Ph.D. Research 

Thesis Title: Asymmetric Information in Higher Education in India: A Study of Screening, Signaling, and Inequality in Access

PART - I

Chapter 1: Desirability and Disparity in Higher Education Attainment in India (Under review at Journal of Quantitative Economics)

Abstract: This paper examines the determinants of higher education participation and the persistence of disparities in attainment in India. The study uses nationally representative data from the Comprehensive Annual Modular Survey (CAMS) of the National Sample Survey Office, focusing on individuals aged 18 years and above. The empirical strategy combines descriptive analysis, logistic regression models to examine the determinants of higher education attainment, and the Fairlie non-linear decomposition technique to quantify the contribution of socioeconomic, demographic, and spatial factors to observed attainment gaps across groups defined by caste, gender, religion, wealth, and rural--urban residence. The results show that household economic status is the single most powerful determinant of higher education attainment and the primary driver of disparity across social groups. While caste, religion, gender, and spatial location remain significant axes of inequality, their effects are deeply intertwined with economic deprivation. Women now exhibit a modest advantage in aggregate higher education enrollment, indicating a reversal of historical gender gaps at the entry level; however, this advantage is highly uneven and erodes sharply with marriage, rural residence, and lower economic status. Rural location substantially amplifies caste- and religion-based disadvantages, and economic resources can partially mitigate—but not eliminate—structural barriers. Overall, expansion has increased participation without fundamentally dismantling entrenched inequalities. 

Chapter 2: The Causal Impact of Socioeconomic Status (SES) on Higher Education Attainment in India


Chapter 3: Non-Traditional Students & Inequality in Higher Education Attainment: Recent Estimates

PART - II

Chapter 4: Estimating Returns to Higher Education in India


Chapter 5: Higher Education as a Screening Device


Chapter 6: Higher Education as a Signal 

WP: The Devaluation Of Degrees In Post-Massification India

Abstract: We argue that the massification of higher education in India after 1995 triggered a devaluation of academic credentials, reflected in declining wage returns for post-expansion cohorts. Leveraging an event-study and IV strategy based on exposure to the reform, we estimate a 0.53 log point decline in wage premiums. The effect is strongest for women, disadvantaged caste groups, rural residents, and wage employees. We find evidence consistent with Collins (2019)’s Credential Society theory in the Indian context, with an inverted U- U-shaped relationship between massification and wage returns.

WP: The Credential Premium: Evidence of Sheepskin Effects of Higher Education in India (Under Review at Education Economics)

Abstract: Do higher education credentials confer wage premiums relative to drop-outs in India? Using nationally representative data from the Periodic Labour Force Survey (PLFS) for 2021–2023, this paper examines the presence and magnitude of sheepskin effects in the Indian labor market. We estimate causal returns to higher education using step-wise and spline regression models, while our main identification strategy instruments higher education attainment based on exposure to massification policies, proxied by potential graduation year.  We find a strong wage–education relationship, with each additional year of schooling associated with a 6.6% increase in wages. Wage returns increase gradually at lower schooling levels but jump sharply at degree completion: 42.2% at higher secondary, 65.4% at diploma, 86.2% at graduate, and 120.4% at postgraduate or higher levels. Cohort analysis reveals shifting signaling strength over time, with bachelor’s degrees gaining importance among younger cohorts. Heterogeneity analysis shows that gender, caste, and religion significantly shape returns to higher education credentials, indicating that these wage premiums are unequally distributed across demographic groups.

Chapter 8: Gender Based Sorting & Higher Education in the Indian Labour Market



Working Papers (Outside Thesis)

Abstract: This paper delves into the factors influencing the preference for private education among households, focusing on quantifying inter and intra-group disparities in private education attainment. Leveraging data from the Indian Household Development Survey II, focusing on children aged 8 to 11, our study employs confirmatory factor analysis to construct psychometric indicators of school experience and perception. Through Fairlie decomposition, we dissect the observed disparities across gender, region, and caste. We find that positive school experience and negative school perception are associated with the likelihood of a child attending a private school in India. We highlight a higher rural-urban disparity (33%) compared to gender-based disparity (9.6%) in private school attainment. When compared with other castes, our analysis uncovers substantial disparities of 20.5% in Scheduled Castes (SCs), 26.5% in Scheduled Tribes (STs), a discrepancy of 22.2% of SCs and STs, combined, in private education attainment. Our analysis identifies disparities in household wealth and income indicators as significant drivers of these disparities. However, interaction analysis indicates that policy interventions aimed at increasing asset endowment could mitigate disparities across regions and genders, suggesting that income and asset ownership improvements could effectively bridge the gap in private education in India.

Abstract: In this paper, I develop a refinement of stability for matching markets with incomplete information. I introduce Information-Credible Pairwise Stability (ICPS), a solution concept in which deviating pairs can use credible, costly tests to reveal match-relevant information before deciding whether to block. By leveraging the option value of information, ICPS strictly refines Bayesian stability, rules out fear-driven matchings, and connects belief-based and information-based notions of stability. ICPS collapses to Bayesian stability when testing is uninformative or infeasible and coincides with complete-information stability when testing is perfect and free. I show that any ICPS-blocking deviation strictly increases total expected surplus, ensuring welfare improvement. I also prove that ICPS-stable allocations always exist, promote positive assortative matching, and are unique when the test power is sufficiently strong. The framework extends to settings with non-transferable utility, correlated types, and endogenous or sequential testing.



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