Gender inequality amid educational expansion in India: an analysis of gender differences in the attainment of reading and mathematics skills.
Gender inequality in education is a persistent problem in Indian society, especially for girls from rural areas and lower socioeconomic backgrounds. During the past several decades, India has achieved success in moving toward universal school enrollment and in enacting policies to address educational inequalities such as those based on gender. However, education gaps still exist. This paper seeks to identify the factors through which educational gender inequality continues to operate and the social contexts that are associated with girls, who may be left behind academically.
Using data from the 2005 India Human Development Survey (IHDS), this study analyzes how social background factors, access to learning resources, time devoted to formal learning activities, and cultural attitudes regarding the education of girls contribute to ongoing gender gaps in learning. This study is an attempt to go beyond more commonly found descriptive studies of country-wide achievement and attainment patterns by measuring a more diverse set of indicators available through the IHDS dataset, including the identification of statistical interactions among key variables. We hope the results will provide increased insight into the status of educational gender inequality in India, offer useful information to policymakers as they develop targeted policies to address areas of gender inequality where it persists, and identify areas for further study using more fine-grained analyses among a narrower range of variables.
Prior research reveals educational disparities by various demographic and school-related factors such as gender, social background, and access to educational resources. To build on this foundation, additional research is needed to further examine factors that are associated with gender gaps, and to assess how the effects of India's increasing educational attainment, public policies targeted to girls, and changing educational landscape are having an impact.
Several important questions emerge from the literature regarding gender inequality in education. For example, although socioeconomic and other family background factors have been shown to influence educational attainment, it is less clear how these factors differentially affect boys and girls. Time devoted to learning and other educational resources are also important to investigate, and it may be the case that parents are prioritizing sons' education over daughters' education through the allocation of these factors. Finally, the role of attitudes toward the education of girls is underexplored. Female students with parents who look favorably upon the education of girls might be expected to exhibit higher educational achievement relative to those without such parents. In order to answer these questions, this paper will explore the relative contributions that social background factors, learning resources, time devoted to learning, and cultural attitudes make to academic outcomes.
Educational Expansion in India
Attempts to increase the educational achievement of girls are taking place amid a backdrop of sweeping educational expansion in India. During the last half of the twentieth century, India made great strides in improving its education infrastructure--an achievement representative of a worldwide educational expansion by newly independent states and the importance of education within the emerging nation-state model (Meyer, Ramirez, and Soysal, 1992). This has included a rapid expansion in the number of primary schools in the decade preceding the IHDS data collection in 2005 (De et al., 2011).
India's educational expansion is also reflective of the United Nation's Economic, Social, and Cultural Organization (UNESCO) program Education for All and the push to achieve universal primary education by the year 2015 under the Millennium Development Goals program (Govinda, 2002; United Nations, 2010; 2015). In addition, expansion efforts are guided by India's Constitution, which mandates universal education for those under the age of fourteen, as well as court decisions and policies that secure the right to education and increase educational investments for girls and other disadvantaged groups. Complementing these policy imperatives are government and NGO efforts to universalize enrollment, improve learning, and promote gender equality in education. Specific policies have included the expansion of educational funding, the provision of free educational resources such as textbooks and uniforms, an increase in the number of female teachers, and the introduction of local schools, single sex schools, and special facilities (including in non-formal settings) for girls and the non-enrolled (Government of India (GOI), 2000; Govinda, 2002; Kingdon, 2007; Nayar, 2002; Rao, Cheng, and Narain, 2003; and Vaughan, 2013). Finally, increased availability of early care facilities has the potential to improve the school attendance of girls who provide childcare for younger siblings, as does the decline in fertility rates in India overall (GOI, 2000; United States Census Bureau, 2014).
A primary outcome of this increased focus on education and learning has been a significant improvement in primary school enrollment rates, which approached universal levels and included the achievement of near gender parity at the time that the IHDS was conducted in 2005 (De et al., 2011; UNESCO, 2016b). (1) Expanded access was not universally shared, however, with rural girls in particular continuing to be less likely to be enrolled than their female urban counterparts or their male peers (De et al., 2011).
In addition to this progress in enrollment, there was a sizable increase in literacy rates among the Indian population as a whole from approximately 18% to 65% in the fifty years ending in 2001. However, a significant gender gap of nearly 22% still remained at the beginning of the 21st century (GOI, 2000; GOI, 2011). According to census estimates, the literacy rate has continued to climb to 73% in 2011; although, the gender gap has narrowed only slightly, with women still at literacy levels 16% below men (GOI, 2011). Literacy rates among youths age 15-24 were higher still, at 81% in 20052008, yet a 14% gender gap remained (UNESCO, 2011).
The continued presence of educational gaps despite improvement in educational access and infrastructure is perhaps unsurprising given the historical prevalence of gender inequality in a patriarchal Indian society (Desai et al., 2010). Notwithstanding recent progress, educational quality and learning outcomes also remain major concerns (United Nations, 2015). Educational disparities in India are also striking given their contrast to a worldwide pattern of less marked gender inequality in education. The gap in reading skills in India is especially noteworthy as girls in most other countries typically outscore boys in reading as measured on international tests of comparative educational achievement (Baer et al., 2007; Lynn and Mikk, 2009; Organisation for Economic Co-operation and Development (OECD), 2012; Thompson et al., 2012).
It is imperative to remedy educational inequalities since they can lead to inequality in economic and other adult domains. Education is linked to increased future wages for women (Kingdon, 2007), and is seen as a protective factor that is associated with child investments as well as other health and civic outcomes (Desai et al., 2010). Education can also help women realize their full participation and leadership potential in economic, social, and political life (UNESCO, 2016a). Importantly, educational inequalities have been shown to be amenable to remediation through policies geared toward increasing girls' academic achievement (Marks, 2008).
Factors Associated with Educational Achievement
Social background factors
The education research literature has focused on the relative contributions of both social background and school environment to learning and academic achievement. In the United States, Coleman et al. (1966) were among the first to establish the importance of students' family backgrounds to the academic success of children. In India, despite improvements in educational access over the past several decades, social background is also found to be associated with learning outcomes. Achievement gaps based on gender, region, and social background factors often arise in primary school, and many Indian children still struggle against historical inequalities such as those based on gender and caste (Azam, 2015; De et al., 2011; Desai, Adams, and Dubey, 2009; Desai et al., 2010; Desai and Thorat, 2013; Probe Team, 1999; Rao, Cheng, and Narain, 2003). First generation learners and those from impoverished backgrounds may also enter school with a diminished readiness to learn (Kaul, 2002).
