Social Norms and Aspirations: Age of Marriage and Education in Rural India

World Development Vol. 47, pp. 1–15, 2013 Ó 2013 Elsevier Ltd. All rights reserved. 0305-750X/$ - see front matter www.elsevier.com/locate/worlddev

http://dx.doi.org/10.1016/j.worlddev.2013.01.027

Social Norms and Aspirations: Age of Marriage and Education in Rural India ANNEMIE MAERTENS * University of Pittsburgh, USA Summary. — Using a unique dataset that I collected in three villages in semi-arid India, I analyze the role of perceived returns to education and social norms regarding the ideal age of marriage in the educational plans, i.e., aspirations, parents have for their children. I show that perceptions of the ideal age of marriage significantly constrain the education that parents aspire to have for their daughters, but not their sons. Furthermore, aspirations are sensitive to the perceived returns to higher education in the case of boys, but not in the case of girls. Ó 2013 Elsevier Ltd. All rights reserved. Key words — female education, social norms, Asia, India

1. INTRODUCTION

levels of educational completion for each of their children, and (c) their plans to invest in education, henceforth referred to as aspirations, for each of their children. I find that only 39% of the girls would be allowed (by their parents) to pursue higher education, compared to 71% of the boys, and only 8% of the girls are expected to complete higher education versus 22% of the boys. The socially acceptable age of marriage is perceived to be, on average, 18.3 years for girls and 22.7 years for boys. I show that, controlling for perceived returns, perceptions about the acceptable age of marriage significantly constrain the desired education for daughters, but not for sons. In addition, the results indicate that while parents do perceive the returns to education to be different for boys and girls, their aspirations are sensitive to perceived returns in a specific way: For boys, aspirations are only affected by the returns to “higher” (i.e., college and above) education, but for girls, aspirations are only sensitive to the returns to “lower” (i.e., high-school and below) education. Because parents may be unconsciously factoring in unobserved aspects of their child’s quality in their stated “ideal

The institution of early marriage is thought to be a contributing factor toward low educational attainments of women in India. 1 Research by anthropologists and sociologists emphasizes the importance of marriage-related social customs that result in social pressure to marry off one’s daughter at a young age (Srinivas, 2000). Traditionally, when a female child reached the age of menarche, the child was taken out of school and arrangements for her marriage were made (Caldwell, Reddy, & Caldwell, 1983). 2 Demographic pressure (see Sautman, 2009) and family composition also play a role in determining the age of marriage; Vogl (2010) finds that parents tend to rush the marriage of their daughter if she has a younger sister, while Caldwell et al. (1983) note the existence of social norms which requires parents to marry off their daughters before choosing a bride for their son(s). At the same time, both early marriage and low levels of education are intertwined with norms that inhibit the participation of women in the workplace, which might act to lower the returns (actual or perceived) to higher education. Disentangling the separate effects of perceived returns and marriage-related norms on educational attainment is therefore difficult to the extent that observed age of marriage may be correlated with unobserved (by the econometrician) perceived returns. In addition, observed returns are often an imperfect proxy for perceived returns, both because of selection based on heterogeneous abilities/characteristics and because of imperfect information about returns (see Nguyen, 2008; Attanasio & Kaufmann, 2009; Jensen, 2010). Indeed, Jensen (2010) reports that measured (actual) returns to schooling in the Dominican Republic are high, whereas the returns perceived by students are extremely low. Attanasio and Kaufmann (2009) confirm that, in Mexico, youths’ and parents’ expectations regarding the returns to education matter for high school and college attendance decisions (as long as credit constraints are not binding). In this study, I analyze unique data that I collected in three villages in rural India to shed light on the role of marriage-related norms and perceived returns in the educational aspirations that parents have for their children. I directly elicit parents’ perceptions of (a) the “socially acceptable” age of marriage for boys and girls (b) the perceived returns to various

* The data for this paper were gathered in India during 2007–2008, in collaboration with International Crop Research Institute of the Semi-Arid Tropics. This dissertation research was funded through a NSF Doctoral Dissertation Research Improvement Grant (Grant No. 0649330), an AAEA McCorkle Fellowship, a Cornell University Mario Einaudi Center for International Studies International Research Travel Grant, Graduate School Research Travel Grant, International Student and Scholar Office Grant and funds provided by Chris Barrett. I thank the research assistants on the field: Sanjit Anilesh, Shraavya Bhagavatula, Pramod Bangar, Sana Butool, V.D. Duche, Shital Duche, Madhav Dhere, Anand Dhumale, Nishtha Ghosh, Navika Harshe, Meenal Inamdar, Shilpa Indrakanti, Sapna Kale, Jessica Lebo, Labhesh Lithikar, Nishita Medha, Ramesh Babu Para, Abhijit Patnaik, Gore Parmeshwar, Amidala Sidappa, Nandavaram Ramakrishna, K. Ramanareddy, P.D. Ranganath, Arjun Waghmode and Yu Qin. I have benefited from the comments of the seminar participants at Cornell University, the University of Leuven, the University of Pittsburgh and my discussions with A. V. Chari, Chris Barrett, Kaushik Basu, Stephen Coate, Christopher Ksoll, George Jakubson, Robert Jensen, and Hope Michelson. Any errors and omissions are my own. Final revision accepted: January 20, 2013. 1

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WORLD DEVELOPMENT

age of marriage,” I demonstrate the robustness of the results to an instrumental variable strategy in which I instrument the stated ideal age of marriage with the average (gender-specific) stated ideal age of marriage of other households within the same subcaste (using the fact that marriages are typically arranged within one’s subcaste; see Caplan (1984) and Srinivas (1984)). I also establish the robustness of the results to an alternative set of controls and an alternative specification in which educational aspirations are treated as being chosen from an (ordered) set of outcomes, instead of treating the number of years of desired education as a dependent variable in a linear regression framework. The remainder of this article is structured as follows. The next section introduces the data. Section 3 discusses selected descriptive statistics, drawing on conversations with survey respondents. Section 4 presents the methodology and results and Section 5 concludes with a discussion of the results. 2. DATA 2.1 The ICRISAT-VLS villages I collected the data used for this study in 2007–08 in three villages in India. These three villages are part of the Village Level Studies program of the International Crop Research Institute of the Semi-Arid Tropics (ICRISAT). In this program, ICRISAT collected detailed panel data from 300 households in six villages during the period 1975–85 with the goal of gaining a better understanding of socio-economic life in semi-arid India (see Singh, Binswanger, & Jodha (1985) and Walker & Ryan (1990) for an overview of the goals and findings of this first generation VLS). The household sample was selected in four stages. In the first stage, three contrasting dryland agricultural regions were identified on the basis of cropping, soil, and climatic criteria: Telangana in Andhra Pradesh and Bombay Deccan and Vidarbha in Maharashtra. Within these regions, representative districts were selected: Mahabubnagar in Telangana, Solapur in Bombay Deccan, and Akola in Vidarbha. In a second stage, typical talukas (administrative divisions smaller than districts) were chosen on the basis of 40 socio-economic variables. In the third stage, villages representing the characteristics considered in the selection of talukas were picked: Aurepalle and Dokur in Mahabubnagar, Kanzara and Kinkhed in Akola, and Kalman and Shirapur in Solapur. In the fourth stage, the village census provided the basis for drawing the sample of households, stratified according to landholding size (as the focus of the program was on agricultural markets and relations). In 2002, ICRISAT restarted the panel, revisiting all households of the original sample if the head of the household was alive, in addition to household split-offs and new households to ensure a representative sample (Rao, 2008; Rao & Charyulu, 2007). 3 ICRISAT’s goal of restarting the study was to investigate the impact of the changes in markets, policies, and technologies on these village economies. Globalization of agricultural markets and changes in government price support policies has resulted in a decline of output price for traditional dryland crops such as millet and sorghum, resulting in smaller profit margins despite technological advancements in terms of genetically modified seeds, improvements in infrastructure and electricity subsidies. Drought remains a dominant constraint for crop production, and while the households continue to invest in water exploration, they are also diversifying their investment into education, health and non-agricultural businesses (the average years of education of the household head in-

