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Analyzing Small Business Trends in the Quarantine Economy

Daniel Sternberg Head of Data Science, Gusto 

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. The COVID-19 pandemic and the necessary government actions to reduce the spread of the virus have significantly impacted businesses across the country. We’ve compiled data from Gusto’s people platform to show changes to employment at small businesses since local governments began to issue stay-at-home and shelter-in-place mandates. 

Gusto is in a unique position to provide insight into the reaction of small businesses to the pandemic, and the hard decisions they’ve had to make to reduce or eliminate their workforces as a result. We’ve examined trends across specific industries, geographies, and sizes of business in order to enable increased support to segments that need it most. Note that the data in this report includes the first half of March ‘20, when many cities and states had not yet announced any disruption in business operations or restrictions on consumer behavior.

We believe that the trends observed on the Gusto platform are indicative of broader trends across the country, and that payroll data can act as an early warning for broader economic trends that will later surface in government unemployment levels. 

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

  1. Small businesses have been forced to implement multiple strategies beyond layoffs to reduce headcount costs in order to make it through the first shockwave of sheltering in place and stay-at-home orders. Gusto data shows a 50% increase in the number of employees experiencing a significant reduction in hours worked for March ’20 compared to the year prior, and a 25% increase in employees experiencing furloughs. Reduction in hours worked and furloughs both led to substantial decreases in earned wages for the month. 
  2. Nearly every small business category has experienced higher levels of layoffs in the month of March ‘20. The effects go beyond sectors that require foot traffic such as Food and Beverage. Businesses that are less dependent on foot traffic, including those in Manufacturing, Wholesale, Communications, and Education, all experienced headcount reductions of 3%. 
  3. Geographically, workforces in states with both big and small populations have experienced serious declines in a short-period of time. For example: Alaska, Hawaii, Nebraska, and New Mexico, which are among the least populous states, rank among the biggest percentage increases in layoffs in March ’20.

3 Small Business Trends in March ‘20

Over the course of March ’20 the COVID-19 crisis began to ripple through the labor market. According to the Bureau of Labor Statistics, non-farm payroll employment fell by 701,000 in March, and the unemployment rate rose by 0.9% to 4.4%. Based on the timeframe of the BLS surveys that drive monthly unemployment reports, this data understates the actual unemployment rate at this moment. An unprecedented 10 million initial unemployment claims were filed in the two weeks that ended March 27, and some estimates have placed our current unemployment rate as high as 13%. 

Layoffs are only one way that this crisis has affected employees at small businesses. The response to the COVID-19 pandemic has put massive pressure on many small businesses across the country, resulting in less foot traffic and forced shutdowns, thus reducing revenue. 

To cope with these losses, small businesses took additional actions beyond layoffs to immediately reduce payroll costs, including temporary furloughs and decreasing employee hours. Underreported thus far, the reduction in hours worked by employees was the biggest contributor to lower earned wages in March ‘20. Table 1 compares the rates of these employer actions in March ‘20 compared to March ‘19.

Table 1. Rates of employer actions affecting employee wages for March 2019 and March ’20

March ‘19March ‘20
Furlough rate3.7%4.7%
Reductions in hours9.5%14.3%
Termination rate4.6%8.7%
Layoff rateN/A (not captured)3.0%
Net change in headcount+1.5%-3.7%

1. Overall reduction in wages

Gusto’s payroll data provides a lens into the actions small businesses have taken to control their costs during this extraordinary period of time. In March ’20, we saw a 9% gap in overall wages paid to small business employees compared to what we would expect based on the previous month and year-over-year comparisons. 

2. Widespread reduction in hours worked

The largest driver of reduced wages in March was a widespread reduction in hours worked, which is separate from furloughs or headcount reductions. In March ’20, we saw a 50% year-over-year increase in employees who experienced a significant reduction in hours worked compared to 2019. These workers averaged a third less pay in March ’20 compared to the previous month. This reduction in hours accounted for nearly 70% of the reduction in overall wages earned in March.

