Older Workers' Employment and Social Security Spillovers through the Second Year of the COVID-19 Pandemic (Working Paper 30567)
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NBER Working Paper No. 30567, Older Workers' Employment and Social Security Spillovers through the Second Year of the COVID-19 Pandemic, by Gopi Shah Goda, Emilie Jackson, Lauren Hersch Nicholas and Sarah Stith, dated October 2022, Revised February 2023. The abstract states that the authors use Current Population Survey and Social Security application data to study employment among older adults and their participation in disability and retirement insurance programs through the second year of the pandemic. It states that they find ongoing improvements in employment outcomes among older workers in the labor force, sustained higher levels in the share no longer in the labor force, and that applications for Social Security disability benefits remain depressed, particularly for Supplemental Security Income.
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NBER WORKING PAPER SERIES
OLDER WORKERS’ EMPLOYMENT AND SOCIAL SECURITY SPILLOVERS THROUGH
THE SECOND YEAR OF THE COVID-19 PANDEMIC
Gopi Shah Goda
Emilie Jackson
Lauren Hersch Nicholas
Sarah Stith
Working Paper 30567
http://www.nber.org/papers/w30567
NATIONAL BUREAU OF ECONOMIC RESEARCH
1050 Massachusetts Avenue
Cambridge, MA 02138
October 2022, Revised February 2023
We would like to thank Sara Ji, Saniya Mahate, Rosemary Rhodes, and Bradley Strauss for
outstanding research assistance, and seminar participants at the NBER meetings on the Labor
Market for Older Workers, NBER Summer Institute, MEA annual meetings, and WEAI annual
conference for helpful comments. The research reported herein was performed pursuant to grant
RDR18000003 from the US Social Security Administration (SSA) funded as part of the Retirement
and Disability Research Consortium. The opinions and conclusions expressed are solely those of
the authors and do not represent the opinions or policy of SSA, any agency of the Federal
Government, or the National Bureau of Economic Research. Neither the United States Government
nor any agency thereof, nor any of their employees, makes any warranty, express or implied, or
assumes any legal liability or responsibility for the accuracy, completeness, or usefulness of the
contents of this report. Reference herein to any specific commercial product, process or service by
trade name, trademark, manufacturer, or otherwise does not necessarily constitute or imply
endorsement, recommendation or favoring by the United States Government or any agency
thereof.
At least one co-author has disclosed additional relationships of potential relevance for this research.
Further information is available online at http://www.nber.org/papers/w30567
NBER working papers are circulated for discussion and comment purposes. They have not been
peer-reviewed or been subject to the review by the NBER Board of Directors that accompanies
official NBER publications.
© 2022 by Gopi Shah Goda, Emilie Jackson, Lauren Hersch Nicholas, and Sarah Stith. All rights
reserved. Short sections of text, not to exceed two paragraphs, may be quoted without explicit
permission provided that full credit, including © notice, is given to the source.
Older Workers’ Employment and Social Security Spillovers through the Second Year of the
COVID-19 Pandemic
Gopi Shah Goda, Emilie Jackson, Lauren Hersch Nicholas, and Sarah Stith
NBER Working Paper No. 30567
October 2022, Revised February 2023
JEL No. H31,H5,J14,J26
ABSTRACT
The COVID-19 pandemic triggered a large and immediate drop in employment among US workers,
along with major expansions of unemployment insurance and work from home. We use Current
Population Survey and Social Security application data to study employment among older adults
and their participation in disability and retirement insurance programs through the second year of
the pandemic. We find ongoing improvements in employment outcomes among older workers in
the labor force, along with sustained higher levels in the share no longer in the labor force during
this period. Applications for Social Security disability benefits remain depressed, particularly for
Supplemental Security Income. In models accounting for the expiration of expanded
unemployment insurance, we find some evidence that the loss of these additional financial supports
resulted in an increase in disability claiming. Social Security retirement benefit claiming is
approximately 3 percent higher during the second year of the pandemic.
Gopi Shah Goda Lauren Hersch Nicholas
Stanford University Colorado School of Public Health
SIEPR CU Anschutz Fitzsimons Building
366 Galvez St. 13001 East 17th Place
Stanford, CA 94305 3rd Floor, Mail Stop B119
and NBER Aurora, CO 80045
gopi.shah.goda@gmail.com lauren.h.nicholas@cuanschutz.edu
Emilie Jackson Sarah Stith
Michigan State University University of New Mexico
486 W. Circle Dr Department of Economics
25D Marshall-Adams Hall 1915 Roma Ave. NE 1019
East Lansing, MI 48824 Albuquerque, NM 87131
emiliej@msu.edu sss1000000@gmail.com
1 Introduction
The first year of the COVID-19 pandemic in the United States was characterized by an
immediate drop in employment among all workers, followed by a gradual recovery. Older
workers between age 50 and 70, who were particularly susceptible to severe illness from
COVID-19, were approximately 10 percent less likely to be employed during the first year of
the pandemic (Goda et al., 2022). Unlike prior recessions, where older workers have turned
to Social Security retirement or disability insurance benefits, the drop in employment was
accompanied by a decline in applications for disability insurance, and no significant change
in retirement applications (Goda et al., 2022).
A number of factors could account for this departure from past trends. Rapid public
policy responses, including stimulus payments and extended unemployment benefits, likely
provided additional financial security in the short term. Higher levels of uncertainty due
to changes in availability of vaccines, remote work options, and other factors may have led
older adults to adopt “wait and see” attitudes before making more permanent decisions
about work and benefit application.
In this paper, we analyze monthly survey data from the Current Population Survey (CPS)
spanning March 2020 to March 2022 to describe patterns in employment, unemployment,
and labor force participation for older adults 50-70. We also analyze Social Security Ad-
ministration (SSA) administrative data on monthly applications received for retirement and
disability applications. In both cases, we compare monthly data in each of the two years
following the start of the pandemic to their expected patterns had the pandemic not oc-
curred under various sets of assumptions. By the end of our study period, we see recovery in
employment and unemployment, with labor force participation remaining depressed relative
to pre-pandemic levels. Overall applications for Social Security disability benefits remain
depressed through the second year of the pandemic (14 percent lower than pre-pandemic
levels), with the drop driven by applications for Supplemental Security Income (SSI). Ap-
plication rates for Social Security retirement benefits in the first year of the pandemic were
1
overall unchanged, but approximately 3 percent higher during the second year.
Early in the pandemic, Unemployment Insurance (UI) was expanded at the Federal level
to provide an additional $600 of weekly benefits along with expanded eligibility for job
classes that would typically be ineligible for state programs through September 2021. Ini-
tially available in all states, termination of these uncharacteristically generous benefits varied
as twenty-four states opted out of these programs during June-August 2021, in advance of
the federal end date in September 2021. We test whether the expiration of expanded UI
benefits, which led to an estimated 36.3 percentage point drop in UI receipt among cus-
tomers of a financial services company (Coombs et al., 2022), was associated with changes
in Social Security disability applications. We find evidence that applications for concurrent
Social Security Disability Insurance (SSDI) and SSI applications reversed earlier declines.
Under the assumption that the expiration of expanded UI programs was not driven by dif-
ferences in factors that affect transitions from UI to disability programs across states and
that disability applications would have evolved similarly in the absence of early expiration,
these results provide suggestive evidence that the expanded unemployment benefits between
March 2020 and September 2021 dissuaded some individuals from applying for disability
insurance benefits.
Our paper builds on several strands of literature. A large body of work examines the
impacts of the pandemic on labor market outcomes (e.g., Bartik et al. (2020); Cajner et al.
(2020); Coibion et al. (2020); Forsythe et al. (2020); Montenovo et al. (2022); Davis (2022);
Larrimore et al. (2021); Lee et al. (2021)). Some of this work looks specifically at older
workers, the population we examine, (e.g., Bui et al. (2020), Quinby et al. (2021), Goda et
al. (2022)), but only Goda et al. (2022) examines SSI and SSDI applications. Analyses of
early labor market effects show dramatic declines in employment (Bartik et al., 2020; Cajner
et al., 2020; Montenovo et al., 2022)), labor force participation (Coibion et al., 2020), and
labor demand (Forsythe et al., 2020). In general, this literature finds evidence of considerable
labor market disruption from the COVID-19 pandemic that broadly hit populations of all
2
ages, with somewhat more concentrated effects among the youngest (Montenovo et al., 2022)
and oldest workers (Bui et al., 2020; Quinby et al., 2021). Lower wage earners, ethnic and
racial minorities, and other vulnerable workers were consistently harder hit (Cajner et al.,
2020; Montenovo et al., 2022; Davis, 2022; Larrimore et al., 2021).
A smaller literature has explored the effects of the pandemic on retirement and disability
benefit claiming. Evidence supports at least a small increase in retirement (Goda et al.,
2022; Cortes and Forsythe, 2022; McEntarfer, 2022) and sustained levels of retirement savings
(Derby et al., 2022), but no effect on Social Security retirement (SSR) benefit claims (Quinby
et al., 2021; Goda et al., 2022). Goda et al. (2022) also find that the widely documented
labor market exits following the onset of COVID-19 were not matched by major changes
in the likelihood of individuals reporting not being in the labor force due to disability. In
addition, disability applications declined rather than increased during the first year following
the onset of COVID-19, and this reduction was driven by SSI, a program targeting low- to
no-wage earners.
Prior to the Great Recession, it was thought that older workers were generally less likely
to be displaced than younger workers during economic downturns due to the larger losses
from laying off older workers that firms had invested in over many years (Farber et al., 2005).