Within India, large regional differences in educational outcomes also exist, with rural females and those living in urban poverty historically representing those who are illiterate and those who are not enrolled in school (Nayar, 2002). Sundaram and Vanneman (2008) consider regional variation in educational achievement and find that the level of economic development is associated with a narrowing of gender gaps in literacy, with level of district wealth as well as number of teachers in a district largely responsible for this difference. Additional state specific initiatives (not addressed by this analysis), such as the successful social and political efforts to promote female literacy and education in the state of Kerala, have also resulted in the achievement of higher literacy levels for both boys and girls (De et al., 2011; Probe Team, 1999).
Access to high-quality education resources
Educational research highlights the importance of school-level resources in student learning (Greenwald, Hedges, and Laine, 1996; Hedges, Laine, and Greenwald, 1994), although some question whether additional resources are associated with improvements in school quality and educational outcomes once family background factors are considered (Banerjee et al., 2007; Hanushek, 1989, 1995, 1997). In addition, research indicates that the influences of socioeconomic background and learning contexts and resources are often interrelated (Duncan and Murnane, 2011). Moreover, research in developing countries indicates that quality schooling can be especially influential in promoting the academic achievement of students (Gamoran and Long, 2006; Heyneman and Loxley, 1983).
School quality is important to consider given research that suggests Indian girls may experience lower quality school environments than boys. Research finds that despite the expansion of private school enrollment in recent years, a persistent gender gap remains (Chudgar and Creed, 2016; Maitra, Pal, and Sharma, 2016; Woodhead, Frost, and James, 2013). In addition to being enrolled in private schools at somewhat lower rates, girls may also be less engaged with private tutoring. Together these factors contribute to higher overall education expenditures for boys than for girls, even with the existence of special fee reduction policies for girls in some areas (De et al., 2011; Desai et al., 2009; Desai et al., 2010).
Girls' under-enrollment in private schools is of special concern given that private schools and government schools differ in educational quality and outcomes (De et al., 2011; Gouda et al., 2013). Controlling for student intake factors, attendance at a private school is associated with a higher level of student achievement (Desai et al., 2009; Kingdon, 2007), with higher beneficial returns of private school attendance for lower income students (Desai et al., 2009). Chudgar and Quin (2012) also find that private school attendance is associated with better learning outcomes, although much variability in quality exists among private schools.
Educational discrimination based on gender also results from fewer intrahousehold resources allocated to girls (Saha, 2013; Zimmermann, 2012). Lower-income parents can experience the additional cost of sending a child to school (e.g. paying for school materials, uniforms) as a financial hardship in addition to the opportunity cost of girls not fulfilling other time intensive household and child care responsibilities (De et al., 2011; GOI, 2000; Probe Team, 1999; Rao, Cheng, and Narain, 2003). An additional financial resource is money for private tuitions, and as Banerji and Wadhwa (2013) find, this supplemental help can also raise educational outcomes.
Finally, differences in educational expenditure between boys and girls are related to level of urbanization. Kingdon (2005) finds that inequality in educational expenditure within households in rural areas is primarily the result of enrollment differentials between boys and girls. Azam and Kingdon (2013) also reveal that gender disparities in educational expenditure are more prevalent in rural areas and within certain states. In addition, these authors suggest that an important factor related to gaps in education expenditure is the higher level of private school enrollment among boys.
Time devoted to school-related learning activities
Historically, Indian girls enrolled in school at lower rates than boys (GOI, 2000), and when they did enroll, they tended to "enter late and dropout earlier" (Nayar, 2002). Girls also did not progress to or enroll in upper primary levels at the same rate as boys with major impediments to their continued progression being the lack of a nearby upper primary school, cultural attitudes toward female education, and being diverted to household and childrearing tasks (De et al., 2011; GOI, 2000; Probe Team, 1999).
More recently, girls have achieved near enrollment parity with boys at the primary school level as India approached having universal primary enrollment levels for all children (De et al., 2011; UNESCO, 2016b). Despite this progress, however, certain subgroups of Indian girls (such as those from rural backgrounds) may be at higher risk for school withdrawal or absenteeism due to cultural beliefs about gender roles. They may also devote less time to out-of-school learning activities such as completing homework. Reasons for diminished engagement in school-related activities include the need to fulfill household responsibilities such as domestic work and caring for younger siblings. These competing demands for girls' time could present an opportunity cost for parents who wish to employ girls in activities that permit the economic survival of the family. Additional reasons cited for girls dropping out or spending less time in school-related activities include the burden of school expenses, a lack of parental interest in educating girls, the dearth of female teachers, girls not being allowed to travel to distant schools, and the scarcity of early education and care facilities, which can have particularly negative consequences for older girls in large rural families (De et al., 2011; Govinda, 2002; Kambhampati and Rajan, 2008; Kaul, 2002; Nayar, 2002; Probe Team, 1999).
Motiram and Osberg (2010) add further insight into the time available for learning in their analysis of the Central Statistical Organization of India's 1999 Indian Time Use Survey. Overall, they find that girls attending school shared a higher burden for performing household chores than did boys, regardless of age or urban/rural status. These authors also found that the overall time devoted to household chores for both rural and urban girls increased with age. However, rural girls (ages 6-14 and who were attending school) devoted more time to household chores than their urban counterparts. Rural girls also experienced the lowest rates for both enrollment and school attendance, with higher percentages of rural girls missing from school as they got older. In addition, the percentage of all children who do any homework is lowest for rural girls. This provides evidence for the hypothesis that the opportunity cost of sending children to school (as opposed to engaging them in household activities) is higher for girls than for boys, and highest for rural girls.
Cultural attitudes regarding the education of girls
There is a fairly robust research literature that establishes the link between cultural attitudes and academic achievement. Weiner (1985) finds that achievement motivation, or the striving and persistence to learn, is related to an individual's own belief, as well as the beliefs and attributions of others that one can be a successful learner. According to the expectancy value model, girls' achievement-related decisions are also influenced by whether learning is consistent with self-image, and whether learning fits with other interests and the perceived utility and cost of engaging in learning activities (Eccles, 2005). In addition, Steele (1997) finds that expectations of gender roles and gender stereotypes can have an effect on an individual's educational achievement. And finally, the beliefs and aspirations of parents and teachers in particular are found to influence perceived self-efficacy, and the perception of inequity can reduce girls' self-confidence in their capabilities as learners (Bandura et al., 1996; Bussey and Bandura, 1999).
Gender differences in educational outcomes are also related to community and family attitudes regarding the education of girls. These attitudes are embedded in cultural norms and are influenced by marriage and kinship patterns that may lead parents to invest more emotional and financial resources in educating sons rather than daughters (Desai et al., 2010). The centrality of preparing girls for marriage is particularly pronounced in the north of India, where parents have historically held lower aspirations for educating daughters rather than sons (Probe Team, 1999).
