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The Impact of Fertility Rate on the Education Attainment Level of Children
in the State of Uttar Pradesh, India: The Quantity-Quality Tradeoff
Senior Thesis
Presented to
The Faculty of the School of Arts and Sciences Brandeis University
Undergraduate Program in Department of Economics
Professor Kathryn Graddy, Advisor Professor Elizabeth Brainerd, Advisor
Professor Sarah Lamb, Reader
In partial fulfillment of the requirements for the degree of Bachelor of Arts
by Neelanjana Gupta
May 2013
Copyright by Neelanjana Gupta
Abstract
India’s total fertility rate (TFR) has fallen by 19% over the last ten years. Between 2000-2010 the percentage decline in TFR in the state of Uttar Pradesh has been 23%. Meanwhile, literacy rate has risen by 13.5% in the state over the last decade. Using the framework suggested in Becker’s Quantity-Quality Tradeoff Model (1960), this study provides evidence of a correlation between a woman’s fertility rate and the education attainment level of the children in the household. As there are more children born into a household, the resources of the parents get divided amongst the children. Thus, expanding the family size has worsening prospects for the children. Keywords: Fertility, Education, Family size, Quantity versus Quality !
Table of Contents Acknowledgments………………..………………………………………………………………..3
Personal Motivation……………………………………………………………………………….4
Introduction………………………………………………………………………………………..5
Poverty in India—The State of Affairs...……………….………………………………..……..…6
The State of Uttar Pradesh………….......………………………………………………….……...7
Fertility………….…...…………………………………………………………………………….9
Child Labor………….…...…………………………………………………………………..…..11
The Right of Children To Free And Compulsory Education Act, 2009………….…...………....12
Gender Bias………….…...………………………………………………………………………13
Literature Review…....……………………………………………………………………...……14
Data.……………..…….……………...….…..…..………………………………………………28
Variables.………..…….……………...….…..…..………………………………………………30
Methodology………….…...…………………………………………………………………..…33
Results..………….…...………………………………………………………………………..…36
Discussion and Critique………….……………...….…..…..……………………………………44
Conclusion ……...…….……………...….…..…..………………………………………………47
Further Analysis: Thoughts for Further Study of the Topic …...………………….…………….48
Tables……………..……………………………………………………………………..……….50
Appendix A: Statistics for India and Uttar Pradesh………….…...………...……...…...……..…62
Bibliography…....……………………………………………………………………………..…65!
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Acknowledgments This study would not have been possible without the guidance of my primary advisor,
Professor Kathryn Graddy. She encouraged me all along to think creatively and analytically
about such a routine subject as the topic of my thesis would appear to be. I thank her for her
excellent mentorship, inexhaustible patience, and accessibility.
I would like to express my deep gratitude to Professor Elizabeth Brainerd for her
continuous support and innovative ideas over the three years that she has advised me. The
Economics of Race and Gender course, specifically focusing on Becker’s model, taught by her
stimulated my thinking about women’s economic difficulties.
I am thankful to the TA, Jeremy Kronick, for his assistance with the data analysis. I thank
him for being so generous with his time, and for suggesting to me several more ways of
analyzing my data and undertaking my study.
I extend my heartfelt gratitude to Professor Sarah Lamb, my third Reader from the
Department of Anthropology. Her knowledge of India proved to be invaluable for my study. I
thank her for her contribution.
I also thank Ms. Meredith Robitaille for her constant reminders to make sure I keep up
with the deadlines. In addition, I thank Natasha for taking out the time to proofread and
appreciate my work- her inputs were very helpful. I am grateful to my friends- Jay, Nabila,
Robyn, and Sarah for their friendship and company all along.
I dedicate my thesis to my wonderful parents for supporting me, as always, also in my
college years and urging me on. They have criticized me and loved me, seen me fall and helped
me stand, and worried for me, yet trusted me. I thank them for their love and encouragement,
instilling in me the value of education, and having faith in me.
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Personal Motivation
Born and brought up in New Delhi, India, I was not ignorant of the sufferings of the less
fortunate people of my society. On the one hand, I was provided with the best resources
necessary for my personal development that included exposure to quality education at one of the
premier academic institutions of New Delhi. On the other hand, I also had occasion to witness
the misery and deprivation of many children from the slums who do not have a roof above their
heads or enough food to last them through the day, let alone other basic and necessary facilities
such as education and healthcare.
A burning question arose again and again in my mind at that time—why the deprivation?
This study is a first step to answer that question. My research for the study has driven me to
analyze how the number of children in a household is an important factor that contributes in the
decision making process of the parents, which in turn inevitably affects the level of education
attained by the child. The resources of the parents get diluted with a higher number of offspring,
deterring the child’s access to assets considered necessary for a healthy and human existence.
Instead of attending school, children are compelled to engage in unskilled manual labor to
supplement the income of the family. I have observed that child labor and poverty are
inextricably linked. Parents in low-income households are forced to send their children to work
out of economic necessity. The children are rendered incapable of reaching their maximum
potential. Hence, I have strong reasons to feel convinced that the uncontrolled number of
offspring in poor households creates a vicious circle of poverty. This study is an attempt to
discover some of the true causes of poverty in India. Hopefully, that would be a beginning for
the true solutions also.
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Introduction
“The ‘capability’ of a person is a concept that has distinctly Aristotelian roots. The life of
a person can be seen as a sequence of things the person does, or states of being he or she
achieves and these constitute a collection of ‘functionings’- doings and beings the person
achieves. ‘Capability’ refers to the alternative combinations of functionings from which a person
can choose. Thus, the notion of capability is essentially one of freedom- the range of options a
person has in deciding what kind of life to lead.”—(Drèze and Sen, 1995)
In 1960, Gary Becker studied the importance of understanding fertility by observing the
interaction between child quantity and quality. A decade later, Becker and Lewis established that
“one can only cite a negative correlation between quantity and quality of children per family”
(Becker and Lewis, 1970). Parents reallocate resources consistent with Becker’s Quantity and
Quality model when they make a decision regarding changing their family size, i.e. how many
children to have. Most studies in the past have assumed that couples agree to have fewer children
in order to provide a higher quality of life to their offspring. The ‘dilution model’ (Blake, 1981)
suggests that a higher number of children in a household implies a lower quality of life for each
child. When making decisions regarding family size, most background factors are fixed, but it is
imperative to study whether or not parents can provide their offspring with a decent standard of
living— and this largely entails the child’s education attainment level.
This paper seeks to add to the existing literature on this recurring household debate. It is
an attempt to study whether or not family size is inversely related to the quality of life of the
child—specifically to the highest level of education (in number of years) that a child receives.
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This study uses the household level National Family Health Survey Data (International
Institute for Population Sciences) from the years 1992-93, 1998-99, and 2005-06 to look at the
impact of fertility on children’s education attainment level in a household. We expect a negative
relationship between the number of children in the household and the level of education attained
by each child. The data set used in this study is specific to the state of Uttar Pradesh, India, a
state that has seen significant demographic transition between 1990 and 2010. The analysis is
carried out using an ordinary least squares model.
This paper is structured as follows: a short history of India, and specifically, Uttar
Pradesh’s demographic structure and transition over the years are presented. Next, I have
explained various other factors contributing to children’s education attainment level with context
to India. Following that is a review of literature of similar studies. Then, the data and variables
are described along with the methodology used to analyze the subject, and the results follow. A
critique of the analysis and a short conclusion are presented. At the end, I have discussed
possibilities for further study.
Poverty in India—The State of Affairs
In the context of India, poverty has been studied through the lens of ‘capability
deprivation’. Human Development Indicators are dismal for the second most populated country
in the world with 32.7% of the population living below the international poverty line.i
The Oxford Poverty and Human Development Initiative and the United Nations
Development Program developed the Multidimensional Poverty Index (MPI) replacing the
Human Poverty Index (HPI) in 2010. It was an attempt to determine poverty beyond income-
based lists. The MPI uses the same dimensions as the Human Development Index (HDI)—health
(child mortality and nutrition), education (years of schooling, children enrolled), and standard of !!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!i World Bank, 2010
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living (cooking fuel, water, electricity, toilet, floor, and assets). It is an index of acute
multidimensional poverty. While the HPI is an indicator of the standard of living in a country,
the HDI is a synthesized statistical measure of longevity, knowledge, and income indices that
ranks countries into four tiers of human development. Thus, the HDI better reflects the extent of
deprivation in developing countries compared to HDI. As of 2005, India’s MPI was 0.283, and
53.7% of the population was expected to be poor.ii
Uttar Pradesh, the most populated state in the country, accounts for almost 70% of the
country’s poor population. About 134.7 million people are expected to be MPI poor, contributing
to 21.3% of overall poverty in the country The MPI for the state of Uttar Pradesh is 0.386, the
fifth highest in the country.iii It indicates that the MPI poor suffer from deprivation in 38.6% of
the indicators.
The State of Uttar Pradesh
Historically, Uttar Pradesh was considered to be the pacesetter for India’s economic and
social development. Rich in human resources and natural resources, the state was at the peak of
development in the 1980s with large amounts of money invested in encouraging agricultural
research, expansion, building roads: thus promoting irrigation and improving infrastructure. By
the end of the 1980s, growth accelerated, and the incidence of poverty fell. However, twenty
years down the line, today, Uttar Pradesh shows less promise. After 1990, the state has fallen
behind as its economic growth has faltered. When several government reports brought attention
to this, efforts were made to address the problem, but the level of poverty did not change. The
absence of agency, vulnerability, and social exclusion have added to the material and human
deprivation of the people in the state. In order to level with the goals of the Government of
!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!ii UN MPI: 2011 Data. Oxford Poverty and Human Development Initiative iii UN MPI: 2010 Data. Oxford Poverty and Human Development Initiative!
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India’s Tenth Five Year Plan and the United Nations’ Millennium Development Goals, it is
essential that the problem be addressed at its roots for multidimensional progress. For a state that
is larger than many other countries, it is a matter of global significance to meet this challenge.
Selected indicators for the state of Uttar Pradesh and India can be found in Appendix A. As seen
in Table 1, the population of Uttar Pradesh was 199,581,471 in the year 2011.iv An estimated 8%
of the world’s poor live in the state of Uttar Pradesh alone.v A large North Indian state such as
Uttar Pradesh can be considered to be in the same league as the world’s least developed countries
in terms of all demographic indicators (Murthi, Giao, and Drèze, 2009).
The 2011 Population Census showed a 75% increase in literacy rate in the state of Uttar
Pradesh between 1991 and 2011, a significant progress; however, it still falls below the all-India
average of 74.04% (Table 4). The female literacy rate was estimated at 43%, in comparison to
the all-India average of 54% (Table 2). The school enrollment rate has also increased, but the
less fortunate children are not as likely to attend school (Table 7). In the late 1990s, only half the
girls among the poorest 20% were enrolled in school; whereas for the wealthiest 20% of the
households, this number was approximately 85%. Indeed, just as elsewhere, poor men and
women are highly vulnerable. At the household level, it gives rise to hardships such as lack of
access to basic amenities for growth and development. Studying selected indicators for human
development show improving results. In Uttar Pradesh, life expectancy at birth, 60 years, and
infant mortality rate, 61 per 1000 live births—both are above the all-India average of 63.5 years
and 47 per 1000 live births respectively. Even birth rate and death rate are higher than the all-
India averages (Table 3).
!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!iv Economic Survey of India 2010-11, Government of India v Based on international poverty line of $1.08 per person per day, 1998 estimates
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Between 1990 and 2000, the state’s expenditure on elementary education increased by a
small margin, from 1.7% of GSDP (gross state domestic product) to 1.8%. Thus, there is an
urgent need to upgrade educational performance in Uttar Pradesh. This requires additional
expenditure that will improve physical, economic and social access for all children, especially to
those from poor and socially isolated families. When making household decisions, poor couples
fail to realize that the expected returns to sending a child to school and educating him are more
than the opportunity cost of child’s labor along with the cost of schooling. Given their limited
resources, these families cannot afford books, school supplies and uniforms. Studies done in
other countries have shown that government-funded scholarships work in favor of female
children and other minorities, and raise the enrollment rate in school. Once these changes have
been implemented, it is expected that incidents of child labor will drop. Later, the issue of
improving the quality of teaching can be addressed after expanding school access to children.
