US Commute Times & Remote Work (2021 vs. 2019)

Although remote work was not unheard of prior to the COVID-19 pandemic lockdowns, the ample wave that rose in 2020 continues to ripple through the national workscape. And, although there is still some debate regarding a fully flexible future of work, the movement has so far brought certain undeniable benefits: Whether it has to do with evolving energy costs, health concerns, or investing that time toward more issues of personal value, cutting down on at least some of the previous commute time has been highly appreciated.

But, how much did things actually change? To find out if an increase in the number of remote workers has put a dent in commute times for those still traveling to work, we looked at the most recent U.S. Census data on how 2021 numbers compared to 2019.

The National Average Commute Time Drops 7%, Remote Workforce Grows 12ppt

In 2019, Americans spent an average of 55.2 minutes per day on a round-trip commute (27.2 minutes each way). Two years later in 2021, Census survey data revealed that the same commute time had dropped to 51.2 minutes (or 25.6 minutes each way) — a 7% decrease in the national average, which added up to roughly 17 hours in commute time saved per year.

Meanwhile, the remote workforce increased 12 percentage points (ppt) — from nearly 6% in 2019 to just below 18% of the national workforce in 2021. In plain numbers, we estimated that, in 2019, there were nearly 9 million workers aged 16 years and older who were doing their jobs outside of a centralized workplace. For comparison, in 2021, that portion of the workforce increased to roughly 27.6 million workers. As a result, there were about 18.6 million fewer commuters across the country last year.

However, things look very different at the local level. Granted, both commute times and workforce distribution can vary greatly depending on a multitude of factors, such as: the size of the city; the local transit infrastructure; the makeup of the local economy and how much of it is compatible with remote work; local geography; seasonal weather patterns and more.

In particular, among the 50 most populous U.S. cities, those that saw the most significant contractions in average commute time were in the Western U.S. These were followed by major cities in the Southern region, where commuters saved at least a day’s worth of commute time last year, compared to 2019.

The graphic below shows the workforce distribution — remote (in blue, at right) versus non-remote (in green, at left) — and highlights the most notable local changes in average commute times:

  • The top graphics, which contain nationwide totals in 2021 compared to 2019, are followed by four sections, each representing one of the four main Census-designated U.S. regions: West, South, Northeast and Midwest.
  • Each regional section features the three cities that recorded the largest decreases in average commute time during the timeframe of reference.
  • For each of the three cities featured, we also included a breakdown of changes in the remote versus non-remote workforce distribution in 2021 compared to 2019.

Beyond the Commute: How Remote & Flexible Work Can Usher in Positive Change

Decoupling work from geography has had an undeniably transformational effect on the national employment landscape, particularly in the knowledge economy: Remote recruiting has expanded accessible talent pools for employers, while simultaneously broadening employment opportunities for talent located outside of the traditional clusters of their target industry.

What’s more, a flexible, remote-friendly work environment is in high demand with younger generations. Some of the most attractive aspects of it are that it’s more inclusive and accessible than the average centralized workplace; is an option that respects workers’ time; and grants each individual the ability to tap into their own patterns of best productivity.

Another notable benefit has been the wider adoption of modern workplace technology. The “great remote work experiment” provides real-world use case data that is essential to boosting the progress and refinement of today’s technology, as well as ample practical opportunity to improve technological literacy across the multigenerational workforce.

Beyond the work environment itself, long-term workplace flexibility has the potential to reshape and revitalize communities. In fact, in a permanently flexible work landscape, amenities would no longer be largely concentrated in the high-density urban cores, but rather would expand outward to serve people where they are.

To that end, coworking space already does a great job of complementing traditional office clusters by securing the third space that flexibility creates a need for — the space between working from home and congregating at a centralized workspace. Over time, the reliable ability to work near home for a larger flexible workforce could result in fewer food deserts, as well as reduce the lack of other day-to-day service amenities that many large metro suburban areas are challenged with today.

Methodology

The data used for the study was extracted from the latest U.S. Census American Community Survey. We focused on travel time to work and the distribution of the workforce in terms of remote and non-remote workers. For both commuting and workplace characteristics, we analyzed 2019 and 2021 data for the 50 most populous cities in the U.S.

