Racial and Ethnic Disparities in Unemployment Benefits
Summary
An original research article, Racial and ethnic disparities in who receives unemployment benefits during COVID-19, by Don Mar, Paul Ong, Tom Larson and James Peoples, published in SN Bus Econ (2022) 2:102. The authors analyze over 1.3 million US Census Household Pulse Survey interviews from June 11, 2020 to December 22, 2020 to examine who among workers displaced by the pandemic received Unemployment Insurance. They report that Black workers are 12.0% of the employed but 17.5% of displaced workers without UI, and Hispanic workers are 15.6% of the employed but 23.4% of displaced workers without UI. The article reviews prior studies of recessions and UI receipt, and describes data limitations, including that the survey was administered online in only English and Spanish. It is 17 pages with a reference list.
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SN Bus Econ (2022) 2:102
https://doi.org/10.1007/s43546-022-00283-6
ORIGINAL ARTICLE
Racial and ethnic disparities in who receives
unemployment benefits during COVID‑19
Don Mar1 · Paul Ong2 · Tom Larson3 · James Peoples4
Received: 20 December 2021 / Accepted: 6 July 2022 / Published online: 23 July 2022
© The Author(s), under exclusive licence to Springer Nature Switzerland AG 2022
Abstract
The impact of COVID-19 on job displacement in the United States has been une-
venly experienced by race, ethnicity, and the socioeconomically disadvantaged.
Although unemployment benefits may mitigate the effects of job displacement, this
social safety net is also unevenly distributed across workers. We examine racial/
ethnic differences in receiving unemployment benefits among workers displaced by
the pandemic. We use data from the US Census Household Pulse Survey (HPS),
which is specifically designed to capture the real time effects of the pandemic across
a wide spectrum of social issues. (US Census, 2020) Unlike the Current Population
Survey (CPS) data used in the monthly unemployment rate calculations, the HPS
data allow us to identify workers directly displaced from their jobs by the pandemic.
We analyze over 1.3 million HPS interviews from the first stage of the pandemic
when the disruptions to the labor market were the most severe, covering the period
from June 11, 2020 to December 22, 2020. We contribute to the literature on the
labor market effects of the pandemic in two ways. One, the HPS data allow us to
identify workers who directly experienced job loss as a result of the disruptions cre-
ated by COVID-19 and to determine who did not receive unemployment insurance.
Two, we present both bivariate and multivariate analyses to examine racial/ethnic
disparities for five groups: non-Hispanic whites, Blacks, Hispanic, Asian, and non-
Hispanic Other workers. We find that Black and Hispanic workers are more likely
to be unemployed without Unemployment Insurance (UI). Black workers are 12.0%
of the employed but 17.5% of displaced workers without UI. Hispanic workers are
even more affected. Hispanic workers are 15.6% of the employed, but are 23.4% of
all displaced workers without UI. Although there are limitations to using the HPS
data—the survey was administered online in only English and Spanish and occu-
pational and industry data are not available for displaced workers, the results still
provide valuable insights informing the current policy debate about the effects of
expanding UI.
Keywords COVID-19 · Unemployment · Racial disparities · Unemployment
benefits
Extended author information available on the last page of the article
Vol.:(0123456789)
102 Page 2 of 17 SN Bus Econ (2022) 2:102
Introduction
The COVID-19 pandemic has had devastating health and economic impacts on
the US population. As of June 17, 2022, the Center for Disease Control reported
nearly 85 million cases and over a million deaths (CDC 2022). According to the US
Bureau of Economic Activity (US BEA 2020), GDP declined at an annualized rate
of 32.9% for the second quarter of 2020. Moreover, the morbidity, mortality, social,
and employment effects of the pandemic have not been evenly distributed across
racial and ethnic groups. Although the augmentation of Unemployment Insurance
(UI)1 benefits is an important policy tool to mitigate the pandemic’s effects, UI ben-
efits have also been unevenly distributed across to the population during the course
of the pandemic. We utilize the US Census Bureau’s Household Pulse Survey (HPS)
to analyze racial and ethnic disparities in who receives UI benefits during the ini-
tial stage of the pandemic (US Census 2020a). The HPS is a unique, experimental
survey to specifically measure the effects of the pandemic across many aspects of
society including employment and social welfare. The HPS data allow us to analyze
racial/ethnic differences in the ability to collect UI directly caused by COVID-19
job losses. Previous studies of COVID unemployment using the Current Population
Survey (CPS) unemployment data do not allow a distinction between non-COVID
and COVID related unemployment.
We begin with a brief overview of prior studies of racial and ethnic differences in
unemployment during recessions, the impact of COVID on minority and disadvan-
taged populations, and differences in UI recipients by race and ethnicity. We then
present a discussion of the data, methodology, simple share analyses, and finally the
multivariate analyses of disparities by race/ethnicity in receiving UI during 2020.
Race/ethnicity, unemployment, and unemployment benefits
under the pandemic
Economists have a long history of studying the effects of recessions on minority and
disadvantaged populations. Smith et al. (1974) used CPS data from 1967 through
1973 to analyze employment, unemployment, and labor force participation over the
course of the business cycle. They found that Black, women, and younger workers
were likely to experience more unemployment than other groups during recessions.
Couch and Fairlie (2010) linked monthly CPS data to create panel data from 1989
to 2004 to analyze men’s labor market transitions during the business cycle. They
found that Black men are not only more likely to lose their jobs during a down-
turn, but also to be more likely to leave the labor force when unemployed. Couch
1
In the United States, unemployment insurance is a federal program administered by state agencies.
Employers fund the program with an unemployment insurance tax. Cash payments are normally made
to eligible recipients for up to 26 weeks. Eligibility and level of benefits are normally based on earnings
during a period before job loss. Eligibility is also based on a job loss due to an involuntary separation.
During the pandemic, the 2020 Coronavirus Aid, Relief, and Economic Security (CARES) Act expanded
both eligibility, duration, and benefits.
