The Effect of Working from Home on the Agglomeration
Economies of Cities: Evidence from Advertised Wages
Please click here to download the latest version.

Yichen Su†

Sitian Liu*

This Draft: August 9, 2023
First Draft: May 13, 2022

Abstract
Using job posting wage data, we find a substantial decrease in the urban wage premium for occupations with high working-from-home (WFH) adoption following the COVID-19 pandemic. This decline
is accompanied by an employment shift away from large employment centers. Through the lens of a stylized model, our empirical findings suggest that WFH adoption weakens the agglomeration economies ofcities. A decomposition exercise reveals that the urban wage premium drop is partly driven by reduced
wage returns for interpersonal skills in large cities, suggesting that the weakening of the agglomeration
effect stems partially from reduced interactive activities.

Keywords: Agglomeration, Productivity, Spillover, Urban Wage Premium, Working from Home, Remote, Virtual, WFH, Wages, Job Posting, COVID-19, Pandemic
JEL Codes: R12, R23, J24, J31* Queen’s University. Email: sitian.liu@queensu.ca.
†

Federal Reserve Bank of Dallas. Email: yichensu@outlook.com. The views expressed in this article are those of the authorsand do not necessarily represent the views of the Federal Reserve Bank of Dallas or the Federal Reserve System. We thankNathaniel Baum-Snow, Jan Brueckner, Andra Ghent, Edward Glaeser, Emma Harrington, Jack Liebersohn, Andrii Parkhomenko,
Esteban Rossi-Hansberg, the anonymous referees, and participants at Fed system applied micro meeting, the CPD Social Statistics
and Population Dynamics seminar at McGill University, seminar at Toronto Metropolitan University, SITE, Stanford Remote Work
Conference, AREUEA National Conference, MEA, UEA Meeting, and SOLE conference for their helpful comments.

1

Introduction

It is well documented that productivity and wages tend to be higher in large and densely populated cities
compared with smaller cities or rural areas (Ciccone and Hall, 1996; Glaeser and Mare, 2001; Baum-Snowand Pavan, 2012; Moretti, 2013; Diamond, 2016). The elevated productivity in large cities is primarily
attributed to the “agglomeration effect” resulting from the geographical clustering of workers and firms.
Increased interaction and proximity among workers in large cities enables easier knowledge transfer andskill acquisition, which elevates local productivity (Glaeser, 1999; Wheaton and Lewis, 2002; Jaffe et al.,
2003; Charlot and Duranton, 2004; Kerr and Kominers, 2010; Akcigit et al., 2018; Davis and Dingel, 2019;
Baum-Snow et al., 2021; Jarosch et al., 2021; Emanuel et al., 2023). Additionally, the co-agglomeration offirms and industries facilitates professional networking and the establishment of new business relationships,
which further boosts productivity within these large urban areas and industry clusters (Ellison et al., 2010).
This paper studies the impact of working from home (WFH) on the agglomeration economies of cities
and its aggregate productivity implication. On the upside, WFH enhances job flexibility, which has beenshown to boost productivity for certain workers (Bloom et al., 2015; Barrero et al., 2021; Emanuel andHarrington, 2022). It also allows workers to access high-productivity firms in large cities without incurring
the high costs of urban living. This could increase the labor supply to these large-city firms, potentiallydriving up aggregate productivity, wages, and output.
Conversely, WFH could dilute the positive productivity spillovers arising from spontaneous in-person
interactions. The lack of informal “coffee talks” could limit the exchange of knowledge and ideas, bothwithin and between firms clustered in large cities. Furthermore, the decrease in physical presence mayundermine the role of large cities in fostering professional networks and facilitating complex business relationships. If WFH weakens the agglomeration effect, it could negatively impact productivity and wages inlarge-city firms, potentially causing workers to shift to smaller-city firms. This could not only reduce theurban wage premium but also lead to a decline in aggregate productivity, wages, and output.
We present a highly stylized spatial equilibrium model to illustrate the competing forces through which
WFH affects the strength of agglomeration economies, the urban wage premium, and aggregate economic
output. The model assumes that the agglomeration effect is fueled by the productivity externalities generated
by onsite workers. The model predicts that the adoption of WFH reduces the wage premium in large cities,
regardless of whether WFH weakens the agglomeration effect in large cities. Specifically, if the agglomer-1

ation effect remains strong despite a reduction in onsite workers, WFH adoption could increase the labor
supply for firms in large cities. This increase would result in more workers servicing large-city firms while
residing in smaller cities, which enables broader access to these firms’ high productivity and thus elevatesaggregate productivity, wages, and output. In contrast, if the switch from onsite to remote work significantly
weakens agglomeration economies in large cities, this could lead to diminished productivity in large cities.
In such a scenario, workers might be incentivized to switch to firms in smaller cities. The decreased productivity in large cities and the equilibrium reallocation of workers could lead to lower aggregate productivity,
wages, and output.
Based on our model, we derive two empirical predictions to validate the model and to examine whether
the adoption of WFH weakens the agglomeration economies. First, the urban wage premium should decrease with the advent of WFH, regardless of whether agglomeration economies in large cities are weakened. Second, the direction of employment shift between cities depends on the primary driver of the reduced
urban wage premium. If the decrease is primarily due to a weakened agglomeration effect in large cities, employment (based on employers’ locations) should move from large to small cities. However, if an increasedlabor supply to large cities is the main cause, employment should shift from small to large cities.1
To empirically assess whether the adoption of WFH affects local productivity and whether the effect isdue to a decrease in agglomeration economies, we use the COVID-19 pandemic as an exogenous shock to
the prevalence of WFH. The pandemic forced many employers across various occupations to suddenly andunexpectedly shift to a WFH model, while other occupations, due to the nature of their work, were unable
to do so (Bartik et al., 2020; Bick et al., 2022; Brynjolfsson et al., 2020). These differential levels of WFH
adoption provide an empirical setting to examine the effects of WFH on economic outcomes such as wages
and employment in large cities compared with smaller cities.
Our empirical method assumes that shocks unrelated to the WFH adoption during the pandemic did notdisproportionately affect wages of jobs located in large cities within high-WFH-adoption occupations. We
are aware that other pandemic shocks unrelated to WFH adoption could have distinct impacts on high- andlow-WFH-adoption occupations. However, if the effects operate at the occupational level, they should notalter the urban wage premium or employment growth differently by city size. Similarly, other pandemic
1

It is worth noting that observing an increase in employment in large cities does not necessarily contradict a decrease in the
agglomeration effect. This is because the surge in labor supply might outweigh the reduced agglomeration effect. Conversely, adisproportionate decrease in both employment and the wage premium in large cities would provide compelling evidence for the
decline of agglomeration economies in large cities.

2

shocks might have affected wages and employment differently in large and small cities. However, ourconclusion should hold as long as these city-specific effects do not differ systematically by occupations’level of WFH adoption.2
We test our model’s urban wage premium prediction using data on advertised wages from Emsi Burning
Glass (now Lightcast). Our findings indicate a significant decrease in urban wage premium for jobs in
occupations that heavily adopted WFH after the pandemic shock. This decrease was observed for bothcollege-degree-required and non-degree jobs. Furthermore, the reduced urban wage premium in high-WFHadoption occupations did not bounce back post-2020 and remained well below pre-pandemic levels as of
mid-2023. Conversely, for occupations with low WFH adoption, the urban wage premium remained mostly
unchanged after the pandemic shock. These findings validate our model’s first prediction.
One potential concern is that our results might be influenced by spatial sorting of skill supply or demandduring and post-pandemic, potentially reflecting changes in skill sorting rather than an actual urban wage
premium reduction. To mitigate the concern, we show that even after controlling for jobs’ observable skill
needs, there is a significant decrease in the urban wage premium within high-WFH-adoption occupations.
Additionally, to rule out any confounding effects due to geographical changes in job postings, we examine
the location-specific industry-level average weekly earnings from the Quarterly Census of Employment
and Wages (QCEW). We find a similarly disproportionate drop in the urban wage premium in high-WFHadoption industries over the three years following the pandemic.
Another potential concern is that the decrease in the urban wage premium within high-WFH-adoption
occupations might simply be a mechanical result of larger cities offering a greater share of WFH-compatible
jobs. If WFH functions as a job amenity that provides compensating wage differentials, a disproportionate
increase in WFH-compatible jobs offered by large-city firms could automatically reduce the urban wage
premium without any decrease in agglomeration economies or increase in labor supply in large cities. To
demonstrate that the drop in the urban wage premium is not mechanically driven by a higher WFH adoption
in large cities, we show that the urban wage premium decreased among both WFH-compatible and noncompatible jobs within high-WFH-adoption occupations. For added robustness, we use the latest American
Community Survey (ACS) and find that hourly wages reported by onsite workers in high-WFH-adoption
occupations witnessed a significant urban wage premium drop in 2021, compared with 2018 and 2019.
2

It is noteworthy that this paper does not intend to analyze the short- or long-run effect of the pandemic per se, such aspredicting the extent to which on-site work will return in the long term or whether large cities will make a come-back eventually.
The pandemic is used as the empirical setting of the study, not the subject.

3

We next explore whether the decreased urban wage premium among jobs in high-WFH-adoption occupations was mainly driven by diminished agglomeration effects in large cities or by an increased labor
supply (of remote workers) in these cities. We use the QCEW to analyze the location-specific employment
growth. Since the QCEW reports employment by industry (not by occupation), we examine whether employment growth in high-WFH-adoption industries was faster in larger employment clusters compared withsmaller ones post-pandemic. Our finding suggests a disproportionate decline in employment (based on employers’ locations) in high-WFH-adoption industries in larger cities compared to smaller cities relative tothe pre-pandemic period. In other words, high-WFH-adoption industries in large cities not only experienced
a relative wage decrease but also saw an outward employment shift. According to our model, this finding
implies that the decreased relative wage in large cities is likely attributable, at least partially, to weakened
agglomeration economies in these cities.3
Lastly, we move away from empirical model tests and shift our focus to indirectly evaluating the weakening of agglomeration economies using an alternative approach. We employ a Gelbach decomposition
exercise, drawing from Gelbach (2016), to identify which skills listed in posted jobs contribute the mostto the overall decline in the urban wage premium within high-WFH-adoption occupations. Our hypothesisis that if labor supply to large cities increased due to WFH adoption, we should observe disproportionate
decreases in the urban wage premium of skills that are specifically complementary to remote work, suchas information technology skills. In contrast, if the weakening of agglomeration economies in large cities
plays a more important role, we should anticipate disproportionate decreases in the urban wage premium ofskills supportive of knowledge spillovers, network building, or fostering business relationships.
Our decomposition results suggest that several skill cluster families, including “Marketing and Public
Relations,” “Customer and Client Support,” “Building Relationship,” “Communications,” “Business Management,” and “Information Technology,” play a crucial role in the decline of the urban wage premium.
Notably, skills related to “Marketing and Public Relations,” “Customer and Client Support,” “Building Relationship,” and “Communications” tend to be particularly productive in interactive activities (Deming, 2017;
Deming and Kahn, 2018). The relative decline in wage returns to these skills in large cities suggests that
3

Some studies have found higher or unexpectedly high productivity for remote workers compared with onsite workers (Bloom
et al., 2015; Barrero et al., 2021; Emanuel and Harrington, 2022). Our findings do not dispute these results. Specifically, we are
not comparing the productivity of remote and onsite workers, holding all other factors equal. Rather, our results suggest that WFH
adoption may have disproportionately affected the relative productivity of jobs based in larger cities. The productivity reduction
could affect both onsite and remote workers because fewer onsite workers due to WFH could decrease the degree of knowledge
spillovers for all workers.

4there has likely been a decrease in the frequency of productive interactive activities requiring these skills infirms located in large cities. In addition to the decrease in the urban wage premium for these skills, we also
observe a decline in the frequency that these skills are listed in job postings in large cities compared withsmaller ones, further suggesting reduced demand for these skills in large-city workplaces. These findings
constitute indirect evidence that large cities saw their agglomeration economies weakened due to reduced
productive interactions.
In contrast, “Information Technology” skills typically involve using electronic tools and applications,which complement the feasibility of remote work. These skills also saw a significant decrease in the urban
wage premium. This finding suggests that the decline in the urban wage premium could also be partiallyattributed to an increase in the labor supply of remote workers to large cities.
Our paper contributes to several strands of literature. First, we add to the ongoing discussions surrounding the impact of the pandemic-induced rise in WFH on cities and productivity. Many studies havehighlighted a shift in housing demand from city centers to suburbs and from larger to smaller cities due tothe increasing prevalence of WFH (Gupta et al., 2021; Liu and Su, 2021; Ramani and Bloom, 2021; Althoff
et al., 2022; Delventhal et al., 2022; Li and Su, 2022; Howard et al., 2023; Monte et al., 2023). Other studies
explore how the endogenous change in productivity brought about by the WFH shock affects the well-being
and inequality of the U.S. population (Behrens et al., 2021; Davis et al., 2021; Delventhal and Parkhomenko,
2022). Especially relevant to our paper, Brueckner et al. (2021) present a spatial equilibrium model with
WFH and show that remote work is likely to equalize wages across regions. We further demonstrate thatboth the weakening of agglomeration economies in large cities and the increased labor pool accessible tofirms in these cities are likely important drivers behind the diminished wage differentials across space.
In addition, our paper complements the extensive body of research exploring the agglomeration economies
of cities and the urban productivity premium. This literature seeks to unravel why workers and firms in larger
cities tend to be more productive. Prior studies provide evidence that the productivity advantage in large
cities arises from both the effect of sorting of more productive firms and workers into these areas and the
productivity-boosting effect of the large cities themselves (Combes et al., 2008; D’Costa and Overman,
2014; Gaubert, 2018; Martellini, 2022). Notably, research by Glaeser and Mare (2001), De La Roca andPuga (2017), and Eckert et al. (2022) demonstrates that experience in large cities not only increases workers’ productivity and wages but also bolsters wage growth even after individuals leave these areas. Our
paper offers additional insight into the mechanisms behind cities’ agglomeration effects. Previous research5has established micro-foundations and provided evidence for various mechanisms that drive agglomeration
economies, with knowledge spillovers, input-output linkages, and labor pooling being the most prominent(Rosenthal and Strange, 2003; Duranton and Puga, 2004; Bleakley and Lin, 2012). Our paper adds to thisliterature by studying the effect of the removal of in-person interactions on agglomeration effects.
The rest of the paper is organized as follows. Section 2 presents a stylized model and its predictions.
Section 3 describes the data. Section 4 presents the empirical results that test the model predictions. Section 5 presents additional skill-level analyses to further shed light on the mechanism behind the weakened
agglomeration effect. Section 6 concludes.

2

Stylized Model of Working from Home and Agglomeration

To demonstrate how increased WFH adoption could affect agglomeration economies, the urban wage premium, and productivity, we introduce a stylized model, which captures the key mechanisms at work andsummarizes the main implications of WFH in the presence of local agglomeration externalities.
Assume there are two locations: H and L. H represents a large or high-density city, and L representsa small or low-density city. People who work for firms in H can either be onsite by living in location H
or work remotely by living in location L.4 However, if they live and work in different locations, they incura long-distance cost ϕ. We assume that people who work for firms in location L must also live in L.5 All
workers are identical. The total number of workers in the economy sums up to 1.
Notation-wise, let NHH be the number of workers who work for firms in H and live in H; let NHL be
the number of workers who work remotely for firms in H and live in L; let NLL be the number of workers
who work for firms in L and live in L.
4

Note that our model assumes that remote workers of firms in H can only live in location L. Without the presence of amenities,
if location H has higher housing costs, any workers who work from home would choose to live in L in equilibrium, even if they
could opt to live in H. In Appendix A3, we present an alternative model allowing workers employed by firms in H to choose
between living in H or L while WFH, effectively separating the concepts of WFH and remote work. We show that the core insightsfrom our baseline model persist even with this additional feature.
5
We assume away the margin between WFH and onsite work in L for simplicity. The assumption is consistent with the factthat agglomeration economies are largely a large-city phenomenon. Moreover, even if WFH is modeled in L, under the assumptionof perfect mobility and that rent is higher in H, workers who work in L will all choose to live in L.

6

2.1

Production

Large/High-Density City H: The production function in the large and high-density location H is givenby the following equation:
FH (BH , NHH , NHL ) = BH (NHH + NHL )γ ,where BH is the productivity level in location H, which firms in H take as given. Given the level of
BH , firms use labor, either provided onsite NHH or remotely NHL , as the input for production. Beyond
each firm’s control, the presence of onsite workers generates productivity externalities. Specifically, BH =BH (NHH ) is a function of the number of onsite workers located in H:
θ
BH (NHH ) = B0H NHH,

where θ > 0, which represents the intensity of productivity externalities, influenced by the agglomeration
effect of workers in location H. This can be interpreted as externalities resulting from spontaneous physicalinteractions and the ease of building relationships in large and densely populated areas.
Firms’ profit-maximization problem implies that the wage is equal to the marginal product of labor:θ
WH = γB0H NHH(NHH + NHL )γ−1 .

We can see that the wage level of location H decreases as the labor supply increases due to the diminishingmarginal return of labor. However, owing to the production externality term, a greater number of onsite
workers can boost wages due to the agglomeration effect.

Small/Low-Density City L:

The production function in the small and low-density location L is simpler,

as only onsite workers are used in production:γ
FL (BL , NLL ) = BL NLL.

We assume that the productivity level in location L only contains an exogenous component BL , which isequivalent to assuming θ = 0. We believe this is a reasonable assumption because production externalities

7

are usually facilitated by intense communication and knowledge exchanges, which are more common inlarge cities and industry clusters. In addition, previous research suggests that the strength of agglomeration
tends to exhibit some degree of convexity with respect to city size (Moretti, 2021).
Firms’ profit-maximizing problem yields that the wage level in location L is
γ−1
WL = γBL NLL.

2.2

Housing Market

Local housing costs adjust based on local housing demand, varying according to the local housing supply
elasticity. The housing demand in location j, j ∈ {H, L}, is the sum of the population who chooses to livein location j regardless of the location of their labor supply.
The rent of local housing services in location H isrH = π0H + πH ln(NHH ).

The housing demand in H is the number of workers who work and live in H. The rent in location L isrL = π0L + πL ln(NHL + NLL ).

Slightly different from location H, the housing demand in L is the sum of workers who supply labor remotely for firms in H while living in L and workers who work and live in L.

2.3

Workers’ Location Choice

Workers can make one of the following choices: (i) working and living in H: HH, (ii) working in H andliving in L: HL, or (iii) working and living in L: LL. We use an exogenous cost of remote work, denoted by
ϕ, to model the shock of WFH adoption—the cost decreases exogenously when WFH becomes prevalent.
We then examine the impact of WFH on the equilibrium outcomes by evaluating the comparative statics of adecrease in ϕ.6 Appendix A2 presents an alternative setup where workers adopt WFH as a result of both an
6
Our model of WFH is more simplified compared with some other studies that explore the impact of WFH on productivity,
such as Davis et al. (2021) and Delventhal and Parkhomenko (2022). In our baseline model, we assume the marginal productivityof remote and onsite workers is identical; we use the cost of WFH (ϕ) to induce a shift in the fraction of remote workers. A decreasein ϕ can be viewed as a reduced-form way to account for both the relative productivity increase of WFH as described by Davis et al.

8exogenous decrease in the cost of WFH and an exogenous increase in the productivity of remote workers.
We show that ϕ can capture both sources of exogenous shocks in a reduced-form way.
Workers can attain the following utility levels based on their work and residential location choices:

UHH = wH − βrH ,UHL = wH − βrL − ϕ,
ULL = wL − βrL ,where wH and wL are the log wages in H and L, respectively; rH and rH are log rents in H and L,
respectively; ϕ is the cost of working remotely from another city.
Since all workers are assumed to be identical, in equilibrium, all three levels of utility must equalize(assuming we are not in a corner solution where UHL is too low such that no one works remotely):Ū = wH − βrH = wH − βrL − ϕ = wL − βrL .

The equalization property of the homogeneity assumption allows for easy comparative statics.

2.4

Effect of WFH in Equilibrium

Urban Wage Premium

From the equalized utility levels, it is evident that a decrease in ϕ would narrow

the spatial gaps in both rents and wages. Specifically, taking the difference between the first and second
equations, we can see that the rent premium between H and L is a function of ϕ:rH − rL =ϕ.
βTaking the difference between the third and second equations, we can see that the wage premium between
H and L is exactly ϕ:
wH − wL = ϕ.
(2021) and the shift in social norms as described by Delventhal and Parkhomenko (2022). Our analysis does not require explicit
assumptions on these factors. In addition, our stylized model abstracts away from commuting and hybrid arrangement. While ourmodel simplifies many aspects of the work environment, it is designed with the specific aim of illustrating how WFH can affectagglomeration economies. As such, we intentionally streamline other forces to present a focused picture.

