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 children’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]

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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 tradeoff, 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.

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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 1

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

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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. 2

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

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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 daugh3

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

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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).

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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 4

A 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.

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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 5

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

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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 + βa Aj + βf Fij + βm Mj + βh Hj + βx Xij + ij

(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 ij 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 + βa Aj + βw Wij + ij

(2)

0

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.

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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 + αz Zj + αw Wij + ηj

(3)

Yij = β0 + βa Aj + βw Wij + ij

(4)

The first stage is given by the equation (3), and equation (4) is the structural equation. 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 socioeconomic 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 members, 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 continuous, 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 6

Birth year fixed effects can also account for any exposures to environmental determinants of age at menarche. 7 We construct an asset index using Principal Component Analysis from the asset data. 8 We use data from the Gender, Marriage,and Kinship Survey conducted by NCAER in 1995. This data

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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). 9 See Field and Ambrus, 2008 for a detailed discussion

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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. 10

Children were awarded scores based on their skills, these scores were integers between 0 and 4. For example 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, the highest math score. 11 Arithmetic 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 more complex number manipulation; (c) Score 2:can subtract a two digit number from another; (d) Score 3: can divide a three digit number between by a single digit number.

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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 12

Scores in reading are classified under five categories; (a)Score 0: cannot read at all; (b)Score 1: can recognize 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.

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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. 13

We evaluated the effects on nutrition and health outcomes as well. While the nutritional outcomes are statistically significantly influenced by mother’s age of marriage, health outcomes such as incidence of respiratory diseases are not. Results are available on request. 14 The average age at marriage for our sample is 17 years. We also check for non-linear effects of mother’s age at marriage on test scores of her child. Our estimations reject presence of any non-linear effects.

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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 15

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

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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.

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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.

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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 relationship 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 17

Results 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 literature 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 18

IHDS 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 of India (Uttar Pradesh and Karnatka). 19 Since the states in Gender, Marriages,and Kinship Survey vary in terms of geography, climate, and many other attributes, we plot the distributions of residual age at menarche after controlling for state fixed effects and height of the women. 20 See 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 21

96% 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 neighboring districts in the regressions, we try to maximize the probability that the climate condition of the natal home is 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 between 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 column (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 marriage 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 coefficients 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 compare 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 coefficient 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 estimates 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 capital 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 improvement in her children’s human capital. A one year delay in woman’s marriage increases the 22

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.

25

probability that her children will be able to perform higher level cognitive tasks by 3 percentage 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] Ag¨ uero, 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 Consumption: 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: Contrasts 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 Transmission 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”, Demography, Vol. 47, pp. 667-687 [12] Duflo, E. (2003), “Grandmothers and Granddaughters: Old-Age Pensions and Intrahousehold 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 Demographic 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 Schooling 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), “Strategies to Meet the Health Needs of Adolescents: A Review”, Operations Research Project, Health and Population Extension Division, Special Publications No. 91. Dhaka, International 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 Resources 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 Status and Household Charateristics: Evidence from Brazil”, Journal of Development Economics, 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.

.2 .1 0

Kernel density

.3

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

0

10

20

30

40

50

Age Age at first marriage Age at menarche kernel = epanechnikov, bandwidth = 0.6000

Note: Data from Indian Human Development Survey, 2005. Sample includes all ever married women between 15-49.

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

.4

.3

.2

.1

0 Less than 15

Betw. 15 and 17 Cannot do math Subtraction

Betw. 18 and 19

Above 20

Number Division

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.

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

.5

.4

.3

.2

.1

0 Less than 15

Betw. 15 and 17 Cannot read Word Story

Betw. 18 and 19

Above 20

Letter Paragraph

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.

.1 .05 0

Kernel density

.15

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

0

10

20

30

40

50

Age at first marriage Rural Area Urban Area

.2 .1 0

Kernel density

.3

kernel = epanechnikov, bandwidth = 0.8000

10

15

20

25

Age at menarche Rural Area Urban Area kernel = epanechnikov, bandwidth = 0.8000

Note: Data from Indian Human Development Survey, 2005. Sample includes all ever married women between 15-49.