Several factors influence negative attitudes toward the education of girls. One concern relates to savings for dowry, which may limit the amount of funds that parents have to spend on daughters' education or create a fear that having educated daughters could result in having to pay higher marriage costs and dowry. In addition, differences in educational investment may result from parents' reliance upon a son's support in old age, leading to a differential investment in the child who would be responsible for the parents' financial security as they grow older (Desai et al., 2010; Probe Team, 1999).
Within schools, girls historically experienced a less challenging curriculum than boys, reflecting the traditional expectation that schools should prepare women for a more gendered role of homemaking and motherhood. In addition to this alienating curriculum, girls may have had fewer female teachers to serve as role models (especially in rural areas) and experienced gender stereotyping and less attention from their teachers (Basu, 1996; Jeffery and Basu, 1996; Nayar, 2002; Probe Team, 1999; Rampal, 2002).
An emphasis on promoting a more diverse curriculum and increasing female teachers is an attempt to reverse gender bias that girls experience in schools (GOI, 2000). At the same time, rising educational aspirations overall and social changes are challenging traditional beliefs and practices in the home. Education is increasingly seen as important in the marriage prospects of girls, who may be valued for their higher earning potential as well as their improved ability of marrying into modern families, although these factors are still subject to community specific norms. In addition, education is seen as a social norm of good child rearing, and the skills developed through education can serve as a protective factor for financial independence, especially in widowhood (Behrman et al., 1999; De et al., 2011; Desai et al., 2010; Probe Team, 1999; Rao, Cheng, and Narain, 2003). In light of these changing social norms and trends, an open question for research is how cultural attitudes toward female education in India are currently associated with girls' learning outcomes.
Given historical gender discrimination in India, and a research base showing the negative influence that cultural attitudes can have on educational achievement, it is important to understand how and under what circumstances gender bias is associated with the educational outcomes of Indian girls. India has experienced large gains in expanding educational access to its children nationwide. The result has been the achievement of nearly universal primary school enrollment for boys and girls and reduced gender differences in literacy and other educational outcomes. However, previous research reviewed in this section has also shown persistent educational gaps based on gender and other social background factors, such as caste, income, and level of urbanization. Rural girls appear to be the most disadvantaged, as research indicates that they spend the least amount of time in educational activities. Given the trend toward improved educational access and equity over the past few decades, and taking into consideration these persistent gaps, it is important to understand how factors historically linked to educational inequality for girls, including the financial and emotional investments that parents make, are currently related to girls' educational achievement.
This analysis explores factors related to gender bias in Indian education with a specific focus on the development of literacy and numeracy skills. The preceding review indicates that despite improvement in educational access, many Indian girls experience lower quality school environments, are afforded fewer educational resources to support their learning, and struggle with family attitudes that do not encourage them to excel academically. Girls' time may also be diverted to household and childrearing tasks, leading to a decreased amount of time available for learning. As a population of special concern, girls from rural areas appear to have the least time devoted to learning, and have the lowest rates of enrollment, school attendance, and homework completion.
Given that the quality of learning opportunities available to girls may be fundamentally distinct from those of boys, gender serves as a primary factor to be analyzed in this study. This paper explores the impact of four sets of factors thought to influence the educational outcomes of boys and girls. These factors include social background and socioeconomic status, access to learning resources, time devoted to formal learning activities, and cultural attitudes regarding the education of girls. Social background and socioeconomic status are quantified by the child's age, number of younger siblings in the household, rural/urban residence, level of household education, family assets, and caste. Access to learning resources is measured by the type of school attended and the level of household educational expenditures. A child's time available for learning is assessed by homework completion rates, school absenteeism, and amount of accumulated schooling. Lastly, household cultural attitudes will be explored to determine whether higher aspirations for girls' achievement translate into improved educational success. Cultural attitudes are measured by two relevant variables available in the dataset: an adult household female's attitude regarding girls' education, and a school distance variable, which is considered important since many families are reluctant to permit girls to travel long distances.
We hypothesize that areas of persisting gender inequality in education result from negative cultural attitudes regarding the education of girls, as well as parents choosing to prioritize sons' education over daughters' education due to a hidden opportunity cost of engaging girls in out of school activities that have practical and economic value for the family. Given these competing demands for girls' time, we further hypothesize that gender differences will be the highest for girls for whom time demands are the greatest, which would include girls from rural environments, girls with a larger number of younger siblings, and girls from families with lower incomes. These hypotheses lead to the following research questions to be explored. First, in what ways is continued educational inequality influenced by social context, and are gender differences in educational outcomes affected by presence of younger siblings, income, and level of urbanization? Second, how is access to school resources (e.g. type of school attended and educational expenditure) and the time devoted to formal learning activities (e.g. homework completion and school attendance) associated with learning outcomes? Finally, to what extent are family aspirations for girls' learning responsible for differences in the development of reading and mathematics skills? Since gender roles emerge from interdependent social influences, including the roles played by both parents and schools (Bussey and Bandura, 1999), this analysis investigates the unique contribution of each independent variable and explores how inequality may result from the intersection (or in statistical terms "interaction") of multiple categorical dimensions of social influences (Riley and Desai, 2007).
Data and Methods
The data used in this analysis is from the 2005 India Human Development Survey (IHDS), an instrument designed and administered by researchers from the University of Maryland and the National Council of Applied Economic Research (NCAER) (Desai, Vanneman, and NCAER, 2005). The comprehensive nature of the social, economic, and cultural variables that are measured in the IHDS provide an excellent opportunity to expand the educational research literature in India by permitting the investigation of how social and contextual factors influence educational outcomes based on gender.
The IHDS was administered by trained interviewers to 41,554 households within 1,503 villages as well as 971 urban neighborhoods located throughout India. The survey includes embedded reading and mathematics assessments that were administered to household children ages 8-11. In all, the dataset includes 12,356 children who completed the reading assessment and 12,306 children who completed the mathematics assessment. Children with missing data on either the reading or mathematics assessments are excluded from each respective analysis. (2) Reasons for missing data include lack of consent from parents and/or agreement to participate by children and lack of time to administer the assessment after a long household interview (Desai et al., 2009). Finally, given the complex sampling design of the IHDS, a design weight is included in statistical analyses.
Broad administration of reading and mathematics assessments is difficult to perform in India given the wide disparity of educational attainment among children. To address this concern, survey designers based the reading and mathematics assessments on those that have been successfully developed and used by Pratham, a non-governmental organization that conducts the Annual Status of Education Report (ASER) (Desai et al., 2009; Pratham, 2005). The reading and mathematics assessments in the IHDS were designed to be simple enough to be administered across a wide range of ability, and the educational assessments were also translated into twelve languages common among the sampled population in addition to English. Finally, interviewers were carefully trained by Pratham to administer the assessments, and were taught to distinguish behaviors such as shyness, which may have affected the ability to complete the academic assessments (Desai et al., 2009).
