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The Real-Time Impact of COVID-19 on Small Business Employees

Daniel Sternberg Head of Data Science, Gusto 
The Real-Time Impact of COVID-19 on Small Business Employees - Gusto

Small businesses across the country continue to grapple with ongoing uncertainty and difficult decisions tied to the COVID-19 pandemic. Gusto is continuing to track the economic hardships small businesses are facing—with particular attention to the hardest-hit groups. 

In this report, we look at the impact of the nation’s ongoing economic crisis on small businesses and their employees based on age, income, and industry. We will soon be releasing our full April report that analyzes additional trends in the quarantine economy, including furloughs, wage reduction, and terminations by industry, geography, and business size in the first half of May. Our March report can be found here.

Gusto enables more than 100,000 small businesses across the United States to take care of their teams, with full-service payroll, benefits, compliance, and expert HR. We believe that the trends observed on Gusto’s platform are indicative of broader trends across the country, and that payroll data can act as an early warning for economic trends that will later surface.

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.

Key Report Findings

The economic stress and hardship bearing down on small businesses amid the COVID-19 pandemic is pushing many to the brink. Many small businesses have been forced to make the difficult decision to let go of staff. Gusto’s analysis shows that those who make the least are being hit the hardest by COVID-19-related layoffs:

  1. Lower-income employees are more likely to be laid off. In particular, hourly workers making less than $20 per hour have experienced a 115% higher rate of layoffs compared to those making $30 or more per hour. This can be partly attributed to the over-representation of hourly employees on lower wages across service-based industries, which have borne the brunt of COVID-19-related business closures. 
  2. Gen Z and new entrants to the workforce are seeing deep cuts. Workers under the age of 25 have experienced a 93% higher rate of layoffs than workers above the age of 35. This difference is at least partially driven by the fact that younger workers are disproportionately employed in lower wage jobs in industries that depend on foot traffic.
  3. Jobs in low-Income areas are most at-risk. Employees who work in businesses located in lower-income areas are 25% more likely to have been laid off, compared with employees who work in higher-income areas.  

Employee Wage

Gusto data shows a distinct difference in impact between salaried and hourly employees across wage groupings, with lower wage earners in both groups disproportionately affected by layoffs. Since COVID-19 took hold in March, Gusto data shows that from March 2 through April 26, the layoff rate for hourly employees (5.2%) was double that of salaried employees (2.5%) across-the-board.

Hourly workers making $20 or less per hour were laid off at more than 2 times the rate of higher earners making $30 or more per hour (5.8% vs. 2.7%). For salaried workers, 3.5% of those making less than $50,000 were laid off compared to 3% of workers in the $50,000 to $75,000 salary range lost their jobs. Figures 1 and 2 below highlight the differences between the layoff rates for Salaried versus Hourly wage groups.

Figure 1. Cumulative % of layoffs by Wage Group for Hourly EEs

layoffs_wage_hourly

Figure 2. Cumulative % of Layoffs by Wage groups for Salaried EEs

layoffs_wage_salaried

The higher layoff rate among hourly employees appears to be driven by the impact of COVID-19-related closures across service industries that primarily depend on foot traffic to make money. The vast majority of workers in Food and Beverage, Accommodations, and Salon and Spa are paid hourly (80% of workers or more across the three), and more than 75% of hourly employees in these industries earn less than $15 an hour.

Lenore Estrada, co-founder of Three Babes Bakeshop in San Francisco, knows firsthand the difficulty of having to let staff go. Week-by-week, as all of their corporate wholesale orders were canceled, Estrada and her team made phased decisions, each one more difficult than the next, starting with reduced hours, progressing to furloughs, and then, as the situation worsened, being forced to enact layoffs. “Those were the toughest weeks,” says Estrada. “It was terrible.”

With so much of the food industry laying off its hourly, minimum-wage workers, and food insecurity on the rise citywide, Estrada shifted gears to create the SF New Deal, which pairs restaurants with online delivery services to provide meals to the city’s most vulnerable populations, from the homeless and people living in single-room occupancy hotels, as well as low-income families and newly laid-off workers struggling to get by. So far, the SF New Deal has served more than 98,000 meals and helped 43 small restaurants keep operations going. Tables 1 and 2 provide more detail on layoff percentages by industry across wage groups, and Appendix Table A2 provides additional detail on the percentage of hourly employees per industry and their wage distribution.

The vast majority (82%) of those employed in the Sports, Fitness, and Recreation industries are hourly workers. Among them, 76% make less than $20 per hour. This group experienced an 8% layoff rate, versus a 3% layoff rate among those making more than $20 per hour.

