Intergenerational Consequences of Early Age Marriages of Girls ...

Intergenerational Consequences of Early Age Marriages of Girls:

Effect on Children’s Human Capital ∗

Sheetal Sekhri† and Sisir Debnath‡

This Draft: December, 2010

Abstract

We use a nationally representative data from India on test scores in an instrumental

variable framework to isolate the causal effects of early age marriages of girls on human

capital of their children. Early age marriages reduce mother’s educational attainment

which can adversely impact the education outcomes of their children. On the other

hand, better marriage prospects of young brides may compensate and improve chil-

dren’s educational outcomes by way of resource provision. Consequently, the effect of

early age marriages of girls on their children is theoretically ambiguous and warrants an

empirical examination. In our empirical analysis, we use plausibly exogenous variation

in age at menarche to instrument for marriage age. Our estimates show that a delay

of one year in the marriage age of the mother increases the probability of being able

to do the most challenging arithmetic and reading tasks on the administered test by 3

percentage points.

Key words: Early-Age Marriages, Child Development, Human Capital

∗We would like to thank Leora Friedberg, Sarah Turner and the participants of the the Labor Economics

Research group at University of Virginia for their insightful suggestions. We also wish to thank Virendra

Rao for sharing the Gender, Marriages, and Kinship Survey data with us.

†University of Virginia, Email: [email protected]

‡University of Virginia, Email: [email protected]

1

1 Introduction

Marriage practices in developing countries, particularly in rural areas, often involve early

marriages of adolescent girls. In many countries around the world, this practice remains

widespread. In India, about 5 percent of girls between the ages of 10 and 14, and over 35

percent girls between the ages of 15 and 19, are married (Census of India, 1991). Similarly,

51 percent of women in Bangladesh and as high as 74 percent of women in Niger are married

before the age of 18 (UNFPA, 2007). Poverty, social norms, lack of security for young

adolescent girls, and parental attitudes toward girls have been identified as potential reasons

for early marriage of females in developing countries. Early age marriages can also have

implications for children. In this paper, we empirically investigate how mother’s age at

marriage influences their children’s welfare.

Early marriages and subsequent early motherhood constraints human capital formation of

women (Field and Ambrus, 2008). Young brides also tend to have less control over resources

in their husband’s families and experience more domestic violence (Jenson and Thornton,

2003). On the other hand, there is a premium on age in the marriage markets. All else equal,

younger brides may be able to marry into relatively richer households. Given this trade-

off, the effect on child welfare is not clear. A number of early studies show that mother’s

education improves the child’s human capital. There is also a growing literature which shows

that income or assets controlled by women are associated with improvements in child health

and greater household spending on nutrients, health and housing (Thomas 1994, Duflo 2003).

Therefore, more schooling and greater control over household resources for women could

translate into greater human capital for the next generation. But, better endowed households

may be able to compensate for the lack of mother’s education. Therefore, theoretically the

effect of mother’s age at marriage on children’s outcomes is ambiguous. Our paper focuses on

the empirical examination of the intergenerational consequences of early marriages of girls.

2

We use a nationally representative data from India, the India Human Development Survey

of 2005, which includes data on test scores of children to isolate the causal effects of early

age marriages of girls on human capital of their children. The main empirical challenge in

identifying the causal effect is that marriage age may be endogenous. In order to address this,

we use age at which girls experience their first menstrual cycle as an instrument for their age

at marriage. Variation in the age at menarche generates a quasi-random difference in the age

at which a girl enters the marriage market.1 Mothers coming from an economically strong

background may receive higher nutrition, and hence would be more likely to menstruate early,

and their offspring might have better health and education resulting from the economic status

of their grandparents. However, conditioning on the nutritional status of the mother, age at

menarche would provide plausibly exogenous source of variation in age at marriage. We use

mothers’ height as a proxy for the nutrition she received in her childhood. It is worthwhile to

point out that since the economic status of the natal family is negatively correlated with age

at menarche, any bias resulting from omission of the economic status will tend to attenuate

the results and the estimates will underreport the effect.

Our estimates show that a delay of one year in the age of marriage of the mother increases

the probability of her child being able to do the most challenging arithmetic and reading

tasks by 3 percentage points, and increases the likelihood of being enrolled in school by 3.5

percentage points.

Our paper makes important contributions to the literature in two ways. First, we identify

the causal estimates of the effect of mother’s age at marriage on children’s educational

outcomes. To the best of our knowledge, our paper is the first to examine the causality. In

addition, the data we use provides comprehensive set of variables including performance in

tests that measure basic human cpaital . We present evidence that mother’s age at marriage

1This instrument was used by Field and Ambrus (2008) to measure the effect of early marriage of girlson their school attainment in Bangladesh. She found that delay in marriage by an additional year increaseseducation by 0.22 years and increases the probability of literacy by 5.66 percent

3

affects school choice, time spent on homework, and household outlays on education related

items. Secondly, we bring together two important strands of literature with important

policy implications. Previous research has shown that women’s age at marriage affects their

educational attainment (Field and Ambrus, 2008). Also, a number of other papers have

shown that mother’s education influences the education and health outcomes of children

(Rosenzweig and Wolpin 1994; Currie and Moretti, 2003). We show that controlling for child,

mother’s and household’s characteristics, the reduced form effect of mother’s age at marriage

on children’s educational outcomes is positive. Our results indicate that women’s age at

marriage affects the likelihood of being enrolled in school, and improves the human capital

of their children over and above the effect of the family resources. These intergenerational

externalities warrant that minimum age laws that prevent under-age marriages of women

be passed and enforced. In India, there is a mandated legal minimum age of marriage.

Prohibition of Child Marriage Act in India of 2006 bans child marriage. It also empowers

civil courts to annul such marriages and to make penal provisions for people who solemnize

these marriages.2 The stated objective of a minimum age at marriage is to prevent maternal

mortality and to increase human capital of women. If effectively enforced, such policies can

improve the educational outcomes of the children as well.

The rest of the paper is organized as follows. Section 2 provides background information

on marriage practises in India. In Section 3, we provide the details of the data used in our

empirical analysis. We specify our identification strategy in Section 4, and the results are

reported in Section 5. We discuss robustness of our estimation in Section 6 and Section 7

discusses suggestive channels through which age at marriage may affect children’s human

capital. We conclude in Section 8.

2Apart from legal provisions, many social welfare programs are designed to provide incentives for parentsto delay marriages of their daughters. A prominent micro-finance program in India excludes borrowers whosedaughters marry before 17, and national education vouchers in Bangladesh exclude married girls.

4

2 Motivation and Background

India, like many other developing countries, is a hot-spot for early age marriages of girls.

Historically, the age at marriage for women in India has been very low. The median age

at marriage was 14.5 in 1951, one of the lowest in the world (Agarwala, 1957). According

to National Family Health survey, in 2005-06, the average age at first marriage for Indian

women stood at 17.96 years and 38% of them were married below the age of 18. A number of

explanations have been provided to account for such widespread practice of early marriage

for girls. Traditional customs that evolved to protect kinship networks are often cited as an

important factor (Dyson and Moore, 1983). Parents enter their daughters in the marriage

markets early, when they are young so they can control spousal choices in order to protect

the kinship network. Another proposed explanation is that parents marry the daughters

young due to economic motives. Marriage outcomes are often determined by the size of the

gifts or dowry that parents of the bride can offer to the groom and his family. These dowry

payments can constitute a substantial burden for poor households.3 Younger brides often get

better matches with lower dowry price (Dyson and Moore 1983, Coale 1992). In addition,

due to a patriarchal social structure, women leave their parents house after their marriage,

and reside with the husband’s family. Marrying young daughters can reduce the number of

children to be looked after and fed. Poorer natal families may exercise this option to reduce

their economic burden.

2.1 Menarche and Marriage Outcomes

Most of the marriages are solemnized soon after the girl child reaches menarche. While 9%

of the women in our sample report pre-menarche marriage, around 52% of the marriages

take place within 3 years of puberty. Parents feel concerned about the safety of their daugh-

3In certain parts of India, dowry prices might be over 50 percent of household assets (Rao, 1993).

5

ters as virginity is highly priced in marriage markets (Caldwell, 2005; Desai and Andrist,

2010; Sheela and Audinarayana, 2003). As the girl starts her menstrual cycle, the kinship

network is informed immediately so that the search for the groom can commence (Sheela

and Audinarayana, 2003).

As shown in Figure 1, the average age of first marriage by age at menarche, age of

marriage is closely followed by age at menarche. In Figure 2, we plot the distributions of

age at marriage and menarche, showing that compared to age at menarche the distribution

of age at marriage has a higher variance but it is shifted to the right. Onset of menarche

predicts age at which girls are married.

2.2 Menarche and Changes in Life Style

Reaching menarche becomes a life changing event for the girls. Typically, religious and

social sanctions are imposed on the girl child. For example, hindu girls are forbidden to

enter temples when menstruating. They are often asked to change the way they dress, and

forbidden to play with male children. Parents are also reported to withdraw their daughters

from schools. Girls are refrained from going out of the house alone. Some studies also report

that girls feel traumatized by these changes and tend to remember the timing of menarche

very well (Nahar et al., 1999).

