Male members 42356 1.545 0.821 1 11 25766 1.612 0.869 1 10

Number of Adult Female members 42356 1.544 0.821 1 13 25766 1.638 0.909 1 11

Number of adult male & females in HH 42356 3.089 1.402 2 19 25766 3.250 1.500 2 16

Number of children 42356 1.897 1.613 0 17 25766 1.817 1.623 0 15

Number of adult males with education 42356 2.235 1.188 1 14 25766 2.310 1.220 1 14

Number of adult females with education 42356 2.103 1.168 1 14 25766 2.217 1.236 1 12

Number of members with education 42356 2.872 1.991 0 27 25766 3.220 1.991 0 17

Household Size 42356 5.007 2.556 1 26 25766 5.112 2.664 1 24

Land possessed 42356 4.228 2.095 1 12 25766 4.430 2.311 1 12

HH headed by females 42356 0.061 0.239 0 1 25766 0.075 0.263 0 1

HH males with primary and below schooling 42356 0.282 0.450 0 1 25766 0.247 0.431 0 1

HH males with middle and high school 42356 0.295 0.456 0 1 25766 0.347 0.476 0 1

HH males with higher education 42356 0.144 0.351 0 1 25766 0.200 0.400 0 1

HH males with technical education 42356 0.014 0.119 0 1 25766 0.025 0.156 0 1

HH females with primary and below schooling 42356 0.245 0.430 0 1 25766 0.236 0.425 0 1

HH females with middle and high school 42356 0.184 0.388 0 1 25766 0.247 0.432 0 1

HH females with higher education 42356 0.061 0.240 0 1 25766 0.107 0.309 0 1

HH females with technical education 42356 0.004 0.061 0 1 25766 0.009 0.097 0 1

Muslim 42356 0.040 0.195 0 1 25766 0.040 0.197 0 1

Christian 42356 0.025 0.157 0 1 25766 0.031 0.174 0 1

Sikh 42356 0.005 0.068 0 1 25766 0.022 0.145 0 1

Other religion 42356 0.009 0.097 0 1 25766 0.011 0.105 0 1

Scheduled Tribes 42356 0.077 0.266 0 1 25766 0.050 0.217 0 1

Consumption Variables

Cereals & cereal products 42341 685.635 400.855 10 15000 25745 649.3091 407.1295 20 6000

Pulses & pulses products 41925 120.337 80.2904 4 2000 25499 135.2086 88.45742 4 3300

Edible oil 42196 153.113 89.422 4 4400 25421 172.8233 106.7603 3 2200

Intoxicants, pan and tobacco 34046 119.414 138.466 3 5000 18072 167.7373 202.2132 4 7050

Fuel and light 42105 324.292 172.493 9 4850 25599 394.7693 218.9147 4 5500

Entertainment 13447 86.3677 93.7579 4 4000 9239 120.4679 97.56365 5 2100

Vegetable and fruits 42264 268.937 171.505 6 4200 25719 299.7245 203.5115 10 8000

Salt, spices, condiments and other food 42349 210.592 160.082 4 6150 25764 286.5586 207.0537 15 9262

Meat, milk and milk products 40849 382.834 345.957 4 13000 25466 598.5484 549.7724 10 9000

Medical expenditure 31030 949.223 5024.39 2 250500 19020 1684.086 8925.278 3 375913

School expenditure 30879 1931.37 4116.53 2 125045 21209 2727.583 6094.921 2 215136

Personal, toiletry and miscellaneous articles 42219 135.892 110.527 4 9000 25682 175.9877 131.306 5 3000

Clothing, bedding and footwear 42291 2774.39 2175.91 17 100000 25716 3326.568 2692.659 50 100000

Durable goods 41556 1409.01 6257.68 3 818700 25511 2087.521 10767.01 3 605500

Districts - Phase 3 Districts - Phase 1 & 2

Notes: The table shows the differences in trends in the control districts (districts covered in phase 1 and 2) and the treatment districts (districts covered in phase 3) in 2007-08.

Dummy variables containing information about education levels, caste and religion of the households are included. Dummy for households with female head = 1 if household is

headed by female, otherwise 0. Muslim takes value 1 if household religion is Muslim. Christian = 1 if household religion is Christian, otherwise 0. Sikh = 1 if households religion

is Sikh, otherwise 0. Other religion = 1 if the household religion falls under any of the other categories like Jainism, Buddhism, Zoroastrianism, and others. Scheduled Tribes = 1 if

household caste is scheduled tribe, otherwise all other castes (SCs, OBCs and general) take value 0 because for several districts no data was available for other castes.

109

Tab

leB

.2.

Impac

tof

NR

EG

Aon

Exp

endit

ure

Shar

es-

Fra

ctio

nal

Log

itM

odel

wit

hC

orre

late

dR

andom

Eff

ects

Appro

ach

Vari

ab

les

Cere

als

P

uls

es

Ed

ible

Oil

Fu

el

&

Lig

ht

Into

xic

an

tsE

nte

rtain

men

t

Veg

&

Fru

its

Co

nd

imen

ts

Meat

&

Mil

k

Med

ical

Ex

pd

Sch

oo

l

Ex

pd

Pers

on

al

Clo

thin

g &

bed

din

g

Du

rab

le

Go

od

s

NR

EG

A0.0

46**

*0.0

08

0.0

09

-0.0

16

-0.0

98**

*-0

.088**

*0.0

08

-0.0

60**

*-0

.106**

*-0

.032

0.0

66*

-0.0

33**

*0.0

47**

0.0

53**

(0.0

12)

(0.0

12)

(0.0

11)

(0.0

10)

(0.0

19)

(0.0

17)

(0.0

12)

(0.0

11)

(0.0

16)

(0.0

25)

(0.0

36)

(0.0

10)

(0.0

20)

(0.0

27)

NR

EG

A0.0

09**

*0.0

02

0.0

02

-0.0

03

-0.0

24**

* -

0.0

22**

*0.0

02

-0.0

14**

* -

0.0

21**

*-0

.008

0.0

15*

-0.0

08**

*0.0

08**

0.0

10**

(0.0

02)

(0.0

03)

(0.0

03)

(0.0

02)

(0.0

05)

(0.0

04)

(0.0

03)

(0.0

03)

(0.0

03)

(0.0

06)

(0.0

08)

(0.0

02)

(0.0

04)

(0.0

05)

Oth

er C

on

tro

ls

Yes

Yes

Yes

Yes

Yes

Yes

Yes

Yes

Yes

Yes

Yes

Yes

Yes

Yes

Lan

d in

clud

edY

esY

esY

esY

esY

esY

esY

esY

esY

esY

esY

esY

esY

esY

es

N81898

81049

81234

81373

59134

37835

81821

81915

80101

65201

67975

81823

81743

81276

No

tes:

*p

<0.1

0,**

p<

0.0

5,**

*p

<0.0

1.E

stim

atio

nis

via

frac

tio

nal

logi

tm

od

elw

ith

corr

elat

edra

nd

om

effe

cts.

Th

esa

mp

leis

rest

rict

edto

incl

ud

eh

ouse

ho

lds

wit

hat

leas

to

ne

adult

fem

ale

and

mal

em

emb

er.D

epen

den

tvar

iab

les

are

inth

e

form

of

bud

get

shar

essp

ent

on

14

sep

arat

eco

mm

odit

yca

tego

ries

out

of

the

tota

lm

on

thly

spen

din

gb

ya

ho

use

ho

ldin

ad

istr

ict

ata

par

ticu

lar

po

int

inti

me.

Ad

dit

ion

alco

ntr

ols

incl

ud

edin

each

spec

ific

atio

n-

dis

tric

tfi

xed

effe

cts,

ho

use

ho

ldsi

ze,ag

eo

fth

eh

ead

of

the

ho

use

ho

ld,ag

esq

uar

ed,n

um

ber

of

child

ren

,n

um

ber

of

liter

ate

mal

ean

dfe

mal

em

emb

ers,

num

ber

of

mal

ean

dfe

mal

em

emb

ers

wit

hp

rim

ary,

mid

dle

,h

igh

eran

dte

chn

ical

educa

tio

n,

Sch

edule

dT

rib

e

(ST

),Sch

edule

dC

aste

(SC

),O

ther

Bac

kw

ard

Cla

ss(O

BC

),H

indu,

Isla

m,

Ch

rist

ian

ity,

Sik

his

m,

and

oth

erre

ligio

n,

and

mea

ns

of

all

con

tro

lsat

dis

tric

tle

vel

acro

ssti

me.

Sta

nd

ard

erro

rsar

ecl

ust

ered

atd

istr

ict

level

and

rep

ort

edin

par

enth

esis

. Sam

ple

is

rest

rict

ed t

o in

clude

on

ly h

ouse

ho

lds

wit

h a

tlea

st 1

mal

e an

d f

emal

e ad

ult

mem

ber

wh

o h

ave

sch

oo

l go

ing

child

ren

fo

r th

e m

od

el w

her

e o

utc

om

e is

sch

oo

l ex

pd

endit

ure

Marg

inal

Eff

ects

of

NR

EG

A

Co

eff

icie

nts

110

Tab

leB

.3.

Het

erog

eneo

us

Impac

tsof

NR

EG

Aon

Exp

endit

ure

Shar

es:

Fem

ale

Shar

eof

NR

EG

AE

mplo

ym

ent

-F

ract

ional

Log

itM

odel

wit

hC

orre

late

dR

andom

Eff

ects

Appro

ach

Vari

ab

les

Cere

als

P

uls

es

Ed

ible

Oil

Fu

el

&

Lig

ht

Into

xic

an

tsE

nte

rtain

men

t

Veg

&

Fru

its

Co

nd

imen

ts

Meat

&

Mil

k

Med

ical

Ex

pd

Sch

oo

l

Ex

pd

Pers

on

al

Clo

thin

g &

bed

din

g

Du

rab

le

Go

od

s

Co

eff

icie

nts

NR

EG

A0.1

23**

*0.0

60**

*0.0

55**

*0.0

05

-0.2

21**

*-0

.090**

*0.0

35**

-0.0

78**

*-0

.200**

*-0

.067**

-0.0

40

-0.0

55**

*0.0

23

-0.0

21

(0.0

17)

(0.0

33)

(0.0

14)

(0.0

13)

(0.0

27)

(0.0

21)

(0.0

15)

(0.0

16)

(0.0

25)

(0.0

31)

(0.0

40)

(0.0

13)

(0.0

22)

(0.0

29)

NR

EG

A*F

emal

e sh

are

of

NR

EG

A e

mp

loym

ent

-0.2

27**

*-0

.123**

*-0

.129**

*-0

.081**

*0.3

82**

*-0

.034

-0.0

34

0.0

27

0.3

18**

*0.1

02*

0.3

97**

*0.0

40

0.0

62

0.3

21**

*

(0.0

36)

(0.5

12)

(0.0

27)

(0.0

27)

(0.0

51)

(0.0

33)

(0.0

28)

(0.0

28)

(0.0

46)

(0.0

59)

(0.0

67)

(0.0

25)

(0.0

44)

(0.0

52)