Tanya Hartman is the owner of Gilded Social, a bridesmaid special order dress shop in downtown Columbus, Ohio. The busiest time of year for her company is January through April, which means COVID-19 cut her business’ high season in half. Rather than lay her employees off, Ms. Hartman decided that she would reduce her employees’ hours and immediately file for a PPP loan. She did this because she felt it was the best option for her employees and her business. Ms. Hartman believes it is her responsibility to ensure that her employees emerge as unscathed as possible.

3. Unprecedented reductions in staff 

Terminations and likely furloughs split the remaining payroll cost reductions roughly equally, but in different ways. We estimated likely furloughs as employees who earned wages in February ‘20, but earned no income on their final paycheck in March ‘20. Likely furloughs increased 25% year-over-year, and in 2020 we saw furloughed individuals collect only half as much in March on average compared to their February paycheck.

Terminations in March ’20 increased by 94% compared to February ‘20. Additionally, layoffs increased by 1,021%. As a result, Gusto data shows a 3.7% decline in overall headcount amongst small businesses. While terminations and layoffs had a smaller impact on lost March wages compared to reduction in hours, the effects of the former are expected to have broader and longer-term impacts that will significantly affect the earnings of workers in future months. Figure 1 shows the net change in headcount along with hiring, termination, and layoff rates from January ‘19 – March ’20.

Suzanna Cameron is a single mother and the owner of Stems, a floral shop in Brooklyn. When a shelter-in-place order was issued in New York, she pivoted her business to do virtual workshops: delivering flowers + vases locally and teaching flower arranging classes via livestream. Because her shop was deemed non-essential, Ms. Cameron has been forced to lay off her entire staff. 

You try so hard to do your best, to do right by your team, and to plan ahead for rainy days. But this rainy day has turned into a worldwide hurricane that has swooped in and swallowed up dreams and hopes and ambition—leaving very little in her trail but tears. There’s a worldwide health pandemic and a very personal, very real pandemic simultaneously happening to our businesses.”

Figure 1. Monthly employee net change in headcount, hiring, termination and layoff rates from January ‘19 – March ’20

Small business headcount changes by week

In order to better understand how small businesses responded to the unfolding crisis over the course of the month, we analyzed hiring and termination rates. We did this for two reasons:

  • First, change in headcount is a more effective leading indicator than wage data itself – employees can be hired or terminated at any time, whereas wages only appear when companies run payroll, which happens on a set schedule (most commonly weekly, bi-weekly, or semi-monthly.
  • Second, headcount changes have longer-term broader impacts that we would expect to significantly affect earnings in future months. 

Terminations and layoffs were heavily weighted towards the second half of the month. The week of March 16 is when many state and local governments started implementing social distancing, closing non-essential businesses, and prohibiting sit-down dining at restaurants. The impact of these measures on small businesses was nearly instantaneous. 

The increase in terminations and layoffs over the prior baseline has continued, with an even greater number of terminations and layoffs the week of March 23, and a slightly lower rate the week of March 30. Figure 2 shows the cumulative decline in small business employer headcount during this period, along with the hiring, terminations and layoffs over the most recent five weeks.

Figure 2. Cumulative change in headcount since the week of March 2nd and hiring, termination and layoff rates for the same period

The greater than eightfold increase in layoffs for the week of March 16th (844%) tracks closely in terms of timing with the overall trends we’ve seen in seasonally-unadjusted unemployment claims, which “for the week ending March 21” showed an increase of 1,053% over the prior week and nearly doubled again the following week.

However, while the increase in layoffs in our data are indeed dramatic, they are not as dramatic as the increases in unemployment claims. This could be due to the fact that furloughed employees and those with significant hours reductions can also be eligible to claim unemployment. Our payroll data also shows increases in both hour reductions and employees remaining on the roster who were not paid at all in March, though it isn’t possible for us to directly map these changes onto expected unemployment claims. 

