This post was co-authored by Daniel Sternberg and Sarah Gustafson.

With ongoing gridlock over extended relief creating new waves of economic uncertainty, concerns for a potential COVID surge during fall flu season, and cold weather looming on the horizon with the potential to limit outdoor operations, small businesses are being forced to make a new round of urgent, and oftentimes difficult decisions to stay afloat. 

New platform data from Gusto—the people platform offering full-service payroll, benefits, compliance, and expert HR services for 100,000+ small businesses nationwide—for the month of August shows overall small business headcount growth stalling (with just a 1.0% increase), smaller numbers of workers returning to the workforce within a month of being furloughed, and a growing wage gap emerging—with both salaried and hourly workers making less money and working fewer hours than they did pre-COVID. Increasingly worrisome is the economic disparity Gusto data reveals between neighborhoods and communities, with employees living in lower-earning areas more likely to be furloughed and/or terminated from their jobs than those living in higher-earning areas. 

This report analyzes leading indicators and changes to employment at small businesses for the full month of August 2020. The findings reported below represent data points with meaningful sample sizes[1], but based on the distribution of Gusto’s customers, they may not be fully representative of the industry and geographic breakdown of small businesses across the United States.

Key Findings

  • No evidence of a V-shaped recovery: Six months into the pandemic, overall small business headcount growth has stalled with only a 1.0% increase in August ‘20. This marks the third straight month that the growth rate has declined (1.2% in July; 2.5% in June; 3.4% in May). Overall headcount growth over the six month period remains 70% below 2019 levels. 
  • Low-income workers are more likely to be furloughed or lose their job: Workers living in the lowest-income zip codes are 28% more likely to be furloughed and 57% more likely to be terminated as of August compared to employees living in higher-earning areas. More than a quarter (27%) of workers from lower-earning areas who were employed at the onset of the pandemic lost their jobs by August. 
  • Furloughed workers facing dead-end as PPP funds run out: With federal aid running out, fewer small business owners are able to rehire furloughed workers or bring them back at their pre-COVID wages. Only 33.5% of workers furloughed in July have been able to return to work in August (that’s down from 41% of workers furloughed in June who returned to work by July). 27% of those workers who returned to their jobs in August after being furloughed in July came back earning lower wages.

August ’20 Small Business Trends

For the third straight month, overall small business headcount is stagnating with growth creeping to a crawl at +1.0% in August (down slightly from +1.2% overall headcount growth in July). After record-high furloughs in April (9.5% furlough rate, a 215% spike compared to April 2019), furlough rates had been steadily improving over the past 3 months. August, however marks the first time during the pandemic that we’ve seen this trend reverse, with the year-over-year furlough rate (41%) climbing higher than what we saw in July (36%) and on par with what we saw in June (41.5%). Gusto data also shows that workers’ hours are being reduced–8.8% of workers had their hours slashed by 10% or more in August compared to just 6.5% in July.

The rate of employer actions that affect employee wages (furloughs, terminations, reductions in hours and hourly pay rate) are shown in Table 1 below. Reduced hours, along with furloughs and hourly pay cuts, are the main contributors to the overall reduction of total wages in August. Furloughs accounted for 27% of the total reduction in wages experienced by workers in August ‘20, reduction in hours contributed another 31%, and reduction in hourly pay rate accounted for 21%.

Table 1. Rates of employer actions affecting employee wages for 2019 and 2020 as compared to February of the same year[2]


Voluntary Terminations (Quitting) Continue to Rise

As shown in Figure 1, the voluntary termination rate continued to tick marginally upwards in August ‘20 and is now slightly higher than Gusto recorded in pre-COVID months (4.1% in August). By August ‘20, 73% of overall terminations were voluntary terminations (roughly the same proportion that was recorded in February ‘20). At the height of layoffs in March ‘20, only 42% of terminations were voluntary. The cause for the increase in quitting over the past several months may be due to several factors: first, that some employees are feeling comfortable seeking out new job opportunities elsewhere again; second, because of the typical seasonal churn we see at the end of summer; and third, because workers in hard-hit industries, such as Arts and Entertainment, may be transitioning to industries that are faring better.

Figure 1. Monthly net change in headcount and hiring, termination, and layoff statistics. 