SN Bus Econ (2022) 2:102 Page 3 of 17 102
et al. (2016) later use a similar methodology to expand the study to include men and
women and Hispanic workers. Black and Hispanic workers are found to be more
likely to be unemployed during recessions. In addition to Black workers, Hispanic
workers were also more likely to leave the labor force after being unemployed dur-
ing the Great Recession. More recently, Cajner et al. (2017) also used linked CPS
data from 1976 to 2016 to examine the differential effects of recessions on racial
groups. Like Couch et al., they also found that Blacks and Hispanics experience
higher than average job loss rates. Black men and Latinas were again found to be
more likely to leave the labor force after being unemployed.
A number of researchers have recently documented the general labor market
impact of the current pandemic. For instance, Coibion et al. (2020) used household
level Neilsen Homescan data to estimate that 20 million jobs were lost by early
April 2020. They estimated that job losses were greater than the 16.5 million unem-
ployment insurance claims by April 4, 2020. Forsythe et al. (2020) found that labor
demand fell by over 40% by late April using job vacancy data from Burning Glass
Technologies. Industry sector analysis by Cajner et al. (2020) used administrative
data from a private human resources company (ADP) to analyze job losses from late
April to late June of 2020. They found that in late April that employment in the Lei-
sure and Hospitality industries fell by more than 45%; employment in Retail fell by
almost 30%; and employment in “Other Services” fell by 25%. Furthermore, Cajner
et al. showed that these employment declines disproportionately fell on low wage
workers, women, and workers at smaller firms.
Economists have also found ethnic and racial disparities in COVID-19’s labor
market impact. Montenovo et al. (2020) used CPS data to show greater employ-
ment losses and increases in unemployment for Hispanic workers, younger work-
ers, and workers with less than college degrees for April 2020. In the subsequent
months, re-employment of Black workers was slower than for other groups. They
concluded that occupational segregation explains a substantial part of these dif-
ferences. Fairlie et al. (2020) provided an extensive analysis of racial and ethnic
differences in unemployment using data from the CPS. They measured the impact
of the pandemic on racial and ethnic unemployment rates using two methodol-
ogies. The first method measured unemployment using the standard Bureau of
Labor Statistics (BLS) reporting methodology. Black and particularly Hispanic
unemployment rates were higher than white unemployment rates for April 2020
using the BLS methodology. Their second method measured unemployment by
counting workers who were absent from jobs and wanted jobs in an effort to
adjust for BLS misclassification of workers. This second method found the April
2020 national rate to be 26.5% as opposed to the official BLS’ estimate of 14.7%.
Furthermore, the Black and Hispanic unemployment rates were considerably
higher at 31.8% and 31.4%, respectively, using the second method. Using an Oax-
aca type decomposition method to control for differences in industry, occupation,
education, and potential experience they found Hispanic workers to be the most
impacted group of workers by ethnicity. Gezici and Ozay (2020) also used CPS
data to examine racial and ethnic differences in pandemic unemployment with an
additional focus on gender effects. After controlling for differences in individual
characteristics, occupations and industry, they found that women and particularly
102 Page 4 of 17 SN Bus Econ (2022) 2:102
non-white women were more likely to be unemployed in the early part of the pan-
demic. Anyamele et al. (2021) used the HPS data to examine racial, ethnic, and
gender differences on the impact of COVID on household incomes. They also uti-
lized an Oaxaca–Blinder type analysis and found that Hispanics, “Other” ethnics,
and Blacks experienced much greater income losses compared to whites.
While Hispanics and Blacks are consistently found to suffer higher unemploy-
ment and greater income losses during recessions and during the pandemic, they are
also less likely get relief from unemployment and income loss. Nichols and Simms
(2012) used data from the Survey of Income and Program Participation (SIPP) to
find that during the Great Recession, Black and Hispanic workers were less likely
to receive UI benefits compared to whites after controlling for individual character-
istics. Kuka and Stuart (2021) used a larger sample of SIPP data from 1986 to 2014
to analyze Black–white differences in the receipt of UI benefits. After controlling for
individual characteristics including region, education, pre-unemployment earnings,
industry, and gender, they found a large Black–white difference in UI receipt. Differ-
ences in the receipt of UI benefits have also been noted during the current pandemic.
Grooms et al. (2020) found that among workers unemployed in March 2020, only
29% of Black unemployed workers had received unemployment benefits by mid to
late March compared to 35% of white unemployed workers using the National Panel
Study of COVID-19 data. Acks and Karpman (2020) used the Urban Institute’s Cor-
onavirus Tracking survey to show that low income families, particularly low income
Hispanic families, suffered greater job and income losses during the early stage of
the pandemic induced recession. Acks and Karpman also found that only 36% of
unemployed workers said they received UI benefits within 30 days of job loss. A
recent Department of Labor (2021) report using CPS data also noted that Black and
Hispanic workers were less likely to receive UI benefits during the pandemic.
In addition to providing a source of income and means of consumption during a
recession, unemployment benefits have been shown to provide other social benefits.
Kuka (2020) used SIPP data to find that workers receiving UI benefits reported bet-
ter self-reported health as well as higher rates of health insurance and use of health
services. Raifman et al. (2021), using a national health survey of COVID’s effects,
found that households receiving UI benefits experienced a large and significant
reduction in food insecurity. Confining their study to households earning less than
$75,000 in 2020, their study found that having UI is associated with a 35% decline
in the percentage of households reporting food insecurity. Berkowitz and Basu
(2021) used HPS data from June and July 2020 to show that respondents receiving
unemployment benefits were less likely to experience food insecurity, miss housing
payments, and delay health and mental health care.
