Commuting in America 2021 - Transportation

Commuting in America The National Report on Commuting Patterns and Trends

2021

Brief 21.1. The Changing Nature of Work

http://www.transportation.org

http://ctpp.transportation.org

About the AASHTO Census Transportation Planning Products ProgramEstablished by the American Association of State Highway and Transportation Officials (AASHTO) and the U.S. Department of Transportation (U.S. DOT), the AASHTO Census Transportation Planning Products Program (CTPP) compiles census data on demographic characteristics, home and work locations, and journey-to-work travel flows to assist with a variety of state, regional, and local transportation policy and planning efforts. CTPP also supports corridor and project studies, environmental analyses, and emergency operations management, and many other efforts.

In 1990, 2000, 2006, 2013, and again in 2019, AASHTO partnered with all of the states on a project to sup-port the development of special census products and data tabulations for transportation. These census transpor-tation data packages have proved invaluable in understanding characteristics about where people live and work, their journey-to-work commuting patterns, and the modes they use for getting to work. In 2012, the CTPP was established as an ongoing technical service program of AASHTO.

CTPP provides a number of primary services:

• Special Data Tabulation from the U.S. Census Bureau—CTPP oversees the specification, purchase, and delivery of this special tabulation designed by and for transportation planners.

• Outreach and Training—The CTPP team provides training on data and data issues in many formats, from live briefings and presentations to hands-on, full-day courses. The team has also created a number of electronic sources of training, from e-learning to recorded webinars to downloadable presentations.

• Technical Support—CTPP provides limited direct technical support for solving data issues; the pro-gram also maintains a robust listserv where many issues are discussed, dissected, and resolved by the CTPP community.

• Research—CTPP staff and board members routinely generate problem statements to solicit research on data issues; additionally, CTPP has funded its own research efforts. Total research generated or funded by the current CTPP since 2006 is in excess of $1 million.

Staff• Penelope Weinberger, CTPP Program Manager• Matt Hardy, Program Director, Policy and Planning• Joung Lee, Director of Policy and Government Relations

Brief Team• Nancy McGuckin, Author, InvestigatorPanel• Phil Mescher, IA DOT• Guy Rousseau, ARC• Jessie Jones, AR DOT• Joe Hausman, FHWA• Thomas Hill, FDOT• Elizabeth Robbins, WSDOT• Clara Reschovsky, BTS• Krishnan Viswanathan, Cambridge Systematics

ContactPenelope Weinberger, e-mail: [email protected], phone: 202-624-3556; or [email protected]

© 2021 by the American Association of State Highway and Transportation Officials. All rights reserved. Duplication is a violation of applicable law.

Pub Code: CA01-5 ISBN: 978-1-56051-766-5


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PurposeThe 2020 COVID-19 pandemic has radically changed the nature of work in the United States. Essential workers and many who could work from home retained employment, while tens of millions whose work was in person or on-site were thrown into unemploy-ment. Travel, of course, was radically curtailed because of stay-at-home orders, dropping as much as 90 percent at times. The long-term effects of these sudden shifts are uncertain.

Even before the pandemic, major generational, demographic, economic, and cultural shifts in the U.S. were already altering the nature of work and travel. As a result of these long-term shifts, the number of workers who usually worked from home and those who could sometimes work from home had already been rising, enabled by new technology, the demands of digital workers, and the working retirement of many baby boomers. In addition, new digital platforms enabled workers to directly contract with customers and employers for short-term contract work (gigs).

The economy will recover and create a new normal, and the way that the work world will adapt is unknown. Perhaps workers will demand more work-at-home and telecommut-ing options, having been shown that possibility; perhaps more workers will enter the gig economy as a supplement or a substitute for the job lost; perhaps there will long-standing shifts in the major economic sectors. This brief is a snapshot of the time before, ready to be

Brief 21.1.The Changing Nature of Work

Commuting in America 2021


revisited in some undetermined time after when we are ready to assess what changes have remained.

The purpose of this brief is to provide a snapshot of the nature of work in 2019, with focus on the single most important trend in work travel: the shift to nontraditional work. The goal is to help researchers and policymakers interested in quantifying the amount and type of changes in the U.S. workforce in 2020 and later. In addition, this brief explores how workers involved in each of these sectors of nontraditional work commute and travel for their daily life tasks. Statistics with margins of error are presented at the 90th confidence limit (±10 percent).

IntroductionIn the before time, many (especially younger) workers pushed for greater flexibility to bal-ance work and family life, including telecommuting options, nontraditional hours, and the ability to work from anywhere. In a tight employment market, employers responded with more of these types of benefits.

Employers had also been shifting more workers to contract jobs without the traditional benefits of pension, health insurance, and paid time off. This shift to defining more jobs as contractor, 1099, or gig work was just starting to see policy interventions by state and local authorities to protect workers in 2019.

In combination with that, a plethora of new online platforms were launched to directly connect workers and work opportunities without formal employment. What started with Uber ballooned into thousands of sites offering one-time or short-term gigs to work in food delivery; pet, child, and elder care; handyman services; and multitudes more. There was such growth in the amount of “electronically enabled work” (a definition used by the Bureau of Labor Statistics) and the shift to contract employees that some projections (such as McKinsey Global Institute) were that fully half of the U.S. workforce would be involved in some sort of gig work in the next decade or so.

But in the spring of 2020, the world changed, and the nature of work changed with it. The stay-at-home advisories meant most workers had to find ways to work from home, including teachers (as schools and universities shuttered), doctors (as office visits moved online), and entertainers and musicians. Some jobs, by their very nature, were neither deemed essential or not able to be done off site, for example workers in food and beverage sales; retail and shop workers; personal service providers; airline and transportation work-ers; and hospitality, travel, and tourism workers.

4 Commuting in America 2021: The National Report on Commuting Patterns and Trends© 2021 by the American Association of State Highway and Transportation Officials. All rights reserved. Duplication is a violation of applicable law.

Figure 1. Online Platforms that Enable Gig Work

It is important to keep in mind that while the supply of hotels, restaurants, and service jobs disappeared as establishments were shuttered, demand for many goods and services disappeared as well. One of the many unknowns is how the demand and supply balance might look going forward.

Trends toward Nontraditional WorkThe number of people engaged in nontraditional work grew over the last few decades. For instance, between 1990 and 2018 the number of workers who worked at home increased substantially, and faster than the overall growth in the workforce, especially since 2010 (U.S. Census, 1990, 2000, and 2010; American Community Survey (ACS), 2018). Overall, the trend data shows that in 1995 few workers (just over 2 percent of all) worked only at home (not including occasional telecommuting) and not much variation was seen between people of different ages. In addition, the number of workers that sometimes or occasionally telecommuted (but usually went into a regular workplace) nearly doubled since 2001.