Fem

ale

shar

e o

f N

RE

GA

emp

loym

ent

= 2

5%

0.0

23**

*0.0

15**

*0.0

13**

*0.0

01

-0.0

55**

*-0

.022**

*0.0

08**

-0.0

18**

*-0

.041**

*0.0

16**

-0.0

09*

-0.0

13**

*0.0

04

-0.0

04

(0.0

03)

(0.0

04)

(0.0

03)

(0.0

03)

(0.0

07)

(0.0

05)

(0.0

03)

(0.0

04)

(0.0

05)

(0.0

07)

(0.0

09)

(0.0

03)

(0.0

04)

(0.0

06)

Fem

ale

shar

e o

f N

RE

GA

emp

loym

ent

= 7

5%

0.0

24**

*0.0

15**

*0.0

13**

*0.0

01

-0.0

53**

*-0

.022**

*0.0

08**

-0.0

18**

*-0

.038**

*0.0

16**

0.0

09*

-0.0

13**

*0.0

04

-0.0

04

(0.0

04)

(0.0

04)

(0.0

03)

(0.0

03)

(0.0

06)

(0.0

05)

(0.0

03)

(0.0

04)

(0.0

05)

(0.0

07)

(0.0

09)

(0.0

03)

(0.0

04)

(0.0

05)

Oth

er C

on

tro

ls

Yes

Yes

Yes

Yes

Yes

Yes

Yes

Yes

Yes

Yes

Yes

Yes

Yes

Yes

Lan

d in

cluded

Yes

Yes

Yes

Yes

Yes

Yes

Yes

Yes

Yes

Yes

Yes

Yes

Yes

Yes

N80234

79427

79610

79738

57931

37019

80157

80248

78466

63887

52018

80159

80082

79,6

28

Marg

inal

Eff

ects

of

NR

EG

A

No

tes:

*p

<0.1

0,

**p

<0.0

5,

***

p<

0.0

1.

Est

imat

ion

isvia

frac

tio

nal

logi

tm

od

elw

ith

corr

elat

edra

ndo

mef

fect

sat

dis

tric

tle

vel

.T

he

sam

ple

isre

stri

cted

toin

clud

eh

ouse

ho

lds

wit

hat

leas

to

ne

adult

fem

ale

and

mal

em

emb

er.

Dep

enden

tvar

iab

les

are

in

the

form

of

bud

get

shar

essp

ent

on

14

sep

arat

eco

mm

odit

yca

tego

ries

out

of

the

tota

lm

on

thly

spen

din

gb

ya

ho

use

ho

ldin

adis

tric

tat

ap

arti

cula

rp

oin

tin

tim

e.A

ddit

ion

alco

ntr

ols

incl

uded

inea

chsp

ecif

icat

ion

-dis

tric

tfi

xed

effe

cts,

NR

EG

Ajo

bs

wo

men

toto

tal

emp

loym

ent

rati

oin

tera

cted

wit

hN

RE

GA

,h

ouse

ho

ldsi

ze,

age

of

the

hea

do

fth

eh

ouse

ho

ld,

age

squar

ed,n

um

ber

of

child

ren

,n

um

ber

of

liter

ate

mal

ean

dfe

mal

em

emb

ers,

num

ber

of

mal

ean

dfe

mal

em

emb

ers

wit

hp

rim

ary,

mid

dle

,

hig

her

and

tech

nic

aled

uca

tio

n,

Sch

edule

dT

rib

e(S

T),

Sch

edule

dC

aste

(SC

),O

ther

Bac

kw

ard

Cla

ss(O

BC

),H

ind

u,

Isla

m,

Ch

rist

ian

ity,

Sik

his

m,

and

oth

erre

ligio

nan

dm

ean

so

fal

lco

ntr

ols

atd

istr

ict

level

acro

ssti

me.

Sta

ndar

der

rors

are

clust

ered

at

dis

tric

t le

vel

an

d r

epo

rted

in

par

enth

esis

. Sam

ple

is

rest

rict

ed t

o in

clude

on

ly h

ouse

ho

lds

wit

h a

tlea

st 1

mal

e an

d f

emal

e ad

ult

mem

ber

wh

o h

ave

sch

oo

l go

ing

child

ren

fo

r th

e m

odel

wh

ere

outc

om

e is

sch

oo

l ex

pen

dit

ure

.

111

Tab

leB

.4.

Het

erog

eneo

us

Impac

tsof

NR

EG

Aon

Exp

endit

ure

Shar

es:

Sta

teSti

pula

ted

Min

imum

Wag

es-

Fra

ctio

nal

Log

it

Model

wit

hC

orre

late

dR

andom

Eff

ects

Appro

ach

Vari

ab

les

Cere

als

P

uls

es

Ed

ible

Oil

Fu

el

&

Lig

ht

Into

xic

an

tsE

nte

rtain

men

t

Veg

&

Fru

its

Co

nd

imen

ts

Meat

&

Mil

k

Med

ical

Ex

pd

Sch

oo

l

Ex

pd

Pers

on

al

Clo

thin

g &

bed

din

g

Du

rab

le

Go

od

s

Co

eff

icie

nts

NR

EG

A0.2

67

-0.0

79*

-0.1

08**

-0.0

68

-0.1

00

-0.1

56**

-0.0

35

-0.2

30**

* -

0.2

00**

-0.0

56

0.2

25

-0.0

99**

-0.1

30*

-0.0

92

(0.0

41)

(0.0

44)

(0.0

46)

(0.0

44)

(0.0

75)

(0.0

52)

(0.0

45)

(0.0

38)

(0.0

70)

(0.1

02)

(0.1

44)

(0.0

39)

(0.0

72)

(0.1

00)

NR

EG

A*m

inW

-0.2

21**

*0.0

88**

0.1

21**

0.0

54

0.0

02

0.0

70

0.0

43

0.1

74**

*0.0

91

0.0

29

-0.1

62

0.0

66*

0.1

73**

0.1

39

(0.0

46)

(0.0

41)

(0.0

44)

(0.0

42)

(0.0

71)

(0.0

44)

(0.0

45)

(0.0

38)

(0.0

65)

(0.1

02)

(0.1

32)

(0.0

38)

(0.0

72)

(0.0

94)

Min

imum

Wag

e =

Rs.

82.5

0

per

day

0.0

16**

*-0

.002

-0.0

02

-0.0

05*

-0.0

24**

* -

0.0

24**

*0.0

00

-0.0

20**

* -

0.0

24**

*-0

.008

0.0

21**

-0.0

11**

*0.0

02

0.0

04

(0.0

03)

(0.0

04)

(0.0

03)

(0.0

03)

(0.0

06)

(0.0

05)

(0.0

03)

(0.0

03)

(0.0

04)

(0.0

07)

(0.0

11)

(0.0

03)

(0.0

04)

(0.0

06)

Min

imum

Wag

e =

Rs.

159.4

0

per

day

-

0.0

17**

0.0

15**

0.0

20**

0.0

04

-0

.024**

-0.0

11*

0.0

07

0.0

11*

-0.0

11

-0.0

02

-0.0

07

0.0

01

0.0

27**

0.0

27**

(0.0

06)

(0.0

06)

(0.0

07)

(0.0

05)

(0.0

11)

(0.0

06)

(0.0

07)

(0.0

06)

(0.0

08)

(0.0

15)

(0.0

18)

(0.0

06)

(0.0

09)

(0.0

12)

Oth

er C

on

tro

ls

Yes

Yes

Yes

Yes

Yes

Yes

Yes

Yes

Yes

Yes

Yes

Yes

Yes

Yes

Lan

d in

clud

edY

esY

esY

esY

esY

esY

esY

esY

esY

esY

esY

esY

esY

esY

es

N80234

79427

79610

79738

57931

37019

80157

80248

78466

63887

52018

80159

80082

79,6

28

No

tes:

*p

<0.1

0,

**p

<0.0

5,

***

p<

0.0

1.

Est

imat

ion

isvia

frac

tio

nal

logi

tm

od

elw

ith

corr

elat

edra

nd

om

effe

cts

atd

istr

ict

level

.T

he

sam

ple

isre

stri

cted

toin

clud

eh

ouse

ho

lds

wit

hat

leas

to

ne

adult

fem

ale

and

mal

em

emb

er.

Dep

end

ent

var

iab

les

are

inth

efo

rmo

fb

udge

tsh

ares

spen

to

n14

sep

arat

eco

mm

od

ity

cate

gori

eso

ut

of

the

tota

lm

on

thly

spen

din

gb

ya

ho

use

ho

ldin

ad

istr

ict

ata

par

ticu

lar

po

int

inti

me.

Ad

dit

ion

alco

ntr

ols

incl

uded

inea

chsp

ecif

icat

ion

-dis

tric

tfi

xed

effe

cts,

min

imum

wag

es,

ho

use

ho

ldsi

ze,

age

of

the

hea

do

fth

eh

ouse

ho

ld,

age

squar

ed,

num

ber

of

child

ren

,n

um

ber

of

liter

ate

mal

ean

dfe

mal

em

emb

ers,

num

ber

of

mal

ean

dfe

mal

em

emb

ers

wit

hp

rim

ary,

mid

dle

,h

igh

eran

dte

chn

ical

educa

tio

n,

Sch

edule

dT

rib

e(S

T),

Sch

edule

dC

aste

(SC

),O

ther

Bac

kw

ard

Cla

ss(O

BC

),H

ind

u,

Isla

m,

Ch

rist

ian

ity,

Sik

his

m,

and

oth

erre

ligio

nan

dm

ean

so

fal

lco

ntr

ols

atd

istr

ict

level

acro

ssti

me.

Sta

ndar

der

rors

are

clust

ered

atd

istr

ict

level

and

rep

ort

edin

par

enth

esis

. Sam

ple

is

rest

rict

ed t

o in

clude

on

ly h

ouse

ho

lds

wit

h a

tlea

st 1

mal

e an

d f

emal

e ad

ult

mem

ber

wh

o h

ave

sch

oo

l go

ing

child

ren

fo

r th

e m

od

el w

her

e o

utc

om

e is

sch

oo

l ex

pen

dit

ure

.

Marg

inal

Eff

ects

of

NR

EG

A

112

Tab

leB

.5.

