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This article is adapted from “Remote Work and the Coronavirus,” by Roberto Gallardo and Richard Florida, published by the Purdue University Center for Rural Development. The complete study is available online.

In the wake of the coronavirus crisis, large segments of the U.S. workforce have shifted to remote work. A substantial share of Americans will be working remotely for the foreseeable future. 

But not all workers and communities are equally prepared for remote work or e-learning. 

Two factors in particular affect workers’ ability to work remotely in their communities. The first is the availability of ubiquitous, adequate and affordable digital connectivity, which varies widely across the country. The second is the share of jobs that can be done via remote work. This too varies across the country, with remote-work amenable jobs concentrating in large metros and tech hubs.

So, how ready are America’s communities for the new realities of remote work?

To get at this we looked at two factors: 1) the availability of digital connectivity and 2) the share of workers employed in industries and occupations that are amenable to remote work. We focused on how vulnerable counties are by looking at the places that have inadequate digital connectivity—including access to the internet and to digital devices—and places where a higher share of workers are employed in industries/occupations that are not remote work friendly.

If a particular county has both limited digital connectivity and a higher share of non-remote work workers, it will more than likely struggle to leverage e-learning and remote work. We looked at the geography of remote work across America’s 3,000 plus counties.

What did we find?

Counties best positioned for success with remote work are more urban, have larger economies, more educated workers, and higher incomes. Conversely, those that are most vulnerable are smaller, more rural, suffer from high rates of unemployment and have less educated workers.

That said, even the least vulnerable places across the country have some percentage of residents and workers who are not well suited for remote work, who suffer from inadequate connectivity, and work in occupations or industries that are far less amenable to remote work.

County Type

First, rural counties were more likely to face difficulties shifting their residents to remote workplaces. Figure 3 breaks down the vulnerability groups by type of counties using the urban influence codes developed by the United States Department of Agriculture Economic Research Service. 

Roughly two-thirds of counties (63.4%) with no vulnerability were large/small metro areas compared to one-fifth of the high vulnerability counties (20.3%). On the other hand, two-thirds (66.4%) of counties with a high vulnerability were rural counties.

Source: Purdue Center for Regional Development; ACS 5 Yr. 2014-2018

Age Groups

Figure 4 breaks down the data by age groups. It shows a slightly higher share of those ages 65 and over in counties with a high vulnerability (18.2%) compared to those with no (14.9%) or low (15.4%) vulnerability. In addition, about one-fifth of residents (22.6%) in high vulnerability counties were children (under 18).

Race and Ethnicity

Figure 5 breaks the data by race and ethnicity. It shows that a higher share of White Non-Hispanic residents lived in high vulnerability counties compared to 58.5% living in no vulnerability counties. The share of minorities was higher in no and low vulnerability counties compared to areas with high vulnerability.


Figure 6 shows the share of household in each of the vulnerability groups by annual income. Close to one-third of homes in no vulnerability counties made $100,000 or more per year compared to 13% in counties with a high vulnerability. On the flip side, 43.5% of homes in high vulnerability counties made less than $35,000 per year compared to 27.2% in counties with no vulnerability as defined by this study.

Gross Domestic Product

Figure 7 shows the gross domestic product (in billions of dollars) for each of the vulnerability groups. The cumulative GDP of no vulnerability counties was a little more than $16 trillion compared to $236 billion of high vulnerability counties. Although it may seem that the economic impact could not be that high, keep in mind that economic multipliers and supply chains affect all vulnerability groups.

Unemployment and Not in the Labor Force

Figure 8 shows that high vulnerabilities counties had a lower employment rate among those ages 16 to 64, a higher unemployment rate, and a higher share of those not in the labor force.

Educational Attainment

Figure 9 shows that roughly one-fifth of those ages 25 or older did not have a high school degree in high vulnerability counties compared to 11.3% in no vulnerability counties. On the flip side, the share of those with a bachelor’s degree or higher was three times higher in no vulnerability counties compared to high (35.9 versus 13.3%).


Remote work and the challenges and inequities it brings will be with us for a while. Pandemics like the current coronavirus unfold in waves lasting 12 to 18 months. During that time, many of us will continue to engage in remote work. And even after, many may continue to do remote work as a growing number of workers value its flexibility and a growing number of firms realize that it can save them money. It is also likely to benefit large congested cities by reducing traffic and enabling the conversion of some office space into residential uses.

To conclude, it is important to remember that remote work is part of the broader challenge of economic and geographic inequality. As we shift to more remote work, it is important that mayors, economic developers, community leaders, businesses, unions, and workers develop strategies to make remote work as inclusive as possible and undertake efforts to ameliorate the economic and spatial inequality that it reflects.

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