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
We would like to thank Sara Ji and Rosemary Rhodes 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 NBER.
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. The views expressed herein are those of the authors
and do not necessarily reflect the views of the National Bureau of Economic Research.
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.ack
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 They have not been
peer-reviewed or been subject to the review by the NBER Board of Directors that accompanies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
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 that the loss of these additional
financial supports is associated with a drop in older adult unemployment rates and an increase in
Social Security Disability Insurance claiming. Social Security retirement benefit claiming has
rebounded to pre-pandemic levels, but has shifted from offline to online applications.
Gopi Shah Goda
Stanford University
SIEPR
366 Galvez St.
Stanford, CA 94305
and NBER
gopi@stanford.edu
Emilie Jackson
Michigan State University
486 W. Circle Dr
25D Marshall-Adams Hall
East Lansing, MI 48824
emiliej@msu.edu
Lauren Hersch Nicholas
Colorado School of Public Health
CU Anschutz Fitzsimons Building
13001 East 17th Place
3rd Floor, Mail Stop B119
Aurora, CO 80045
lauren.h.nicholas@cuanschutz.edu
Sarah Stith
University of New Mexico
Department of Economics
1915 Roma Ave. NE 1019
Albuquerque, NM 87131
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. 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. Appli-
cations for Social Security disability benefits remain depressed through the second year of
the pandemic (17 percent lower than pre-pandemic levels), with the drop more pronounced
in applications for Supplemental Security Income (SSI) relative to Social Security Disability
Insurance (SSDI) benefits.
1
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. Initially
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 nationally (Coombs
et al., 2022), was associated with changes in labor market outcomes and Social Security
disability applications. Among older adults, we find that unemployment dropped by 0.5
percentage points in state-months where expanded UI benefits had expired. In addition,
applications for Social Security Disability Insurance (SSDI) increased by 5.2 percent and
applications for concurrent SSDI and Supplemental Security Insurance (SSI) by 8.8 percent.
While these results cannot be interpreted causally since program expiration was not random,
they provide suggestive evidence that the expanded unemployment benefits between March
2020 and September 2021 dissuaded some individuals from working or 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 unemployment (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
ages, with somewhat more concentrated effects among the youngest (Montenovo et al., 2022)
2
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 larger for SSI, a program targeting low- to
no-wage earners, than for SSDI, which requires some work history for eligibility.
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;
Charles et al., 2018; Munnell and Rutledge, 2013). These studies generally find that higher
3
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
pandemic, when many older workers appeared to be taking a “wait and see” approach to
4
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
was obtained (e.g., due to absence, refusal, or impassable roads); and distributional weights
5
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, i.e., the week including the 12th of the month. Households
are surveyed for four consecutive months, then not surveyed for the next eight months,
before being surveyed again for a final four consecutive months, after which they exit the
sample. Surveys are conducted via telephone and in-person interviews with a single “refer-
ence” 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, Unemployed, and Not In Labor Force (NILF ).1 For those
not in the labor force, we create {0,1} dichotomous variables breaking out the reasons re-
ported for being NILF into Retired,Disabled, and Other. We also obtained demographic
controls for the households in our sample, including 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
poverty rates (Hokayem et al., 2015), and potentially affected by COVID-19 and COVID-
19-related policies. This issue appears to have been significantly exacerbated 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
1The employed-absent response may include measurement error, particularly between February and May
2020—many workers were furloughed and some furloughed workers may have been recorded as Employed−
Absent rather than Unemployed (Montenovo et al., 2022)
6
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).
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 2,871,418 observations, 1,724,862 from individuals between 50
and 61 and 1,146,556 from individuals between 62 and 70.
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
are 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 NILF 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 NILF and
7
accounts for 40% of this group, followed by 31% who considered themselves retired, and
finally 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 NILF 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 represented by the 62–70 cohort is smaller than that
represented by the 51–60 cohort for Employed, Employed− Absent, and Unemployed, but
is larger for NILF .
Figure 2 splits out NILF 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
shock among 50–61 year-olds than among 62–70 year-olds. However, Disabled as a share of
the population appears to have somewhat rebounded for the younger cohort, while it has
remained lower 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.
