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Q U I T Y

Uma Sarada Kambhampati

A B S T R A C T

This paper tests three hypotheses about how mothers’ autonomy in India affects their children’s participation in school and the labor market. To do so it extends the concept of mothers’ autonomy beyond the household to include the constraints imposed by the extent of gender equity in the regions in which these women live. This study began with the expectation that increased autonomy for Indian mothers living in heterosexual households would increase child schooling and decrease child work. However, the results are mixed, indicating that mother’s autonomy can be reinforced or constrained by the environment. The paper concludes that mothers and fathers in India make different decisions for girls vis-a`-vis boys and that the variables reflecting mothers’ autonomy vary in their impact, so that mothers’ level of education relative to fathers’ is not often statistically significant, while mothers’ increased contributions to household expenditure decrease the probability of schooling and girls’ work.

K E Y W O R D S

Child labor, gender roles, intrahousehold inequality

JEL Codes: D13, J16

I N T R O D U C T I O N

This paper considers whether increased autonomy for mothers in India improves child welfare, specifically in terms of whether children attend school or participate in the labor market. In this context, the factors used to determine how much autonomy a mother possesses are her education and employment status, her education and income contributions relative to her spouse, and the extent of gender equity that prevails in the region in which she lives. The paper asks whether mothers and fathers make symmetric decisions with regard to child work and schooling, whether mothers with greater autonomy make ‘‘better’’ decisions than those with less autonomy, and whether kinship systems are important in determining these decisions. Feminist Economics15(4), October 2009, 77–112

Feminist EconomicsISSN 1354-5701 print/ISSN 1466-4372 onlineÓ2009 IAFFE http://www.tandf.co.uk/journals

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Analysis of the study data leads to the conclusion that mothers and fathers in India make different decisions for girls than they do for boys. The variables used to proxy for a mother’s autonomy vary in their impact on the probability of children attending school and children working. More specifically, female autonomy measured by how much education a mother has relative to the level the father has achieved is not often statistically significant but when it is, higher autonomy of the mother measured by her education leads to increased child work and decreased schooling. When mothers’ contributions to household expenditures increase, especially in

households with incomes below the Indian poverty line,1the probability of

schooling for their children decreases. This surprising result might well reflect the fact that mother’s contribution to household expenditure might be higher in poorer households. However, with mothers working and contributing to household expenditure, daughters may not need to work. Once again, this is reflected in a decrease in the probability that daughters younger than 15 years will be working. Overall, this study finds that the education and employment characteristics (primary, secondary, and tertiary education and employment) of the mother and father matter independently. Their positions relative to each other (mother’s expendi-ture contribution to the household and relative education) also matter as does the level of gender equity in the region.

Our analysis is undertaken in the social context of India, where gender equity varies considerably both across households and across regions because kinship systems vary across castes, religions, and regions. Naila Kabeer argues, for instance, that households based on patriarchy-patriliny-patrilocality are most common in the northern plains of India, among Muslims, upper-caste Hindus, and landowning classes (2003: 116). Tim Dyson and Mick Moore (1983) divide the country into three separate kinship systems – the North Indian System, the South Indian system, and the East Indian System – based on their approaches to female independence. In the North Indian system, spouses are unrelated in terms of kinship, men cooperate with and receive help only from those men who are blood relatives, and women do not inherit property. These kinship characteristics create a system in which groups of patrilineally related men rigidly control the household roles of women within their groups through restrictions like purdah (the physical seclusion of women), as a means of maintaining their honor, reputations, and power. In this environment women have little freedom and are very carefully protected from outside influences. In contrast, within the South Indian kinship system, spouses are often cross-cousins (that is, the children of a parent’s opposite-sex sibling), close socioeconomic relations exist between men who are related by blood and by marriage (see also Lupin Rehman and Vijayendra Rao 2004), and women may inherit property. Dyson and Moore (1983) argue that this system results in less rigid control of women’s movements. Relative to their

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status in the north, daughters in southern families are more valued, both economically and socially. They are more likely to survive infancy and childhood, to be educated, to work, to marry later, and to marry into households located closer to their natal homes, enabling them to maintain ties with their parents after marriage (David E. Sopher 1980; Barbara D. Miller 1981; Patricia Jeffrey, Roger Jeffrey, and Andrew Lyon 1988). Revisiting the thesis that daughters in South India enjoy higher status and greater autonomy that those in North India, Rehman and Rao (2004) draw somewhat different conclusions. They find that village exogamy is common in both North and South India and has mixed effects on female autonomy. Consanguinity, on the other hand, is seen to have negative effects rather than the positive ones identified by Dyson and Moore (1983).

In their earlier summing up of the debate, Jean Dre`ze and Amartya Sen (1996) argued that the highly unequal gender relations that exist in many parts of the country are reflected in very low female labor-force participation, a large gender gap in literacy rates, extremely restricted

female property rights, strong boy preference in fertility decisions,2 and

widespread neglect of female children (Dre`ze and Sen 1996: 142). This paper makes three main contributions to the literature. First, while a large and growing literature looks at the factors influencing the incidence of child work including household poverty status, household asset owner-ship, and characteristics of the child (age and gender), much less has been said about the impact of female autonomy in the region on child work. Second, the literature on female autonomy has focused on its impact on fertility, infant mortality, child health, and household-expenditure patterns. However, few scholars have examined the impact of female autonomy on child school attendance and work participation. The author knows of only a handful of papers in this specific area (Kaushik Basu and Ranjan Ray 2002; Farzana Afridi 2006; Geoffrey Lancaster, Pushkar Maitra, and Ranjan Ray 2006). Third, despite Agnes R. Quisumbing and John A. Maluccio’s (1999) conceptual broadening of female autonomy to include the characteristics of the extended family and of the kinship system, most applied studies (with the exception of Øystein Kravdal [2004]) of female autonomy have tended to concentrate on autonomy within the household. Applied to India, where large interregional differences in female autonomy exist, such an extension is both interesting and fruitful, allowing this study to test the influence both of individual autonomy characteristics and of the environments in which the women live. The paper assumes that regional differences in gender

equity help establish norms that many households find difficult to ignore.3

D A T A

The data analyzed here are from Round 50, Schedule 10 of the house-hold socioeconomic survey conducted by the National Sample Survey

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Organisation (NSSO) in India (1993). The data set is large and complex, covering all the states and union territories in India. It includes socio-economic information for 356,352 individuals belonging to 69,231 house-holds in rural India. While Round 50 of the survey focused on consumer expenditure and employment, Schedule 10 within it concentrates on education and employment issues and offers detailed information on the educational status and economic activity of members of each of the households (NSSO 1993). The data set thus provides exhaustive infor-mation on children’s activities and on the education of parents as well as their current and usual employment including their occupation, hours worked, and wages earned. This study also obtained information relating to the Gender Equity Index of the various states from the Human Development Report for India (Planning Commission, Government of India 2002).

This study defines children as those between 5–15 years of age, which conforms to the decision put forward by the International Labour

Organi-zation (ILO) and the United Nations Children’s Fund (ILO 2009).4Since

the paper focuses on child labor, the under-5 category is not considered. The current analysis concentrates on a sample of 93,825 children. Appendices A and B provide summary statistics of the binary variables (Appendix A) and continuous variables (Appendix B) used in the analysis. Although the data from the NSSO is rich and comprehensive, particularly for a household data source, some limitations with regard to the measures for child work need to be kept in mind. In rural areas child work is often highly seasonal and may be misreported. If it occurs in conjunction with schooling, there is potential for ambiguity when the principal and secondary activity statuses of children are recorded (Shakti Kak 2004: 50).

