The Inequitable Burden of the COVID-19 Pandemic Among Marginalized Older Workers in the United States: An Intersectional Approach

Abstract

Objectives

The COVID-19 pandemic has profoundly affected the lives of people globally, widening long-standing inequities. We examined the COVID-19 pandemic’s impact on employment conditions by race/ethnicity, gender, and educational attainment and the association between such conditions and well-being in older adults in the United States.

Methods

Using data from the Health and Retirement Study respondents interviewed between May 2020 and May 2021 when they were ≥55 years of age, we examined intersectional patterns in COVID-19-related changes in employment conditions among 4,107 participants working for pay at the start of the pandemic. We also examined the compounding nature of changes in employment conditions and their association with financial hardship, food insecurity, and poor self-rated health.

Results

Relative to non-Hispanic White men with greater than high school education (>HS), Black and Latinx men and women were more likely to experience job loss irrespective of education; among those who did not experience job loss, men with ≤HS reporting Black, Latinx, or “other” race were >90% less likely to transition to remote work. Participants who experienced job loss with decreased income or continued in-person employment with decreased income/shift changes had greater prevalence of financial hardship, food insecurity, and poor/fair self-rated health than others.

Discussion

The impact of COVID-19 on employment conditions is inequitably patterned and is associated with financial hardship, food insecurity, and adverse health in older adults. Policies to improve employment quality and expand social insurance programs among this group are needed to reduce growing inequities in well-being later in life.

The COVID-19 pandemic has affected the lives of people globally, widening long-standing inequities. In the United States, research has highlighted how racially and socioeconomically marginalized people are not only at greater risk of exposure to SARS-CoV-2 (Webb Hooper et al., 2020), but are also more likely to be hospitalized and die as a result (Siegel et al., 2022). The role of occupation has featured prominently in this context, with those employed in lower income or otherwise more precarious employment—that is, with more instability, limited interpersonal power, and limited access to benefits like health insurance and paid leave—at greater risk of illness and death from SARS-CoV-2 infection (Adler & Bhattacharyya, 2021; Chen & Krieger, 2021; Hawkins, 2020; Hawkins et al., 2021; McClure et al., 2020), particularly those over the age of 50 (Bellotti et al., 2021). However, the implications of the pandemic for marginalized workers extend far beyond their risk of infection and death from COVID-19.

The pandemic has had a substantial effect on the labor market, with unprecedented global job losses (International Labour Organization, 2021). By April 2020, the U.S. unemployment rate hit the highest level since recording began in 1948 (U.S. Bureau of Labor Statistics, 2020). Moreover, as many as 60% of those who did not lose their jobs experienced wage cuts or freezes (Cajner et al., 2020). Like inequities in illness and mortality, the burden of adverse COVID-19-related changes in employment conditions have not been equally distributed across the population. One recent U.S. study found that about half as many Black and/or Latinx workers were employed in jobs that could be done from home relative to White workers; income inequities in such conditions were also substantial (Gould & Shierholz, 2020). As such, racial, gender, and class inequities in COVID-19-related unemployment were apparent from the outset of the pandemic, with Black women facing the largest losses in employment between February and April 2020 (Gould & Wilson, 2020). Additionally, 44% of Latinx and 32% of Black Americans reported in an August 2020 survey that someone in their household had taken a pay cut because of the pandemic, in contrast to 29% of White Americans (Parker et al., 2020). Moreover, 60% of respondents who indicated they lost wages due to the pandemic indicated they were still earning less at the time of the survey (Parker et al., 2020). Concomitantly, the switch to remote work during the pandemic may not be feasible for older workers who tend to be employed in manual jobs (Johnson & Wang, 2017). Researchers have found that although older workers’ monthly job loss rates from January through April 2020 were lower than younger workers’, rates for older workers have not recovered during the pandemic (Gould, 2021). Moreover, given what has been observed in previous recessions (Bui et al., 2020), it is likely older workers who lose their jobs will face more difficulties finding a new job than others (Moen et al., 2020).