More recently, evidence shows that older workers are particularly vulnerable to recessions,
and are more likely to leave the labor force and collect SSR benefits sooner (Coile and Levine,
2007; Munnell et al., 2009; Coile and Levine, 2011; Johnson, 2012). It is thought that this
shift is related to reductions in tenure among older workers and higher displacement of older
workers employed in manufacturing (Munnell et al., 2009). Other work has found that older
workers delay retirement in an effort to recover lost earnings and wealth (Helppie McFall,
2011; Chan and Stevens, 1999; Gustman et al., 2010; Goda et al., 2011).
A large existing literature has found that disability claiming through SSI and SSDI is
sensitive to economic conditions (Stapleton et al., 1998; Autor and Duggan, 2003; Coe et
al., 2010; Cutler et al., 2012; Maestas et al., 2015, 2018; Schmidt, 2012; Black et al., 2002;
3
Charles et al., 2018; Munnell and Rutledge, 2013). These studies generally find that higher
rates of unemployment lead to larger numbers of applications for SSI and SSDI, increasing
both processing costs and benefit obligations substantially. Carey et al. (2022) find that a
1 percentage point increase in unemployment rates increases SSDI receipt by 4.2 percent.
The additional benefit claimants induced to apply during times of higher unemployment
use less healthcare after entering Medicare, suggesting that the marginal applicants during
recessions are healthier and that these applications may partly reflect the need for income
support when fewer jobs are available.
Prior work suggests that generous UI benefits are also associated with reduced SSDI
claiming (Rutledge, 2011; Lindner and Nichols, 2014; Lindner, 2016). Although the expi-
ration of UI benefits did not lead to meaningful increases in SSDI applications during the
Great Recession (Mueller et al., 2016), there is evidence that it led to increases in self-
reported disability (Rothstein and Valletta, 2017). In addition, evidence from Austria shows
that extended UI benefits increased the probability of future DI takeup while decreasing use
concurrently (Inderbitzin et al., 2016), and Couch et al. (2014) find that extended jobless
spells are associated with a higher likelihood of DI benefits 20 years later.
Finally, a small amount of prior work has examined the effects of UI generosity on older
workers retirement decisions (Hamermesh, 1980; Coile and Levine, 2007; Inderbitzin et al.,
2016; Rothstein and Valletta, 2017). In general, the evidence is mixed with some studies
showing a positive correlation between UI benefits and retirement (Hamermesh, 1980; Inder-
bitzin et al., 2016; Rothstein and Valletta, 2017), and others finding no consistent evidence
of UI generosity affecting retirement decisions (Coile and Levine, 2007).
Our study adds to this literature by examining how work and benefit application deci-
sions are being made by older workers during the second year of the pandemic, a period
where vaccine availability, new coronavirus variants, pandemic-induced restrictions, and the
availability of other social insurance programs like UI were rapidly changing. This period is
especially interesting to study given the high level of uncertainty during the first year of the
4
pandemic, when many older workers appeared to be taking a “wait and see” approach to
retirement and Social Security application decisions (Goda et al., 2022). In addition, several
other features of the 2021-22 period differ from prior periods of economic recovery, such as
relatively low unemployment rates, supply shortages, the continuation of SSA office closures,
and climbing inflation.
The rest of our paper proceeds as follows. Section 2 summarizes the data sources used in
our analysis and provides summary statistics. We describe the empirical methods in Section
3. Section 4 reports our results, and 5 concludes.
2 Data
We use three primary datasets to assess the effect of COVID-19 on labor market outcomes
among older adults and associated spillovers to Social Security—the Current Population
Survey (CPS), SSA’s State Monthly Workload Data, and SSA’s Monthly Data for Retirement
Insurance Applications. Our sample period begins in January 2015, to allow a sufficient
pre-period to establish pre-existing trends in our outcome variables that likely would have
continued but for the COVID-19 pandemic. Our sample ends in March 2022, two years after
the onset of COVID-19 pandemic.
2.1 CPS Data and Variables
The labor market outcome variables we analyze come from the CPS fielded by the U.S.
Census Bureau. We source these data from IPUMS (Flood et al., 2020). The CPS sur-
veys 60,000 households, using a probability selected sampling approach. The data include
individual-level survey weights, which roughly correlate with the number of individuals in
the population represented by the sampled individual. The survey weights adjust for sub-
sampling, in which a small area may be significantly over-represented in terms of the number
of households; non-interview adjustment to account for households for which no information
5
was obtained (e.g., due to absence, refusal, or impassable roads); and distributional weights
based on characteristics such as sex, age, race, and state of residence, which are derived from
the known distribution of these characteristics in the population, as extrapolated from the
decennial U.S. Census.
The survey is fielded the week of the 19th of each month and the questions refer to ac-
tivities during the prior week.1 Households are surveyed for four consecutive months, then
not surveyed for the next eight months, before being surveyed again for a final four con-
secutive months, after which they exit the sample. Surveys are conducted via telephone
and in-person interviews with a single “reference” household member answering questions
for all eligible household members. Eligible individuals are age 15 or older, in the civilian
population, and not institutionalized (United States Census Bureau, 2019). We use the
unharmonized labor force outcome variable created by IPUMS, which directly reflects the
underlying CPS variables obtained from the Census Bureau and Bureau of Labor Statistics.
Unharmonized IPUMS variables differ only from the underlying data in that IPUMS applies
consistent naming conventions across multiple samples (IPUMS, 2022). From this variable,
we generate {0,1} dichotomous variables for Employed, Employed − Absent, U nemployed,
and N ot In Labor F orce (N ILF ).2 For those not in the labor force, we create {0,1} dichoto-
mous variables breaking out the reasons reported for being N ILF into Retired, Disabled,
and Other.3 We also obtained demographic controls for the households in our sample, in-
cluding age, sex, race, ethnicity, education, marital status, household size, metro area, and
state.
Non-response bias is a known issue with the CPS, likely leading to underestimation of
1
This is usually, but not always, the week including the 12th of the month.
2
The CPS categorizes anyone temporarily away from work, regardless of the reason as Employed−Absent.
However, during the pandemic, the employed-absent response may include measurement error, as many
workers were furloughed and some furloughed workers may have been recorded as Employed − Absent rather
than U nemployed in the March and April 2020 CPS. Coding errors resulted in individuals out of work due
to the pandemic in some cases being coded as “employed-absent” rather than unemployed(Montenovo et al.,
2022).
3
Reasons for N ILF – Other include gainful activities such as education and training, as well as what the
American Time Use Survey categorizes as “socializing, relaxing, and leisure” (Eberstadt, 2022).
6
poverty rates (Hokayem et al., 2015). This issue appears to have been significantly exacer-
bated by a pause in in-person interviews starting March 20, 2020, with in-person interviews
partially resuming in July but still remaining below historical levels even as late as October
2020. The change in survey method appears to have skewed the non-response rates such that
unemployed and those not participating in the labor force were less likely to participate. This
may have led to oversampling of non-Hispanic whites, older respondents, and more educated
respondents (Ward and Edwards, 2021), as well as higher income respondents (Rothbaum
and Bee, 2021). The underrepresentation of low-income respondents may affect our results
since these individuals may be more likely to move in and out of unemployment and the
labor force.
We restrict our sample to civilian individuals aged 50–70, to focus on the sub-population
with a high likelihood of applying for Social Security disability or retirement benefits. The
average SSI and SSDI applicants were aged 40 and 50 between January 2015 and December
2020, respectively (Goda et al., 2022). SSDI rates among insured workers increase signifi-
cantly with age with the highest claiming rates among those 60–66 (Center for Budget and
Policy Priorities, 2021), and SSA retirement benefits are only available to those 62 or older.
For our analyses, we further split this age group into 50-–61-year-olds and 62–70-year-olds
due to differing access to SSA retirement benefits and different baseline employment levels.
Our analysis sample includes 2,847,633 observations, 1,701,077 from individuals between 50
and 61 and 1,146,556 from individuals between 62 and 70.4
Tables 1 and 2 report descriptive statistics for our demographic and outcome variables.
Although the sample does not differ much across age groups in Table 1, clear differences in
labor market outcomes exist, as shown in Table 2. In the pre-COVID period, the average
age in the younger group is 56 and 66 in the older group. The remaining demographics are
relatively similar across age groups. A few small differences of note include: the older cohort
4
These numbers reflect the raw number of observations in our sample. All of our analyses utilize
individual-level survey weights. The corresponding weighted observations are 7,210,386,432.36, of which
4,422,889,675.123 are between 50 and 61 and 2,787,496,757.235 are between 62 and 70.
7
is slightly more likely to be female (53% vs. 51%), less likely to be Hispanic (9% vs. 13%),
and more likely to be disabled (18% vs. 13%).
Prior to COVID-19 the older cohort is much less likely to be employed (36% vs. 69%) or
unemployed (1% vs. 2%). This is not surprising and these differences are offset by reporting
higher levels of not being in the labor force (60% vs. 26%). Retirement is the primary driver
of the much higher rate of labor force nonparticipation in the older cohort and accounts
for 81% of individuals not in the labor force, followed by 13% due to disability, and 6% for
other reasons. Among the younger cohort, disability is the primary reason for individuals
reporting labor force nonparticipation and accounts for 35-40% of this group. About 31%
of this group considered themselves retired, and 29% report reasons other than disability or
retirement.
