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Which Jobs Can Be Done Remotely?: A JobsEQ Analysis of Remote Occupations

Knowing which occupations can be performed remotely—that is, by working from home—is valuable for understanding the future of labor force supply in the post-COVID economy.

Working from home was already becoming more common, but the COVID-19 crisis forced many employers to quickly and drastically alter policies to enable more workers to perform their jobs from home. This “forced experimentation” with remote work may open some employers eyes to the benefits of an increasingly remote workforce. Shopify, for example, announced in May that its 5,000 employees would be allowed to work from home indefinitely. More employers following this example could induce a stark and fundamental shift in the nature of work.

Occupation Analysis

To begin exploring the ramifications of these potential changes, a first step is to understand which occupations are more suitable for working from home. Unfortunately, the bulk of the data available for this analysis is pre-COVID. The coronavirus lockdown radically changed operating requirements for jobs regarding what is considered acceptable. Teaching K-12, for example, was predominantly considered something that needed to be done on site; with COVID-19, however, many school systems adapted and moved to remote learning arrangements. Nevertheless, even if our occupation characteristics data are coming from the pre-COVID world, this information will give us a solid baseline for further analysis.

To define which occupations can be performed remotely,[1] we leveraged two data sets: the American Time-Use Survey and the Department of Labor’s O*NET. The Time-Use Survey asks if respondents can work from home, if they actually have worked from home, and the reasons for working from home. Using the available responses, we were able to exclude cases where someone, for example, occasionally takes additional work home when they get behind and to focus on those instances where someone more regularly performs work from their home. Specifically, we focused on identifying individuals that actually do work from home (rather than those who say they can work from home but don’t) and especially those who often work from home.[2]

To supplement these data—both for validation purposes as well as obtaining data for occupations with insufficient sample sizes in the survey data—we used information from O*NET, a compendium of data on worker characteristics. Specifically, O*NET contains information on the extent that workers “…perform job tasks in close physical proximity to other people.” In addition, O*NET has information on other working conditions that would typically preclude working from home, such as working outdoors or in an enclosed vehicle or needing to spend working time standing or wearing safety equipment. In all, we used 21 characteristics from the O*NET data to assist in defining which occupations can or cannot be performed from home based upon the nature of the job.

 

Top Occupations by Remote-Work Category Based on Number of People Employed

Non-Remote Jobs

Partial-Remote Jobs

Remote Jobs

41-2031

Retail Salespersons

43-9061

Office Clerks, General

43-3031

Bookkeeping, Accounting, and Auditing Clerks

35-3023

Fast Food and Counter Workers

31-1122

Personal Care Aides

15-1256

Software Developers and Software Quality Assurance Analysts and Testers

41-2011

Cashiers

11-1021

General and Operations Managers

13-2011

Accountants and Auditors

29-1141

Registered Nurses

43-6014

Secretaries and Administrative Assistants, Except Legal, Medical, and Executive

13-1198

Project Management Specialists and Business Operations Specialists, All Other

53-7062

Laborers and Freight, Stock, and Material Movers, Hand

43-1011

First-Line Supervisors of Office and Administrative Support Workers

11-9198

Personal Service Managers, All Other; Entertainment and Recreation Managers, Except Gambling; and Managers, All Other

43-4051

Customer Service Representatives

41-4012

Sales Representatives, Wholesale and Manufacturing, Except Technical and Scientific Products

13-1111

Management Analysts

35-3031

Waiters and Waitresses

43-4171

Receptionists and Information Clerks

23-1011

Lawyers

37-2011

Janitors and Cleaners, Except Maids and Housekeeping Cleaners

41-3091

Sales Representatives of Services, Except Advertising, Insurance, Financial Services, and Travel

13-1161

Market Research Analysts and Marketing Specialists

53-7065

Stockers and Order Fillers

25-2031

Secondary School Teachers, Except Special and Career/Technical Education

11-3031

Financial Managers

53-3032

Heavy and Tractor-Trailer Truck Drivers

11-9013

Farmers, Ranchers, and Other Agricultural Managers

15-1211

Computer Systems Analysts

Source: Chmura

 

Combining the Time-Use Survey and O*NET data, we classified all occupations[3] into one of three buckets: remote jobs, partial-remote jobs, and non-remote jobs. (See the link at the bottom of this blog for the full list of occupations and classifications. These categories will also be available soon for regional analysis within JobsEQ.) The above table shows the ten largest occupations (by employment) in each of these groupings. About 12% of all U.S. employment falls under remote job occupations with 26% in the partial-remote bucket and the remaining 62% in non-remote occupations.