Uttar Pradesh has failed to sustain family planning programs and make imperative
advances in education. This makes the state an intriguing environment to study the tradeoff
families have to make in order to provide a standard quality of life for all members of the
household.
Fertility
Given the current demographics of the country, it is hard to believe that India was one of
the first few countries in the world to introduce a national family planning program.
“Development is the best contraceptive” (Drèze and Murthi, 2001). There is a need for
immediate social development, which would supplement economic growth with drastic changes
in the field of public health and elementary education. Over the years, many Indian states have
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made reasonable progress with the decline in fertility: however, absolute figures of fertility rate
still remain alarming.
Total fertility rate (TFR), or fertility rate, is defined as the average number of children
that a woman bears during her reproductive lifetime, given that—she experiences the exact
current age-specific fertility rates through her lifetime, and she survives all the births through the
end of her reproductive life. The TFR for Uttar Pradesh was 4.2 in 2005 and fell to 3.3 in 2011;
India’s TFR was 2.9 in 2005 and 2.4 in 2011. In Uttar Pradesh, while the total fertility rate in
rural areas was 3.7, and in urban areas it was 2.7 in 2011.vi
Female education plays a key role in social development. Despite vast amounts of
literature in the field, the association between female education and low fertility is often
confused, and remains unclear.
An increase in female education reduces desired family size. An educated woman is more
aware of modern social norms, feels economically independent and secure about her future, and
incurs a high opportunity cost of time spent at home (considered to be unproductive labor work
that does not add value to the economy since consumption is greater than production). While
improvements in male education also decrease fertility, the influence is smaller compared to that
for females since women are assumed to bear the primary responsibility of childcare. In
developing countries, on the one hand, a higher income makes it more affordable to have
children, on the other hand, there are also negative income effects associated with fertility rate.
Female literacy has a significantly negative effect on the fertility rate, after controlling for
male literacy. An increase in adult female literacy from its base level of 22% (1981), to 65%
(2001) would reduce TFR by one child per woman (Drèze and Murthi, 2001). Fertility decline is
!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!vi National Family Health Survey-3, International Institute of Population Sciences (IIPS), Mumbai, designated by the Ministry of Health and Family Welfare, Government of India, 2011.
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not just a byproduct of economic growth; it depends on improvements in the specific conditions
that are conducive to social development.
Child Labor
The 2001 Population Census showed that the number of child laborers in India increased
by 11.61%, from 11.28 million (1991) to 12.59 million (2001) (Appendix A). Today, India is
home to the largest number of child laborers in the world. When making household decisions,
poor couples fail to realize that the expected returns to sending a child to school are greater than
the opportunity cost of child’s labor. Children in rural India are engaged in paid or unpaid forms
of unskilled manual labor. This is a violation of children’s rights. In poor families, children are
forced to stay out of school, and they are seen as extra earning hands in the family, employed on
a casual basis with low wages and long work hours. Despite the government’s intervention
program, established in an effort to abolish child labor, a significant number of children still
remain under the evil shadow of child labor.
Interestingly the percentage of child laborers is not uniform across states in India: in fact,
Uttar Pradesh accounts for the largest share of children’s workforce (Table 8, Appendix A).
Poverty and the absence of quality universal education are two leading causes of child labor.
Privatization of basic services has further widened the income gap between the rich and the poor,
which has, as a result, affected children aged between 4 to 18 years more than any other age
group. Children are discouraged from staying in school and are more likely to enter the work
force because of limited academic and school enrollment opportunities. In many cases, female
children are unwillingly forced into domestic labor in their own homes to carry out daily
household chores and look after younger siblings. It is unclear that whether or not parents choose
to have more children so they can put them to child labor and earn additional income.
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Many families live below the poverty line and send their children to work. Appendix A
shows that Uttar Pradesh accounts for 22.4% share of child labor in India (2004-05). 61.24% of
the child laborers in the state of Uttar Pradesh are engaged in agricultural activities, while others
are involved in glass, carpet and bangle industries, firecracker factories, and other unorganized
sectors.vii The United Nations Children’s Fund (UNICEF) is preparing to implement programs in
the state with the aim of: reducing gender disparity, promoting access to education for
disadvantaged children, and delivering quality education.
The Right of Children To Free And Compulsory Education Act, 2009
Since the inception of the Republic of India, the government has made provisions for
establishing equal opportunities to all individuals. In 2009, the Government of India enforced
The Right of Children To Free and Compulsory Education Act. The Directive Principles of State
Policy enumerated in the Constitution of India that “the State shall provide free and compulsory
education to all children up to the age of fourteen years”.viii This legislation identifies the
importance of strengthening the social fabric of democracy. With the insertion of article 21A in
the Constitution under the 86th Amendment, it became imperative that the State provide
education and implement this provision under the law.
Universal elementary education plays a crucial role in development and growth. Over the
years, elementary schools in India have expanded immensely; however, access to basic
elementary education remains a distant dream for the economically weaker section of the society.
In some cases, children are made to drop out of school even before completing elementary
education.
!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!vii See Appendix A: Sectoral Distribution of India’s Child Labor, 2004-05 viii The Constitution of India. Delhi: Manager of Publications, 1949
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Under the ‘compulsory education’ provision the Government is obligated to provide and
ensure admission, attendance, and completion of elementary education to all children up to the
age of fourteen years. The law ensures that a child’s family is not liable to pay a fee or incur
expenses that would prevent the child from pursuing and completing his elementary education.
The legislation aims at creating a just and humane society that can be achieved only through the
provision of inclusive elementary education to all. It is the government’s responsibility to make
accommodations for free and compulsory education of satisfactory quality for the children from
disadvantaged and economically weaker section of the society.
It is disappointing to see that despite such arrangements a large number of children,
especially from the less fortunate families, drop out of school. Unfortunately, this study does not
show the impact of this legislation on education attainment level of the children. Data is
available for the years 1992-93, 1998-99, and 2005-06. Since this legislation was introduced in
2009, more recent statistics may show improved results.
Gender Bias
“The bias against the girl child is reflected in every indicator of basic education both in
rural and in urban areas. The neglect of girl’s education is greater in rural than in urban areas”
(Mehrotra, 2006). Eliminating gender bias from the society can help catalyze economic growth.
Ensuring that females have equal rights as males, such as the right to possess and inherit land,
will lead to security and economic independence of women.
In September 2005, the Government of India amended The Hindu Succession Act in an
attempt to abolish gender discriminatory provisions in the previous versions of the law. Under
the new revised legal framework daughters are given equal rights as sons to inherit ancestral and
family property coming from their parents. Seeing that parents invest more in their male
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offspring, this legislation is expected to improve conditions for the female child, who is
otherwise denied proper nutrition and education, thus worsening future prospects for females.
Literature Review
Previous researchers have suggested various implications of an increase in family size on
the number of years of education a child receives. Apart from this significant factor, I discuss
how birth order, income, quality of education (achievement rate and success rate), education
attainment level of the parents, gender of child, women’s literacy and female labor force
participation have affected the education attainment level of children.
a. Family size and Birth Order
Becker and Lewis (1970) introduced the relationship between child quantity and child
quality. The shadow price of children, i.e. the cost of an additional child, holding their quantity
constant, is proportional to the quality. With a higher number of children in the household, it is
more expensive to increase the quality of each child. The increase in quality has to apply to more
units. Accordingly, it is more expensive to increase the quantity of children in the household if
the existing children are of higher quality, since higher quality children cost more.
Black, Devereux and Salvanes (2004) studied the effect of family composition on
children’s education in Norway. Analyzing the much-speculated tradeoff between child quantity
and quality within a family, the study concluded that family size impacts the marginal child
through the effect of birth order. However, once birth order is controlled for, there is a negligible
causal effect of family size on education attainment level. This can be studied using two different
approaches—first, by including controls for family background characteristics and birth order,
we see that family size effects are weak once birth order is controlled for; and second, the birth
of twins is included as a source of exogenous variation in family size. Overall, on controlling for
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birth order the study finds that family size has a negligible effect on children’s education
attainment level. The study also introduced a certain Optimal Stopping Model. It suggests that if
a “good quality” child is born first, this may induce parents to have more children who will not
be of as high quality. In contrast, if early children are “poor quality”, parents may decide to
discontinue child bearing. Thus, according to birth order effects, earlier-born children have better
endowments. Iacovou’s (2001) study in Britain also suggests that later-born children have poorer
outcomes than earlier-born. There is a steady decline in education received by every successive
child. Hence, the effect of being a “second child” is large and negative for all family sizes.
Lee (2007) examines the trade-off between child quantity and quality and finds that while
the first child’s gender is an indicator of sibling size and fertility timing, a higher number of
children has adverse effects on per-child investment in education. In African and South Asian
countries, a low literacy rate among women results in a higher fertility level. A higher number of
siblings in a household exerts a negative effect on each child’s educational attainments—this is
called the dilution effect. Overall, the study concludes that lower fertility rate leads to a higher
investment in children’s education.
In Buenos Aires, Argentina, Lanus (2009) studied the effect of overwhelmed housing on
children’s educational attainment and attendance. Using a linear probability model it showed that
there is a strong negative relationship between living in a house with more than two people and
the probability of completing secondary education and high school attendance. Several factors-
in-school (better teachers, better schools, pedagogical improvements) and out-of-school (peer
effect, neighborhood, housing or family characteristics) contribute to higher education attainment
level. Lanus observes a statistically significant association between poor quality housing and low
education attainment level. This is alarming given that the education system in Argentina is such
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that there is universal enrollment up to the age of 13 years, irrespective of the family income.
The study controls for the age of youth, total family income, ownership status of the house, and
the education level of the parents. The findings reveal that a 17 year old is around 17% less
likely to attend school in comparison to a 13 year old. The age coefficients remain consistent- as
young people get older they are less likely to remain in school because they either they move
into the work force or they dropout. While there is a strong positive relationship between school
attendance and household income, there is a negative and highly statistically significant
association between school attendance and overcrowded houses. The model explains 11%
variation between overwhelmed housing condition and school enrollment of the children.
In India, Bhat (2002) observes an unfavorable effect of the family size on child schooling,
especially for the female children and the first-born of either sex. In the case of large families,
the girls and the first-borns are either not sent to school or withdrawn early from school,
considering the existing low family income, or to look after their younger siblings. Thus, this
relationship shows that the first female child stands to gain from decline in fertility rate. Children
from large families receive less schooling because of resource constraints. There are direct and
indirect costs involved with raising children. Bhat provides evidence for peasant families where
high levels of child mortality are observed. Parents prefer to retain children in traditional
occupations since historical statistics show that not many children are expected to survive till
adult ages. Couples are forced to make efforts to reduce fertility in an attempt to achieve higher
level of schooling for their children. With binary dependent variables, a logistic regression has
been used for separate analysis for the first son, later-born sons, the first daughter, and later-born
daughters. Family size has a strong negative effect on the current school enrollment of the
children. The resource dilution in a large family affects the schooling of girls more than boys
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because in the Indian context girls are more unwanted. Family size also has a significant negative
effect on the completion of primary-level schooling, especially among female children and the
first-born child of the household.
Using and supporting the models of Becker (1960), Becker and Lewis (1973), and Willis
(1973), Hanushek (1992) studied how birth rates could fall with increasing income even though
children are not inferior goods. Parents do not show favoritism to first-born children: they treat
all children “evenhandedly”- no special attention is purposely given to the first-born or to the
youngest child of the family. However, data shows that being early in birth order implies a
distinct advantage, entirely due to a higher probability of being in a small family. A tradeoff
occurs because parents’ time and other resources must be spread thinner with more children in
the household. In large families, while the first-born has an advantage (access to a smaller family
and more household income) early on in life, the last-born has the same advantage later in life.