3 Data

The principal data-set we use in our empirical analysis is the India Human Development

Survey (IHDS) of 2005, a nationally representative data-set spanning 41,554 households

over 25 states and union territories of India (with the exception of Andaman/Nicobar and

Lakshyadweep islands). The survey covered 1503 villages and 971 urban neighborhoods.4

4A stratified random sampling technique was used to construct the sampling frame. See Desai, S., Dubey,A., Vanneman, R., and Banerji, R. (2009) for details about the survey.

6

The survey provides comprehensive data that we need for our analysis at both individual

and household level. Ever married women between ages of 15 and 49 were asked to provide

complete information about their marital and reproductive history.

Among other modules, the survey also covered topics concerning health, education, and

employment for all members of the household. Data were collected on children’s school

enrollment, type of school, medium of instruction and hours spent in school, homework,

private tuition, and number of days absent from school in the last month. Expenditure

incurred on school fees, private tuition and on other school accessories were collected as well.

In addition to these self reported measures, children aged 8 to 11 were administered short

reading, writing and arithmetic tests. Children were classified according to their ability to

read, in one of the following five categories: (a) Cannot read at all; (b) Can recognize letters

but cannot read words; (c) Can read words but cannot read entire sentence; (d) Can read a

short paragraph of two to three sentences but cannot read a short story; (e) Can read a one

page short story. The mathematical skill of the children were classified into four categories:

(a) Cannot read numbers above 10; (b) Can read two digit numbers but unable to do more

complex number manipulations; (c) Can subtract a two digit number from another; (d) Can

divide a three digit number by a single digit number. In addition to the maths and reading

tests children were also administered writing tests. The writing scores were classified into

two categories: (a) Unable to write; (b) Can write with two or less mistakes.5

3.1 Sample Construction and Main Outcomes of Interest

The main outcome variables that we analyze are the test scores of the children. Since the

tests were administered only to 8 to 11 year children, we restrict our sample to them while

analyzing test score outcomes. We restrict our sample to children without missing values

5Since these categories are not exhaustive, the scores are not meaningfully reported for all children.Hence, we do not analyze effects on writing scores.

7

for the test scores. The IHDS data consists of 29,263 children. Among them, 9126 children

are between ages 8 to 11. However, the tests were administered to only 75.4% of them.

Children could not be tested without parental consent which shrinks the sample size for this

analysis. We examine whether the parents and households where these variables are missing

are systematically different from those which report the test scores and find no evidence of

observable differences. Table 1.1. compares the summary statistics across all children, those

who took the test and those who did not. From a comparison of Columns (iii) and (v), we

find no systematic differences between the children whose parents consented versus those

whose parents did not consent to administer the tests.

3.2 Summary Statistics

Summary statistics for the children between 8 to 11 years with test scores are presented in

the last two columns in Table 1.1. There are 6884 children in this age group, belonging to

5787 mothers. The average age of marriage for the mothers is 17, which is one year below

the legal age. The average years of schooling for mothers is 3.93. Fathers are 5.2 year older

than mothers on average. 80 % of the households are Hindu and 26 % of them are below the

official poverty line. The average number of sibling for a child is 3.5. 53 % of the children

are boys.

Table 1.2. provides the summary statistics of children’s outcome by the age at first

marriage of their mother. The table shows that the the mothers who got married at the

age 18 or later are more likely to enroll their children in private schools and their offsprings

spend more time at school, homework and private tuition. On an average their children

score 0.37 and 0.43 points more in math and reading tests respectively, compared to those

who got married at 17 or earlier.

We further explore the relationship between age at marriage of mothers on test score

outcomes of their children in Figures 3 and 4. These figures plot the distribution of children’s

8

math and reading skills by age of marriage of their mother. In Figure 3, the average number

of children who Cannot do math are unfamiliar with basic mathematical concepts and those

who know only Numbers decrease as the age at marriage of the mother increases. However,

the average number of children who can do Subtraction and Division, which require greater

skills increases steadily with the age at marriage of the mother. The same patterns are

reflected in Figure 4 for reading scores. We formally test these observations in the following

sections.

4 Identification Strategy

Our goal is to evaluate the intergenerational effects of early age marriages of women. We

want to isolate the causal effect on the human capital of their children. The empirical model

is specified as follows:

Yij = β0 + βaAj + βfFij + βmMj + βhHj + βxXij + εij (1)

where Yij is the outcome of child i born to mother j, Aj is the age at marriage of the mother,

Fij are the characteristics of the father, Mj are the characteristics of the mother, Hj are

household characteristics, Xij are the characteristics of the child i and εij is a random error

term. The coefficient βa on age at marriage Aj, is the parameter of interest. We rewrite

equation (1) as follows:

Yij = β0 + βaAj + βwWij + εij (2)

where βw = (βf βm βh βx) and Wij = [Fij Mj Hj Xij]′. Wij includes all regressors except

age at marriage of the mother and βw is the coefficient on Wij.

The are two main empirical challenges. First, age of marriage may be endogenous.

Omitted variables may affect both the age at marriage of the mother and the child outcomes.

9

For example, a father who prefers to invest in his children may also have stronger preferences

not to marry a very young woman. In principal, there might be other potential omitted

variables which are not orthogonal to age of marriage of the mother and might be correlated

with the children’s outcomes. The second issue relates to the accuracy of the report of age

of marriage. During the survey age at marriage was self reported. Inaccurate reports would

generate measurement error in the explanatory variable and could attenuate the estimates

of the coefficient of interest. To address these concerns, we follow an instrument variable

(IV) approach. We use age of menarche as an instrument for marriage age of the mother.

4.1 Instrumental Variable Approach

The IV approach involves estimating a two stage model which is specified as follows:

Aj = α0 + αzZj + αwWij + ηj (3)

Yij = β0 + βaAj + βwWij + εij (4)

The first stage is given by the equation (3), and equation (4) is the structural equa-

tion. The mother’s age at marriage Aj is instrumented by Zj, her age at menarche, and

Yij are the children’s outcome of interest. As above, Wij is a set of control variables that

include child’s, mother’s and father’s characteristics, household background and socioeco-

nomic status. Child’s characteristics include number of siblings, gender, grade and birth

order. Mother’s characteristics include age and height. Father’s characteristics include age

and education, and in the background of the family we include number of household mem-

bers, place of residence (urban/rural), land ownership and dummies for below poverty line

status and religion.

We use a standard two stage estimation procedure when the outcome variable is con-

tinuous, ordered probit when the outcome variable is an ordered categorical variable, and a

10

probit model when the outcomes are binary. We cluster standard errors at the village level.

We perform a number of robustness checks to test for the validity of the instruments.

4.2 Validity of the Instrument

First, we examine whether age at menarche predicts age at marriage which is the endogenous

regressor. Consistent with Field and Ambrus’s findings for Bangladesh, we find that age at

menarche significantly predicts age at marriage in India. The results from the regression of

women’s age at first marriage are presented in Table 2. Column (i) reports the coefficient

on age at menarche without additional controls. The coefficient of 0.32 is highly statistically

significant, and the F Statistic is 133.7, eliminating concerns about ‘weak instruments’.

Next, we examine the threats to the validity of this instrument. Acute malnutrition in

early childhood can result in delayed onset of menarche. Exposure to acute malnutrition

could potentially affect long term health of the mother and consequently her child. This

could undermine our instrument. Medical evidence suggests that severe loss in food intake

can result in stunting, and in some cases delayed onset of puberty. The changes in nutrition

that could result in delayed onset of menarche are also likely to result in stunting (Stathopolu

et al, 2003). We explore this correlation in our sample. Figure A2 in the Appendix shows

adult heights by age at menarche among the mothers in our sample. We do not observe any

evidence of correlation between adult height and age at menarche.

Volatility in exposure to malnutrition also affects maturation (Field and Ambrus, 2008).

Agriculture and allied activities, that employ majority of the Indians, are overly weather

dependent. Extreme weather conditions in the mother’s birth year like drought and flooding

might lead to crop failure resulting in transitory but severe malnutrition. Therefore, females

born during these unprecedented weather events may experience delayed age at menarche

as they are more likely to be malnourished. We control for this possibility in our first stage

regression. In column (ii), we add birth year fixed effects for the mother to account for

11

extreme weather events at the time of birth.6 The point estimates and standard errors are

remarkably similar across columns (i) and (ii). Next, we include adult height in the regression

in column (iii) as a proxy for acute malnutrition in childhood. Neither the point estimates,

nor the standard errors change. We condition all subsequent results on adult height and

mother’s birth year fixed effects.