Even before the pandemic, labor markets have generally shifted toward more precarious employment conditions, with workers in recent decades increasingly subjected to unstable temporary arrangements, inadequate wages, and meager access to benefits—often clustering together simultaneously among the most disadvantaged segments of the workforce (Benach et al., 2014). These trends have coincided with growing wealth and health inequities, and these features of employment quality are increasingly recognized as social determinants of health (Benach et al., 2014; Howell & Kalleberg, 2019). However, the limited literature assessing the impact of COVID-19 on employment conditions have examined conditions in isolation, without considering how these conditions co-occur, or the potential implications of continuing disruptions for later-life financial security and well-being. Financial shocks close to retirement can reduce retirement income, compromise health-insurance coverage, and shorten longevity (Bui et al., 2020). Given the inequitable effects of the pandemic on the labor market, adverse COVID-19-related changes in employment conditions may exacerbate existing inequities in later-life financial security and health.

Our work further documents the contribution of the COVID-19 pandemic in exacerbating inequities through three objectives. First, we aimed to better characterize the nature of COVID-19-related impacts on employment conditions experienced by older workers interviewed between May 2020 and May 2021. Second, we aimed to examine heterogeneity across employment conditions by race, gender, and education. Finally, we aimed to examine the relationship between COVID-19-related changes in employment conditions and experiences of financial hardship, food insecurity, and poor self-rated health. A better understanding of the impact of COVID-19 on employment conditions and well-being is critical for informing policy approaches to mitigate growing inequities exacerbated by the pandemic.

Method

We used data from the Health and Retirement Study (HRS), a nationally representative biennial panel survey of U.S. adults aged 51 or older (Juster & Suzman, 1995). HRS employs a steady-state design, replenishing the sample every 6 years with younger cohorts (Health and Retirement Study, 2022). A cohort born between 1960 and 1965 was added in 2016, making the youngest participants in our sample at the time of data collection 55 years old. HRS added COVID-19-related questions to the 2020 core interview. Of the 10,321 participants who were administered the COVID-19 survey module between May 2020 and May 2021, 4,107 participants reported they were working for pay at the start of the pandemic (March 2020) and/or at some point between the start of the pandemic and the time of interview, irrespective of hours and months worked. This study used publicly available, deidentified data. While exempt from Institutional Review Board review, research falls under a broader research project approved by the UW Institutional Review Board (study ID 5330).

Measures

COVID-19-related change in employment conditions

We focused on objective measures of change in employment conditions wrought by the COVID-19 pandemic. The COVID-19 module included questions about changes to income (increase, decrease, or no change), any change to hours or days worked (yes/no), any change from in-person to remote work (yes/no), and whether the participant had to stop working (temporary or permanent job loss vs. did not have to stop working). Questions regarding changes to hours or days worked and changes from in-person to remote work were only asked of participants who were not forced to discontinue working. Additional details on key questions from the COVID-19 module and how they were coded for analyses are available in Supplementary Tables A1 and A2, respectively.

Because we were also interested in the compounding nature of these changes in employment conditions, we subsequently created a six-level categorical variable featuring combinations of these conditions: “Switched to remote only,” “Switched to remote with income decrease/shift changes,” “Status quo,” “Income decrease/shifts changed only,” “Job loss with stable income,” and “Job loss with income decrease.” We operationalized “Status quo” (no transition to remote and either had no change to hours/shifts worked or had no decrease in income) as our referent group.

Characteristics of current or most recent employment

We retrieved variables corresponding to the quality of the participant’s current or most recent employment relationship including whether the participant owned their own business, had access to employer-sponsored health insurance, retirement contributions, paid leave, and union representation, as well as their annual income, hours worked per week, weeks worked in the previous year, and years of employment.

Intersectional identities

Intersecting social factors of interest included race/ethnicity, gender, and education as proxies for structural racism, sexism, and classism, respectively. Additionally, educational credentials are required for many occupations, including those that may be more (or less) susceptible to COVID-19-related changes in employment conditions. HRS reports participants as Hispanic or not Hispanic, White, Black, or Other. Although HRS records participant race in more granular detail, those reporting American Indian, Alaskan Native, Asian, Native Hawaiian, or Pacific Islander are aggregated into a single “other” category in publicly available data. Sparse cell counts necessitated dichotomizing education into those with a high school degree or less (≤HS) and those with greater than high school (>HS). Supplementary Table A3 provides cell counts and additional details regarding small subgroups.