There has been an increase in the percentage of workers who hold multiple/part-time jobs—while the percent employed at single full-time work had been decreasing (in workers over the age of 21). Related to that, the Bureau of Labor Statistics data indicates growth in the percentage of workers in jobs with no benefits.1

Importantly, workers in nontradtional work have different travel behavior and commute characteristics and substantial growth in these nontraditional arrangements could change

1 Current Population Survey March Supplement (1995–2017) and Katz and Krueger (2019). Data from the Center for Retirement Research, Boston College. Published at: https://crr.bc.edu/working-papers/how-do-older-workers-use-nontraditional-jobs/

5Brief 21.1. The Changing Nature of Work© 2021 by the American Association of State Highway and Transportation Officials. All rights reserved. Duplication is a violation of applicable law.

https://crr.bc.edu/working-papers/how-do-older-workers-use-nontraditional-jobs/

https://crr.bc.edu/working-papers/how-do-older-workers-use-nontraditional-jobs/

choices for residential location, affect work tenure, and alter the proportion of work travel during peak periods and on weekends.

Who Are the Nontraditional Workers?Nontraditional workers in 2019 included workers who had flexible work schedules, had the option of telecommuting, or usually worked at home, and workers who worked multiple and/or part-time jobs; together these represent a plurality of workers in the U.S.

In all, about 42 percent of workers could set or change their work times, i.e. flextime (see Figure 2), and this category overlaps workers who have the option to telecommute (14 percent of all workers) and workers who usually work at home (12 percent of all work-ers). In addition, trends show a growing portion of workers who worked multiple and/or part-time jobs and some portion of these are (also or only) gig workers.

Workers with Part-Time or Multiple Jobs

26%

Workers with Flexible Schedules

42%

Workers Who Have the Option to Telecommute

14%

WorkersWho Only

Work Gig Jobs??%

Workers Who Usually

Work at Home12%

Figure 2. Proportion of Workers in Nontraditional Work Arrangements, 2017


There were important demographic differences between the workers involved in non-traditional work and those in more traditional arrangements.

• Younger workers (between 21 and the median age of 40 years) were less likely to have flextime in their jobs; 36 percent of younger workers could set their own schedules compared to 43 percent of older workers. Younger workers were much less likely to usually work at home compared to older workers; 9 percent of younger workers compared to 14 percent of older workers.

• Non-white workers were less likely to have flextime or the option to telecommute compared to their white counterparts; only 34 percent of non-white workers could set their own schedules compared to 43 percent of white workers.

• Lower-income workers (who have incomes under the median of $47,0002) had even more differences compared to their counterparts: only 27 percent of work-ers with lower than median incomes had the ability to set or change their work start times (flextime) compared to 45 percent of higher-income workers. Over a third of lower-income workers worked multiple/part-time jobs compared to less than one out of five of higher-income workers. Lower-income workers were less than half as likely to have the option to telecommute but nearly the same propor-tion of higher- and lower-income workers usually worked at home.

• Traditionally, women have been more likely to work part-time; 31 percent of women worked multiple/part-time jobs in 2017 compared to 19 percent of men. Women were also much less likely than men to have had the option of telecom-muting (only 12 percent of women workers had the option of telecommuting) or to set their own work schedules.

Figure 3 shows the participation rate by demographic group in these nontraditional work categories; the outlines indicate statistically different participation rates.

2 State of Working America Wages 2019, February 2020. Elise Gould, Economic Policy Institute. https://www.epi.org/publication/swa-wages-2019/


https://www.epi.org/publication/swa-wages-2019/

Workers with Flextime41.8% of all

Multiple/Part-Time Jobs26.4% of all

Option of Telecommuting13.9% of all

Usually Works at Home11.5% of all

Percentage of Workers

OlderYounger White Lower Inc. Higher Inc. Men Women48% 42%36% 43% 43% 34% 27% 45%

25% 23% 23% 25% 34% 19% 19% 31%

15% 15% 18% 15% 8% 21% 17% 12%

9% 12%12%11%11%13%14% 12%

Non-White

OlderYounger White Lower Inc. Higher Inc. Men WomenNon-White



Figure 3. Percentage of Workers Who Have Nontraditional Work Arrangements by Age, Race, Income, and Gender, 2017

Figure 4 shows the distribution of workers in each of the nontraditional arrangements, highlighting the demographic differences even more:

• More than half of workers with the option to telecommute and two thirds of those who usually worked from home were older than the median age of 40.

• Non-whites are over-represented across all the nontraditional work arrange-ments but especially in the category of workers with multiple/part-time jobs. Among these workers, 38 percent were non-white compared to 22 percent in the total workforce.

• Three quarters or more of workers who had flextime, the option to telecommute, or usually worked at home were higher-income (i.e. above the median).

• About three out of five (around 60 percent) of the workers who had flextime, the option to telecommute, or usually worked from home were men (the workforce is about 47 percent women and 53 percent men).


50%50%69%

31% 21%

76%57%

43%

49%51% 62%38% 45%54%

41%59%

46%54%68%

32% 15%

83%61%

39%

34%66% 68%

32% 29%

68%52%48%

Workers with Flextime41.8% of all

Multiple/Part-Time Jobs26.4% of all

Option of Telecommuting13.9% of all

Usually Works at Home11.5% of all

Older Younger Lower Inc.Higher Inc. MenWomenNon-White White

Figure 4. Distribution of Workers Who Have Nontraditional Work Arrangements by Age, Race, Income, and Gender, 2017

How Do Nontraditional Workers Travel?Nontraditional workers traveled differently than their counterparts. Figure 5 summarizes the average passenger miles traveled (PMT) by all means of travel and for all purposes. The differences in travel were co-related to the demographics of workers in each group, such as income and occupation, and include:

Workers who usually worked at home. Home workers traveled nearly the same as workers who usually worked at a regular work-place. While home workers spent 40 percent fewer miles commuting, they spent more miles for shopping, family, and personal errands.

Men had the same likelihood of working at home whether or not they had children in the household. In contrast, women with children were more likely to work at home compared to women in households without children. Women who worked at home trav-eled very similarly to women who worked at a workplace—there was just a 1.2-mile differ-ence per day between them. On the other hand, men who worked at home traveled almost 10 miles more per day (56.9 compared to 47.0 miles) compared to men who worked at a regular workplace.