Offspring are expected to provide economic and biological benefits to parents in the long
run. While these benefits increase with offspring number and quality of the children, and time
and resources are limited, parents face a tradeoff between having fewer “high-quality” versus
more “low-quality” offspring. Better-educated children can obtain higher-paying jobs, as
opposed to more less-educated children who work on the family farm. Hagen, Barrett and Price
(2005) analyze the impact of the number of siblings on children’s anthropometry—the
measurement of the human individual. The study uses anthropometry as a proxy for child’s
physical and mental fitness in the Shuar community in Ecuador. They find that large family sizes
have a direct negative impact child anthropometry, specifically because it is more difficult to
feed larger families, let alone providing other necessary resources to the children in the
household.
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If there is a “quantity-quality trade-off”, then policies that discourage large families
should lead to increased human capital, higher earnings, and—at the macro level—promote
economic development. Angrist, Lavy and Schlosser (2005) look at the causal effect of family
size on completed educational attainment, fertility, and earnings. As parents become wealthier
they demand children of higher “quality” (more productive children), without necessarily
demanding more of them. An increase in quality can be interpreted as making children more
expensive, thus the quantity-quality tradeoff explains why families might get smaller as parents
get richer. Using uniquely constructed datasets by linking the Israeli Census Data with the
demographic structures of the family, the outcome variable of interest captures the effects of
family-size on economic well being and social status. Estimates of effects of family size on the
level and quality of schooling are very close to zero. There are negative effects of having three or
more children on completed educational attainment; effects on the probability of having any
children or having more than two children are small and not significantly different from zero.
The absence of an adverse effect of family size on child quality in this sample is noteworthy in
view of the non-western characteristics of the population and the efforts made to promote smaller
families in many developing countries. While the OLS estimates show strong adverse effects, IV
strategies show little evidence for a quantity-quality tradeoff. IV strategies imply a causal link
between sibship size—the number of children produced by a pair of parents—and outcome
variables describing the human capital, earnings, or social status of first- and second- born child.
Bhamarbagwala and Ranger (2009) noted that in the last five decades, India has
experienced two striking demographic features: a rapid decline in fertility and falling female-
male child ratios. India’s sex ratio, i.e. number of female to males (927:1000), is sufficiently
lower than that for the United States (950:1000). Using the ‘intensification effect’ the study
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suggests a positive correlation between family size and female-male offspring sex ratios. It exists
for all social groups, irrespective of education level of the parents, wealth, sector of residence,
and maternal wellbeing. While maternal education, maternal undernourishment, and urban
residence weaken the intensification effect for most social groups, higher paternal education and
greater wealth strengthen it for all groups. The study concludes that three or more children
exhibit gender equality in offspring sex ratios. However, in families with one or two children,
there are less than 800 daughters for every 1000 sons. Upon simultaneously estimating family
size and sex ratios as a function of socioeconomic characteristics of household and identifying
variables that affect both outcomes, the coefficient estimates in this study cannot be interpreted
as causal due to the possibility of reverse causation. This analysis provides evidence of a robust
positive correlation between the family size and female-male offspring sex ratios.
Qian (2009) studies exogenous changes in family size caused by the relaxation of China’s
One Child Policy. She estimates the causal effect of the family size on the school enrollment of
the first child. Using time variation in China’s one-child policy, as well as multiple births to
estimate the effects, her analysis suggests that the relaxation of the One Child Policy increased
the school enrollment rate of the first-born children. An additional child significantly increased
the school enrollment of the first-born children, by approximately 16 percentage points. Both
China and India, the world’s two most populous countries, have experimented with different
family planning policies to limit the family size. Standard theoretical models that predict the
quantity-quality tradeoff often assume that the cost of child quality and child rearing increases
with the number of children. Qian’s study, however, contradicts this; she find that there are
economies of scale in raising children. For households with two or more children, the number of
siblings is negatively correlated with the quality of child; however, children with no siblings do
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worse than children with one or two siblings. If parents are more likely to have a second child
when the first child is of high quality, the OLS estimate of the family size effect will be biased
upwards. This model allows for economies of scale and shows how having a second child could
increase the school enrollment of the first child.
Caceres (2004) observed that when making decisions regarding family size, parents
reallocate resources consistent with Becker’s Quantity and Quality model. For large families,
particularly generated by a twin, there is a lower chance that the older children attend private
school. Caceres’ “quantity-quality” model finds a negative influence of family size even on
measures of child wellbeing, such as private school enrollment rate. Caceres uses a bivariate
regression model, restricted to the oldest siblings in households that are not from a multiple
birth—since being part of a multiple birth or being a younger sibling is conditional on the
occurrence of multiple births in the household. The trade-off is expected to be lower for the
oldest child, since the first-born child would belong to a smaller family than the rest of the
siblings, thereby generating an advantage for them.
Rosenzweig and Wolpin (1980) used multiple births to analyze the quantity-quality
tradeoff in a small sample from India. Their estimates point to a negative effect of multiple births
on education attained by the child, but their sample consisted of children who may not have
completed their schooling, and included children born after a multiple birth as well. Parents want
to provide an environment that fosters high, yet equal quality for each child. A rise in income
reduces fertility: thus, quality and the number of children tend to be negatively correlated across
households. The analysis went on to explain that households automatically adopt the stopping
rule—they reduce the number of pregnancies below the optimal when twin births occur prior to
the optimal pregnancy, thus leading to a decrease in subsequent fertility. Their study confirms
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the hypothesis that an exogenous decrease in family size would increase schooling levels of
Indian children.
As mentioned earlier, the dilution model, introduced by Blake (1981), suggests that
parents provide environment, opportunities and treatment in different ways—personal attention,
intervention, or teaching their children. It is only correct to assume that causal arrows are
unidirectional, from the parents to the children. Child quality tends to go down with each
successive child, but the rate of decline tapers off after the second child because each successive
child experiences less of a loss. However, consistent with the family-size-decision-making model,
increasing the family size has a significant negative influence on the quantity of children. Thus,
for couples, choosing their family size can improve the quality of their children. Data shows that
a single child is not disadvantaged; it may be easier to avoid the negative consequences of larger
families, even if one is well off.
In summary, the literature suggests that large families are more detrimental to a child’s
education attainment level.
b. Income
The observed income elasticity of demand for quality of children is high whereas the
observed income elasticity of demand for quantity of children is low and often negative. Becker
and Tomes (1976) suggested that an increase in the rate of growth of income over time has
additional implications because it increases the endowment of children relative to the income of
their parents. An increase in child endowment reduces a parent’s investment in the child, which
reduces the shadow cost of the children produced. Therefore, the number of children would be
positively related and parental investment per child would be negatively related to the rate of
growth of income. Parents invest more human capital in better-endowed children—in accordance
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with the ‘rotten-kid’ theorem (Becker 1974). The rotten-kid theorem suggests that even selfish
children take account of their parents’ desires if they receive transfers form their parents. Better-
endowed children would recognize that their parents invest more human capital in them.
Lanus (2009) found that the housing conditions have stronger relationships with positive
educational achievement. It is not the lack of adequate housing that causes a hypothetical
detrimental effect on the educational attainment but rather the unobservable factors that influence
both educational outcomes and housing characteristics. On accounting for observed household
characteristics, such as income, helps reduce the extent of any bias when estimating the
relationship between the quality of housing and educational outcomes. “A decent place for a
family becomes a better platform for dignity and self-respect and a base for hope and
improvement”.ix Home ownership has a positive effect on educational outcomes, measured by
the years of schooling, chances of attending high school, and a negative effect on the probability
of being a welfare recipient. Living in an overcrowded space is a source of stress and favors
illness linked to anxiety; family members transmit their infections to one another more easily,
weakening each other’s immune systems. Thus, children’s educational achievements are also
strongly correlated with those of their neighbors.
Hagen, Barrett and Price (2005) studied the tradeoff in the Shuar community in Ecuador
by operationalizing family wealth in different capacities such as family garden productivity,
father’s wealth, and father’s social status in the village. Parental investment theory assumes a
tradeoff between the quantity of offspring and their quality. As family size grows, more hectares
can be brought under cultivation. Thus, garden productivity may simply increase to
accommodate the family size. The increasing evidence for the negative impact of family size on
child growth suggests that these variables are promising candidates for inclusion in such models. !!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!ix National Housing Task Force, 1988, 3
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In rural China, parents rely on their children for income during old age. As a result, an
additional child means an increase in permanent income (Qian, 2009). This has led to an increase
in the school enrollment rate, assuming that parents are not credit constrained and view
children’s schooling as a form of consumption by parents themselves. The primary results are
driven by an income effect—if parents are not credit constrained, any income effects should
cause a positive effect on the enrollment of the first child.
c. Education Quality
Lunas (2009) observed that there is a statistically significant association between poor
quality housing and poor educational attainment in Buenos Aires, Argentina. Although there is
universal school enrollment up to the age of 13 years irrespective of family income, there are
disparities in access to schooling for the lowest socioeconomic classes and performance in
secondary level education, especially in the last three years of schooling. Finally, the education
that the young people are receiving has produced very poor results.
Hanushek (1992) developed a straightforward maximization model in which parents
choose time allocations to maximize an objective function—the total academic achievement of
their children. Parents make time allocations based on the ‘then-existent’ number of children.
Parental optimizing decisions are allocations of two types of educational inputs: public time and
private time. Consumption of public time by one child does not lower the amount of time
available to other children. Private time is more expensive, since private time for one child
subtracts from the total time available to other children.
Kingdon’s (1996) analysis of the quality and productivity of public and private school
education indicates that the quality aspects of education deserve attention. Critiquing the
literature, she has focused on economic consequences of how the number of years of education
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may be an imperfect indicator of human capital acquired if schooling quality varies greatly, as in
the case of many developing countries. Kingdon successfully documented evidence for India,
pointing to very low schooling quality, given educational inputs (poor teaching materials) and
educational outputs (cognitive achievement levels). For Uttar Pradesh specifically, the popularity
of fee-charging private schools is explained by their superior quality. Government and privately
aided schools are similar in their cost-efficiency but compare unfavorably with private unaided
schools. Thus, the quality and cost-efficiency of government-funded schools needs to be
improved as the state is forgoing economic growth because of its poor quality of investment in
education.
Caceres’ (2004) study shows a negative correlation between family size and child
achievement while implying a causal relationship. In a household where siblings interact, they
learn from each other such that the “price” of quality could decrease with the family size. The
older siblings are more likely to obtain skills that could be highly profitable to them in the future.
d. Gender
Lee’s (2007) study in South Korea uses an instrumental variable for fertility—first child’s
sex. Preference for sons leads to a certain pattern in family planning practices. Parents want to
have another child in families where having a son is more preferable than having a daughter, and
the first child is not a son. Thus, as long as the first child’s sex is not an indicator of parental
investment in children’s education, it remains a good IV predictor for the actual number of
children in a family. Given same-sex children in a household, parents are more likely to have
additional children.
Wealth and the education level of parents increase parents’ access to and affordability of
sex-selection technologies. These have allowed parents to choose both the sex of their children
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and the desirable family size (Bhamarbagwala and Ranger, 2009). Higher paternal education and
greater wealth capture family income and economic status. Families with fewer economic
constraints may be able to raise more daughters, and may be more likely to use technology in
order to obtain their ideal family size and gender composition.
Qian’s (2009) examination of the implementation versus the relaxation of the One Child
Policy in China implies that among the first-born children, girls on an average have more
siblings, more educated parents, and a higher level of school enrollment. Furthermore, only
children are more likely to be male, more likely to be enrolled in school, and have more educated
parents. After the relaxation of the One Child Policy, parents were allowed to have a second
child only if the first-born child was a girl: this was introduced as a measure to curb sex selection.
