©2013

Tanu Kohli

ALL RIGHTS RESERVED

IMPACT OF MIGRANT REMITTANCES ON FERTILITY AND EDUCATION IN

THE SOURCE COMMUNITY: EMPIRICAL EVIDENCE FROM INDIA

by

TANU KOHLI

A Dissertation submitted to the

Graduate School-Newark

Rutgers, the State University of New Jersey

in partial fulfillment of the requirements

for the degree of

Doctor of Philosophy

Graduate Program in Global Affairs

written under the direction of

Professor Carlos Seiglie

and approved by

___________________________________

Carlos Seiglie, Ph.D.

___________________________________

Kusum Mundra, Ph.D.

___________________________________

Mariana Spatareanu, Ph.D.

___________________________________

Jun Xiang, Ph.D.

___________________________________

Ira Gang, Ph.D., Rutgers University-New Brunswick

Newark, New Jersey

October, 2013

ii

Abstract

Impact of Migrant Remittances on Fertility and Education In The

Source Community: Empirical Evidence from India

By Tanu Kohli

Dissertation Director

Carlos Seiglie, Ph.D.

This dissertation studies the impact of migrant remittances on two measures of human

development- fertility and education. Remittances help recipient households to earn extra

income and increase their standards of living over time. If by augmenting household

income, remittances lead to an increase in the number of children in the household, the

long term development impact of remittances will be undermined. Comparatively, if

remittance incomes allow households to spend more on the education of each child in the

household, it will be better for the migrant-sending household in terms of long term

development. The two essays in this dissertation attempt to evaluate the impact of

remittances on fertility and the impact of remittances on education expenditures made by

remittance receiving households, and compare these outcomes with households that do

not receive remittances. The dataset used for this analysis is the 64th

Round of National

Sample Survey conducted by the Government of India. It is seen that remittance incomes

lead to a lower probability of birth in the remittance receiving household while increasing

the share of education related expenditures in the household and education investments in

each child, which are desirable outcomes for a developing community characterized by

high population and low human capital.

Keywords- remittance, fertility, education expenditure, India

iii

Acknowledgement

I would like to thank Dr. Carlos Seiglie, who not only advised this dissertation, but stood

by me helped me through the highs and lows of my graduate experience at Rutgers

University-Newark. He has been a kind teacher, a generous guide and most importantly a

fatherly figure, without whom I would not have become the researcher and professional

that I am today. He continually inspires me to work hard, dedicate myself to research and

teaching and above all, be a better person. His unwavering support can by no means be

summarized on paper.

I would like to thank Dr. Kusum Mundra, to whom I owe my knowledge of

econometrics and of migration studies in general. She has been instrumental is pushing

me to work harder on the development of my analysis, thinking like a researcher, and

trying till I succeed. She has also helped me through my personal decisions and I am

immensely grateful to her for providing perspective when I needed it the most. I am

thankful to Dr. Mariana Spatareanu for generating my interest in the topic of remittances

as a development strategy. She has taught me to be ambitious, value my research and

helped me envision a life as an academician and a researcher. I am grateful to Dr. Jun

Xiang, whose first words to me were “writing a dissertation is more about discipline than

anything else.” These words have never left me through the years and I hope to never

forget them. His advice extended beyond research to entering the job market, publishing

and being a good writer, which was instrumental to my progress. I am also extremely

thankful to Dr. Ira Gang, who was kind enough to join my committee despite his busy

schedule. His keen insight into writing and representing results has been extremely

iv

helpful. I also appreciate that he pushed me to be more confident about my research and

carry on despite small failures.

I want to offer special thanks to Minglu Wang at Dana Library, who helped me

with the first steps of statistical analysis. She devoted her time and her knowledge to help

build mine, to which I am deeply indebted. I am keen to thank M.L. Philip at the Data

Dissemination Unit of Ministry of Statistics and Programme Implementation, India

without whose instrumental support, the process of data cleaning and interpretation

would have taken much longer. I also want to thank Ann Martin and Desiree Gordon at

the Division of Global Affairs for making my academic journey smooth. It was the

continuing funding of the Division of Global Affairs that helped me complete my

education without financial constraints and valuable teaching experience. Jiping ‘Jeannie’

Wang at the Office of International Student and Scholar Services was instrumental in

cheering me at each step and providing valuable administrative support. Dr. John Graham

and Pearl Johnson at the Department of Economics have advised me and blessed me all

the way, to which I am extremely thankful.

I would like to thank my friends who shared my joys and anxieties through my

tenure at Rutgers University. Yi-Chun Lin, John Handal, Jyldyz Kasymova, Reagan

Barron, Helyett Harris, Aparna Dutt, Piyush Modak and Harish Damodaran, listened to

me and gave me vital support. Special thanks to my sisters, Dr. Meha Kohli-Mishra,

Shivani Kapoor and Gunjan Goyal who not only helped with critical feedback but also

held my hand through my worst fears and made life easy for me. I am deeply indebted to

my parents, Dr. Ajay Kohli and Dr. Neera Kohli, whose success in their professional life,

dedication towards their fields of study and work ethic inspired me to put in my best

v

effort as well. Brainstorming with them was fun, enlightening and immensely rewarding.

Lastly, I would like to thank my husband and my companion for life, Gaurav Bagwe,

who has been there from the beginning, supporting and respecting my decisions, and

loving me unconditionally.

vi

This dissertation is dedicated to my parents,

Dr. Ajay Kohli and Dr. Neera Kohli

and my beloved sister, Dr. Meha Kohli-Mishra

vii

Table of Contents

Abstract ii

Acknowledgement iii

Table of Contents vii

List of Tables and Figures ix

Introduction 1

Theories of Labor Migration 4

Motivations behind Remittance Flows 7

Data Collection Anomalies and the National Sample Survey 15

National Sample Survey 18

Snapshot of the Surveyed Households 28

Demographic Characteristics of All Households 28

Demographic Characteristics of Migrant-Sending Households 31

Migrant Histories 34

Migrants and Remittances 37

Consumption Patterns 41

Impact of Migrant Remittances on Household Fertility 46

Literature Review 47

Hypotheses and Model 55

Data and Summary Statistics 57

Results from the Probit Analysis 65

Results from the IV Analysis 75

Discussion 84

viii

Impact of Migrant Remittances on Education Outcomes 88

Literature Review 89

Hypotheses and Model 97

Data and Summary Statistics 99

Results from OLS Analysis 107

Results from the IV Analysis 115

Alternative Instruments 119

Discussion 125

Summarizing the Results and Future Work 127

Future Work 131

Works Cited 133

Appendix A 139

Curriculum Vitae 152

ix

List of Tables and Figures

Figures

Figure 2.1 - Remittances transferred through RBI 17

Tables

Table 3.1 - Overview of the household sample 29

Table 3.2 - Demographic characteristics of surveyed households 30

Table 3.3 – Demographics of Migrant Sending Households 31

Table 3.4 - Demographic characteristics of migrant-sending households 32

Table 3.5 - Household migration and remittances profiles 35

Table 3.6 - Reasons to migrate 37

Table 3.7 - Destination of Migrant Individuals (Percentages) 37

Table 3.8- Information on Remittance Sending Migrants 39

Table 3.9 - Utilization of Remittances (Percentages) 40

Table 3.10 - Monthly Consumption Expenditure and Income by Household Type 42

Table 3.11 - Expenditure Categories by Household Type 44

Table 4.1 - Expected behavior of variables 63

Table 4.2 - Descriptive statistics for fertility model 64

Table 4.3 - Probit results for all households 68

Table 4.4 - Probit results for selected households with married women 72

Table 4.5 - IV probit results for all households 78

Table 4.6 - IV probit results for selected households with married women 81

x

Table 5.1 - Variable definition and expected behavior 104

Table 5.2 - Descriptive statistics for schooling models 106

Table 5.3 - OLS estimates for share of schooling expenses and schooling expense per

child 110

Table 5.4 - Durbin-Wu-Hausman test for endogenous variables 115

Table 5.5 - IV estimates for share of schooling expenses and schooling expense per child

118

Table 5.6 - Post-estimation tests for weak instruments 119

Table 5.7 - IV estimates for share of schooling expenses and schooling expense per child

using unemployment and district-wise concentration of post offices as instruments 122

1

Introduction

Labor migration, domestic and international, is one of the most controversial features of

globalization. Cheap migrant labor is believed to take away employment opportunities

from the residents of a community and bring down their wage levels. Advanced countries

impose strict immigration laws to thwart the flow of illegal migrants and regulate the

flow of legal migrants to include only the most productive cohorts. For example, United

States of America (USA) is trying to implement immigration reforms with respect to

illegal migrants in the country as well as make the process of getting a green card faster

for legal immigrants; while countries such as Australia and Canada already have

migration policies that strongly favor the skill and ability of prospective migrants.

Developing countries on the other hand, struggle with the sentiments of resistance

attached to the migration of rural workers to urban areas and its impact on the labor

market of the latter. For example, workers from the states of Uttar Pradesh and Bihar are

often blamed for higher unemployment and higher crime rates in metropolises of Mumbai

and Delhi in India. Rural migrants are seen to create resource pressures in urban areas in

China where housing them and providing them with public services creates visible

distress among the urban populations.

At the other end of the spectrum are the migrant populations who act as agents of

globalization. Successful economic assimilation of migrants in their host community

makes migration a desirable attribute of the process of economic growth for both

destination and source communities. Their movement across states and nations also

brings about a change in the societal norms at both the source and destination. For

2

example, migrant diasporas can act as links between the destination and source

community to facilitate the flow of investments, goods and services. They can also

facilitate long term infrastructure development in their source community; or ‘fix’ local

vices by utilizing their exposure to other communities. The role of migrants in the

process of economic development is therefore, extremely crucial to understand.

Migrants can exercise their effectiveness by two methods. First is through

frequent visitations to the home community, thus becoming the agents of change. In this

case, migrants transfer knowledge, norms and techniques of the host community to the

home community; and help the two societies become more homogenous. Such transfer is

however impeded by the ability of migrants to travel back to their host communities

frequently. Additionally, it is also common to see that economic migrants settle down in

the host community and start their own family, which gradually weakens the relationship

between the migrant and his household in the host community. In such a scenario,

migrants can still influence the household in the host community by remitting a part of

their income. Remittances can thus help the household in the recipient community to

become more like the host community. Eventually, as the behavior of remittance

receiving households change, the non-remittance receiving households also change their

consumption behaviors, thus helping the peripheral economy to develop.1

With respect to the effects of migration on development, social scientists as well

as public policy creators have traditionally focused on the labor market and social

1 The continued dependence on remittance could also a have a negative effect on

the labor markets and economy of the host community. If the flow of remittances makes

one set of households more well-off than others, the latter might also decide to engage in

migration; thus aiding the creation of a migration economy. Such economies can lose on

account of brain drain and missing youth which would be a worse outcome in terms of

development.

3

assimilation of migrants in the destination community (at the domestic and international

levels) or observed the effect of brain drain on the migrant’s source community (usually

international migration from developing countries). In the past decade and a half

however, more studies have started focusing on remittances, rather than the migrant, in

facilitating economic growth in the source community. This dissertation is an attempt to

understand the impact of remittances on fertility and on education in India. The primary

objective is to compare the consumption practices of remittance-receiving households

with non-remittance receiving households and interpret the role of remittances in

ensuring long term development in the source community of the migrant.

This dissertation proposes to add to the current literature on remittances in three

prominent ways. First, the impact of remittances on fertility is a new area of academic

studies. There are only three studies2 that explore the possibility of link between

remittances and fertility and utilize a panel of countries. Comparatively, the essay on

fertility in this dissertation is the first to explore the effect of remittances on fertility using

migrant stock of one country. Second, the essay on education studies schooling

expenditures and expenditure per child. Both these variables have not been studied before

with respect to the impact of remittances. Majority of the studies exploring the effect of

remittances on schooling outcomes focus on school enrolments rather than schooling

expenses at any given level of education. Third, this dissertation utilizes a micro-level

dataset from India which is not the focus of migration and remittance based studies. Most

2 Fargues, “Demographic Benefit of International Migration.”

Beine, Docquier, and Schiff, "International Migration, Transfers of Norms and Home

Country Fertility”

Naufal, and Vargas-Silva, "Influencing Fertility Preferences One Dollar at a Time”

4

of the migration studies concentrate on South American migrants, principally the

Mexico-USA corridor. The dataset provided by the National Sample Survey Organization

(NSSO) has been used to study labor market conditions however; this dissertation is the

first to utilize it for remittance studies.

Theories of Labor Migration

Migration refers to movement of people across state and national boundaries due to

economic and/or socio-political reasons. A migrant, according to the United Nations’

Statistics Division, is “a person who has entered the country with the intention of

remaining for more than a year and who either must never have been in that country

continuously for more than one year or, having been in the country at least once

continuously for more than one year, must have been away continuously for more than

one year since the last stay of more than one year.”3 Individual countries have their own

definition of migrant, which guides their data collection processes. For example,

according to the Government of India Census Data 2001, a migrant is defined as “a

person who has moved from one politically defined area to another similar area… Thus a

person who moves out from one village or town to another village or town is termed as a

migrant provided his/her movement is not of purely temporary nature on account of

casual leave, visits, tours, etc.”4 The latter definition for a migrant refers to international

as well as domestic migrants while the former refers to international migrants only. Since

the dataset used in this dissertation is a national sample, the second definition is adhered

3 Recommendations on Statistics of International Migration, p. 13,

4 Government of India Census Data 2001

5

to. Accordingly, households are characterized as migrant-sending if at least one person

left the household on a permanent basis for a non-tourist purpose.

Prior to studying the impact of migration however, it is crucial to understand the

nature and motivations behind the movement of persons. Migration may be caused by

external factors, such as a natural calamity or war, or due to social reasons such as

marriage. Economic migration however, is usually motivated by the possibility of earning

a higher wage in the destination community and enjoying higher standards of living.

Within academic literature, at least three streams of economic literature discuss the

intentions behind labor migration. These include discussions made by the neo-classical

theorists, popularized by Todaro (1969) and Harris and Todaro (1970); the new

economics of labor migration that gained importance in 1980s mainly through the

pioneering work of Stark and Bloom (1985) and; the world systems theorists. The neo-

classical theory of labor migration adheres to the simple logic of returns to the factor of

production. Labor flows from labor-abundant market to the capital- abundant market

because the returns to labor are higher in the advanced economic centers. Promoted by

Todaro and Harris and Todaro,5 this theory suggests that the expectations of a greater

urban wage drive rural- urban migration, even if there is unemployment in the migrant

receiving sector. This flow of labor eventually slows down as income differentials

become narrower and there is no incentive for the labor to move from one market to the

other. Neo-classical theorists treat labor migration as an individual decision based on a

cost-benefit analysis of moving from one market to the other.

5 Todaro, "A Model of Labor Migration and Urban Unemployment in Less

Developed Countries."

Harris and Todaro, "Migration, Unemployment and Development."

6

Towards 1980s economists such as Stark and Bloom, Stark and Katz, Borjas and

Taylor6 direct attention towards the new economics of labor migration that attribute

migration to be a family decision. They propose that migration is driven by the idea of

relative deprivation which is experienced by low income families in a community. In

order to overcome this sense of deprivation, families send migrants to economic centers.

Migration is thus seen as a “calculated strategy”7 with the objective of creating monetary

returns in the form of remittances and overcoming relative deprivation with respect to

others in the source community. New economics of labor migration also assumes that

migrants’ home market structures are labor abundant and that migration will in fact help

to relieve the pressure from the home country labor market as more jobs would be

available to the labor that chooses not to migrate.

The third theory focusing on labor migration concentrates on the “pull factors”8 of

advanced economies that act as magnets to labor from the periphery and thus facilitate

migration. Known as the world systems theory, it emphasizes that migration is a natural

outcome of the process of globalization. As the search for new materials spreads

development to the traditional sectors lying at the periphery, some migrant workers get

attracted to the economically advanced core. Once in the economic core, the migrant

labor could start by becoming a part of the informal sector. The formal workforce on the

other hand, occupies the unionized, more stable positions, allowing the migrants to take

up jobs not taken by the former. The organizational hierarchy stays intact and the migrant

6 Stark and Bloom, “The New Economics of Labor Migration.”

Katz and Stark, “On Fertility, Migration and Remittances in LDCs.”

Borjas, “Economic Theory and International Migration.”

Taylor, “New Economics of Labour Migration and the Role of Remittances.” 7 Stark and Bloom, “The New Economics of Labor Migration,” 175.

8 Massey et al, “Theories of International Migration,” 440.

7

and native workers survive in the labor market.9 Massey et al also emphasize on the

importance of networks and institutions as a reason for labor migration. However, it

might be more appropriate to count them as reasons behind continued movement of labor

rather than the primary cause of migration.

From the literature review above, following conclusions are drawn. First, the main

motive to migrate is economic well-being. If the prospective migrant sees that his income

might increase due to such movement, he will bear the cost of leaving. Second, the costs

of migration are an important determinant for movement. The poorest will not migrate

because of high costs and the richest don’t need to migrate as they are well off in their

current situation. The highest movement will thus be from the middle income group of

the source community. Third, as communications between developed and developing

societies increase and transportation costs reduce, the flow of migration will increase till

an equalization of wages is brought between the urban core and the semi-urban/ rural

periphery.

Motivations behind Remittance Flows

Remittances are the part of migrant remuneration that is sent back to the migrant’s family

members in the home community. Katz and Stark label it as the ‘reward’ for undertaking

migration.10

The International Monetary Fund formally defines remittances as

representing “household income from foreign economies arising mainly from the

9 Massey et al, “Theories of International Migration,” 448-451.

10 Katz and Stark, “Desired Fertility and Migration in LDCs.”

Katz and Stark, "On Fertility, Migration and Remittances in LDCs."

8

temporary or permanent movement of people to those economies.”11

Remittances are

thus, a derived product of the export of labor from labor surplus to labor deficient

countries. The national accounting estimates of each country might vary with respect to

categorizing what can be included as remittances. For example, remittances are usually

used in reference to cash transfers, but depending on the country, can also include gifts

such as computers, cars or other household items.

In order to understand the motivations behind why a migrant would send a part of

his income to his home community, it is useful to view migration as a family decision,

than as an individual decision. If a migrant is a selfish economic agent, he will enjoy

higher wages and have no inclination to send money back to the source community.

Despite this, many migrants do remit a part of their income. The proponents of new

economics of labor migration such as Stark, Stark and Taylor and Stark and Lucas

attribute this willingness to intangible characteristics such as the altruistic desire to

support the family in the home community.12

This willingness can be backed by

economic considerations that can vary from the perceived obligation of a migrant towards

his parents/sibling, to the need to overcome economic hardships in the home community

where remittances become a tool for removing credit constraints. Such co-dependency

between a migrant and his family in the source community can be explained by four

factors- altruism, old-age insurance, risk-diversification and implicit loan repayments.

11

International Monetary Fund, “International Transactions in Remittances,” 1. 12

Stark, “Rural-to-Urban Migration in LDCs.”

Stark and Taylor, "Relative Deprivation and International Migration."

Stark and Lucas, "Migration, Remittances, and the Family."

Lucas and Stark, "Motivations to Remit”

9

Altruism is often cited as the most common reason to remit. Family emergencies

such as sickness, marriages, natural disasters, and the mere act of supporting someone

because they are a part of the family; are all altruistic reasons to remit. Using remittances

as the means to create post-retirement security for the migrant is suggested by the old-age

insurance argument. So if a migrant plans to retire in the home community or has

property at the source which he wants to stay secured till his return, he would remit to his

family members, keeping his stakes secure for the long run. If the migrant does not plan

to retire at the source community he would severe their ties with the household and not

remit money. It is also seen that a migrant would remit continuously if he leaves his wife

or children behind, especially in a multi-generational household with joint land and

property ownership, to make sure they are treated well by other family members of the

household. However, Banerjee, in his study of rural-urban migrants in New Delhi finds

that land ownership and separation from wife and children does not have a substantial

impact on continued remittance transfer by the migrant to the family in the source

community.13

Remittances can also be interpreted as a method for risk diversification

undertaken by both migrant-sending households as well as the migrant sending

remittances. Taylor stress that, “…migration is hypothesized to be partly an effort by

households to overcome market failures that constraint local production.”14

Children act

as ‘assets’ in such a case, with remittances as the expected returns. Households, lured by

the prospect of higher incomes, have more children and diversify their future risks by

13

Banerjee, "Rural‐Urban Migration and Family Ties," 350-351. 14Taylor, “The New Economics of Labour Migration and the Role of

Remittances,” 74.

10

sending migrants to different destinations. This risk diversification strategy can also work

the other way. A migrant could remit money to his family at the source for financial or

property investment as a means to save a part of the earned income and earn a return on

them. Another reason for transfer of remittances is given by Poirine who suggests that

remittances are “implicit loan repayments” by the migrant to his family for investing in

his human capital and helping him to relocate to a destination with greater returns to

human capital. Remittances are therefore, a payback by the migrant to compensate for the

consumption that his parents might have enjoyed, if they had not invested in his human

capital.15

Irrespective of the motivations behind remittance transfers, they have an

integral role to play in the migration decision and have a direct impact on the household,

once it starts receiving remittance incomes.

The role of remittances in the economic development of source communities has

been closely followed by economists at the World Bank16

and scholars interested in

growth studies. To augment this process, in the last 20 years, recordkeeping of

remittances flows at the international level has been pioneered by the World Bank; while

at the national level many developing countries have integrated and improved migration

and remittance trends in national level census and housing surveys. A brief look at the

remittance data shows that between 1980 and 2010, worldwide receipt of remittances

increased by approximately 92%, mainly because of increased movements of populations

around the world. Socio-political changes such as the break-up of the Soviet Union also

added to this increase in international remittance transfers. The improvements in record

keeping and increased use of official channels for transfers have also contributed to the

15

Poirine, "A Theory of Remittances as an Implicit Family Loan Arrangement." 16

Notably the Migration and Remittances team at World Bank led by Dilip Ratha.

11

increases in recorded remittances. Over the years India, China and Mexico have

maintained their dominance as the main recipients of international remittances. While

remittances form a very small part of the gross domestic product of these countries, for

some others they make up as high as 35% of the gross domestic product.17

Such high

dependence on remittances is expected to reflect in the impact on labor markets,

consumption behaviors, family structures and human capital outcomes for these

countries. Numerous studies that have evaluated the nature of remittance flows and their

effect at the household and national levels come to the following important conclusions:

1. Stable source of development finance- The World Bank finds remittances to be more

consistent than foreign direct investment and foreign aid as a source of external

finance. For example- Yang observed that during the East Asian crises, while foreign

direct investment was withdrawn from the Philippines, remittances to the country

witnessed an increase.18

This counter-cyclical characteristic of remittances is also

observed by Ratha, Sayan and Ratha and Mohapatra while studying a panel of

developing countries.19

While this observation seems plausible, remittances can fail to

exhibit such counter-cyclical nature if the migrant’s destination country is witnessing

an economic downturn as well. Additionally, while such counter-cyclicality has not

been studied for micro-data samples for a country, remittances are still a relatively

stable source of income for recipient households.

17

Remittances make up 35% of Tajikistan’s gross domestic product. 18

Yang, "International Migration, Remittances and Household Investment.” 19

Ratha, "Workers’ Remittances”

Sayan, "Business Cycles and Workers' Remittances.”

Ratha and Mohapatra, "Increasing the Macroeconomic Impact of Remittances.”

12

2. Reachability- Remittances are person-to-person flows and are not attached with

interest obligations, like personal loans given by banks and microcredit institutions.

Hence, their outreach is greater and affects consumption and savings directly. For

example, in a panel study of 39 developing countries, Pradhan, Upadhyay and

Upadhyaya recognize that remittances directly and positively affect consumption,

productive activity, increase educational retention in school and lead to greater

savings.20

Ratha and Mohapatra also reach the same conclusion about the role of

remittances towards greater investment in education, entrepreneurship and health in

migrant sending households.21

3. Role in poverty reduction- Due to their accessibility and freedom of the household

members in deciding the use of these remittances, the latter are more effective in

reducing poverty. When remittances are used for consumption, they increase the

aggregate demand at the national level, and when invested in productive activities,

additions are made to the output growth in the economy. For example, a panel study

of developing countries done by Adams and Page finds that “a 10% increase in per

capita official international remittances will lead, on average, to a 3.5% decline in the

share of people living in poverty.”22

4. Role in foreign exchange stability- At the national level, remittances help in building

a country’s foreign exchange reserves. Case studies from India show that workers’

remittances from the Middle East and Gulf countries helped the country to evade a

20

Pradhan, Upadhyay, and Upadhyaya, "Remittances and Economic Growth in

Developing Countries." 21

Ratha and Mohapatra, "Increasing the Macroeconomic Impact of Remittances.” 22

Adams and Page, "Do International Migration and Remittances Reduce Poverty

in Developing Countries?” 1660.

13

balance of payment crisis for a large period in 1980s. Similar proof is found by

Taylor et al for countries such as Bangladesh, Yemen, El Salvador and Sudan whose

foreign exchange was being financed by remittance receipts in absence of sufficient

FDI flows.23

It is clear that remittances have an effect on the development outcomes for migrant-

sending communities. The intensity of these impacts on consumption behaviors and

different aspects of development have been studied by many academic scholars. The

focus can be on health outcomes, schooling enrolment and retention, environment and

investment. This dissertation isolates the effect of remittances on fertility (by observing

the event of birth in the remittance receiving household in the survey year) and the effect

of remittances of education (by observing the schooling expenses incurred by a

remittance receiving household and investments made in the education of each child).