Workers with the option of telecommuting. In contrast, workers who had the option of telecommuting traveled more miles than work-ers who did not—almost 20 percent more overall. Men who had the option to telecommute traveled much further for work and work-related purposes in an average day—20 miles compared to 17 for men who could not telecommute. Women with the option to telecom-mute had work-related travel closer in distance in mileage their counterparts, just 2.5 miles more (14.8 compared to 12.3 miles in work and work-related travel).

Workers with multiple/part-time jobs. Workers with multiple/part-time jobs traveled about 10 percent fewer miles per day compared to workers with full-time jobs. These workers had shorter commutes but they reported more miles of non-categorized travel, indicating that the typical purposes allowed in the survey were inadequate for describing their travel. Importantly, the time-of-day pro-file for these workers was quite different compared to full-time workers.

Workers with flextime. Workers who could set their own work schedule traveled about 10 percent more than work-ers who did not have that option. Women with flextime traveled just 5 percent more miles than women who abided a work schedule, whereas men with flextime traveled 11 percent more miles compared to men who abided a work schedule. The commute time-of-day peaks for workers with flextime were quite similar to those without, with just a slight shift to late morning and evening (on the shoulder of the peaks).


-3% -2% -1%

Workers with Flextime

Workers with Multiple/Part–Time Jobs

Workers Who Can Telecommute

Workers Who Usually Work at Home

Fewer Miles than All Workers More Miles than All Workers

Travel by Non–Traditional Workers(PMT per Day Compared to All Workers)

WomenMenAll Workers

0% 1% 2% 3%

Figure 5. Travel by Nontraditional Workers: Percent Difference in Miles per Day (PMT), 2017

For reference, baseline travel rates by purpose by all workers is shown in Appendix A, Table A1. The remainder of this brief dives into more detail for each of these nontraditional groups of workers.

1. Workers with FlextimeOver four out of ten workers in the National Household Travel Survey (NHTS) could set or change their work time (flextime) but this option was highly related to the type of job, the level of income, and the gender of the worker, as well as whether or not the work is in a large metro area (see Appendix A, Table A2 for details). For example, about a third of workers in sales and service had flextime, compared to well over half (55 percent) of those in professional, technical, or managerial occupations. Less than a third of workers in low-er-income households had flextime compared to more than half of workers in the high-est-income group. Men were more likely than women to have the option of setting their own work hours.

Workers with flextime had remarkably similar time-of-day profiles for their commutes compared to workers without it. Overall, workers in both groups traveled during the morning and evening peaks, with a slight tendency for later morning arrivals for workers with flextime (Figure 6). Such a similar time-of-day pattern indicates that flextime has little effect on commute times, which may be more related to extraneous factors such as dropping children at school, scheduled meetings and co-work, and even after-work plans (whole-day scheduling).


0

5

10

15

20

25

30

35

Mid–6AM 6–9AM 9–NOON NOON–3PM 3–6PM 6–9PM 9–Mid

Percentage of W

orkers

Had Flextime Did Not Have Flextime

Lorem ipsum

Figure 6. Percentage of Workers by Commute Time: Workers with and without Flextime, 2017 NHTS

In their daily tasks, workers with flextime traveled more miles than workers overall: about 37 miles per day on average compared to 34, or about a 9 percent more for workers with flextime. Women with flextime traveled just 5 percent more miles than women who abided a work schedule, whereas men with flextime traveled 11 percent more miles com-pared to men who abided a work schedule (Figure 7).


40.6All14.9Commute and Work Related

10.1Shop/Family/Personal10.3Social/Recreational

5.3Other44.0All

17.3Commute and Work Related9.8Shop/Family/Personal10.4Social/Recreational

6.4Other36.1All

11.6Commute and Work Related10.6Shop/Family/Personal

10.0Social/Recreational3.9Other

All

Miles by Purpose Per Day:Men and Women Workers with Flextime

Men

Wom

en

Figure 7. Miles by Purpose per Day: Men and Women Workers with Flextime, 2017 NHTS

2. Workers Who TelecommuteIn 2019, about 26 million Americans—16 percent of the total workforce—had the option of telecommuting at least part of the time.3 According to the NHTS, the number of workers that sometimes or occasionally telecommute has nearly doubled in the last two decades. There are a lot of definitional issues related to telecommuting (Mokhtarian et al., 2005). In addition to working at home, telecommuters can work in other locations, such as coffee shops, libraries, or co-working spaces. Workers take work home after hours, or catch up on the weekends, or work part of their workday remotely. Many large employers encourage full days of telework; for example, since 2010 the Federal Government has encouraged telecom-muting and 43 percent of Federal employees were deemed eligible to do so in 2017.4

While telecommuting often means working from home, in this report workers who usually worked at home and those who telecommuted are differentiated by frequency. As shown in Figure 8, nearly a quarter of the workers who indicated that they had the option of telecommuting or sometimes working from home had not done so in the previous month, another third telecommuted once a month or so, while two out of five workers took this option once a week or so. Only 2 to 3 percent of workers indicated that they

3 American Time Use Survey. Bureau of Labor Statistics, 2019. See Table 6 at: https://www.bls.gov/news.release/atus.t06.htm

4 Status of Telework in the Federal Government: Report to Congress, Fiscal Year 2017. Office of Personnel Management, 2019. https://www.telework.gov/reports-studies/reports-to-congress/2018-report-to-congress.pdf


https://www.bls.gov/news.release/atus.t06.htm

https://www.bls.gov/news.release/atus.t06.htm

https://www.telework.gov/reports-studies/reports-to-congress/2018-report-to-congress.pdf

telecommuted every day; these could be workers who do not work at their regular work-place but also do not work at home.

MenWomenTotal

0%

5%

10%

15%

20%

25%

30%

35%

40%

45%Pe

rcen

tage

of W

orke

rs

Not in the Last Month Once a Month or So Once a Week or So Almost Every Day

Figure 8. Number of Days Telecommuted in the Last Month by Workers with the Option to Telecommute, 2017 NHTS

The option to sometimes work from home had been increasing over the last two decades, inspired by the growth of digital work, the benefit to work/life balance, and ever-better technology to support telecommuting. Telecommuting showed particular expansion as an option for higher-income workers in professional and technical fields; by 2017, a quarter of high-income workers and workers in professional/technical/managerial occupations had the option to telecommute, compared to just 8 percent of lower-income workers and 10 percent or less of workers in other occupations (see Figures 9 and 10).


