Graduation Year

2018

Document Type

Dissertation

Degree

Ph.D.

Degree Name

Doctor of Philosophy (Ph.D.)

Degree Granting Department

Economics

Major Professor

Kwabena Gyimah-Brempong, Ph.D.

Co-Major Professor

Gabriel Picone, Ph.D.

Committee Member

Giulia La Mattina, Ph.D.

Committee Member

Susan Kask, Ph.D.

Keywords

quota, caste, Gross Enrollment Ratio, dropout rate, infant mortality, under five mortality

Abstract

Article 334 of the Constitution of India (1950) stipulates that certain electoral districts in each state should be reserved for minority groups, namely the “Scheduled Caste”(SC) and the Scheduled Tribe”(ST), through the reservation of seats in the states' legislative assemblies. Even though the original article stated that the reservation policy would be in place for just twenty years, it has been amended several times and is still in effect. This dissertation examines the impact of the policy on the education and health outcomes of the SC population. Variations in seat quotas are generated by the timing of elections in different states and the states’ fluctuating SC populations. The first paper on education uses data from 25 Indian States and 3 Union Territories for the years 1990-2011 to form a panel dataset to estimate the impact of the quota system on both enrollment and dropout rates among SC students in all levels of schooling. I use the fixed effect regression to test the mechanisms through which an elected SC legislator could have an influence on the education outcomes for the SC population in the represented state. I then use the resulting variables as my controls to identify the causal relationship using the dynamic panel data model. I find that a SC legislator has the potential to influence the number of schools built, as well as the amount of education and welfare expenditure allocated to the SC population. Moreover, I find that the SC political reservation has a positive and statistically significant impact on the SC enrollment rates and a negative and significant impact on the dropout rates, in all levels of schooling. Likewise, I use the NFHS-3 dataset and the Cox Proportional Hazard Model to estimate the hazard rates (risks of dying) of children under the age of 12 months (IMR) and under the age of 60 months (U5MR) as influenced by different SC quota share quintiles. I find that the 50-60% quota-share quintile has the biggest impact in reducing the IMR and U5MR among the SC children.

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