We graph the raw data on the CPS outcomes over time in Figure 1 and Figure 2. Figure
1a shows that the share of the population reporting being employed dropped with the onset
of the COVID-19 pandemic, but has been trending upward ever since, subject to seasonality
throughout the sample period from January 2015–March 2022. Employed-Absent exhibited
an uptick early on in the pandemic, depicted in Figure 1b, but fluctuations in 2021 are
similar to those in pre-pandemic years. As graphed in Figure 1c, following a large spike
in response to the COVID-19 shock, Unemployed has steadily trended downward. Figure
1d shows that N ILF generally has been flat with possibly a slight increase among 50–61-
year-olds immediately after COVID-19 that then plateaued higher than the average level in
2019, but in line with earlier years in the sample period. Changes in outcomes over time
were similar across age groups, although baseline levels differ significantly across the two
age cohorts—the share of the population age 62–70 Employed, Employed − Absent, and
U nemployed is smaller than for the 51–60 cohort, while N ILF is significantly higher.
Figure 2 splits out N ILF into the three categories: Retired, Disabled, or Other. Retired
as a share of the population was flat over time, but much larger for the 62–70 age group. Both
Disabled (Figure 2b) and Other (Figure 2c) show much sharper responses to the COVID-19
8
shock among 50–61 year-olds than among 62–70 year-olds. Despite a larger initial COVID
shock, Disabled as a share of the population rebounded to a trend comparable to the several
months prior to the pandemic for the younger cohort, while it has remained lower than
trend post-COVID-19 for the older cohort. Similarly, the share reporting reasons for not
participating in the labor force other than disability and retirement jumped in the 50–61-
year-old cohort before declining to levels slightly higher than pre-pandemic with a more
muted response in the 62–70 cohort.
2.2 SSA Administrative Claims Data
Although the CPS outcomes Retired and Disabled offer some insight into labor market
dynamics affecting Social Security claiming, we turn to the SSA’s administrative claims
databases to more directly measure these changes (Social Security Administration, 2022a,b).
In particular, we assess changes in applications for Supplemental Security Income (SSI),
Social Security Disability Insurance (SSDI), Concurrent SSI and SSDI, and Social Security
Retirement (SSR). Both the SSI and SSDI programs have the same medical requirements, but
target different subgroups. The SSI program focuses on low-income individuals below age 65
with disabilities regardless of work history as well as low-income adults 65 and older without
disabilities. The SSDI program supports individuals with disabilities with a sufficient work
history, i.e., approximately ten years of work history with at least five within the last ten
years, although the precise amount of work history varies somewhat over time (Social Security
Administration, 2023a). Individuals who are attempting to qualify for both programs are
captured by the Concurrent SSI and SSDI applications data. Individuals at least 62 years
of age are eligible for SSR with the monthly payment amount dependent upon work history
and age of claiming (Social Security Administration, 2023b).
We focus on applications as a leading indicator of labor market changes that affect SSA,
because factors such as administrative processing time and a potentially lengthy appeals
process can create a significant, even multi-year, lag from when an individual decides to
9
apply for benefits until actual receipt of benefits. The SSA data are provided at a monthly
frequency and follow the federal fiscal calendar from October 1st through September 30th,
with all “months” ending on a Friday, leading to four- and five-week months that do not
directly correspond to actual months.5 Using this information, we first calculate the average
weekly number of claims for each SSA month. We then transform the SSI, SSDI, and
Concurrent SSI and SSDI into weekly rates per 100,000 population aged 20–64, and the SSR
data into weekly rates per 100,000 population aged 60–69.6 In addition to total applications
for retirement benefits, SSA also provides information on the quantity of applications filed
online. These data allow us to examine potential substitution from in-person to online
applications in the context of SSA field office closures during the pandemic, providing insight
into mechanisms behind changes in disability applications.
The State Average Monthly Workload Data capture all SSI, SSDI, and concurrent SSI
and SSDI applications and are available at the state-month level, yielding 4,350 observations
between January 2015 and March 2022. The SSA Monthly Data for Retirement Insurance
Applications are provided at the national level and distinguish between applications filed via
the internet and those filed offline (phone and in-person). The lack of state information in
the SSR data leaves us with only 87 observations for analysis. Tables 3 and 4 show pre- and
post-COVID means for these variables. In an average week, 25.5 SSI, SSDI, and concurrent
applications were received per 100,000 people age 20 to 64 prior to the pandemic; in March
2020 and later, the rate declined to 22.4 applications. Approximately 40 percent of these
applications were for SSDI only, while applications for SSI only constitute about a third of
all applications and concurrent SSI and SSDI applications account for the remainder. Prior
to March 2020, approximately 145.2 SSR applications were received per 100,000 population
aged 60-69; this rate increased slightly to 147.6 during the first two years after the pandemic
5
Exceptions to this rule exist, but the only exception during our sample period occurred in September
2016 and we adjusted accordingly. More details are available at https://www.ssa.gov/disability/data/
ssa-sa-mowl.htm#TimePeriodDescription.
6
While the disability application data represent data for all ages, direct communication with SSA indicates
that the correlation between applications for ages 50+ and overall applications were highly correlated over
the study period, with correlation coefficients above 0.90.
10
as shown in Table 4. While approximately half of applications were filed via the internet
prior to the pandemic, this rate increased slightly to 56 percent in the two years after the
onset of the pandemic.
Figure 3a and 3b displays SSA disability applications over time. The raw data in 3a
suggest that the seasonal pattern exhibited in SSI, SSDI, and concurrent SSI and SSDI
applications prior to COVID-19 became less regular post-COVID-19 with reduced seasonality
in the first year, but a return to historical seasonality patterns by 2021. SSR data graphed
over time in 3b exhibit a noisy but generally flat rate of retirement applications over time.
Shifts in application methods from offline to online have occurred, both prior to and following
the onset of the COVID-19 pandemic. Online applications appear to be trending upward
toward the end of the sample period with offline applications dropping further.
2.3 Expanded Unemployment Insurance Benefits
We augment our data with information on the availability of expanded Unemployment Insur-
ance (UI) benefits. The Coronavirus Aid, Relief, and Economic Security (CARES) Act was
passed by Congress on March 25, 2020 and signed into law on March 27, 2020. The CARES
Act provided fast and direct economic assistance through several programs, some of which
expanded the eligibility and generosity of UI benefits. Specifically, the Federal Pandemic
Unemployment Compensation (FPUC) provided unemployed workers with expanded weekly
benefits, and the Pandemic Unemployment Assistance (PUA) program provided income to
unemployed workers ineligible for regular unemployment benefits, such as the self-employed,
and those who had already exhausted their state UI benefits. The exact nature of the ex-
panded UI benefits changed over the course of the pandemic. For example, the $600 weekly
supplement amount was available between March and July 2020, absent for a period of time,
and then reduced to $300 weekly between January and September 2021.7
All of the federal programs expired on September 6, 2021, but some states opted to end
7
A $300 weekly supplement was also available in Lost Wages Assistance in September and October 2020
as a FEMA disbursement authorized through executive order.
11
programs prior to that date. Expiration reduced benefit generosity in terms of the dollar
amounts claimants would receive and the types of workers eligible for UI. We construct two
state-level, time-varying binary variables: UI Expiration Month st , which equals 1 during
the month where these expanded programs expired in state s, and 0 otherwise; andPost UI
Expiration st , which equals 1 in months after expanded UI programs were expired in state
s, and 0 otherwise. This allows us to examine the month during which the regime changed
separately from months where expanded UI programs were never in place.
Figure 4 shows the month of UI expiration for each state. As shown in the figure, 22
states opted out of at least one program in June 2021, and four in July. The remaining
states kept both FPUC and PUA in effect until September 6, 2021.
3 Empirical Methods
We estimate the evolution of labor market outcomes for older workers and SSA applications
over the COVID-19 pandemic using an event study framework similar to that used in recent
work (Bacher-Hicks et al., 2021; Goda et al., 2022). Given the different levels of aggregation
across our data sets, we use the three specifications below for our CPS, SSI and SSDI, and
SSR analyses, respectively.
−1
X 25
X
Yist = βk × 1[e(t) = k] + βk × 1[e(t) = k] + γ × 1[e(t) < −5]
k=−5 k=1
+ θm(t) + τ t + ωs + ξXist + εist (1a)
−1
X 25
X
Yst = βk × 1[e(t) = k] + βk × 1[e(t) = k] + γ × 1[e(t) < −5]
k=−5 k=1
+ θm(t) + ωs + εst (1b)
−1
X 25
X
Yt = βk × 1[e(t) = k] + βk × 1[e(t) = k] + γ × 1[e(t) < −5]
k=−5 k=1
12
+ θm(t) + εt (1c)
To assess the effects of COVID-19 on employment outcomes using the CPS data, we
employ Equation 1a, regressing our outcome variables Yist on a series of event-time dummy
variables, with e(t) representing the time relative to February 2020 and ranging from -5 to
25, where -5 refers to time periods 5 months or more before February 2020. We further
control for month-level indicators ωm(t) to control for seasonality, a time trend denoted by
τ to capture pre-existing trends in our outcome variables, and demographic variables Xist ,
which include single-year-of-age fixed effects, race, Hispanic ethnicity, education, metro area,
and household family size, to reduce the risk that changes in population composition may
be affecting our results.
In measuring the effects of COVID-19 on Social Security applications, we use two spec-
ifications. For disability applications, we employ Equation 1b, using the same strategy for
our event-time variables. We further control for month and state fixed effects. Equation 1c
details our analysis for SSR data and is the same as Equation 1b except that state fixed
effects are not included because the data are at the national level.
In all three specifications, our coefficients of interest are represented by the βk ’s. These
coefficients measure the deviation between observed outcomes and what we would have
expected after controlling for seasonality, changes in the demographic composition, and
time-invariant state characteristics, and in our CPS analyses, prior trends.