9Thus, when the cost of WFH (ϕ) decreases, the urban wage premium also decreases. However, this condition alone does not directly indicate how much the aggregate wages and output levels are impacted by thedecrease of ϕ. Below, we analyze these effects.

Agglomeration and Aggregate Productivity To examine the effect of a reduction in ϕ on equilibrium
productivity, wages, and output, we totally differentiate the sum of production in both locations with respect
to ϕ. This allows us to understand the mechanisms through which ϕ affects output. Since we assume aconstant and equal labor share (γ) in both H and L, and the total population is normalized to one, thedirection of change for output aligns with the direction of change in wages and productivity.
The effect of a decrease in ϕ on the aggregate output is∂(NHH + NHL )∂(FH + FL )
θ−1 ∂NHH= θB0H NHH(NHH + NHL )γ + (WH − WL )
.
∂(−ϕ)
∂(−ϕ)
∂(−ϕ)
|
{z
} |
{z
}
Weakening of Agglomeration Economies

Reallocation of Labor from L to H<0

< 0 or > 0(1)

The effect can be decomposed into two components: (i) a decrease in the aggregate output due to weakened
agglomeration economies in H and (ii) a change in the output due to the reallocation of labor between L
and H (in terms of job location). Below, we show that the first component is definitively negative if θ > 0.
However, the second component’s sign depends on the direction of labor reallocation between H and L,
which is not definitive. The intuition of Equation 1 is as follows: The rise in WFH reduces the number ofonsite workers, thereby lowering the agglomeration effect in H and the output. The extent of this effectdepends on the sensitivity of productivity in H with respect to the number of onsite workers, i.e., θ. If
the reduction in H’s agglomeration effect is minimal and WFH allows a sufficient number of workers toremotely supply their labor to the high-productivity location H while residing in L, the gains from suchreallocation could compensate for the productivity loss from a weakened agglomeration effect. However, if
the reduction in H’s agglomeration effect is so significant that it drives workers to relocate from H to L, theaggregate output will definitively decrease. It is important to note that a scenario exists where there is a netincrease of workers moving to firms in H, but the weakened agglomeration economies still result in a net
loss of output.
To analyze the sign of each component in Equation 1, we need to know the direction of changes in∂(NHH +NHL )HH. Appendix A1 presents
the number of onsite workers ∂N∂(−ϕ) and the reallocation of labor
∂(−ϕ)

10

the derivation. Here, we show the effect of a decrease in ϕ on the numbers of onsite and remote workers
servicing firms in H:
∂NHH
=− 
∂(−ϕ)
β
∂NHL
= 
∂(−ϕ)
β

1
πL
πH
1−NHH + NHH

1πH
πL
1−NHH + NHH

β
+

β





 < 0;πL
πH
1−NHH + NHH

πL
πH
1−NHH + NHH



(2)



θ
− NHH

1−γ
1−γ
NLL + NHH +NHL

.

(3)

In line with the discussed intuition, we demonstrate that reducing the cost of WFH decreases the number ofonsite workers (NHH ). However, the effect on the total labor supply for production in H is not certain:∂(NHH + NHL )
= 
∂(−ϕ)
β

β



πL
πH
1−NHH + NHH

πH
πL
1−NHH + NHH





θ
− NHH

1−γ
1−γ
NLL + NHH +NHL

.

(4)

Interestingly, the impact of a decrease in the cost of WFH on labor supply to location H can be either
positive or negative, depending on the intensity of agglomeration, θ. If θ = 0 (no externality spillovers),
a reduction in ϕ will definitely increase the total labor supply to location H. The intuition is that before
WFH adoption, workers employed by firms in H have to bear the cost of housing in H, thereby limiting
the number of workers who can take advantage of the productivity offered in H. The adoption of WFH
allows more workers to relocate to L while still working for firms in H, effectively relieving the congestion
problem associated with working in H.
Conversely, if θ > 0 (where agglomeration externalities exist), the positive effect of reducing ϕ on the
total labor supply to H diminishes and could even reverse if θ is large enough. This happens because as moreworkers switch from onsite to remote work, the productivity spillover from onsite workers decreases, which
lowers the marginal product of both onsite and remote workers. Since these externalities are not internalized
in firms’ profit maximization problem and hence not priced in wages, the number of onsite workers and thetotal number of workers at firms in H will decrease and will fall below the optimal level.

Model Predictions

In sum, our model predicts that the rise of WFH (a decrease in ϕ) will invariably

reduce the urban wage premium, irrespective of potential weakening in agglomeration economies of large
cities. This can be attributed to WFH expanding the labor pool available to firms in these large cities, which
could also drive down the urban wage premium.
To further test whether agglomeration economies have declined in large cities, we need to assess whether
11∂(NHH +NHL )< 0. If we observe not only a reduced urban wage premium but also a decline in employment∂(−ϕ)

by firms in large cities due to WFH, this would suggest a drop in productivity in these cities so that workers
are encouraged to switch to firms in smaller cities. In this scenario, the weakened agglomeration economies
in large cities would reduce aggregate productivity, output, and wages.

2.5

Empirical Tests in the Context of the COVID-19 Pandemic

We use the sudden and unprecedented surge in WFH prevalence during the COVID-19 pandemic as anempirical setting. The adoption of WFH during this period varied widely across occupations. To test ourmodel predictions, we examine changes in the spatial patterns of wages and employment separately foroccupations with different levels of WFH adoption.

Occupations with High WFH Adoption

Figure 1a illustrates how the labor demand and supply curves

would theoretically shift during the pandemic in large cities for high-WFH-adoption occupations and theeffects on the equilibrium wage and employment. For high-WFH-adoption occupations, the model predicts
that the reduced number of onsite workers in large cities during the pandemic may lower productivity due
to weakened agglomeration economies, which could lead to decreased labor demand. Meanwhile, the labor
supply of remote workers for high-WFH-adoption occupations may increase in large cities as they remotely
work for large-city jobs due to the increased prevalence of WFH. The reduced labor demand and expanded
labor supply in large cities will lower the wage level (w → w′′′ ) and thus reduce the urban wage premium
within high-WFH-adoption occupations.7 Crucially, if we observe a decrease in employment within highWFH-adoption occupations in large cities post-pandemic, this implies that the labor demand in large cities
must have shifted downward (because the labor supply curve moves upward due to the advent of WFH).

Occupations with Low WFH Adoption

We now turn to the local labor markets for occupations with

low WFH adoption. It is noteworthy that the COVID-19 pandemic, besides increasing WFH adoption, also
sparked a surge in migration from large cities to smaller ones. This was not only fueled by the shift awayfrom locations of employers due to the prevalence of WFH but also driven by the pandemic-induced decline
in urban amenities and activities. This migration away from large cities likely shrunk the local labor supply
in large cities for onsite positions while having less impact on occupations with high WFH adoption.
7

In smaller cities, labor supply is likely to shift downward due to the reallocation of labor, resulting in higher wages. This
could further reduce the urban wage premium.

12In addition, the migration would naturally redistribute demand for local services, like restaurants, fromlarge to smaller cities. This shift would likely decrease local labor demand in local service sectors oflarge cities, which predominantly require the onsite presence of workers. In contrast, high-WFH-adoption
occupations, usually in professional services, are less impacted by local service demand shift (Eckert et al.,2022).
Figure 1b illustrates the potential impact of these labor demand and supply shifts on equilibrium wage
and employment in large cities for low-WFH-adoption occupations. The concurrent downward shifts could
reduce employment in these occupations within large cities (M → M ′ ). However, equilibrium wages might
not shift definitively in any direction. Thus, the urban wage premium for low-WFH-adoption occupations
may remain relatively stable if the demand and supply effects offset each other.
Table 1 summarizes the expected changes in the urban wage premium and employment in large and small
cities separately for occupations with high and low WFH adoption and the underlying economic forces.

Identifying Assumption

The key assumption behind our empirical approach of using the pandemic shock

to identify the effect of WFH adoption is that other pandemic shocks unrelated to WFH adoption did notdisproportionately affect wages and employment for jobs in high-WFH-adoption occupations in large cities.
Given this assumption, if we observe a decrease in the urban wage premium for high-WFH-adoption occupations post-pandemic, while it remains largely unchanged for low-WFH-adoption occupations, we canattribute the decline in urban wage premium to WFH adoption, thereby validating our model’s prediction.
It is important to acknowledge that during the pandemic, other shocks unrelated to WFH adoption,such as the exacerbated labor shortage in the low-skilled market, could have affected high- and low-WFHadoption occupations differently. However, as long as these effects did not vary systematically by city size,
we would not expect to see a disproportionate change in the urban wage premium for high-WFH-adoption
occupations. Similarly, other pandemic-driven shifts, such as altered preferences for low-density housing
and changes in urban amenities, may have affected the labor supply of large and small cities differently,
subsequently affecting wage levels in these cities differently. While these factors could potentially inducechanges in the urban wage premium, they are not expected to cause a disproportionate shift in high-WFHadoption occupations.8
8

One could argue that workers in high-WFH occupations were leading the suburbanization wave, which may have decreased
the total labor supply in these occupations to large cities instead of increasing it. If this were the case, it should have led to anincrease in the urban wage premium. Under this scenario, the observed decline in the urban wage premium among high-WFH

13

3

Data

3.1

Advertised Wages by Occupation and Geography: Burning Glass (Lightcast)

We use data from various sources to test the model’s predictions. Our main wage data come from Lightcast,formerly known as Burning Glass Technologies, which we will refer to as Burning Glass data henceforth.
Burning Glass is a company that scrapes and cleans job postings from about 40,000 online job boards andcompany websites across the U.S. The aim is to capture the universe of job postings in the U.S. A deduplication algorithm is used to avoid repetition. We use the Burning Glass data from January 2018 to
May 2023. The dataset represents approximately 70% of U.S. vacancies (Carnevale et al., 2014).9 Around
20% of the postings in the Burning Glass data contain wage information, including total and hourly salaryprovided as a range (maximum and minimum value). We calculate a job’s wage by taking the mid-point
between the maximum and minimum hourly salary for the job (Hazell et al., 2022).
Notably, the job postings include highly detailed occupation codes (SOC) and industry codes (three-digit
NAICS), which are generated by Burning Glass based on the text within each job listing. Additionally, thedata include the county associated with each job posting, which we take as the primary job location.10
The job postings also offer various job-level details such as minimum degree requirement, full-/parttime status, salary types, tax terms, and an extensive array of skill requirements.11 These detailed job
characteristics enable us to study changes in the urban wage premium controlling for job characteristics.
With the specific skill requirements, we can distinguish between changes in the composition of workers or
local skill demand and shifts in local wage premium, holding constant skill demand. The absence of suchinformation in most datasets, including government administrative data, makes Burning Glass data a unique
resource for our analysis.
For the sake of computational efficiency, we randomly sample 10% of the raw Burning Glass data for
occupations could only be explained by a significant decrease in productivity in large cities.
9
Carnevale et al. (2014) show that online job ads can overrepresent higher-skilled, white-collar positions, a potential bias in the
Burning Glass data. However, we do not use the Burning Glass data to study the overall job number or local composition. Instead,
we use the wage information of the posted jobs to analyze how local wage changes vary across different occupations.
10
Some might question whether the location associated with each job posting truly reflects the primary job location or the firm’s
location, rather than the location of workers targeted by the job ads. Given that Burning Glass scrapes online job postings, there
is no direct method to confirm whether the location information indeed represents the actual job or firm location. We indirectly
verify this by comparing the local industry shares in the Burning Glass data with the those in the Quarterly Census of Employment
and Wages (QCEW), which is based on employer locations. We compute the 3-digit NAICS industry shares in each metropolitan
statistical area (MSA) using both datasets. Figure A1 shows the binned scatterplot of the shares, separately for January 2020 (prepandemic) and July 2020 (during the pandemic). The industry compositions in the Burning Glass data closely correspond withthose derived from employers’ locations in the QCEW, both before and after the pandemic began.
11
Salary types include base pay, bonus, commission, and shift premium. Tax terms include employee and contractor.

14

our statistical analysis, including binned scatterplots and regression analyses.

3.2

Spatial Patterns of Employment

Our employment data are from the Quarterly Census of Employment and Wages (QCEW). The data provide
quarterly employment counts, covering over 95% of all U.S. jobs, across industries defined by NAICS codes.
Nevertheless, the QCEW data do not provide breakdowns in employment numbers by occupation.
Our primary measure of the relevant local labor market size for each job is the employment size of the
job’s occupation and metropolitan statistical area (MSA). Thus, we need to impute employment size by
occupation and MSA. To do so, we use the Burning Glass data to generate a crosswalk between the threedigit NAICS code and the SOC occupation code. Since each job posting in the Burning Glass data is assigneda three-digit NAICS code and SOC code, we calculate the empirical distribution of the NAICS conditionalon each SOC code. Using the probabilistic crosswalk, we impute employment by SOC occupation andcounty. We then use a county-MSA crosswalk to compute the employment size by occupation and MSA.
However, to analyze differential employment growth across MSAs, we directly use industry-level employment data from the QCEW. We use industry-level employment data rather than the imputed occupationlevel data for employment analysis because the imputation process using the NAICS-SOC crosswalk canmute a substantial amount of variations in cross-industry employment growth found in the raw industry-level
data. Therefore, to capture any variations accurately, we reply on the original industry-level data to analyze
spatial patterns of employment.
Importantly, because the QCEW data are drawn from employment information reported by businessestablishments participating in the Unemployment Insurance programs, the employment counts reflect thelocations of employers, not workers. This distinction is crucial for analyzing how the rise of WFH has
influenced labor supply to firms in large cities compared with smaller ones.

3.3

Measuring the Adoption of Working from Home (WFH)

Burning Glass We categorize each job as WFH-compatible or not using the original texts of job postingsfrom 2018 to 2022. We use a dictionary approach, where we search for keywords associated with WFH orremote work. To ensure our categorization is immune to negations (e.g., employers reference WFH-related
topics to indicate the lack of such options), we exclude jobs from the WFH-compatible category if any

15

negation words appear just before or immediately following the WFH keywords. We discuss our dictionary
approach in details in Appendix A4.1.
After classifying each job as WFH-compatible or not, we calculate the fraction of WFH-compatible
jobs within each SOC occupation. We define an occupation’s WFH adoption level as high, moderate, or
low based on the post-pandemic increase in the fraction of WFH-compatible jobs. In our main analysis,
occupations with high WFH adoption are defined as those that saw an over 20 percentage point increase in
the national share of WFH-compatible jobs, as determined by comparing pooled data from 2018 to March
2020 with pooled data from April 2020 to May 2023 in the Burning Glass data (representing the 90th
percentile of the distribution in change of WFH-compatible job share). Occupations with moderate WFH
adoption saw an 11 to 20 percentage point increase in the share of WFH-compatible jobs (representing the50th and 90th percentiles), while occupations with low WFH adoption saw less than an 11 percentage point
increase.

American Community Survey (ACS)We use the ACS from 2018 to 2021 to supplement the Burning

Glass data. The ACS collects information on how respondents usually commute to work in the previous
week (Ruggles et al., 2022). The survey reports whether a respondent “worked from home” during theprior week. Combining this information with occupational codes (OCC2010), we compute the fraction of
respondents from each occupation who worked from home each year up to 2021. Therefore, by assessing
the change in the fraction of WFH workers by occupation before and after the pandemic’s onset, we canalso classify each occupation’s WFH adoption level in the ACS data.12 We use the levels of WFH adoption
by occupation measured with the ACS data to conduct a number of robustness checks.13
12

In ACS, we define an occupation’s WFH adoption level based on the change in the national share of remote workers within theoccupation between 2019 and 2021. We classify occupations as high-WFH-adoption occupations if they saw an over 28 percentage
point increase in the national share of WFH workers, comparing data from 2019 and 2021 (representing around the 90th percentileof the distribution in the change of WFH worker share within the ACS data). Moderate-WFH-adoption occupations saw an 8 to
28 percentage point increase in the share of WFH workers (representing the 50th and 90th percentiles), while low-WFH-adoption
occupations saw less than an 8 percentage point increase.
13
For robustness checks that use the ACS to evaluate changes in the urban wage premium, we compute the occupation-level
WFH adoption directly from the ACS. In other checks, we measure the level of WFH adoption by occupation using the ACS andmerge the measurement with the Burning Glass data. However, a challenge arises because the ACS uses the broader Census Occupation Code (OCC2010), while the Burning Glass uses the more detailed SOC codes. Thus, a large number of SOC occupationsin the Burning Glass data cannot be directly matched to OCC2010 codes. To leverage the ACS’s WFH-adoption information, we
combine 57 SOC-occupation-level work context characteristics from the Occupational Information Network (O*NET) data with theobserved occupation-level WFH adoption in the ACS. Then using observed O*NET work contexts for all occupations, we imputethe level of WFH adoption for all SOC occupations. Please refer to Appendix A4.2 for more details on the imputation process.

16

American Time Use Survey (ATUS)

The ATUS provided by the Bureau of Labor Statistics is another re-source for tracking WFH adoption by occupation (Hofferth et al., 2020). The ATUS measures the amount oftime that respondents spend on various activities over a 24-hour period. Hence, the data allow us to calculate
the fraction of working hours spent at home by occupation over time. With its annual data releases, we cancompare the prevalence of WFH before and after the pandemic. However, the ATUS has its limitations as
the number of respondents is often too small for occupation-level analyses. Thus, we mainly use the ATUS
to validate our findings from the Burning Glass data and ACS.

Cross-Validations To ensure the credibility of our primary WFH adoption measurement from the Burning
Glass data, we cross-validate it using alternative datasets. It is crucial to note that our study does not focus on
the precise level of WFH adoption but rather the relative changes in WFH adoption levels across occupations
post-pandemic. In other words, it is the ranking of WFH adoption across occupations that is relevant for ouranalysis. For validation, we compute WFH prevalence by occupation group using the ACS and ATUS data
and the fraction of WFH-compatible jobs by occupation or occupation group in the Burning Glass data overthe first two years of the pandemic (2020–2021). Figures 2a and 2b plot the fraction of WFH-compatible
jobs by occupation group against the workers’ WFH prevalence by occupation group in the ATUS and ACS
data, respectively, both indicating strong correlations. Figure 2c compares our estimated shares of WFHcompatible jobs by occupation with those from Bloom et al. (2023), suggesting a high correlation with an
R-squared exceeding 0.75.14 Lastly, Figure 2d shows that the WFH prevalence measures across occupationgroups using the ACS and ATUS data align closely with an R-squared close to 0.9.15

4Empirical Evidence

4.1

The Adoption of WFH Arrangement

We begin our empirical analysis by documenting changes in WFH adoption over time. Using the ACS and
ATUS data from 2005–2021, we first calculate the aggregate share of remote workers by year.16 Figure 3a14

Bloom et al. (2023) develop a machine-learning technique to categorize WFH-compatible jobs with increased precision.
For the validation of the imputed levels of WFH adoption based on the ACS and O*NET occupational characteristics, please
see Appendix A4.2.
16
For 2020, to highlight changes in WFH during the pandemic, we impute Q2-Q4 share of remote workers, assuming the Q12020 share is equal to the 2019 share. Specifically, assume that the 2019 share of remote workers is share2019 and observed 20202019
share is share2020 . Then the imputed share of remote workers in Q2-Q4 2020 is shareQ2−Q4,2020 = share2020 −0.25share
.
0.75
15

17

reveals a dramatic rise in WFH adoption starting in 2020 and a high level maintained into 2021. The patternis reflected in both ACS and ATUS.
Consistent with predictions of Dingel and Neiman (2020) and findings from other studies, we documentthat the level of WFH adoption varied widely across different types of workers, occupations, and industries(Barrero et al., 2021; Bick et al., 2022; Brynjolfsson et al., 2020).17 Figure 3b shows that college-educatedworkers were more likely to transition to WFH in 2020 than their non-college-educated counterparts. Acrossoccupations, Figure 3c shows a very high WFH uptake by computer and mathematical occupations, followedby business and financial occupations. In contrast, occupations related to food services and health care saw
a much lower level of WFH adoption. Similarly, Figure 3d shows industry-level disparities, with finance,
information, and professional and business services sectors leading in WFH adoption, contrasting withsectors such as accommodation and food services, as well as health care and social assistance.

