Long-distance work and compensation
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This content was first published by the ADP Research Institute on May 22, 2024
Long-distance work is splitting geographic cost savings between workers and employers and improving the match between them.
Long-distance work didn’t begin with the coronavirus lockdown; people were collaborating between offices well before then. But four years after the pandemic’s normalization-by-necessity of remote work, many long-distance workers never went back, and their ranks are growing.[1]
So, does remote work pay? And if it does, for whom? Employees who go remote can raise their standard of living overnight by moving to a city with less expensive housing and an overall lower cost of living. But our research found that employers can share in the geographic cost savings, too. Perhaps most important, the widespread acceptance of long-distance labor has improved the odds that workers and jobs will be well-matched, which stands to help the labor market as a whole.
In earlier studies, we examined cross-metropolitan work—in which the worker and the manager live in different metro areas—to document the unprecedented scale and scope of long-distance work and its geographic implications for cities.[2] In this study, we examined payroll and HR data of some 1.3 million workers each month to see how the rise of long-distance work has influenced compensation.[3]
We found that job-stayers who stayed with their team but relocated to less-expensive communities had slower average wage growth than their on-site teammates. That discount suggests that employees are sharing the geographic cost savings of relocation with their employers.
In contrast, people newly hired into long-distance positions won bigger average pay gains than similar job-switchers who took local positions. In some cases, that premium reflects the transition from local work to long-distance work for employers located in higher-paying markets. But we found bigger average pay gains for long-distance workers hired to companies in lower paying markets as well, which suggests something different altogether: Long-distance positions might be helping both workers and employers find a better fit with each other.
The big picture is that the normalization of remote work has created opportunities for employers and workers alike and has the potential to make the labor market more efficient by better matching the right person to the right job.
Sharing the benefits of long-distance work
People can transition into long-distance jobs in multiple ways. The most straightforward scenario is one in which a person relocates without giving up their current job.[4] When a worker moves to a community with cheaper housing and a lower cost of living, do they maintain their pay level and reap the full geographic cost savings, or does their pay take a hit, giving their employer part of the savings?
We tracked the gross wages of job-stayers who transitioned from local to long-distance work by relocating to communities with cheaper housing. We compared their earnings in the months before and after the move.[5,6,7] We did the same for teammates who didn’t relocate.[8] We statistically controlled for factors such as personal attributes, job function, and location to obtain an approximate yet meaningful apples-to-apples comparison of average wage growth.[9]
Figure 1
In the four years preceding the pandemic, from 2016 to 2019, average wage growth for job-stayers who relocated to affordability was 0.3 percentage points below that of their teammates, a difference statistically indistinguishable from zero. The difference remained indistinguishable from zero for 2020-2021.
But for 2022-2023, the wage growth deficit increased to 2.8 percentage points.[10] Given that the median pay gain for job-stayers was 7.3 percent in 2022 and 5.2 percent in 2023, this 2.8 percentage point deficit is considerable.[11,12]
While some employers might have recalibrated workers’ pay in proportion to cost-of-living differences, most did not, because cost-of-living differences are generally far more substantial than the wage growth deficit we found.[13] That means employers, at least on average, are splitting the geographic savings that flow from relocation with existing workers, with the lion’s share going to the latter.
Finding the right fit
Reducing an employee’s pay can be difficult; setting the wages of new employees less so. With that in mind, employers can better capitalize on geographic cost savings by hiring new long-distance workers for far less than they pay their existing workers.
As with our job-stayers exercise, we used ADP data to track the gross wages of job-switchers who transitioned into long-distance work and compared their wages before and after the transition.[14] Unlike job-stayers, who generally transition into long-distance work by relocating, job-switchers can transition into long-distance work with or without relocating.