B A C K G R O U N D T O S C H O O L I N G A N D C H I L D W O R K I N I N D I A

While free public schools exist in most regions, parents whose children attend them incur considerable hidden costs for transport, uniforms, books, and tuition fees. Thus, Jandhyala B.G. Tilak (2002) found that households with children in school spent approximately 2.93 percent of household income on education, with the proportion being 3.16 percent for boys and 2.57 percent for girls.

Turning to consider the school and work participation of children, we define work in this paper as including only market-based activities. Based on this definition, Table 1 indicates that 59 percent of girls and 72 percent of boys only go to school, while 5 percent of girls and 7 percent of boys only work. These figures and those in the rest of this paper relate to a child’s principal activity. To identify the activities being undertaken by each child, we consider the Usual Principal Activity Status, which indicates the main

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activity that the person is engaged in. According to the NSS, the usual activity status relates to the activity of a person during the reference period of 365 days preceding the date of survey. The activity in which a person spent more time during the year preceding the date of survey is the one that is considered to be the primary activity status of the person. In addition, the dataset also provides the Usual Subsidiary Activity Status of the child. This variable indicates whether children are doing more than one activity. A child whose principal activity is determined on the basis of the major time criterion may also have pursued some other economic activity for thirty days or more during the reference period of 365 days preceding the date of survey. This is identified as the secondary activity of the child. In our dataset, a very small proportion of girls (0.86 percent) and boys (1.62 percent) were involved in more than one activity in 1993. In this paper, therefore we concentrate entirely on the main activity of the child.

Appendix A provides summary statistics for the levels of schooling and work in regions with different levels of gender equity. It shows that in regions with high gender equity, on average, 74 percent of children have schooling as their primary activity, and 9 percent have work as their primary activity; in areas with low gender equity the corresponding statistics are 65 percent (in school) and 5 percent (working). Clearly, higher proportions of children work and go to school in regions with higher gender equity than in regions with lower gender equity. While the schooling statistics are as expected, that is, more children go to school in regions with greater gender equity, the work statistics are unexpected. They indicate that more children also work in more equitable regions. To consider whether these differences in percentages of working children are significant, we test whether these patterns hold up after controlling for household character-istics. Even greater differences exist between families living above the poverty line and those below, with 59 percent of children in the poorer

Table 1School, work, and household chores done by children in rural households in

India (for age group 5–15 yrs)

No. of girls Girls (%) No. of boys Boys (%)

School 25858 59.2 36208 72.2

Work 2310 5.3 3628 7.2

Chores 4684 10.7 358 0.7

More than 1 principal activitya 605 1.4 894 1.8

None 10215 23.4 9082 18.1

TOTAL 43672 100 50170 100

Notes:aThis variable denotes children who do more than one principal activity where the principal

activity accounts for a certain number of hours of a child’s time during the week. This classification is different from that of subsidiary activities used in Table 3.

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families attending school while 81 percent of children from families above the poverty line do so. Similarly, 8 percent of children in the poorer families work compared with 5 percent in the families that are not poor. Further details about the data underlying these statistics are provided in the section on Empirical Estimation. Thus both regional gender equity and household income/expenditure have statistically significant effects on percentage of children participating in school or the labor market in a certain region. As indicated above, we will consider whether this result holds up once we control for parental (and other family) characteristics.

In considering these figures, it is important to note there are incentives to underreport child work in India. First, many types of child work are illegal in India. The Indian Constitution prohibits child work in certain sectors and in many hazardous industries (the Indian Child Labour Prohibition and Regulation Act [Government of India 1986]). The Act also regulates the number of hours worked by children and the conditions in which they work. Thus, children are not allowed to work in two establishements on the same day; they are not permitted to work more than three hours without a break; and employing children at night (between 7 pm and 8 am) is not permitted. However, the Indian government has not attempted to abolish labor by children under the age of 14 years and most laws rarely extend to the rural informal sector where children are employed on farms, often under parental supervision. Therefore, while state attempts to regulate child labor might cause some underreporting in the current sample, it would be surprising if the effect were marked. Second, some under-reporting may be the result of a household’s attempt to take advantage of the midday meal scheme in schools (Kak 2004). Thus, households may send children to school for only part of the day and keep them at work for the rest of the day. Third, in these statistics children who are engaged in household chores are not reported as working. Instead, they are reported under a separate category of household chores. Thus, in Table 1, we can see that 10 percent of girls and 0.7 percent of boys indicate that their primary activity is doing household chores. Finally, many children (23 percent of girls and 18 percent of boys) are reported neither as going to school nor as working. Instead, this category of children may well be those who would work if employment existed but are not able to do so because of labor market conditions (Uma S. Kambhampati and Raji Rajan 2006, 2008).

T H E O R E T I C A L B A C K G R O U N D A N D E S T I M A T I O N I S S U E S Traditionally, economists undertook analyses of household behavior within the unitary model of the household (Gary S. Becker 1965), which saw the household as a single altruistic unit in which decisions were made by the household head. In essence, it assumed the congruence of decisions made

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by different members of a household. By the late 1980s, however, the unitary model was overtaken by models that argued that the decisions made within households varied according to whether a father or a mother made them. This literature developed within a game theoretic framework in which household members could be seen as playing a bargaining game (Marilyn Manser and Murray Brown 1980; Marjorie B. McElroy and Mary Jean Horney 1981) or as negotiating to achieve some form of efficiency (Pierre-Andre Chiappori 1988, 1992). This paper also tests the hypothesis that mothers’ and fathers’ wages have different impacts on household decisions (Hypothesis 1). If proved, this hypothesis will allow for the rejection of a unitary household model of decision making and confirm that some bargaining is occurring within the households.

A recent advance in this literature has been the recognition that the bargaining power held by different household members is itself endogen-ous. Thus, Kaushik Basu (2006) argues that female labor supply is both a factor in household decision making and a determinant of the household balance of power. In a hypothetical, heterosexual, nuclear household, with a woman who is only interested in spending on one good (he calls it milk) and a man on another (alcohol), Basu assumes that both the man and the woman would find it painful to send their child out to work. Maximizing the household’s utility function subject to a budget constraint that includes the income earned by the child, Basu finds that as the woman’s power increases, the household will spend more and more of its income on the good for which she has a preference (milk). The opposite will be true as the man’s power within the household increases. This conclusion provides the intuition behind Basu’s results. When all the power in the household rests with one agent (whether the man or the woman), the child present is more likely to work, because this single agent reaps all the benefits of the added income (in terms of increases in the goods for which that individual

has a preference).5However, when the power is equally divided between

the man and the woman, a single agent does not reap all the benefits of an increase in household income, and therefore the child is less likely to work. This reasoning leads to the second hypothesis (Hypothesis 2) tested in this paper: that when the household balance of power in terms of relative wages of the spouses and their relative education levels tilt in favor of the mother, there will be a decrease in children working and an increase in the probability of children going to school. In this context, this paper tests for the possibility that the impact of the woman’s contribution to household expenditure is not linear.