It is conjectured that hard physical labor in early childhood can influence menarcheal

age (Pellerin-Massicotte et al., 1997). However, the children who work in India do not do

strenuous physical work like construction. Detailed data on child labor collected by Das

from northern India show that 99.8 % of working girls of age 6 to 14 are engaged in domestic

work while 0.001 % of them work for wage (Basu, Das and Dutta, 2010). Economic status

of the woman’s natal family might affect the age at which she reacheed puberty as it might

affect whether the women worked strenuously as a child or not. We do not directly observe

the economic status of the parents of the mother. However, married women were asked if

the economic status of their natal family was the same as their husband’s family. After

restricting the sample to those women who were married within same economic status, we

control for an asset index of the husband’s family to account for the socioeconomic status of

the natal family.7 For this sample, we report the OLS results for age at menarche conditioned

on adult height with mother’s birth year fixed effects in column (iv). The results from the

regression in which we additionally control for the family’s asset index are reported in column

(v). The coefficient of age of menarche on age at marriage is still highly significant.

To further substantiate the observation that conditional on mother’s height and birth year

fixed effects, the age of menarche is not influenced by the characteristics of the natal family,

we examine the relation between age of menarche and characteristics of the natal family using

an additional survey data from India.8 We first show that the distributions of age of menarche

6Birth year fixed effects can also account for any exposures to environmental determinants of age atmenarche.

7We construct an asset index using Principal Component Analysis from the asset data.8We use data from the Gender, Marriage,and Kinship Survey conducted by NCAER in 1995. This data

12

and age of marriage across the two datasets - IHDS and Gender, Marriage, and Kinship

Survey are similar. We plot the kernel densities of age at menarche and age at marriage in

the top panel in the appendix Figure A3. This indicates a similar relation between the two

variables as indicated by Figure 2, based on IHDS-2005 (the data we use in our empirical

analysis). The bottom panel shows the relationship between the age at menarche by literacy

of father and whether father owned irrigated land before the marriage of the girl in the

Gender, Marriage, and Kinship survey data. None of figures in the bottom panel show any

systematic relation between socioeconomic characteristics of the natal family and the age of

menarche of the girl. This is suggestive that conditional on height and birth year fixed effects,

age at menarche may not be influenced by the socioeconomic characteristics of the natal

family. Most importantly, since economic status and age at menarche would be negatively

correlated, exclusion of socioeconomic status of mother’s natal family, would attenuate our

estimates of age at marriage on child outcomes. If anything, we would underreport the effect

of age of marriage on child outcomes.

Geographical features like temperature and altitude also may influence age at puberty.9

The data do not report the location of the mother’s natal family. However, we rely on

estimated size of the marriage markets to predict natal family’s location. We re-estimate

the first stage and regress age at marriage on age at menarche controlling for the average

temperature and elevation of the proxied natal locations. A detailed discussion of the method

followed and the results is presented in section 6.2.

These robustness tests lend further credibility to our hypothesis that conditional on adult

height and birth year fixed effects, the residual variation in age at menarche is plausibly

exogenous.

is collected from rural areas of two large states of India (Uttar Pradesh and Karnatka).9See Field and Ambrus, 2008 for a detailed discussion

13

5 Empirical Results

In this section, we provide empirical evidence that a mother’s age at marriage affects the

human capital, school outcomes of her children and household investments in their human

capital. Human capital is measured by a child’s performance in arithmetic, and reading tests

that were administered during the interview. School outcomes are measured by enrollment

status and the type of the school. Investment in human capital is measured by outlays on

education related items and time spent studying.

5.1 Children’s Human Capital

To measure the impact of early marriage on a child’s human capital, we estimate the effect

of a mother’s age at marriage on a child’s test scores. The tests were administered during

the survey to 8 to 11 year old children which measured arithmetic and reading skills. These

scores are ordered categorical variables.10

The results from OLS and Ordered Probit regressions for test scores on mother’s age at

marriage are reported in Table 3. These estimates are not causal, as age at marriage of

mother is potentially endogenous. The OLS coefficient for math scores, reported in column

(i), is 0.013 and it is highly significant at 1% level.11 Since the scores are ordered we present

the estimates from an Ordered Probit model in column (ii). The Ordered Probit coefficient

for math score is 0.018 and it is significant at 1% level. The coefficient does not measure

the direct effect of mother’s age at marriage on child’s math score but it provides crucial

information about the sign of the effect for the lowest and highest categories of the score.

10Children were awarded scores based on their skills, these scores were integers between 0 and 4. Forexample children who could recognize only single digit number were given a score of zero, the lowest score,and those who could divide a three digit number with a two digit number were awarded a score of 3, thehighest math score.

11Arithmetic score, is an ordered categorical variable. The four categories of the score are: (a) Score 0:cannot read numbers above ten; (b) Score 1: can recognize two digit numbers but not able to do morecomplex number manipulation; (c) Score 2:can subtract a two digit number from another; (d) Score 3: candivide a three digit number between by a single digit number.

14

The positive coefficient suggests that increase in mother’s age at marriage increases the

probability that her child will score the highest possible score and decreases the probability

that the child will score the lowest possible score.

The results from the OLS and Ordered Probit regressions on reading scores are reported in

columns (iii) and (iv). The coefficients from both the regressions are positive and significant

at 1% level.12 The coefficient estimates for mother’s age at marriage are the same from both

the models at 0.019. This suggests that the marginal effect of mother’s age at marriage is

negative for the lowest math and reading score categories, but it is positive for the highest

category.

As the estimates presented in Table 3 potentially suffer from endogeneity, we use age

at menarche as an instrument for the endogenous regressor and since the scores are ordinal

we estimate the effect of marriage timing with a IV-Ordered Probit model. We estimate

a Seemingly Unrelated Regression model, given by equation (3) and (4). Only the final

stage, equation (4) is structural, and the estimators are obtained by maximizing a Limited

Information Likelihood function (LIML). The results from the IV-Ordered Probit regression

on math score are reported in Table 4.1. Panel A of the table reports the first stage of the

regression. The coefficient on age at menarche is highly significant and positive. One year

delay in onset of menarche increases age at marriage by 0.34 years. Panel B in the same table

reports the marginal effect of mother’s age at marriage for several categories of math score.

All the estimates are highly statistically significant. The estimates show that an increase in

age at marriage by one additional year decrease the probability that the child will receive the

lowest score (a child will be able to recognize a two digit number but will not be able to do

more complex number manipulation) by 1.8 percentage points and increases the probability

12Scores in reading are classified under five categories; (a)Score 0: cannot read at all; (b)Score 1: canrecognize letters but cannot read words; (c) Score 2:can read words but cannot read entire sentence; (d)Score 3: can read a short paragraph of two to three sentences but cannot read a short story; (e) Score 4:can read a one page short story.

15

of receiving the highest score (a child will be able to divide a three digit number by a single

digit number) by 3 percentage points.

Table 4.2. reports the estimates for reading scores. The first stage of the estimation is

presented in Panel A. The first stage estimates differ from those in the Table 4.1. as we

include test language fixed effects in our model. The test language for math test and reading

test could be different. age at menarche is again significant and positively influences age at

marriage. The F statistics is high at 95.9. Panel B reports marginal effects of mother’s age

at marriage for several categories of reading score. Similar to the previous result, delay in

marriage increases the chance that the child will fare better in terms of reading skills. In

particular, delay in marriage of a mother by one year increases the probability that her child

will have the highest reading score (a child will be able to read a one page story) by 3%

points.

The results presented above indicate that after controlling for parents, household, and

child characteristics, mothers’ age at marriage influences children’s human capital.13 If we

assume these effects to be linear with age at marriage, a 2 year delay in marriage could

translate into 6 percentage point increase in probability that a child will have the division

skills and the skill to read a one page story.14

5.2 School Enrollment and School Choices

In this section, we provide estimates of the effects of mother’s age at marriage on school

enrollment and school choices for her children. We explore if the choice of schools in terms

of public versus private, and in terms of English medium versus local language, are affected

by mother’s age at marriage.

13We evaluated the effects on nutrition and health outcomes as well. While the nutritional outcomesare statistically significantly influenced by mother’s age of marriage, health outcomes such as incidence ofrespiratory diseases are not. Results are available on request.

14The average age at marriage for our sample is 17 years. We also check for non-linear effects of mother’sage at marriage on test scores of her child. Our estimations reject presence of any non-linear effects.

16

The results of the estimation are reported in Table 5. Panel A of the table reports the first

stages of the estimation and Panel B reports the second stages. In column (i) the dependent

variable is school enrollment status of a child, which is binary. We estimate an iv-probit

model and use mother’s age at menarche as an instrument for her age at marriage. Column

(i) in Panel B reports the marginal effect of mothers age at marriage on the probability that

a child will be enrolled in school. A one year delay in marriage increases the probability

that a child will be enrolled in school by 3.5 percentage points. The estimate is significant

at 10% level. Similarly, in Column (ii) the dependent variable is whether the chosen school

is private. The estimate is highly significant and one year delay in marriage increases the

probability that a child will be enrolled in a private school by 6.3 percentage points. Finally,

Column (iii) reports the effects of age at marriage of mother on medium of instruction in the

school of her children. The coefficient is negative but it is statistically insignificant. These

estimates suggest that delay in marriage age is likely to improve the chance that the child is

in school, and is more likely to be enrolled in a private school.

5.3 Effects on Investments in Human Capital

We also investigate whether age of marriage of a mother influences expenditure on education

for her children. In particular we measure the effect on school fees, books, and private

tuition.15 We also examine the effect of mothers’ age at marriage on time spent by children

at school, private tuition and homework.