We compared the wage growth of job-switchers into long-distance work with that of job-switchers who took new positions locally with the same employers.[15]
Here, too, new hires can vary in terms of personal attributes such as age and income level, job function, and location. The tendency to transition into long-distance work can correlate with those factors, so we controlled for them to obtain a meaningful apples-to-apples comparison.[16]
Job-switchers hired into long-distance roles saw substantially greater wage growth than similar job-switchers who took local positions with the same employers.
Figure 2
Since the pandemic, this gap has widened substantially. From 2016 to 2019, wage growth for people hired into long-distance work was more than 8 percentage points higher than for similar people who took on new jobs locally with the same employers.[17] By 2022-2023, the difference had risen to nearly 16 percentage points.
Why might the match between workers and jobs matter for pay?[18] Increasing the number and variety of job openings and candidates available—a thickening of the labor market— increases the potential for workers and employers to find better matches than they could otherwise.[19]
The explosion of long-distance work has significantly expanded the pool of jobs that applicants can consider and has enabled people to find better-matching jobs than they might otherwise. It also has helped employers find better candidates.[20] A better match ultimately creates value, which can manifest with improved productivity, greater job satisfaction, lower turnover, higher employer profits, and greater worker pay.[21]
And one worker-job match may be better than another because the applicant lives in a less expensive area and is willing to accept less pay. The post-pandemic jump in the wage growth differential of job-switchers into long-distance work can be attributed to geographic cost savings as well as better matching in a thicker market.
How might we separate the two? If the alternative to hire a long-distance worker is to hire locally, near the manager, then a long-distance worker-job match has the potential for geographic cost savings only when workers live in less-expensive places than their managers, not the other way around. If we examine job-switchers who take on long-distance roles reporting to managers in both more- and less-expensive places, any wage growth differential of the latter can be attributed only to better matching.[22] Figure 3 repeats the job-switcher exercise for those two groups.[23]
Figure 3
The wage growth differential for job-switchers taking long-distance work increased for both groups but was bigger for people reporting to managers in more-expensive areas. This result is consistent with that greater wage growth differential reflecting both geographic cost savings and better matching.[24]
Crucially, however, we also see an increase in the wage growth differential for remote job-switchers who report to managers in less-expensive places, where there are no geographic cost savings to be had. This higher relative wage growth suggests that long-distance work began producing better worker-job matches after the pandemic. The thickening of the market for long-distance work—the greater availability of non-local candidates and job openings due to the normalization of remote work—strengthened the match-quality advantage of long-distance work.
Looking ahead
Improved worker-job matching can benefit the economy and society because people who are in the right jobs tend to be more productive and satisfied.[25]
Long-distance work has its problems, including isolation and the challenges of remote managing, but its increasing prevalence suggests that the benefits outweigh the frictions.[26,27]
As long-distance workers multiply and gain experience, new career trajectories might emerge. People could transition between in-person and long-distance careers, for example, the way lawyers move between partner- and non-partner career tracks, reflecting a trade-off between higher pay and better work-life balance.
And while both substance and presentation will continue to matter, long-distance work might lend itself to showcasing one’s work more prominently than one’s social skills, perhaps taking the market closer to meritocracy.
But as one of our previous studies noted, lack of proximity may come at a cost to society. The tremendous growth of long-distance work has turbocharged the process of domestic offshoring, which concentrates value-intensive jobs in the nation’s most expensive cities while sending rank-and-file ones to more affordable parts of the country. Inasmuch as opportunity depends on geographic proximity and interaction in-person, long-distance work that fuels domestic offshoring runs the risk of separating most Americans from opportunity by a barrier of distance. The many advantages of long-distance work may or may not be enough to surpass that barrier.
Notes:
We use the terms long-distance work and cross-metropolitan work synonymously to describe the relationship between a worker and a manger who live in different metro areas. Those terms are distinct from remote work, which refers to work that is regularly performed off-site, regardless of which metro the worker or their manager reside in. For more details, see the first study in this series.
