We investigate the role of information frictions in migration. Using novel moment inequalities and data on internal migration in Brazil, we estimate worker preferences and migration costs while allowing for unobserved worker-specific information sets. We find that common estimation procedures overestimate migration costs and underestimate the importance of expected wages in migration decisions. Model specification tests indicate that workers often have limited information on location-specific wages. However, those living in regions with better internet access and larger populations have more precise wage information, and information precision decreases with distance. According to our estimated model, workers' limited wage information plays a quantitatively important role in reducing migration flows and worker welfare, and limits the effect of policies that reduce migration costs.

Relative to remote work, working downtown facilitates valuable interactions with other in-office workers, but entails commuting costs. The resulting coordination mechanism can lead to multiple stationary equilibria with different levels of remote work. Temporary reductions in commuter shares, such as the COVID-19 pandemic, can then lead to persistently large fractions of remote workers. Consistently, using cell-phone-based mobility data for the U.S., we document that trips in the largest cities have stabilized at levels that are only about 60% of pre-pandemic levels, while smaller cities have returned to pre-pandemic levels. U.S. cities that exhibit multiplicity experience average welfare losses of 2.3%.

Information is critical for migration decisions. Yet, depending on where individuals reside and who they interact with, they may face different costs of accessing information about employment opportunities. How does this imperfect and heterogeneous information structure affect the spatial allocation of economic activity and welfare? To address this question, I develop a quantitative dynamic model of migration with costly information acquisition and local information sharing. Rationally inattentive agents optimally acquire more information about nearby locations and learn about payoffs in other locations from the migrants around them. I apply this model to internal migration in Brazil and estimate it using migration flows between regions. To illustrate its quantitative implications, I evaluate the counterfactual effects of the roll-out of broadband internet in Brazil. By allowing workers to make better mobility choices, expanding internet access increases average welfare by 1.6%, reduces migration flows by 1.2%, reduces the cross-sectional dispersion in earnings by 4%.

We investigate the socioeconomic impacts of plausibly exogenous eviction from rent-controlled housing in California. Under the Ellis Act, landlords are allowed to evict all tenants from a building and withdraw it from the rental market. We assemble panel data on address histories, employment, income, and neighborhood characteristics for all Ellis-evictees in San Francisco and a control group of non-evictees in the same block. We confirm that those large-building evictions appear orthogonal to evictees’ individual characteristics after controlling for observable household and neighborhood effects. Comparing those two groups with a difference-in-difference approach, we find that evicted tenants between 1998 and 2012 not only exhibit a higher propensity to exit the city but also endure a reduction in nominal income that reaches 20 percent eight years after the eviction, the end of our analysis window. Those income losses are also experienced, in the same magnitude but more gradually, by evictees who remain in San Francisco. Those large income losses contrast with the income improvement we observe for non-eviction-related relocations. The negative impact extends to their residential destinations post-eviction, which tend to be neighborhoods with lower job density, higher unemployment rates, and diminished school quality, particularly for tenants with lower pre-eviction income. Specifically, children from evicted households face significant setbacks, evidenced by diminished earnings in early adulthood, suggesting a persistent intergenerational impact.

As states have diverged in their relative demand for skilled workers, the children born in these states have also diverged in their probability of graduating from college. We ask how much of this spatial divergence in college attainment can be explained by the growing differences in the types of people who live in these locations, or by the increased importance of frictions to geographic mobility, such as migration costs and out-of-state tuition. First, using panel data on individual education choices and college characteristics, we show that children are less likely to go to college when they are located in a labor market, at age 17, with a lower skill premium and lower access to quality colleges. Second, we build a dynamic spatial equilibrium model with college choice, financial frictions, migration, and endogenous heterogeneity across locations in the higher-education system. We calibrate the model and show how a spatially uneven skill-biased technical change shock can reproduce most of the observed divergence in college attainment. We find that migration costs, rather than financial constraints, out-of-state tuition, differences in college quality, or increased sorting, is most important for explaining the growing gap in college attainment.