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Spring 5-19-2018
Essays in Development Economics Essays in Development Economics
Manini Ojha Southern Methodist University, [email protected]
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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
0.0
38
0.1
91
Oth
er r
elig
ion
(1 =
yes
)0.0
19
0.1
35
0.0
06
0.0
75
0.0
11
0.1
03
0.0
04
0.0
66
Fat
her
wit
h p
rim
ary
educa
tio
n (
1 =
yes
)0.1
67
0.3
73
0.1
59
0.3
66
0.1
48
0.3
55
0.1
50
0.3
57
Fat
her
wit
h s
eco
nd
ary
educa
tio
n (
1 =
yes
)0.2
51
0.4
34
0.3
72
0.4
83
0.2
24
0.4
17
0.3
53
0.4
78
Fat
her
wit
h s
enio
r se
c. e
duca
tio
n (
1 =
yes
)0.0
54
0.2
25
0.1
29
0.3
35
0.0
42
0.2
01
0.1
07
0.3
09
Fat
her
w
ith
co
llege
ed
uca
tio
m (
1 =
yes
)0.0
32
0.1
75
0.1
07
0.3
09
0.0
36
0.1
85
0.0
89
0.2
85
Mo
ther
wit
h p
rim
ary
educa
tio
n (
1 =
yes
)0.1
78
0.3
82
0.1
70
0.3
75
0.1
61
0.3
67
0.1
54
0.3
61
Mo
ther
wit
h s
eco
nd
ary
educa
tio
n (
1 =
yes
)0.3
71
0.4
83
0.3
09
0.4
62
0.3
70
0.4
83
0.2
57
0.4
37
Mo
ther
wit
h s
enio
r se
c. e
duca
tio
n (
1 =
yes
)0.1
11
0.3
14
0.0
78
0.2
68
0.0
85
0.2
79
0.0
67
0.2
49
Mo
ther
wit
h c
olle
ge e
duca
tio
n (
1 =
yes
)0.0
84
0.2
77
0.0
51
0.2
20
0.0
82
0.2
75
0.0
54
0.2
26
No
tes:
Sam
ple
isre
stic
ted
toh
ouse
ho
lds
wh
ere
child
ren
bet
wee
nth
eag
egr
oup
5to
18
year
s.T
he
tab
lesh
ow
ssu
mm
ary
stat
isti
csin
the
con
tro
ld
istr
icts
and
trea
tmen
td
istr
icts
in2004-0
5an
d2011-1
2fo
rth
ein
div
idual
level
dat
a.D
um
my
var
iab
les
con
tain
ing
info
rmat
ion
abo
ut
gen
der
,sc
ho
ol
faci
litie
s,
par
enta
led
uca
tio
nle
vel
s,ag
e,ca
ste
and
relig
ion
of
the
ind
ivid
ual
sar
ein
clud
ed.
Musl
imta
kes
val
ue
1if
ind
ivid
ual
isM
usl
im,
0o
ther
wis
e.C
hri
stia
n=
1if
ind
ivid
ual
isC
hri
stia
n,0
oth
erw
ise.
Sik
h=
1if
ind
ivid
ual
isSik
h,0
oth
erw
ise.
Oth
erre
ligio
n=
1if
the
ind
ivid
ual
falls
un
der
any
of
the
oth
erca
tego
ries
like
Jain
ism
,B
ud
dh
ism
,Z
oro
astr
ian
ism
,an
do
ther
s,0
oth
erw
ise.
ST
=1
ifin
div
idual
'sca
ste
issc
hed
ule
dtr
ibe,
0o
ther
wis
e.SC
=1
ifin
div
idual
'sca
ste
is
sch
edule
dca
ste,
0o
ther
wis
e.O
BC
=1
ifin
div
idual
bel
on
gsto
oth
erb
cakw
ard
cast
e,0
oth
erw
ise.
Oth
erre
ligio
n=
1if
ind
ivid
ual
bel
on
gsto
oth
erca
stes
or
gen
eral
cat
ego
ry, 0 o
ther
wis
e.
Co
ntr
ol
Dis
tric
tsT
reate
d D
istr
icts
2004-0
42011
-12
2011
-12
2004-0
5
34
Figure 1.1. Pre-trends at district level
35
Tab
le1.
3.Im
pac
tof
RSB
Yon
hou
sehol
dsc
hool
exp
endit
ure
(1)
Sch
oo
l ex
pd
.
Bu
dg
et
Sh
are
(2)
Lo
g S
ch
oo
l ex
pd
.
Levels
(3)
Lo
g T
ota
l co
nsu
mp
tio
n
ex
pd
. L
evels
(4)
Sch
oo
l ex
pd
.