The estimates of the effects of mothers’ age at marriage on outlays on human capital

are reported in Table 6.1. As before, we use mothers’ age at menarche as an instrument.

Columns (i), (ii), and (iii) in Panel A report the first stage of the estimation. The coefficient

on age at menarche is positive and highly significant. Columns (i), (ii) and (iii) in Panel

B report the second stage of the instrumental variable estimation on expenditure on school

15Expenditure on books include expenditure on uniform and transportation (eg. bus fare).

17

fees, books, and private tuition respectively. Our estimates suggest that one year delay

in marriage increases outlays on school fees and books by Rs. 88 and 138 respectively.16

The effect of mothers’ age at marriage on expenditure on private tuition is positive but not

significant.

We also examine the effect of age at marriage on allocation of children’s time for study.

Table 6.2 reports the results. Columns (i), (ii), and (iii) in Panel A report the first stage

of the instrumental variable estimation. The coefficient on age at menarche is positive and

highly significant. Columns (i), (ii), and (iii) in Panel B report the second stage of the

instrumental variable estimation on time spent by children at school, private tuition, and

homework per week respectively. We find that conditional on parent, household, and child

characteristics, a child would spend 40 additional minutes at school per week if his mother

were married one year later. The estimate is marginally significant at 10 % level. Time

spent by children at private tuition, and on homework seems to decline with mothers’ age

at marriage. But this is inconclusive since the estimates are insignificant.

6 Robustness

In this section, we address two additional concerns about our empirical analysis. First, two

variables that we use intensively, age at marriage and menarche, are collected retrospectively.

This raises a concern about strategic misreporting and recall bias in them. Secondly, variety

of medical literature suggests that other than socioeconomic factors puberty is also affected

by geographical and climatic conditions. If the unobserved error in the structural equation

is correlated with climatic variables, such as elevation and temperature, then our instrument

could be potentially invalid. We present substantiating evidence to allay these concerns.

16$1 is equivalent to Rs. 47.

18

6.1 Recall Bias & Strategic misreporting

One concern with our estimates is the presence of systematic measurement error in the

variables that were collected retrospectively, for example, the age of a woman at her marriage

and menarche. One concern might be that the measurement error in the reported age at

marriage may increase with current age of a woman. We conduct a test to check if this

might be of concern. Marriages in India are closely followed by motherhood and pregnancies

outside marriages are rare. Therefore, one should expect the age gap between first birth and

marriage to be small and positive. We calculate the age at first birth for each woman from

current age profile of their children. For 8.75 % women in the full sample, the difference

between age at marriage and age at birth is negative indicating that there could be some

reporting error in the variable. Figure A1 (Appendix) plots the difference between age at

first birth and reported age at marriage by the current age of a women. However, this figure

does not reveal any stark relationship between current age and time gap between age at first

birth and age at marriage. The lowess smoother is almost horizontal at zero, implying there

is no systematic recall bias that increases with age of the women.

Another concern about reported age at marriage is that women who were married under

the legal minimum age of marriage might overstate their marriage age. The legal minimum

age at marriage for females in India is 18. According to Indian Penal Code, conjugal rela-

tionship with a minor girl is a punishable offence. Women who were married below 18 might

not reveal their true age at marriage on the suspicion that the information might be revealed

to the authorities. If such strategic misreporting were prevalent in our data,we would see

a break in the distribution of age at marriage at age 18. We formally test if there is any

discontinuity in the distribution of age at marriage at 18 using McCrary’s DC Density test.17

We do not find any significant jump in the reported age at marriage at 18.

The variable age at menarche is also collected retrospectively. If respondents do not

17Results are available upon request.

19

remember their age at menarche, they might approximate it with their age at marriage and

that might confound the correlation between age at marriage and menarche. The concerns

about systematic measurement error in age at menarche is less severe. First, medical lit-

erature suggests that women are generally able to recall their age at menarche accurately

(Field and Ambrus, 2008). Secondly, as we discussed earlier menarche ushers dramatic life

style changes for a Indian girls. Hindu girls are forbidden to enter temples and to participate

in any other religious activity when they are menstruating. Muslim girls are instructed to

pray five times a day, to keep fast and cover most of their body. In some parts of India,

menarche is celebrated with gifts of jewelry and traditional dresses to the girl. Additionally,

anthropological accounts suggest that most of the girls are unaware of menstruation before

it begins and are traumatized by the event. Therefore, women tend to remember their age

at puberty.

An additional concern might be that menarchial and marriage ages might be misreported

in rural areas or not remembered by women in rural areas. We separate the families according

to their area of residence (urban or rural) and plot the distribution of age at marriage and

age at menarche for these two groups of women in Figure 5. The top panel of the figure

shows the distribution of age at marriage by area of residence and the bottom panel shows

the distribution of age at menarche by area of residence. It is clear that women in urban

areas marry later but no such difference is noticed in the distribution of age at menarche.

This also suggests that age of menarche is less prone to measurement error.

Finally, we follow Field and Ambrus’s strategy to check if we discern differences in age of

menarche across two groups distinguished by a pre-existing preference for different marriage

ages but presumably orthogonal to age at puberty. Since menarche is unrelated to the

preference for early marriage in first group, we do not expect to find significant difference in

the distribution of reported age of menarche. If we notice a difference in age at menarche,

it would be suggestive of recall bias or strategic misreporting. To test this we compare the

20

distribution of age at marriage and age at menarche by parent’s literacy (Field and Ambrus,

2008 used literacy of the mother) using another survey data from India and subsequently

compare the distributions of those variables with IHDS data.18 The idea is to show that

there is no recall bias in age at menarche in the Gender, Marriages,and Kinship Survey data

(refered as NCAER data in the figures) and show that the distribution of age at menarche

and age of marriage is similar across IHDS and this data-set.19 The top panel of Appendix

Figure A4 shows the the distribution of age at marriage varies with literacy of the parent, but

the distribution of age at menarche is similar across these groups characterized by literacy

of the parents. The bottom panel compares the distributions of residuals of age at marriage

and age at menarche across (i) the Gender, Marriages,and Kinship Survey data, (ii) IHDS

data restricted to the districts from which the Gender, Marriages,and Kinship Survey data

was collected, and (iii) the entire sample from the IHDS data. The distributions of residuals

of age at marriage are different across the three data-sets, but the distribution of residual of

age at menarche are remarkably similar.

6.2 Climate & Age at Menarche

According to medical literature, exposure to endocrine disrupting chemicals (direction of

influence varies by compound), strenuous physical activity (delay menarche), abrupt changes

in diet in utero or in childhood (delay puberty), altitude and temperature (high altitude and

cold weather delay puberty) are the major determinants of age at puberty.20 The first two

factors are less likely to confound our results. Exposure to chemicals are strongly correlated

with area of residence, since we control for a indicator for residence, exposure to chemicals

18IHDS does not collect information for women’s natal family. We use data from the Gender Marriage,Kinship Survey conducted by NCAER in 1995. This data is collected from rural areas of two large states ofIndia (Uttar Pradesh and Karnatka).

19Since the states in Gender, Marriages,and Kinship Survey vary in terms of geography, climate, andmany other attributes, we plot the distributions of residual age at menarche after controlling for state fixedeffects and height of the women.

20See Field and Ambrus, 2008 for a comprehensive discussion

21

are also unlikely to confound our results. We also control for mothers’ birth year fixed effects

in all our specifications, thus eliminating concerns for extreme weather events affecting age

at menarche of mother. Lastly, our sample spans all over India with significant geographical

and climatic variation. Therefore, if high altitude delays age at puberty, and thereby age at

marriage, and if schools are difficult to access in high altitude areas, then our results could

be driven by a spurious correlation in these variables introduced by omission of altitude.

Unfortunately, we do not have the geographical location of mother’s natal family. But

in order to confirm whether our estimates are robust to these concerns, we predict the natal

family from estimated size of marriage markets in India. Bolch et al (2002) estimates that

the average distance between husband’s home and wife’s natal home is 21.1 miles for India.

Given that estimate and a 2077 square miles average area of a district, we can assume that

a woman in India is most likely to get married within her natal district. Therefore, we

include geographic and climatic control for the district in which a woman was surveyed to

our regressions. In particular we include altitude and temperature averaged at the district

level of the current location of the women in the sample. In addition, we also construct the

averages of these variables for all neighboring districts that border the district of current

residence. Figure A5 in the Appendix shows a map to serve as an example of neighboring

districts used to construct this measure.21

Appendix Table A1, Panel A reports the coefficients from a regression of age at menarche

on probable predictors of menarche including district averages of temperature and altitude.

In Column (i), we control for the average altitude and temperature of the current residing

district and in Column (ii), we control these variables averaged for the residing and the

neighboring districts. Across these specifications, adult height of the women is statistically

2196% of the women in IHDS report that it takes 10 hours or less to reach their natal home. Therefore,by including the average temperature and altitude of the current residence district and the neighboringdistricts in the regressions, we try to maximize the probability that the climate condition of the natal homeis controlled.

