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STATUS OF SECONDARY EDUCATION IN INDIA: AN ANALYSIS

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Parthajeet Das

Contemporary Education Dialogue

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The universalisation of secondary education is emerging as one of the important policy imperatives across nations. The challenges relating to the universalisation of secondary education are manifold and interlinked and have to be tackled simultaneously. This paper examines some of these challenges in India

Kanna Velusamy

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Secondary education serves as a link between the elementary and higher education, and plays a very important role in this respect. A child's future can depend a lot on the type of education she/he receives at the secondary level. Apart from grounding the roots of education of a child, secondary education can be instrumental in shaping and directing the child to a bright future. This stage of education serves to move on higher secondary stage as well as to provide generic competencies that cut across various domains of knowledge as well as skills. Providing secondary education to all, both boys and girls, with a focus on quality education assumes greater meaning today, when we consider the emerging challenges in our society. For instance, rising levels of socioeconomic aspirations and also the democratic consciousness particularly among marginalized sections of population such as the dalits, tribals, OBCs, religious and linguistic minorities and girls seek space in the secondary education system for greater access, participation and quality. The recent significant development viz., Universal Elementary Education (UEE) being achieved through Sarva Shiksha Abhiyan (SSA) and also the impact of globalisation and rapid growth of new technologies have led to reassessment of India’s preparedness to generate required technical manpower, develop new knowledge and skills, and remain competitive at global level. The secondary and higher secondary education system has a key role to play in enabling the nation to move towards these objectives. Given the high transition rate of about 85% from class VIII to IX and the anticipated progress in UEE, which is now widely acknowledged, that the time has arrived for taking proactive measures to plan and provide for universal access to secondary education and senior secondary education in a phasedwise manner. Keywords: Secondary education, Sarva Shiksha Abhiyan, Educational Reforms.

Gender Segregation in Education: Evidence From Higher Secondary Stream Choice in India

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Soham Sahoo , Stephan Klasen; Gender Segregation in Education: Evidence From Higher Secondary Stream Choice in India. Demography 1 June 2021; 58 (3): 987–1010. doi: https://doi.org/10.1215/00703370-9101042

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This paper investigates gender-based segregation across different fields of study at the senior secondary level of schooling in a large developing country. We use a nationally representative longitudinal data set from India to analyze the extent and determinants of gender gap in higher secondary stream choice. Using fixed-effects regressions that control for unobserved heterogeneity at the regional and household levels, we find that girls are about 20 percentage points less likely than boys to study in science (STEM) and commerce streams as compared with humanities. This gender disparity is unlikely to be driven by gender-specific differences in cognitive ability, given that the gap remains large and significant even after we control for individuals' past test scores. We establish the robustness of these estimates through various sensitivity analyses: including sibling fixed effects, considering intrahousehold relationships among individuals, and addressing sample selection issues. Disaggregating the effect on separate streams, we find that girls are most underrepresented in the study of science. Our findings indicate that gender inequality in economic outcomes, such as occupational segregation and gender pay gaps, is determined by gendered trajectories set much earlier in the life course, especially at the school level.

Introduction

Various forms of gender inequality are observed in different parts of the world. In South Asia, such inequalities have manifested throughout the life course of individuals: sex imbalance at birth due to sex-selective abortions, unequal survival rates, differential human capital investments, discrimination in the labor market, and so on. The last few decades, however, have seen progress toward gender equality, most notably in education. Although the gender gap in enrollment rates at all levels of education has diminished, it has not translated into a commensurate improvement in women's labor market outcomes. For instance, female labor force participation, which is viewed as one of the important indicators of inclusive development and female economic empowerment, has remained very low and stagnant and has sometimes declined in India, despite the nation's rapid economic growth, female educational expansion, and fertility decline in the last two decades ( Klasen and Pieters 2015 ). Underparticipation of women will restrict the country from properly utilizing its demographic gift of having a high proportion of the population at working ages. Additionally, occupational and sectoral segregation of employment by gender is remarkably persistent and is a key issue behind perpetuating female disadvantage, such as the gender pay gap in the labor market ( Borrowman and Klasen 2020 ).

Against this backdrop, we show here that the gender gap in economic participation in adulthood in India is shaped by gendered trajectories set earlier in life, especially at the school level. Specifically, we identify the gender gap in science, technology, engineering, and mathematics (STEM) and commerce-related fields at the senior secondary level of education, which is likely to have a significant and far-reaching effect on individuals' adult-life outcomes.

The literature has analyzed the determinants and consequences of stream choice at the postsecondary and tertiary levels of education ( Arcidiacono 2004 ; Beffy et al. 2012 ; Fuller et al. 1982 ). Some studies have also recognized the link between educational segregation and occupational segregation and have reflected on the life course processes that determine women's career trajectories relative to men's ( Schneeweis and Zweimüller 2012 ; Xie and Shauman 2003 ). However, the causes of gender gap in stream choice are not fully understood, and they constitute an area of active research in the literature ( Kahn and Ginther 2017 ; Xie et al. 2015 ). In addition, barring a few exceptions, most of this research has focused on developed countries. 1

By investigating this issue in the context of an emerging economy, we contribute to the literature in various ways. First, India offers an important case study given the importance of STEM education in its economy. Over the last few decades after economic liberalization, the growth of the Indian economy has been led by the services sector, where the information technology–related industry has been a prime contributor ( Panagariya 2004 ). The nature of economic growth during this period has potentially had a spillover effect on education participation ( Jensen 2012 ; Oster and Steinberg 2013 ; Shastry 2012 ). India, along with China, has accounted for the majority of the world's recent STEM graduates, constituting a much larger share than the European Union and the United States ( UNCTAD 2018 ). Yet, the gender composition of these STEM students and their labor market prospects have remained unexplored in the literature.

Cross-country studies have shown variation in the overall levels and patterns of sex segregation in stream choice (e.g., Charles and Bradley 2009 ), indicating that the processes that generate this form of gender inequality in advanced and developing nations may be distinct. This set of findings justifies an empirical analysis in a developing country setting, such as India, where the prevalence of male-favoring gender norms affects individual decisions at various life stages. In a society where economic and cultural factors drive underinvestment in girls' education ( Azam and Kingdon 2013 ; Kingdon 2005 ), the magnitude and determinants of gender gap in STEM participation might be different from those in more gender-egalitarian countries. Moreover, from a demographic point of view, the Indian scenario differs from developed countries in its very low and stagnating female labor force participation. In this context, although a growing body of literature has examined the role of education in female labor force participation, it has focused on the level of education and not the type of education ( Klasen and Pieters 2015 ; Sarkar et al. 2019 ). The current paper contributes to this discourse by highlighting the persistence of gender segregation of academic fields despite improvements in female education levels.

The Indian education system also has distinct structural features that imply greater importance of stream choice made at the school level. After secondary schooling, completed after 10 years of education, students entering at the higher secondary level (lasting another two years) must specialize in one of the following streams: humanities, science, commerce, engineering/vocational, and other. 2 Unlike many developed countries, including the United States, stream choice at the tertiary level in India is made before admission to college, and because of eligibility requirements, this choice is largely determined by the stream studied at the higher secondary level. 3 Therefore, school-level stream choice is a crucial juncture in an individual's career because it determines the subsequent course of study at the college level and the nature of jobs that the individual may obtain in the future. Thus, the life course approach proposed by Xie and Shauman (2003) is especially relevant in the context of India, where the prevailing education system implies that choices made in adolescence affect adult-life outcomes. In fact, Sahoo and Klasen (2018) showed that stream choice at the higher secondary level in India strongly influences later labor market outcomes, including participation, occupational choice, and earnings.

Another contribution of this study is its exploration of the gender gap in the commerce stream, which is equivalent to a business major. Although a vast literature has focused on the gender gap in STEM, the gender gap in business studies is less explored. Analyzing trends in college major choice in the United States, Gemici and Wiswall (2014) found that women are significantly less likely than men to choose a business major, despite the documented overall rise in women's participation in tertiary education ( Goldin et al. 2006 ). We extend this literature by investigating gender disparity in the choice of the commerce stream at the higher secondary school level in India.

We use a nationally representative household-level panel data set that tracks the same households and individual members at two time points: 2005 and 2012. The novelty of this survey is that it asks all individuals about their performance in the secondary school leaving certificate (SSLC) examination and subsequently asks what stream they studied at the higher secondary level. Additionally, individuals aged 15–18 years (the ages corresponding to higher secondary schooling) in 2012 can be matched with information on their prior skills in mathematics, reading, and writing from an independent test conducted in the earlier round of survey in 2005. Thus, we have a unique setting to investigate individuals' higher secondary stream choice after controlling for their past academic performances, which serve as reasonable proxies of their cognitive ability.

Estimating fixed-effects regression models, we find a significant gender disparity of about 20 percentage points in the choice of nonhumanities streams (i.e., STEM and commerce) at the higher secondary level among youth aged 15–18 years. In addition to a rich set of covariates, we account for unobserved heterogeneity at the regional and household levels by including fixed effects in the regression. The gender gap remains unchanged even after we control for SSLC exam performance and lagged test scores from the previous survey. We establish the robustness of the estimates by considering the intrahousehold relationships of individuals, estimating sibling fixed-effects models, and addressing sample selection issues using an inverse probability weighting (IPW) framework.

We further investigate the determinants of gender difference in stream choice. Given the persistence of the gender gap even after we take into account the effect of cognitive ability as measured by past exam performance and test scores, we explore the roles of other relevant characteristics. We find that the gender gap does not vary with household income, suggesting that gender-based sorting into different streams is equally prevalent in richer and poorer households. Rather, the gender gap is significantly reduced when there is greater educational parity between parents, captured by the difference in education level between mother and father. We also show that better access to STEM-related education benefits girls more than boys, thus narrowing the gender gap. Additionally, investigating the choice of separate study tracks, we show that the pro-male gender bias is largest in science, followed by commerce and engineering/vocational streams.

Background and Related Literature

The last few decades have seen considerable progress in bridging the gender gap in educational attainment around the developing world. At the same time, trends in female labor force participation have been rather uneven, with South Asia actually experiencing declining female labor force participation rates ( Klasen 2019 ). Moreover, women have continued to be employed predominantly only in few sectors and occupations ( Borrowman and Klasen 2020 ). This perpetuating trend in occupational and sectoral segregation is a major reason for the persistence of the male-female earnings gap ( Blau and Kahn 2017 ). This pattern of gender stratification has also been found in the Indian labor market ( Duraisamy and Duraisamy 2014 ).

India has experienced a major expansion in education provision, resulting in a significant rise in school enrollment of both boys and girls. The Indian education system has a common structure throughout the country: students progress through primary, middle, and secondary education in their first 10 years of schooling, followed by another two years of higher secondary schooling and subsequently three to five years of tertiary education. Data from the National Sample Survey (NSS) show that in the mid-1990s, the average enrollment rate among children in the age group corresponding to elementary (i.e. primary and middle) schooling was about 70%, with a gender gap of 10 percentage points favoring boys. Over the next 20 years, this enrollment rate increased to 93%, with the gender gap declining to only 2 percentage points. The same pattern is visible in secondary and higher secondary levels: over the last two decades, the enrollment rate increased from 50% to 77%, and the gender gap declined from 16 percentage points to 2 percentage points.