The National Sample Survey from India is used to examine these hypotheses. The dataset

provides information on domestic and international migrants and surveys a diverse set of

economic groups. The dataset also provides sufficient religious and caste diversity and

allows focusing on the multi-generational family structure, which is usually not observed

in many countries and is also not covered in majority of migration and remittance related

empirical studies.

The remainder of the dissertation is arranged as follows. The next chapter

provides a brief overview of the Indian domestic and international migrant stock in the

recent years. It also includes data on remittance flows at the international level. The

23

Taylor et al, "International Migration and National Development."

14

problem of data unavailability at the national level is reported after which the National

Sample Survey is introduced. Following part provides a detailed description of the

demographic, economic and consumption behaviors of sampled households. The fertility

model is introduced in part four, and it applies probit regression and instrumental

variables regression methods to determine the role of remittance receipt in the increased/

decreased likelihood of birth in the household. It is seen that remittances reduce the

likelihood of births in a remittance receiving household, which is desirable with respect

to long term economic growth of the migrant-sending community. Thereafter, the

education expenditure models are introduced and linear regression results show a positive

impact of remittance receipt on different education variables. The last section

summarizes the results and suggests future work in this direction, given the lack of

availability of good instruments for the education expenditure models in part five.

15

Data Collection Anomalies and the National Sample Survey

India has been a migrant sending nation since the early 19th

century due to its

colonization by Great Britain. Indentured labor from India traveled to other

commonwealth nations in the Caribbean, United Kingdom and South Africa through the

19th

and 20th

centuries, usually on a permanent basis. The largest wave of domestic

migration in India was recorded at the time of partition of the country in 1947.

Approximately 10 million people moved from provinces that now lie in Pakistan, to

mainland India.1 There was a parallel movement of international migrants from India to

the United Kingdom at this time. The second wave of immigration from India came after

the immigration reform in the USA in 1965, where an increasing number of educated

Indians migrated, again on a permanent basis. The flow of migrant population to the USA

intensified after the economic reforms of 1991 which were paralleled by changing work

requirements in the former. The most important migration corridor, that helped to

recognize the importance of remittances as development tool however, came with the

flow of migrants from southern states of Kerala, Andhra Pradesh and Tamil Nadu to the

Gulf countries on a short-term basis. The migration of these workers, physicians and

nurses directly affected the standards of living of their families in India and at the

macroeconomic level, helped India maintain its balance of payments accounts during the

1980s. The migration to Gulf countries is usually for a shorter time period of about three

to five years for less-skilled workers and longer for highly skilled workers especially

engineers. Over the years, Indian diasporas in the UAE have swelled to make it the

1 "Sixty Bitter Years After Partition."

16

largest population group, residing in the cities of Sharjah, Abu Dhabi and Dubai.

Comparatively, the Indian immigrants in the USA, Australia and United Kingdom tend to

be more skilled and choose to migrate on a permanent basis.

Information on domestic migration in India is however, not as well chronicled as

international migration. Metropolitan cities of Mumbai, New Delhi, Chennai and Kolkata

have traditionally been most popular with domestic migrants. However, a substantial

movement is also seen from the rural and semi-urban areas to the capital cities in each

state. In last two decades, cities such as Bangalore, Hyderabad and Ahmadabad have

started witnessing large flows of internal migrants from all over the country, apart from

attracting regional migrants as before.

Migrants and remittances have traditionally not been a focus of the National

Sample Surveys in India. The Government of India has collected information on migrants

only three times since 1950; in 1993, in 1999-2000 and in 2007-08. Of these, only the

2007-08 dataset collects information on remittance transfers made by the migrants.

Reports from these migration surveys find the dominance of urban-to-urban migration at

the domestic level. For example the urban migration rate was 30.65% in the 1993 survey,

33% in 1999-2000 and 35% in 2007-08. Comparatively, only 22.74% people migrated

from rural areas in 1993, 24% in 1999-2000 and 26% in 2007-08. These reports also

show that women tend to migrate at a much higher rate than men. For example, in 1993,

77% of the migrants were female, which fell to 48% in rural areas and 46% in urban

areas in 2007-08.2 These migration rates however, are more likely indicative of mobility

2 Department of Statistics. Migration in India January-June 1993, 14.

Ministry of Statistics and Programme Implementation, Migration in India 1990-2000, 4.

Ministry of Statistics and Programme Implementation, Migration in India 2007-2008, 22.

17

due to marriage rather than economic mobility. The results from the 2007-08 migration

survey are covered in detailed in the next part.

Comparatively, the data on domestic remittances is largely absent. For

international remittance inflows, data from the Reserve Bank of India (RBI) shows that

India’s receipt of remittances peaked in the year 2005-06 and has steadily declined

thereafter. This decline could be due to the onset of the global financial crisis in the later

2008. Despite this decline in remittances, India remains the largest receipt of remittances

in the world.

Figure 2.1 - Remittances transferred through RBI

There is no parallel data collection on domestic remittance flows. Additionally, the

dataset is not rich in information about the destination of migrants. As will be covered in

the essay on education expenditures, the lack of information about the destination states

and countries of permanent migrants makes it slightly difficult to use destination-based

instrumental variables.

0

2000

4000

6000

8000

10000

12000

14000

16000

18000

20000

1970-7

1

1975-7

6

1980-8

1

1985-8

6

1990-9

1

1995-9

6

20

00-0

1

2005-0

6

2010-1

1

Bil

lion R

upee

s

Year

18

National Sample Survey

This dissertation uses the 64th

Round of the National Sample Survey (NSS) on

Employment & Unemployment and Migration Particulars conducted in 2007-08

(henceforth referred to as the NSS). These socio-economic surveys are conducted by the

NSSO under the Ministry of Statistics and Programme Implementation (MOSPI),

Government of India. The NSSO conducts annual surveys on industries and agriculture

and decadal socio-economic survey rounds. The socio-economic surveys are devoted to

data collection on characteristics such as livestock, debt, employment, manufacturing &

trade and social consumption. Each survey is conducted one to four times in the 10 year

period, depending on the relevance of the estimates. For example, land & livestock

surveys and social consumption surveys are conducted only once in 10 years while

manufacturing & trade is surveyed four times in 10 years. Two years in the 10 year

period of socio-economic surveys are devoted to the study of employment &

unemployment. Migration particulars are recorded in the employment & unemployment

surveys and have been conducted only three times since 1950, the latest being the 64th

Round.

The 2007-2008 NSS interviews 125,578 households and provides information on

572,254 individuals, of which this dissertation focuses on only remittance receiving and

non-remittance receiving households. The unit of analysis is the household rather than the

individual. The NSS questionnaire is attached in the Appendix A for reference. The

information collected by the NSS is spread over seven levels, each level corresponding to

different characteristics of the surveyed households and the individuals residing in them.

A unique household identification number is assigned to each household which enables

19

recognizing the household’s information and characteristics of each individual within the

household. A brief summary of these variables is provided below.3

Household-level Demographic Variables

1. Household size- includes all the members that resided in the household at the time of

the survey. It does not include the permanent short-term or long-term migrants from

the household, but only individuals that can claim residency in the household at the

time of the survey. This information is used to create variables on proportion of

female children, female adults and proportion of children in the surveyed household.

2. Household sector- shows whether the household is located in the rural sector or the

urban sector. In the final analysis, this variable assumes a dummy value with

residence in the rural sector as the reference category.

3. Household type- gives the employment type of the household in rural and urban areas.

For example, a household can be self-employed in agriculture or non-agricultural

activities if in rural area, or can be self-employed in urban area or can be a regular

wage earning household in an urban area. This information is not utilized in the final

analysis, but can be useful with respect to further analyze regional level data on

remittances.

4. Religion- gives the religion that the household follows. Individual religious

preferences are not recorded because in the Indian society it is commonly observed

that the family’s religion is also the individual’s religion. There are five main

3 Please note that not all the variables are included in the final analysis of fertility

and education outcomes. They are however used in the creation of other variables used in

the analysis.

20

religions in India- Hinduism, Islam, Christianity, Sikhism and Buddhism.

Approximately 77% of the sampled households followed Hinduism, making it the

reference religion for the empirical analysis.

5. Social group- asks a question about the reserved caste status of the household. The

Indian society is divided into social groups that identify population according to their

backward economic statutes. The root of these social groups lies in the Indian caste

system that divided a society into upper caste and lower castes (untouchables).

Scheduled Castes (SCs) and Other Backward Classes (OBCs) consist of lowest castes

in the caste system hierarchy in India while Scheduled Tribes (STs) are peoples of

indigenous tribes that have needed government protection for survival in the post-

independence era. The status of SC/ST/OBC provides members of these communities

with privileged access to education, government employment and political offices

through the system of reservation. “Others” in the survey refers to the general

category and other non-reserved castes, which are not protected by special status of

the government. As will be seen later, these caste based classifications are expected to

reflect the difference economic opportunities of a given household and hence the

difference in preference for children and child education.

6. Possession of land- gathers information on any land owned, rented or encroached

upon by the household. Land ownership can serve as a good proxy for the wealth of

the household. The NSS however, includes encroached and rented property while

collecting information on land ownership. This creates an upward bias in the wealth

estimation for the surveyed households. Therefore, the variable is not included in the

final analysis.

21

7. Total wages of the household- from all activities and for all individuals in the week

before the survey is conducted. Individual incomes are added to derive the

household’s weekly wage and annual wage. This income data however, is highly

sporadic. Only 51% of the households report their income in the survey, and almost

66% of the remittance-receiving households do not report any income for the week

before the survey is conducted. Such missing information can be attributed to two

factors. First, the households do not prefer to disclose their incomes and choose not to

answer these questions. Since the survey itself puts more information of consumption

details, the income data is left unnoticed. Secondly, the households that report any

income might not be a true representation of the household’s income because of the

nature of information collected. If the household depends on daily wages or bi-

weekly wages, the household might not have earned anything in the week preceding

the survey but could have earned more incomes in other weeks of the year. This is

also true for seasonal workers who utilize income from the peak-income season

throughout the year. If the survey was conducted during the off-peak season, these

workers could report no income, even if they are using their incomes earned in some

other time period. This recording error creates a downward bias in the income

estimates and thus, not truly representative of the household’s standard of living.

8. Monthly household consumption expenditure- is the total monthly spending of a

household on different consumption goods, a systemic breakdown of which is

provided on page 12 of the NSS questionnaire in Appendix A and discussed in brief

below. In the absence of reliable data on household income and the detailed

breakdown of consumption habits of the surveyed household, consumption

22

expenditure might be used as a proxy for standard of living. A substantial amount of

academic literature supports the inclusion of consumption expenditure as a proxy for

income in analyzing population surveys from developing countries. For example,

Deaton provides insight into this problem especially in reference to the NSS.4

Deaton’s main hypothesis is that developing countries are mainly agrarian in nature

which provides households with an uneven flow of income; compared to which their

consumption stays relatively stable.5 The current sample is also overwhelmingly rural

(68%) than urban. His theory thus, directly applies to the current study sample.

Deaton also points at erroneous income reporting by households where incomes are

reported as zero for a given time period. This happens because the amounts earned by

the household are often entirely spent on consumption and since the value added stays

zero, the income reported by households is also reported as zero. The 2004-05 NSS is

used by Das and Mukherjee to study child labor outcomes in India and they also use

monthly per capita expenditure as a proxy for income due to the difficulties in

collecting accurate income data by the NSS.6

9. Multigenerational households- and collateral households are ones in which more than

two generations or more than one cohort of parents reside in the same house and pool

income and consumption resources. For example, a couple living with the son’s

4 Deaton, "Saving in Developing Countries."

Deaton, "Household Saving in LDCs."

Deaton, and Zaidi, “Guidelines for Constructing Consumption Aggregates for Welfare

Analysis” 5 Deaton says that developing country households save and dis-save at relatively

high rates due to uncertain incomes, thus allowing them to smooth consumption around

the year. 6 Das and Mukherjee, "Role of Women in Schooling and Child Labour Decision.”

Das and Mukherjee, "Measuring Deprivation Due to Child Work and Child Labour.”

23

parents and their own children or a brother co-residing with other brothers and their

wives and children and any unmarried siblings. Approximately 36% of the surveyed

households in the NSS reported to be multi-generational households. Such

households are characteristic of the Indian family system and thus an important

determinant of fertility and education outcomes.

Decision making in these households is relatively decentralized, and the

household head is usually expected to consult other members before making a

decision that would affect the entire household. For example- a grandchild from a

three generational family is to be sent outside the state for receiving higher education.

The decision making process would involve the parents of that child, the household

head and his/her spouse, and the highest educated elder of the family. The opinion of

the most educated will matter more than the decisions of the parents because of

his/her expertise in the area of education. The final decision, made by the household

head, will be heavily influenced by the former’s expertise. Such hierarchy is often not

challenged, due to the cultural as well as socio-economic benefits of the arrangement.

10. Education expenses- are listed as one of the consumption expenditures conducted by

households. This includes expenses incurred on tuition, fees, school supplies, library

charges etc. This information is used to create the two dependent variables of the

education essay, share of education expenditures out of total consumption

expenditure and education expense per child.

Household-level Migration Variables

24

11. Migrant household- is different from a migrant-sending household. If the entire

household migrated to the current place of survey in the last 365 days, they are

labeled as migrant households. This variable is not utilized in the present essays but is

useful with respect to studying return migration.

12. Former migrants- refer to permanent migrants that have been out of the house for a

year or more (and not return migrants as the name might suggest). A household with

these migrants is a migrant-sending household. Information on these former migrants

include the sex of the migrant, age of the migrant, current location of the migrant7 and

the time since they migrated. These variables are used to measure the strength of the

relationship between the migrant and the household in the source community by

including the time duration since the migrant left the household, as well the number

of migrants who have left the household. These variables are discussed in detail in

later sections.

13. Amount of remittances- are monies sent by each migrant to the household and the

total amount (from all migrants) received by each household in the last year. This

variable distinguishes a remittance receiving from a non-remittance receiving

household. The receipts and their use are distinguished from any other income source

with the objective of identifying the exclusive effect of remittances. There are no non-

migrant sending households that report remittance receipt.

7 The current location of the migrant does not include the state or country the

migrant is currently residing in. Instead, the data collected points out whether the migrant

is in the same district, a different district in the same state, a different state or a different

country.

25

14. Employment status of permanent migrants- indicates which permanent migrant

economically active at their destination community. This variable is used to study the

impact on education expenses of the household.

15. Temporary migrants- include individuals who left the household for employment

purposes for a period of one to six months in the survey year. Information is provided

on their employment and industry of employment during these spells of temporary

migration. While this variable is not included in the final analysis, they can also serve

as good variables to study education outcomes in a household.

16. Return migrants- and their individual migration history is recorded in the survey as

well. This information is separate from the information on whether the household was

a migrant household (summarized on p. 24 above). This data on return migrants can

be used for future studies, especially because it reports the last location of the migrant

(state or country) which makes comparative studies extremely useful. In the current

study however, these variables are not of much utility.

Individual-level characteristics

17. Age and gender of each household member- is recorded which enables the creation of

household level variables such as the total number of adults (male and female), total

number of children (male and female), proportion of females adults and children and

as will be seen later, number of employed adults and number of educated adults. This

variable also helps to estimate the event of birth in the survey year.

18. Relation of each individual to the household head- is recorded and arranged in a

particular hierarchy in the household so that one family sub-unit can be distinguished

26

from others in a multi-generational household. This variable is not used in the

analysis but is important to the recognition of a multi-generational household.

19. Highest level of schooling completed by each individual- is used to create the

education variables on maximum educational attainment achieved in the household

by any individual. This member might or might not be the same as the household

head. It is also used to create variables of proportion of adults and females out of total

adults, at each level of completed schooling.

The original the survey divides education attainment of each individual into 14

specific categories (p. 5 of the NSS). These categories are broadly grouped into- not

literate, literate without formal schooling and through government sponsored adult

education programs, primary and middle schooling (till eighth grade), secondary and

higher secondary schooling (grades ninth to 12th

) and; college educated (bachelors

degree and above).

20. Employment status of each individual- the survey categorizes economic activity of

each individual as self-employed, unpaid family worker, salaried employee, casual

labor, student or unemployed in the labor force, attended to household duties, retirees,

and disabled. These categories are used to create a variable on the employment status

of each individual in the household. More details on these variables are provided in

later sections.

The following section provides a snapshot of the characteristics of the surveyed

households, focusing on the variables that have been outlined above. Additional

information is provided with respect to consumption behavior and utilization of

27

remittances. These summary statistics are useful in predicting what is to come with

respect to the economics analysis of remittances, fertility and education in the surveyed

households.

28

Snapshot of the Surveyed Households

The NSS is used for the purpose of labor market studies in India but the availability of

information on migration and remittances, enables the use of the latter for empirical

analysis of the hypothesis proposed by this dissertation. As previously mentioned, the

employment-unemployment Surveys are conducted every five years, but migration

particulars have been collected only two times prior to the current survey, in the 49th

Round (1993) and 55th

Round (1999-2000). The 2007-2008 NSS collects information on

the employment, unemployment and migration details of 125,578 households and

572,254 individuals in this dataset. The survey stretches through 7 levels of information

ranging from household demographics, migration histories for households as well as

individuals within a household, education and occupational status of each member in a

household, their employment and income statuses and finally household consumption

levels for different goods and services. The variables that might be of interest with

respect to this study were summarized above. This section provides further detail on the

variables introduced earlier.

Demographic Characteristics of All Households

Out of the 125,578 households that were interviewed in this NSS, approximately 63%

were rural households while only 37% could be classified as urban. The survey covers all

the 35 states and Union Territories in India, including the national capital of New Delhi.

The survey design is such that equal weight is provided to each state. Majority of the

interviews were conducted in Uttar Pradesh, the largest state in India by population

29

(12,603) followed by Maharashtra (10,044 interviews). The lowest number of interviews

conducted was in the least populated Union Territories of Lakshadweep (240) and

Daman& Diu and Dadra & Nagar Haveli (320 each).

Table 3.1 - Overview of the household sample

Total Percentage

Sector Rural 79091 62.98

Urban 46487 37.02

Religion

Hindu 97,230 77.43

Muslim 14,801 11.79

Christian 8,418 6.70

Sikh 2,290 1.82

Buddhist 1,445 1.15

Others 1,391 1.11

Social Group

Scheduled Tribes 17,267 13.75

Scheduled Castes 20,917 16.66

Other Backward

Classes 46,768 37.25

Others 40,615 32.35

Like many developing countries, for India as well, a simple distinction by rural and urban

sectors and states is not sufficient. Economic opportunities to migrate have traditionally

differed for people of different ethnicities and religion and by gender, making them an

important study criterion as well. In the current sample, 77.43% of the households

surveyed followed Hinduism (the most prominent religion in the country) while 11.79%

of the families practiced Islam. Alternatively, almost 68% identified themselves to be

from reserved classes of SC, ST and OBC. Table 3.2 below summarizes these

characteristics.

30

Table 3.2 - Demographic characteristics of surveyed households

Total Rural Urban

Mean S.D. Mean S.D. Mean S.D.

Household Size 4.56 2.33 4.73 2.34 4.25 2.29

By Religion

Hindu 4.44 2.29 4.64 2.33 4.08 2.18

Muslim 5.2 2.67 5.34 2.59 5.18 2.79

Christian 4.62 1.96 4.79 1.93 4.30 1.97

Sikh 4.69 2.27 4.95 2.33 4.20 2.08

By Social Group

Scheduled Castes 4.54 2.22 4.59 2.21 4.39 2.23

Scheduled Tribes 4.74 2.09 4.80 2.07 4.52 2.17

Other Backward

Classes

4.65 2.43 4.80 2.45 4.34 2.34

Others 4.38 2.36 4.67 2.42 4.11 2.28

The average household size is noted to be 4.5 persons. However, households that did not

follow Hinduism, on an average; have bigger families than those who follow Hinduism as

their primary religion. The largest households are for households that follow Islam, with

the average exceeding in both rural and urban sectors. Similarly, reserved castes

generally have bigger families than other castes, except in the case of rural areas where

SC households will marginally lesser than the ‘others’ caste grouping. These religious

and caste differences reflect the cultural difference among various social groups within

the country. For example, contraception access and use is more acceptable in Hinduism

than in Islam or access to economic resources has traditionally been lower for SC, ST and

OBC (reserved castes).1

1 The caste distinction is lesser in urban areas as compared to rural areas, where

opportunities might vary according to the household’s caste status in the village.

31

Demographic Characteristics of Migrant-Sending Households

The segregation of households according to remittance receiving and non-remittance

receiving households in Table 3.3 shows that majority of the remittance receiving

households resided in rural areas than in urban areas. The values are similar for non-

remittance receiving households. This is perhaps not a very substantial difference as the

number of households surveyed is larger in rural areas than in urban areas.

Table 3.3 – Demographics of Migrant Sending Households

Remittance receiving Non-remittance

receiving

Total Percentage Total Percentage

Sector Rural 20237 67.54 16427 68.45

Urban 9726 32.46 7571 31.55

Religion

Hindu 23101 77.10 18949 78.96

Muslim 3609 12.05 2376 9.90

Christian 1903 6.35 1586 6.61

Sikh 707 2.36 545 2.27

Buddhists 331 1.10 318 1.33

Others 310 1.03 224 0.93

Social

Group

Scheduled Tribes 3749 12.51 2997 12.49

Scheduled Castes 4572 15.26 4041 16.84

Other Backward

Classes 11303 37.73 9149 38.13

Others 10335 34.50 7810 32.55

Religion wise distribution shows that majority of the migrant sending households

followed Hinduism, which does not reflect anything extraordinary since the majority of

the households sampled follow Hinduism. However, greater number of Hindu households

does not receive remittances while households following Islam tend to receive

remittances from migrants. Within reserved castes, the proportion of remittance receiving

households is generally higher than proportion of non-remittance receiving households.

32

These values do not present a different story from the household summarized in Table 3.1

above.

Table 3.4 below summarizes the household size for remittance receiving and non-

remittance receiving households segregated according to different religious and caste

groups. It is seen that the average household size of remittance receiving households in

urban areas is smaller than the sample average of 4.73 persons per household. Non-

remittance receiving households have a greater household size for both rural and urban

households at 4.97 and 4.71 persons respectively, as compared to the sample average of

4.73 for rural areas and 4.25 of urban areas.

Table 3.4 - Demographic characteristics of migrant-sending households2

Migrant-sending Remittance

receiving

Non-remittance

receiving

Rural Urban Rural Urban Rural Urban

Household Size 4.73

(36664)

4.39

(17297)

4.53

(20237)

4.13

(9726)

4.97

(16427)

4.71

(7571)

By Religion

Hindu 4.63

(29211)

4.17

(12839)

4.42

(15933)

3.92

(7168)

4.88

(13278)

4.49

(5671)

Muslim 5.48

(3565)

5.54

(2420)

5.20

(2195)

5.14

(1414)

5.95

(1370)

6.09

(1006)

Christian 4.74

(2183)

4.39

(1306)

4.65

(1164)

4.25

(739)

4.84

(1019)

4.58

(576)

Sikh 4.96

(887)

4.07

(365)

4.84

(513)

3.90

(194)

5.12

(374)

4.27

(171)

By Social Group

Scheduled Castes 4.51

(6744)

4.54

(1869)

4.30

(3585)

4.29

(987)

4.76

(3159)

4.81

(882)

Scheduled Tribes 4.81

(5318)

4.84

(1428)

4.65

(2927)

4.78

(822)

5.01

(2391)

4.92

(606)

Other Backward

Classes

4.81

(14569)

4.53

(5883)

4.63

(7963)

4.31

(3340)

5.03

(6606)

4.81

(2543)

Others 4.70

(10030)

4.17

(8115)

4.47

(5760)

3.86

(4575)

5.00

(4270)

4.58

(3540)

2 Average household size is reported. The number of households is reported in the

parenthesis.

33

Remittance receiving households in rural areas, on average have a smaller household size

than the sample household average, irrespective of the religion they follow. Religion wise

distribution of non-remittance receiving households however shows the average

household size to be greater than the sample average for each religion reported in Table

3.2 above. A similar result is seen for the average household size for different religious

groups categorized as remittance receiving and non-remittance receiving households in

urban areas. Non-remittance receiving households exhibit a larger household size than

remittance-receiving households as well as the average sample size. This larger average

household size for non-remittance receiving households could be indicative of two things.

For rural areas a larger household size might be indicative of using migrants as an asset

diversification strategy. That is, the households are larger because some of the members

are expected to become migrants in the future. Without a corresponding flow of

remittance incomes as a return for this asset diversification however, it is difficult to

prove this intent. For urban areas a larger household size and non-receipt of remittances

can be indicative of the wealth of the household. That is larger, richer households can

afford to send more migrants and do not require remittances in return.

Similarly, remittance receiving households, when categorized according to

reserved castes, show an average household size smaller than the sample average. On the

other hand, non-remittance receiving households tend to be larger than the sample

average for all castes, in rural as well as urban areas.