22

significantly correlated with age at menarche, though the correlation is small. However, the

temperature and altitude do not seem to be correlated with age at menarche. The coefficients

are very small and only altitude is marginally significant in the case where we include the

averages of the current residing district.

Panel B reports the results from a regression of age at marriage on age at menarche

including the geographical variables in the regression. This shows that the correlation be-

tween age at menarche and age at marriage is highly significant even after controlling for the

climatic variables. In column (i), we report the coefficients from a specification where we

control for average temperature and elevation in the current residing district. The coefficient

and the standard error on age at menarche is similar to the benchmark case reported in col-

umn (iii) of Table 2. Marriage age is independently correlated with average temperature in

the residing district. We see the same patterns in column (ii) where we include the average

for current residing and neighboring districts. Columns (iii) to (v) restrict the sample to

those women who report that the economic status of their husband’s family is similar to

the natal family. In column (iv), we additionally control for assets owned by the husband’s

family. Neither the coefficient nor the standard error on age at menarche changes.

Finally, we include these geographical variables in our second stage regressions. Appendix

Table A2 reports the results of our estimates for mathematics and reading scores of the child

when we include additional geographical controls. Column (i) and (iii) report the estimates

for maths score while column (ii) and (iv) report the same for reading scores. In column

(i) and (ii), we control for average temperature and altitude of the residing district, and in

column (iii) and (iv), we control the same variables averaged for the residing district and

the neighboring districts. Panel A reports the first stage estimates. The coefficient on age

at menarche is positive and highly significant for both the scores. The coefficient on age at

menarche is different across columns as we control for test language fixed effects. Panel B

reports the second stage estimates. Column (i) shows that an increase in age at marriage

23

by one additional year decreases the probability that the child will receive the lowest math

score (a child will be able to recognize a two digit number but will not be able to do more

complex number manipulation) by 1.9 percentage points and increases the probability of

receiving the highest score (a child will be able to divide a three digit number by a single

digit number) by 3 percentage points. Similarly, column (iii) shows that an increase in age

at marriage by one additional year increases the probability of receiving the highest reading

score by 2.7 percentage points. These results are similar to the benchmark results reported

in Panel B of Tables 4.1 and 4.2.

7 Mechanisms

This section provides suggestive evidence on the possible channels through which early mar-

riage of mothers’ can affect human capital of their children. Previous literature identifies

lower human capital and lower autonomy of women as a consequence to early marriage (Field

and Ambrus 2008, Jenson and Thornton 2003). We explore if early marriage translates into

lower human capital for the next generation through these channels.

The approach we follow to understand the channels involves OLS estimation of the coef-

ficients of mother’s age at marriage on test scores of their children with additional controls

measuring mother’s autonomy and her human capital successively. Subsequently, we com-

pare the results across the specifications with the baseline case where none of the additional

controls are used. If inclusion of a set of additional controls leads to a decline in the coef-

ficient on mother’s age at marriage, then it would suggest that the additional control may

be one of the operational intermediate pathways. This method provides only suggestive

insights to identify the channels. Therefore, the results described in this section should be

interpreted as suggestive.

Table 7 reports the OLS estimates of arithmetic and reading scores of children with addi-

24

tional controls measuring autonomy and human capital of mothers. We use four indicators

as measure of mothers’ autonomy. These variables take the value one if a mother decides (a)

what is to be cooked in the household on a daily basis, (b) whether to purchase an expensive

item, (c) the number of children she bears, and (d) what ought to be done if her children fall

sick. Mother’s years of education is used to measure her human capital. Column (i) and (v)

report the coefficient of mother’s age at marriage on arithmetic and reading scores without

any additional controls.22 A one year delay in mother’s marriage increases arithmetic and

reading scores of the child by 1.2 and 2.1 percentage points respectively. None of the esti-

mates change significantly after controlling for mother’s autonomy additionally as reported

in column (ii) and (vi). However, in column (iii) and (vii), the coefficient of mother’s age

at marriage changes both in magnitude and significance when mother’s years of education

is used as an additional control. Column (iv) and (viii) report the estimates when both

mother’s autonomy and years of education are controlled. The value of R-square across all

the specifications remain remarkably unchanged. These results suggest that a substantial

part of inter-generational effects of early marriage are mediated through lower human capi-

tal of mothers’ as inclusion of mother’s years of education in the regressions attenuates the

effects of mother’s age at marriage. We find little evidence that the early marriage effects

on test scores are mediated through mother’s autonomy.

8 Conclusion

This paper provides empirical evidence that early marriage of girls affects educational and

health outcomes of her children. Delay in age at marriage of a woman leads to an improve-

ment in her children’s human capital. A one year delay in woman’s marriage increases the

22All regressions control for mother’s height, birth year FE and age, indicator for poverty, land ownership,residence (urban/rural), religion, # household members, father’s age and education, # sibling of the child,birth order, gender, grade and test language FE.

25

probability that her children will be able to perform higher level cognitive tasks by 3 per-

centage points. We also show that mothers who marry later are more likely to send their

children to private schools and they spend more on education related items. These effects are

over and above the compensation offered by the household to the child in terms of resources.

Our results suggest that mandating a minimum marriage age and strictly enforcing it will

improve the education outcomes of children.

26

References

[1] Agarwala,S. N. (1957), “The age at marriage in India”, Population Index, Vol. 23, pp.

96-107

[2] Aguero, J. and Ramachandran M. (2010), “The Intergenerational Effects of Increasing

Parental Schooling: Evidence from Zimbabwe”, Working Paper

[3] Basu, K., Das, S., and Dutta, B. (2010), “Child labor and household wealth: Theory

and empirical evidence of an inverted-U”, Journal of Development Economics, Vol. 91,

pp. 8-14

[4] Bolch, F., Rao, V., Desai, S., (2002), “Wedding Celebartions as Conspicuous Consump-

tion: Signaling Social Status in Rural India”, The Journal of Human resources, Vol. 39,

pp. 675-695

[5] Card, D. (1999), “Chapter 30 The causal effect of education on earnings”, Handbook of

Labor Economics, Vol. 3, pp. 1801-1863

[6] Caldwell, K. B. (2005),“Factors affecting female age at marriage in South Asia: Con-

trasts between Sri Lanka and Bangladesh”, Asian Population Studies, Vol. 1, pp. 283-301

[7] Caldwell, J. C. (1979), “Education as a Factor in Mortality Decline An Examination of

Nigerian Data”, Population Studies, Vol. 33, pp. 395-413

[8] Coale, A. J. (1992), “Age of Entry into Marriage and the Date of the Initiation of

Voluntary Birth Control”, Demography, Vol. 29, pp. 333-341

[9] Currie, J. and Moretti, E. (2003), “Mother’s Education and the Intergenerational Trans-

mission of Human Capital: Evidence from College Openings”, The Quarterly Journal

of Economics, Vol. 118, pp. 1495-1532

27

[10] Desai, S., Dubey, A., Vanneman, R., and Banerji, R. (2009), “Private Schooling in

India: A New Educational Landscape”, India Policy Forum, vol. 5, pp. 1-58

[11] Desai, S. and L. Andrist (2010), “Gender Scripts and Age at Marriage in India”, De-

mography, Vol. 47, pp. 667-687

[12] Duflo, E. (2003), “Grandmothers and Granddaughters: Old-Age Pensions and Intra-

household Allocation in South Africa”, World Bank Economic Review, Vol. 17, pp.

1-25

[13] Dyson, T. and Moore M. (1983), “On Kinship Structure, Female Autonomy and Demo-

graphic Behavior in India”, Population and Development Review, Vol. 9, pp. 35-60

[14] Field, E. and Ambrus, A. (2008), “Early Marriage, Age of Menarche, and Female School-

ing Attainment in Bangladesh”, The Journal of Political Economy, Vol. 116, pp. 881-930

[15] Jensen, R. (2000), “Agricultural Volatility and Investments in Children”, American

Economic Review Papers and Proceeding, Vol. 90, pp. 399-404

[16] Jensen, R. and Thornton, R. (2003), “Early Female Marriage in the Developing World”,

Gender and Development, Vol. 11, pp. 9-19

[17] Nahar Q., Amin S., Sultan R., Nazrul H., Islam M., Kane T.T. et al. (1999), “Strate-

gies to Meet the Health Needs of Adolescents: A Review”, Operations Research Project,

Health and Population Extension Division, Special Publications No. 91. Dhaka, Inter-

national Centre for Diarrhoeal Diseases Research, Bangladesh.

[18] Pellerin-Massicotte, Jocelyne, Guy R. Brisson, Celine St.-Pierre, Pierre Rioux, and

Denis Rajotte. (1987), “Effect of Exercise on the Onset of Puberty, Gonadotropins, and

Ovarian Inhibin”, Journal of Appllied Physiology, Vol. 63, pp. 1165−1173

28

[19] Sheela, J. and N. Audinarayana (2003), “Mate Selection and Female Age at Marriage:

A Micro Level Investigation in Tamil Nadu, India”, Journal of Comparative Family

Studies, Vol. 34

[20] Stathopolu, E., J. Antony H., and David C. (2003), “Difficulties with Age Estimation

of Internet Images of Southeast Asians.” Child Abuse Review Vol. 12, pp. 4657.