Clearly, receiving UI benefits helps mitigate the effects of the pandemic. How-
ever, minority and disadvantaged workers are also less likely to receive these ben-
efits. The current literature has documented the pandemic’s effects on earnings,
employment, and unemployment, but to our knowledge, there has not been an
analysis of the racial/ethnic differences on receipt of unemployment benefits as a
direct result of COVID job displacement. Our use of the HPS data also allows us
to directly link COVID job displacement to who receives UI benefits and also to
SN Bus Econ (2022) 2:102 Page 5 of 17 102
expand the literature to include Asians and non-Hispanic Other workers—workers
who do not identify as White, Black, Hispanic, or Asian.
Data and methodology
We analyze data from HPS interviews conducted by the U.S. Census from June 11,
2020 to December 22, 2020 to examine differences in UI recipiency during the first
stage of the pandemic. The data are publicly available from the US Census Bureau,
https://www.census.gov/data/experimental-data-products/household-pulse-survey.
html. Each period’s survey is commonly referred to as a “week” with consecutive
numbering, even though there can be varying time gaps between surveys and sur-
veys have slightly differing lengths of survey time. We pool data from “weeks” 7
through 21, resulting in over 1.3 million observations. We do not use the first
6 weeks as the survey did not contain questions regarding receipt of UI.
There are some limitations to the HPS data. One, the survey was administered
online. As a result, the unweighted responses were more likely to be women, afflu-
ent, better educated, and from smaller sized households compared to the nation as a
whole. Two, the questionnaire is available only in English and Spanish. This limi-
tation means that limited-English-language Asians and other non-Hispanic immi-
grants are likely underrepresented in the sample. To overcome some of these limita-
tions, responses were weighted by the Census to make the results representative of
the nation. The Census bureau weighted responses by applying adjustments for non-
response, estimates of occupied housing, and other demographic adjustments based
on the 2018 American Community survey. (Fields et al. 2020; US Census 2021)
We used the Census bureau’s HPS developed weights to analyze the data. A third
data limitation is that questions regarding industry of employment and occupation
of respondents are asked only of employed workers, so no industry and occupational
data are available for unemployed workers.
Our analyses focus on workers who were specifically unemployed by COVID-19
and whether they received unemployment benefits. We conservatively counted as
displaced by the pandemic only respondents who answered “No” to the question,
“In the last 7 days, did you do ANY work for either pay or profit?” and gave the fol-
lowing survey responses (US Census 2020b) for not working:
• “I did not have work due to coronavirus pandemic related reduction in business
(including furlough).”
• “I am/was laid off due to coronavirus pandemic.”
• “My employment closed temporarily due to the coronavirus pandemic.”
• “My employment went out of business due to the coronavirus pandemic.”
This method allows us to separate unemployment due directly to COVID from
non-pandemic related unemployment, which is not possible with CPS data. Using
this definition, COVID only unemployed—defined as a percentage of COVID
unemployed and employed workers—is 15.3%. This figure is higher than the official
BLS unemployment estimates which ranged from 14.8% in April 2020 to 6.7% by
102 Page 6 of 17 SN Bus Econ (2022) 2:102
December 2020. These two statistics, however, are not directly comparable because
of differences in HPS and CPS unemployment questions. However, the higher dis-
placement numbers are more in keeping with the higher rates of unemployment
found by Fairlie, Couch, and Xu (ibid).
In our analyses, we create three categories of workers: employed, displaced and
receiving UI, and displaced but not receiving UI. We first use a percentage share
analysis of each of the three categories by race/ethnicity. We then use logit regres-
sions to further analyze the determinants of displaced workers with and with-
out unemployment insurance to control for individual characteristics and state of
residence.
We define employed workers as respondents who answered “Yes” to the ques-
tion, “Now we are going to ask about your employment. In the last 7 days, did you
do ANY work for either pay or profit?” In addition, we also added non-working
respondents who answered “No” to the employment question in the last 7 days,
but answered “Yes” when asked “Are you receiving pay for the time you are not
working?”.
We use two methods to determine if respondents were receiving UI payments.
The first method uses the question, “Thinking about your experience in the last
7 days, which of the following did you use to meet your spending needs?” Respond-
ents who said “Yes” to using UI benefits as part of their spending were counted as
receiving UI payments. The use of UI payments for spending was asked of respond-
ents for weeks 7 through 21.
The second method uses the question, “Since March 13, 2020, did you receive
Unemployment Insurance (UI) benefits?” This question was added to the survey for
weeks 13 through 21.
We construct mutually exclusive racial and ethnic categories: non-Hispanic
whites, Blacks, Asians, and Hispanics, and non-Hispanic Other (self-identified in
the survey). The fifth category, non-Hispanic Other, are non-Hispanics who did not
self-identify as exclusively non-Hispanic white, Black, Asian, or Hispanic. We use
this approach to both clearly define ethnic and racial groups and to specifically dis-
tinguish Hispanic workers from white workers.
To validate the results, we compare the final weighted frequency counts for the
employed and unemployed receiving UI with the CPS estimates for comparable
weeks. Weighted employment and workers with UI estimates from the HPS com-
pared closely to the CPS employment and workers with continued UI claims num-
bers for the reference week in June 2020. The employment count from the HPS data
for the week of 6/18/20 thru 6/23/20 was 146.8 million compared to the CPS esti-
mate for the same June reference week of 142.8 million. The HPS count of 13.7
million workers receiving unemployment benefits for the same reference week also
compared closely to the BLS continued UI claims of 16.3 million.
We use both simple share analyses and logit analyses to examine disparities by
race/ethnicity. The share analyses compare the five ethnic/racial groups’ shares
across three categories—employed, unemployed with UI, unemployed without UI
using both methods. To further analyze the racial and ethnic disparities in the
displacement of workers, we use logit regressions to control for the independ-
ent effects of individual worker characteristics and place of residence. Place of
SN Bus Econ (2022) 2:102 Page 7 of 17 102
residence has a significant impact on COVID related unemployment due to dif-
ferences in shelter-in-place policies, UI programs, and industry mix differences.
We also include survey week dummies to account for differences across time.