Table 1.1: Summary Statistics Sample:

Mother's Characteristics Age Height Education (years) Age at marriage Age at menarche Father's Characteristics Age Education (years) Household Characteristics If religion is Hindu If caste is Brahmin If Below poverty line If in urban area If own land No. of Household members Children's characteristics Gender Birth order Age No. of Siblings No. of mothers No. of fathers No. of households No. of Child

All

Children between 8-11 yrs. Missing Non-missing Test Score Test Score

(Mean) (i)

(sd) (ii)

(Mean) (sd) (iii) (iv)

(Mean) (v)

(sd) (vi)

33.55 151.50 4.43 17.31 13.74

5.87 6.83 4.77 3.55 1.31

33.30 151.51 4.03 17.24 13.67

4.98 7.00 4.45 3.42 1.30

33.62 151.34 3.93 17.09 13.77

5.05 6.23 4.57 3.44 1.27

38.77 6.70

6.76 4.88

38.63 6.15

5.99 4.77

38.84 6.27

5.93 4.84

0.80 0.06 0.23 0.37 0.42 5.94

0.40 0.23 0.42 0.48 0.49 2.32

0.79 0.05 0.26 0.33 0.41 6.21

0.41 0.21 0.44 0.47 0.49 2.43

0.80 0.05 0.26 0.34 0.43 6.19

0.40 0.22 0.44 0.47 0.50 2.31

0.53 2.53 9.50 3.43

0.50 1.58 2.89 1.53

0.52 2.46 9.55 3.43

0.50 1.48 1.11 1.48

0.53 2.58 9.45 3.48

0.50 1.61 1.06 1.50

16316 16316 16314 29263

1948 1948 1948 2242

Note: Data used from India Human Development Survey 2005.

5787 5787 5787 6884

Table 1.2. Summary Statistics

Variable If enrolled in private school Hours Spent in school (per week) Hours Spent on homework (per week) Hours at private tuition (per week) If enrolled in English medium school Days absent from school Expenditure on school fees (Rs.) Expenditure on books, uniform and transport (Rs.) Expenditure on private tuition (Rs.) Arithmetic test score Reading test score If repeated any grade Height (cm.) Weight (Kg.) N

Mother's married older than 18

Mother's married older than 18

Mean

Mean

0.22 30.73 7.28 1.49 0.07 3.21 310.76 509.2 130.04 1.5 2.55 0.11 123.76 23.27

Std. Dev. 0.42 7.55 5.33 4.05 0.25 5.2 786.69 821.74 510.74 1.02 1.34 0.32 12.99 5.18

3860

3860

Note: Data used from Human Development Survey 2005.

All

0.36 30.87 8.88 2.22 0.2 2.2 897.57 969.44 297.15 1.87 2.98 0.1 124.21 24.17

Std. Dev. 0.48 7.45 5.95 4.72 0.4 4.13 1834.15 1300.84 1170.86 0.96 1.16 0.29 13.24 5.71

Mean 0.28 30.79 7.98 1.81 0.13 2.77 568.53 711.37 203.45 1.66 2.74 0.11 123.96 23.66

Std. Dev. 0.45 7.51 5.66 4.37 0.33 4.79 1381.78 1083.5 869.04 1.01 1.28 0.31 13.1 5.44

3024

3024

6884

6884

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

Universe Age at Menarche

All (i)

All (ii)

All (iii)

0.32*** (0.03)

0.30*** (0.03)

0.29*** (0.03) 0.039*** (0.0)0

0.28*** (0.03) 0.044*** (0.01)

N 66588 0.013 133.7

Y 66588 0.022 123.5

Y 66588 0.028 93.3

Y 46213 0.028 66.3

Height

Inter Status Marriage (iv) (v)

Asset Index Birth year fixed effects N R-sq F

0.20*** (0.03) 0.013** (0.00) 1.36*** (0.04) Y 46213 0.13 342.1

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. Model Mother’s age at mrrg. If below pov. line If own land Urban # Household mem. Mother’s age Mother’s height Father’s age Father’s edu. (yrs.) # Sibling Birth order Gender (1: Male) Grade N R-sq F Pseudo R-sq Wald Chi-sq

Arithmatic Score OLS Ordered-Probit (i) (ii) 0.013*** 0.018*** (0.00) (0.01) -0.23*** -0.29*** (0.03) (0.04) 0.041 0.057 (0.03) (0.04) 0.24*** 0.32*** (0.03) (0.04) 0.011* 0.015** (0.01) (0.01) -0.019 -0.034 (0.04) (0.06) 0.0006 0.00086 (0.00) (0.00) 0.015*** 0.020*** (0.00) (0.00) 0.037*** 0.049*** (0.00) (0.00) -0.029** -0.036** (0.01) (0.02) -0.051*** -0.070***

