Affirmative action in education: Evidence from engineering college admissions in India

Abstract

Many countries mandate affirmative action in university admissions for traditionally disadvantaged groups. Little is known about either the efficacy or costs of these programs. This paper examines affirmative action in engineering colleges in India for "lower-caste" groups. We find that it successfully targets the financially disadvantaged: the marginal upper-caste applicant comes from a more advantaged background than the marginal lower-caste applicant who displaces him. Despite much lower entrance exam scores, the marginal lower-caste entrant does benefit: we find a strong, positive economic return to admission. These findings contradict common arguments against affirmative action: that it is only relevant for richer lower-caste members, or that those who are admitted are too unprepared to benefit from the education. However, these benefits come at a cost. Our point estimates suggest that the marginal upper-caste entrant enjoys nearly twice the earnings level gain as the marginal lower-caste entrant. This finding illustrates the program's redistributive nature: it benefits the poor, but costs resources in absolute terms. One reason for this lower level gain is that a smaller fraction of lower-caste admits end up employed in engineering or advanced technical jobs. Finally, we find no evidence that the marginal upper-caste applicant who is rejected due to the policy ends up with more negative attitudes towards lower castes or towards affirmative action programs. On the other hand, there is some weak evidence that the marginal lower-caste admits become stronger supporters of affirmative action programs.

Estimating the distortionary effects of ethnic quotas in Singapore using housing transactions

“…40 These results echo some of the findings in the literature on the distortionary effects of diversity-enhancing policies. In addition to the results discussed above by Marion (2009) and Matsa and Miller (2013), Bertrand, Hanna, and Mullainathan (2010) finds that castebased affirmative action quotas in India reduce the number of females entering engineering colleges. On the other hand, McCrary (2007) finds that court-ordered hiring quotas increased the fraction of African Americans among newly hired police officers while city crime rates appear unaffected.…”

“…The potential for allocative inefficiencies depends on how different the groups are. It is conceivable that the skill distribution for police candidates might be more similar across ethnic groups, compared to the distribution of test scores (the main admission criteria in Bertrand, Hanna, and Mullainathan (2010)) for different castes in India or the distribution of WTP in housing markets for different ethnic groups.There is a long literature on differences in test scores between ethnic groups (Clotfelter, Ladd, and Vigdor, 2009) and differences in housing preferences (Bayer, Ferreira, and McMillan, 2007). 42 See, for example, Holzer and Neumark (2000); Corns and Schotter (1999); McAfee and McMillan (1989).…”

“…Chay and Fairlie (1998) report that it is hard to identify the dates of adoption of a particular affirmative action program. Bertrand, Hanna, and Mullainathan (2010) point out the lack of datasets that comprise both the favored group who were admitted and the non-favored group who were not admitted due to affirmative action quotas in colleges. Marion (2009) studies the impact of procurement policies favoring disadvantage business enterprises but lacks data on which firms are owned by minorities.…”

Resolving the Systemic Bias against Students from Disadvantaged Classes in Higher Education in India: The Case of Indian Institutes of Technology

“…Even though the IITs have continuously churned out global leaders they have been plagued with the problem of inequitable treatment of SSDCs. Studies conducted by [4] and [5] elaborate on the ineffectiveness of these institutes in ensuring equivalent professional development of students gaining access through reservations when compared to the non-reserved category students. Reports such as "90% of students expelled from IIT Roorkee belong to reserved categories" [6] provide further evidence on how the IITs have not been able to address this discrepancy.…”

“…Also amongst these a large percentage come from households lingering in poverty of which a large proportion suffer from extreme poverty 4 , which takes us to the second aspect of the bias. Of the students getting admitted to the IITs around 50% to 60% [8] [10] and makes external private coaching inaccessible to students from low-income households, who earn less than INR 200,000 [11] a year.…”