Finally, most studies have concentrated on female autonomy within households. As noted earlier, Quisumbing and Maluccio (1999) broaden the notion of autonomy, arguing that bargaining power within a household is determined by control over resources, influences over the bargaining process, mobilization of interpersonal networks, and basic attitudinal

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attributes. They also argue that legal rights, education levels, or bargaining skills may influence the bargaining process and that ‘‘in societies where the extended family is a key player in intra-household allocation, such as those in South Asia, the characteristics of the extended family may affect intra-household allocation outcomes’’ (Quisumbing and Maluccio 1999: 10). In a study of child mortality in India, Kravdal (2004) finds that not only the education level of mothers but also the average education level of women in the area have a statistically significant impact on child mortality. In this paper, we hypothesize that the extent of female autonomy in the region will also influence child work and school participation (Hypothesis 3).

M E T H O D O L O G Y

To summarize, this paper aims to test three hypotheses arising from the literature:

Hypothesis 1: Fathers and mothers make similar decisions about child welfare (as reflected in child schooling and work in this study). This would be similar to arguing that the household is a unitary one where all incomes are pooled and all decisions are jointly made.

Hypothesis 2: Mothers with greater autonomy within the household make decisions that will increase child schooling and decrease child work.

Hypothesis 3: The gender equity conditions that exist in a region play an important role in determining the probability of child schooling and work.

To address these issues, this study estimates a bivariate probit model of child work and schooling in India. The model is estimated separately for boys and for girls and for children living in households above and below the

poverty line.6

The mother’s autonomy within the household is proxied by including her relative monetary contribution to the household as well as her edu-cation relative to the father’s. The former makes a good proxy for the mother’s influence because there might well be many sources of income (both wage and non-wage), and it is the mother’s monetary contribution to overall household expenditure that is likely to determine how much power

she weilds in decision making.7 We also include the mother’s absolute

education level as well as her education relative to that of her spouse. Any influence she derives from the kinship system in the region or from the sociocultural environment is captured by the inclusion of a state-level

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gender equity index. While this index allows for variation in gender equity across states, its inclusion makes the implicit assumption that gender equity does not vary within states. This is, of course, not entirely appropriate. However, it is the most disaggregated level at which the index is currently

available (Planning Commission, Government of India 2002).8 The

measurement of female autonomy in the study is therefore quite limited. It is possible, for instance, that the presence of other household members, especially a mother-in-law as well as the characteristics of the mother-in-law (her education and employment), may influence the mother’s autonomy in the household. Similarly, the education of fathers may increase female autonomy in the household. Our measures are therefore only a first approximation to the extent of female autonomy that exists within the household.

Empirical estimation

To consider the impact of female autonomy on child work and schooling, this study estimates a standard bivariate probit model in which school and work are two binary dependent variables specified according to the principal activity status of the child (see Appendix A). A child can only have one principal activity (unless the child spends exactly half the time in each activity). As Table 1 indicates, there is a very small proportion of children in this category (1.8 percent of boys and 1.4 percent of girls). The child can also be engaged in one or more subsidiary activities but again, there are few children who do this. 0.86 percent of girls and 1.62 percent of boys are engaged in more than one activity. The vast majority of children, therefore, are engaged in only one activity. For the purposes of this paper, we are interested in the probability of this activity. Child work is said to occur when the principal activity of the child refers to any one of those activities categorized as ‘‘employment’’ within the data. Here the dependent vari-able, Work, is coded 1 if the child is working and 0 otherwise. When the principal activity of the child refers to attending educational institutions the

child is categorized as attending School (School¼1). This classification is

based on parents reporting children’s activities.9

This paper divides the sample by gender as well as by poverty status. For the second category it uses the poverty line set by the Indian government in 1992 of Rs.296 per capita per month in urban areas and Rs.276 per capita per month in rural areas. The poverty line is a per capita figure. Since our data in this paper relates to the rural sample in 1992/3, it is the poverty line for the rural sector in 1992 that is the appropriate one. The current study also uses per-capita expenditure rather than income, as is the norm in the literature, because the expenditure figure takes into account informal income sources and provides a longer-term income profile, one not affected by short-term changes in income levels. Households with monthly

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per-capita expenditures above the official poverty line are in the above poverty line sample. These divisions result in four subsamples: girls in households above the poverty line, girls in households below the poverty line, boys in households above the poverty line, and boys in households below the poverty line. Separate estimations for each subsample allow this study to determine whether the impact of the female autonomy variables varies according to the gender of the children as well as across the poverty classes – for example, might women among the poorer groups have greater autonomy or might women have greater autonomy over decisions relating to daughters rather than sons?

Variables included

Although the primary concern here is the influence mothers may have on the probability of child work, this study also controls for personal charac-teristics of the child (including age and sex), for household traits (such as religion, social status, illiteracy rates of both male and female residents, number of adult dependants, land ownership, and debt status), and for regional characteristics (average village wages and regional dummies). Included also are those variables that reflect maternal autonomy at two levels: the autonomy of women in general in the region and the autonomy of mothers within their households (see also Appendix A and 1b). The former is reflected in the Gender Equity Index, a measure of female autonomy devised by the United Nations Development Programme (UNDP) and measured across Indian states, while the latter is proxied by including the mother’s own education and employment characteristics. Thus, mothers’ education levels (primary, secondary, and tertiary) and mothers’ wages are both included, as are mothers’ contributions to household expenditure and mothers’ education levels relative to fathers’. Finally, each of these variables is interacted with the Gender Equity Index to capture whether educated mothers who live in regions with greater female autonomy have different impacts on child work and schooling than educated mothers in areas where women have limited autonomy. The rationales underlying these variables are discussed in detail below.

Autonomy of women in the region

There are great differences in the levels of autonomy women enjoy in different parts of India, as reflected by the fact that their literacy and employment levels vary according to region. The Gender Equity Index captures the disparities between men and women in education, health, employment, and income: the higher the index, the more equitable are gender relations. The contrast between Kerala, a state in the country’s southwestern tip, which in 1991 had a Gender Equity Index of 0.825, and

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Bihar, in the northeast, with a 1991 index of 0.469 epitomizes these regional differences in Indian women’s autonomy. If female autonomy in a region increases the welfare of children within individual households, then we would expect that rates of child work are lower and rates of child schooling are higher in regions where the gender equity index is high. However, female autonomy in the region may not have such a straightforward impact on child work and schooling, particularly because the definition of child welfare this study employs (more school and less work) might not match the needs of individual households. Households functioning under an income constraint may not be able to afford to keep children out of work. Thus, in high Gender Equity Index regions, there is on average more child schooling but also more child work. This is because these are regions where adult women are better educated and also more likely to work. They are therefore likely to be aware of the importance of education for their children and to reinforce this. However, given the household’s income constraint, and given that these women are also working, they are better placed to introduce their children to the labor market. Therefore, while at first glance one might expect increased schooling and decreased work, this may not be the final outcome. The impact of the Gender Equity Index variable may depend on the characteristics of the individual mothers (their education and employ-ment) and of the households (spouse education, employment, social groupings, number of dependents, etc.) they operate in. To allow for these effects, this study interacts the index with the mother’s education and employment variables.