34

Migrant Histories

Majority of the migrants are seen to travel to the nearest urban center, despite the vast

geography, the ease of transportation and access to different parts of the country. The

exception to these movements is the attraction to metropolises like Delhi, Mumbai and

Bangalore. International migration is noted to the USA, Canada, Britain and oil rich

Middle Eastern countries, apart from the small expatriate communities living in South

America, South Africa and Australia.

Maximum migration is observed in Himachal Pradesh (approximately 57%),

followed by Haryana at 56% and Kerala at 54%. Least permanent migration is witnessed

in the national capital of New Delhi at 9.9%, primarily because of the availability of

economic opportunities in the city. Large volume of intra-district migration is indicative

of two things. First is the lack of inclination of the migrant to separate from the

household in the native community. Migrating to a closer city allows the migrant to

exercise greater control over the household left behind, especially if there are substantial

financial ties between the two units. Second possibility could be the presence of a big

urban center within the same district which overcomes the need to relocate over a greater

geographical distance. Himachal Pradesh had maximum intra-state-district migration at

47% followed by Andaman & Nicobar Islands as well as Arunachal Pradesh at 43%.

Substantial intra-district migration was also seen in more prosperous states of

Maharashtra and Gujarat at 38% and Andhra Pradesh at 37%. These states have more

than two or three important urban centers that could attract migrant labor. Intra-state

migration is high in the states of Maharashtra and Tamil Nadu at 47% followed by

Meghalaya at 45%. This high inter-district/ intra-state migration can again be explained

35

by the presence of an over-arching urban center in the state. For example, in Maharashtra

it is the presence of Mumbai and in Tamil Nadu, Chennai is the major economic center.

Inter-state migration is highest in the states of Bihar (66.7%), Jharkhand (56%) and Uttar

Pradesh (40%) which also had one of the lowest per capita incomes in the country at the

time of the survey. A brief look at international migrants shows that Kerala and Punjab

lead the country with approximately 25% and 28% of the migrant stock leaving these

states respectively.

Table 3.5 below summarizes the migrant profiles for India in the survey year

2007-08. It is seen that of the surveyed households, almost 43% households have

permanent migrants that left the house more than one year ago. Of these 43% households,

approximately 55% are remittance-receiving households. Gender-wise, of the 100,647

migrating individuals, 54% are men and 46% are women.

Table 3.5 - Household migration and remittances profiles

Migration

Migrants sending Non-migrant sending

Total Percentage Total Percentage

Households 53,961 42.97 71,617 57.03

Remittances

Remittance-receiving Non-remittance receiving

Total Percentage Total Percentage

Households 29963 55.52 23998 44.47

Migrant Gender-Distribution

Male Female

Individual Total Percentage Total Percentage

54175 53.82 46471 46.17

Since exposure to migration by itself can change the expectations of the household and

make them behave differently, the migration history of each household is recorded. It is

seen that around 30,800 households have migrants that left the household recently i.e. in

36

the last five years, while approximately 10,700 households are seen to have participation

in migration for six to 10 years. The exposure of these households to migration is thus,

fairly recent. It is also seen that the intensity of migration is stronger in the short run i.e.

on average more migrants left the sample household in the last five years as compared to

previous years. This lesser number of migrants leaving the household in the medium term

of six to 10 years can be indicative of higher mobility due to higher rates of economic

growth in India in the last decade.

The data also allows looking at individuals who undertook temporary migration

(more than one month but less than six months) for economic purposes3 in the survey

year 2007-08. Temporary migration with the intention of employment is reported by

18,806 individuals from the sample population of which 16,407 are men and 2,399 are

women. Of these individuals 85.5% report to be gainfully employed during their time

outside the household, with the employment rate for men at 88.6%.

Majority of the migration in India is of economic nature, where individuals move

to other regions and internationally to either search for employment or to take up better

employment. The flow of labor is to the economically active city centers, or the capital

cities in a given state. As seen in Table 3.6 below, 85% of the economic migrants are

men. Women dominate the category of migration due to marriage. This is one of the

reasons why women have been found to be not as economically active and not remitting

money to their families.

3 Migration for economic purpose is defined as migrating to take up new

employment or in search of new employment.

37

Table 3.6 - Reasons to migrate

Total Male Female

Economic Reasons4 48.84 85.09 6.65

Education 4.35 5.41 3.11

Marriage 32.55 0.68 69.64

Migration of earning member 12.44 6.88 18.90

Others5 1.83 1.94 1.71

This lack of movement of female migrants can also be seen in the destination of migrant

individuals. Compared to men, almost 50% of the female migrants move within the same

district in a state in the year of the survey whereas men moved to another state (40%) and

even international destinations (8%). The lack of economic participation and lack of

mobility thus undermines the role of women in the process of labor migration. For men

however, economic mobility and their contribution to remittance sending are stronger.

Table 3.7 - Destination of Migrant Individuals (Percentages)

Total Male Female

Same district, same

state

32.41 17.76 49.48

Another district,

same state

33.72 34.49 32.84

Another state 28.59 39.73 15.60

Another country 5.13 7.8 2.02

Not known 0.14 0.22 0.05

Migrants and Remittances

The survey examines the remittances received by households in the last one year

counting back from the date when the survey was conducted. An individual break down

4 This category was created by merging economic reasons that include- in search

of employment, in search of better employment, to take new/ better employment, transfer

of service, business and proximity to work. 5 Others was modified to include negligible amounts of migration conducted due

to natural disasters, development project displacement, acquisition of new house,

healthcare, post-retirement and socio-political problems.

38

of these remittances by frequency and by amounts is also provided. There is however, a

data recording discrepancy at this step of information recording. Since the survey collects

information on only those individuals who were residing in the house at the time of the

survey, there is no additional demographic record for the migrated individuals who are

sending remittances to the surveyed household. This creates a problem on account of

being unable to determine the education level of the migrant or his/her relationship to the

household head and obtain further information on the migrant’s remittance behavior.

Data on the sex, age, destination, reason for migration and time since migration for these

households on the other hand helps in aiding the empirical analysis.

As seen in Table 3.5 above, there are 53,961 households that report sending at

least one migrant in the last year or earlier (before the survey year). Of these households,

29,963 report the receipt of remittances. At the individual level, 100,647 migrants are

accounted for and their characteristics are summarized in Table 3.8 below. It is seen that

majority of the migrant individuals are male who remit to their families and are

economically active. In all three categories women lack by a greater margin, for example-

only 4% women remitted money to their families and only 20% of them were actively

engaged in economic activity. This discrepancy in remittance sending behavior of women

can be attributed to the non-economic nature of migration of women.

39

Table 3.8- Information on Remittance Sending Migrants

Migrants Economically

Active Migrants6

Remittance Sending

Migrants7

Total Percentage Total Percentage Total Percentage

Migrant

Population

54,084 53.74 35,091 34.87

Male

Migrants

54,175 53.83 44,828 82.76 33,

046

61

Female

Migrants

46,471 46.17 9,256 19.92 2,044 4.40

Due to greater economic mobility men also remit more than women, as seen in Table 3.8

(61% men remit as compared to just 4% women). On average, migrants remit 23,141

INR8 with men averaging around 23,595 INR and women sending an average of 15,812

INR as remittances. This measure slightly differs at the household level as households in

some cases receive remittances from more than one migrant. For a remittance receiving

household the amount of remittances averages to 25,849 INR. Due to the presence of

some large amounts of remittances (maximum amount received by a family was

3,000,000 INR) variation in these amounts is high. Thus, a look at the median amount of

remittances shows 13,500 INR received as remittances by a household, with median

remittance amount for male migrants averaging at 12,000 INR and for female migrants at

6,000 INR.

The survey also collects information on specific uses these remittances are put

into by recipient households. These uses range from food and commodity consumption to

education, healthcare, loan repayment and entrepreneurial investments. A preliminary

6 Percentages not involved in economic activity were- 44.91% of total migrants,

15.95% of male migrants and 78.67% of female migrants. The remaining declined to

answer and there were three missing values. 7 Percentages not sending remittances were- 65.13% of total migrants, 39% of

male migrants and 95.60% of female migrants 8 Indian National Rupee (INR) is the currency of India

40

analysis suggests that the first instinct of households is to spend the extra remittance

income on food items. As seen in the table below, 67% households spent their remittance

income on increasing food consumption.

Table 3.9 - Utilization of Remittances (Percentages)

Primary Use Secondary Use Tertiary Use

Food 67.06 4.92 5.09

Education 1.62 31.23 5.00

Durables 0.91 16.16 11.53

Marriage 1.93 1.58 1.50

Healthcare 6.83 20.27 20.09

Other consumption expenditure 9.10 17.29 38.68

House repairs and purchase of

property

3.49 2.69 3.57

Debt repayment 3.13 2.37 3.58

Finance working capital 0.47 0.42 0.44

New entrepreneurial activity 0.10 0.08 0.14

Saving/ investment 2.48 2.28 6.27

Others 2.88 0.72 4.11

31% households on the other hand, spend on education expenses after they have spent

remittance income on food. Health care and other consumption are listed as tertiary

source of remittance spending by 20% and 39% households respectively. Most of the

families however do not put remittances to secondary and tertiary uses which could be

either indicative of a shortage of remittance amounts, or their transfer for specific

activities only.

Among the households that list food as their primary consumption category,

36.38% state education as the second activity to be funded by remittances and 24.06%

fund healthcare services through remittance income. For households that use education as

the first activity to be funded by remittances, 27.84% list expenditure on food as their

preferred second use of remittances closely followed by health care expenses, which is

listed by 25.75% as the second use of remittances. These families also tend to save and

41

invest their remittances 1.8% more, as compared to migrants who spent on food items.

For the 6.8% remittances receiving families that spend primarily on healthcare, the

second use of remittances is on household items for 27.56% households, on consumer

durables for 24.13% households and 19.31% households prefer to invest in education as

the second use of their remittance incomes.9

Consumption Patterns

The comparison of consumption behaviors of remittance receiving households with non-

remittance receiving households and non-migrant sending households provides a path

into looking at their specific consumption patterns with respect to fertility and education

expenses in the following chapters. If these three types of households have sufficiently

different consumption patterns, then divergent human development paths will be easier to

predict.

In the table 3.10 below, 71,617 of the sampled households have no permanent

migrants10

at the time of the survey, or these are the non-migrant sending households.

While on average all three types of households spent 4,182 INR on consumption goods,

the non-migrant sending households consume lesser than average, at around 4,004 INR

per month. Remittance-receiving households on the other hand consume more than the

average population spending roughly 4,416 INR per month. Non-remittance receiving

9 The values presented for second and third use of remittances are not given in Table 3.7.

10 According to the 64

th Socio-Economic NSS Survey field instructions manual,

migrant is any individual who left the household to take up residence in another village/

town/ district/ state or country and it does not include members who came back to be

members of household at the time of the survey. This category thus includes migrants

who have permanently migrated or plan to stay outside of the family for a long time

period.

42

households have the highest monthly consumption expenditure at 4,629 INR. This large

difference in consumption patterns could occur if non-remittance receiving households

are better off to start with, and along with high consumption also enjoy higher income

levels, thus not requiring remittances to cover the spending gap.11

Table 3.10 - Monthly Consumption Expenditure and Income by Household

Type

All Non-

migrant

sending

Non-

remittance

receiving

Remittance

receiving

Number 125,578 71,617 23,998 29,963

Average Monthly

Consumption

Expenditure (INR)

4182.14 4003.91 4461.41 4384.46

Average Monthly

Income, Cash & Kind

(INR)

6244.54 6223.90 6436.66 6280

As seen in the table above, the assertion is true for income differences between the three

households. It could be derived that non-remittance receiving households tend to earn

more, spend more and rely less on remittance transfers, thereby making them a stronger

economic group as compared to remittance receiving households and non-migrant

sending households. Non-remittance receiving households also exhibit greater education

achievements with 21% households having members who are college graduates as

compared to 17% in remittance-receiving households and 15% in non-migrant sending

households. The non-remittance receiving households thus seem to be most self-

sufficient among the three household types.

To further investigate household consumption behavior the survey compiles

information on 19 expenditure categories. For the purpose of this study the categories are

11

These households also have larger average household size as noted in Table 3.4

on p. 32.

43

collapsed into 11 groups that include food expenses, expenses on alcohol and tobacco,

expenses on fuel and light, entertainment expenses, personal hygiene expenses, consumer

services expenditure, household rent and taxes, clothing expenses and expenditure on

consumer durables along with medical expenses and schooling expenses. Table 3.11

below gives a summary of how different households spend their incomes.

Among the three household types, non-remittance receiving households are seen

to enjoy higher levels of expenditure than remittance receiving households and non-

migrant sending households. Substantial difference is seen in the food expenses incurred

by non-remittance receiving households and other two household types. Non-remittance

receiving household spend approximately 685 INR more on food than remittance

receiving households and more than 2200 INR than non-migrant households.

Among other expense categories such as alcohol and tobacco and consumer

services as well, non-remittance receiving household spend substantially more than

remittance receiving households and non-migrant sending households.

44

Table 3.11 - Expenditure Categories by Household Type

All households

(125578)

Non-migrant

sending

households

(71617)

Remittance

receiving

households

(29963)

Non-

remittance

receiving

households

(23998)

Institutional and

non-institutional

medical

4256.66

3512.34

5136.84

5159.58

Schooling 4487.36

4317.96 5011.67 4345.40

Food 24126.45 23342.52 24862.32 25546.2

Alcohol and

tobacco

1815.08 1790.64 1773.84 1930.08

Fuel and light 4848.84 4659 4981.2 5244.48

Entertainment 1805.23 1789.8 1800.6 1859.4

Personal effects 2144.11 2084.76 2210.64 2237.64

Consumer

services

4579.21 4219.92 4902.72 5236.08

Household rent

and taxes

3619.28 4466.88 2614.56 2241.12

Clothing 3327.00 3156.04 3521.44 3594.56

Consumer

durables

2190.97 1809.14 2736.98 2628.33

Overall, non-migrant households spend substantially lesser on annual consumption

expenditure, especially with respect to medical expenses, schooling expenses, food, fuel

and light, entertainment, personal effects, clothing and consumer durables. Remittance

receiving households on the other hand spend the highest on education expenses as

compared to non-remittance receiving households and non-migrant sending households.

The summary statistics above show that migrant sending households enjoy higher

consumption standards than non-migrant sending households. Within the migrant sending

households, remittance receiving households enjoy smaller household sizes and higher

consumption levels. Non-remittance receiving households on the other hand have larger

45

household sizes and perhaps due to that, higher consumption expenditures as well. In

terms of income as well, remittance-receiving household report highest income levels,

followed by non-remittance receiving households. This income data is however sporadic

and not highly reliable.

It is also seen that most migration is for economic reasons or for marriage with

male dominating the former category and female the latter. It is also seen that women

tend to undertake intra-district and intra-state migration while men tend to be more active

with respect to intra-state migration, inter-state migration and international migration. As

a result, they also remit more.

It would be misleading however, to concentrate only on these samples due to the

difference in the sample sizes of migrant sending households and non-migrant sending

households. A more rigorous exercise is therefore required to analyze the effect of

remittances on consumption patterns in households. The next essay focuses on one of

these effects; that of remittance receipt on household fertility, followed by the essay on

education.

46

Impact of Migrant Remittances on Household Fertility

Economic migration allows households to expand their income possibilities and achieve a

higher standard of living. Migration also acts as a tipping point for households to adopt

cultural practices that bring the source communities and destination communities closer

to each other. Therefore, apart from enabling an equalization of wages over time,

migration also promotes social homogeneity between peripheral economies and

economies at the core of economic development. While majority of the academic

literature focuses on the effect of migration on the social assimilation and economic

performance of migrants in their destination communities; an increasing amount of

research is being devoted to the developmental impact of migration on the source

community of the migrant. This essay is an extension of the existing studies on migration,

focusing on the impact of remittance incomes on the fertility levels in the source

community. The primary objective is to analyze the role of remittance receipt in

increasing or decreasing the fertility levels in the households that receive these income

transfers. The NSS data described in previous chapters is used for this analysis. The

results from the probit analysis show that remittances cause approximately 0.6% to 1%

increase in the probability of having a birth in the remittance receiving household. The

results from the instrumental variable (IV) analysis however show a negative impact of

remittance receipt on fertility.

This essay utilizes the theory of fertility presented by Becker (1960, 1973) and

applies it to the case of remittance receiving migrant households with the assumption that

children are normal goods, such that their consumption increases with an increase in

47

income.1 A sample of remittance receiving households from India is selected for this

analysis and controls are provided for caste, religion, education and, migration history of

the household. The primary focus is thus on the income effect of remittances, rather than

the act of migration. The rest of the essay is arranged as follows: section II presents the

literature review; section III introduces the hypotheses and the fertility model; section IV

explains the dataset and the variables used for the analysis; section V summarizes the

results from probit analysis; section VI introduces the instruments and presents the results

of the IV probit analysis; section VII concludes.

Literature Review

Household income, fertility and migration have been extensively researched through the

1960s till present, albeit not together. Seminal works conducted by Becker, Duesenberry

and Okun and Becker and Lewis concentrate on household income and fertility and

suggest that when a household’s income increases, there is an income effect in favor of

having more children.2 Becker, Duesenberry and Okun are of the opinion that children

provide an altruistic utility to parents, such that a greater quantity of children will bring

greater happiness. This consumption utility, combined with the expectation of children

being future economic agents, leads to greater fertility in a household. Becker,

Duesenberry and Okun also suggest that limits to fertility come with respect to the cost of

raising children. Therefore, there is a trade-off between quantity and quality of children,

1 The proposition of children as normal good was first made by Professor Gary

Becker in his analysis of fertility. 2 Becker, Duesenberry, and Okun, "An Economic Analysis of Fertility."

Becker and Lewis, "On the Interaction between the Quantity and Quality of Children."

48

such that in order to provide each child with higher human capital and better income

prospects, the parents would choose to have fewer children. Becker and Lewis in a

similar vein, suggest that as a household’s income increases along the budget line, there

will be an increase in overall consumption; including bearing a greater number of

children and higher human capital per child, ceteris paribus. The interaction of these pure

income effects with societal changes such as the use of contraception and increased

participation of women in the workforce however, leads to a substitution away from a

greater quantity of children causing the household fertility to fall.3

Katz and Stark extend Becker’s analysis of household fertility to migrant

households’ fertility. They study the risk diversification strategies and expectations about

future income returns adopted by the parents of a migrant in determining household

fertility in the current time period. They postulate that risk-averse parents have fewer

children but, most parents view children as productive assets and prospective migrants

who will remit, thus increasing the probability of having more children in the present.

Katz and Stark develop a theoretical model where parents’ fertility decisions in time

period t are affected by their expectation of remittance receipt from their children in time

period t+1. The altruism of parents and their expectations to have remittance funded

financial security in t+1 will positively affect the fertility decisions of parents.4

From a sociological perspective, a migrant is studied as the entity that links origin

and destination communities through norm transference. For example, Hervitz (1985)

3 Becker and Lewis, "On the Interaction between the Quantity and Quality of

Children." 4 Katz and Stark, "Labor Migration and Risk Aversion in Less Developed

Countries."

49

analyzes the effect of migration and its effect on fertility through socialization,

selectivity, adaptation and disruption. He uses a sample of married women in Brazil to

observe the number of children born to migrant women in the host community and

compares these values to children born to non-migrant women of the host and source

communities respectively. His results show that migrant fertility lies between the source

and host community fertility, and as adaptation increases, migrant fertility comes closer

to the host community fertility. 5

Similar norm transference was suggested by Visaria and

Visaria while explaining the factors that might have affected fertility decline in India

during the 1980s. Their observations are made with respect to international migration

from Kerala, Gujarat and Punjab to the USA and Great Britain.6 Initial literature on

migration and fertility thus focuses exclusively on migrant fertility in the destination

community rather than the effect of migration on the family left behind in the source

community.

Some of the recent literature that explores the effect of migration on fertility on

the families in the source communities, especially those in developing economies

include7 Yadava, Yadava and Yadawa, Hampshire and Randall, Omondi and Ayiemba

and Lindstorm and Munoz-France. They study the impact of migration on the fertility of

household members that are left behind in India, Burkina Faso, Kenya and Guatemala

respectively. Yadava, Yadava and Yadawa study the impact of migration led separation

between husband and wife on household level fertility in India. They observe that

5 Hervitz, "Selectivity, Adaptation, or Disruption?”

6 Visaria and Visaria, "Demographic Transition”

7 This interest is renewed around the same time when World Bank recognized

remittances as a prominent source of development finance for extremely poor

communities in developing countries.

50

migrant-sending households generally have lower fertility than non-migrant households,

due to adaptation to the urban lifestyle as well as disruption in marriage. Within migrant

groups however, fertility results vary based on caste distinctions. While upper castes

exhibit lower fertility than backward castes, the fertility difference between upper caste

migrants and non-migrants are much smaller than the fertility differentials between

migrants and non-migrants from the backward castes. Their analysis indicates the faster

adaptability of backward castes in comparison to the upper castes.8 Hampshire and

Randall study the impact of temporary male migration from Burkina Faso on the rural

communities in the year 1995-96. Their analysis compares four ethnic sub-groups and

their fertility differentials due to the migration patterns of men in the household, their

chosen destination city and inherent differences in education and culture among these

groups. Using multiple logistic models they conclude that while migration might delay

marriage for men, the social status attached to being a parent dominates the overall

fertility in migrant-sending families. Migration related fertility differentials are thus,

found to be very small.9 Omondi and Ayiemba make similar observations about western

and central provinces of Kenya. They conclude that while both western and central

Kenya have high rates of migration, the latter’s geographical proximity to Nairobi has

allowed modernization and the spread of contraception knowledge, leading to fertility

decline in the region. For western Kenya however, there is a continued dependence on

remittance income, and in order to keep this flow of money, the families tend to maintain

8 Yadava, Yadava, and Yadawa, "Effect on Fertility of Husband- Wife Separation

Due to Migration." 9 Hampshire, Kate, and Sara Randall, "Pastoralists, Agropastoralists and

Migrants.”

51

higher fertility rates. Migration therefore, has an ambiguous effect on fertility.10

Lindstorm and Muñoz‐Franco utilize multilevel logistic regression models to examine the

contact of women in source communities with social networks of migrants and its impact

on increased contraception knowledge and thus declining fertility rates in source

communities.11

Of the four studies reviewed above, none focus on the role of remittances in

changing fertility preferences of the household. Their primary focus is to compare the

number of children born in a migrant sending household to the children born in a non-

migrant sending household. Yadava, Yadava and Yadawa focus on caste distinctions to

observe migrant adaptability, while Hampshire and Randall exercise ethnic influences to

understand fertility differentials between migrant groups. Omondi and Ayiemba on the

other hand, concentrate on distance from the largest center as a tool to measure migrant

influence. One important conclusion from Yadava, Yadava and Yadawa however, is the

maintenance of the fertility rate between migrant upper caste and non-migrant upper

caste. While they suggest that lower castes have faster adaptability to urban norms; it is

equally plausible that upper castes have higher income endowments to start with and can

afford more children. In such a scenario, migration would not substantially change the

fertility preference of the household.

The utilization of remittances as a crucial variable influencing fertility in the

source community is first observed in a study of MENA countries done by Fargues. His

work is followed by Beine, Docquier and Schiff and Nafaul and Vargas-Silva who study

10

Omondi and Ayiemba, "Migration and Fertility Relationship.” 11

Lindstrom and Muñoz‐Franco, "Migration and the Diffusion of Modern

Contraceptive Knowledge and Use in Rural Guatemala."

52

a panel data of countries. These studies find an ambiguous effect of remittance- receipt

on fertility but a positive norm transference effect.12

For example, in the study done by

Fargues,13

he suggests that when migration is of permanent nature, remittances provide as

a strong link for transference of fertility norms. He also suggests that fertility will not

always reduce in the source community. If the migrant chooses to go to a country where

fertility rates are higher than those in the source community, fertility is bound to rise for

the migrant sending family. His study on Morocco and Turkey (with prominent migrant

flow to Western Europe) and Egypt (with majority migration to the Gulf and Saudi

Arabia) shows that, “Egyptian migration to the Gulf did not bring home innovative

attitudes regarding marriage and birth…On the contrary in Morocco, emigration to

Europe has coincided with a fundamental change of attitudes…”14

This study therefore,

establishes a correlation between the household fertility norms in the destination country

and the source country, treating migrant remittances as the catalyst for change. Beine,

Docquier, and Schiff on the other hand, reach the same conclusion as Fargues, by

including expected remittance returns to their theoretical model along with the effect of

destination country norms and altruistic intentions of parents15

as other independent

variables influencing fertility. Through the use of ordinary least squares and instrumental

variables analysis, Beine, Docquier, and Schiff conclude that norms positively affect

12

Norm transference refers to the non-monetary influence a migrant can have on

the decision making process in a household. Even if a migrant does not remit money, he

can play a role in altering the preferences of his household in the source community by

introducing new ideas, goods and lifestyle. 13

Fargues, “Demographic Benefit of International Migration.” 14

Fargues, “Demographic Benefit of International Migration,” 20. 15

They study altruism and old-age insurance intentions of parents by using

investments in adult human capital and their probability to migrate. If the human capital

of current generation is high, their probability to migrate is high, and thus their focus on

fertility will be negative.

