Non-Metropolitan Areas Lag Behind in Work-from-Home Rates

By John V. Winters

Recent years have seen an explosion in the prevalence of individuals who work from home following social distancing efforts to reduce the spread of COVID-19 (Hegde and Van Parys 2024). Many workers were heavily exposed to new work patterns and communication methods, including more flexible work hours and virtual meetings via Zoom and other tools. During this experience, many firms and workers found working from home to be beneficial, and elevated work-from-home rates that began during the pandemic have largely stuck in the following years (Barrero, Bloom, and Davis 2023).1

However, the option to work from home is not available to all workers. Most jobs still involve many tasks that must be done in a traditional workplace. Remote jobs tend to be information- and communication-intensive professional service occupations that were previously done in office settings. Workers in many fields such as manufacturing, retail, health care, and others do not have much ability to perform their jobs from home.

This short article documents and attempts to explain work-from-home differences between metropolitan and non-metropolitan area residents. Not too long ago, work-from-home rates were similar between metro and non-metro areas. However, increased work-from-home rates have been much more pronounced in metro than non-metro areas. I consider differences by worker characteristics and the possible role that these play in metro-non-metro differences and find that worker demographics do not play a major role. However, education, occupation, and industry differences do play important roles in explaining the lower rates of work from home in non-metro areas. Broadband internet also appears to be an important factor. Thus, non-metro areas may still have untapped potential in increasing work-from-home rates to improve labor market outcomes for non-metro residents and strengthen local economies. Improved education and high-speed internet access are likely key policy goals to help achieve this.

Work-from-home trends

Figure 1 presents work-from-home trends for workers ages 18–64 in the annual American Community Survey (ACS) from 2010 to 2022. In 2010, work-from-home rates were actually slightly higher for non-metro area residents (4.1%) than metro area residents (4.0%).2 Work-from-home rates grew over time; and, by 2019, work-from-home rates had surpassed previous years in both metro and non-metro areas at 5.5% and 4.4%, respectively. Work-from-home rates skyrocketed during the pandemic and peaked in 2021 at 19.4% for metro areas but only 8.3% for non-metro areas. In 2022, the most recent ACS year available for this analysis, work-from-home rates had fallen somewhat to 16.2% for metro areas and to 8.1% for non-metro areas.3 

Figure 1. Work-from-home trends in metro and non-metro areas.
Figure 1. Work-from-home trends in metro and non-metro areas.
Source: Based on author estimates using workers ages 18–64 in the American Community Survey.

Working from home is more practical in some occupations and industries than others. Additionally, work-from-home opportunities vary by education level. Figure 2 illustrates work-from-home trends for college graduates and non-college graduates in metro and non-metro areas. I define college graduates as persons with a bachelor’s degree or higher. Multiple patterns emerge. The two groups of college graduates consistently have the highest work-from-home rates. However, there are major differences between college graduates in metro and non-metro areas. For 2022, 25.0% of metro college graduates worked from home, while only 13.6% of non-metro college graduates worked from home. Among persons with less than a bachelor’s degree (i.e., non-graduates), 10% of metro workers worked from home in 2022, while only 6.3% of non-metro workers worked from home.

Figure 2. Work-from-home trends by college graduation and metro status.
Figure 2. Work-from-home trends by college graduation and metro status.
Source: Based on author estimates using workers ages 18–64 in the American Community Survey.


Work-from-home opportunities also depend on whether one is a paid employee or self-employed. Figure 3 documents differences between these groups over time and by metro and non-metro status. Self-employed workers have historically much higher work-from-home rates than paid employees. However, the increased prevalence of working from home that began during the pandemic is more pronounced for paid employees, especially employees residing in metro areas. In fact, the work-from-home rate for self-employed non-metropolitan residents did not meaningfully increase over time—the rate for this group was 19.8% in 2022, a slightly lower rate than in most years before the pandemic.4

Figure 3. Work-from-home trends by self-employment and metro status.
Figure 3. Work-from-home trends by self-employment and metro status.
Source: Based on author estimates using workers ages 18–64 in the American Community Survey.