The first 10 years of education in India include a common, nonselective curriculum for all students. After that, each student enrolling in higher secondary level must specialize in a particular stream; most choose the humanities, science, or commerce stream, and a minority opt for other tracks, such as engineering or vocational education. After completing the higher secondary level, students who continue to tertiary education enroll in colleges for bachelor's and master's degrees in a chosen stream. A crucial aspect of the Indian education system is that stream choice at the higher secondary level largely determines subsequent major choice at the college level. Particularly, students who have studied in the humanities stream in higher secondary school are deemed ineligible for a STEM or commerce major in almost all colleges. Therefore, stream choice at the higher secondary level is an important decision in an individual's career because it drives the field choice at subsequent levels of education, which in turn affects labor market outcomes through occupational choice. National-level statistics from repeated cross-sectional surveys of the NSS show that the proportion of students enrolled in higher secondary level choosing humanities declined from 56% in 2007–2008 to 42% in 2014. In contrast, science enrollment increased from 31% to 39% during this period, and commerce enrollment increased from 13% to 16%. These aggregate statistics also reveal that girls have a higher propensity to study humanities than science or commerce, and boys are more likely than girls to study science ( Figure 1 ). This gender disparity in school-level stream choice also leads to subsequent gender gaps in undergraduate studies: the share of women in STEM is only 37%, and the share in commerce is 45% ( Government of India 2016 ).

The literature on postsecondary stream choice, mostly based on developed countries, highlights that educational choices at this level are closely linked to labor market outcomes. First, stream choice is affected by the expected future earnings from different streams ( Beffy et al. 2012 ; Boudarbat 2008 ). Second, such educational choices also cause much of the variation in earnings later in life ( Dustmann 2004 ; Joensen and Nielsen 2009 ). Specifically, evidence suggests that a STEM or business major yields higher returns than studying humanities ( Flabbi 2011 ). This pattern is corroborated in the Indian context when we compare the earnings distributions of individuals who studied STEM/commerce with those of individuals who studied humanities at the higher secondary level ( Figure 2 ).

Focusing on gender, studies have shown gender disparities in stream choice: girls are especially underrepresented in STEM at the postsecondary level of education in most countries ( Hill et al. 2010 ; World Bank 2012 ). The incidence of gender segregation in education and its relation to occupational segregation has also been explored using data from the United States and Europe ( Bieri et al. 2016 ; Daymont and Andrisani 1984 ; Eide 1994 ; Flabbi 2011 ; Van Puyenbroeck et al. 2012 ). These studies have found that men's and women's college major choice largely explains occupational choices and accounts for a significant part of the gender wage gap. For the case of India, Sahoo and Klasen (2018) found that, even with controls for exam results and household fixed effects, women who choose a STEM or commerce stream in higher secondary education have substantially higher chances of participating in the labor force, securing salaried employment, choosing a male-dominated occupation, and having higher earnings. The choice of STEM or commerce stream in turn leads to a reduction of gender gap within households in terms of all these economic outcomes. Also, among different streams, science appears to have the most significant effect.

One potential reason that girls are less likely to choose STEM subjects is that boys may have a comparative advantage in mathematics. Evidence shows that a male advantage in mathematics achievement starts manifesting in middle school and increases with age ( Bharadwaj et al. 2012 ; Kahn and Ginther 2017 ), but mathematical ability does not fully account for the gender gap in STEM choice ( Dickson 2010 ; Friedman-Sokuler and Justman 2016 ; Riegle-Crumb et al. 2012 ; Turner and Bowen 1999 ). Rather than inherent gender differences in cognitive ability, other societal, psychosocial, and preference-related factors play a larger role in explaining the underrepresentation of women in math-intensive STEM subjects ( Antecol and Cobb-Clark 2013 ; Buser et al. 2014 ; Zafar 2013 ). In fact, a large part of the observed gender gap can be attributed to the stereotypical beliefs about girls' mathematical ability and gendered preferences that are often shaped by cultural norms ( Charles and Bradley 2009 ; Kahn and Ginther 2017 ).

The salience of societal factors implies that contextual analysis is essential for understanding the incidence and determinants of gendered educational choices. In addition, the theoretical perspectives on the relationship between economic development and gender stratification in education do not always converge ( Hannum 2005 ). Modernization or neoclassical theory suggests that the expansion of market forces reduces discriminatory cultural practices that are linked to economic inefficiency, thereby reducing gender disparities in education ( Forsythe et al. 2000 ). On the other hand, Boserup (1970) hypothesized that inequality would first increase and then decrease in the process of development. Initially, men with better access to market opportunities may reap greater benefits of economic prosperity, and progress toward gender equality would be achieved as the structural transformation proceeds ( Lantican et al. 1996 ). Traditional institutions also mediate the effect of economic development on women's educational responses ( Munshi and Rosenzweig 2006 ). Particularly for school-age children, decisions are influenced by parents, who are likely to consider factors beyond labor market returns to education. In South Asia, these factors include dowry payment for daughters' marriage, a higher likelihood of receiving old-age support from sons than from daughters due to patrilocality, and gender norms about women's participation in activities outside the household ( Alderman and King 1998 ; Jayachandran 2015 ).

Our study contributes to the literature in two ways. First, we identify the pattern of gender segregation in stream choice in an emerging economy where such evidence has been lacking.

Second, we explore the plausible determinants of the gender disparity. Specifically, we analyze the role of cognitive ability, measured by past exam performance and test scores. In addition, we examine the influence of other pertinent factors in this context. Household income is likely to be a constraining factor for poorer students while choosing a STEM education, which is more costly to study than humanities. Indeed, the NSS data reveal that the average expenditure incurred by students in the science and commerce streams is more than twice the expenditure of those studying humanities at the higher secondary level. 4 Variation in household income may lead to gendered choices depending on whether resource constraints are binding and how son preference varies along with income ( Alderman and King 1998 ; Garg and Morduch 1998 ). Therefore, we investigate whether household income determines the gender difference in stream choice.

Another potential determinant we consider is parental education gap. A large literature has explored the intergenerational transmission of human capital, but this research has mostly analyzed the effect of parental education on children's years of schooling or grade progression rather than stream choice ( Holmlund et al. 2011 ). In addition, we introduce the gender dimension by focusing on the gap in educational attainment between mothers and fathers. We postulate that greater parity in parental education would induce equality in stream choice between boys and girls.

Finally, we consider access to STEM-related education, which is especially important in a developing country where students are often constrained by the availability of specific streams in the local schools. Reviewing the literature on several developing countries, Glick (2008) noted that access to education, despite being a gender-neutral factor, may disproportionately affect girls' participation. This possibility is plausible in the context of a patriarchal society like India, where strong gender norms may discourage adolescent girls from traveling long distances to attend school ( Muralidharan and Prakash 2017 ). Safety concerns may also dissuade girls from enrolling in their preferred stream if it involves traveling longer distances ( Borker 2017 ). Using regional variation in the availability of STEM colleges as a proxy for access, we test whether better access reduces the gender gap in stream choice.

Data Description

We use the India Human Development Survey (IHDS), a nationally representative, two-period longitudinal data set ( Desai et al. 2010 , 2015 ). 5 The first round of data was collected in 2004–2005 on 41,554 households in 1,503 villages and 971 urban neighborhoods across India. In 2011–2012, the second round of survey reinterviewed 83% of the same households; for households that could not be tracked, a replacement sample was used. Thus, the second round of survey covered 42,152 households across India. For brevity, we refer to the first round as 2005 data and the second round as 2012 data . IHDS is a multitopic survey collecting detailed information at the individual, household, and community levels. Our analysis mainly uses the sample from the 2012 survey and uses the 2005 survey to account for past characteristics of the same individuals.

We explore whether the choice of study stream exhibits a gender bias at the higher secondary level. In India, the official school entry age is 6 years, and the (lower) secondary level ends after 10 years of schooling. In the IHDS sample, the enrollment rate of children of secondary school age (14–15 years) is 87%, and the gender gap in the enrollment rate is only 2 percentage points. Because the higher secondary (or senior secondary) level consists of two years of schooling succeeding the secondary level, we concentrate on the sample of individuals who are in the corresponding age group of 15–18 years. 6 Information on stream choice at the higher secondary level is available only for individuals who have passed the secondary level and enrolled in the subsequent level of education. The secondary pass rate for our sample is 39.4% for males and 40.6% for females; a t test reveals that the gender difference in the secondary pass rate is not statistically significant. After we drop observations with missing values, the final analysis sample is 5,203 children.

The first step toward specialization begins at the higher secondary level of education, when students have to choose a stream mainly from the following options: arts/humanities, commerce, science, engineering/vocational, and others (e.g., home science, craft, and design). 7 Estimates from the IHDS data show patterns of stream choice that are similar to the national-level statistics around this period. Summary statistics presented in Table 1 show that 50% of students in the sample chose the humanities stream. The next most popular stream is science, followed by commerce, engineering/vocational, and others, the latter of which are chosen by very few. In the sample, 58% girls but only 41% boys chose humanities, indicating that girls are underrepresented in science, commerce, and engineering/vocational streams. Because these average differences may be confounded by various observable and unobservable factors that are correlated with both gender and stream choice, we next lay out an econometric model to identify the gender gap.

Empirical Model

We estimate a linear probability model where the dependent variable ( ⁠ S t r e a m i h v d k ⁠ ) is a binary indicator of whether an individual of higher secondary school age (15–18 years) has chosen to study stream k ⁠ , where k ∈ { H u m a n i t i e s , C o m m e r c e , S c i e n c e , E n g i n e e r i n g / V o c a t i o n a l , O t h e r } ⁠ . The subscripts i , h , v , and d (respectively) denote individual, household, village/town, and district. The main explanatory variable is an indicator variable ( ⁠ F e m a l e ⁠ ) denoting whether the individual is female. In addition, we control for individual-level covariates ( ⁠ X i h v d ⁠ ): age, birth order, number of siblings, mother's years of education, father's years of education, and dummy variables indicating relationship to the household head. Household-level covariates ( ⁠ Z h v d ⁠ ) include household size, wealth, dummy variables for social group (caste and religion), and whether the household is in a rural area. To control for regional characteristics, we first include fixed effects at the district level ( ⁠ μ d ⁠ ) and then the village/town level ( ⁠ φ v d ⁠ ). Inclusion of village/town fixed effects also helps us to control for access to education in the locality, which is important because some schools may not offer higher secondary education or may not offer all the streams at this level. Other regional characteristics, such as local labor market conditions and societal norms toward girls' education, are also subsumed by these fixed effects.

Because household-level factors, including unobserved tastes and preferences for different types of education, potentially affect the stream choice, we control for household-level heterogeneity by including household fixed effects ( ⁠ ϕ h v d ⁠ ) in an additional set of regressions. 8 This control is especially important in the context of India, where the household's unobserved preferences are correlated with gender inequality. For example, female children in India are often more likely to be found in larger families because fertility decisions are endogenously determined; parents keep having children until they have at least one boy ( Basu and de Jong 2010 ; Clark 2000 ; Yamaguchi 1989 ). If STEM education requires higher investments, then comparisons across households may artificially show a gender gap because girls belong to larger families, who invest less in the human capital of each child. For these and related reasons, studies investigating gender discrimination in educational investments have advocated using household fixed effects ( Jensen 2002 ; Kingdon 2005 ; Sahoo 2017 ). Although it includes household fixed effects, our model also takes into account the potential nonindependence of observations belonging to the same household by clustering the standard errors at the household level. 9

Gender differences in the choice of STEM education may be driven by girls' lower cognitive ability compared with boys, especially in mathematics. The literature on gender gaps in mathematics achievement suggests that most of the observed gap is explained by background factors ( Benbow and Stanley 1980 ; Nollenberger et al. 2016 ). In India, because of systematic and continual underinvestment in girls' human capital from early childhood, girls' cognitive ability may lag behind that of boys at the higher secondary level. A novel feature of our data is that they allow us to account for an individual's cognitive ability using two distinct measures.

The first measure of cognitive ability is given by the individual's performance in the secondary level board examination, which is potentially an important predictor of stream choice at the higher secondary level. In India, a standardized examination is conducted by the education board (at the state or national level) to which each school belongs. Every student must pass this examination and obtain the SSLC to be able to continue at higher secondary levels of education. The results of this examination are typically categorized into divisions 1, 2, and 3, in the declining order of the quality of grade obtained. We use this SSLC performance indicator to control for the individual's cognitive ability.