Individual characteristics of parents: Mother’s education

In all countries, better-educated parents are generally assumed to have greater abilities and incentives than less-educated ones to improve their children’s educations. They are also considered more likely to value education. Mark R. Rosenzweig and Kenneth I. Wolpin (1982) argue that there is a strong intergenerational transfer of educational achievement from parents to children. To allow for this, both father’s and mother’s levels of education are included in the model. They are included as three sepa-rate binary categorical variables (mother’s primary education, mother’s secondary education, and mother’s tertiary education; and father’s primary education, father’s secondary education, and father’s tertiary education)

that identify primary, secondary, and tertiary (higher) education,10 with

uneducated mothers and fathers being the excluded category. We expect to show that higher levels of parent education result in increased child schooling and decreased child work because we assume that educated parents place an intrinsic value on their children’s educations.

In the high Gender Equity Index regions, the proportion of mothers with any education is higher (see Appendix A). This is also true in households

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above the poverty line compared to those below the poverty line. Since this study also considers whether the impact of the education variable varies in regions where female autonomy exists relative to those where it does not, it interacts this variable with the Gender Equity Index of the region by including three related variables as follows:

Female autonomy within a household¼Gender Equity Index6mother’s

education, where mother’s education is defined as primary, secondary, or tertiary education.

This study proposed that the impact of gender equity would reinforce that of mothers’ education. Thus, we expect that if a mother’s education inclined her toward more education for her children, when this occurred together with regional gender equity, the latter would empower the mother to work toward fulfilling her preference for better-educated children.

Individual characteristics of parents: Employment and wages

Mothers’ wages increase household incomes and could decrease the need to send children out to work. This variable is likely to be endogenous and

has therefore been instrumented (see Table 2).11 The higher a mother’s

wage, given all other wages in the household, the higher child schooling would be expected to be and the lower child work would be expected to be, if India is like other countries.

The inclusion of mothers’ wages also allows this study to consider

whether thesourceof household income has an impact on child schooling

and child work: that is, do wages earned by fathers have the same impact on the probabilities of work and schooling for their children as those earned by mothers? Many writers studying developing countries argue that a higher proportion of mothers’ wages is spent on goods for children and a higher proportion of fathers’ wages on so-called adult goods like alcohol and cigarettes (John Hoddinott 1992; see also Cheryl R. Doss [1996a] for the role played by assets in determining female autonomy and household expenditure patterns). In the current analysis, this claim implies that a higher proportion of mothers’ wages than of fathers’ will be spent on schooling and preventing child work. This study tests whether this is the case by formally testing in its model whether the coefficient of mother’s wage is equal to that of father’s wage. A rejection of this hypothesis would imply a rejection of the unitary household model.

Autonomy within the household

This study uses two variables to capture the autonomy mothers have in decision making compared with that of fathers in the household. These variables relate to the mothers’ own education and employment charac-teristics relative to those of fathers.

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Table 2 Tobit and sample selection estimation o f instruments for father’s and mother’s wages Tobit Sample select Mother’s wage Father’s wage Mother’s wage Father’s wage Variable Coefficient Standard error Coefficient Standard error Variable Coefficient Standard error Coefficient Standard error Constant -384.74*** 11.71 -1223.85*** 22.68 Constant -60.34*** 8.43 -230.79*** 67.26 Age 12.84*** 0.57 32.28*** 0.95 Age 2.56*** 0.39 8.15** 3.14 Age squared -0.22*** 0.01 -0.43*** 0.01 Age squared -0.03*** 0.01 -0.09*** 0.03 Primary education -95.62*** 4.01 -59.32*** 3.57 Primary education -11.66*** 1.69 -0.21 4 .42 Secondary education -22.09*** 4.92 32.79*** 4.00 Secondary education 64.87*** 2.55 67.23*** 6.19 Tertiary education 36.85*** 7.39 373.89*** 5.64 Tertiary education 139.57*** 3.94 296.55*** 14.09 Village wage 0.64*** 0.06 102.71*** 1.54 Village wage 14.01*** 0.27 25.71*** 1.12 Land -0.05*** 0.002 -0.12*** 0.003 Land -0.01*** 0.001 -0.04*** 0.01 Sigma 228.71*** 1.84 351.85*** 1.59 LAMBDA 2.13 2.26 0.01 0.80 LM test [df] for Tobit 3509.38 (8) 22679.80 (8) R squared 0.22 0.19 Anova based fit measure 10.22 0.30 Adj-R squared 0.22 0.19 Decomp based fit measure 0.49 0.36 F test 2651.4 (0.0) 751.32 (0.0) Log likelihood chi-square test v.high (0.0) 5404.22 (0.000) Notes : *** denotes significance at 1 percent, * * a t 5 percent, and * a t 1 0 percent. In the father equation, age and education refer to the father, the village wag ei st h e average male village wage, and land is household landholdings. In the mother’s wage equation, age and education refer to the mother, the village wage is the average female village wage, and land is household landholdings. C H I L D S C H O O L I N G A N D W O R K

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Education relative to spouse

This variable is included as a measure of female autonomy within the household. While mother’s primary education, mother’s secondary education, and mother’s tertiary education proxy a mother’s preferences with respect to education, the mother’s education relative to father’s is assumed to influence her ability to negotiate in defense of her preferences with respect to her children’s work and schooling. Appendix A shows that on average, even mothers in high gender equity regions are less educated than fathers, with 0.26 years of education for every year of education the father has. In fact, this study finds that only 1.3 percent of mothers in our sample have more education than fathers do; 12.3 percent of mothers have less. As expected, the number of years of education the mother has relative to her husband is higher in the high Gender Equity Index regions (0.26) and in families living above the poverty line (0.35). Once again, to allow for the possibility that a mother’s education relative to a father’s may have a greater impact on children’s welfare in regions with more female autonomy than in those with less, this study interacts this variable with the Gender Equity Index. The interacted variable captures the above possibility.

Mother’s contribution to household expenditure

While the impact mothers’ incomes may have is tested by looking at mothers’ wages, holding fathers’ wages constant, looking at mothers’ contribution to household expenditure also allows this study to consider what happens as mothers increase their contributions to household expenditures. Researchers have generally argued that the more a mother contributes to the household budget, the more bargaining power she will have within the household. However, this premise is not often tested in countries like India, where traditionally women do not work. Summary statistics for our sample, for example, reveal that mothers contribute more to household expenditure in high Gender Equity Index regions (0.19) and in households living below the poverty line (0.19) than in low Gender Equity Index regions (0.13) or more prosperous households (0.096). Thus, both a household’s need to survive and an environment of higher gender equity increase a mother’s contribution to household expenditure. A rise in a mother’s contributions may be tied to a decrease in the father’s; that is, the mother works because she must. In this case, a mother’s contributions could be interpreted as symptomatic of the marginality of the household.

Becoming a breadwinner may increase a mother’s autonomy within the

household, but the autonomy of the family in general (and of the mother in particular) outside the household may decrease owing to its reduced circumstances. Alternatively, the mother’s contributions may increase in the context of a relatively prosperous household. In such a case, on the

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other hand, a mother’s contributions to expenditure might be interpreted as reflecting increased female autonomy both within the household and in the community. If these two assumptions are true, one might expect this variable to have a different impact on households above and below the poverty line. This study also tests whether this variable (mother’s contribution to household expenditure) has a nonlinear impact on child welfare, as Basu and Ray (2002) hypothesize by including a quadratic term in it. Since this variable is also likely to be endogenous, just as mothers’ and fathers’ wages are, it is derived from the instrumented mother’s wage.