Bu
dg
et
Sh
are
(5)
Lo
g S
ch
oo
l ex
pd
.
Levels
(6)
Lo
g T
ota
l co
nsu
mp
tio
n
ex
pd
. L
evels
RSB
Y*P
ost
0.0
05**
*0.3
02**
*0.0
77**
* -
0.0
03*
-0.1
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
.047**
-0
.829*
-0.2
24**
*
(0.0
24)
(0.4
99)
(0.0
84)
RSB
Y*P
ost
*Lo
w I
nco
me
0.0
07**
*0.4
22**
*0.0
98
(0.0
01)
(0.1
88)
(0.0
86)
Un
der
iden
tifi
cati
on
tes
tp
=0.0
00
p=
0.0
00
p=
0.0
00
p=
0.0
01
p=
0.0
01
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
11.6
07
11.5
74
6.5
80
6.5
43
6.5
53
En
do
gen
eity
tes
tp
=0.0
10
p=
0.0
00
p=
0.0
25
p=
0.0
10
p=
0.0
02
p=
0.0
27
Oth
er C
on
tro
lsY
YY
YY
Y
Dis
tric
t fi
xed
eff
ects
YY
YY
YY
Tim
e fi
xed
eff
ects
YY
YY
YY
Dis
tric
t*In
com
e fi
xed
eff
ects
YY
Y
Tim
e*In
com
e fi
xed
eff
ects
YY
Y
N47421
47421
47421
47421
47421
47421
Pan
el
A.
DID
Pan
el
B.
DD
D
*p
<0.1
0,**
p<
0.0
5,**
*p
<0.0
1.T
he
sam
ple
isre
stri
cted
toH
Hw
ith
child
ren
and
wh
ere
age
of
the
hea
dis
bet
wee
n18
to90
year
s.P
anel
Aan
dB
pro
vid
eth
eD
IDan
dD
DD
resu
lts
resp
ecti
vel
y.E
stim
atio
nis
usi
ng
IVap
pro
ach
Co
l(1)
and
(4):
dep
end
ent
var
iab
leis
bud
get
shar
eo
fh
ouse
ho
ld's
sch
oo
lex
pen
dit
ure
(sch
oo
lex
pen
dit
ure
/to
tal
con
sum
pti
on
exp
end
iture
).C
ol.
(2)
and
(5)
:d
epen
den
tvar
iab
leis
the
inver
se
hyp
erb
olic
sin
etr
ansf
orm
atio
no
fsc
ho
ol
exp
end
iture
inle
vel
s.C
ol.
(3)
and
(6):
dep
end
ent
var
iab
leis
the
inver
seh
yper
bo
licsi
ne
tran
sfo
rmat
ion
of
tota
lco
nsu
mp
tio
nex
pen
dit
ure
inle
vel
s.A
dd
itio
nal
con
tro
ls
incl
ud
e:R
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
t
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,
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
t b
y in
com
e fi
xed
eff
ects
(fo
r D
DD
), t
ime
by
inco
me
fixed
eff
ect
(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.
36
Tab
le1.
4.Im
pac
tof
RSB
Yon
child
school
enro
llm
ent
(1)
(2)
(3)
(4)
En
roll
men
t
En
roll
men
t w
ith
gen
der
dif
fere
nti
al
En
roll
men
t
En
roll
men
t w
ith
gen
der
dif
fere
nti
al
RSB
Y*P
ost
0.0
17**
*0.0
27**
*-0
.024
-0.0
23
(0.0
05)
(0.0
06)
(0.0
17)
(0.0
17)
Bo
y0.0
53**
*0.0
60**
* 0
.054**
*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
.328
-3.5
76
(9.6
08)
(9.5
77)
RSB
Y*P
ost
*Bo
y -0
.019**
*
(0.0
05)
RSB
Y*P
ost
*Lo
w I
nco
me
0.0
42**
0.0
46**
(0.0
19)
(0.0
20)
RSB
Y*P
ost
*Lo
w I
nco
me*
Bo
y-0
.009**
*
(0.0
01)
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
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
Eff
ects
YY
YY
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
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
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
.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
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
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.
Addit
ion
alco
ntr
ols
incl
uded
inea
chsp
ecif
icat
ion
-dis
tric
tfi
xed
effe
cts,
ho
use
ho
ldsi
ze,ag
eo
fth
eh
ead
of
the
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
),H
ind
u,
Isla
m,
Ch
rist
ian
ity,
Sik
his
m,
and
oth
erre
ligio
n.
Sta
ndar
d e
rro
rs a
re c
lust
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
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**
*0.0
03
-0.0
06**
*0.0
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
182374
181215
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
ad
istr
ict
ata
par
ticu
lar
po
int
inti
me.