Furthermore, in the 2005 IHDS round, children who were aged 8–11 years were given cognitive tests on mathematics, reading, and writing ability. In the 2012 survey, these children are in the age group corresponding to the higher secondary level and are considered in the regression. Therefore, we are able to control for their past cognitive ability by including their performance on these tests. 10 Consequently, we control for achievement scores collected by two independent tests: one from the SSLC examination and the other conducted by IHDS enumerators in 2005. Hence, we believe that our regression adequately captures the differences in children's abilities and identifies the gender gap in stream choice.

A potential concern that remains is that stream choice is defined only for those individuals who have passed the secondary level and enrolled at the higher secondary level. In the age group considered, 40% of children passed the secondary level. These children are likely to be systematically different from those who have education below the secondary level. However, disaggregating this pass rate by boys and girls, we find that there is no gender gap in the secondary level pass rate. We also estimate a regression (see Table A2 , online appendix) similar to Eq. (1) but with the dependent variable being a binary indicator of whether a child has passed the secondary level (and hence is eligible for higher secondary stream choice). The coefficient on gender in this regression is almost always insignificant, and the magnitude is almost zero, suggesting that the probability of selecting into the sample for our main regression (stream choice) does not vary by gender. Hence, this selection is unlikely to confound the effect of gender in the regression of STEM/commerce stream choice.

Main Results

We begin by investigating the gender difference in the choice of STEM/commerce streams, combining science, engineering/vocational, and commerce into one category and comparing it with the humanities and other streams. The results, presented in Table 2 , show a statistically significant female disadvantage of about 20 percentage points in the choice of STEM/commerce streams compared with the humanities. This estimate remains stable across different specifications. Although all regressions include observable control variables and SSLC results to control for cognitive ability, we sequentially add fixed effects at the level of the district, village/town, and household. 11

Our final model further includes test scores from the 2005 survey. 12 Among all boys and girls, 50% study STEM/commerce streams; thus, the estimated gender gap translates into a magnitude of 40% of the mean participation, which is substantial. As expected, we find that students who scored better on the SSLC examination are more likely to study STEM/commerce at the higher secondary level. Students who in 2005 scored at the highest level of difficulty in mathematics (i.e., division) also have a higher probability of choosing these quantitative streams. 13 Because the estimate of the gender gap remains significant and stable even after we take into account the variation in cognitive abilities captured by two different measures, the gender gap in stream choice is unlikely to be driven by the intrinsic ability of students.

Robustness Analysis

Our main results reveal a gender gap in stream choice after we control for explanatory factors. We further investigate the intrahousehold differences in outcomes when we include household fixed effects in the analysis. In this section, we test whether our estimates of the intrahousehold gender gap remain robust after we take household structures into account.

First, we consider the relationship of individuals in the household more explicitly. In the sample of adolescents included in the analysis, 84% are children and 12% are grandchildren of the household heads. 14 To ensure that intrahousehold relationships do not confound the effect of gender, all the regressions control for dummy variables denoting an individual's relation to the household head. Moreover, we conduct a sensitivity analysis by restricting the comparison between individuals who are in a similar position within the household; in particular, we compare direct siblings by using a sibling fixed-effects model. 15 The observations pertaining to the siblings sharing the same parents may not be independent because the siblings are likely to have common unobservable characteristics. To address this issue, our model estimates cluster-robust standard errors, allowing the error terms to be correlated among siblings who share the same parents. Results presented in columns 1–2 of Table 3 reveal that the estimates remain almost unchanged in this analysis. In an additional exercise, we restrict the sample to sons and daughters of the household head and estimate the model. We again find a similar estimate of the gender gap, as shown in the last two columns of Table 3 . These analyses establish that the magnitude and precision of the estimated gender gap are not affected by the household structure and relationships among individuals in the household.

Next, we investigate whether the estimates from the household fixed-effects models are generalizable. Because the coefficients in these models are estimated using variation within households, observations belonging to households with multiple children contribute to this estimation. Moreover, for identification of the intrahousehold gender gap in stream choice, at least some of these households must have both multiple children and children of opposite gender. If the characteristics of these households systematically vary from those of the overall sample, then the estimates may not be generalizable. 16 To address this issue, we adopt IPW, which has been widely used in the literature in similar contexts ( Fitzgerald et al. 1998 ; Jones et al. 2006 ; Wooldridge 2010 ). This estimation technique follows two steps. In the first step, using our main sample of 5,203 adolescents, we model the probability of belonging to a household with multiple children, conditional on a set of covariates. These covariates include the observable explanatory variables used in Eq. (1) and their interaction with the gender dummy variable. In the second step, we use the inverse of these predicted probabilities as weights for the observations while estimating a household fixed-effects model restricting the sample to those households with multiple children. In another instance, we apply the IPW model for households with multiple children of opposite gender for the second step.

The findings of this robustness analysis are summarized graphically in Figure 3 , which juxtaposes the estimates that do not use IPW with those using IPW. We find that the point estimates and the confidence intervals are remarkably similar even after we use IPW to correct for any potential nonrandom selection of households when fixed effects are used. This analysis bolsters our main results and indicates that the estimated gender gap is robust to the issue of sample selection.

Heterogeneity Analysis Exploring the Determinants of the Gender Gap

To explore what drives the gender gap in stream choice, we augment our main empirical model (i.e., Eq. (1) ) by including interaction terms of gender with some key explanatory factors. Estimating how the effect of gender varies along with these factors sheds light on the underlying determinants of the gender gap. We investigate variations with respect to factors that have high contextual relevance: household affluence, parental educational parity, and access to STEM education. The first two factors are related to the demand for education, and the third factor reflects the supply of education, which is also policy-relevant.

Studying STEM or related streams likely involves a higher cost, which wealthier households are better able to pay ( Chandrasekhar et al. 2019 ). Indian households are also likely to make greater educational investments on boys ( Azam and Kingdon 2013 ; Kingdon 2005 ). Therefore, the higher cost of STEM-related education may discourage households from enrolling girls in such streams, especially when households have limited resources for children's education. To check whether resource constraint leads to gender disparity, we interact the gender dummy variable in our model with household income (per capita). We mitigate the potential endogeneity in household income by using baseline income from the earlier round rather than contemporaneous income. As revealed in Table 4 , household income has no significant effect on the gender gap in stream choice, although households with higher income are more likely to enroll boys in the STEM/commerce streams. 17 Thus, we find that the gender gap is quite pervasive, given that it is observed both in richer and poorer households. This result implies that either resource constraint is relatively less crucial than other determinants of the gender gap, or the gendered preference concerning stream choice does not change with respect to household income.

Because the decision of stream choice is made in adolescence, parents are likely to influence it ( Alderman and King 1998 ; Dustmann 2004 ). In a patriarchal society like India, parental attitudes toward gender equality in education are likely to affect the study choice of girls vis-à-vis boys. To capture this aspect, we next consider parental educational parity, as defined by the difference in years of education between the mother and father. Because mothers usually have lower levels of education than fathers, a greater parity implied by relatively higher education of the mother may reduce the gender disparity in their children's education. By interacting the female dummy variable with parental educational parity in our model, we find support for this hypothesis. On average, a mother has 1.7 fewer years of education than a father; when educational attainment between the parents is equal, it reduces the gender gap in their children's STEM/commerce stream choice by 2.2 percentage points (column 4, Table 4 ).

Another pertinent question from the supply side of education is whether the gender disparity declines when STEM-related education is made more accessible. Although various government policies over the last few decades have universalized access to education at the elementary levels, access to higher secondary education still varies substantially. In addition, educational institutions that offer higher secondary level grades may not offer all the streams. In many places, students have to travel long distances to study their desired stream, especially science or commerce. 18 Although any such variation in access to education is captured by village/town fixed effects in our model, access may have a differential effect on girls than boys. To estimate the differential effect of access by gender, we interact with gender a variable that measures the total number of science and technical colleges in the district at the time the stream choice was made. 19 The results show that districts with a higher number of colleges providing science or technical education have a smaller gender gap in stream choice (columns 5–6, Table 4 ). A 1 standard deviation increase in the number of science/technical colleges per 1 million population in the district is associated with a reduction of 7 percentage points in the gender gap in higher secondary stream choice.

Gender Gap in the Choice of Individual Streams

We also estimate a linear probability model given by Eq. (1) separately for each stream. Table 5 presents results that include village/town fixed effects (panel A) and household fixed effects (panel B). Girls are 20 percentage points more likely than boys to study humanities, as estimated from both models. Underrepresentation of girls is most prominent in science (8.5–10 percentage points), followed by commerce (6–8 percentage points) and engineering/vocational education (about 3.5 percentage points). Ability sorting is also significant across streams: the humanities stream seems to attract students with worse grades in SSLC exam results, whereas science attracts the best performing students. Nonetheless, the effect of gender remains significant even after we take the effect of ability into account.

Our study provides quantitative evidence on the gender segregation in higher secondary stream choice in an emerging economy. In addition to showing that girls are substantially underrepresented in STEM and commerce streams as compared with the humanities, we shed light on the plausible determinants of the gender gap. Our findings are based on data from India, which accounts for a large share of the world's STEM graduates, thus expanding on the literature, which has focused mostly on developed countries. Also, by reflecting on the underlying processes that cause the gender gap, our findings have implications beyond the Indian setting.

A recent international comparison of test scores in mathematics and science found significant heterogeneity in the relative performance of girls versus boys across different countries ( UNESCO 2017 ). Because of the importance of math skills in STEM fields, one may presume that the extent of the gender gap in math performance would predict an underrepresentation of women in STEM fields. However, we show that variations in cognitive skills, as measured by prior exam performance and math test scores, do not subsume the effect of gender on stream choice. In addition, we show that the gender gap in STEM/commerce participation is equally prevalent among richer and poorer households, in line with the pervasiveness of women's underrepresentation in STEM fields across societies with varying levels of economic prosperity. Our results imply that individual performance or household affluence need not be the main determining factor behind gendered educational choices, and it is necessary to consider other background and societal factors in this context.

Exploring the role of other factors, we show that parental educational parity helps to reduce the gender gap in STEM education. This result underscores the influence of parents, especially in settings where streams are chosen at an early age. Unlike the United States, many European countries require students to choose a field of study in secondary school (e.g., see Dustmann [2004] for Germany; see Dahl et al. [2020] for Sweden). That gender parity in parental education encourages girls to pursue a male-dominated field indicates an intergenerational transmission of gender attitude toward education. However, intergenerational mobility may be limited if parental background determines the education choice of the next generation, as shown by Dustmann (2004) in the context of Germany. Hence, there is scope for policies to play an instrumental role in bridging the gender gap by providing equal opportunities to boys and girls. As our results highlight, one avenue through which policies can be effective is by increasing the number of local educational institutions offering STEM and commerce streams, especially in underserved areas. Such an approach is particularly relevant for developing countries, where girls may be disproportionately affected by the lack of access to STEM education.

It is important to point out that there may be other potential determinants that we have not examined here because appropriate data are not available. These factors include individual preferences or behavioral traits; for instance, gender differences in competitiveness may explain the gender gap in STEM choice ( Buser et al. 2014 ). Teachers may also influence stream choice, but without matched teacher-student data, it is not possible to analyze this aspect. The labor market opportunities for women studying different streams can be another relevant determinant. Investigation of these additional determinants constitutes an agenda for future research.