Second, small businesses may have represented a smaller share of layoffs and furloughs during this period compared to larger corporations. For example, major hotel chains, airlines and retailers announced layoffs affecting hundreds of thousands of employees during the earlier part of this period. 

The remainder of this report provides more context on how small business hiring and termination rates differed across different types of businesses and in areas that have responded differently to the crisis.

Affected industries

Much of the conversation has focused on the negative impact to Food and Beverage businesses that have been affected by COVID-19. Our data shows additional industries have experienced significant losses including: Sports and Recreation; Salon and Spa; and Arts and Entertainment.

The highest concentration of small businesses affected are those that rely on high amounts of foot traffic and would be expected to be affected by the social distancing and shelter-in-place ordinances that were passed over this period. 

In the month of March, among industries with at least 1,000 Gusto customers, we saw the largest net reductions of headcount in:

  1. Food and Beverage (-13.1%)
  2. Salon and Spa (-8.7%)
  3. Sports and Recreation (-8.6%)
  4. Arts and Entertainment (-7.7%)
  5. Retail (-5.5%)

These reductions in headcount were driven by large increases in termination and layoffs. For the month of March, we saw the following percentage increases in terminations and layoffs compared to February:

Table 2. Month-over-month percentage increases in terminations and layoffs in March ’20

Industry% Increase in terms.% Increase in layoffs
Food & Beverage155%9,040%
Salon & Spa163%7,209%
Sports & Recreation212%5,597%
Arts & Entertainment123%1,981%
Retail109%1,635%

We saw even larger net reductions in headcount in Tourism (24.3%) and Accommodations (18.3%) than in Food and Beverage during this period, but these categories represent a much smaller part of Gusto’s customer base. However, when we did include these categories, we saw evidence that the layoffs started earlier among businesses focused on Tourism or Accommodations, with small net declines starting the week of March 9. These patterns fit with observed reductions in travel that occurred prior to the widespread adoption of social distancing and stay-at-home ordinances. These trends accelerated the week of March 16 across all majorly affected industries, and accelerated for Salon and Spa and Arts and Entertainment the week of March 23 (see Figure 3). In the last week, the declines in these industries were not as steep as the prior week. We continue to see overall declines in headcount across industries and weekly employee terminations continue to be nearly double their baseline values. Figure 3 shows cumulative changes in headcount for the past five weeks across industries.

Figure 3. Cumulative changes in headcount since the week of March 2nd across industries

Tables A1 through A4 in the appendix provide detailed stats on hiring and termination rates across industries for the month of March and for the most recent three weeks. Each of the weekly tables also includes the layoffs and terminations for the week of March 8, before the large increases were seen, as a comparison.

Geographic trends

States and local governments across the United States have responded differently to the COVID-19 pandemic. In the last two weeks of March, many states passed “stay-at-home” or “shelter-in-place” orders, and nearly all passed orders restricting large gatherings, sit-down dining at restaurants, and closing non-essential businesses.

Table 3. States with highest reductions in headcount in March ’20

RankState% Change in headcount Actions taken by state and local governments
1New York-7.5%Stay-at-home order issued on March 22
2Nebraska-7.4%No stay-at-home order. Omaha restaurants and bars required to move to takeout/delivery only as of March 19th
3New Mexico-6.5%Stay-at-home order issued on March 24 
4Alaska-6.1%Stay-at-home order issued on March 28
5North Dakota-5.8%No stay-at-home order
6Pennsylvania-5.6%Stay-at-home orders issued for multiple counties in Philadelphia and Pittsburgh metros on March 23. PA statewide order issued on April 1.
7Hawaii-5.6%Stay-at-home order issued on March 24
8Oregon-5.3%Stay-at-home order issued on March 23
9New Jersey-5.3%Stay-at-home order issued on March 24
10District of Columbia-5.2%Non-essential businesses closed on March 24. Stay-at-home order issued March 30.