Financial hardship and food insecurity

The COVID-19 module included additional questions regarding hardship. We classified participants as having financial hardship if they responded affirmatively to missing any regular payments on rent or mortgage, credit cards or other debt, other regular payments such as utilities or insurance, or being unable to pay medical bills. Participants responding affirmatively to not having enough money to pay for food were classified as having food insecurity.

Poor self-rated health

We classified participants as having poor/fair self-rated health if their response to “Would you say your health is excellent, very good, good, fair, or poor?” was “poor” or “fair.”

Statistical Analyses

Analyses proceeded in four steps. First, we descriptively examined the proportion of respondents experiencing each employment condition. Second, we used modified Poisson regression (Zou, 2004) with interaction terms to examine heterogeneity in the risk of individual COVID-19-related changes in employment conditions by intersecting social factors. In all models examining heterogeneity by intersecting social factors, we included the three-way interaction between four-level race, two-level gender, and two-level education as well as all lower-level interactions and main effects; we utilized the group with the best employment conditions on average (non-Hispanic White men with >HS) as the referent group. Because our aim was to capture the total magnitude of inequities across intersecting subgroups, we did not adjust for confounding variables (Bauer, 2014; Conroy & Murray, 2020). Third, we descriptively examined the compounding nature of employment conditions by visualizing the distribution of sociodemographic characteristics, current or most recent employment attributes, and outcomes of interest by different combinations of COVID-19-related changes in employment conditions relative to our overall study population. Finally, we examined whether individual or combined changes in employment conditions were associated with financial difficulties, food insecurity, and/or poor self-rated health by estimating prevalence ratios (PRs) in separate modified Poisson regression models. We left these regressions unadjusted, knowing the associations would partially reflect respondents’ baseline precarity, as well as their membership in oppressed or privileged social groups. In sensitivity analyses, we adjusted for participant race/ethnicity, gender, highest level of education, age, region of residence, childhood socioeconomic status, and childhood self-rated health (Supplementary Table A7).

All analyses were performed in Stata MP Version 16.1 (StataCorp, College Station, TX) and incorporated strata and sampling units to account for HRS’s complex sampling design. Survey year respondent-level sampling weights were applied such that estimates are representative of the U.S. civilian, noninstitutionalized population aged 55 years and older. We used multiple imputation (m = 20 replicates) to address missingness in study variables. Participant sociodemographic characteristics as well as employment quality attributes from their current or last job were used to inform imputation. Estimates and their respective standard errors were adjusted for the imputation uncertainty using Rubin’s rules. In our interpretation of estimated associations, we focus on the direction, magnitude, and precision of the estimated risk ratios (RRs) and PRs rather than statistical significance (Stang et al., 2010).

Results

Table 1 displays characteristics of the 4,107 participants who were working for pay between March 2020 and May 2021. Participants were 70% White with a mean age of 63.3 years. Fourteen percent reported COVID-19-induced permanent or temporary job loss and 29% reported decreases in their income. Of those participants who did not have to discontinue working, 15% reported changes in their hours, and 30% reported switching completely from in-person to remote work. Relative to all respondents who participated in the HRS 2020 COVID-19 Module, those in the labor force—and thus in our analytical sample—were younger on average and disproportionately men (Supplemental Table A4).

Table 1.

Characteristics of 4,107 Participants Who Were Working for Pay at Some Point Between March 2020 and May 2021, Health and Retirement Study