Acknowledgments

We are grateful to the editors and four anonymous reviewers of Demography for their constructive comments that helped us to improve the paper. We thank the participants of GrOW Workshop 2016 at Stellenbosch University, Contemporary Issues in Development Economics Conference 2016 at Jadavpur University, PEGNet Conference 2017 at ETH Zurich, GREThA International Conference on Economic Development 2018 at University of Bordeaux, Sustainability and Development Conference 2018 at University of Michigan, and CSAE Conference 2019 at University of Oxford for helpful comments. We also thank Rahul Lahoti, Abhiroop Mukhopadhyay, Nishith Prakash, Sudipa Sarkar, and Hema Swaminathan for helpful discussions. We gratefully acknowledge funding from the Growth and Economic Opportunities for Women (GrOW) initiative, a multifunder partnership between the United Kingdom's Department for International Development, the Hewlett Foundation, and the International Development Research Centre.

An exception is Sookram and Strobl (2009) , who analyzed this topic for Trinidad and Tobago.

We categorize science and engineering/vocational as STEM. Subjects like accountancy and finance that involve mathematical tools are included in the commerce stream. Hence, some of our comparisons in this paper involve humanities versus nonhumanities, including STEM and commerce. We also report analysis for each stream separately.

Estimates from the data we use suggest that 93% and 85% of students who are currently studying, respectively, engineering and science in college studied a STEM stream at the higher secondary level. Among students studying humanities in college, 85% studied humanities in higher secondary school as well.

The difference in expenditure is mainly driven by higher school fees and private tutoring costs. Compared with humanities students, students in the science and commerce streams pay, respectively, 2.7 and 2.5 times more on school fees and 2.9 and 2.2 times more on private tutoring. Science and commerce students also incur marginally higher expenses on books, school supplies, and transportation, although these expenses are relatively smaller in proportion to the total expenditure.

The IHDS was carried out jointly by the University of Maryland and the National Council of Applied Economic Research, New Delhi. The data set is publicly available. More details can be found online at https://ihds.umd.edu/ .

Strictly speaking, the ages corresponding to the higher secondary level should be 16–17 years. However, we include one year below and one year above this range to allow for the possibility that some children may finish the secondary level earlier or later. The enrollment rate among children aged 16–17 is 74%; however, many of them haven’t yet completed secondary-level schooling. In an alternative specification, we remove the age restriction and estimate the regression for all individuals enrolled in the higher secondary level; the results are unchanged.

Students choose from physics, chemistry, mathematics, and biology/computer science/economics in the science stream; business studies, accountancy, economics, and business mathematics in the commerce stream; and fields such as history, geography, political science, sociology, and philosophy in the humanities stream. In addition, all students study languages at the higher secondary level.

The inclusion of household fixed effects implies that only households with at least two individuals contribute to identification in this regression. To ensure that our estimates are not biased due to the selection of such households, we also present results from analyses excluding household fixed effects. To further address this issue, we check the sensitivity of our estimates using an inverse probability weighting technique in our robustness analysis.

We also check the robustness of the estimates by clustering the standard errors at the level of district and village/town in the earlier specifications. We thereby take into account any potential heteroskedasticity and correlation in the error terms within the clusters ( Angrist and Pischke 2009 : chapter 8).

The sample size is reduced substantially, by about 50%, when we control for past test scores from the previous round of the survey due to many missing values in the variables capturing past test scores (see Table 1 ). Some individuals (about 11% of the sample) could not be found in the 2012 survey, and others may have misreported their age in the previous survey, leading to missing values for test scores among this age group. Later, we show that our estimates are not driven by variations in sample size.

We provide additional estimates based on an intrahousehold comparison in the subsequent section on robustness analysis. The results are also robust to the inclusion of a control variable indicating whether there was a sibling who married and left the household (results not shown).

Although the sample size drops after the inclusion of past test scores, which are not available for the entire sample, a comparison yields no significant difference in key variables between the entire sample and the reduced sample. Also, if we estimate regressions from columns 1–3 ( Table 2 ) on the reduced sample, the estimates are almost unchanged. See Table A1 in the online appendix.

We do not find any significant effect of other measures of cognitive ability (i.e., reading and writing scores) on stream choice. This result is consistent with Arcidiacono’s (2004) finding that math ability was more important than verbal ability in explaining sorting into particular majors in the context of the United States.

In addition, 2% are nephews/nieces of the household head, and the sample reflects very few other relationships (each less than 1%), such as daughter-in-law, brother/sister, or other relatives.

These direct siblings share the same parents. In India, sometimes multiple families coreside in a household, forming an extended or joint family; hence children from multiple parents may be coresiding in a household. A sibling fixed-effects model controls for heterogeneity across different parents within a household; few such cases are found in the sample, however, given that 84% of the sample is formed by children of the household head.

A comparison of the key characteristics between the sample of households with multiple children and the whole sample is provided in Table A3 in the online appendix. For the samples in the stream choice analysis (i.e. 15- to 18-year-old adolescents enrolled in higher secondary schooling), there is no significant difference in the mean of the outcome variable (i.e., stream choice) across these households. However, some of the household characteristics predictably differ across these samples, although the differences are not large.

We find similar results if instead of household income we use household wealth measured by durable assets.

Data from the 2014 NSS show that in rural areas, 43% and 41% of girls in, respectively, the science and commerce streams have to travel more than 5 kilometers to reach their schools; 30% of girls who study humanities travel more than 5 kilometers for school.

We use data from the All India Survey of Higher Education to construct this variable. The measure is lagged with respect to the individual’s stream choice decision and is normalized by the population of the district.

Supplementary data

Data & figures.

Fig. 1 National-level statistics on stream enrollment at the higher secondary level, by gender. Source: Authors' calculations using National Sample Survey (NSS) data 2007–2008 and 2014. All individuals ages 15–18 years who are enrolled at the higher secondary level are considered. The 2007–2008 NSS reported only the main three streams because of very low enrollment in the “other” category, which is included in 2014 data.

National-level statistics on stream enrollment at the higher secondary level, by gender. Source: Authors' calculations using National Sample Survey (NSS) data 2007–2008 and 2014. All individuals ages 15–18 years who are enrolled at the higher secondary level are considered. The 2007–2008 NSS reported only the main three streams because of very low enrollment in the “other” category, which is included in 2014 data.

Fig. 2 Kernel density estimates of log of annual and hourly earnings of individuals aged 25–60 years, by higher secondary stream choice and gender. Source: Authors' calculations using 2011–2012 IHDS data. All working-age individuals (ages 25–60 years) are considered for these plots.

Kernel density estimates of log of annual and hourly earnings of individuals aged 25–60 years, by higher secondary stream choice and gender. Source: Authors' calculations using 2011–2012 IHDS data. All working-age individuals (ages 25–60 years) are considered for these plots.

Fig. 3 Household fixed-effects (FE) estimates addressing potential sample selection using inverse probability weighting (IPW). Household FE Model 1 considers households with multiple children ages 15–18. Household FE Model 2 further restricts the sample to households with multiple children of the opposite gender age 15–18. The coefficient on female is reported from regressions that control for age, birth order, number of siblings, parental education, relationship with household head, and measures for ability. The ability measure includes secondary exam results in one set of regressions, and both secondary exam results and past test scores in another set of estimations. Two types of estimates are reported for comparison: estimates with and those without inverse probability weights (IPW). The table also shows 95% confidence intervals along with the point estimate for the coefficient on gender from the regressions.

Household fixed-effects (FE) estimates addressing potential sample selection using inverse probability weighting (IPW). Household FE Model 1 considers households with multiple children ages 15–18. Household FE Model 2 further restricts the sample to households with multiple children of the opposite gender age 15–18. The coefficient on female is reported from regressions that control for age, birth order, number of siblings, parental education, relationship with household head, and measures for ability. The ability measure includes secondary exam results in one set of regressions, and both secondary exam results and past test scores in another set of estimations. Two types of estimates are reported for comparison: estimates with and those without inverse probability weights (IPW). The table also shows 95% confidence intervals along with the point estimate for the coefficient on gender from the regressions.

Summary statistics of variables from the estimation sample (15- to 18-year-olds)

Sources: IHDS data, 2011–2012. Past scores are obtained from IHDS data, 2004–2005.

Effect of gender on higher secondary stream choice of STEM/commerce (vs. humanities)

Notes: The results are from linear probability models for adolescents ages 15–18. All regressions control for age, birth order, number of siblings, parental education, dummy variables indicating relationship with household head, household size, household wealth, caste dummy variables, religion dummy variables, and whether household lives in a rural area. The regression in column 4 additionally controls for past reading scores (dummy variables are included to denote whether the person can read a word, paragraph, or story) and writing score (whether the person can write). The third division is the omitted category for secondary result. Robust standard errors, clustered at the level of fixed effects (district in column 1, village/town in column 2, and household in columns 3 and 4), are shown in parentheses.

* p < .05; ** p < .01; *** p < .001

Robustness analysis of the effect of gender on higher secondary stream choice of STEM/commerce (vs. humanities)

Notes: The results are from linear probability models taking adolescents ages 15–18. All regressions include control variables, as specified in Table 2 . Regressions in columns 2 and 4 additionally control for past reading scores. Robust standard errors, clustered at the level of fixed effects (sibling in columns 1 and 2 and household in columns 3 and 4), are shown in parentheses.

† p < .10; * p < .05; ** p < .01; *** p < .001

Heterogeneous effect of gender on higher secondary stream choice of STEM/commerce (vs. humanities)

Notes: The results are from a linear probability model for adolescents ages 15–18. All regressions include control variables, as specified in Table 2 . Robust standard errors, clustered at the level of fixed effects (village/town shown in columns 1, 3, and 5; household shown in columns 2, 4, and 6), are shown in parentheses.

Effect of gender on choice of stream at higher secondary level

Notes: The results are from a linear probability model for adolescents ages 15–18. All regressions include control variables, as specified in Table 2 . Robust standard errors, clustered at the level of fixed effects (village/town in panel A and household in panel B), are shown in parentheses.

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Determinants of School dropouts among adolescents: Evidence from a longitudinal study in India

Pradeep kumar.

1 Research & Data Analysis, Population Council, India Habitat Centre, New Delhi, India

Sangram Kishor Patel

2 Population Council, India Habitat Centre, New Delhi, India

Solomon Debbarma

3 Magic Bus India Foundation, Hill Side Tuikhuahtlang, Aizawl, Mizoram

Niranjan Saggurti

Associated data.

Data were collected as part of Population Council’s UDAYA study which is publicly available on the site of Harvard Dataverse (DOI: 10.7910/DVN/RRXQNT ).

Introduction

India has the largest adolescent population in the world. However, many unprivileged Indian adolescents are still unable to complete schooling. Hence, there is a need to understand the reasons for school dropout among this population. The present study is an attempt to understand the determinants of school dropout among adolescents and identify the factors and reasons that contribute to it.

Material and methods

Longitudinal survey data- Understanding Adults and Young Adolescents (UDAYA) for Bihar and Uttar Pradesh has been used to identify the determinants of school dropout among adolescents aged 10–19. The first wave of the survey was conducted in 2015–2016, and the follow-up survey in 2018–2019. Descriptive statistics along with bivariate and multivariate analysis was used to observe school dropout rates and factors associated with it among adolescents.

Results show that the school dropout rate was highest among married girls aged 15–19 years (84%), followed by unmarried girls (46%), and boys (38%) of the same age group. The odds of school dropout among adolescents decreased with an increase in household wealth status. School dropout was significantly less likely among adolescents whose mothers were educated as compared to mothers who had no education. Younger boys [AOR: 6.67; CI: 4.83–9.23] and girls [AOR: 2.56; CI: 1.79–3.84] who engaged in paid work were 6.67 times and 2.56 times more likely to drop out of school than those who were not. The likelihood of school dropout was 3.14 times more likely among younger boys [AOR: 3.14; CI: 2.26–4.35], and it was 89% more likely among older boys [AOR: 1.89; CI: 1.55–2.30] who consumed any substances as compared to those who did not consume any substances. Both younger [AOR: 2.05; CI: 1.37–3.05] and older girls [AOR: 1.30; CI: 1.05–1.62] who acknowledged at least one form of discriminatory practice by parents were more likely to drop out of school than their counterparts. Lack of interest in studies/education not necessary (43%) was the predominant reason among younger boys for school dropout, followed by family reasons (23%) and paid work (21%).