The dependent variables used in this analysis are literacy and numeracy skill assessment scores for children ages 8-11 and who are residing throughout India. The reading assessment score is a five-level ordinal variable that reflects the following assessment determination made by the interviewer administering the reading test: 0=Cannot Read; 1=Recognizes Letters; 2=Recognizes Words; 3=Can Read a Paragraph; and 4=Can Read a Story. As the dependent variable for the second set of analyses, the mathematics assessment score is a four-level ordinal variable that represents the following assessment determination made by the interviewer administering the mathematics test: 0=Cannot Perform Mathematics; 1=Recognizes Numbers (above 10); 2=Can Perform Subtraction Problems; and 3=Can Perform Division Problems. Although both measures are coded as ordinal variables, they represent latent underlying continuums within each learning domain.
Independent variables that are explored in these analyses represent socioeconomic, time for learning, school resource, and attitudinal factors that may have an impact on how gender interacts with the development of reading and mathematics skills. The social background factors that are examined include: gender, age, number of younger siblings, level of urbanization based on census designation (Rural, Urban), highest level of household education for any adult age 21+ in the household (None, 1st-fh Standard, 5th-9th Standard, ioth-11th Standard, 12th Standard-Some College, Graduate), a household asset index (an SES measure coded as a numeric index of the number of household possessions held by a family from a standard list), and caste (Higher Castes, Other Backward Classes, Dalit, Adivasi, Muslim, Other Religion). The following independent variables measure time engaged in learning factors: average number of hours spent on homework and private tutoring in a week, average number of days absent per month, and highest education level attained by the student. School resource factors that will be explored include type of school attended (Government, Private, Other) and per child expenditure on school-related fees (measured continuously). Finally, an attitudinal measure is included that represents an attribution that an adult female in the household makes regarding the importance of educating girls relative to boys (Same, Boys Should be More Educated, Girls Should be More Educated), as well as a variable measuring the distance between home and school (Less than 1 km, 1-2 km, More than 2 km).
An ordered logit model is used to assess the relationships between gender and other covariates with the two outcome variables, the reading assessment score and mathematics assessment score. An ordered logistic regression model can estimate ordinal outcome values along an underlying continuum as a function of each model's independent variables in which the latent outcome variables (ranging from -[infinity] to [infinity]) are mapped to observable ordinal outcome variables (Cohen et al., 2003; Long, 1997; Winship and Mare, 1984). The model is described by:
[y.sup.*.sub.i] = [x.sub.i][beta] + [[epsilon].sub.i] where [y.sub.i] = m if [[tau].sub.m - 1] [less than or equal to] [y.sup.*.sub.i] < [[tau].sub.m] for m = 1 to J
The notation [tau] represents cut points or thresholds of moving from one ordinal category to another (Long, 1997).
An important assumption underlying the ordered logit regression model is that of proportional odds, in which the effect of a covariate on the odds of moving from one outcome to the next is the same between all possible outcomes. Because this assumption may be violated in this data, generalized ordered logit models, which allow the coefficients to vary between outcomes, are also run (Williams, 2006).
The results of the ordered logistic regression models are presented in full model form and include the results of tested gender interactions. Models stratified by gender are also presented to facilitate a comparison of effects for boys and girls. The results from the generalized ordered logit models, which largely support the findings from the ordered logit models discussed in the next section, are shown in Appendices A and B.
Table 1 displays the mean values (or distributions) by gender for each of the explanatory variables used in the analysis. Girls and boys are both well represented in the survey, with boys representing a slightly higher proportion (53%) of the sample. The majority of the sample is rural, and the children are predominantly enrolled in government schools.
Tables 2a and 2b present the distribution of reading and mathematics assessment scores broken down by age and gender and display the percentage of each group attaining a given score category. A Wilcoxon non-parametric test is performed on these results and shows that boys outperform girls at every age except age 9 in reading and at every age in mathematics.
Table 3 shows odds ratios and gender interactions from the ordered logit model for the reading assessment score, with models stratified by gender shown in Table 4.
In reviewing the results related to social background, the reading assessment model shows a positive association between level of household education and reading skills with increasingly larger odds ratios as household education level increases. The full model also shows a strong negative association of lower caste background and reading scores for Dalit and Muslim children. Indeed, both Dalit and Muslim children are approximately a third less likely than those from higher castes of moving from one reading assessment category to the next highest level. In addition, level of urbanization does not appear to have a significant association with reading attainment.
In measuring access to high-quality resources and its impact on reading skill differences, there is a strong positive relation between private school attendance and reading skills. Children attending private school are approximately 60% more likely to be in a higher reading assessment category than are their government-schooled peers. However, educational expenditure appears to exhibit little relation with reading score net of the effect of other factors included in the model.
In terms of time available for learning activities, the number of hours per week devoted to both private tuitions and homework appears to have a small positive association with reading score. In addition, the number of days absent per month does not bear a statistically significant relationship. Not surprisingly, education level attained by the child has a strong positive relation with reading score.
The next set of reading results relate to the extent that family aspirations for girls' learning and school distances are responsible for differences in the development of reading skills. The full model shows a strong positive association between prioritizing girls' education and reading skill development. Children who have an adult female in the household agreeing that girls should be educated more are approximately 45% more likely to be in a higher reading assessment category than are children with an adult female who does not hold this belief. Lastly, there is an unexpected finding in that boys and girls who live more than 2 km from school have a 33% increased likelihood of being in a higher assessment category.
While the coefficient for gender is not significant, several interesting gender interactions are found and reported with the full model of reading assessment score on the set of independent variables. There is a highly significant interaction between being female and number of younger siblings. With each additional younger sibling, girls are less likely than boys to move from one reading assessment category to the next. There is also a significant gender interaction with the household asset index variable, suggesting that an increased level of household assets benefits the reading scores of girls to a greater extent than boys.
A similar set of analyses were conducted using mathematics assessment score as the dependent variable. Table 5 displays the results from the full model and associated gender interactions, and Table 6 shows the gender-stratified models.
The significant full model odds ratio for being female is 0.682 (p<0.01), which translates to girls being 32% less likely than boys of moving from one mathematics assessment category to the next. There is again a significant negative interaction between gender and the number of younger siblings; with each additional younger sibling, girls are less likely than boys to move from one mathematics assessment category to the next. Unlike the results for reading, the mathematics assessment results show a positive interaction between gender and urban status with urban girls more likely to be in a higher assessment category than their male peers. Although the gender-stratified models reveal that urban girls are not significantly more likely to move to a higher mathematics category than are rural girls, urban boys are significantly less likely to do so than are rural boys. Household education again exhibits a pattern of increasingly larger odds ratios for higher levels of household education. Lastly, the full model for mathematics confirms a negative association between lower caste background and mathematics score, as well as a small positive association between household assets and mathematics score.
