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 varying levels of remote work. Temporary reductions in commuters, as in the COVID-19 pandemic, can then lead to persistently large fractions of remote workers. Cell-phone-based mobility data for the U.S. shows that commuting trips in the largest cities, which are more likely to exhibit multiplicity, have stabilized at only 60% of pre-pandemic levels, while they are fully back in smaller cities. Cities with permanently low commuting experience average welfare losses of 2.3%.
Both large establishments and large cities are known to offer workers an earnings premium. In this paper, we show that these two premia are closely linked by documenting a new fact: when workers move to a large city, they also move to larger establishments. We then ask how much of the city-size earnings premium can be attributed to transitions to larger and better-paying establishments. Using administrative data from Spain, we find that 38 percent of the city-size earnings premium can be explained by establishment-size composition. Most of the gains from the transition to larger establishments realize in the short-term upon moving to the large city. Establishment size explains 29 percent of the short-term gains, but only 5 percent of the medium-term gains that accrue as workers gain experience in the large city. The small contribution to the medium-term gains is due to two facts: first, within large cities workers transition to large establishments only slightly faster than in smaller cities; second, the relationship between earnings and establishment size is weaker in large cities.
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.
How important is stable housing near high-paying jobs for long-run career growth? We study San Francisco Ellis Act withdrawals, which remove rent-controlled buildings from the rental market, and compare displaced tenants with tenants in nearby rent-controlled buildings. Six years after eviction, displaced workers earn about $13,000 less (20 percent of baseline earnings) and live in lower-value housing and neighborhoods. Losses are largest for younger workers and remain large even for movers of 5-25 km, who transition to smaller, lower-paying firms and face longer commutes. We develop an equilibrium model of frictional housing and job search, costly commuting, localized search, and human-capital accumulation, calibrated to the Bay Area. The model quantitatively matches the eviction effects because central locations offer higher wages and faster wage growth; displacement reduces access to both and slows progression up the spatial job ladder. Counterfactuals show that improving access to central housing reduces losses more than temporary rent or commuting subsidies.
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% and reduces the cross-sectional dispersion in earnings by 4%.
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.