4.9% 5.4%8.0%

9.3%12.1%

14.5%

18.1%

23.2%25.6%

Has the Option to Sometimes Work at Home/Telecommute

2001 2009 2017 2001 2009 2017 2001 2009 2017

Less than $50K $50–100K More than $100K

Figure 9. Percentage of Workers with the Option to Telecommute by Income, 2017 NHTS

2001 2009 2017 2001 2009 2017 2001 2009 2017 2001 2009 2017

Sales and Service Clerical or Administrative Const./Warehouse/Maint. Prof/Manager/Tech

7.0%8.3% 9.0%

4.6%6.4%

10.4%

3.7% 4.3%6.1%

13.2%

20.1%

25.0%

Trends in Telecommuting by Occupational Category

Figure 10. Percentage of Workers with the Option to Telecommute by Occupational Category, 2017 NHTS


Because workers who had the option to telecommute were more likely to be higher-in-come professionals in larger metro areas, the average miles per day workers who telecom-muted traveled for all purposes was greater than workers who did not have the option to telecommute. For instance, telecommuters had longer commute distances on average than workers without the telecommuting option, 18 miles compared to 14.8 (Figure 11). However, statistical testing showed that while a clear pattern of telecommuters having longer commutes was evident, the margins of error in this group were wide. Only workers engaged in sales and service occupations had statistically longer commutes when they had the option of telecommuting (see Appendix A, Table A6). Further detailed analysis could offer more insight.

All

Commute and Work Related

Shop/Family/Personal

Social/Recreational

0.0 5.0 10.0 15.0 20.0 25.0 30.0 35.0 40.0 50.045.0

Number of Miles per Worker per Day *Travel coded as "Other" not included

44.0

18.0

9.7

10.4

35.4

14.8

8.1

8.5

Figure 11. Miles per Day of Travel for Workers Who Had the Option to Telecommute and Those Who Did Not, 2017 NHTS


3. Workers with Multiple and/or Part-Time JobsOnline digital platforms accelerated policymakers’ interest in the effects of nontraditional work on overall travel. A plethora of new apps match a large pool of labor and service pro-viders with a wide pool of buyers or potential customers. In this gig labor market, workers act as independent contractors or freelancers for short-term or multiple jobs, structured unlike formal employment.

For businesses, defining who is an employee and who is a contractor has become increasingly difficult5 and there is no fixed definition of who is part of the gig economy. However, the literature points to a growing number of workers engaged in jobs that are not traditional payroll style. In this section, we use data from the Bureau of Labor Statistics (BLS), which defines “electronically-mediated” and “contingent” workers, and from the NHTS, where this category includes workers with multiple and/or part-time jobs.

Generally, gig workers can be divided into two catego-ries: Labor providers (for example: drivers, housecleaners, handymen, health aide workers) and goods producers (for example: writers, artists, craftsmen, designers). In 2019, about half of electronically-mediated workers did their work online and half in person.6 Contract or contingent workers were found across all industries but particu-lar growth has been seen in the transportation and warehousing sectors (Uber/Lyft and Amazon).

The NHTS data shows that younger, lower-income, non-white workers are more likely than their counterparts to hold multiple/part-time jobs; they may piece together multiple jobs for their entire livelihood, whereas higher-income and more-educated workers may use gig work as supplemental income.

5 The IRS has guidance on their site: The general rule is that an individual is an independent contractor if the payer has the right to control or direct only the result of the work, not what will be done and how it will be done. Businesses providing employee-type benefits, such as insurance, a pension plan, vacation pay, or sick pay have employees. Businesses generally do not grant these benefits to independent contractors. The permanency of the relationship is important. An expectation that the relationship will continue indefinitely, rather than for a specific project or period, is generally seen as evidence that the intent was to create an employer–employee relationship. In general, determination is made on a case-by-case basis.

6 A Look at Contingent Workers. September 2018. https://www.bls.gov/spotlight/2018/contingent-workers/

“Online, picking up a ‘gig’ (or a temporary work engagement) is as easy as making plans for dinner or finding a date… These companies make it easy for workers to find a quick, tempo-rary job (i.e., a gig), which can include any kind of work, from a musical performance to fixing a leaky faucet… However… that gig is a temporary work engage-ment, and the worker is paid only for that specific job.”

National Association of Counties (NACO) “The Future of Work:

The Rise of the Gig Economy”https://www.naco.org/featured-resources/

future-work-rise-gig-economy


https://www.bls.gov/spotlight/2018/contingent-workers/

https://www.naco.org/featured-resources/future-work-rise-gig-economy

https://www.naco.org/featured-resources/future-work-rise-gig-economy

According to the 2018 American Time Use Survey, multiple jobholders were more likely to work on an average weekday than were single jobholders—90 percent of these workers reported working on the sample day compared to 82 percent of workers with a single job. Multiple jobholders were also more likely to work on an average weekend day—56 percent, compared with 28 percent.7

Workers with multiple/part-time jobs traveled about 10 percent fewer miles per day compared to workers with full-time jobs. The commute distances for these workers was much less than that of full-time workers, but they reported more miles of non-categorized travel, indicating that the typical purposes contained in the survey were inadequate to describe their travel.

-30% -20% -10%

Fewer Miles than All Workers

More Miles than All Workers

0% 10% 20%-40%

All



Social/Recreational

Other

Travel by Workers with Multiple/Part-Time Jobs(PMT per Day Compared to Full-Time Workers)

Figure 12. Travel by Workers with Multiple/Part-Time Jobs (PMT per Day Compared to Full-Time Workers), 2017 NHTS

Importantly, the time-of-day profiles for the commute trips by workers with multiple/part-time jobs was quite different compared to traditional workers with a single full-time job. Figure 13 shows that only 40 percent of workers in this category arrived at work during the morning peak of 6 to 9 am, compared to 70 percent of traditional workers.

7 American Time Use Survey. Bureau of Labor Statistics, 2019. See Table 4. https://www.bls.gov/news.release/pdf/atus.pdf


https://www.bls.gov/news.release/pdf/atus.pdf

https://www.bls.gov/news.release/pdf/atus.pdf

Gig WorkersAll Others

0.0%

10.0%

20.0%

30.0%

40.0%

50.0%

60.0%

70.0%

80.0%

Perc

enta

ge o

f Wor

kers

Arrival Time at Work (On Workdays) for Workerswith Multiple/Part-Time Jobs and All Others

Mid–6AM 6–9AM 9–NOON NOON–3PM 3–6PM 6–9PM 9–Mid

Figure 13. Arrival Time at Work (on Workdays) for Workers with Multiple/Part-Time Jobs and All Others, 2017 NHTS

4. Workers Who Usually Work at HomeThe number of people who usually worked at home had been increasing since the 1990s. Both demographics and technology advances contributed to this trend. Between the 1990 and the 2018 ACS, the number of workers who worked at home increased substantially, and faster than the overall growth in the workforce, especially since 2010.