In terms of educational resources, the full model yields a similarly strong positive association between private school attendance and mathematics skill attainment, with both boys and girls benefiting from private school attendance. As with reading, time devoted to private tutoring and homework appears to have a small positive association with mathematics score. However, unlike reading, the full model for mathematics generates a statistically significant, small negative relation between number of absences per month and mathematics score. Education expenditure again appears to have no discernible impact.
Finally, the association of gender attitudes on mathematics skill development is similar to that found for reading. However, in this model there is a statistically significant negative association between mathematics assessment score for all students and the presence of an adult female in the household who agrees that boys should be educated more.
The results of this analysis reveal that in 2005, 8-11 year-old Indian girls were underperforming boys on tests of reading and mathematics achievement. The results also reiterate findings from the Indian educational literature that show persistent learning gaps operating along social and contextual lines despite India's progress in improving its educational infrastructure and expanding educational access for girls and other disenfranchised groups.
The descriptive statistics and ordered logit regressions performed in this study also offer insight into the factors historically linked to gender inequality in education and provide evidence to address the research questions that have motivated this study. First, it does appear that the development of reading and mathematics skills is influenced by social context, and that gender differences are more pronounced for girls burdened with increased time demands outside of school. Girls with a larger number of younger siblings are less likely to advance to higher levels of reading and mathematics than boys from similarly sized families. As family size grows, many girls may be disproportionately spending time caring for their younger siblings at the expense of their academic learning. In addition, the interaction related to household assets (for reading) points to this variable serving as a factor that may encourage girls' achievement, since those from more resourced homes have an elevated chance of being in a higher reading assessment category than their male peers. Finally, the set of interaction results related to gender and urban status provide less insight than desired. While a significant gender interaction indicates that urban status increases the likelihood of girls advancing in mathematics relative to boys, the results do not provide clear evidence regarding the association of level of urbanization on girls' achievement overall. In fact, urban boys are found to be faltering compared to their rural counterparts in mathematics and may be a population of special concern for further research.
This analysis provides mixed results regarding the roles of high quality school resources and time devoted to formal learning activities. The findings strongly indicate that students who attend private schools are scoring higher in reading and mathematics. This result illustrates a major theme in the sociology of education literature in that schools themselves can serve as sources of inequality, especially given financial barriers that some families face to provide their children with higher quality learning environments. Beyond type of school attended, the level of educational expenditure does not appear to exhibit any discernable relation once other variables are controlled for in the models. Furthermore, time devoted to homework and private tuitions has modest positive associations with both reading and mathematics scores, while the number of school absences has a small negative association with mathematics only.
The last set of results address the role of family attitudes and aspirations in promoting achievement, and in particular, whether pro-female attitudes may be beneficial for the educational development of girls. For the ordered logit models focused on reading, there is a strong positive association between an adult female prioritizing girls' learning and reading skill development overall, although this outcome appears to be most consequential for girls. There is also a negative association between mathematics score and having an adult female in the household agreeing that boys should be educated more, revealing that such an attitude depresses mathematics skill development.
In summary, this analysis adds further evidence that gender inequality persists in the development of reading and mathematics skills for 8-11 yearold children in India. The research findings confirm that gender gaps remain for girls with many younger siblings who are faring worse academically than similarly situated boys. In addition, the results show that household asset level is associated with girls advancing in reading, and that having a positive attitude towards girls' education may be an important contributor to learning outcomes, especially for the reading achievement of girls. Unfortunately, the results related to rural residence yield less understanding than desired for this hypothesized factor.
Future investigations can build upon these findings and further explore the effects of socioeconomic, cultural, and other schooling factors on girls' academic achievement in India. This analysis can also serve as a baseline for comparative studies using subsequent waves of IHDS data. In addition, the results are potentially useful to policymakers in developing targeted policies to address gender inequality where it still remains, particularly in support of lower income girls and those with many younger siblings. Finally, this study reveals the importance of attitudes that prioritize girls' education, and the positive consequences that these have for learning.
University of Maryland, College Park
University of Louisville
JOAN R. KAHN
University of Maryland, College Park
University of Maryland, College Park
Received 10 September 2016 * Received in revised form 21 December 2016
Accepted 21 December 2016 * Available online 22 December 2016
The authors would like to acknowledge and thank Sonalde Desai and Reeve Vanneman of the University of Maryland, College Park for their insights and comments during the development of this article.
(1.) The gender parity index in primary level gross enrollment was .97 in 2003 and .98 in 2007. Source: UIS.STAT Data Base, UNESCO Institute for Statistics. http://data.uis.unesco.org/7queryidM42.
(2.) There is potential concern that children not attending school (and who may have thus scored the lowest on these assessments) may be disproportionately missing from the data. An earlier analysis of this data performed by Desai, Dubey, Vanneman, and Banerji (2009) determined that children not enrolled in school had the lowest proportion completing the reading and mathematics assessments. These authors did not otherwise find large differences in test completion between children of different background characteristics including gender.
