As Jobs Become More Portable, Many Workers Move to New Neighborhoods — with Surprising Results for Overall Welfare

Of the many ways in which the COVID pandemic reconfigured labor markets, the rise of remote work was among the most consequential. For workers who transitioned to remote jobs, the relationship between their personal and professional lives took on a new dimension. For this cohort, decisions about work were accompanied by a sense of freedom about where to live. For higher-earning households in big metropolitan areas, suburbs and small towns became increasingly attractive, setting in motion waves of migration.

As more people moved, there were plenty of questions for economists and analysts to consider. What would happen to wages? Would some workers have more opportunities than others? How would housing prices behave in communities receiving new residents as well as in those losing them? In their paper, “The Great Reshuffle: Remote Work and Residential Sorting,” Wenli Li and Yichen Su adopt a novel accounting framework to answer these questions.¹ Their findings challenge the narrative of widening income inequality that tends to appear in post-COVID analyses of labor-market behavior. Although higher earners clearly benefited by relocating from big cities to smaller communities, the authors write, lower-earning households experienced notable gains as well: “…migration-driven ‘decongestion’ of neighborhoods heavily populated by low-income individuals resulted in greater welfare gains for the average low-income person relative to their high-income counterparts, thereby contributing to a narrowing of welfare inequality.”

To assess migration flows, the authors combined nationwide data sets from academic institutions, government agencies, and corporate organizations. These data comprise real-world observations of relocation choices made by individuals from late 2019 through the fall of 2024. For their evaluation of this data, they focused on people between 25 and 65 years of age, “retaining only those who are observed in every quarter during [the] period.”² Li and Su find that migration “constitutes an additional channel through which welfare inequality evolves, beyond the direct effects of remote work itself.”

The authors also find that benefits and drawbacks were distributed in unexpected ways. Take, for instance, shifts in housing costs between suburban and urban areas. Li and Su discover that out-migration from urban centers reduced demand for urban housing, which allowed prices to ease. This in turn reduced the cost burden for people who lived in these neighborhoods. Although home prices in suburbs may have risen in response to the influx of new residents, such gains were tempered because real estate markets are generally more elastic in the suburbs. (That is to say, when demand for housing increases in the suburbs, supply can be more responsive, whereas in central cities, supply is constrained by limited space, long-established neighborhood configurations, and other factors.) As house prices softened in cities, urban residents accrued benefits, while in the suburbs, the increase in home prices was somewhat checked by the relative elasticity of housing supply. This duality, the research shows, was unexpectedly helpful in mitigating some of the repercussions of post-COVID migration.

Li and Su also analyzed how migration patterns affected job growth. They distinguished between two broad types of jobs: those in local service industries (such as restaurants and retail) and those in the professional services sector (finance and law, for instance). They find that, during the first two years after the onset of the pandemic, “…counties with very low population density saw positive job growth in [the local services] sector,” while high-density counties “experienced notable employment losses.” As the years wore on, urban employment in this sector rebounded. “However,” the authors write, “the densest counties still had not returned to their [early] 2020 employment levels” even four years later.

The outcomes are considerably different in the professional services sector, where job growth showed little variation between high-density and low-density residential areas. Li and Su attribute this lack of variation to remote work, which is more prevalent in this sector. (Even though remote workers moved to low-density counties, their employers did not; most employers remained in their original facilities. The eventual migration of employers to lower-density counties was modest, at best.)

Li and Su find mixed implications for job accessibility, another aspect of migration they investigate. For instance, as remote workers in professional service industries dispersed to less-populated areas, they relocated away from neighborhoods that offered convenient access to major employment centers, which detracted from welfare for this group of workers. (Living farther from job markets is viewed as a welfare loss for workers of all stripes, even those who work remotely.) For local service employment in small towns, the effect of migration to small towns was positive, particularly when population gains resulted in more local service jobs becoming available for workers already living in those communities.³ This positive effect was offset by weaker demand for local service jobs in major urban areas, which resulted in welfare losses for service workers in those communities.

After looking at migration patterns and their effect on jobs and housing markets, the authors introduce a method for quantifying the total combined effects of remote-work adoption on welfare. Referred to as a welfare accounting framework, it enables Li and Su to “translate detailed empirical changes in housing costs and employment access into explicit measures of resident welfare.” This framework, which expresses gains and losses in wage-equivalent units, shows, in aggregate, a shrinking inequality between high and low earners. (This part of the analysis also distinguishes between two different types of migration flows: from large cities to small towns, and from urban cores to suburban neighborhoods. The authors point out, for instance, that overall welfare tends to be better for lower-income workers in the first type of migration and worse in the second.)

To bring their results into sharper focus, Li and Su discuss what happens in big, well-known metropolitan areas such as New York and San Francisco. In high-profile areas like these, the total combined effects of migration over the entire four-year period are positive for low-income residents. Indeed, the effects of migration are so strong that stripping them away results in welfare losses for these residents.

In their work, Li and Su produce fresh insights into the effects of remote work on the labor force, particularly when remote jobs provoke waves of migration from densely populated, job-rich communities to suburbs and small towns. Their research shows that on its own, the adoption of remote work leads to greater inequality across income groups, but when the effects of migration are factored in, “the rise in welfare inequality is significantly moderated.”