Dismantling the Legacy of Caste - Centre For Development Economics

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Let p j (a i,j ) = E( i,j ) be the probability, conditional on aptitude a i,j , that an applicant in caste j obtains admission to a more elite institution. Then we can write utility of the non-engineering college option as: (7) U n ,j(a i,j ,c a i,j) = p j (a i,j )( u a j(a i,j , c a i,j) - u 0 j(a i,j )) + u 0 j(a i,j ) + n i,j , where (8) n i,j = ’ i,j + ( i,j - p j (a i,j ))( u a j(a i,j ,c a i,j) - u 0 j(a i,j )). Thus, n i,j impounds both an idiosyncratic preference shock, ’ i,j, as well as idiosyncratic factors that influence whether a member of caste j with aptitude a i,j is admitted to a superior alternative academic institution. Now an applicant who gains admission to an engineering college will matriculate if: (9) u i,j = (u e j(a i,j ,c e i,j r i,j ) - u 0 j(a i,j )) - p j (a i,j )( u a ,j(a i,j ,c a i,j) - u 0 j(a i,j )) > n i,j - e i,j. The first term in the middle expression is the difference in the deterministic components of utility between and engineering college and the no-college option. The second term is the probability of admission to an academic institution preferred to an engineering college multiplied by difference in the deterministic components of utility between the alternative academic institution and the no-college option. We assume that the idiosyncratic shocks are i.i.d. normal, implying a probit specification for the binary choice of attending or not attending an engineering college. For a large majority of students who take the entry examination for engineering colleges, an engineering college will be their best academic option. The probability of admission to an IIT or comparable institution will be an increasing and convex function that is near zero throughout most of its domain and increases sharply for aptitudes in the far right tail of the distribution. Thus, for most applicants, the second term of the middle expression in equation (9) will be approximately zero, implying a choice between an engineering college and the no-college option: (10) u i,j ≈ u e j(a i,j ,c e i,j ,r i,j ) - u 0 j(a i,j ). 19
If the gain from attending an engineering college is greater for more able students, the preceding expression will be increasing in a i,j . The gain will also be increasing in r i,j because those of higher rank within their caste have higher priority in engineering college choice. Hence, we expect the probability of matriculation in an engineering college to be increasing in a i,j until a i,j becomes large enough that the probability of admission to IIT begins to rise above zero. High-aptitude students who gain admission to an IIT will generally prefer attending an IIT to attending an engineering college. Hence, for sufficiently high a i,j , we expect the second term in the middle expression in equation (9) will come to dominate. At that point, the probability of matriculation at an engineering college will begin to decline as a i,j increases. Overall, then, we expect the probability of matriculation at an engineering college to be increasing in r i,j and to be an inverted U-shaped function in a i,j . We will estimate the admission probability using a flexible functional form in a i,j to capture this anticipated U-shaped pattern. Note that we have retained a subscript j on all deterministic components of utility, permitting potentially different valuations of the benefits of college by members of different castes. Let college attendance, y, take the value of if a student is admitted and matriculates in an engineering college and 0 otherwise. As explained previously, we have college attendance data for all students. Hence, we can evaluate the effect of affirmative on college attendance. As detailed above, we expect that the probability of matriculation in engineering colleges will be a nonlinear function of an individual’s academic ability. To capture this anticipated nonlinear pattern, we include a polynomial in latent aptitude for engineering, , defined in equation (2). We will permit the coefficients of to differ between the attendance and achievement equations, since the components of aptitude may differentially effect attendance decisions and performance in engineering colleges. In addition, we include a polynomial in the entry examination scores: (11) s q q q . s s m m p p Our motivation for including this additional term is the following. The high school examination scores exhibit substantial bunching at the upper end of the distribution as shown in the 20
Page 1 and 2: Dismantling the Legacy of Caste: Af
Page 3 and 4: institutions where their academic p
Page 5 and 6: Our paper is organized as follows.
Page 7 and 8: (2007) find no adverse impact of af
Page 9 and 10: As we have mentioned, India is an i
Page 11 and 12: ability within classrooms, and sugg
Page 13 and 14: As is evident from Table 1, there a
Page 15 and 16: group of each student and the wheth
Page 17 and 18: (3) p r for j = O, ST, SC, B
Page 19: 4.2. College Attendance An applican
Page 23 and 24: perspective of studying the effects
Page 25 and 26: In presenting our model of achievem
Page 27 and 28: expectation, the ordering of the si
Page 29 and 30: coefficients of the interactions of
Page 31 and 32: open caste. In Table 6, we provide
Page 33 and 34: References Akerlof, George (1976),
Page 35 and 36: Fernandez, R. and Rogerson, R. (200
Page 37 and 38: Appendix A: Ranking Within Caste an
Page 39 and 40: Appendix B. Unbiased Estimation of
Page 41 and 42: Figure 1. Impact of School Choice o
Page 43 and 44: Figure 3. 42
Page 45 and 46: Attendance Index Figure 5 Relations
Page 47 and 48: Table 1. Summary Statistics Full Sa
Page 49 and 50: Table 3. Regression Results: Attend
Page 51 and 52: Table 5. Changes in Attendance from

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