4.2

The Effect of WFH Adoption on the Urban Wage Premium

In this section, we examine the effect of WFH adoption during the COVID-19 pandemic on the urban wage
premium. Based on the empirical tests outlined in Section 2.5, we expect a decrease in the urban wage
premium for occupations with high WFH adoption during the pandemic. Conversely, occupations with low
or moderate WFH adoption are expected to see smaller changes.
We begin by presenting binned scatterplots of residualized log posted hourly wages against residualized
log employment of each job’s occupation and MSA. We use employment size by occupation and MSA as ameasure of the relevant local labor market size for each job.18 To residualize the variables, we control fordummy variables for SOC occupation code, three-digit NAICS code, job’s required education level, salarytype, full-/part-time status, tax terms, and the posting month. Hourly wages are from the Burning Glassdata, and employment size is from the QCEW. Figure 4 presents these plots, where the slopes of the curves
represent the urban wage premium.19
17

A few recent papers use customized surveys to document differential changes in the prevalence of WFH during the pandemic—
e.g., the Survey of Working Arrangements and Attitudes by Barrero et al. (2021). We do not use the survey data because our analysis
requires a highly detailed occupation code, which is not available in these surveys.
18
We also present similar binned scatterplots using total MSA employment (rather than by MSA and occupation) to measure
the job’s relevant local labor market size. These plots are presented in Figure A2.
19
We validate the urban wage premium estimated from the Burning Glass data using the 2019 ACS. We use the ACS for the
pre-pandemic year because estimating the urban wage premium requires the location of jobs, and the ACS reports the job locations
only for onsite workers. The prevalence of WFH before the pandemic was very low, so using the ACS to measure the urban wage
premium before the pandemic is unlikely to introduce significant bias. Figure A3 compares the urban wage premium estimatedfrom the two datasets and shows that they are highly comparable.

18

Figure 4a presents the plot for all jobs from two periods: the pre-pandemic era (January 2018 to March
2020) and the period after the pandemic hit (April 2020 to May 2023). In a cross-sectional view, the slopes of
the curves are positive, indicating that residual wages are generally higher in larger labor markets, a findingthat aligns with prior empirical evidence. Following the pandemic, the urban wage premium experienced adrop, decreasing from 0.0276 to 0.0212, and the decline is statistically significant.
Figures 4b and 4c present the plots for jobs that require a college degree and those that do not, respectively. While both plots show a decrease in the urban wage premium, the decline is not statisticallysignificant for the jobs that require a college degree.
Figures 4d and Figure 4e present the plots for jobs in occupations with high and low levels of WFH
adoption, respectively. From Figure 4d, we observe a large and statistically significant decline in the urban
wage premium for occupations with high WFH adoption. The drop is from 0.0417 to 0.0309, approximately
a 26% decline. In contrast, for jobs in low-WFH-adoption occupations (as shown in Figure 4e), the decline
in the urban wage premium is much smaller, decreasing from 0.0174 to 0.0167. This corresponds to roughly
a 4% decrease from a much lower base number. This finding—i.e., a more dramatic decrease in the urban
wage premium for occupations with high WFH adoption compared with those with low WFH adoption—isconsistent with the prediction of our model as described in Section 2.5.20
Lastly, Figure 5 presents the urban wage premium annually from 2018 to 2023, separated into fourgroups of job postings according to their level of WFH adoption and educational requirements. Each group’surban wage premium is normalized by the 2018 estimate. We find a notable drop in the urban wage premium
among occupations with high WFH adoption, regardless of whether a college degree is required. Moreover,this decrease did not rebound since the pandemic’s outbreak. Conversely, the urban wage premium remained
relatively stable among occupations with low WFH adoption regardless of the need for a college degree.

Spatial Sorting of Skill Demand

One might be concerned that the observed decline in the urban wage

premium could be due to factors other than the adoption of WFH, such as the pandemic causing an exodusof higher-wage firms from large cities for reasons unrelated to WFH. This means that the reduction in therelative wages in larger cities does not necessarily indicate a true decline in the urban wage premium for agiven set of jobs but could instead be a result of higher-skill jobs leaving these large cities.
20

Figure A4 presents binned scatterplots of changes in the urban wage premium of four selected occupation groups. To see
which cities experienced wage declines most during the pandemic, in Figure A5, we present changes in residualized log posted
wages for jobs in four occupation groups across various MSAs. More details are presented in Appendix A5.1.

19

To further investigate this possibility, we analyze the extent to which our estimates of the decline in
the urban wage premium are driven by spatial sorting of skill demand and how much they are driven by agenuine decrease in the urban wage premium for a given set of worker skills. Fortunately, the Burning Glassdata provide a rich array of skill requirements associated with each job posting, allowing us to perform thisanalysis.21 Specifically, we estimate the following equation:ln(wikjt ) = α0 ln Mkj + α1 ln Mkj × P ostt + α2 ln Mkj × M odk + α3 ln Mkj × P ostt × M odk(5)+ α4 ln Mkj × Highk + α5 ln Mkj × P ostt × Highk + α6 P ostt + α7 M odk + α8 Highk+ α9 P ostt × M odk + α10 P ostt × Highk + Xikjt Θ + εikjt ,where wikjt is the posted hourly wage of job i in occupation k MSA j and time t; Mkj is the employment
size of occupation j in MSA j (as measured in 2019 Q1); P ostt is an indicator of the post-pandemic period(i.e., 1 if t is after March 2020); M odk is an indicator that k is an occupation with moderate WFH adoption; Highk is an indicator that k is an occupation with high WFH adoption; Xikjt is a vector of job-level
characteristics, including dummy variables for SOC occupation code, three-digit NAICS code, years of education required by the job, salary type, full-/part-time status, tax terms, posting month, and required skills.
The parameter α1 represents the change in the urban wage premium post-pandemic for low-WFH-adoption
occupations; α1 + α3 represents the change in the urban wage premium for moderate-WFH-adoption occupations; α1 + α5 represents the change in the urban wage premium for high-WFH-adoption occupations;
α3 (α5 ) represents the differential change in the urban wage premium for moderate (high) WFH-adoption
occupations compared with low-WFH-adoption occupations. We estimate the equation using the Burning
Glass data from 2018–2023.
Table 2 presents the estimates of Equation 5. Column 1 includes basic job characteristics.22 Column
2 further controls for the skill fixed effects. Both specifications show small and statistically insignificant
changes in the urban wage premium for jobs in low-WFH-adoption occupations. However, for high-WFHadoption occupations, the change in the urban wage premium is strongly negative. Even after controlling for
21
The added complexity of the data is that some job postings specify only a few skill requirements while others list more thanten distinct skills. To simplify our computations, we focus on the first 20 skills mentioned in each job posting, ranked by the overallfrequency of mentions across all postings. About 90% of the jobs in the sample mention fewer than 20 skills. In job postings withfewer than 20 skills, the remaining slots are filled as “na.”22
The regression results are slightly different from the slopes of the binned scatterplots in Figure 4d and 4e. This is because in
the figures, we residualize log hourly wage and log M separately for the sample before and after the pandemic began.

20

detailed skill fixed effects, the magnitude remains largely unchanged. This suggests that the considerable
decline in the urban wage premium among high-WFH-adoption occupations is likely a true reflection of adecrease in the relative price of labor in larger cities, holding skills constant.
For further validation, Table A1 columns 1 and 2 present the results after controlling for the interaction
between occupation fixed effects and the post-pandemic dummy, as well as the interaction between MSA
fixed effects and the post-pandemic dummy. These controls aim to capture any pandemic-related changes
that have affected an entire occupation or MSA. The results remain largely consistent with those in Table 2,
thereby strengthening the robustness of our findings.23

Alternative Definitions of WFH Adoption There may be concerns that our results hinge on how we define the levels of WFH adoption. To address the concern, in Table 2 column 3, we use an alternative indicatorof high WFH adoption that classifies an occupation as “high WFH adoption” if it belongs to business/financeor computer/mathematics occupation groups. As shown in Figure 3c, these two occupation groups exhibiteda dramatic increase in WFH adoption from 2020 onward. When using this alternative categorization, theregression results confirm that these two occupation groups experienced a disproportionate decrease in theurban wage premium compared with other occupations, corroborating the robustness of our baseline results.
In Table A1 columns 3 and 4, we use the same baseline specification but define WFH adoption levels
based on each job’ three-digit NAICS industry code, rather than the SOC occupation code. The results
closely align with those in Table 2 columns 1 and 2.24

Selection Bias

Because only a subset of job postings provide wage information, there may be concernsof selection bias in our estimated regression coefficients. Specifically, if high-skilled jobs in large cities areless likely to list wages post-pandemic, this could result in a spurious decrease in our urban wage premium
estimates. To address that concern, in column 4, we apply a Heckman correction to rectify the potential
bias. In the first stage, we use the triple interaction between dummy variables for jobs’ required years of
23

To address concerns that changes in the urban wage premium might occur at a geographical level below the MSA, such as
the county level, we perform the same regression analysis as in Equation 5, but redefine M at the occupation-county level. The
estimates are reported in Table A2. Column 1 includes only job-level controls, while Column 2 further controls the interaction
term of MSA fixed effects, the high-WFH-adoption dummy, and the post-pandemic dummy. The interaction term absorbs any
MSA-level changes in the urban wage premium. The results suggest that the decline in the urban wage premium has occurred bothwithin and across MSAs.
24
In Table A3, we use the baseline specification but categorize WFH adoption levels using the imputed change in the fractionof WFH workers—the imputation is based on the observed change in the fraction of WFH workers in the ACS and the O*NET
occupational characteristics. The results are also consistent with those in Table 2. Please see footnote 13 and Appendix A4.2 fordetailed discussions of the imputation.

21

education, the post-pandemic period, and occupation’s WFH adoption level across all job postings to predict
the availability of wage information for a given posting. In the second stage, we estimate the conditional
expectation of wages according to Equation 5, given that wage information is available for a job. The resultsin column 4 indicate that, after accounting for wage selection based on observed job characteristics, theestimated decrease in the urban wage premium for high-WFH-occupations is comparable to the baseline
estimates in columns 1 and 2. Although we cannot exclude the possibility of other forms of selection, ourfindings suggest that the estimated decline in the urban wage premium using the Burning Glass data isunlikely to be attributed to a selection bias based on the skill level of jobs.
To further ensure that our findings are robust to selection bias from wage postings, in Table A4, we
estimate changes in the urban wage premium using the average weekly earning data from the QCEW. The
QCEW data offer several benefits. They provide earnings information likely free from selection bias, andthe earnings information is based on employers’ locations. However, some cautionary points need to beconsidered: First, the data do not provide hourly wage information; weekly earnings might be confoundedby changes in the number of part-time workers during the pandemic. Moreover, while QCEW reflects theearnings of existing workers, Burning Glass pertains to new job postings. Despite these differences, theresults in Table A4 point to a substantial and statistically significant decrease in the urban wage premium forhigh-WFH-adoption industries.25 The magnitude of the decrease intensified from 2020 to 2022, possibly
because wages for existing workers adjust at a slower pace compared with those for new jobs.

Mechanical Effect of Larger Cities’ Disproportionate WFH Adoption

Another potential concern isthat the observed decrease in the urban wage premium among high-WFH-adoption occupations could bea mechanical result of larger cities’ disproportionate adoption of WFH and the fact that WFH may serve
as a type of amenities compensating for lower wages. If jobs based in larger cities were more likely toadopt WFH than similar jobs in smaller cities, this could have led to a greater wage reduction in large cities,
creating a spurious decrease in the urban wage premium that is unrelated to weakened agglomeration or
increased labor supply.
25

Here, we estimate changes in the urban wage premium by industries with different WFH adoption levels rather than byoccupations, because the QCEW only includes NAICS industry codes. We define high, moderate, and low WFH-adoption industries
using each industry’s fraction of WFH-compatible jobs in the Burning Glass data: Industries (three-digit NAICS) with high WFH
adoption saw an increase of more than 20 percentage points in the national share of WFH-compatible jobs, as determined bycomparing pooled data from 2018 to March 2020 with pooled data from April 2020 to May 2023. Industry with moderate WFH
adoption saw an increase between 11 and 20 percentage points, while industries with low WFH adoption saw an increase of lessthan 11 percentage points.

22

To examine this concern, we delve into the WFH options at the individual job level. If the decrease in
the urban wage premium among high-WFH-adoption occupations is not solely due to larger cities’ increasing WFH adoption and compensating wage differentials, we should observe a decrease in the urban wage
premium not just overall in high-WFH-adoption occupations but also specifically in both job categories that
do and do not provide WFH options within these occupations. The rationale behind this is that if the decline
in the urban wage premium is driven by weakened agglomeration economies and diminished knowledge
spillovers, then the productivity of onsite workers could be negatively impacted if a substantial proportionof their colleagues or workers in the same occupation shifted to remote work. Moreover, if labor supply tofirms based in large cities increased due to the “virtual” influx of remote workers, the wages of both remoteand non-remote workers in the same local labor market may face downward pressure.
We conduct the test using both the Burning Glass data and the ACS. First, using the Burning Glass data,
we estimate changes in the urban wage premium separately for jobs allowing and not allowing WFH within
occupations with the same WFH adoption level. Table 3 Panel A presents these results. Columns 1 and 2
show a large and statistically significant decrease in the urban wage premium for both WFH-compatible andnon-WFH-compatible jobs in high- or moderate-WFH-adoption occupations. The magnitude of the decline
is even slightly larger for non-WFH-compatible jobs in high-WFH-adoption occupations. In contrast, forjobs within low-WFH-adoption occupations (column 3), although we observe a notable decrease in theurban wage premium for WFH-compatible jobs, it remained unchanged for non-WFH-compatible jobs. The
findings present evidence that the decreased urban wage premium among high-WFH-adoption occupations
is unlikely to have been mechanically driven by larger cities’ disproportionate WFH adoption.
Second, using the ACS data from 2018 to 2021, we estimate changes in the urban wage premium by
occupation’s WFH adoption level among onsite workers.26 We only focus on onsite workers because the
ACS provides the MSA of workers’ workplaces, which only represents the job location for onsite workers.
In other words, using the ACS, we are not able to estimate changes in the urban wage premium basedon job locations for those working remotely.27 If the urban wage premium reduction was purely a result
26

The sample is restricted to onsite workers aged from 25–65 who worked at least 35 hours per week. The dependent variable isa worker’s log hourly wage. M is the employment size of the worker’s occupation in the MSA of the workplace. The post-pandemic
dummy is set to 1 for the years 2020 and 2021. Footnote 12 details the categorization of high-, moderate-, and low-WFH-adoption
occupations. We control for workers’ characteristics, including dummy variables for age, gender, race, Hispanic origin, marital
status, educational attainment, and occupation code.
27
One might suggest estimating changes in the urban wage premium based on workers’ residential locations. Pre-pandemic,
the estimates based on residential and workplace locations likely overlapped significantly. However, during the pandemic, withwidespread WFH adoption and subsequent worker migration away from workplace cities, these estimates likely diverged. Jobs withthe most significant drop in the urban wage premium are likely those where workers move away toward smaller cities. Therefore,23

of larger cities’ greater WFH adoption, we would not anticipate any changes in the urban wage premium
among onsite workers. Table 3 Panel B presents the results. Consistent with the results from the Burning
Glass data, the results in the ACS show a decrease in the urban wage premium among onsite workers inhigh-WFH-adoption occupations. While we also observe a decrease in the urban wage premium among
onsite workers in low-WFH-adoption occupations, onsite workers in high- and moderate-WFH-adoption
occupations saw a substantially greater decline.

Reduced Compensating Wage Differentials

Lastly, a remaining alternative explanation for the decreased

urban wage premium in high-WFH-adoption occupations could be that adopting WFH offers greater amenity
value to workers working for employers based in large cities than they do in smaller cities. After adopting
WFH, firms might pay employees less because of the reduced commuting burden they enjoy. Since commutetime tends to be longer in large cities prior to the pandemic, the switch to WFH could have led to a greater
reduction in commute time for jobs based in large cities, potentially leading to a more substantial wage
decrease in these cities due to changing compensating differentials.
We examine the plausibility of this explanation by exploiting the idea that the reduction in commutetime due to WFH adoption should be larger for workers who initially had longer commutes. If reduced
commute time led to wage reductions, we would expect the largest drops in wages among workers who
likely had the greatest reductions in commuting—those working in high-WFH-adoption occupations living
in neighborhoods with traditionally long commutes for their occupations. Using ACS data from 2015–
2021, we estimate changes in reported wages based on occupation category and the average pre-pandemic
commute time for workers’ residential PUMA and occupation category. Table A5 shows that workers inhigh-WFH-adoption occupations residing in neighborhoods with long pre-pandemic commutes experienced
a greater reduction in their commute time. However, despite the gains in commuting amenities, we do notfind that these workers had greater wage decreases. These findings provide some evidence that our observed
reduction in the urban wage premium is unlikely solely driven by reduced compensating wage differentials
in large cities. Appendix A5.2 provides more details on this analysis.
estimating the urban wage premium based on residual locations could severely underestimate the actual decrease.

24

4.3

The Effect of WFH on Local Employment

Our model suggests that the surge in WFH could reduce the urban wage premium, whether because of
weakened agglomeration economies in large cities or increased labor supply to firms in these cities due tothe surge in WFH availability. To investigate whether the agglomeration economies in large cities have been
weakened, we can explore whether employment (based on firms’ locations) shifted away from large cities
among the high-WFH-adoption occupations. If firms in large cities employed relatively fewer workers inhigh-WFH-adoption occupations and relative wages in large cities declined, it would imply that the local
productivity must have declined among these occupations.
We conduct the employment analysis using the QCEW data. Since QCEW only provides employment data by industry, not occupation, to avoid potential measurement errors from imputed occupation-level
employment growth, we define jobs’ WFH adoption levels based on their industries, rather than by occupations.28 We use the following simple regression to examine whether employment in MSAs with large initial
employment sizes increased or decreased across industries with different levels of WFH adoption:∆ ln Empkjt =X

0× Lowk +at1 ln Mkjt

t=2019,2022

+

X

X

0
× M odk
at2 ln Mkjt

(6)

t=2019,2022
0× Highk + ηkt + θj + ekjt ,at3 ln Mkjt

t=2019,2022

where ∆ ln Empkjt is the change in log employment in industry k and MSA j between the 1st quarter of2017 and the 4th quarter of 2019 (t = 2019), or between the 1st quarter of 2020 and the 4th quarter 2022
0 is the employment size at the beginning of each period; Low is an indicator that industry(t = 2022); Mkjtkk has low WFH adoption; M odk is an indicator that industry k has moderate WFH adoption; Highk is
an indicator that industry k has high WFH adoption. Parameters a2019, a2019, and a2019
represent how
12
3
employment changed between 2017 and 2019 by the labor market size for low-, moderate-, and high-WFH
industries, respectively. Similarly, parameters a2022, a2022, and a2022
represent how employment changed
12
3
between 2020 and 2022 by the labor market size for low-, moderate-, and high-WFH industries, respectively.
We control for industry × time period fixed effects.
Table 4 presents the results. Column 1 shows that employment growth of all three industry groups was
relatively slower in larger MSAs over the three years pre-pandemic—estimates of a2019, a2019, and a2019
all
1
2
3
28

The categorization of WFH adoption levels by industry is detailed in footnote 25.

25

are negative, though not statistically significant for a2019. However, with the onset of the pandemic, there3
was a more pronounced decline in employment in larger MSAs for both types of occupations—estimates ofa2022, a2022and a2022
are all strongly negative, which surpass the magnitudes of a2019, a2019, and a2019
. In
1
2
3
1
2
3
Column 2, high-WFH-adoption industries are classified as “Finance,” “Information,” and “Professional andBusiness Services.” The finding remains the same.
It is worth noting that industries with low or moderate levels of WFH adoption also saw a sizable shift in
employment from large to small cities post-pandemic. This finding is consistent with the predictions detailedin Section 2.5. However, the reasons for the disproportionate employment drop in large cities differ between
low-/moderate-WFH-adoption industries and high-WFH-adoption industries, as elaborated in Table 1.
In conclusion, the substantial decrease in the urban wage premium for occupations and industries withhigh WFH adoption, combined with the disproportionate negative employment growth in larger cities forthese industries, suggest that the adoption of WFH weakened the productivity advantage of large cities
during the pandemic.29

5

Decomposition by Skill

Next, to provide additional evidence for changes in agglomeration economies and spatial labor supply due to
WFH adoption, we shift our focus from directly testing the model predictions. We implement an alternative
approach by examining the urban wage premium associated with specific skills required in posted jobs. We
consider each job as a package of required skills, and the wage return for each job can be considered as
the sum of the wage returns for all required skills on the job. With this framework, we can decompose theurban wage premium for the high-WFH-adoption occupations into the sum of the urban wage premium of
individual skills and identify which specific skills contribute the most to the overall decline in the urban
wage premium among these jobs.
What can we learn from such a decomposition exercise? If skills that foster idea exchange, relationshipbuilding, and networking saw a disproportionate wage reduction in large cities (i.e., a decreased urban wage
premium for these skills), it would suggest that productive interactive activities, where these skills are highly
useful, have likely diminished in workplace in these locations. This supports the hypothesis that the rise of
WFH weakened the agglomeration economies in large cities. On the other hand, if skills complementary
29

Figures A6 show employment growth by industry in selected MSAs. Appendix A5.1 presents more discussions.