53

fertility such that the fertility trends in destination societies will be transmitted to source

societies. They also find a positive but weak impact of remittances on fertility, such that

remittance income will encourage households to have more children. However, the

strength of the norm transference coefficient suppresses the weak impact of remittances.16

Nafaul and Vargas-Silva also find similar dominance of norm transference effect over

remittance income effect in their study of panel data from 59 countries. They use

remittances to reflect the income effect in favor of fertility; and norm transference to

reflect the substitution effect away from fertility. They find that host country and home

country fertility are directly related, while remittances have an inverse impact on home

country fertility.17

The studies summarized above utilize international migration data from several

countries, and not a micro-data sample being utilized in this essay. Davis and Lopez-

Carr, however study the impact of remittances on fertility in Guatemala, with the

objective of exploring the long term impact of changed fertility and consumption on

Guatemala’s environmental balance. They conduct a qualitative analysis examining the

impact of remittances on fertility. Their main argument is that remittances provide a

boost to household consumption but do not translate to a higher fertility rate due to rising

costs of education, a theory also suggested in the work of Becker. They however,

simultaneously point to the lack of use of contraception that might not reduce fertility

16

Beine, Docquier, and Schiff, "International Migration, Transfers of Norms and

Home Country Fertility.” 17

Naufal, and Vargas-Silva, "Influencing Fertility Preferences One Dollar at a

Time”

54

among Guatemalan households, leading to an ambiguous effect of remittance-receipt on

fertility.18

Four conclusions can be drawn from the literature review above. First, migrant

fertility rates usually lie between destination community fertility levels and the source

community fertility levels. This median rate is indicative of the adaptability of the

migrants to the destination community norms. Thus, while remittances can have a

positive income effect on fertility, the transference of ideas about modernization,

contraception and emphasis on a smaller family size can counteract the fertility effect.

Second, migrants act as agents of change in their source communities by facilitating a

transfer of fertility and household practices. This warrantees the inclusion of migration

related variables that measure the strength of the relationship between the migrant and the

household in the source community. Third, at the country level, the impact of migration

on fertility might be ambiguous because of the inclusion of socio-economic variations

between different regions of the country. Such ambiguity is however not confirmed and

the results can vary from one country to the other. Fourth, studies till now have viewed

remittances as a reward for migration or as the tool for the transference of norms between

the host and home community, if the migration is of permanent nature and remittances

serve as the only link between the migrant and his family. None of these studies therefore

use remittance receipt as the sole cause of changed fertility preference in the recipient

household.

18

Davis and Lopez-Carr, "The Effects of Migrant Remittances on Population–

Environment Dynamics in Migrant Origin Areas.”

55

Hypotheses and Model

Panel data provided by surveys such as the Mexican Migration Project (MMP) and Panel

Study of Income Dynamics (PSID), allow for a multi-level hypothesis creation with

regards to household fertility. Unlike these data-sets however, the NSS used for this

analysis concentrates on only one survey year and does not track the same households

over the years. This leads to the formation of the following hypothesis-

Did remittance receipt in the survey year increase the probability of having a birth in the

remittance receiving household as compared to a non-remittance receiving household?

Assuming that remittance receipt is a permanent addition to the household budget and

children are normal goods; it is possible that when remittance incomes are received, the

households witness an increased sense of economic well-being and tend to increase their

fertility as well. Along with the impact of remittances, the effect of other demographic

factors, such as migration, education, religion and family type are also explored to

provide a more comprehensive picture of the consumption behaviors of remittance-

receiving households.

Based on the assumptions and hypothesis listed above, the empirical model can be

summarized as-

Bi 0 1remittancei 2economici 3demographici

4educationi 5migration

i

56

where, Bi is the dummy dependent variable birth, which assumes the value 1 if the

household had a birth in the survey year and 0 otherwise. The independent variable of

interest, remittancei assumes the value 1 if the household receives remittances and 0

otherwise. Other control variables include economic variables such as consumption levels

of the household and employment status of the adults in the household; demographic

factors such as religion and caste of the household; education factors include the

educational attainment of adults in the household and; migration factors study the

strength of migrants’ relationship with the household left behind.

Birth is chosen as a dependent variable, instead of total number of children to

eliminate any ambiguities that may be introduced in the analysis due to lack of

information provided on the remittance receipt for a given household before the survey

year. It is possible, for example, that a household has been receiving remittances

consistently for the last five years (an assumption which is also made above) which will

affect the fertility decision for all children under the age of five in that household. Despite

this possibility, the relationship between current remittance receipt and current fertility is

observed to make the model more accurate.19

19

When total children, defined as household members below the age of 18 years,

is chosen as a dependent variable, the coefficient for remittance receiving is significant at

0.1386 showing an overall positive effect of remittance receipt on fertility. However, the

migration experience of each household is different, i.e. for a household that sent a

migrant one year ago is less likely to affect the birth of a 16 year old child, as compared

to a migrant that left 20 years ago. Hence, birth in the survey year is used as a dependent

variable to keep the model more dynamic. It is however, possible to use an interaction

term that take into account the average duration for which a household has faced

migration and the number of children born in the household thereafter. The use of such

interaction terms is not explored at this level of analysis.

57

Data and Summary Statistics

The NSS attached in Appendix A is used to conduct this analysis. The NSS questionnaire

gathers information on the children born in the survey year 2007-08 which helps in the

creation of the dependent variable. Data on the receipt of remittances, and on the amount

of remittances is also available, which helps to determine the independent variable of

interest.

Other control variables include economic variables such as employment status of

the household head, employment status of other adults in the household and; per capita

annual household consumption expenditure as a proxy for the standard of living of the

household. Despite the increase in the parents’ utility with the birth of a child, the

quantity of children that will be born in a household is closely linked to the economic

viability of having children. Households will have children if they can economically

accommodate the cost of raising a child and if the children are expected to add to the

future income of the family. Thus, while making the fertility decision, parents will

measure the cost of having a child against the benefit that comes from being a parent and

the future income expectations from that child. If the perceived benefits exceed the cost,

fertility decision will be affirmative. To this effect, the first economic variable included is

the employment status of the head of the household. Since the household head is also the

principal economic agent of the household, the fertility decision could be affected by

their ability to get sustained income to the household. Based on the responses listed in the

survey, a dummy is created for the employment status of the head of the household. If the

head of the household is employed and responds to the economic status as self-employed

58

or as working in a household enterprise or as a regular salaried/ wage employee or

reported to have worked in casual wage labor, the dummy takes the value 1. If however,

the employment status of the household head includes responses such as did not work but

was available for work, attended educational institution, attended domestic duties,

retirees and remittance recipients and disabled , they were included as unemployed and

their employment status is coded as 0. However, some households in the sample are

multi-generational, with a retired parent residing with sons/ daughters who might be

active economic agents. In such a case the employment status of the head of the

household is neither sufficient nor an accurate measure. Thus, the second economic

variable included is total number of employed adults in a surveyed household, which acts

as a substitute to the variable indicating the employment status of the head of the

household.

Lastly, per capita annual household consumption expenditure is added as a proxy

for the standard of living of the household and its ability to afford another child. Annual

income is an alternative measure for capturing this effect, but the income data collected

in the survey is highly sporadic and inaccurate. Comparatively, annual household

consumption expenditure is more reliable, and widely preferred (to income) when

studying developing countries.20

The expected relationship between per capita

consumption expenditure and the dependent variable will be positive if consumption is a

good proxy for capturing the standard of living. If however, the assumption is inaccurate

20

Refer to the works done by Deaton, "Saving in Developing Countries.";

Deaton, "Household Saving in LDCs." and; Deaton, and Zaidi, “Guidelines for

Constructing Consumption Aggregates for Welfare Analysis.” to understand why

substituting consumption expenditure for income can work as a valid strategy.

Alternatively, a summary is provided on page 22-23.

59

and consumption expenditure is a poor proxy for standard of living, the probability of

birth will be negative.

The demographic variables measure the social and cultural characteristics of the

household that might shape their consumption preferences with respect to fertility. For

example, a parent might have a child despite their economic inability to do so, because

their religion does not allow them to undertake abortions or the parent prefers to have a

male child to carry on the family lineage. To address these influences, six demographic

variables are used. The first demographic variable is a religion dummy variable,

Hinduism, which equals 1 if the household follows Hinduism and 0 otherwise. Hinduism

is chosen because it is the majority religion (77% of the surveyed households) and Hindu

households tended to have a smaller household size than other religions.21

Second

variable is the reserved caste status of a household. If the household belongs to a

reserved caste22

holding a scheduled tribes, scheduled castes and other backward classes’

status, their value is coded as 1 and 0 otherwise. It is expected that since reserved castes

have been economically backward and late recipients of social benefits and better

education, they would consider children as future economic agents and tend to have

greater fertility than other castes. Third variable is a dummy for household located in

rural area, assuming the value 1 if the household is located in a rural area and 0 if it is in

21

Refer to summary statistics on page 29. 22

Indian society is historically divided into four occupation based categories that

can act as a proxy to the economic status of the household. The less privileged castes

have been given a reserved status on the basis of which they might claim an equal

opportunity status through government plans. A parallel can be drawn between the

reserved castes in India and minority ethnicities that exist in Western societies.

Traditionally, these castes were denied social mobility and equal economic opportunities

because of their birth in a lower economic stratum. Since independence however, these

caste distinctions have been blurred significantly, especially in urban sectors and in

private industry. Castes are however, still used as a major leverage point in Indian politics

60

an urban area. Due to the physical nature of work in rural areas, the lack of educational

institutions and lower use of contraception methods, it is expected that households in

rural areas would have higher fertility rates compared to a household residing in the

urban area. The fourth demographic variable for a multigenerational family addresses the

unique nature of the Indian society where grandparents, parents and children voluntarily

reside in the same house and share the resources that are brought forward by the

economic members in the household.23

Such co-residence is expected to have a positive

influence on the fertility in a household. The reasons could be numerous- it is cheaper to

bring up a child in a multigenerational household as grandparents can take care of the

children when parents are away working. Multigenerational households also tend to be

more traditional and encourage the continuation of family lineage, thus encouraging

births. A dummy is created to indicate if a family is multigenerational (= 1) or a nuclear

family structure (=0). Fifth variable is the sex of the head of the household, a dummy

variable where male household head acquires the value 1 and 0 if the household head is

female. The last variable added in this category is the proportion of female children

already present in the household. Being a patriarchal society, the preference for a male

child is strong, across all religions in India. If a household has a higher proportion of

female children, the probability of gambling with another birth are higher.

The education of parents will affect the human capital of children and their

decision regarding the number of children in the household. Thus, education variables

are divided in two broad categories- maximum educational attainment in the household

and the proportion of educated adults at each level of educational attainment. While the

23

Multi-generational families are not limited to the followers of Hinduism. Other

religions also consist of joint families.

61

education of the household head will directly impact the fertility decision, in a

multigenerational household, decisions are made in consultation with the household

member with an expertise in a particular area. Therefore, maximum educational

attainment in the household is included to aptly capture the expertise of the household

member with highest education instead of looking at an aggregate measure of

household’s education. Maximum educational attainment dummies for each level of

educational attainment are created with illiterate as the reference category. Accordingly,

primary education takes the value 1 if the maximum education attained by any household

member is the completion of primary schooling (up to 8 years of schooling) otherwise 0;

secondary education takes the value 1 if the maximum education attained by any

household member is completion of secondary school (9 to 12 years of schooling)

otherwise 0 and; graduate education takes the value 1 if the maximum education attained

by any household member is the completion of graduate or post-graduate education

otherwise 0. The proportion of educated adults at each level of educational attainment

records the proportion of adults with primary education in the household, proportion of

adults with secondary education in the household and; the proportion of adults with

graduate education or higher in the household.

Irrespective of the nature of the education measure used, the relationship with

fertility can exhibit either a positive or a negative relationship. Higher educational

attainment in the household will bear a negative relationship with fertility in the

household if along with higher income households also adopt a cultural practice of

having lesser children. This cultural practice can emerge if with increasing education,

female labor force participation increases, use of contraception increases and children are

62

seen as detrimental to economic progress. Higher educational attainment will bear a

positive effect on fertility when with greater education the income potential of the

household also increases, thus increasing the affordability of each additional child; while

the cultural practices that attach importance to the utility of children do not change. In

such a scenario, education serves as a proxy to household income such that as education

increases, the probability of having more children will also increase.

Lastly, migration variables are used to measure the strength of relationship

between the migrant and the remittance receiving household. These include a migration

history variable which measures the average years the household has witnessed

migration. This variable measures the exposure of a household to the practice of

migration. If a household has had the tradition of sending migrants to urban areas, their

exposure to norms of modernity will be greater. These households will also be more

adaptive to changes because of the ideas the migrant would bring to the household. Thus,

the longer a household is exposed to migration, lower the likelihood of having birth in the

in the household. The second migration variable is total migrants which measures the

intensity of migrant influence on behaviors of the household left behind. If the number of

migrants is larger, the impact on preferences will be stronger as well. The total number

of migrants can also represent a higher remittance potential which will positively impact

the consumption behaviors of the households. For example, if a household sends three

economic migrants, their expectations about remittance incomes will be greater and effect

their consumption positively, as compared to a situation where only one migrant leaves

the household.

63

The expected relationship of the independent and control variables, with the

dependent variable are listed in Table 4.1 below.

Table 4.1 - Expected behavior of variables

Variable Nature Expected

relationship

Remittance receipt Dummy

Remittance-receiving=1;

Non- remittance

receiving= 0

Positive

Log of annual per capita

consumption expenditure

Log Ambiguous

Employment status of the head of

the household

Dummy

Employed= 1;

Unemployed =0

Positive

Total employed adults in the

household

Numeric Positive

Hinduism Dummy

Hinduism =1; Other

religion=0

Negative

Reserved caste Dummy

Reserved caste= 1;

Others= 0

Positive

Multi-generational household Dummy

Multigenerational=1;

Nuclear=0

Positive

Rural household Dummy

Rural= 1; Urban= 0

Positive

Sex of the head of the household Dummy

Male= 1; Female =0

Ambiguous

Proportion of female children in the

household

Ratio; Female children

Total children

Positive

Maximum education for the

household

Primary schooling

Secondary schooling

Graduate schooling

Dummy;

Primary=1; Others=0

Secondary= 1; Others=0

Graduate= 1; Others= 0

Reference category-

Illiterate

Ambiguous

Proportion of educated adults

-with primary schooling

-with secondary schooling

-with graduate education

Ratio

Ambiguous

Migration history Numeric Negative

Total migrants Numeric Positive

64

Descriptive statistics for these variables is given in Table 4.2 below. The results are

summarized according to remittance receiving and non-remittance receiving households.

Table 4.2 - Descriptive statistics for fertility model

Variable Remittance receiving

households

Non- remittance receiving

households

Mean S.D. Mean S.D.

Births in the surveyed

household

0.0625 0.2422 0.0638 0.2445

Remittance receipt 1 0 -- --

Annual per capita

consumption

expenditure

52613.49 45174.34 53536.89 48721.67

Employment status of

the head of the

household

0.6601 0.4736 0.8582 0.3487

Total employed adults in

the household

1.3146 1.1610 1.8804 1.1439

Hinduism as household

religion

0.7710 0.4201 0.7896 0.4075

Reserved caste 0.6550 0.4753 0.6745 0.4685

Rural household 0.6753 0.4682 0.6845 0.4647

Multigenerational

household

0.4295 0.4950 0.4750 0.4993

Sex of the head of the

household, male=1

0.6484 0.4774 0.8766 0.3287

Proportion of female

children in the

household

0.4648 0.3722 0.4582 0.3763

Maximum education for

the household

Primary schooling

Secondary schooling

Graduate education

0.4056

0.3145

0.1723

0.4910

0.4643

0.3776

0.4121

0.3126

0.1808

0.4922

0.4636

0.3849

Proportion of educated

adults with-

Primary schooling

Secondary schooling

Graduate education

0.3472

0.2014

0.0841

0.3553

0.2918

0.2148

0.3525

0.1879

0.0826

0.3245

0.2640

0.2055

Migration history 5.9871 5.8646 7.0317 7.0284

Total migrants 1.8830 1.5275 1.8429 1.3781

65

Results from the Probit Analysis

The results of the fertility model are summarized in Table 4.3 and 4.4 below. Table 4.3

summarizes results for all households while a sample selection criterion is applied to

Table 4.4. Columns 1 and 3 of Table 4.3 use employment status of the household head as

a control variable while columns 2 and 4 use the total number of employed adults in the

household. Also, columns 1 and 2 of Table 4.3 summarize the results with maximum

education level attained in the household as one of the controls. This variable is replaced

by proportion of educated adults at each level of schooling completed in columns 3 and 4

of Table 4.3. Two values are reported in the parentheses, the standard errors and the

marginal effects generated by the fertility model. As per the expectation, remittance

receiving household have a 0.6% to 1% greater probability of having a birth. Columns 2

and 4 present a stronger result as compared to columns 1 and 3 due to the inclusion of

more comprehensive household participation via total employed adults and proportion of

adults at each education level instead of employed household head and maximum

education in the household. If a household head is employed, the likelihood of having a

birth in the household is more. That is, a household with an employed head has 2.7%

greater probability of having a child. These results however, are not significant. Columns

2 and 4, substitute the variable for employed household head with the total number of

employed adults. As the total number of employed adults in the household increases, the

likelihood of having a birth increases as well, by approximately 0.9% to 1%. This

behavior was expected as greater number of employed adults would bring more income

to the household, making the upbringing of an additional child affordable.

66

Per capita consumption shows the reduced likelihood of having birth in the

household. The use of per capita consumption as a proxy for standard of living is

therefore inappropriate. The results show that an increase in per capita consumption

would decrease the probability of having a child by 4.4% to 5% (columns 1 through 4).

Per capita consumption here indicates that if the household is already consuming at

higher levels for the existing household members, their preference for an extra person

would be negative. This result does not imply that the findings of previous studies are

wrong. It is merely indicative of the fact that the dependent variable being used is

capturing a different relationship than previous studies that use the NSS and

consumption.

Demographic variables behave in the expected manner with Hindu households

having a 0.9% lower probability of having a birth. That is, Hindu households are less

likely to bear children than their religious counterparts. This characteristic was also seen

in summary statistics24

where the average household size of a Muslim or a Christian

household was found to be larger than a Hindu household. Households from the reserved

caste, as expected, are more likely to have a birth in the household, with the probability

of birth being 0.43% to 0.68% higher as compared to household not belonging to the

reserved caste. This substantiates the earlier claim that children serve as economic asset

to lower castes that had traditionally lesser economic opportunities to grow. Households

residing in rural areas exhibit non-significant and ambiguous relationship with a higher

likelihood of birth reported in columns 1 and 3, but a lower likelihood exhibited in

columns 2 and 4. Membership in a multi-generational household is related to a greater

likelihood of having a birth, which was expected given the ability to pool resources and

24

Refer to p. 30 and p.32.

67

importance to family lineage. These households thus increase the probability of having

births by an average of 11.5% as seen in columns 1 through 4. A male headed household

lowers the probability of having births by 1% to 1.8%. The last demographic variable

included was the proportion of female children in the household and the expectation was

that a household with larger number of female children will increase the likelihood of

birth due the desired preference for a male child. This expectation is correct with

households that have a higher number of female children out of total children, having a

1.5% to 1.6% higher probability of having another birth.

68

Table 4.3 - Probit results for all households

1 2 3 4

Economic Variables

Remittance receiving household 0.0439**

(0.0207)

[0.0064]

0.0700***

(0.0208)

[0.0102]

0.0442**

(0.0207)

[0.0064]

0.0712***

(0.0209)

[0.0103]

Log of annual per capita

consumption expenditure

-

0.3280***

(0.0235)

[-0.0480]

-

0.3024***

(0.0238)

[-0.0441]

-

0.3487***

(0.0246)

[-0.0509]

-

0.3262***

(0.0248)

[-0.0476]

Employed household head 0.0155

(0.0277)

[0.0022]

-- 0.0187

(0.0277)

[0.0027]

--

Total employed adults in the

household

-- 0.0669***

(0.0083)

[0.0097]

-- 0.0688***

(0.0083)

[0.0100]

Demographic Variables Household follows Hinduism -0.0623*

(0.0238)

[-0.0091]

-

0.0623***

(0.0239)

[-0.0091]

-

0.0647***

(0.0239)

[-0.0094]

-

0.0653***

(0.0239)

[-0.0095]

Household belongs to a reserved

caste

0.0384*

(0.0225)

[0.0056]

0.0294

(0.0226)

[0.0043]

0.0470**

(0.0226)

[0.0068]

0.0389*

(0.0227)

[0.0056]

Household resides in rural area 0.0038

(0.0250)

[0.0005]

-0.0126

(0.0250)

[-0.0018]

0.0145

(0.0253)

[0.0021]

-0.0011

(0.0253)

[-0.0001]

Household is multi-generational 0.8025***

(0.0241)

[0.1174]

0.7535***

(0.0243)

[0.1100]

0.8313***

(0.0241)

[0.1215]

0.7744***

(0.0243)

[0.1129]

Household head is male -

0.0841***

(0.0271)

[-0.0123]

-

0.1270***

(0.0253)

[-0.0185]

-

0.0752***

(0.0269)

[-0.0109]

-

0.1208***

(0.0253)

[-0.0176]

Proportion of female children in

the household

0.1130***

(0.0260)

[0.0165]

0.1117***

(0.0261)

[0.0163]

0.1104***

(0.0260)

[0.0161]

0.1090***

(0.0261)

[0.0159]

69

Table 4.3 continued

Education Variables I- Maximum education dummies with illiterate as the

reference category

Dummy for primary schooling as

maximum education

0.0562*

(0.0310)

[0.0082]

0.0289***

(0.0313)

[0.0042]

-- --

Dummy for secondary schooling

as maximum education

0.1624***

(0.0326)

[0.0237]

0.1279***

(0.0330)

[0.0186]

-- --

Dummy for graduate education as

maximum education

0.2916***

(0.0395)

[0.0426]

0.2500***

(0.0399)

[0.0365]

-- --

Education Variables II- Proportion of educated adults from each education

group

Adults with primary schooling -- -- 0.1446***

(0.0356)

[0.0211]

0.1224***

(0.0361)

[0.0178]

Adults with secondary schooling -- -- 0.2551***

(0.0457)

[0.0373]

0.2370***

(0.0460)

[0.0345]

Adults with graduate education -- -- 0.4952***

(0.0684)

[0.0724]

0.4730***

(0.0649)

[0.0690]

Migration Variables- Migration history of the

household

-0.0036**

(0.0015)

[-0.0005]

-0.0034**

(0.0015)

[-0.0005]

-0.0033**

(0.0015)

[-0.0004]

-0.0032**

(0.0015)

[-0.0004]

Total migrants from the

household

0.0202***

(0.0065)

[0.0029]

0.0197***

(0.0065)

[0.0028]

0.0222***

(0.0065)

[0.0032]

0.0216***

(0.0065)

[0.0031]

Number of observations 33245 33245 33209 33209

Pseudo R-square 0.0806 0.0835 0.0809 0.0840

Correctly classified 90.15 90.15 90.15 90.15

(Standard errors); [Marginal effects]

*** significant at 1% level; ** significant at 5% level; * significant at 10% level

Education variables present support for education as a proxy for income. Both categories

of education variables, maximum level of education attained by any household member

and proportion of educated adults at each level of schooling, exhibit an increased

likelihood of having birth in the household. Additionally, as the education level increases,

the probability of having a birth increases. Therefore, in columns 1 and 2, a household

70

with graduate education as the maximum education level attained increases the

probability of having a birth by 3.6% to 4.2% as compared to households where

secondary education is the maximum education level attained (probability of having birth

is 1.8% to 2.3% higher). Similarly, as seen in columns 3 and 4, a household with higher

proportion of individuals with secondary education have 3.4% to 3.7% higher probability

of having a birth, as compared to a household with higher proportion of primary educated

adults. Education, therefore, is a more appropriate measure of standard of living of the

household than consumption expenditure, as was expected earlier. Thus, as the

educational attainment of household member increases, their income levels also increase,

making a greater quantity of children more affordable than before.25

The total number of migrants seems to increase the likelihood of birth in the

household, by a probability of around 0.2%. This could mean that the expectation of

receiving more remittances from more migrants in the household has a positive impact on

the consumption habits of the household. Exposure of the household to migration, as

captured by the migration history of the household on the other hand, reduces the

likelihood of birth as the average number of years a household faces migration increases.

This impact is almost negligible; the longer a household is exposed to migration the

probability of birth is reduced by merely 0.05%. If migration history is divided into short

term, medium term and long term migrants, as slightly different picture is presented.

Short migration history is if the household has witnessed migration for an average of five

years or lesser, medium migration history is when the exposure is over five years but less

25

Most of the studies reviewed for this essay did not include parents’ education in

the estimate. Beine, Docquier, and Schiff use it and find an inverse relationship between

education of the parent and fertility

71

than 10 years and long migration history is when average migration exposure is greater

than 10 years. The breakdown reflects that as the migration history increases, the

likelihood of having births goes down. This result supports the proposition that exposure

to migration transfers ideas about modernity to the recipient household and changes

fertility preferences inversely.

