Abstract: I study how language barriers impact aggregate and distributional economic outcomes using rich microdata from India applied to a quantitative spatial general equilibrium framework. I first document three empirical facts, which are strongest for unskilled workers: first, workers migrate less often to locations where they face high language barriers; second, migrants with high language barriers are employed less often in speaking-intensive occupations; and third, migrants with high language barriers get a wage premium. To rationalize these facts, I then develop and estimate a static migration model in which heterogeneous workers sort across occupations and locations by skill and language, with wages accounting for worker selection and adjusting in general equilibrium. I show through the lens of the model how language barriers, by increasing worker sorting and selection, significantly obstruct internal migration and augment the skill premium; removing them would raise aggregate welfare by 10.9 percent. Finally, I calibrate costs of both program provision and learning languages to evaluate potential benefits of language programs for unskilled migrants. Using the calibrated model, I argue that welfare benefits of implementing language programs would outweigh costs.

Abstract: We study how criminally accused candidates win three times more often than non-criminals upon nomination using data from four recent parliamentary elections in India. We write a simple model of party nomination choice, which predicts that criminals are nominated only when they are needed to win and not otherwise. Using local linear regressions, we confirm this prediction in the data. In particular, we find that the predicted probability from the ex post decision to nominate a criminal has an inverse-U relationship with a party's ex ante margin of victory. This may explain why criminal candidates are more successful than non-criminal candidates upon nomination: they are selected by political parties to do so.

Using district-to-district migration data from the Indian Census, we show that areas with higher mean and variance of adverse climate conditions experience increased out-migration, with many households engaging in temporary migration. We develop a dynamic spatial equilibrium model with agricultural productivity uncertainty, migration costs, and concave household utility to explain these patterns. The model predicts that greater agricultural income uncertainty increases rural out-migration. We show that moderate uncertainty encourages temporary migration as a risk-sharing strategy, while severe uncertainty pushes entire households to migrate permanently. Using the estimated model, we quantify how climate variability affects migration patterns and household structure. In future work, we plan to evaluate whether migration could serve as an effective adaptation strategy to climate-induced agricultural productivity losses and analyze its implications for climate policy in developing countries.