The Long-Term Effects of Teachers’ Gender Bias

This paper studies the effects of teachers’ gender biases on students’ long-term outcomes, including high school completion, college attendance, and formal sector employment. I measure teacher bias using differences in gender gaps between teacher-assigned and blindly-graded tests, and validate the assessment-based measure with novel data on teachers’ attitudes, as captured by the Implicit Association Test (IAT). I develop a large-scale online portal available to teachers and students in Peruvian public schools to collect IAT scores nationwide. This analysis provides evidence that math teachers who strongly associate males with scientific disciplines give higher scores to male students, when compared to blindly-graded test scores, while language arts teachers who strongly associate females with humanities-based disciplines award higher grades to female students. Next, using graduation, college enrollment, and matched employer-employee data on 1.7 million public high school students who were expected to graduate between 2015 and 2019, I find that female students who are assigned to more biased teachers are less likely to complete high school and apply to college than male students. Moreover, female students assigned to more biased teachers in high school are less likely to hold a job in the formal sector after graduation and have fewer paid working hours relative to their male classmates. Exposure to gender-biased teachers also leads to monthly earnings losses for women, further exacerbating the gender pay gap.

Caste Heterogeneity in the Effects of Political Affirmative Action in India

This paper studies the limitations of political affirmative action policies. In India, certain state legislature seats are restricted for the historically-discriminated lower castes (Dalits). Dalits are a large and heterogeneous group and there is little understanding of how different castes have been impacted by such enfranchisement, due to a lack of data on the individual caste of beneficiaries. Exploiting the link between names and caste membership, I create a new dataset including the caste of workers involved in a public workfare program (NREGA). Because constituencies are reserved for Dalit legislators based on a population cutoff rule, I use a regression discontinuity design to estimate the effect of having a Dalit state representative on the timing of payments to low-caste laborers in NREGA. I explore this effect on all Dalit workers and differentially by the individual caste of the worker. I find that Dalit workers represented by a Dalit state legislator experience a 12% higher probability of receiving their payments late. This effect is constant across all individual castes, except when considering constituencies won by parties that expressly favor Dalit voters. In this instance, I estimate that Dalit workers receive earlier payments in reserved constituencies and that those belonging to the state’s largest Dalit caste are even more advantaged. The deleterious effects of having a Dalit representative on Dalit workers are borne entirely by areas where the legislator has lower bargaining power over the local bureaucrat who directly manages the processing of payments. Given the high desirability of stable public employment, often these bureaucrat postings attract people from a more advantaged social background, relative to the Dalit legislators. Hence, my findings point to the importance of considering vertical power structures when designing policies aimed at empowering under-represented minorities around the world.