creased from two years at the end of the 70s to five years in 2007). As a result, the occupational structure has become more diversified over the last thirty years with non-farm activities accounting for over 50% of the income in 2004 (Rao & Charyulu, 2007). Relatedly, migration, both temporary and permanent, has become an important source of income and has impacted the way of life in the villages: Joint families have given way to nuclear families and informal village networks are declining in importance (Badiani, 2007). Overall, income, assets, and consumption have increased substantially over the last thirty years (Badiani, Dercon, Krishnan, & Rao, 2007). 2.2 Data collection 2007–08 2.2.1 Sample households To obtain information on marriage-related social customs, expectations, and aspirations with regard to education, I surveyed 339 ICRISAT-VLS households in Dokur and Kalman and Shirapur in 2007–08 (for the purpose of this study, I defined the household to include also the daughters under the age of 26 who married and moved out). In this survey I elicited, for each individual up to the age of 25, details about their education and activities since age six. For household members older than 25 years and in-laws, I obtained basic information about their current occupation and education obtained. I included questions on household composition, caste, and subcaste (jati), wealth, income, social network, 4 and time preferences. 5 I also completed a village questionnaire and school questionnaires among the top five most attended schools in each village with the assistance of the ICRISAT-VLS investigators, the school principals, and the sarpanch (i.e., the democratically elected head of a village level statutory institution of the local self-Government called the Gram Panchayat). This included information on village infrastructure, educational programs in the village, the direct cost of education, school infrastructure, facilities, and instruction. Table 1 introduces the sample. With a sampling frame of 1720 households and a sample size of 339 households, the sampling rate is almost 20%. Of the 1,876 individuals included in the sample, 835 individuals are under 26 years of age. Excluding daughters-in-law, over 90% of the children between the ages of six and 15 years are enrolled in school (as a comparison, all-India primary school (gross) enrollments are 112% for girls and 115% for boys in 2007 according to World Development Indicators (WDI) data) and 28% of the young adults between the ages of 15 and 25 years are enrolled in an educational institute (as a comparison, all-India secondary school (gross) enrollments are 52% for girls and 61% for boys, and tertiary school (gross) enrollment is 16% for boys and 11% for girls in 2007 according to WDI data). 6 There is little gender gap in enrollment up to the age of 15 years, but compared to male young adults, fewer female young adults are enrolled in an educational institute (24% versus 30%). In total, there are 556 individuals either currently enrolled in an educational institute or under the age of six (and, as such, planning to go to school). The average size of a household is 5.6 members and the average kharif 7 income is 51,176 Rs (about $1,280 at the time of the survey, compared to all-India per capita income of $1,055 in 2007 according to WDI). About 42% of household members older than 25 years (excluding retirees) are active in the agricultural sector, and about 10% work in agricultural-related industries (e.g. sugarcane mills) or as a daily laborers in the construction or other non-agricultural sectors. Traditional caste occupations such as carpentry, basket-weaving, washing, clothing, and tailoring

SOCIAL NORMS AND ASPIRATIONS: AGE OF MARRIAGE AND EDUCATION IN RURAL INDIA

3

Table 1. Selected descriptive statistics Number of households in the villages Number of households in sample Average number of household members Average Kharif income (Rs)a Average education level of respondent (in years)b Children enrolled in school (%)c Young adults enrolled in school (%)c

1,720 339 5.55 51,176 4.77 93 28

a

The Kharif season is the rainy season. The respondent is the main decision maker with regard to the education of the children under 25 year in the household. c An adult is defined as an individual over the age of 25, a young adult is defined as an individual between the ages of 15 and 25 and a child is defined as an individual under the age of 15; children under the age of 6 were excluded from this calculation as school starts at age 6 in India. b

are on the rise, as are occupations in the service-sector such as driver, teacher, policeman, contractor, or even artist (see also Rao & Charyulu, 2007). 8 2.2.2 Education: Perceived earnings and aspirations The education system in India comprises school education from 1st to 12th standard, and higher education, beyond 12th standard, also referred to as 12+. A child typically enrolls in school at the age of six. Up to the age of 14 (which typically corresponds to elementary school—1st to 8th standard), school education is compulsory. The recently passed Right to Education Act made elementary education a right of every child and specified minimum norms for every school. The last two years of school education are referred to as higher secondary education, while 10th and 11th are referred to as lower secondary education. Before entering 11th standard the child has to pass a national or state level administered exam and from the 11th standard onward, the student chooses three to four subjects in which he specializes. The student completes his school education by taking another national or state level administered exam after 12th standard. Following this, there are several options for a student to continue education. One can enroll in a two-year diploma course to become, for instance, a teacher or a textile designer or opt for technical training at an I.T.I. (Industrial Training Institution), a two-year diploma course after which one can become an electrician, mechanic, painter, welder, etc. Alternatively, one can pursue a three year program at a college or university to obtain a bachelor’s degree in sciences, commerce, or the arts. A few degrees take four to five years, such as an engineering, law or medicine degree. After finishing a bachelor’s degree a student can pursue a Master’s degree. Dokur has one public school, established in 1952, which offers education up to 10th standard. Kalman has two public schools, one offers 1st up to 4th standard and the other 5th to 12th standard. The former was established in 1950 and the latter in 1965. Shirapur has two schools as well. The public school offers 1ste up to 5th standard and a private-aided school offers 5th to 10th standard. The former was established in the late 19th century and the latter in 1972. None of the villages are geographically isolated. Dokur is 7 km from the town of Deverakadra, Shirapur is 12 km from Mohol and Kalman is 15 km from Vairag. Public transportation and three-wheelers connect the villages with these towns in less than 40 minutes. The nearby towns offer options for higher education, including engineering, diploma, and B.A. One individual was interviewed in each household: the selfreported primary decision maker with regard to the education

of the household members younger than 26 years (in 73% of the cases this was the father, in 7% of the cases this was the mother, and in the remaining 20% of the cases this was another relative). In the remainder of this article I refer to this decision maker as the “parent” of the child. The average education level of this respondent is low, 4.8 years. As the majority of the children complete elementary education and lower secondary education, I focus on future plans to invest in upper secondary and higher education. Considering these future plans to invest in education, i.e., educational aspirations, instead of the educational attainments of those who have completed schooling have two main advantages. First, current aspirations relate more closely to current perceptions of social norms and returns. Second, from a cost perspective, eliciting both perceived returns to education and aspirations in one survey is preferred over a panel survey. To elicit the parent’s educational aspirations for each child (currently enrolled in school or planning to go to school), I distinguish between the minimum amount of education the parent wants the child to complete and the maximum amount of education the child would be allowed to complete. According to the parent’s own account, the actual level of education which will be obtained is a complex function of many factors, several of which are currently unknown (at the time I conducted the survey). The minimum and maximum amount of education then corresponds to the bounds of what the parent perceives plausible at the time of the interview (I return to this interpretation in the next section). For instance, if the child appears to be very bright, highly motivated, and the family does not face any financial difficulties, the actual education level will be close to the maximum mentioned. Conversely, with financial difficulties, or poor ability the actual education level will be close to the minimum mentioned. Hence, I elicited the aspirations with the following questions: “What is the minimum amount of education you want this particular child to obtain?” and “What is the maximum amount of education you would allow this particular child to complete?”. I elicited current beliefs regarding the returns to education, conditional on the child’s ability and characteristics, but unconditional on the nature of the employment. To obtain a density function of future earnings for each education level and for each child, I used a method based on Dominitz and Manski (1996) and Lybbert, Barrett, McPeak, and Luseno (2007). I first elicited the minimum and maximum earnings the respondent imagined the child would earn when finishing a particular schooling milestone (net of costs, on a monthly basis). During this exercise, the respondent was asked to imagine the various options possible, i.e., various types of employ-