Table 4. States with headcount that has been least affected in March ’20

RankState% Change in headcount  Actions taken by state and local governments
1Mississippi+0.4%Stay-at-home order issued on April 3
2Missouri+0.6%Stay-at-home order issued on April 6
3Iowa-0.8%No stay-at-home order issued, some non-essential businesses closed 
4Oklahoma-0.9%No stay-at-home order issued– Oklahoma City and Tulsa enacted them on March 28. 
5Alabama-1.4%Stay-at-home issued order on April 4
6Arkansas-1.4%No stay-at-home order issued
7Maryland-1.7%Stay-at-home order issued on March 30
8Michigan-2.0%Stay-at-home order issued on March 24
9Vermont-2.2%Stay-at-home order issued on March 25
10South Carolina-2.3%Stay-at-home order issued April 7, Charleston and Columbia issued orders on March 24th and 26

New York and Pennsylvania especially stand out, being the fourth and fifth most populous states in the US, and showing significantly larger losses in headcount than the median state (median change = -3.3%). In New York in particular, terminations increased by 155% in March compared to February, and layoffs increased by 1,285%. 

These trends generally track with the level of response the states have made. Eight of the 10 most affected states issued stay-at-home orders either statewide or in major metros during the last two weeks of March. None of the five most affected states had statewide stay-at-home orders by the end of March. However, all of these states with the exception of Iowa and Arkansas have passed stay-at-home orders in the past week, so we expect to see similar trends in these states over the coming weeks. 

Headcount trends for March in the fifty largest metropolitan areas in the United States show an even stronger effect of government action. Metropolitan areas with the largest declines in headcount all are either centered in states that issued stay-at-home orders during the second or third week in March, or the anchor cities themselves issued these orders.

  1. New Orleans-Metairie, LA (-7.4%): Louisiana issued March 20 stay-at-home order.
  2. New York-Newark-Jersey City, NY-NJ-PA (-7.1%): New York issued March 22 stay-at-home order.
  3. Philadelphia-Camden-Wilmington, PA-NJ-DE-MD (-6.8%): Pennsylvania issued March 23 stay-at-home order.
  4. Louisville/Jefferson County, KY-IN (-5.7%): Kentucky issued March 25 stay-at-home order.
  5. Miami-Fort Lauderdale-West Palm Beach, FL (-5.4%): Miami issued March 24 stay-at-home order, Florida issued stay-at-home order April 3.

The smallest reductions (and even two increases) were seen in metropolitan areas located in states that did not issue stay-at-home orders until later, with one exception.

  1. St. Louis, MO-IL (+1.3%): Missouri issued stay-at-home order April 6.
  2. Virginia Beach-Norfolk-Newport News, VA-NC (+0.2%): State issued stay-at-home order March 30.
  3. Jacksonville, FL (-0.3%): Florida issued stay-at-home order April 3.
  4. Memphis, TN-MS-AR (-0.7%): Tennessee stay-at-home order issued April 3.
  5. Charlotte-Concord-Gastonia, NC-SC (-0.9%): City issued stay-at-home order March 26 andNorth Carolina issued stay-at-home order March 30. South Carolina did not issue an order until April 6.

Impact by business sizes

Businesses with smaller levels of employees fared better than those with more employees. 

We saw the biggest headcount decline among businesses with 25-49 employees at -6.94%. Businesses with fewer than five employees saw the smallest decline at 1.02%.

Table 5. Net change in the employee base for different small business size segments in March ’20.

SizeTerms.Layoffs% Change in terms.% Change in layoffs% Change in headcount
1-44.681.3861%658%-1.02
5-98.022.6883%1195%-3.07
10-2410.43.47101%1060%-5.24
25-4912.615.09137%1631%-6.94
50+11.684.26105%721%-6.1

One potential explanation for why the smallest businesses had relatively lower headcount reductions may be that many of these businesses have few to no employees who they can stand to lose without needing to fully shut down operations. As a result, if these businesses experience significant enough revenue shocks, they may close down entirely. 