Conclusions

Dropout was prevalent among lower social and economic strata. Mother’s education, parental interaction, participation in sports and having role models reduce school dropout. Conversely, factors such as being engaged in paid work, substance abuse among boys, and gender discriminatory practices towards girls, are risk factors for dropout among adolescents. Lack of interest in studies and familial reasons also increase dropout. There is a need to improve the socio-economic status, delay the marital age of girls, and enhance the government incentives for education, give rightful work to girls after schooling, and provide awareness.

Education is one of the primary determining factors of development for any country [ 1 , 2 ]. It plays a significant role in enriching people’s understanding of themselves and the world. Also, education plays a crucial role in securing economic and social progress and improving income distribution [ 1 ]. No country in the world can achieve sustainable economic development without substantial investment in human capital [ 2 ]. So, considering the need and importance of the education, targets was set at the global level; in Goal 4 of the Sustainable Development Goals (SDG) framework, which talks about quality of education, and one of the targets of this goal is to ensure that all the girls and boys complete free, equitable, and quality primary and secondary education [ 3 ]. Therefore, it is essential to understand how this goal can be achieved and what progress has already been made in this regard. So, pertaining to this; the scenario at the national level as per National Education Policy (NEP) report indicates that the gross enrolment ratio (GER) for grades 6–8 was 91%, while for grades 9–10 and 11–12, it was 79% and 57%, respectively [ 4 ]. Clearly, efforts to bring children within the formal education system through primary schooling have been successful. However, the increasing dropout rates among Indian children, especially after 8th grade, has put the long-term benefits of such gross enrolment into question [ 5 ].

A longitudinal study in the US has shown that adolescent employment and school dropouts are strongly associated after adjusting for the individual- and labor-market-level factors [ 6 ]. Previous literature has also demonstrated that intensively employed students tend to be less academically successful, less engaged in school, and more likely to drop out [ 7 , 8 ]. Moreover, research in north Karnataka revealed that economic factors (household poverty; girls’ work-related migration) were associated with school dropout among adolescent girls [ 9 ]. Another author also substantiates financial obstacles as one of the reasons behind dropout [ 10 ].

Poor learning environment and bullying/harassment at school was found associated with an increased odds of school dropout among adolescent girls [ 9 ]. While, others factors like distance to school, lack of basic facilities, poor quality of education, inadequate school environment and building, overloaded classrooms, improper languages of teaching, carelessness of teachers, and security problems in girls’ schools are major causes of student dropout in different countries [ 10 ]. A cross-sectional community-based study in Raipur, Chhattisgarh, found 11% scholastic dropouts among adolescents [ 11 ]. While, poor academic performance is another determining factor [ 11 ].

Social norms and practices (child marriage; the value of girls’ education) [ 9 ], parents’ unwillingness [ 10 ], socioeconomic status, mother’s education, family violence [ 11 ] and household’s income have significant association with school dropouts [ 12 ]. In one prospective study, it was found that social relations were strongly related to the non-completion of secondary education. For example, 18-year-old girls who found family conflicts difficult to handle had a 2.6-fold increased risk of not completing secondary education. Moreover, young people from low-income families were almost three times more likely to not complete secondary education than those from high-income families [ 13 ].

Earlier literature has established a link between adolescents engaging in non-academic risky behaviors (e.g. delinquency, drug, alcohol, or cigarette use; sexual involvement, and unintended pregnancies) [ 14 – 17 ], substance abuse [ 11 ] and subsequently dropping out of high school [ 15 ]. A panel data analysis shows that children whose parents did not participate in Parent-teacher Association (PTA) meetings, discuss academic progress with school teachers, and supervise their children’s homework in the first round had a higher risk of dropout in their adolescence (round II) [ 5 ]. Poor relations with teachers and classmates at age 18 explained a substantial part of the association between income and dropouts among both girls and boys [ 13 ]. A longitudinal study found that students’ academic and behavioral engagement and achievement in 10 th grade were associated with a decreased likelihood of dropping out of school in 12 th grade [ 18 ].

Dropout can lead to several consequences as mentioned in the various studies. One of the studies mentioned that dropout from school is an issue that affects not only students who make this decision but also affects their family, the community, and society as a whole [ 19 ]. Dropping out of school also leads to under-employment and a lower quality of life for young people [ 15 , 20 ]. Globally, a large number of students drop out of school every year [ 21 , 22 ]. While, a significant number of them are found living in poverty or receiving public assistance, imprisoned, unhealthy, divorced, or single parents of children who are likely to repeat the cycle themselves [ 21 , 23 , 24 ]. Dropouts are also at a greater risk of experiencing mental health problems [ 25 ] and delinquency [ 26 ]. However, it is not clear that risky behavior negatively affects educational achievement and increases the risk of school dropout [ 27 , 28 ]. One interesting finding from earlier studies reveals that boys who dropped out of school generally worked on family farms, entered the labor market, or undertook vocational training, whereas girls tended to marry [ 29 , 30 ].

A few decades ago there was a global call to ensure ‘education for all’ under Millennium Development Goal 2, and now under SDG 4 emphasis is on quality of education; but school dropouts continue to increase in low- and middle-income countries [ 31 ]. School dropout is very common in rural India due to various underlying factors. On the other hand, India has the largest adolescent population in the world [ 32 ]. This population can benefit the country socially, politically and economically, if they are healthy, safe, educated and skillful. However, many unprivileged Indian adolescents, particularly girls, are still unable to complete schooling. Hence, there is a need to understand the reasons for school dropout among this population. There are a good number of research papers on school dropout in India, but very few focus on the adolescent population. Problems like school dropout can be a major factor in determining adolescents’ future perspectives regarding personal and social achievements. The present study is an attempt to understand the determinants of school dropout among adolescents and identify the factors and reasons that contribute to it.

Data and methods

This study utilized data from the unique longitudinal survey of adolescents aged 10–19 (Understanding the lives of adolescent and young adults study—hereafter referred to as UDAYA study/survey) in Bihar and Uttar Pradesh. The first wave of the survey was conducted in 2015–2016, and the follow-up survey in 2018–2019. A state-representative sample of unmarried boys and girls aged 10–19 and married girls aged 15–19 was collected in the 2015–16 survey. The study used a multi-stage stratified sampling design to draw sample areas for rural and urban areas separately. In each state, 150 primary sampling units (PSUs)—villages in rural areas and census wards in urban areas—were chosen as the sampling frame, based on the 2011 census list of villages and wards. Households to be interviewed were chosen by systematic sampling in each primary sampling unit (PSU). Each PSU was subjected to a comprehensive mapping and household listing operation (or in selected segments or linked villages as appropriate). The PSU was divided into two nearly equal segments based on the list; one segment was randomly chosen for conducting interviews of females, and the other for interviews of males (married girls were interviewed from both male and female segments). Detailed information about data collection, sampling design of the study has been published elsewhere [ 33 ]. The field investigators interviewed 20,594 adolescents using a structured questionnaire; the response rate for the survey was 92 percent, and 1% of selected respondents refused to participate.

In 2018–19, the study re-interviewed those who were successfully interviewed in 2015–16, and who gave consent. The UDAYA study re-interviewed 4,567 boys, and 12,251 girls out of the 20,594 respondents who were eligible. The final follow-up sample consisted of 4,428 boys and 11,864 girls, resulting in an effective follow-up rate of 74% for boys and 81% for girls. The study excluded three percent of respondents who gave inconsistent responses to questions related to age and education during the follow-up survey. The main reasons for loss-to-follow-up were that the participant had migrated (10% for boys and 6% for girls), and the participant or his/her parent or guardian refused (7% for boys and 6% for girls). We note that the characteristics of those who were re-interviewed and those who could not be re-interviewed differed significantly in terms of age, education, place of residence, caste, and religion (see Table 1 in S1 Appendix for attrition bias). The analysis presented in this paper drew on data from the subset of adolescents. The present study considered the sample of adolescents who were enrolled in school at wave 1. The sample size for boys was 3676 and 6178 for girls.

Outcome variable

School dropout was the outcome variable of this study. It was defined as a binary variable (yes/no)—whether adolescents dropped out of school between wave-1 and 2. Data pertaining to school dropout was obtained from binary indicators of the school enrolment status collected during both waves of UDAYA. The study included only those adolescents who were enrolled/correspondence in a school during wave 1 [ 5 ]. Adolescents who were enrolled in school during wave-1 but not during wave-2 were classified as “yes” (school dropout), while those who were enrolled in both waves were classified as “no” [ 5 ].

Exposure variables

The explanatory variables included in this study were: place of residence, caste, religion, wealth index, mother’s education, engaged in paid work, substance use, state, role model, parental interaction, participation in sports activities, and gender discriminatory practices at home. Place of residence was classified as urban and rural. Caste was categorized as scheduled caste/tribe, other backward class, and others. Religion was grouped into two categories: Hindu and non-Hindu. Household wealth index was constructed based on selected durable goods and amenities with possible scores ranging from 0–57; households were then divided into quintiles, with the first quintile representing households of the poorest wealth status and the fifth quintile representing households with the wealthiest status [ 34 ]. Mother’s education was coded as ‘no education’ and ‘educated’. Work status (paid work in last one year) was coded as no and yes. Substance use included consumption of tobacco products, alcohol, and drugs; if the respondent consumed any one of the products, it was coded as “yes”, otherwise “no”. The survey was conducted in two states—“Uttar Pradesh” and “Bihar”. Adolescents reported having a role model (Yes/No). The role models reported were categorized as family members/relatives, teachers, professionals, friends, army/police, sports personalities, friends, actors, politicians and others. Adolescents were considered to have parental interaction (yes/no) if they discussed any of the following topics with their mother or father in the year preceding the interview—school performance, friendship, experience of bullying, physical changes during adolescence, or how pregnancy occurs. Participation in sports activities was coded as ‘yes’ and ‘no’. The respondent was asked—“Do you play any sports or games or engage in physical activities like walking, skipping, running, yoga, etc.?” Respondents were also asked if they experienced any gender discriminatory practices at home where parents favored sons over daughters in any of the following situations—the quantity or quality of food items given, the amount of pocket money given, the type of school in which they were enrolled, and parental aspirations for the respondent’s education [ 34 ].

Statistical analysis

Descriptive statistics were used to observe the school dropout rates among adolescents. Moreover, bivariate analysis was done to find the factors associated with school dropout. A chi-square test was performed to test the significance of the association between outcome variable and predictors of school dropout. Finally, a binary logistic regression analysis was used to observe the relationship between school dropout and other explanatory variables.

The equation for logistic distribution

Where, β 0 ,….., β n , are regression coefficients indicating the relative effect of a particular explanatory variable on the outcome variable. These coefficients change as per the context in the analysis in the study.

Ethics approval and consent to participate

The study protocol was approved by the Institutional Review Board of the Population Council. We took several measures to ensure that research ethics were strictly followed. Interviews of boys and girls were undertaken in separate segments of each primary sampling unit to avoid any risk of teasing, harassment and harm to girls’ reputation if interviews of boys and girls were conducted in the same geographical segments. Interviews were conducted separately but simultaneously in cases more than one respondent was selected from a household. In order to minimise discomfort during questioning, the scenarios and terminologies described by adolescents were adapted for use in our questionnaire on sensitive topics. Based on our earlier experiences of working with young adolescents, we made the survey questions age-appropriate—for example, we did not ask about sexual and reproductive health matters with young adolescents. Interviewers underwent extensive training in ethical issues, and teams were instructed to apprise community leaders about the study and seek their support for its implementation in the community. Consent was sought from each individual to be interviewed, and for unmarried adolescents aged 10–17, consent was also sought from a parent or guardian. Names were never recorded in the computer form in which data were collected. In order to preserve the confidentiality of the respondent or the parent/guardian, signing the consent form was optional; however, the interviewer was required to sign a statement that she or he had explained the content of the consent form to the respondent or parent. Interviewers were instructed to skip to relatively non-sensitive sections in case the interview was observed by parents or other family members, call upon a fellow interviewer to conduct parallel discussion sessions with bystander, conduct interviews in locations that offered privacy for the interview and terminate interviews if privacy could not be ensured. Finally, the study team approached NGOs that conduct youth or health-related activities at the district level, help lines that work at national or sub-national levels and public health authorities and referred study participants in need of information or services.