We further assess changes in our outcome variables using a difference-in-differences ap-
proach. This approach collapses the event-time dummies into two aggregated measures,
Post-Covid 1, representing the time period from March 2020 to March 2021, which overlaps
with our prior analysis of initial COVID-19-related changes in labor market outcomes and
SSA claiming among older adults (Goda et al., 2022), and Post-Covid 2, which corresponds
to the period from April 2021 through March 2022, roughly representing the second year
after the onset of the COVID-19 pandemic. The first year is dominated by the initial ef-
13
fects of the pandemic with generally increasing case counts and vaccines only first becoming
available to older adults in December 2020. The second period begins in April 2021, when
most individuals became eligible for COVID-19 vaccines and COVID-19 cases were declining.
This period also includes the rise of variants that led to significant increases in COVID-19
case counts, such as the Delta variant in fall 2021 and the Omicron variant in late 2021 and
early 2022. Equations 2a–2c below detail our difference-in-differences approach.
Yist = α1 P ostCovid1ist + α2 P ostCovid2ist + θm(t) + τ t + ωs + ξXist + εst (2a)
Yst = α1 P ostCovid1st + α2 P ostCovid2st + θm(t) + ωs + εst (2b)
Yt = α1 P ostCovid1t + α2 P ostCovid2t + θm(t) + εst (2c)
Equations 2a–2c include the same set of controls as Equations 1a–1c. For all specifications
using the CPS and disability insurance outcomes, we cluster our standard errors at the state-
level to control for heteroskedasticity and arbitrary correlation across observations within
a state. For regressions using the SSR data, for which we do not have state identifiers, we
include heteroskedasticity-robust standard errors.
Our analysis of CPS data includes time trends to reflect the fact that our outcomes
were changing prior to the pandemic, with employment at older ages on a steady upward
trajectory and unemployment and labor force nonparticipation trending down. The inclusion
of time trends means that the counterfactual levels of our outcomes in the absence of the
pandemic assume the continuation of these trends. However, given the ongoing recovery from
the Great Recession occurring over this period, a more conservative benchmark would be to
assume that labor force outcomes would have been at the average during the pre-pandemic
period. We therefore investigate alternative benchmarks where we omit time trends and
define the pre-pandemic period as the average in the 2015-2019 period as well as the two
years prior to the start of the pandemic.
14
Our last set of analyses assesses whether the expiration of expanded unemployment in-
surance programs resulted in changes in disability insurance application outcomes. A causal
interpretation relies on the assumption that, conditional on controls, outcomes in states that
ended expanded UI programs early would have evolved similarly to states that did not end
them early. We examine how trends in our outcomes evolved in the months preceding UI
program expiration, and conclude that there is more support for UI expiration decisions be-
ing unrelated to claiming Federal disability benefits than state-level employment outcomes;
thus, we restrict these analysis to our disability applications data.8
To conduct these analyses, we limit our focus to the post-pandemic period from March
2020 onwards and estimate a version of Equation 2b that includes U I Expiration M onthst ,
a binary variable with a value of 1 in the month t in which state s allowed one or more of their
expanded UI benefits to expire, and P ost U I Expirationst , a binary variable that equals 1
for all months following the expiration of expanded UI benefits in state s. We differentiate
between these two variables because U I Expiration M onthst represents a partially treated
month while subsequent months are fully treated. As these specifications are limited to
only the post-pandemic period, a time when seasonality patterns were less of a concern than
other time-varying factors such as Covid surges from new variants, we include month of
year (month x year) fixed effects rather than month fixed effects. These controls allow more
flexibility than month fixed effects but were not possible in our main specifications because
our Post Covid variables do not have temporal variation across states. The coefficient on
P ost U I Expirationst summarizes the change in the outcome variable in months where the
state no longer had special pandemic UI programs in place relative to average values of the
outcome during the pandemic period prior to the program expiration.
To further explore the effect of UI expiration on disability claiming in particular, we
estimate an event study with the month prior to UI expiration as the omitted period (t=-1),
tracking the periods five months prior and five months after UI expiration. Months outside
8
We cannot conduct a similar analysis with the SSA Retirement applications as our data are only at the
national-level data, and so we cannot leverage state-level differences in month of expiration.
15
this window are grouped into the t=-5 and t=+5 periods. These figures show the evolution
of disability applications both before and after the expanded UI programs expired.
4 Results
4.1 Labor Market Outcomes
We first estimate the effect of the COVID-19 pandemic on labor market outcomes for older
workers. In Figure 5, we present coefficients from our event study estimation in equation 1a.
Beginning with Figure 5a, we examine how the probability of being employed has evolved
compared to what would have been expected if prior trends and seasonality had continued,
after controlling for changes in demographic composition. February 2020 is assumed to be
the period prior to the pandemic, and all prior and following months are shown relative to
this baseline. We present the coefficients for the 50-61-year-olds in the black line and with
a gray line for 62-70-year-olds.
When comparing the overall dynamics in the second year of the pandemic to the first
year, we see the deviation in employment steadily trending back toward zero, indicating a
recovery of employment from the sharp drop and depressed levels seen in year one. By March
2022, the results suggest that employment is returning to what would have expected given
pre-pandemic patterns. In fact, the deviations in employment levels for 62-70-year-olds are
not statistically different from zero in most months in the latter portion of year two. As seen
in Figure 5b, employed but absent only deviated from expectations in the beginning of year
one and did not exhibit any unexpected patterns from June 2020 through the second year
of COVID-19.
Deviations in unemployment, as seen in Figure 5c, generally display inverse dynam-
ics to those seen with employment above in Figure 5a. While initially peaking around 4-7
percentage points higher than would have been predicted in April 2020, the deviations in un-
employment have declined throughout the second year of the pandemic. For 62-70-year-olds,
16
our monthly estimates for deviations in unemployment are no longer statistically different
from zero starting in September 2021, suggesting that unemployment rates are not signifi-
cantly different from what would have been predicted given trends, seasonality, and changes
in demographic composition since February 2020.
Finally, we examine the dynamics of individuals classified as not in the labor force (NILF).
The dynamics of labor force non-participation differ from that of unemployment and em-
ployment. In particular, for the 50-61-year-olds, we do not see a continual reduction in labor
force non-participation throughout the second year of the pandemic. Instead, we observe a
steady and higher level of labor force non-participation than we would have expected. By
the beginning of 2022, the point estimates begin to suggest a slight, mild trend of recovery
toward what we would have expected, but the coefficients are not statistically different from
earlier in year two. Results for the 62-70-year-olds are noisier; however, relative to February
2020, labor force non-participation is slightly higher for this group in March 2022.
Next we collapse our event study estimates into two post-COVID-19 indicators, one for
the first year and one for the second year of the pandemic. These results are presented in
Table 5, with the estimates for 50-61-year-olds in Panel A and 62-70-year-olds in Panel B.
Starting with Column (1) of Panel A, comparing the coefficient estimates for year one and
year two, we observe an estimate almost half as large in the second year as the first year
of the pandemic. In year two, employment was about 3.1 and 2.5 percentage points lower
than predicted levels for 50-61-year-olds and 62-70-year-olds, respectively. These differences
amount to a 4.5 and 7 percent reduction relative to the pre-pandemic mean. The magnitude
of the deviation in year two is significantly smaller than the deviation in year one for both
age groups.
The lower levels of employment are due to both increases in unemployment and increases
in labor force non-participation. While the increase in unemployment was approximately half
of the reduction in employment during year one, unemployment constituted approximately
30-40 percent of the reduction in employment in the second year.
17
By contrast, the increase in labor force non-participation accounts for the majority of
the reduction in employment for both age groups. This increase is consistent with some
individuals who were previously unemployed and looking for work subsequently choosing to
leave the labor force all together in the second year of the pandemic.
We now turn to Table 6 where we separate the labor force non-participation by the
reason the respondent noted, namely that they were out of the labor force due to being
retired, disabled, or for reasons other than retirement or disability. The results in Table 6
show a shift between year one and two of the pandemic. In the first year of the pandemic,
there was a significant portion not looking for work for other reasons. By the second year
of the pandemic, the deviation in non-participation due to reasons other than retirement
or disability declined, while the deviation in non-participation due to retirement increased.9
Non-participation for disability reasons returned to predicted levels for 50-61 year olds,
eliminating the slight reduction seen during the first year in the pandemic, and held steady
during the second year of the pandemic for 62-70 year olds. Together, these results indicate
a transition towards retirement and away from exits due to other reasons. We note, however,
that differential nonresponse during the pandemic among low-income populations discussed
earlier may affect our results by understating labor force exits, as the populations who
were more affected by nonresponse may be those who are more likely to move along the
employment and participation margin.
We also conduct two additional versions of our analysis with alternative counterfactual
benchmarks. The first omits time trends from the specification and thus compares outcomes
9
It is possible that a component of the reduction in disability as a reason for labor force nonparticipation
among 50 to 61 year-olds in Table 6 comes from individuals who received a negative determination from
SSA, and therefore, changed their disability status. However, in general, SSI and SSDI receipt is reported
more frequently than are work limitations or affirmative answers to the six-part question on difficulties with
daily activities, the two primary measures of self-reported disability in the CPS. Burkhauser et al. (2012)
shows that, among those reporting SSI and SSDI receipt, only 84.1 percent reported a work limitation and
only 63.3 percent reported a difficulty with hearing, vision, memory, physical difficulty, mobility limitations
or personal care; 92 percent of those reporting SSI or SSDI receipt answer affirmatively to either the work
limitation question or one of the parts of the six-part difficulty question. These percentages suggest that
the factors driving SSI and SSDI disability determinations may differ from those leading to self-determined
disabilities.