Het

erog

eneo

us

Impac

tsof

NR

EG

Aon

Exp

endit

ure

Shar

es:

Cro

pR

egio

ns

-F

ract

ional

Log

itM

odel

wit

hC

orre

late

d

Ran

dom

Eff

ects

Appro

ach

Vari

ab

les

Cere

als

P

uls

es

Ed

ible

Oil

Fu

el

&

Lig

ht

Into

xic

an

tsE

nte

rtain

men

t V

eg

& F

ruit

s C

on

dim

en

ts

Meat

&

Mil

k

Med

ical

Ex

pd

Sch

oo

l

Ex

pd

Pers

on

al

Clo

thin

g &

bed

din

g

Du

rab

le

Go

od

s

NR

EG

A

-0.0

15*

0.0

36**

-0.0

10

-0.0

5**

-0.0

68**

-0.0

49*

-0.0

23

-0.0

13

-0.0

44

-0.0

30

0.1

04

-0.0

36*

-0.0

54

0.1

38**

(0.0

24)

(0.0

28)

(0.0

21)

(0.0

19)

(0.0

32)

(0.0

28)

(0.0

20)

(0.0

19)

(0.0

34)

(0.0

53)

(0.0

78)

(0.0

21)

(0.0

34)

(0.0

67)

NR

EG

A*R

ice

0.0

71**

-0.0

43

-0.0

10

0.0

28

0.0

55

-0.0

48

0.0

10

-0.0

98**

*-0

.057

-0.0

75

-0.0

08

-0.0

30

0.1

42**

-0.1

17*

(0.0

30)

(0.0

36)

(0.0

28)

(0.0

26)

(0.0

45)

(0.0

34)

(0.0

27)

(0.0

26)

(0.0

40)

(0.0

71)

(0.0

98)

(0.0

26)

(0.0

51)

(0.0

71)

NR

EG

A*B

oth

0.0

02

-0.0

70

-0.0

24

0.0

20

0.2

07**

-0.0

19

0.0

04

0.0

12

0.1

15

0.0

68

-0.1

57

0.0

05

0.0

43

-0.0

32

(0.0

40)

(0.0

41)

(0.0

29)

(0.0

33)

(0.0

70)

(0.0

69)

(0.0

27)

(0.0

36)

(0.0

57)

(0.0

98)

(0.1

35)

(0.0

33)

(0.0

57)

(0.0

85)

Wh

eat

Reg

ion

s0.0

05

0.0

00

-0.0

05

-0.0

06*

-0.0

01

-0.0

20**

*-0

.004

-0.0

17**

* -

0.0

12**

-0.0

15

0.0

18

-0.0

13**

*0.0

07

0.0

12

(0.0

04)

(0.0

04)

(0.0

04)

(0.0

04)

(0.0

07)

(0.0

06)

(0.0

04)

(0.0

04)

(0.0

04)

(0.0

10)

(0.0

14)

(0.0

03)

(0.0

06)

(0.0

07)

Ric

e R

egio

ns

0.0

10**

-0.0

02

-0.0

05

-0.0

05

-0.0

03

-0.0

24**

*-0

.003

-0.0

26**

* -

0.0

20**

* -

0.0

25*

0.0

22

-0.0

16**

*0.0

16*

0.0

04

(0.0

05)

(0.0

06)

(0.0

05)

(0.0

05)

(0.0

09)

(0.0

07)

(0.0

05)

(0.0

06)

(0.0

05)

(0.0

14)

(0.0

18)

(0.0

04)

(0.0

08)

(0.0

09)

Reg

ion

s p

roduci

ng

bo

th-0

.003

-0.0

08

-0.0

08

-0.0

06

0.0

34**

-0.0

17

-0.0

04

0.0

00

0.0

15

0.0

09

-0.0

12

-0.0

07

-0.0

02

0.0

20

(0.0

06)

(0.0

08)

(0.0

05)

(0.0

06)

(0.0

16)

(0.0

16)

(0.0

05)

(0.0

08)

(0.0

10)

(0.0

21)

(0.0

26)

(0.0

06)

(0.0

09)

(0.0

12)

Oth

er C

on

tro

ls

Yes

Yes

Yes

Yes

Yes

Yes

Yes

Yes

Yes

Yes

Yes

Yes

Yes

Yes

Lan

d in

cluded

Yes

Yes

Yes

Yes

Yes

Yes

Yes

Yes

Yes

Yes

Yes

Yes

Yes

Yes

N38141

37536

37722

37866

28802

16616

38103

38146

37211

30407

25213

38112

38081

37814

No

tes:

*p

<0.1

0,**

p<

0.0

5,**

*p

<0.0

1.E

stim

atio

nis

via

frac

tio

nal

logi

tm

od

elw

ith

corr

elat

edra

ndo

mef

fect

sat

dis

tric

tle

vel

.T

he

sam

ple

isre

stri

cted

toin

clud

eh

ouse

ho

lds

wit

hat

leas

to

ne

adult

fem

ale

and

mal

em

emb

er.Sam

ple

isfu

rth

erre

stri

cted

to

incl

ud

eo

nly

tho

sere

gio

ns

that

are

rice

pro

duci

ng,

wh

eat

pro

duci

ng

and

tho

seth

atp

roduce

bo

thri

cean

dw

hea

t.D

Ric

e=1

for

rice

regi

on

s.If

DR

ice=

0,th

enD

Bo

this

also

equal

toze

ro.

Dep

enden

tvar

iab

les

are

inth

efo

rmo

fb

udge

tsh

ares

spen

to

n14

sep

arat

eco

mm

od

ity

cate

gori

eso

ut

of

the

tota

lm

on

thly

spen

din

gb

ya

ho

use

ho

ldin

adis

tric

tat

ap

arti

cula

rp

oin

tin

tim

e.A

ddit

ion

alco

ntr

ols

incl

ud

edin

each

spec

ific

atio

n-

dis

tric

tfi

xed

effe

cts,

dum

my

for

rice

regi

on

s,dum

my

for

regi

on

sth

atp

roduce

bo

thri

cean

dw

hea

t,h

ouse

ho

ldsi

ze,

age

of

the

hea

do

fth

eh

ouse

ho

ld,

age

squar

ed,

num

ber

of

child

ren

,n

um

ber

of

liter

ate

mal

ean

dfe

mal

em

emb

ers,

num

ber

of

mal

ean

dfe

mal

em

emb

ers

wit

hp

rim

ary,

mid

dle

,h

igh

eran

dte

chn

ical

educa

tio

n,

Sch

edule

dT

rib

e(S

T),

Sch

edule

dC

aste

(SC

),O

ther

Bac

kw

ard

Cla

ss(O

BC

),H

indu,

Isla

m,

Ch

rist

ian

ity,

Sik

his

m,

and

oth

erre

ligio

nan

dm

ean

so

fal

lco

ntr

ols

atdis

tric

tle

vel

acro

ssti

me.

Sta

ndar

der

rors

are

clust

ered

atdis

tric

tle

vel

and

rep

ort

edin

par

enth

esis

. Sam

ple

is

rest

rict

ed t

o in

clude

on

ly h

ouse

ho

lds

wit

h a

tlea

st 1

mal

e an

d f

emal

e ad

ult

mem

ber

wh

o h

ave

sch

oo

l go

ing

child

ren

fo

r th

e m

odel

wh

ere

outc

om

e is

sch

oo

l ex

pen

dit

ure

.

Marg

inal

Eff

ects

of

NR

EG

A

Co

eff

icie

nts

113

Tab

leB

.6.

Impac

tof

NR

EG

Aon

Exp

endit

ure

inL

evel

s

Vari

ab

les

Cere

als

P

uls

es

Ed

ible

Oil

Fu

el

&

Lig

ht

Into

xic

an

tsE

nte

rtain

men

t

Veg

&

Fru

its

Co

nd

imen

ts

Meat

&

Mil

k

Med

ical

Ex

pd

Sch

oo

l

Ex

pd

Pers

on

al

Clo

thin

g &

bed

din

g

Du

rab

le

Go

od

s

NR

EG

A0.0

16

0.0

13

-0.0

07

-0.0

50**

*-0

.021

-0.1

99**

*0.0

34

-0.1

07**

*-0

.038

0.0

01

0.1

82**

*-0

.024

0.0

08

0.1

72**

*

(0.0

21)

-0.0

26

-0.0

20

(0.0

19)

(0.0

35)

(0.0

40)

-0.0

24

-0.0

28

(0.0

25)

(0.0

60)

(0.0

52)

(0.0

24)

(0.0

24)

(0.0

51)

Oth

er C

on

tro

ls

Yes

Yes

Yes

Yes

Yes

Yes

Yes

Yes

Yes

Yes

Yes

Yes

Yes

Yes

Dis

tric

t F

ixed

Eff

ects

Yes

Yes

Yes

Yes

Yes

Yes

Yes

Yes

Yes

Yes

Yes

Yes

Yes

Yes

Un

der

iden

tifi

cati

on

Tes

t p

= 0

.000

p =

0.0

00

p =

0.0

00

p =

0.0

00

p =

0.0

00

p =

0.0

00

p =

0.0

00

p =

0.0

00

p =

0.0

00

p =

0.0

00

p =

0.0

00

p =

0.0

00

p =

0.0

00

p =

0.0

00

Wea

k I

den

tifi

cati

on

Tes

t:

Cra

gg-D

on

ald

Wal

d F

sta

tist

ic2009.4

11982.2

64

1991.0

78

1988.9

31393.1

0676.0

81372.1

40

2003.0

64

1873.1

91541.9

71013.9

01998.3

61963.0

21923.6

8

Kle

iber

gen

-Paa

p r

k W

ald F

sta

tist

ic469.8

4464.2

80

463.1

97

468.7

7347.7

7202.9

5468.5

19

471.2

80

436.2

1411.2

4336.6

9473.8

5475.2

9478.0

8

En

do

gen

eity

Tes

tp

= 0

.001

p =

0.2

69

p =

0.2

97

p =

0.0

23

p =

0.0

51

p =

0.0

21

p =

0.6

89

p =

0.0

00

p =

0.0

00

p =

0.0

00

p =

0.0

17

p =

0.0

08

p =

0.0

00

p =

0.0

00

N80234

79427

79610

79738

57931

37019

80157

80248

78466

63887

52018

80159

80083

79628

No

tes:

*p

<0.1

0,

**p

<0.0

5,

***

p<

0.0

1.

Est

imat

ion

isvia

Intr

um

enta

lV

aria

ble

app

roac

h.

Th

esa

mp

leis

rest

rict

edto

incl

ud

eh

ouse

ho

lds

wit

hat

leas

to

ne

adult

fem

ale

and

mal

em

emb

er.D

epen

den

tvar

iab

les

are

inn

atura

llo

gfo

rm-

log

of

mo

nth

ly

exp

end

iture

.T

he

coef

fici

ent

for

NR

EG

Ash

ould

be

inte

rpre

ted

as(e^(β)-1).

Th

eim

pac

tin

per

cen

tage

term

sis(e^(β)-1)*100.A

ddit

ion

alco

ntr

ols

incl

uded

inea

chsp

ecif

icat

ion

-dis

tric

tfi

xed

effe

cts,

log

of

tota

lco

nsu

mp

tio

n,

ho

use

ho

ldsi

ze,

age

of

the

hea

do

fth

eh

ouse

ho

ld,

age

squar

ed,

num

ber

of

child

ren

,n

um

ber

of

liter

ate

mal

ean

dfe

mal

em

emb

ers,

num

ber

of

mal

ean

dfe

mal

em

emb

ers

wit

hp

rim

ary,

mid

dle

,h

igh

eran

dte

chn

ical

educa

tio

n,

Sch

edule

dT

rib

e(S

T),

Sch

edule

dC

aste

(SC

),

Oth

erB

ackw

ard

Cla

ss(O

BC

),H

indu,Is

lam

,C

hri

stia

nit

y,Sik

his

m,an

do

ther

relig

ion

.In

stru

men

tfo

rto

talco

nsu

mp

tio

nis

lan

dp

oss

esse

d.Sta

ndar

der

rors

are

clust

ered

atdis

tric

tle

vel

and

rep

ort

edin

par

enth

esis

.U

nder

iden

tifi

cati

on

Tes

tre

po

rts

the

p-v

alue

of

the

Kle

iber

gen

-Paa

p(2

006)

rkst

atis

tic

wit

hre

ject

ion

imp

lyin

gid

enti

fica

tio

n;E

nd

oge

nei

tyT

est

rep

ort

sth

ep

-val

ue

wit

hn

ull

bei

ng

var

iab

leis

exo

gen

ous;

F-s

tat

rep

ort

sth

eK

leib

erge

n-P

aap

Fst

atis

tic

and

Cra

gg-D

on

ald

Wal

dF

stat

isti

cfo

r

wea

k iden

tifi

cati

on

. Sam

ple

is

rest

rict

ed t

o in

clude

on

ly h

ouse

ho

lds

wit

h a

tlea

st 1

mal

e an

d f

emal

e ad

ult

mem

ber

wh

o h

ave

sch

oo

l go

ing

child

ren

fo

r th

e m

odel

wh

ere

outc

om

e is

sch

oo

l ex

pen

dit

ure

.