	N (Col. %) or mean (SE)
Age (years)	63.3 (0.2)
Gender
Men	1,765 (51.6)
Women	2,194 (48.4)
Missing	148 (0.0)
Race
Non-Hispanic White	1,921 (69.8)
Non-Hispanic Black	958 (10.8)
Latinx	406 (6.4)
Non-Hispanic Othera	666 (12.9)
Missing	156 (0.1)
Educational attainment
Less than high school	405 (6.6)
GED	177 (3.6)
High school graduate	900 (21.1)
Some college	1,204 (28.5)
College graduate	1,273 (40.3)
Missing	148 (0.0)
COVID-19-related changes in employment conditions
Did not have to stop working	2,986 (74.9)
Lost job/permanently laid-off	129 (2.2)
Furloughed/laid-off temporarily	555 (12.0)
Quit	34 (0.7)
Other (details not publicly available)	311 (7.6)
Missing	92 (2.5)
Changes to income
Income increased	387 (9.1)
No change in income	2,438 (61.9)
Income decreased	1,267 (28.6)
Missing	15 (0.4)
Required to change hours or day workedb
No	2,531 (84.6)
Yes	455 (15.4)
Missing	0 (0.0)
In-person work transitioned to remoteb
No	2,235 (69.5)
Yes	751 (30.5)
Missing	0 (0.0)
Economic and health outcomes
Financial hardship
No	3,297 (86.4)
Yes	791 (13.3)
Missing	19 (0.4)
Food insecurity
No	3,747 (94.9)
Yes	341 (4.7)
Missing	19 (0.4)
Poor self-rated health
No	3,322 (85.6)
Yes	784 (14.4)
Missing	1 (0.0)

Notes: Analyses incorporated strata and sampling units to account for HRS’s complex sampling design. Survey year respondent-level sampling weights were applied such that estimates are representative of the U.S. civilian, noninstitutionalized population aged 55 years and older.

^aAmerican Indian, Alaskan Native, Asian, Native Hawaiian, or Pacific Islander.

^bThese questions were only asked of participants who were not forced to discontinue working (e.g., 15.4% of respondents who did not have to stop working reported changes to hours or shifts worked).

Heterogeneity in COVID-19-Related Changes in Employment Conditions by Race, Gender, and Education

The likelihood of each employment condition varied considerably by race, gender, and education (Figures 1 and 2).

Figure 1.

Association between intersecting group identity and COVID-19-related job loss or changes in income, Health and Retirement Study May 2020–May 2021. Relative risk estimated with modified Poisson regression in separate models for each employment condition. Analyses incorporated strata and sampling units to account for HRS’s complex sampling design and were adjusted for imputation uncertainty using Rubin’s combination rules. NH White men with >HS education were the reference group in all models. For consistency in modeling approach and estimate interpretation as a risk ratio, we estimated associations with changes in income in two separate models, that is, increase in income versus no change in income and decrease in income versus no change in income. ≤HS = high school or less; >HS = greater than high school; NH = non-Hispanic.

Figure 2.

Association between intersecting group identity and COVID-19-related changes in hours worked or transition to remote work among those who did not experience job loss, Health and Retirement Study (HRS) May 2020–May 2021. Relative risk estimated with modified Poisson regression in separate models for each employment condition. Analyses incorporated strata and sampling units to account for HRS’s complex sampling design and were adjusted for imputation uncertainty using Rubin’s combination rules. NH White men with >HS education were the reference group in all models. Of the Latinx men with ≤HS who did not experience job loss, none reported transitioning from in-person to remote work. As such, relative risk is 0 and confidence intervals are not estimable. NH = non-Hispanic.

Job loss

Relative to non-Hispanic White men with >HS, Black and Latinx men and women were more likely to experience job loss irrespective of education with stronger associations observed for Latinx men with >HS (RR: 2.79; 95% confidence interval [CI]: 1.50, 5.20; Figure 1; Supplementary Table A5) and Black women with ≤HS (RR: 2.76; 95% CI: 1.63, 4.66) and weaker associations observed for Black men with >HS (RR: 1.89; 95% CI: 1.09,3 .25) and Latinx men with ≤HS (RR: 1.95; 95% CI: 0.75, 5.08). Those reporting another race were also more likely to experience job loss; however, associations for men with >HS were weaker (RR: 1.37; 95% CI: 0.76, 2.46).

Changes in income

With the exception of Latinx men and women with ≤HS, generally all subgroups exhibited a greater likelihood of experiencing increases in income relative to White men with >HS. Estimates were generally imprecise, ranging from 12% more likely (Black men with ≤HS; RR: 1.12; 95% CI: 0.59, 2.13) to 99% more likely (women reporting another race with >HS; RR: 1.99; 95% CI: 1.06, 3.74). Latinx workers and those workers reporting another race were also generally more likely than White men with >HS to experience decreases in income, irrespective of gender and education (Figure 1; Supplementary Table A5). In contrast, Black men with ≤HS were 37% less likely to report decreased income; however, this estimate was imprecise (RR: 0.63; 95% CI: 0.40, 1.00).