Figure 1

Rehiring Rate Stays Low in August

Less than 11% of hires by the end of August were re-hires, That’s down from May—at the height of PPP funding distribution—when more than 25% of hires in May ‘20 were re-hires. This would indicate that if an employee was terminated in the midst of the strictest lockdowns in March and April and hasn’t been hired back already, it appears unlikely that they will be rehired by that employer. 

Figure 2. Re-hires as a percentage of hires, on a weekly basis. 

Figure 2

Gains in Employment Remain Below Expectations 

Between March and August, headcount overcame a deep deficit to surpass its pre-COVID levels by 3% (as shown in the 2020 line in black in Figure 3). Yet, 3% is still far below the growth we would expect in a typical year. In fact, it’s 70% lower than headcount growth we saw between March and August ‘19. 

Figure 3. Cumulative % change in headcount between the beginning of March and end of August from 2017-2020.

Figure 3

Industries Seeing Especially Low Headcount Growth 

Headcount growth in industries that have been hit hardest by the pandemic continues to plateau.  Employment in Accommodations, Arts & Entertainment, Food & Beverage, Salon & Spa, Sports & Recreation, and Tourism have all maintained relatively flat growth since July and are still operating with fewer employees than they were at the beginning of March. Other industries have fared better and have experienced more growth in recent months, particularly Retail, which is +4.3% compared to its headcount in early March. 

Figure 4. Cumulative change in headcount since the week of March 2, 2020 across industries heavily affected by COVID and all others.

Figure 4

The growth deficit in different sectors is made most clear when looking at 2020 headcount growth versus the same period last year. Food & Beverage (shown below in Figure 5), for instance, has seen a change in headcount that is still -147% below the growth rate seen in 2019.  Other industries that are far below expectations for February-August employment include Accommodations (-101%) and Salon & Spa (-156%). 

Retail has fared slightly better, likely thanks in part to shift in consumer spending from restaurants and entertainment to at-home cooking and activities. However, its headcount remains -53% below expectations based on data from prior years. Other industries that have had moderate losses to their expected headcount growth by August include desk-based industries such as Technology (-46%) and Legal (-21%).

Healthcare & Social Assistance (includes daycares, dentist and doctor offices, therapists and counselors, etc.) has nearly overcome a moderate dip in headcount growth this year by August, but is still -20% below the growth seen in 2019. Construction has fared very well compared to other industries, and by the end of August ‘20 has entirely made up the headcount growth deficit incurred earlier in the year. 

Cumulative change in headcount in 2020 vs. 2019 plotted by industry is provided in Appendix A4. 

Figure 5: Cumulative % change in headcount of Food & Beverage workers between the beginning of March and end of August from 2019-2020. YoY% change as of end of August: -147%

Rate of Furloughs Stays Elevated Compared to Last Year 

Hourly workers remain 4 times more likely than salaried workers to be furloughed (7.9% for hourly workers in August vs. 2.0% for salaried workers) and 2.5 times more likely to have been terminated by August (30% for hourly workers versus 12.2% for salaried workers).   

While salaried workers have experienced less severe employment outcomes compared to hourly workers, these workers have still experienced higher rates of reduced earnings than in prior years.  For instance, in August ‘20, salaried workers experienced a reduction in their hourly pay rate that was 42% higher than the same time last year (4.4% of salaried workers in Aug ‘20 vs. 3.1% in Aug ‘19).   Salaried workers are also experiencing reduction in hours at a higher than usual rate in August ‘20, but adjusting hours for salaried workers is still relatively uncommon practice as compared to hourly workers (5.0% of salaried workers had their hours reduced in August, as opposed to 12.1% of hourly workers). 

Table 2: Percent of workers that experienced the following changes in employment as compared to February of the same year, cut by FLSA Status (salaried vs. hourly).


For workers returning to work from furlough by August ‘20, a high proportion are returning to reduced hours or reduced hourly pay rate

29% of all employees furloughed between March ‘20 and July ‘20 have been terminated by the end of August ‘20, whereas the termination rate for the same time period last year was 37%. Those who returned to work have been much more likely to return to lower wages than they received before their furlough began (either as an outcome of reduced hours or reduced hourly pay rate).  Of those that have returned to work this year, 29% have returned to lower wages, as opposed to 20% for the same time in 2019. 