114

Tab

leB

.7.

Het

erog

eneo

us

Impac

tsof

NR

EG

Aon

Exp

endit

ure

inL

evel

s:F

emal

eShar

eof

NR

EG

AE

mplo

ym

ent

Vari

ab

leC

ere

als

P

uls

es

Ed

ible

Oil

Fu

el

&

Lig

ht

Into

xic

an

tsE

nte

rtain

men

t

Veg

&

Fru

its

Co

nd

imen

ts

Meat

&

Mil

k

Med

ical

Ex

pd

Sch

oo

l

Ex

pd

Pers

on

al

Clo

thin

g &

bed

din

g

Du

rab

le

Go

od

s

NR

EG

A-0

.002

0.0

03

0.0

05

-0.0

71**

*-0

.084*

-0.0

83

-0.0

30

-0.0

92**

-0.0

14

0.1

87**

-0.0

82

-0.0

36

-0.021

0.0

29

(0.0

29)

(0.0

37)

(0.0

28)

(0.0

26)

(0.0

49)

(0.0

59)

(0.0

37)

(0.0

45)

(0.0

36)

(0.0

90)

(0.0

93)

(0.0

33)

(0.0

35)

(0.0

70)

NR

EG

A*F

emal

e sh

are

of

NR

EG

A e

mp

loym

ent

0.0

29

0.0

20

-0.0

20

0.0

27

0.1

81**

-0.2

53**

*0.1

76**

*-0

.044

-0.0

46

-0.3

91**

*0.7

29**

*0.0

10

0.0

79

0.3

93**

*

(0.0

61)

(0.0

67)

(0.0

56)

(0.0

48)

(0.0

86)

(0.0

96)

(0.0

59)

(0.0

71)

(0.0

74)

(0.1

51)

(0.1

72)

(0.0

58)

(0.0

69)

(0.1

29)

Oth

er C

on

tro

ls

Yes

Y

es

Yes

Y

es

Yes

Y

es

Yes

Y

es

Yes

Y

es

Yes

Y

es

Yes

Y

es

Dis

tric

t F

ixed

Eff

ects

Yes

Y

es

Yes

Y

es

Yes

Y

es

Yes

Y

es

Yes

Y

es

Yes

Y

es

Yes

Y

es

Fem

ale

shar

e o

f N

RE

GA

emp

loym

ent

= 2

5%

0.0

05

0.0

08

0.0

00

-0.0

65**

*-0

.038

-0.1

46**

*0.0

14

-0.1

03**

*-0

.025

0.0

90

0.1

00

-0.0

33

-0.0

01

0.1

27**

(0.0

22)

(0.0

28)

(0.0

21)

(0.0

20)

(0.0

38)

(0.0

44)

(0.0

27)

(0.0

33)

(0.0

26)

(0.0

66)

(0.0

73)

(0.0

25)

(0.0

25)

(0.0

53)

Fem

ale

shar

e o

f N

RE

GA

emp

loym

ent

= 7

5%

0.0

19

0.0

18

-0.0

10

-0.0

51*

0.0

52

-0.2

73**

*0.1

02**

*-0

.125**

*-0

.048

-0.1

06

0.4

64**

*-0

.028

0.0

39

0.3

24**

*

(0.0

34)

(0.0

37)

(0.0

29)

(0.0

27)

(0.0

46)

(0.0

50)

(0.0

29)

(0.0

34)

(0.0

40)

(0.0

74)

(0.0

99)

(0.0

32)

(0.0

36)

(0.0

71)

Un

der

iden

tifi

cati

on

Tes

tp

= 0

.000

p =

0.0

00

p =

0.0

00

p =

0.0

00

p =

0.0

00

p =

0.0

00

p =

0.0

00

p =

0.0

00

p =

0.0

00

p =

0.0

00

p =

0.0

00

p =

0.0

00

p =

0.0

00

p =

0.0

00

Wea

k I

den

tifi

cati

on

Tes

t:

Cra

gg-D

on

ald

Wal

d F

stat

isti

c1962.1

11940.4

21950.3

21945.9

41369.6

4657.4

81958.3

31960.5

21830.4

71515.0

51409.8

21953.4

61919.7

21879.5

0

Kle

iber

gen

-Paa

p r

k W

ald

F

stat

isti

c459.5

9455.1

8454.3

4459.3

9336.0

5207.5

4459.0

2461.9

4427.3

2404.5

5378.6

8464.2

3466.2

2469.5

6

En

do

gen

eity

Tes

tp

= 0

.001

p =

0.3

05

p =

0.4

10

p =

0.0

17

p =

0.0

29

p =

0.0

63

p =

0.5

09

p =

0.0

00

p =

0.0

00

p =

0.0

00

p =

0.0

03

p =

0.0

14

p =

0.0

00

p =

0.0

00

N78436

77787

77919

77971

56617

36164

78356

78448

76716

62531

65037

78366

78287

77885

Marg

inal

Eff

ects

of

NR

EG

A

No

tes:

*p

<0.1

0,

**p

<0.0

5,

***

p<

0.0

1.

Est

imat

ion

isvia

Intr

um

enta

lV

aria

ble

app

roac

hin

Dif

f-in

-Dif

f.T

he

sam

ple

isre

stri

cted

toin

clud

eh

ouse

ho

lds

wit

hat

leas

to

ne

adult

fem

ale

and

mal

em

emb

er.

Dep

enden

tvar

iab

les

are

inn

atura

l

log

form

- lo

g o

f m

on

thly

exp

endit

ure

; th

us,

th

e co

effi

cien

t fo

r th

e dum

my

var

iab

les

sho

uld

be

inte

rpre

ted

as

e^(β

)-1. T

he

imp

act

in p

erce

nta

ge t

erm

s is

(e^

(β)-

1)*

100. A

ddit

ion

al c

on

tro

ls in

clud

ed in

eac

h s

pec

ific

atio

n -

dis

tric

t fi

xed

eff

ects

,

log

of

tota

lco

nsu

mp

tio

n,

ho

use

ho

ldsi

ze,

age

of

the

hea

do

fth

eh

ouse

ho

ld,

age

squar

ed,

num

ber

of

child

ren

,n

um

ber

of

liter

ate

mal

ean

dfe

mal

em

emb

ers,

num

ber

of

mal

ean

dfe

mal

em

emb

ers

wit

hp

rim

ary,

mid

dle

,h

igh

eran

dte

chn

ical

educa

tio

n,Sch

edule

dT

rib

e(S

T),

Sch

edule

dC

aste

(SC

),O

ther

Bac

kw

ard

Cla

ss(O

BC

),H

ind

u,Is

lam

,C

hri

stia

nit

y,Sik

his

m,an

do

ther

relig

ion

.In

stru

men

tfo

rto

talco

nsu

mp

tio

nis

lan

dp

oss

esse

d.Sta

ndar

der

rors

are

clust

ered

atd

istr

ict

level

and

rep

ort

edin

par

enth

esis

.U

nd

erid

enti

fica

tio

nT

est

rep

ort

sth

ep

-val

ue

of

the

Kle

iber

gen

-Paa

p(2

006)

rkst

atis

tic

wit

hre

ject

ion

imp

lyin

gid

enti

fica

tio

n;

En

do

gen

eity

Tes

tre

po

rts

the

p-v

alue

wit

hn

ull

bei

ng

var

iab

leis

exo

gen

ous;

F-s

tat

rep

ort

sth

eK

leib

erge

n-P

aap

Fst

atis

tic

and

Cra

gg-D

on

ald

Wal

dF

stat

isti

cfo

rw

eak

iden

tifi

cati

on

.Jo

int

sign

ific

ance

test

sre

po

rtth

est

atis

tica

lsi

gnif

ican

ceo

fth

eto

tal

imp

act

of

NR

EG

Aev

aluat

edat

the

max

imum

of

stat

est

ipula

ted

stan

dar

diz

dm

inim

um

wag

esas

wel

las

the

min

imum

bo

un

d.

Sam

ple

isre

stri

cted

toin

clud

eo

nly

ho

use

ho

lds

wit

hat

leas

t1

mal

ean

dfe

mal

ead

ult

mem

ber

wh

oh

ave

sch

oo

lgo

ing

child

ren

for

the

mo

del

wh

ere

outc

om

eis

sch

oo

l

exp

end

iture

.

115

Tab

leB

.8.

Het

erog

eneo

us

Impac

tsof

NR

EG

Aon

Exp

endit

ure

inL

evel

s:Sta

teSti

pula

ted

Min

imum

Wag

es

Vari

ab

leC

ere

als

P

uls

es

Ed

ible

Oil

Fu

el

&

Lig

ht

Into

xic

an

tsE

nte

rtain

men

t

Veg

&

Fru

its

Co

nd

imen

ts

Meat

&

Mil

k

Med

ical

Ex

pd

Sch

oo

l

Ex

pd

Pers

on

al

Clo

thin

g &

bed

din

g

Du

rab

le

Go

od

s

NR

EG

A0.1

79**

-0.2

01**

-0.2

08**

*-0

.143*

-0.0

61

-0.4

98**

*-0

.219**

-0

.380**

*-0

.122

0.0

54

0.0

44

-0.0

97

-0.2

10**

-0.0

18

(0.0

79)

-0.1

01

-0.0

78

(0.0

81)

(0.1

42)

(0.1

36)

-0.0

96

-0.0

91

(0.0

97)

(0.2

25)

(0.2

17)

(0.0

93)

(0.0

88)

(0.1

83)

NR

EG

A*m

inW

-0.1

66**

0.2

10**

0.2

02**

*0.0

94

0.0

40

0.3

02**

*0.2

47**

*0.2

81**

*0.0

79

-0.0

37

0.1

40

0.0

70.2

10**

0.1

68

(0.0

72)

-0.0

92

-0.0

77

(0.0

79)

(0.1

41)

(0.1

15)

-0.0

95

-0.0

89

(0.0

96)

(0.2

15)

(0.2

13)

(0.0

88)

(0.0

84)

(0.1

73)

Oth

er C

on

tro

ls

Yes

Yes

Yes

Yes

Yes

Yes

Yes

Yes

Yes

Yes

Yes

Yes

Yes

Yes

Dis

tric

t F

ixed

Eff

ects

Yes

Yes

Yes

Yes

Yes

Yes

Yes

Yes

Yes

Yes

Yes

Yes

Yes

Yes

NR

EG

A*m

inW

(at

Min

W =

Rs.