Ad
dit
ion
alco
ntr
ols
incl
uded
inea
chsp
ecif
icat
ion
-D
um
my
for
sect
or
(rura
l=1,
urb
an=
2),
inte
ract
ion
bet
wee
nse
cto
ran
dN
RE
GA
trae
tmen
t(t
rip
ledif
fere
nce
),d
istr
ict
fixed
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(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
wh
oh
ave
sch
oo
lgo
ing
child
ren
fo
r th
e m
odel
wh
ere
outc
om
e is
sch
oo
l ex
pd
endit
ure
.
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
02)
(0.0
94)
(0.0
25)
(0.0
13)
NR
EG
A*f
emal
e sh
are
of
NR
EG
A
emp
loym
ent
0.0
60
(0.0
18)
NR
EG
A*m
inW
0.0
32
(0.0
26)
NR
EG
A*R
ice
0.0
52**
(0.0
23)
NR
EG
A*B
oth
-0.0
11
(0.0
20)
H0: F
emal
e sh
are
of
NR
EG
A
emp
loym
ent
= 2
5%
p=
0.7
32
H0: F
emal
e sh
are
of
NR
EG
A
emp
loym
ent
= 7
5%
p=
0.7
86
H0:N
RE
GA
+N
RE
GA
*min
W
(at
Rs.
82.5
0 p
er d
ay)=
0p
= 0
.367
H0:N
RE
GA
+N
RE
GA
*min
Wag
e
(at
Rs.
159.4
0 p
er d
ay)=
0p
= 0
.349
H0:N
RE
GA
+N
RE
GA
*Ric
e =
0p
= 0
.118
H0:N
RE
GA
+N
RE
GA
*Bo
th=
0p
= 0
.063
N80279
78471
80279
38164
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
t
var
iab
les
inea
chsp
ecif
icat
ion
isa
bin
ary
var
iab
lein
dic
atin
gw
het
her
the
ho
use
ho
ldis
hea
ded
by
afe
mal
eo
rn
ot
-ta
kes
val
ue
1if
itis
hea
ded
by
fem
ale
and
0o
ther
wis
e.Sp
ecif
icat
ion
(1)
per
tain
sto
the
bas
elin
em
od
el.
Sp
ecif
icat
ion
(2)
per
tain
sto
the
mo
del
incl
ud
ing
rati
oo
fw
om
ento
tota
lem
plo
ymen
tth
rough
NR
EG
Ajo
bs
atdis
tric
tle
vel
.Sp
ecif
icat
ion
(3)
per
tain
sto
mo
del
incl
ud
ing
stat
est
ipula
ted
min
imum
wag
es.Sp
ecif
icat
ion
(4)
per
tain
sto
the
mo
del
incl
udin
gri
cep
roduci
ng
area
s,w
hea
tp
roduci
ng
area
san
dth
ose
that
pro
duce
bo
th.
Co
ntr
ols
incl
uded
insp
ecif
icat
ion
(1)
-d
istr
ict
fixed
effe
cts,
log
(to
tal
con
sum
pti
on
),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
ind
u,Is
lam
,C
hri
stia
nit
y,Sik
his
m,an
do
ther
relig
ion
.A
ddit
ion
alco
ntr
ol
incl
ud
edin
spec
ific
atio
n(2
)co
mp
ared
to(1
)is
shar
eo
fw
om
ento
tota
lem
plo
ymen
t
thro
ugh
NR
EG
Ajo
bs.
Ad
dit
ion
alco
ntr
ol
incl
ud
edin
spec
ific
atio
n(3
)co
mp
ared
to(1
)is
stat
em
inim
um
wag
es.
Add
itio
nal
con
tro
lsin
clud
edin
spec
ific
atio
n(4
)co
mp
ared
to(1
)ar
e
dum
my
for
rice
pro
duci
ng
area
san
ddum
my
for
area
sp
rod
uci
ng
bo
thri
cean
dw
hea
t.Sta
ndar
der
rors
are
clust
ered
atd
istr
ict
level
and
rep
ort
edin
par
enth
esis
.A
smal
lfr
acti
on
of
ho
use
ho
lds
are
fem
ale
hea
ded
asco
mp
ared
toth
eto
tal
num
ber
of
ho
use
ho
lds
-ap
roxim
atel
y8%
of
ho
use
ho
lds
for
the
full
sam
ple
and
app
roxm
atel
y9%
are
fem
ale
hea
ded
for
cro
p
regi
on
s sa
mp
le.
62
Tab
le2.