Results for Limited Household Sample- A brief overview of the sample shows that many

of the remittance receiving households did not have women in the reproductive age and

could not have reported a birth in the survey year. Their inclusion therefore might create

a downward bias in the impact of remittance receipt on birth. A second version of the

fertility model is then analyzed by applying a selection criterion to the surveyed

households. Households which have married women between the age of 18 and 45 years

are chosen and a sample of remittance-receiving and non-remittance receiving

households is created.

The results of probit analysis for these households are summarized in columns 1

and 2 of Table 4.4 below. The standard errors and marginal effects are reported in the

parentheses. In column 1, the education variables include dummies for maximum

educational attainment in the household and in column 2 these values are replaced by

proportion of educated adults at different levels of schooling completed. The remaining

economic and demographic control variables stay the same.

72

Table 4.4 - Probit results for selected households with married women

1 2

Economic Variables Remittance receiving household 0.04957**

(0.0220)

[0.0085]

0.0514**

(0.0220)

[0.0088]

Log of annual per capita consumption

expenditure

-0.2677***

(0.0247)

[-0.0462]

-0.2894***

(0.0257)

[-0.0499]

Total employed adults in the household 0.0577***

(0.0088)

[0.0099]

0.0589***

(0.0087)

[0.0101]

Demographic Variables

Household follows Hinduism -0.0739***

(0.0249)

[-0.0127]

-0.0767***

(0.0249)

[-0.0132]

Household belongs to a reserved caste 0.0508**

(0.0235)

[0.0087]

0.0584**

(0.0235)

[0.0100]

Household resides in rural area -0.0182

(0.0259)

[-0.0031]

-0.0085

(0.0262)

[-0.0014]

Household is multi-generational 0.7125***

(0.0266)

[0.1229]

0.7234***

(0.0262)

[0.1248]

Household head is male -0.1246***

(0.0269)

[-0.0215]

-0.1217***

(0.0268)

[-0.0210]

Proportion of female children in the

household

0.1462***

(0.0281)

[0.0252]

0.1440***

(0.0281)

[0.0248]

Education Variables I- Maximum

education dummies with illiterate as

the reference category

Dummy for primary schooling as

maximum education

-0.0266

(0.0330)

[-0.0045]

--

Dummy for secondary schooling as

maximum education

0.0573

(0.0351)

[0.0098]

--

Dummy for graduate education as

maximum education

0.1711***

(0.0426)

[0.0295]

--

73

Table 4.4 continued

Proportion of educated adults from

each education group

Adults with primary schooling -- 0.0709*

(0.0376)

[0.0122]

Adults with secondary schooling -- 0.1671***

(0.0479)

[0.0288]

Adults with graduate education -- 0.3994***

(0.0673)

[0.0689]

Migration Variables-

Migration history of the household -0.0053***

(0.0016)

[-0.0009]

-0.0052***

(0.0016)

[-0.0009]

Total migrants from the household 0.0314***

(0.0068)

[0.0054]

0.0327***

(0.0068)

[0.0056]

Number of observations 27688 27688

Pseudo R-square 0.0712 0.0714

Correctly classified 88.34 88.34

(Standard errors); [Marginal effects]

*** significant at 1% level; ** significant at 5% level; * significant at 10% level

Remittance receipt increased the likelihood of birth in the limited sample as well. The

households that receive remittances therefore, have a 0.8% greater probability of having

births as compared to non-remittance receiving households. Per capita consumption

expenditure exhibits similar relationship as in Table 4.3 with higher per capita

consumption leading to a lower likelihood of having a birth in the household. A higher

number of employed adults in the household increase the likelihood of having birth by

0.9% to 1%. These results are similar to the results in Table 4.3 as well.

Within demographic variables, households that follow Hinduism have a lower

likelihood of having a birth, with the probability being 1.2% to 1.3% lower as compared

74

to households that follow other religions. Reserved caste households are more likely to

have a birth and so do households that are multi-generational. Therefore, reserved caste

households have 0.8% to 1% greater probability of having a birth while multi-

generational households have 12.2% greater probability of births. If the household has a

greater proportion of female children in the stock of total children, the probability of

having a birth is 2.5% higher for that household. Residence in a rural area and a male

headed household exhibit similar results as listed in columns 2 and 4 of Table 4.3.

For education variables indicating the maximum educational attainment in the

household, households that indicate primary schooling as the maximum educational

attainment have a lower likelihood of having a birth. Thus, for less educated households,

instead of a lower premium is placed on the expected returns to an additional child.

Additionally, as it was seen earlier, as the education level increases, the likelihood of

having a birth in the household increases. Thus, households with secondary schooling as

maximum education are 0.9% more likely to make a positive fertility decision as

compared to 2.9% greater probability for graduate households of making a positive

fertility decision. These results are replicated in column 2 of Table 4.4 with the variable

for proportion of household members with primary, secondary and graduate education,

with the likelihood of birth increasing as the education level of the household increases.

Education therefore, is a better indicator of the household’s income levels, such that as

the former rises; the income potential of the household also increases, thus enhancing the

ability to afford an additional child.

75

Results from the IV Analysis

While the preliminary analysis shows the positive impact of remittances on fertility in a

household, the data provides information on just one survey year, where remittance and

birth are contemporaneous. The simultaneous occurrence of the two can be indicative of

reverse causality between births and remittances. A household could receive money from

migrated family members as financial help or as a gift owing to the birth of a child. In the

absence of remittance data on preceding or following years, a possible solution is conduct

an instrumental variables analysis to isolate the true effect of remittance receipt on birth.

In order to conduct this analysis, two instrumental variables are used. The first is

the district-wise concentration of scheduled commercial banks in India during the survey

year 2007-08 obtained from the Reserved Bank of India. Scheduled commercial banks

are more popular than private banks and have a much deeper outreach to semi-urban and

rural areas as compared to the latter. This enables them to facilitate the easy transfer of

remittances. The second instrument is the district-wise concentration of post offices in

India. This data is collected from the Indian Postal Services. Post offices are commonly

used for money transfer services as well, and are highly popular with the poorer

households with respect to the transfer of lower amounts of money. These instruments are

expected to facilitate the transfer of remittances between the migrant and the family in the

source community, but are not expected to affect the number of births in the household.

Using these instruments, the IV probit analysis is conducted for all the households and

for households limited by the selection criteria. Table 4.5 and 4.6 below summarize the

results from IV probit analysis for all households and selected households respectively.

76

Columns 1 to 4 for Table 4.5 report the impact of remittances on birth in a

household when district-wise concentration of banks and district-wise concentration of

post offices is used as instruments. The standard errors and marginal effects are in

parentheses. The receipt of remittances now reduces the likelihood of having birth in a

household such that remittance receiving households have a 10.2% to 10.5% lower

probability of births. Consumption per capita decreases the likelihood of births by similar

probabilities (4.4% to 4.6%) as reported earlier. For the employment status of household

head, it is seen that the likelihood of birth reduces if the household head is employed.

These results are however, vary from being less significant (column 1) to insignificant

(column 3).26

The employment status of the adults in the household still tends to increase

the likelihood of a birth in the household. Thus, as the number of employed adults in the

household increases, the probability of having a birth in that household increases by

0.25% to 0.28%. These estimates are marginally stronger than that reported in the probit

analysis in table 4.3 and table 4.4 above.

The marginal effects for demographic variables do not change substantially with

the IV analysis. Hindu households report an approximately 1% lower probability of

having birth as compared to a non-Hindu household while being a multi-generational

household increases the likelihood of having a birth by 11.6% to 12.13%. If the

household belongs to a reserved caste, the likelihood of birth increases as expected.

These results are not significant in columns 1 and 2 but only for columns 3 and 4, where

the probability of birth in a household with reserved caste status is 0.6% to 0.7% more

than other households. Households residing in rural areas increase the likelihood of births

26

The marginal effects reported in column 1 yield insignificance of the employed

household head variable.

77

but this effect is not significant (as in Table 4.3 and 4.4), except in column 3 where the

marginal effect of the household residing in rural area is only 0.8 percentage points. If the

household head is male, the likelihood of having a birth is negative; which seemed to be a

rather unique result. The effect of this variable almost doubles in the IV results, with the

probability of having a birth in the household falling by 4.4% to 5.6% if the household

head is male. Proportion of female children in the household increases the likelihood of

having a birth as reported previously. The probability of birth is marginally higher than

reported in Table 4.3 at 1.8%.

78

Table 4.5 - IV probit results for all households

1 2 3 4

Economic Variables

Remittance receiving household -0.6139**

(0.2677)

[-0.1053]

-0.6064**

(0.2757)

[-0.1040]#

-0.6074**

(0.2724)

[-0.1038]

-0.5993**

(0.2781)

[-0.1024]#

Log of annual per capita

consumption expenditure

-

0.2650***

(0.0392)

[-0.0425]

-

0.2525***

(0.0350)

[-0.0405]

-

0.2890***

(0.0396)

[-0.0462]

-

0.2778***

(0.0358)

[-0.0445]

Employed household head -0.0790*

(0.0471)

[-0.0130]#

-- -0.0739

(0.0474)

[-0.0121]

--

Total employed adults in the

household

-- 0.0157

(0.0235)

[0.0025]

-- 0.0180

(0.0238)

[0.0028]

Demographic Variables Household follows Hinduism -

0.0623***

(0.0231)

[-0.0102]

-

0.0656***

(0.0230)

[-0.0107]

-

0.0644***

(0.0232)

[-0.0105]

-

0.0680***

(0.0231)

[-0.0111]

Household belongs to a reserved

caste

0.0352

(0.0220)

[0.0056]

0.0303

(0.0219)

[0.0048]

0.0446**

(0.0221)

[0.0070]

0.0399*

(0.0221)

[0.0063]

Household resides in rural area 0.0446

(0.0290)

[0.0070]

0.0320

(0.0302)

[0.0051]

0.0552*

(0.0294)

[0.0087]

0.0429

(0.0303)

[0.0068]

Household is multi-generational 0.7806***

(0.0353)

[0.1180]

0.7679***

(0.0276)

[0.1163]

0.8054***

(0.0368)

[0.1213]

0.7884***

(0.0274)

[0.1190]

Household head is male -

0.2722***

(0.0793)

[-0.0448]

-

0.3248***

(0.0813)

[-0.0577]

-

0.2651***

(0.0821)

[-0.0461]#

-

0.3176***

(0.0824)

[-0.0562]

Proportion of female children in

the household

0.1153***

(0.0261)

[0.0185]

0.1148***

(0.0261)

[0.0184]

0.1129***

(0.0261)

[0.0180]

0.1125***

(0.0261)

[0.0180]

79

Table 4.5 continued

Education Variables I- Maximum education dummies with illiterate as the

reference category

Dummy for primary schooling as

maximum education

0.0341

(0.0316)

[0.0055]

0.0250

(0.0302)

[0.0040]

-- --

Dummy for secondary schooling

as maximum education

0.1372***

(0.0350)

[0.0228]

0.1257***

(0.0328)

[0.0209]

-- --

Dummy for graduate education as

maximum education

0.2488***

(0.0455)

[0.0448]

0.2391***

(0.0407)

[0.0429]

-- --

Education Variables II- Proportion of educated adults from each education

group

Adults with primary schooling -- -- 0.1361***

(0.0344)

[0.0217]

0.1294***

(0.0339)

[0.0207]

Adults with secondary schooling -- -- 0.2479***

(0.0450)

[0.0396]

0.2453***

(0.0443)

[0.0393]

Adults with graduate education -- -- 0.4516***

(0.0697)

[0.0723]

0.4508***

(0.0665)

[0.0722]

Migration Variables- Migration history of the

household

-

0.0100***

(0.0029)

[-0.0016]

-

0.0097***

(0.0029)

[-0.0015]

-

0.0097***

(0.0030)

[-0.0015]

-

0.0095***

(0.0029)

[-0.0015]

Total migrants from the

household

0.0260***

(0.0067)

[0.0041]

0.0262***

(0.0068)

[0.0042]

0.0280

(0.0068)

[0.0044]

0.0282***

(0.0069)

[0.0045]

Number of observations 33245 33245 33209 33209

First stage correlation tests-

F- statistic 85.58 81.78 83.42 81.16

Prob > F 0.0000 0.0000 0.0000 0.0000

Over-identification tests

Sargan score 0.6643

(p =

0.4150)

0.6605

(p =

0.4164)

0.9124

(p =

0.3396)

0.8585

(p

=0.3541)

Basmann score 0.6640

(p =

0.4151)

0.6602

(p =

0.4165)

0.9119

(p =

0.3396)

0.8581

(p =

0.3543)

(Standard errors); [Marginal effects]

# denotes lower significance for marginal effects

*** significant at 1% level; ** significant at 5% level; * significant at 10% level

80

The education variables show that there is an increased likelihood of birth as education

stock of the household increase (columns 1 to 4), which is not significantly different from

the results derived in the probit analysis in the previous section. The migration history

variable exhibits a stronger likelihood than previous estimates but the relationship is still

negative. A breakdown of migration history according to short term, medium term and

long term, exhibits results similar to the probit analysis. That is, as the period of exposure

to migration increases, the fertility preferences of the household change from positive to

negative, indicating the transfer of modernity from more developed to less developed

areas.

81

Table 4.6 – IV probit results for selected households with married women

1 2

Economic Variables Remittance receiving household -0.545*

(0.3041)

[-0.1055]^

-0.5557*

(0.3041)

[-0.1079]^

Log of annual per capita consumption

expenditure

-0.2558***

(0.0358)

[-0.0413]

-0.2460***

(0.0370)

[-0.0451]

Total employed adults in the household 0.0151

(0.0243)

[0.0027]

0.0151

(0.0245)

[0.0027]

Demographic Variables Household follows Hinduism -0.0762***

(0.0242)

[-0.0143]

-0.0785***

(0.0243)

[-0.0148]

Household belongs to a reserved caste 0.0512**

(0.0229)

[0.0092]

0.0587**

(0.0231)

[0.0106]

Household resides in rural area 0.0242

(0.0333)

[0.0044]

0.0341

(0.0332)

[0.0062]

Household is multi-generational 0.7360***

(0.0282)

[0.1226]

0.7460***

(0.0275)

[0.1244]

Household head is male -0.3254***

(0.1032)

[-0.0654]#

-0.3271***

(0.1033)

[-0.0659]#

Proportion of female children in the

household

0.1459***

(0.0281)

[0.0267]

0.1437***

(0.0280)

[0.0263]

Education Variables I- Maximum education dummies with illiterate as the

reference category

Dummy for primary schooling as

maximum education

-0.0335

(0.0324)

[-0.0061]

--

Dummy for secondary schooling as

maximum education

0.0514

(0.0351)

[0.0095]

--

Dummy for graduate education as

maximum education

0.1594***

(0.0432)

[0.0312]

--

82

Table 4.6 continued

Proportion of educated adults from each education group Adults with primary schooling -- 0.0693*

(0.0358)

[0.0127]

Adults with secondary schooling -- 0.1673***

(0.0466)

[0.0307]

Adults with graduate education -- 0.3745***

(0.0698)

[0.0687]

Migration Variables- Migration history of the household -0.0111***

(0.0032)

[-0.0020]

-0.0111***

(0.0032)

[-0.0020]

Total migrants from the household 0.0380***

(0.0074)

[0.0069]

0.0394***

(0.0073)

[0.0072]

Number of observations 27688 27688

First stage correlation tests-

F- statistic 66.64 66.19

Prob > F 0.0000 0.0000

Over-identification tests

Sargan score 0.7898

(p = 0.3741)

0.8670

(p = 0.3518)

Basmann score 0.7894

(p = 0.3743)

0.8665

(p = 0.3519)

(Standard errors); [Marginal effects]

# denotes lower significance of marginal effect;

^ denotes change of marginal effect to non-significance

*** significant at 1% level

** significant at 5% level

* significant at 10% level

An increase in total number of migrants increases the probability of birth by

approximately 0.4% in columns 1 through 4, which is similar to the earlier results. The

intensity is indicative of the expectation of receiving more remittances as more migrants

83

leave the household, which would encourage the household to have more children. The

impact however, is extremely negligible.

Results for Limited Household Sample- Table 4.6 present the result of the IV analysis if

only households with married women between the age of 18 and 45 years are included in

the sample. This sample is chosen to remove the bias that might be created by remittance

receiving households that do not have any probability of having a birth because of having

older members. Standard errors and marginal effects are reported in the parenthesis.

The application of instruments to the limited household sample show that the

likelihood of a having a birth in a remittance receiving household is lower than in a non-

remittance receiving household. However, the marginal effect of this variable (10.5% to

10.7%) is not significant, such that the probability by which the event of birth is expected

to reduce is not known with absolute certainty. The remaining variables report

coefficients that are very similar to the values report in IV probit in Table 4.5 above. The

few exceptions include, the marginal effects for households following Hinduism that

change marginally from 1.2% in Table 4.4 and 1% in Table 4.5 to 1.4% in Table 4.6. The

probability that a household belonging to the lower caste will have a birth in the survey

year increases when the household sample is limited and exhibits the strongest effect of

approximately 1% compared to other regressions. The coefficients for a male household

head predict the likelihood of birth in these households to be lower, but the marginal

effects are non-significant such that the extent of this reduced probability cannot be

accurately measured. Education estimates predict similar effect on fertility but do not

exhibit a significant value except in the case of graduate education dummy variable for

maximum education attained in a household. Education variables reflecting proportions

84

are significant, but the marginal effects are smaller as compared to IV results in Table

4.5. The migration history variable has a greater marginal effect of 0.2% as compared

with probit estimates in Table 4.4 and IV estimates in Table 4.5. The impact of total

migrants on probability of having a birth also changes marginally to 0.6% from the

previous average of 0.4% in other regressions, as the elimination of ineligible households

is done.

Post-estimation tests- Over-identification tests are conducted to confirm the exogeneity

of instruments chosen for the analysis. The estimates derived are listed at the end of

columns 1 to 4 in Table 4.5 and columns 1 and 2 for Table 4.6. The default null

hypothesis is that the instruments are uncorrelated with the error and the scores listed are

from Sargan and Basmann tests. The p-values are high enough for all households as well

as the dataset with limited households and the null cannot be rejected; implying that the

model is correctly identified.

Discussion

This essay investigated the claim that remittance receipt increases probability of having

birth in the remittance-receiving household. The primary objective was to challenge the

usefulness of remittances in improving the standards of living of households receiving

these remittances. The probit regression models show that households not only rely on

remittance receipts while making a fertility decision but also exhibit increased likelihood

to reproduce. Two sets of regressions were conducted, one with the complete dataset for

remittance receiving and non-remittance receiving households; and the second limited the

85

sample to remittance receiving households and non-remittance receiving households with

only married women between the age of 18 years and 45 years. It was seen that compared

to a non-remittance receiving household, remittance receiving households were 0.6% to

1% more likely to have a birth in both data samples. This relationship, while positive, has

a very small impact on the probability of increasing household fertility.

Despite the negligible impact of remittances on fertility, there is the possibility of

reverse causation between the receipt of remittances and the event of birth in the

household. To deal with this problem, two instruments, district-wise concentration of

scheduled commercial banks and district-wise concentration of post offices were used to

isolate the effect of remittance receipt. The results of the IV analysis showed a lower

likelihood of having birth in the remittance receiving household, once the instruments are

applied. In fact, the probability of fertility going down in remittance receiving households

is much larger (approximately 10%) than the probability of remittances positively

affecting births in the simple regression analysis. These results show that the pure income

effect of remittance might be overshadowed by the transference of fertility preferences

from the migrant’s host community to the source community as has been suggested by

the works of Fargues, Beine, Docquier, and Schiff, and Nafaul and Vargas-Silva.

There were two outliers to this analysis- the assumption about consumption

expenditure as a proxy of standard of living was incorrect and; education was discovered

to be a better proxy for income than for changes preferences that come with higher

education. Consumption expenditure was seen to reduce the likelihood of births in the

surveyed households, which is indicative of the preference of the household to not have

an additional child if it is already spending a lot of money. On the other hand, the

86

assumption that parents with higher education or a household with more educated adults

will prefer children with high quality i.e. human capital but a lower number of children in

general was found to be wrong. It is seen that since higher education also promises higher

income, there is a preference for more children. The remaining demographic variables

seemed to affect the birth variable in the expected manner.

In terms of policy formulation on the role of remittances in long term development,

this impact of remittances on reducing fertility is important. The income effect of

remittances being overshadowed by the modernization preferences transferred by the

migrant to his/her source community is useful to encourage the flow of remittances from

the migrant to the family back home. In such as scenario, remittances, as noticed in many

studies (some of which are reviewed above) is the most efficient tool to keep the migrants

and their family in contact. The results above show that while remittances might

encourage recipient households to spend more on consumption, they serve as a medium

to reduce fertility in the long run. This outcome is favorable for highly populated

developing societies such as those in rural India where remittances can help the

community to reduce the fertility rates. As fertility declines, the standard of living in the

source community would go up, allowing investments in education, health and food.

The result is also important with respect to the application of conventional

wisdom that income increases, ceteris paribus, will increase fertility. As seen from the

results above, this is not true for remittance receiving households. In order to further

substantiate this claim, it will be useful to apply the same methodology with other

instruments to confirm the negative impact of remittances on fertility. Also, using

interaction terms between the exposure of a household to migration and remittance

87

receipt and exposure to migration and number of children born after migration takes

place can help in determining the impact of remittances on fertility in a more accurately.

88

Impact of Migrant Remittances on Education Outcomes

The role of remittance incomes in stabilizing and improving the consumption patterns of

recipient households is widely observed in academic as well as non-academic literature.

It is also well accepted that remittances contribute to higher investments in human capital

through increased investments in education and health. This impact of remittances on

human capital is crucial because sustained contributions to education in the present will

aid the creation of a better, productive workforce in the future; thus promoting the

economic development of the country in the long run. Most of the initial work studying

the relationship between education expenses and migrant remittances focused on the

motivations to remit and motivations to maximize returns from expected migration. For

example, Stark and Lucas find that families educate migrants in expectation of higher

remittances in the future, which the migrant provides, as a contractual obligation.68

As a

result, the greater the education levels of the migrant, the higher the amount of

remittances that the migrant will send back. Rapoport and Docquier also find support for

their hypothesis suggesting that the expectation of remittances would encourage parents

to invest in their children’s education in the current time period.69

Even if not the all

children who are groomed for future migration actually leave the household, there is a

definite increase in the human capital stock of a community.

The current essay attempts to understand the long-term human capital investments

by studying the schooling expenses made by remittance receiving households as

compared to non-remittance receiving households. The first set of analysis studies the

68

Stark and Lucas, “Migration, Remittances, and the Family." 69

Rapoport and Docquier, "The Economics of Migrants' Remittances.”

89

impact of remittance receipt on schooling expenditure as a share of total expenditure

while the second set of analysis explores the impact of remittance receipt on the

schooling expense per child in a given household. The primary objective to see whether

remittance receiving household tend to invest more in child schooling as compared to

other households. The data utilized for this study is the 64th

round of the National Sample

Survey (NSS) of the Government of India conducted in 2007-08. This data stands as an

outlier to the usual remittance-education studies focusing on the Mexico-USA migration

corridor. The results from ordinary least square (OLS) analysis show that remittance

receipt has a positive impact on education expenditures, thus leading to higher human

capital outcomes for these households. The results from instrumental variables (IV)

analysis are inconclusive and warrantee the use of better instruments to deal with the

problem of endogenous variables.

Rest of the essay is arranged as following: section II presents the literature

review; section III introduces the hypothesis and the education expenditure models;

Section IV elaborates on the dataset and the variables used for the analysis; section V

summarizes the results of the OLS analysis; section VI introduces the instrumental

variables (IVs) and presents the results and; section VII concludes with data shortages

and future work in this direction.

Literature Review

In the past decade, there has been an increased focus on the impact of migration and

remittances on the schooling outcomes of children in migrant sending households.

Empirical studies address these effects by observing different parameters of education

90

such as retention rates, academic performance and gender-based differences in school

enrolments. The academic literature on schooling outcomes can be divided in two

categories. First studies the impact of migration on schooling outcomes, not utilizing

remittances in the empirical work. The second kind of literature focuses on the impact of

remittance incomes or of remittance receipt on school enrolment and retention rates.

Studies concentrating on the former usually reflect an ambiguous impact of

parental migration on educational attainment such as reduction in college aspirations but

an increase in educational aspirations and retention. On the other hand, the latter category

usually finds a positive impact of the receipt of remittances on schooling outcomes.

Increased expenditure towards schooling indicates the choices made by remittance

receiving households with respect to building future human capital; but are not indicative

of the choices made by households in the long run, with respect to the migration

aspirations of the children. That is, while parents can use remittances to invest in

children’s education in the short run, remittances alone cannot explain whether these

children will go to college or drop out and become migrants like their parents/siblings.

Evidence from these two streams of literatures also suggests that the impact on

human capital will differ between countries. For example, one of the main conclusions

Kandel and Kao make in their study of Mexico is that U.S. migration of a family member

positively impacts academic performance of children left behind, while on the other hand

Meyerhoefer and Chen in their study of rural China conclude that parental labor

migration reduces the educational attainment among girls. Alternatively, studies such as

that of Edwards and Ureta find a positive impact of remittances and increased schooling

enrolment in El Salvador. In some cases, the impacts also vary within the same country.

91

For example, in opposition to the findings Kandel and Kao, McKenzie and Rapoport find

a negative impact of migration on attendance and completion of high school in Mexico.