Sample distribution of the study population ( Table 1 )

Note: Wave 1 refers to 2015–16

A higher proportion of adolescents lived in rural areas (81–88%), belonged to Hindu religion (82–96%), and more than half of the adolescents belonged to other backward classes (53–67%). About one-third of the adolescents’ mothers were educated (30–40%). A higher percentage of older adolescents engaged in paid work irrespective of their gender. About half of the older adolescents had a role model. A high percentage of adolescents had parental interaction and participated in sports activities.

Fig 1 shows that the overall school dropout rate was highest among married girls aged 15–19 years (84%), followed by unmarried girls (46%), and boys (38%) of the same age group.

An external file that holds a picture, illustration, etc.
Object name is pone.0282468.g001.jpg

*overall dropouts: those who were in schooling/correspondence at wave 1 and discontinued at wave 2.

Table 2 presents bivariate association of school dropout among adolescents (different age groups and gender) and their background characteristics. Results showed that school dropout was significantly higher among older boys (39%) and girls (49%) who lived in rural areas compared to those who lived in urban areas. Caste has a significant association with adolescents’ school dropout. For instance, dropout was more prevalent among adolescents who belonged to SC/ST caste than other castes irrespective of their age and gender. Household wealth has a negative relationship with school dropout among adolescent boys and girls; the dropout was significantly higher among both adolescent boys and girls who belonged to the poorest wealth quintile and it decreases with increase of wealth status of the households. Mother’s education also has a significant association with school dropout among adolescents—it was more prevalent among both adolescent (younger and older) boys and girls whose mother had no education. Adolescents who engaged in paid work experienced higher school dropout than those who were not. The most significant difference (paid work and not in paid work) was observed among older boys (59% vs. 33%) and girls aged 15–19 years (42% vs. 21%). Similarly, both younger (41%) and older boys (51%) who consumed any substance had a significantly higher likelihood of school dropout than those who did not. Dropout among younger boys was significantly higher in Bihar (20%), however, it was higher in Uttar Pradesh among married girls (90%). Dropout was lower among adolescents who had any role model irrespective of their age and gender. Parental interaction and participation in sports among unmarried adolescents were significantly associated with school dropout–those who played sports and interacted with parents were less likely to drop out of school.

Note: p-values are based on chi-square test; N/A: Not applicable

Estimates from logistic regression analysis for school dropouts among adolescent boys and girls ( Table 3 )

@: reference category

***p<0.0001

**p<0.05

*p<0.10; AOR: adjusted odds ratio; CI: confidence interval; N/A: Not applicable

The likelihood of school dropout was significantly higher among older girls who lived in rural areas as compared to their urban counterparts [AOR: 1.30; CI: 1.12–1.50]. Moreover, the odds of school dropout were significantly higher among both boys (younger-AOR: 1.77; CI: 1.16–2.69; older-AOR: 1.54; CI: 1.16–2.05) and girls (younger-AOR: 1.78; CI: 1.20–2.64; older-AOR: 1.38; CI: 1.16–1.63) who belonged to a non-Hindu religion as compared to those who belonged to Hindu religion. The likelihood of school dropout among adolescents decreased with an increase in household wealth status. Mother’s education plays a significant role in reducing school dropout among adolescent boys and girls and married girls. School dropout was significantly less likely among adolescents whose mothers were educated as compared to mothers who had no education. Younger boys [AOR: 6.67; CI: 4.83–9.23] and girls [AOR: 2.56; CI: 1.79–3.84] who engaged in paid work were 6.67 times and 2.56 times more likely to drop out of school than those who were not. Similarly, the risk of school dropout was significantly more likely among older boys who engaged in paid work than those who were not engaged in paid work [AOR: 2.86; CI: 2.35–3.49]. The likelihood of school dropout was 3.14 times more likely among younger boys [AOR: 3.14; CI: 2.26–4.35], and it was 89% more likely among older boys [AOR: 1.89; CI: 1.55–2.30] who consumed any substances as compared to those who did not consume any substances. The odds of school dropout were 65% higher among younger boys who belonged to Bihar [AOR: 1.65; CI: 1.21–2.27]. The risk of school dropout was 22% and 13% less likely among older boys and girls, respectively, who had a role model than those who did not have. Moreover, parental interaction and participation in sports activities were significant predictors of dropout among adolescents. Both younger [AOR: 2.05; CI: 1.37–3.05] and older girls [AOR: 1.30; CI: 1.05–1.62] who acknowledged at least one form of discriminatory practice by parents were more likely to drop out of school than their counterparts.

Reasons for school dropouts among adolescent boys and girls, and married girls ( Table 4 )

ⴕ included got job and work for payment in cash or kind

¥ included household work, work on form/family business, care of siblings, and illness or death of a family member

£ included school too far away, no proper school facilities for boys and girls, transport not available, costs too much, not safe to send girls/boys and poor quality of teaching/education

€ included illness and not consider education/further education is necessary

₭ included, pregnancy related reason for girls and others; @: frequency less than 25; N/A: not applicable.

Lack of interest in studies/education is not necessary (43%) was the predominant reason among younger boys for school dropout, followed by family reasons (23%) and paid work (21%). Among older boys, paid work (32%) was the primary reason for school dropout, followed by lack of interest in studies/ education not necessary (29%). Among younger girls, family reasons (31%) were the main factor for school dropout, followed by school-related reasons (31%) and lack of interest in studies/ education is not necessary (26%). In contrast, school-related reasons (32%) played a significant role in school dropout among older girls, followed by family-related reasons (26%) and failures (23%). Among married girls, getting married/engaged (38%) was the major reason for school dropout, followed by failures (25%) and family-related reasons (23%).

Adolescents who lived in rural areas, belonged to SC/ST caste group, belonged to the poorest wealth quintile, and whose mother was not educated, reported more family-related reasons for school dropout compared to their counterparts irrespective of their gender and marital status. Similarly, personal reasons for school dropout were reported more by unmarried adolescents who lived in rural areas, belonged to a lower caste group, and whose mother was not educated ( Table 5 ). Moreover, paid work as a reason for school dropout was more reported by boys who lived in rural areas, who belonged to non-Hindu religions, and whose mother was uneducated compared to their counterparts ( Table 6 ).

This study examines the determinants of school dropout among adolescents in Uttar Pradesh and Bihar, based on data from the longitudinal UDAYA (Understanding the lives of adolescents and young adults) study. School dropout cannot be justified by one single reason; rather, it has several contributing factors. The main finding of this study highlights that dropout was high among married girls, and in rural areas, it was high for both boys and girls. Higher the social (Caste) and economic (Wealth quintile) strata lower the dropout rate and children from other religious background (other than Hindu) were found to have higher dropout rates in the study area. Dropout was also high among those who were engaged in paid work. Mother’s education and parental interaction were found to reduce dropout rates and the same is true with the participation in sports activities. The main reasons for dropout are ‘not interested’ in studies, family reasons, paid work and personal reasons.

At the global level, sustainable development goals have identified girl’s education as a priority, but the present study among adolescents found that school dropout rate was higher among married girls, followed by unmarried girls and younger boys. There are several possible reasons for this–an earlier study found that in Bihar, girls are married at an early age (Paul, 2021). This is further qualified with the finding that risk of dropout among girls was associated with marriage [ 35 ]. Moreover, it was found that Indian households invest equally in boys and girls at primary school level, but at secondary level of education sons are given priority above girls to study further [ 36 ]. Costs of education at secondary level are higher, which may be the factor for girls to discontinue [ 37 ].

For many health indicators, the reason for rural-urban differential is mainly due to socio-economic status of the household and parent’s education [ 38 , 39 ]. Similarly, we may attribute the higher dropout of older boys and girls in rural areas as compared to their urban counterparts to the low socio-economic status and parental education. A majority of families in rural areas are economically poor and may have food insecurity, which results in children engaging in farming and household work, thus leading to dropout [ 40 – 44 ].

Caste had a significant association with adolescent school dropout and it was prevalent more among lower social strata. This result may be substantiated by findings of UNICEF & UNESCO 2014, Prakash, Bhattacharjee, Thalinja, & Isac, 2017, wherein higher dropout rates were seen among adolescent girls of low income families living in rural areas, and belonging to a lower caste [ 9 , 45 ]. Children from different caste groups do not attend classes together, and that can lead to dropout of lower caste groups [ 46 ]. Moreover, children of scheduled caste have intrinsic disadvantages that result in less chance of going to school, even after controlling factors like wealth, parental education and motivation, and school quality, etc. [ 47 ].

Dropout was higher among adolescent boys and girls who belonged to the poorest wealth quintile—similar results have been found in other studies [ 10 , 37 , 48 ]. Furthermore, poverty interacts with other social disadvantages and pressures vulnerable children to dropout [ 49 ]. Mother’s education has a significant impact on school dropout. As found in an earlier study, children of educated parents are likely to continue schooling for longer [ 49 ]. While a mother’s educational level influences length of the girls schooling, it has also been found that illiterate parents are unable to guide their children and that results in low performance and school dropout [ 42 ].

This study found that dropout was higher among those who were engaged in paid work rather than unpaid work. As found by Agarwal, many Indian households engage in different kinds of work from an early age to support their families—girls often work as wage laborers and help their mothers in household work, and girls who engage in work frequently remain absent [ 50 ]. Time spent on paid or domestic work may leave children with less time for school and learning—as a result, paid work or domestic work leads to school dropout as found in earlier research [ 51 ].

The study found that parental interaction among unmarried adolescents plays a significant role in reducing school dropout. Parent-child interaction can help to encourage schooling and to work hard, especially among low social and economically disadvantaged families who otherwise suffer from lack of motivation and low self-esteem. Another significant finding of this study is that participation in sports activities reduced the school dropout among adolescents. This is consistent with the results of the previous literature [ 52 , 53 ]. Schools/colleges provide the platform to students for sports activities and this might be the reason for fewer dropouts among adolescents who participated in sports activities. Moreover, the present study revealed that both younger and older girls who acknowledged at least one form of favorable discriminatory practices towards boys by parents had higher chances of school dropout. Previous research also shows that gender discrimination is a major reason for school dropout along with poverty and domestic or household responsibilities [ 54 ]. The states of Bihar and Uttar Pradesh have a patriarchal value system and an earlier study shows that socio-cultural issues pertinent to gender imbalance, a patriarchal value system, and educational issues disfavored female students [ 55 ].

The present study found that engagement in paid work among adolescent boys was the major reason for school dropout. However, among girls, family-related reasons are predominant. Lack of interest in studies was another reason for dropout among adolescents. These findings are consistent with previous literature wherein multiple household duties for girls, early marriage, and poverty were the main reasons for school dropout [ 56 – 59 ]. Conversely, other studies cited financial difficulties as a reason for dropping out for both girls and boys [ 56 , 58 , 60 ].