On an average, the relaxation increased family size of the first-born girls by approximately 0.25
children.
e. Women’s Literacy
Osili and Long (2007) provide evidence that educating young women reduces growth in
population, thus creating sustainable economic and social welfare in developing countries.
Giving females access to schooling increases the opportunity cost of childbearing and child-
rearing among educated women. Educated females are more knowledgeable about use of
contraceptive methods, which increases a woman’s bargaining power in fertility decisions. Their
study analyzed the implementation of the Universal Primary Education (UPE) program in
Nigeria that altered schooling costs and primary classroom sizes. This had a strong impact on
female education and fertility rate; increase in education of females reduced the number of early
births. They also concluded that if there is discrimination against girls in terms of educational
expenditure one would expect to observe significant educational differences in gender outcomes.
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However, total expenditure increases with the number of children. Investment in children’s
education is considered a normal good in Nigeria; thus, it is a priority household expenditure.
Bhat (2002) found that there has been a substantial decline in fertility among illiterate
women in India. Total fertility is highest among the illiterates and lowest among women
educated through or beyond matriculation. As expected, educated parents beget educated
children. There is an increased likelihood that a child will attend school if even only one of the
parents is literate. The study finds evidence that 49% of the children are enrolled in school when
both the parents are illiterate. It rises to 73% when the father alone is literate, and 92% when
both the parents are literate. 40% of the female children go to school when both parents are
illiterate, 64% if the father is literate, and 90% when both the parents are literate; more children
go to school when the mother alone is literate. In the case of illiterate parents who send their
children to school, as fertility rate begins to fall, there is an increase in school enrollment. There
has been a substantial decline in fertility among illiterate women in India and a larger number of
literate parents have begun to send their children to school. Bhat’s study concluded that couples
have begun to reduce their family size in order to invest more in schooling of their children.
f. Parents’ Education
In South Korea, Lee (2007) observed that more educated parents invest more in their
children’s education: this investment depends more on the mother’s education. More educated
mothers have a smaller number of children and invest more in each child’s education. Father’s
education increases this investment but also the number of children in the household.
Bhat’s (2002) study of India’s demographic transition shows that there is an inverse
relationship between child schooling among illiterate parents and the family size. Given the
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rising awareness about the quantity-quality tradeoff model, fertility rate is declining and child
schooling is rising among illiterate couples.
g. Female Labor Female Participation
In families where a mother is engaged in wage labor, she cannot afford to hire someone
else to perform household chores. Thus, the mother’s work status has a strong negative effect on
the schooling level of the first daughter (Bhat, 2002). Hanushek’s (1992) data supports that the
rise in female labor force participation and rising incidents of one-parent families have impacted
transmission of human capital.
Angrist, Lavy and Schlosser (2005) revealed that for the Israeli population, mothers’
withdrawal from the labor force in response to childbirth is ultimately a net plus for the older
siblings. Parents may reduce the expenditure on inputs of low value to their children.
Having a younger sibling affects the first child’s level of education through mother’s
labor supply. If having a second child increases household needs for monetary income, then an
additional child may cause the mother to enter the labor force and send the older child to school.
In many cases children with younger siblings may attend school earlier if parents wish to
decrease the amount of at-home child care needed during the day; furthermore, the parents also
have to hold back the first child with the belief that that there are economies of scale to having
two children in school at the same time. With an additional child, the mother is less likely to stay
at home and more likely to participate in the labor market. These results of Qian’s (2009) study
are consistent with the hypothesis that the parents view school as an alternative source of
childcare for the first child, and send him to school while the mother enters the labor force.
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Caceres’ (2004) OLS estimates demonstrate that childbearing has a negative impact on
the female labor force participation rate. For mothers who have two or more children, an
additional child reduces child labor participation by 8.6%.
h. Other Variables
Bhat’s (2002) study of India’s demographics concluded that household variables (caste,
religion, size of land owned, and proportion of irrigated land), community-level variables
(village population, having a bus stop and middle school in village, and village infrastructure),
and rising opportunities for non-agricultural employment—all affect parents’ decision-making
process whether or not to send their children to school. Landholding has a positive effect on
schooling of both boys and girls. Overall, infrastructural improvement at the village level also
show strong positive effect on child’s years of schooling.
Summary of Review of Literature
Putting together the existing research in the field, we find that factors such as the birth
order, the household income, the quality of education, the education attainment level of the
parents, the child’s gender, women’s literacy and the female labor force participation have
significant impact on children’s education attainment level. Large families provide their children
with less schooling because they face resource constraints. Thus, family size has a strong
negative effect on child’s schooling (Bhat, 2002). Moreover, in the Indian context, daughters are
at a disadvantage—families are likely to reach their ideal family size and gender composition
while providing more resources to the sons (Bhamarbagwala and Ranger, 2009).
Data
The principal datasets used for this study have been obtained from Demographic and
Health Surveys (DHS) household level database. The International Institute For Population
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Sciences (IIPS) carried out these surveys for three time periods: 1992-93, 1998-99, and 2005-06.
The datasets were then recoded in different phases: DHS-II, DHS-IV and DHS-V respectively.
Using a core questionnaire for Uttar Pradesh only, the National Family Health Survey
(NFHS) identifies the members of the household by prescribing a precise household schedule
and selecting eligible respondents for individual interviews. The chosen ones were ever-married
women, aged between 13 to 49 years. ‘Ever-married women’ include all women who have either
been previously married and there marriages have been dissolved, or all women who are
currently married. In addition, community level data was collected with the help of a village
questionnaire.
Each household observation has an identifying code and a case identity number. The data
set has been prepared after getting results from three different questionnaires: the household
questionnaire, the women’s questionnaire, and the village questionnaire. Individually, certain
state-specific questions were included pertaining to that state, in this case, Uttar Pradesh.
The household questionnaire consisted of a description of the household location, the
number of household members, the household itself, and the births and deaths in the household
in the past two years. The list of the household schedule prepared from this questionnaire was
referred to deduce basic information about each individual member of the household—the
relationship to household head, sex, age, marital status, education received by, occupation of and
health conditions of the individual.
The women’s questionnaire collected information from eligible women (ever-married,
aged 13-49 years, and usual resident of the household). It consisted of—background details
regarding a woman’s age, her marital status and education; the woman’s reproductive history-
number of live births and still births, number of sons and daughters, abortions, current pregnancy,
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and the birth and death history of children; contraception use; children’s health; fertility
preferences—ideal family size, and preferred birth intervals.
The village questionnaire consisted of community level information. It included
information about services available in the village such as access to electricity, water, sanitation,
transportation, education, and health.
For this study, the obtained household level datasets consisted of about 2000
variables each, and this study focuses on about 20-25 of those variables; these include the age,
gender, level of education, residence type, birth order of the child etc. The time frame of this
study is restricted because the latest available dataset is for the year 2005-06. As a result, the
impact of The Right of Children To Free and Compulsory Education Act, introduced in 2009,
cannot be observed. Note that this study does not necessarily require matching the household
questionnaire to the women’s or village questionnaire.
This study restricts the pool of children to those enrolled in school, i.e., between the ages
of 4 to 18 years. The 1992-93 dataset holds information for 10,110 households in Uttar Pradesh,
the 1998-99 dataset has 8,682 households and there are 10,026 households studied in 2005-06.
Table 1 presents summary statistics for the datasets used in the study, and Table 2 shows the
distribution of family sizes in the samples. Table 3 shows the education attainment level (in
years) for children aged 4-18 years over three different time periods. These variables and
indictors are discussed in detail in the following section.
Variables
This study analyzes the effect of number of children in a household on the level of
education attained by each child (in number of years), while accounting for the gender of the
child, the age of the child, proxy variables for household income (electricity, radio, refrigerator,
television, bicycle, motorcycle, car and telephone), the area of residence, the household head’s
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education attainment level, the number of sons in a household, the birth order of the child (first-
born or last-born), proxy for the household occupation (whether the household owns agricultural
land), and whether or not the child is still in school. The sample of children for all three datasets
is based on the children in the household who are between the ages of 4 and 18 years. Unlike
some countries, the Indian education system includes two years of kindergarten, therefore, the
children start going to school at the age of 4 years.x In India, specifically the state of Uttar
Pradesh, the minimum age of enrollment for primary school is also 4 years, and the children
graduate from high school at the age of 18 years.
Table 2 shows the number of children in households. We see that 17.87% and 18.95% of
the households in 1992-93 and 2005-06 respectively have an average of 3 children, and 17.78%
households had an average of 4 children in 1998-99. These numbers are higher, considering that
ideally a family would be expected to have 2 children in each household. In Table 3, we see that
across all three datasets almost two-thirds of our sample has 0 years of schooling for all children
between the ages of 4 and 18 years, and the maximum number of years of schooling was 14
years. Furthermore, the years of education received by children is highly skewed to the left in all
three periods. Only 4.34%, 5.41%, and 4.65% of the children had only 1 year of schooling in the
years studied respectively.
Unfortunately, we do not have any good measures of family income for our observations;
therefore, this study uses a set of variables that imply asset ownership in the household; however,
there are still some missing variables in this study. This analysis is a form of a regression
analysis that uses possible independent variables to explain effect on a dependent variable. Thus,
in this case, asset ownership—indicated by variables such as the household’s access to
electricity, radio, refrigerator, television, bicycle, motorcycle, car and telephone—is used as a !!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!x Age criteria for admission to school, Central Board of Secondary Education (CBSE), India
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proxy for household income. While these regressors do not explain household income (the
default independent variable) entirely, they can be used as a measure of wealth, which implies an
interesting statistical pattern between the independent and dependent variables. As a result, eight
dummy variables were created for households that have access to these resources and the results
are shown in Table 4. For the year 1992-93 there is no data for electricity and telephone access.
As expected, access to these basic amenities has increased with improvement in infrastructure,
even in rural areas, over time. More than half of the households had access to electricity by
2005-06, and approximately three-fourths of them had a bicycle. Households’ access to radios
was almost consistent between 1992-93 and 2005-06; whereas access to refrigerator and
motorcycle more than tripled in this period. Access to television sets also increased by almost
26% from 19.1% (1992-93) to 45.3% (2005-06). However, only 3.2% (2005-06) of the
households had access to cars (1% in 1992-93), and 11.5% (2005-06) of the households had a
telephone.
There are six more dummy variables in our dataset. The gender variable is a dummy
variable with 0 for male and 1 for female. Across all three datasets, as seen in Table 5, more than
half the children in the age group of 4 to 18 years are males. Gender bias still prevails in India
because parents prefer having sons to daughters. The variable for the area of residence is 0 for
urban areas and 1 if the household is in a rural area. Uttar Pradesh has not developed as fast and
is still home to several poor people residing in rural areas. Thus, most of the households in this
study are located in rural areas. First-born and last-born are dummy variables for the first-born
child of the family and the last-born respectively. In the broader Indian context, parents invest
more in their last-born child as the first-born child is expected to stay at home, carry out
household chores, work on the farm, and look after younger siblings. Ownership of agricultural
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land is used as a proxy variable for the household occupation. It is expected that if a household
owns agricultural land, they engage in agricultural activities, and in the production of primary
goods. Conversely, non-ownership of agricultural land implies involvement in secondary and
tertiary sectors. If agricultural land is owned, this variable is 1, and 0 otherwise. The last dummy
variable is used to observe whether or not the child is still in school or not. There is no
information about the child attending school in 2005-06. Table 5 reflects the results of these
variables.
The number of children, the number of sons, the education level of the child and of the
household head, and the age of the child are continuous variables. Table 1 shows that across the
three datasets, households have approximately a mean of 4 children each with an average age of
10 years. This analysis uses an interaction term that allows to study the marginal effect for
female child and male child on education attainment level. Furthermore, a term for age squared
is introduced in order to augment the linear regression model and study the effect of age of
child’s education attainment level.
