This post was co-authored by Daniel Sternberg and Sarah Gustafson.
June ‘20 is the second consecutive month that Gusto data has shown an uptick in small business headcount nationwide since mandatory shelter in place orders were issued as a necessary response to COVID-19. June also marks a shift in the terms for federal aid, with new changes to the Paycheck Protection Program (PPP) signed into law early providing more flexibility and more time for small businesses to ensure their loans are forgiven.
Total small business headcount grew 2.4% in June ‘20 compared to the previous month and is now hovering closer to pre-COVID levels—a first since the crisis took hold in March ‘20. Yet, while overall small business headcount is climbing towards pre-COVID levels, wages and hours are not. Small businesses are bringing on more staff, but often at reduced pay and fewer hours. Hour and pay rate reductions accounted for the largest percentages of reduced wages in June (33% and 27% respectively).
With surging cases of COVID stalling reopening plans for small businesses nationwide, and new waves of government action and aid still on the horizon, time will tell whether upticks in rehiring will prove to be temporary or indicative of a larger, more sustainable rebound.
Gusto enables more than 100,000 small businesses across the United States to take care of their teams with full-service payroll, benefits, compliance, expert HR, and more. This report analyzes leading indicators and changes to employment at small businesses for the full month of June ‘20. The findings reported below represent data points with meaningful sample sizes, 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.
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Key Findings
- Headcount upswing continues: Total headcount grew 2.4% in June compared to the previous month, representing the second straight month in positive headcount growth since the crisis took hold in March ‘20. As a result, overall small business headcount is hovering closer to pre-COVID baseline levels. Headcount growth in June ‘20 was driven by new hires, which represented 82% of all workers hired in the month.
- Back to work, but making less money: More people are back at work, but they’re still working fewer hours and for less money than they did pre-COVID. The number of salaried workers that have experienced at least a 10% wage cut increased by 80% compared to pre-COVID levels. Hour and pay rate reductions accounted for the largest percentages of reduced wages in June (33% and 27% respectively), with furloughs accounting for 25% of reduced wages.
- Quitting is on the rise: More workers are choosing to quit their jobs—a first in the COVID era. In June, 3.4% of employees left their companies voluntarily, which represented a 32% increase over May.
June ’20 Small Business Trends
Small business headcount grew for the second consecutive month, with a 2.4% increase in June, which drove overall headcount above the pre-COVID baseline for the first time since early March. Upticks in overall headcount correlate with the first and second waves of payroll-focused federal relief. Small businesses started to receive PPP funds in April and May, which initially propped up payroll costs for eight weeks. Now, with new changes signed into law that offer more flexibility and more time for small businesses to use those relief funds, it remains to be seen if hiring will continue to climb or stall out as companies exhaust these loan funds over the course of the summer.
Table 1. Rates of employer actions affecting employee wages for 2019 and 2020 as compared to February of the same year.
| March ‘19 | April ‘19 | May ‘19 | June‘19 | March ‘20 | April ’20 | May ‘20 | June‘20 | YoY March | YoY April | YoY May | YoYJune | ||
| Furlough rate | 3.6% | 3.0% | 3.4% | 4.3% | 4.2% | 9.5% | 6.8% | 6.2% | 16.7% | 217.8% | 102.4% | 45.6% | |
| Reduction in hourly wages of at least 10% | 2.3% | 2.3% | 2.6% | 2.3% | 2.2% | 3.1% | 3.5% | 3.3% | -6.5% | 32.9% | 36.4% | 43.7% | |
| Reductions in hours of at least 10% | 9.4% | 10.6% | 7.6% | 10.4% | 13.3% | 11.7% | 9.6% | 9.2% | 40.4% | 10.9% | 27.7% | -11.3% | |
| Termination rate | 4.0% | 7.6% | 11.0% | 14.3% | 8.2% | 11.3% | 13.6% | 15.8% | 104.7% | 50.1% | 24.2% | 10.4% | |
| Layoff rate | – | – | – | 3.0% | 1.8% | 0.7% | 0.6% | – | – | – |
Hiring Drives Headcount Growth
Overall small business headcount growth in June continued to be driven mostly by recovered hiring. Figure 1 shows monthly headcount statistics since January 2019. The hiring rate in June ‘20 (7.5%) was very similar to May ‘20 (7.3%), though rehires did not make up as large a percentage of hires in June (18%) as they did in April and May (22%); however, this number was still elevated compared to pre-COVID baseline (see Figure 2). On a weekly basis, we saw consistent growth in headcount and stable hiring, termination, and layoff rates throughout June. Figure 3 shows headcount statistics on a weekly basis since January 2019.