We estimate the following logit models using both methods to determine UI
recipients:
(1) COVIDUNwUI = f(AGE AGE_SQUARED, WOMEN, CHILDRENLT18,
WOMEN_CHILDREN, HSGRAD, AADEGREE, BADEGREE, GRADD-
EGREE, MARRIED, HISPANIC, BLACK, ASIAN, NHISPANICOTHER,
WEEK_DUMMIES, STATE_DUMMIES)
(2) COVIDUNnoUI = f(AGE AGE_SQUARED, WOMEN, CHILDRENLT18,
WOMEN_CHILDREN, HSGRAD, AADEGREE, BADEGREE, GRADD-
EGREE, MARRIED, HISPANIC, BLACK, ASIAN, NHISPANICOTHER,
WEEK_DUMMIES, STATE_DUMMIES)
(3) COVIDUNwUI2 = f(AGE AGE_SQUARED, WOMEN, CHILDRENLT18,
WOMEN_CHILDREN, HSGRAD, AADEGREE, BADEGREE, GRADD-
EGREE, MARRIED, HISPANIC, BLACK, ASIAN, NHISPANICOTHER,
WEEK_DUMMIES, STATE_DUMMIES)
(4) COVIDUNnoUI2 = f(AGE AGE_SQUARED, WOMEN, CHILDRENLT18,
WOMEN_CHILDREN, HSGRAD, AADEGREE, BADEGREE, GRADD-
EGREE, MARRIED, HISPANIC, BLACK, ASIAN, NHISPANICOTHER,
WEEK_DUMMIES, STATE_DUMMIES)
where the variables are defined as follows:
• COVIDUNwUI equals 1 if unemployed due to COVID and spending unem-
ployment benefits; equals 0 if employed.
• COVIDUNnoUI equals 1 if unemployed due to COVID and not spending
unemployment benefits;
equals 0 if employed.
• COVIDUNwUI2 equals 1 if unemployed due to COVID and receiving unem-
ployment benefits; equals 0 if employed.
• COVIDUNnoUI2 equals 1 if unemployed due to COVID and not receiving
unemployment benefits; equals 0 if employed.
• AGE is the respondent age calculated by subtracting year of birth from 2020.
• AGE_SQUARED is the square of age.
• WOMEN is a dummy variable equal to 1 for women respondents.
• CHILDRENLT18 is a dummy variable equal to 1 if the respondent lives in a
household with children under 18 years of age.
• WOMEN_CHILDREN is and interaction term equal to 1 if the respondent is
a woman and lives in a household with children under 18 years of age; other-
wise, equal to 0.
• HSGRAD is a dummy variable for high school graduates.
• AADEGREE is a dummy variable for workers with associate degrees.
102 Page 8 of 17 SN Bus Econ (2022) 2:102
• BADEGREE is a dummy variable for workers with BA or BS degrees.
• GRADDEGREE is a dummy variable for workers with a graduate degree.
• MARRIED is a dummy variable equal to 1 for married respondents.
• HISPANIC is a dummy variable for Hispanic workers.
• BLACK is a dummy variable for Black workers.
• ASIAN is a dummy variable for Asian workers.
• NHISPANICOTHER is a dummy variable for workers who do not self-identify
as white, Hispanic, Black or Asian.
• WEEK_DUMMIES are dummy variables for the HPS survey week.
• STATE_DUMMIES are dummy variables for the state of residence.
For weeks 7 thru 21 the excluded week is week 7 in the logits using the unem-
ployment benefit spending definition and for weeks 13 thru 21 using the receiving
UI benefits definition, week 13 is excluded. The excluded educational category is
less than a high school education. The excluded racial/ethnic category is non-His-
panic whites.
The model differences are summarized in Table 1. Models 1 and 2 allow us to
analyze the effects of the pandemic closer to the onset of COVID’s labor market
impact, although the measure of determining who is receiving UI benefits is indirect.
Model 1 uses the UI spending question to determine receipt of UI based on a sample
of employed workers and unemployed workers reporting spending UI benefits for
HPS weeks 7–21. Model 2 also uses the UI spending question but includes only
employed workers and unemployed workers who do not report spending UI benefits
for the same weeks. Models 3 and 4 allow us to directly determine who is receiving
UI benefits and to validate the first method of determining UI receipt. Model 3 uses
the receipt of UI question and a sample of employed workers and unemployed work-
ers receiving UI for HPS weeks 13–21. Finally, model 4 also uses the receipt of UI
question and a sample of employed workers and unemployed worker not receiving
UI benefits for HPS weeks 13–21.
Results: employment, COVID job loss, and unemployment insurance
benefits by race/ethnicity
There are significant racial and ethnic differences in who receives UI due to
COVID-19 related job losses. Table 2 shows the percentage shares of the three labor
force categories by race/ethnicity using both methods. Both methods show Blacks
and Hispanics as having a larger share of workers unemployed with and without UI
compared to whites. Using the first method, Black workers make up 12.4% of the
employed worker category but 16.0% of the COVID unemployed workers with UI
and 17.9% of COVID unemployed workers without UI. Hispanic workers are 15.6%
of the employed worker category, but are 17.2% of COVID unemployed workers
with UI and an even larger 23.4% of all COVID unemployed workers without UI.