The degree of employment interconnectivity between U.S. cities has exploded since 2020, and that has turbocharged the process of domestic offshoring, whereby value-intensive jobs are increasingly concentrated in the nation’s most expensive cities, while rank-and-file positions locate in more affordable parts of the country.
This study and earlier studies in the series employ a sample of ADP payroll and HR records corresponding to subset of employers serviced by ADP with at least 1,000 employees in the United States. Within that employer group, the sample was further limited to individuals who are members of teams with a reliable set of reporting structure fields by limiting the data to teams in the United States in which no more than 10 employees report to the same manager. Although the number varies over time, approximately 1.33 million employees at 3,500 businesses, on average, met these criteria each month from January 2016 to June 2023.
A worker can also transition into long-distance status passively if their local manager relocates to another metro area, or actively by taking on a new long-distance job, i.e. one in which they reporting to a manager living in another metro area, with or without relocating themselves.
Following the logic of event study models, we determined workers’ pre- and post-transition metro of residence, employer, and team identity (as reflected by the identity of the manager), by considering a six-month window before and after each month of observation (the current month of observation was included in the after window). Only records with fully observed before and after windows (i.e. with six consecutive monthly observations, containing all necessary information), and that were also internally consistent with respect to the metro, employer, and team identity were kept in the sample. Cases in which a manager’s location changed during the before or after window or between the two were omitted as well to better ensure continuity of the worker’s role. Finally, records were omitted when they corresponded to a job spell with average monthly wages below a $20,000 annual income, when they overlapped with another observed employment spell for the same worker having higher average earnings (i.e. when a record did not belong to a worker’s primary job), or when they reflected the first or last month of an employment spell, when earnings are likely to reflect only a partial month. Wage growth was obtained as the percent difference between the average gross wages in the last five months of the after window and the first five months of the before window. The current and preceding months were omitted from the wage growth calculation to avoid assigning pre- and post-transition pay incorrectly given uncertainty around the exact timing of the relocation and the potential wage update within those two months. To eliminate outliers, wage growth for job-stayers was truncated below halving and above doubling.
Relocation was determined based on metro areas defined broadly. Specifically, metropolitan area definitions used for determining relocation and long-distance status (i.e. for comparing workers’ and managers’ metro of residence) refer to Consolidated Statistical Areas (CSAs) where those are applicable, and to Core-Based Statistical Areas (CBSAs) elsewhere. By using the broadest available definitions of metropolitan areas, we minimize situations in which workers separated by arbitrary lines within broader metros, e.g. between the San Francisco and San Jose metro areas. The downside of this approach is that the largest metro areas internalize employment relationships that are cross-metro in kind. In contrast, relocation up and down the housing price gradient was determined based on workers’ narrowly defined metro area, i.e. CBSAs. That is a pragmatic choice, because housing price indices are readily available for CBSAs but not CSAs. It also allows us to better capture the actual local housing cost levels faced by individuals. Housing price levels associated with each CBSA were obtained from the public-facing Zillow Home Value Index. To avoid muddying inter-metropolitan housing price comparisons with housing price growth during the years observed, we assigned each metro area its housing prices from February 2020—on the eve of the pandemic—regardless of the timing of the associated record in the data. Relocations were designated as being up the housing price gradient when the origin to destination housing price difference was greater than 10 percent, and as down the housing price gradient when it was below negative 10 percent.
We measured near-immediate wage growth around the transition. It is possible that workers transitioning into long-distance status would experience a different trajectory of wage growth than they would otherwise over time. In that case, the changes in their wage growth owing to the transition could compound gradually over time, causing them to deviate from the near-immediate wage growth that we observe.
The estimates were performed via linear regression with team fixed effects. For details, see regression appendix.
We know from an earlier study, for example, that the age of workers flowing into long-distance status changed markedly during the pandemic. Inasmuch as wage growth tends to steepen or flatten at certain points of the lifetime career trajectory, a changing mix of ages among those transitioning into long-distance work could influence their observed wage growth relative to those staying local. For details of the controls used, see regression appendix.