Table 3.  Rate of Employer Actions Impacting Employees Furloughed Between March and July by August of the same year

For workers Furloughed between March and July, Employment Outcome by August 2020 2019 YoY % Difference
Terminated 29% 37% -21%
Still furloughed 23% 18% 25%
Returned to work at normal wage 27% 30% -11%
Returned to work at reduced wage (either fewer hours or lower hourly pay rate) 11% 7% 63%
% of all workers who returned to work that returned at reduced wage 29% 20% 42%
Other[3] 9% 7% 27%

Employment outcomes for workers who have been furloughed have varied depending on the industry. Workers in industries that are typically not based at a desk (such as Food & Beverage, Facilities, Retail, etc.), tend to see higher termination rates following furloughs.  

Table 4 below shows the Termination rates by August for workers who were furloughed between March and July this year versus the same period last year. Very few industries saw an increase in termination rates compared to last year — only workers in the Real Estate and Technology industries had an increased likelihood of termination this year, although their termination rates were still below the average across all industries. 

Table 4. Rate of workers furloughed between March & July who have been terminated by August, cut by select Industries

Industry 2020 2019 YoY
Real Estate 27.8% 26.2% 6.0%
Technology 26.5% 25.2% 5.2%
Non-Profits & Associations 22.6% 22.5% 0.3%
Finance & Insurance 26.7% 28.5% -6.5%
Utilities 42.4% 46.0% -8.0%
Tourism & Accommodations 38.5% 45.1% -14.6%
Professional Services 27.4% 33.7% -18.7%
Facilities 41.5% 52.2% -20.5%
Entertainment & Recreation 28.5% 36.5% -22.0%
Retail 31.3% 40.7% -23.3%
Education 27.6% 37.9% -27.2%
Construction 27.4% 39.4% -30.4%
Food & Beverage 37.0% 54.6% -32.2%
Healthcare & Social Assistance 24.4% 36.7% -33.5%
Personal Services 27.0% 45.4% -40.6%

Table 5 below shows the rate of workers that were furloughed between March and July ‘20 and remain so as of August ‘20. Workers in Entertainment and Recreation (subcategories of which include Sports & Recreation and Arts & Entertainment) recorded the highest rate of remaining furloughed in 2020, at nearly 28% of all workers who were furloughed remaining so by August. However, prolonged furloughs do not appear out of the ordinary for workers in this industry based on prior year data. When considering 2019 furlough rates, more atypical industries this year include Non-Profits and Food & Beverage workers. The rate of remaining furloughed by August was more than 40% higher this year than the same period last year for both these industries. 

Table 5. Rate of workers furloughed between March & July who remain furloughed by August, cut by select Industries

Industry 2020 2019 YoY
Non-Profits & Associations 26.5% 18.0% 47.6%
Food & Beverage 20.5% 14.3% 43.1%
Retail 21.9% 16.9% 29.0%
Facilities 18.3% 14.9% 23.1%
Utilities 15.7% 12.9% 21.5%
Education 27.3% 22.5% 21.4%
Professional Services 23.0% 19.3% 19.3%
Construction 20.8% 17.5% 18.5%
Entertainment & Recreation 27.7% 23.3% 18.5%
Personal Services 18.9% 16.8% 12.7%
Tourism & Accommodations 16.4% 14.9% 10.3%
Real Estate 20.8% 19.2% 8.6%
Finance & Insurance 21.9% 20.2% 8.6%
Technology 17.2% 16.1% 6.6%
Healthcare & Social Assistance 19.0% 18.7% 1.4%

Table 6 below shows the rate that workers who were furloughed between March and July returned to work by August.   