The Asian American employment share is 6.0% while the COVID unemployment
share with UI is 6.5% and their share of the COVID unemployed without UI group
SN Bus Econ (2022) 2:102
Table 1 Sample frames for unemployment benefit recipients using HPS data
Model 1 Model 2 Model 3 Model 4
Definition of UI recipient Spending UI benefits: Receiving UI benefits:
Method 1 Method 2
HPS weeks 7–21 (6/11–12/22/2020) 13–21 (8/19–12/22/2020)
Definition of unemployed Furloughed; laid off, temporary closure, permanent closure due to Furloughed; laid off, temporary closure, permanent closure due to
due to COVID COVID COVID
In the analysis sample Employed & unemployed spend- Employed & unemployed not Employed & unemployed receiv- Employed & unemployed not
ing UI benefits spending UI benefits ing UI benefits receiving UI benefits
Page 9 of 17 102
102 Page 10 of 17 SN Bus Econ (2022) 2:102
Table 2 Employed and COVID displaced by race/ethnicity, household pulse data, 2020
Non-His- Blacks (%) Asians (%) Hispanics Non-His-
panic whites (%) panic Oth-
(%) ers (%)
Employed: method 1 62.1 12.4 6.0 15.6 3.9
COVID displaced with UI: method 1 56.0 16.0 6.5 17.2 4.3
COVID displaced no UI: method 1 48.0 17.9 5.7 23.4 4.9
Employed: method 2 62.0 12.3 6.1 15.7 3.8
COVID displaced with UI: method 2 53.8 17.1 6.5 18.2 4.4
COVID displaced no UI: method 2 46.5 17.5 5.6 24.9 5.6
is 5.7%. Non-Hispanic Other workers are 3.9% of employed workers but are 4.3%
and 4.9% of COVID unemployed with and without UI, respectively.
The second method shows similar results. Black workers are 12.0% of the
employed worker category but 17.0% of the COVID unemployed workers with UI
and 17.5% of COVID unemployed workers without UI.
Method 2 finds Hispanic workers composing 15.7% of employed workers, but
18.2% of COVID unemployed workers with UI, and 24.9% of the COVID unem-
ployed workers without UI. Using the second method, Asian Americans are 6.5%
of the employed workers, 6.5% of COVID unemployed with UI workers, and 5.6%
of the COVID unemployed without UI. Non-Hispanic Other workers are 3.8% of
employed workers using the second method and 4.4% of workers unemployed with
UI and 5.6% of COVID unemployed without UI. These results show that, unadjusted
for individual characteristics, Black, Hispanic, and non-Hispanic Other workers are
more likely to have been unemployed by COVID without receiving UI when com-
pared with their share of employment.
Table 3 shows the means and standard deviations of the variables used in the
logit analyses. Using the spending question to determine receipt of UI, COVID dis-
placed workers are equally split between receiving UI and not receiving UI with
each group accounting for 6.9% of each sample. Using the receiving UI question
method, COVID displaced workers are more likely to be receiving UI (6.8%) com-
pared to workers not receiving UI (4.8%). The two percentage point difference of
COVID unemployed workers not receiving UI using the second method may reflect
the expansion of UI programs through the Coronavirus Aid and Economic Secu-
rity (CARES) Act that authorized a $600/week supplement to state UI benefits, as
well as expanding UI eligibility and duration toward the latter half of 2020. Finally,
a comparison of the general demographic characteristics shows little differences
across the samples in each analysis.
Table 4 shows the logit results using both methods of determining receipt of
unemployment benefits. We omit reporting of the week and state dummies for con-
venience and clarity. The results for individual characteristics are in keeping with
expectations. The age coefficients are significant in both models. Younger workers
are more likely to be unemployed without UI. Workers with lower levels of educa-
tion have greater probability of being unemployed with and without UI. All the race
SN Bus Econ (2022) 2:102 Page 11 of 17 102
Table 3 Descriptive statistics of logit variables
Dependent variable Model 1 Model 2
COVIDUNwUI COVIDUNnoUI
Variable Mean Std. dev. Mean Std. dev.
Method 1 (HPS weeks 7–21)
COVIDUNwUI 0.069 0.254
COVIDUNnoUI 0.069 0.254
AGE 44.147 14.845 44.08 14.918
AGE_SQUARED 2169.33 1385.37 265.84 1390.33
WOMEN 0.486 0.500 0.484 0.500
CHILDRENLT18 0.429 0.495 0.432 0.495
WOMEN_CHILDREN 0.215 0.410 0.215 0.411
HSGRAD 0.496 0.500 0.487 0.500
AADEGREE 0.096 0.294 0.096 0.295
BADEGREE 0.188 0.391 0.191 0.393
GRADDEGREE 0.146 0.353 0.152 0.360
MARRIED 0.549 0.498 0.558 0.497
HISPANIC 0.178 0.383 0.177 0.382
BLACK 0.129 0.336 0.128 0.339
ASIAN 0.061 0.239 0.060 0.237
NHISPANICOTHER 0.039 0.194 0.040 0.195
Dependent variable Model 3 Model 4
COVIDUNwUI2 COVIDUNnoUI2
Variable Mean Std. dev. Mean Std. dev.