[21] Thomas, D. (1994), “Like Father Like Son; Like Mother Like Daughter: Parental Re-

sources and Child Height”, The Journal of Human Resources, Vol. 29, pp. 950-988

[22] Thomas, D., Strauss, J. and Henriques, M-H. (1989), “Child Survival. Nutritional Sta-

tus and Household Charateristics: Evidence from Brazil”, Journal of Development Eco-

nomics, Vol.33, pp. 197-234

[23] Rao, V. (1993), “The Rising Price of Husbands: A Hedonic Analysis of Dowry Increases

in Rural India”, The Journal of Political Economy, Vol. 101, pp. 666-677

[24] Rosenzweig, Mark R. and Wolpin, Kenneth I. (1994), “Are There Increasing Returns

to the Intergenerational Production of Human Capital? Maternal Schooling and Child

Intellectual Achievement”, The Journal of Human Resources, Vol. 29, pp. 670-693

[25] United Nations Population Fund. (2007), “State of the World Population

2007, Unleashing the Potential of Urban Growth”, New York. Retrieved from

http://www.unfpa.org/swp/2005/presskit/factsheets/facts child marriage.htm#ftn5.

29

Figure 1: Average Age at First Marriage by Age at Menarche

Note: Sample includes all ever married women between 15 - 49. The black line in the left figure

shows fitted age at first marriage for age at menarche. The gray area depicts the confidence

interval. The right figure shows the average age at first marriage for age at menarche.

Figure 2: Density, Age at First Marriage and Age at Menarche

Note: Data from Indian Human Development Survey, 2005. Sample includes all ever married

women between 15-49.

0.1

.2.3

Ker

nel

den

sity

0 10 20 30 40 50

Age

Age at first marriage

Age at menarche

kernel = epanechnikov, bandwidth = 0.6000

Figure 3: Distribution of Child’s Arithmetic Skill by Age at Marriage of Mother

Note: Data from India Human Development Survey, 2005. Horizontal axis represents categories

of mother’s age at first marriage and vertical axis measures average number of children. Sample

includes 8 to 11 year old children of ever married women between 15 to 49. Scores of the math

test were ordinal. A score of 0 implies child is unable to do any math, 1 implies child recognizes

numbers, scores of 2 and 3 were rewarded for subtraction and division skills.

0

.1

.2

.3

.4

Less than 15 Betw. 15 and 17 Betw. 18 and 19 Above 20

Cannot do math Number

Subtraction Division

Figure 4: Distribution of Child’s Reading Skills by Age at Marriage of Mother

Note: Data from India Human Development Survey, 2005. Horizontal axis represents categories

of mother’s age at first marriage and vertical axis measures average number of children. Sample

includes 8 to 11 year old children of ever married women between 15 to 49. Scores of the reading

test were ordinal. A score of 0 implies child is unable to read, 1 implies child recognizes letters,

scores of 2, 3 and 4 were rewarded for successfully reading words, paragraph and stories

respectively.

0

.1

.2

.3

.4

.5

Less than 15 Betw. 15 and 17 Betw. 18 and 19 Above 20

Cannot read

Letter

Word Paragraph

Story

Figure 5: Distribution of Age at Menarche and Age at Marriage by Area of Residence

Note: Data from Indian Human Development Survey, 2005. Sample includes

all ever married women between 15-49.

0

.05

.1.1

5

Kern

el

densi

ty

0 10 20 30 40 50

Age at first marriage

Rural Area

Urban Area


0.1

.2.3

Kern

el

densi

ty

10 15 20 25

Age at menarche

Rural Area

Urban Area


Table 1.1: Summary Statistics

Sample: All Children between 8-11 yrs.

Missing

Test Score

Non-missing

Test Score

(Mean) (sd) (Mean) (sd) (Mean) (sd)

(i) (ii) (iii) (iv) (v) (vi)

Mother's Characteristics

Age 33.55 5.87 33.30 4.98 33.62 5.05

Height 151.50 6.83 151.51 7.00 151.34 6.23

Education (years) 4.43 4.77 4.03 4.45 3.93 4.57

Age at marriage 17.31 3.55 17.24 3.42 17.09 3.44

Age at menarche 13.74 1.31 13.67 1.30 13.77 1.27

Father's Characteristics

Age 38.77 6.76 38.63 5.99 38.84 5.93

Education (years) 6.70 4.88 6.15 4.77 6.27 4.84

Household Characteristics

If religion is Hindu 0.80 0.40 0.79 0.41 0.80 0.40

If caste is Brahmin 0.06 0.23 0.05 0.21 0.05 0.22

If Below poverty line 0.23 0.42 0.26 0.44 0.26 0.44

If in urban area 0.37 0.48 0.33 0.47 0.34 0.47

If own land 0.42 0.49 0.41 0.49 0.43 0.50

No. of Household members 5.94 2.32 6.21 2.43 6.19 2.31

Children's characteristics

Gender 0.53 0.50 0.52 0.50 0.53 0.50

Birth order 2.53 1.58 2.46 1.48 2.58 1.61

Age 9.50 2.89 9.55 1.11 9.45 1.06

No. of Siblings 3.43 1.53 3.43 1.48 3.48 1.50

No. of mothers 16316 1948 5787

No. of fathers 16316 1948 5787

No. of households 16314 1948 5787

No. of Child 29263 2242 6884

Note: Data used from India Human Development Survey 2005.

Table 1.2. Summary Statistics

Mother's married

older than 18

Mother's married

older than 18

All

Variable Mean Std.

Dev.

Mean Std.

Dev.

Mean Std.

Dev.

If enrolled in private school 0.22 0.42 0.36 0.48 0.28 0.45

Hours Spent in school (per week) 30.73 7.55 30.87 7.45 30.79 7.51

Hours Spent on homework (per week) 7.28 5.33 8.88 5.95 7.98 5.66

Hours at private tuition (per week) 1.49 4.05 2.22 4.72 1.81 4.37

If enrolled in English medium school 0.07 0.25 0.2 0.4 0.13 0.33

Days absent from school 3.21 5.2 2.2 4.13 2.77 4.79

Expenditure on school fees (Rs.) 310.76 786.69 897.57 1834.15 568.53 1381.78

Expenditure on books, uniform and transport (Rs.) 509.2 821.74 969.44 1300.84 711.37 1083.5

Expenditure on private tuition (Rs.) 130.04 510.74 297.15 1170.86 203.45 869.04

Arithmetic test score 1.5 1.02 1.87 0.96 1.66 1.01

Reading test score 2.55 1.34 2.98 1.16 2.74 1.28

If repeated any grade 0.11 0.32 0.1 0.29 0.11 0.31

Height (cm.) 123.76 12.99 124.21 13.24 123.96 13.1

Weight (Kg.) 23.27 5.18 24.17 5.71 23.66 5.44

N 3860 3860 3024 3024 6884 6884

Note: Data used from Human Development Survey 2005.

Table 2: Regressions of Age at Marriage on Age at Menarche

Universe All All All Inter Status Marriage

(i) (ii) (iii) (iv) (v)

Age at Menarche 0.32*** 0.30*** 0.29*** 0.28*** 0.20***

(0.03) (0.03) (0.03) (0.03) (0.03)

Height 0.039*** 0.044*** 0.013**

(0.0)0 (0.01) (0.00)

Asset Index 1.36***

(0.04)

Birth year fixed effects N Y Y Y Y

N 66588 66588 66588 46213 46213

R-sq 0.013 0.022 0.028 0.028 0.13

F 133.7 123.5 93.3 66.3 342.1

Note: Data used from India Human Development Survey (IHDS), 2005. Col. (i) and (ii) are OLS

estimates of age at first marriage on age at menarche for all ever married women without and with birth

year fixed effects. In Col. (iii) we add woman’s height as an additional regressor. In Col. (iv) and (v) we

restrict our sample to women who were married within same economic class and in Col. (v) we

additionally control for an asset index of the husband’s family. Standard errors are clustered at village

level.