Table 6. Return-to-work rates by August for workers furloughed between March & July, cut by select Industries

Industry 2020 2019 YoY
Non-Profits & Associations 43.5% 53.3% -18.4%
Technology 44.9% 52.5% -14.5%
Tourism & Accommodations 32.0% 36.7% -12.8%
Entertainment & Recreation 32.8% 35.5% -7.6%
Education 34.0% 36.5% -6.8%
Utilities 34.5% 36.2% -4.7%
Real Estate 40.6% 41.8% -2.9%
Professional Services 39.5% 40.4% -2.2%
Finance & Insurance 41.0% 39.4% 4.1%
Facilities 33.0% 28.6% 15.4%
Retail 38.4% 33.0% 16.4%
Construction 43.4% 35.6% 21.9%
Healthcare & Social Assistance 51.8% 39.8% 30.2%
Food & Beverage 33.1% 24.2% 36.8%
Personal Services 44.8% 32.5% 37.8%

Table 7. For workers furloughed between March & July that returned to work by August, what proportion returned to reduced wages?

industry 2020 2019 YoY %
Entertainment & Recreation 26% 13% 104%
Healthcare & Social Assistance 42% 23% 85%
Utilities 24% 13% 85%
Retail 36% 22% 64%
Facilities 36% 22% 62%
Construction 30% 19% 53%
Finance & Insurance 24% 16% 50%
Food & Beverage 22% 15% 48%
Professional Services 28% 19% 46%
Tourism & Accommodations 24% 17% 45%
Real Estate 20% 14% 42%
Technology 19% 14% 41%
Personal Services 23% 17% 39%
Non-Profits & Associations 35% 27% 28%
Education 12% 10% 22%

Lower Return-to-Work Rates Among Workers Furloughed in July 

Gusto’s August data reveals a worrying reversal in what was previously a recovering trend: workers furloughed in July are being brought back to work in August at a lower rate than workers furloughed earlier in the year.  Prior to August, employment metrics showed a continued, albeit slowing, recovery to pre-COVID employment, with the proportion of employees remaining furloughed after one month decreasing steadily.

However, August data indicates workers furloughed in July were not brought back to work within one month of being furloughed at as high of a rate as workers who were furloughed in April, May or June.  As shown in Table 8, Only 34% of all workers who were furloughed in July were brought back to work by the end of August–an 18% relative drop in the return-to-work rate since June; and a 27% drop in the return-to-work rates for furloughed employees during the same period last year.

Table 8. Return-to-work rates within one month for employees whose furloughs began in the months listed in 2020 and 2019.

Month Furlough Began: 2020 2019 YoY %
Furloughed March 30% 51% -40%
Furloughed April 40% 45% -10%
Furloughed May 40% 38% 3%
Furloughed June 41% 47% -14%
Furloughed July 34% 46% -27%

Table 9. Proportion of those workers that returned to work within one month who returned on reduced wages

Month Furlough Began: 2020 2019 YoY %
Furloughed March 42% 27% 55%
Furloughed April 49% 25% 97%
Furloughed May 35% 26% 32%
Furloughed June 23% 22% 8%
Furloughed July 27% 20% 35%

 As shown in Table 9 above, of the 33.5% of workers who were furloughed in July but had returned to work by August ‘20, 27% of these workers were returning to wages that were lower than their pre-furlough wages (either due to a reduction in hours or a reduction in hourly pay rate). While this proportion is lower than the March-May ‘20 reduced wage rates for workers coming off furlough, it’s higher than the rate we recorded in June – a reversal in a month-over-month decreasing trend.   

Workers Furloughed in March were Most Likely to be Terminated 

Among employees furloughed since February ‘20, those furloughed in March have largely fared the worst by August. More than one third of employees who were furloughed in March have now been terminated, and only 36% have returned to work, a share lower than those furloughed in any month except July. 

An even smaller number of those workers that have now returned to work have returned to the same wages they were receiving prior to being furloughed (meaning they either had their hours reduced or their hourly pay rate cut upon return). While 36% of employees that were furloughed in March have returned to work by August, 28% of those employees have returned to reduced wages. For comparison, 40% of employees that were furloughed in March of ‘19 had returned to work by August‘19.  Of those, only 18% returned at reduced wages. 

Employees furloughed in April have consistently enjoyed the highest return-to-work rates: as of August, 46% are back to work. Despite these high return rates, only 68% of those furloughed employees who returned are back at a wage comparable to their pre-pandemic baseline, the lowest of any monthly cohort (32% of those employees who are back at work are working on reduced wages).  