Method 2 (HPS weeks 13–21)
COVIDUNwUI2 0.068 0.251
COVIDUNnoUI2 0.048 0.213
AGE 44.122 14.757 44.076 14.871
AGE_SQUARED 2165.53 1372.52 2163.87 1385.33
WOMEN 0.486 0.500 0.485 0.500
CHILDRENLT18 0.434 0.496 0.436 0.496
WOMEN_CHILDREN 0.217 0.412 0.218 0.413
HSGRAD 0.489 0.500 0.485 0.500
AADEGREE 0.098 0.298 0.097 0.296
BADEGREE 0.196 0.397 0.195 0.396
GRADDEGREE 0.150 0.357 0.152 0.359
MARRIED 0.562 0.496 0.562 0.496
HISPANIC 0.174 0.379 0.177 0.382
BLACK 0.126 0.332 0.126 0.332
ASIAN 0.062 0.240 0.061 0.239
NHISPANICOTHER 0.038 0.192 0.039 0.193
102 Page 12 of 17 SN Bus Econ (2022) 2:102
Table 4 Logit results on COVID displaced with UI and without UI—selected variables
Dependent variable METHOD 1 for determining UI METHOD 2 for determining UI
benefits benefits
Model 1 Model 2 Model 3 Model 4
COVIDUNwUI COVIDUNnoUI COVIDUNwUI2 COVIDUNnoUI2
Independent variable Coefficient Coefficient Coefficient Coefficient
AGE 0.0414*** −0.0618*** 0.0367*** −0.0623***
(0.0049) (0.0055) (0.0055) (0.0075)
AGE_SQUARED −0.0004*** 0.0007*** −0.0004*** 0.0007***
(0.0001) (0.0001) (0.0001) (0.0001)
WOMEN 0.0107 −0.0545 −0.0694* −0.1762***
(0.0294) (0.0334) (0.0328) (0.0472)
CHILDRENLT18 −0.1153** −0.0484 −0.1704*** −0.0543
(0.0390) (0.0422) (0.0450) (0.0645)
WOMEN_CHILDREN 0.0439 0.0649 0.1080 0.0964
(0.0491) (0.0533) (0.0562) (0.0759)
HSGRAD 0.1011 −0.3960*** 0.0384 −0.5358***
(0.0684) (0.0543) (0.0735) (0.0794)
AADEGREE −0.0322 −0.6649*** −0.1559* −0.8395***
(0.0716) (0.0589) (0.0770) (0.0874)
BADEGREE −0.2836*** −0.9282*** −0.4307*** −1.1250***
(0.0695) (0.0566) (0.0749) (0.0816)
GRADDEGREE −1.0346*** −1.1832*** −1.1531*** −1.3342***
(0.0727) (0.0598) (0.0774) (0.0821)
MARRIED −0.3902*** −0.3348*** −0.3934*** −0.3944***
(0.0255) (0.0292) (0.0292) (0.0427)
HISPANIC −0.1154** 0.3418*** −0.0654 0.3919***
(0.0390) (0.0366) (0.0427) (0.0525)
BLACK −0.0984** 0.4094*** 0.2808*** 0.3889***
(0.0379) (0.0369) (0.0419) (0.0528)
ASIAN 0.1763*** 0.1656** 0.0676 0.1750*
(0.0355) (0.0567) (0.0584) (0.0842)
NHISPANICOTHER 0.0563 0.3541*** 0.0949 0.5206***
(0.0557) (0.0599) (0.0647) (0.0890)
Number of observations 876,455 871,055 520,466 509,223
Log likelihood −210,353.37 −208,467.76 −123,473.81 −92,407.99
Pseudo R squared 0.0464 0.0452 0.0405 0.0509
***
denotes p < .001; ** denotes p < .01; * denotes p < .001; robust standard errors in parentheses; week
and state variables omitted in table
and ethnicity coefficients, including Asians, are positive and significant in models in
determining who is COVID unemployed without UI using both methods of deter-
mining unemployment without UI. Moreover, the magnitude of the racial/ethnic
coefficients are very similar between models 2 and 4 for the COVID unemployed
without UI. This indicates that the two methods are consistent in determining who is
not receiving UI benefits. In addition, the differences in the signs and significance of
the racial/ethnic coefficients between models 1 and 3 may indicate some change in
SN Bus Econ (2022) 2:102 Page 13 of 17 102
Table 5 Logit simulations—COVID unemployed without UI
Sample estimated Simulated COVID Sample estimated Simulated COVID
COVID Unemployed no COVID Unemployed no
Unemployed no UI—method 1 (%) Unemployed no UI—method 2 (%)
UI—method 1 UI—method 2 (%)
(%)
NON-HISPANIC 5.4 6.2 3.6 4.8
WHITE
HISPANIC 10.0 8.8 7.3 6.3
BLACK 9.7 7.7 6.6 5.4
ASIAN 6.7 6.4 4.3 4.2
NON HIS- 8.7 7.3 6.9 5.2
PANIC OTHER
who receives UI benefits over the time. Overall, the logit results show that minority
workers were significantly less likely to receive UI benefits during the pandemic.
As a robustness test of these results, we also estimate logits for each educational
category to examine the effects of education on receiving UI benefits. The results
are similar to the pooled samples across the educational categories. The only notable
exceptions are the race/ethnicity parameter estimates for workers with less than a
high school education using both methods were not significant but did remain posi-
tive in models 2 and 4.
We also simulate how the labor market treats workers controlling for these same
differences in individual characteristics, survey weeks, and states of residence to
determine the robustness of the racial effects. We first estimate logit models 2 and
4 for COVID unemployed without UI using both methods, but without the race/eth-
nicity variables. We then use the estimated models to simulate the COVID unem-
ployed percentage without UI using the individual characteristics, survey weeks,
and states of residence for the five racial/ethnic groups—non-Hispanic whites, His-
panic, Black, Asian, and non-Hispanic others. This method is similar to the Oax-
aca–Blinder method using the estimated coefficients of all groups combined as the
average benchmark coefficients as opposed to using different racial/ethnic group
coefficients. We then compare the sample COVID unemployed without UI percent-
ages to the simulated percentages. These results are shown in Table 5.
The simulations show that if the labor market affected workers equally, control-
ling for measurable individual characteristics and state of residence, the percentages
of COVID unemployed workers without UI for Hispanic, Black, and Non-Hispanic
Other workers would improve, decreasing by 1% to 2%, while the percentages of
non-Hispanic white COVID unemployed workers without UI would increase by
approximately 1% using both methods. The Hispanic percentages unemployed with-
out UI decreases from the sample percentage of 10% to the simulated 8.8% using
the logit coefficients from method 1 and from the sample percentage of 7.3% to a
simulated 6.3% using the logit coefficients from method 2. This still leaves a 1.5%
to 1.6% difference between Hispanic workers and non-Hispanic whites when com-
paring the simulated results for both groups and a 2.7% to 3.4% difference when
102 Page 14 of 17 SN Bus Econ (2022) 2:102
comparing the simulated Hispanic percentage to the non-Hispanic white sam-
ple percentages. Black workers also have a persistent difference in COVID unem-
ployment without UI. The simulated Black unemployment without UI percentage
of 7.7% using method 1 is 2.3% above the unadjusted non-Hispanic white sample
percentage and 1.5% greater when compared to the non-Hispanic white simulated
percentage. Using method 2, the simulated Black COVID unemployment percent-
age of 5.4% is 1.8% above the unadjusted non-Hispanic sample white percentage
and 0.6% greater when compared to the non-Hispanic white simulated percentage.