In contrast, job-stayers transitioning into long-distance work by relocating up the housing price gradient, i.e. to more expensive places, experienced average wage growth around the transition that was about 2 percentage points below that of their teammates in 2016-2019. That difference turned positive in 2020-2021, then reverted to its 2016-2019 level in 2022-2023. It is not obvious how to interpret that result. The key probably is to ask what kind of selection determines who moved up the housing price gradient within jobs in each period. Internal transitions up the housing price gradient pre-pandemic were rather uncommon and perhaps included an over-represented contingent of relatively junior employees who were more tolerant to expensive cities prior to having children, and whose wage growth may have been lower than that of their more senior peers. During 2020-2021—but much less so in 2022-2023—such moves probably included a very different pool of people, as the typical motivation suddenly became living near family again, including for workers whose families are located in expensive places.
The median pay changes for job-stayers correspond to year-over-year changes as of January 2023 and January 2024, drawn from the ADP Research Institute’s Pay Insights report as of February 28, 2024.
As noted in footnote 5, we are measuring the near-immediate wage growth deficit around the transition. If that wage growth deficit were to persist over time and compound, it could become even more meaningful.
Housing costs—a proxy for the cost of living—are highly dispersed across metros. For example, the interquartile range across CBSAs of the February 2020 Zillow Home Value Index is a 2.4-fold multiple.
This exercise followed the same lines as the previous one, as detailed in footnote 5, with some differences. Here we used a three-month window before and after each observation to determine the pre- and post-transition metro of residence and employer identity for workers and their distinct managers. We also allowed up to a six-month gap between the end of the pre-transition job and the beginning of the post-transition one, using the first month of the post-transition spell to time the transition. Overlapping jobs held by the same individuals were more fully purged, with any job spell observed to overlap with another removed in its entirety to reduce instances of part-time roles populating transitions. To eliminate outliers, wage growth for job-stayers was truncated below two-thirds reductions and above tripling (reflecting the wider wage swings of job-switchers relative to job-stayers).
Estimates were performed via linear regression with post-transition employer fixed effects.
The controls used here are the same as those used in the regression informing Figure 1, with one exception. The previous regression controlled for job-stayer location only prior to the transition to avoid inadvertently controlling for the potential for geographic cost savings. The current regression controls for the pairing of job-switchers’ pre- and-post transition locations, because in this case the employer’s potential for geographic cost savings depends largely on the housing price gradient between the manager’s location and the worker’s location. For job-stayers transitioning from local to long-distance work, the origin-destination housing price gradient is generally identical to the manager-worker housing price gradient, but for job-switchers into long-distance work, the two are often different. Here, we control for the worker’s origin-destination housing price gradient which can differ across workers joining the same employer and could potentially correlate with post-transition long-distance status, but we don’t control for the manager-worker housing price gradient, which is the main determinant of the employer’s geographic cost savings. For more details of the controls used, see regression appendix.
The wage growth differential for job-switchers transitioning into long-distance versus local work could be attributed to better matching in a thicker market even before the pandemic. The normalization of remote work during the pandemic changed that not qualitatively, but quantitatively: By vastly increasing the number of long-distance job openings and candidates, it thickened the market, increasing match quality and raising the premium for long-distance work. That said, the pre-pandemic result could be attributed, at least in part, to omitted variable bias, as the controls included in the regression cannot ultimately hold all else fixed.
It is beyond the scope of this study to identify and disentangle with certainty all the causes of the post-pandemic jump in the excess wage growth of job-switchers into long-distance work, however we can make some headway. First, the jump could stem from changes in the mix of long-distance employers. That is why we compare job-switchers’ transitioning into long-distance work with those switching into local work for the same employers. Second, the jump could stem from changes in the mix of long-distance workers. That is why we control for worker attributes, including their locations before and after the job transition (See footnote 16, as well as the regression appendix). Finally, the jump could stem from changes that the reflect the pairing of jobs and workers, i.e. the match between them. That is what we are concerned with.