Figure 1. Monthly net change in headcount and hiring, termination, and layoff statistics.
Figure 2. Rehires as a percentage of hires, on a weekly basis.
Figure 3. Weekly net change in headcount and hiring, termination, and layoff statistics.
Returning to Work with Less Pay, Fewer Hours
Overall small business headcount is approaching pre-COVID baseline levels, but wages earned and hours worked are not. In fact, hour and pay rate reductions accounted for the largest percentages of reduced wages in June (33% and 27% respectively), with furloughs accounting for 25% of reduced wages. This is a shift from earlier in the crisis when furloughing was the top driver of reduced wages. The impact of terminations also continued to decline, accounting for 9.1% of reduced wages in June. This data suggests that many employees at small businesses have been able to return to work over the past few months, but in a reduced capacity.
The way small businesses reduce wages varies between hourly and salaried workers. Salaried employees are 3.8 times more likely than hourly workers to have their wages cut, while hourly workers are 3.5 times more likely than salaried workers to experience reduced hours.
Furloughs Fall For Second Straight Month
The percentage of employees temporarily furloughed fell again in June, as employees returned to work, from 6.8% in May to 6.2%. This furlough rate represents a 35% decline from their peak during the crisis, but still 45% above the same period last year. In April ’20, we saw a 3.2x year-over-year overall increase in employees who were furloughed (9.8% versus 3.1%) and more than double the rate seen in March of this year (4.4%).
Furlough rates for hourly employees continued to decline for the second consecutive month, though the decline was not as steep as in May, and still represents a 59% year-over-year increase in the furlough rate compared to June ‘19. Furlough rates for salaried employees ticked up just slightly (from 2.1% to 2.3%) in the month. Hourly workers remain more likely to be furloughed than salaried workers, though the gap has narrowed as hourly furlough rates have continued to decline (from a 5x gap in May to a 4x gap in June). Table 2 shows the various changes in employment broken down by FLSA status.
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).
| Salaried (exempt) | March ‘19 | April ‘19 | May ‘19 | June‘19 | March ‘20 | April ’20 | May ‘20 | June‘20 | YoY March | YoY April | YoY May | YoYJune | |
| Furlough rate | 2.5% | 1.3% | 1.5% | 2.3% | 2.3% | 3.2% | 2.1% | 2.3% | -7.7% | 137.9% | 44.9% | -1.5% | |
| Reduction in hourly wages of at least 10% | 2.9% | 2.9% | 3.1% | 2.8% | 3.0% | 5.1% | 5.7% | 5.4% | 5.4% | 77.0% | 86.1% | 91.8% | |
| Reductions in hours of at least 10% | 3.9% | 4.2% | 3.4% | 6.0% | 6.6% | 5.2% | 4.4% | 4.0% | 69.9% | 21.9% | 27.0% | -33.0% | |
| Termination rate | 2.5% | 4.7% | 7.0% | 9.1% | 4.0% | 6.3% | 7.7% | 9.0% | 60.7% | 33.0% | 9.8% | -1.1% |
| Hourly (non-exempt) | March ‘19 | April ‘19 | May ‘19 | June‘19 | March ‘20 | April ’20 | May ‘20 | June‘20 | YoY March | YoY April | YoY May | YoYJune | |
| Furlough rate | 4.9% | 4.5% | 5.1% | 6.0% | 6.0% | 15.1% | 11.0% | 9.6% | 23.2% | 234.8% | 117.1% | 59.4% | |
| Reduction in hourly wages of at least 10% | 1.7% | 1.6% | 1.8% | 1.5% | 1.3% | 1.2% | 1.5% | 1.4% | -21.7% | -24.8% | -16.0% | -7.9% | |
| Reductions in hours of at least 10% | 15.0% | 16.9% | 11.7% | 15.0% | 19.3% | 17.5% | 14.3% | 13.9% | 28.9% | 3.9% | 22.8% | -7.5% | |
| Termination rate | 5.6% | 10.6% | 15.3% | 19.9% | 12.1% | 16.2% | 19.4% | 22.3% | 115.3% | 52.7% | 26.6% | 12.0% |
Uptick in Quitting
Voluntary terminations recovered to close to their pre-COVID baseline, indicating that employees at small businesses were more likely to leave their current jobs for new opportunities. In June, 3.4% of employees left their company voluntarily, which represented a 32% increase over May and a 4% increase compared to February. The uptick in voluntary terminations in June was one major difference from May, suggesting that employees felt more comfortable taking new job opportunities, and that there were some openings available for them. Involuntary terminations in general and layoffs in particular remained slightly elevated over pre-COVID baseline—1.6% of employees were involuntarily terminated in June, a 4% increase compared to May and a 32% increase over February. This suggests there is still elevated layoff activity occurring, though at a much smaller scale.