4.H
eter
ogen
eous
Impac
tsof
NR
EG
Aon
Exp
endit
ure
Shar
es:
Fem
ale
Shar
eof
NR
EG
AE
mplo
ym
ent
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
39**
* 0
.008*
0.0
14**
*0.0
06**
-0.0
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
on
gto
top
30%
and
0o
ther
wis
e(f
or
DD
D),
HH
size
,p
aren
tal
educa
tio
nch
arac
teri
stic
s,in
dic
ato
rsfo
rre
ligio
no
fH
H,
ind
icat
ors
for
cast
eo
fH
H,
dum
my
for
urb
anar
eas,
sch
oo
lfa
cilit
ies
and
sch
ola
rsh
ips
off
ered
,
dis
tric
tan
dti
me
fixed
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
db
yth
ege
nd
ero
fth
efi
rst
child
.Sta
nd
ard
erro
rsre
po
rted
are
clust
ered
stan
dar
d e
rro
rs.
104
Tab
leA
.11.
Sen
siti
vit
yan
alysi
s:Im
pac
tof
RSB
Yon
hou
sehol
dsc
hool
exp
endit
ure
and
child
school
enro
llm
ent
-R
ura
lvs
urb
an
Pan
el
I -
Sch
oo
l ex
pen
dit
ure
Base
lin
eU
rban
Ru
ral
Base
lin
eU
rban
Ru
ral
RSB
Y*P
ost
0.0
05**
*0.0
07**
0.0
04**
* -
0.0
03*
-0.0
01
-0.0
04
(0.0
01)
(0.0
03)
(0.0
01)
(0.0
02)
(0.0
04)
(0.0
03)
Lo
w I
nco
me
(=1 f
or
bo
tto
m 7
0%
)-0
.047**
0.0
00
0.0
01
(0.0
24)
(0.0
06)
(0.0
03)
RSB
Y*P
ost
*Lo
w I
nco
me
0.0
07**
*0.0
02
0.0
08**
(0.0
01)
(0.0
05)
(0.0
03)
Oth
er C
on
tro
lsY
YY
YY
Y
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
N47421
11219
28897
47421
11205
28897
Pan
el
II.
Sch
oo
l en
roll
men
tR
SB
Y*P
ost
0.0
27**
*-0
.005
0.0
40**
*-0
.023
-0.0
43**
*-0
.027
(0.0
06)
(0.0
11)
(0.0
07)
(0.0
17)
(0.0
14)
(0.0
18)
Bo
y0.0
60**
*0.0
23**
*0.0
72**
*0.0
55**
*0.0
22**
*0.0
68**
*
(0.0
04)
(0.0
06)
(0.0
06)
(0.0
04)
(0.0
05)
(0.0
06)
RSB
Y*P
ost
*Bo
y -0
.019**
*-0
.005
-0.0
24**
*
(0.0
05)
(0.0
08)
(0.0
06)
Lo
w I
nco
me
(=1 f
or
bo
tto
m 7
0%
)-0
.042
-0.1
76
-0.0
08
(0.0
23)
(0.1
64)
(0.1
05)
RSB
Y*P
ost
*Lo
w I
nco
me
0.0
46**
0.0
43**
0.0
66**
*
(0.0
20)
(0.0
20)
(0.0
24)
RSB
Y*P
ost
*Lo
w I
nco
me*
Bo
y-0
.009**
*-0
.007
-0.0
15**
(0.0
01)
(0.0
12)
(0.0
07)
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
44.0
22
30.5
25
27.4
62
42.4
620.8
92
27.7
81
En
do
gen
eity
tes
tp
= 0
.570
p=
0.4
02
p=
0.5
66
p=
0.4
14
p=
0.5
98
p =
0.5
16
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
YN
83221
22760
60461
83221
22760
60461
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.P
anel
Ip
rovid
esth
ere
sult
ses
tim
ated
usi
ng
IVap
pro
ach
.D
epen
den
tvar
iab
leis
bud
get
shar
eo
fh
ouse
ho
ld's
sch
oo
l
exp
end
iture
.T
he
sam
ple
isre
stri
cted
toH
Hw
ith
child
ren
and
wh
ere
age
of
the
hea
dis
bet
wee
n18
to90
year
s.P
anel
IIp
rovid
esth
ere
sult
ses
tim
ated
usi
ng
aL
PM
.D
epen
den
tvar
iab
leis
sch
oo
len
rollm
ent
of
ach
ildin
a
ho
use
ho
ld.In
div
idual
sam
ple
isre
stri
cted
toch
ildre
nab
ove
the
age
of
5an
db
elo
wth
eag
eo
f18.A
dd
itio
nal
con
tro
lsin
clud
edin
Pan
elI
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
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,
ind
icat
ors
for
cast
eo
fH
H,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
e
HH
has
ab
ank
acco
un
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
).C
on
tro
lsin
Pan
elII
spec
ific
atio
nin
clud
ea
gen
der
dum
my
=1
for
ab
oy
and
0fo
ra
girl
,R
SB
Y,
dum
my
for
Lo
wIn
com
e,H
Hsi
ze,
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,
dum
my
for
urb
anar
eas,
sch
oo
lfa
cilit
ies
and
sch
ola
rsh
ips
off
ered
,d
istr
ict
and
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
).H
Hsi
zeis
inst
rum
ente
db
yth
ege
nd
ero
fth
efi
rst
child
. Sta
nd
ard
err
ors
rep
ort
ed a
re c
lust
ered
sta
nd
ard
err
ors
.