26

to remote work (e.g., using electronic technologies) saw a substantial drop in their urban wage premium, it
would suggest that the labor supply has expanded in large cities due to remote technologies.
In addition to assessing spatial wage changes linked to skills, we also explore how the listing intensity of skill requirements among jobs in high-WFH-adoption occupations shifted geographically. If theagglomeration economies in large cities were indeed weakened, we should expect the listing intensity for
relationship-building, communication, and networking skills to decrease in large cities. Conversely, if WFH
led to an expanded labor pool in large cities, the listing intensity for skills complementary to remote work
should increase in those areas.

5.1

Gelbach Decomposition

To identify which skills contribute to the decline in the urban wage premium the most, we use the Gelbach
decomposition method (Gelbach, 2016). We focus on jobs in occupations with high WFH adoption (with atleast a 15 percentage point rise in the share of WFH-compatible jobs).30 To estimate the change in the urban
wage premium, we use the following equation:ln(wikjt ) = γ0 ln Mkj + γ1 P ostt + γ2 ln Mkj × P ostt + Xikjt Ψ + ϵikjt .

(7)

The variables are defined the same as in Equation 5. The change in the urban wage premium for high-WFHadoption occupations post-pandemic is γ2 .31
If we consider changes in the skill-specific urban wage premium as the underlying driving forces behindthe overall urban wage premium, then the fully specified equation isln(wikjt ) = γ̃0 ln Mkj + γ̃1 P ostt + γ̃2 ln Mkj × P ostt + Xikjt Ψ̃
X
X
s
s
+
β0s ln Mkj × Skillit+
β1s P ostt × Skillit
s

+

X

(8)

s
s
β2s ln Mkj × P ostt × Skillit+ ϵ̃ikjt ,s
30
We use a threshold of 15 percentage points instead of 20 (in the Section 4 analyses) to increase the sample size for thedecomposition. This criterion includes approximately 28% of job postings. Some occupations from the upper range of moderateWFH-adoption occupations are also included in this analysis.
31
This is a simpler version of Equation 5, excluding occupations with low/moderate WFH adoption. The estimate of γ2 is very
similar to the estimate of α1 + α3 in Equation 5, which pools all jobs. The estimation results of Equation 7 are not reported due tospace limitations.

27

s is an indicator that skill s is required in job i. β s represents the change in the skill-specificwhere Skillit
2

wage premium among high-WFH-adoption occupations post-pandemic. After controlling for the skill interaction terms, the change in the residual urban wage premium is likely to drop from γ2 to γ̃2 . This reduction
signifies the portion of the decline in overall urban wage premium that can be attributed to the decrease in
the skill-specific urban wage premiums. The gap between γ2 and γ̃2 can also be understood as the size ofthe contribution of adding back the “omitted” covariates.
However, as demonstrated by Gelbach (2016), when covariates are statistically correlated, we cannot
simply add or subtract each covariate to individually decompose its contribution. According to his method,
to determine the contribution of each covariate, we need to estimate the effect of each covariate on theoutcome variable and how each covariate correlates with the main regressors in the equation. In our context,
this implies that if skill s is very frequently required in high-WFH-adoption occupations, a large estimate ofβ2s (i.e., a substantial decline in the urban wage premium for skill s) would suggest that the decline in theurban wage premium for skill s greatly contributes to the overall decrease in the urban wage premium. In
contrast, if skill s is rarely required in high-WFH-adoption occupations, even a large estimate of β2s would
not significantly contribute to the overall decline in the urban wage premium.
To apply the Gelbach decomposition, we estimate the following equation separately for each skill s:s
ln Mkj × P ostt × Skillit= Γs0 ln Mkj + Γs1 P ostt + Γs2 ln Mkj × P ostt + Xikjt Γx + ηikjt ,(9)where Γs2 represents how much each added covariate of skill s correlates with the key regressors. The
contribution of the change in the urban wage premium for each skill s to the overall change in the urban
wage premium in high-WFH-adoption occupations is as follows:π̂ s =

Γ̂s2 · β̂2s
,
γ̂2

(10)

where Γ̂, β̂, and γ̂ represent the estimated coefficients.

Results For computational feasibility, we define s as a skill cluster family, as defined in the Burning Glassdata. There are 35 skill cluster families in total. Detailed definitions and assignments of these skill cluster
families are provided in Appendix A4.3.

28

Table 5 presents the Gelbach decomposition results.32 We conduct the decomposition analysis for threetime periods to assess the contribution of changes in skill returns to the overall decline in the urban wage
premium from 2018–2019 to 2020, from 2018–2019 to 2021, and from 2018–2019 to 2022–2023 (up until
May of 2023). We separate the three time periods to account for potential changes in the returns to skills
over time as work dynamics evolved to accommodate developments such as hybrid work arrangements,
especially those skills linked to interpersonal interactions.
For each period, we rank the skill cluster families based on their contributions to the decline in the urban wage premium. Our findings suggest that among the skill cluster families contributing the most to thedecrease in the urban wage premium are “Marketing and Public Relations,” “Business Management,” “Information Technology,” “Customer and Client Support,” “Building Relationship,” and “Communications,”
although “Information Technology” drops off over the horizon to 2021.33
Among the skills identified, WFH likely enabled more workers with “Information Technology” skills
to remotely work for firms in large cities or industry clusters. These skills are typically compatible withelectronic tools, making remote work more feasible (Bloom et al., 2015; Dingel and Neiman, 2020; Barrero et al., 2021). The increased labor supply enabled by these technologies to large cities would likely
drive down the wage returns to skills complementary to these technologies in large cities, which is largely
consistent with the evidence.
In contrast, other prominent skills such as “Communications,” “Building Relationship,” “Marketing andPublic Relations,” and “Customer and Client Support” do not inherently benefit from WFH. These skills,
rather, are typically useful with productive interactive activities at the workplace. They often play a key rolein facilitating knowledge spillovers, forming professional networks, and establishing client relationships(Deming, 2017; Deming and Kahn, 2018). The large decrease in the urban wage premium for skills predominantly used in interactive activities suggests that the demand for these skills declined disproportionately
among employers based in large cities, which constitutes indirect evidence that in-person interactive activities at the workplace for which these skills are useful have most likely reduced. The decline in the urban
wage premium due to the absence of interactive workplace activities among large-city employers supports
the hypothesis that WFH weakened the agglomeration effects of cities.34
32

Tables A10 and A11 in the Appendix present the Gelbach decomposition results for jobs that require a college degree andjobs without a degree requirement, respectively.
33
Tables A8 and A9 present the most frequently listed skills that belong to these skill cluster families.
34
In Table A13, we conduct the same decomposition exercise, but use the sample of high-WFH-adoption occupations basedon the imputed WFH adoption levels (from the ACS and O*NET data). The results also suggest that the interactive skills and29

Note that “Business Management” skills, which are ranked high on the list, might be versatile enoughto facilitate both remote work and face-to-face interactions. There could be a dual effect where the rise of
WFH increases the labor supply in large cities for jobs needing such skills, while the decline in interpersonal
activities lowers their productivity. Thus, both factors could contribute to reduced urban wage premium
associated with “Business Management” skills.

5.2

Skill Intensity

Lastly, we investigate how the listing intensity of the few key skill groups within high-WFH-adoption jobs
has shifted geographically. If agglomeration economies have diminished in large cities, we would expect
reduced demand for relationship-building and networking skills in these cities. Hence, in addition to adecreased urban wage premium for these skills, we should also expect decreased listings of these skills in
the job ads made by firms based in large cities. Conversely, if WFH has expanded the labor pool in large
cities due to remote technologies, then the supply of workers possessing IT skills should increase in large
cities. Therefore, not only should the jobs that require these skills see a decreased urban wage premium due
to an increased supply of workers possessing these skills, we should also expect an increase in the listingsof these skills by employers in large cities.
We assess the spatial changes in skill demand using the following equation:s= λs0 ln Mkj + λs1 P ostt + λs2 ln Mkj × P ostt + Xikjt Λx + uikjt ,Skillikjt(11)

where the outcome variable is an indicator that a skill in skill cluster family s was listed in job posting
i within occupation k in MSA j at time t. Xikjt is a vector of basic job characteristics (excluding skill
dummies). Other variables are defined the same as in Equation 5. We again focus on jobs in occupations
with high WFH adoption (with at least a 15 percentage point rise in the share of WFH-compatible jobs).
We focus on the six skill cluster families that contribute the most to the overall decline in the urban
wage premium among jobs in high-WFH-adoption occupations, as indicated by our Gelbach decomposition
results. These are “Information Technology,” “Business Management,” “Building Relationship,” “Communications,” “Customer and Client Support,” and “Marketing and Public Relations.” These skills are either
inherently suited to remote work, conducive to interactive activities, or both. Our prediction is that “Infor“Information Technology” rank prominently on the top of the list (with “Information Technology” dropping off in 2021).

30

mation Technology” skills may have been listed more intensively in large cities, whereas “Building Relationship,” “Communications,” “Customer and Client Support,” and “Marketing and Public Relations” skills
may have seen reduced demand due to diminished workplace interactions. As for “Business Management”
skills, they could either see an increase or decrease in demand, depending on which effect is stronger: theexpanded labor pool or the weakened agglomeration economies.
Results Table 6 presents the results.35 Column 1 shows that “Information Technology” skills were listed
more intensively by job postings in high-WFH-adoption occupations in large cities by 2021, but no statisticalsignificance over other time horizons. Columns 2–6 show a general decrease in the demand for “Business
Management,” “Building Relationship,” “Communications,” and “Marketing and Public Relations” skills
by 2022–2023. These findings are, for the most part, consistent with our prediction that the demand forskills enhancing interactive activities would decrease post-pandemic, lending support to the hypothesis of
weakened agglomeration economies due to diminished workplace interactions.36

6

Conclusion

This paper studies how WFH adoption affects the agglomeration economies of large cities. Using a stylized
model, we demonstrate that WFH lowers the urban wage premium through two mechanisms. On the one
hand, WFH increases labor supply to high-productivity firms in large cities because workers can work
remotely for these firms without incurring high housing costs. The boosted employment at high-productivity
firms can raise aggregate productivity, wages, and output. On the other hand, if the number of onsite workers
in large cities decreases significantly, it could lead to a decline in agglomeration economies. This may lower
the productivity of large cities and thus encourage workers to shift from large cities to smaller ones, which
could reduce aggregate productivity, wages, and output. We derive two testable predictions to disentangle
the effects of WFH.
Using wage data from advertised job postings, we find a substantial decrease in the urban wage premiumfor occupations with high WFH adoption during the COVID-19 pandemic, which did not bounce back asof mid-2023. In contrast, occupations with low WFH adoption saw a much smaller decline in the urban
35

Table A12 presents the results separately for jobs that require a college degree and jobs without a degree requirement.
In Table A14 we use the same specification, but the sample of high-WFH-adoption occupations based on the imputed WFH
adoption levels (from the ACS and O*NET data). Similar to our baseline results, these results also indicate reduced listings ofinteractive skills in large cities.
36

31

wage premium. Furthermore, among jobs in high-WFH-adoption industries, employment shifted from large
to small cities (based on firm locations). According to our model’s predictions, the decline in the urban
wage premium and employment reallocation away from large cities indicate a reduction in the productivityof large cities due to WFH adoption, which is consistent with the weakening of agglomeration effects.
To provide additional empirical support that the reduced productivity in big cities has indeed been driven
by weakened agglomeration economies, we perform an additional decomposition analysis. The analysis
aims to identify skills that contribute the most to the decline in the urban wage premium within high-WFHadoption occupations. We find that the overall decline in the urban wage premium can be attributed to botha decline in the urban wage premium of both skills conducive to interactive activities (e.g., relationshipbuilding skills) and skills compatible with remote work technologies (e.g., information technology skills).
Moreover, we observe a decrease in the intensity of job requirements for relationship-building skills in large
cities. These findings lend support to our hypotheses: (i) the agglomeration economies of large cities werelikely weakened by the adoption of WFH, and (ii) firms in large cities likely benefited from an expanded
labor pool due to WFH adoption.
One limitation of our paper is that we study the adoption of WFH driven by the COVID-19 pandemic,
which may not accurately reflect firms’ work model in long-run equilibrium. During the pandemic, the decision to adopt WFH was largely determined by the compatibility of job tasks with existing WFH technologies,
such as Zoom or Slack. However, in the long run, firms and workers are expected to make adjustments totheir work tasks and WFH technologies, considering the benefits of in-person interactions and the expanded
labor pool offered by remote work technologies. Hybrid work arrangements may become more prevalentas the pandemic ends. These creatively designed hybrid arrangements may mitigate the loss of productive
in-person interactions while still providing the benefit of an expanded labor pool for productive firms.
It is important to note that our paper may have limited direct implications for the effect of hybrid workon agglomeration economies, and we leave this topic for future research. Despite this limitation, our paper
highlights the unintended negative effect of full WFH arrangement, namely reduced productivity spillovers
due to reduced workplace interaction. Our findings suggest that considering agglomeration externalities
when designing future hybrid work arrangements could lead to positive effects on aggregate productivity.

32References
Akcigit, U., Caicedo, S., Miguelez, E., Stantcheva, S., and Sterzi, V. (2018). Dancing with the stars:
Innovation through interactions. NBER Working Paper No.24466.
Althoff, L., Eckert, F., Ganapati, S., and Walsh, C. (2022). The geography of remote work. Regional Scienceand Urban Economics, 93(103770).
Barrero, J. M., Bloom, N., and Davis, S. (2021). Why working from home will stick. Working Paper.
Bartik, A., Cullen, Z., Glaeser, E., Luca, M., and Stanton, C. (2020). What jobs are being done at homeduring the COVID-19 crisis? Evidence from firm-level surveys. Harvard Business School Working Paper,
20:138.
Baum-Snow, N., Gendron-Carrier, N., and Pavan, R. (2021). Local productivity spillovers. Working Paper.
Baum-Snow, N. and Pavan, R. (2012). Understanding the city size wage gap. Review of Economic Studies,
79(1):88–127.
Behrens, K., Kichko, S., and Thisse, J.-F. (2021). Working from home: Too much of a good thing? Working
Paper.
Bick, A., Blandin, A., and Mertens, K. (2022). Work from home after the COVID-19 outbreak. Federal
Reserve Bank of Dallas Working Paper No. 2017.
Bleakley, H. and Lin, J. (2012). Thick-market effects and churning in the labor market: Evidence from US
cities. Journal of Urban Economics, 72(2-3):87–103.
Bloom, N., Hansen, S., Lambert, P., Davis, S., Sadun, R., and Taska, B. (2023). Remote work across jobs,
companies and space. Working Paper.
Bloom, N., Liang, J., Roberts, J., and Ying, Z. J. (2015). Does working from home work? Evidence from achinese experiment. Quarterly Journal of Economics, 130(1):165–218.
Brueckner, J., Kahn, M., and Lin, G. (2021). A new spatial hedonic equilibrium in the emerging work-fromhome economy? American Economic Journal: Applied Economics, page Forthcoming.
Brynjolfsson, Erik amd Horton, J., Ozimek, A., Rock, D., Sharma, G., and TuYe, H.-Y. (2020). Covid-19
and remote work: An early look at us data. NBER Working Paper, 27344.
Carnevale, A., Jayasundera, T., and Repnikov, D. (2014). Understanding online job ads data. Georgetown
University, Center on Education and the Workforce, Technical Report (April).
Charlot, S. and Duranton, G. (2004). Communication externalities in cities. Journal of Urban Economics,
56(3):581–613.
Chen, J. and Chen, Z. (2008). Extended bayesian information criteria for model selection with large model
spaces. Biometrika, 95(3):759–771.
Ciccone, A. and Hall, R. (1996). Productivity and the density of economic activity. American Economic
Review, 86(1):54–70.
Combes, P.-P., Duranton, G., and Gobillon, L. (2008). Spatial wage disparities: Sorting matters! Journal of
Urban Economics, 63(2):723–742.
33

Davis, D. and Dingel, J. (2019). A spatial knowledge economy. American Economic Review, 109(1):153–
170.
Davis, M., Ghent, A., and Gregory, J. (2021). The work-from-home technology boon and its consequences.
NBER Working Paper, 28461.
D’Costa, S. and Overman, H. (2014). The urban wage growth premium: Sorting or learning? Regional
Science and Urban Economics, 48:168–179.
De La Roca, J. and Puga, D. (2017). Learning by working in big cities. Review of Economic Studies,
84(1):106–142.
Delventhal, M., Kwon, E., and Parkhomenko, A. (2022). Jue insight: How do cities change when we workfrom home? Journal of Urban Economics, 127.
Delventhal, M. and Parkhomenko, A. (2022). Spatial implications of telecommuting. Working Paper.
Deming, D. (2017). The growing importance of social skills in the labor market. Quarterly Journal of
Economics, 132(4).
Deming, D. and Kahn, L. (2018). Skill requirements across firms and labor markets: Evidence from job
postings for professionals. Journal of Labor Economics, 36(S1).
Diamond, R. (2016). The determinants and welfare implications of US workers’ diverging location choices
by skill: 1980-2000. American Economic Review, 106(3):479–524.
Dingel, J. and Neiman, B. (2020). How many jobs can be done at home? Journal of Public Economics, 189.
Duranton, G. and Puga, D. (2004). Micro-foundations of urban agglomeration economies. Handbook ofRegional and Urban Economics, 4(48):2063–2117.
Eckert, F., Hejlesen, M., and Walsh, C. (2022). The return to big city experience: Evidence from refugees
in denmark. Journal of Urban Economics, (103454).
Ellison, G., Glaeser, E., and Kerr, W. (2010). What causes industry agglomeration? Evidence from coagglomeration patterns. American Economic Review, 100(3):1195–1213.
Emanuel, N. and Harrington, E. (2022). “Working” remotely? Selection, treatment, and the market for
remote work. Working Paper.
Emanuel, N., Harrington, E., and Pallais, A. (2023). The power of proximity. Working Paper.
Gaubert, C. (2018). Firm sorting and agglomeration. American Economic Review, 108(11):3117–3153.
Gelbach, J. (2016). When do covariates matter? And which ones, and how much?
Economics, 34(2):509–543.

Journal of Labor

Glaeser, E. (1999). Learning in cities. Journal of Urban Economics, 46(2):254–277.
Glaeser, E. and Mare, D. (2001). Cities and skills. Journal of Labor Economics, 19(2):316–342.
Gupta, A., Mittal, V., Peeters, J., and Van Nieuwerburgh, S. (2021). Flattening the curve: Pandemic-induced
revaluation of urban real estate. Journal of Financial Economics, Forthcoming.
Hazell, J., Patterson, C., Sarsons, H., and Taska, B. (2022). National wage setting. Working Paper.
34

Hofferth, S., Flood, S., Sobek, M., and Backman, D. (2020). American time use survey data extract builder:
Version 2.8 [dataset]. college park, md: University of maryland and minneapolis, mn.
Howard, G., Liebersohn, J., and Ozimek, A. (2023). The short- and long- run effects of remote work on u.s.
housing markets. Working Paper.
Jaffe, A., Trajtenberg, M., and Henderson, R. (2003). Geographic localization of knowledge spillovers as
evidenced by patent citations. Quarterly Journal of Economics, 108(3):1–28.
Jarosch, G., Oberfield, E., and Rossi-Hansberg, E. (2021). Learning from coworkers. Econometrica,
89(2):647–676.
Kerr, W. and Kominers, S. D. (2010). Agglomerative forces and cluster shapes. NBER Working Paper
16639.
Li, W. and Su, Y. (2022). The great reshuffle: Residential sorting during the COVID-19 pandemic and its
welfare implications. Working Paper.
Liu, S. and Su, Y. (2021). The impact of the COVID-19 pandemic on the demand for density: Evidence
from the U.S. housing market. Economics Letters, 207(110010).
Martellini, P. (2022). Local labor markets and aggregate productivity. Working Paper.
Monte, F., Porcher, C., and Rossi-Hansberg, E. (2023). Remote work and city structure. Working Paper.
Moretti, E. (2013). Real wage inequality. American Economic Journal: Applied Economics, 5(1):65–103.
Moretti, E. (2021). The effect of high-tech clusters on the productivity of top inventors. American Economic
Review, 111(10):3328–3375.
Ramani, A. and Bloom, N. (2021). The donut effect: How COVID-19 shapes real estate. SIEPR Policy
Brief.
Rosenthal, S. and Strange, W. (2003). Geography, industrial organization, and agglomeration. Review of
Economics and Statistics, 85(2):377–393.
Ruggles, S., Flood, S., Goeken, R., Schouweiler, M., and Sobek, M. (2022). IPUMS USA: Version 12.0
[dataset]. Minneapolis, MN: IPUMS.
Wheaton, W. and Lewis, M. (2002). Urban wages and labor market agglomeration. Journal of Urban
Economics, 51(3):542–562.