Explanatory correlates

I next turn to considering the role of individual characteristics in explaining work-from-home rate differences between metro and non-metro areas in 2022 using multivariate regression analysis that controls for an increasing number of possible explanatory factors and seeing how the remaining work-from-home gap between metro and non-metro areas changes. More details are provided in the appendix with regression results in table A1.

In 2022, the work-from-home rate was 16.2% for metro workers and 8.1% for non-metro residents, resulting in a gap of 8.1 percentage points (i.e., the rate was twice as high in metro areas). Adding regression controls for age and sex produces a gap of 8.0 percentage points, which is virtually unchanged. Further adding controls for race and Hispanic ethnicity actually increases the metro-non-metro work-from-home gap to 8.9 percentage points. Also controlling for self-employment further pushes the gap between metro and non-metro areas to 9.0 percentage points.  Thus, these factors discussed thus far do not explain the gap—race, ethnicity, and self-employment actually mask some of the gap that would occur if metro and non-metro areas did not differ along these dimensions.

I next add further controls for education, which reduces the gap to 6.5 percentage points. Adding detailed controls for industry and occupation of employment reduces the estimated work-from-home gap between metro and non-metro areas to 3.4 percentage points. Thus, education, occupation, and industry differences between metro and non-metro areas collectively explain the majority of the work-from-home difference between these areas in 2022. However, some portion remains unexplained even after accounting for these individual factors.

As a final piece of analysis, I next consider the potential role of broadband internet access by including local broadband rates as an additional explanatory variable. The results suggest that broadband has an important positive relationship with work-from-home rates. Broadband rates are also significantly lower in non-metro areas. Adding the broadband explanatory variable further reduces the work-from-home difference between metro and non-metro areas to only 0.2 percentage points and the difference is not statistically significant (i.e., the difference is not statistically distinguishable from zero). This analysis is illustrative and may not provide perfectly accurate estimates due to the exclusion of other potentially important variables. However, at face value, it appears that broadband access may be an important factor explaining work-from-home differences between metro and non-metro areas.


Many non-metro residents have limited employment options in their area and have to settle for lower-paid work than they could get if they lived in a metro area (Winters 2020). Lack of employment opportunities is also a key factor for rural-urban migration as workers leave rural areas for better jobs and higher incomes in urban areas (Artz and Yu 2011). Working from home has potential to increase employment opportunities and incomes for rural residents by giving them access to employers and markets farther away—they can live in a rural area and work for an employer hundreds or even thousands of miles away. Increased incomes for remote workers in rural areas could also help fuel rural economic development due to employment multipliers. Rural remote workers who earn more money have more money to spend in their local community, which can create additional jobs and income for their neighbors. Remote work could one day be a central part of rural economies.

However, work-from-home rates in non-metro areas have been unspectacular thus far. Work-from-home rates did increase in non-metro areas during the COVID-19 pandemic, but by 2022 only 8.1% of non-metro workers worked remotely. Furthermore, non-metro work-from-home rates lag behind the 16.2% rate for metro area workers. Education, occupation, and industry differences between metro and non-metro workers collectively explain the majority of their differing work-from-home rates, but observable worker characteristics do not explain some portion of it. Differences in local broadband rates appear to explain the remaining work-from-home rate difference between metro and non-metro workers.

This article suggests that non-metro areas have untapped potential to increase work-from-home rates and there is likely some role for public policy. First, rural education and training programs should increasingly prioritize access to and familiarity with remote work opportunities and practices. This can include familiarity with software for virtual meetings, file sharing, communication, etc. It also likely includes providing job seekers with information on where and how to find, apply for, and get hired for remote jobs. Finally, high-speed internet access appears to be a significant obstacle to working from home for many rural residents. Improved internet speeds and reliability may have a plethora of benefits for rural residents, and remote work opportunities may be especially important among them. Increasing work-from-home rates may be critical for the economic health of non-metro areas in the coming years and decades.