105
Tab
leA
.12.
Sen
siti
vit
yan
alysi
s:Im
pac
tof
RSB
Yon
hou
sehol
dsc
hool
exp
endit
ure
and
child
school
enro
llm
ent
-V
aria
tion
by
cast
es
Pan
el
I -
Sch
oo
l ex
pen
dit
ure
Base
lin
eG
en
era
lO
BC
SC
ST
Oth
er
Base
lin
eG
en
era
lO
BC
SC
ST
Oth
er
RSB
Y*P
ost
0.0
05**
*0.0
09**
*0.0
04*
0.0
00
0.0
04
0.0
03
-0.0
03*
-0.0
09
-0.0
02
0.0
01
0.0
00
-0.0
05
(0.0
01)
(0.0
02)
(0.0
02)
(0.0
02)
(0.0
03)
(0.0
02)
(0.0
02)
(0.0
08)
(0.0
03)
(0.0
03)
(0.0
06)
(0.0
04)
Lo
w I
nco
me
(=1 f
or
bo
tto
m 7
0%
)
-0.0
47**
-0
.006**
* -
0.0
07**
-0
.004
-0.0
08
-0.0
05
(0
.024)
(0.0
01)
(0.0
02)
(0.0
04)
(0.0
08)
(0.0
04)
RSB
Y*P
ost
*Lo
w I
nco
me
0.0
07**
*0.0
19**
*0.0
04
0.0
02
0.0
06
0.0
06*
(0.0
01)
(0.0
06)
(0.0
03)
(0.0
03)
(0.0
06)
(0.0
03)
Oth
er C
on
tro
lsY
YY
YY
YY
YY
YY
Y
Dis
tric
t F
ixed
Eff
ects
YY
YY
YY
YY
YY
YY
Tim
e F
ixed
Eff
ects
YY
YY
YY
YY
YY
YY
Dis
tric
t*In
com
e F
ixed
Eff
ects
YY
YY
YY
Tim
e*In
com
e F
ixed
Eff
ects
YY
YY
YY
N47421
2206
15091
14565
7025
8534
47421
2206
15085
14565
7025
8534
Pan
el
II.
Sch
oo
l en
roll
men
tR
SB
Y*P
ost
0.0
27**
*0.0
24
0.0
62**
*0.0
46**
*0.0
54
-0.0
15
-0.0
23
0.0
11
-0.0
24
-0.0
24
-0.1
34**
*0.0
14
(0.0
06)
(0.0
22)
(0.0
12)
(0.0
13)
(0.0
44)
(0.0
25)
(0.0
17)
(0.0
22)
(0.0
34)
(0.0
38)
(0.0
35)
(0.0
42)
Bo
y0.0
60**
*0.0
29**
*0.0
77**
*0.0
51**
*0.0
41**
*0.0
61**
*0.0
55**
*0.0
26**
*0.0
64**
*0.0
53**
*0.0
32**
*0.0
60**
*
(0.0
04)
(0.0
10)
(0.0
09)
(0.0
08)
(0.0
14)
(0.0
09)
(0.0
04)
(0.0
10)
(0.0
09)
(0.0
07)
(0.0
10)
(0.0
08)
RSB
Y*P
ost
*Bo
y -0
.019**
*-0
.011
-0.0
70**
*0.0
08
0.0
01
0.0
01
(0.0
05)
(0.0
14)
(0.0
08)
(0.0
08)
(0.0
14)
(0.0
18)
Lo
w I
nco
me
(=1 f
or
bo
tto
m 7
0%
)-0
.042
-0.4
72*
-0
.014
-0.1
81*
-0
.111
0.2
37
(0.0
23)
(0.2
50)
(0.1
67)
(0.1
01)
(0.1
86)
(0.1
79)
RSB
Y*P
ost
*Lo
w I
nco
me
0.0
46**
0.0
34
0.0
72
0.0
70*
0.1
98**
*-0
.046
(0.0
20)
(0.0
23)
(0.0
47)
(0.0
38)
(0.0
52)
(0.0
42)
RSB
Y*P
ost
*Lo
w I
nco
me*
Bo
y-0
.009**
*-0
.007
-0.0
61**
*0.0
04
-0.0
21*
-0
.008**
*
(0.0
01)
(0.0
20)
(0.0
11)
(0.0
09)
(0.0
13)
(0.0
01)
Un
der
iden
tifi
cati
on
tes
tp
=0.0
00
p=
0.0
00
p=
0.0
00
p =
0.0
41
p =
0.0
40
p=
0.0
00
p=
0.0
00
p=
0.0
00
p=
0.0
00
p=
0.0
56
p=
0.0
03
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
20.4
44
12.6
67
4.1
31
4.1
11
17.6
56
42.4
618.7
93
11.3
18
5.7
17
8.1
25
25.4
2
En
do
gen
eity
tes
tp
= 0
.570
p =
0.4
47
p =
0.5
30
p =
0.7
40
p =
0.6
05
p =
0.0
24
p=
0.4
14
p =
0.4
33
p =
0.5
63
p =
0.8
49
p =
0.4
15
p =
0.0
10
Oth
er C
on
tro
ls
YY
YY
YY
YY
YY
YY
Dis
tric
t F
ixed
Eff
ects
YY
YY
YY
YY
YY
YY
Tim
e F
ixed
Eff
ects
YY
YY
YY
YY
YY
YY
Dis
tric
t*In
com
e F
ixed
Eff
ects
YY
YY
YY
Tim
e*In
com
e F
ixed
Eff
ects