Table 3: Effect of Marriage Age on Children’s Test Scores

Dep. Var. Arithmatic Score Reading Score

Model OLS Ordered-Probit OLS Ordered-Probit

(i) (ii) (iii) (iv)

Mother’s age at mrrg. 0.013*** 0.018*** 0.019*** 0.019***

(0.00) (0.01) (0.01) (0.01)

If below pov. line -0.23*** -0.29*** -0.28*** -0.27***

(0.03) (0.04) (0.04) (0.04)

If own land 0.041 0.057 0.12*** 0.11***

(0.03) (0.04) (0.04) (0.04)

Urban 0.24*** 0.32*** 0.26*** 0.26***

(0.03) (0.04) (0.04) (0.04)

# Household mem. 0.011* 0.015** 0.0062 0.0049

(0.01) (0.01) (0.01) (0.01)

Mother’s age -0.019 -0.034 0.051 0.04

(0.04) (0.06) (0.06) (0.06)

Mother’s height 0.0006 0.00086 0.00085 0.0017

(0.00) (0.00) (0.00) (0.00)

Father’s age 0.015*** 0.020*** 0.011** 0.013***

(0.00) (0.00) (0.00) (0.00)

Father’s edu. (yrs.) 0.037*** 0.049*** 0.036*** 0.038***

(0.00) (0.00) (0.00) (0.00)

# Sibling -0.029** -0.036** -0.074*** -0.069***

(0.01) (0.02) (0.02) (0.02)

Birth order -0.051*** -0.070*** -0.030* -0.032**

(0.01) (0.02) (0.02) (0.02)

Gender (1: Male) 0.12*** 0.16*** 0.036 0.031

(0.02) (0.03) (0.03) (0.03)

Grade 0.24*** 0.32*** 0.33*** 0.34***

(0.01) (0.01) (0.01) (0.01)

N 6884 6884 6884 6884

R-sq 0.32 - 0.289 -

F 62.24 - 46.55 -

Pseudo R-sq - 0.142 - 0.115

Wald Chi-sq - 1973.71 - 1627.69

Note: The dependent variables, test scores of the children are ordered categorical. Score in arithmetic can

take four values. Col. (i) and (ii) are the estimates of the coefficients from OLS and Ordered Probit

models on arithmetic score. Scores in reading are classified under five categories. Col. (iii) and (iv) are

the estimates of the coefficients from OLS and Ordered Probit models on reading score. Standard errors

are clustered at village level. Regressions also include mother’s birth year, religion and test language

fixed effects.

Table 4.1: Effect of Mother's Age at Marriage on Children's Math Score

Panel A: First Stage Estimates

Dependent variable: Age at Marriage

Model 2SLS IV Ordered-IV Probit

(i) (ii) (iii) (iv) (v)

Age at Menarche 0.34*** 0.34*** 0.34*** 0.34*** 0.34***

(0.03) (.036) (.036) (.036) (.036)

F Statistic 87.91 . . . .

R-sq 0.41 . . . .

Wald Chi sq . 2111.52 2111.52 2111.52 2111.52

Observations 6884 6884 6884 6884 6884

Panel B: Second Stage Estimates

Dependent Variable: Math Score

Model

2SLS IV

Ordered-IV Probit

Score 0 Score 1 Score 2 Score 3

(i) (ii) (iii) (iv) (v)

Predicted Mother's 0.081 -0.018*** -0.023*** 0.011*** 0.030***

Age at Marriage (0.03) (0.01) (.00) (.01) (.00)

R-sq 0.29 . . . .

Wald Chi-sq 3251.69 2111.52 2111.52 2111.52 2111.52

Observations 6884 6884 6884 6884 6884

Note: Panel A and B present the first and the second stages of a Two Stage Least Square and an Ordered

IV-Probit estimation of arithmatic score on mother’s age at marriage. We use mother’s age at menarche

as an instrument for her age at marriage. The columns in Panel A report the first stages of the estimations.

Panel B reports the second stage. The dependent variable, arithmetic score, in Panel B is an ordered

categorical variable. Column (i) in Panel B reports the marginal effect of mother's age at marriage on

arithmetic score from a 2SLS-IV regression model. All other columns in Panel B are marginal effects of

mother’s age at marriage on each of the categories of math score. All regressions control for mother’s

adult height and birth year fixed effects. Standard errors are clustered at village level.

Table 4.2: Effect of Mother's Age at Marriage on Children's Reading Score

Panel A: First Stage Estimates

Dependent variable: Age at Marriage

Model 2SLS IV Ordered IV Probit

(i) (ii) (iii) (iv) (v) (vi)

Age at Menarche 0.361*** 0.36*** 0.36*** 0.36*** 0.36*** 0.36***

(0.03) (0.04) (0.04) (0.04) (0.04) (0.04)

F Statistics 95.97 . . . . .

R-sq 0.43 . . . . .

Wald Chi-sq . 75788.4 75788.4 75788.4 75788.4 75788.41

Observations 6884 6884 6884 6884 6884 6884


Dependent Variable: Reading Score

Model

2SLS IV

Ordered IV Probit

Score 0 Score 1 Score 2 Score 3 Score 4

(i) (ii) (iii) (iv) (v) (vi)

Predicted 0.071** -0.007** -0.01*** -0.01** -0.01*** 0.030***

Mother's Age at

Marriage

(0.04) (0.00) (0.01) (0.01) (0.00) (0.01)

R-sq 0.28 . . . . .

Wald Chi-sq 2538.3 75788.4 75788.4 75788.4 75788.4 75788.41

Observations 6884 6884 6884 6884 6884 6884

Note: Panel A and B present the first and the second stages of a Two Stage Least Square and an Ordered

IV-Probit estimation of reading score on mother’s age at marriage. We use mother’s age at menarche as

an instrument for her age at marriage. The columns in Panel A report the first stages of the estimations.

Panel B reports the second stage. The dependent variable, reading score, in Panel B is an ordered

categorical variable. Column (i) in Panel B reports the marginal effect of mother's age at marriage on

reading score from a 2SLS-IV regression model. All regressions control for mother’s adult height and

birth year fixed effects. Standard errors are clustered at village level.

Table 5: Effect of Mother's Age at Marriage on Children's Educational Outcomes

Panel A: Estimates from Mothers' Age at Marriage Equation

Dependent Variable: Age at Marriage

(i) (ii) (iii)

Mother's age at menarche 0.27*** 0.27*** 0.27***

(0.036) (0.036) (0.036)

Panel B: Estimates from School Outcome Equations

Dependent Variable: Enrolled in Enrolled in Enrolled in

school Private school English medium

(i) (ii) (iii)

Mother's age at marriage 0.035* 0.063*** -0.014

(0.02) (0.02) (0.01)

Observations 6884 6884 6884

Chi-sq 32.95 1010.43 700.35

Note: Sample includes children of age 8 to 11. We estimate the effect of mother's age at marriage on

various school outcomes of her child. The outcome variables are binary. We use mother's age at menarche

as an instrument for her age at marriage. All point estimates reported in the table are marginal effects

derived from iv-probit model using Maximum Likelihood estimation. Panel A reports the estimates from

the mother's age at marriage equation and Panel B reports the estimates from school outcome equations.

Col. (i) reports the effect of mothers age at marriage on current school enrollment of children. School

enrollment takes value one if a child is currently enrolled in school. Col - (ii) reports the effect of

mothers’ age at marriage on type of school children is currently enrolled. In Col. (iii) we report the effect

of mothers’ age at marriage on the medium of instruction in school in which children are currently

enrolled. The outcome variable takes value one if it is an English medium school. All errors are clustered

at village level. All regressions include parents, household and child characteristics as regressors.

Table 6.1: Effect of Mother's Age at Marriage on Expenditure on Education



(i) (ii) (iii)


(0.027) (0.027) (0.027)

Panel B: Estimates from Expenditure on Education Equations

Dependent Variable: Expenditure on Expenditure on Expenditure on

school fees books private tuition

(i) (ii) (iii)

Mother's age at marriage 88.2* 138.0*** 1.14

(50.5) (48.54) (25.82)


Chi-sq 640.78 661.34 269.71

Note: Sample includes children of age 8 to 11. We estimate the effect of mother's age at marriage on

outlays on education of her child. The outcome variables are measured in Rs.. We use mother's age at

menarche as an instrument for her age at marriage. Panel A reports the estimates from the mother's age at

marriage equation and Panel B reports the estimates from expenditure equations. Col. (i) reports the effect

of mothers age at marriage on expenditure on school fees of children. Col. (ii) reports the effect of

mothers’ age at marriage on expenditure on books, uniform and transportation. In Col. (iii) we report the

effect of mothers’ age at marriage on expenditure on private tuition. All errors are clustered at village

level. All regressions include parents, household and child characteristics as regressors.

Table 6.2: Effect of Mother's Age at Marriage on Time Spent on Study



(i) (ii) (iii)


(0.027) (0.027) (0.027)

Panel B: Estimates from Time Use Equations

Dependent Variable: Hours spent Hours spent Hours spent

at school at private tuition doing homework

(i) (ii) (iii)

Mother's age at marriage 0.66* -0.21 -0.96

(0.38) (0.19) (0.29)


Chi-sq 133.81 296.9 410.15

Note: Sample includes children of age 8 to 11. We estimate the effect of mother's age at marriage on time

use of her child. The outcome variables are measured in hours per week. We use mother's age at

menarche as an instrument for her age at marriage. Panel A reports the estimates from the mother's age at

marriage equation and Panel B reports the estimates from time use equations. Col. (i) reports the effect of

mothers’ age at marriage on number of hours spent at school by her children in a week. Col. (ii) reports

the effect of mothers age at marriage on hours spent at private tuition. In Col. (iii) we report the effect of

mothers’ age at marriage on hours spent by her children doing homework. All errors are clustered at

village level. All regressions include parents, household and child characteristics as regressors.

Dep. Var.

