Research

Abstract

I study the effects of immigration on the performance of local firms and their workers leveraging a sharp increase in Canada's immigration targets in 2016. The policy led to an influx of predominantly high-skilled workers and generated unexpected variation in the growth of the foreign-born population across regions and nationalities. Motivated by the role of immigrant networks as vital channels for transmitting job information, I quantify firms' exposure to the immigration shock using a shift-share instrument that combines these differential inflows with the ethnic and spatial composition of firms' existing workforce. Using a novel decomposition of this measure, I draw comparisons across firms that operate within the same labor market based on differences in worker origins. Employers more exposed to the shock accelerated the hiring of recent arrivals who lacked locally accumulated human capital, increased employment and compensation for both immigrant and native workers, and experienced expansions in total output and output per worker. These results are consistent with firms benefiting from immigration through workplace ethnic networks, which may help identify workers' productivity characteristics that are otherwise overlooked in the labor market.

Abstract

Misspecification is theoretically linked with updating failures, but empirical evidence has been lacking. We document the empirical relevance and estimate the impact of misspecification on updating. We collect a novel high-frequency dataset on students' beliefs about grades in a freshman course. Students are overconfident, their beliefs do not improve over time, and they overestimate the testing noise by a factor of 3. Our RCT exogenously shocks and improves students' belief in the testing noise. Treated students reduce their prediction errors by 32%. We estimate the impact of misspecification structurally and find that a lower bound of 25% of prediction errors can be attributed to misspecification. Our finding suggests that misspecification is a major obstacle to processing information correctly, but it can be alleviated via simple interventions. 

Despite an extensive body of literature documenting the importance of early childhood education and care for mothers and children, little is known about the unique labor market of its providers. We investigate how the introduction of universal childcare in Quebec impacted the welfare and composition of workers in the childcare sector. The predicted effects of subsidies on providers are ambiguous. On the one hand, such programs create an unexpected and large surge in the demand for childcare labor, which, in theory, may lead to improved compensation and working conditions. On the other hand, if the government emerges as a major employer of childcare workers, such positive effects may be blunted. Individuals’ decisions to enter and remain in this profession therefore depend on how policymakers address the tension between the quality and the quantity of publicly funded care. Using administrative data from individual municipalities in Quebec, our research design exploits the distribution of available childcare slots prior to the rollout of subsidies. We further supplement this analysis by comparing childcare workers in Quebec and in other provinces in Canada.  This paper provides policy-relevant evidence on how government decisions affect a highly gender-segmented and under-researched workforce.