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WORLD DEVELOPMENT

ment, including self-employment, and various locations where the child might live in the future, anticipating migration. I opted for such a set-up as separately eliciting the various employment options appeared infeasible: the respondents had a cascade of sorts in mind (if the child cannot find job X, he or she will try for job Y etc.) and had difficulties figuring out the entire set of possibilities as well as the corresponding probabilities. Then, I made three boxes, evenly distributed between this minimum and maximum and I asked the respondent to use 20 stones (each stone representing a 5% probability) and place the stones in the three boxes, with more stones into the boxes representing the event they consider more likely to happen, i.e., essentially to form an earnings density function (see Delavande, Gine´, & McKenzie (2011a, 2011b) on various methods to elicit beliefs in developing countries). This question was repeated for the various levels that the child still had ahead of him/her, and could include, 8th standard, 10th standard, 12th standard, diploma, bachelor’s, engineering, medical doctor, and master’s. Thus, for a child currently enrolled in 11th standard, one was asked to reconstruct the density function for 12th standard, diploma, bachelors, engineering, medical doctor, and masters, but not for 8th or 10th standard. To ensure that the respondent understood the set-up, I started this game with some simple stone exercises, practising the concept of likelihood. Then, I did two trial games applied to popular topics (elections, weather, exam success and credit matches), such as, “What is the chance it will rain tomorrow?” If the respondent looked confused, put all his stones onto one pile, or left an empty box at any of the two ends of the distribution, the question was rephrased. In general, the respondents appeared to enjoy the game and have a good understanding, so that I had to repeat the set-up only a couple of times. I should note that understanding the game did not imply knowing the returns for all levels. There are 121 children,

mostly in Dokur, for whom the respondent answered “no idea” for one or more of these “stone game” questions. Comparing these respondents with the other respondents in Dokur, it appears that they are, on average, somewhat less educated, older, and have younger children. Their aspirations for their children (minimum and maximum) are lower compared to the other children in Dokur (see also Maertens (2011) for a discussion on what determines these (lack of) expectations). The direct costs of education can be substantial, ranging from 17% of the average annual income per capita for elementary education to over 80% for secondary school education, and over 100% for higher education. While several respondents mentioned they would be willing to borrow money for the education of their sons and daughters, these type of loans are non-existent up to 12th standard. Banks provide loans only for higher education and incomplete land markets might imply that land cannot be sold easily to finance education. Due to time constraints, I was only able to elicit the perceived direct cost of the various levels of education using a similar set-up as the returns question for one of the villages, Dokur. While the direct cost of education is largely fixed at the caste level in each village up to 12th standard (as the various prices are fixed by the respective state governments), the direct costs of higher education are institution specific. I hence rely on school level cost data up to 12th standard and the direct costs, as recalled by the respondents, for higher education. 9 2.2.3 Marriage norms The question with regard to the “ideal”, i.e., socially acceptable age of marriage, was asked as follows, without reference to any particular child: “What is the ideal, socially acceptable, age for a girl/boy to get married these days?” In addition, I asked the respondent how much he expects each of his children to contribute to their old age pension. The respondent was asked to keep in mind the total financial contribution from his children on a monthly basis (in Rs) and then asked

Percentage

60 50 40 30 20 10 master 2

master 1

eng 4

eng 3

eng 2

eng 1

bachelor 3

bachelor 1

bachelor 2

dip 2

dip 1

stand 12

stand 11

stand 10

stand 9

stand 8

stand 7

0

Minimum level aspired

child's wish

phd

medical doctor

master 2

master 1

eng 4

eng 3

eng 2

eng 1

bachelor 3

bachelor 2

bachelor 1

dip 2

dip 1

stand 12

stand 11

60 50 40 30 20 10 0 stand 10

Percentage

Fig. 1. Minimum level of education aspired, N = 556. See also notes to Table 2.

Maximum level aspired

Fig. 2. Maximum level of education aspired, N = 556. See also notes to Table 2.

SOCIAL NORMS AND ASPIRATIONS: AGE OF MARRIAGE AND EDUCATION IN RURAL INDIA

to rank his children in terms of expected share contributed. Once the respondent decided on the ranking, I asked him/ her to attribute an expected percentage to each child. The child-level questionnaire also included a module on dowry (the transfers from the bride’s family to the groom’s family at the time of marriage) among the married household members up to the age of 25 years. The dowry could depend on the education level of bride and groom and hence increase or decrease the net returns to education indirectly, a cost which needs to be taken into account (see also Maertens & Chari, 2012). I control for these dowry costs in the analysis by including the predicted dowry based on the difference between wealth in the two families, the education level of the bride and groom, the family composition of the household the bride joins, and caste and village fixed effects.

As a comparison, Figure 3 shows the distribution of the highest level of education obtained for all the individuals who have completed their education. 11 About 36% of these individuals did not complete first standard and only 6% completed at least one year of higher education. Comparing these numbers with the all-India figures, of all individuals between the ages of 15 and 65 years, 40% did not complete primary education, 29% completed primary or lower secondary education, 22% completed higher secondary education and 8% have a higher education degree (Agrawal, 2011 using the 2005 Indian Human Development Survey). Comparing Figs. 1 and 2 with Fig. 3, I note the discontinuous nature of these aspirations. This suggests that while the parents might not plan on stopping their child’s education before a program or level is completed, in practice many children do drop out. 12 Also note the lower average level of education of Fig. 1 compared to Figs. 2 and 3. If minimum and maximum aspirations correspond to the bounds of the parent’s aspirations, one would expect the difference between these two aspirations to decline as the child grows older and the parent learns about the child as well as about household resources available. I regress the difference between the maximum and minimum aspirations (converted to years of education) as a function of age, and plot the predicted difference in Fig. 4, and find exactly this for girls, and to a lesser extent for boys, when focusing on the age 5–15 range (possibly the most relevant stretch in terms of learning).

3. A FIRST LOOK AT THE DATA Figures 1 and 2 show, respectively, for the children currently enrolled or planning to enroll in school in the future (including the children under the age of six), the minimum and maximum levels of educational aspirations the parent has for the child. In 80% of the cases the parent wants the child to complete a minimum of 10 or 12 years of education (Fig. 1) and in 55% of the cases the child would be allowed to continue for a bachelor’s degree or “as long as the child wants” (Fig. 2). 10

60 Percentage

50 40 30 20 10 master 2

eng 4

master 1

eng 3

eng 1

eng 2

bachelor 3

bachelor 2

bachelor 1

dip 1

dip 2

stand 12

stand 11

stand 9

stand 10

stand 7

stand 8

stand 6

stand 5

stand 4

stand 3

stand 2

none

stand 1

0

Highest level completed

Predicted difference in max and min aspirations (years) 2 2.5 3 3.5

Fig. 3. Highest level of education completed by individuals who have finished their education, N = 1329.

0

5

5

10

15

Age of child (years)

Fig. 4A. Scatter plot of the predicted difference between maximum and minimum aspirations as a function of the child’s age (boys), N = 213.