The correlation matrices determine the correlation between all the variables observed in
this study. The higher the correlation between two variables, the better our model is. We see that
the correlation between both—the education received in years and log of number of children in
the household, and the years of education received and the gender of the child (female)—is
negative across all three datasets (shown in Tables 9, 10, and 11). Variables that study asset
ownership, as a proxy for household income, are generally positively correlated with each other,
and more negatively correlated with the number of children. The household location in rural
areas is also inversely correlated with asset ownership and education received by the child.
Methodology
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This study uses an ordinary least squares regression model to assess the effect of number
of children in a household on each child’s education attainment level. The regression is run only
for school going children, i.e., in the age group of 4-18 years, while controlling for other
variables that affect children’s education attainment level. The following 4 models are used to
determine the impact:
1. Education (in years) = β0 + β1 (Log(Number of children in household)) + εi
2. Education (in years) = β0 + β1 (Log(Number of children in household)) + β2 (Female child) +
εi
3. Education (in years) = β0 + β1 (Log(Number of children in household)) + β2 (Female child) +
β3 (Log(Number of children in household) * Female child) + εi
4. Education (in years) = β0 + β1 (Log(Number of children in household)) + β2(Female child) +
β3(Log(Number of children in household) * Female child) + β4(Electricity) + β5(Radio) + β6
(Television) + β7 (Refrigerator) + β8 (Bicycle) + β9 (Motorcycle) + β10 (Car) + β11 (Telephone) +
β12(Rural Household) + β13(Household head’s education) + β14 (Log(Number of sons)) +
β15(First born) + β16(Last born) + β17(Age of child) + β18 (Age of child2) + β19 (Agricultural land
owned) + β20 (Member still in school) + εi
Here, β1 is the coefficient of interest, which shows by how many years a child’s
education level is affected with a percentage increase in the number of children in a household.
The expected sign of this coefficient is negative, because with a higher number of children,
resources available to each child are diluted. However, β1 may be biased upward because of
omitted variable bias (especially in regression models 1, 2, and 3). Furthermore, there are women
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who value children’s education and prefer to have a small size; this leads to correlation between
the dependent and the independent variable. We also do not consider the mother’s labor force
participation in the model, which leads to omitted variable bias. Some variables in the regression
model are correlated with a variable that has been omitted from the analysis. All OLS estimators
(β̂ coefficients) are assumed to be unbiased and consistent estimators of the β coefficients.
εi is an error term. The error term in all the above regression models is implicitly assumed
to be independent and identically distributed to simplify the statistical analysis.
The sign for β2, the coefficient for female child is also expected to be negative. As
discussed earlier, in the Indian context, male children are given priority over their female
counterparts, and parents invest more in their upbringing. In regression model 3 and 4 we
introduce an interaction term for log of number of children and the gender of the child. The
coefficient β3 is the difference in the effect of the number of children for females (β1 + β3) versus
males (β1) on the educational attainment level. We expect the sign on β3 to be positive because
there is a marginal effect of the number of children on the education attainment level for females
but not for males. Given that the household is located in rural areas, we predict a negative sign
for β11— people living in rural areas either do not have access to schools around their village
(and good quality schooling), or do not have the resources and means to send their children to
school. If the child is the first-born, it has a negative impact of education, whereas, last-born
children stand to gain from their birth order and receive a higher level of education. Families
who own agricultural land will also have a negative impact on children’s education attainment
level—ownership of agricultural land implies that the household engages in agricultural
activities, and perhaps, the children of the family also work instead of attending school. Children
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are seen as extra helping hands on farmlands; furthermore, children are unskilled, underpaid, and
do not benefit overall.
For coefficients whose variables are proxies for income—electricity, radio, refrigerator,
television, bicycle, motorcycle, car and telephone—we expect a positive relationship with the
years of education received. Ownership of these assets implies access to resources that the
individuals, especially the children in the household, benefit from. Furthermore, a child is
expected to stay in school longer if the household head himself has a higher level of education.
Thus, β12 is expected to be positive. If the child is still in school, there is a positive effect on the
education attainment level of the child.
The following section will discuss the findings of the study.
Results
This section discusses the results of the study in three separate sections for the three
different years studied. The results are significantly different in each of the years studied; thus,
the results are presented separately; matching or combining the households across the datasets
cannot be justified. Tables 6, 7, and 8 show that the standard errors are calculated using Stata’s
robust estimation method. Upon checking for robustness, we find that the coefficients are not
significantly different from one another. Thus, all standard errors are adjusted to account for
heteroskedasticity.
1992-93:
In the 1992-93 household dataset, families had an average of 4.24 children in each
household, with each child getting 1.55 years of education. 17.81% of the 10,110 households
have 4 children. 69.26% of the children aged between 4-18 years received no education (Table 1,
2 and 3).
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For the year 1992-93, the OLS regression on the child’s education attainment level shows
that if the number of children in the household increases by 100%, the child receives 0.46 less
years of education, at the 1% significance level (Table 6). For instance, in a household, if the
number of children increases from 2 to 4 children, then each child will lose 0.46 years of
education. This regression (1) does not control for any other variables and explains merely 0.7%
variation in the number of years of education a child receives.
When the regression is separately run to include the gender of the child, the model (2)
explains 26.19% variation in education level of a child. On controlling for the number of
children and the gender of the child, we find that if the number of children increase by 100%, the
child is expected to receive 0.37 less years of education; and moreover, the female child receives
2.91 less years of education compared to the male, both significant at the 1% level. Adding an
interaction variable to this (3), the effect of number of children in the household on education
attainment level of each child is higher for females.
Using additional controls, and running a complete regression (4) decreases the observed
effect of the number of children on the years of education, and it is significantly different at the
1% level. If the number of children in a household increases by a 100%, then each child receives
0.37 less years of education, controlling for all other factors. For instance, if a household with 2
children now has 4 children, then each child gets 0.37 less years of education. We observe an
inverse relationship between the years of education received by a child and the number of
children in the household. Each child loses about 4 months of schooling if there are double the
children in the house. Yet again, the girl child stands to lose about 3.83 years of education,
significant at the 1% level. Like regression model 3, this regression result also finds that the
effect of number of children in the household on education attainment level of each child is
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! 38 | Gupta
higher for females, also significant at the 1% level. As expected, residing in rural households has
a negative impact on the years of education received, however it is not significant at the 1%, 5%
or 10% levels. If the number of sons increases by 100%, each child loses 0.18 years of education
at the 1% significance level. The first-born and last-born children have limited access to
education: if the child is the first-born in the household, they receive 0.06 less years of education,
not significant at the 1%, 5% or 10% level; but the last-born child loses 0.16 years of education,
significant at the 1% level. In addition, while access to radio, television and bicycle have a
positive impact at the 1% significance level on the number of years of education the child
receives, having a refrigerator in the household also contributes to this effect and is significant at
the 5% level. Access to a car and motorcycle also have a positive outcome, however the effect is
insignificant. Unfortunately, this dataset does not include any statistics about household’s access
to electricity and telephone, which would also be expected to have a positive effect on the
dependent variable. The coefficients for the age of the child and the age of the child squared both
show a positive effect on the years of education received, at 10% and 1% significance level
respectively. A positive coefficient for both these variables implies that as the child gets older,
the effect of age is stronger, i.e. if the child is older he is expected to achieve a higher level of
education. The household head’s education attainment level also has a positive effect on the
years of schooling of their child, significant at the 1% level. Furthermore, the ownership of
agricultural land has a significant impact on the years of education at the 1% level. If the
household owns agricultural land, the child’s education increases by 0.26 years. This goes
against our expectations since we expect parents to be involved in agricultural activities and the
child to work on the farm instead of attending schooling. However, two factors may have offset
this reasoning: the land is a measure of household wealth, and moreover, the same piece of land
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brings in additional income to the house such that parents can support the child’s education. It is
important to observe whether or not the child is still enrolled in school, if the child is still in
school it affects schooling by an additional 0.65 years. Considering all these parameters, this
model explains 48.99% of the variation in the years of schooling of the child. Overall, this model
found that given other factors, having additional children in the household has a negative
consequence on the years of education for the observed child.
1998-99:
The average number of children in a household in 1998-99 in Uttar Pradesh was 4.27,
with each child receiving 1.63 years of schooling. Of the 8,682 families in our dataset, 17.78% of
the households have 4 children. 66.62% of the children in the age group of 4-18 years have not
been to school (Table 1, 2 and 3).
The 1998-99 data is analyzed using similar OLS regression models as in the previous
year’s dataset. When the regression model (1) controls only for the number of children, it merely
explains 1.05% variation in the years of education. We find that if the number of children in the
household rises by 100%, the child receives 0.58 less years of education at the 1% significance
level (Table 7).
On running a separate regression (2) that also includes the gender of the child, we can
explain 28.9% variation in the model. Controlling for the number of children in each household
and the gender of the child, we observe that the child’s schooling level falls by 0.45 years if the
number of children in the household double. In addition, if the child being observed is a female,
she loses 3.07 more years of education than the male child. Both these variables are significant at
the 1% level. The marginal effect of number of children is higher for females than for males (3).
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After controlling for all other factors in our model that affect children’s years of
schooling, we see that that if the parents agree to have 100% more children in the household (say
increase the number of children from 2 to 4), then each child will receive 0.45 less years of
education, significant at the 1% level. There is a negative relationship between the two variables,
as expected. A female child secures 4.38 less years of education, also significant at the 1% level.
Again, as expected the marginal effect of number of children on the years of education is higher
for females than for males by 0.88 years, significant at the 1% level. According to the above
literature discussion, this result is predictable—yet dismal—in the South Asian context in
general. There is a positive effect observed at the 5% significance level for the children of
households located in rural areas. The child gains 0.1 years of schooling compared to a child
from the urban areas. There is a positive collinearity between the rural household and the number
of children (Table 10). The number of years of education received decreases by 0.06 years if the
number of sons in the household increases by 100%; however, this is insignificant at the 1%, 5%
or 10% levels. The first-born of the house is denied 0.16 years of schooling at the 1% level in
comparison to other children; the last-born also loses a few years of education, the effect
however is insignificant. If the child has access to facilities such as electricity and bicycle, he
gains 0.18 and 0.16 years of education respectively, both significant at the 5% level. Having a
refrigerator in the household also has a positive effect at the 5% significance level; and if there is
a television in the house, the child’s education level rises by 0.14 years at the 10% significance
level. It is interesting to see that access to a motorcycle, car, and telephone have a negative effect
on the years of education, however the effect is insignificant. We anticipate that these variables
will have a positive effect in our model since they are assets that add to household wealth and are
necessary resources. This particular dataset stands out in our study due to the unexpected
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relationship between the age of the child and the years of education received by the child. As the
child grows older, we expect him to pursue his education further. However, in this model, given
that the age of the child shows a negative effect on the years of schooling, and the age of child
squared has a positive effect of years of schooling, both significant at the 1% level, we can tell
that as the child gets older, the effect of age on education received declines, that is, the child is
expected to drop out of school soon before he or she turns 18. It is possible that the year this data
was collected parents invested in their children’s primary or secondary education but not in their
higher education. This is a common phenomenon observed, especially is rural areas. Once
children receive education up to middle school level, they are either employed in unskilled
manual labor tasks, or girls especially are often made to stay at home, carry out household chores,
and look after younger siblings. As we expect, the more educated the child’s parents are, the
more educated will the child be. The number of years of household head’s education has a strong
positive relationship with the number of years of education the child receives, significant at the
1% level. As explained in the 1992-93 dataset results, if the household owns agricultural land,
the child’s education level increases by 0.14 years: this implies a strong positive relationship at
the 1% level. While this sign is not predicted, the justification provided above stands true in this
case as well. Lastly, if the child was enrolled in school at the time of data collection, he is
expected to have 1.15 additional years of schooling, significant at the 1% level. When all the
above factors were incorporated in the model (4), the variables explained 55% variation in the
years of education received by a child. Again, as we expect, this data analysis for the year 1998-
99 also implied that having a higher number of children in a household would have detrimental
effects on the children’s education attainment level.