35

Figure 1: Changes in Labor Demand and Supply in Large Cities During COVID-19
Wage (w)SupplyDemand

w

←−

−→


 ′
 w ∼ w′′

y
w′′′

M′

Employment (M )M ∼ M ′′′ M ′′(a) Occupations with High Levels of WFH Adoption

Wage (w)SupplyDemand←−←−w′ ∼ w

M′←−

M

Employment (M )(b) Occupations with Low or Moderate Levels of WFH Adoption
Note: The figures present graphical illustrations of how the local labor markets in large cities respond to the COVID-19
pandemic. We illustrate occupations with high levels of WFH adoption in Figure 1a and occupations with low/moderate levelsof WFH adoption in Figure 1b. The solid lines represent the labor demand and supply curves before the pandemic. The dashedlines represent the shifted labor demand and supply curves during the pandemic.

36

Figure 2: Comparison of WFH Shares: Data from Burning Glass, ACS, and ATUS (2020–2021)(a) Burning Glass vs. ATUS(b) Burning Glass vs. ACS(c) Burning Glass vs. Bloom et al. (2023)(d) ACS vs. ATUS

Note: These figures compare WFH shares computed from several datasets. Specifically, we compute the share of WFHcompatible jobs among those posted in 2020 (after the first quarter) and 2021 for each occupation or occupation group using
the Burning Glass data. We also compute the share of workers who worked from home using the ACS and ATUS data for 2020
(after the first quarter) and 2021. Figure 2a presents the share of WFH-compatible jobs from the Burning Glass against theshare of WFH workers from the ATUS. Figures 2b presents the share of WFH-compatible jobs from the Burning Glass against
the share of WFH workers from the ACS. Figure 2c presents the share of WFH-compatible jobs from the Burning Glass (basedon our dictionary approach) against the share computed by Bloom et al. (2023) based on a mechine learning approach. Figure
2d presents the share of WFH workers from the ACS against the share from the ATUS.

37

Figure 3: Share of Working-from-Home Workers(a) All Workers(b) Workers by Education (ACS)(c) Workers by Selected Occupation Groups (ACS)(d) Workers by Selected Industry Groups (ACS)

Note: The figures plot the share of workers who worked from home from 2005 to 2021. In Figure 3a, we use the American
Community Survey (ACS) and the American Time Use Survey (ATUS) to calculate the share of all workers who worked fromhome in each survey year. For the year 2020, to highlight the share of workers who worked from home during the pandemic,
we impute the numbers for the period after the first quarter of 2020. Specifically, we assume that both the ACS and the ATUS
surveyed respondents randomly in each month of 2020 and that the share of workers who worked from home in the first quarterof 2020 is identical to the share estimated for 2019. For the ACS, we restrict the sample to workers who worked at least 35
hours a week and were aged between 25 and 65. For the ATUS, we calculate the share of workers who worked from home by
dividing the number of workers whose working activities all occurred at home by the number of workers who recorded working
activities during the period surveyed. Figure 3b shows the share of workers with or without college degrees who worked fromhome using the ACS data. Figure 3c shows the share of workers for four selected occupation groups who worked from homeusing the ACS data. Figure 3d shows the share of workers for four selected NAICS industry groups who worked from homeusing the ACS data.

38

Figure 4: Urban Wage Premium: 2018–2019 vs. 2020–2023(a) All Jobs(b) With College Degree Requirement(c) No College Degree Requirement(d) High WFH Adoption(e) Low WFH Adoption

Note: The figures present binned scatterplots of residualized log posted hourly wage against residualized log employment of
the occupation and MSA of a job separately for jobs posted between 2018 and 2019 and those posted between 2020 and May
2023, using the Burning Glass data. We residualize log wage and log employment by regressing these variables on dummies for
SOC occupation code, NAICS code, years of education required, salary type, full-/part-time status, tax terms, and job posting
month. We then add back the means of the original variables. Figure 4a presents the plot for all posted jobs. Figures 4b and4c present the plot for jobs that require a college degree and those without a degree requirement, respectively. Figures 4d and4e present the plot for jobs in high-WFH-adoption occupations (more than 20 percentage points increase in the fraction of
WFH-compatible jobs) and those in low-WFH-adoption occupations(fewer than 11 percentage points increase in the fraction
39
of WFH-compatible jobs), respectively.

Figure 5: Urban Wage Premium Over Time

Note: This figure shows how the urban wage premium, normalized to the 2018 level, has changed over time by job type, basedon education requirement and the level of WFH adoption during the pandemic. For each year and job type, we estimate theurban wage premium, controlling for dummies of SOC occupation code, NAICS code, years of education required, salary type,full-/part-time status, tax terms, and job posting month. We estimate the urban wage premiums using a 10% random sample of
the Burning Glass data. Occupations with high WFH adoption are defined as those that have seen an increase of more than 20
percentage points in the national share of WFH-compatible jobs, as determined by comparing pooled data from 2018 to March
2020 with pooled data from April 2020 to May 2023. Occupations with low WFH adoption are defined as those that have seen
an increase of less than 11 percentage points.

40

Table 1: Testable Predictions during the COVID-19 Pandemic
Occupations with High WFH Adoption
Urban Wage Premium

Employment by City Size

Productivity Decreases in Large Cities↓↓ in H; ↑ in L

Labor Supply Increases in Large Cities↓↑ in H; ↓ in L

Occupations with Low WFH Adoption
Urban Wage Premium

Employment by City Size

Labor Demand Decreases in Large Cities↓↓ in H; ↑ in L

Labor Supply Decreases in Large Cities↑↓ in H; ↑ in L

Note: This table summarizes the expected changes in the urban wage premium (column 1) and employment inlarge/high-density (H) and small/low-density (L) cities (column 2) in occupations with high WFH adoption (upper
panel) and low/moderate WFH adoption (lower panel). Different rows indicate the effects of different underlying driving forces. Section 2.5 presents more detailed discussions.

41

Table 2: Changes in Urban Wage Premium by Occupation’s WFH Adoption Level
Log Posted Hourly Wages
(2)
(3)

(1)

(4)

Log M

0.0169***(0.00405)

0.0175***(0.00400)

Log M × Moderate WFH

0.0193***(0.00165)

0.0141***(0.00132)

Log M × High WFH

0.0267***(0.00316)

0.0223***(0.00254)

0.0262***(0.00377)

0.0282***(0.00323)

Log M × Post

0.00176(0.00108)0.00068(0.0011)-0.00261***(0.00087)

0.00255***(0.00116)

Log M × Moderate WFH × Post

-0.00944***(0.00075)-0.00628***(0.00066)

Log M × High WFH × Post

-0.0123***(0.00157)-0.0127***(0.00136)-0.00834***(0.00179)-0.0130***(0.00159)

Controls: Job characteristics

X

X

X

X

Controls: Skill Requirements

0.0244***(0.00409)

0.0186***(0.00428)
0.0209***(0.00165)-0.0103***(0.000756)

X

Specification

Baseline

Baseline

Alt. HighWFH Def.

HeckmanCorrection

Observations

7,316,072

5,996,752

7,316,072

20,434,736

Note: This table presents the estimates of the urban wage premium pre- and post-pandemic by occupation category,based on the level of WFH adoption (i.e., α0 –α5 in Equation 5). The sample includes job postings from a 10%
random selection of the Burning Glass data, from 2018 to May 2023. The dependent variable is log posted hourly
wage of a job posting. M is employment size (in 2019 Q1) of the occupation in the MSA of the posted job. Post
is a post-pandemic dummy (i.e., months after March 2020). Moderate WFH is a dummy variable that is equal
to 1 if the occupation of the posted job has moderate WFH adoption (i.e., occupations with an increase of 11–20
percentage points in the national share of WFH-compatible jobs, as determined by comparing pooled data from2018 to March 2020 with pooled data from April 2020 to May 2023); High WFH is a dummy variable that is equal
to 1 if the occupation of the posted job has high WFH adoption (i.e., occupations with an increase of more than 20
percentage points in the national share of WFH-compatible jobs). Column 1 controls for basic job characteristics,
including dummy variables for SOC occupation code, NAICS industry code, years of education required, salarytype, part-/full-time status, tax term, and posting month. Columns 2 further controls for dummy variables for 20
skill requirements. Column 3 presents results in which an occupation is defined as a high-WFH-adoption occupation
if it belongs to either “Computer and Mathematical Occupations” or “Business and Financial Operations.” Column
4 presents the results of the Heckman correction regression. Standard errors are clustered at the MSA level. ***p < 0.01, ** p < 0.05, *p < 0.1.

42

Table 3: Changes in Urban Wage Premium by Occupation’s WFH Adoption Level
and Job’s WFH Compatibility
Log Hourly Wages
(2)

(1)

(3)

Panel A: Burning Glass Data 2018–2023 (All Jobs)
Log M

Log M × Post

WFH Jobs

0.0312***(0.00484)

0.0409***(0.00451)

0.0233***(0.00425)

Other Jobs

0.0426***(0.00438)

0.0345***(0.00434)

0.0180***(0.00411)

WFH Jobs-0.00595**(0.00269)-0.0131***(0.00262)-0.00668***(0.00232)

Other Jobs

-0.0113***(0.00212)-0.00725***(0.00101)-0.00002(0.00121)

X

X

X

Occupations withHigh WFH Adoption

Occupations with
Moderate WFH Adoption

Occupations with
Low WFH Adoption

563,2442,573,786

2,893,292

Controls: Job Characteristics
SampleObservations

Panel B: ACS Data 2018–2021 (Onsite Workers Only)
Log M

0.0796***(0.00900)

0.0620***(0.00763)

0.0371***(0.00696)

Log M × Post

-0.0123***(0.00283)-0.0116***(0.00219)-0.00595***(0.00151)

X

X

X

Occupations withHigh WFH Adoption

Occupations with
Moderate WFH Adoption

Occupations with
Low WFH Adoption

315,494

1,044,938

1,140,382

Controls: Worker Characteristics
SampleObservations

Note: This table presents the estimates of the urban wage premium pre- and post-pandemic by each job’s WFH-compatibility andby occupation’s overall WFH adoption level. Panel A is based on the sample of all job postings from a 10% random selection of the
Burning Glass data, from 2018 to May 2023. The panel presents the estimates for WFH-compatible jobs and other jobs, separately,
for occupations with high, moderate, and low levels of WFH adoption. The dependent variable is the log posted hourly wage of eachjob posting. M is employment size (in 2019 Q1) of the occupation in the MSA of the posted job. Post is a post-pandemic dummy(i.e., months after March 2020). All regressions control for basic job characteristics, including dummy variables for SOC occupation
code, NAICS industry code, years of education required, salary type, part-/full-time status, tax term, and posting month. Panel B
is based on the sample of onsite workers aged from 25–65 who worked at least 35 hours per week in the ACS from 2018–2021.
The dependent variable is the log hourly wage of a worker. M is employment size of the occupation in the MSA of the worker’s
workplace. P ost is a post-pandemic dummy (i.e., years 2020 and 2021). All regressions control for basic worker characteristics,
including dummy variables for age, gender, race, Hispanic origin, marital status, educational attainment, and occupation code.
Section 3.3 describes how we define high-, moderate-, and low-WFH-adoption occupations in both datasets. Standard errors are
clustered at the MSA level. *** p < 0.01, ** p < 0.05, *p < 0.1.

43

Table 4: Employment Growth by Local Employment Size: Before and Afterthe Pandemic for Different Industry Groups
Changes in Log Number of Jobs
(1)
(2)
Log M × 2017–2019 × Low WFH-0.0265**(0.0105)

Log M × 2020–2022 × Low WFH-0.0550***(0.00696)

Log M × 2017–2019 × Moderate WFH-0.00741*(0.00382)

Log M × 2020–2022 × Moderate WFH-0.0250***(0.00470)

Log M × 2017–2019 × High WFH-0.00433(0.0129)

Log M × 2020–2022 × High WFH-0.0373*(0.0196)

Log M × 2017–2019 × Other Ind-0.0218***(0.00808)

Log M × 2020–2022 × Other Ind-0.0457***(0.00571)

Log M × 2017–2019 × Fin./Info./Prof.

-0.00512(0.00690)

Log M × 2020–2022 × Fin./Info./Prof.

-0.0314***(0.00753)

Observations

97,01597,015

Note: This table presents the estimates of how employment growth changes with respect to employment
size, pre- and post-pandemic by industry category (i.e., a2019, a2022, a2019, a2022, a2019, a2022
in
1
1
2
2
3
3
Equation 6), using employment counts from the QCEW. Each observation is an industry-MSA cell(based on three-digit NAICS industry). The dependent variable is the change in log employment byindustry and MSA between the 1st quarter of 2017 and the 4th quarter of 2019 or the change between
the 1st quarter of 2020 and the 4th quarter of 2022. M is employment size by industry and MSA at
the beginning of each period. 2017–2019 (2020–2022) is a dummy variable that is equal to 1 if theemployment change in the dependent variable is between 2017 and 2019 (between 2020 and 2022).
We report the coefficients on the interactions between Log M and the time period dummies and the
WFH adoption dummies. Fin./Info./Prof. is a dummy variable that is equal to 1 if the three-digit
NAICS industry belongs to “Information” (NAICS 51x), “Finance and Insurance” (NAICS 52x), or“Professional and Business Services” (NAICS 54x). Each estimate represents how employment growthvaries with respect to the the relevant local initial employment size by time period and industry group.
In both columns, we control for three-digit industry × period fixed effects. The regressions are weighted
by the employment size of each MSA at the beginning of each period. Standard errors are clustered at
the MSA level. *** p < 0.01, ** p < 0.05, *p < 0.1.

44

Table 5: Gelbach Decomposition: Contribution of Changes in Skill-Specific Urban Wage Premium
to the Decrease in the Urban Wage Premium among High-WFH Jobs
2020
Skill
Marketing and Public Relations
Business Management
Information Technology
Physical Abilities
Finance
Building Relationship
Maintenance, Repair, and Installation
Engineering
Agriculture
Creativity
Environment
Education and Training
Manufacturing and Production
DesignPublic Safety and National Security
Legal
Economics, Policy, and Social Studies
Health Care
Decision Making
Energy and Utilities
Personal Care and Services
Human Resources
Media and Writing
Planning
Architecture and Construction
Leadership
Industry Knowledge
Administration
Communications
Analysis
Organizational Skills
Customer and Client Support

π
13.5%
11.0%
10.1%
5.2%
4.5%
4.1%
3.5%
1.3%
1.2%
1.0%
0.7%
0.5%
0.4%
0.4%
0.4%
0.1%
0.1%
0.0%
0.0%
0.0%
-0.1%
-0.1%
-0.2%
-0.3%
-0.8%
-2.5%
-2.5%
-6.4%
-8.5%
-10.6%-14.7%-21.5%

2021
Skill
Customer and Client Support
Finance
Marketing and Public Relations
Building Relationship
Business Management
Communications
Maintenance, Repair, and Installation
Administration
Physical Abilities
Decision Making
Leadership
Education and Training
Environment
DesignPersonal Care and Services
Public Safety and National Security
Economics, Policy, and Social Studies
Legal
Energy and Utilities
Agriculture
Creativity
Engineering
Manufacturing and Production
Media and Writing
Architecture and Construction
Analysis
Health Care
Industry Knowledge
Planning
Human Resources
Organizational Skills
Information Technology

π
33.3%
23.5%
23.2%
17.5%
13.7%
11.9%
9.1%
8.1%
3.4%
1.0%
0.6%
0.5%
0.5%
0.4%
0.4%
0.2%
0.1%
0.0%
-0.1%
-0.1%
-0.1%
-0.2%
-0.4%
-0.8%
-1.2%
-1.2%
-2.1%
-4.3%
-4.6%
-6.2%
-19.2%-24.7%

2022–2023
Skill
Communications
Information Technology
Customer and Client Support
Building Relationship
Administration
Marketing and Public Relations
Business Management
Maintenance, Repair, and Installation
Physical Abilities
Human Resources
Creativity
Engineering
Decision Making
Personal Care and Services
Education and Training
Media and Writing
DesignPublic Safety and National Security
Agriculture
Economics, Policy, and Social Studies
Energy and Utilities
Manufacturing and Production
Legal
Organizational Skills
Architecture and Construction
Environment
Finance
Leadership
Health Care
Planning
Analysis
Industry Knowledge

π
22.5%
22.2%
21.4%
16.1%
15.9%
14.1%
11.6%
6.6%
4.6%
3.1%
2.9%
2.3%
2.2%
2.1%
1.8%
0.8%
0.6%
0.5%
0.2%
0.1%
0.0%
0.0%
-0.2%
-0.3%
-0.4%
-0.5%
-0.6%
-1.7%
-6.2%
-7.2%
-10.3%-11.2%

Note: This table presents the Gelbach decomposition results, i.e., contributions of changes in the urban wage premium of various skills tothe overall decline in the urban wage premium of high-WFH-adoption occupations. We conduct the analysis seperately for three periods,
between 2018–2019 and 2020, between 2018–2019 and 2021, and between 2018–2019 and 2022–May 2023. The sample includes all job
postings in high-WFH-adoption occupations (i.e., occupations with an increase of more than 15 percentage points in the national shareof WFH-compatible jobs, as determined by comparing pooled data from 2018 to March 2020 with pooled data from April 2020 to May2023) in the Burning Glass data. For each time period, we rank skill cluster families by their contributions to the overall decline in theurban wage premium (i.e., π in Equation 10).

45

Table 6: Spatial Shift in Skill Listing Intensity
IT

Business

Communication(2)

Building
Relations(3)Marketing(4)

Customer
Support
(5)

(1)
Log M

0.00717***(0.000999)

0.00523***(0.00142)

0.00574***(0.00141)

0.00901***(0.00104)-0.00194(0.00160)

0.00711***(0.000774)

Log M × 2020

-0.000173(0.000648)-0.000408(0.000667)

0.00180***(0.000578)-0.000823(0.000709)

0.00132***(0.000453)-0.00178***(0.000630)

Log M × 2021

0.00154*(0.000806)-8.31e-05(0.000510)

0.000119(0.000519)-0.00180**(0.000720)-0.000143(0.000654)-0.00205***(0.000496)

Log M × 2022–2023

0.000136(0.000773)-0.00118**(0.000586)-0.00281***(0.000747)-0.00143*(0.000832)

0.000188(0.000580)-0.00221***(0.000570)

Observations

1,792,510

1,792,510

1,792,510

1,792,510

1,792,510

1,792,510(6)

Note: The table presents the estimates of changes in the listing intensity of various skill cluster families with respect to employment size
over time, based on Equation 11. The sample includes all job postings in high-WFH-adoption occupations (i.e., occupations with anincrease of more than 15 percentage points in the national share of WFH-compatible jobs, as determined by comparing pooled data from2018 to March 2020 with pooled data from April 2020 to May 2023) in the Burning Glass data. The dependent variable is an indicatorof whether a skill belonging to the skill cluster family is listed in a job posting. M is employment size (in 2019 Q1) of the occupationin the MSA of the posted job. 2020 is an indicator that is equal to 1 if the job was posted in 2020, similarly for 2021, and 2022–2023.
All columns control for basic job characteristics, including dummy variables for SOC occupation code, NAICS industry code, years ofeducation required, salary type, part-/full-time status, tax term, and the posting month. Standard errors are clustered at the MSA level.
*** p < 0.01, ** p < 0.05, *p < 0.1.

46

Appendix
A1

Comparative Statics of the ModelIn this section, we detail the derivation process of the comparative comparative statics for the baseline modelof WFH and agglomeration presented in Section 2.
We begin with the equalized utility levels in equilibrium:Ū = wH − βrHŪ = wH − βrL − ϕŪ = wL − βrL .
We simplify the equation set by taking the difference between the first and second equations and between thethird and second equations. taking the difference between the first and second equations and the difference
between the third and the second equations. We then plug in the equilibrium wage and rent equations:

0 = −β(π0H + πH ln(NHH )) + β(π0L + πL ln(1 − NHH )) + ϕ

0 = c + (γ − 1) ln(1 − NHH − NHL ) − θ ln(NHH ) − (γ − 1) ln(NHH + NHL ) + ϕ.
HH
Our primary interest lies in ∂N∂ϕand ∂N∂ϕHL . As NHH + NHL + NLL = 1, there is not need to
LLcompute compute ∂N∂ϕ . In these equations, NHH and NHL are endogenous and ϕ is exogenous. The

functional forms are smooth and differentiable. Thus, we use the implicit function theorem to calculated thecomparative static. We define

G1 = −β(π0H + πH ln(NHH )) + β(π0L + πL ln(1 − NHH )) + ϕ

G2 = c + (γ − 1) ln(1 − NHH − NHL ) − θ ln(NHH ) − (γ − 1) ln(NHH + NHL ) + ϕ.