18
during the pandemic to the average for the 2015-2019 period. The second is similar but
shortens the pre-pandemic period to the same number of months as in the post-pandemic
period, resulting in a sample period of February 2018 - March 2022, with 25 months in each
of the pre- and post-pandemic era.
The results are included in Appendix A. We present results for the first alternative in
Figure A.1 and Tables A.1 and A.2. The results are qualitatively similar though smaller
in magnitude owing to the fact that the counterfactual employment rate is lower and coun-
terfactual unemployment and nonparticipation rates are higher in the 2015-2019 period on
average relative to what the continuation of the trend over those years would imply. In
Figure A.2 and Tables A.3 and A.4, we present the results for the second alternative bench-
mark. When we shorten our pre-pandemic period to be the same number of months in the
post-pandemic period, the results are both qualitatively and quantitatively in line with our
baseline results.
4.2 SSA Applications
We now examine changes in applications for SSI, SSDI, and SSR. We first estimate Equa-
tion 1b to evaluate the month by month deviations relative to February 2020 in disability
applications. Starting with Figure 6a, we observe a sharp reduction in total SSI and SSDI
applications that levels off in the end of year one around five fewer applications per 100,000
individuals aged 20-64 than would have been predicted. In the second year of the pandemic,
we continue to see a depressed level of applications for SSI and/or SSDI relative to predicted
levels that is similar in magnitude.
Next, we decompose total disability applications into three subgroups: concurrent SSI and
SSDI applications (Figure 6b), SSDI only applications (Figure 6c), and SSI only applications
(Figure 6d). There are several patterns of note when examining the decomposition. First,
the dynamics in Figure 6b and 6d follow closely to that of total applications, and drive the
majority of the total effect. Both stay at a depressed level of applications relative to what
19
pre-pandemic patterns would have anticipated. Second, as seen in Figure 6c, applications for
SSDI only follow a different pattern from applications for SSI or concurrent applications. We
do not observe a significant decline in SSDI only applications relative to the pre-pandemic
levels.
In Table 7, we estimate the changes in the outcomes during the Covid-19 pandemic
relative to the pre-pandemic period and compare the coefficients for Post-Covid 1 and Post-
Covid 2 to differentiate the magnitude of the effects in year one and year two. The results in
Column (1) show that the deviation in total applications in year two is 3.63 fewer applications
per 100,000 people aged 20 to 64, and this coefficient is significantly different (at the 10%
level) from the 2.82 reduction estimated in year one. Column (2) shows that SSDI only
applications were not significantly lower than predicted in either post-covid period. However,
deviations in SSI only (Column 3) and concurrent SSI and SSDI applications (Column 4) both
grew in magnitude during the second year of the pandemic remaining significantly lower than
pre-pandemic levels. Column 3 indicates that there were 2.33 fewer SSI only applications in
year two, a significant decrease from year one which saw 1.66 fewer applications. Column
4 indicates that there were 1.34 fewer concurrent SSI and SSDI applications in year two, a
significant decrease from 1.02 fewer applications in year one. These results reinforce findings
from the first year of the pandemic, during which time SSI represented a significant share of
“missing” applications for disability insurance (Goda et al., 2022).
Turning to Figure 7, we evaluate deviations in applications for SSR benefits. In year
one, we found no evidence of a statistically significant change in total SSR applications,
but some evidence that applications filed via the internet substituted for applications filed
offline. While results in Figure 7 are noisy given the national level data, the estimates
suggest a slightly higher level of applications in late 2021 and early 2022 relative to earlier
in the pandemic. This pattern is more noticeable in Figure 7b, where we see higher levels
of applications filed via the internet in late 2021 compared with late 2020. In Figure 7c, we
see persistently lower levels of applications filed offline through March 2022. These patterns
20
across mode of application likely result from the fact that Social Security offices did not
reopen until April 7, 2022.
In Table 8 Column (1), when we collapse the monthly coefficients into Post-Covid 1
and Post-Covid 2, we find that while in the first year of the pandemic, applications shifted
from online to offline with no change in the overall total, in the second year, there was a
statistically significant increase in total applications. Specifically, in the second year there
are 4.75 more applications per 100,000 individuals age 60 to 69 relative to predicted levels.
The total effect in Column (1) is almost entirely driven by increases in applications filed via
the internet. These results are consistent with a larger share of older individuals reporting
labor force non-participation due to retirement in Table 6.
The fact that SSA offices remained closed until April 2022 and applications for SSR
shifted from online to offline during the pandemic provides another possible mechanism for
the decline in SSI and concurrent applications shown in the previous section. Specifically, if
applicants for SSI and concurrent benefits face barriers in applying for benefits online, office
closures could instead lead to reduced applications overall.
4.3 Expiration of Expanded UI Benefits
As explained in Section 2.3, while federal programs expanding UI expired in September 2021,
there were several states that allowed one or both of their UI programs to expire in June, July,
or August 2021. We estimate a version of Equation 2b that limits our sample period to only
include the pandemic period from March 2020 onwards and incorporates the state-level vari-
ation in when expanded UI benefit expired to assess whether there were associated changes
in disability insurance applications. As described in Section 3, U I Expiration M onthst has
a value of 1 in the month t in which state s allowed one or both of their expanded UI bene-
fits to expire. P ost U I Expirationst has a value of 1 for all months following the expiration
of expanded UI benefits in state s. The specifications also include state fixed effects and
month-of-year fixed effects to control for fixed differences across states and time patterns
21
during the pandemic that may have resulted in overall higher or lower applications in any
given month.
In Table 9 we evaluate the relationship between UI expiration and deviations in appli-
cations for disability. During the month of program expiration, which represents a period
where expanded UI programs were partially in place, there is no evidence that application
rates differ from earlier in the pandemic. During the months following program expiration,
concurrent SSI and SSDI application rates are statistically significantly higher (without cor-
rection for multiple comparisons). The magnitude of the increase, 0.62 applications per
100,000 individuals age 20-64, represents a reversal of approximately half of the decline that
was seen over the course of the pandemic in Table 7. While the corresponding coefficients in
the other columns are positive and suggestive of a behavioral response, they are not statis-
tically significant. Decomposing the post-UI expiration effect in line with Goodman-Bacon
(2021) indicates that, while most of the effects of UI expiration on applications are positive,
substantial heterogeneity exists and negative effects cannot be ruled out for SSDI only and
SSI only applications.
In Figure 8, we plot event study figures that show the evolution of Social Security dis-
ability applications before and after UI expiration that come from modified versions of our
main specification that include event time binary variables for five months before and after
UI expiration, where time -5(+5) include all periods earlier (later) than five months before
(after) UI expiration, and 0 represents the month during which the programs first expired.
Consistent with the results in Table 9, we see that applications are slightly elevated in the
months following UI expiration. These effects appear to be concentrated in the first few
months after expanded UI programs expire. In general, these figures do not show evidence
that application rates were evolving differently in the months preceding the expiration of the
expanded UI programs.
22
5 Conclusion
The COVID-19 pandemic that began in March 2020 led to unprecedented disruption in the
economy. Employment levels dropped dramatically in the two months after its onset before
a rapid, partial recovery towards pre-pandemic levels.
The prevalence of labor force nonparticipation remained elevated throughout both years
and reasons for nonparticipation remained fairly constant for ages 50 to 61 and 62 to 70,
with some evidence of a shift from nonparticipation due to reasons other than retirement
and disability towards nonparticipation due to retirement. Reductions in nonparticipation
due to disability persisted in the second year for the 62- to 70-year-old age group, but
reverted to pre-pandemic levels among the 50- to 61-year-old age group. Consistent with
the idea that some who had left the labor force during the first year were waiting some time
before reporting retirement, we see no change in retirement applications in the first year
of the pandemic followed by a statistically significant increase in retirement applications of
approximately 3 percent during the second year.
Applications for disability benefits remained depressed during the first two years of the
pandemic, driven by SSI and concurrent SSI and SSDI applications. SSDI applications
appear largely unaffected by the pandemic. The distributional shift towards online rather
than offline retirement applications and the fact that SSA offices did not reopen for in-
person appointments until April 2022, after the end of our sample period, suggest some of
the reduction in applications for SSI and concurrent SSI and SSDI applications may reverse
with increased access to in-person SSA application services.
We also examine the role of expanded UI benefits in the labor market recovery during
the second year of the pandemic. While UI expansions were in place during the full first year
of the pandemic in some capacity, these enhanced benefits began to expire in the summer
of 2021. We find evidence that concurrent SSI and SSDI applications increased after these
programs expired, reversing approximately half of their decline during the pandemic.
The pandemic recession differs in many ways from prior recessions, and the second year
23
of the pandemic came with widespread vaccine availability, new coronavirus variants that
generated large increases in infections, and policy changes. As the COVID-19 pandemic
continues to evolve, it will be important to continue to monitor how labor market outcomes
among older workers and spillovers to Social Security change as the COVID-19 pandemic
transitions to an endemic state.
24
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Schmidt, Lucie, “The Supplemental Security Income Program and Welfare Reform,” Tech-
nical Report 12-3, Federal Reserve Board of Boston Public Policy Discussion Paper 2012.
Social Security Administration, “SSA State Agency Monthly Workload Data [dataset],”
May 2022a.
, “SSA Monthly Data for Retirement Insurance Applications Filed via the Internet
[dataset],” May 2022b.