Among those who did not experience job loss, we observed heterogeneous experiences of changes to hours worked and switching to remote work (Figure 2; Supplementary Table A5).

Changes to hours worked

Relative to White men with >HS, women of all races with >HS were 14% (Black women; RR: 1.14; 95% CI: 0.66,1.98) to 59% (Latinx women; RR: 1.59; 95% CI: 0.76, 3.35) more likely to report changes to hours or days worked, whereas women of color with <HS were 11% (women reporting another race; RR: 0.89; 95% CI: 0.44, 2.38) to 19% less likely (Black women; RR: 0.81; 95% CI: 0.41, 1.60). The experience of men of color was more heterogeneous ranging from 65% less likely (Black men with ≤HS; RR: 0.35; 95% CI: 0.14, 0.85) to 75% more likely (men with ≤HS reporting another race; RR: 1.75; 95% CI: 0.98, 3.15).

Switching to remote work

Relative to White men with >HS, participants with ≤HS—irrespective of their race or gender—were less likely to have switched from in-person to remote work during the pandemic; the strongest association was observed for Latinx men with ≤HS, of whom not a single respondent indicated switching from in-person to remote work (RR: 0.00). The experience for women with >HS was more heterogeneous, ranging from 18% less likely (Black women; RR: 0.82; 95% CI: 0.58, 1.14) to 24% more likely (Latinx Women; RR: 1.24; 95% CI: 0.79, 1.95).

Compounding COVID-19-Related Changes in Employment Conditions

Supplementary Table A6 displays sociodemographic and employment characteristics stratified by different combinations of COVID-19-related changes in employment conditions. Relative to all participants in our study population, men were most overrepresented among those who experienced “income decrease/shift changes only” (51.6% of sample, 58.7% of subgroup), whereas women were most overrepresented among those who experienced “job loss with stable income” (48.4% of sample, 59.8% of subgroup). While non-Hispanic White participants were most overrepresented among those who “switched to remote only” (69.9% of sample, 78.6% of subgroup), non-Hispanic Black participants were most overrepresented among those who experienced “job loss with stable income” (10.8% of sample, 17.5% of subgroup) and both Latinx (6.4% of sample, 11.5% of subgroup) and non-Hispanic participants reporting another race (12.9% of sample, 16.9% of subgroup) were most overrepresented among those who experienced “job loss with income decrease.” Finally, participants with a college degree were most overrepresented among those who “switched to remote only” (40.3% of sample, 70.0% of subgroup), whereas participants with less than a high school degree were most overrepresented among those who experienced “income decrease/shift changes only” (6.5% of sample, 9.1% of subgroup).

Participants’ current or most recent employment quality also varied by different combinations of COVID-19-related changes in employment conditions. Mean annual income was highest for those who “switched to remote with income decrease/shift changes” and lowest for those who experienced “job loss with stable income,” whereas mean hours per week, weeks per year, and years of tenure were highest for those who switched to remote and lowest for those who experienced “job loss with income decrease.” Finally, relative to all participants in our study population, employer-sponsored health insurance, pensions, and paid leave were most overrepresented among those who “switched to remote only” and most underrepresented among those who experienced “job loss with income decrease.”

Associations Between COVID-19-Related Changes in Employment Conditions and Financial Hardship, Food Insecurity, and Poor Self-rated Health

Permanent or temporary job loss as well as COVID-19-related changes to hours or days worked and decreases in income were associated with greater prevalence of all three outcomes (Figure 3; Supplementary Table A7). In contrast, transitioning to remote work was associated with a lower unadjusted prevalence of financial hardship (PR: 0.64; 95% CI: 0.44, 0.92), food insecurity (PR: 0.28; 95% CI: 0.14, 0.59), and poor self-rated health (PR: 0.78; 95% CI: 0.55, 1.09). The magnitude and direction of estimates were generally robust to covariate adjustment, with the exception of the association between switching to remote and poor/fair self-rated health (unadjusted PR: 0.78; adjusted PR: 1.09).