2005-06:
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In the data collected in the year 2005-06, the average number of children in each
household was 3.79. A child received a mean of 1.8 years of education. We observe 10,026
families in this dataset. About 18.95% of the households have 3 children, and 18.31% of the
households have 4 children each. 65.35% of the children aged 4-18 years had no education or
school experience (Table 1, 2 and 3).
An OLS regression (1) model is used for the year 2005-06 as well to estimate the effect
of the number of children on children’s education attainment level in a household. To study the
pure effect, irrespective of other controls, we run a bivariate regression. This model, however,
explains only 1.8% variation in years of education given only the number of children in the
household. A 100% increase in the number of children implies that each child gets 0.80 less
years of education, significant at the 1% level (Table 8).
Next, this model is extended to include the child’s gender, which is expected to have
unfavorable results for the female child. This regression model (2) accounts for 32.53% variation
in the years of education a child receives. The child’s education attainment level and the number
of children in the household continue to have a negative relationship at the 1% significance level.
Each child receives 0.64 less years of schooling if the number of children increases by a 100%,
significant at the 1% level. In addition, the female child continues to lose—she receives 3.42 less
years of education than the male child in the household, also significant at the 1% level.
Extending this model to add an interaction variable to this regression (3) shows that marginal
effect of the number of children is yet again higher for females than for males.
Finally, we run a regression (4), like in the previous cases, to determine the inclusive
effect of the number of children in the household on children’s education attainment level. With
a 100% rise in the number of children in the household, i.e. from 2 children to 4, the number of
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years of education for each child decreases by 0.79years, significant at the 1% level. The child’s
gender and the years of educational attainment also have a significant negative relationship at the
1% level. A female child receives 4.9 less years of education than the male child. The coefficient
for the interaction variable implies that the marginal effect of the number of children on the
child’s years of education is yet again higher for females than for males, significant at the 1%
level. As observed in the year 1998-99, the effect of residing in rural areas is positive and
significant at the 5% level in 2005-06 also. The child’s years of schooling increase by 0.13 years
if the household is located in a rural area. With a 100% increase in the number of sons in the
house, the education attainment level of each child goes down by 0.08 years, however this effect
is insignificant. Both the first-born and last-born children receive 0.29 and 0.19 less years of
education respectively. These are both significant at the 1% level respectively. Considering asset
ownership as a proxy for household income, having a radio, television and car in the household
have insignificant effects on the education attainment level. Access to electricity and having a
motorcycle and a telephone has positive results on the number of years of education at the 10%
significance level. Moreover, access to a refrigerator and a bicycle also show positive results at
the 1% significance level. The child’s age and age squared, both have a positive effect on the
years of schooling at the 1% significance level. It implies that as the child gets older, the effect of
age on the years of education gets stronger. As the child’s age increases, he or she is expected to
achieve a higher level of education. With the increase in years of education of the household
head, we expect the child’s level of education to rise by 0.05 years, significant at the 1% level.
Yet again, we observe agricultural land ownership positively contribute to the educational level
attainment at the 1% level. The child receives 0.17 more years of education if the family owns
household land. This regression model (4) explains 55.96% variation in the education attainment
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level of the child. It implies an inverse relationship between the educational attainment in years
and the number of children in a household while controlling for other factors.
Summary of Results
A quick analysis of the data from the three different periods shows that a rise in the
number of children in the school has a negative effect on the child’s education attainment level,
significant at the 1% level. However, the coefficient is larger for 2005-06 than for 1992-93 and
1998-99. Perhaps, this can be attributed to the increasing population rate and increased life
expectancy at birth in the state of Uttar Pradesh. As a result, parents have more children in the
household, and are able to provide fewer resources. We would ideally expect this trend to
become less negative over time.
In addition, if the child is a female, she receives less education than her male counterpart.
The effects of an increase in the number of children in the household are higher for females than
for males. Further, the first-born and the last-born, both stand to lose years of schooling. Asset
ownership largely has a positive effect on the number of years of child’ schooling. Agricultural
land ownership and attendance at the school at the time of survey also have a positive
relationship with the educational attainment level of the child.
Discussion and Critique
To restate the results, a higher number of children in a family leads to lower level of
educational attainment for each child. As expected, in the Indian context, there is also significant
difference in the number of years of education received by the female child than the male child.
Previous literature, studies and reports match the result of this study fairly closely. This study
found no significant change in the years of education received by the child due to household
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income, area of residence, birth order or household occupation. Thus, these factors may have
skewed the results of this study and led to omitted variable bias.
This study considers several variables that affect the number of years of education a child
receives, but there are still some missing variables. Let us see why we observe divergences or
lack of significance in the results obtained in our regression analysis:
• Income is measured using an exclusive list of variables available for these time periods
(electricity, radio, refrigerator, television, bicycle, motorcycle, car and telephone). While
these measures display access to amenities, they do not entirely dictate what the
household head or parent earns in order to accommodate their child’s education.
• In the relevant cultural context, investment in a female child’s education is seen as a
wasteful expenditure. Furthermore, since girls are usually married away earlier than the
legal age of 18 years, this dataset excludes those children who might have gone on to be
listed as wives or mothers. These observations are not accounted for, and hence the
variable for the gender of the child does not pick the complete and accurate effect on the
years of education received by the child.
• For the area where household is located, urban or rural, we find positive effects for the
years 1998-99 and 2005-06 in rural areas. This goes against our expectations. But it must
be recalled that it is troublesome and even expensive to find a reasonable school for
children if families have recently migrated to urban areas. The cost of living in these
regions is already high enough, apart from the other necessary expenditures involved in
the process of moving. Hence, on a comparative scale, people in rural areas marginally
win in this case, only suggesting an unconditional relationship between the area of
residence and the years of education received.
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• This study postulates that the ownership of agricultural land is indicative of the
household head’s occupation level, i.e., the household engages in primary production
activities that do not pay as high an income. Hence, affording an education for the
children might be expensive. One can argue that access to agricultural land is a measure
of family wealth, hence showing significant positive effect on the years of education
received. Perhaps, several landlords give their pieces of land to peasants to cultivate
them; the landlords earn an income, which can be additional revenue contributing to
making the child’s enrollment in school affordable.
As mentioned earlier, there are more variables than mentioned in this study that affect
children’s education attainment level.
• This study lacks an analysis of fees charged by public or private schools.xi This could be
the primary reason why parents cannot afford to send their children to school. The cost of
living for many individuals, especially farmers, is too high; bearing an additional cost of
sending their child to school is too demanding. Although the fee structure for schools has
changed over time, it still remains unaffordable for a large section of the society.
• The study does not consider the effect of multiple (twin/triplet) births. This can have an
additional negative impact on the years of education for the child, and in many cases even
pressurize parents against having more children so that they can sustain the existing
family.
• We do not have access to information regarding quality of education and the child’s
achievement level that are important contributors to child’s education attainment level.
The education children are receiving today, especially students who reside in and attend
!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!xi School fess charged by public schools in India is heavily subsidized, and may even cost nothing in some cases; however, private schools are expensive
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schools in poor neighborhoods, is producing poor results. Apart from government’s
efforts to improve access to schooling and education, quality and cost efficient schooling
must be ensured at all levels in order to avoid a negative causal relationship.
• This analysis does not account for mother’s labor force participation and the effect it has
on the years of education received by the child. In poor households mothers find wage
employment to sustain household income and cannot afford to hire household help.
Therefore, the mother’s work status implies a negative relationship on the schooling level
of the first child, especially the girl child who is expected to entirely be able to fill in for
the mother.
• Community level variables such as village infrastructure and presence of a school in the
village were not incorporated in our models of analysis. These too are expected to have a
significant impact on child’s education attainment level.
Conclusion
This study provides evidence of a correlation between a woman’s high fertility rate and
the low levels of education among her children observed in the state of Uttar Pradesh. The study
determines that with an additional child in the household there is a damaging effect on the
education attainment level of each child. The hypothesis behind this finding was that as there are
more children born into a household, parents have to divide the available resources, and
hopefully spread them evenly across their children in order to provide them with a decent
standard of living. All in all, continuing to have more children means putting each child’s future
at risk by not providing him with the necessary facilities. This prevents the child from reaching
his maximum potential, and renders them incapable.
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While most studies analyze the effect of women’s education on their fertility rate—
especially in developing countries—this study is unique because unlike other literature it looks at
the relationship between mother’s fertility rate and children’s education attainment level, not the
mother’s education.
The statistical conclusions drawn from our analysis are parallel to our theoretical
hypothesis that expanding the family size, beyond the affordable capacity, will have worsening
prospects for the children of the family. The coefficient of interest is statistically significant at
the 10% level in 1992-93 and 1% level in 1998-99 and 2005-06, demonstrating the disadvantage
of having more children in the household.
Thus, there is an urgent need to employ severe measures to regulate the family size and
generate awareness about literacy—aiming at giving every child a better tomorrow and a secure
future.
Further Analysis: Thoughts for Further Study of the Topic
While the evidence in this study implies that upon controlling for the number of children
in the household parents can provide each child with a higher number of years of education,
further studies can benefit from examining this trend over a longer time frame, perhaps even
making use of panel data. More specifically, it will be interesting to see how the Right of
Children To Free And Compulsory Education Act, implemented nationally in 2009, has dictated
the trend of this relationship in recent years. One can also then go on to study how the legislation
itself has affected children’s education attainment level. A major drawback of this study was that
the available data was restricted over the time frame in which the analysis could be conducted.
Another avenue for further study would be to take a look at the child’s birth order paired
with the child’s gender. Researchers have carried out analyses studying the effects of being the
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first-boy, second-boy, first-girl and second-girl child on the education attainment level. Upon
extending this model, it would be interesting to determine to what extent having twins in the
family can affect siblings’ resources.
Furthermore, the effect on the years of education significantly varies with the child’s age.
Supplementary studies can benefit from using a pool of children who begin attending school with
primary school, i.e., children who are in the age group of 6 to 18 years.
One last suggestion would be to see how a policy similar to China’s One Child Policy, if
introduced in our sample, would curb the growing family size and help households consume
their disposable resources optimally.