8
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). 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 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.2 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.
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
2Exceptions 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.
9
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
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 In-
surance (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 workers an extra $600 per week
in 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 federal programs expired on September 6, 2021, but some states opted to allow
10
these programs to expire prior to that date. We construct a state-level, time-varying binary
variable, UI Expirationst, that equals 1 in states and months where these expanded programs
were no longer in effect, and 0 otherwise.3 In order to harmonize these data with the timing
of the CPS reference week, we code a state’s expanded UI programs as expired in a given
month if the expiration date was prior to the 12th of that month.4
Figure 7 shows the month of UI expiration for each state. As shown in the figure, 18
states opted out of at least one program in June 2021, five in July, and one in August. 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.
Yist =
−1∑
k=−5
βk × 1[e(t) = k] +
10∑
k=1
βk × 1[e(t) = k] + γ × 1[e(t) < −5]
+ θm(t) + τt+ ωs + ξXist + εist (1a)
Yst =
−1∑
k=−5
βk × 1[e(t) = k] +
10∑
k=1
βk × 1[e(t) = k] + γ × 1[e(t) < −5]
+ θm(t) + ψy(t) + ωs + εst (1b)
3There are four states that terminated one of the two federal programs early – we group these states into
the month where they ended at least one federal unemployment insurance program.
4For example, Tennessee ended both FPUC and PUA on July 3, 2021. Because expiration occurred prior
to the 12th of the month, UI Expirationst is equal to 1 in July 2021 and later months. However, Alaska
terminated the FPUC on June 19, 2021. Therefore, UI Expirationst for Alaska is 0 in June 2021, but 1 for
July 2021 and later months.
11
Yt =
−1∑
k=−5
βk × 1[e(t) = k] +
10∑
k=1
βk × 1[e(t) = k] + γ × 1[e(t) < −5]
+ θm(t) + ψy(t) + εst (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 month-level time trend
τt to capture pre-existing trends in our outcome variables, and demographic variables Xist,
which include age, 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 specifi-
cations. For disability applications, we employ Equation 1b, using the same strategy for our
event-time variables. We further control for month, state, and year fixed effects 5 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 based on a continuation of prior trends, after controlling for seasonality, changes
in the demographic composition, and time-invariant state characteristics.
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
5We include year fixed effects rather than a month-level time trend because we adjust our applications
data by population, which is measured annually. Demographic information is not available for these data.
12
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-
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 = α1PostCovid1ist + α2PostCovid2ist + θm(t) + τt+ ωs + ξXist + εst (2a)
Yst = α1PostCovid1st + α2PostCovid2st + θm(t) + ψy(t) + ωs + εst (2b)
Yt = α1PostCovid1t + α2PostCovid2t + θm(t) + ψy(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 last set of analyses assesses whether the expiration of expanded unemployment in-
surance programs was associated with changes in both labor market and disability insurance
application outcomes. To do so, we first augment Equations 2a, 2b and 2c by including UI
Expirationst, a binary variable that equals 1 if expanded UI benefits were expired in a given
month in a given state as described earlier. As all of the expanded UI programs expired in
2021, the UI coefficient effectively divides the second year after the onset of the pandemic
Post-Covid 2 period into pre- and post-UI expiration. Months during the second year when
expanded UI benefits were available are captured by the coefficient on Post-Covid 2, and
13
the total effect during months when expanded UI benefits had expired are given by the sum
of the coefficients for Post-Covid 2 and UI Expiration. To further explore the effect of UI
expiration on disability claiming in particular, we generate an event study framework with
the month of UI expiration as the omitted period (t=0), tracking the periods five months
prior and five months after UI expiration. Months outside this window are grouped into the
t=-5 and t=+5 periods. We adjust for state, year and month fixed effects.
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 4, we present coefficients from our event study estimation in equation 1a.
Beginning with Figure 4a, 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 4b, 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.
14
Deviations in unemployment, as seen in Figure 4c, generally display inverse dynam-
ics to those seen with employment above in Figure 4a. 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,
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.