Industry Trends
Small business headcount grew across industries in June, just as it did in May. The following industries experienced the highest growth levels:
- Sports & Recreation (+4.9%)
- Facilities (+4.3%)
- Food & Beverage (+4.2%)
- Transportation (+3.7%)
- Manufacturing (+3.3%)
Figure 4 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 4. Net change in headcount for March through June by industry.
Hardest-Hit Industries Are Still the Hardest Hit
As headcount has continued to grow in June, small businesses in many industries that experienced losses in March and April have fully recovered on Gusto, but a number of the hardest hit industries still have between 5–10% declines over the period—these industries include Food & Beverage, Salon & Spa, Sports & Recreation, Accommodations, and Arts & Entertainment. Figure 5 shows the cumulative net change in headcount for heavily affected industries over the course of the crisis compared to others.
Figure 5. Cumulative change in headcount since the week of March 2 across heavily affected industries and all others.
Geographic Trends
Over the course of June, nearly all US states with at least 1,000 employees on the Gusto platform also showed net increases in headcount. The only exceptions were Hawaii and Mississippi, which showed slight declines.
Figure 6. Net percentage change in headcount for March–May by state.
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.
Appendix
Table A1: Hires, terminations, and layoffs per 100 employees by industry, for the month of June ‘20, ranked in decreasing order by net change in headcount.
| Industry | Hires | Terms | Layoffs | % Change in Hires | % Change in terms | % Change in layoffs | Net headcount change |
| Sports & Recreation | 11.43 | 6.54 | 0.57 | 39% | 11% | -56% | 4.9% |
| Facilities | 13.85 | 9.59 | 0.25 | -5% | 17% | -48% | 4.3% |
| Food & Beverage | 15.05 | 10.85 | 1.64 | 9% | 19% | -20% | 4.2% |
| Transportation | 9.7 | 6.02 | 0.85 | -7% | -4% | 11% | 3.7% |
| Manufacturing | 7.95 | 4.61 | 0.56 | 3% | 14% | -18% | 3.3% |
| Construction | 8.17 | 4.91 | 0.35 | -5% | 16% | -28% | 3.3% |
| Salon & Spa | 10.53 | 7.68 | 0.74 | 0% | 6% | -51% | 2.8% |
| Healthcare & Social Assistance | 7.87 | 5.14 | 0.27 | 4% | 26% | -11% | 2.7% |
| Insurance | 6.15 | 3.51 | 0.09 | 8% | 54% | -55% | 2.6% |
| Finance | 5.48 | 2.94 | 0.5 | 24% | 50% | 35% | 2.5% |
| Retail | 8.71 | 6.21 | 0.51 | -5% | 20% | -29% | 2.5% |
| Legal | 4.45 | 2.82 | 0.24 | -5% | 25% | -19% | 1.6% |
| Other Professional Services | 7.57 | 5.94 | 0.6 | 1% | 25% | 1% | 1.6% |
| Wholesale | 5.89 | 4.27 | 0.53 | -16% | 26% | -42% | 1.6% |
| Technology | 4.34 | 2.8 | 0.46 | -7% | 29% | 36% | 1.5% |
| Other Personal Services | 6.56 | 5.03 | 0.68 | -15% | 8% | -3% | 1.5% |
| Consulting | 4.8 | 3.43 | 0.57 | -22% | 12% | -12% | 1.4% |
| Real Estate | 5.1 | 3.77 | 0.49 | -7% | 33% | 30% | 1.3% |
| Communications | 4.04 | 2.89 | 0.69 | -16% | 35% | 44% | 1.2% |
| Arts & Entertainment | 5.52 | 4.6 | 0.91 | 14% | 13% | -17% | 0.9% |
| Education | 7.87 | 6.97 | 0.35 | 23% | 92% | -21% | 0.9% |
| Accounting | 3.65 | 2.89 | 0.25 | -20% | -4% | -47% | 0.8% |
| Non-Profits & Associations | 4.24 | 3.84 | 0.4 | 13% | 91% | 76% | 0.4% |
Table A2: Hires, terminations, and layoffs per 100 employees across the most populous 50 Metropolitan Statistical Areas for the month of June ‘20, ranked in decreasing order by net change in headcount.