82.5

0 p

er d

ay)

-0.0

97**

0.1

11*

0.0

80

-0.0

18

0.0

44

-0.0

35

0.2

08**

*0.0

23

-0.0

52

-0.0

29

0.2

89*

0.0

02

0.1

26

0.2

67**

(0.0

47)

-0.0

61

-0.0

53

(0.0

5)

(0.1

01)

(0.0

76)

-0.0

63

-0.0

67

(0.0

65)

(0.1

55)

(0.1

71)

(0.0

59)

(0.0

59)

(0.1

15)

NR

EG

A*m

inW

(at

Min

W =

Rs.

159.4

0 p

er d

ay)

0.0

37

-0.0

22

-0.0

28

-0.0

58**

0.0

02

-0.2

55**

*-0

.015

-0.1

33**

*-0

.037

-0.0

16

0.1

36

-0.0

31

-0.0

15**

0.1

45**

(0.0

26)

-0.0

34

-0.0

24

(0.0

24)

(0.0

41)

(0.0

55)

-0.0

29

-0.0

32

(0.0

31)

(0.0

74)

(0.0

88)

(0.0

30)

(0.0

29)

(0.0

61)

Un

der

iden

tifi

cati

on

Tes

tp

= 0

.000

p =

0.0

00

p =

0.0

00

p =

0.0

00

p =

0.0

00

p =

0.0

00

p =

0.0

00

p =

0.0

00

p =

0.0

00

p =

0.0

00

p =

0.0

00

p =

0.0

00

p =

0.0

00

p =

0.0

00

Wea

k I

den

tifi

cati

on

Tes

t:

Cra

gg-D

on

ald

Wal

d F

sta

tist

ic2025.8

81998.6

74

2014.2

01

2005.7

31399.1

6688.5

62019.6

81

2020.9

39

1889.6

91555.8

81017.0

63

2014.8

41979.8

81940.5

8

Kle

iber

gen

-Paa

p r

k W

ald F

stat

isti

c474.6

3469.0

85

470.6

25

473.7

1351.6

4203.6

6473.3

44

476.2

08

440.1

7415.5

3335.5

4478.8

6480.1

7483.2

7

En

do

gen

eity

Tes

tp

= 0

.001

p =

0.3

26

p =

0.3

47

p =

0.0

24

p =

0.0

50

p =

0.0

30

p =

0.5

70

p =

0.0

00

p =

0.0

00

p =

0.0

00

p =

0.0

16

p =

0.0

10

p =

0.0

00

p =

0.0

00

N80234

79427

79610

79738

57931

37019

80157

80248

78466

63887

52018

80159

80083

79628

No

tes:

*p

<0.1

0,

**p

<0.0

5,

***

p<

0.0

1.

Est

imat

ion

isvia

Intr

um

enta

lV

aria

ble

app

roac

hin

Dif

f-in

-Dif

f.T

he

sam

ple

isre

stri

cted

toin

clude

ho

use

ho

lds

wit

hat

leas

to

ne

adult

fem

ale

and

mal

em

emb

er.

Dep

enden

tvar

iab

les

are

inn

atura

llo

gfo

rm-

log

of

mo

nth

lyex

pen

dit

ure

;th

us,

the

coef

fici

ent

for

the

dum

my

var

iab

les

sho

uld

be

inte

rpre

ted

ase^(β)-1.T

he

imp

act

inp

erce

nta

gete

rms

is(e^(β)-1)*100.A

dd

itio

nal

con

tro

lsin

clud

edin

each

spec

ific

atio

n-

dis

tric

tfi

xed

effe

cts,

min

imum

wag

es,lo

g

of

tota

lco

nsu

mp

tio

n,

ho

use

ho

ldsi

ze,

age

of

the

hea

do

fth

eh

ouse

ho

ld,

age

squar

ed,

num

ber

of

child

ren

,n

um

ber

of

liter

ate

mal

ean

dfe

mal

em

emb

ers,

num

ber

of

mal

ean

dfe

mal

em

emb

ers

wit

hp

rim

ary,

mid

dle

,h

igh

eran

dte

chn

ical

educa

tio

n,

Sch

edule

dT

rib

e(S

T),

Sch

edule

dC

aste

(SC

),O

ther

Bac

kw

ard

Cla

ss(O

BC

),H

indu,

Isla

m,

Ch

rist

ian

ity,

Sik

his

m,

and

oth

erre

ligio

n.

Inst

rum

ent

for

tota

lco

nsu

mp

tio

nis

lan

dp

oss

esse

d.

Sta

ndar

der

rors

are

clust

ered

atdis

tric

tle

vel

and

rep

ort

edin

par

enth

esis

.U

nder

iden

tifi

cati

on

Tes

tre

po

rts

the

p-v

alue

of

the

Kle

iber

gen

-Paa

p(2

006)

rkst

atis

tic

wit

hre

ject

ion

imp

lyin

gid

enti

fica

tio

n;

En

do

gen

eity

Tes

tre

po

rts

the

p-v

alue

wit

hn

ull

bei

ng

var

iab

leis

exo

gen

ous;

F-s

tat

rep

ort

sth

eK

leib

erge

n-P

aap

Fst

atis

tic

and

Cra

gg-D

on

ald

Wal

dF

stat

isti

cfo

rw

eak

iden

tifi

cati

on

.Jo

int

sign

ific

ance

test

sre

po

rtth

est

atis

tica

lsi

gnif

ican

ceo

fth

eto

tal

imp

act

of

NR

EG

Aev

aluat

edat

the

max

imum

of

stat

est

ipula

ted

stan

dar

diz

dm

inim

um

wag

esas

wel

las

the

min

imum

bo

un

d. S

amp

le is

rest

rict

ed t

o in

clude

on

ly h

ouse

ho

lds

wit

h a

tlea

st 1

mal

e an

d f

emal

e ad

ult

mem

ber

wh

o h

ave

sch

oo

l go

ing

child

ren

fo

r th

e m

od

el w

her

e o

utc

om

e is

sch

oo

l ex

pen

dit

ure

.

Marg

inal

Eff

ects

of

NR

EG

A

116

Tab

leB

.9.

Het

erge

neo

us

Impac

tsof

NR

EG

Aon

Exp

endit

ure

inL

evel

s:C

rop

Reg

ions

Vari

ab

les

Cere

als

P

uls

es

Ed

ible

Oil

Fu

el

&

Lig

ht

Into

xic

an

tsE

nte

rtain

men

t

Veg

&

Fru

its

Co

nd

imen

ts

Meat

&

Mil

k

Med

ical

Ex

pd

Sch

oo

l

Ex

pd

Pers

on

al

Clo

thin

g &

bed

din

g

Du

rab

le

Go

od

s

NR

EG

A-0

.076*

0.0

21

0.0

00

-0.0

85**

*-0

.026

-0.0

85

0.0

33

0.0

13

0.0

00

0.0

53

0.2

32**

-0.0

39

-0.0

49

0.2

38**

(0.0

41)

-0.0

49

-0.0

35

(0.0

33)

(0.0

58)

(0.0

66)

-0.0

49

-0.0

44

(0.0

48)

(0.1

32)

(0.1

04)

(0.0

52)

(0.0

64)

(0.1

21)

NR

EG

A*R

ice

0.1

41**

*-0

.063

-0.0

42

0.0

71

-0.0

3-0

.136

-0.1

05*

-0.3

14**

*-0

.120**

-0.1

82

0.0

54

-0.0

70.1

29*

-0.1

04

(0.0

52)

-0.0

71

-0.0

47

(0.0

45)

(0.0

86)

(0.0

86)

-0.0

63

-0.0

61

(0.0

61)

(0.1

66)

(0.1

18)

(0.0

59)

(0.0

77)

(0.1

32)

NR

EG

A*B

oth

-0.0

18

-0.0

84

0.0

67

0.0

04

0.3

28**

*-0

.144

0.0

71

0.0

02

0.0

30.4

05

-0.2

25

-0.0

47

0.1

05

-0.1

78

(0.0

81)

-0.0

69

-0.0

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er C

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ls

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Yes

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Yes

Yes

Yes

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tric

t F

ixed

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ects

Yes

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Yes

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Yes

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eat

Reg

ion

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e R

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tes:

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pen

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Marg

inal

Eff

ects

of

NR

EG

A

117

Appendix C

THE EFFECT OF QUALITY OF EDUCATION ON CRIME: EVIDENCE FROM

COLOMBIA

Figure C.1. Crime Rate 2007

118

Figure C.2. Education Quality 2007

119

Figure C.3. Crime Rate 2013

120

Figure C.4. Education Quality 2013

121

Tab

leC

.1:

Sum

mar

yst

atis

tics

Vari

able

Mean

Std

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ev.

Min

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ax.

N

Car

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ate

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012

4.68

156

42

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378

51

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563

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978

51

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62.7

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65

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guag

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edia

nSco

re31

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14.8

490

57.3

277

65

Con

tinued

onnex

tpag

e

122

Tab

leC

.1–

conti

nued

from

pre

vio

us

pag

e

Vari

able

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Std

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ev.

Min

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ax.

N

Mat

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nSco

re28

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14.1

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69.0

1077

65

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58.7

7577

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090

51.8

977

65

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53.1

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65

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32.4

3462

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7764

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guag

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2.62

423

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65

Con

tinued

onnex

tpag

e

123

Tab

leC

.1–

conti

nued

from

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us

pag

e

Vari

able

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Std

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Rura

lity

Index

0.57

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(log

)7.

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127

3.49

713

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tten

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(log

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51

Per

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ita

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xp

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7689

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stm

ent

inQ

ual

ity

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(200

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$)75

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7854

Per

Cap

ita

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rage

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stm

ent

inQ

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ity

ofN

eigh

bor

s26

38.4

1087

0.2

015

6671

.577

70

Con

tinued

onnex

tpag

e

124

Tab

leC

.1–

conti

nued

from

pre

vio

us

pag

e

Vari

able

Mean

Std

.D

ev.

Min

.M

ax.

N

Per

Cap

ita

Shif

tShar

eof

Inve

stm

ent

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ual

ity

(miles

)62

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3.9

0.00

118

369.

873

57

Sou

rce:

DA

NE

,C

ED

E,

ICF

ES,

DN

P,

IPU

MS,

Nat

ional

Pol

ice

and

auth

or’s

calc

ula

tion

s

125

Table C.2. Crime and Education Quality (Without Bogota)

Crime

Crime Property Crime Violent Crime

(1) (2) (3)

Average Score in Subjects -5.73*** -6.05*** 0.24

(2.00) (2.17) (0.98)

Municipality FE Yes Yes Yes

Controls Yes Yes Yes

Trend Yes Yes Yes

Adjusted-R2 -2.62 -2.89 -0.19

Observations 4486 4491 6134

Underidentification 0.011 0.011 0.001

Weak Identification 24.198 24.125 22.439

Overidentification 0.472 0.575 0.816

Notes: Standardized coefficients from Instrumental Variable (IV) regression.Heteroskedasticity robust standard error estimates clustered at municipality levelare reported in parentheses; *** denotes statistical significance at the 1% level,** at the 5% level, and * at the 10% level, all for two-sided hypothesis tests.Underidentification Test reports the p-value for the Kleibergen-Paap (2006) rkstatistic with rejection implying identification; F-stat reports the Kleibergen-Paap F statistic and Cragg-Donald Wald F statistic for weak identification;Overidentification test reports the p-value for the Hansen J statistic with thenull being that the instruments are jointly valid.

