Understanding the varied impact of work from home across society is important for businesses, workers, and policymakers alike. This column examines patterns in the US before the COVID-19 pandemic and since. Before the pandemic, the relationship between potential work from home rates and actual rates was weak. During the pandemic, many industries reached their full work from home potential. Post-pandemic, many industries reverted to lower levels but the relationship between potential work from home rates and actual rates remains stronger than pre-pandemic. Long-term adoption depends on additional factors, such as productivity, collaboration, and business needs.
The ability to work from home (WFH) varies greatly among workers. Some job tasks can be performed remotely, while others require a physical presence (Neiman and Dingel 2020). Some firms have a policy that forbids WFH, while others permit or even encourage it (Bick et al. 2023). Some workers’ homes can accommodate a quiet and dedicated workspace, while others cannot. Variation in WFH ability leads to different WFH rates across demographic groups, occupations, industries, and locations (Mondragon and Wieland 2022, Barrero et al. 2023, Bick et al. 2023). As a result, the economic impact of WFH is unevenly distributed.
Understanding the varied impact of WFH across society is an important area of research. This column examines WFH patterns using six major nationally representative US surveys with information on WFH. We analyse which groups of workers were more likely to WFH before the COVID-19 pandemic, how these patterns have evolved since, and the extent to which different data sources produce consistent findings. 1
In our recent paper (Bick et al. 2024a), we document WFH rates in six major nationally representative surveys with information on WFH in the US. Four of these surveys are run by the US Census: the American Community Survey, the American Time Use Survey, the Current Population Survey, and the Survey of Income and Programme Participation. The other two are online surveys by teams of academic economists: the Real-Time Population Survey by Bick and Blandin (2023), and the Survey of Working Arrangements and Attitudes by Barrero et al. (2021).
Because datasets measure WFH differently and survey different population samples, we first harmonise measures and samples across surveys. Using these comparable measures and samples, we find that our six datasets are mostly consistent with one another regarding differences in WFH across demographics (age, sex, education), geography, and firm size distribution. This holds true for both full-time WFH and hybrid work. Below we highlight some of the key patterns we document in our research paper.
One notable trend is that while women already exhibited higher rates of WFH before the pandemic, this gap expanded post-pandemic. For example, in the Real-Time Population Survey, the share of women WFH every workday pre-pandemic was 0.5 percentage points higher than for men, whereas in 2024, this gap had widened to 2.9 percentage points. Similarly, workers with a four-year degree consistently had higher full-time WFH rates than those without such a degree. In the Real-Time Population Survey, this gap grew from 0.5 to 6.6 percentage points in 2024. While the magnitude of these estimates differs somewhat across the datasets, we find similar patterns in all of them.
WFH patterns also vary considerably by firm size. In three of the four datasets (Survey of Income and Programme Participation, Real-Time Population Survey, Survey of Working Arrangements and Attitudes), full-time WFH follows a U-shaped pattern, with medium-sized firms (10–499 employees) having the lowest WFH rates. In the Current Population Survey alone, full-time WFH is similar across small and medium-sized firms and most prevalent in large firms.
Additionally, data from the Survey of Income and Programme Participation and Real-Time Population Survey enable a comparison of WFH rates by firm size pre- and post-pandemic, revealing that WFH increased most in small and large firms. For instance, in the Real-Time Population Survey, full-time WFH rates for small and large firms increased from about 8.6% pre-pandemic to 11.0% and 14.6%, respectively, in 2024. The rise in medium-sized firms was more modest, from 5.2% to 6.7%.
WFH also differs across industries. To facilitate cross-dataset comparisons, we focus on full-time WFH, which is available in five of our six datasets. Figures 1 (pre-pandemic) and 2 (post-pandemic) illustrate that different datasets provide a consistent ranking of industries by WFH prevalence.
