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Gender differences in students’ progress from elementary to secondary education in India: who are performing better?

A Correction to this article was published on 28 August 2021

This article has been updated

Abstract

This paper analyzes gender differences in the progress of students from elementary to secondary education in India by using India Human Development Survey (2004–05 and 2011–12) panel data. Using a logit model analysis, we have examined how post-enrollment, a child’s family background, household educational inputs and process indicators determine his/her elementary and secondary school completion (SSC). Our findings suggest that even after accounting for school accessibility, family socioeconomic status plays an important role in the manifestation of gender inequality in school progression. Secondary school completion has emerged as the major stumbling block for scheduled castes, scheduled tribes and Muslim children, particularly for girls belonging to low-economic-status households. Family educational inputs and student process indicators are also significant influencers of SSC. We find a significant gap in the performance of private and government school children that narrows as family economic status improves.

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Data availability

The data that support the findings of this study are openly available in Inter-university Consortium for Political and Social Research [ICPSR] at https://doi.org/10.3886/ICPSR36151.v6 [doi], V6 [2018–08-08] and https://doi.org/10.3886/ICPSR22626.v12 [doi] [2018–08-08]. Desai, S., & Vanneman, R. (2015). India Human Development Survey-II (IHDS-II), 2011–12. ICPSR36151-v6.Ann Arbor, MI: Inter-university Consortium for Political and Social Research [distributor], 31.

Change history

Notes

  1. The gross enrollment ratio for girls in 2014–15 was 99.20 percent (UDISE, 2015–2016).

  2. The annual average drop rate in secondary education in 2013–14 was 17.86 percent (UDISE, 2015–16).

  3. According to the Ministry of Human Resource Development (MHRD), in 2012–13, state governments allocated 54% of their education budget to elementary education and only 33.84% to secondary education. Central government’s allocation was even more skewed with 54.37% going to elementary education and only 33.84% to secondary education.

  4. IHDS data is a nationally representative multi-topic panel survey that has been conducted in 2004–5 and 2011–12. This panel survey makes the data suitable for tracking the progress of both male and female children over time.

  5. It includes expenditure on school/college fees, private tuition fees, expenditure on books and other educational articles (Azam & Kingdon 2013). It may also include the cost of conveyance from home to school and vice-versa.

  6. Split households are those that got split from the parent household (in 2005) within the two surveys period, and they were staying in different houses in 2011. See IHDS-II User’s Guide for more information.

  7. This selection of sample has taken only those students who were aged 9 to 18 years and excluded the outliers that were below or upper to this age category. Also, the majority of students who were enrolled in the 6th, 7th and 8th classes belong to the age group of 9–18 years.

  8. Thus, the sample size becomes 7132, for which we have done the descriptive statistics analysis.

  9. For instance, 46 percent of students enrolled in upper primary school were female in IHDS-I. This proportion fell to 40 percent in our final ‘tracked’ sample.

  10. The full sample for 2004–05 had 54.5% males and 45.5% females.

  11. These may include cultural practices and norms regarding marriage, which are not explanatory variables in our study. In addition, the existing literature (Marphatia, 2019; Chugh, 2011) suggests that such practices and norms may impact girl children across socioeconomic categories in similar ways.

  12. Under a logit model: P (Yi = 1) / 1–P (Yi = 1) = e (βXi) ⇒ P (Yi = 1) = (e (βXi) / 1 + e (βXi)) = F (βXi) (1) Where: Xi = {Xi j, j = 1... J} represents the vector of observations, for individual ‘i’ on ‘j’ variables, andβ = {βj, j = 1... J} is the associated vector of coefficient estimates (Amemiya, 1981; Greene, 2003).

  13. It is calculated using the margins command in Stata, as suggested by Karaca-Mandic, Norten and Dowd (2012).

  14. The data on ownership of resources as a household asset index is available in IHDS 2004–05 that contains data on different variables of goods and house owned by the household, and the quality of housing. This index is based on the values of 36 different kinds of household assets like 'pakka' or 'kaccha' house, TV, refrigerator, car, laptop/computer and AC.

  15. One reason for this emerging as a choke point may be that the class 10 board exam is the first board exam that has to clear by a student. In other words, this is the first ‘public’ test of a student’s abilities, and the shortcomings in the learning outcomes for the students from the marginalized sections come to the fore, preventing them from continuing to higher secondary school.

  16. Recent data for Delhi schools showed that government schools (passing rate of 90 percent) are performing better than private schools in class 12th results. However, the data also show that more than 40 percent of the government school students dropped out before completing class 9 or 10. Source: https://www.newslaundry.com/2018/06/09/delhi-government-schools-print-filtering-students-aam-aadmi-party.

  17. E.g. Karnataka government has promised to provide free education to those female students studying in the government run educational institutions of the state till the post-graduation level (The New Indian Express, 2018). Punjab has also promised to provide free education for girls from nursery up to doctoral studies (India Today, 2017).

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The original online version of this article was revised: the affiliation of the authors “Deepak Kumar and Bhanu Pratap” was incorrectly published. The corresponding author "Bhanu Pratap" is affiliated with "Patna Women’s College, Patna, Bihar, India" and the first author “Deepak Kumar” is affiliated with “Centre for Economic Studies and Planning, Jawaharlal Nehru University, New Delhi, India.

Appendix

Appendix

See Tables 2, 3.

Table 2 Comparison of descriptive statistics for both ‘final study sample (in 2012)’ and ‘total enrolled children full sample (in 2004–05)’
Table 3 Notation and definition of variables

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Kumar, D., Pratap, B. & Aggarwal, A. Gender differences in students’ progress from elementary to secondary education in India: who are performing better?. Educ Res Policy Prac 21, 217–241 (2022). https://doi.org/10.1007/s10671-021-09302-z

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Keywords

  • Elementary education
  • Secondary education
  • Gender
  • Socioeconomic status
  • Right to education
  • Caste