(i) (ii) (iii) (iv) (v) (vi) (vii) (viii)

Mother’s age at mrrg. 0.012*** 0.012*** 0.0076* 0.0076* 0.021*** 0.020*** 0.014** 0.014**

(0.00) (0.00) (0.00) (0.00) (0.01) (0.01) (0.01) (0.01)

Cooking decision 0.17** 0.16** 0.20** 0.18**

(0.07) (0.07) (0.09) (0.09)

Purchase decision -0.027 -0.030 0.031 0.028

(0.04) (0.04) (0.05) (0.05)

# child to bear decision -0.0035 -0.0064 -0.027 -0.032

(0.04) (0.04) (0.06) (0.06)

Child's medical decision 0.013 0.014 -0.0066 -0.0050

(0.05) (0.05) (0.06) (0.06)

Mother's education (yr.) 0.019*** 0.019*** 0.028*** 0.028***

(0.00) (0.00) (0.00) (0.00)

N 6528 6528 6528 6528 6528 6528 6528 6528

F 58.7 55.0 57.6 54.1 43.9 41.7 44.2 41.9

R-sq 0.32 0.32 0.32 0.32 0.29 0.29 0.29 0.29

Note: The dependent variables, test scores of the children are ordered categorical. All regressions control

for mother's height, birth year FE and age, indicator for poverty, land ownership, residence (urban/rural),

religion, # household members, father's age and education, # sibling of the child, birth order, gender,

grade and test language FE. Standard errors are clustered at the village level. The decision variables are

binary, they take the value one if mother decides the described activity. Col. (i) and (v) are the OLS

estimates of the coefficient of mother's age at marriage on arithmetic and reading scores without

controlling for mother's autonomy and education. Col. (ii) and (vi) are the OLS estimates on the same

outcomes after additionally controlling for mother's autonomy. Col. (iii) and (vii) are the OLS estimates

of the coefficients after controlling for mothers education. Col. (iv) and (viii) present similar esimates on

the scores after controlling for both mother's autonomy and education.

Table 7: Effect of Marriage Age on Children’s Test Scores after Controlling for Mother's Autonomy and

Arithmatic Score Reading Score

APPENDIX

Figure A1: Difference Between Age at First Birth and First Marriage by Current age of Mothers.

Note: Data from Indian Human Development Survey, 2005. Sample includes all ever

married women between 15 to 49.

Figure A2: Mother’s Age at Menarche and Mother’s height

Note: Data used from India Human Development Survey, 2005. Sample includes

ever married women between 15 to 49.

Figure A3: Distributions of Age at Marriage and Age at Menarche by Father’s Education and

Possession of Irrigated

Note: Data used from Gender Marriage, Kinship Survey conducted by NCAER in 1995 in the states of

Uttar Pradesh and Karnataka in India. The top panel plots the densities of age at menarche and age at first

marriage. The first figure in the bottom panel plots the densities of age at menarche by literacy of the

parents, while the second figure plots the densities of age at marriage by possession of irrigated land by

father.

0

.05

.1.1

5.2

Ker

nel

den

sity

0 10 20 30Age at Menarche

Age at Menarche

Age at Marriage

0

.05

.1.1

5.2

.25

Kern

el

densi

ty


Parent Illiterate

At least one of the parent literate

0

.05

.1.1

5.2

.25

Kern

el

densi

ty


Father had irrigated land

Father did not have irrigated land

Figure A4: Distributions of Residual Age at Marriage and Residual Age at Menarche

Panel A: Distributions of Residual Age at Marriage and Residual Age at Menarche by Parents’ Literacy using

NCAER data

Panel B: Distributions of Residual Age at Marriage & Age at Menarche: NCAER, IHDS (U.P. & Karnataka)

and IHDS (Full Sample)

Note: Panel A plots residual age at marriage and residual age at menarche (after controlling for state FE and height) from

NCAER, 1995 data. Panel B plots residual age at marriage and residual age at menarche from NCAER, IHDS (Uttar

Pradesh) and IHDS (full sample) data-sets respectively.

0

.05

.1.1

5

Kern

el

densi

ty

-20 -10 0 10 20Age at Marriage (Residual, after controlling for state fixed effects and height)

Parent Illiterate


0

.05

.1.1

5.2

.25

Kern

el

densi

ty-20 -10 0 10 20

Age at Menarche (Residual, after controlling for state fixed effects and height)

Parent Illiterate


0

.05

.1.1

5

Kern

el

densi

ty

-20 -10 0 10 20

Age at Marriage (Residual, after controlling for state fixed effects and height)

NCAER Data

IHDS - U.P & Karnataka

IHDS - All India

0

.05

.1.1

5.2

Kern

el

densi

ty

-20 -10 0 10 20

Age at Menarche (Residual, after controlling for state fixed effects and height)

NCAER Data

IHDS - U.P & Karnataka

IHDS - All India

Figure A5: Neighboring Districts of the District Katihar in the State of Bihar.

Table A1: Regressions of Age at Menarche and Age at Marriage with

Temperature and Altitude Panel A

Dependent variable: Age at menarche

Universe All Intra status marriage

(i) (ii) (iii) (iv) (v)

Height (c.m.)

0.0082***

0.0082*** 0.0089*** 0.0060*** 0.0063***

(0.00) (0.00) (0.00) (0.00) (0.00)

Asset index - - - 0.12*** 0.12***

(0.02) (0.02)

Avg. temp. of residing district -0.0059 - -0.0082 -0.0029 -

(0.01) (0.02) (0.02)

Avg. altitude of residing district 0.00021* - 0.0001 0.00012 -

(0.00) (0.00) (0.00)

Avg. temp. of residing & neighboring districts - -0.03 - - -0.032

(0.02) (0.02)

Avg. altitude of residing & neighboring districts - 0.00011 - - 0.0000046

(0.00) (0.00)

N 66470 66588 46108 46108 46213

R-sq 0.019 0.022 0.013 0.019 0.022

F 22.1 28.1 11.3 19.3 20.9

Panel B

Dependent variable: Age at marrige

Universe All Intra status marriage

(i) (ii) (iii) (iii) (iv) (v)

Age at menarche 0.29*** 0.28*** 0.27*** 0.27*** 0.20*** 0.19***

(0.03) (0.03) (0.03) (0.03) (0.03) (0.03)

Height (c.m.) 0.039*** .039*** 0.038*** 0.045*** 0.014** 0.014**

(0.0)0 (0.00) (0.00) (0.01) (0.00) (0.00)

Avg. temp. of residing district -.16*** - -0.18*** -0.13*** -

(0.03) (0.03) (0.03)

Avg. altitude of residing district -.0006*** - -0.0006** -0.0004* -

(0.00) (0.00) (0.00)

Avg. temp. of residing & neighboring districts - -0.15*** - - -0.09**

(0.03) (0.03)

Avg. altitude of residing & neighboring districts - -0.00034 - - -0.00001

(0.00) (0.00)

Asset index - - 1.33*** 1.33***

(0.00) (0.00)

N 66588 66470 66588 46108 46108 46213

R-sq 0.028 0.035 0.036 0.036 0.13 0.13

F 93.3 69.1 74.1 50.7 212.6 214.9

Note: Data used from India Human Development Survey, 2005. Sample consists of ever married women of

age 15-49. All regressions control for birth year fixed effects.

Table A2: Effect of Mother's Age at Marriage on Children's Math & Reading Score with

District Level Geographical and Climate Controls

Panel A: First Stage Estimates (Dependent variable: Age at Marriage)

Avg. Temp. and Altitude at

the level of: Residing district

Residing and neighboring districts

(i) (ii) (iii) (iv)

Age at Menarche 0.32 0.35 0.31 0.34

(0.03) (0.04) (0.04) (0.04)

Wald Chi sq 2161.11 62925.26 2176.93 35053.11

Observations 6876 6876 6884 6884


Dependent Variable: Math Score Reading Score Math Score Reading Score

(i) (ii) (iii) (iv)

Mar

gin

al E

ffec

ts f

or

Pre

dic

ted

Ag

e at

M

arri

age

Score 0 -0.019*** -0.0060* -0.018** -0.0065*

(0.01) (0.00) (0.01) (0.00)

Score 1 -0.023*** -0.010* -0.023*** -0.011**

(0.01) (0.01) (0.01) (0.01)

Score 2 0.012*** -0.011** 0.011*** -0.011**

(0.00) (0.01) (0.00) (0.01)

Score 3 0.030*** 0.000046 0.030** 0.000047

(0.01) (0.00) (0.01) (0.00)

Score 4 - 0.027* - 0.028**

- (0.01) - -0.01

Wald Chi-sq 2161.11 62925.26 2176.93 35053.11

Observations 6876 6876 6884 6884

Note: Panel A and B present the first and the second stages of Ordered IV-Probit estimations of arithmetic

and reading score on mother’s age at marriage. In columns (i) and (ii) in both panels the average

temperature and altitude of the district of residence are controlled, whereas in columns (iii) and (iv) the

average temperature and altitude of district of residence and neighboring districts has been controlled.

The columns in Panel A report the first stages of the estimations. Panel B reports the second stage. All

columns in Panel B are marginal effects of mother’s age at marriage on each of the categories of math and

reading scores. All regressions control for mother’s adult height and birth year fixed effects. Standard

errors are clustered at village level.

Intergenerational Consequences of Early Age Marriages of Girls ...

Documents

Transcript of Intergenerational Consequences of Early Age Marriages of Girls ...