A contributing demographic factor is the number of people who continue to work past the traditional retirement age, many of whom work only at home. According to the BLS, more than half of people aged 60 to 64—54.7 percent—were working at least part time in 2017 and a third of those aged 65 to 69—31.2 percent—were working. According to the NHTS, a quarter of workers aged 65 and older reported working only from home.

Overall, the trend data shows that in 1995 few workers (just over 2 percent of all) worked only at home (not including occasional telecommuting) and not much variation was seen between people of different ages. Since then, the increase in home workers has been substantial in every age group but the growth in older workers working from home is notable. By 2017, the oldest workers showed the highest levels of work at home but even for prime-age workers (30 to 64, shown in different age groups), between 10 and 15 percent reported usually working at home (Figure 14).


0%

5%

10%

15%

20%

25%

30%

Perc

enta

ge o

f Wor

kers

Who

Wor

k at H

ome

Source: BTS Analaysis of NHTS Data Series

Trends in the Percentage of Workers byAge Group Who Work Only at Home

75 and older

65 to 74 years old

55 to 64 years old45 to 54 years old30 to 44 years old

16 to 29 years old

1995 2001 2009 2017

Figure 14. Trends in the Number of Workers by Age Group Who Work at Home, NHTS Data Series 1995–2017

Another factor related to working at home is the presence of children. Workers aged 21 to 54 make up 71 percent of all workers (in the NHTS) and more than half of workers in this age group (54 percent) are living in a household with one or more children. The presence of children is well known to constrain workers’ commutes; for example, dropping children at school on the way to work is a common purpose of trip chaining (McGuckin, et al., 2004; McDonald, 2014).

A little over 10 percent of workers in this age group worked at home in 2017, whether or not there were children present. (The estimates of 10.2 and 10.7 percent are within the margin of error.) Interestingly, men in households with and without children had the same likelihood of working at home. On the other hand, women in households with children were more likely to usually work from home compared to women in households without children, as shown in Figure 15 (these estimates are statistically different).


WAH in Households with Child(ren) WAH in Households w/o Child(ren)

9.9%

12.6%

10.7%9.9%

10.5% 10.2%

Men Women All

Figure 15. Percentage of Workers Aged 21 to 54 Who Work at Home, Comparing Men and Women in Households with Children, 2017 NHTS

One of the important questions about travel behavior of people who usually worked at home is whether they traveled less than workers who went to work. Logic says that home workers who did not make a work trip—for most people the longest trip of the day—would travel fewer miles than workers who commuted to work. And while this is true, it is only a marginal difference.

According to the 2017 NHTS, workers traveled just under 45 miles per day on average; 34 percent were spent commuting (15.3 miles traveling to and from work including work-ers who went to work and those that did not). Working men travel farther in an average day (48 miles on average) than working women (41 miles on average). (Baseline data for PMT by workers by purpose is in Appendix A, Table A1—Daily PMT by All Workers by Gender and Purpose.)

Figure 16 compares the daily miles of travel by purpose for men and women who worked at home and who did not. Overall, workers who usually worked at home traveled just 1.3 fewer miles per day than workers who did not work at home. While home workers traveled nearly half the miles for work-related tasks—such as client meetings, site visits, or service calls—they traveled more miles for shopping and errands and for social and recre-ational purposes.

There was a significant difference in the daily miles of travel by men and women home workers but the overall pattern was similar: home workers shifted the miles spent com-muting to household errands and social and recreational travel. The age of the worker also made a significant difference; men aged 21 to 54 and 55 to 69 and women aged 55 to 69


traveled nearly the same number of miles per day when they were home workers or not. That is, workers in these age groups seemed to replace the lost commute trip with other travel. On the other hand, home workers who were women aged 21 to 54 and the oldest home workers of both genders (those aged 70 and older) traveled much less than their counterparts for all daily tasks.

35.5

8.0

12.2

10.6

4.8

36.8

15.3

8.3

8.8

4.3

0.0 5.0 10.0 15.0 20.0 25.0 30.0 40.035.0

All



Social/Recreational

All Other Purposes

Person Miles of Travel per Worker per Day

Did Not Work at Home Work at Home

Figure 16. Daily Miles of Travel (PMT) for Workers Who Worked at Home Compared to Those Who Do Not, 2017 NHTS


Conclusions

FlextimeFlextime was an option for a plurality of workers but seems unlikely to increase to occupa-tions reliant on shift work (e.g. hospitals, retail, hospitality, manufacturing, warehousing). In addition, flextime had little effect on travel rates or time of day of commuting—that is, workers with flextime seemed to travel to work at about the same time as those without.

Working at Home and TelecommutingIn 2019, the option to telecommute or primarily work from home was tied to higher-income professions in large metro areas. The stay-at-home orders forced most workers to conduct their daily work only from home. In the future, there will be workers who continue to work only at home and more dispersion of the option to telecommute across income groups, across occupations, and in metro areas large and small. Working at home and the option to telecommute are the most likely to permanently increase in the workforce after 2020.

Multiple/Part-Time JobsAs online platforms continue to grow, the ability of businesses and individuals to connect with skilled and unskilled labor directly will also grow. Participation by workers through online platforms will undoubtedly continue to increase but the increase may come in the form of lower-wage, labor-based services. While in 2019 about half of contract or contin-gent workers were working online, there may be a shift to more in-person work such as delivery services; elder, child, and pet care; handyman; and other services.

Critically, not only is this sector likely to increase, workers in these jobs exhibit the greatest differences in travel compared to traditional workers. For example, they are more likely to travel from worksite to worksite, to travel for work on weekends and at non-peak times, and to use their vehicle for commercial passenger or freight delivery.

Likely Overall ImpactLooking forward, the changes to the workforce would undoubtedly have had a profound impact on travel behavior, even without the pandemic. There was a latent demand prior to 2020 for more telecommuting and work-at-home and many more workers will do so if their occupations can accommodate it. Some form of flextime or new shift work may evolve over the next year to help workplaces maintain social distancing. While hospitality, tourism, and entertainment businesses may reopen, we cannot predict the demand for gathering or traveling in crowds. Finally, workers who turned to online platforms while unemployed may retain that relationship as a supplement even if their regular job resumes. Contract and


contingent workers were the focus of some policy interventions prior to 2020 and we may expect more worker protections alongside more workers engaged in gig work.

