We study the primary employment-based affirmative action regulation in the U.S.: Executive Order 11246. In particular, we estimate the causal effects of the regulation on workers’ earnings and future employment prospects. We do so by exploiting different features specified by the policy in a regression discontinuity setting. The project circumvents prior data limitations by constructing the first large-scale administrative database containing worker-level information (from the restricted access matched employer-employee LEHD data) together with the federal contractor status of their employers (from EEOC Data and Federal Procurement Data). Motivated by our empirical findings, we develop a theoretical model in order to identify the different mechanisms through which the policy operates. Our results may suggest important margins to consider when designing policies to address racial inequalities.

This paper evaluates the short-term causal effects of a high-quality, intensive, part-time computer coding bootcamp for women on skill acquisition and employment outcomes. Spots were offered in an oversubscribed coding course to a random subset of applicants in Buenos Aires, Argentina, and Bogota, Colombia. The applicants who were chosen received a scholarship that covered most of the tuition costs of the course. Follow-up data collected shortly after the bootcamp ended indicate that the program increased participants’ coding skills, as well as their probability of finding a job in technology. Compared with other jobs, technology jobs are more likely to offer flexible hours and work-from-home arrangements, and generate higher job satisfaction. These results are interpreted as an improvement in overall job quality. Moreover, the paper compares the employment situation of the sample before and during the first months of the COVID-19 outbreak. The evidence indicates that the program increased participants’ resilience to a downturn in the labor market.

This paper estimates the impact of an agricultural technology adoption program on production and income using an experimental approach. We exploit a two-stage randomized experiment conducted at the geographic- and farmer-level to measure both direct and spillover effects of technology adoption. We find different patterns of adoption and significant impacts on production-related outcomes. The assessment of indirect effects validate the hypotheses that knowledge spillovers might take place among farmers in close proximity to program beneficiaries, especially through social networks.