WORLD DEVELOPMENT Predicted difference in max and min aspirations (years) 1.5 2.5 3 3.5 2

6

0

5

10

15

Age of child (years)

Fig. 4B. Scatter plot of the predicted difference between maximum and minimum aspirations as a function of the child’s age (girls), N = 184. Table 2. Educational aspirations By gender:

Boys

Girls

Pooled

Min Max Min Max [percentage of individuals currently enrolled in school, or planning to go to school] 10th or lower 12th Higher than 12th

40 37 22

7 20 71

68 22 8

21 38 39

Min

Max

52 30 16

13 27 57

Notes: The full sample includes 311 boys and 245 girls; Totals do not add up to 100% due to a small fraction of respondents who answered “don’t know” or “it depends”; The minimum aspiration was elicited as the response to the question: “What is the minimum amount of education you want this particular child to obtain?”; The maximum aspiration was elicited as the response to the question: “What is the maximum amount of education you would allow this child to obtain?”.

Table 3. Expectations with regard to earnings [in Rs/month] [including all individuals currently enrolled in school, or planning to go to school] Boys Girls Pooled 8th standard 10th standard 12th standard Diploma Bachelor’s Medical doctor Engineer Master’s

2033 (102) 3080 (188) 4274 (244) 8709 (446) 10140 (314) 27140 (3141) 19784 (842) 15244 (805)

1787 (128) 2857 (235) 3714 (228) 7807 (445) 8696 (292) 24434 (3763) 16567 (832) 13909 (965)

1920 (81) 2978 (148) 4022 (169) 8311 (318) 9499 (220) 25940 (2415) 18359 (600) 14663 (619)

Notes: Standard errors reported in parenthesis under the average. For each individual the perceived average monthly earnings are calculated assuming a step-wise distribution with the minimum and maximum of the distribution of earnings as specified by the respondent for each level of education completed. These earnings are equivalent to the wage in the case of wage employment and are earnings net of costs in the case of selfemployment. The numbers presented in this table are the sample averages and standard errors of these individual expected averages for each level, excluding the cases in which the respondent answered “don’t know”.

Figs. 1 and 2 conceal a substantial amount of gender-based variation. Table 2 reveals that only 39% of the girls would be allowed to pursue higher education, compared to 71% of the boys, and only 8% of the girls is expected to complete higher education versus 22% of the boys. These gender-based differences in aspirations might be partially explained by a difference in expectations with regard to the returns to education. Table 3 reports the summary statistics with regard to the parent’s perceived returns to education. Table 3 shows that the parent believe that ‘graduates’ (i.e., someone with a bachelor’s degree) earn, on average, 9,499 Rs/month, which is more than double what someone who completed 12th standard is expected to earn, on average (4,022 Rs/month). According to the respondents’ own account, medical doctor and engineering degrees are considered a ticket out of poverty. This is reflected in their beliefs. Medical doctors are expected to earn, on average, 25,940 Rs/month and engineers are expected to earn, on average, 18,359 Rs/month. Girls are expected to earn less compared to boys, independent of the level of education. Girls are expected to earn, on average, 8,696 Rs/month after completing a bachelor’s degree versus 10,140 Rs/month for boys. After completing an engineering degree, girls are expected to earn, on average, 16,567 Rs/month versus 19,784 Rs/month for boys. This between-gender difference is significantly different from zero at the 5% level only for the bachelor’s and engineering degrees. 13 Comparing these statistics with the actual earnings, it may appear that our respondents are optimistic regarding the returns to higher education: in 2005 (using data from the Indian Human Development Survey), graduates earn an average of

7

20 0

10

Percentage

30

40

SOCIAL NORMS AND ASPIRATIONS: AGE OF MARRIAGE AND EDUCATION IN RURAL INDIA

14

16

18 20 22 Ideal age of marriage girls (years)

24

Fig. 5. Histogram of the stated ideal (socially acceptable) age of marriage for girls, N = 448.

5,500 Rs/month in Andhra Pradesh and 6,500 Rs/month in Maharashtra. 14 Perhaps the respondents did not believe these averages applied to their children and had more prestigious jobs in mind (e.g. graduates from the Indian Institute of Management earn close to 100,000 Rs/month (Chakravarty & Somanathan, 2007), and working as an enumerator for a research project—as the respondents knew all too well—pays about 15,000 Rs/month). The discrepancies between perceived and actual earnings may reflect information constraints, discrimination, overoptimism, or simply heterogeneity in ability (see also Maertens, 2011 on the role of social networks in determining these beliefs). According to the respondents’ own account, marriage and being-in-school are incompatible (especially in the case of girls), indicating that a low socially acceptable age of marriage for girls might limit the parents’ educational aspirations for their daughters. Many respondents mentioned that a family that has an unmarried older daughter at home is often criticized, sometimes compromising the marriage prospects of the other children and resulting in social exclusion of the adult household members. As such, it is in the parents’ interest to marry their daughter around the ‘ideal’ age of marriage, i.e., the socially acceptable age of marriage. 15 This ideal age of marriage is, on average, 18.3 years for girls and 22.7 years for boys. At a household level, these two numbers are highly correlated (the correlation coefficient is 0.66). Figures 5 and 6 show, respectively, the histogram of the ideal age of marriage for girls and boys. Almost 60% of the parents considers the ideal age of marriage for girls to be 16 years or below. For boys, 17.5 was the lowest acceptable age mentioned and about 65% of the parents mention 21 years and above. In both cases, over 25% of the total variation in the ideal age of marriage is between subcaste (jati) variation. As fewer than 1% of the parents mention that they would be willing to consider a marriage outside of their subcaste, this does not come as a surprise. For married individuals under the age of 26, the average age of marriage is 17.5 for girls and 20.3 for boys; these averages are lower than the average stated ideal age for each gender. The difference is largely due to sample selection: The married sample only captures those who married before the age of 25. The average age of marriage has been on the increase in the villages, especially for girls: Rao (1993) recorded an average age of marriage on 21.07 years for boys and 14.40 years for

girls in 1983. This is consistent with the experience in the rest of India. According to the 2001 Census, the average age of marriage (SMAM or Singulate Mean Age of Marriage) is 20.2 years for girls and 24.8 years for boys. In 1991 and 1981, these numbers were, respectively, 19.3 and 18.3 for girls and 23.8 and 23.3 for boys (see also Sautman, 2009 2009 for an analysis covering 1911 to 1991). I conclude this section with a note on the custom of patrilocality. With the exception of five respondents who had access to savings or an old age pension, respondents reported that they would have to rely on their children for old-age support. Among the households that have both sons and daughters (there are 228 such cases), 61% of the parents expect more care from their sons than their daughters (relative to the number of sons and daughters in the household). Male children are expected to contribute, on average, 78% of the financial support during old age. The female children are, contrary to popular beliefs, not unimportant: they are expected to contribute, on average, 37%. Traditionally, in the patrilocal marriage system in which married couples reside with or near the family of the husband, daughters are expected to leave the household while sons (or one of the sons) are expected to stay with their parents. This does imply that (the majority of) the returns to education after marriage might go to the in-law’s family, thereby decreasing the benefits accruing to the parents of the girl (Srinivas, 1984; Lahiri & Sharmistha, 2007). 16 Nonetheless, the data indicate that it is no longer the case that it is only boys who are expected to take care of their parents during old age (as Foster & Rosenzweig, 2001 assume to be the case in their analysis of India’s marriage markets during the Green Revolution). Of the households with only male children (there are 59 such cases), 42% expect the same contribution from all their sons. Of the households with only female children (there are 24 such cases), 33% expect the same contribution of all their daughters. 17 These numbers suggest that whom the parent expects support from does not merely depend on family composition: for example, the parent does not always expect the same share from all his sons. Neither does the data support an order story: younger or older sons do not contribute systematically more or less. I find a negative relationship between the average age of children and the likelihood of an equal division between the male children which suggest that the parent learns about