YY
YY
YY
N83221
4486
25892
25189
12291
15363
83221
4486
25892
25189
12291
15363
Pan
el
B.
DD
DP
an
el
A.
DID
*p
<0.1
0,**
p<
0.0
5,**
*p
<0.0
1.P
anel
Aan
dB
pro
vid
eth
eD
IDan
dD
DD
resu
lts
resp
ecti
vel
y.P
anel
Ip
rovid
esth
ere
sult
ses
tim
ated
usi
ng
IVap
pro
ach
.D
epen
den
tvar
iab
leis
bud
get
shar
eo
fh
ouse
ho
ld's
sch
oo
l
exp
end
iture
.T
he
sam
ple
isre
stri
cted
toH
Hw
ith
child
ren
and
wh
ere
age
of
the
hea
dis
bet
wee
n18
to90
year
s.P
anel
IIp
rovid
esth
ere
sult
ses
tim
ated
usi
ng
aL
PM
.D
epen
den
tvar
iab
leis
sch
oo
len
rollm
ent
of
a
child
ina
ho
use
ho
ld.In
div
idual
sam
ple
isre
stri
cted
toch
ildre
nab
ove
the
age
of
5an
db
elo
wth
eag
eo
f18.A
dd
itio
nal
con
tro
lsin
clud
edin
Pan
elI
spec
ific
atio
nin
clud
eR
SB
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,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
,
dum
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
).
Co
ntr
ols
inP
anel
IIin
clud
ea
gen
der
dum
my
=1
for
ab
oy
and
0fo
ra
girl
,R
SB
Y,
Lo
wIn
com
e,H
Hsi
ze,
par
enta
led
uca
tio
nch
arac
teri
stic
s,in
dic
ato
rsfo
rre
ligio
no
fH
H,
dum
my
for
urb
anar
eas,
sch
oo
lfa
cilit
ies
and
sch
ola
rsh
ips
off
ered
,d
istr
ict
and
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
).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
41
(0.0
42)
(0.1
11)
(0.2
00)
-0.0
61
-0.0
88
(0.0
71)
(0.2
69)
(0.1
43)
(0.0
86)
(0.0
81)
(0.1
69)
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
Wh
eat
Reg
ion
s-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)
Ric
e R
egio
ns
0.0
65
-0.0
42
-0.0
42
-0.0
14
-0.0
56
-0.2
21**
-0.0
72
-0.3
01**
* -
0.1
2**
-0.1
29
0.2
86**
-0.1
09**
0.0
80*
0.1
34
(0.0
43)
(0.0
63)
(0.0
45)
(0.0
40)
(0.0
72)
(0.0
82)
(0.0
54)
(0.0
64)
(0.0
57)
(0.1
37)
(0.0
95)
(0.0
46)
(0.0
52)
(0.0
93)
Reg
ion
s p
roduci
ng
bo
th-0
.094
-0.0
63
0.0
67*
-0.0
81**
0.3
02**
-0.2
29
0.1
04**
0.0
15
0.0
30
0.4
58*
0.0
07
-0.0
86
0.0
56
0.0
60
(0.0
48)
(0.0
47)
(0.0
40)
(0.0
33)
(0.0
91)
(0.2
16)
(0.0
54)
(0.0
80)
(0.0
60)
(0.1
93)
(0.1
25)
(0.0
68)
(0.0
57)
(0.1
72)
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
ic751.0
5743.4
72
742.8
72
739.9
3549.4
8215.0
4743.2
72
746.4
10
661.9
3574.2
7405.9
2747.2
5725.1
4720.7
1
Kle
iber
gen
-Paa
p r
k W
ald F
sta
tist
ic176.4
4175.1
69
172.1
77
173.7
5130.6
363.2
7175.4
50
175.1
33
163.6
6152.4
0125.0
7178.4
3178.9
1176.9
9
En
do
gen
eity
Tes
tp
= 0
.067
p =
0.0
00
p =
0.0
70
p =
0.6
91
p =
0.0
00
p =
0.0
00
p =
0.0
00
p =
0.0
01
p =
0.0
00
p =
0.0
54
p=
0.4
018
p =
0.1
27
p =
0.3
84
p =
0.0
00
N38141
37536
37722
37866
28802
16616
38103
38146
37211
30407
25213
38112
38082
37814
No
tes:
*p
<0.1
0,
**p
<0.0
5,
***
p<
0.0
1.