Table 1. Top 10 highest industries in terms of layoffs of salaried positions, % of workers laid off by income group

* Data excludes salaried non-exempt employees

Industry% Salaried EEsLess than $50KBetween $50K–$75KBetween $75K–$100KGreater than $100K
Tourism45%20%17%13%10%
Accommodations20%11%12%8%13%
Food & Beverage8%9%11%9%6%
Sports, Fitness and Recreation11%6%5%4%1%
Manufacturing37%5%4%2%1%
Communications70%5%4%4%2%
Salon & Spa7%5%8%2%4%
Other Personal Services24%5%4%4%3%
Retail26%5%4%3%2%
Wholesale47%4%5%4%2%

Table 2. Top 10 highest industries in terms of layoffs of hourly positions, % of workers laid off by income group

Industry% Hourly EEsLess than $15Between $15–$20Between $20–$30Greater than $30
Tourism46%19%27%14%4%
Accommodations75%14%14%9%0%
Food & Beverage89%9%13%12%7%
Other Personal Services66%8%4%3%2%
Sports, Fitness and Recreation82%8%9%3%3%
Arts & Entertainment46%8%6%6%3%
Manufacturing57%8%7%4%3%
Transportation59%7%6%4%1%
Wholesale44%6%8%5%4%
Retail67%6%6%7%2%

Employee Age

Much has been made of the impact a second financial crisis would have on the lives of younger people, especially one hitting so close to the 2008 Recession. In a Data for Progress survey, 52% of workers under the age of 45 reported having been laid off, furloughed, or having had their hours reduced due to the crisis.

Looking at how COVID-19 has affected employees across age groups, our data shows that Gen Z workers under the age of 24, including new entrants into the workforce, lost their jobs at a 45% higher rate than workers in the 25 to 34 age range, and at a 93% higher rate than workers 35 and older. Figure 1 below shows the cumulative layoff percentage across all age groups from March 1, 2020 onward.

The outsized impact on hourly workers follows the same wage and industry trends described earlier in this report. First, younger workers are more likely to be paid by the hour—83% of those aged 24 or below are paid hourly, compared to 52% of workers between ages 25 and 34, and 46% of those aged 35 or older.

Younger workers who are paid hourly also earn lower wages—75% of the hourly workers younger than 24 make less than $15 per hour compared to 45% for those over the age of 25. Table A1 in the Appendix shows the distribution of wages for hourly employees across ages.

Finally, younger workers are also far more likely to work in the industries that have been most affected by the crisis compared to other age groups. Younger workers make up 17% of the workforce but account for a bigger percentage of employees in the Food and Beverage, Sports, Fitness and Recreation, Salon and Spa, and Retail sectors—accounting for at least 25% of the workforce in these industries.

Table 3 shows data for industries with the highest layoff percentage, and the numbers for Tourism and Accommodations indicate impact across all age groups while also highlighting the impact to younger workers in Manufacturing and Other Personal Services.

Figure 3. Cumulative % of layoffs by EE Age Group

layoffs-age

Table 3. Top 10 Industries by highest % of layoffs within each age group (minimum of 1000 EEs each Industry)

Table A3 in the Appendix contains more data on representation of the age groups across all industries.

Industry% LayoffsBetween 16–19Between 20–24Between 25–34Between 35–44Between 45–5455 or older
Tourism19%33%24%21%15%16%20%
Accommodations12%12%13%14%9%10%9%
Food & Beverage9%7%9%10%9%8%7%
Mining & Oil7%0%19%7%5%10%3%
Utilities6%13%8%7%2%7%4%
Sports, Fitness and Recreation6%8%8%6%4%4%5%
Salon & Spa6%5%6%6%6%4%4%
Retail5%7%6%5%4%4%4%
Other Personal Services5%9%8%6%4%4%3%
Manufacturing5%9%6%5%4%4%3%

Business Location vs. Layoffs

Small businesses located in areas with lower economic prosperity had a 25% higher layoff rate (4.5%) compared to businesses located in more affluent areas (3.6%). Figure 4 below shows the cumulative layoff rates during the period for each group.

As a note, economic prosperity within a given zip code was categorized using ACS five-year data comparing the median household income in the zip code to the median household income for the Metropolitan Statistical Area (MSA) in which the zip code was located (Low = <50% of MSA median, Moderate = 50–80% of MSA median, Middle = 80–120% of MSA median, Upper = >=120% of MSA median).