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 for the month of March ’20 by industry, ranked by net reduction in headcount by industry

IndustryHiresTerms.Layoffs% Change in terms. vs. February% Change in layoffs vs. February% Change in headcount
Food & Beverage6.9820.058.49155%9041%-13.07
Salon & Spa4.6413.325.67163%7209%-8.68
Sports & Recreation5.1413.774.56212%5597%-8.64
Arts & Entertainment4.3212.053.45123%1981%-7.73
Retail5.6611.24.19109%1636%-5.54
Other Personal
Services
5.110.253.55117%2143%-5.15
Manufacturing5.118.793.93108%1082%-3.69
Wholesale4.718.243.85120%797%-3.53
Communications4.548.032.87155%491%-3.5
Education4.327.672.52112%4548%-3.35
Transportation6.259.233.0171%1935%-2.98
Other Professional
Services
6.138.92.5260%874%-2.77
Healthcare &
Social Assistance
5.227.932.1785%1496%-2.72
Real Estate4.326.322.0161%575%-2
Consulting4.756.722.1371%1109%-1.98
Construction6.128.072.0969%499%-1.94
Accounting3.2650.7440%75%-1.73
Finance4.295.271.8279%1095%-0.98
Non-Profits &
Associations
3.784.730.84%-59%-0.96
Legal3.74.471.2654%1514%-0.77
Insurance4.865.621.5439%252%-0.76
Technology4.34.31.2553%357%0
Facilities13.0612.622.8541%234%0.44

Table A2

Hires, terminations, and layoffs per 100 employees for the week of March 16, 2020, by industry, ranked by net reduction in headcount by industry

IndustryHiresTerms.LayoffsTerms. week of March 8thLayoffs week of March 8th% Change in terms. versus March 8th% Change in layoffs versus March 8th% Change in headcount in week
Food & Beverage1.248.184.72.010.24306%1877%-6.93
Sports & Recreation0.844.91.781.330.06268%2776%-4.06
Salon & Spa0.924.251.771.40.06203%2888%-3.32
Retail1.163.321.511.350.13145%1025%-2.16
Other Personal Services0.912.791.091.350.14106%658%-1.88
Arts & Entertainment0.522.290.822.140.357%133%-1.77
Manufacturing1.122.791.641.160.22141%640%-1.68
Education0.8520.780.980.02104%3576%-1.15
Communications0.631.761.080.840.12110%787%-1.14
Wholesale0.952.071.331.050.1297%1053%-1.12
Other Professional Services1.532.591.071.610.3461%212%-1.06
Transportation1.662.621.321.290.16103%724%-0.96
Accounting0.61.490.240.88069%Inf-0.89
Consulting0.951.760.6410.1276%424%-0.81
Healthcare &
Social Assistance
0.971.740.511.070.0463%1160%-0.77
Finance0.821.50.590.720.05110%1034%-0.68
Real Estate0.871.510.461.180.0828%448%-0.64
Construction1.311.840.481.830.081%513%-0.53
Insurance0.731.130.311.620.6-30%-48%-0.41
Non-Profits &
Associations
0.560.840.321.450.06-42%437%-0.28
Legal0.761.020.340.70.0445%695%-0.26
Facilities2.612.860.572.180.1131%408%-0.25
Technology0.851.070.330.790.1235%178%-0.21

Table A3

Hires, terminations, and layoffs per 100 employees for the week of March 23, 2020, by industry, ranked by net reduction in headcount by industry