Est
imat
ion
isvia
Intr
um
enta
lV
aria
ble
.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
too
nly
incl
ud
ere
gio
ns
that
are
rice
pro
duci
ng,
wh
eat
pro
duci
ng
and
tho
seth
atp
rod
uce
bo
th.
IfR
ice=
0,
then
Bo
this
also
equal
toze
ro.
Dep
end
ent
var
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.
Th
eim
pac
tin
per
cen
tage
term
sis(e^(β)-1)*100.
Add
itio
nal
con
tro
lsin
cluded
inea
chsp
ecif
icat
ion
-d
istr
ict
fixed
effe
cts,
dum
my
var
iab
les
for
regi
on
sth
atp
rod
uce
rice
,re
gio
ns
that
pro
duce
bo
thri
cean
dw
hea
t,lo
go
sto
tal
con
sum
pti
on
,h
ouse
ho
ldsi
ze,
age
of
the
hea
do
fth
eh
ouse
ho
ld,ag
esq
uar
ed,n
um
ber
of
child
ren
,
num
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
atd
istr
ict
level
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
t
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
rw
eak
iden
tifi
cati
on
.Jo
int
sign
ific
ance
test
sre
po
rtst
atis
tica
lsi
gnif
ican
ceo
fth
eto
tal
imp
act
of
NR
EG
Agi
ven
the
inte
ract
ion
of
NR
EG
Aw
ith
rice
pro
duci
ng
regi
on
san
din
tera
ctio
no
fN
RE
GA
wit
hre
gio
ns
that
pro
duce
bo
thri
cean
dw
hea
t.Sam
ple
isre
stri
cted
toin
clude
on
lyh
ouse
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 ex
pen
dit
ure
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
.D
ev.
Min
.M
ax.
N
Car
Thef
tR
ate
5.66
611
.21
012
4.68
156
42
Com
mer
ceT
hef
tR
ate
16.1
3325
.982
040
5.07
773
62
Thef
tson
Per
son
Rat
e44
.353
73.7
70
632.
972
7606
Hou
sehol
dT
hef
tR
ate
22.4
4138
.738
047
1.88
474
74
Tot
alK
idnap
pin
gsR
ate
1.03
25.
103
018
5.56
378
51
Pol
itic
alK
idnap
pin
gR
ate
0.45
73.
770
185.
563
7851
Non
Pol
itic
alK
idnap
pin
gR
ate
0.57
53.
375
013
0.71
978
51
Hom
icid
eR
ate
31.6
938
.232
048
5.79
476
06
Ave
rage
Sco
rein
Sub
ject
s29
.678
14.1
480
55.0
1777
65
Ave
rage
Sco
rein
Cog
nit
ive
Are
as29
.943
14.3
80
62.7
877
65
Lan
guag
eM
edia
nSco
re31
.055
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
Mean
Std
.D
ev.
Min
.M
ax.
N
Mat
hM
edia
nSco
re28
.83
14.1
390
69.0
1077
65
Ave
rage
Sco
rein
Soci
alA
reas
28.0
7913
.673
052
.455
7765
Soci
alSci
ence
sM
edia
nSco
re29
.469
14.0
940
58.7
7577
65
Philos
ophy
Med
ian
Sco
re26
.689
13.5
090
51.8
977
65
Bio
logy
Med
ian
Sco
re30
.687
14.5
490
53.1
977
65
Tot
alSco
re47
.63.