Tables
Table 1: Summary Statistics
Variable
1992-93 1998-99 2005-06 Mean SD Min Max Mean SD Min Max Mean SD Min Max
1 Education received
in years 1.555 2.882 0 15 1.632 2.908 0 14 1.807 3.086 0 15
2 Number of
children 4.247 2.593 0 17 4.276 2.523 0 15 3.797 2.426 0 19
3 Female child 0.469 0.499 0 1 0.472 0.499 0 1 0.477 0.499 0 1
4 Electricity
0.381 0.485 0 1 0.543 0.498 0 1
5 Radio 0.369 0.482 0 1 0.352 0.477 0 1 0.375 0.484 0 1
6 Refrigerator 0.058 0.235 0 1 0.077 0.266 0 1 0.190 0.392 0 1
7 Television 0.191 0.393 0 1 0.289 0.453 0 1 0.453 0.497 0 1
8 Bicycle 0.611 0.487 0 1 0.653 0.475 0 1 0.757 0.428 0 1
9 Motorcycle 0.079 0.270 0 1 0.094 0.292 0 1 0.224 0.416 0 1
10 Car 0.010 0.100 0 1 0.012 0.110 0 1 0.032 0.177 0 1
11 Telephone
0.045 0.207 0 1 0.115 0.319 0 1
12 Rural Household 0.785 0.410 0 1 0.797 0.401 0 1 0.602 0.489 0 1
13 Household head's
education 4.307 4.947 0 21 4.759 5.028 0 23 5.413 5.477 0 22
14 Number of sons 2.218 1.673 0 12 1.736 1.433 0 8 1.972 1.564 0 13
15 First Born 0.139 0.346 0 1 0.138 0.345 0 1 0.1474 0.354 0 1
16 Last Born 0.139 0.346 0 1 0.138 0.345 0 1 0.1474 0.354 0 1
17 Age of child 10.560 4.317 4 18 10.572 4.383 4 18 10.706 4.271 4 18
18 Agricultural land
owned 0.682 0.465 0 1 0.678 0.467 0 1 0.531 0.498 0 1
19 Member still in
school 0.384 0.486 0 1 0.536 0.498 0 1
Table 2: Number of children in the Household (by Household) 1992-93 1998-99 2005-06 Frequency Percentage Frequency Percentage Frequency Percentage 0 2,366 3.63 2,229 3.98 3,335 5.69 1 4,977 7.63 3,981 7.10 5,547 9.46 2 8,962 13.74 7,457 12.31 9,054 15.43 3 11,653 17.87 9,670 17.26 11,116 18.95 4 11,616 17.81 9,966 17.78 10,742 18.31 5 9,255 14.19 8,069 14.40 7,371 12.57 6 6,279 9.63 5,593 9.98 4,793 8.17 7 3,944 6.05 3,347 5.97 2,705 4.61 8 2,227 3.41 2,058 3.67 1,782 3.04 9 1,125 1.72 1,368 2.44 973 1.66 10 852 1.31 885 1.58 433 0.74 11 632 0.97 547 0.98 202 0.34 12 456 0.70 526 0.94 179 0.31 13 409 0.63 258 0.46 115 0.20 14 217 0.33 42 0.07 141 0.24 15 185 0.28 42 0.07 19 0.03 16 47 0.07 24 0.04 17 24 0.04 69 0.12 18 62 0.11
Total 65,226 56,038 58,662
Table 3: Education Attainment Level (in Years) 1992-93 1998-99 2005-06 Frequency Percentage Frequency Percentage Frequency Percentage 0 17,108 69.26 14,850 66.62 14,938 65.35 1 1,072 4.34 1,207 5.41 1,063 4.65 2 997 4.04 978 4.39 938 4.10 3 784 3.17 841 3.77 782 3.42 4 701 2.84 672 3.01 815 3.57 5 850 3.44 823 3.69 941 4.12 6 662 2.68 559 2.51 661 2.89 7 579 2.34 555 2.49 587 2.57 8 692 2.80 621 2.79 699 3.06 9 642 2.60 633 2.84 641 2.80 10 296 1.20 262 1.18 321 1.40 11 193 0.78 146 0.66 287 1.26 12 97 0.39 110 0.49 142 0.62 13 13 0.05 23 0.10 29 0.13 14 13 0.05 10 0.04 11 0.05 15 1 0.00 2 0.01
Total 24,700 22,290 22,857
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Table 4: Assets Ownership Implying Household Income (in Percentages)
Asset 1992-93 1998-99 2005-06 Electricity 38.1 54.3
Radio 36.9 35.2 37.5 Refrigerator 5.8 26.6 19.0 Television 19.1 28.9 45.3
Bicycle 61.1 48.7 75.7 Motorcycle 7.9 9.4 22.9
Car 1.0 1.2 3.2 Telephone 4.5 11.5
Total Observations 65,226 56,038 56,882
Table 5: Other dummy variables (in Percentages)
Variable 1992-93 1998-99 2005-06 Female child 46.9 49.9 47.7
Rural residence 78.5 79.7 60.2 First-born 13.9 13.8 14.74 Last-born 13.9 13.8 14.74
Agricultural land owned 68.2 67.8 53.1 Member still in school 38.4 53.6
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Table 6: Education received in years, 1992-93 Variable: Education received 1 2 3 4
Log(Number of children) -0.4656*** -0.3737*** -0.6953*** -0.3710*** (0.0388) (0.0330) (0.0609) (0.0600)
Female child
-2.9173*** -3.9254*** -3.8349***
(0.0296) (0.0946) (0.0939)
Interaction Variable
0.6953*** 0.6172***
(0.0609) (0.0582)
Radio
0.1597***
(0.0325)
Television
0.2798***
(0.0495)
Refrigerator
0.1857*
(0.0924)
Bicycle
0.2440***
(0.0295)
Motorcycle
0.0807
(0.0746)
Car
0.2593
(0.1854)
Rural Household
-0.0387
(0.0462)
Household head's education
0.0606***
(0.0034)
Log(Number of sons)
-0.1876***
(0.0349)
First Born
-0.0609
(0.0406)
Last Born
-0.1616***
(0.0431)
Age of child
0.0649**
(0.0232)
(Age of child)2
0.0114***
(0.0012)
Agricultural land owned
0.2599***
(0.0356)
Member still in school
0.6539***
(0.0332)
Constant 2.2295*** 3.4650*** 3.9254*** 0.5774*** (0.0616) (0.0582) (0.0946) (0.1381)
R2 0.0070 0.2619 0.2657 0.4899 Degrees of freedom 24,698 24,697 24,696 23,082
Number of observations 24,700 24,700 24,700 23,101 Standard errors in parentheses and are calculated using Stata's robust estimation method to account for heteroskedasticity; *p<0.05 , **p<0.01 , ***p<0.001
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Table 7: Education received in years, 1998-99 Variable: Education received 1 2 3 4
Log(Number of children) -0.5811*** -0.455*** -0.8427*** -0.4579*** (0.0421) (0.0351) (0.0639) (0.0580)
Female child
-3.0768*** -4.3068*** -4.3850***
(0.0310) (0.1001) (0.1024)
Interaction Variable
0.8427*** 0.8846***
(0.0639) (0.0626)
Electricity
0.1822***
(0.0381)
Radio
0.0657
(0.0345)
Television
0.01412**
(0.0441)
Refrigerator
0.1781*
(0.0862)
Bicycle
0.1614***
(0.0306)
Motorcycle
-0.0373
(0.0692)
Car
-0.2304
(0.1645)
Telephone
-0.0166
(0.1031)
Rural Household
0.1050*
(0.0512)
Household head's education
0.0440***
(0.0034)
Log(Number of sons)
-0.0617
(0.0342)
First Born
-0.1642***
(0.0428)
Last Born
-0.0034
(0.0444)
Age of child
-0.1507***
(0.0250)
(Age of child)2
0.0228***
(0.0012)
Agricultural land owned
0.1402***
(0.0346)
Member still in school
1.1581***
(0.0369)
Constant 2.4784*** 3.7500*** 4.3068*** 1.1830*** (0.0676) 0.0624 (0.1001) (0.1464)
R2 0.0105 0.2890 0.2945 0.5500 Degrees of freedom 22,288 22,287 22,286 19,989
Number of observations
22,290 22,290 22,290 20,010
Standard errors in parentheses and are calculated using Stata's robust estimation method to account for heteroskedasticity; *p<0.05 , **p<0.01 , ***p<0.001
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! 55 | Gupta
Table 8: Education received in years, 2005-06
Variable: Education received 1 2 3 4 Log(Number of children) -0.8040*** -0.6473*** -1.1999*** -0.7944***
(0.0439) (0.0357) (0.0646) (0.0606) Female child
-3.4287*** -5.0807*** -4.9075***
(0.0319) (0.0967) (0.0953) Interaction Variable
1.1999*** 1.0070***
(0.0646) (0.0616) Electricity
0.1145**
(0.0389) Radio
0.0125
(0.0324) Television
-0.0082
(0.0392) Refrigerator
0.2588***
(0.0579) Bicycle
0.2780***
(0.0341) Motorcycle
0.1385**
(0.0482) Car
-0.1394
(0.1163) Telephone
0.02254**
(0.0711) Rural Household
0.1322*
(0.0434) Household head's education
0.0529***
(0.0033) Log(Number of sons)
-0.0881*
(0.0365) First Born
-0.2941***
(0.0408) Last Born
-0.1996***
(0.0423) Age of child
0.2710***
(0.0201) (Age of child)2
0.0039***
(0.0010) Agricultural land owned
0.1731***
(0.0352) Constant 2.9121*** 4.3344*** 5.0807*** 0.5662***
(0.0677) (0.0615) (0.0967) (0.1391) R2 0.0180 0.3253 0.3352 0.5596
Degrees of freedom 22,855 22,854 22,853 21,351 Number of observations 22,857 22,857 22,857 21,371
Standard errors in parentheses and are calculated using Stata's robust estimation method to account for heteroskedasticity; *p<0.05 , **p<0.01 , ***p<0.001
Table 9: Correlation Matrix, 1992-93,"(* 0.05 Significance)
Variables
Education received in years
Log(Number of children)
Female child Radio Refrigerator Television Bicycle Motorcycle Car
Rural Household
Education received in years 1.0000
Log(Number of children) -0.0837* 1.0000
Female child -0.5073* 0.0327* 1.0000 Radio 0.0967* 0.0114* 0.0181* 1.0000
Refrigerator 0.0822* -0.1126* 0.0079 0.2292* 1.0000 Television 0.1220* -0.0529* 0.0083 0.3986* 0.4325* 1.0000
Bicycle 0.0940* 0.1413* -0.0008 0.2441* 0.0649* 0.1690* 1.0000 Motorcycle 0.0821* -0.0501* 0.0112 0.2880* 0.5210* 0.4565* 0.0954* 1.0000
Car 0.0397* 0.0054 -0.0028 0.1051* 0.3032* 0.1549* 0.0172* 0.2362* 1.0000
Rural Household -0.0552* 0.0899* -0.0031 -
0.2092* -0.4064* -0.4953* -
0.0181* -0.2720* -
0.1242* 1.0000 Household head's
education 0.1562* -0.0878* 0.0060 0.2867* 0.3292* 0.3968* 0.1321* 0.2208* 0.1141* -0.2945*
Log(Number of sons) 0.0472* 0.6979* -
0.2218* 0.0113* -0.0760* -0.0340* 0.1175* 0.0096* 0.0255* 0.0611*
First Born 0.2062* -0.1468* 0.0117 0.0184* 0.0027 -0.0083* -
0.0364* -0.0057 -0.0063 -0.0093*
Last Born -0.0213* -0.1468* 0.0117 0.0184* 0.0027 -0.0083* -
0.0364* -0.0057 -0.0063 -0.0093* Age of child 0.4211* -0.0946* 0.0151* 0.0410* 0.0350 0.0545* 0.0470* 0.0312* 0.0215* -0.0297*
(Age of child)2 0.4129* -0.1030* 0.0179* 0.0448* 0.0340* 0.0561* 0.0507* 0.0322* 0.0204* -0.0297* Agricultural land
owned 0.0227* 0.1029* 0.0011 -
0.0517* -0.2510* -0.2695* 0.0632* -0.1249* -
0.0628* 0.5687*
Member still in school 0.1227* 0.0172* -
0.1500* 0.1183* 0.0857* 0.1454* 0.0641* 0.0901 0.0254* -0.0814*
(Continued)
!!
! 57 | Gupta
Table 9 (continued): Correlation Matrix, 1992-93,"(* 0.05 Significance)
Variables
Household head's
education Log(Number
of sons) First Born
Last Born
Age of child
(Age of child)2
Agricultural land owned
Member still in school
Education received in years
Log(Number of children)
Female child Radio Refrigerator Television Bicycle Motorcycle Car Rural Household Household head's
education 1.0000 Log(Number of sons) -0.0710* 1.0000
First Born 0.0081* -0.0952* 1.0000 Last Born 0.0081* -0.0952* 0.0005 1.0000
Age of child 0.0182* -0.0362* 0.4697*
-0.1244* 1.0000
(Age of child)2 0.0160* -0.0403* 0.4978*
-0.1167* 0.9845* 1.0000
Agricultural land owned -0.0994* 0.0676*
-0.0208*
-0.0208* 0.0083 0.0080 1.0000
Member still in school 0.2163* 0.0262*
-0.1596* 0.0419*
-0.1338*
-0.2273* 0.0103 1.0000
!!