Kandel and Kao study Mexican children who had U.S. temporary migration

experience or belonged to families with migration experience to the U.S. Their empirical

analysis utilizes OLS regression and logistic regression for two primary dependent

variables- changes in student GPA (indicative of the immediate financial impact of

migration) and changes in college aspirations (indicative of long term non-monetary

impact of migration) respectively. The study concludes that while migration to the USA

facilitates has a positive impact on academic performance, it is in fact negatively related

to college aspirations. Migration of an extended family member would thus increase a

student’s GPA by anywhere between 14.3% to 16.9% for different levels of education70

;

and migration of an immediate family member would increase the student’s GPA by

18.3% to 23.0% for all education levels. If the student himself had an international

migration experience, his GPA would increase by an average of 11.9% to 46% depending

on the duration of their migration experience. For domestic migration experience, GPA is

seen to decline at the primary and secondary schooling levels but not for the preparatory

levels. With respect to college aspirations, the migration of an extended family member

reduces college aspirations by 1.9% to 23% for all education levels while the migration

of an immediate family member reduces college aspirations by 37% to 38% for different

education levels, with the impact being most severe if the migrating family member is the

father. However, if the student himself had a migration experience, his college aspirations

increase by 30.5% to 73% for international migrants from all education groups and by

70

Kandel and Kao divide academic levels into three groups- primary (grade 6),

secondary (grade 9) and preparatory (grades 10 to 12)

92

19% to 78.5% for domestic migrants from all education groups.71

These values are highly

significant and perhaps the most optimistic, as will be seen from the studies reviewed

below.

Hanson and Woodruff also study schooling completion in Mexican households

but find an ambiguous relationship between migration and schooling. The ambiguity is

rendered from the positive impact of remittances on the ability to make educational

investments (financial effect), combined with a negative impact of parental absence on

the “scholastic progress”72

of the children (non-monetary effect). Applying an

instrumental variable analysis using historical Mexican migration rates as instruments,

they compare the accumulated schooling73

of children in migrant-sending households

with non-migrant sending households. The instrumental variable results show an overall

positive impact of migration on accumulated schooling for both males and females

between 10 to 15 years of age, approximately 8.1% for females and 4.4 % for males.

Ambiguity is also created by the impact of maternal education and migration history to

the USA. While migration to the USA of an eligible household member74

leads to a

17.7% to 26.4% increase in accumulated schooling of the children, if the mother is a

migrant to the USA, the accumulated schooling reduces by an average of 23.05% for

female children and by an average of 26.6% for male children. This result seems

plausible given the absence of a parent from the household would weaken parental

control and could encourage the child to skip school. Further dividing the maternal

71

Kandel and Kao, "Impact of Temporary Labor Migration on Mexican

Children's Educational Aspirations and Performance." 72

Hanson and Woodruff, "Emigration and Educational Attainment in Mexico," 7. 73

Defined as the grades successfully completed by a child. 74

Any individual between 16 to 65 years of age who migrated in the last 5 years.

93

education by years of education and children by age groups, Hanson and Woodruff find

that households where the mother’s education is lower (3 to 5 years) tend to have higher

accumulated schooling as compared to households with more educated mothers (9 to 12

years of education), with negative impact being the strongest for 13 to 15 years age group

for both boys and girls.75

This result is opposite to the common expectation that a

household with strong education history will impact schooling outcomes positively.

McKenzie and Rapoport study the effect of Mexican migration to the USA on

school attendance and high school completion rates using instrumental variable analysis.

Their primary finding is that migration has a negative effect on school attendance with

membership in a migrant-sending household leading to a 16% - 21% decline for males

between the age of 12 and 18 years and a decline of 20% for females between the age of

16 and 18 years. For years of education completed, the authors find that living in a

migrant sending household lowers the probability of completing 9 years of schooling by

22.5% for males between 12 to 15 years of age and by 14.5% for females in the same age

group. In the age group of 16 to 18 years, the probability of completing 9 years of

education is lowered by 7.9% for males and 12.2% for females. For the age group of 12

to 15 years, the probability of completing 10 years of education reduces by 12% for

males and 10% for females. These results are indicative of possibility that when a parent

migrates, the onus of taking care of the household falls on the older child. McKenzie and

Rapoport find support for this argument observing higher workforce participation for

younger males, or a greater rate of migration of these males (2.2% for 12 to 15 year old

males and 7.3% for 16 to 18 year old males) and greater participation in housework by

75

Hanson and Woodruff, "Emigration and Educational Attainment in Mexico."

94

female children (9.3% for 12 to 15 year old females and 34.6% for 16 to 18 year old

females).76

Antman makes similar observations as McKenzie and Rapoport with respect

to father’s migration from Mexico to the U.S. and its impact on weekly work and study

outcomes for children left behind. The study hours reduce by 35.5 hours and work hours

increase by 60.6 hours for all boys and girls with the effect being more severe on younger

boys and girls, ages 12 to 15 years. This group witnesses a decline of approximately 53

hours in their study hours as compared to a gain in work hours of approximately 32 hours

for boys and 25.5 hours for females.77

Meyerhoefer and Chen focus on the impact of

parental migration on the schooling lags created for school children in rural China and

reveal a similar story. Their primary focus is on female children, where the application of

OLS and IV analysis shows a 0.7 grade lag in the education of female children. That is,

the migration of a parent from rural area to urban area pushes a female child behind by

0.7 grade level or more than half a year of schooling. The probit specification utilized by

the authors also concludes that the probability of a female child from a migrant-sending

household to be behind by a year in schooling is almost 37% higher than for a female

child from a non-migrant sending household. The corresponding result for boys is also

negative but is not statistically significant.78

The literature focusing on the relationship of remittances and schooling decisions

reflect a clear and positive impact of former on the latter. Edwards and Ureta study the

impact of remittance incomes on schooling in El Salvador utilizing the Cox proportional

76

McKenzie and Rapoport, "Can Migration Reduce Educational Attainment?” 77

Antman, “The Intergenerational Effects of Paternal Migration on Schooling and

Work.” 78

Meyerhoefer and Chen, "The Effect of Parental Labor Migration on Children’s

Educational Progress in Rural China."

95

hazard method. They find a significantly large impact of remittances on schooling

retention rates. Segregating their sample by rural and urban areas and grade levels 1-6

and 7-12; they report a 54% and 27% lower hazard of dropping out of school for urban

areas for the two grade categories respectively. For rural areas, this number averages at

14% for the two grade categories.79

Acosta also reaches similar conclusions as Edwards

and Ureta, using probit regression for a sample from El Salvador. The receipt of

remittances (not the amount of remittances) increases the probability of remittance

receiving households to keep children enrolled in school by 4.6%, when all other

demographic controls such as number of children and parental education are applied.

Acosta also uses migrant networks and return migrants as instruments to deal with the

contemporaneous relationship between school retention and remittance income. The

impact of remittances on retention in this case reduces to 3.5% and becomes statistically

insignificant. With respect to labor force participation Acosta finds that remittances

reduce labor force participation for children between 11 and 17 years of age by

approximately 1.3% (probit) to 6.7% (IV probit) for both males and females.80

Amuedo-

Dorantes and Pozo study the impact of remittance receipt on school attendance in the

Dominican Republic by comparing the dependent variable outcome for households that

have migrants with those that do not have migrants. Isolating the effect of remittances on

children in non-migrant sending households, the authors predict better schooling

outcomes for these children, compared to the households where one of the members

undertook migration, thus leaving the child susceptible to hardships. Returns to education

79

Edwards and Ureta, "International Migration, Remittances, and Schooling.” 80

Acosta, "Labor supply, School Attendance, and Remittances from International

Migration.”

96

and expectations about future migration also affects school attendance, with low returns

to education in the destination countries and higher expectation to migrate leading to

lesser school attendance. The positive financial impact of remittances is thus overcome

by the negative, non-monetary impact of migration.81

Perhaps an outlier to this generally

positive impact of remittances on education expenses is the study of Albanian households

by Cattaneo who utilizes the Engel curve framework and parametric and semi-parametric

tobit models to find that remittance incomes do not have any impact on education

expenditures. The author attributes this non-preference of education to spending

conditions put forward by migrants to send remittances and to low returns to education in

the Albanian labor market combined with the importance given to other more urgent

consumption expenditures by the households.82

Three broad conclusions that can be made from the studies reviewed above. First,

the impact of remittances on schooling outcomes is still a less explored area even though

the impact of remittances on education is more or less predictable. Second, most of the

studies exploring the relationship between remittances and schooling (or even migration

and schooling) are concentrated in exploring the Mexican education outcomes, making

studies for other countries virtually negligible. Third, the studies focusing on remittances

and education outcomes focus on the receipt of remittances and not the amount of

remittances. Thus, within the remittance receiving households, the magnitude to which

remittances effect schooling is not explored and can definitely be utilized for

comparisons of outcomes for different remittance receiving households.

81

Amuedo-Dorantes and Pozo, "Accounting for Remittance and Migration

Effects on Children’s Schooling." 82

Cattaneo, "Migrants’ International Transfers and Educational Expenditure.”

97

Hypotheses and Model

In this essay, the impact of remittances on schooling outcomes is examined by using two

dependent variables. The first dependent variable is the share of total consumption

expenditure devoted to schooling expenses in the survey year; henceforth referred as

share of schooling expenses. It is measured as the ratio of the household’s annual

schooling expenses to the household’s annual consumption expenditure. Share of

schooling expenses variable is indicative of the choice made by remittance receiving

households towards higher human capital investments, as compared to non-remittance

receiving households. The second dependent variable is the annual schooling expenditure

per child in a given household. This variable measures the quality of education each child

receives in a remittance receiving household compared to a non-remittance receiving

household. The two dependent variables are created from a common known indicator-

schooling expenses of a household. The NSS data provides information on the annual

schooling expenses83

made by the surveyed household, total annual household

consumption expenditure and total number of children of school going age to conduct

this analysis. While most of the studies reviewed above focus on schooling enrolment,

this data set provides information of grade of schooling completed only. At any given

time thus, the continued status of school enrolment is not known. Hence, annual

schooling expenses become the most plausible tool to estimate a household’s preference

for education. Consequently, two main hypotheses can be developed-

83

Schooling expenses include expenditure on tuition, fees, tutoring costs, school

supplies etc.

98

Do remittance receiving households contribute a greater share of annual consumption

expenses towards schooling expenses and thus, are more conducive to the creation of

human capital.

and;

Do remittance receiving households invest more in towards the schooling of each child

than non-remittance receiving household and thus have qualitatively better children?

According to the hypotheses above, two models are created to address the slightly

differentiated dependent and independent variables. The general form of the education

expenditure model can be summarized as below-

In Model 1, the dependent variable is given by, log share of schooling expenditure =

[

] and, the primary independent variable of interest

, is remittance receipt, a dummy variable which assumes value 1 if the

household receives remittances and 0 if the household does not receive remittances.

For Model 2, the dependent variable assumes the following value.

[

] The total number of

school going children is calculated as children between the ages of 6 years to 17 years in

99

the household. The independent variable of interest assumes a dummy

value = 1 if the household receives remittances, and 0 otherwise.

Other control variables include economic variables such as employment status of

the head of the household and employment status of the adults in the household;

demographic variables capture the household and individual characteristics; education

variables measure the educational attainment of adults in the household and; migration

variables study the strength of influence migrating members of the household have on the

household.

Data and Summary Statistics

As mentioned in the previous section, the NSS questionnaire gathers information on

annual schooling expenses, number of children in the household and annual consumption

expenditure in the household, which enables the creation of the dependent variables.

Additionally, the information on receipt of remittances by a household and the amount of

remittances received in a given a year are also available, allowing the use of the former as

an independent variable.

Among other control variables, economic variables include employment status of

the head of the household and employment status of adults in the household. An

employed household head can ensure continued flow of income, enabling the household

to spend more money on tuition and school supplies and satisfying schooling

requirements of each child. Based on the responses listed in the survey, a dummy is

created for the employment status of the head of the household. If the head of the

100

household is employed and responds to the economic status as self-employed or as

working in a household enterprise or as a regular salaried/ wage employee or reported to

have worked in casual wage labor, the dummy takes the value 1. If however, the

employment status of the household head includes responses such as did not work but

was available for work, attended educational institution, attended domestic duties,

retirees and remittance recipients and disabled, they were included as unemployed and

their employment status is coded as 0.This variable takes a dummy value equal to 1 if the

household head is employed and 0 otherwise. Since many family structures in India can

be multi-generational families, the head of the household might not always be employed.

For example, the head of the household can be a retired grandfather with working sons

and daughters. In order to account for this possibility, proportion of employed adults in

the household is also used as an economic variable. This variable is calculated as the ratio

of number of employed adults to total adults in the household. The higher the proportion

of employed adults in the households, greater is the assurance that schooling of the

children in the household will not be disrupted.

Demographic variables influence the consumption patterns of a household via the

caste the household belongs to, family structure of the household, location of the

household and; female participation in household decisions. A household residing in the

rural area will spend lesser of schooling because of two reasons. First, the concentration

of schools is generally lower in rural areas than in urban areas. Second, rural areas tend to

have more government-run schools that are completely funded and do not require

students to spend anything extra. Meanwhile in the urban areas, private schools exist

along with government schools which tend to tip the balance of schooling expenditure in

101

favor of urban areas further. If the household is in a rural area, the dummy assumes the

value 1, otherwise 0. The caste system in India, which is less segregating at present than

it was a decade ago, still reflects the difference in economic opportunities among

households from the reserved castes and the general castes. Since India has free and

compulsory schooling for children from ages 6 to 14 years84

, the caste of household will

not affect the enrolment of children in schools. Caste, via inherent difference in economic

opportunities will however, affect the access to non-tuition education expenses on school

supplies. The household’s caste is thus included as a dummy variable which equals 1 if

the household belongs to any of the reserved backward castes and 0 otherwise. The

dummy variable for multigenerational family (=1) or not (=0) is expected to have a

negative relationship with share of schooling expenses, as schooling children can pool

their schooling resources and use them more efficiently. It is also possible that the

presence of a greater number of household members diverts consumption to other kind of

consumption needs, thus reducing educational expenditures. Multigenerational family is

expected to have a negative relationship with expense per child as well; since a greater

number of children in the household will lead to lesser investment in the education of

each child. It is possible however, that schooling expense on each child also falls because

of sharing books, supplies and reduced infrastructure costs such as tutoring and

expenditure on school uniforms. To further address decision making process in a multi-

generation family, where spending decisions can be influenced by more than one parent

84

Free and compulsory education for children between 6 to 14 years of age was

written into the Indian Constitution via the 86th

Amendment Act, 2002. In 2009, universal

education for children between 6 to 14 years of age or up to grade VIII of schooling was

made a fundamental right by the enactment of the Right of Children to Free and

Compulsory Education Act. For more information see, http://ssa.nic.in/quality-of-

education/right-of-children-to-free-and-compulsory-education-act-2009

102

or couple, a variable measuring the proportion of adult females in the household is added.

It is calculated as the ratio of total adult women and total adults in a household. If a larger

proportion of adults are women, they can influence spending decisions with greater

bargaining power. Additionally, three variables addressing the role of children in the

household are added to the analysis. Total number of school going children is expected to

reduce the schooling expense per child and increase the share of schooling expenses. The

proportion of female children is expected to be negatively related to the share of

schooling expenses and expense per child, due to the preference to educate a male child.

Ratio of total children to total members in the household is expected to exhibit a positive

relationship with share of schooling expenses as well as schooling expense per child

since the household comprises of more children than adults, naturally tipping the

expenditure in favor of education expenses.

Education variables in the model are divided in two main groups- maximum

education level of the household and proportion of educated adult females at the primary,

secondary and graduate levels. The household member who has completed the highest

level of education will influence a household’s perspective towards education spending.

Maximum education is added in lieu of education level of the parent in the household as a

multigenerational family will have more than one parent who can influence the spending

decisions of the household. The maximum educational attainment is divided in three

dummy categories- primary education takes the value 1 if the maximum education

attained by any household member is the completion of primary schooling (up to 8 years

of schooling) otherwise 0; secondary education takes the value 1 if the maximum

education attained by any household member is completion of secondary school (9 to 12

103

years of schooling) otherwise 0 and; graduate education takes the value 1 if the maximum

education attained by any household member is the completion of graduate or post-

graduate education otherwise 0. The reference category for the maximum education

variable is given by no educational attainment or illiteracy of adults. The second group of

education variables account for the proportion of women educated at each education

level. The education expenditure outcomes will be worse for a household with a greater

proportion of women who completed primary education than with a household with

greater proportion of women who completed secondary education or graduate education.

Lastly, the migration variables are used to measure the strength of relationship

between the migrant and the remittance receiving household. The survey design allows

creating a migration history variable which measures the average years the household has

witnessed migration. This variable is indicative of the changing preferences of a

household that has been exposed to more developed societies. Since more developed

communities also exhibit better human capital, the effect of the migration history variable

on human capital expenditure in the source community should be positive. The second

migration variable is the proportion of employed migrants in the household. If there are

more employed migrants, they will remit more money, which in turn will have a positive

impact on the spending abilities of the household.

Table 5.1 below provides a summary of the variables chosen for the final analysis

and show the predicted sign of the coefficient.

104

Table 5.1 - Variable definition and expected behavior

Variable Nature of the Variable Expected sign of the

coefficient

Share of

schooling

expenditure

Schooling

expense per

child

Remittance receipt Dummy

Remittance receiving=1

Non- remittance

receiving=0

Positive Positive

Employment status of the

head of the household

Dummy

Employed=1;

Unemployed= 0

Positive Positive

Proportion of employed

adults in the household

Ratio; Employed adults

Total adults

Positive Positive

Rural household Dummy

Rural=1; Urban= 0

Negative Negative

Caste

Dummy

Reserved=1; Others= 0

Negative Negative

Multigenerational

household

Dummy

Joint=1; Nuclear= 0

Negative Negative

Sex of the head of the

household

Dummy

Male= 1; Female= 0

Ambiguous Ambiguous

Proportion of female

adults in the household

Ratio; Female adults

Total adults

Positive Positive

Number of school-aged

children (6 years to 17

years)

Numeric Positive Negative

Proportion of female

children in the household

Ratio; Female children

Total children

Negative Negative

Proportion of total

children in the household

Ratio; Total children

Household size

Positive Positive

105

Table 5.1 continued

Maximum education

Primary schooling

Secondary schooling

Graduate education

Dummy

Primary=1; Others=0

Secondary=1; Others= 0

Graduate=1; Others= 0

Reference category-

illiterate

Positive Positive

Proportion of adult

females with

-primary

schooling

-secondary

schooling

-graduate

education

Ratio

Female adults with --

education

Total female adults

Positive Positive

Migration history Numeric Positive Positive

Proportion of employed

migrants

Ratio; Employed adult

migrants

Total migrants

Positive Positive

The descriptive statistics for all variables is given in table 5.2 below. The sample

breakdown is provided according to remittance receiving and non-remittance receiving

households to get an estimation of the characteristics of each kind of household.

106

Table 5.2 - Descriptive statistics for schooling models

Variable Remittance receiving

households

Non- remittance receiving

households

Mean S.D. Mean S.D.

Annual schooling

expenses

5011.67 10040.35 4345.40 10232.46

Share of schooling

expenses in total

consumption

expenditure

0.0670 0.0769 0.0556 0.07115

Schooling expense per

child

2989.19 6109.25 2650.34 7314.48

Remittance receipt 1 0 0 0

Amount of remittances 27102.13 49732.58 0 0

Employment status of

the head of the

household

0.6601 0.4736 0.8582 0.3487

Proportion of employed

adults in the household

0.4722 0.3452 0.6017 0.2729

Rural household 0.6845 0.4647 0.6753 0.4682

Reserved caste 0.6550 0.4753 0.6745 0.4685

Multigenerational

household

0.4295 0.4950 0.4750 0.4993

Sex of the head of the

household, male=1

0.6484 0.4774 0.8766 0.3287

Proportion of female

adults in the household

0.6282 0.2493 0.5053 0.1811

Number of school-aged

children (6 years to 17

years)

1.0490 1.3119 1.0450 1.3016

Proportion of female

children in the

household

0.4680 03722 0.4582 0.3763

Ratio of total children 0.2913 0.2612 0.2492 0.2224

Maximum education

Primary schooling

Secondary schooling

Graduate education

0.4056

0.3145

0.1723

0.4910

0.4645

0.3776

0.4121

0.3126

0.1808

.4922

.4636

.3849

Education of female

members

Primary schooling

Secondary schooling

Graduate education

0.2070

0.1632

0.0636

0.2868

0.3092

0.2084

0.1557

0.1370

0.0561

0.2075

0.2844

0.1948

Migration history 5.9871 5.8646 7.0317 7.0284

Proportion of employed

migrants

0.8398 0.2391 0.3982 0.4494

107

Results from OLS Analysis

The education expenditure models are first estimated using a simple OLS method, the

results of which are summarized in Table 5.3 below. Robust standard errors are reported

in the parentheses and the results are reported to be significant at the 1% level. For Model

1, with share of schooling expenditure as the dependent variable, the number of

observations is 26,436 and for Model 2 with schooling expense per child as the

dependent variable, the number of observations is 23,685. This discrepancy in

observations occurs if the household is incurring educational expenses on children below

6 years of age or above 18 years of age; observations that the sample selection does not

include. Columns 1 and 2 in Table 5.3 summarize the results for Model 1, using different

combinations of independent variables listed in Tables 5.1 and 5.2 above. Columns 3 and

4 summarize the results for Model 2. Variables excluded from columns 2 and 4 are

employment status of the household head which is replaced by the proportion of

employed adults in the household in columns 2 and 4 to capture the multi-generational

household effect. The multi-generational household dummy in columns 1 and 3 is

replaced by proportion of adult women in the household in columns 2 and 4 to account

for the multi-generational effect, as well as measure the relative bargaining power of

females versus males in the household. Instead of using the total number of school going

children (used in columns 1 and 3) the ratio of total children in the household is used in

columns 2 and 4. This variable, along with the proportion of female adults and proportion

108

of employed adults can account for the effects of a multi-generational family.85

Other

variables such as dummy for remittance receiving household, social characteristics of the

household, proportion of female children in the household and education and migration

variables are included in all the columns.

Remittance receipt has a consistently strong impact on the share of schooling as

well as schooling expense per child. Columns 1 and 2 show that remittance receipt can

increase the share of consumption expenditure devoted to education expenditures

anywhere from 9.6% to 16.6%. This result was predicted above since remittance receipt

was expected to reduce credit constraints and increase consumption for a remittance

receiving household. These households also tend to spend 16% to 19% more on the

education of each child in the household, as compared to a non-remittance receiving

household. Thus, the remittance experience not only relives credit constraints but also

encourages households to invest in the human capital of the children in the household in

order to secure a better future. Such tendency might come from the exposure of the

migrant to a better living environment, thus pushing the family left behind to aspire for

similar standards via long run human capital investments. It is also possible that these

households already give importance to education and the extra income helps them realize

their education goals.

Households with an employed head (columns 1 and 3) and a larger number of

working adults (columns 2 and 4) seem to have a negative impact on the two dependent

variables by an average of 15.9% and 41.5% respectively. This result goes against

conventional wisdom of consistency of incomes and higher investments in education. The

85

Inclusion of the dummy for a multi-generational family however does not

distort the effects of these variables.

109

proportion of employed migrants in a household also exhibits a similar negative

relationship with the dependent variables. A greater number of employed migrants would

thus reduce share of education expenditure by approximately 6% and reduce schooling

expense on each child by 9.2% to 16.2%. One plausible reason for such unexpected

behavior of the employment variable could be that households prefer that the children get

into the labor force as soon as possible, instead of investing many years in obtaining

education. Such expectations could lead to lesser investment in schooling. It is also

plausible that if the migrant from the household did not acquire higher education but is

economically successful, the household might not give importance to education as well

and groom the children to be economic agents instead. For example, 17% of the

households had no literate adult in the household while 37% households had adults who

completed primary education. On the other hand, only 16.29% of the household had

adults with bachelor degree or higher. Thus, the household can give more importance to

entering the labor force rather than obtain education.