Table 10.  Rate of Employer Actions Impacting Cohorts of Furloughed Employees in Subsequent Months of 2020


Employees Living in Lowest Income Zip Codes Remain Hardest Hit

Six months into the pandemic, workers in the lowest-income areas within a city have experienced significantly higher rates of furlough and terminations, with similar portions seeing reductions in hours and hourly pay rate. 

Figure 13 places workers in four income buckets based on the median household income of their zip code relative to the median income of the larger Metropolitan Statistical Area (MSA), and examines employment outcomes in August among those employed as of February 2020[4]. 28% of those employed in February in the lowest-income neighborhoods have been terminated, almost 1.5 times the share for workers in the highest-income neighborhoods (18.1%). The percentage of workers experiencing furloughs is highest amongst workers in the lowest-income areas, also a worrying trend given the worsening rates of furlough re-hiring. 

Figure 6. Distribution of Employment Outcomes, By Neighborhood Income Level (February vs. August 2020) 

Figure 6

Furthermore, as shown in Appendix A.3, workers in low-income zip codes suffered the highest growth in furlough rates relative to August of 2019: furloughs are 70% higher than this time last year in low-income areas, whereas they are only 32% higher year-over-year in the highest-income zip codes.

Industry Trends

Small business headcount grew across nearly all industries in August, at a pace similar to that seen in July. A few industries saw a reduction in headcount in August compared to July (Arts & Entertainment, Sports & Recreation, and Education), the first quarter of decline since April for each of those industries except Sports & Recreation. Still, on the whole, companies across industries are growing. Some of the largest gains were made in the following industries:

  1. Healthcare & Social Assistance (+2.4%)
  2. Insurance (+1.9%)
  3. Transportation (+1.7%)

Figure 8 shows net headcount changes for all industries with 1,000 Gusto customers over the past month, ranked by the cumulative change in headcount over the period.

Figure 7. Net change in headcount for March through June by industry.

Figure 7

Geographic Trends

Similar to July, nearly all U.S. states with at least 1,000 employees on the Gusto platform also showed net increases in headcount for the month of August ‘20. There were a handful of exceptions, but the only state with more than 1% decline was Vermont (-3.6%).

Figure 8. Net percentage change in headcount for March-August by state.

Figure 8

About Gusto

Gusto is a modern, online people platform that helps small businesses take care of their teams. In addition to full-service payroll, Gusto offers health insurance, 401(k)s, compliance and expert HR, and more. The company serves over 100,000 businesses nationwide and has offices in Denver, New York City, and San Francisco.


Table A1: Hires, terminations, and layoffs per 100 employees by industry, for the month of August ‘20, ranked in decreasing order by net change in headcount.

Industry Hires Terminations Layoffs % Change in hiring vs. march Percentage change in terminations Percentage change in layoffs Net change in employees
Facilities 14.81 8.19 0.44 0.33 -0.11 -0.74 6.62
Food & Beverage 13.79 9.25 2.11 1.33 -0.05 -0.45 4.54
Construction 8.55 4.28 0.49 0.5 -0.19 -0.65 4.28
Retail 9.08 5.13 0.69 0.47 -0.16 -0.63 3.95
Transportation 10.4 6.59 0.75 0.47 -0.13 -0.68 3.8
Manufacturing 7.77 4.01 0.67 0.52 -0.21 -0.66 3.76
Wholesale 6.89 3.34 0.89 0.64 -0.16 -0.46 3.55
Healthcare & Social Assistance 7.52 4.05 0.31 0.88 -0.15 -0.78 3.46
Insurance 5.68 2.26 0.21 0.55 -0.35 -0.74 3.42
Salon & Spa 10.3 7.18 1.45 3.63 0.6 -0.11 3.12
Other Personal Services 7.73 4.73 0.72 1.01 -0.33 -0.78 3
Consulting 5.97 2.97 0.62 0.41 -0.24 -0.55 2.99
Education 6.46 3.56 0.43 0.98 -0.32 -0.75 2.9
Other Professional Services 7.51 4.81 0.59 0.63 -0.17 -0.72 2.7
Real Estate 5.51 2.85 0.38 0.7 -0.35 -0.68 2.66
Communications 4.77 2.2 0.42 0.78 -0.55 -0.86 2.57
Technology 4.63 2.15 0.35 0.38 -0.35 -0.71 2.48
Finance 4.38 1.94 0.37 0.29 -0.39 -0.66 2.43
Legal 4.63 2.24 0.3 0.79 -0.27 -0.6 2.39
Sports & Recreation 8.27 6.22 1.3 1.6 -0.28 -0.61 2.06
Non-Profits & Associations 3.73 2.1 0.23 0.69 -0.46 -0.77 1.63
Accounting 4.59 2.99 0.45 0.9 -0.4 -0.79 1.6
Arts & Entertainment 4.71 3.91 1.06 0.83 -0.46 -0.63 0.8