! 58 | Gupta
Table 10: Correlation Matrix, 1998-99,"(* 0.05 Significance)
Variables
Education received in years
Log(Number of children)
Female child Electricity Radio Refrigerator Television Bicycle Motorcycle Car
Education received in years 1.0000
Log(Number of children) -0.1026* 1.0000
Female child -0.5316* 0.0422* 1.0000 Electricity 0.0935* -0.0331* 0.0096 1.0000
Radio 0.0839* 0.0104* 0.0035 0.2646* 1.0000 Refrigerator 0.0739* -0.0998* 0.0053 0.3424* 0.2383* 1.0000
Television 0.1080* -0.0378* 0.0080 0.5523* 0.3672* 0.3788* 1.0000 Bicycle 0.0719* 0.0995* 0.0004 0.1053* 0.2201* 0.0778* 0.1413* 1.0000
Motorcycle 0.0544* -0.0069 0.0114 0.2877* 0.2872* 0.4737* 0.4082* 0.1191* 1.0000 Car 0.0212* -0.0036 0.0012 0.1276* 0.1077* 0.2159* 0.1627* 0.0407* 0.2557* 1.0000
Telephone 0.0507* -0.0697* 0.0021 0.2510* 0.2069* 0.5482* 0.2978* 0.0643* 0.4431* 0.3198*
Rural Household -0.0630* 0.1006* -0.0048 -0.5189* -
0.1610* -0.4537* -0.4579* -
0.0180* -0.2605* -
0.1092* Household head's
education 0.1337* -0.1240* -0.0023 0.3066* 0.2478* 0.3079* 0.3482* 0.0970* 0.2926* 0.1405*
Log(Number of sons) 0.0920* 0.5757* -
0.2262* -0.0007 0.0165* -0.0470* -0.0025 0.0592* 0.0039 0.0043
First Born 0.2055* -0.1470* 0.0154* -0.0078 -
0.0198* -0.0046 -0.0134* -
0.0266* -0.0171* -
0.0085*
Last Born -0.0180* -0.1470* -
0.0563* -0.0078 -
0.0198* -0.0046 -0.0134* -
0.0266* -0.0171* -
0.0085* Age of child 0.4465* -0.1057* 0.0136* 0.0603* 0.0522* 0.0521* 0.0757* 0.0468* 0.0240* 0.0103
(Age of child)2 0.4390* -0.1185* 0.0181* 0.0579* 0.0553* 0.0498* 0.0764* 0.0509* 0.0229* 0.0072* Agricultural land
owned 0.0153* 0.0669* 0.0081 -0.2585 0.0183* -0.2208* -0.2051* 0.0800* -0.0748* -
0.0331*
Member still in school 0.1947* 0.0010 -
0.1257* 0.1234* 0.0903* 0.0946* 0.1207* 0.0481* 0.0960* 0.0530*
!!
! 59 | Gupta
Table 10 (continued): Correlation Matrix, 1998-99,"(* 0.05 Significance)
Variables Telephone Rural
Household
Household head's
education Log(Number
of sons) First Born
Last Born
Age of child
(Age of child)2
Agricultural land owned
Member still in school
Education received in years
Log(Number of children)
Female child Electricity Radio Refrigerator Television Bicycle Motorcycle Car Telephone 1.0000
Rural Household -0.3109* 1.0000 Household head's
education 0.2521* -0.2667* 1.0000 Log(Number of sons) -0.0389* 0.0621* -0.0639* 1.0000
First Born -0.0058 -0.0056 0.0089* -0.0745* 1.0000 Last Born -0.0058 -0.0056 0.0089* -0.0745* -0.0069 1.0000
Age of child 0.0259* -0.0652* 0.0298* 0.0108* 0.4567*
-0.1318* 1.0000
(Age of child)2 0.0219* -0.0616* 0.0273* -0.0011 0.4862*
-0.1211* 0.9845* 1.0000
Agricultural land owned -0.1404* 0.4819* -0.0415* 0.0248*
-0.0171*
-0.0171* -0.0068 -0.0050 1.0000
Member still in school 0.0831* -0.0718* 0.1835* 0.0364*
-0.1149* 0.0050 -0.0088
-0.1109* 0.0308* 1.0000
!!
! 60 | Gupta
Table 11: Correlation Matrix, 2005-06,"(* 0.05 Significance)
Variables
Education received in years
Log(Number of children)
Female child Electricity Radio Refrigerator Television Bicycle Motorcycle Car
Education received in years 1.0000
Log(Number of children) -0.1343* 1.0000
Female child -0.5600* 0.0472* 1.0000 Electricity 0.0901* -0.1073* -0.0092 1.0000
Radio 0.0669* 0.0404* 0.0019 0.1776* 1.0000 Refrigerator 0.1046* -0.1878* -0.0089 0.4268* 0.1720* 1.0000
Television 0.0962* -0.1181* -0.0071 0.6087* 0.2745* 0.4683* 1.0000 Bicycle 0.0836* 0.0821* -0.0072 0.0068* 0.1412* -0.0374* 0.0656* 1.0000
Motorcycle 0.0967* -0.0917* -0.0122 0.3516* 0.2483* 0.5443* 0.4239* 0.0341* 1.0000 Car 0.0300* -0.0745 -0.0003 0.1543* 0.1111* 0.3115* 0.1822* -0.0181* 0.3002* 1.0000
Telephone 0.0948* -0.1495* -0.0111 0.3112* 0.1835* 0.5472* 0.3561* 0.0111* 0.4813* 0.3885*
Rural Household -0.0573* 0.1615* 0.0186* -0.5733* -0.0669* -0.4910* -0.4787* 0.1128* -0.2970* -
0.1656* Household head's
education 0.1410* -0.2114* -0.0050 0.3303* 0.2197* 0.4099* 0.3676* 0.0614* 0.3964* 0.2366*
Log(Number of sons) 0.0375* 0.6821* -
0.2131* -0.0592* -0.0300* -0.1318* -0.0769* 0.0779* 0.0564 -
0.0428*
First Born 0.1845* -0.1373* 0.0279* -0.0084* -0.0134* -0.0047 -0.0120* -0.0173* -0.0175* -
0.0088*
Last Born -0.0048 -0.1373* -
0.0703* -0.0084* -0.0134* -0.0047 -0.0120* -0.0173* -0.0175* -
0.0088* Age of child 0.4511* -0.1074* 0.0130* 0.0846* 0.0627* 0.0624* 0.0874* 0.06360* 0.0401* 0.0038
(Age of child)2 0.4446* -0.1182* 0.0164* 0.0881* 0.0669* 0.0673* 0.0924* 0.0637* 0.0470* 0.0048 Agricultural land
owned 0.0161* 0.1009* 0.0217* -0.3330* 0.0323* -0.2778* -0.2550* 0.1676* -0.0811* -
0.0633*
!!
! 61 | Gupta
Table 11 (continued): Correlation Matrix, 2005-06,"(* 0.05 Significance)
Variables Telephone Rural
Household
Household head's
education Log(Number
of sons) First Born
Last Born
Age of child
(Age of child)2
Agricultural land owned
Education received in years
Log(Number of children)
Female child Electricity Radio Refrigerator Television Bicycle Motorcycle Car Telephone 1.0000
Rural Household -0.3043* 1.0000 Household head's
education 0.3609* -0.2514* 1.0000 Log(Number of sons) -0.0939* 0.1074* -0.1712* 1.0000
First Born -0.0122* -0.0050 0.0086* -0.0901* 1.0000 Last Born -0.0122* -0.0050 0.0086* -0.0901* 0.0172* 1.0000
Age of child 0.0514* -0.0679* 0.0461* -0.0429* 0.4393*
-0.1491* 1.0000
(Age of child)2 0.0550* -0.0681* 0.0488* -0.0492* 0.4708*
-0.1401* 0.9843* 1.0000
Agricultural land owned -0.1394* 0.5594* -0.0516* 0.0507*
-0.0157*
-0.0157* 0.0067 0.0074 1.0000
Appendix A: Statistics for India and Uttar Pradesh
Table 1: Population of India (1951-2011) (in thousand) 1951 1961 1971 1981 1991 2001 2011 Uttar Pradesh
60274 70144 83849 105137 132062 166198 199581
India 361088 439235 548160 683329 846421 1028737 1210193 Source: Economic Survey of India 2010-11, Government of India
Table 2: Selected Social Indicators for India 1990-91 2010-11
Population (million) 679 1201 Birth Rate (per 1000) 33.9 22.1 Death Rate (per 1000) 12.5 7.2
Life expectancy at birth (in years)
58.7 63.5 Male 58.6 62.6
Female 59 64.2 Education: Literacy
Rate (%) 52.2 74
Male 64.1 Female 39.3
Source: Economic Survey of India 2010-11, Government of India
Table 3: Selected Indicators of Human Development Life Expectancy at Birth
(2002-2006) Infant Mortality Rate (per 1000 live births) (2010)
Male Female Total Male Female Total Birth Rate (per 1000) (2010)
Death rate (per 1000) (2010)
Uttar Pradesh
60.3 59.5 60 58 63 61 28.3 8.1
India 62.6 64.2 63.5 46 49 47 22.1 7.2 Source: Economic Survey of India 2010-11, Government of India
Table 4: State-wise Literacy Rates (1951-2011) (in per cent) 1951 1961 1971 1981 1991 2001 2011 Uttar Pradesh
12.02 20.87 23.99 32.65 40.71 56.27 69.72
India 18.33 28.30 34.45 43.57 52.21 64.84 74.04 Source: Economic Survey of India 2010-11, Government of India
!!
! 63 | Gupta
Table 5: State-wise Infant Mortality Rates (2009-2010) (in per cent) 2009 2010 Male Female Person Male Female Person Uttar Pradesh
41 42 41 37 39 38
India 49 52 50 46 49 47 Source: Economic Survey of India 2010-11, Government of India
Table 6: Number of Recognized Educational Institutions in India (2009-10) (provisional)
Pre-Degree/Junior Colleges/Higher Sec. Schools
High/Post Basic Schools
Middle/Senior Basic Schools
Primary/Junior Basic Schools
Universities
Uttar Pradesh
8547 7889 51948 123403 36
India 66917 123726 367745 823162 436 Source: Economic Survey of India 2010-11, Government of India
Table 7: Gross Enrollment Ratio in Grade I-V and VI-VIII and I-VIII (2009-10) Grade I-V (6-10 years) Grade VI-VIII (11-13 years) Grade I-VIII (6-13 years) Boys Girls Total Boys Girls Total Boys Girls Total Uttar Pradesh
106.6 114.7 110.4 74.3 65.9 70.3 94.7 96.3 95.4
India 115.6 115.4 115.5 84.5 78.3 81.5 103.8 101.1 102.5 Source: Economic Survey of India 2010-11, Government of India
Table 8: Child Labor (2004-05) (in thousands) Rural Urban All % Share of Child
Labor Uttar Pradesh 1620 459 2074 22.9
India 7445 1525 9075 100 Source: Derived from Unit Level Records of National Sample Survey Organization
(NSSO), 2004-05, Magnitude of Child Labor in India
!!
! 64 | Gupta
Table 9: Sectoral Distribution of India’s Child Labor (2004-05) (in per cent)
Agri. Mining & Quarrying
Mfg. Elec. &
Water
Cons. Trade &
Hotel
Transport Fin. Com. &
Soc.
Total
Uttar Pradesh
61.24 0.00 25.34 0.00 0.40 9.73 0.68 0.50 2.11 100
India 68.14 0.25 16.55 0.02 1.95 8.45 0.66 0.57 3.41 100 Source: Estimated from Unit Level Records of National Sample Survey Organization
(NSSO), 2004-05, Magnitude of Child Labor in India
Table 10: Census Data for Uttar Pradesh, 2010 (for children up to 14 years) Year 1971 1981 1991 2001
Child Labor 10753985 13640870 11285349 12666377 Source: Censes Data, 2010, UNICEF
!!
! 65 | Gupta
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