The simulated non-Hispanic Other COVID unemployment percentage of 7.3% using
method 1 is 1.9% above the unadjusted non-Hispanic white sample percentage and
1.1% greater when compared to the non-Hispanic white simulated percentage. Using
method 2, the simulated non-Hispanic Other unemployment percentage of 5.2% is
1.6% above the unadjusted non-Hispanic white sample percentage and 0.4% greater
when compared to the non-Hispanic white simulated percentage. The Asian simu-
lated percentages of COVID unemployed without UI do not vary much from the
sample percentages. Individual and state characteristics do matter and explain some
of the racial and ethnic differences, but racial/ethnic differences still persist even
when controlling for these differences.
Conclusion and policy
There are significant racial and ethnic differences in who collects unemployment
relief during the first stage of the pandemic. Both Black, Hispanic, and non-His-
panic Other workers are more likely to be unemployed than whites without UI.
Black workers are 12.0% of the employed but 17.5% of displaced workers without
UI. Non-Hispanic Other workers are 3.9% of employed workers but are 4.9% of
COVID unemployed without UI. Hispanic workers are even more affected. Hispanic
workers are 15.6% of the employed, but are 23.4% of all displaced workers with-
out UI. The logit simulations, controlling for individual characteristics and state of
residence, show that Hispanics, Blacks, and non-Hispanic Others are less likely to
receive unemployment insurance by one to two percentage points of the labor force.
Unemployment benefits provide not just a source of income and consumption for
recipients, but also have effects on housing security, food security, and health. These
differences in who receives UI benefits by race are part of the increasing racial
dimension of economic inequality in the US. The HPS data allow us to contribute
to the growing literature on the labor market effects of the pandemic by identify-
ing workers directly displaced by COVID and linking these workers to whether they
received UI benefits. We are also able to expand this literature with the inclusion of
Asians and non-Hispanic Other workers who do not self-identify with a single race
or ethnicity.
These results have an impact on public policy. Although the CARES Act
did increase benefits, eligibility, and the duration of UI benefits, this expansion
still did not remove racial and ethnic disparities. In addition to augmenting UI
benefits, policies should include better community outreach to minority and
low income communities as well as language and assistance programs to better
SN Bus Econ (2022) 2:102 Page 15 of 17 102
explain UI eligibility and enrollment. Past studies have shown that younger work-
ers, Black workers, and Hispanic workers are more likely to leave the labor force
when unemployed during a recession. UI policies to keep these workers in the
labor force would help to mitigate economic inequities in incomes and employ-
ment by race and ethnicity in the aftermath of the pandemic. In the short-run,
policies include extended benefit duration, increased family assistance payments,
and job search assistance. Over the longer run, programs could be developed to
preserve jobs and small businesses, provide job skill development, and expand
social services for lower-income and minority workers who have been heavily
impacted but underserved by traditional social welfare programs. Although there
is currently a debate over the labor market effects of expanded UI benefits during
the pandemic (Altonji et al. 2020; Coombs et al. 2022), these studies show that
racial economic inequalities have been exacerbated by the pandemic.
This study contributes to the knowledge of how the pandemic has had a dif-
ferential impact on the labor market by specifically examining the direct effects of
COVID job displacement and who received unemployment benefits. Along with
previous studies, we find that Blacks, Hispanics and non-Hispanics Other workers
in particular face a greater labor market impact as a result of the pandemic. Use
of the HPS data allows us to identify workers directly unemployed by COVID
and to control for individual characteristic. Using the HPS data does have some
limitations. We are unable to analyze the effects of industry and occupation as the
HPS data does not include this information for unemployed workers. Industries
employing a large percentage of Black and Hispanic workers—hospitality and
leisure, personal services, and retail industries—were hard hit by the pandemic. It
is likely that omitting the industry and occupational effects would lower the mag-
nitude of the racial and ethnic effects on unemployed workers without UI. How-
ever, studies of the pandemic labor market effects on unemployment and earnings
still find significant racial and ethnic effects even with industry and occupation
controls. On the other hand, the online survey collection method has likely under-
represented disadvantaged households in the data which may increase the magni-
tude of racial/ethnic disparities. Finally, the HPS data also do not allow analysis
of the critical question of why workers do not receive UI. Reasons include dif-
ficulty in accessing UI applications, knowledge of the UI program, linguistical
problems, immigration status, differences in local administration of UI, and dis-
crimination. To develop appropriate policies as the pandemic continues, future
research is necessary to determine specific reasons different ethnic/racial groups
have accessing UI.
Data availability The datasets generated during and/or analyzed during the current study are available
from the corresponding author on reasonable request. These datasets were derived from the following
public domain resources: https://www.census.gov/data/experimental-data-products/household-pulse-sur-
vey.html.
Declarations
Conflict of interest On behalf of all authors, the corresponding author states that none of the authors have
a financial or personal relationship with a third party whose interests could be positively or negatively
102 Page 16 of 17 SN Bus Econ (2022) 2:102
influenced by the article’s content.
References
Acs G, Karpman M (2020) Employment, income, and unemployment insurance during the Covid-19
pandemic. Urban Institute, pp 1–11. https://www.urban.org/research/publication/employment-
income-and-unemployment-insurance-during-covid-19-pandemic. Accessed 12 June 2022
Altonji J, Contractor Z, Finamor L, Haygood R, Lindenlaub I, Meghir C, O’Dea C, Scott D, Wang
L, Washington E (2022) Employment effects of unemployment insurance generosity during the
pandemic. Tobin Center for Economic Policy, Yale University. https://tobin.yale.edu/sites/defau
lt/files/files/C-19%20Articles/CARES-UI_identification_vF(1).pdf. Accessed 8 Apr 2022
Anyamele O, McFarland S, Fiakofi K (2021) The disparities on loss of employment income by US
households during the COVID-19 pandemic. J Econ Race Policy
Berkowitz S, Basu S (2021) Unemployment insurance, health-related social needs, health care access,
and mental health during the COVID-19 pandemic. JAMA Intern Med 181(5):699–702
Cajner T, Radler T, Ratner D, Vidangos I (2017) Racial gaps in labor market outcomes in the last
four decades and over the business cycle. In: Finance and economics discussion series 2017-071.