Other economic and demographic trends will continue and some may reflect reces-sionary adaptations. For example, in the near future we can expect larger household size as families consolidate; especially, college-aged children may remain at home. Slower popu-lation growth (especially in the prime working age) will result from lowered immigration. Lost income during the stay-at-home period may force older workers to delay retiring even longer. Delays in marriage, childbearing, and household formation may result from uncer-tain economic times. Urban population growth may slow.

For transportation-related changes, the most impactful will probably be a slow recov-ery of long-distance air travel and related hospitality and tourism. Transit and ridesharing, especially the carpool and vanpool options, may face some obstacles. Home delivery of gro-ceries and other daily goods will gain a greater market share along with continued growth in e-commerce.

In short, as the number of people involved in nontraditional work continues to rise, some important transportation impacts could include:

• Greater number of workers working at home and greater participation in tele-commuting across income groups and occupations

• Greater number of workers engaged in part-time gig work • Shrinking portion of workers making traditional commutes during peak period• Fewer opportunities for transit and carpool to serve workers• More vehicle miles traveled (VMT) for deliveries, transport services, and non-

peak commuting• More day-to-day variation in travel; more work travel on the weekends• Greater number of workers who are now marginal (e.g. older, younger,

differently-abled)

Implications for DataAll the federal datasets need to examine the questions related to worker status, occupation, and work location for their relevance in light of the growth in nontraditional work. To understand the total impact on travel, researchers need more detailed information about the work life of sampled people. For example, direct questions about the use of online plat-forms (and whether travel was needed to complete the job), questions about the number of hours worked day to day, and better information on time spent working at home. The NHTS also should try to identify people who drive to deliver goods or serve passengers as part of their daily activity.


Notes on the Data SourcesNote that the NHTS data subset workers aged 21 years and older. For PMT estimates, the variable TRPMILES was capped at 480 miles per trip to remove outliers (the 2017 NHTS method of obtaining trip distance used a google API to calculate network distance from geocoded origin to geocoded destination which resulted in some outliers). The character-istics under analysis: “Usually works at home,” “Has the option of working at home,” and “Has a flexible work schedule” are coded as single variables for workers in the NHTS but those engaged in gig work were constructed from multiple variables.

Notes on Teleworking Definitions (from “The Future of Work,” Cambridge Systematics for Southern California Association of Governments)One of the challenges of combining analyses from various survey data is that each survey can ask common questions in different ways that might lead to different responses from the same person. A publication from the Census Bureau explains differences estimates of home-based workers from SIPP and ACS. Table 1 details the data sources and definitions used in this report.

The estimates of home-based workers from the Survey of Income and Program Participation (SIPP) and ACS are not directly comparable because each survey queries workers about home-based activities differently. The SIPP asks workers aged 15 and over to indicate which days of the work week they work entirely from home. Thus, to be regarded as an at-home worker by this survey, a respondent must report having worked only at home on a given workday. Individuals who check email or carry out other work activities at home but outside of their normal work hours are not counted as home-based workers in SIPP.

In the ACS, workers aged 16 and over are asked to report how they “usually” got to work last week. Those who used several methods of getting to work, either in the same week or in the same day, are asked to list the mode used most often. If two or more modes are used with the same frequency, the respondent selects the mode used for the longest distance. Respondents who select work at home, presumably, work the majority of the week from home. This measure of home-based work is more conservative than the SIPP measure and excludes respondents who work at home during off hours or those who sometimes telework from home but for less than the majority of a workweek.

Home-based worker estimates between the two surveys may also differ because of differences in labor force definitions and survey design. In SIPP, the labor force estimates in the Work Schedule Topical Module refer to a typical week in the month prior to the inter-view month but the ACS estimates are based on work activities that occur during the week prior to the interview week. The SIPP also includes more extensive labor force questions


aimed at measuring contingent and unpaid family work. Lastly, the SIPP uses two interview modes (personal visit and telephone), while the ACS uses three (mail, phone, and personal visit). Taken together, these differences may increase the likelihood that SIPP identifies respondents who work irregular schedules. Some additional differences, the effects of which are more difficult to speculate about, included the survey collection period—1 year for the ACS and 4 months for SIPP—editing and imputation procedures, and the calcula-tion of survey weights.

Table 1. Commuter Type Definitions from Various Data Sources

Data Source Definitions

Telecommuter

SIPP A worker who is not flagged as working from home but worked from home 1 to 4 days per week

NHTS 2009 A worker who has a teleworking option and telecommuted at least 1 day per week

NHTS 2017 A non-home worker who has a teleworking option and telecommuted at least 1 day per week

Home Worker

SIPP A worker who is flagged as working from home or worked from home 5 or more days a week

ACS Those who reported working from home as their commute last week

NHTS 2009 and 2017 Those who reported working from home full time or part time

Flexible Worker

NHTS 2009 and 2017 Workers who reported having “the ability to set or change their own start time.”


References

Asgari, H. and X. Jin. Toward a Comprehensive Telecommuting Analysis Framework. In Transportation Research Record 2496. Transportation Research Board, National Research Council, Washington, DC, 2015. Available from https://www.academia.edu/21009023/Toward_a_Comprehensive_Telecommuting_Analysis_Framework?email_work_card=view-paper

Desilver, D. Before the Coronavirus, Telework Was an Optional Benefit, Mostly for the Affluent Few. Fact Tank: News in the Numbers, Pew Research Center, March 2020. Available from https://www.pewresearch.org/fact-tank/2020/03/20/before-the-coronavirus-telework-was-an-optional-benefit-mostly-for-the-affluent-few/

Ernst & Young. “The workforce is changing.” Available from https://www.ey.com/Publication/vwLUAssets/The_future_of_work_is_changing/$FILE/ey-the-future-of-work-is-changing-will-your-workforce-be-ready.pdf

McDonald, N. Household Interactions and Children’s School Travel: The Effect of Parental Work Patterns on Walking and Biking to School. Journal of Transport Geography, Vol. 16, 2008, pp. 324–331. Available from http://mcdonald.web.unc.edu/files/2014/12/McDonald_HHInteract_JTG_2008.pdf

McGuckin, N., J. Zmud, and Y. Nakamoto. Trip Chaining Trends in the US—Understanding Travel Behavior for Policy Making. In Transportation Research Record 1917. Transportation Research Board, National Research Council, Washington, DC, 2005. Available from https://www.researchgate.net/publica-tion/245561588_Trip_Chaining_Trends_in_the_US-Understanding_Travel_Behavior_for_Policy_Making

