Abstract Book of the 2nd International Conference on Social Sciences
Year: 2025
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Educational Inequality Among the Scheduled Castes and Scheduled Tribes in India in 2022: a State – Wise Analysis
Bhadra A H
ABSTRACT:
Education plays a key role in shaping human capital. It improves productivity, innovation, and long-term economic growth. Research shows that when education is distributed fairly, growth happens more quickly. On the other hand, inequality in education slows investment and development. The benefits of human capital depend on the policy environment, with liberal economies tending to gain more. Public education systems generally support a fairer income distribution than private ones.
Additionally, technological changes can create wider skill gaps, reinforcing the connections between ability, wealth, and growth. In India, Scheduled Castes (SCs) and Scheduled Tribes (STs) still face barriers to education. This is evident in their low literacy rates, high dropout rates, and lack of representation in higher education. While states like Kerala have seen SC/ST literacy rates exceed the national average, challenges remain in technical and professional education.
Enrolment tends to focus on traditional arts and science courses while high educational costs, mostly unrelated to fees, further restrict access. The quality of education is also a problem as SC/ST students often have lower pass rates, underperform in competitive exams, and drop out of professional courses at high rates. Despite constitutional protections and state initiatives aimed at supporting these groups, cultural differences, systemic barriers, and inconsistent implementation have left the educational disparities largely unaddressed. This study aims to study the extent of educational inequality among the SCs and STs in India, focusing on its implications on economic growth and human capital formation. Drawing from the Periodic Labour Force Survey (PLFS) 2022- 23, it employs quantitative methods, including Gini coefficient analysis and a limited dependent variable model, to investigate the extent, patterns, and determinants of educational inequality among SC and ST populations. With the key objective is quantifying the state-level disparities in years of schooling and assessing differences in higher education attainment, this paper also aims to identify the influence of social group identity, gender, and rural–urban location on the issue.
Methodologically, this study applies two quantitative tools. Firstly, it applies the Gini coefficient analysis to measure how schooling years are distributed within each social group at the state level. This adapts the traditional income inequality measure for education. Secondly, it uses a logit regression model to estimate the likelihood of achieving higher education based on social group identity, gender, and sector. PLFS 2022–23 provides large-scale, nationally representative microdata disaggregated by education, social group, gender, and location. This data source will be the best suited for our study as it provides rich socio-economic variables that include education attainment data, labour market outcomes and demographic variables. Variables employed in the analysis include years of schooling, education level categories, and demographic identifiers like area of inhabitance and gender. The study focuses on 22 states selected for ensuring coverage of both high-performing and low-performing regions. State-level Gini coefficients and mean years of schooling are computed separately for SC and ST populations, enabling comparison with the general population and across geographies, while the logit regression identifies statistically significant predictors of higher education attainment. The findings of the paper highlight that ST has recorded the lowest mean years of education accompanied with highest Gini coefficient value of 0.528 indicating wide gap of educational accessibility among this social group. Conversely, the General Category records the lowest inequality with a Gini coefficient value of 0.462 and highest mean years of education (6.754). The OBC category establishes an intermediate position between the SCs and General category necessitating targeted measures aimed at reducing overall educational inequality. The national level aggregate of education, that is, 0.48 suggests moderate educational inequality at the national level, even though it masks the inter group disparities that are persistent. Most importantly, marginal effects from the logit regression model shows that predicted probability of attaining higher education is 21.2% for SC/ST individuals compared to 25.9% for OBC/General category individuals. This logit model purely identifies the effect of social identities on the higher education outcomes of an individual as changes in gender and area of livelihood have been statistically controlled for. By integrating distributional metrics with econometric modelling, this study contributes to empirical literature on educational inequality in India and underscores the importance of addressing social identity–based barriers to learning. The evidence from PLFS 2022 and 2023 will give policymakers a clear, data-based understanding of where help is most needed. It will also highlight the socio-demographic factors that limit access to higher education for marginalized groups, thereby driving focused, evidence-backed strategies for creating a fairer and productive India.
Keywords: Formation Educational Inequality, Scheduled Castes, Scheduled Tribes, Economic Growth, Human Capital