Table A2: Hires, terminations, and layoffs per 100 employees across the most populous 50 Metropolitan Statistical Areas for the month of August ‘20, ranked in decreasing order by net change in headcount.

msa_title Hires Terminations Layoffs Percentage change in hires Percentage change in terminations Percentage change in layoffs Net change in employees
Louisville/Jefferson County, KY-IN 8.71 2.86 0.43 0.6 -0.2 -0.86 5.85
Columbus, OH 9.8 4.27 0.21 0.98 -0.27 -0.81 5.54
Houston-The Woodlands-Sugar Land, TX 9.91 4.54 0.5 1.39 -0.18 -0.71 5.37
Detroit-Warren-Dearborn, MI 9.16 3.84 0.65 1.16 -0.34 -0.81 5.32
Cleveland-Elyria, OH 9.09 3.82 0.07 0.18 0.08 -0.92 5.27
Kansas City, MO-KS 8.05 3.16 0.26 0.5 -0.38 -0.73 4.89
Hartford-West Hartford-East Hartford, CT 7.89 3.11 0.5 0.96 -0.26 0.17 4.79
Salt Lake City, UT 8.55 4.13 0.33 0.59 -0.24 -0.82 4.42
Tampa-St. Petersburg-Clearwater, FL 9.53 5.17 0.47 0.66 -0.1 -0.65 4.36
Milwaukee-Waukesha-West Allis, WI 8.83 4.48 0.22 1.42 -0.14 -0.82 4.35
Providence-Warwick, RI-MA 8.29 3.95 0.2 1.17 -0.19 -0.9 4.34
Jacksonville, FL 8.64 4.48 0.16 0.91 -0.26 -0.92 4.16
Austin-Round Rock, TX 7.9 3.95 0.53 0.91 -0.33 -0.78 3.95
Dallas-Fort Worth-Arlington, TX 9.31 5.39 0.45 0.77 0.03 -0.69 3.92
Atlanta-Sandy Springs-Roswell, GA 8.24 4.4 0.57 1.19 0.06 -0.47 3.84
Charlotte-Concord-Gastonia, NC-SC 7.51 3.67 0.19 0.92 -0.43 -0.94 3.83
San Antonio-New Braunfels, TX 8.7 4.93 0.46 0.74 -0.07 -0.64 3.77
Miami-Fort Lauderdale-West Palm Beach, FL 8.46 4.74 0.84 0.98 -0.03 -0.31 3.72
Baltimore-Columbia-Towson, MD 6.7 3.01 0.26 1 -0.35 -0.76 3.69
Denver-Aurora-Lakewood, CO 7.04 3.38 0.46 0.61 -0.34 -0.75 3.67
Virginia Beach-Norfolk-Newport News, VA-NC 6 2.36 0.09 0.14 -0.38 -0.87 3.64
Orlando-Kissimmee-Sanford, FL 7.94 4.49 0.56 1.21 -0.27 -0.47 3.46
Phoenix-Mesa-Scottsdale, AZ 8.65 5.21 0.51 0.86 -0.02 -0.6 3.45
Indianapolis-Carmel-Anderson, IN 6.38 3 0.28 0.49 -0.61 -0.93 3.39
Cincinnati, OH-KY-IN 7.27 3.97 0.31 0.75 0.09 -0.72 3.3
New York-Newark-Jersey City, NY-NJ-PA 7.27 4.01 0.86 1.11 -0.38 -0.7 3.26
Nashville-Davidson–Murfreesboro–Franklin, TN 9.16 5.92 1.09 0.31 0 -0.47 3.25
San Diego-Carlsbad, CA 6.72 3.51 0.48 0.89 -0.2 -0.72 3.21
Birmingham-Hoover, AL 7.12 3.97 0.58 1.56 0.1 2.91 3.15
Las Vegas-Henderson-Paradise, NV 7.7 4.55 1.36 0.47 -0.37 -0.6 3.15
New Orleans-Metairie, LA 6.39 3.42 0.59 0.45 -0.53 -0.51 2.97
Sacramento–Roseville–Arden-Arcade, CA 6.7 3.82 0.45 0.86 -0.22 -0.62 2.88
Portland-Vancouver-Hillsboro, OR-WA 6.36 3.53 0.67 0.64 -0.26 -0.6 2.83
Minneapolis-St. Paul-Bloomington, MN-WI 6.44 3.67 0.68 0.62 -0.38 -0.43 2.77
Los Angeles-Long Beach-Anaheim, CA 6.25 3.58 0.73 0.74 -0.19 -0.61 2.66
Washington-Arlington-Alexandria, DC-VA-MD-WV 6.27 3.65 0.98 0.64 -0.12 -0.25 2.62
Philadelphia-Camden-Wilmington, PA-NJ-DE-MD 6.78 4.18 0.42 0.89 -0.19 -0.81 2.6
St. Louis, MO-IL 8.02 5.44 1.54 0.14 -0.3 -0.28 2.58
Boston-Cambridge-Newton, MA-NH 6.02 3.55 0.7 0.51 0 -0.4 2.47
Chicago-Naperville-Elgin, IL-IN-WI 5.66 3.26 0.73 0.4 -0.38 -0.63 2.41
Pittsburgh, PA 5.92 3.6 0.3 0.26 -0.53 -0.93 2.31
Richmond, VA 6.68 4.64 0.37 0.76 -0.19 -0.73 2.03
San Francisco-Oakland-Hayward, CA 5.31 3.33 0.89 0.5 -0.31 -0.52 1.98
Memphis, TN-MS-AR 8.85 6.89 0.15 0.31 -0.02 -0.93 1.96
Seattle-Tacoma-Bellevue, WA 5.97 4.16 0.79 0.65 -0.38 -0.7 1.82
Raleigh, NC 7.22 5.52 0.56 1.06 0.09 -0.68 1.7
San Jose-Sunnyvale-Santa Clara, CA 5.04 3.62 0.82 0.87 -0.31 -0.66 1.42
Riverside-San Bernardino-Ontario, CA 5.97 4.64 0.98 0.53 -0.09 -0.6 1.33
Buffalo-Cheektowaga-Niagara Falls, NY 3.77 2.9 0.43 0.27 -0.71 -0.94 0.87
Oklahoma City, OK 7.91 8.48 0.7 0.71 0.48 -0.5 -0.57