110

Table 5.3 - OLS estimates for share of schooling expenses

and schooling expense per child

1

Share of

schooling

expenses

2

Share of

schooling

expenses

3

Schooling

expense

per child

4

Schooling

expense

per child

Economic Variables

Remittance receiving household 0.1667***

(0.0159)

0.0969***

(0.0166)

0.1924***

(0.181)

0.1652***

(0.0183)

Employed household head -

0.1791***

(0.1617)

-- -

0.1398***

(0.0184)

--

Proportion of employed adults in

the household

-- -

0.3579***

(0.0226)

-- -

0.4734***

(0.0252)

Demographic Variables Household resides in rural area -

0.2402***

(0.0153)

-

0.2385***

(0.0156)

-

0.5023***

(0.0176)

-

0.4741***

(0.0176)

Household belongs to a reserved

caste

-

0.1259***

(0.1427)

-

0.1152***

(0.0146)

-

0.2355***

(0.0163)

-

0.2053***

(0.0162)

Household is multi-generational -0.4302

(0.0133)

-- -

0.0907***

(0.0152)

--

Proportion of adult women in the

household

-- -

0.1157***

(0.1926)

-- 0.4456***

(0.0440)

Total children of school age (6 to

17 years)

0.1928***

(0.0053)

-- -

0.2119***

(0.0063)

--

Proportion of total children in the

household

-- 1.1980***

(0.0478)

-- -

1.7794***

(0.0527)

Proportion of female children in

the household

-

0.1221***

(0.0185)

-

0.1157***

(0.0192)

-

0.1108***

(0.0211)

-

0.0941***

(0.0210)

Education Variables I- Maximum education dummies with illiterate as the

reference category

Dummy for primary schooling as

maximum education

0.7634***

(0.0748)

0.9267***

(0.0740)

0.9437***

(0.0835)

0.7935***

(0.0841)

Dummy for secondary schooling

as maximum education

1.3035***

(0.0757)

1.4940***

(0.0752)

1.7531***

(0.0846)

1.4481***

(0.0854)

Dummy for graduate education as

maximum education

1.3194***

(0.0789)

1.4701***

(0.0790)

2.0331***

(0.0888)

1.6072***

(0.0898)

111

Table 5.3 continued

Education Variables II- Proportion of educated adult females from each

education group

Adult females with primary

schooling

0.4102***

(0.0250)

0.2797***

(0.0268)

0.6296***

(0.0285)

0.6528***

(0.0297)

Adult females with secondary

schooling

0.3680***

(0.0272)

0.2544***

(0.0285)

0.6637***

(0.0321)

0.7385***

(0.0324)

Adult females with graduate

education

0.4680***

(0.0482)

0.3404***

(0.0510)

0.9530***

(0.0570)

1.2129***

(0.0573)

Migration Variables- Migration history of the

household

0.0086***

(0.0009)

0.0044***

(0.0009)

0.0098***

(0.0011)

0.0063***

(0.0010)

Proportion of employed migrants

from the household

-

0.0608***

(0.0182)

-

0.0585***

(0.0187)

-

0.1623***

(0.0207)

-

0.0929***

(0.0206)

Number of observations 26436 26436 23685 23685

R-square 0.2171 0.1722 0.3977 0.4045

Standard errors are in the parenthesis; ***Significant at 1% level

Among the demographic variables, the coefficients behave in the expected manner.

Households in rural areas tend to spend approximately 24% less on schooling expenses

and on an average, invest 48.8% lesser on each child as compared to a household residing

in the urban area. This difference in spending pattern can arise due to two reasons. First,

rural areas have more government sponsored schools that do not require any additional

investment on schooling or schooling supplies from the parents. This shrinks the share of

schooling expense and schooling expense per child in rural areas compared to households

in urban areas where the children might go to private schools and spend on their own

books, extra tuition and other school fees. Second, rural areas in general have lesser

number of schools, which along with a household’s requirement for farm and non-farm

labor can lead to lesser children enrolled in schools and thus, lower education expenditure

for rural areas. Households from reserved backward castes devote 11.5% to 12.5% lesser

112

consumption towards education expenditure and spend approximately 20% to 23% lesser

on each child than a non-reserved caste household. As mentioned in section IV above,

this can be due to differences in economic opportunities of the household because of

being a lower caste household instead of lack of access to education per se. A multi-

generation household also contributes 43% lesser to education expenditure as compared

to a nuclear household and also spend about 9% lesser on each child in terms of

education expenditure. This however, does not imply that multi-generational households

assign lower importance to human capital. A more likely explanation is that the

household shares education resources and thus has to spend lesser portion of the

consumption budget on school supplies. For example, siblings can share school supplies,

recycle the same books for years before discarding them and the teach each other thus

eliminating the need for tutoring.

The variables related to children in the household behave more or less as

expected. A larger number of school-going children in the household leads to a greater

share of consumption expenditure devoted to education expenses (19.2%). It however,

negatively impacts the investment made in each child (21.1% lesser); confirming the

expectation that as the number of children will increase, the quality of education each

child receives will fall. In columns 2 and 4, the total number of school going children is

replaced by the proportion of children in the household and it is expected that a greater

share of children in the household will increase the share of consumption on schooling

expenditure and reduce the quality of each child’s education by negatively affecting the

expense per child. These coefficients behave as expected with greater proportion of

children increasing the share of schooling expenditure by almost 119% and reducing

113

expense per child by approximately 177% respectively. Lastly, higher the number of

female children in the household lesser is the share of schooling expenditure (average

11.8% lesser) and lesser is the expense per child (average 10.2% lesser) in that

household. This reflects the preference for investing in the human capital of a male child

compared to a female child. The general opinion is that while a male child will have to be

the bread-winner of his family in the future, a female child will be fine without work

since she can get married and secure her future.

Variables related to the education of adults in the household also behave as

expected of them. As the maximum education obtained by any member in a household

increases, the share of schooling expenditure as well as the share of schooling expense

per child increased. For share of schooling expenditure out of total consumption

expenditure, these values range between 76.3% to 92.6% for households with maximum

educational attainment at the primary level; 130.3% to 149.4% for maximum educational

attainment at the secondary level and; 131.9% to 147% for maximum educational

attainment at the graduate level. For schooling expense per child, these values range

between 79.3% to 94.3% for maximum educational attainment at the primary level;

114.8% to 175.3% for maximum educational attainment at the secondary level and;

160.7% to 203.3% for maximum educational attainment at the graduate level.

Households with higher education levels therefore, lay much higher premium on

obtaining schooling for their children as compared to households with lower or no

education.86

It is also seen that as more women acquire higher education in the

household, the education outcomes for the children in the household also improve. Thus,

86

Reference category for these variables was if the maximum education in the

household was no-schooling/ illiteracy.

114

while households where a greater number of women completed secondary education

spend an average of 31.2% of their consumption expenditure on education expenses

while households with a greater number of women with graduate education spend an

average of 40.4% of their consumption expenditure on education expenses.

Comparatively, for schooling expense per child households with larger number of

graduate women spend almost 38% more than households with a larger number of

women with secondary education.

Migration history of a household has a small but positive impact on education

expenditures- average 0.6% for share of education expenditure and 0.7% for expenditure

per child. Thus, the exposure to a more developed society encourages households to

reach human capital outcomes similar to those societies. However, the weak relationship

shows that this variable might not be a crucial determinant of education outcomes. On the

other hand, as the proportion of employed migrants from a household increases, the

schooling expenses incurred by the household decreases by 5% to 16%. If the households

see the economic benefits of migration, they might substitute away from investing in

schooling and push children to become migrants, thus not requiring investments in

schooling. This negative impact of migration but positive impact of remittances on

education aspirations is similar to the results of studies by Kandel and Kao, Hanson and

Woodruff and Amuedo-Dorantes and Pozo.87

87

Refer to p.88 to p.92 above.

115

Results from the IV Analysis

OLS estimates provide an extremely optimistic picture regarding the effect of remittance

receipt on the dependent variables. This model however, suffers from potential

endogeneity issues. Remittance receipt will change the consumption patterns and increase

schooling investments, but in some cases, where the migrant might be a close relative,

remittances might be received specifically to improve schooling outcomes for the

children (to pay for a tutor for a poor performing child or to buy a computer). The IV

analysis is built on the results presented by OLS regression analysis in columns 1 and 3

of Table 5.3. The Durbin-Wu-Hausman test for endogenous variables yields an F statistic

greater than 10 and p-value less than 0.05, the results of which are summarized in Table

5.4 below.

Table 5.4 - Durbin-Wu-Hausman test for endogenous variables

F-statistic p- value

IV for Model 1, Column 1 29.49 0.0000

IV for Model 2, Column 3 100.145 0.0000

To address the problem of endogenous variables, district-wise concentration of scheduled

commercial banks in the survey year 2007-08 (as reported by the Reserve Bank of India)

is chosen as an instrument. State-owned commercial banks are widely used in India as a

medium of saving, investment and money-transfers in India. Their outreach is more wide-

spread than that of private banks and thus can facilitate the easy transfer of remittances.

Table 5.5 below summarizes the results of the IV analysis using the two dependent

variables. Results of the 2SLS regression are reported. Thereafter, tests for weak

116

instruments and over-identification tests are also conducted, the values of which are

reported in Table 5.6.

It is see that after remittance receiving is instrumented, its relationship with the

two dependent variables (share of schooling expenses in column 5 and schooling expense

per child in column 6) becomes negative. The receipt of remittance now seems to push

down the educational expenditures in the remittance receiving household, indicating that

children in these households might face worse human capital outcomes in the future than

their counterparts from non-remittance receiving households. Such negative relationship

seems to indicate that households that receive remittances give less important to human

capital and more leverage to becoming economic agents as soon as possible. The

coefficient for employment status of the head of the household magnifies as well, though

the nature of the relationship does not change. There is a downward movement in the

coefficient if the household is in a rural area for both share of schooling expenditure (-

0.18 in column 5 from -0.24 in column 1) and schooling expense per child (-0.38 in

column 6 from -0.50 in column 3). Similar reduction in coefficient is observed for multi-

generational households (from -0.43 in columns 1 to -0.39 in column 5 and from -0.09 in

columns 3 to 0.01 in column 6). For total school going children, households still increase

the share of schooling expenditure as number of the former increase, and this increase is

only slightly from 19.2% in OLS results to 21.1% in the IV analysis. The impact of total

school children on schooling expense per child is still negative (-.16 in column 6) and is

only slightly lesser than that of OLS results (-0.21 in column 3). Education variables

behave as earlier but witness an upward bias of approximately 20% in each category. The

effect of migration history of the household becomes even weaker when compared to

117

OLS results in columns 1 and 3 of Table 5.3. The proportion of employed migrants in the

household however, now significantly and positively impact education expenditures in

columns 5 and 6. This variable had a negative coefficient in the OLS regressions reported

in Table 5.3.

118

Table 5.5 - IV estimates for share of schooling expenses and schooling expense

per child

1

Share of schooling

expenses

2

Schooling expense

per child

2SLS regressions

Economic variables

Remittance receiving household -1.6490***

(0.4085)

-3.8306***

(0.7067)

Employed household head -0.4917***

(0.0729)

-0.8739***

(0.1327)

Demographic variables

Household resides in rural area -0.1891***

(0.2170)

-0.3868***

(0.0365)

Household belongs to a reserved caste -0.1467***

(0.0179)

-0.2789***

(0.0293)

Household is multi-generational -0.3912***

(0.0187)

-0.0151

(0.0297)

Total children of school age (6 to 17

years)

0.2118***

(0.0075)

-0.1679

(0.0133)

Proportion of female children in the

household

-0.1126***

(0.0216)

-0.0708**

(0.0367)

Education Variables I- Maximum education dummies with illiterate as the

reference category Dummy for primary schooling as

maximum education

0.6730***

(0.0784)

0.7240***

(0.1384)

Dummy for secondary schooling as

maximum education

1.2168***

(0.0793)

1.5419***

(0.1397)

Dummy for graduate education as

maximum education

1.1871***

(0.0862)

1.7404***

(0.1507)

Education Variables II- Proportion of educated adult females from each

education group

Adult females with primary schooling 0.6220***

(0.0572)

1.0664***

(0.9185)

Adult females with secondary schooling 0.5184***

(0.0482)

0.98644***

(0.0788)

Adult females with graduate education 0.6269***

(0.0680)

1.2957***

(0.1158)

Migration variables

Migration history of the household 0.0046***

(0.0015)

0.0017

(0.0024)

Proportion of employed migrants from

the household

1.0540***

(0.2515)

2.2942***

(0.4326)

Number of observations 26436 23685

R-squared 0.3592 0.3601

Standard errors in the parentheses; *** Significant at 1% level; ** Significant at 5% level

119

While these results are slightly disturbing and undermine the positive impact of

remittances on human capital development, one last step before finalizing the analysis is

to test for the validity of the IV. As seen in Table 5.6 below, the instrument chosen does

not seem to be a strong one.

Table 5.6 - Post-estimation tests for weak instruments

Share of schooling

expenses

Schooling expense per

child

R-squared 0.3592 0.3601

F-statistic 60.193 48.0175

Prob > F 0.0000 0.0000

Shea’s partial R-squared 0.0023 0.0020

As seen in the table above, while an F-statistic greater than 10 suggests that the IV used is

not weak, the Shea’s partial R-squared value is extremely low, leaving the IV model

undetermined.

Alternative Instruments

The results with district-wise concentration of commercial banks exhibit a weak result,

thus leaving the model undetermined. As a result, two more instruments are tested to see

the impact of remittance receipt on schooling expenses. The first alternative instrument is

district-wise concentration of post offices, which was also used in the essay on fertility.

The second alternative instrument is the state-wise and sector-wise unemployment rate in

the survey year 2007-08. While a stronger network of post office will facilitate the

transfer of remittances, the unemployment rates, high unemployment at the source will

encourage the migrant to remit money to the household. There is however, a strong

possibility that high unemployment will affect the household income, and thus schooling

120

expenses. To test that unemployment and the dependent variable do not share a strong

relationship, the correlation between them is calculated. Unemployment and the two

dependent variables, share of schooling expenses and schooling expense per child exhibit

a weak correlation, thus allowing the use of these two instruments as an alternative to

district-wise concentration of commercial banks. The results from these tests are

summarized in Table 5.7 below. Column 1 corresponds to share of schooling expenses as

the dependent variable and column 2 corresponds to schooling expense per child as the

primary dependent variable of interest. The independent variables are the same as in

Table 5.5 above and include, remittance receipt (0/1 dummy), employment status of the

head of the household, rural or urban location of the household, reserved caste status of

the household, multi-generational household and total children and proportion of female

children in the household. Education variable include maximum education dummies with

illiterate as the reference category and proportion of educated females at each level of

schooling completed. Migration variables include average years the household has

witnessed migration and the proportion of employed migrants in the household.

IV results show a positive impact of remittance receipt on share of schooling

expenses out of the total household budget. This result is in contrast to the one derived in

Table 5.5 with the use of district-wise concentration of commercial banks as an

instrument. Here, if the household receives remittances, it tends to invest 141.5% more

than non-remittance receiving household towards share of education expenses, as shown

in column 1. While this positive relationship is encouraging, the value of the coefficient

is extremely high, which seems to raise some concerns. For the second dependent

variable on schooling expense per child, reported in column 2, remittance receiving

121

household seem to invest 267.1% more in each child that non-remittance receiving

household. This positive relationship, while encouraging, is extremely high as well.

Among other economic variables, an employed household head is expected to

devote 3.5% more towards education expenses out of the total household budget, but this

value is insignificant. In column 2 however, a household with an employed head is seen

to invest 31.2% more in the education of each child as compared to a household where

the head is not employed. This result is expected, as an employed head will be able to

invest more in the educational attainment of the children.

The remaining variables do not behave differently from the OLS results in Table

5.3 and IV results in Table 5.5 above. If the household resides in a rural area, it will

invest 27.5% lesser consumption expenditure towards schooling and 57.3% lesser in

education of each child. Similarly, membership in the reserved caste shows that

households contribute 11.1% lesser towards share of schooling expenses and 20.8%

lesser towards schooling of each child as compared to a non-reserved household. Multi-

generational households spend a lesser portion of their entire consumption expenditure on

schooling expenses and 13.7% in the schooling expenses per child, which supports the

previous assumption that such households might be pooling resources and older children,

might be helping their younger siblings which reduces the need to spend more on

education expenses. As the number of school going children increases, the share of

schooling expenditure increases by approximately 18% but the expense on each child

decreases by approximately 24%. A household where a higher number of children are

female, the share of schooling expenses as well as education spending on each child is

lesser by 12.8% and 13.5% respectively.

122

Table 5.7 - IV estimates for share of schooling expenses and schooling expense

per child using unemployment and district-wise concentration of

post offices as instruments

1

Share of schooling

expenses

2

Schooling expense

per child

2SLS regressions

Economic variables

Remittance receiving household 1.4155***

(0.2026)

2.6712***

(0.2805)

Employed household head 0.0358

(0.0390)

0.3126***

(0.0566)

Demographic variables

Household resides in rural area -0.2754***

(0.0176)

-0.5739***

(0.0244)

Household belongs to a reserved caste -0.1117***

(0.0159)

-0.2087***

(0.0218)

Household is multi-generational -0.4570***

(0.0156)

-0.1375***

(0.0209)

Total children of school age (6 to 17

years)

0.1798***

(0.0060)

-0.2390***

(0.0088)

Proportion of female children in the

household

-0.1287***

(0.0196)

-0.1356***

(0.0275)

Education Variables I- Maximum education dummies with illiterate as the

reference category Dummy for primary schooling as

maximum education

0.8255***

(0.0696)

1.0790***

(0.1024)

Dummy for secondary schooling as

maximum education

1.3632***

(0.0705)

1.8834***

(0.1036)

Dummy for graduate education as

maximum education

1.4101***

(0.0751)

2.2125***

(0.1099)

Education Variables II- Proportion of educated adult females from each

education group

Adult females with primary schooling 0.2644***

(0.0372)

0.3604***

(0.0490)

Adult females with secondary schooling 0.2586***

(0.0350)

0.4642***

(0.0474)

Adult females with graduate education 0.3591***

(0.0555)

0.7428***

(0.0791)

Migration variables

Migration history of the household 0.0114***

(0.0019)

0.0148***

(0.0016)

Proportion of employed migrants from

the household

-0.8276

(0.1255)

-1.6762***

(0.1729)

Number of observations 26434 23683

123

Table 5.7 continued

First stage correlation tests-

F- statistic 101.66 88.94

Prob > F 0.0000 0.0000

Over-identification tests

Sargan score 2.4832

(p = 0.1151)

5.2081

(p = 0.0225)

Basmann score 2.4818

(p = 0.1152)

5.2055

(p = 0.0225)

Standard errors in the parentheses; *** Significant at 1% level; ** Significant at 5% level

As the years of completed schooling by a household increases, the percentage

share of consumption expenditure on schooling expenses also increases. Thus, while

households where at least one adult completed primary school will spend 82.5% more on

share of schooling expenses and 107.9% more on education of each child, than household

where none of the adults were educated; for households that had at least one graduate the

share of schooling expenses is approximately 141% higher and expense per child is 221%

higher. Similarly, as the proportion of women with completed primary, secondary and

graduate education increase, the share of schooling expenses of the household increase.

Therefore, a household with greater proportion of female with graduate education would

devote 35.9% of the consumption expenditure to schooling and 74.2% more on schooling

expense per child, as compared to a household where the larger proportion of women

completed only secondary education. These education variables present a picture similar

to the expectations that were set for them earlier in the essay. A household with higher

educational attainment will place higher premium on schooling.

The two migration variables, migration history and proportion of employed

migrants do not change substantially from the results reported in Table 5.3. As a

124

household’s average year of exposure to migration increases, it invests approximately

1.1% share towards schooling expenses and 1.4% toward schooling expense per child. In

order to test the validity of this result, migration history of the household is divided into

three periods. If the household has had exposure to migration in the last five years, the

migration history of the household is short, while medium term exposure implies an

average of five to 10 years since the household sent a migrant. Breaking down this

variable provides a clearer picture of household’s schooling expenses. It is seen that a

household with short history of migration would in fact reduce the share of schooling

expenses and invest less in each child as compared to a household that has been exposed

to migration for a longer time. That is households with a long term migration history

spend 17.7% more on share of schooling expenses and 23.1% more on schooling expense

per child as compared to a household with recent exposure to migration. These results

seem to indicate that as soon as a migrant leaves the household, there is a disruption in

the household budget, which would affect the schooling expenses as well.88

However, as

the migrant settles at the destination, and sends regular remittances, the share of

schooling expenses tend to increase. Antman, McKenzie and Rapoport and Meyerhoefer

and Chen find similar disruptions and reduction in schooling attainment in migrant

sending households.89

Additionally the household, witnessing the benefits of migration

(especially if the migrant has high human capital), will tend to increase the schooling

investments of the current generation.

88

This disruption could occur if the household had to divert resources from regular consumption

towards costs of migration. 89

Refer to p. 90 and p.91 above.

125

Post-estimation tests- Over identification tests for the instruments listed at the end of

Table 5.7 show that the first model for schooling expenses with share of schooling

expenses as the dependent variable is correctly identified by using state-wise and sector-

wise unemployment rates and district-wise concentration of post offices as instruments.

The Sargan-Basmann scores are reported in column 1. For the second model with

schooling expenses per child however, these instruments fail to correctly identify the

model (column 2). This warranties the use of alternative instruments that can better

predict the impact of remittance receipt on schooling outcomes.

Discussion

The primary objective of this essay was to observe if the receipt of remittances by

surveyed households leads to higher investments in education in the household. A

positive impact of remittances on education expenditure would mean that not only

remittance incomes enable households to enjoy a higher level of consumption, but also

enable them to enjoy sustained development by assisting the creation of higher human

capital of the children. Two dependent variables were chosen to explore this impact of

remittances, share of schooling expenditure out of total consumption expenditure and the

schooling expenditure incurred on each child of school-going age in the household. These

variables were chosen as a measure of educational attainment in the household due to

lack of data available on enrolment rates. The results from the OLS analysis showed that

remittance receiving households devote more money towards educational expenditure

and invest more in the human capital of each child. Other control variables, except

126

employment status of household members and migrants behaved in the way it was

expected.

The OLS model was seen to suffer from endogeneity and to correct for this error,

district-wise concentration of scheduled commercial banks in India was used as an

instrument. The inclusion of this instrument changed the relationship of the endogenous

regressor, remittance receipt with the dependent variables. The receipt of remittance by a

household started exhibited a negative impact on the human capital outcomes of the

household. Testing for the strength of the instrument however showed a very small

Shea’s partial R-squared which makes the true impact of remittance receipt on

educational expenditure unidentifiable.

As an alternative to district-wise concentration of scheduled commercial banks,

state-wise sectoral unemployment rates and district-wise concentration of post offices are

introduced as instruments. The model is recalculated and it is seen that remittance receipt

has a positive impact on both the share of schooling expenses as well as schooling

expense per child. The model is correctly identified for the first dependent variable, share

of schooling expenses but the results for the second dependent variable on schooling

expense per child are not significant. Additionally, the value of both the coefficients is

extremely high, which begs for further investigation in terms of better instruments that

can provide more accurate results. Possible instruments could include the district-level

data on natural calamities such as rainfall90

or droughts. Many studies use destination

community unemployment rates, but the lack of data on migrant destination stops the use

of this IV.

90

Munshi uses rainfall in the origin communities in Mexico as an IV.

127

Summarizing the Results and Future Work

The essays in this dissertation focused on two factors that determine long term human

development in a country. First was the impact of remittance receipt on fertility and the

second was the impact of remittance receipt on investments in education. Remittances

have been found to have positive impact on the standards of living of recipient

households, as measured by their higher propensity to consume. This higher propensity to

consume however is not beneficial unless it adds to the productivity of each household

and a country in the foreseeable future. Therefore, fertility propensities of remittance

receiving household and their tendency to spend on education, compared to other

consumption categories are analyzed. If remittance receiving households give less

importance to fertility and more importance to human capital investments, they can serve

as a seat of human development in a society. These households, through their better

development outcomes, can encourage their communities to adopt such behaviors as

well.

The dataset used for this analysis is obtained from the Government of India’s 64th

National Sample Survey on Employment, Unemployment and Migration Particulars from

the year 2007-08. This data is rarely used by researchers focusing on economic

development and human development; almost never by researchers outside India and; has

never been used to analyze the impact of remittances on human development before. This

dissertation therefore, is a pioneering study for developing an interest in the Indian

migrant stock and how their internal and international mobility can help the country to

develop at the micro level. The NSS dataset is also rich in terms of number of surveyed

households, their labor market information, consumption behaviors and demographic

128

characteristic of each individual from these households. The scope of data analysis from

this dataset is therefore immense.

The dissertation provided a summary of seminal works done in the field of

migration and remittances in the first part. The role of remittances in improving living

standards in recipient households is reviewed along with the development of literature on

economic labor migration and the motivations of a migrant to remit to a household that

they leave behind. Economic migration is seen to be motivated by the attraction to higher

wages and usually, a better lifestyle at the destination. Remittances on the other hand are

motivated by altruistic behavior of the migrant, a way to pay back the family which

invested in him/her undertaking migration successfully. Often the purpose of remittance

would be to insure future stability by maintaining continuous contact with the family at

source and supporting them financially at present to be supported by them in the future.

Despite the motivations to remit, the extra income was seen to help households achieve

consumption stability and sometime, even save some income. It was also seen that

remittances have a deeper outreach because they are person to person transfers and

enable the resolution of credit constraints without any collateral attached. In case of

international migrants, remittances were also seen to contribute to exchange rate stability,

especially in the case of India where during the 1980s current account stability was

largely maintained by remittance flows from the Gulf countries.

The second part of the dissertation provides a snapshot of the Indian migrant

stock and remittance flows, both domestic and international. Despite witnessing

migration since early 19th

century and the presence of a strong and large Indian Diaspora

outside the country, the data collection about these migrants was seen to be almost

129

negligible. Additionally, despite the reliance on remittance flows for maintaining

macroeconomic stability, micro level data on the former was found to be lacking as well.

This lack of data on domestic migrants and remittances was attributed to the sporadic

data collection efforts in this direction. This is followed by providing a brief outline of

the NSS focusing on the information later utilized for empirical analysis.

The detailed findings of the NSS are discussed in the third part of the dissertation,

focusing on the separation of household between remittance receiving, non-remittance

receiving and non-migrant sending households. Households were seen to be similar in

many regards, with their demographic characteristics varying slightly among different

groups. With respect to consumption behavior however the remittance receiving

households were observed to have different preferences when compared to the other kind

of households.