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Appendix A Odds ratios from generalized ordered logit model of reading assessment score on set of independent variables 0-1 1-2 Female 1.318 0.919 (0.301) (0.159) Age 1.166 *** 1.122 *** (0.056) (0.042) # Younger Siblings 1.063 1.055 (0.075) (0.043) Rural/Urban (Urban=1) 0.719 0.894 (0.150) (0.115) Highest Household Education 1st-4th Std 1.004 1.398 ** (0.171) (0.190) 5th-9th Std 1.120 1.331 *** (0.139) (0.123) 10th-11th Std 1.497 * 1.790 *** (0.344) (0.246) 12th Std-Some Colleqe 1.235 1.892 *** (0.436) (0.356) Graduate 1.225 2.042 *** (0.567) (0.474) Household Asset Index 1.083 *** 1.030 ** (0.024) (0.013) Caste OBC 0.617 *** 0.743 ** (0.115) (0.096) Dalit 0.558 *** 0.611 *** (0.115) (0.076) Adivasi 0.828 0.963 (0.210) (0.168) Muslim 0.531 *** 0.666 *** (0.128) (0.093) Other Religion 0.902 1.230 (0.541) (0.337) Type of School Private 1.708 *** 1.342 *** (0.278) (0.136) Other 1.337 0.371 ** (0.619) (0.150) Education Expenditure 1.000 * 1.000 (0.000) (0.000) Hrs. Homework & Tutoring 1.053 *** 1.053 *** (0.011) (0.008) Days Absent / Month 0.960 *** 0.982 * (0.013) (0.009) Education Level 1.702 *** 1.605 *** (0.079) (0.051) School Distance 1 to 2 km 1.015 1.112 (0.267) (0.179) Beyond 2 km 1.530 1.605 *** (0.402) (0.244) Adult Female Attitude Boys More 1.174 0.862 (0.162) (0.086) Girls More 1.432 1.323 (0.442) (0.286) Female * # Younger Siblings 0.785 *** 0.820 *** (0.069) (0.052) Female * Urban 2.106 *** 1.293 (0.588) (0.237) Female * HHA Index 0.961 1.017 (0.027) (0.017) N = 11,238 2-3 3-4 Female 0.927 0.852 (0.140) (0.129) Age 1.159 *** 1.225 *** (0.040) (0.041) # Younger Siblings 0.972 1.016 (0.031) (0.035) Rural/Urban (Urban=1) 0.993 0.867 (0.104) (0.091) Highest Household Education 1st-4th Std 1.337 ** 1.351 ** (0.155) (0.178) 5th-9th Std 1.420 *** 1.348 *** (0.130) (0.123) 10th-11th Std 1.655 *** 1.906 *** (0.194) (0.239) 12th Std-Some Colleqe 1.994 *** 2.110 *** (0.289) (0.274) Graduate 2.264 *** 2.089 *** (0.359) (0.285) Household Asset Index 1.037 *** 1.023 ** (0.011) (0.010) Caste OBC 0.856 0.966 (0.083) (0.084) Dalit 0.599 *** 0.758 *** (0.076) (0.077) Adivasi 0.853 0.808 (0.134) (0.124) Muslim 0.604 *** 0.619 *** (0.076) (0.079) Other Religion 0.818 1.077 (0.168) (0.210) Type of School Private 1.554 *** 1.689 *** (0.139) (0.139) Other 0.742 1.393 (0.243) (0.482) Education Expenditure 1.000 1.000 (0.000) (0.000) Hrs. Homework & Tutoring 1.040 *** 1.034 *** (0.006) (0.005) Days Absent / Month 0.986 ** 1.001 (0.006) (0.007) Education Level 1.737 *** 1.559 *** (0.053) (0.041) School Distance 1 to 2 km 1.322 ** 1.078 (0.187) (0.119) Beyond 2 km 1.314 * 1.245 ** (0.189) (0.131) Adult Female Attitude Boys More 0.911 0.785 ** (0.094) (0.082) Girls More 1.280 1.598 ** (0.229) (0.294) Female * # Younger Siblings 0.857 *** 0.866 *** (0.040) (0.041) Female * Urban 1.076 1.169 (0.148) (0.155) Female * HHA Index 1.024 * 1.021 * (0.013) (0.011) N = 11,238 Notes: (a) The reading assessment score is a five-level ordinal variable: 0 = Cannot Read; 1 = Recognizes Letters; 2 = Recognizes Words; 3 = Can Read a Paragraph; and 4 = Can Read a Story. (b) Numbers in parentheses are standard errors. * p < .05. ** p < .01. *** p < .001. (c) Reference groups: Household Education = None; Caste = Higher Castes; School Type = Government; School Distance = Castes; School Type = <1 km; and Attitude=Educate Both Genders the Same. Appendix B Odds ratios from generalized ordered logit model of mathematics assessment score on set of independent variables 0-1 1 -2 2-3 Female 0.843 0.689 ** 0.612 *** (0.167) (0.117) (0.110) Age 1.119 *** 1.233 *** 1.274 *** (0.043) (0.040) (0.046) # Younger Siblings 1.107 0.962 1.052 (0.050) (0.037) (0.038) Rural/Urban (Urban=1) 0.706 ** 1.106 0.776 ** -0.106 (0.114) (0.090) Highest Household Education 1st- 4th Std 1.216 1.279 * 1.101 -0.163 (0.161) -0.173 5th-9th Std 1.085 1.412 *** 1.344 *** -0.109 (0.120) -0.147 10th-11th Std 1.492 ** 1.856 *** 2.003 *** (0.262) (0.233) (0.251) 12th Std-Some College 1.39 1.979 *** 2.293 *** -0.281 (0.253) (0.321) Graduate 4.188 *** 2.242 *** 2.456 *** -1.208 (0.340) (0.348) Household Asset Index 1.047 *** 1.026 ** 1.024 ** -0.016 (0.012) (0.011) Caste OBC 0.756 ** 0.948 0.756 *** -0.108 (0.088) (0.070) Dalit 0.631 *** 0.740 *** 0.559 *** -0.109 (0.081) (0.060) Adivasi 0.670 ** 0.843 0.610 *** -0.124 (0.119) (0.108) Muslim 0.691 ** 0.732 *** 0.592 *** -0.115 (0.083) (0.085) Other Religion 1 .528 1.307 0.541 *** (0.640) (0.330) (0.115) Type of School Private 1.169 1.477 *** 1.567 *** -0.157 (0.141) (0.139) Other 1.424 1.039 2.1 50 *** (0.537) (0.340) (0.593) Education Expenditure 1.000 *** 1.000 *** 1.000 ** (0.000) (0.000) (0.000) Hrs. Homework & Tutoring 1.037 *** 1.051 *** 1.047 *** (0.009) (0.006) (0.005) Days Absent/Month 0.953 *** 0.981 *** 1.017 ** (0.008) (0.006) (0.008) Education Level 1.546 *** 1.513 *** 1.596 *** (0.058) (0.043) (0.046) School Distance 1 to 2 km 1.294 1.152 1.126 (0.243) (0.160) (0.139) Beyond 2 km 1.803 *** 1.218 * 0.968 (0.388) -0.131 (0.115) Adult Fem ale Attitude Boys More 0.941 0.802 * 0.732 *** -0.113 (0.096) (0.084) Girls More 0.950 1.399 * 1.161 -0.218 (0.263) (0.207) Female * # Younger Siblings 0.858 ** 0.092 0.911 * (0.052) (0.051) (0.049) Female * Urban 2.026 *** 1.211 1.196 -0.431 -0.163 (0.182) Female * HHA Index 0.978 1.021 * 1.021 * (0.022) -0.012 (0.013) N = 11,191 Notes: (a) The mathematics assessment score is a four-level ordinal