Specifically, the reported travel time to work includes time spent by workers not working from home picking up passengers in carpools, time spent navigating public transportation, and time spent in any other activities related to getting to work. We calculated the time spent traveling between home and work both ways (Census data multiplied by two). Hours spent in traffic were calculated by multiplying the minutes by working days (we used an average of 250 days) and dividing the result by 60 (minutes in an hour).

For workforce distribution estimates, we employed work-from-home percentages from total workers, as listed in Census report and calculated the difference between 2019 and 2021 values as a percentage point change. We estimated the number of workers who work outside a centralized workplace using the WFH percentage and number of total workers as per Census.

2021 vs. 2019 Commute Times & Remote/Non-Remote Workforce Distribution in the 50 Largest U.S. Cities

The cities included in the table below are presented in decreasing order of commute time saved in 2021 compared to 2019. Use the horizontal scroll feature to view data in all columns. See the full article methodology for further details.
Data sources: U.S. Census Bureau.

City 2019 Travel Time (minutes) 2021 Travel Time (minutes) Time Saved/Year (hours) 2021 vs. 2019 (minutes) 2019 WFH % 2021 WFH % 2021 vs. 2019 (ppt) 2019 WFH Workers 2021 WFH Workers 2021 vs. 2019 (workers) 2021 vs. 2019 (%)
Chicago, IL 31.7 24.4 -60.8 -7.3 4.2 29.7 25.5 22,091 143,676 121,585 550%
Minneapolis, MN 34.4 28.3 -50.8 -6.1 6.8 33.3 26.5 15,456 72,698 57,242 370%
Columbus, OH 34.7 29.2 -45.8 -5.5 7.4 45.6 38.2 38,926 200,158 161,232 414%
Milwaukee, WI 31.5 27.3 -35.0 -4.2 4.5 24.1 19.6 12,345 63,595 51,249 415%
Omaha, NE 32.0 28.3 -30.8 -3.7 5.1 20.0 14.9 11,911 44,880 32,969 277%
Indianapolis, IN 31.7 28.3 -28.3 -3.4 7.4 48.3 40.9 28,555 170,998 142,443 499%
Kansas City, MO 33.0 29.8 -26.7 -3.2 6.5 23.8 17.3 131,369 432,254 300,885 229%
Wichita, KS 27.0 23.8 -26.7 -3.2 8.0 20.6 12.6 19,992 50,476 30,483 152%
Detroit, MI 34.3 31.1 -26.7 -3.2 5.1 24.3 19.2 36,274 170,514 134,240 370%
Philadelphia, PA 25.5 22.4 -25.8 -3.1 8.1 25.7 17.6 60,817 183,557 122,740 202%
Boston, MA 35.3 32.2 -25.8 -3.1 6.2 27.1 20.9 84,169 355,049 270,879 322%
New York City, NY 27.1 24.3 -23.3 -2.8 8.9 32.3 23.4 37,227 130,422 93,195 250%
Baltimore, MD 24.4 21.6 -23.3 -2.8 10.5 33.1 22.6 26,297 81,248 54,951 209%
Washington, D.C. 27.8 25.1 -22.5 -2.7 9.6 38.7 29.1 25,002 100,015 75,013 300%
Raleigh, NC 26.3 23.6 -22.5 -2.7 9.1 34.9 25.8 33,348 122,984 89,636 269%