47

Based on the implicit function theorem,






∂NHL
∂ϕ

−1 



∂NHH
 ∂ϕ 

∂G1
 ∂NHH = −

∂G1∂NHL 

∂G2
∂NHH

∂G2
∂NHL



∂G1
 ∂ϕ 





∂G2
∂ϕ



Expanding the matrices, we get:











∂NHH
∂ϕ 





 = −





∂NHL
∂ϕ

∂G
∂G
∂G2 ∂G1− ∂N 1 ∂ϕ2∂NHL ∂ϕ
HL
∂G1
∂G2
∂G
∂G
− ∂N 1 ∂N 2
∂NHH ∂NHL
HL
HH∂G1 ∂G2∂G∂G− ∂N 2 ∂ϕ1∂NHH ∂ϕ
HH
∂G2
∂G
∂G1
∂G
− ∂N 1 ∂N 2
∂NHH ∂NHL
HL
HH










Plugging in G1 and G2 , we get











∂NHH
∂ϕ 



 
=
 
 

∂NHL
∂ϕ



1
β

− 
β

1
πL
π
+N H
1−NHH
HH



 −

πL
π
+N H
1−NHH
HH







πL
π
+N H −N θ
1−NHH
HH

 HH

π
π
1−γ
β 1−NL + N H+ N 1−γ
N
+N
HH
HH
LL
HH
HL

β









Note that ϕ denotes the cost of WFH. As a result, to determine the impact of reducing the cost of WFH, anegative sign should be applied to each derivative.

A2

Alternative Model Framework: Productivity Shock on WFH

In this section, we present an alternative model of WFH and agglomeration. In this framework, the shockthat propels worker to adopt WFH also stems from an increase in WFH productivity, rather than only fromthe disutility of WFH as outlined in the baseline model in Section 2.
This model adopts all the setups from the baseline model, with one exception in the production function
for the large city or high-density location H. Here, we allow the marginal productivity of onsite and remoteworkers to be different—the marginal productivity of remote workers NHL is δ instead of 1. It is reasonable
to anticipate that on average, δ would be less than 1, but we do not explicitly impose such a restriction.

48

Specifically, the production function for location H is
FH (BH , NHH , NHL ) = BHH (NHH + δNHL )γIn this framework, we assume that a WFH shock could either decrease the cost of WFH, ϕ, or increase themarginal productivity of remote works relative to onsite workers, δ.
Given the potentially different marginal productivity among the three types of workers, the competitive
labor market implies that there would be three equilibrium wages: WHH —the wage for onsite workers in
H; WHL —the wage for remote workers working for H but living in L; WL —the wage for workers working
and living in L.
Given that the choices available to workers remain unchanged, we continue to assume that all three
options provide equal utility at equilibrium. The only difference is that wage offerings from location H are
different for onsite and remote workers:

UHH = wHH − βrH ,UHL = wHL − βrL − ϕ,
ULL = wL − βrL ,Based on the specification of the production function in H, the marginal products of NHL and NHH
maintain a constant ratio:
WHL
= δ.
WHH
This implies that log wages wHL = wHH + ln δ. If δ < 1, then the log wage of remote workers is always
lower than that of onsite workers. Plugging this relationship back into remote workers’ utility function, we
get
UHL = wHH − βrL +ln δ − ϕ.
| {z }

Reduced form
cost of remote work: ϕ̃

It is worth noting that this utility function resembles that in the baseline model, with one exception: theconstant in UHL that imposes a penalty for remote work now incorporates both the productivity discount of
remote work, ln δ, and the utility cost of remote work ϕ. Therefore, in this alternative model framework, an49

exogenous increase in productivity δ operates much like an exogenous decrease in ϕ in the baseline model,
in terms of influencing the workers’ choices among the three options.
As a result, the rent premium in equilibrium is

rH − rL =ϕ − ln δ.
βBecause there are two wage offerings from location H, the urban wage premiums are:wHH − wL = ϕ − ln δ,
wHL − wL = ϕ.

The intuition behind the difference between the two wage gaps is that the wage gap between the remoteworkers residing in L and those employed by L should only compensate for the utility cost of remote work.
Nevertheless, the wage gap between onsite workers in H and workers employed by L should reflect theadditional productivity premium for working onsite at H (if δ < 1).

Agglomeration and Aggregate Productivity The effect of an increase in the relative productivity of
remote workers on aggregate output is∂(NHH + NHL )∂(FH + FL )
θ−1 ∂NHH= θB0H NHH(NHH + δNHL )γ + (WHH − WL )
∂δ
∂δ
∂δ
|
{z
} |
{z
}
Weakening of Agglomeration Economies

Reallocation of Labor from L to H<0

< 0 or > 0∂NHL−(WHH − WHL )+WHH NHL| {z }∂δ }|{z
Direct Productivity Effect of δ

Adjustment for Reallocation into WFHThe effect of an increase in the relative productivity of remote workers on the reallocation of labor is
∂NHH
=− 
∂δ
δβ

1
πL
πH
1−NHH + NHH

50

,

(12)

∂NHL
1


=
(13)
πL
πH
∂δ
δβ 1−N
+
NHH
HH

 



1−γ
πL
πH
πL
πH
θ
β 1−N
+
−
N
δβ
+
+
(1
−
δ)
HL
NHH
1−NHH
NHH
NHH +δNHL − NHH
HH



.
+
δ(1−γ)1−γπLπH
δβ 1−N++
NHH
NLL
NHH +NHL
HH
Similar to the baseline model results, the effect of increasing δ on the total labor supply to location H is notdefinitive:∂(NHH + NHL )
=
∂δ

A3

β



πL
πH
1−NHH + NHH







1−γ
πL
πH
θ
− NHL δβ 1−N
+
+
(1
−
δ)
NHH
NHH +δNHL − NHH
HH



.
δ(1−γ)1−γπLπH
δβ 1−N++
NHH
NLL
NHH +NHL
HH(14)

Alternative Model Framework: Distinguishing Between
WFH and Remote WorkIn our baseline model, we assume that if a worker opts to work from home for firms in H, they are required toreside in L and work remotely for H. In such a scenario, remote workers employed by H would choose notto live in H. This is because the baseline model does not include location-specific amenities, and therefore
there are no reasons for workers to endure the high housing cost in H while opting not to work onsite. We
set up the baseline model to streamline the equilibrium characterization by reducing the number of choices
a workers needs to consider. However, there may be concerns as to whether the predictions and intuition of
the baseline model would hold if we allowed people to work from home while residing in the same city as
their employers. In this section, we present an alternative model in which workers can choose to work fromhome for firms in H while still residing in H. We demonstrate that all the predictions remain the same.
In this framework, we maintain the production functions in H and L, but we allow workers living in Hoand working for firms in H the option to work either onsite or from home. Let NHH
be the number of onsitehworkers who live and work in H, and let NHH
be the number of workers who live in H but work from home

for firms in H. Same as the baseline model, NHL denotes the number of workers who work remotely forfirms in H and live in L, and NLL denotes the number of workers who work for firms in L and live in L.
Rather than having three options, workers now have four: working onsite in H, working from home for
H while residing in the same city (thus, not remotely), working remotely for H (thus, living and working in51different cities), and working onsite in L. We define the utility associated with each choice as follows:oo
UHH= wH − βrH − C(NHH),
h
UHH= wH − βrH − ϕ,

UHL = wH − βrL − φϕ,
ULL = wL − βrL .

In this alternative model, we introduce the following elements: First, we add congestion as a utility cost ofo ), which we assume to strictly increase in the number of onsite workers, N o . This
onsite work, C(NHH
HH

term may capture the reality that if fewer people work onsite, the utility of working onsite may enhance due
h
o
would
and NHH
to lighter traffic, less workplace noise, etc. The term is introduced to ensure that NHH

be non-zero in equilibrium. Second, to account for the likely higher cost of living and working in different
cities compared with working from home in the same city, we introduce a utility cost of cross-city remotehwork as φϕ, with φ > 1. This term would ensure that NHH> 0. Without φ > 1 or modeling amenities, no

one would choose to work from home in H.
As workers are perfectly mobile and homogeneous, utility levels will equalize in equilibrium. Equating
the first two equations implies thatoC(NHH
) = ϕ.

Since C(.) is a strictly increasing function, the inverse function C −1 (.) should also be increasing:o∂NHH′= −C −1 (ϕ) < 0.
∂(−ϕ)

In other words, an exogenous decrease in the cost of WFH (ϕ) would reduce the number of onsite workers
in H.

Urban Wage and Rent Premiums By equalizing the utility equations, we can get the urban wage andrent premiums:
wH − wL = φϕ,rH − rL =(φ − 1)ϕ.
β52

Same as the baseline model predictions, here too, an exogenous decrease in ϕ will unambiguously reduce
both the urban wage and rent premiums.

Labor Reallocation Equalizing the second and third utility functions and then differentiating the resulting
equation with respect to ϕ, we gethoh )(1 − N o
h
o
∂NHH
(NHH
+ NHH
∂NHH
HH − NHH )= −(φ − 1).
−oh ) + βπ (N o
h
∂(−ϕ)
∂(−ϕ)
βπH (1 − NHH− NHH
L
HH + NHH )Equalizing the third and fourth utility functions and then differentiating the resulting equation withrespect to ϕ, we can determine the effect of ϕ on total employment in H:
o
h
∂(NHH
+ NHH
+ NHL )
=−
∂(−ϕ)



o
h
(NHH
+ NHH
+ NHL )NLL
γ−1


φ−

o
∂NHH
o
NHH
∂ϕ

θ


.

We can see that the effect of the WFH shock on total employment (either onsite, WFH in the same city, or
working remotely) is theoretically uncertain. If θ = 0, i.e., there are no agglomeration spillovers, the effectwould be positive. In that case, a lower cost of WFH/remote work would bring a larger labor supply tolocation H. In contrast, if θ > 0 and is sufficiently large, the decrease in the number of onsite workers may
significantly reduce productivity in H, leading to a possbile shift of total employment from H to L. The
model’s prediction and the underlying intuition regarding the impact of ϕ on employment remains exactly
the same as in the baseline model.

A4

Data Appendix

A4.1

Determining WFH-Compatible Jobs Using Original Texts from Job Postings

We evaluate the content of job descriptions in the Burning Glas data to determine whether the job is likely toaccommodate remote work. We start with scanning the text of each job posting for keywords that indicate
a remote work setup. These keywords include “remote,” “telework,” “work from home,” “work at home,”“wfh,” “home office,” “virtual,” “work anywhere,” and “mobile office.” If any of these keywords appear, thejob is initially classified as being compatible with remote work.
However, a serious concern is the potential misclassification because these keywords may appear in anegated context. For instance, a job description stating “You cannot work from home” would be incorrectly53be labeled as remote-compatible. To mitigate the issue, we conduct a search for negation words such as“cannot,” “couldn’t,” or “don’t” within 20 characters preceding each keyword. Similarly, we look for “no”
or “not” immediately following any keywords. If any of these negation words are found, we exclude the job
posting from the remote-compatible classification.
Lastly, we search for a separate list of keywords that indicate onsite work. These keywords include “fully
onsite,” “fully on-site,” “attendance,” “physical appearance,” “physically,” “show up on time,” “in office,”“in person,” “requires onsite,” “requires on-site,” “require onsite,” “require on-site,” “onsite required,” “onsite required,” “onsite only,” and “on-site only.” If any of these keywords appear, the job is classified as
being not WFH-compatible.
We acknowledge that our classification method may result in measurement errors. Specifically, there is
the possibility of false positives and false negatives. For instance, a false positive might occur in situations
when the remote work keywords appear without any negation but may be construed in contexts other thanwork arrangements. For example, if a job ad mentions that “our team also serves geographically remoteareas of the state”, where the word “remote” does not indicate remote work. Conversely, a false negative
might occur in cases where remote work is implied in the ads but none of the keywords appear. For example,
if a job ad says that “locations of work are flexible” or “our team members are located across the nation.”
Despite the potential presence of measurement errors, it is important to note that the goal of our empirical
exercise is not to quantify the prevalence of WFH per se, in which these false-positive and false-negative
errors would skew the summary statistics of WFH-compatibility. Instead, our aim is to capture variations
in WFH adoption across occupations, based on their relative changes in the levels of WFH adoption after
the pandemic started. We use these variations to analyze whether the adoption of WFH disproportionately
affects the wages and productivity of firms in large cities. In other words, it is the ranking of WFH adoption
across occupations that is relevant for our analysis, not the exact the level. Figure 2c compares our estimatedshares of WFH-compatible jobs by occupation group with those estimated by Bloom et al. (2023), who
address these measurement errors through a combination of manual classification and machine-learning
approach, suggesting a high correlation between the two measures with an R-squared value exceeding 0.75.

Potential Threat to Identification from Measurement Errors There exists a potential threat to identification if occupations with an increase in false-positives also saw a disproportionate reduction in the urban
wage premium for reasons unrelated to local productivity changes. This may incorrectly attribute such a54

disproportionate decrease in the urban wage premium among incorrectly measured “high-WFH-adoption”
occupations to the adoption of WFH. As previously mentioned, certain occupations might have seen an increase in jobs that require interaction with customers in remote areas post-pandemic. This may result in asurge of false positives in WFH-compatible jobs due to the use of the term “remote.” If the demand for these“false-positive” jobs in large cities declined relative to smaller cities since the start of the pandemic, then thedisproportionate decrease in the urban wage premium among the “high-WFH-adoption” occupations may
not entirely be a result of WFH adoption.
Our empirical strategy assumes that the occupations with a rise in false positive or drop in false negativeclassifications do not see a disproportionate drop in labor demand or disproportionate increase in labor
supply in large cities for reasons unrelated to remote work. Under this assumption, the measurement errors
present in our classification should dilute the estimated change in the urban wage premium among the highWFH occupations. This means that the more severe the measurement error is, the more likely the true effectof WFH on urban wage premium is actually larger than the number implied by our estimate.
In our analysis, we also include a number of robustness checks where we use alternative definitions of
WFH adoption that do not rely on the above-mentioned classification procedure. Results are all consistent
with our baseline findings.

A4.2

Imputing WFH Adoption Levels with ACS and O*NET Data

For robustness checks, we also measure the levels of WFH adoption using the change in the fraction ofworkers who report that they work from home before and after the start of the pandemic. Since the ACS
data categorize occupations by the Census Occupation Code, there are many SOC occupation codes in the
Burning Glass data that cannot be matched to the occupations categorized in the ACS. To enable all of
the SOC occupations in the Burning Glass data to be matched to a level of WFH adoption measured by
the information provided by the ACS data, we combine the ACS data with the Occupational Information
Network (O*NET) work context characteristics.
The O*NET is developed by the U.S. Department of Labor and the Employment and Training Administration. The data report the levels and importance of skills required for each occupation, the activities
involved in performing the jobs, and the work context in terms of the nature of human interaction, physical work conditions, and structural job characteristics. Each occupation is scored across 57 work context
characteristics. Because of the universal coverage of occupations in the O*NET, we use the occupational55

characteristics to impute changes in WFH prevalence based on the similarity of occupational characteristics
in the Burning Glass occupations to the occupations observed in the ACS data.
The imputation procedure works as follows:
1. We use the Lasso regression to select the O*NET occupational characteristics that can best predictchanges in WFH adoption based on occupations that can be matched between the ACS and O*NET.
We then estimate an OLS regression model using the selected characteristics. Table A6 shows theLasso coefficients and the OLS coefficients post-estimation. Figure A7 plots the predicted changes
in the fraction of workers who WFH against the observed changes in the fraction workers who WFH
in the ACS among occupations that can be matched between SOC and Census Occupation Code. Itshows that the predicted changes in the share of WFH workers line up well with the observed changes
from the ACS.
2. We then match the SOC occupations in the Burning Glass data to the same set of work context occupational characteristics. We use the estimated OLS regression coefficients from step 1 to impute thepredicted change in the fraction of workers who WFH for each SOC occupation occupation.

A4.3

Definition and Assignment of Skill Cluster Families

The Burning Glass data provide over 13,000 distinct skills associated with the job postings, extracted directlyfrom the job descriptions. These skills serve a dual purpose in our paper. First, they allow us to include
detailed job-level controls when estimating changes in the urban wage premium. Second, they facilitate ouranalysis of changes in the urban wage premium by skill and the shift of skill listing intensity, as detailed in
Section 5.
The data structure poses a challenge for estimation due to the varying lengths of skill vectors across job
postings; some list only one or two skills, while others feature nearly 20. To tackle this, we include controls
for the top 20 skills associated with each job, ranked by each skill’s overall frequency of appearance in thedataset. About 90% of jobs in the sample mention fewer than 20 skills. In job postings with fewer than 20
skills, the remaining slots are filled as “na.”To decompose the overall decrease in the urban wage premium into changes in the urban wage premiumby skill, we need to interact ln M with skill dummies. However, using the detailed skill dummies is not
feasible due to the large number. The Burning Glass data organize skills into broader categories known as56

skill clusters and skill cluster families. There are over 650 skill clusters and 29 skill cluster families. We
use these skill cluster families for our decomposition analysis. Notably, not all skills are assigned to a skill
cluster family, so we manually allocate some commonly listed unassigned skills. Table A7 presents ourmanual assignment of these unassigned skills.
Lastly, we note that the skill cluster family “Business” in the Burning Glass data includes a broad rangeof skills, mostly related to management. To improve the interpretability of this category, we reclassify
skills typically related to team and people management under “Human Resources.” These reassignments areshown in Table A7.

A5

Robustness Tests

A5.1

Changes in Wages and Employment in Selected MSAs

Figure A5 presents changes in residualized log posted hourly wage for four occupation groups across various
selected MSAs, between the pre-pandemic and pandemic periods. Notably, the most pronounced relative
wage declines in computer and mathematical occupations are evident in cities traditionally considered hubs
for the computer industry. Similarly, cities renowned as significant business and financial centers suffered thegreatest wage reductions in business and financial occupations. However, such patterns are not discernible
in food preparation, service, and health occupations. Correspondingly, Figure A6 shows a substantial departure of employment from cities traditionally associated with the finance and information industries for the
associated industry groups.
While the patterns generally hold true, there are exceptions. For example, despite a significant wage
decline in high-WFH-adoption occupations in San Jose and San Francisco, the employment decrease in therelevant finance and information industry was comparatively mild in San Jose, and San Francisco actually
saw some employment growth in that industry. This suggests that San Jose and San Francisco’s wage decline
might have been in part due to a labor supply increase from remote workers residing elsewhere. Austin, TX
serves as another exception. Although it saw a wage drop in high-adoption occupations, it also experienced
an exceptionally high employment growth in these fields. This could be attributed to an increased labor
supply through remote working or possibly due to the inflow of high-tech firms into Austin during the
pandemic.
Figures A5 and A6 also highlight a notable case in the food preparation and service occupations and57

the food and accommodation industries. While the wage decline does not show a strong correlation withcity size, the employment drop is more pronounced in larger cities and business hubs. This is likely a resultof both declining labor demand and supply in service sectors in large cities, leading to a steep decrease inemployment but ambiguous wage changes, as illustrated in Figure 1b.

A5.2

Reduced Compensating Differentials: An Alternative Source of Decreasing Urban
Wage Premium

Another reason for the observed decline in the urban wage premium for jobs in high-WFH-adoption occupations could be a reduction in compensating wage differentials. Firms might have reduced wages following
WFH adoption because workers could now avoid lengthy commutes. Since commute time was likely longer
in larger cities or dense areas pre-pandemic, the drop in the urban wage premium might be driven by thedisproportionate decrease in compensating differentials in large cities, in addition to (or instead of) the reduced agglomeration economies. We cannot directly disentangle this possibility from our hypothesis with
our main analysis because the Burning Glass data do not include workers’ residential locations.
However, we can examine this alternate explanation indirectly using the ACS data from 2015–2021. We
exploit the idea that the reduction in commuting due to WFH adoption should be larger for workers who
previously had longer commutes. Therefore, if reduced commute time led to wage reductions, we would
expect the largest drops in wages among workers who likely had the greatest reductions in commuting—those working in high-WFH-adoption occupations living in neighborhoods with traditionally long commutes
for their occupations. Specifically, we estimate the following triple-difference regression:

yikjmt = λ0 Highk + λ1 Highk × P ostt + λ2 Ckjm + λ3 Ckjm × P ostt + λ4 Highk × Ckjm

(15)

+ λ5 Highk × Ckjm × P ostt + ξm × ζt + Λt Xi + ϵikjmt ,where yikjmt is log hourly wage or commute time of individual i in occupation k in PUMA j of MSA min year t; Highk is an indicator that occupation k has high WFH adoption during the pandemic; P ostt is
an indicator for post-pandemic period (i.e., t = 2020 or 2021); Ckjm is the average commute time faced byworkers in occupation k living in PUMA j in MSA m between 2015 and 2019. We approximate Ckjm by
calculating the average commute time experienced by two broad occupation groups: high-WFH-adoption

58

occupations and low/moderate-WFH-adoption occupations.37 Xi is a vector of workers’ characteristics, including indicators of sex, age, race, Hispanic status, marital status, and education. We allow the coefficients
Λt to vary over time to account for changes in sorting of workers over time. The coefficient of interest in λ5 .
Table A5 presents the estimates of λ0 –λ5 . The sample comprises workers aged from 25–65 who worked
at least 35 hours per week. In Columns 1–2, the dependent variable is the log hourly wage. In Columns
3–4, the dependent variable is the commute time. All columns control for the interaction between year fixed
effects and MSA fixed effects. Columns 2 and 4 further control for workers’ demographic characteristics.
The estimates of λ5 in Columns 1 and 2 suggest that during the pandemic, workers in high-WFH-adoption
occupations who lived in neighborhoods with high commute time pre-pandemic saw a wage increase relative
to others, although the magnitude is small. Adding workers’ characteristics does not significantly affect theresult. This finding is contradict the hypothesis of changing compensating differentials. In contrast, as
Columns 3 and 4 show, these workers experienced a more substantial decrease in commute time, translating
to increased amenities of reduced commuting, as expected. If the change in compensating differentials was
driving the observed reduction in the urban wage premium, the increased amenities should have led to awage decrease. Therefore, the results Table A5 offer some indirect evidence that our finding of reduced
urban wage premium is unlikely solely driven by reduced compensating wage differentials.