15
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.
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.
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.
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 5a, we observe a sharp decrease in total SSI and SSDI
applications that levels off in the end of year one around four 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
16
levels that is similar in magnitude.
Next, we decompose total disability applications into three subgroups: concurrent SSI and
SSDI applications (Figure 5b), SSDI only applications (Figure 5c), and SSI only applications
(Figure 5d). There are several patterns of note when examining the decomposition. First,
the dynamics in Figure 5b and 5d 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
pre-pandemic patterns would have anticipated. Second, as seen in Figure 5c, applications for
SSDI only follow a diverging pattern from applications for SSI or concurrent applications in
year two and begin to recover slowly. In fact, from July 2021 onwards, most coefficients are
statistically indistinguishable from zero, suggesting that SSDI only applications are similar
to what would have been predicted for this time period.
In Table 7, we 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 4.09 fewer applications per 100,000 people
aged 20 to 64, and this coefficient is not significantly different from that estimated in year
one.
Column (2) shows that SSDI only applications have recovered to predicted levels; how-
ever, the deviations in SSI and concurrent SSI and SSDI applications have slightly increased
during the second year of the pandemic and remain significantly lower. These results re-
inforce 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 6, 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 6 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 6b, where we see higher levels
17
of applications filed via the internet in late 2021 compared with late 2020. In Figure 6c, we
see persistently lower levels of applications filed offline through March 2022. These patterns
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 a statistically significant increase in total applications in the second
year of the pandemic. We find that in the second year there are 10.61 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.
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 versions of Equations 2a and 2b that incorporate
UI Expirationst to differentiate between months during the second year of the pandemic
with and without expanded UI benefits available. We caution that because the expiration
of expanded UI benefits occurred non-randomly, these coefficients should not be interpreted
causally.
First, we estimate a modified version of Equation 2a to examine the association between
UI expiration and labor market outcomes. In Table 9, we see that the expiration of expanded
UI benefits is associated with lower levels of unemployment. More precisely, in the months
after UI expiration unemployment is 0.5pp (panel A) and 0.4pp (panel B) lower, accounting
for about one-third to one-half of the year two deviation. While employment appears to
increase somewhat during months when expanded UI benefits were not available, the dif-
ferences are not statistically different, and in general, the other labor market outcomes we
18
examine do not vary during these months.
Second, in Table 10 we evaluate the association between UI expiration and deviations
in applications for disability. Starting with Column (1), the coefficient on UI Expirationst
indicates that in state-months during which expanded UI benefits have expired, we observe
1.9 more applications per 100,000. This effect reverses about 37 percent (1.9/5.13) of the
year two effect on total applications. When breaking down the types of applications, this
effect is largely driven by SSDI and concurrent applications. We see 0.86 more SSDI appli-
cations per 100,000 individuals age 20 to 64 in months after expanded UI benefits expired in
a state. In magnitude, this is a reversal of the entire deviation in SSDI applications in year
two (0.86/0.81). We see a similar magnitude change in concurrent applications, 0.75 more
applications per 100,000 individuals, which offsets half of the second year deviation in con-
current applications (0.75/1.50). By contrast, only one tenth of SSI applications (0.30/2.83)
are offset by UI expiration. Overall, Table 10 suggests a significant rebound in applications
for SSDI and concurrent SSDI and SSI in months after expanded UI benefits have expired.
Finally, in Figure 8, we plot event study figures that show the evolution of Social Security
disability 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. Consistent with the results in Table 10, 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.
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 approaching pre-pandemic levels.
19
Older workers experienced many of these patterns during the first year of the pandemic,
but unlike previous recessions, Social Security applications for retirement and disability did
not significantly increase. Our analysis extends earlier work into the second year of the
pandemic to determine whether labor market outcomes exhibited changes or more people
turned to Social Security as COVID-19 persisted.