McGuckin, N. and A. Fucci. Summary of Travel Trends: 2017 National Household Travel Survey. FHWA PL-18-019. Federal Highway Administration, U.S. Department of Transportation, Washington, DC, July 2018, Table 27, p. 79. Available from https://nhts.ornl.gov/assets/2017_nhts_summary_travel_trends.pdf

Mokhtarian, P., I. Salomon, and S. Choo. Data and Measurement Issues in Transportation, with Telecommuting as a Case Study. Research Report UCD-ITS-RR-04-29. Institute of Transportation Studies, University of California at Davis, 2004. Available from https://escholarship.org/uc/item/9pt8s9jv

Mokhtarian, P., I. Salomon, and S. Choo. Measuring the Measurable: Why Can’t We Agree on the Number of Telecommuters in the U.S.? Quality and Quantity, Vol. 39, No. 4, 2005, pp. 423–452. Available from https://escholarship.org/content/qt7mb104c1/qt7mb104c1.pdf


https://www.academia.edu/21009023/Toward_a_Comprehensive_Telecommuting_Analysis_Framework?email_work

https://www.academia.edu/21009023/Toward_a_Comprehensive_Telecommuting_Analysis_Framework?email_work

https://www.pewresearch.org/fact-tank/2020/03/20/before-the-coronavirus-telework-was-an-optional-ben

https://www.pewresearch.org/fact-tank/2020/03/20/before-the-coronavirus-telework-was-an-optional-ben

https://www.researchgate.net/publication/245561588_Trip_Chaining_Trends_in_the_US-Understanding_Travel_Behavior_for_Policy_Making

https://www.researchgate.net/publication/245561588_Trip_Chaining_Trends_in_the_US-Understanding_Travel_Behavior_for_Policy_Making

https://nhts.ornl.gov/assets/2017_nhts_summary_travel_trends.pdf

https://escholarship.org/uc/item/9pt8s9jv

https://escholarship.org/content/qt7mb104c1/qt7mb104c1.pdf

https://mcdonald.web.unc.edu/wp-content/uploads/sites/8583/2014/12/McDonald_HHInteract_JTG_2008.pdf

28 Commuting in America 2021: The National Report on Commuting Patterns and Trends

Appendix A. Statistical Testing

Table A1. Daily PMT by All Workers by Gender and Purpose, NHTS 2017

Gender Purpose Total Daily PMT

Total Number of

Workers

PMT per

Worker per Day

Percent-age of

PMT by Purpose

All ALL 2,565,098,709,327 156,988,243 44.8 100.0%


873,677,306,296 156,988,243 15.2 34.1%


561,843,569,668 156,988,243 9.8 21.9%

Social/Recreational 568,916,983,513 156,988,243 9.9 22.2%

Other 560,660,849,850 156,988,243 9.8 21.9%

Men Commute and Work Related

552,201,581,551 83,588,762 18.1 37.6%


274,831,738,598 83,588,762 9.0 18.7%


Other 335,413,110,065 83,588,762 11.0 22.8%

Women Commute and Work Related

321,475,724,745 73,399,481 12.0 29.3%


287,011,831,070 73,399,481 10.7 26.2%


Other 225,247,739,785 73,399,481 8.4 20.6%


29Brief 21.1. The Changing Nature of Work

Table A2. Percentage of Workers with Flextime by Gender, Geography, Income, and Occupation (2017 Estimate with 90 percent Confidence Limits), NHTS 2017

Percentage of Workers Who Can

Change Work Start Time

Margin of Error ±

Gender

Men 46.0% 0.1%

Women 40.4% 0.4%

Total 43.4% 0.1%

Metro Area Size

MSA Less Than 250K* 39.0% 0.6%

MSA 250K to Less Than 500K* 39.5% 1.0%

MSA 500K to Less Than 1 mil. 42.1% 0.4%

MSA or CMSA 1 mil. to Less Than 3 mil. 44.4% 0.7%

MSA or CMSA Greater Than 3 mil. 47.3% 0.3%

Not in an MSA 37.4% 1.0%

Household Income

Income Not Reported 46.8% 2.0%

Less Than $50K 32.1% 0.5%

$50K to $99,999 41.2% 0.4%

$100,000 and Over 55.1% 0.3%

Occupational Category

Sales and Service 33.4% 0.5%

Clerical or Administrative 36.2% 0.9%

Construction/Warehouse/Maintenance 29.9% 0.8%

Professional, Managerial, or Technical 54.7% 0.2%

*The percentage of workers with flextime in these categories is not significantly different.


30 Commuting in America 2021: The National Report on Commuting Patterns and Trends

Table A3. Daily PMT by Workers with Flextime by Gender and Purpose, NHTS 2017

Total Daily PMT

Total Num-ber of

Workers

PMT per Worker per Day

Percent of PMT by Purpose

Both Men and Women

All 2,021,303,530,573 151,093,348 36.7 100.0%


795,952,935,990 151,093,348 14.4 39.4%


485,087,840,988 151,093,348 8.8 24.0%

Social/Recreational

498,232,351,863 151,093,348 9.0 24.6%

Other 242,030,401,733 151,093,348 4.4 12.0%

Men All 1,162,379,136,144 80,890,669 39.4 100.0%


492,448,722,304 80,890,669 16.7 42.4%


245,927,691,726 80,890,669 8.3 21.2%

Social/Recreational

270,450,105,543 80,890,669 9.2 23.3%

Other 153,552,616,570 80,890,669 5.2 13.2%

Women All 858,924,394,429 70,202,680 33.5 100.0%


303,504,213,686 70,202,680 11.8 35.3%


239,160,149,261 70,202,680 9.3 27.8%

Social/Recreational

227,782,246,320 70,202,680 8.9 26.5%

Other 88,477,785,163 70,202,680 3.5 10.3%


31Brief 21.1. The Changing Nature of Work

Table A4. Percent of Workers with the Option to Telecommute by Gender, Geography, Income, and Occupation (2017 Estimate with 90 percent Confidence Limits), NHTS 2017

Percent of Workers Who

Have the Option to Telecommute

Margin of Error ±

Gender

Men 18.5% 0.4%

Women 13.8% 0.8%

Total 16.3% 0.3%

Metro Area Size



MSA 500K to Less Than 1 mil.* 15.0% 1.5%




Household Income


Less Than $50K 8.0% 0.9%

$50K to $99,999 14.5% 0.8%

$100,000 and Over 25.6% 0.3%


Sales and Service* 9.0% 1.8%

Clerical or Administrative* 10.4% 1.7%



*Percentage of workers who can telecommute in these categories is not significantly different.