Figure 3.

Associations between individual COVID-19-related changes in employment conditions and financial hardship, food insecurity, and poor self-rated health, Health and Retirement Study (HRS) May 2020–May 2021. Prevalence ratios estimated with modified Poisson regression in separate models for each employment condition and outcome. Analyses incorporated strata and sampling units to account for HRS’s complex sampling design and were adjusted for imputation uncertainty using Rubin’s combination rules. For each condition, the reference group was the absence of change, that is, did not experience job loss, no change in income, no change in hours or days worked, and no transition to remote work. Questions regarding changes to hours or days worked and changes from in-person to remote work were only asked of participants who did not discontinue working.

Relative to participants experiencing the “status quo,” those who experienced the most precarious combinations were all more likely to report financial hardship (Figure 4; Supplementary Table A7). Those who experienced “income decrease/shift changes only” or “job loss with income decrease” were also more likely to report food insecurity and poor self-rated health. In contrast, those who “switched to remote with income decrease/shift changes” were less likely to report food insecurity than participants experiencing the “status quo”; however, the estimate was imprecise (RR: 0.57; 95% CI: 0.18, 1.75).

Figure 4.

Associations between different combinations of COVID-19-related changes in employment conditions and financial hardship, food insecurity, and poor self-rated health, Health and Retirement Study (HRS) May 2020–May 2021. Prevalence ratios estimated with modified Poisson regression in separate models for each employment condition and outcome. Analyses incorporated strata and sampling units to account for HRS’s complex sampling design and were adjusted for imputation uncertainty using Rubin’s combination rules. For each combination, “Status Quo” was used as the reference group. ¹Transitioned to remote and had no change to hours/shifts worked AND no decrease in income; ²Transitioned to remote and had changes to hours/shifts worked OR decrease in income; ³No transition to remote and had no change to hours/shifts worked OR no decrease in income; ⁴No transition to remote and had changes to hours/shifts worked OR decrease in income; ⁵Job loss with no decrease in income; ⁶Job loss with decrease in income.

Discussion

Using a nationally representative sample of older adults in the United States, we observed the impact of COVID-19 on employment conditions was multifaceted and pervasive, with one in five workers experiencing work discontinuation and 29% experiencing decreased income. Moreover, adverse COVID-19-related changes in employment conditions were inequitably patterned by race, gender, and education, with Black, Latinx, and other non-White women with ≤HS experiencing the greatest burden of temporary or permanent job loss; and Black, Latinx, and other non-White men with ≤HS more than 90% less likely to switch to remote work than non-Hispanic White men with >HS. In fact, none of the 98 Latinx men with ≤HS interviewed between May 2020 and May 2021 reported transitioning from in-person to remote work. Additionally, we observed some evidence that adverse COVID-19-related changes in employment compounded with each other; in particular, the combination of job loss and decreases in income were somewhat more strongly associated with financial hardship and food insecurity compared with their individual associations.

Our findings suggest that employment-related impacts of the pandemic were felt more strongly by already marginalized working populations. Workers who experienced some combination of decreases in income, schedule changes, and job loss were more likely to be engaged in jobs with less income, hours, union representation, and access to employment benefits such as health insurance, paid leave, or pensions. Thus, the workers most affected had the least resources to shoulder these employment disruptions. Racist, classist, and sexist policies and norms exacerbate inequities and constrain employment opportunities and pathways that compound over time (Bailey et al., 2017; DiPrete & Eirich, 2006), exacerbating later-life inequities in employment and health (Andrea et al., 2022; Oddo et al., 2021). For example, financial downturns tend to impact the same subgroups of the population throughout their life courses, making their well-being increasingly precarious. Data suggest that for older women of color with ≤HS, racism, sexism, and classism intersect and reinforce each other, amplifying the harms of complex structural inequities (Bauer, 2014; Cho et al., 2013; Collins, 2015).