, “Disability Benefits - How you Qualify,” Technical Report 2023.
28
, “Retirement Benefits,” Technical Report 05-10035 2023.
Stapleton, David, Kevin Coleman, Kimberly Dietrich, and Gina Livermore, “Em-
pirical analyses of DI and SSI application and award growth,” Growth in Disability Ben-
efits: Explanations and Policy Implications, 1998, pp. 31–92.
United States Census Bureau, “Current Population Survey Design and Methodology,”
Technical Report 77, Technical Paper October 2019.
Ward, Jason and Kathryn Ann Edwards, “CPS Nonresponse During the COVID-19
Pandemic: Explanations, Extent, and Effects,” Labour Economics, 2021, 72, 102060.
29
6 Figures
Figure 1: Employment Outcomes for Ages 50-61 and 62-70, 2015-2022
(a) Employed (b) Employed-Absent
(c) Unemployed (d) NILF
Notes: Sample contains civilians ages 50-70 from the January 2015-March 2022 CPS living
in the United States. Figures depict the share of individuals in an employment category in
each month. Estimates are weighted using survey weights.
30
Figure 2: NILF Outcomes for Ages 50-61 and 62-70, 2015-2022
(a) Retired (b) Disabled
(c) Other
Notes: Sample contains civilians ages 50-70 from the January 2015-March 2022 CPS living
in the United States. Figures depict the share of individuals in an employment category in
each month. Estimates are weighted using survey weights.
31
Figure 3: Social Security Disability and Retirement Application Rates, 2015-2022
(a) Weekly Disability Applications Rate
(b) Weekly Retirement Applications Rate
Notes: Panel (a) displays aggregated SSA State Agency Monthly Workload data and ranges
from January 2015 to March 2022. Application rates are number of weekly applications per
100,000 people aged 20 to 64. Panel (b) displays aggregated SSA Monthly Data for Retire-
ment Insurance Applications data and ranges from January 2015 to March 2022. Application
rates are number of weekly applications per 100,000 people aged 60-69.
32
Figure 4: Federal Pandemic Unemployment Insurance Expiration Month by State
Notes: The Federal Pandemic Unemployment Compensation (FPUC) and the Pandemic
Unemployment Assistance (PUA) programs were established through the Coronavirus Aid,
Relief, and Economic Security (CARES) Act and signed into law on March 27, 2020. All
benefits under the FPUC expired on September 6, 2021. The FPUC provided workers with
an extra $600 per week in addition to regular state UI benefits or PUA benefits. PUA
provided income to unemployed workers who are otherwise ineligible for regular state UI or
have previously run out of state UI benefits. Various states opted to allow these programs
to expire prior to their initial date. This figure depicts the month in which a state opted out
of at least one of the two federal UI programs.
33
Figure 5: Event Studies of Employment Outcomes from the CPS Among 50-70 Year Olds
(a) Employed (b) Employed-Absent
(c) Unemployed (d) NILF (Disability, Retired, Other)
Notes: Sample contains civilians ages 50-70 from the January 2015-March 2022 CPS living in
the United States. Outcome variable is whether or not an individual is employed, employed
but absent, unemployed, or not in the labor force. An individual is classified as employed-
absent if they are absent from their job for a temporary reason during the survey reference
week. Standard errors are robust and clustered at the state level. Estimates are weighted
using survey weights and 95% confidence intervals are shown. The event time is relative to
February 2020. Regressions include a time trend, month and state fixed effects and adjust
for age, sex, race, Hispanic ethnicity, education, and household family size.
34
Figure 6: Event Study of Social Security Disability Applications
(a) Total SSI + SSDI (b) Concurrent SSI and SSDI
(c) SSDI Only (d) SSI Only
Notes: Sample comes from the SSA State Agency Monthly Workload and ranges from Jan-
uary 2015 to March 2022. Outcome variable is weekly applications per 100,000 people aged
20 to 64. Standard errors are robust and clustered at the state level. 95% confidence inter-
vals are shown. Regressions include month and state fixed effects and event time relative to
February 2020.
35
Figure 7: Event Study of Social Security Retirement Applications
(a) Total Applications (b) Applications Filed via Internet
(c) Applications Filed Offline
Notes: Sample comes from the SSA Monthly Data for Retirement Insurance Applications
and ranges from January 2015 to March 2022. Outcome variable is weekly applications per
100,000 people aged 60 to 69. Standard errors are robust. 95% confidence intervals are
shown. Regressions include month fixed effects and event time relative to February 2020.
36
Figure 8: Event Study of Social Security Disability Applications: UI Expiration
(a) Total SSI + SSDI (b) Concurrent SSI and SSDI
(c) SSDI Only (d) SSI Only
Notes: Sample comes from the SSA State Agency Monthly Workload and ranges from March
2020 to March 2022. Outcome variable is weekly applications per 100,000 people aged 20
to 64. Standard errors are robust and clustered at the state level. 95% confidence intervals
are shown. Regressions include month of year and state fixed effects. Months since UI
expiration is equal to zero in the month that expanded UI programs expired; -5 denotes 5
or more months prior to expiration; 5 denotes 5 or more months after expiration.
37
38
7 Tables
Table 1: CPS Demographic Summary Statistics
Ages 50-61 Ages 62-70
Pre-Covid Post-Covid Pre-Covid Post-Covid
Age 55.50 55.56 65.75 65.77
(3.42) (3.47) (2.57) (2.57)
Female 0.51 0.51 0.53 0.53
(0.50) (0.50) (0.50) (0.50)
White 0.80 0.79 0.82 0.81
(0.40) (0.41) (0.38) (0.39)
Black 0.12 0.12 0.11 0.11
(0.33) (0.33) (0.31) (0.32)
Other 0.08 0.09 0.07 0.07
(0.27) (0.28) (0.25) (0.26)
Hispanic 0.13 0.15 0.09 0.10
(0.34) (0.36) (0.29) (0.30)
≤ High School 0.10 0.09 0.10 0.09
(0.30) (0.29) (0.30) (0.28)
High School 0.30 0.29 0.30 0.30
(0.46) (0.46) (0.46) (0.46)
Some College 0.16 0.15 0.17 0.16
(0.37) (0.35) (0.38) (0.37)
College + 0.33 0.36 0.33 0.34
(0.47) (0.48) (0.47) (0.47)
Associates 0.11 0.11 0.10 0.11
(0.31) (0.31) (0.30) (0.31)
Disabled 0.13 0.12 0.18 0.17
(0.33) (0.32) (0.39) (0.38)
Married 0.65 0.65 0.65 0.64
(0.48) (0.48) (0.48) (0.48)
Divorced/Separated 0.19 0.19 0.18 0.18
(0.39) (0.39) (0.38) (0.38)
Widowed 0.04 0.03 0.09 0.09
(0.19) (0.18) (0.29) (0.29)
Single 0.12 0.13 0.08 0.09
(0.32) (0.34) (0.27) (0.29)
Household Size 2.61 2.64 2.16 2.17
(1.36) (1.37) (1.13) (1.14)
Observations 1286653 414424 838723 307833
Notes: Sample contains civilians ages 50-61 and 62-70 from the January 2015-March 2022 CPS living in the
United States. Share of each relevant demographic is listed and weighted using survey weights. Pre-Covid
captures the mean outcome in the pre-period January 2015-February 2020. Post-Covid captures the mean
outcome in the post-period March 2020-March 2022.
39
Table 2: CPS Employment Summary Statistics
Ages 50-61 Ages 62-70
Pre-Covid Post-Covid Pre-Covid Post-Covid
Employed 0.688 0.669 0.363 0.353
(0.463) (0.471) (0.481) (0.478)
Employed-Absent 0.026 0.030 0.019 0.020
(0.160) (0.169) (0.136) (0.142)
Unemployed 0.024 0.039 0.013 0.022
(0.152) (0.194) (0.112) (0.147)
Not in Labor Force-Retired, Disabled, and Other 0.262 0.262 0.606 0.605
(0.440) (0.440) (0.489) (0.489)
Retired 0.080 0.083 0.491 0.490
(0.271) (0.276) (0.500) (0.500)
Disabled 0.105 0.092 0.079 0.074
(0.306) (0.289) (0.270) (0.261)
NILF-Other 0.078 0.087 0.036 0.042
(0.268) (0.282) (0.186) (0.200)
Observations 1286653 414424 838723 307833
Notes: Sample contains civilians ages 50-61 and 62-70 from the January 2015-March 2022
CPS living in the United States. Share of each employment status is listed and weighted
using survey weights. An individual is classified as employed-absent if they are absent from
their job for a temporary reason during the survey reference week. Pre-Covid captures the
mean outcome in the pre-period January 2015-February 2020. Post-Covid captures the mean
outcome in the post-period March 2020- March 2022.
40
Table 3: Social Security Disability Application Summary Statistics
Pre-Covid Post-Covid
All 25.49 22.37
(8.348) (8.133)
SSDI 9.549 9.527
(2.788) (2.947)
SSI 9.539 7.585
(3.793) (3.417)
Concurrent 6.406 5.256
(2.430) (2.289)
Observations 3100 1250
Notes: Sample comes from the SSA State Agency Monthly Workload data from January 2015-March 2022.
Variables denote weekly applications per 100,000 people aged 20 to 64. Pre-Covid captures the mean outcome
in the pre-period January 2015-February 2020. Post-Covid captures the mean outcome in the post-period
March 2020-March 2022.