Table A5. Daily PMT for Workers Who Have the Option to Telecommute by Gender and Purpose, NHTS 2017

Workers Who Have the Option to Work at Home (Telecommute)

Gender Purpose Daily PMT

Total Workers with the Option of Telecom- muting

PMT per

Worker per Day

Both Men and Women

ALL 349,558,925,794 21,756,325 44.0

Commute and Work Related 143,307,320,259 21,756,325 18.0

Shop/Family/Personal 77,099,038,221 21,756,325 9.7

Social/Recreational 82,708,435,553 21,756,325 10.4

Other 46,444,131,762 21,756,325 5.8

Men ALL 230,329,881,745 13,247,470 47.6




Other 34,343,942,156 13,247,470 7.1

Women ALL 119,229,044,049 8,508,855 38.4




Other 12,100,189,605 8,508,855 3.9


Table A6. Distance to Work for Telecommuters and Non-Telecommuters, NHTS 2017

Had Option to Telecom-

mute

No Option to Tele-

commute

MOE‡ for Telecom-muters

MOE‡ for Never

Telecom-mutes

Gender Men 26.5 23.3 8.3 4.2

Women 28.4 16.7 13.8 3.4

Income Less than $50K 27.5 19.1 9.2 2.3

$50K to $100K 27.1 29.7 9.2 8.1

Over $100K 26.9 23.6 8.0 3.6

Occupation Sales and Service 45.9 15.1 20.9 2.5

Clerical or Administrative

22.7 14.4 15.8 3.6

Construction/Warehouse/Maintenance

48.0 28.1 62.6 10.7

Professional, Managerial, or Technical

22.4 22.0 9.4 4.1

‡ Margin of Error +/-


Table A7. Percent of Workers with Multiple/Part-Time Jobs by Gender, Geography, Income, and Occupation (2017 Estimate with 90 percent Confidence Limits), NHTS 2017

Percent of Workers who Have Multiple/

Part-time Jobs Margin of Error ±

Gender

Men 46.0% 0.1%

Women 40.4% 0.4%

Total 43.4% 0.1%

Metro Area Size



MSA 500K to Less Than 1 mil. 42.1% 0.4%




Household Income


Less Than $50K 32.1% 0.5%

$50-$99,999 41.2% 0.4%

$100,000 and Over 55.1% 0.3%






*Percentage of workers with multiple/part-time jobs in these categories is not significantly different.


Table A8. Daily PMT for Workers with Multiple/Part-Time Jobs by Gender and Purpose, NHTS 2017

Gender Purpose Total Daily PMT

Total Number of

Workers


Percent of PMT

by Purpose

Both Men and Women

All 2,081,934,719,676 156,988,243 36.3 100.0%


801,034,364,562 156,988,243 14.0 38.5%


509,952,056,413 156,988,243 8.9 24.5%

Social/Recreational

522,586,198,000 156,988,243 9.1 25.1%

Other 248,362,100,701 156,988,243 4.3 11.9%

Men All 1,188,410,340,719 83,588,762 39.0 100.0%


495,638,744,926 83,588,762 16.2 41.7%


256,515,453,793 83,588,762 8.4 21.6%

Social/Recreational

279,552,798,973 83,588,762 9.2 23.5%

Other 156,703,343,027 83,588,762 5.1 13.2%

Women All 893,524,378,957 73,399,481 33.4 100.0%


305,395,619,637 73,399,481 11.4 34.2%


253,436,602,619 73,399,481 9.5 28.4%

Social/Recreational

243,033,399,027 73,399,481 9.1 27.2%

Other 91,658,757,674 73,399,481 3.4 10.3%


Table A9. Percent of Workers Who Usually Work at Home by Gender, Income, Geography, and Occupation (2017 Estimate with 90 percent Confidence Limits), NHTS 2017

Percent of Workers who Work at Home Margin of Error ±

Gender

Men* 11.2% 0.5%

Women* 11.7% 0.6%

Total 11.5% 0.5%

Metro Area Size




MSA or CMSA 1 mil. to Less Than 3 mil.* 11.3% 0.6%



Household Income


Less Than $50K* 10.7% 0.9%

$50K to $99,999* 9.7% 0.7%

$100,000 and Over 13.5% 1.2%

Occupational Category*





* Percentage of workers who work at home in these categories is not significantly different.


Table A10. Daily Miles for Men and Women Who Usually Work at Home, NHTS 2017

PMT per Day

Gender Purpose

Workers Who Usually Work

at Home

Workers Who DO NOT

Usually Work at Home

Both Men and Women

All 35.5 36.8

Commute and Work Related 8.0 15.3

Shop/Family/Personal 12.2 8.3

Social/Recreational 10.6 8.8

Other 4.8 4.3

Men All 38.2 39.5




Other 5.9 5.1

Women All 32.5 33.6




Other 3.5 3.4


Table A11. Characteristics of Workers in Households with Children Who Usually Work at Home by Gender, Geography, Income, and Occupation, NHTS 2017

Percent of Workers Aged 21–54 in House-

holds with Children Who Work at Home

Margin of Error ±

Gender

Men 9.6% 1.1%

Women 12.0% 1.2%

Total 10.7% 1.0%

Metro Area Size




MSA or CMSA 1 mil. to Less Than 3 mil.* 10.8% 1.0%

MSA or CMSA Greater Than 3 mil.* 12.0% 0.6%

Not in an MSA* 9.7% 2.6%

Household Income

Income Not Reported* 12.2% 6.0%

Less Than $50K* 9.4% 2.6%

$50-$99,999* 8.4% 2.0%

$100,000 and Over 13.4% 1.5%


Sales and Service* 13.1% 1.7%

Clerical or Administrative* 8.3% 2.5%

Construction/Warehouse/Maintenance* 8.8% 3.6%


*Percentage of working parents who usually work at home in these categories is not significantly different.


Table A12. Daily PMT for Workers Who Usually Work at Home, NHTS 2017

Gender Purpose Daily PMT

Total Workers Who Usually

Work at Home


Both Men and Women

All 233,326,008,865 18,007,392 35.5


52,258,311,591 18,007,392 8.0



Other 31,227,431,729 18,007,392 4.8

Men All 130,951,719,339 9,383,399 38.2


34,068,515,538 9,383,399 9.9



Other 20,067,729,632 9,383,399 5.9

Women All 102,374,289,526 8,623,993 32.5


18,189,796,053 8,623,993 5.8



Other 11,159,702,097 8,623,993 3.5


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