126

Table C.3. Disaggregated Crime and Education Quality (Without Bogota)

Car Commerce Household Person Kidnap. Pol. Kidnap. Non Pol. Kidnap. Homid.

(1) (2) (3) (4) (5) (6) (7) (8)

Average Score in Subjects -6.32*** 0.69 0.07 -3.64 -3.27** -0.23 -4.56** 0.69

(2.16) (1.21) (0.85) (2.68) (1.59) (0.82) (2.10) (1.02)

Municipality FE Yes Yes Yes Yes Yes Yes Yes Yes

Controls Yes Yes Yes Yes Yes Yes Yes Yes

Trend Yes Yes Yes Yes Yes Yes Yes Yes

Adjusted-R2 -1.75 -0.21 -0.19 -1.50 -0.51 -0.19 -0.83 -0.22

Observations 4586 5962 6036 6130 6213 6213 6213 6134

Underidentification 0.013 0.001 0.001 0.001 0.001 0.001 0.001 0.001

Weak Identification 20.330 22.125 22.376 22.590 22.610 22.610 22.610 22.439

Overidentification 0.568 0.190 0.210 0.291 0.531 0.821 0.483 0.987

Notes: Standardized coefficients from Instrumental Variable (IV) regression. Heteroskedasticity robust standard error estimatesclustered at municipality level are reported in parentheses; *** denotes statistical significance at the 1% level, ** at the 5% level,and * at the 10% level, all for two-sided hypothesis tests. Underidentification Test reports the p-value for the Kleibergen-Paap (2006)rk statistic with rejection implying identification; F-stat reports the Kleibergen-Paap F statistic and Cragg-Donald Wald F statisticfor weak identification; Overidentification test reports the p-value for the Hansen J statistic with the null being that the instrumentsare jointly valid.

Table C.4. Crime and Education Quality (Without State Capitals)

Crime Property Crime Violent Crime

(1) (2) (3)

Average Score in Subjects -3.60*** -3.87*** 0.32

(1.31) (1.48) (0.99)

Municipality FE Yes Yes Yes

Controls Yes Yes Yes

Trend Yes Yes Yes

Adjusted-R2 -0.94 -1.02 -0.20

Observations 4324 4329 5954

Underidentification 0.016 0.016 0.004

Weak Identification 15.755 15.708 18.554

Overidentification 0.210 0.260 0.875

Notes: Standardized coefficients from Instrumental Variable (IV) regression.Heteroskedasticity robust standard error estimates clustered at municipalitylevel are reported in parentheses; *** denotes statistical significance at the 1%level, ** at the 5% level, and * at the 10% level, all for two-sided hypothe-sis tests. Underidentification Test reports the p-value for the Kleibergen-Paap(2006) rk statistic with rejection implying identification; F-stat reports theKleibergen-Paap F statistic and Cragg-Donald Wald F statistic for weak iden-tification; Overidentification test reports the p-value for the Hansen J statisticwith the null being that the instruments are jointly valid.

127

Table C.5. Disaggregated Crime and Education Quality (Without State Capitals)

Car Commerce Household Person Kidnap. Pol. Kidnap. Non Pol. Kidnap. Homid.

(1) (2) (3) (4) (5) (6) (7) (8)

Average Score in Subjects -5.33* -0.11 0.43 -0.73 -3.44* -0.29 -4.74** 0.80

(2.89) (1.09) (0.93) (0.86) (1.81) (0.92) (2.34) (1.03)

Municipality FE Yes Yes Yes Yes Yes Yes Yes Yes

Controls Yes Yes Yes Yes Yes Yes Yes Yes

Trend Yes Yes Yes Yes Yes Yes Yes Yes

Adjusted-R2 -1.18 -0.17 -0.20 -0.21 -0.54 -0.19 -0.87 -0.23

Observations 4424 5782 5856 5950 6033 6033 6033 5954

Underidentification 0.018 0.004 0.005 0.004 0.004 0.004 0.004 0.004

Weak Identification 13.057 18.201 17.686 18.296 18.667 18.667 18.667 18.554

Overidentification 0.461 0.228 0.187 0.128 0.523 0.872 0.479 0.955

Notes: Standardized coefficients from Instrumental Variable (IV) regression. Heteroskedasticity robust standard error estimatesclustered at municipality level are reported in parentheses; *** denotes statistical significance at the 1% level, ** at the 5% level,and * at the 10% level, all for two-sided hypothesis tests. Underidentification Test reports the p-value for the Kleibergen-Paap(2006) rk statistic with rejection implying identification; F-stat reports the Kleibergen-Paap F statistic and Cragg-Donald WaldF statistic for weak identification; Overidentification test reports the p-value for the Hansen J statistic with the null being thatthe instruments are jointly valid.

Table C.6. Violence and Education Quality (With Population <200,000 Inhabitants)

Crime

Violence Property Crime Violent Crime

(1) (2) (3)

Average Score in Subjects -3.67*** -3.90*** 0.32

(1.27) (1.41) (0.99)

Municipality FE Yes Yes Yes

Controls Yes Yes Yes

Trend Yes Yes Yes

Adjusted-R2 -0.99 -1.07 -0.20

Observations 4336 4341 5984

Underidentification 0.015 0.015 0.004

Weak Identification 15.896 15.847 18.556

Overidentification 0.221 0.274 0.882

Notes: Standardized coefficients from Instrumental Variable (IV) regression. Het-eroskedasticity robust standard error estimates clustered at municipality level arereported in parentheses; *** denotes statistical significance at the 1% level, **at the 5% level, and * at the 10% level, all for two-sided hypothesis tests. Under-identification Test reports the p-value for the Kleibergen-Paap (2006) rk statisticwith rejection implying identification; F-stat reports the Kleibergen-Paap F statis-tic and Cragg-Donald Wald F statistic for weak identification; Overidentificationtest reports the p-value for the Hansen J statistic with the null being that theinstruments are jointly valid.

128

Table C.7. Disaggregated Crime and Education Quality (With Population < 200, 000 In-

habitants)

Car Commerce Household Person Kidnap. Pol. Kidnap. Non Pol. Kidnap. Homid.

(1) (2) (3) (4) (5) (6) (7) (8)

Average Score in Subjects -5.47* -0.14 0.31 -0.74 -3.42* -0.24 -4.76** 0.79

(2.97) (1.07) (0.88) (0.84) (1.80) (0.92) (2.34) (1.04)

Municipality FE Yes Yes Yes Yes Yes Yes Yes Yes

Controls Yes Yes Yes Yes Yes Yes Yes Yes

Trend Yes Yes Yes Yes Yes Yes Yes Yes

Adjusted-R2 -1.20 -0.17 -0.19 -0.21 -0.54 -0.19 -0.88 -0.23

Observations 4436 5812 5886 5980 6063 6063 6063 5984

Underidentification 0.017 0.004 0.005 0.004 0.004 0.004 0.004 0.004

Weak Identification 13.200 18.215 17.679 18.289 18.668 18.668 18.668 18.556

Overidentification 0.467 0.234 0.194 0.130 0.520 0.850 0.473 0.949

Notes: Standardized coefficients from Instrumental Variable (IV) regression. Heteroskedasticity robust standard error estimatesclustered at municipality level are reported in parentheses; *** denotes statistical significance at the 1% level, ** at the 5% level,and * at the 10% level, all for two-sided hypothesis tests. Underidentification Test reports the p-value for the Kleibergen-Paap(2006) rk statistic with rejection implying identification; F-stat reports the Kleibergen-Paap F statistic and Cragg-Donald Wald Fstatistic for weak identification; Overidentification test reports the p-value for the Hansen J statistic with the null being that theinstruments are jointly valid.

Table C.8. Crime and Education Quality (Rural Areas)

Violence Property Crime Violent Crime

(1) (2) (3)

Average Score in Subjects -4.16* -5.48** 0.87

(2.38) (2.62) (1.12)

Municipality FE Yes Yes Yes

Controls Yes Yes Yes

Trend Yes Yes Yes

Adjusted-R2 -0.87 -1.34 -0.24

Observations 2588 2588 3961

Underidentification 0.001 0.001 0.018

Weak Identification 10.409 10.409 11.827

Overidentification 0.288 0.454 0.932

Notes: Standardized coefficients from Instrumental Variable (IV) regression. Het-eroskedasticity robust standard error estimates clustered at municipality level arereported in parentheses; *** denotes statistical significance at the 1% level, **at the 5% level, and * at the 10% level, all for two-sided hypothesis tests. Under-identification Test reports the p-value for the Kleibergen-Paap (2006) rk statisticwith rejection implying identification; F-stat reports the Kleibergen-Paap F statis-tic and Cragg-Donald Wald F statistic for weak identification; Overidentificationtest reports the p-value for the Hansen J statistic with the null being that theinstruments are jointly valid.

129

Table C.9. Disaggregated Crime and Education Quality (Rural Areas)

Car Commerce Household Person Kidnap. Pol. Kidnap. Non Pol. Kidnap. Homid.

(1) (2) (3) (4) (5) (6) (7) (8)

Average Score in Subjects -5.84 0.45 1.95 -0.80 -4.01 -0.68 -4.48* 1.43

(3.93) (1.52) (1.35) (1.53) (2.53) (1.67) (2.67) (1.17)

Municipality FE Yes Yes Yes Yes Yes Yes Yes Yes

Controls Yes Yes Yes Yes Yes Yes Yes Yes

Trend Yes Yes Yes Yes Yes Yes Yes Yes

Adjusted-R2 -1.07 -0.19 -0.33 -0.22 -0.65 -0.20 -0.76 -0.31

Observations 2668 3805 3858 3946 4029 4029 4029 3961

Underidentification 0.002 0.018 0.011 0.018 0.017 0.017 0.017 0.018

Weak Identification 9.245 11.418 12.650 11.263 11.791 11.791 11.791 11.827

Overidentification 0.472 0.240 0.242 0.293 0.672 0.803 0.576 0.968

Notes: Standardized coefficients from Instrumental Variable (IV) regression. Heteroskedasticity robust standard error estimatesclustered at municipality level are reported in parentheses; *** denotes statistical significance at the 1% level, ** at the 5% level,and * at the 10% level, all for two-sided hypothesis tests. Underidentification Test reports the p-value for the Kleibergen-Paap(2006) rk statistic with rejection implying identification; F-stat reports the Kleibergen-Paap F statistic and Cragg-Donald Wald Fstatistic for weak identification; Overidentification test reports the p-value for the Hansen J statistic with the null being that theinstruments are jointly valid.