Atlanta, GA 27.1 24.5 -21.7 -2.6 6.4 22.4 16.0 53,387 177,428 124,041 232%
San Antonio, TX 31.2 28.6 -21.7 -2.6 4.1 30.3 26.2 16,437 105,977 89,540 545%
Louisville, KY 25.5 23.1 -20.0 -2.4 3.8 15.1 11.3 28,146 102,314 74,168 264%
Houston, TX 23.4 21.0 -20.0 -2.4 8.5 18.5 10.0 20,734 45,304 24,570 119%
Austin, TX 23.0 20.8 -18.3 -2.2 4.5 15.6 11.1 13,512 46,749 33,237 246%
Charlotte, NC 28.5 26.4 -17.5 -2.1 4.4 15.6 11.2 49,366 169,877 120,511 244%
Miami, FL 28.4 26.3 -17.5 -2.1 7.9 46.8 38.9 36,458 205,259 168,802 463%
Nashville, TN 24.1 22.1 -16.7 -2.0 7.1 34.7 27.6 17,356 81,567 64,211 370%
Dallas, TX 25.3 23.3 -16.7 -2.0 10.8 38.8 28.0 61,572 219,322 157,750 256%
Jacksonville, FL 26.2 24.2 -16.7 -2.0 10.0 34.6 24.6 47,929 162,521 114,591 239%
Virginia Beach, VA 41.7 39.8 -15.8 -1.9 4.6 24.1 19.5 185,503 890,498 704,995 380%
Fort Worth, TX 28.3 26.4 -15.8 -1.9 6.1 18.4 12.3 14,968 42,034 27,066 181%
Arlington, TX 25.1 23.4 -14.2 -1.7 8.3 22.8 14.5 31,013 83,314 52,302 169%
Memphis, TN 27.4 25.7 -14.2 -1.7 5.2 18.7 13.5 34,991 119,629 84,637 242%
Oklahoma City, OK 26.9 25.2 -14.2 -1.7 5.5 23.9 18.4 13,741 59,214 45,473 331%
El Paso, TX 24.8 23.3 -12.5 -1.5 5.7 16.0 10.3 25,812 73,622 47,811 185%
Tulsa, OK 24.1 22.7 -11.7 -1.4 4.7 16.0 11.3 11,605 38,798 27,193 234%
San Jose, CA 23.0 21.6 -11.7 -1.4 5.1 23.7 18.6 24,915 111,215 86,300 346%
Oakland, CA 27.2 25.9 -10.8 -1.3 5.0 15.3 10.3 22,113 68,589 46,477 210%
San Francisco, CA 22.6 21.4 -10.0 -1.2 3.6 15.8 12.2 9,709 40,265 30,555 315%
Long Beach, CA 26.8 25.7 -9.2 -1.1 4.6 13.5 8.9 13,667 38,133 24,466 179%
Los Angeles, CA 20.1 19.2 -7.5 -0.9 4.6 18.3 13.7 11,294 45,803 34,509 306%
Mesa, AZ 22.5 21.7 -6.7 -0.8 3.8 18.6 14.8 10,402 51,125 40,723 392%
San Diego, CA 26.9 26.1 -6.7 -0.8 5.1 13.7 8.6 10,044 26,952 16,907 168%
Denver, CO 22.2 21.5 -5.8 -0.7 3.0 10.5 7.5 8,747 28,677 19,930 228%
Portland, OR 24.1 23.6 -4.2 -0.5 4.0 16.5 12.5 16,732 70,888 54,156 324%
Phoenix, AZ 22.2 21.7 -4.2 -0.5 5.5 19.9 14.4 14,194 52,325 38,131 269%
Colorado Springs, CO 22.8 22.4 -3.3 -0.4 5.6 12.2 6.6 12,247 27,176 14,929 122%
Seattle, WA 22.7 22.3 -3.3 -0.4 6.2 17.4 11.2 15,911 44,476 28,565 180%
Sacramento, CA 19.0 18.7 -2.5 -0.3 3.1 9.2 6.1 5,883 17,166 11,283 192%
Las Vegas, NV 24.4 24.1 -2.5 -0.3 3.8 11.0 7.2 6,034 18,671 12,637 209%
Albuquerque, NM 25.4 25.5 0.8 0.1 3.5 15.1 11.6 8,994 32,606 23,611 263%
Fresno, CA 21.8 22.1 2.5 0.3 3.9 13.6 9.7 12,419 45,279 32,860 265%
Tucson, AZ 23.5 23.8 2.5 0.3 3.0 10.1 7.1 9,107 29,748 20,642 227%
Bakersfield, CA 18.6 19.0 3.3 0.4 4.1 13.6 9.5 7,841 25,400 17,558 224%

 

Last modified: September 22, 2022



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