Reading Score OLS Ordered-Probit (iii) (iv) 0.019*** 0.019*** (0.01) (0.01) -0.28*** -0.27*** (0.04) (0.04) 0.12*** 0.11*** (0.04) (0.04) 0.26*** 0.26*** (0.04) (0.04) 0.0062 0.0049 (0.01) (0.01) 0.051 0.04 (0.06) (0.06) 0.00085 0.0017 (0.00) (0.00) 0.011** 0.013*** (0.00) (0.00) 0.036*** 0.038*** (0.00) (0.00) -0.074*** -0.069*** (0.02) (0.02) -0.030* -0.032**

(0.01)

(0.02)

(0.02)

(0.02)

0.12***

0.16***

0.036

0.031

(0.02) 0.24*** (0.01) 6884 0.32 62.24 -

(0.03) 0.32*** (0.01) 6884 0.142 1973.71

(0.03) 0.33*** (0.01) 6884 0.289 46.55 -

(0.03) 0.34*** (0.01) 6884 0.115 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 Model Age at Menarche F Statistic R-sq Wald Chi sq Observations

Dependent variable: Age at Marriage 2SLS IV Ordered-IV Probit (i) 0.34*** (0.03) 87.91 0.41 . 6884

(ii) 0.34*** (.036) . . 2111.52 6884

(iii) 0.34*** (.036) . . 2111.52 6884

(iv) 0.34*** (.036) . . 2111.52 6884

(v) 0.34*** (.036) . . 2111.52 6884

Panel B: Second Stage Estimates Model

Predicted Mother's Age at Marriage R-sq Wald Chi-sq Observations

Dependent Variable: Math Score Ordered-IV Probit 2SLS IV (i)

Score 0 (ii)

Score 1 (iii)

Score 2 (iv)

Score 3 (v)

0.081 (0.03) 0.29 3251.69 6884

-0.018*** (0.01) . 2111.52 6884

-0.023*** (.00) . 2111.52 6884

0.011*** (.01) . 2111.52 6884

0.030*** (.00) . 2111.52 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)

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

Age at Menarche

Panel B: Second Stage Estimates Dependent Variable: Reading Score Model

Predicted

Ordered IV Probit 2SLS IV (i)

Score 0

Score 1

Score 2

Score 3

Score 4

(ii)

(iii)

(iv)

(v)

(vi)

0.071**

-0.007**

-0.01***

-0.01**

-0.01***

0.030***

Mother's Age at Marriage R-sq

(0.04)

(0.00)

(0.01)

(0.01)

(0.00)

(0.01)

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) Mother's age at menarche

0.27*** (0.036)

(iii)

0.27*** (0.036)

0.27*** (0.036)

Panel B: Estimates from School Outcome Equations Dependent Variable:

Enrolled in school (i)

Enrolled in Private school (ii)

Enrolled in English medium (iii)

Mother's age at marriage

0.035* (0.02)

0.063*** (0.02)

-0.014 (0.01)

Observations Chi-sq

6884 32.95

6884 1010.43

6884 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 Panel A: Estimates from Mothers' Age at Marriage Equation Dependent Variable: Age at Marriage (i) (ii) Mother's age at menarche

0.28*** (0.027)

(iii)

0.28*** (0.027)

0.28*** (0.027)

Panel B: Estimates from Expenditure on Education Equations Dependent Variable:

Expenditure on school fees (i)

Expenditure on books (ii)

Expenditure on private tuition (iii)

Mother's age at marriage

88.2* (50.5)

138.0*** (48.54)

1.14 (25.82)

Observations Chi-sq

6884 640.78

6884 661.34

6884 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 Panel A: Estimates from Mothers' Age at Marriage Equation Dependent Variable: Age at Marriage (i) (ii) Mother's age at menarche

0.28*** (0.027)

0.28*** (0.027)

(iii) 0.28*** (0.027)

Panel B: Estimates from Time Use Equations Dependent Variable:

Hours spent at school (i)

Hours spent at private tuition (ii)

Hours spent doing homework (iii)

Mother's age at marriage

0.66* (0.38)

-0.21 (0.19)

-0.96 (0.29)

Observations Chi-sq

6884 133.81

6884 296.9

6884 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.