We find evidence that while employment and unemployment rates for older workers have
largely recovered to predicted levels by March 2022, labor force non-participation remains
elevated. Further, we see a compositional shift among respondents towards reporting retire-
ment as the reason for not participating in the labor force rather than reasons other than
disability or retirement. Consistent with the idea that some who had left the labor force dur-
ing the first year were waiting some time before reporting retirement, we see a small increase
in retirement applications during the second year of the pandemic. However, applications for
disability benefits remain depressed relative to pre-pandemic levels. The distributional shift
towards online rather than offline retirement applications and the fact that SSA offices did
not reopen to in-person appointments until April 2022, after the end of our sample period,
suggest some of the reduction in 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. Leveraging variation in when states chose to allow these benefits to expire, we find that
months after the expiration of expanded UI benefits are associated with a small reduction
in unemployment and a partial reversal of the reduction in SSDI applications. However,
these results cannot be interpreted causally due to the non-random variation driving benefit
expiration.
The pandemic recession differs in many ways from prior recessions, and the second year
of the pandemic came with widespread vaccine availability, new coronavirus variants that
20
generated large increases in infections, and policy changes. As the COVID-19 pandemic
continues to result in more than 100,000 confirmed cases of COVID-19 each day as of June
2022, it will be important to continue to monitor how labor market outcomes among older
workers and spillovers to Social Security continue to evolve as the COVID-19 pandemic
transitions to an endemic state.
21
References
Autor, David H and Mark G Duggan, “The rise in the disability rolls and the decline
in unemployment,” The Quarterly Journal of Economics, 2003, 118 (1), 157–206.
Bacher-Hicks, Andrew, Joshua Goodman, and Christine Mulhern, “Inequality in
household adaptation to schooling shocks: Covid-induced online learning engagement in
real time,” Journal of Public Economics, 2021, 193, 104345.
Bartik, Alexander W, Marianne Bertrand, Feng Lin, Jesse Rothstein, and Matt
Unrath, “Measuring the labor market at the onset of the COVID-19 crisis,” Technical
Report 27613, National Bureau of Economic Research Working Paper 2020.
Black, Dan, Kermit Daniel, and Seth Sanders, “The impact of economic conditions on
participation in disability programs: Evidence from the coal boom and bust,” American
Economic Review, 2002, 92 (1), 27–50.
Bui, Truc Thi Mai, Patrick Button, and Elyce G Picciotti, “Early Evidence on the
Impact of Coronavirus Disease 2019 (COVID-19) and the Recession on Older Workers,”
Public Policy & Aging Report, 2020, 30 (4), 154–159.
Cajner, Tomaz, Leland Dod Crane, Ryan Decker, Adrian Hamins-Puertolas, and
Christopher Johann Kurz, “Tracking labor market developments during the covid-19
pandemic: A preliminary assessment,” Technical Report 2020-030, Federal Reserve Board
Finance and Economics Discussion Series 2020.
Carey, Colleen, Nolan H Miller, and David Molitor, “Why Does Disability Increase
During Recessions? Evidence from Medicare,” Technical Report, National Bureau of
Economic Research 2022.
Center for Budget and Policy Priorities, “Chart Book: Social Security Disability In-
surance,” February 2021.
Chan, Sewin and Ann Huff Stevens, “Employment and retirement following a late-
career job loss,” American Economic Review, 1999, 89 (2), 211–216.
Charles, Kerwin Kofi, Yiming Li, and Melvin Stephens Jr, “Disability benefit take-
up and local labor market conditions,” Review of Economics and Statistics, 2018, 100 (3),
416–423.
Coe, Norma B, Kelly Haverstick et al., “Measuring the spillover to disability insurance
due to the rise in the full retirement age,” Boston College Center for Retirement Research
Working Paper, 2010, 21.
Coibion, Olivier, Yuriy Gorodnichenko, and Michael Weber, “Labor markets during
the COVID-19 crisis: A preliminary view,” Technical Report 27017, National Bureau of
Economic Research Working Paper 2020.
22
Coile, Courtney C and Phillip B Levine, “Labor market shocks and retirement: Do
government programs matter?,” Journal of Public Economics, 2007, 91 (10), 1902–1919.
and , “Recessions, retirement, and social security,” American Economic Review, 2011,
101 (3), 23–28.