Table C.10. Crime and Education Quality (Urban Areas)

Violence Property Crime Violent Crime

(1) (2) (3)

Average Score in Subjects -1.67 -1.10 -2.41***

(1.45) (1.41) (0.72)

Municipality FE Yes Yes Yes

Controls Yes Yes Yes

Trend Yes Yes Yes

Adjusted-R2 -0.50 -0.26 -0.92

Observations 1893 1897 2165

Underidentification 0.500 0.501 0.319

Weak Identification 18.799 18.566 24.835

Overidentification 0.799 0.718 0.111

Notes: Standardized coefficients from Instrumental Variable (IV) regression. Het-eroskedasticity robust standard error estimates clustered at municipality level arereported in parentheses; *** denotes statistical significance at the 1% level, **at the 5% level, and * at the 10% level, all for two-sided hypothesis tests. Under-identification Test reports the p-value for the Kleibergen-Paap (2006) rk statisticwith rejection implying identification; F-stat reports the Kleibergen-Paap F statis-tic and Cragg-Donald Wald F statistic for weak identification; Overidentificationtest reports the p-value for the Hansen J statistic with the null being that theinstruments are jointly valid.

130

Table C.11. Disaggregated Crime and Education Quality (Urban Areas)

Car Commerce Household Person Kidnap. Pol. Kidnap. Non Pol. Kidnap. Homid.

(1) (2) (3) (4) (5) (6) (7) (8)

Average Score in Subjects -2.03 -0.94 -1.06 -0.54 0.71 0.91 -0.39 -2.54***

(1.43) (0.63) (0.97) (1.43) (1.27) (0.63) (2.64) (0.76)

Municipality FE Yes Yes Yes Yes Yes Yes Yes Yes

Controls Yes Yes Yes Yes Yes Yes Yes Yes

Trend Yes Yes Yes Yes Yes Yes Yes Yes

Adjusted-R2 -0.57 -0.23 -0.28 -0.17 -0.23 -0.25 -0.20 -1.04

Observations 1912 2148 2169 2175 2175 2175 2175 2165

Underidentification 0.497 0.333 0.458 0.318 0.318 0.318 0.318 0.319

Weak Identification 18.973 24.340 23.947 24.692 24.692 24.692 24.692 24.835

Overidentification 0.492 0.127 0.155 0.267 0.313 0.560 0.343 0.044

Notes: Standardized coefficients from Instrumental Variable (IV) regression. Heteroskedasticity robust standard error estimatesclustered at municipality level are reported in parentheses; *** denotes statistical significance at the 1% level, ** at the 5% level,and * at the 10% level, all for two-sided hypothesis tests. Underidentification Test reports the p-value for the Kleibergen-Paap (2006)rk statistic with rejection implying identification; F-stat reports the Kleibergen-Paap F statistic and Cragg-Donald Wald F statisticfor weak identification; Overidentification test reports the p-value for the Hansen J statistic with the null being that the instrumentsare jointly valid.

Table C.12. Crime and Education Quality (Total Transfers as Instruments)

Crime Rate

Crime Property Crime Violent Crime

(1) (2) (3)

Average Score in Subjects -2.19 -2.76** 1.05

(1.38) (1.41) (1.16)

Municipality FE Yes Yes Yes

Controls Yes Yes Yes

Trend Yes Yes Yes

Adjusted-R2 -0.49 -0.67 -0.26

Observations 4642 4647 6390

Underidentification 0.001 0.001 0.001

Weak Identification 23.106 23.101 15.720

Overidentification 0.053 0.088 0.127

Notes: Standardized coefficients from Instrumental Variable (IV) regression. Het-eroskedasticity robust standard error estimates clustered at municipality level arereported in parentheses; *** denotes statistical significance at the 1% level, **at the 5% level, and * at the 10% level, all for two-sided hypothesis tests. Under-identification Test reports the p-value for the Kleibergen-Paap (2006) rk statisticwith rejection implying identification; F-stat reports the Kleibergen-Paap F statis-tic and Cragg-Donald Wald F statistic for weak identification; Overidentificationtest reports the p-value for the Hansen J statistic with the null being that theinstruments are jointly valid.

131

Table C.13. Disaggregated Crime and Education Quality (Total Transfers as Instruments)

Crime Rate

Car Commerce Household Person Kidnap. Pol. Kidnap. Non Pol. Kidnap. Homid.

(1) (2) (3) (4) (5) (6) (7) (8)

Average Score in Subjects -3.14 -0.27 0.03 -1.76 -2.84 0.18 -4.44 1.46

(2.22) (1.04) (0.65) (2.22) (2.09) (0.86) (3.12) (1.22)

Municipality FE Yes Yes Yes Yes Yes Yes Yes Yes

Controls Yes Yes Yes Yes Yes Yes Yes Yes

Trend Yes Yes Yes Yes Yes Yes Yes Yes

Adjusted-R2 -0.57 -0.17 -0.19 -0.46 -0.43 -0.19 -0.79 -0.32

Observations 4754 6183 6274 6380 6469 6469 6469 6390

Underidentification 0.002 0.001 0.001 0.001 0.001 0.001 0.001 0.001

Weak Identification 13.591 16.427 23.803 16.888 15.978 15.978 15.978 15.720

Overidentification 0.082 0.912 0.106 0.208 0.279 0.232 0.588 0.089

Notes: Standardized coefficients from Instrumental Variable (IV) regression. Heteroskedasticity robust standard error estimatesclustered at municipality level are reported in parentheses; *** denotes statistical significance at the 1% level, ** at the 5% level,and * at the 10% level, all for two-sided hypothesis tests. Underidentification Test reports the p-value for the Kleibergen-Paap(2006) rk statistic with rejection implying identification; F-stat reports the Kleibergen-Paap F statistic and Cragg-Donald Wald Fstatistic for weak identification; Overidentification test reports the p-value for the Hansen J statistic with the null being that theinstruments are jointly valid.

132

Table C.14. Crime and Education Quality (Total Transfers as an Additional Regressor)

Violence Property Crime Violent Crime

(1) (2) (3)

Average Score in Subjects -9.62** -9.50** -0.97

(4.10) (4.19) (1.27)

Per Capita Total Transfers 0.21 0.18 0.09

(0.14) (0.13) (0.06)

Municipality FE Yes Yes Yes

Controls Yes Yes Yes

Trend Yes Yes Yes

Adjusted-R2 -7.27 -7.08 -0.25

Observations 4486 4491 6134

Underidentification 0.056 0.056 0.016

Weak Identification 6.739 6.671 14.352

Overidentification 0.454 0.533 0.213

Notes: Standardized coefficients from Instrumental Variable (IV) regression. Het-eroskedasticity robust standard error estimates clustered at municipality level arereported in parentheses; *** denotes statistical significance at the 1% level, **at the 5% level, and * at the 10% level, all for two-sided hypothesis tests. Under-identification Test reports the p-value for the Kleibergen-Paap (2006) rk statisticwith rejection implying identification; F-stat reports the Kleibergen-Paap F statis-tic and Cragg-Donald Wald F statistic for weak identification; Overidentificationtest reports the p-value for the Hansen J statistic with the null being that theinstruments are jointly valid.

133

Table C.15. Disaggregated Crime and Education Quality (Total Transfers as an Additional

Regressor)

Car Commerce Household Person Kidnap. Pol. Kidnap. Non Pol. Kidnap. Homid.

(1) (2) (3) (4) (5) (6) (7) (8)

Average Score in Subjects -10.89** 1.18 -0.62 -5.42 -3.92** -0.29 -5.46* -0.53

(4.55) (1.57) (1.70) (3.46) (1.97) (0.85) (2.92) (1.31)

Per Capita Total Transfers 0.23 -0.03 0.07 0.14 0.05 0.00 0.07 0.09

(0.16) (0.05) (0.06) (0.10) (0.10) (0.04) (0.14) (0.06)

Municipality FE Yes Yes Yes Yes Yes Yes Yes Yes

Controls Yes Yes Yes Yes Yes Yes Yes Yes

Trend Yes Yes Yes Yes Yes Yes Yes Yes

Adjusted-R2 -4.84 -0.27 -0.21 -3.19 -0.65 -0.19 -1.09 -0.21

Observations 4586 5962 6036 6130 6213 6213 6213 6134

Underidentification 0.070 0.016 0.014 0.014 0.016 0.016 0.016 0.016

Weak Identification 5.738 13.958 15.028 14.235 14.129 14.129 14.129 14.352

Overidentification 0.496 0.260 0.085 0.120 0.308 0.853 0.287 0.292

Notes: Standardized coefficients from Instrumental Variable (IV) regression. Heteroskedasticity robust standard error estimates clus-tered at municipality level are reported in parentheses; *** denotes statistical significance at the 1% level, ** at the 5% level, and* at the 10% level, all for two-sided hypothesis tests. Underidentification Test reports the p-value for the Kleibergen-Paap (2006) rkstatistic with rejection implying identification; F-stat reports the Kleibergen-Paap F statistic and Cragg-Donald Wald F statistic forweak identification; Overidentification test reports the p-value for the Hansen J statistic with the null being that the instruments arejointly valid.

134

Table C.16. Crime and Education Quality (Total Transfers instead of Total Expenditures)

Crime Property Crime Violent Crime

(1) (2) (3)

Average Score in Subjects -1.53 -1.89 0.71

(1.29) (1.29) (1.51)

Per Capita Total Transfers -0.05 -0.06 0.02

(0.08) (0.07) (0.05)

Municipality FE Yes Yes Yes

Controls Yes Yes Yes

Trend Yes Yes Yes

Adjusted-R2 -0.31 -0.38 -0.22

Observations 4642 4647 6390

Underidentification 0.023 0.023 0.023

Weak Identification 4.837 4.811 6.638

Overidentification 0.042 0.075 0.118

Notes: Standardized coefficients from Instrumental Variable (IV) regression.Heteroskedasticity robust standard error estimates clustered at municipalitylevel are reported in parentheses; *** denotes statistical significance at the 1%level, ** at the 5% level, and * at the 10% level, all for two-sided hypothesistests. Underidentification Test reports the p-value for the Kleibergen-Paap(2006) rk statistic with rejection implying identification; F-stat reports theKleibergen-Paap F statistic and Cragg-Donald Wald F statistic for weak iden-tification; Overidentification test reports the p-value for the Hansen J statisticwith the null being that the instruments are jointly valid.

135

Table C.17. Disaggregated Crime and Education Quality (Total Transfers instead of Total

Expenditures)

Car Commerce Household Person Kidnap. Pol. Kidnap. Non Pol. Kidnap. Homid.

(1) (2) (3) (4) (5) (6) (7) (8)

Average Score in Subjects -2.78 -0.08 -0.75 -1.68 -3.12 0.33 -5.03 1.07

(2.25) (1.23) (0.69) (1.94) (2.66) (1.27) (4.19) (1.49)

Per Capita Total Transfers -0.03 -0.01 0.05* -0.00 0.02 -0.01 0.04 0.02

(0.09) (0.05) (0.03) (0.08) (0.10) (0.05) (0.14) (0.05)

Municipality FE Yes Yes Yes Yes Yes Yes Yes Yes

Controls Yes Yes Yes Yes Yes Yes Yes Yes

Trend Yes Yes Yes Yes Yes Yes Yes Yes

Adjusted-R2 -0.49 -0.17 -0.21 -0.43 -0.48 -0.20 -0.95 -0.26

Observations 4754 6183 6274 6380 6469 6469 6469 6390

Underidentification 0.038 0.018 0.011 0.021 0.024 0.024 0.024 0.023

Weak Identification 4.346 6.595 7.149 6.965 6.547 6.547 6.547 6.638

Overidentification 0.075 0.874 0.069 0.188 0.297 0.198 0.621 0.083

Notes: Standardized coefficients from Instrumental Variable (IV) regression. Heteroskedasticity robust standard error estimatesclustered at municipality level are reported in parentheses; *** denotes statistical significance at the 1% level, ** at the 5% level,and * at the 10% level, all for two-sided hypothesis tests. Underidentification Test reports the p-value for the Kleibergen-Paap(2006) rk statistic with rejection implying identification; F-stat reports the Kleibergen-Paap F statistic and Cragg-Donald Wald Fstatistic for weak identification; Overidentification test reports the p-value for the Hansen J statistic with the null being that theinstruments are jointly valid.

