Essays in labor economics

This dissertation contains three essays on labor economics. In the first chapter, co-authored with Nicholas Bloom and Mihai Codreanu, we run randomized controlled trials on a panel of 7,300 small U.S. firms to test if we can improve their sales forecasting. At baseline, only 17.4% of entrepreneurs can forecast their firm's sales over the next three months within 10% of the realized value, with 1% of the mean squared error attributable to bias and the remaining 99% attributable to noise. Our first intervention rewards entrepreneurs up to 00 for accurate forecasts, our second requires respondents to review historical sales data, and our third provides forecasting training. Increased reward payments significantly reduce bias but do not affect noise, despite successfully making entrepreneurs spend more time thinking about their predictions. The historical sales data intervention does not affect bias but significantly reduces noise. Since bias is only a minor part of overall forecasting errors, we find that the reward payments have small effects on mean squared error, while the historical data intervention reduces it by 12.4%. The training intervention has negligible effects on bias, noise, and mean squared error. Our results suggest that while offering financial incentives that increase effort make forecasts more realistic, firms may not fully realize the benefits of having easy access to past performance data. The second chapter, co-authored with Nicholas Bloom and Ethan Yeh, uses survey data to assess the impact of COVID-19. We find a significant negative sales impact that peaked in Quarter 2 of 2020, with an average loss of 29% in sales. The large negative impact masks significant heterogeneity, with over 40% of firms reporting zero or a positive impact, while almost a quarter report losses of more than 50%. These impacts also appear to be persistent, with firms that reported the largest sales drops in mid-2020 still forecasting large sales losses a year later in mid-2021. In terms of business types, we find that the smallest offline firms experienced sales drops of over 40% compared to less than 10% for the largest online firms. Finally, in terms of the owners, we find female and black owners reported significantly larger drops in sales. Owners with a humanities degree also experienced far larger losses, while those with a STEM degree experienced the smallest impact. In the third chapter, I explore the extent to which American geographic political polarization is caused by internal migratory patterns, using a novel political campaigning dataset spanning the universe of American voting-age citizens. Contrary to popular belief that migration drives geographic polarization, I estimate that migration reduces it by mixing individuals across politically homogeneous areas. This effect is largely explained by regression to the mean - the United States has become geographically polarized beyond what even strong preferences for self-sorting can sustain. Furthermore, I demonstrate that the political beliefs of internal migrants tend to increasingly reflect the average political beliefs of their destinations the longer they live there. This effect amplifies the depolarizing effect of the migration patterns as individuals who move to less politically homogeneous areas adopt less polarized views

Keywords: Economics.
Advisor: Bloom Nick
Dupas Pascaline
Morten Melanie