IndustryHiresTerms.LayoffsTerms. week of March 8thLayoffs week of March 8th% Change in terms. versus March 8th% Change in layoffs versus March 8th% Change in headcount
Food & Beverage0.997.413.32.010.24268%1287%-6.42
Salon & Spa0.355.763.781.40.06310%6282%-5.41
Sports & Recreation0.615.542.371.330.06316%3737%-4.92
Arts &
Entertainment
0.554.171.742.140.3595%396%-3.62
Retail1.154.111.841.350.13203%1274%-2.96
Other Personal
Services
0.763.511.591.350.14160%1004%-2.75
Manufacturing0.993.091.731.160.22167%679%-2.1
Wholesale1.013.031.681.050.12190%1357%-2.02
Healthcare &
Social Assistance
0.952.911.251.070.04173%2984%-1.96
Education0.792.561.30.980.02160%6016%-1.77
Real Estate0.752.041.11.180.0873%1218%-1.29
Consulting1.042.251.0810.12125%791%-1.21
Communications0.952.121.170.840.12153%855%-1.17
Legal0.511.520.660.70.04116%1453%-1
Facilities2.813.81.662.180.1174%1387%-0.99
Transportation1.322.220.971.290.1672%508%-0.9
Construction1.2120.81.830.089%934%-0.79
Other Professional
Services
1.282.060.71.610.3428%106%-0.79
Non-Profits &
Associations
0.711.460.331.450.061%464%-0.75
Accounting0.511.180.410.88034%N/A-0.67
Finance1.11.570.870.720.05119%1561%-0.47
Technology0.911.280.510.790.1262%328%-0.37

Table A4

Hires, terminations, and layoffs per 100 employees for the week of March 30, 2020, by industry, ranked by net reduction in headcount by industry

IndustryHiresTerms.LayoffsTerms. week of March 8thLayoffs week of March 8th% Change in terms. versus March 8th% Change in layoffs versus March 8th% Change in headcount
Food & Beverage1.054.932.342.010.24145%884%-3.88
Arts & Entertainment0.73.71.242.140.3573%253%-3
Other Personal
Services
0.753.612.121.350.14168%1376%-2.86
Communications0.963.421.770.840.12307%1347%-2.46
Sports &
Recreation
0.582.981.431.330.06123%2218%-2.4
Salon & Spa0.311.830.621.40.0631%942%-1.52
Healthcare &
Social Assistance
0.962.440.881.070.04128%2066%-1.47
Retail1.212.641.211.350.1395%798%-1.44
Education0.832.230.90.980.02127%4155%-1.4
Other Professional Services1.052.41.251.610.3449%265%-1.35
Wholesale0.882.161.251.050.12106%982%-1.28
Facilities2.893.880.962.180.1178%756%-0.99
Manufacturing1.092.020.861.160.2274%288%-0.93
Accounting0.681.590.290.88079%Inf-0.9
Non-Profits &
Associations
0.441.280.41.450.06-11%584%-0.84
Consulting1.031.880.9110.1288%651%-0.84
Construction1.42.180.931.830.0819%1092%-0.78
Real Estate0.751.520.621.180.0829%644%-0.77
Transportation1.542.180.851.290.1669%431%-0.65
Finance0.861.40.510.720.0596%867%-0.54
Legal0.691.20.410.70.0471%868%-0.52
Technology0.891.30.590.790.1266%394%-0.41
Insurance1.21.440.311.620.6-11%-48%-0.24

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 and March (had no termination effective during that time), had earned money in the final paycheck of February but had not earned money in the final paycheck of March. Non-terminated, non-furloughed employees were labeled as work reduced if the total hours of their March paychecks were less than 90% of the hours on their February 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.

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] https://www.bls.gov/news.release/archives/empsit_04032020.pdf
[3] https://www.nytimes.com/2020/04/03/upshot/coronavirus-jobless-rate-great-depression.html
[4] The 9% gap is based on a year-over-year comparison of 2019 and 2020 wages. In 2020 March wages were 3.4% lower than in February, whereas in 2019, March wages were 5.9% higher than in February.
[5] Layoff rates were only available starting in January, 2020.
[6] https://www.dol.gov/ui/data.pdf
[7] West Virginia, South Dakota and Wyoming were excluded from this analysis, due to sample size concerns (less than 1,000 employees represented).
[8] West Virginia, South Dakota and Wyoming were excluded from this analysis, due to sample size concerns (less than 1,000 employees represented).

Updated: April 9, 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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