Finally, we interact the mother’s contribution to household expenditure variable with the Gender Equity Index variable to detect its impact on child welfare when other women in the region also have some autonomy (that is, gender equity is high).

R E S U L T S

We began by estimating two models, one with the Gender Equity Index alone and the other with the Gender Equity Index interacted with maternal characteristics such as education and income. Since a number of the interaction terms were statistically significant, this study presents only the

results for the latter model.12 Before discussing the results, I will briefly

explain the instruments estimated for mothers’ and fathers’ wages.

Instruments for father’s and mother’s wages

A major problem for any study of female autonomy is the endogeneity of wages (see Doss [1996a] for a discussion of this problem and of possible solutions). This study corrects for this problem by instrumenting mothers’ wages using mother’s age, average village female wages, mother’s edu-cation, and household landholdings (see Table 2 for results of these estimations). However, the wage data are plagued by relatively large num-bers of zero values. These might arise because the subjects are unemployed, not looking for work, or working in a family enterprise or in a subsistence manner on the family farm. In all these cases, their wage entry may well show a zero value. Estimation using Ordinary Least Squares will result in biased estimates. I correct for this using both the Sample Selection and Tobit methods. The Tobit method corrects for the left truncation of the data by having a likelihood function with two parts: the first being the Log Likelihood summed over uncensored observations (identical to the log likelihood for OLS) and the second being the likelihood for the censored observations. The second method is the Heckman sample selection model, which models the probability of a variable being zero explicitly and then includes the Inverse Mills Ratio from this estimation as an independent regressor into the wage model. This study also instruments fathers’ wages

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(which are likely to be endogenous) in a similar manner, using the relevant variables (a father’s age, village male wages, a father’s education, and household landholdings). Table 2 presents the results for both estimations. The diagnostics (see LM Test for Tobit) reject the Tobit model for both fathers and mothers. This test considers whether the log likelihood of the Tobit model is significantly different from the sum of the log likelihood for the constituent Probit and truncated regressions. The result (a chi-square value of 3,509 with 8 degrees of freedom) indicates that we can reject the hypothesis that a Tobit model fits with 99 percent probability (the critical chi-square value for 95 percent probability being 15.51). We therefore use the sample selection predictions as instruments for wage equations in the rest of the paper.

The results of the sample selection estimation indicate that mothers’ wages increase with age but the rate of growth tapers off. While primary education decreases the wage earned by mothers, secondary and tertiary education have the expected positive effects. The higher the average village female wage, the higher a mother’s wage is; however, in households that own land, the mother’s wage is lower. Fathers’ wages, too, increase with age and with average village wage. Village wage affects child schooling and labor largely via its impact on adult wages, and including it as a determinant makes this channel of causation explicit. While primary education has no statistically significant impact on fathers’ wages, both secondary and tertiary education have a positive and statistically significant impact on this variable. Finally, fathers’ wages also decrease when the household owns land. These results are all highly statistically significant and are as expected. The insignificance of lambda implies that selection is not a significant determinant of wages. The Log Likelihood Chi Square test for the model confirms that it is highly statistically significant, as does the F test of the joint significance of the coefficients. We therefore conclude that our instru-ments for mother’s wage and father’s wage are good.

Results for child schooling and work

The estimated instruments for fathers’ and mothers’ wages are included in the bivariate probit model for school and work. The marginal effects from this model are shown in Table 4, which presents only the coefficients that are pertinent to the hypotheses in this paper. The full set of results (including the controls) is given in Appendix 2. Since many of the variables of interest have both a direct and an indirect effect (through the Gender Equity Index) this paper considers the size of the net effect separately in Table 6 and in the section ‘‘Regional and household autonomy: The net impact.’’

We test the division of the sample into four sub-groups – girls below the poverty line, girls above the poverty line, boys below the poverty line, and

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boys above the poverty line – using the Likelihood Ratio test (see Table 3). The results confirm that the separation of the sample by gender as well as by poverty status is appropriate. The Chi-Square test shows that the estimation is significantly different statistically for boys and for girls and also for children in households above and below the poverty line.

I will discuss the results of the four subsamples in the context of the hypotheses set out previously.

Hypothesis 1: Fathers and mothers make symmetric decisions with regard to child work and schooling

The influence of fathers and mothers on these decisions is captured in the variables relating to their wages and to their individual education levels. The results (Table 4) indicate that fathers’ and mothers’ wages have very different impacts. While a higher mother’s wage significantly increases the probability of schooling for both boys and girls below the poverty line, the size of the coefficient is very small. Mothers’ wages would have to rise by Rs.100 (from an average of less than Rs.50 for all four subsamples) to increase the probability of schooling by 0.3 percent. Fathers’ wages do not have a statistically significant influence on schooling in any of the four sub-samples.

On the other hand, the results indicate that a rise in mothers’ wages increases the probability that girls will work in households both above and below the poverty line, while an increase in fathers’ wages increases the probability of work only for boys in households below the poverty line.

Table 3Testing the division of the sample into subgroups: likelihood ratio tests

URSS RRSS RRSS-URSS

LR¼2 (RRSS-URSS)

Probability (w25LR) Model for boys: above and

below poverty line being equal

-19515.10 -10694.30 8820.77 17641.54 0.999

Model for girls: above and below poverty line being equal

-20510.50 -11476.20 9034.28 18068.56 0.999

Model for children above poverty line: boys and girls equal

-12357.00 -6386.46 5970.56 11941.12 0.999

Model for children below poverty line: boys and girls equal

-27673.60 -15829.30 11844.31 23688.62 0.999

Notes: The null hypothesis is that running two separate models is equivalent to running a model across the two subsamples. URSS¼unrestricted sum of squares of the two separate models. RRSS¼Restricted sum of squares of a model in which all coefficients are constrained to be equal in the two subsamples.

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Table 4 The impact of mother’s education and employment: marginal effects (with interactive terms with GEI) – subset of results Girls below poverty line Boys below poverty line G irls above poverty line Boys above poverty line Variable Coefficient Standard error Coefficient Standard error Coefficient Standard error Coefficient Standard error SCHOOL SCHOOL SCHOOL SCHOOL Mother’s primary education 0.11 0.13 -0.01 0 .14 -0.11 0.15 -0.43** 0.16 Mother’s secondary education 0.88*** 0.24 0.62*** 0.26 0.3 0 .24 0 .2 0.24 Mother’s tertiary education 2.11*** 0.67 5.16*** 2.42 -0.17 0 .41 0 .61 1 .06 Father’s employment 0.02 0.04 0.09*** 0.04 -0.09 0 .09 0 .20*** 0.08 Mother’s wage 0.003*** 0.001 0.003*** 0.001 -0.001 0.002 0.001 0.002 Father’s wage -0.001 0.001 -0.002*** 0.001 0.000 0.001 0.001 0.001 Mother’s expenditure contribution -2.48*** 0.35 -1.512*** 0.3 -0.34 0.95 -2.76*** 0.98 Mother’s expenditure contribution squared -0.31 0 .43 -1.43*** 0.46 1.65 2.04 3.79 2.79 Father’s primary education 0.43*** 0.04 0.43*** 0.04 0.17*** 0.05 0.18*** 0.06 Father’s secondary education 0.68*** 0.08 0.70*** 0.08 0.40*** 0.11 0.19* 0.12 Father’s tertiary education 0.91*** 0.28 1.46*** 0.3 0 .43 0 .38 -0.18 0.41 Relative education -0.1 0.09 -0.24*** 0.11 -0.2* 0 .12 0 .05 0 .13 Gender Equity Index -0.58*** 0.08 -0.21*** 0.09 -0.62*** 0.11 -0.36*** 0.12 Gender Equity Index *mother’s primary education 0.56*** 0.16 0.69*** 0.17 0.66*** 0.18 0.74*** 0.19 Gender Equity Index *mother’s secondary education -0.53 0 .35 -0.08 0.37 0.50* 0.31 0.17 0.33 Gender Equity Index *mother’s tertiary education -2.34*** 1.02 -7.03** 3.78 0.80 0.59 -0.49 1 .56 Gender Equity Index *mother’s expenditure contribution 3.03*** 0.38 0.93*** 0.43 1.39 1.00 2.29*** 1.13 ( continued )