Coombs, Kyle, Arindrajit Dube, Calvin Jahnke, Raymond Kluender, Suresh
Naidu, Michael Stepner et al., “Early Withdrawal of Pandemic Unemployment Insur-
ance: Effects on Employment and Earnings,” in “AEA Papers and Proceedings,” Vol. 112
American Economic Association 2022, pp. 85–90.
Cortes, Guido Matias and Eliza Forsythe, “Heterogeneous Labor Market Impacts of
the COVID-19 Pandemic,” ILR Review, 2022, pp. 1–26.
Couch, Kenneth A, Gayle L Reznik, Christopher R Tamborini, and Howard M
Iams, “Economic and health implications of long-term unemployment: Earnings, disabil-
ity benefits, and mortality,” in “New Analyses of Worker Well-Being,” Emerald Group
Publishing Limited, 2014.
Cutler, David M, Ellen Meara, and Seth Richards-Shubik, “Unemployment and
disability: Evidence from the Great Recession,” NBER Retirement Research Center Paper,
2012, No. NB 12-12.
Davis, Owen, “Employment and Retirement Among Older Workers During the Covid-19
Pandemic,” Technical Report 6, Schwartz Center for Economic Policy Analysis 2022.
Derby, Elena, Lucas Goodman, Kathleen Mackie, and Jacob Mortenson, “Changes
in Retirement Savings During the COVID Pandemic,” Technical Report, U.S. Congress
Joint Committee on Taxation 2022.
Farber, Henry S et al., “What do we know about job loss in the United States? Evidence
from the Displaced Workers Survey, 1984–2004,” Economic Perspectives, 2005, 29 (2),
13–28.
Flood, Sarah, Miriam King, Renae Rodgers, Steven Ruggles, and J. Robert
Warren, “Integrated Public Use Microdata Series, Current Population Survey: Version
8.0 [dataset],” 2020.
Forsythe, Eliza, Lisa B Kahn, Fabian Lange, and David Wiczer, “Labor demand in
the time of COVID-19: Evidence from vacancy postings and UI claims,” Journal of public
economics, 2020, 189, 104238.
Goda, Gopi Shah, Emilie Jackson, Lauren Hersch Nicholas, and Sarah See Stith,
“The Impact of Covid-19 on Older Workers’ Employment and Social Security Spillovers,”
Technical Report 29083, National Bureau of Economic Research Working Paper 2022.
, John B Shoven, and Sita Nataraj Slavov, “What explains changes in retirement
plans during the Great Recession?,” American Economic Review, 2011, 101 (3), 29–34.
23
Gustman, Alan L, Thomas L Steinmeier, and Nahid Tabatabai, “What the stock
market decline means for the financial security and retirement choices of the near-
retirement population,” Journal of Economic Perspectives, 2010, 24 (1), 161–82.
Hamermesh, Daniel S, “Unemployment Insurance and the Older American.,” 1980.
Hokayem, Charles, Christopher Bollinger, and James P. Ziliak, “The Role of CPS
Nonresponse in the Measurement of Poverty,” Journal of the American Statistical Associ-
ation, 2015, 110 (511), 935–945.
Inderbitzin, Lukas, Stefan Staubli, and Josef Zweimu¨ller, “Extended unemploy-
ment benefits and early retirement: Program complementarity and program substitution,”
American Economic Journal: Economic Policy, 2016, 8 (1), 253–88.
IPUMS, “IPUMS CPS Unharmonized Variables,” May 2022.
Johnson, Richard W, “Older workers, retirement, and the great recession,” New York:
Russell Sage Foundation, 2012, pp. 1–7.
Larrimore, Jeff, Jacob Mortenson, and David Splinter, “Earnings Shocks and Stabi-
lization During COVID-19,” Technical Report 2021-052, Board of Governors of the Federal
Reserve System 2021.
Lee, Sang Yoon, Minsung Park, and Yongseok Shin, “Hit Harder, Recover Slower?
Unequal Employment Effects of the COVID-19 Shock,” Technical Report, National Bureau
of Economic Research 2021.