Table 4: Retirement Application Summary Statistics
Pre-Covid Post-Covid
Total Retirement Applications 145.2 147.6
(7.891) (9.262)
Retirement Application Filed via Internet 74.69 83.04
(6.673) (8.672)
Retirement Application Filed non-Internet 70.53 64.56
(5.740) (4.640)
Observations 62 25
Notes: Sample comes from the SSA Monthly Data for Retirement Insurance Applications from the January
2015-March 2022. Variables denote weekly applications per 100,000 people aged 60 to 69. Retirement
Application Filed non-Internet denotes all retirement applications filed not through internet, including filing
by phone. Pre-Covid captures the mean outcome in the pre-period January 2015-February 2020. Post-Covid
captures the mean outcome in the post-period March 2020-March 2022.
41
Table 5: Changes in Employment Outcomes Following COVID-19 Pandemic
A. 50-61-Year-Olds
(1) (2) (3) (4)
Employed Employed-Absent Unemployed NILF
Post-Covid 1 -0.055∗∗∗ 0.006∗∗∗ 0.034∗∗∗ 0.014∗∗∗
(0.003) (0.001) (0.002) (0.003)
Post-Covid 2 -0.031∗∗∗ 0.002∗∗ 0.013∗∗∗ 0.016∗∗∗
(0.003) (0.001) (0.002) (0.004)
Observations 1701077 1701077 1701077 1701077
Pre-Covid Mean 0.688 0.026 0.024 0.262
T-test PC1 = PC2 0.000 0.000 0.000 0.488
B. 62-70-Year-Olds
(1) (2) (3) (4)
Employed Employed-Absent Unemployed NILF
Post-Covid 1 -0.038∗∗∗ 0.003∗∗∗ 0.019∗∗∗ 0.016∗∗∗
(0.004) (0.001) (0.002) (0.003)
Post-Covid 2 -0.025∗∗∗ -0.000 0.007∗∗∗ 0.018∗∗∗
(0.004) (0.001) (0.002) (0.004)
Observations 1146556 1146556 1146556 1146556
Pre-Covid Mean 0.363 0.019 0.013 0.606
T-test PC1 = PC2 0.000 0.000 0.000 0.459
Standard errors in parentheses
∗
p < 0.10, ∗∗ p < 0.05, ∗∗∗ p < 0.01
Notes: Samples contains civilians ages 50-61 and 62-70 from the January 2015-March 2022 CPS living in
the United States. Outcome variable is whether or not an individual is employed, unemployed, or not in
the labor force due to disability, retirement, or another reason respectively. An individual is classified as
employed-absent if they are absent from their job for a temporary reason during the survey reference week.
Standard errors are robust and clustered at the state level. Estimates are weighted using survey weights.
Post Covid 1 equals 1 between March 2020 and March 2021, and Post Covid 2 equals 1 between April 2021
and March 2022. Regressions include a time trend, and month and state fixed effects, and adjust for age,
sex, race, Hispanic ethnicity, education, and household family size. Pre-Covid means captures the mean of
the dependent variable in the pre-period January 2015-February 2020.
42
Table 6: Changes in NILF Following COVID-19 Pandemic
A. 50-61-Year-Olds
(1) (2) (3) (4)
NILF Retired Disabled Other
Post-Covid 1 0.014∗∗∗ 0.003∗ -0.005∗∗∗ 0.017∗∗∗
(0.003) (0.002) (0.002) (0.001)
Post-Covid 2 0.016∗∗∗ 0.006∗∗ 0.000 0.010∗∗∗
(0.004) (0.003) (0.003) (0.002)
Observations 1701077 1701077 1701077 1701077
Pre-Covid Mean 0.262 0.080 0.105 0.078
T-test PC1 = PC2 0.488 0.086 0.041 0.000
B. 62-70-Year-Olds
(1) (2) (3) (4)
NILF Retired Disabled Other
Post-Covid 1 0.016∗∗∗ 0.012∗∗∗ -0.004∗ 0.008∗∗∗
(0.003) (0.004) (0.002) (0.001)
Post-Covid 2 0.018∗∗∗ 0.018∗∗∗ -0.005∗ 0.005∗∗∗
(0.004) (0.006) (0.002) (0.002)
Observations 1146556 1146556 1146556 1146556
Pre-Covid Mean 0.606 0.491 0.079 0.036
T-test PC1 = PC2 0.459 0.075 0.694 0.031
Standard errors in parentheses
∗
p < 0.10, ∗∗ p < 0.05, ∗∗∗ p < 0.01
Notes: Samples contains civilians ages 50-61 and 62-70 from the January 2015-March 2022 CPS living in
the United States. Outcome variable is whether or not an individual is not in the labor force as well as each
subcategory of NILF: disability, retirement, or another reason. Standard errors are robust and clustered at
the state level. Estimates are weighted using survey weights. Post Covid 1 equals 1 between March 2020
and March 2021, and Post Covid 2 equals 1 between April 2021 and March 2022. Regressions include a
time trend, and month and state fixed effects, and adjust for age, sex, race, Hispanic ethnicity, education,
and household family size. Pre-Covid means captures the mean of the dependent variable in the pre-period
January 2015-February 2020.
43
Table 7: Changes in Disability Applications During COVID-19 Pandemic
(1) (2) (3) (4)
All SSDI SSI Concurrent
Post-Covid 1 -2.82∗∗∗ -0.15 -1.66∗∗∗ -1.02∗∗∗
(0.466) (0.180) (0.186) (0.141)
Post-Covid 2 -3.63∗∗∗ 0.04 -2.33∗∗∗ -1.34∗∗∗
(0.482) (0.187) (0.199) (0.148)
Observations 4350 4350 4350 4350
Pre-Covid Mean 25.49 9.55 9.54 6.41
T-test PC1 = PC2 0.09 0.33 0.00 0.02
Robust and clustered (at state level) standard errors in parentheses
∗
p < 0.10, ∗∗ p < 0.05, ∗∗∗ p < 0.01
Notes: Sample comes from the SSA State Agency Monthly Workload and ranges from January 2015 to
March 2022. Outcome variable is weekly applications per 100,000 people aged 20 to 64. Standard errors
are robust and clustered at the state level. Regressions include month and state fixed effects. Post Covid 1
equals 1 between March 2020 and March 2021, and Post Covid 2 equals 1 between April 2021 and March
2022.
Table 8: Changes in Retirement Applications During COVID-19 Pandemic
(1) (2) (3)
Total Filed via Internet Filed offline
Post-Covid 1 -0.09 5.33∗∗∗ -5.43∗∗∗
(2.457) (1.699) (1.453)
Post-Covid 2 4.75∗∗ 11.62∗∗∗ -6.87∗∗∗
(2.138) (2.227) (1.351)
Observations 87 87 87
Pre-Covid Mean 145.23 74.69 70.53
T-test PC1 = PC2 0.13 0.02 0.41
Robust and clustered (at state level) standard errors in parentheses
∗
p < 0.10, ∗∗ p < 0.05, ∗∗∗ p < 0.01
Notes: Sample comes from the SSA Monthly Data for Retirement Insurance Applications and ranges from
January 2015 to March 2022. Outcome variables represent weekly applications per 100,000 people aged 60
to 69. Standard errors are robust. Regressions include month fixed effects. Post Covid 1 equals 1 between
March 2020 and March 2021, and Post Covid 2 equals 1 between April 2021 and March 2022.
44
Table 9: Changes in Disability Applications During COVID-19 Pandemic: UI Expiration
(1) (2) (3) (4)
All SSDI SSI Concurrent
UI Expiration Month 0.28 0.34 -0.10 0.03
(0.746) (0.336) (0.270) (0.206)
Post UI Expiration 1.62 0.78 0.23 0.62∗∗
(1.123) (0.472) (0.399) (0.296)
Observations 1250 1250 1250 1250
Pre-Covid Mean 25.49 9.55 9.54 6.41
Robust and clustered (at state level) standard errors in parentheses
∗
p < 0.10, ∗∗ p < 0.05, ∗∗∗ p < 0.01
Notes: Sample comes from the SSA State Agency Monthly Workload and ranges from March 2020 to March
2022. Outcome variable is weekly applications per 100,000 people aged 20 to 64. Standard errors are robust
and clustered at the state level. Regressions include month of year and state fixed effects. UI Expiration
Month equals 1 if expanded UI programs expired during the month; Post UI Expiration equals 1 in months
following expanded UI program expiration.
45
Appendix A: Alternative Benchmarks
A.1 Counterfactual: 2015-2019 average
Figure A.1: Event Studies of Employment Outcomes from the CPS Among 50-70 Year Olds
(a) Employed (b) Employed-Absent
(c) Unemployed (d) NILF (Disability, Retired, Other)
Notes: Sample contains civilians ages 50-70 from the January 2015-March 2022 CPS living in
the United States. Outcome variable is whether or not an individual is employed, employed
but absent, unemployed, or not in the labor force. An individual is classified as employed-
absent if they are absent from their job for a temporary reason during the survey reference
week. Standard errors are robust and clustered at the state level. Estimates are weighted
using survey weights and 95% confidence intervals are shown. The event time is relative to
February 2020. Regressions include month and state fixed effects and adjust for year of age
fixed effects, sex, race, Hispanic ethnicity, education, and household family size.