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Table 4 ( Continued ) Girls below poverty line Boys below poverty line G irls above poverty line Boys above poverty line Variable Coefficient Standard error Coefficient Standard error Coefficient Standard error Coefficient Standard error SCHOOL SCHOOL SCHOOL SCHOOL Gender Equity Index *mother’s expenditure contribution squared 0.44 0.54 2.14*** 0.71 -4.23 3 .12 -3.75 4.89 Gender Equity Index *relative education 0.13 0.15 0.09 0.18 0.03 0.20 -0.01 0 .21 Controls Yes Yes Yes Yes WORK WORK WORK WORK Mother’s primary education -0.37 0 .25 0 .79*** 0.27 0.25 0.29 0.46** 0.24 Mother’s secondary education 0.6 0 .64 0 .71 0 .63 0 .13 0 .43 -0.41 0.53 Mother’s tertiary education 0.88 0.75 -2.99 170506.06 -0.26 0 .75 -0.34 1.47 Father’s employment 0.16*** 0.06 0.03 0.05 0.01 0.12 -0.2*** 0.1 Mother’s wage 0.004*** 0.001 -0.002 0.001 0.01*** 0.003 0.001 0.003 Father’s wage -0.001 0.001 0.002** 0.001 -0.001 0.002 -0.001 0.002 Mother’s expenditure contribution -1.95*** 0.36 0.01 0.65 -3.50*** 1.72 2.18 1.68 Mother’s expenditure contribution squared -0.38* 0.23 1.62 1.67 3.05 3.25 -3.66 6 .02 Father’s primary education -0.13*** 0.05 -0.42*** 0.05 -0.1 0.08 -0.09 0 .08 Father’s secondary education -0.25*** 0.12 -0.67*** 0.10 -0.33** 0.17 -0.23 0 .17 Father’s tertiary education -0.10 0 .49 -1.31*** 0.44 -0.16 0 .63 -0.21 0.62 Relative education 0.11 0.14 -0.01 0 .19 0 .42*** 0.19 0.09 0.21 Gender Equity Index 0.66*** 0.12 1.120*** 0.14 0.52*** 0.21 0.64*** 0.17 ( continued ) C H I L D S C H O O L I N G A N D W O R K

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Table 4 ( Continued ) Girls below poverty line Boys below poverty line G irls above poverty line Boys above poverty line Variable Coefficient Standard error Coefficient Standard error Coefficient Standard error Coefficient Standard error WORK WORK WORK WORK Gender Equity Index * mother’s primary education -0.35 0 .30 -1.59*** 0.29 -0.98*** 0.35 -0.89*** 0.26 Gender Equity Index * mother’s secondary education -1.86*** 0.87 -1.26 0 .88 -1.70*** 0.67 0.21 0.66 Gender Equity Index * mother’s tertiary education -13.06 119105.19 0.27 363568.63 -1.42* 0.89 0.29 2.00 Gender Equity Index * mother’s expenditure contribution 2.36*** 0.45 0.58 0.95 2.75 1.83 -2.22 1 .67 Gender Equity Index * mother’s expenditure contribution squared 0.56* 0.32 -2.49 2 .48 -0.71 5.17 0.61 8.76 Gender Equity Index * relative education 0.35* 0.21 -0.14 0 .23 0 .14 0 .3 -0.21 0 .31 Controls Yes Yes Yes Yes Notes : *** denotes 1 percent statistical significance, ** for 5 percent, and * for 10 percent. Results relating to the control variables have been excluded fr om the table to make it less unwieldy. The variables include age, age squared, sex, sex of household head, birth order, number of siblings, Hindu, Muslim, schedule d caste and tribe dummy, debt status of household, female and male illiteracy levels w ithin households, amount of land held, number of dependants o lder than 60 ye ars, village wage, and a dummy to indicate region (South o r North).

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Again, the magnitude of the coefficients is relatively small. These results confirm the findings of other studies that show mothers’ employment is complementary with daughters’ employment (Olga Nieuwenhuys 1996). Complementarity also seems to exist between the employment of boys and their fathers in households below the poverty line.

To formally test whether mothers’ and fathers’ wages have different impacts on the probability of child work and schooling, this study uses a Wald test of the restrictions that the coefficients of mothers’ and fathers’ wages are insignificantly different in the various subsamples. Our results (see Table 5) indicate that fathers’ and mothers’ wages have a significantly different impact in statistical terms on boys in households both above and below the poverty line but a very similar impact on girls. Thus, the household is clearly not unitary in all dimensions. While parents’ wages seem to have a symmetric impact on girls, they are not symmetric in their impact on boys.

Hypothesis 2: Mother’s autonomy within the household increases child schooling and decreases child work

As mentioned, this study measures female autonomy using a mother’s contribution to household expenditure and her education level relative to that of a father in the same household. Turning first to consider whether mothers’ education relative to fathers’ has a statistically significant effect, this study finds that, contrary to expectations, increases in mothers’ education relative to fathers’ significantly decreases the probability of schooling of girls in households above the poverty line (by 20 percent) and of boys in households below this line (by 24 percent). Thus, while positive changes in a mother’s absolute education have a positive impact on child schooling and education in households below the poverty line, increases in mothers’ education relative to fathers’ have a negative impact on child schooling and education. Mother’s relative education has no statistically significant impact on work probabilities except in the subsample of girls in

Table 5Wald test for the equality of the coefficient of mother’s and father’s wages

Sample

Wald Statistic for

b(Mother’s Wage)-b(Father’s Wage)¼0

Probability from Chi-Squared [1]

Girls below poverty line 0.10 0.75

Boys below poverty line 4.82 0.03

Girls above poverty line 0.95 0.33

Boys above poverty line 5.66 0.02

All below poverty line 2.47 0.12

All above poverty line 0.77 0.38

All girls 0.001 0.98

All boys 10.20 0.001

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households above the poverty line. In this subsample, the higher the level of education a mother has attained compared with that of the father, the more likely their daughter is to work. Looking next at whether the impact of this variable varies by the level of gender equity prevalent in the state, this study finds that the interaction term is statistically insignificant in all cases (except one) for both school and work. Thus, while the impact of the mother’s absolute education level varies with the gender equity of the state (see discussion on Hypothesis 3), the impact of her education level relative to her spouse’s does not. In the only case where the latter factor is statistically significant (girls in households above the poverty line), it actually decreases schooling probabilities and increases work probabilities, though this variable is statistically insignificant in all other cases.