Lindner, Stephan, “How do unemployment insurance benefits affect the decision to apply
for social security disability insurance?,” Journal of Human Resources, 2016, 51 (1), 62–94.
and Austin Nichols, “The impact of temporary assistance programs on disability rolls
and re-employment,” in “Safety Nets and Benefit Dependence,” Emerald Group Publishing
Limited, 2014.
Maestas, Nicole, Kathleen J Mullen, and Alexander Strand, “Disability insurance
and the great recession,” American Economic Review, 2015, 105 (5), 177–82.
, , and , “The effect of economic conditions on the disability insurance program:
Evidence from the great recession,” Technical Report 25338, National Bureau of Economic
Research Working Paper 2018.
McEntarfer, Erika, “Older Workers, Retirement, and Macroeconomic Shocks,” Technical
Report, Pension Research Council of the Wharton School of the University of Pennsylvania
2022.
McFall, Brooke Helppie, “Crash and wait? The impact of the Great Recession on the
retirement plans of older Americans,” American Economic Review, 2011, 101 (3), 40–44.
24
Montenovo, Laura, Xuan Jiang, Felipe Lozano Rojas, Ian M Schmutte, Kos-
ali I Simon, Bruce A Weinberg, and Coady Wing, “Determinants of Disparities in
COVID-19 Job Losses,” Demography, 2022, (9961471), 935–945.
Mueller, Andreas I, Jesse Rothstein, and Till M Von Wachter, “Unemployment
insurance and disability insurance in the Great Recession,” Journal of Labor Economics,
2016, 34 (S1), S445–S475.
Munnell, Alicia H and Matthew S Rutledge, “The effects of the Great Recession on the
retirement security of older workers,” The Annals of the American Academy of Political
and Social Science, 2013, 650 (1), 124–142.
Munnell, Alicia H., Dan Muldoon, and Steven A. Sass, “Recessions and Older
Workers,” Technical Report, Center for Retirement Research at Boston College 2009.
Quinby, Laura, Matthew S. Rutledge, and Gal Wettstein, “How Has COVID-19
Affected the Labor Force Participation of Older Workers?,” Technical Report, Center for
Retirement Research at Boston College 2021.
Rothbaum, Jonathan and Adam Bee, “Coronavirus Infects Surveys, Too: Nonresponse
Bias During the Pandemic in the CPS ASEC,” Technical Report WP2020-10, United
States Census Bureau SEHSD 2021.
Rothstein, Jesse and Robert G Valletta, “Scraping by: Income and program partici-
pation after the loss of extended unemployment benefits,” Journal of Policy Analysis and
Management, 2017, 36 (4), 880–908.
Rutledge, Matthew S, “The impact of Unemployment Insurance extensions on Disability
Insurance application and allowance rates,” Boston College Center for Retirement Re-
search Working Paper, 2011, (2011-17).
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.
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.
25
A 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.
26
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.
27
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.
28
Figure 4: 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.
29
Figure 5: 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 in-
tervals are shown. Regressions include month, year, and state fixed effects and event time
relative to February 2020.
30
Figure 6: 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 and year fixed effects and event time relative to February
2020.
31
Figure 7: 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.
32
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 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 intervals
are shown. Regressions include month, year, and state fixed effects, relative to the month of
UI expiration. Months since UI expiration that are ∓ 5, inclusive, are grouped together.
33
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.
34
Table 2: CPS Employment Summary Statistics
Ages 50-61 Ages 62-70
Pre-Covid Post-Covid Pre-Covid Post-Covid
Employed 0.688 0.670 0.363 0.353
(0.463) (0.470) (0.481) (0.478)
Employed-Absent 0.026 0.030 0.019 0.020
(0.160) (0.171) (0.136) (0.142)
Unemployed 0.024 0.035 0.013 0.022
(0.152) (0.184) (0.112) (0.147)
Not in Labor Force-Retired, Disabled, and Other 0.262 0.249 0.606 0.605
(0.440) (0.432) (0.489) (0.489)
Retired 0.080 0.085 0.491 0.490
(0.271) (0.279) (0.500) (0.500)
Disabled 0.105 0.095 0.079 0.074
(0.306) (0.294) (0.270) (0.261)
NILF-Other 0.078 0.082 0.036 0.042
(0.268) (0.275) (0.186) (0.200)
Observations 1286653 438209 838723 307833
Notes: Sample contains civilians ages 25-49, 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.