Board of Governors of the Federal Reserve System, Washington. https://www.federalreserve.
gov/econres/feds/racial-gaps-in-labor-market-outcomes-in-t he-last-four-decades-and-over-t he-
business-cycle.htm. Accessed 12 June 2022
Cajner T, Crane L, Decker R, Grigsby J, Hamins-Puertolas A, Hurst E, Kurz C, Yildirmaz A (2020)
The U.S. labor market during the beginning of the pandemic recession. In: Brookings papers on
economic activity. https://www.brookings.edu/wp-content/uploads/2020/06/Cajner-et-al-Confe
rence-Draft.pdf. Accessed 15 June 2022
Center for Disease Control (2022) https://covid.cdc.gov/covid-data-tracker. Accessed 20 June 2022
Coibon O, Gorodnichenko Y, Weber M (2020) Labor markets during the Covid-19 crisis: a prelimi-
nary view. In: NBER working paper 27017, NBER
Coombs K, Dube A, Jahnke C, Kluender R, Naidu S, Stepner M (2021) Early withdrawal of pandemic
unemployment insurance: effects on earnings, employment, and consumption. In: Harvard Busi-
ness School working paper 22-046
Couch KA, Fairlie R, Xu H (2016) Racial differences in labor market transitions and the great reces-
sion. In: IZA discussion paper no. 9761, 2016. https://ssrn.com/abstract=2742549. Accessed 12
June 2022
Couch K, Fairlie R (2010) Last hired, first fired? Black-white unemployment and the business cycle.
Demography 47(1):227–47. https://www.jstor.org/stable/25651498. Accessed 12 June 2022
Fairlie, R., Couch, K, Xu H (2020) The impacts of Covid-19 on minority unemployment: first evi-
dence from April 2020 CPS microdata. In: NBER working paper 27246, NBER, May 2020.
http://www.nber.org/papers/w27246
Fields JF, Hunter-Childs J, Tersine AA, Sisson J, Parker E, Velkoff V, Logan C, Shin H (2020)
Design and operation of the 2020 household pulse survey. U.S. Census Bureau. https://www2.
census.gov/programs-surveys/demo/technical-documentation/hhp/2020_HPS_Background.pdf.
Accessed 26 July 2020
Forsythe E, Kahn L, Lange F, Wiczer D (2020) Labor demand in the time of Covid-19: evidence from
vacancy postings and UI claims. In: NBER working paper 27061, NBER
Gezici A, Ozay O (2020) An intersectional analysis of COVID-19 unemployment. J Econ Race Policy
3(4):270–281
Grooms J, Ortega A, Rubalcava J (2020) The COVID-19 public health and economic crises leave
vulnerable populations exposed. Brooking. https://www.brookings.edu/blog/up-front/2020/08/
13/t he-c ovid-1 9-p ublic-h ealth-a nd-e conom ic-c rises-l eave-v ulner able-p opula tions-expose d/.
Accessed 8 Apr 2022
Kuka E (2020) Quantifying the benefits of social insurance: unemployment insurance and health. Rev
Econ Stat 102(3):490–505
Kuka E, Stuart B (2021) Racial inequality in unemployment receipt and take-up. In: NBER working
paper 29595, NBER
SN Bus Econ (2022) 2:102 Page 17 of 17 102
Montenovo L, Jiang X, Lozano Rojas F, Schmutte I, Simon K, Weinberg B, Wing C (2020) Determi-
nants of disparities in Covid-19 job losses. In: NBER working paper 27132, NBER
Nichols A, Simms M (2012) Racial and ethnic differences in receipt of unemployment insurance ben-
efits during the great recession. Urban Institute
Raifman J, Bor J, Venkataramani A (2021) Association between receipt of unemployment insurance and
food insecurity among people who lost employment during the COVID-19 pandemic in the United
States. Public Health 4(1):35884
Smith RJE, Vanski J, Holt C (1974) Recession and the employment of demographic groups. In: Brook-
ings papers on economic activity, vol 3, pp 737–60
US Bureau of Economic Activity (2020). https://www.bea.gov/news/2020/gross-domestic-product-2nd-
quarter-2020-advance-estimate-and-annual-update. Accessed 16 Aug 2020.
US Census Bureau (2020a). https://www.census.gov/programs-surveys/household-pulse-survey.html.
Accessed May to Dec 2020a
US Census Bureau (2020b). https://www.census.gov/data/experimental-data-products/household-pulse-
survey.html. Accessed May to December 2020
US Census Bureau (2021). https://www.census.gov/programs-surveys/household-pulse-survey/technical-
documentation.html. Accessed 11 Apr 2022
US Department of Labor (2021) Management report: preliminary information on potential racial and eth-
nic disparities in the receipt of unemployment insurance benefits during the COVID-19 pandemic.
https://www.gao.gov/products/gao-21-599r. Accessed 8 Apr 2022
Authors and Affiliations
Don Mar1 · Paul Ong2 · Tom Larson3 · James Peoples4
* Don Mar
dmar@sfsu.edu
1
Economics Department, San Francisco State University, San Francisco, CA, USA
2
UCLA Luskin School of Public Affairs, Los Angeles, CA, USA
3
Department of Economics and Statistics, California State University Los Angeles, Los Angeles,
CA, USA
4
Department of Economics, University of Wisconsin-Milwaukee, Milwaukee, WI, USA
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