variable: 0 = Cannot Mathematics; 1 = Recognizes Numbers; 2 = Can Perform Subtraction Problems; and 3 = Can Perform Division Problems. (b) Numbers in parentheses are standard errors. * p < .05. ** p < .001. (c) Reference groups: Household Education = None; Caste = Higher Castes; School Type = Government; School Distance= <1 km ; and Attitude = Educate Both Genders the Same. Table 1 Descriptive statistics (sample distribution) Total Age 9.480 (1.067) # Younger Siblings 1.373 (1.330) Rural/Urban Rural 76.3% Urban 23.7% Highest Household None (Ref) 24.6% Education 1st- 4th Std 9.6% 5th-9th Std 34.6% 10th--11th Std 13.9% 12th Std--Some College 8.1% Graduate 9.3% Household Asset 10.656 Index (5.671) Caste Higher Castes (Ref) 18.9% OBC 36.3% Dalit 23.7% Adivasi 6.5% Muslim 12.8% Other Religion 1.8% Type of School Gov. (Ref) 69.1% Private 30.0% Other 0.9% Educ. Expenditure 1109.547 (2166.702) Hrs. Homework & Tutoring/Wk. 8.242 (7.441) Days Absent/Mo. 3.669 (6.187) Education Level 3.073 (1.498) School Distance Within 1 km (Ref) 83.0% 1 to 2 km 7.7% Beyond 2 km 9.4% Adult Female Educ. Same (Ref) 84.8% Attitude Boys More 13.0% Girls More 2.3% Females Age 9.477 (1.054) # Younger Siblings 1.520 (1.361) Rural/Urban Rural 75.9% Urban 24.1% Highest Household None (Ref) 24.0% Education 1st- 4th Std 9.4% 5th-9th Std 35.9% 10th--11th Std 13.1% 12th Std--Some College 7.8% Graduate 9.9% Household Asset 10.625 Index (5.670) Caste Higher Castes (Ref) 18.3% OBC 35.7% Dalit 25.1% Adivasi 6.6% Muslim 12.7% Other Religion 1.7% Type of School Gov. (Ref) 71.3% Private 27.8% Other 0.8% Educ. Expenditure 1004.701 (1813.777) Hrs. Homework & Tutoring/Wk. 7.968 (7.471) Days Absent/Mo. 3.642 (6.220) Education Level 3.069 (1.518) School Distance Within 1 km (Ref) 84.7% 1 to 2 km 7.4% Beyond 2 km 7.9% Adult Female Educ. Same (Ref) 86.0% Attitude Boys More 11.3% Girls More 2.7% Males Age 9.482 (1.079) # Younger Siblings 1.243 (1.289) Rural/Urban Rural 76.7% Urban 23.3% Highest Household None (Ref) 25.1% Education 1st- 4th Std 9.8% 5th-9th Std 33.4% 10th--11th Std 14.6% 12th Std--Some College 8.4% Graduate 8.8% Household Asset 10.683 Index (5.672) Caste Higher Castes (Ref) 19.5% OBC 36.8% Dalit 22.5% Adivasi 6.5% Muslim 12.9% Other Religion 1.8% Type of School Gov. (Ref) 67.1% Private 32.0% Other 0.9% Educ. Expenditure 1202.852 (2434.537) Hrs. Homework & Tutoring/Wk. 8.486 (7.406) Days Absent/Mo. 3.693 (6.158) Education Level 3.077 (1.480) School Distance Within 1 km (Ref) 81.5% 1 to 2 km 7.9% Beyond 2 km 10.7% Adult Female Educ. Same (Ref) 83.7% Attitude Boys More 14.4% Girls More 1.9% Notes: (a) N=12,356; 5,798 Females (46.9%) and 6,558 Males (53.1%) (b) Distributions are based on the sub-sample used for the reading assessment score outcome variable. The sub-sample used for the mathematics assessment score outcome variable produces a nearly identical distribution. Table 2a Distribution of reading assessment scores by age and gender Reading % of Group Obtaining Score Assessment 0 1 2 3 4 All Female 10.3 13.7 19.8 21.7 34.6 Ages Male ([dagger]) 8.1 12.4 20.5 22.1 36.9 Age 8 Female 16.3 19.0 25.1 19.6 20.1 Male ([dagger]) 12.8 18.0 27.3 20.4 21.6 Age 9 Female 9.0 12.9 19.5 25.6 32.9 Male 7.7 14.4 22.0 22.3 33.7 Age 10 Female 8.7 12.2 19.7 20.9 38.5 Male ([dagger]) 6.9 10.0 17.9 24.5 40.8 Age 11 Female 6.7 10.4 13.3 21.2 48.4 Male ([dagger]) 4.6 7.3 14.5 19.9 53.8 N=12,356 (5,798 Females and 6,558 Males) Notes: (a) The reading assessment score is a five-level ordinal variable: 0=Cannot Read; 1=Recognizes Letters; 2=Recognizes Words; 3=Can Read a Paragraph; and 4=Can Read a Story. ([dagger]) Males score significantly (p<0.05) higher than females based on Wilcoxon-Mann-Whitney non-parametric test. Table 2b Distribution of mathematics assessment scores by age and gender Mathematics % of Group Obtaining Score Assessment 0 1 2 3 All Female 19.7 31.9 26.9 21.6 Ages Male ([dagger]) 14.5 31.9 28.2 25.4 Age 8 Female 26.9 39.2 24.2 9.7 Male ([dagger]) 22.3 41.4 24.3 12.0 Age 9 Female 18.0 33.4 29.6 19.0 Male ([dagger]) 14.8 32.7 32.9 19.6 Age 10 Female 17.8 29.6 27.1 25.5 Male ([dagger]) 11.9 29.1 29.0 30.1 Age 11 Female 15.4 24.6 26.8 33.2 Male ([dagger]) 8.5 23.3 26.8 41.5 N=12,306 (5,777 Females and 6,529 Males) Notes: a The mathematics assessment score is a four-level ordinal variable: 0=Cannot Perform Mathematics; 1=Recognizes Numbers; 2=Can Perform Subtraction Problems; and 3=Can Perform Division Problems. ([dagger]) Males score significantly (p<0.05) higher than females based on Wilcoxon-Mann-Whitney non-parametric test. Table 3 Odds ratios and significant gender interactions from ordered logit model of reading assessment score on set of independent variables Significant Full Model Gender Interactions Gender (Female=1) 0.887 NA (0.107) Age 1.168 *** NA (0.032) # Younger Siblings 1.016 *** (0.032) Rural/Urban (Urban=1) 0.893 (0.080) Highest Household Education 1st.