| MSA | Hires | Terms | Layoffs | % Change in hires | % Change in terms. | % Change in layoffs | Net headcount change |
| Virginia Beach-Norfolk-Newport News, VA-NC | 11.74 | 4.84 | 0.24 | 0.92 | 1.05 | 1.85 | 6.91 |
| Milwaukee-Waukesha-West Allis, WI | 11.91 | 5.4 | 0.21 | 0.33 | 0.2 | -0.04 | 6.52 |
| Pittsburgh, PA | 11.24 | 5.11 | 0.32 | 0.87 | 0.41 | 0.05 | 6.12 |
| Cleveland-Elyria, OH | 9.16 | 4.34 | 0.07 | 0 | 0.12 | -0.09 | 4.81 |
| Philadelphia-Camden-Wilmington, PA-NJ-DE-MD | 8.91 | 4.26 | 0.31 | 0.3 | 0.01 | -0.25 | 4.65 |
| Providence-Warwick, RI-MA | 8.95 | 4.68 | 0.09 | 0.09 | 0.18 | -0.53 | 4.27 |
| Oklahoma City, OK | 10.06 | 5.83 | 0.17 | 0.27 | -0.31 | -0.75 | 4.23 |
| Charlotte-Concord-Gastonia, NC-SC | 9.04 | 4.82 | 0.28 | 0.2 | 0.31 | 0.44 | 4.22 |
| Detroit-Warren-Dearborn, MI | 9.23 | 5.47 | 0.51 | 0 | 0.42 | -0.22 | 3.76 |
| Minneapolis-St. Paul-Bloomington, MN-WI | 8.15 | 4.4 | 0.36 | 0.26 | 0.2 | -0.46 | 3.75 |
| Indianapolis-Carmel-Anderson, IN | 8.34 | 4.78 | 0.11 | 0.3 | 0.59 | -0.61 | 3.56 |
| Las Vegas-Henderson-Paradise, NV | 9.47 | 6.08 | 0.45 | 0.23 | 0.33 | -0.67 | 3.38 |
| St. Louis, MO-IL | 8.89 | 5.61 | 0.45 | 0.1 | 0.03 | -0.71 | 3.28 |
| Dallas-Fort Worth-Arlington, TX | 8.91 | 5.75 | 0.55 | -0.05 | 0.06 | 0.22 | 3.16 |
| Richmond, VA | 6.86 | 3.7 | 0.25 | 0.04 | -0.2 | -0.31 | 3.15 |
| Jacksonville, FL | 8.51 | 5.36 | 0.18 | -0.03 | 0.19 | 0.11 | 3.15 |
| San Antonio-New Braunfels, TX | 9.41 | 6.3 | 0.15 | 0.06 | 0.27 | -0.68 | 3.1 |
| Austin-Round Rock, TX | 7.59 | 4.51 | 0.73 | -0.05 | 0.13 | 0.38 | 3.08 |
| Louisville/Jefferson County, KY-IN | 8.18 | 5.14 | 0.41 | -0.06 | 0.79 | 0.12 | 3.04 |
| Salt Lake City, UT | 6.31 | 3.29 | 0.25 | -0.27 | -0.2 | -0.25 | 3.02 |
| Tampa-St. Petersburg-Clearwater, FL | 8.1 | 5.16 | 0.42 | -0.15 | 0 | -0.11 | 2.93 |
| Riverside-San Bernardino-Ontario, CA | 7.43 | 4.67 | 0.37 | 0.25 | 0.02 | -0.62 | 2.76 |
| Chicago-Naperville-Elgin, IL-IN-WI | 6.77 | 4.01 | 0.47 | 0.19 | 0.23 | -0.35 | 2.76 |
| Buffalo-Cheektowaga-Niagara Falls, NY | 6.03 | 3.37 | 0.7 | 0.56 | 0.17 | 0.63 | 2.66 |
| New Orleans-Metairie, LA | 8.18 | 5.68 | 0.51 | 0.28 | 0.66 | -0.15 | 2.49 |
| Seattle-Tacoma-Bellevue, WA | 7.33 | 4.84 | 0.53 | 0.22 | 0.16 | -0.33 | 2.48 |
| Denver-Aurora-Lakewood, CO | 7.55 | 5.29 | 0.87 | 0.06 | 0.56 | 0.89 | 2.27 |
| Phoenix-Mesa-Scottsdale, AZ | 7.43 | 5.18 | 0.56 | -0.14 | -0.01 | 0.11 | 2.25 |
| Washington-Arlington-Alexandria, DC-VA-MD-WV | 6.34 | 4.12 | 0.48 | 0.01 | 0.13 | -0.51 | 2.22 |