The other proxy for female autonomy that we include in our model is mother’s expenditure contribution. The results show that as mothers’ con-tributions to household expenditure increase, the probability of schooling in three out of four subsamples decreases. The exception is for girls above the poverty line. This study also finds that while a rise in mothers’ wages alone increases the probability of school for both girls and boys below the poverty line, an increase in mothers’ contributions to household expendi-ture has the opposite effect: it decreases the probability that boys and girls will attend school. However, as mothers’ expenditure contribution increa-ses, the probability of work for girls in households both above and below the poverty line decreases. The results therefore indicate that greater autonomy for the mother as reflected in higher household expenditure contributions actually decreases schooling in the cases where such contributions have a statistically significant impact; on the other hand, greater female autonomy decreases the probability of work for girls. Note also that the quadratic term is statistically insignificant in six of eight estimations and in both cases where it is significant, the impact of the quadratic term is to reinforce rather than mitigate the effect of the linear term.

In examining whether the impact of this variable varies across states with different levels of gender equity, this study finds that, in three of the four subsamples, when both gender equity and increases in a mother’s contribution to household expenditure are high, the probability of schooling for both boys and girls is high. Thus, the autonomy women derive from their household expenditure contributions is reinforced by regional equity levels. This study also finds that this variable does not have a statistically significant impact on the probability of work for any subsample of children, except for girls below the poverty line. For this group, the probability of work increases when mother’s contribution to household expenditure and the Gender Equity Index are both high.

Thus, one cannot straightforwardly accept or reject Hypothesis 2. The pattern depends on the subsample – girls/boys, children living in house-holds above and below the poverty line, etc. – and on mothers’ autonomy

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within the household. Note, however, that mothers’ autonomy within the household does not automatically incline mothers toward seeking more education and less work for their children. One possible interpretation of this finding might be that mothers who seemingly have greater autonomy within the household may actually be highly constrained externally. Under the constraints posed by their economic circumstances, both mothers and fathers make similar decisions regarding child work and schooling.

Hypothesis 3: Regional gender equity increases child schooling and decreases child work

The results indicate that holding all other factors constant, an increase in the Gender Equity Index decreases the probability of child schooling and increases the probability of child work in all four subsamples. Sensitivity analysis estimating two versions of the bivariate school and work model – first with the Gender Equity Index as the only variable in the model and second, with this index in the model together with all other variables but excluding the interaction terms – confirms that this is a robust result. Of the six subsamples for which we estimated the impact of the Gender Equity Index on school and work, the effect was negative in five cases. These results therefore confirm that even relatively empowered mothers may prefer to send their children to work rather than to school. The reasons for this choice are beyond the scope of the current study. However, to test whether it is caused by a lack of schools in the region, this study considers the correlation between school availability (number of schools per 1,000 children) and the Gender Equity Index. The results indicate a correlation of 0.568 for primary schools and 0.523 for upper schools. Though this correlation is not very high, it is clearly positive. There is therefore no indication that the lower levels of child schooling in high Gender Equity Index regions might arise from a lack of schools in these regions. It might therefore simply be that mothers see better opportunities for their children in employment than in schooling – that is, the education available is of poor quality or gives poor returns (Jean Dre`ze and Haris Gazdar 1997). Of course, the Gender Equity Index has more than a stand-alone impact. Its effect is mediated through the level of education and the employment characteristics of mothers within the household. Thus, a mother’s edu-cation may have a different impact on regions where few women have gone to school than it does where the vast majority have received some education. In the latter case, the mother’s autonomy is reinforced by the autonomy of other women in the region. While the stand-alone impact of a mother’s education on child school probabilities was positive in households below the poverty line, its impact as mediated through the Gender Equity Index of a given state is more complicated. The coefficients of the interaction variables [Gender Equity Index * mother’s primary education;

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Gender Equity Index * mother’s secondary education; Gender Equity Index * mother’s tertiary education] indicate that mother’s primary and tertiary education have a statistically significant impact on child schooling but mother’s secondary education does not. Our results indicate that mothers with primary education living in regions with higher gender equity increase the probability of children going to school. When mothers have tertiary education in regions with higher gender equity, then the probability of schooling for boys and girls in households below the poverty line is significantly lower than for mothers with tertiary education in regions

with lower gender equity.13 Thus, mothers with tertiary education have

more impact on child schooling when few women in the neighborhood are highly educated, while less educated mothers (with primary education alone) have greater impact when they live in regions or communities where more equitable gender relations prevail.

Examining the impact of the interaction between mother’s education and regional gender equity on the probability of child work, this study finds that when mothers with primary education live in regions with high gender equity, then the probability that their children will work decreases in all subsamples except for girls below the poverty line. Thus, in states where there is greater gender equity, mothers with primary education decrease child work. Mothers with primary education living in states with lower gender equity have a smaller impact. Moreover, mothers with secondary education who live in states with greater gender equity have a greater impact than they would in states with low gender equity on decrea-sing the probability of girls’ employment, although they have no statistically significant impact on boys’ employment. Overall, therefore, regional gender equity is extremely important in determining the effect mothers’ education (primary, secondary, and tertiary education) may have on the work and school probabilities of both boys and girls.

Regional and household autonomy: The net impact

In the estimated model, the net impact of a mother’s income and education depends on the coefficient of the variable itself as well as the coefficient of the interaction term with the Gender Equity Index. For a single variable (say, Mother’s Primary Education), the final effect will therefore be as follows:

Schooli¼aþb(mother’s primary education)iþg½Gender Equity Index

(mother’s primary education)i þZZiþei

¼aþ(mother’s primary education)iðbþgGender Equity Index)

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where Z denotes all the other variables in the model and i denotes the individual.

Thus, the net coefficient of mother’s primary education is not a constant but depends on the Gender Equity Index of the state. Since conceptualizing the size of this impact is not straightforward, this study considers the range within which the effect falls by calculating the size of the coefficient of mother’s primary education (and of other relevant variables) in the state with the lowest Gender Equity Index (Bihar with 0.469) and the state with the highest Gender Equity Index (Kerala with 0.825). Table 6 presents the results of this calculation and of the other variables of interest.

To interpret these results, in all cases, this study considers whether the net coefficient (the value in each cell) increases or decreases between Bihar and Kerala. Since all other states in the sample are ranged between Bihar and Kerala in terms of their Gender Equity Index, their coefficients must also fall between those of these two states.

Mother’s education (primary, secondary, and tertiary) and state Gender Equity Index

Thus, Table 6 shows that the impact of mother’s primary education on children’s schooling is increasing in all samples (girls and boys above and below the poverty line). While the net impact of mother’s primary education on girls’ schooling in households below the poverty line is 0.367 in Bihar, it increases to 0.565 in Kerala. Similarly, the net probability of mother’s primary education on boys’ schooling below the poverty line is 0.314 in Bihar, but it increases to 0.561 in Kerala. Thus, while mothers with primary educations have a positive impact on their children’s schooling in both states, they have a larger positive effect in Kerala, the state with the highest Gender Equity Index in India. This finding confirms the pattern from the marginal effects, which indicated that women (including mothers) with primary educations or low levels of education gain greater autonomy by living in regions with gender equity. Mother’s primary education is more effective when it occurs in states with greater gender equity.