WORLD DEVELOPMENT

20 0

10

Percentage

30

8

18

20 22 24 Ideal age of marriage boys (years)

26

28

Fig. 6. Histogram of the stated ideal (socially acceptable) age of marriage for boys, N = 338.

the attributes of his children, and this learning translates itself into revised beliefs with regard to whom to rely on during old age (these results are available on request). 4. ANALYSIS AND RESULTS 4.1 Conceptual framework I briefly outline a conceptual framework that will motivate the regression analysis to follow. The decision to invest in education can be analyzed using a cost-benefit framework, broadly conceived. Consider a household that must decide on the education level and the age of marriage of their child (for simplicity, I am assuming they have only one child). When making this decision, the household takes as given (i) Its own income, assets, and demographic characteristics as well as time preferences, denoted by the vector X, (2) The characteristics of the child, denoted by the vector Z, (3) The returns to various levels of education, captured by a vector R, (4) The direct costs associated with each of the levels of education, denoted by the vector C (for instance, school fees), and (5) Social norms regarding the “acceptable” age of marriage of a child of that gender, denoted by N. Social norms are usually thought of as being sustained by a penalty on deviations from the norm. In this sense, the norm N may be considered as imposing an indirect cost associated with education. 18 I begin by noting that the distribution of perceived educational returns, summarized in R, embeds a number of elements, such as the child’s (perceived) ability, the quality of education, local employment rates, and wages and discrimination in the labor market. As I noted earlier, marriage norms may themselves be related to the returns to education, which in turn matter directly for educational aspirations. Being able to control for these returns is therefore extremely important in order to avoid omitted variable bias, and this is one of the strengths of the data. While the productive characteristics of the child plausibly enter the household’s decision problem through R, there may be other non-productive characteristics that may play a role in determining marriage decisions, such as child order and physical beauty (clearly, these are not all observable). These non-productive characteristics constitute the vector Z.

The solution to the household’s decision problem is represented by a pair of functions of the given parameters: E ¼ /1 ðX; Z; R; C; N Þ A ¼ /2 ðX; Z; R; C; N Þ

ð1Þ ð2Þ

where E* and A* represent the chosen education level and age of marriage, respectively. Eqs. (1) and (2) represent the reduced form solution to the household’s decision problem. While I do not have information on A* for any of the unmarried children, I do have information on the educational aspirations, E*, as well as on X,Z,R,C and N. I am therefore interested in estimating the reduced form Eq. (1), in order to understand how marriage norms and the perceived returns to education affect educational aspirations for boys and girls. In the next section, I adopt a regression approach, relating E* to the explanatory variables X,Z,R,C and N. 4.2 Regression analysis I begin by relating educational aspirations (translated into years of education) 19 to the expected returns to education for boys and girls separately. To keep the presentation simple, I aggregate the returns to various levels of education into two broad levels: “higher” education, which includes education beyond high school, and “lower” education, which includes schooling levels up to and including high school. Recall that the aspirations were only elicited for children who had not already dropped out of or completed school. To avoid selection issues arising from dropout, I restrict the analysis to children under 15 years of age (dropout rates tend to increase dramatically after the 8th grade). In addition, the perceived returns to education were only elicited for the levels the child still has ahead of him/her, so using a sample with children up to 8th standard ensures that this set of perceived returns is comparable for all children in the sample. To control for potential confounding factors at the household level that may be correlated with returns, I employ a fixed-effects approach, regressing aspirations on returns in the presence of household fixed effects (i.e., using the variation among siblings within each family). The regression specification is:

SOCIAL NORMS AND ASPIRATIONS: AGE OF MARRIAGE AND EDUCATION IN RURAL INDIA

y ij ¼ a þ bL RLij þ bH RHij þ cZij þ gj þ eij where yij denotes the educational aspiration for child i in household j, RLij and RHij represent the child-specific expected returns to lower and higher education respectively, gj represents a household fixed effect and eij denotes an unobserved error. The inclusion of gj absorbs the variables X,C and N which are common to children within the same household. The child-specific variables included in Zij are the age of the child and his/her birth order and birth order within gender, in addition, I control for the standard deviation of the child-specific returns to education of the various educational levels. The coefficients of interest in this regression are bL and bH, which represent the causal effect of returns to education on the household’s educational aspirations for the child. Table 4 reports the results from this specification, separately for boys and girls and separately for the household’s minimum and maximum educational aspirations. Increasing the returns to lower education by 1,000 Rs increases the minimum aspirations for girls by 0.48 years (significant at the 1% level), but has no statistically significant effect on the minimum aspirations for boys (Columns 1 and 2). Increasing the returns to higher education by 1,000 Rs increases the maximum aspirations for boys by 0.12 years (significant at the 10% level) but the effect on aspirations for girls is not statistically significant. The coefficients on the returns to higher education are comparable for boys and girls when looking at minimum aspirations (Columns 1 and 2), but these effects are not statistically significant. I now turn to the main focus of the paper: the relation between aspirations and the perceived ideal age of marriage. Because the perceived acceptable age of marriage is not childspecific, I cannot utilize a household fixed effects regression as before. I therefore start with the following specification to be estimated by ordinary least squares (OLS): y ij ¼ a þ bL RLij þ bH RHij þ bN N j þ cZij þ dXJ þ eij where the household variables Xj include (among others) measures of wealth, income, household composition, time preferences, and education of the household and its network, and child variables Zij include child age, child order, within gender child order. I also control for the direct and dowry cost of education, as well as the child’s expected contribution to the parents’ retirement income (the descriptive statistics of the

9

variables are presented in Appendix Table A1). In this specification, interest centers on the coefficient bN which captures the effect of norms regarding age of marriage on the household’s educational aspirations for the child. Table 5 presents the results of OLS regressions for the minimum and maximum educational level aspired to (Appendix Table A2 presents the full set of regression coefficients). The coefficient on “ideal age of marriage” for boys is small and insignificant in the case of both maximum and minimum aspirations (Columns 1 and 3), while in the case of girls this effect is estimated to be 0.20 years (significant at the 1% level) in the case of minimum aspirations and not significantly different from zero in the case of maximum aspirations (Columns 2 and 4). Interpreting these coefficients as causal would imply that an increase in the socially acceptable age of marriage by one year would increases the minimum level of education aspired to for girls by on average 0.20 years. A potential concern with the OLS regression is that even though the ideal age of marriage was elicited without reference to the household’s children, the decision maker when answering the question may have been influenced by the characteristics of his/her children. This creates a correlation between the stated ideal age of marriage and child characteristics. To the extent that the stated ideal age of marriage is correlated with productive characteristics, this should not create a bias in the OLS regressions because productive characteristics have been effectively controlled for by the inclusion of the returns variables, R. However correlation between the ideal age of marriage and the child’s non-productive characteristics is more problematic because these characteristics are not well observed. The resulting endogeneity may bias the coefficients of any explanatory variables that are correlated with the ideal age of marriage. To address this possibility, I repeat the estimation using the socially acceptable age of marriage of the remaining households within one’s jati in the village as an instrumental variable (IV). The motivation for this strategy is that because marriage-related norms are typically jati-specific, there should be a strong correlation between perceptions of the ideal age of marriage across households within the group. At the same time, other households’ perceptions of the ideal age are unlikely to be related to household i’s characteristics. One may worry, however, that the instrument, being a within-subcaste average, may be correlated with (i) group-based educational