-4th std 1.316 ** NA (0.133) 5th-9th Std 1.342 *** NA (0.099) 10th-11th std 1.768 *** NA (0.178) 12th Std-Some College 1.924 *** NA (0.249) Graduate 1.993 *** NA (0.275) Household Asset Index 1.031 *** * (0.009) Caste OBC 0.865 NA (0.068) Dalit 0.667 *** NA (0.064) Adivasi 0.899 NA (0.107) Muslim 0.650 *** NA (0.063) Other Religion 0.982 NA (0.164) Type of School Private 1.596 *** NA (0.117) Other 0.783 NA (0.260) Education Expenditure 1 NA (0.000) Hrs. Homework & Tutoring 1.040 *** NA (0.005) Days Absent / Month 0.984 NA (0.009) Education Level 1.644 *** NA (0.039) School Distance 1 to 2 km 1.155 NA (0.127) Beyond 2 km 1.328 ** NA (0.128) Adult Female Attitude Boys More 0.885 NA (0.068) Girls More 1.452 * NA (0.240) N= 11,238 Notes: (a) Numbers in parentheses are standard errors. * p < .05. ** p < 01. *** p < .001. (b) Reference groups: Household Education = None; Caste = Higher Castes; School Type=Government; School Distance= <1 km; and Attitude=Educate Both Genders the Same. (c) Gender interactions tested with the follow ing variables: # of Younger Siblings; Rural/Urban; and Household Asset Index. Table 4 Odds ratios from ordered logit models of reading assessment score on set of independent variables (stratified by gender) Girls Boys Only Only Age 1.204 *** 1.138 *** (0.048) (0.041) # Younger Siblings 0.855 *** 1.014 (0.027) (0.034) Rural/Urban (Urban=1) 1.096 0.878 (0.104) (0.078) Highest Household Education 1st-4th Std 1.377 * 1.288 (0.183) (0.180) 5th-9th Std 1.295 * 1.412 *** (0.149) (0.134) 10th-11th Std 1.708 *** 1.867 *** (0.239) (0.272) 12th Std-Some College 1.844 *** 2.021 *** (0.280) (0.412) Graduate 1.707 * 2.394 *** (0.386) (0.354) Household Asset Index 1.057 *** 1.025 * (0.011) (0.010) Caste OBC 0.871 0.864 (0.093) (0.089) Dalit 0.739 * 0.612 *** (0.092) (0.073) Adivasi 0.956 0.862 (0.147) (0.130) Muslim 0.656 *** 0.643 ** (0.082) (0.092) Other Religion 0.924 1.053 (0.248) (0.215) Type of School Private 1.451 *** 1.716 *** (0.152) (0.167) Other 0.604 1.013 (0.217) (0.515) Education Expenditure 1.000 1.000 (0.000) (0.000) Hrs. Homework & Tutoring 1.038 *** 1.043 *** (0.006) (0.006) Days Absent / Month 0.984 0.985 (0.010) (0.013) Education Level 1.688 *** 1.617 *** (0.059) (0.049) School Distance 1 to 2 km 0.930 1.367 * (0.150) (0.180) Beyond 2 km 1.719 *** 1.115 (0.255) (0.138) Adult Female Attitude Boys More 0.890 0.885 (0.091) (0.092) Girls More 1.520 * 1.367 (0.315) (0.374) N= 5,255 5,983 Notes: (a) Numbers in parentheses are standard errors. * p < .05. ** p < .01. *** p < .001. (b) Reference groups: Household Education=None; Caste=Higher Castes; School Type=Government; School Distance = <1 km; and Attitude=Educate Both Genders the Same. Table 5 Odds ratios and significant gender interactions from ordered logit model of mathematics assessment score on set of independent variables Significant Full Model Gender Interactions Gender (Female=1) 0.682 ** NA (0.088) Age 1.203 *** NA (0.032) # Younger Siblings 1.009 ** (0.032) Rural/Urban (Urban=1) 0.850 (0.078) Highest Household Education 1st-4th Std 1.199 NA (0.115) 5th-9th Std 1.274 ** NA (0.096) 10th-11th std 1.804 *** NA (0.191) 12th Std-Some College 1.938 *** NA (0.242) Graduate 2.257 *** NA (0.255) Household Asset Index 1.029 ** (0.010) Caste OBC 0.826 * NA (0.063) Dalit 0.650 *** NA (0.061) Adivasi 0.714 ** NA (0.086) Muslim 0.677 *** NA (0.064) Other Religion 0.796 NA (0.130) Type of School Private 1.518 *** NA (0.123) Other 1.465 NA (0.362) Education Expenditure 1.000 ** NA (0.000) Hrs. Homework & Tutoring 1.048 *** NA (0.005) Days Absent / Month 0.976 *** NA (0.007) Education Level 1.552 *** NA (0.038) School Distance 1 to 2 km 1.172 NA (0.126) Beyond 2 km 1.185 NA (0.103) Adult Fem ale Attitude Boys More 0.835 * NA (0.076) Girls More 1.196 NA (0.180) N= 11,191 Notes: (a) Numbers in parentheses are standard errors. * p < .05. ** p < .01. *** p < .001. (b) Reference groups: Household Education = None; Caste = Higher Castes; School Type=Government; School Distance= <1 km; and Attitude=Educate Both Genders the Same. (c) Gender interactions tested with the following variables: # of Younger Siblings; Rural/Urban; and Household Asset Index. Table 6 Odds ratios from ordered logit models of mathematics assessment score on set of independent variables (stratified by gender) Girls Boys Only Only Age 1.161 *** 1.236 *** (0.044) (0.047) # Younger Siblings 0.901 ** 1.008 (0.029) (0.035) Rural/Urban (Urban=1) 1.145 0.831 * (0.113) (0.077) Highest Household Education 1st-4th Std 1.239 1.185 (0.174) (0.146) 5th-9th Std 1.281 * 1.294 ** (0.146) (0.128) 10th-11th std 2.025 *** 1.680 *** (0.312) (0.241) 12th Std-Some College 1.942 *** 1.969 *** (0.314) (0.352) Graduate 2.100 *** 2.527 *** (0.331) (0.376) Household Asset Index 1.049 *** 1.026 * (0.013) (0.011) Caste OBC 0.842 0.830 (0.087) (0.090) Dalit 0.771 0.565 *** (0.108) (0.064) Adivasi 0.802 0.665 ** (0.141) (0.102) Muslim 0.738 * 0.635 ** (0.107) (0.091) Other Religion 0.739 0.868 (0.158) (0.194) Type of School Private 1.423 ** 1.602 *** (0.169) (0.153) Other 1.110 1.842 (0.296) (0.678) Education Expenditure 1.000 * 1.000 * (0.000) (0.000) Hrs. Homework & Tutoring 1.044 *** 1.053 *** (0.007) (0.007) Days Absent / Month 0.983 0.971 * (0.010) (0.012) Education Level 1.577 *** 1.539 *** (0.056) (0.046) School Distance 1 to 2 km 1.103 1.222 (0.165) (0.183) Beyond 2 km 1.528 ** 0.999 (0.205) (0.127) Adult Female Attitude Boys More 0.820 0.850 (0.116) (0.088) Girls More 1.115 1.305 (0.227) (0.289) N= 5,236 5,955 Notes: (a) Numbers in parentheses are standard errors. * p < .05. ** p < .01. *** p < .001. (b) Reference groups: Household Education = None; Caste = Higher Castes; School Type=Government; School Distance = <1 km; and Attitude=Educate Both Genders the Same.
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|Author:||White, Gregory; Ruther, Matt; Kahn, Joan R.; Dong, Dian|
|Publication:||Journal of Research in Gender Studies|
|Date:||Jul 1, 2016|
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