A prerequisite is that workers and employers are able to search for each other effectively, i.e., that they should be able to sift through the vast pool of potential partners and find those that offer the best match, or at least a better one than they might find otherwise. Search can be conducted effectively today only to the extent that it is well-mediated by the internet, for example through online job and hiring search engines. In some ways, that is true for localized search as well, though it seems likely that there is a benefit to greater local familiarity that might be lost in long-distance search.
Strictly speaking, the normalization of remote work since the pandemic increased the number and variety of job openings and candidates, but it might have limited local options for workers and employers because of competition. That limitation of options is likely to be the most noticeable for workers in the most expensive places, who might have fewer local job options as local employers opted to hire remotely, and for employers in places with the lowest wages, who might have lost local candidates to competition from remote employers. That said, because all local labor markets are small compared to the national (or global) labor market, the increase in remote options seems likely to dominate any reduction in local options nearly everywhere. All participants experienced a thickening.
Better matching is not confined to the kinds of skills and job requirements that might appear on a resume or job description. It also includes myriad other criteria that matter for the people involved, such as their schedules, habits, expertise, and more.
Note that in the case of job-stayers—workers relocating while keeping their existing jobs—the geographic aspect of the match changed while all other aspects remained largely unchanged.
See footnote 4 for details on the assignment of housing price levels to CBSAs. Worker-manager relationships were designated as being up the housing price gradient when the housing price difference between the manager’s CBSA and the worker’s was greater than ten percent, and as down the housing price gradient when it was below negative ten percent.
Specifically, the excess wage growth of job-switchers into long-distance work up the housing price gradient reflects the worker’s portion of the geographic cost savings and the improved matching. Presumably, the employer benefits from both, too.
In fact, even for workers and employers who do not engage in long-distance work directly, the age of remote work could shape the set of outside options, affecting them indirectly. For example, if local workers have a new option of earning more from distant employers, local employers trying to retain a sufficient workforce might need to raise wages for employees who continue working locally. This can hold even in lines of work which don’t lend themselves to being performed remotely as long as worker options include the ability to transition to other lines of work that do. Similarly, if employers have a new option of hiring distant workers at lower cost, that might exert downward pressure on local workers’ wages. The potential indirect effects of the new bargain on workers and employers not engaging directly in long-distance work are closely related to the factor-price equalization theorem in the Heckscher-Ohlin model of comparative advantage in trade. And while such factor-price equalization with respect to wages in different places is unlikely to occur in isolation—the world is more complicated, as illustrated by the evidence of domestic offshoring—it nevertheless is likely to be an important element in what unfolds.
Sensitivity to those frictions can vary significantly across different types of work. As we argued in a previous study, the degree of sensitivity to those frictions is likely a key element in determining the pattern of domestic offshoring, i.e. the geographic sorting of work between the groups of U.S. cities we dubbed leadership hubs and workforce nodes.
Long-distance work offers individuals at least two non-pecuniary benefits: Optionality and opportunity. Optionality is improved because a broad swath of jobs are more geographically footloose and job choices are less dependent on where one lives. With limitations, a Wall Street career can now be had in the Midwest, permitting workers to separate their careers from the needs, wants, and circumstances that determine where they live. Opportunity is another benefit. A thicker market means workers searching for jobs will have more gold nuggets to find, so to speak, if they’re able to tackle a more complicated search process. Those who engage the long-distance job market simply by applying to the same handful of superstar firms as everyone else likely will be wasting their time. New strategies and wisdom will emerge around job search, which is likely to be a higher ROI activity than it used to be. Employers, too, will need to adapt recruitment in the long-distance job market. Familiarity with the nation’s best universities will not suffice; employers will need to familiarize themselves with sources of talent that more local and less known.
Regression Appendix