Table 4. Aspirations and perceived returns to education (fixed effects regressions) Fixed effects regression

Returns to lower education (1000 Rs) Returns to higher education (1000 Rs) Contribution to old age pension (%) Constant R-squared Number of households

Minimum

Maximum

(1) Boys

(2) Girls

(3) Boys

(4) Girls

0.31 (0.19) 0.04 (0.04) 0.00 (0.00) 11.87*** (0.35)

0.48*** (0.11) 0.04 (0.04) 0.01 (0.02) 9.35*** (0.80)

0.35 (0.30) 0.12* (0.07) 0.01 (0.01) 14.01*** (0.82)

0.18 (0.33) 0.14 (0.11) 0.02 (0.04) 11.21*** (1.82)

0.323 122

0.443 106

0.317 121

0.387 106

Notes: * p < 0.1; ** p < 0.05; *** p < 0.01. Robust standard errors reported in parentheses. Includes all children up to the age of 14. Lower education comprises 12th, 10th, and 8th standard. Higher education comprises diploma, bachelor’s, medical doctor, engineering, and master’s/PhD. Averages across higher and lower returns are computed as simple averages for each child of the average returns for the levels concerned. Additional controls (not reported above) are age of the child, child order, child order within gender, and the standard deviation of the individual returns to education (excluding the 8th standard so as to include all children up to the age of 14 years).

10

WORLD DEVELOPMENT Table 5. Aspirations and the ideal age of marriage (OLS regressions) OLS regression

Minimum

Maximum

(1) Boys

(2) Girls

(3) Boys

(4) Girls

0.01 (0.06) 0.04 (0.08) 0.09*** (0.02) 0.00 (0.00) 0.14 (0.09) 0.29*** (0.10) 0.10 (0.09) 1.47*** (0.54) 1.09 (0.68)

0.20*** (0.06) 0.09* (0.04) 0.04* (0.02) 0.00 (0.00) 0.01 (0.08) 0.12 (0.10) 0.03 (0.08) 0.35 (0.35) 1.01* (0.54)

0.07 (0.07) 0.04 (0.12) 0.08** (0.04) 0.01* (0.01) 0.08 (0.14) 0.52*** (0.19) 0.14 (0.11) 0.03 (0.64) 0.23 (0.94)

0.10 (0.11) 0.05 (0.08) 0.05 (0.05) 0.00 (0.01) 0.03 (0.18) 0.15 (0.20) 0.16 (0.15) 0.38 (0.53) 0.93 (0.94)

Observations R-square

208 0.681

171 0.652

208 0.522

171 0.551

F tests (joint signifiance at 10% level) Direct costs of education Education of social network HH composition and wealth Education of HH

No Yes Yes Yes

Yes Yes Yes No

Yes Yes No No

Yes No No No

Ideal age of marriage Returns to lower education (1000 Rs) Returns to higher education (1000 Rs) Contribution to old age pension (%) Measure of impulsivity (1–5 range with 5 most impulsive) Measure of planning (1–5 range with 5 least planning) Measure of inhibition (1–5 range with 5 lowest inhibition) Other backward castes Scheduled caste

Notes: *p < 0.1; ** p < 0.05 ; ***p < 0.01. Robust standard errors reported in parentheses. Dependent variable is minimum/maximum aspirations— planned education (in years); The regression sample includes all children up to the age of 14; Lower education comprises 12th, 10th and 8th standard. Higher education comprises diploma, bachelor’s, medical doctor, engineering, and master’s/PhD; Averages across higher and lower returns (cost) are computed as simple averages for each child of the average return (cost) for the levels concerned, using all the returns (costs) available; The additional controls (not reported above) are income; land; value of other assets (equipment, durables, animals, stock, savings, net-lendings); direct costs, and dowry cost of higher and lower education; age decisionmaker; education level decisionmaker and maximum education level in household; education level of extended family (measured as % of members who obtained lower and higher education degrees) and people known with higher/lower education in total/ village/among relatives; houshold composition; age of child; child order, and child order within gender. For the full set of results see Appendix Table A3.

norms, (ii) labor market discrimination on the basis of caste, or (iii) differences in cost and quality of education. To address (i), I include controls for educational norms. Specifically, I control for the number of people that the respondent is aware of who have completed lower and higher education degrees (in total, within the village and within the set of relatives) and the percentage of extended family members who have completed lower and higher education. With respect to (ii) and (iii), the system of positive discrimination instituted by the Government of India provides educational subsidies for children from lower caste groups and creates quotas (also called “reservations”) in public sector employment and institutes of higher education for members of lower caste groups. Because castebased labor market variation is embedded in the returns variables R, (ii) is unlikely to be a significant concern for the identification. Nevertheless, I control in the regression for a set of three dummies for Forward, Scheduled, and Other Backward Castes, as these are the official basis of affirmative action policies in India. With respect to (iii), school quality varies greatly in India and is known to affect educational investments (see e.g., Kingdon, 1999; Dre`ze & Kingdon, 2000; Glewwe, Hanushek, Humpage, & Ravina, 2011). Once again, however, school quality should indeed matter for aspirations, but only to the extent that it affects parents’ perceptions of the returns to schooling. Controlling for these perceived returns is therefore sufficient to control for all the factors that may affect

the returns to schooling, including school quality. Thus, any correlation between caste membership and school quality should not undermine the validity of the instrument. In addition, note that I also explicitly control for cost of education. Table 6 presents the results of the IV regressions (the first stage regression results are reported in Table 7). The F-statistic associated with testing for the significance of the instrument in the first stage is high for both boys as well as girls (and the associated p-values are low), indicating that the instrument is strongly correlated with the (potentially) endogenous variable. The table also reports the results of the Durbin–Wu–Hausman test for exogeneity of the stated ideal age of marriage. While the hypothesis of exogeneity is rejected for girls, there is not sufficient information to reject exogeneity in the case of boys. Columns 1 and 3 of Table 6 show that, once again, increasing the “ideal age of marriage” for boys does not have a significant effect on either minimum or maximum aspirations. In contrast, Columns 2 and 4 indicate that increasing the “ideal age of marriage” for girls increases the minimum aspirations by 0.55 years (significant at the 1% level), and the maximum aspiration by 0.73 years (significant at the 1% level). These are evidently large effects, and are consistent with the qualitative evidence that for girls norms regarding the ideal age of marriage are strict and binding, while for boys they are not strict and/or not binding.