35
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, and contains civilians ages 20-64 from
the January 2015-March 2022 living in the United States who applied for social security disability benefits
on the state level. Outcome variable is weekly applications per 100,000 people aged 20 to 64. 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.
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, and contains
civilians ages 60-69 from the January 2015-March 2022 living in the United States who applied for retirement
insurance on the country level. Outcome variable is weekly applications per 100,000 people aged 60 to 69.
Retirement Application Filed non-Internet stands for 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.
36
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.
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 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.
37
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. 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 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.
38
Table 7: Changes in Disability Applications During COVID-19 Pandemic
(1) (2) (3) (4)
All SSDI SSI Concurrent
Post-Covid 1 -3.78∗∗∗ -0.61∗∗∗ -2.08∗∗∗ -1.10∗∗∗
(0.513) (0.196) (0.219) (0.156)
Post-Covid 2 -4.09∗∗∗ -0.34 -2.66∗∗∗ -1.08∗∗∗
(0.861) (0.313) (0.376) (0.235)
Observations 4350 4350 4350 4350
Pre-Covid Mean 25.49 9.55 9.54 6.41
T-test PC1 = PC2 0.63 0.28 0.02 0.92
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. 95% confidence intervals are shown. Regressions include month,
year, 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 -4.18 14.76∗∗∗ -18.94∗∗∗
(2.904) (2.345) (1.602)
Post-Covid 2 10.61∗∗ 24.54∗∗∗ -13.93∗∗∗
(5.299) (4.609) (2.564)
Observations 87 87 87
Pre-Covid Mean 145.23 74.69 70.53
T-test PC1 = PC2 0.00 0.01 0.00
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 and year 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.
39
Table 9: Changes in Employment Outcomes Following COVID-19 Pandemic: UI Expiration
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 -0.033∗∗∗ 0.001 0.016∗∗∗ 0.017∗∗∗
(0.003) (0.001) (0.002) (0.004)
UI Expiration 0.003 0.002∗∗ -0.005∗∗∗ -0.000
(0.004) (0.001) (0.001) (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 0.491
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 -0.028∗∗∗ -0.001 0.009∗∗∗ 0.020∗∗∗
(0.006) (0.001) (0.002) (0.006)
UI Expiration 0.005 0.000 -0.004∗∗ -0.002
(0.005) (0.001) (0.001) (0.005)
Observations 1146556 1146556 1146556 1146556
Pre-Covid Mean 0.363 0.019 0.013 0.606
T-test PC1 = PC2 0.069 0.002 0.000 0.482
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. The unemployment insurance (UI) regressor represents when an individual state opted-out of
extended benefits (June through August for early opt-out states and September for other states). 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.
40
Table 10: Changes in Disability Applications During COVID-19 Pandemic: UI Expiration
(1) (2) (3) (4)
All SSDI SSI Concurrent
Post-Covid 1 -3.78∗∗∗ -0.61∗∗∗ -2.08∗∗∗ -1.10∗∗∗
(0.513) (0.196) (0.219) (0.156)
Post-Covid 2 -5.13∗∗∗ -0.81∗∗ -2.83∗∗∗ -1.50∗∗∗
(0.857) (0.306) (0.378) (0.230)
UI Expiration 1.90∗∗∗ 0.86∗∗∗ 0.30∗∗ 0.75∗∗∗
(0.380) (0.158) (0.145) (0.114)
Observations 4350 4350 4350 4350
Pre-Covid Mean 25.49 9.55 9.54 6.41
T-test PC1 = PC2 0.04 0.42 0.01 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. 95% confidence intervals are shown. Regressions include month, year,
and state fixed effects. Post Covid 1 equals 1 between March 2020 and March 2021, Post Covid 2 equals 1
between April 2021 and March 2022, and UI Expiration equals 1 if the UI status is expired in a state in a
given month.