The study has a few limitations and strengths. This study is based on two Indian states (Uttar Pradesh and Bihar), limiting the generalizability of the findings. The dropout rate may be an overestimate because of the short interval of the survey. Unmeasured factors may have biased the results. For example, information on the educational attainment of adolescents’ fathers and their occupations were not available in the study. The variable—‘parental interaction’ was constructed based on the discussion of following topics—school performance, friendship, experience of bullying, physical changes during adolescence, or how pregnancy occurs—with either the mother or father in the year preceding the interview. There is no direct question related to parental interaction on education matters or their involvement in school activities. Finally, more research is needed to understand the socioeconomic, familial, and other school-related characteristics of adolescents. Despite these limitations, this study has the strengths of a prospective design, the longitudinal nature of data, and large sample size which allows examination of a detailed picture of school dropout, and the use of multiple covariate adjustments.

In conclusion, it is found that substance use, engagement in paid work, and gender discrimination in families are the risk factors for school dropout. Conversely, factors such as higher economic status, mother’s education, having a role model, parental interaction, and participation in sports activities, are protective factors that reduce dropout among adolescent boys and girls. Girl’s schooling is a serious concern and there is a need for immediate action. This study found higher dropout rates in rural areas, specifically among girls. From this finding, one could estimate some of the underlying factors of dropout as follows—in rural areas parents are mostly illiterate and unaware about the importance of education, which results in a lack of parent-child interaction. Further, in rural areas, most households are economically poor and socially backward, so this may lead to early child marriage and pressure to engage in paid work. For boys, substance abuse is a major contributing factor towards dropout. Hence, all of these factors directly or indirectly affect dropout, and this study confirms these factors by citing existing literature.

Lastly, to reduce dropout of girls in particular, it is essential to stop child marriages and give awareness to the parents and improve socio-economic status. This can be achieved by giving rightful work to girls after their education, so that both the children and the parents will be motivated. There is also a need for gender sensitivity. The government should give proper awareness and improve girls’ incentives for education and introduce some programs that focus on the return of married girls to school.

Supporting information

S1 appendix, acknowledgments.

The authors are grateful to Sanjay Patnaik for his editorial support on the earlier version of this paper. The authors would also like to acknowledge the contributions of other members of the UDAYA study team at the Population Council.

Funding Statement

The authors received no specific funding for this work.

Data Availability

PLoS One. 2023; 18(3): e0282468.

Decision Letter 0

21 Nov 2022

PONE-D-22-13862Transitional School Dropouts among Adolescents: Evidence from a Longitudinal StudyPLOS ONE

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Reviewer #1: This paper uses a unique longitudinal data set to explore factors underlying school discontinuation in two states of India. Data are state representative and the two waves of the survey were conducted in 2015-16 and 2018-19. Explanatory factors are a range of socioeconomic characteristics, as well as individual characteristics reported in Wave 1, and discontinuation in the intervening period is assessed from Wave 2 data. Findings suggest that substance use, paid work participation, and gendered socialisation place adolescents at elevated risk of discontinuation, while household economic status, maternal education, as well as individual factors such as having a role model, interaction with parents, participation in sports activities have a protective effect.

While this is an interesting topic, my main concern is that currently, the paper is somewhat superficial, hypothesis are not well articulated, and findings not interpreted in sufficient depth. Evidence on five groups of adolescents (10-14, 15-19, unmarried boys and girls, married girls) is provided, but aside from pointing out that married girls are an outlier, gender and age differences suggested by determinants are hardly discussed, and the discussion disappointingly does not offer hypotheses for similarities/differences. Socioeconomic background factors (rural-urban residence, economic status, religion, caste etc are well known factors influencing schooling. But while confirming the relationship using longitudinal data is no doubt interesting, what is new and exciting about these findings is that parent-child indicators (interaction, socialisation, perhaps even mother’s education as a proxy for education) and even individual behavioural factors (substance use, having a role model, engaging in sports) are key factors influencing school discontinuation, even after confounding background factors are controlled. I would strongly recommend that the paper is recast to highlight the importance of these factors in explaining school dropout, if the same relationship emerges when other concerns described below are taken into consideration. These concerns are:

1. I found the title baffling. What is meant by “transitional” school dropouts? The paper does not explain, and I would recommend a clear title, perhaps just “Determinants of school dropout…”

2. The literature review is somewhat disjointed, it needs to be reorganised so as to synthesise what the leading correlates/determinants are, rather than just describe various articles and their conclusions.

3. The dropout indicator needs to be clearer:

a. In Table 2, the three indicators of drop out shown need to be better explained. Is an older adolescent who has completed Class 10 or Class 12 and has discontinued his/her education considered a dropout, and if so, why? Surely a cutoff of Class 10 (or 12) should not denote dropout. Perhaps this is already done, but if so, it is not described. If not done, authors need to redo their analysis, or at most, justify their use of this broad indicator.

b. Table 2 shows three measures of discontinuation – its not clear to me why overall dropout is so much greater than the other two indicators for older adolescents?

c. How is “dropout” operationalised in the multivariate analyses? Three different indicators are provided in Table 2, some clarity needed. If we assume that the minimum required level of education is Class 10 in order for adolescents to make a successful transition to adulthood, then this, or the more stringent dropout before Class 12, should be used.

4. Findings are interesting, showing that even after place of residence, religion/caste and household economic status are controlled, several reflecting parent-child relations (interaction, gendered socialisation) and individual (substance use, engaging in sports, having a role model), and confirming that both domains are important determinants of school discontinuation. However, some refinement would be helpful among the explanatory variables:

a. Parental interaction and gendered socialisation are described as dichotomous indicators, but each comprises a number of areas of interaction or gendered socialisation. Authors need to be clear – does the indicator reflect at least one of these, or does it refer to interaction on all activities probed, or gendered socialisation on all the situations discussed. Table 1 suggests that parental interaction (>80% report interaction) in particular may well be scaled or at least modified to reflect interaction on all/some items.

b. Maternal education is an important determinant of child outcomes, and it would be good, if a sufficient number of better educated women is available, to show at least three categories of education.

c. Just 1-3% of all groups aside from older boys used substances in the first survey. Does it make sense to include this indicator in the study? And what does substance mean – alcohol? Drugs? Tobacco?

5. Could authors include in the analysis any variable(s) that reflect school related obstacles to continuation, as measured in the first survey?

6. Reasons for dropout are interesting, but would authors like to reconsider the reasons clubbed under various headings. For example, “other” represents a huge chunk (19-31%), but the items shown appear to be school related reasons (no transport, cost, not safe…), why not include these as school related reasons? Likewise, education not considered necessary is quite different from illness, and should be separated.

Overall, this is an interesting paper, with new and exciting findings derived from a unique dataset. It has the potential to contribute to what is known about factors influencing premature school discontinuation in India using a far more relevant set of explanatory indicators than are typically available. However, a clear hypothesis needs to be articulated, and interpretation of findings, including gender and age similarities and differences, needs to be more thoughtful. Author may want to clarify some of the comments noted above.

Reviewer #2: The paper examines drop out decisions in India with a focus on Bihar and Uttar Pradesh, two most educationally backward states of India. To their credit, the authors also use longitudinal data. According to the manuscript, between round 1 and round 2 (or 2018-19 vs 2015–16), the UDAYA study had an effective follow-up rate of 74% for boys and 81% for girls.

So those who dropped out from school b/c of out-migration are mostly absent from the very sample that the authors used to model drop out decisions. Therefore, it is important to discuss the implications of lost to follow up from different characteristics on the drop out from school and the study findings.

Further, authors may highlight the key findings in the conclusion section for better understanding of the manuscript results. N/A need to define in the footnote of the tables.

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Author response to Decision Letter 0

Response: Thanks for the suggestion. Amendment has been done.

Response: Modification has been made as per the suggestion in revised manuscript.

Response: Here in this study, School dropout was defined as whether adolescents dropped out of school between wave-1 and 2. Adolescents who were enrolled in school during wave-1 but not during wave-2 were classified as school dropout, while those who were enrolled in both waves were classified as not dropouts. We have removed other dropouts (10 or 12) for a better understanding of the reader.

Response: We have removed other two measures of school dropouts in the revised manuscript.

Response: Authors are agree with your suggestion, however, if we take class 10 as minimum required level of successful transition then we will lose the sample of 10-14 years adolescent as they are not eligible for 10th standard. Keeping this in mind, authors defined dropouts, who were enrolled in school during wave-1 but not during wave-2.

Response: The parental interaction reflects at least one of these (interaction). The number of interactions on all the activities was very less; therefore, we chose at least one interaction on the items. Similarly, for gendered socialization, we took at least one item for the selection.

Response: The sample was not enough to make three categories of mothers’ education with five-age cohort of the adolescent therefore authors made it into two group.

Response: Substance use is important indicator or one of the reason for school dropouts. Therefore, we took it as a predictor and results show that adolescent who consumed substances had higher likelihood of school dropout. Substance use included consumption of tobacco products, alcohol, and drugs. It is mentioned in the variable description as well.

Response: Authors tried to include all possible available factors in the survey, which affect school dropout/continuation.

Response: Thanks for the suggestion. Amendment has been done in the revised manuscript.

Response: Thanks for the suggestion. This study already articulated the important findings coming from this longitudinal data. However, we again look at the findings through the gender and age differences along with covariates lenses and revised further. The hypothesis is clear to us and stated already in paper that there is differences in school drop outs by gender, age and socio-economic and behavioural characteristics. The findings are also very clearly highlighted those in the manuscript. Add further to it, these findings are also linked to the global call to ensure ‘education for all’ under millennium development goal 2, and now under SDG 4 emphasis is on quality of education. These young population can benefit the country socially, politically and economically, if they are healthy, safe, educated and skilful. However, many unprivileged Indian adolescents, particularly girls, are still unable to complete schooling. Hence, there is a need to understand the reasons for school dropout among this population. There are a good number of research papers on school dropout in India, but very few focuses on adolescent population using longitudinal data. Problems like school dropout can be a major factor in determining adolescents' future perspectives regarding personal and social achievements. The present study is an attempt to understand the determinants of school dropout among adolescents and to identify the factors and reasons that contribute to it.

Response: In UDAYA longitudinal study, in 2018-19, we interviewed again those who were successfully interviewed in 2015-16, and who consented to be re-interviewed. Of the 20,594 who were eligible for re-interview, we re-interviewed 4,567 boys and 12,251 girls. We excluded respondents (3%) who gave inconsistent response to questions related to age and education at the follow-up survey; therefore, the final follow-up sample comprised 4,428 boys and 11,864 girls, thus resulting in an effective follow-up rate of 74% for boys and 81% for girls. The main reasons for loss-to-follow-up were that the participant had migrated (10% for boys and 6% for girls), and the participant or his/her parent or guardian refused (7% for boys and 6% for girls). We note that the characteristics of those who were re-interviewed and those who could not be re-interviewed differed significantly in terms of age, education, place of residence, caste, and religion (see Appendix Table 1 for attrition bias). The analysis presented in this paper drew on data from the subset adolescents.

Appendix Table 1. The characteristics at wave 1 of adolescents who were re-interviewed and who were not

Baseline Variable Respondents lost to follow up Respondents interviewed in the follow-up sample Mean difference

Years of education (mean) 7.33 7.37 0.04

Completed 8 or more years of education (%) 58.70 58.60 0.10

Currently in School (%) 57.00 64.80 7.8***

Mothers level of education (mean) 2.91 2.51 0.40***

Place of residence (%) 45.20 57.50 12.3***

Social group (% SC\\ST) 21.60 24.30 2.7***

Religion (% Hindu) 73.70 80.00 6.3***

HH wealth Score (mean) 22.57 21.51 1.06***

Total number of respondents 4302 16292

*** p<0.01, ** p<0.05, * p<0.1

Submitted filename: Response to Reviewers.docx

Decision Letter 1

16 Feb 2023

Determinants of School Dropouts among Adolescents: Evidence from a Longitudinal Study in India

PONE-D-22-13862R1

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Invitation for Proposals: Development of 2024 State of Education Report for India on Culture and Arts Education

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UNESCO is looking for interested parties for the submission of proposals concerning the development of the 2024 State of Education Report for India focusing on Culture and Arts Education .