136

Table C.18. Crime and Education Quality (Total Transfers Instrumented)

Crime Property Crime Violent Crime

(1) (2) (3)

Average Score in Subjects 0.70 0.00 1.79

(1.66) (1.48) (1.47)

Total Transfers 0.34*** 0.32*** 0.15

(0.06) (0.06) (0.19)

Municipality FE Yes Yes Yes

Controls Yes Yes Yes

Trend Yes Yes Yes

Adjusted-R2 -0.23 -0.15 -0.39

Observations 4486 4491 6117

Underidentification 0.140 0.140 0.077

Weak Identification 2.965 2.966 4.983

Overidentification 0.005 0.014 0.364

Notes: Standardized coefficients from Instrumental Variable (IV) regression. Het-eroskedasticity robust standard error estimates clustered at municipality level arereported in parentheses; *** denotes statistical significance at the 1% level, **at the 5% level, and * at the 10% level, all for two-sided hypothesis tests. Under-identification Test reports the p-value for the Kleibergen-Paap (2006) rk statisticwith rejection implying identification; F-stat reports the Kleibergen-Paap F statis-tic and Cragg-Donald Wald F statistic for weak identification; Overidentificationtest reports the p-value for the Hansen J statistic with the null being that theinstruments are jointly valid.

137

Table C.19. Disaggregated Crime and Education Quality (Total Transfers Instrumented)

Car Commerce Household Person Kidnap. Pol. Kidnap. Non Pol. Kidnap. Homid.

(1) (2) (3) (4) (5) (6) (7) (8)

Average Score in Subjects -0.79 0.84 1.40 0.06 -5.75** -1.04 -7.34** 2.59

(2.51) (1.11) (0.94) (0.94) (2.57) (1.19) (3.57) (1.69)

Total Transfers 0.26** 0.04 0.17* 0.38*** -0.33 -0.12 -0.36 0.20

(0.11) (0.18) (0.09) (0.13) (0.33) (0.14) (0.40) (0.22)

Municipality FE Yes Yes Yes Yes Yes Yes Yes Yes

Controls Yes Yes Yes Yes Yes Yes Yes Yes

Trend Yes Yes Yes Yes Yes Yes Yes Yes

Adjusted-R2 -0.22 -0.23 -0.30 -0.18 -1.19 -0.22 -1.81 -0.61

Observations 4586 5945 6019 6113 6196 6196 6196 6117

Underidentification 0.139 0.073 0.050 0.076 0.071 0.071 0.071 0.077

Weak Identification 2.123 5.004 8.650 4.806 4.980 4.980 4.980 4.983

Overidentification 0.140 0.415 0.149 0.003 0.189 0.463 0.321 0.539

Notes: Standardized coefficients from Instrumental Variable (IV) regression. Heteroskedasticity robust standard error estimatesclustered at municipality level are reported in parentheses; *** denotes statistical significance at the 1% level, ** at the 5% level,and * at the 10% level, all for two-sided hypothesis tests. Underidentification Test reports the p-value for the Kleibergen-Paap(2006) rk statistic with rejection implying identification; F-stat reports the Kleibergen-Paap F statistic and Cragg-Donald Wald Fstatistic for weak identification; Overidentification test reports the p-value for the Hansen J statistic with the null being that theinstruments are jointly valid.

Table C.20. Crime and Education Quality (Cognitive Areas)

Violence Property Crime Violent Crime

(1) (2) (3)

Average Score in Cognitive Areas -11.77*** -12.28*** 0.30

(3.44) (3.70) (1.24)

Municipality FE Yes Yes Yes

Controls Yes Yes Yes

Trend Yes Yes Yes

Adjusted-R2 -13.45 -14.64 -0.20

Observations 4486 4491 6134

Underidentification 0.032 0.031 0.019

Weak Identification 11.530 11.618 7.805

Overidentification 0.845 0.981 0.814

Notes: Standardized coefficients from Instrumental Variable (IV) regression. Het-eroskedasticity robust standard error estimates clustered at municipality level are re-ported in parentheses; *** denotes statistical significance at the 1% level, ** at the5% level, and * at the 10% level, all for two-sided hypothesis tests. UnderidentificationTest reports the p-value for the Kleibergen-Paap (2006) rk statistic with rejection im-plying identification; F-stat reports the Kleibergen-Paap F statistic and Cragg-DonaldWald F statistic for weak identification; Overidentification test reports the p-value forthe Hansen J statistic with the null being that the instruments are jointly valid.

138

Tab

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.21.

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139

Tab

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Cri

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(Soci

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ep

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efo

rth

eH

an

sen

Jst

ati

stic

wit

hth

enu

llb

ein

gth

at

the

inst

rum

ents

are

join

tly

valid

.

140

Tab

leC

.23.

Dis

aggr

egat

edC

rim

ean

dE

duca

tion

Qual

ity

(Soci

alA

reas

)

Car

Com

mer

ceH

ou

seh

old

Per

son

Kid

nap

.P

ol.

Kid

nap

.N

on

Pol.

Kid

nap

.H

om

id.

(1)

(2)

(3)

(4)

(5)

(6)

(7)

(8)

Ave

rage

Sco

rein

Soci

alA

reas

-3.5

8***

0.4

20.0

6-2

.21

-2.0

0**

-0.1

4-2

.79**

0.4

2

(1.1

6)

(0.7

2)

(0.5

2)

(1.5

5)

(0.9

7)

(0.5

0)

(1.2

9)

(0.6

3)

Mu

nic

ipal

ity

FE

Yes

Yes

Yes

Yes

Yes

Yes

Yes

Yes

Con

trol

sY

esY

esY

esY

esY

esY

esY

esY

es

Tre

nd

Yes

Yes

Yes

Yes

Yes

Yes

Yes

Yes

Ad

just

ed-R

2-1

.23

-0.2

1-0

.19

-1.1

8-0

.44

-0.1

9-0

.69

-0.2

2

Ob

serv

atio

ns

4586

5962

6036

6130

6213

6213

6213

6134

Un

der

iden

tifi

cati

on0.0

17

0.0

01

0.0

02

0.0

01

0.0

01

0.0

01

0.0

01

0.0

01

Wea

kId

enti

fica

tion

21.3

54

25.6

84

26.0

29

26.2

13

26.0

32

26.0

32

26.0

32

25.8

64

Ove

rid

enti

fica

tion

0.4

99

0.1

95

0.2

04

0.2

65

0.5

04

0.8

29

0.4

57

0.9

98

Note

s:S

tan

dard

ized

coeffi

cien

tsfr

om

Inst

rum

enta

lV

ari

ab

le(I

V)

regre

ssio

n.

Het

erosk

edast

icit

yro

bu

stst

an

dard

erro

res

tim

ate

scl

ust

ered

at

mu

nic

ipality

level

are

rep

ort

edin

pare

nth

eses

;***

den

ote

sst

ati

stic

al

sign

ifica

nce

at

the

1%

level

,**

at

the

5%

level

,an

d*

at

the

10%

level

,all

for

two-s

ided

hyp

oth

esis

test

s.U

nd

erid

enti

fica

tion

Tes

tre

port

sth

ep

-valu

efo

rth

eK

leib

ergen

-Paap

(2006)

rkst

ati

stic

wit

hre

ject

ion

imp

lyin

gid

enti

fica

tion

;F

-sta

tre

port

sth

eK

leib

ergen

-Paap

Fst

ati

stic

an

dC

ragg-D

on

ald

Wald

Fst

ati

stic

for

wea

kid

enti

fica

tion

;O

ver

iden

tifi

cati

on

test

rep

ort

sth

ep

-valu

efo

rth

eH

an

sen

Jst

ati

stic

wit

hth

enu

llb

ein

gth

at

the

inst

rum

ents

are

join

tly

valid

.

141

Table C.24. Crime and Education Quality (Total Score)

Violence Property Crime Violent Crime

(1) (2) (3)

Total Score -0.60*** -0.64*** 0.05

(0.21) (0.23) (0.21)

Municipality FE Yes Yes Yes

Controls Yes Yes Yes

Trend Yes Yes Yes

Adjusted-R2 -0.48 -0.51 -0.19

Observations 4486 4491 6134

Underidentification 0.003 0.003 0.007

Weak Identification 18.026 18.031 9.135

Overidentification 0.189 0.223 0.811

Notes: Standardized coefficients from Instrumental Variable (IV) regres-sion. Heteroskedasticity robust standard error estimates clustered atmunicipality level are reported in parentheses; *** denotes statisticalsignificance at the 1% level, ** at the 5% level, and * at the 10% level,all for two-sided hypothesis tests. Underidentification Test reports the p-value for the Kleibergen-Paap (2006) rk statistic with rejection implyingidentification; F-stat reports the Kleibergen-Paap F statistic and Cragg-Donald Wald F statistic for weak identification; Overidentification testreports the p-value for the Hansen J statistic with the null being thatthe instruments are jointly valid.

142

Table C.25. Disaggregated Crime and Education Quality (Total Score)

Car Commerce Household Person Kidnap. Pol. Kidnap. Non Pol. Kidnap. Homid.

(1) (2) (3) (4) (5) (6) (7) (8)

Total Score -0.70** 0.13 0.00 -0.75 -0.69* -0.05 -0.96* 0.14

(0.27) (0.23) (0.15) (0.52) (0.35) (0.17) (0.50) (0.21)

Municipality FE Yes Yes Yes Yes Yes Yes Yes Yes

Controls Yes Yes Yes Yes Yes Yes Yes Yes

Trend Yes Yes Yes Yes Yes Yes Yes Yes

Adjusted-R2 -0.41 -0.18 -0.19 -0.80 -0.33 -0.19 -0.48 -0.20

Observations 4586 5962 6036 6130 6213 6213 6213 6134

Underidentification 0.007 0.006 0.003 0.007 0.007 0.007 0.007 0.007

Weak Identification 13.668 8.966 13.095 9.203 9.065 9.065 9.065 9.135

Overidentification 0.290 0.188 0.218 0.270 0.553 0.809 0.514 0.971

Notes: Standardized coefficients from Instrumental Variable (IV) regression. Heteroskedasticity robust standard error esti-mates clustered at municipality level are reported in parentheses; *** denotes statistical significance at the 1% level, ** atthe 5% level, and * at the 10% level, all for two-sided hypothesis tests. Underidentification Test reports the p-value for theKleibergen-Paap (2006) rk statistic with rejection implying identification; F-stat reports the Kleibergen-Paap F statistic andCragg-Donald Wald F statistic for weak identification; Overidentification test reports the p-value for the Hansen J statisticwith the null being that the instruments are jointly valid.

143

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