1. Notable benefits discussed include reduced time commuting and reduced need for office space (Aksoy et al. 2023; Behrens, Kichko, and Thisse 2024). 
2. Some workers live and work in different areas. The current analysis measures metro and non-metro status based on where workers live. The ACS work-from-home question is based on where an individual “usually” works. Thus, an individual who works from home some but spends most of their work time at a worksite away from home would not be coded as working from home.
3. The ACS is conducted throughout a calendar year, but the publicly available data do not disclose the timing of individual surveys. Thus, the ACS is useful for annual averages but cannot be used to track changes within a given year. 
4. Rates for this group exhibit some moderate year-to-year fluctuations but do not systematically trend up or down over the full period.


Aksoy, C.G., J.M. Barrero, N. Bloom, S.J. Davis, M. Dolls, and P. Zarate. 2023. “Time Savings when Working from Home.” AEA Papers and Proceedings 113:597–603. doi: 10.1257/pandp.20231013.
Artz, G. and L. Yu. 2011. “How ya Gonna Keep ’em Down on the Farm: Which Land Grant Graduates Live in Rural Areas? Economic Development Quarterly, 25(4):341–352. Available at: doi: 10.1177/0891242411409399.
Barrero, J.M., N. Bloom, and S.J. Davis. 2023. “The Evolution of Work from Home.” Journal of Economic Perspectives 37(4):23–49. doi: 10.1257/jep.37.4.23.
Behrens, K., S. Kichko, and J.F. Thisse. 2024. “Working from Home: Too Much of a Good Thing?” Regional Science and Urban Economics article 103990. doi: 10.1016/j.regsciurbeco.2024.103990.
Hegde, S.S. and J. Van Parys. 2024. “The Impact of Municipal Broadband Restrictions on COVID-19 Labor Market Outcomes.” NBER Working Paper No. 32257.
Ruggles, S., S. Flood, M. Sobek, D. Backman, A. Chen, G. Cooper, S. Richards, R. Rogers, and M. Schouweiler. IPUMS USA: Version 14.0 [dataset]. Minneapolis, MN: IPUMS, 2023. doi: 10.18128/D010.V14.
Winters, J.V. 2020. “What You Make Depends on Where You Live: College Earnings across States and Metropolitan Areas.” Thomas B. Fordham Institute.

Suggested citation

Winters, J. 2024. "Non-Metropolitan Areas Lag Behind in Work-from-Home Rates." Agricultural Policy Review, Spring 2024. Center for Agricultural and Rural Development, Iowa State University.


Table A1 uses multivariate regression analysis to examine the influence of individual characteristics and local broadband rates in explaining work-from-home differences between metro and non-metro workers ages 18-64 in the 2022 American Community Survey (ACS). The dependent variable is an indicator equal to 1 if a worker works from home and 0 otherwise. The key explanatory variable is a Non-Metro indicator variable equal to 1 for workers residing in a non-metro area and 0 for metro residents. The key variable captures differences in work-from-home rates between metro and non-metro residents.

Table A1. Work-from-Home Differences in 2022 for Non-Metro Relative to Metro Areas with Increasing Regression Controls
Notes: The sample includes workers ages 18–64 in the 2022 American Community Survey. Local broadband rate is computed for Census Public Use Microdata Areas (PUMAs). Standard errors are clustered by PUMA. **Significant at the 1% level.
Local Broadband Rate       0.227**


Table A1 includes seven columns with generally increasing sets of controls for age, sex, race/ethnicity, self-employment status, education, industry, and occupation—Yes and No at the bottom of the table indicate inclusion for each column. Controls are detailed dummy variables. The change in the Non-Metro coefficient reflects the influence of additional regression controls. The final column also includes a variable for the local broadband rate in the 2022 ACS computed as the percentage of adults (ages 18+) who have broadband internet in their home. The results in the final column indicate that the residual work-from-home rate difference between metro and non-metro areas (-0.002) is small and not statistically significant after including the full set of explanatory variables. Additionally, the coefficient of 0.227 for the local broadband rate suggests that a 10 percentage point increase in broadband would increase the work-from-home rate by 2.27 percentage points. For workers in the sample, the mean local broadband rate is 78.9% for metro residents and 64.3% for non-metro residents, a difference of 14.6 percentage points. Multiplying the broadband coefficient (0.227) by the broadband rate difference between metro and non-metro areas (0.146) indicates that broadband explains about 3.3 percentage points (0.033) of the work-from-home difference.