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Other Coverage: Breaking the Mold: India's Untraveled Path to Prosperity (Raghuram Rajan and Rohit Lamba)
How quantitatively important are screening practices in driving socioeconomic gaps in access to elite jobs, and what aspects of screening generate such disparities? To answer this question, I use recruitment data from over 1000 jobs at elite multinational corporations, primarily based in the United States or Europe, that hire from an elite Indian college. Each employer conducts four eliminatory screening rounds. I show that nearly 90% of the caste disparities in hiring arise in the final screening round, which comprises informal, non-technical personal interviews that often assess "fit." Thus, the three screening rounds prior to personal interviews together explain only a modest portion of hiring disparities. These three rounds are application reading, written aptitude tests, and large group debates that assess socio-emotional skills. The caste penalty following personal interviews closely parallels caste revelation. In a small subsample where last names provide clearer caste signals, the penalty emerges much earlier—during the application reading round. However, the overall caste penalty during application reading is negligible because 85% of the sample has non-distinctive caste names. Instead, the caste penalty emerges during personal interviews, where students are screened based on background, hobbies, and cultural fit: characteristics strongly correlated with caste. Furthermore, caste disparities in hiring outcomes are not explained by differences in job productivity. Disadvantaged castes have about 15% higher promotion rates, even among the most selective jobs. By studying the entire entry-level hiring process among elite jobs, this paper shows that disparities in hiring predominantly arise in the final screening round—personal interviews—something that audit studies would not capture. My findings elucidate the economic behavior underlying elite hiring outcomes and pave the way for policies that reduce socioeconomic disparities in access to elite jobs by effectively targeting the source of the inequality, in India and elsewhere.
This paper studies how socio-emotional skills affect academic outcomes and eventual job placements among MBA students at a top U.S. business school. Using detailed measures of socio-emotional traits taken before students matriculate, the paper evaluates how such traits affect academic performance and course choices, job search behavior, and industry and compensation of the accepted job offer. Initial descriptive results show that socio-emotional skills are predictive of behavior in each of these three areas, commonly out-predicting the effects of standardized test scores. Moreover, the relative importance of different skills varies across the three areas of outcomes considered. Based on the descriptive evidence, we estimate a sequential model of the key stages of the MBA program, including academic performance, course specialization, job search, and job placements. Using the model, we then decompose the total effects of socio-emotional skills into direct returns and indirect returns through differences in both academic performance and job search behavior.
Most environmental justice movements emphasize the exposure of communities to sources of pollution. Following suite, recent policy proposals aim to direct funding to specific communities to alleviate pollution burdens. Yet, the selection of communities and the potential value of place-based policies to tackle inequality stemming from environmental issues are empirically unclear, even though economic theory traditionally favors individual-level transfers over place-based redistribution. In the context of a major environmental justice initiative of the U.S. government, we discuss how the unique spatial correlation of pollution and environmental risks warrant place-based policies on equity grounds and directly impact their efficiency costs.
We examine the Petviashvilli method for solving the semilinear elliptic partial differential equation on a bounded (potentially, multidimensional) domain with Dirichlet boundary conditions. We prove a local convergence result, using spectral analysis, akin to the result for the problem on the one-dimensional domain by Pelinovsky & Stepanyants, 2004. We also prove a global convergence result by generating a suite of nonlinear inequalities for the iteration sequence, and we show that the sequence has a natural energy that decreases along the sequence.