46
Table A.1: Changes in Employment Outcomes Following COVID-19 Pandemic
A. 50-61-Year-Olds
(1) (2) (3) (4)
Employed Employed-Absent Unemployed NILF
Post-Covid 1 -0.038∗∗∗ 0.005∗∗∗ 0.027∗∗∗ 0.005∗∗
(0.003) (0.001) (0.002) (0.002)
Post-Covid 2 -0.009∗∗∗ 0.001 0.003∗∗ 0.005
(0.003) (0.001) (0.001) (0.003)
Observations 1701077 1701077 1701077 1701077
Pre-Covid Mean 0.688 0.026 0.024 0.262
T-test PC1 = PC2 0.000 0.000 0.000 0.793
Standard errors in parentheses
∗
p < 0.10, ∗∗ p < 0.05, ∗∗∗ p < 0.01
B. 62-70-Year-Olds
(1) (2) (3) (4)
Employed Employed-Absent Unemployed NILF
Post-Covid 1 -0.021∗∗∗ 0.003∗∗∗ 0.016∗∗∗ 0.002
(0.003) (0.001) (0.001) (0.002)
Post-Covid 2 -0.002 -0.000 0.003∗∗ -0.001
(0.003) (0.001) (0.001) (0.003)
Observations 1146556 1146556 1146556 1146556
Pre-Covid Mean 0.363 0.019 0.013 0.606
T-test PC1 = PC2 0.000 0.000 0.000 0.448
Standard errors in parentheses
∗
p < 0.10, ∗∗ p < 0.05, ∗∗∗ p < 0.01
Notes: Samples contains civilians ages 50-61 and 62-70 from the January 2015-March 2022 CPS living in
the United States. Outcome variable is whether or not an individual is employed, unemployed, or not in
the labor force due to disability, retirement, or another reason respectively. An individual is classified as
employed-absent if they are absent from their job for a temporary reason during the survey reference week.
Standard errors are robust and clustered at the state level. Estimates are weighted using survey weights.
The Post-Covid estimate captures the change in employment outcome using January 2015-February 2020 as
the pre-period and March 2020-March 2021 as the first post-period and April 2021-March 2022 as the second
post-period. Regressions include month and state fixed effects, and adjust for year of age fixed effects, sex,
race, Hispanic ethnicity, education, and household family size. Pre-Covid means captures the mean of the
dependent variable in the pre-period January 2015-February 2020.
47
Table A.2: Changes in NILF Following COVID-19 Pandemic
A. 50-61-Year-Olds
(1) (2) (3) (4)
NILF Retired Disabled Other
Post-Covid 1 0.005∗∗ 0.001 -0.010∗∗∗ 0.014∗∗∗
(0.002) (0.001) (0.001) (0.001)
Post-Covid 2 0.005 0.004∗∗∗ -0.006∗∗∗ 0.007∗∗∗
(0.003) (0.001) (0.002) (0.001)
Observations 1701077 1701077 1701077 1701077
Pre-Covid Mean 0.262 0.080 0.105 0.078
T-test PC1 = PC2 0.793 0.080 0.129 0.000
Standard errors in parentheses
∗
p < 0.10, ∗∗ p < 0.05, ∗∗∗ p < 0.01
B. 62-70-Year-Olds
(1) (2) (3) (4)
NILF Retired Disabled Other
Post-Covid 1 0.002 -0.003 -0.003∗∗ 0.008∗∗∗
(0.002) (0.003) (0.002) (0.001)
Post-Covid 2 -0.001 -0.001 -0.004 0.004∗∗∗
(0.003) (0.003) (0.002) (0.001)
Observations 1146556 1146556 1146556 1146556
Pre-Covid Mean 0.606 0.491 0.079 0.036
T-test PC1 = PC2 0.448 0.550 0.868 0.016
Standard errors in parentheses
∗
p < 0.10, ∗∗ p < 0.05, ∗∗∗ p < 0.01
Notes: Samples contains civilians ages 50-61 and 62-70 from the January 2015-March 2022 CPS living in
the United States. Outcome variable is whether or not an individual is not in the labor force as well as each
subcategory of NILF: disability, retirement, or another reason. Standard errors are robust and clustered at
the state level. Estimates are weighted using survey weights. The Post-Covid estimate captures the change
in employment outcome using January 2015-February 2020 as the pre-period and March 2020-March 2021
as the first post-period and April 2021-March 2022 as the second post-period. Regressions include month
and state fixed effects, and adjust for age, sex, race, Hispanic ethnicity, education, and household family
size. Pre-Covid means captures the mean of the dependent variable in the pre-period January 2015-February
2020.
48
A.2 Counterfactual: February 2018-February 2020 average
Figure A.2: Event Studies of Employment Outcomes from the CPS Among 50-70 Year Olds
(a) Employed (b) Employed-Absent
(c) Unemployed (d) NILF (Disability, Retired, Other)
Notes: Sample contains civilians ages 50-70 from the February 2018-March 2022 CPS living
in the United States. Outcome variable is whether or not an individual is employed, employed
but absent, unemployed, or not in the labor force. An individual is classified as employed-
absent if they are absent from their job for a temporary reason during the survey reference
week. Standard errors are robust and clustered at the state level. Estimates are weighted
using survey weights and 95% confidence intervals are shown. The event time is relative to
February 2020. Regressions include month and state fixed effects and adjust for age, sex,
race, Hispanic ethnicity, education, and household family size.
49
Table A.3: Changes in Employment Outcomes Following COVID-19 Pandemic
A. 50-61-Year-Olds
(1) (2) (3) (4)
Employed Employed-Absent Unemployed NILF
Post-Covid 1 -0.047∗∗∗ 0.006∗∗∗ 0.031∗∗∗ 0.010∗∗∗
(0.003) (0.001) (0.002) (0.002)
Post-Covid 2 -0.017∗∗∗ 0.002∗∗ 0.006∗∗∗ 0.010∗∗∗
(0.003) (0.001) (0.001) (0.003)
Observations 901574 901574 901574 901574
Pre-Covid Mean 0.688 0.026 0.024 0.262
T-test PC1 = PC2 0.000 0.000 0.000 0.774
Standard errors in parentheses
∗
p < 0.10, ∗∗ p < 0.05, ∗∗∗ p < 0.01
B. 62-70-Year-Olds
(1) (2) (3) (4)
Employed Employed-Absent Unemployed NILF
Post-Covid 1 -0.030∗∗∗ 0.004∗∗∗ 0.017∗∗∗ 0.010∗∗∗
(0.003) (0.001) (0.002) (0.003)
Post-Covid 2 -0.011∗∗∗ -0.000 0.004∗∗∗ 0.007∗∗
(0.003) (0.001) (0.001) (0.003)
Observations 644848 644848 644848 644848
Pre-Covid Mean 0.363 0.019 0.013 0.606
T-test PC1 = PC2 0.000 0.000 0.000 0.481
Standard errors in parentheses
∗
p < 0.10, ∗∗ p < 0.05, ∗∗∗ p < 0.01
Notes: Samples contains civilians ages 50-61 and 62-70 from the February 2018-March 2022 CPS living in
the United States. Outcome variable is whether or not an individual is employed, unemployed, or not in
the labor force due to disability, retirement, or another reason respectively. An individual is classified as
employed-absent if they are absent from their job for a temporary reason during the survey reference week.
Standard errors are robust and clustered at the state level. Estimates are weighted using survey weights.
The Post-Covid estimate captures the change in employment outcome using February 2018-February 2020 as
the pre-period and March 2020-March 2021 as the first post-period and April 2021-March 2022 as the second
post-period. Regressions include month and state fixed effects, and adjust for year of age fixed effects, sex,
race, Hispanic ethnicity, education, and household family size. Pre-Covid means captures the mean of the
dependent variable in the pre-period February 2018-February 2020.
50
Table A.4: Changes in NILF Following COVID-19 Pandemic
A. 50-61-Year-Olds
(1) (2) (3) (4)
NILF Retired Disabled Other
Post-Covid 1 0.010∗∗∗ 0.002∗ -0.007∗∗∗ 0.015∗∗∗
(0.002) (0.001) (0.001) (0.001)
Post-Covid 2 0.010∗∗∗ 0.005∗∗∗ -0.003 0.008∗∗∗
(0.003) (0.002) (0.002) (0.001)
Observations 901574 901574 901574 901574
Pre-Covid Mean 0.262 0.080 0.105 0.078
T-test PC1 = PC2 0.774 0.087 0.131 0.000
Standard errors in parentheses
∗
p < 0.10, ∗∗ p < 0.05, ∗∗∗ p < 0.01
B. 62-70-Year-Olds
(1) (2) (3) (4)
NILF Retired Disabled Other
Post-Covid 1 0.010∗∗∗ 0.005 -0.003∗∗ 0.008∗∗∗
(0.003) (0.003) (0.002) (0.001)
Post-Covid 2 0.007∗∗ 0.007∗ -0.004∗∗ 0.005∗∗∗
(0.003) (0.004) (0.002) (0.001)
Observations 644848 644848 644848 644848
Pre-Covid Mean 0.606 0.491 0.079 0.036
T-test PC1 = PC2 0.481 0.473 0.827 0.014
Standard errors in parentheses
∗
p < 0.10, ∗∗ p < 0.05, ∗∗∗ p < 0.01
Notes: Samples contains civilians ages 50-61 and 62-70 from the February 2018-March 2022 CPS living in
the United States. Outcome variable is whether or not an individual is not in the labor force as well as each
subcategory of NILF: disability, retirement, or another reason. Standard errors are robust and clustered at
the state level. Estimates are weighted using survey weights. The Post-Covid estimate captures the change
in employment outcome using January 2018-February 2020 as the pre-period and March 2020-March 2021
as the first post-period and April 2021-March 2022 as the second post-period. Regressions include month
and state fixed effects, and adjust for year of age fixed effects, sex, race, Hispanic ethnicity, education, and
household family size. Pre-Covid means captures the mean of the dependent variable in the pre-period
February 2018-February 2020.
51
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