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©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
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