The negative impact of mother’s primary education on the probability of child work also increases with the Gender Equity Index of the state, except for the sample of girls below the poverty line. Thus, the net probability of work for girls in households above the poverty line decreases with mother’s primary education (the coefficient is always negative), but this variable has a greater impact in Kerala (-0.810) than in Bihar (-0.461). This is true for three of the four subsamples (except for girls below the poverty line). Thus, mothers with primary educations living in states with gender equity are less likely to send their children out to work.

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Table 6 Net impact on the probability of child work and schooling o f Gender Equity Index and mother’s education and income variables in two states – B ihar and Kerala General mean probability

Mother’s primary education Mother’s secondary education

Mother’s tertiary education Mother’s income contribution Mother’s income contribution squared Relative education Bihar Kerala B ihar Kerala B ihar Kerala B ihar Kerala B ihar Kerala B ihar Kerala B ihar Kerala Girls below school 0.47 0.83 0.37 0.57 0.64 0.45 1.02 0.18 -1.06 0 .02 -0.11 0.05 -0.04 0 .01 Girls above school 0.47 0.83 0.20 0.43 0.53 0.71 0.21 0.5 0 .32 0 .81 -0.33 -1.84 -0.18 -0.17 Boys below school 0.47 0.83 0.31 0.56 0.59 0.56 1.86 -0.64 -1.08 -0.75 -0.43 0.33 -0.19 -0.16 Boys above school 0.47 0.83 -0.08 0 .18 0 .28 0 .34 0 .38 0 .20 -1.68 -0.87 2 .03 0 .7 0.04 0.04 Girls below work 0.47 0.83 -0.37 -0.37 -0.87 -1.53 0.00 0.00 -0.85 -0.01 -0.12 0 .08 0 .16 0 .29 Girls above work 0.47 0.83 -0.46 -0.81 -0.80 -1.40 -0.67 -1.18 -2.21 -1.23 0.00 0.00 0.42 0.42 Boys below work 0.47 0.83 0.06 -0.51 0 .00 0 .00 0 .00 0 .00 0 .00 0 .00 0 .00 0 .00 0 .00 0 .00 Boys above work 0.47 0.83 0.04 -0.28 0 .00 0 .00 0 .00 0 .00 0 .00 0 .00 0 .00 0 .00 0 .00 0 .00 Notes : The values in the cells in this table provide the e ffect of each variable and its interaction w ith Gender Equity Index from the following a þ b (mother’s primary education) i þ g Gender Equity Index*(mother’s primary education) i þ Z Zi þ ei . Where the marginal effects were insignificantly different from zero, they were restricted to zero, giving us, in some cases, no impact of the variable in e ither state.

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On the other hand, the impact of mothers with tertiary education on child schooling is higher in Bihar than in Kerala in all subsamples except girls above the poverty line, and mothers’ tertiary education levels have no

effect on the probability of child work.14This result might be interpreted as

arguing that because there are fewer women with tertiary education in low Gender Equity Index states like Bihar, women with tertiary education in these states might exert greater influence within their households at least as far as child schooling is concerned. They remain ineffective in decreasing child work, however, and this might be because very few children are likely to work in households where mothers have tertiary education. However, with the small number of women with tertiary education, it is not clear that we should give much weight to this result. While mothers’ with secondary education have a positive impact on child schooling in all subsamples regardless of the Gender Equity Index of the state, the impact of mother’s secondary education is larger in Bihar than in other states in two of the four subsamples. Having a mother who has achieved a secondary education decreases the probability of work for girls in all states but the effect is largest in Kerala, that is the magnitude of the effect increases with the Gender Equity Index of the state. Mother’s secondary education has no impact on boys in any state.

Mother’s contribution to household expenditure and Gender Equity Index Turning to the net impact of mother’s contribution to household expenditure, this study finds that the effect of this variable increases with the Gender Equity Index for both school and work for girls in households below the poverty line. Thus, as mothers’ expenditure contributions increase, the probability of girls’ schooling and work will increase as the Gender Equity Index increases. The impact on girls’ schooling is negative in Bihar (-1.057) and positive in Kerala (0.022) for girls below the poverty line. Across all states, between these two extremes, the probability of schooling increases as the Gender Equity Index increases. Similarly, though mother’s contribution to household expenditure decreases the probability of schooling for boys living in households above and below the poverty line in both Bihar and Kerala, its negative impact is smaller in Kerala (-0.746) than in Bihar (-1.078). This could be because in high gender equity states like Kerala, female employment and earnings are likely to reflect female choice and autonomy, while in low gender equity states like Bihar, they may merely reflect the financial constraints of the household concerned.

Mothers’ expenditure contributions also decrease the probability of work for girls below the poverty line in Bihar by -0.845. The net effect of this variable is very close to zero (-0.005). Thus, in states with low gender equity, when mothers’ contribution to household income increases, the probability of work for girls decreases.

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Relative education and Gender Equity Index

The impact of mothers’ educations relative to fathers’ increases with the Gender Equity Index across the two states, except for the subsample of boys in households above the poverty line. Thus, an increase in mothers’ education levels relative to those of fathers decreases schooling for girls living below the poverty line in Bihar (-0.04), while it increases schooling probability for girls living below the poverty line in Kerala (0.005). It also increases the probability of work for girls living in households below the poverty line in both Bihar (0.163) and Kerala (0.287), but the effect is greater in Kerala. Thus, in the subset of households below the poverty line, girls living in Kerala are more likely to work than are those in Bihar, but they are also more likely to go to school as their mothers’ levels of education relative to their fathers’ increases. In households above the poverty line in these two states, the difference in the impacts of this variable on child schooling and work is very small. Thus, the results indicate that the Gender Equity Index has an impact on the outcome, often overshadowing the effect of the mother’s autonomy variable. Also, the impact varies in households above and below the poverty line, largely because in households below the poverty line, the constraints of household finances are likely to be tighter and the role for female choices and autonomy more circumscribed.

D I S C U S S I O N A N D C O N C L U S I O N

This paper set out to test three hypotheses relating to the impact of mother’s autonomy on particular measures of child welfare: participation in school and in the labor market. To do this, it extended the concept of female autonomy beyond the household to include the constraints imposed by the levels of gender equity prevalent in the regions that the women live in. It began with the expectation that increased autonomy for mothers would increase child schooling and decrease child work. This resulted in three hypotheses that we tested in this paper but which yielded mixed results.

First, we tested whether fathers’ and mothers’ wages yield similar outcomes with respect to the schooling and work of their children. Our results indicate this is not the case: mother’s wages increase the probability of schooling but also increase the probability of work especially for girls. Father’s wages have less impact. These findings reinforce the results of previous studies (Kaushik Basu and Ranjan Ray 2002; Patrick M. Emerson and Andre´ Portela Souza 2002; Afridi 2006).

Second, we hypothesized that in households where mothers have greater autonomy, the probability of child schooling would be higher and that of child work would be lower. Our results indicate that reality is more complex than this hypothesis would indicate. Thus, we find that when mothers have

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