089
32.4
3462
.587
7764
Lan
guag
eSco
re47
.703
2.62
423
.389
64.8
0777
64
Mat
hSco
re48
.11
2.59
430
.703
69.0
7877
64
Philos
ophy
Sco
re48
.594
2.35
732
.72
60.0
9677
64
Bio
logy
Sco
re48
.197
2.48
534
.167
61.1
977
64
Soci
alSci
ence
sSco
re48
.176
2.71
227
.883
68.3
6977
64
Tot
alM
edia
nSco
re32
.621
15.2
510
61.3
5477
65
Con
tinued
onnex
tpag
e
123
Tab
leC
.1–
conti
nued
from
pre
vio
us
pag
e
Vari
able
Mean
Std
.D
ev.
Min
.M
ax.
N
Sub
ject
sM
edia
nZ
Sco
re0
0.98
8-2
.071
1.77
377
65
Tot
alP
opula
tion
(log
)9.
545
1.12
65.
509
15.8
5378
51
Bir
thR
ate
13.1
824.
722
052
.217
7842
Infa
nt
Mor
tality
Rat
e21
.987
9.54
36.
507
91.9
778
54
Rura
lity
Index
0.57
40.
244
0.00
11
7851
Agr
icult
ura
lY
ield
7.29
411
.711
013
6.53
576
60
Pro
ject
edP
opula
tion
toA
tten
dP
rim
ary
Sch
ool
(log
)7.
291.
127
3.49
713
.349
7851
Pro
ject
edP
opula
tion
toA
tten
dSec
undar
ySch
ool
(log
)7.
466
1.12
43.
611
13.5
5978
51
Per
Cap
ita
Tot
alE
xp
endit
ure
0.00
40.
012
00.
136
7687
Per
Cap
ita
Tot
alT
axR
even
ue
0.00
20.
008
00.
131
7689
Inve
stm
ent
inQ
ual
ity
ofE
duca
tion
(200
5co
nst
ant
million
$)75
9.7
2,33
5.1
087
388
7854
Per
Cap
ita
Ave
rage
Inve
stm
ent
inQ
ual
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
onQ
ual
ity
(miles
)62
.457
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
leC
.21.
Dis
aggr
egat
edC
rim
ean
dE
duca
tion
Qual
ity
(Cog
nit
ive
Are
as)
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
Cog
nit
ive
Are
as-1
2.7
9***
0.8
6-0
.05
-4.6
8-4
.14*
-0.2
9-5
.78**
0.8
7
(4.6
7)
(1.5
3)
(1.0
0)
(3.8
1)
(2.2
2)
(1.0
5)
(2.9
4)
(1.2
8)
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
2-8
.20
-0.2
4-0
.19
-3.1
9-0
.88
-0.1
9-1
.51
-0.2
5
Ob
serv
atio
ns
4586
5962
6036
6130
6213
6213
6213
6134
Un
der
iden
tifi
cati
on0.0
37
0.0
20
0.0
15
0.0
18
0.0
16
0.0
16
0.0
16
0.0
19
Wea
kId
enti
fica
tion
9.9
87
7.4
72
7.0
18
7.3
51
7.6
60
7.6
60
7.6
60
7.8
05
Ove
rid
enti
fica
tion
0.8
72
0.1
70
0.2
32
0.3
86
0.6
29
0.8
09
0.5
82
0.9
77
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
alsi
gn
ifica
nce
at
the
1%
level
,**
at
the
5%
level
,and
*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
.
139
Tab
leC
.22.
Cri
me
and
Educa
tion
Qual
ity
(Soci
alA
reas
)
Vio
len
ceP
rop
erty
Cri
me
Vio
lent
Cri
me
(1)
(2)
(3)
Ave
rage
Sco
rein
Soci
al
Are
as
-3.2
2***
-3.4
1***
0.1
5
(1.0
1)
(1.1
1)
(0.6
0)
Mu
nic
ipali
tyF
EY
esY
esY
es
Con
trol
sY
esY
esY
es
Tre
nd
Yes
Yes
Yes
Ad
just
ed-R
2-1
.73
-1.9
1-0
.20
Ob
serv
atio
ns
4486
4491
6134
Un
der
iden
tifi
cati
on
0.0
13
0.0
13
0.0
01
Wea
kId
enti
fica
tion
26.3
46
26.2
51
25.8
64
Ove
rid
enti
fica
tion
0.4
08
0.4
95
0.8
21
Note
s:S
tan
dard
ized
coeffi
cien
tsfr
om
Inst
rum
enta
lV
ari
ab
le(I
V)
regre
ssio
n.
Het
-er
osk
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
er-
iden
tifi
cati
on
Tes
tre
port
sth
ep-v
alu
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
s-ti
can
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
.
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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