These classifications are for the occupations on average; not every single job that falls within an occupation will necessarily be the same. For example, while software developers are categorized as a remote occupation, this does not imply that every single software developer position can be performed fully remotely; rather, this designation means that, on average, software developer jobs can be performed remotely. The same can be said for the partial- and non-remote designations. Not every job will strictly fall in line with the overall occupation category designation.

Furthermore, to reiterate, these designations are based upon pre-COVID conditions. As we gather more data on the post-COVID workplace, we will certainly revisit these categorizations as we expect shifts to occur in the range of typical work environments for many of these occupations.

Industry and Regional Analysis

Changing remote work conditions have significant implications for economic development, site selection, and urban planning. Employers shifting to remote work arrangements can drastically change the demand for office space. Likewise, labor supply questions for some occupations need to be approached differently if commute-to-work time is no longer as important a limiting factor. If more people are working from home, shifts in daytime population will have ripple effects for both business districts and neighborhoods.

Industries with Highest Concentration of Remote-Work Employment

NAICS

Industry

5112

Software Publishers

5415

Computer Systems Design and Related Services

5412

Accounting, Tax Preparation, Bookkeeping, and Payroll Services

7114

Agents and Managers for Artists, Athletes, Entertainers, and Other Public Figures

5411

Legal Services

5231

Securities and Commodity Contracts Intermediation and Brokerage

5232

Securities and Commodity Exchanges

5239

Other Financial Investment Activities

5259

Other Investment Pools and Funds

5182

Data Processing, Hosting, and Related Services

5211

Monetary Authorities-Central Bank

5251

Insurance and Employee Benefit Funds

5191

Other Information Services

5414

Specialized Design Services

5416

Management, Scientific, and Technical Consulting Services

Source: Chmura

 

The above table contains the industries (defined at the 4-digit NAICS level) which contain the highest concentrations of remote-work employment as defined by each industry’s component occupations. The software developers industry at the top of the list, for example, is estimated to contain 81% remote employment.[4] Many of the industries on this list are traditional “office-space” employers—that is, operations that are performed in an office rather than in an industrial, warehouse, retail space, or other non-office location.

Despite the typical work characteristics of occupations being in a state of flux following the coronavirus disruption, the industry analysis above can be a helpful baseline for comparing communities. By examining the industry mix of every metropolitan area, for example, we can quantify the mix of remote work jobs by region. This would be helpful for identifying the regions that could be most impacted by further shifts of work-from-home arrangements becoming more commonplace—meaning the traditional office space demand could decline in kind along with such a shift.

  • Remote Work Index

The map above displays all the metropolitan statistical areas (MSAs) in the United States by a Remote Work Index. The index compares the mix of remote-work employment in the region versus the average in the nation.[5] An  index of 100% means the region has the exact same mix as the nation. An index of 110% means the region has 10% more remote jobs than average for a region of its size and an index of 90% means the region has 10% less remote jobs than average for a region of its size.

 

Top and Bottom MSAs: Remote Work Index

MSA

Index

San Jose-Sunnyvale-Santa Clara, CA MSA

145%

California-Lexington Park, MD MSA

134%

Boulder, CO MSA

131%

Washington-Arlington-Alexandria, DC-VA-MD-WV MSA

128%

San Francisco-Oakland-Berkeley, CA MSA

126%

Trenton-Princeton, NJ MSA

125%

Ithaca, NY MSA

124%

Ann Arbor, MI MSA

121%

Bloomington, IL MSA

120%

Durham-Chapel Hill, NC MSA

120%

Boston-Cambridge-Newton, MA-NH MSA

120%

Huntsville, AL MSA

118%

Seattle-Tacoma-Bellevue, WA MSA

117%

Austin-Round Rock-Georgetown, TX MSA

117%

Salt Lake City, UT MSA

116%

Gadsden, AL MSA

73%

Yakima, WA MSA

73%

Merced, CA MSA

73%

Wenatchee, WA MSA

73%

Myrtle Beach-Conway-North Myrtle Beach, SC-NC MSA

72%

Michigan City-La Porte, IN MSA

72%

Morristown, TN MSA

71%

Lake Havasu City-Kingman, AZ MSA

71%

Kahului-Wailuku-Lahaina, HI MSA

71%

Muskegon, MI MSA

70%

Madera, CA MSA

69%

Ocean City, NJ MSA

69%

Kokomo, IN MSA

69%

Visalia, CA MSA

66%

Elkhart-Goshen, IN MSA

66%

Source: Chmura

 

The metro areas with the most or least remote work employment follow several patterns. Of those with higher ratios of remote work jobs, some have high concentrations of tech or office employment (such as San Jose, Boulder, and Washington D.C.) while others have high concentrations of postsecondary employment[6] (Ithaca and Ann Arbor).