The next section introduced the essay on fertility and remittances with the

expectation of making a commentary on the role of remittances in reducing population

growth in a country in the long run. The empirical analysis revealed that remittance

increase the likelihood of a remittance receiving household to have higher fertility levels

than non-remittance receiving households, suggesting that an increase in income would

induce parents to consume a higher quantity of children as well. The contemporaneous

nature of the dependent and the independent variable and the possibility of reverse

causality between them encouraged the use of instrumental variable analysis. Two

instruments, district-wise concentration of scheduled commercial banks and district-wise

concentration of post offices were used. The impact of remittance receipt on fertility

became negative and it was seen that households that receive remittances tend to have

130

10% lower probability of having births than non-remittance receiving households. The

instruments were tested to be valid and the results were significant.

If remittances led to lower likelihood of births in the recipient households, it

would be expected that remittance receiving households use this income towards

consuming a better array of goods and services. Specific interest was in knowing if these

households, with expected number of children falling, would invest more towards

education expenses. The second explores these consumption habits with increased

spending on schooling and schooling expense per child as proxy for human capital

investments in the household. It was seen that remittance receiving households tend to

devote a greater share of their consumption expenditure towards education expenses.

These households also invested more in education expenses per child as compared to

non-remittance receiving households. It was seen that this regression model suffered from

endogeneity issues and to treat this problem, two sets of IV analysis were conducted.

When district-wise concentration of scheduled commercial banks was used as an

instrument, the model was undetermined and the impact of remittance on schooling

expenses was inconclusive. The second IV analysis used unemployment and district-wise

concentration of post offices as instruments. Only one of the models was correctly

determined while the second did not produce pass the post-estimation tests. The impact of

remittance receipt on schooling expenses was however seen to be positive, which is a

desirable outcome for sustained development in the migrant-sending community.

131

Future Work

This dissertation can be expanded and improved in at least four ways. First would be to

further explore the impact of migration histories and remittances with respect to

household fertility. Second would be to use better instruments for the education

expenditure models. Third would be to study the impact of remittance amounts on

education expenditures to understand the difference in consumption patterns of

households that receive higher amounts of remittances. Lastly, the study can be extended

to determine other development variables such as propensity to spend on better health

outcomes for remittance receiving households.

The essay on fertility outcomes presented an encouraging picture in terms of

declining likelihood of births. This analysis can be made more conclusive by including

interaction terms that account for migration history of the household and the number of

children born corresponding to each migration period. This is expected to provide a

clearer relationship between migration, remittance receipt and number of children in the

household.

With respect to instruments that can be applied to the fertility and education

models, concentration of western union agencies, intensity of railway networks serving a

district and the distance of a household to the nearest major city center can be used.

These instruments can be good indicators of convenience of remittance transfers between

the destination and the source community. Additionally, data on these instruments is

more readily available than district-wise rainfall data from India.

Since the data also provides information on the amount and frequency of

remittance receipt, consumption habits of households that receive greater amount of

132

remittances can be compared with those receiving lesser monies or monies with lower

frequency. Additionally, a comparison can be also made between education expenditure

and expenditure on consumer durables to see whether households use remittances for

short term benefits or long term benefits.

Another relationship to explore would be the impact of remittances on health

outcomes by using medical expenses as a proxy. Better access to healthcare will be

indicative of higher productivity of individuals in the household due to better health.

Within the scope of the essays in this dissertation, non-migrant sending

households can be included in the analysis to examine their performance on fertility and

education. Propensity score matching can be used for a smaller sub-sample to have this

comparison between the households.91

A separate analysis can be conducted on

international migrants and how the outcomes for households with the latter differ from

households with domestic migrants.

91

Propensity score matching was tried as an evaluation method for the existing

sample but the values for many variables were unbalanced. The implementation of this

methodology would require more work on the available data.

133

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Appendix A

GOVERNMENT OF INDIA

NATIONAL SAMPLE SURVEY ORGANISATION

SOCIO-ECONOMIC SURVEY

SIXTY FOURTH ROUND: JULY 2007 – JUNE 2008

SCHEDULE 10.2: EMPLOYMENT & UNEMPLOYMENT AND MIGRATION

PARTICULARS

[0] descriptive identification of sample household

1. state/u.t: 5. hamlet name:

2. district: 6. ward /inv. unit /block:

3. tehsil/town *: 7. name of head of household:

4. village name: 8. name of informant:

[1] identification of sample household

Ite

no.

item code ite

m

no.

item cod

e

1. srl. no. of sample village/

block

11. sub-sample

2. round number 6 4 12. FOD sub-region

3. schedule number 1 0 2

13. sample hg/sb number (1/2) 4. sample (central-1, state-

2)

5. sector (rural-1, urban-2) 14. second-stage stratum

6. state-region 15. sample household number

7. district 16. srl. no. of informant (as in col.1, bl. 4)

8. stratum 17. response code

9. sub-stratum 18. survey code

10

.

sub-round 19. reason for substitution of original

household (code)

CODES FOR BLOCK 1:

item 17: response code : informant: co-operative and capable -1, co-operative

but not capable -2, busy -3, reluctant - 4, others - 9

item 18: survey code : original – 1, substitute – 2, casualty – 3

item 19: reason for substitution of original household : informant busy -1,

members away from home -2,

informant non-cooperative -3,

others - 9

* tick mark ( ) may be put in the appropriate place.

RURAL * CENTRAL *

URBAN STATE

140

[2] particulars of field operation

sl.

no. item

investigator/

senior

investigator

superintendent /

senior

superintendent

other supervisory

officer

(1) (2) (3) (4) (5)

1. i) name

(block letters)

ii) code

2. date(s) of : DD M

M

YY DD M

M

YY DD M

M

YY

(i) survey/inspection

(ii) receipt

(iii) scrutiny

(iv) despatch

3. number of additional

sheets attached

4. total time

taken to

canvass

(in minutes)

Schedule

10.2

5. block 7 of

schedule

10.2

6. signature

8. Remarks by investigator/ senior investigator

9. Comments by superintendent/ senior superintendent

10. Comments by other supervisory officers

141

Note: 1 acre = 0.4047 hectare, 1 hectare=10, 000 square metre

Codes for Block 3

item 4: household type:

for rural areas: self-employed in non-agriculture-1, agricultural labour-2, other labour-

3, self-employed in agriculture-4, others-9.

for urban areas: self-employed-1, regular wage/salary earning-2, casual labour-3,

others-9.

item 5: religion: Hinduism-1, Islam-2, Christianity -3, Sikhism-4, Jainism-5, Buddhism-6,

Zoroastrianism-7, others-9.

item 6: social group: scheduled tribe-1, scheduled caste-2, other backward class-3, others-9.

[3] household characteristics

1. household size 9

. if

code

1 in

item 8,

location of last

usual place of

residence (

code)

2. princip

al

industr

y

(NIC-

2004)

description:

1

0

.

pattern of

migration (

code)

code

(5-

digit)

1

1

.

reason for

migration (code)

3. princip

al

occupat

ion

(NCO-

2004)

description: 1

2

.

whether any former

member of the

household migrated out

any time in the past (yes

- 1, no – 2)

code

(3-

digit)

4.

household type (code)

1

3

.

if 1 in item

12, number

of members

who

migrated out

male

5. religion (code)

1

4

.

female

6. social group (code)

1

5

.

amount of remittances

received during the last

365 days (Rs.) (to be

copied from entry

against srl. no. 99,

col.10 of bl. 3.1)

7. land possessed as on date

of survey (code)

1

6

.

if entry>0 in item 15,

use of remittances

(maximum three codes

in descending order of

amount used)

8.

whether the household

migrated to the

village/town of

enumeration during the last

365 days. ( yes- 1, no- 2)

1

7

.

monthly household

consumer expenditure

(Rs.) (to be copied from

item 23, block 7)

142

item 7: land possessed (area in hectare):

item. (9): location of last usual place of residence: same district: rural-1, urban-2; same state

but another district: rural-3, urban-4; another state: rural-5, urban-6; another country-7.

item. (10): pattern of migration: temporary-1, permanent – 2

item. (11): reason for migration:

in search of employment –01, in search of better employment – 02, business – 03, to take up

employment / better employment – 04, transfer of service/ contract – 05, proximity to place of

work – 06, studies – 07, natural disaster (drought, flood, tsunami, etc.) –08, social / political

problems (riots, terrorism, political refugee, bad law and order, etc.) –10, displacement by

development project – 11, acquisition of own house/ flat – 12, housing problems – 13, health

care – 14, post retirement –15, marriage – 16, others –19.

Item 16: use of remittances:

for household consumer expenditure: on food items – 01, education of household

members- 02, , household durable –03, marriage and other ceremonies – 04, health care-

05, others items on household consumer expenditure- 06;

for improving housing condition (major repairs, purchase of land and buildings, etc.)- 07,

debt repayment- 08, financing working capital – 10, initiating new entrepreneurial

activity – 11, saving/investment – 12, others – 19.

less than 0.005 …… 01 2.01 – 3.00 ……………. 07

0.005 - 0.01 …….. 02 3.01 - 4.00 ……………. 08

0.02 - 0.20 …….. 03 4.01 - 6.00 ……………. 10

0.21 - 0.40 …….. 04 6.01 - 8.00 ……….…… 11

0.41 - 1.00 …….. 05 greater than 8.00…..... 12

1.01 – 2.00 …….. 06

143

Codes for Block 3.1

col. (4): present place of residence : same state and within the same district – 1, same state but

another district – 2, outside the state – 3; another country – 4, not known – 9

col. (5): reason for migration:

in search of employment –01, in search of better employment – 02, business – 03, to

take up employment / better employment – 04, transfer of service/ contract – 05, proximity to

place of work – 06, studies – 07, natural disaster (drought, flood, tsunami,

etc.) –08, social / political problems (riots, terrorism, political refugee, bad law and

order, etc.) –10, displacement by development project – 11, acquisition of own

house/ flat – 12, housing problems – 13, health care – 14, post retirement –15,

marriage –16, migration of parent/earning member of the family–17, others –19.

[3.1] particulars of out-migrants who migrated out any time in the past (i.e., for

households with entry 1 in item 12 bl. 3)

srl.

no

Sex

(m

ale

-1,

fem

ale

–

2)

pre

sen

t ag

e (y

ears

)

pre

sen

t p

lace

of

resi

d-

ence

(co

de)

reas

on

for

mig

ra-t

ion

(co

de)

per

iod

sin

ce l

eav

ing

the

hou

seho

ld (

yea

rs)

wh

eth

er p

rese

ntl

y

eng

aged

in

an

y

eco

nom

ic a

ctiv

ity

(ye

s

– 1

, n

o –

2, n

ot

kn

ow

n

– 9

)

wh

eth

er s

ent

rem

itta

nce

s d

uri

ng

th

e

last

36

5 d

ays

(yes

– 1

,

no

–2)

if 1 in column 8,

number

of times

remittanc

es sent

during

the last

365 days

amount

of

remittanc

es sent

during

the last

365 days

(Rs.)

1 2 3 4 5 6 7 8 (9) (10)

01.

02.

03.

04.

05.

06.

07.

08.

09.

10.

11.

12.

13.

14.

15.

99.

tot

al

144

Codes for Block 4

col. (3): relation to head: self-1, spouse of head-2, married child-3, spouse of married child-4,

unmarried child-5, grandchild-6, father/mother/father-in-law/mother-in-law-7,

brother/sister/brother-in- law/sister-in-law/other relatives-8, servants/employees/other

non-relatives-9.

col (6): marital status: never married -1 ; currently married-2; widowed-3;

divorced/separated-4

col. (7): educational level - general:

not literate -01, literate without any schooling: 02, literate without formal schooling:

literate through NFEC/AIEP -03, literate through TLC/ AEC -04, others -05;

literate with formal schooling including EGS: below primary -06, primary -07,

upper primary / middle -08, secondary -10, higher secondary -11,

diploma/certificate course -12, graduate -13, postgraduate and above -14.

col. (8): educational level - technical:

no technical education -1, technical degree (graduate level) in agriculture/ engineering/

technology/ IT/medicine/management, etc.-2; technical degree (postgraduate and above

level) in agriculture/ engineering/ technology/ IT/ medicine/ management, etc.-3;

diploma or certificate (below graduate level) in agriculture/ engineering/ technology/IT/

medicine/ management, etc. -4; diploma or certificate (graduate level) in agriculture/

engineering/ technology/IT/ medicine/ management, etc. -5; diploma or certificate

(postgraduate and above level) in agriculture/ engineering/ technology/IT/ medicine/

management, etc. -6;

col. (9): status:

worked in h.h. enterprise (self-employed): own account worker -11, employer-12,

worked as helper in h.h. enterprise (unpaid family worker) -21; worked as regular

salaried/wage employee -31, worked as casual wage labour: in public works -41, in other

types of work -51; did not work but was seeking and/or available for work -81, attended

educational institution -91, attended domestic duties only -92, attended domestic duties

and was also engaged in free collection of goods (vegetables, roots, firewood, cattle feed,

etc.), sewing, tailoring, weaving, etc. for household use -93, rentiers, pensioners ,

remittance recipients, etc. -94, not able to work due to disability -95, others (including

begging, prostitution, etc.) -97.

col. (11): industry: 5-digit code as per NIC –2004.

col. (12): occupation: 3-digit code as per NCO –2004

col. (14): status: codes as in col. 9 of this block (only codes 11 to 51 are applicable here).

col. (16) : industry : 5-digit code as per NIC-2004.

col. (17) : occupation : 3-digit code as per NCO-2004.

145

Codes for Block 5

col. (4) and (18): status:

codes 11, 12, 21, 31, 51 and 91-95, 97 of col. (9), block 4 and also the following codes:

worked as casual wage labour in public works other than NREG public works – 41,

worked as casual wage labour in NREG public works – 42, had work in h.h. enterprise

but did not work due to: sickness -61, other reasons -62; had regular salaried/wage

employment but did not work due to:sickness -71, other reasons - 72; sought work -81,

did not seek but was available for work -82, did not work due to temporary sickness (for

casual workers only) -98.

col. (5): industry division: 2- digit division codes as per NIC-2004.

col. (6): operation (for rural areas only): manual work in cultivation: ploughing -01,

sowing -02, transplanting -03, weeding -04, harvesting -05,

other cultivation activities -06; manual work in other agricultural activities:

forestry -07, plantation -08, animal husbandry -10, fisheries -11, other

agricultural activities -12; manual work in non-agricultural activities -13,

non-manual work in: cultivation -14, activities other than cultivation -15.

col. (19): industry : 5-digit code as per NIC-2004..

col. (20): occupation : 3-digit code as per NCO-2004

146

[4] demographic and usual activity particulars of household members

srl.

no.

nam

e of

mem

ber

rela

tion t

o h

ead (

code)

sex (

male

-1,

fem

ale

-2)

age

(yea

rs)

mar

ital

sta

tus

(code)

educational level

usual principal activity

whet

her

engag

ed i

n a

ny

work

in s

ubsi

dia

ry c

apac

ity

(yes

-1,

no

-2)

for 1 in col. 13, usual subsidiary economic

activity

gen

eral

(

code)

tech

nic

al (

code)

Sta

tus

(co

de)

industry- occupation

Sta

tus

(co

de)

industry- occupation

des

crip

tion

Indust

ry

(NIC

-

2004

5-d

igit

code)

occ

upat

ion (

NC

O-

2004 -

3-d

igit

co

de)

des

crip

tion

Indust

ry

(NIC

-

2004

5-d

igit

code)

occ

up

a-ti

on

(NC

O-2

004

3-d

igit

code

(1) (2) (3) (4) (5) (6) (7) (8) (9) (10) (11) (12) (13) (14) (15) (16) (17)

147

[5] time disposition during the week ended on ………………….

srl.

no.a

s i

n c

ol.

1,

bl.

4

Age

(yrs

.) a

s in

col.

5,

bl.

4 current day activity particulars

current weekly activity

particulars

srl.

no.

of

acti

vit

y

Sta

tus

(code)

for codes 11 to 72

in col. 4 intensity of activity (full-1.0, half-

0.5)

tota

l no.

of

day

s in

eac

h

acti

vit

y

for codes 31, 41, 42, 51,

71, 72 in col. 4, wage

and salary earnings

(received or receivable)

for the work done

during the week (Rs.)

indust

ry d

ivis

ion

(2-d

igit

NIC

-2004

code)

for

rura

l are

as

on

ly,

type

of

oper

atio

n

(code)

7th

day

6

th

day

5

th

day

4

th

day

3rd

day

2

nd

day

1

st d

ay

Sta

tus

(code)

for codes 11-72 in col. 18

cash

kin

d

Tota

l

(15 +

16) industry

(5-digit

NIC-2004

code)

occupation

(3-digit

NCO-2004

code)

1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16 17 18 19 20

description of industry-

occupation:

T 1 1 1 1 1 1 1 7

description of industry-

occupation:

T 1 1 1 1 1 1 1 7

description of industry-

occupation:

T 1 1 1 1 1 1 1 7

description of industry-

occupation:

T 1 1 1 1 1 1 1 7

148

Codes for Block 6:

col. (5): destination during longest spell: same district: rural-1, urban-2; same state but another district: rural-3, urban-4; another state: rural-5,

urban-6; another country-7.

col. (6) and col. (15): industry division: 2- digit division codes as per NIC-2004

col. (9) nature of movement: temporary: with expected duration of stay less than 12 months – 1, with expected duration of stay 12 months or

more - 2; permanent - 3

col. (11): location of last upr: same district: rural-1, urban-2; same state but another district: rural-3, urban-4; another state: rural-5, urban-6;

another country-7.

col. (13): state/ u.t. code:

country code: Afghanistan – 41, Bangladesh- 42, Bhutan- 43, Maldives- 44, Nepal - 45, Pakistan- 46, Sri Lanka –47, Gulf Countries

(Saudi Arabia, Iran, Iraq, Kuwait, UAE and other countries of the region)- 48, Other Asian Countries- 49, USA- 50, Canada-

51, Other Countries of North and South America- 52, UK- 53, Other Countries of Europe- 54, Countries of Africa- 55, Rest

of the World- 99.

col. (14): usual activity (ps) at the time of leaving last upr:

worked in h.h. enterprise (self-employed): own account worker -11, employer-12, worked as helper in h.h. enterprise

(unpaid family worker) -21; worked as regular salaried/ wage employee -31, worked as casual wage labour: in public

works -41, in other types of work -51; did not work but was seeking and/or available for work -81, attended

educational institution -91, attended domestic duties only -92, attended domestic duties and was also engaged in free

collection of goods (vegetables, roots, firewood, cattle feed, etc.), sewing, tailoring, weaving, etc. for household use -

Andhra Pradesh ….28 Gujarat ….24 Madhya Pradesh ….23 Punjab ….03 West Bengal ….19

Arunachal Pradesh ….12 Haryana ….06 Maharashtra ….27 Rajasthan ….08 A & N Islands ….35

Assam ….18 Himachal Pradesh ….02 Manipur ….14 Sikkim ….11 Chandigarh ….04

Bihar ….10 Jammu & Kashmir ….01 Megahlaya ….17 Tamil Nadu ….33 Dadra & Nagar Haveli ….26

Chhattisgarh ….22 Jharkhand ….20 Mizoram ….15 Tripura ….16 Daman & Diu ….25

Delhi ….07 Karnataka ….29 Nagaland ….13 Uttaranchal ….05 Lakshadweep ….31

Goa ….30 Kerala ….32 Orissa ….21 Uttar Pradesh ….09 Pondicherry ….34

149

93, rentiers, pensioners , remittance recipients, etc. -94, not able to work due to disability -95, others (including

begging, prostitution, etc.) -97.

col. (16): reason for leaving the last usual place of residence:

in search of employment –01, in search of better employment – 02, business – 03, to take up employment / better

employment – 04, transfer of service/ contract – 05, proximity to place of work – 06, studies – 07, natural disaster (drought,

flood, tsunami, etc.) –08, social / political problems (riots, terrorism, political refugee, bad law and order, etc.) –10,

displacement by development project – 11, acquisition of own house/ flat – 12, housing problems – 13, health care – 14, post

retirement –15, marriage –16, migration of parent/earning member of the family–17, others –19.

150

[6] migration particulars of household members

srl.

no.

(as

in c

ol.

1, bl.

4)

Age

(as

in c

ol.

5,

bl.

4)

whet

her

sta

yed

aw

ay f

rom

vil

l./t

ow

n

for

1 m

onth

or

more

but

less

than

6

month

s duri

ng l

ast

365

day

s fo

r em

plo

ym

ent

or

in s

earc

h

of

emplo

ym

ent

(yes

-1,

no-2

) if 1 in col.3,

whet

her

pla

ce o

f

enum

erat

ion

dif

fers

fro

m l

ast

upr

(yes

-1, no

-2) if code 1 in col. 7,

num

ber

of

spel

ls

des

tinat

ion d

uri

ng l

onges

t sp

ell

(code)

if w

ork

ed, in

dust

ry o

f w

ork

for

longes

t dura

tion o

f w

ork

(2-d

igit

NIC

2004)

whet

her

the

pla

ce o

f en

um

erat

ion

was

upr

any t

ime

in t

he

pas

t (y

es-1

,

no

-2)

nat

ure

of

movem

ent

(code)

Per

iod s

since

lea

vin

g t

he

last

upr

(yea

rs) particulars of last

upr

usual activity (ps) at

the time of

leaving last upr

reason

for

leaving

the last

upr

(code)

loca

tion (

code)

state /u.t./

country

stat

us

code for codes

11-51 in

col. 14,

industry

division

(2-digit

NIC 2004)

name code

(1) (2) (3) (4) (5) (6) (7) (8) (9) (10) (11

)

(12) (13) (14) (15) (16)

151

[7] household consumer expenditure

srl.

no

. value of

consumption (Rs)

during

item group last 30

days

last 365

days

(1) (2) (3) (4)

1. cereals & cereal products (includes muri, chira, maida, suji,

noodles, bread (bakery), barley, cereal substitutes, etc.)

2. pulses & pulse products (includes soyabean, gram products,

besan, sattu, etc.)

3. milk and milk products (includes milk condensed/powder,

baby food, ghee, butter, ice-cream, etc.)

4. edible oil and vanaspati 5. vegetables, fruits & nuts (includes garlic, ginger, mango,

banana, coconut, dates, kishmish, monacca, other dry fruits ,

etc.)

6. egg, fish & meat

7. sugar (includes gur, candy (misri), honey, etc.)

8. salt & spices and other food items (includes beverages such

as tea, coffee, fruit juice and processed food such as biscuits,

cake, pickles, sauce, cooked meals, dry chillies, curry powder,

etc.)

9. pan, tobacco & intoxicants

10. fuel & light

11 entertainment (includes cinema, picnic, sports, club fees,

video cassettes, cable charges, etc.)

12 personal care and effects, toilet articles and other sundry

articles (includes spectacles, torch, umbrella, lighter,

toothpaste, hair oil, shaving blades, electric bulb, tubelight,

glassware, bucket, washing soap, agarbati, insecticide, etc.)

13 consumer services and conveyance (includes domestic

servant, tailoring, grinding charges, telephone, legal

expenses, pet animals porter charges, diesel, petrol, school

bus/van, etc.)

14 rent/ house rent, consumer taxes and cesses (includes water

charges, etc.)

15 medical expenses (non-institutional)

16. sub-total (items 1 to 15)

17 medical (institutional)

18 tuition fees & other fees, school books & other educational

articles (includes private tutor, school/college fees, newspaper,

library charges, stationery, internet charges, etc.)

19. clothing, bedding and footwear

20. durable goods

21. sub-total (items 17 to 20)

22. average monthly expenditure for items 17 to 20 [item 21 x

(30÷365)]

23. monthly household consumer expenditure (ite16 + it 22)

152

Curriculum Vitae

Date of Birth- 8 May, 1985

Place of

Birth-

Ludhiana, Punjab, India

Education-

2001-03 Modern School, Lucknow, Uttar Pradesh, India

2003-06 Maitreyi College, University of Delhi, New Delhi, India

Degree- Bachelor of Arts (Honors) Economics

2006-07 Lucknow University, Lucknow, India

Degree-Post-graduate Diploma in Social Duties and Human Rights

Award- Gold Medal for Academic Excellence

2007-09 Rutgers University, Newark, New Jersey

Degree- Master of Science in Global Affairs

2009-13 Rutgers University, Newark, New Jersey

Degree- Ph.D. Global Affairs

Award- Dissertation Fellow, 2012-2013

Employment-

September

2007-May

2009

Center for Law and Justice Library, Rutgers-University, Newark,

New Jersey

Technical Services Cataloging Assistant

May 2008-

September

2008

Consulate General of India, New York, NY

Commerce Division Intern

February 2009-

May 2010

Division of Global Affairs, Rutgers University, Newark, New

Jersey

Graduate Research Assistant

May 2010-

August 2010

Diversity Inc. Media LLC, Princeton, New Jersey

September

2010- June

2012

Department of Economics, Rutgers University, Newark, New Jersey

Teaching Assistant and Instructor of Economics

January 2013-

May 2013

Department of Economics, Rutgers University, Newark, New Jersey

Adjunct Faculty