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Please ensure to include the RFP reference "NDL/ED/SOER/2024 Author - Development of the 2024 State of Education Report for India on Culture and Arts Education" and the closing date and time in the email heading.

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Clean water leads to education: Understanding water scarcity in rural India

On December 18, 2023, I embarked on a journey as a student on the India Winterim: Water Poverty in Rural India, A Freshwater Crisis Case study abroad program. On this study abroad trip, we were tasked with using water quality sensors to measure the levels of things like nitrate, fluoride, pH, salinity, etc. of water sources found in rural villages of India in partnership with the Sehgal Foundation. The testing days were extremely eye opening as we first traveled to the village of Mewat and split up into groups. Here, we walked around the village gathering quite a crowd of individuals interested in seeing what we were doing. With the help of guides, we talked to farmers and villagers, hearing their stories about how the water quality has impacted their lives. One eye opening experience was to be able to measure salinity around a check dam. The check dam works by allowing rainwater to have a higher retention time on the ground to better seep into the aquifers below. The additional rainwater, in theory, dilutes the pollutants found in the waterways to levels safe enough to drink. It was very inspiring to hear how the check dam has given many individuals in the villages safer water.

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One significant impact that I felt on this trip was the impact of clean water systems on young girls' ability to attend school. We visited a few schools that had rainwater harvesting systems that provide water safe for cooking and use in bathrooms. Due to the increase in sanitary bathroom systems, the number of girls attending school has greatly increased. As a woman who has grown up in the United States my whole life, I have had the privilege of attending public school and university. It was difficult to hear how limited education is for women living in India, but inspiring to see how change is occurring to raise the numbers of girls in school. It has inspired me to be a part of the next generation of environmental engineers to design systems to give clean water access to individuals all around the world.

The main challenge on this trip was the water. We were unable to drink the water in the hotels or at meals unless they were clearly sealed. This left us using bottled water for drinking and even to brush our teeth. I typically use a reusable water bottle in the United States to reduce the amount of plastic waste I am producing and that was not an option in India. I felt guilty using the plastic water bottles as garbage and the plethora of litter is another huge concern in India. This trip really made me appreciate the safe drinking water systems I have from my tap here in Iowa. Overall, I loved my study abroad trip to India. I feel that I left that trip with a newfound passion for environmental engineering and creating innovations that can help ensure safe water around the world. In my career, I wish to become an advocate for women in STEM related majors which was further inspired by seeing the young girls in rural India able to attend school.

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CBSE changes exam format for Class 11-12 from this academic year, to focus on concept application questions

The board said it is continuing with aligning of the assessments and evaluation practices with nep- 2020 for the academic session 2024-2025..

The move is expected to herald a paradigm shift in the way students approach their studies, moving away from rote memorization towards a more holistic understanding of concepts. It is envisioned to empower students to think creatively, innovate, and develop a deeper appreciation for the subjects they study.

Percentage of competency-focused questions increased

While the percentage of competency-focused questions in the form of MCQs, case-based questions, source-based integrated questions or any other type has been increased from 40 to 50 per cent, the percentage of constructed response questions including short and long answers has been reduced from 40 to 30 per cent.

What did CBSE Director (Academics) say?

"The board in accordance with National Education Policy, 2020 has taken multiple steps towards implementation of Competency-Based Education in schools, ranging from aligning assessment to competencies, development of exemplar resources for teachers and students as well as continuous capacity building of the teachers etc," said Joseph Emanuel, Director (Academics), CBSE.

"The main emphasis of the board was to create an educational ecosystem that would move away from rote memorization and towards learning that is focused on developing the creative, critical and systems thinking capacities of students to meet the challenges of the 21st century," he added.

No changes in Class 9-10 exam format

Emanuel said the board is continuing with aligning of the assessments and evaluation practices with NEP- 2020 for the academic session 2024-2025. "Consequently, in the forthcoming session, the percentage of Competency Based Questions that assess the application of concepts in real-life situations included in the question papers of the board has been altered," he said. There is no change in the exam format for Classes 9 and 10, the Director added.

(With PTI inputs)

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Ctet july 2024: ctet is held twice a year. ctet 2024 exam will be held for two papers — paper 1 (for class 1 to 5) and paper 2 (for class 6 to 8)..

CTET July registration 2024: The Central Board of Secondary Education (CBSE) today extended CTET July registration dates. The CBSE CTET 2024 application window opened on March 7. Candidates yet to register can apply online for the CTET July 2024 exam till April 5 (11:59 pm). The Central Teacher Eligibility Test (CTET) will be conducted on July 7, 2024. The last date earlier was April 2.

The CTET official website – ctet.nic.in is hosting an information bulletin containing details of the examination, syllabus, languages, eligibility criteria, examination fee, examination cities, and important dates. The CTET July 2024 exam will be conducted in 136 cities across the country in twenty languages. The CTET 2024 exam will be held for two papers — paper 1 (for Class 1 to 5) and paper 2 (for Class 6 to 8).

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CTET July Registration 2024: How to fill the form

Step 1: Visit the official website of CTET – ctet.nic.in

Step 2: Click on the link “Apply online for CTET July 2024”

Step 3: Click on new registration and register

Step 4: Fill out the application form and note the registration number

Step 5: Pay the exam fee and upload the required documents

Step 6: Submit the form and download it for future reference

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CTET 2024: Registration ends today, candidates can apply at ctet.nic.in for CBSE CTET July exam

CBSE CTET July 2024: The Central Board of Secondary Education (CBSE) will conclude the online registration cum application process for the July edition of the Central Teacher Eligibility Test (CTET July 2024) today i.e. April 2 at 11.59 pm.

CBSE CTET July 2024: The registration process for the CTET July 2024 session started on March 7

The registration process for the CTET July 2024 session started in the first week of last month i.e. on March 7 and the CBSE provided a three-week long period for the aspiring candidates to apply for the teacher’s eligibility test which is a mandatory qualification for seeking opportunity in government-run schools from Class 1 to 8.

CBSE CTET July 2024: Examination Date

As per the CTET July 2024 notification the examination will be held on Sunday, July 7, 2024 in two shifts. The first shift will start from 9.30 am to 12.00 noon and the second shift will start at 2.00 pm and end at 4.30 pm.

The CBSE will administer the test in 136 cities and in twenty languages.

The CBSE has asked candidates to check the information bulletin to know about the examination, syllabus, languages, eligibility criteria, examination fee, examination cities and important dates before applying for the test.

CBSE CTET July 2024: Important Dates

CTET July 2024 Registration Start Date: March 7, 2024

CTET July 2024 Registration Last Date: April 2, 2024

CTET July 2024 Admit Card Release Date: To be announced

CTET July 2024 Exam Date: July 7, 2024

CBSE CTET July 2024: Examination Pattern

The Central Teacher Eligibility Test examination consists of two papers for Primary and Secondary classes of teaching:

Paper-I: Designed for candidates seeking to teach classes 1 to 5 (Primary Stage). It assesses knowledge in Child Development and Pedagogy, Language I (chosen from Hindi, English, Sanskrit, or other languages), Language II (another chosen language), Mathematics, and Environmental Studies.

Paper - II: Aims to evaluate candidates aspiring to teach classes 6 to 8 (Secondary Stage). This paper covers subjects like Child Development and Pedagogy, Language I (chosen from Hindi, English, Sanskrit, or other languages), Mathematics, and two chosen elective subjects from a list including Science, Social Science (including History, Geography, Political Science, and Economics), and optional languages.

CTET July 2024 exam: How to apply

Go to ctet.nic.in.
Scroll down till you find the candidate activity board
Open the apply for CTET July 2024 link.
Register by entering the requested information. Once done, your login credentials will be generated. Use it to log in to your account.
Fill out the application form.
Upload photo, signature and other required documents.
Make payment of the examination fee.
Submit the form and download the confirmation page.
For future uses, save the confirmation page, the scanned image of your signature and the photograph used.

The last CETE exam (18th edition) was conducted on January 21 at 3,418 test centres in 135 cities across the country. The CBSE said 26,93,526 candidates were registered for both papers of the CTET January exam, and around 84% attendance was recorded.

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Research on education in India

Open File Educational Research
Published: September 1999
Volume 29 , pages 335–347, ( 1999 )

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Original language: English

R. C. Mishra (India) D.Phil. from Allahabad University. Professor of Psychology at Banaras Hindu University, Varanasi. Post-doctoral Research Fellow and Shastri Research Fellow at Queen's University, Canada. Visiting Fellow at the Jean Piaget Archives and the University of Geneva. Main research interest is the cultural influence on human development. He has contributed numerous articles to journals and books in the field of acculturation, schooling, cognition and crosscultural studies. He is co-author (with J.W. Berry and D. Sinha) of Ecology, acculturation and psychological adaptation (1996) and co-editor (with J.W. Berry and R. C. Tripathi) of Psychology in human and social development: lessons from diverse cultures (in press).

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Mishra, R.C. Research on education in India. Prospects 29 , 335–347 (1999). https://doi.org/10.1007/BF02736959

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UNESCO is looking for interested parties for the submission of proposals concerning the development of the 2024 State of Education Report for India focusing on Culture and Arts Education.. The Request for Proposal (RFP) encompasses various annexes detailing instructions to offers, general conditions of contract, terms of reference, proposal submission forms, price schedules, vendor information ...
Clean water leads to education: Understanding water scarcity in rural India
On December 18, 2023, I embarked on a journey as a student on the India Winterim: Water Poverty in Rural India, A Freshwater Crisis Case study abroad program. On this study abroad trip, we were tasked with using water quality sensors to measure the levels of things like nitrate, fluoride, pH, salinity, etc. of water sources found in rural ...
CBSE changes exam format for Class 11-12 from this ...
Image Source : INDIA TV Central Board of Secondary Education announced a change in the format on April 4. In a significant move aimed at fostering critical thinking and practical understanding ...
AP SSC Result 2024 expected to be out in first half of ...
Class 10 students can follow these steps to check and download their respective AP SSC Result 2024: Step 1: Visit the official website of the board, i.e., bse.ap.gov.in. Step 2: On the homepage, Class 10 students can click the link that reads, 'AP SSC Result 2024' (once the link is active). Step 3: A new page will appear on the screen.
Determinants of school dropouts in India: a study through survival
The present study aims to study the risk of school dropouts in India using retrospective approach to apply Cox proportional hazard model. Using the 75th round of NSSO data, it is observed that around 74 per cent of population aged 18 years and above have dropped out from school before reaching 12th standard. The survival approach provides strong causal evidence that factors like caste division ...
CBSE extends CTET 2024 July registration deadline
To apply for CTET July 2024, applicants under the General and OBC categories will have to pay Rs 1,000 for one paper and Rs 1,200 for both papers. The registration fee for SC, ST, and differently-abled persons is Rs 500 for the single-paper test and Rs 600 for both papers. CTET 2024 January was conducted on January 21. Over 26 lakh candidates ...
CTET 2024: Registration ends today, candidates can apply at ctet.nic.in
CBSE CTET July 2024: The Central Board of Secondary Education (CBSE) will conclude the online registration cum application process for the July edition of the Central Teacher Eligibility Test ...
Research on education in India
Impact of socio-economic factors on the reading habits of students in the secondary schools in Kerala: a pilot study. In: Project report. New Delhi, Indian Council of Social Science Research. Google Scholar. Gautam, N.P. 1988. A study of productivity-oriented education with reference to the new pattern of education.
SSC CHSL 2024 Recruitment: OTR and Application Module ...
SSC CHSL 2024 Recruitment: The Staff Selection Commission (SSC) has issued a press release notifying candidates about the upcoming Combined Higher Secondary (10+2) Level Examination for the year 2024.