Figure 1 Pre-pandemic WFH rates by industry
Figure 2 Post-pandemic WFH by industry
These figures also reveal that variation in WFH adoption increased sharply after COVID-19. Prior to the pandemic, WFH adoption varied little across industries, with only a few exceeding 15% WFH adoption. By contrast, data from 2022 (the most recent year with available data for all five datasets) show substantial divergence across industries, with some surpassing 40% WFH adoption. Notably, all datasets with pre-pandemic data indicate that the financial activities, professional/business services, and information industries experienced the most significant increases in WFH.
One natural explanation for industry-level variation in WFH adoption is that WFH is more feasible in some industries than others. To examine this, we use the concept of WFH potential – the share of jobs in an industry that can feasibly be performed entirely from home. Neiman and Dingel (2020) estimated WFH potential across occupations and scaled their findings to industries, identifying the highest potential in education, financial activities, professional/business services, and information.
Given the substantial WFH growth documented earlier, it is natural to ask whether high WFH potential accounts for this trend. For our analysis, we use the Real-Time Population Survey as it is the only dataset that includes pre-pandemic information, high-frequency data during the pandemic, and already available data for 2024.
The pre-pandemic relationship between WFH potential and actual WFH rates was weak. In February 2020, the correlation between these measures was only 0.35, and each percentage point increase in WFH potential corresponded to just a 0.05-point rise in actual WFH (Figure 3a). This changed dramatically during the pandemic: by May 2020, many industries reached their full WFH potential, driving the correlation to 0.94 (Figure 3b). This underscores how industries where WFH was feasible adapted to crisis conditions, using WFH to maintain business continuity and worker safety.
Figure 3 WFH potential and actual WFH by industry in the Real-Time Population Survey
By mid-2024, many industries reverted to lower levels of WFH. However, the relationship between WFH potential and actual WFH had not fully returned to its pre-pandemic state. While the correlation declined from its peak of 0.94 to 0.54 (Figure 3c), it remained substantially above the pre-pandemic value of 0.35. Moreover, each percentage point increase in WFH potential was associated with a 0.12-point increase in actual WFH – below the pandemic peak, but still more than double the pre-pandemic value.
Sustained high WFH adoption in financial activities, professional/business services, and information suggests that firms in these industries found ways to integrate remote work more permanently. In contrast, other industries with high WFH potential, such as education, have largely returned to in-person work.
The case of education is particularly striking: despite having one of the highest WFH-potential estimates, the sector has returned to in-person work, likely due to concerns over the effectiveness of remote instruction (McKee et al. 2020). Survey evidence from Japan (Morikawa 2022) further reinforces this view, indicating that WFH is only 60–70% as productive as office work.
The variation in WFH adoption across industries carries important implications for policy and economic planning. Persistently high WFH rates in certain sectors are reshaping urban labour markets, commercial real estate demand, and public transportation usage (Glaeser 2022). Early adopters of WFH have made significant investments in remote collaboration tools and flexible work arrangements, while others continue to prioritise in-office work.
A key takeaway is that WFH potential sets an upper bound on actual WFH rates, but long-term adoption depends on additional factors, such as the productivity of WFH relative to in-person work. Even in high WFH-potential industries, prominent firms like Amazon and JP Morgan have pushed for a return to full-time office work (Anand and Binnie 2025, Bindley and Rana 2025).
Work from home has become a defining feature of the post-pandemic labour market, but its adoption varies widely across industries. While some industries have permanently integrated WFH, others have returned to traditional models despite its feasibility. This suggests that WFH potential alone does not determine long-run remote work adoption – productivity, collaboration, and business needs remain critical factors. As the labour market continues to evolve, understanding these patterns will be critical for businesses, workers, and policymakers alike.
Source : VOXeu
As with the informal economy, the lack of data on social capital in developing nations…
Over the past decade, euro area insurers have been challenged by the prolonged period of…
FinTech has transformed finance, but the broader effects of digital payments on consumers, businesses, and…
The global minimum tax represents the most ambitious international effort in decades to curb profit…
Green debt has become a defining feature of sustainable finance, as firms and investors seek…
Have you ever wondered how satellites orbiting thousands of kilometers above Earth can help farmers…