37
We calculate commuting time at a higher occupation group level because commute time at a detailed occupational and geographical level tends to have many sparsely populated cells.

59

Figure A1: Industry Share within MSAs in the Burning Glass Data vs. QCEW

Note: This figure aims to validate that the geographical distribution of job postings in the Burning Glass data is likely torepresent the locations of employers. The y-axis represents the 3-digit NAICS industry share in each MSA from the Burning
Glass data, and the x-axis represents the 3-digit NAICS industry share in each MSA from the Quarterly Census of Employment
and Wages (QCEW), which is based on employer locations. We present the binned scatterplot of the shares, separately for
January 2020 (pre-pandemic) and July 2020 (during the pandemic).

60

Figure A2: Urban Wage Premium: Estimation based on Total Employment by MSA(a) All Jobs(b) High WFH Adoption(c) Low or Moderate WFH Adoption

Note: The figures present binned scatterplots of residualized log posted hourly wage against residualized log employment of
the MSA of a job, separately for jobs posted between 2018 and 2019 and those posted between 2020 and May 2023, using
the Burning Glass data. We residualize log wage and log employment by regressing these variables on dummies for SOC
occupation code, NAICS code, years of education required, salary type, full-/part-time status, tax terms, and job posting month.
We then add back the means of the original variables. Figure A2a presents the plot for all posted jobs. Figures A2b and A2c
present the plot for jobs in high-WFH-adoption occupations and those in low-WFH-adoption occupations, respectively.

61

Figure A3: Urban Wage Premium: Estimation from the Burning Glass Data vs. ACS (2019)(a) All Jobs(b) Business and Finance(c) Computer and Mathematics(d) Food Services(e) Health Care

Note: These figures validate the urban wage premium estimates from the Burning Glass data with the ACS estimates. We
estimate the urban wage premium using the 2019 Burning Glass data by regressing log posted hourly wage on log employmentof the MSA of the corresponding job, controlling for dummy variables for SOC occupation code. We estimate the urban
wage premium using the 2019 ACS data by regressing log hourly wage on log employment of the MSA, controlling for dummy
variables for occ2010 occupation code. Figure A3a presents the demeaned binned scatterplots based on all jobs/workers. Figure
A3b, A3c, A3d, and A3e present the demeaned binned scatterplots for four selected occupation groups.

62

Figure A4: Urban Wage Premium: Selected Occupation Groups(a) Computer and Mathematics(b) Business and Finance(c) Food Prep and Service(d) Health

Note: The figures present binned scatterplots of residualized log posted hourly wage against residualized log employment of theoccupation and MSA of a job, separately for jobs posted between 2018 and 2019 and those posted between 2020 and May 2023,
using the Burning Glass data. We residualize log wage and log employment by regressing these variables on dummies for SOC
occupation code, NAICS code, years of education required, salary type, full-/part-time status, tax terms, and job posting month.
We then add back the means of the original variables. Figure A4a presents the plot for jobs categorized in the occupation groupof “Computer and Mathematical Occupations.” Figure A4b presents the plot for jobs in “Business and Financial Operations
Occupations.” Figure A4c presents the plot for jobs in “Food Preparation and Serving Related Occupations.” Figure A4d
presents the plot for jobs in “Healthcare Practitioners and Technical Occupations.”

63

Figure A5: Residual Wage Growth of Selected Occupation Groups(a) Computer and Mathematical(b) Business and Finance(c) Food Prep and Service(d) Health

Note: The figures present changes in residualized log posted hourly wage between the pre-pandemic period (2018–2019)
and the post-pandemic period (2022–May 2023) for four occupation groups across selected MSAs, using the Burning Glassdata. We residualize log wage by regressing these variables on dummies for SOC occupation code, NAICS code, years ofeducation required, salary type, full-/part-time status, tax terms, and job posting month. We then add back the means of theoriginal variables. Figure A5a presents the residualized wage growth across selected MSAs for jobs in the occupation family of“Computer and Mathematical Occupations.” Figure A5b is for jobs in “Business and Financial Operations Occupations.” Figure
A5c is for jobs in “Food Preparation and Serving Related Occupations.” Figure A5d is for jobs in “Healthcare Practitioners andTechnical Occupations.”

64Figure A6: Employment Growth of Selected Industry Groups(a) Finance and Information(b) Prof. and Business Services(c) Accom. and Food Services(d) Health Care and Social Assist.

Note: The figures present changes in log employment between the pre-pandemic period (2018–2019) and the post-pandemic
period (2022–May 2023) for four industry groups across selected MSAs, using the QCEW. Figure A6a presents employment
growth across selected MSAs for jobs in the industry group of “Finance and Information”, which include NAICS 51x and 52x.
Figure A6b is for jobs in “Professional and Business Services.” (NAICS 54x) Figure A6c is for jobs in “Accommodation andFood Services.” (NAICS 72x) Figure A6d is for jobs in “Health Care and Social Assistance.” (NAICS 62x)

65

Figure A7: Observed WFH Adoption vs. Predicted WFH Adoption

Note: This figure validates the predicted adoption of WFH with the observed adoption of WFH. We use the subset of occupations that the vectors of O*NET occupational characteristics can be matched to the ACS occupation code. Please see section
A4.2 for imputation details.

66

Table A1: Changes in Urban Wage Premium by Occupation’s WFH Adoption Level:
Robustness Checks
Log Posted Hourly Wages
(2)
(3)

(1)

(4)

Log M

0.0275***(0.00842)

0.0204***(0.00658)

0.0169***(0.00405)

0.0175***(0.00340)

Log M × Moderate WFH

0.0122***(0.00129)

0.00844***(0.00114)

0.0193***(0.00165)

0.0141***(0.00132)

Log M × High WFH

0.0130***(0.00234)

0.0108***(0.00210)

0.0267***(0.00316)

0.0223***(0.00254)

Log M × Post

0.00238(0.00480)

0.00277(0.00379)

0.00176(0.00108)0.000676(0.00108)

Log M × Moderate WFH × Post

-0.00538***(0.000818)-0.00363***(0.000678)-0.00944***(0.000753)-0.00629***(0.000658)

Log M × High WFH × Post

-0.00267*(0.00160)-0.00513***(0.00124)-0.0123***(0.00157)-0.0127***(0.00136)

Controls: Job Characteristics

X

X

X

X

Controls: MSA FE × Post, Occ × Post

X

X

Controls: Skill RequirementsXX

WFH Def Based on

SOC Occ.

SOC Occ.

NAICS Ind.

NAICS Ind.

Observations

7,316,061

5,996,739

7,316,072

5,996,752

Note: This table presents the estimates of the urban wage premium pre- and post-pandemic by occupation and industry category,based on the level of WFH adoption, as robustness checks. The sample includes job postings from a 10% random selectionof the Burning Glass data, from 2018 to May 2023. The dependent variable is log posted hourly wage of a job posting. M isemployment size (in 2019 Q1) of the occupation or industry in the MSA of the posted job. Post is a post-pandemic dummy (i.e.,months after March 2020). Moderate WFH is a dummy variable that is equal to 1 if the occupation or industry of the posted
job has moderate WFH adoption (i.e., occupations or industries with an increase of 11–20 percentage points in the national
share of WFH-compatible jobs, as determined by comparing pooled data from 2018 to March 2020 with pooled data from
April 2020 to May 2023); High WFH is a dummy variable that is equal to 1 if the occupation or industry of the posted job has
high WFH adoption (i.e., occupations or industries with an increase of more than 20 percentage points in the national share of
WFH-compatible jobs). All columns control for basic job characteristics (including dummy variables for SOC occupation code,
NAICS industry code, years of education required, salary type, part-/full-time status, tax term, and posting month). Columns
1 and 2 define WFH adoption based on occupation categories and include the interaction between occupation fixed effects andthe post-pandemic dummy, and the interaction between MSA fixed effects and the post-pandemic dummy. Columns 3 and 4
define WFH adoption based on industry categories (three-digit NAICS). Columns 2 and 4 include controls of the indicators ofskill requirements. Standard errors are clustered at the MSA level. *** p < 0.01, ** p < 0.05, *p < 0.1.

67

Table A2: Changes in Urban Wage Premium by Occupation’s WFH Adoption Level:
Cross-County and Within-MSA Comparisons
Log Posted Hourly Wages
(1)
(2)
Log M

0.0117***(0.00278)

0.00794***(0.00174)

Log M × Moderate WFH

0.0192***(0.00196)

0.0170***(0.00129)

Log M × High WFH

0.0268***(0.00353)

0.0290***(0.00259)

Log M × Post

0.00454***(0.000672)

0.00156**(0.000608)

Log M × Moderate WFH × Post

-0.0103***(0.000775)-0.00654***(0.000664)

Log M × High WFH × Post

-0.0118***(0.00155)-0.0127***(0.00122)

Controls: Job Characteristics

X

X

Controls: MSA × M / H WFH × Post
Measurement of M

X
Emp Size by
Occ and County

Emp Size by
Occ and County

7,429,678

7,315,951

Observations

Note: This table presents the estimates of the urban wage premium pre- and post-pandemic by
occupation category, based on the level of WFH adoption. The sample includes job postings from a
10% random selection of the Burning Glass data, from 2018 to May 2023. The dependent variable
is log posted hourly wage of a job posting. M is employment size of the occupation in the countyof the posted job. Post is a post-pandemic dummy (i.e., months after March 2020). Moderate
WFH is a dummy variable that is equal to 1 if the occupation of the posted job has moderate WFH
adoption (i.e., occupations with an increase of 11–20 percentage points in the national share of
WFH-compatible jobs, as determined by comparing pooled data from 2018 to March 2020 withpooled data from April 2020 to May 2023); High WFH is a dummy variable that is equal to 1 if
the occupation of the posted job has high WFH adoption (i.e., occupations with an increase of morethan 20 percentage points in the national share of WFH-compatible jobs). Both columns control
for basic job characteristics (including dummy variables for SOC occupation code, NAICS industry
code, years of education required, salary type, part-/full-time status, tax term, and posting month).
Columns 2 further include the interaction of the MSA fixed effects, dummy for high- or moderate
WFH adoption, and the post-pandemic dummy. Standard errors are clustered at the MSA level. ***p < 0.01, ** p < 0.05, *p < 0.1.

68

Table A3: Changes in Urban Wage Premium by Occupation’s WFH Adoption Level: Imputed WFH
Adoption Levels by O*NET Characteristics(1)

Log Posted Hourly Wages
(2)

Log M

0.0178***(0.00390)

0.0183***(0.00402)

Log M × Moderate WFH

0.0120***(0.00163)

0.00901***(0.00139)

Log M × High WFH

0.0305***(0.00393)

0.0234***(0.00339)

Log M × Post

0.000248(0.00106)0.000117(0.00103)

Log M × Moderate WFH × Post

-0.00451***(0.000744)-0.00341***(0.000655)

Log M × High WFH × Post

-0.00777***(0.00149)-0.00893***(0.00133)

Controls: Job Characteristics

X

X

Controls: Skill Requirements

X

Specification

Baseline

Baseline

Observations

6,913,047

5,661,761

Note: This table presents the estimates of the urban wage premium and its change over time for occupations with different levels of WFH adoption. We present the results from the baseline specification
using the Burning Glass job posting data, similar to columns 1 and 2 in Table 2. The difference is that
we define occupations’ WFH adoption levels using the imputed WFH share based on O*NET occupation characteristics. Please see Appendix A4.2 for the imputation procedure. We define the levelof WFH adoption for an occupation based on changes in the imputed share of remote workers within
the occupation between 2019 and 2021. We classify occupations as high-WFH-adoption occupations if
they experienced an over 25 percentage point increase in the national share of WFH workers, comparing
data from 2019 and 2021 (corresponding to the 90th percentile of the distribution in change of WFH
worker share). Moderate-WFH-adoption occupations experienced an 10 to 25 percentage point increasein the share of WFH workers (representing the 50th and 90th percentiles), while low-WFH-adoption
occupations experienced less than a 10 percentage point increase. *** p < 0.01, ** p < 0.05, *p <
0.1.

69

Table A4: Changes in Urban Wage Premium by Occupation’s WFH Adoption Level:
Average Weekly Earnings from QCEW
Log Average Weekly Earnings(1)(2)
Log M × Post

0.00277***(0.000798)

Log M × 2020

0.00242**(0.000958)

Log M × 2021

0.00200***(0.000728)

Log M × 2022

0.00528***(0.00130)

Log M × Moderate WFH × Post

-0.00399***(0.000783)

Log M × High WFH × Post

-0.0185***(0.00273)

Log M × Moderate WFH × 2020

0.00194(0.00163)

Log M × Moderate WFH × 2021

-0.00127(0.000878)

Log M × Moderate WFH × 2022

-0.00936***(0.00130)

Log M × High WFH × 2020

-0.00861***(0.00258)

Log M × High WFH × 2021

-0.00732(0.00585)

Log M × High WFH × 2022

-0.0290***(0.00303)

Observations

1,921,245

1,921,245

Note: This table presents the estimates of the urban wage premium pre- and post-pandemic by industry’s
WFH adoption level. The sample comprises quarterly average weekly earnings by MSA and industry(3-digit NAICS) from the QCEW for Q1 2018 to Q4 2022. The dependent variable is log average weekly
earning by MSA and industry. Observations are weighted by employment size (2019 Q1) of the MSA
and industry. Moderate WFH is dummy for industries with an increase of 11–20 percentage points in thenational share of WFH-compatible jobs, as determined by comparing pooled data from 2018 to Q1 2020
with pooled data from Q2 2020 to 2022). High WFH is dummy for industries with an increase of morethan 20 percentage points in the national share of WFH-compatible jobs. Standard errors are clustered at
the MSA level. *** p < 0.01, ** p < 0.05, *p < 0.1.
70

Table A5: Compensating Differentials: Alternative Explanation of Changes in the Urban Wage Premium
Log Hourly Wage
(1)
(2)

(3)

Commute Time
(4)

High WFH

0.355***(0.0170)

0.196***(0.0099)0.249**(0.0987)

0.119(0.1000)

High WFH × Post

-0.0135(0.00948)

0.0023(0.00895)

2.838***(0.335)3.671***(0.324)

Pre-Pandemic Commute-0.00746***(0.00114)-0.00344***(0.00066)

0.989***(0.00438)

0.985***(0.00441)

Pre-Pandemic Commute × Post

-0.00052(0.00035)-0.00036(0.00030)-0.060***(0.0138)-0.101***(0.0124)

High WFH × Pre-Pandemic Commute

0.00693***(0.00064)

0.00348***(0.00036)-0.00398(0.00366)-0.00554(0.00366)

Hig -WFH × Pre-Pandemic Commute × Post

0.00059*(0.00033)

0.00064**(0.00031)-0.370***(0.0124)-0.351***(0.0120)

Controls: Year FE × MSA FE

Yes

Yes

Yes

Yes

Controls: Year FE × Worker Characteristics

No

Yes

No

Yes

7,471,296

7,471,296

7,313,590

7,313,590

Observations

Note: This table presents the estimates of changes in log hourly wage and commute time pre- and post-pandemic by occupation’s WFH
adoption level and the average pre-pandemic commute time of the worker’s residential location and occupation group. The sample
comprises workers aged from 25–65 who worked at least 35 hours per week in the ACS from 2015–2021. P ost indicates the pandemic
period (i.e., 2020 or 2021). High W F H is an indicator that is equal to 1 if the occupation of the posted job has a high level of
WFH adoption. P re-P andemic Commute is the average commute time between 2015 and 2019 by workers’ residential PUMA andhigh-WFH indicator. All columns include year fixed effects, MSA fixed effects, and their interaction. Columns 2 and 4 further include
the interactions between year fixed effects and various demographic characteristics, including indicators of sex, age, race, Hispanic
status, marital status, and education. Standard errors are clustered at the PUMA level. *** p < 0.01, ** p < 0.05, *p < 0.1.

71

Table A6: Lasso Selection Results:
Work Context Characteristics as Predictors for WFH Adoption During the Pandemic
Lasso

OLS

Deal With External Customers-0.0086

Deal With Physically Aggressive People-0.0126

Electronic Mail

0.0073

Exposed to Contaminants-0.0121

Exposed to Disease or Infections-0.0035

Exposed to Minor Burns, Cuts, Bites, or Stings-0.0031

Level of Competition

0.0110Physical Proximity-0.0103

Public Speaking

0.0023

Responsible for Others’ Health and Safety-0.0277

Spend Time Sitting

0.0168Spend Time Standing-0.0113

Spend Time Using Your Hands to Handle, Control, or Feel Objects, Tools, or Controls-0.0215Work With Work Group or Team

0.0077

Constant

0.2695-0.0184***(0.00459)-0.0155*(0.00840)
0.0089**(0.00397)-0.0123**(0.00527)-0.0042(0.00443)-0.0032(0.00594)
0.0262***(0.00622)-0.0137**(0.00692)
0.0085(0.00646)-0.0380***(0.00656)
0.0120(0.0115)-0.0144(0.0124)-0.0195***(0.00576)
0.0278***(0.00865)
0.2520***(0.0773)

Note: This table presents the results of the Lasso regression and the OLS regression after selecting variables. We
use the O*NET work context characteristics as predictors for the change in the share of WFH workers during
the pandemic. There are 57 work context characteristics. We show the regression coefficients for the variables
retained by Lasso. The shrinkage parameter λ is searched for based on Extended Bayesian information criterion(EBIC) (Chen and Chen, 2008). *** p < 0.01, ** p < 0.05, *p < 0.1.

72

Table A7: Manual Assignment of Unassigned Skills
Skill

Skill Cluster Family

Building Effective Relationship
Teamwork / Collaboration
Mentoring
Verbal / Oral Communication
Telephone Skills
Written Communication
Writing
Communication Skills
Presentation Skills
Oral Communication
Microsoft Excel
Microsoft Word
Computer Literacy
Problem Solving
Critical Thinking
Creativity
Decision Making
Research
RepairCleaning
Preventive MaintenanceWork Area Maintenance
Planning
Leadership
Organizational Skills
People Management
Staff Management
Supervisory Skills
Conflict Management
Team Management
Personnel Management
Escalation Management
Employee Training
Employee Relations
Employee Engagement
People Development
Employee Coaching
Staff Development
TypingTroubleshooting
Time Management

Building Relationship
Building Relationship
Building RelationshipCommunication
Communication
Communication
Communication
Communication
Communication
Communication
Information Technology
Information Technology
Information Technology
AnalysisAnalysisAnalysisAnalysis
Science and Research
Maintenance, Repair, and Installation
Maintenance, Repair, and Installation
Maintenance, Repair, and Installation
Maintenance, Repair, and Installation
Planning
Leadership
Organizational Skills
Human Resources
Human Resources
Human Resources
Human Resources
Human Resources
Human Resources
Human Resources
Human Resources
Human Resources
Human Resources
Human Resources
Human Resources
Human Resources
Administration
Administration
Administration

Notes: We manually assign some of the unassigned skills to skill cluster families. We select theskills that appear in the skill vectors very frequently but are unassigned to any skill cluster families. Some skill cluster families shown above are created by us because the existing categories
do not fit. “Building Relationship”, “Communication”, “Organizational Skills” are created byus.