Several patterns are prominent among the metros with less remote work employment as well. Of metro areas with lower ratios of remote work jobs, some have high concentrations of tourism industries (such as Myrtle Beach, Kahului, and Ocean City), others are heavy with agriculture (Madera and Visalia), while others have large manufacturing sectors (Elkhart and Morristown).

Implications

Some of the significant impacts of remote work may unfold as the economy settles into its post-COVID phase. This is speculative, of course, and depends upon how strongly employers embrace expanded remote work for their employees. We also may see additional variation by industry develop beyond what the baseline data above suggest.

 

Nevertheless, already we can see clear differentiation in the data for remote work jobs compared to non-remote jobs. Online job ads plummeted during the coronavirus shutdown in March and April 2020. As reported in our May 22 blog and shown in the chart above,[7] the set of remote work occupations generally fared better than average during the first six weeks of the slowdown. While new job ads declined for nearly all occupations, ads didn’t decline as much for this group of remote work occupations. However, the script flipped in the weeks of mid-April and early May. As states moved closer to opening up, ads for non-remote jobs began to rebound while remote-work occupations failed to get traction, skidding even further in some weeks.

 

We can also observe a slight relationship between concentrations of remote employment and the economic health of a region. Comparing the Remote Work Index of MSAs to their April 2020 unemployment rates, those regions with higher concentrations of remote jobs tended to have lower unemployment rates (R2 = 0.14).[8] This correlation isn’t too surprising to find at the regional level since at the occupation level many of the remote careers (such as computer and finance occupations) tend to have lower unemployment rates.

 

 

There are also demographic implications, as some demographic groups are not as prevalent in remote work occupations, and therefore not best positioned to take advantage of these jobs. For example, Blacks account for 12.7% of all workers, but just 7.4% of workers within remote work occupations. Hispanic or Latinos are under-represented as well, accounting for 17.3% of all workers compared to 9.0% within remote work occupations. Asians, in contrast, make up 6.5% of the workforce overall, but 13.7% of workers in remote work occupations.

A strong pathway to remote jobs is higher education. About 35% of all jobs are estimated to typically require a two-year degree or higher for entry. In contrast, 87% of remote work jobs typically require a two-year degree or higher.[9]

Conclusion

As we grapple with the lasting impact of COVID on the U.S. economy, whether or not occupations can be performed remotely is a helpful differentiator. Over the short-term, there have already been recorded variation in impacts among remote versus non-remote jobs. The long-term effects remain to be seen as the direct COVID impact plays out and the responses of employers formulate and take hold in the post-COVID economy.

Remote Work Data Download

These data are available for download in Excel format:

Terms of Use

Free to use if data are sourced to: Chmura Economics & AnalyticsJobsEQ.

 

[1] For other approaches to defining remote work jobs, see https://fivethirtyeight.com/features/the-americans-who-suffered-when-the-economy-shut-down-are-also-in-more-danger-as-it-reopens/ as well as https://bfi.uchicago.edu/wp-content/uploads/BFI_White-Paper_Dingel_Neiman_3.2020.pdf in addition to https://bfi.uchicago.edu/wp-content/uploads/BFI_WP_202051.pdf.

[2] Sample data were obtained from the 2017 and 2018 American Time Use surveys, consisting of 10,040 useable respondent observations—those that were employed and answered the work from home questions.

[3] Occupations at the 6-digit SOC level, using 2018 hybrid SOC codes.

[4] Specifically, for this analysis, we began with the typical staffing patterns for every industry (per JobsEQ) and added 100% of employment of remote occupations and 50% of partial-remote occupation employment to get the total industry mix of remote employment as it compares to total employment in that industry.

[5] Similar to the industry analysis above, 100% of employment of remote occupations and 50% of partial-remote occupation employment is summed to estimate the total remote employment.

[6] Most post-secondary teachers fall into the partial-remote category, tending to boost the index scores of regions with a heavy college and university presence.

[7] For the May 22 blog, we used an earlier version of our remote-job categories which differs somewhat from the one used and shown in this blog. Nevertheless, despite the definition change, there were no substantial changes in the remote-job online ads analysis—meaning the same trends shown there apply to the revised remote-job definition.

[8] A similar relationship is found when comparing the index value to the average unemployment rates in 2019, though the April 2020 relationship was a tad stronger.

[9] Typical minimum education requirements are determined by detailed-level occupation as defined here: https://www.bls.gov/emp/tables/education-and-training-by-occupation.htm.

 

 

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