Table A3: Percent of workers that experienced the following changes in employment as compared to February of the same year, cut by MHI Status (EE home zip code vs. MSA)


Figure A4 Cumulative % change in headcount of workers between the beginning of March and end of August from 2019-2020, broken out by industry

Tourism YoY% change as of end of August: -274%
A4 Tourism
Accommodations YoY% change as of end of August: -101%
A4 Accommodations
Sports & Recreation YoY% change as of end of August: -191%
A4 Sports _ Recreation
Salon & Spa YoY% change as of end of August: -156%
A4 Salon _ Spa
Retail YoY% change as of end of August: -53%
A4 Retail
Technology YoY% change as of end of August: -46%
A4 Technology
Real Estate YoY% change as of end of August: -81%
A4 Real Estate
Healthcare & Social Assistance YoY% change as of end of August: -20%
A4 Healthcare _ Social Assistance
Legal YoY% change as of end of August: -21%
A4 Legal
Construction YoY% change as of end of August: 0%
A4 Construction


The analyses described in this report were based on two separate datasets obtained from Gusto’s small business payroll, benefits, and HR applications. The first dataset used payroll data to capture employee hours, wages, and furloughing (Work Reduction & Furlough). The second dataset used data from employers hiring and terminating employees (Hiring & Termination). When we define outcomes, we’re assigning one outcome per employee per month, which can block certain outcomes (Outcome Rank Ordering).