| New York-Newark-Jersey City, NY-NJ-PA | 6.99 | 4.8 | 0.69 | -0.04 | 0.2 | -0.2 | 2.19 |
| Portland-Vancouver-Hillsboro, OR-WA | 6.8 | 4.61 | 0.78 | 0.07 | 0.31 | 0.16 | 2.19 |
| Orlando-Kissimmee-Sanford, FL | 7.64 | 5.49 | 0.35 | -0.04 | 0.22 | -0.37 | 2.15 |
| Kansas City, MO-KS | 8.1 | 6.04 | 0.19 | 0.01 | 0.91 | -0.26 | 2.05 |
| San Diego-Carlsbad, CA | 7.32 | 5.3 | 0.97 | 0.09 | 0.51 | 1.04 | 2.02 |
| Miami-Fort Lauderdale-West Palm Beach, FL | 7.51 | 5.49 | 0.41 | -0.13 | 0.16 | -0.51 | 2.02 |
| Boston-Cambridge-Newton, MA-NH | 5.92 | 3.91 | 0.37 | -0.02 | 0.1 | -0.46 | 2.01 |
| Atlanta-Sandy Springs-Roswell, GA | 6.89 | 5.02 | 0.44 | -0.17 | 0.14 | -0.23 | 1.86 |
| Houston-The Woodlands-Sugar Land, TX | 7.65 | 6.02 | 0.53 | -0.23 | 0.32 | 0.06 | 1.63 |
| Birmingham-Hoover, AL | 6.09 | 4.46 | 0.14 | -0.15 | 0.12 | -0.77 | 1.62 |
| San Jose-Sunnyvale-Santa Clara, CA | 5.71 | 4.24 | 0.54 | 0.14 | 0.17 | -0.34 | 1.47 |
| Baltimore-Columbia-Towson, MD | 5.93 | 4.49 | 0.71 | -0.11 | 0.48 | 1.74 | 1.44 |
| San Francisco-Oakland-Hayward, CA | 5.66 | 4.36 | 0.72 | 0.06 | 0.3 | -0.2 | 1.31 |
| Nashville-Davidson–Murfreesboro–Franklin, TN | 8.12 | 6.82 | 0.49 | -0.11 | 0.15 | -0.55 | 1.3 |
| Raleigh, NC | 7.39 | 6.3 | 1.11 | 0.03 | 0.14 | 0.99 | 1.09 |
| Sacramento–Roseville–Arden-Arcade, CA | 6.98 | 6.05 | 0.67 | 0.02 | 0.57 | 0.47 | 0.93 |
| Cincinnati, OH-KY-IN | 5.33 | 4.57 | 0.7 | -0.26 | 0.16 | 1.28 | 0.75 |
| Los Angeles-Long Beach-Anaheim, CA | 5.81 | 5.39 | 0.75 | -0.07 | 0.5 | 0.03 | 0.43 |
| Memphis, TN-MS-AR | 5.95 | 5.52 | 0.28 | -0.33 | -0.19 | 0.97 | 0.42 |
| Columbus, OH | 6.31 | 6.62 | 1.18 | -0.38 | 0.48 | 4.31 | -0.32 |
| Hartford-West Hartford-East Hartford, CT | 5.63 | 6.03 | 1.37 | -0.29 | 0.92 | 1.68 | -0.4 |
Methodology
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).
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:
- 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
- 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 work 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 wages. 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.
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] An initial attempt to adjust for industry skew using NAICS codes showed adjusted headcount to still be below baseline (down 1% from the beginning of March).
[3] 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).
[4] Layoff (a subset of overall terminations) data was not collected prior to January ‘20.