SOCIAL NORMS AND ASPIRATIONS: AGE OF MARRIAGE AND EDUCATION IN RURAL INDIA

11

Table 6. Aspirations and the ideal age of marriage (IV regressions) IV Regression

Minimum (1) Boys

Maximum (2) Girls

(3) Boys

(4) Girls

0.08 (0.12) 0.10 (0.10) 0.13*** (0.02) 0.00 (0.00) 0.05 (0.09) 0.24* (0.13) 0.09 (0.08) 0.69** (0.28) 1.79*** (0.45)

0.55*** (0.11) 0.13*** (0.05) 0.00 (0.03) 0.01 (0.01) 0.07 (0.08) 0.03 (0.10) 0.02 (0.13) 0.82** (0.32) 1.61*** (0.35)

0.22 (0.16) 0.11 (0.11) 0.10*** (0.03) 0.01* (0.01) 0.17 (0.13) 0.43** (0.18) 0.16 (0.10) 0.48 (0.45) 1.69*** (0.64)

0.73*** (0.17) 0.13 (0.09) 0.02 (0.05) 0.00 (0.01) 0.06 (0.15) 0.10 (0.17) 0.11 (0.19) 0.57 (0.57) 1.06 (0.68)

Observations R-square

201 0.617

165 0.547

201 0.494

165 0.455

F tests (joint signifiance at 10% level) Direct costs of education Education of social network HH composition and wealth Education of HH

Yes Yes Yes No

Yes No Yes No

Yes Yes Yes No

Yes No Yes No

Ideal age of marriage Returns to lower education (1000 Rs) Returns to higher education (1000 Rs) Contribution to old age pension (%) Measure of impulsivity Measure of planning Measure of inhibition Other backward castes Scheduled caste

First-stage F-test F-value p-Value Robust Durbin–Wu–Hausman test of endogneity F-value p-Value

20.9 0.000

17.61 0.000

20.9 0.000

17.61 0.000

0.17 0.679

4.32 0.040

1.15 0.284

6.18 0.014

Notes: * p < 0.1; ** p < 0.05; ***p < 0.01. Robust standard errors reported in parentheses. Dependent variable is minimum/maximum aspirations— planned education (in years); The estimates correspond to Two-Stage Instrumental Variable Estimation with the “ideal age of marriage within one’s jati” as an instrument for the “ideal age of marriage”; The regression sample includes all children up to the age of 14. See also notes to Table 5.

Finally, the results again suggest that returns to education matter. In the case of boys, Tables 4–6 present a consistent picture: Returns to higher education matter for aspirations (a 1000 Rs increase in these returns would increase aspired years of education by 0.09–0.13 years), but returns to lower education do not. In the case of girls, Tables 4 and 6 indicate that returns to lower education matter for aspirations (a 1000 Rs increase in these returns would increase aspired years of education by 0.13 years), but the returns to higher education do not. The difference between the OLS and IV results is likely due to the endogeneity of stated ideal age of marriage in the case of girls, which may have been biasing the coefficients on other explanatory variables that it was correlated with. The child’s expected contribution at old age does not consistently appear to matter (either in economic or statistical terms). This is reasonable since (controlling for family composition) this variable largely picks up child quality, which should already have been absorbed in the perceived returns to education. Lastly, I note that self-reported time preferences appear to matter as well: parents who plan ahead, on average, report higher levels of aspirations for boys (see also end Note 6). The F-statistics corresponding to the tests of joint significance for the remaining variables are reported in Tables 5 and 6.

4.3 Robustness checks I now implement variations on the basic specifications in order to assess the robustness of the results. In the main regression specifications, I controlled for the education in the household’s network in order to mitigate the possibility that the instrument was simply picking up educational norms. I also included the child’s expected old-age contribution as a way of controlling for the child’s ability/quality over and above the perceived returns. In Appendix Table A4 and A5 I report the results of the IV regression after dropping these two sets of variables. Appendix Table A4 report the results of the IV regression which drops the educational norm variables. Appendix Table A5 further drops the child’s expected contribution from the IV regression. It is notable and reassuring that the results are robust to these alternative specifications. The previous regressions assumed a linear regression framework. Appendix Table A6 reports the results of an ordered logit model that treats aspirations as being chosen from an ordered set of outcomes rather than as a cardinal variable (years of education). The table reports the odds ratios, rather than the coefficients, so that a zero coefficient would imply an odds ratio of 1. While I have not been able to instrument for

12

WORLD DEVELOPMENT Table 7. First-stage results for the IV regression IV Regression (1) Boys Ideal age of marriage within jati (sub-caste) and village Returns to lower education (1000 Rs) Returns to higher education (1000 Rs) Contribution to old age pension (%) Measure of impulsivity Measure of planning Measure of inhibition Other backward castes Scheduled caste Observations R-squared First-stage F-test F-value p-Value

0.71***

(2) Girls 0.54***

(0.16) 0.09 (0.13) 0.04 (0.04) 0.00 (0.01) 0.15 (0.14) 0.38** (0.15) 0.11 (0.13) 0.37 (0.49) 0.37 (1.06)

(0.13) 0.09 (0.08) 0.12*** (0.03) 0.02*** (0.01) 0.02 (0.11) 0.31** (0.15) 0.30** (0.12) 0.17 (0.42) 0.24 (0.58)

201 0.479

165 0.632

20.9 0.000

17.61 0.000

relatives) and the percentage of extended family members who have completed each category of education. Interpreting these coefficients as causal would imply that increasing the “ideal age of marriage” by one year for girls changes the relative odds of aspiring for the 12th standard, rather than 10th standard, by a factor of 1.94. The coefficient on “ideal age of marriage” appears not significantly different from zero for boys. Increasing the returns by 1,000 Rs changes the relative odds of aspiring for that particular level, relative to 10th standard, by a factor of 1.44 in the case of boys. For girls the coefficient on returns appears not significantly different from zero. In Appendix Table A8, the categories included are 10th standard, 12th standard, and bachelor’s degree for both boys and girls. The base category is the 12th standard. Interpreting the coefficients as causal would imply that increasing the “ideal age of marriage” by one year for girls changes the relative odds of aspiring for a bachelor’s degree, rather than 12th standard, by a factor 1.68, and has no effect on the maximum aspirations for boys. Increasing the returns by 1,000 Rs, changes the relative odds of aspiring for that particular level, relative to 12th standard, by a factor of 1.60 in the case of boys. For girls, again, the coefficient on returns appears not significantly different from zero.

5. CONCLUSION

Notes:*p < 0.1; **p < 0.05; ***p < 0.01. Robust standard errors reported in parentheses.The dependent variable is the perceived ideal age of marriage; The regression sample includes all children up to the age of 14. See also notes to Table 6.

the ideal age of marriage in this regression, it is notable that, again, the ideal age of marriage only seems to matter for girls and not for boys. Interpreting the coefficients as causal would imply that increasing the “ideal age of marriage” increases the odds of opting for a higher level (in terms of minimum aspirations) compared to an equal or lower level by a factor of 2.21 for girls. Dropping the proportional odds assumption which underlies the ordered logit model (i.e., that the coefficients that describe the relationship between, say, the lowest versus all higher categories of the response variable are the same as those that describe the relationship between the next lowest category and all higher categories, etc.), one obtains a conditional logit model. Appendix Tables A7 and A8 present the results of the conditional logit model, using, respectively, the minimum and maximum education aspired. In both cases, I need to restrict the sample to the levels of education for which I have a sufficient number of observations (10th standard, 12th standard, and bachelor’s degree) in order to achieve convergence. The fit is considerably better than the ordered logit regression and the Brant test rejects the proportional odds assumption. In Tables A7, the categories included are 10th standard, 12th standard, and bachelor’s degree for boys and only 10th and 12th standard for girls. The base category is the 10th standard. The category-specific variables include the average and standard deviation of the perceived returns to education, the direct costs and indirect dowry costs associated with the various levels of education, the number of people that the respondent is aware of who have completed each category of education (in total, within the village and within the set of