Work Reduction & Furlough

Employees in our dataset were labeled as furloughed if they were employed for the entirety of February, March, April and May (had no termination effective during that time), and either:

  1. had earned money in the final paycheck of the preceding month but had not earned money in the final paycheck of the following month (e.g. final paycheck of February was non-zero, and the final paycheck of March was $0), or
  2. met the definition for part 1 and also continued to earn $0 on payrolls in the subsequent month, thereby on a continued furlough.

Our furlough data in this April report only looks at furloughs that were active in April and were instigated in March or April, not furloughs that began earlier and have continued through this period.

Non-terminated, non-furloughed employees were labeled as hours reduced for the June report if the total hours of their June paychecks were less than 90% of the hours on their February (used as a benchmark for pre-COVID) paychecks. This calculation was done for both 2019 and 2020, where 2019 was used to represent the counterfactual case (what would have happened). 

Presumed COVID-related differences were calculated by subtracting the rates of termination, furlough, and work reduction in 2019 from the same numbers in 2020. This rate difference was used to calculate the number of additional employees affected by each cost-saving strategy. The average dollars saved per affected employee was calculated as the difference between total February and March payrolls, divided by the number of employees, for each type of affected employee, and this dollar amount was multiplied by the COVID-related count of impacted employees to calculate the total dollars saved by each strategy.

Hiring & Termination

A given employee on Gusto can have multiple “employments,” since an employer can potentially hire, terminate, and rehire the same employee multiple times. In our Hiring & Termination dataset, a hire corresponded to an employer creating a new employment with a hiring date, and a termination corresponded to the entry of a termination date for a given employment. Layoffs corresponded to terminations where the employer listed the reason for the termination as a layoff” (one of the choices from a standardized list in Gusto’s termination flow). In order to capture a more time-sensitive view of employer activities, we recorded hires, terminations, and layoffs on the date that they were entered into the system, rather than their effective date. Terminations are also coded as voluntary or involuntary.  

Monthly reporting of termination data may differ slightly from prior reporting, as employers often log a termination several days or weeks after the employee was actually released. For example, more than a third of terminations that were created (logged in the system) in May were actually effective before May. Data in this report may also differ slightly from prior reports due to methodology improvements. Further employment changes are incorporated into this month’s analysis, such as reduction to hourly pay rate. We also separately categorized employees that worked for companies who “paused” their payroll service with Gusto. Employees that fall into this new bucket may have previously not been labeled as impacted, or may have been attributed as “hours reduced” for a given month.

When we assign an outcome to an employee for a given month, we follow this rank ordering:

  1. Employee Terminated
  2. Company Suspended/Paused
  3. Employee Furloughed
  4. Hours Reduced
  5. Hourly Pay Rate Reduced

What this means, in practice, is that if an employee’s hourly pay rate was reduced early in the month and then they were furloughed, we’ll count them toward the “Furloughed” outcome but not the “Hourly Pay Rate Reduced” outcome. The further down the rank order the outcome is, the more likely we are to undercount it in favor of something else.

Employee hires, layoffs, and terminations were aggregated weekly and monthly for a given company and work location. Hiring, termination, and layoff rates reported in this document are based on the number of times each event occurred in a given week or month, divided by the number of employees who were employed at the beginning of the period. 

Locations reported in this document are based on the most recent work location associated with the employee. Industries reported in this document are based on self-report from customers within Gusto’s product.

[1] Values provided in this report were only included for categories (e.g., state, industry, metropolitan area) where we had at least 1,000 employees.

[2] Historical data may differ from earlier Gusto reporting as employers may log employee terminations after the actual termination date (e.g. more than a third of terminations that were logged in April by employers actually occurred in a prior month).

[3] Employees in the Other bucket could have worked for companies that went out of business, left Gusto’s platform, etc.

[4] See Appendix Table A.3 for definitions of income bins and trends over time.

Daniel Sternberg Daniel Sternberg leads the Data Science team at Gusto. Daniel is passionate about using Gusto’s payroll and employment data to help small businesses thrive. Prior to joining Gusto, Daniel earned a PhD in Cognitive Psychology, studying human learning first academically and then in industry. He lives in San Francisco with his wife and daughter.
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