Figure 4. Cumulative % of EE layoffs by Zip-Median Household Income category

layoffs_zip_MHI

Our COVID-19 Small Business Resource Hub has legislation updates, advice, and support.

Appendix

Table A1. Distribution of age vs. wage groups for hourly employees

Age GroupsLess than $15Between $15–$20Between $20–$30Greater than $30
16_1990%8%1%1%
20_2470%22%6%2%
25_3449%27%15%9%
35_4441%26%18%15%
45_5440%25%19%16%
Other46%22%17%15%

Table A2. Distribution of hourly employees by industry and wage groups

Industry% Hourly EEsLess than $15Between $15–$20Between $20–$30Greater than $30
Accommodations75%82%15%2%1%
Accounting49%33%26%25%16%
Agriculture61%57%29%13%2%
Arts & Entertainment46%34%25%22%19%
Automotive66%55%21%18%6%
Communications20%39%30%19%13%
Construction68%23%32%31%15%
Consulting29%25%23%19%32%
Education62%39%28%20%14%
Facilities77%58%30%10%2%
Finance22%38%26%22%14%
Food & Beverage89%85%13%2%0%
Government45%38%27%18%17%
Healthcare & Social Assistance73%38%27%17%18%
Insurance36%54%31%12%3%
Legal39%33%30%25%13%
Manufacturing57%51%31%14%4%
Mining & Oil47%14%36%46%4%
Non-Profits & Associations40%57%22%13%8%
Other Personal Services66%58%24%11%7%
Other Professional Services44%40%29%16%15%
Real Estate37%42%34%19%5%
Retail67%66%25%7%2%
Salon & Spa81%77%15%6%3%
Sports, Fitness and Recreation82%60%16%13%11%
Technology16%25%23%22%30%
Tourism46%53%30%14%3%
Transportation59%41%35%17%7%
Unknown52%54%24%14%9%
Utilities42%18%34%31%17%
Warehousing66%67%26%6%1%
Wholesale44%48%35%14%3%

Table A3. Distribution of employee age groups across industries

IndustryBetween 16–19Between 20–24Between 25–34Between 35–44Between 45–5455 or older
Accommodations3%17%34%21%12%14%
Accounting2%8%29%27%18%17%
Agriculture3%13%39%24%10%10%
Arts & Entertainment2%11%43%26%11%7%
Automotive7%15%29%22%14%13%
Communications2%12%46%24%10%6%
Construction3%11%30%27%16%13%
Consulting1%9%39%27%15%9%
Education7%16%35%21%12%10%
Facilities4%14%34%24%14%10%
Finance1%8%37%26%15%12%
Food & Beverage12%22%36%16%7%6%
Government3%10%31%25%18%14%
Healthcare & Social Assistance2%12%34%24%15%13%
Insurance1%11%33%26%15%14%
Legal1%8%32%29%16%13%
Manufacturing3%12%39%22%13%11%
Mining & Oil1%8%34%25%14%18%
Non-Profits & Associations4%10%29%23%14%19%
Other1%14%34%26%12%13%
Other Personal Services3%15%38%21%12%11%
Other Professional Services1%11%39%25%14%10%
Real Estate1%8%32%27%17%15%
Retail6%17%37%19%10%10%
Salon & Spa4%19%40%20%10%6%
Sports, Fitness and Recreation9%19%38%18%9%7%
Technology1%9%43%28%13%7%
Tourism1%10%43%22%11%12%
Transportation2%9%34%22%17%16%
Unknown4%12%32%24%15%14%
Utilities1%13%37%24%14%10%
Warehousing4%12%42%18%14%10%
Wholesale2%11%35%24%15%13%

Methodology

The data for this analysis comes from Gusto’s customer base as of March 1, 2020, and layoffs as recorded in the system. This data then acts as a baseline to quantify layoffs seen across different age and wage groups across industries over time, which include the peak two-week period of March 16 through March 30.

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 layoffs on the date that they were entered into the system, rather than their effective date.

Layoff rates in this report are based on the number of times each event occurred in a given week or in aggregate, divided by the number of employees who were employed at the beginning of the period. Industries reported in this document are based on self-report from customers within Gusto’s product.

Age segments were created keeping in mind the U.S. Bureau of Labor Statistics standard employment status of the civilian non-institutional population by age, sex, and race. Median Household Incomes based on zip codes and Metropolitan Statistical Areas were calculated based on data from the US Census Bureau—specifically, the ACS five-year Selected Economic Characteristics data profile.

Updated: May 4, 2020

Daniel Sternberg
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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