Table 7: Effect of Marriage Age on Children’s Test Scores after Controlling for Mother's Autonomy and Dep. Var. Arithmatic Score Reading Score (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) 0.17** 0.16** 0.20** 0.18** Cooking decision (0.07) (0.07) (0.09) (0.09) -0.027 -0.030 0.031 0.028 Purchase decision (0.04) (0.04) (0.05) (0.05) -0.0035 -0.0064 -0.027 -0.032 # child to bear decision (0.04) (0.04) (0.06) (0.06) 0.013 0.014 -0.0066 -0.0050 Child's medical decision (0.05) (0.05) (0.06) (0.06) 0.019*** 0.019*** 0.028*** 0.028*** Mother's education (yr.) (0.00) (0.00) (0.00) (0.00) 6528 6528 6528 6528 6528 6528 6528 6528 N 58.7 55.0 57.6 54.1 43.9 41.7 44.2 41.9 F 0.32 0.32 0.32 0.32 0.29 0.29 0.29 0.29 R-sq 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.

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

0

.05

.1

Kernel density

.15

.2

Possession of Irrigated

0

10

20 Age at Menarche

30

.25 .2 0

.05

.1

.15

Kernel density

.15 .1 .05 0

Kernel density

.2

.25

Age at Menarche Age at Marriage

0

10

20

Age at Menarche Parent Illiterate At least one of the parent literate

30

0

10

20

30

Age at Menarche Father had irrigated land Father did not have irrigated land

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.

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

0

0

.05

.1

.15

Kernel density

.1 .05

Kernel density

.2

.15

.25

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

-20

-10

0

10

-20

20

-10

0

10

20

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

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

Parent Illiterate At least one of the parent literate

Parent Illiterate At least one of the parent literate

.2 .15 0

.05

.1

Kernel density

.1 .05 0

Kernel density

.15

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

-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

-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

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.

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 (i)

Height (c.m.)

(ii) 0.0082***

0.0082*** (0.00) -

Asset index Avg. temp. of residing district

-0.0059 (0.01) 0.00021* (0.00) -

Avg. altitude of residing district Avg. temp. of residing & neighboring districts

Intra status marriage (iii) (iv) (v) 0.0089*** 0.0060*** 0.0063***

(0.00) -

(0.00) -

-

-0.0082 (0.02) 0.0001 (0.00) -

(0.00) 0.12*** (0.02) -0.0029 (0.02) 0.00012 (0.00) -

-

-

46108 0.013 11.3

46108 0.019 19.3

-

-0.03 (0.02) 0.00011 (0.00) 66470 66588 0.019 0.022 22.1 28.1 Panel B

Avg. altitude of residing & neighboring districts N R-sq F

(0.00) 0.12*** (0.02) -0.032 (0.02) 0.0000046 (0.00) 46213 0.022 20.9

Dependent variable: Age at marrige Universe Age at menarche Height (c.m.)

(i) 0.29*** (0.03) 0.039*** (0.0)0

Avg. temp. of residing district Avg. altitude of residing district Avg. temp. of residing & neighboring districts

All (ii) 0.28*** (0.03) .039*** (0.00) -.16*** (0.03) -.0006*** (0.00) -

Avg. altitude of residing & neighboring districts

-

Asset index

-

N R-sq F

66588 0.028 93.3

66470 0.035 69.1

(iii) 0.27*** (0.03) 0.038*** (0.00) -0.15*** (0.03) -0.00034 (0.00)

66588 0.036 74.1

Intra status marriage (iii) (iv) (v) 0.27*** 0.20*** 0.19*** (0.03) (0.03) (0.03) 0.045*** 0.014** 0.014** (0.01) (0.00) (0.00) -0.18*** -0.13*** (0.03) (0.03) -0.0006** -0.0004* (0.00) (0.00) -0.09** (0.03) -0.00001 (0.00) 1.33*** 1.33*** (0.00) (0.00) 46108 46108 46213 0.036 0.13 0.13 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 (iii) (iv)

(i)

(ii)

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

Age at Menarche

Panel B: Second Stage Estimates Math Score

Reading Score

Math Score

Reading Score

Score 0

(i) -0.019***

(ii) -0.0060*

(iii) -0.018**

(iv) -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)

0.030***

0.000046

0.030**

0.000047

(0.01)

(0.00)

(0.01)

(0.00)

-

0.027*

-

0.028**

-

(0.01)

-

-0.01

Wald Chi-sq

2161.11

62925.26

2176.93

35053.11

Observations

6876

6876

6884

6884

Marginal Effects for Predicted Age at Marriage

Dependent Variable:

Score 3 Score 4

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.