We investigate the role of information frictions in migration decisions. We show that even without data on what migrants know, most migration models are partially identified under rich patterns of incomplete information. We develop novel moment inequalities estimators for key preference parameters that allow workers' information sets, migration costs, amenities, and price levels to vary flexibly and remain unobserved by the researcher. Applying our estimators to internal migration flows in Brazil, we find that prior work underestimates the importance of expected wages in workers' migration choices and that workers face substantial information frictions. Workers in regions with better internet access and higher population density have more precise wage information, especially about wages in nearby areas. We quantify how information frictions alter migration flows, welfare, and the predicted effect of reducing migration costs.

We study the adoption of remote work within cities and its effect on city structure and welfare. We develop a dynamic model of a city in which workers can decide to work in the central business district (CBD) or partly at home. Working in the CBD allows them to interact with other commuters, which enhances their productivity through a standard production externality, but entails commuting costs. Switching between modes of labor delivery is costly, and workers face idiosyncratic preference shocks for remote work. We characterize the parameter set in which the city exhibits multiple stationary equilibria. Within this set, a coordination mechanism can lead to stationary equilibria in which most workers commute or most of them work partially from home. In these cases, large shocks in the number f commuters, like the recent lockdowns and self-isolation generated by the COVID-19 pandemic, can result in dynamic paths that make cities converge to a stationary equilibrium with large fractions of remote workers. Using cell-phone-based mobility data for the U.S., we document that although most cities experienced similar reductions in CBD trips during the pandemic, trips in the largest cities have stabilized at levels that are only about 60% of pre-pandemic levels. In contrast, smaller cities have, on average, returned to pre-pandemic levels. House price panel data by city show consistent changes in house price CBD-distance gradients. We estimate the model for 274 U.S. cities and show that cities that have stabilized at a large fraction of remote work are much more likely to have parameters that result in multiple stationary equilibria. Our results imply welfare losses in these cities that average 2.7%.

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.