Using a detailed child-level dataset from three villages in India, I analyze the effect of marriage related social norms (in particular, the socially acceptable age of marriage) on the educational aspirations parents have for their children in rural India. I find that the educational aspirations are lower for girls compared to boys. Only 39% of the girls would be allowed (by their parents) to pursue higher education, compared to 71% of the boys, and only 8% of the girls is expected to complete higher education versus 22% of the boys. I find that this gender difference may be partly attributed to social norms that tend to favor early marriage of girls. The estimates obtained in this paper suggest that increasing the socially acceptable age of marriage for girls by one year would increase the years of education aspired to by 0.20 to 0.73. These numbers are comparable to what Field and Ambrus (2008) find in the context of rural Bangladesh: increasing the age of marriage for girls (instrumented by age of menarche) by one year results in, on average, 0.22 additional years of education. My results confirm this aspect of the story for rural India, but also establish that norms regarding age of marriage do not significantly constrain the aspirations that parents have for their sons. For boys, perceived returns to education appear to matter for aspirations, but almost exclusively in the context of returns to higher education. In contrast, aspirations are only responsive to the returns to lower education in the case of girls. This finding is consistent with the traditional division of labor within the household where men are the primary earners whereas women are generally involved in household production and/ or the household business (farm), which requires less formal education. These results are interesting because differences in the labor market returns to education between boys and girls have been speculated to be one of the main causes of the differential investment in education between boys and girls in South Asia (for a discussion, see Rosenzweig & Schultz, 1982; Kingdon, 1998; Dre`ze & Kingdon, 2000; Asadullah, 2006; Chakravarty & Somanathan, 2007; Chamarbagwala, 2008; Aslam, 2009). 20

SOCIAL NORMS AND ASPIRATIONS: AGE OF MARRIAGE AND EDUCATION IN RURAL INDIA

Recent studies have also highlighted the importance of perceived, as opposed to actual returns, to education, by showing that providing information on the “actual” returns to education increases educational investments (Jensen, 2010; Nguyen, 2008), as do policy changes that visibly alter the returns to education (Abramitzky & Lavy, 2011). My results provide a useful caveat to this literature by showing that—in the Indian context—increasing the perceived returns to higher education may not by itself be sufficient to induce greater educational investments in girls. From a policy perspective, it might be useful to consider campaigns to overcome marital norms, in conjunction with better enforcement of the legal age of marriage. It should also be noted that norms regarding education and the age of marriage are likely linked to the social acceptability of female participation in the workforce. The presence of role models as well as the changing nature of work in the economy can be expected to impact the latter and thereby have an impact on marriage and educa-

13

tional decisions. For example, Beaman, Duflo, Pande, and Topalova (2012) show that the presence of female village leaders in West Bengal decreases the gap in career and educational aspirations of both adolescents and their parents, arguably by changing norms. In a slightly different setting, Jensen (2011) randomized the provision of information and recruiting services for jobs in the Business Process Outsourcing (BPO) industry in a set of villages in near Delhi. As opposed to traditional manufacturing jobs, these are service sector (“office”) jobs which are presumably less stigmatized and are also considered to offer a safer work environment for young, umarried women. As a result, Jensen finds that women in the treated group stayed in school longer, and also postponed their marriage. These changes in the nature of work opportunities for women may serve to make female workforce participation more visible and thereby more socially acceptable, and marital norms can also be expected to change as a result.

NOTES 1. The 2001 census reports that the female/male ratios of primary, secondary, and tertiary enrollment in an educational institute are, respectively, 84%, 71%, and 66%. 2. Relatedly, Billig (1992) notes the relationship between the ideal age of marriage and views of “sexual and emotional maturity.” 3. Using National Sample Survey data of 1999–2000, Rao and Charyulu (2007) note that the monthly per capita expenditure (incidence of poverty) in the ICRISAT villages is slightly higher (lower) compared to the corresponding district average. 4. I elicited the education level of all members of the extended family and computed the number of family members who completed a certain level of education as a percentage of all extended family members older than 18 years of age (of the same gender). In addition, I asked the respondent how many people he knows who completed 12th standard, a diploma, bachelor’s degree, engineering degree, medical degree and who are working as a teacher, civil servant, engineer, medical doctor, etc., in different social groups (total, relatives,village). These questions were inspired by Lin (1999). 5. I should note that the measure of time preference that I included is somewhat unconventional. Typically, time preference is elicited by a series of questions along the lines of “Do you prefer X dollars today or Y dollars tomorrow” to derive the underlying discount rate. Time and budget constraints precluded adding such a module in my survey. Instead, I included questions to measure three components of time preferences as outlined by Loewenstein et al. (2001): (i) impulsivity, (ii) planning, and (iii) emotional inhibition, by means of the following (admittedly rudimentary) questions: (i) “Compared to others, how often do you make decisions quickly?,” (ii) “Compared to others, how often do you plan ahead?,” and (iii) “Compared to others, do you get angry easily?” The answers were coded on a 1–5 range with 1 corresponding to most impulsive/least planning/least inhibited. See also Pender, 1996 for estimates of discount rates in the ICRISAT villages. 6. Naturally, enrollment does not imply regular attendance. Of all the children and young adults enrolled in an educational institute, 87% do not miss classes more than 10% of the time. 7. The kharif season is the rainy season, and the main agricultural season in the semi-arid tropics of India.

8. The fact that agriculture as a sector is on the decline is also reflected in the parents aspirations for their children: Very few parents (corresponding to about 8% of the children in the sample) indicated that they would like to see their children occupied in the agricultural sector. 9. The direct cost for school education ranges from an average of about 200 Rs/year for the first standard in a government-run school to an average of about 4,400 Rs/year for the 12th standard in a private school. The direct cost for higher education ranges from less than 2000 Rs/year to obtain a diploma at a local technical institute, to over 100,000 Rs/year for a college degree in the district or state capital. See also Table A9 in the Appendix, which summarizes these costs for the various standards of schooling, separately for private and public schools. 10. These statistics do not include the 14 children for whom the respondent replied “it depends” and/or “I don’t know”. 11. There are a handful of individuals who have stopped their education but have intentions to re-enroll in the future. These individuals are included in Fig. 3. 12. The main reasons mentioned by the parents in this regard are: sudden financial problems (relatedly, Holla (2007) finds that income volatility, as measured by rainfall shocks, disproportionately affects girls’ school drop out); discovering the child is of “low ability” (often after having failed in school); and unexpected marriage prospects. Inspection of the data reveals that drop out is often preceded by failure and low attendance. 13. Relatedly, Mengesha (1990) notes that, in the ICRISAT villages at the end of the 80s, women earned less than men, and typically made their income available to the family with little say on its allocation. 14. I am grateful to Tushar Agrawal for sharing these numbers with me. 15. Relatedly, Dasgupta (1991) also notes the existence of social norms of early marriage for girls in the context of the ICRISAT villages during the period 1975–85. 16. Even though the practice is on the decline, it was not uncommon in South India for Hindu cross cousins to marry, with matrilateral crosscousin (mother’s brother’s daughter) marriages especially being favored (see also Al-Shafaee et al., 2012). Such within-family marriages may have acted to internalize the returns to education for one’s daughters.

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17. These calculations include all the children of the respondent, independent of their age, excluding the children for whom the parent could not report a percentage but only a ranking.

respondents, a master’s and PhD degree are not well known in these villages, and many of the respondents do appear to equate higher education to a bachelor’s degree.

18. The literature to date has shown that educational investement depends on parental education, socio-economic background, work opportunities, village and regional development, school quality, and costs and educational subsidies, all of which affect the cost-benefit calculation of education. See Rosenzweig (1995, 2010) and Schultz (1961, 1989) for an introduction to the economics of education in developing countries. See also Dre`ze and Kingdon (2000)—among others—for an analysis set in India.

20. All these studies, except for Asudullah (using a national representative 1999 household survey from Bangladesh) and Aslam (using a national representative 2002 household survey from Pakistan) find a lower return to education for females compared to males.

19. As noted earlier, some parents are willing to let their children attend school for as long as they (the children) want. I code this as corresponding to the completion of a bachelor’s degree. Based on conversations with the

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APPENDIX A. SUPPLEMENTARY DATA Supplementary data associated with this article can be found, in the online version, at http://dx.doi.org/10.1016/ j.worlddev.2013.01.027.