73Table A8: Most Frequently Listed Skills Under Key Skill Cluster Families (Part 1)
Rank

Custumer and Client Support

Business Management

Marketing and Public Relations

1
2
3
4
5
6
7
8
9
10
11
12
13
14
15
16
17
18
19
20
21
22
23
24
25
26
27
28
29
30
31
32
33
34
35
36
37
38
39
40
41
42
43
44
45
46
47
48
49
50

Customer Service
Customer Contact
Customer Checkout
Cash Handling
Basic Mathematics
Guest Services
Cash Register Operation
Point of Sale System
Claims Knowledge
Customer Accounts
Refunds Exchanges and Adjustments
Customer Complaint Resolution
Processing Item Returns
Needs Assessment
Client Needs Assessment
Customer Experience Improvement
Claims Adjustments
Service Improvement
Payment Collection
Payment Processing
Bagging Items
Checking Out Customers
Satisfaction Failure Correction
Processing Customer Requests
Issuing Receipts
Presenting Solutions
Customer Service Enhancement
Responding to Patient Phone Calls
Product Availability
End-user training
Product Assortment
Account Information Maintenance
Customer Referrals
Claims Processing
Wellness Services
Deposit Collection
Inventory Checking
Pizza Delivery
Customer Relationship Marketing
Settlement Negotiation
Credit Card Transaction Processing
Providing Warranties
Product Features AssistancePrice Checks
Store Communications
Charge and Disbursement Determination
Credit Card Applications
Deposit Preparation
Client Care
Customer Account Review

Project Management
Quality Assurance and Control
Process Improvement
Business Process
Key Performance Indicators (KPIs)
Business Administration
Project Planning and Development Skills
Product Management
Performance Appraisals
Cost Control
Change Management
Performance Management
Stakeholder Management
Operations Management
Strategic Planning
Business Acumen
Performance Analysis
Business Planning
Business Analysis
Thought Leadership
Business Operations
Contract Review
Business Strategy
Property Management
Root Cause Analysis
Business Management
Contract Preparation
Lifecycle Management
Technical Assistance
Service Level Agreement
Event Planning
Contract Management
Process Design
Business Solutions
Restaurant Management
Due Diligence
Real Estate Experience
Professional Services Marketing
Progress Reports
Business Systems Analysis
Resource Management
Business Communications
Profit Targets
Policy Implementation
5S Methodology
Business Case Analysis
Benchmarking
Contract Negotiation
Order Entry
Strategic Development

Social Media
Packaging
Salesforce
Client Base Retention
Marketing
Facebook
Market Strategy
Customer Relationship Management (CRM)
Market Research
Digital Marketing
Newsletters
InstagramMarket Trend
Marketing Materials
LinkedInFundraisingSocial Media Platforms
Customer Retention
Market Analysis
Product Marketing
Brand Experience
Market Planning
Competitive Analysis
Brand Awareness Generation
Community Relations
Google Analytics
Customer Acquisition
Marketing Management
Business-to-Business Sales
YoutubePromotional Materials
Marketing Strategy Development
Copywriting
Crisis Management
Effective Communications
Email Marketing
CRM software
Consumer Behavior
Marketing Communications
Ad Campaigns
Marketing Programs
Focus groups
Social Media Marketing
Direct Mail
Consumer Segmentation
Branding Strategy
Email Campaigns
Account Development
Consumer ResearchSocial Content

74

Table A9: Most Frequently Listed Skills Under Key Skill Cluster Families (Part 2)
rank

Information Technology

1
2
3
4
5
6
7
8
9
10
11
12
13
14
15
16
17
18
19
20
21
22
23
24
25
26
27
28
29
30
31
32
33
34
35
36
37
38
39
40
41
42
43
44
45
46
47
48
49
50

Microsoft OfficeComputer Literacy
Microsoft Word
Microsoft Powerpoint
SQL
Software Development
PythonSpreadsheets
Software Engineering
Java
Technical Support
Microsoft Outlook
Software as a Service (SaaS)
Information Systems
SAP
Enterprise Resource Planning (ERP)
OracleJavaScript
Microsoft Azure
Scrum
Word Processing
Linux
DevOpsData Management
Atlassian JIRA
Git
Telecommunications
Information Security
Microsoft Windows
Microsoft Sharepoint
Microsoft C#
Microsoft Access
Microsoft Project
ServiceNow
Agile Development
Systems Engineering
Amazon Web Services (AWS)C++
Kubernetes
Troubleshooting Technical Issues
Systems Development Life Cycle (SDLC)
Network Hardware/Software Maintenance
System Design
Debugging
Relational Databases
Unit Testing
System AdministrationUNIX
SQL Server
OpenStack75

Table A10: Gelbach Decomposition: Contribution of Changes in Skill-Specific Urban Wage Premium
to the Decrease in the Urban Wage Premium within High-WFH-Adoption Occupations—Jobs Requiring
College Education
2020
Skill
Communications
Business Management
Leadership
Creativity
Planning
Education and Training
Human Resources
Marketing and Public Relations
Media and Writing
Physical Abilities
Decision Making
Public Safety and National Security
Analysis
Agriculture
Maintenance, Repair, and Installation
Legal
Health Care
Energy and Utilities
Environment
DesignEconomics, Policy, and Social Studies
Engineering
FinanceArchitecture and Construction
Manufacturing and Production
Information Technology
Building Relationship
Personal Care and Services
Industry Knowledge
Administration
Organizational Skills
Customer and Client Support

π
35.7%
24.7%
8.9%
5.7%
4.4%
3.2%
2.5%
2.5%
1.8%
1.0%
0.9%
0.8%
0.7%
0.7%
0.7%
0.5%
0.4%
0.2%
0.0%
-0.3%
-0.4%
-0.9%
-1.3%
-1.9%
-2.7%
-4.0%
-5.4%
-7.0%
-14.4%-22.4%-26.4%-54.2%

2021
Skill
FinanceCommunications
Marketing and Public Relations
Business Management
Building Relationship
Customer and Client Support
Leadership
Maintenance, Repair, and Installation
Education and Training
Planning
Analysis
Manufacturing and Production
Economics, Policy, and Social Studies
Agriculture
Administration
Energy and Utilities
Media and Writing
Public Safety and National Security
Environment
Legal
Health Care
Architecture and Construction
Physical Abilities
Decision Making
DesignCreativity
Engineering
Personal Care and Services
Industry Knowledge
Information Technology
Organizational Skills
Human Resources

π
58.4%
49.5%
36.9%
30.3%
27.5%
8.3%
4.8%
4.7%
2.9%
2.6%
1.1%
1.1%
0.2%
0.2%
0.2%
0.1%
0.1%
0.1%
0.0%
0.0%
-0.1%
-0.2%
-0.2%
-0.3%
-0.4%
-0.5%
-1.2%
-3.4%
-4.2%
-10.7%-14.8%-15.6%

2022–2023
Skill
Information Technology
Marketing and Public Relations
Business Management
Building RelationshipCommunications
FinanceCreativity
Analysis
Education and Training
Leadership
Decision Making
Public Safety and National Security
Agriculture
Physical Abilities
Maintenance, Repair, and Installation
Energy and Utilities
Media and Writing
Engineering
Legal
Architecture and Construction
Economics, Policy, and Social Studies
DesignPlanning
Manufacturing and Production
Personal Care and Services
Environment
Health Care
Administration
Organizational Skills
Industry Knowledge
Customer and Client Support
Human Resources

π
58.1%
43.6%
34.0%
21.9%
19.7%
15.9%
9.0%
4.5%
2.6%
1.7%
1.4%
1.3%
0.6%
0.3%
0.2%
0.1%
0.0%
-0.1%
-0.1%
-0.5%
-0.6%
-1.4%
-1.5%
-1.7%
-1.9%
-3.6%
-4.2%
-4.7%
-7.3%
-7.6%
-7.9%
-14.2%

Note: This table presents the Gelbach decomposition results for jobs that require a college degree. We conduct the analysis separately forthree periods: between 2018–2019 and 2020, between 2018–2019 and 2021, and between 2018–2019 and 2022–May 2023. The sample
includes jobs postings that require a college degree in high-WFH-adoption occupations (i.e., occupations with an increase of more than 15
percentage points in the national share of WFH-compatible jobs, as determined by comparing the pooled data from 2018 to March 2020
with pooled data from April 2020 to May 2023) in the Burning Glass data. For each time period, we rank the skill cluster families by theircontributions to the overall decline in the urban wage premium (i.e., π in Equation 10).

76

Table A11: Gelbach Decomposition: Contribution of Changes in Skill-Specific Urban Wage Premium
to the Decrease in the Urban Wage Premium within High-WFH-Adoption Occupations—Jobs Not
Requiring College Education
2020
Skill
Marketing and Public Relations
Information Technology
Building Relationship
Business Management
Physical Abilities
Finance
Maintenance, Repair, and Installation
Customer and Client Support
Industry Knowledge
Engineering
Agriculture
Manufacturing and Production
Administration
Personal Care and Services
Human Resources
Economics, Policy, and Social Studies
DesignEducation and Training
Energy and Utilities
Legal
Creativity
Environment
Public Safety and National Security
Decision Making
Health Care
Architecture and Construction
Planning
Media and Writing
Leadership
Organizational Skills
Analysis
Communications

π
10.9%
8.7%
8.0%
7.7%
6.4%
4.8%
4.0%
3.1%
2.9%
1.9%
1.6%
1.3%
1.0%
0.8%
0.3%
0.2%
0.2%
0.1%
0.1%
0.0%
-0.1%
-0.2%
-0.3%
-0.4%
-0.4%
-0.6%
-0.8%
-1.1%
-6.2%
-10.5%-13.0%-13.9%

2021
Skill
Customer and Client Support
Marketing and Public Relations
Administration
Building Relationship
Finance
Maintenance, Repair, and Installation
Business Management
Physical Abilities
Communications
Decision Making
Personal Care and Services
Engineering
Environment
DesignEconomics, Policy, and Social Studies
Education and Training
Legal
Agriculture
Energy and Utilities
Public Safety and National Security
Creativity
Analysis
Media and Writing
Manufacturing and Production
Human Resources
Architecture and Construction
Leadership
Health Care
Industry Knowledge
Planning
Organizational Skills
Information Technology

π
55.1%
24.2%
23.2%
17.5%
12.6%
10.9%
8.5%
5.2%
4.3%
2.5%
0.5%
0.4%
0.4%
0.4%
0.2%
0.0%
0.0%
-0.1%
-0.2%
-0.3%
-0.8%
-1.0%
-1.2%
-1.3%
-1.4%
-1.7%
-3.0%
-4.7%
-6.8%
-9.5%
-21.1%-31.7%

2022–2023
Skill
Customer and Client Support
Communications
Administration
Building RelationshipInformation Technology
Maintenance, Repair, and Installation
Human Resources
Business Management
Physical Abilities
Marketing and Public Relations
Engineering
Decision Making
Media and Writing
Personal Care and Services
Agriculture
Education and Training
Creativity
DesignOrganizational Skills
Environment
Economics, Policy, and Social Studies
Public Safety and National Security
Energy and Utilities
Manufacturing and Production
Legal
Architecture and Construction
Finance
Leadership
Health Care
PlanningIndustry Knowledge
Analysis

π
42.1%
29.5%
26.5%
14.1%
8.1%
7.7%
7.4%
6.5%
6.1%
4.2%
3.1%
2.9%
1.8%
1.4%
0.8%
0.7%
0.6%
0.3%
0.3%
0.2%
0.1%
0.0%
-0.1%
-0.1%
-0.1%
-0.2%
-2.0%
-3.3%
-6.9%
-12.4%-13.8%-14.2%

Note: This table presents the Gelbach decomposition results for jobs that do not require a college degree. We conduct the analysis separately
for three periods: between 2018–2019 and 2020, between 2018–2019 and 2021, and between 2018–2019 and 2022–May 2023. The sample
includes jobs postings that do not require college education in high-WFH-adoption occupations (i.e., occupations with an increase of morethan 15 percentage points in the national share of WFH-compatible jobs, as determined by comparing the pooled data from 2018 to March
2020 with pooled data from April 2020 to May 2023) in the Burning Glass data. For each time period, we rank the skill cluster families by
their contributions to the overall decline in the urban wage premium (i.e., π in Equation 10).

77

Table A12: Spatial Shift in Skill Listing Intensity: By College Education Requirement
IT

Business

Communication(2)

Building
Relations(3)(1)

Marketing(4)

Customer
Support(5)(6)

Panel A: Jobs Requiring College Education
Log M

0.00808***(0.00126)

0.000456(0.00238)

0.00537***(0.00208)

0.00841***(0.00138)-0.00278*(0.00166)

0.00662***(0.00104)

Log M × 2020

0.00154(0.00111)

0.00510***(0.00133)

0.00124(0.00118)

0.000467(0.00127)

0.00196**(0.000928)

0.00238***(0.000881)

Log M × 2021

0.000508(0.00117)

0.00266**(0.00107)-0.00345***(0.00119)-0.00283**(0.00112)

0.000375(0.000760)

3.51e-05(0.000836)

Log M × 2022-2023-0.000795(0.00118)

0.00352***(0.00103)-0.00352***(0.00132)-0.000789(0.00147)

0.000900(0.000838)-0.00151*(0.000781)

Observations

549,972

549,972

549,972

549,972

549,972

549,972

Panel B: Jobs Not Requiring College Education
Log M

0.00602***(0.00103)

0.00658***(0.00113)

0.00531***(0.00115)

0.00919***(0.00116)-0.00145(0.00163)

0.00653***(0.000785)

Log M × 2020

-0.000203(0.000687)-0.00192***(0.000548)

0.00222***(0.000568)-0.00112(0.000755)

0.00115**(0.000542)-0.00264***(0.000738)

Log M × 2021

0.00191**(0.000808)-0.00101**(0.000476)

0.00166***(0.000535)-0.00181**(0.000739)-0.000364(0.000806)-0.00254***(0.000570)

Log M × 2022-2023

0.000828(0.000767)-0.00270***(0.000597)-0.00182***(0.000618)-0.00194***(0.000748)-0.000152(0.000665)-0.00219***(0.000629)

Observations

1,242,522

1,242,522

1,242,522

1,242,522

1,242,522

1,242,522

Note: The table presents the estimates of changes in the listing intensity of various skill cluster families with respect to employment size
over time, based on Equation 11. Panel A uses the sample of job postings that require a college degree in high-WFH-adoption occupations(i.e., occupations with an increase of more than 15 percentage points in the national share of WFH-compatible jobs, as determined bycomparing pooled data from 2018 to March 2020 with pooled data from April 2020 to May 2023) in the Burning Glass data. Panel B uses
the sample of job postings without a degree requirement in high-WFH-adoption occupations. The dependent variable is an indicator ofwhether a skill belonging to the skill cluster family is listed in a job posting. M is employment size of the occupation in the MSA of theposted job. 2020 is an indicator that is equal to 1 if the job was posted in 2020, similarly for 2021, and 2022–2023. All columns control forbasic job characteristics, including dummy variables for SOC occupation code, NAICS industry code, years of education required, salarytype, part-/full-time status, tax term, and the posting month. Standard errors are clustered at the MSA level. *** p < 0.01, ** p < 0.05,*p < 0.1.

78

Table A13: Gelbach Decomposition: Contribution of Changes in Skill-Specific Urban Wage Premium
to the Decrease in the Urban Wage Premium within High-WFH-Adoption Occupations (Based on
Imputation from O*NET Characteristics)
2020
Skill
Information Technology
Business Management
Marketing and Public Relations
Physical Abilities
Industry Knowledge
Building RelationshipEngineering
Creativity
Education and Training
Environment
Human Resources
Public Safety and National Security
Maintenance, Repair, and Installation
Agriculture
Economics, Policy, and Social Studies
Decision Making
LegalMedia and Writing
Manufacturing and Production
Energy and Utilities
Health Care
DesignArchitecture and Construction
Finance
Planning
Leadership
Personal Care and Services
Analysis
Organizational Skills
Communications
Customer and Client Support
Administration

π
25.4%
11.1%
8.6%
4.4%
3.7%
2.7%
2.7%
2.2%
1.8%
1.3%
0.7%
0.7%
0.5%
0.3%
0.1%
0.0%
0.0%
-0.1%
-0.2%
-0.3%
-0.4%
-0.6%
-0.8%
-0.8%
-0.9%
-3.3%
-3.9%
-4.1%
-8.3%
-17.6%-26.2%-44.0%

2021
Skill
Marketing and Public Relations
FinanceBusiness Management
Building RelationshipCommunications
Customer and Client Support
Leadership
Creativity
Maintenance, Repair, and Installation
Physical Abilities
Personal Care and Services
Environment
Decision Making
Analysis
Industry Knowledge
Education and Training
Manufacturing and Production
Economics, Policy, and Social Studies
Energy and Utilities
Public Safety and National Security
Agriculture
Media and Writing
Legal
Engineering
DesignArchitecture and Construction
Health Care
Information Technology
Planning
Organizational Skills
Human Resources
Administration

π
53.6%
45.4%
37.0%
22.6%
16.8%
16.7%
8.8%
4.6%
3.2%
2.9%
2.6%
2.0%
1.6%
1.5%
0.8%
0.7%
0.3%
0.3%
0.3%
0.1%
0.0%
-0.1%
-0.1%
-0.2%
-0.5%
-2.0%
-2.5%
-2.7%
-7.3%
-13.2%-18.8%-42.0%

2022–2023
Skill
Information Technology
Marketing and Public Relations
Business Management
Communications
Organizational Skills
Building Relationship
Customer and Client Support
Administration
Personal Care and Services
Maintenance, Repair, and Installation
Decision Making
Physical Abilities
FinanceEngineering
Creativity
Human Resources
Education and Training
Public Safety and National Security
Leadership
Agriculture
Legal
Energy and Utilities
Economics, Policy, and Social Studies
Environment
Media and Writing
Manufacturing and Production
Architecture and Construction
DesignPlanning
Health Care
Industry Knowledge
Analysis

π
86.6%
50.5%
31.0%
28.4%
19.9%
19.6%
15.7%
10.8%
10.2%
7.6%
5.1%
4.8%
4.7%
4.6%
3.5%
2.4%
1.2%
1.1%
1.0%
0.4%
0.4%
0.2%
0.1%
-0.3%
-0.3%
-0.7%
-1.0%
-1.3%
-7.9%
-9.9%
-11.3%-16.6%

Note: This table presents the Gelbach decomposition results for jobs with or without college degree requirements. We conduct the analysis
between 2018–2019 and 2020–May 2023. The sample includes jobs postings in high-WFH-adoption occupations as defined by the levelsof WFH adoption predicted by O*NET work context characteristics (occupations with at least 20 percentage points increase in the imputed
fraction of workers WFH). For the detailed imputation procedures, please see section A4.2. For each time period, we rank the skill cluster
families by their contributions to the overall decline in the urban wage premium (i.e., π in Equation 10).

79

Table A14: Spatial Shift in Skill Listing Intensity among High-WFH-Adoption Occupations (Based on
Imputation from O*NET Characteristics)
IT

Business

Communication(2)

Building
Relations(3)Marketing(4)

Customer
Support
(5)

(1)
Ln M

0.00964***(0.000954)

0.00643***(0.00168)

0.00695***(0.00141)0.0105***(0.00106)-0.000939(0.00154)

0.00694***(0.000790)

Ln M × 2020

0.000523(0.000610)

0.000767(0.000570)

0.000702(0.000611)

0.000546(0.000621)

0.000968**(0.000491)-0.000157(0.000491)

Ln M × 2021

0.000190(0.000764)

0.000191(0.000556)-0.00156***(0.000506)-0.00182***(0.000697)-3.72e-05(0.000544)-0.00142***(0.000430)

Ln M × 2022-2023-0.000466(0.000755)-0.00102*(0.000555)-0.00270***(0.000752)-0.000931(0.000831)

3.56e-05(0.000513)-0.00162***(0.000462)

Observations

2,249,696

2,249,696

2,249,696

2,249,696

2,249,696

2,249,696(6)

Note: The table presents the estimates of changes in the listing intensity of various skill cluster families with respect to employment
size over time, based on Equation 11. The sample includes jobs postings in high-WFH-adoption occupations as defined by the levels of
WFH adoption predicted by O*NET work context characteristics (occupations with at least 20 percentage points increase in the imputed
fraction of workers WFH). For the detailed imputation procedures, please see section A4.2. The dependent variable is an indicator ofwhether a skill belonging to the skill cluster family is listed in a job posting. M is employment size of the occupation in the MSA of theposted job. 2020 is an indicator that is equal to 1 if the job was posted in 2020, similarly for 2021, and 2022–2023. All columns control
for basic job characteristics, including dummy variables for SOC occupation code, NAICS industry code, years of education required,salary type, part-/full-time status, tax term, and the posting month. Standard errors are clustered at the MSA level. *** p < 0.01, ** p <0.05, *p < 0.1.

80