We observed that those who experienced permanent or temporary job loss and decreased income were disproportionately Black, Latinx, or another non-Hispanic non-White race. While the current employment outlook is improving as many recover from the employment shocks experienced earlier in the pandemic, Black workers were hit harder and are recovering slower (Lee et al., 2021). This slow recovery is compounded by persisting inequities in recovery from the 2008 recession. As of 2019, Black Americans were still experiencing recession-era unemployment levels despite the national unemployment rate being the lowest it had been in 50 years (Perry, 2019). Concomitantly, Latinx workers—especially those who are noncitizens—have not only experienced higher rates of job loss and earnings loss than their White counterparts, but have also been disproportionately barred from relief efforts and safety-net programs (Gonzalez et al., 2020). Although we were unable to further disaggregate those reporting another non-White race in publicly available HRS data, data from the 2020 American Community Survey indicate that older U.S. residents reporting another race who are still engaged in the labor force are disproportionately Chinese, Vietnamese, American Indian, Alaskan Native, Filipino, Asian Indian, or Korean, with similar composition among those with ≤HS and >HS (Ruggles et al., 2021). Other researchers have found that Asian Americans were more likely to become unemployed than Whites at the onset of the pandemic and were not more likely to regain employment upon reopening (Kim et al., 2021); anti-Asian pandemic-related racism likely contributed to this context (Kandil & Yam, 2020).

In our study, education was the primary driver of inequities in transitioning from in-person to remote work among those who did not experience job loss. Educational inequities in access to telework were further compounded by gender and race/ethnicity such that none of the male Latinx respondents with ≤HS reported transitioning to remote work. Less-educated workers are concentrated in jobs in which working from home is not feasible, including most service, sales, farming, construction, and transportation jobs (Dey et al., 2020). Shaped by ongoing occupational segregation in the United States, Latinx men with ≤HS in particular are crowded into construction, food service, agricultural, and transportation occupations (Bahn & Cumming, 2020; Bucknor, 2016).

The youngest participants in our analyses were 55 years old. Nonetheless, we still observed that those reporting employment and income loss were older on average and those reporting transitioning to remote work were younger. Our findings align with research showing higher rates of job loss and lower rates of recovery among older workers (Bui et al., 2020; Davis et al., 2020). Prolonged unemployment places older workers at risk of involuntary retirement (Schwartz Center for Economic Policy Analysis, 2018). The final years of employment are critical to retirement preparedness, especially for those who have not yet accumulated sufficient resources to retire—disproportionately women, people of color, and those without college degrees. Even if businesses resume hiring, the jobs afforded to these workers tend to be more precarious arrangements in face-to-face in-person jobs and thus risky as highly transmissible variants continue to spread (Bui et al., 2020).

Counterintuitively, we observed that COVID-19-related increases in income were associated with a greater prevalence of financial hardship, food insecurity, and poor self-rated health when compared with no changes in income. It is possible these participants had received their first COVID-19 stimulus check, hazard pay, or the increased unemployment insurance benefit, which were disproportionately allocated to lower income workers and workers in blue- and pink-collar frontline and/or essential jobs (Kinder & Stateler, 2021; Parolin et al., 2022), who are more likely to experience financial hardship generally. Our observed associations between intersecting group identity and reported income increases support this theory as most subgroups generally reported more exposure to income increases relative to White men with higher education, with the exception of Latinx men and women with ≤HS who were disproportionately barred from relief efforts (Gonzalez et al., 2020).

Our study has important limitations. First, respondents were interviewed by HRS between May 2020 and May 2021, so our results may not reflect later phases of the pandemic. Second, based on how questions about financial hardship, food insecurity, and self-reported health were asked, we were unable to affirm their temporal ordering in relation to the COVID-19-related changes in employment conditions. Third, questions about changes to hours or days worked and transition to remote work were only asked of participants who were not forced to discontinue working, excluding respondents who may have had other COVID-19-related changes to their employment conditions prior to work discontinuation. Fourth, we were unable to further disaggregate beyond Black, Latinx, or another non-Hispanic non-White race in our examination of intersectional identities; participants identifying as a race besides “White/Caucasian” or “Black/African American” were coded as “other” in publicly available data. Fifth, small cell sizes for many of the groupings examined in these analyses likely explain some of the imprecision and instability of estimated risk and prevalence ratios. Finally, we were unable to examine the duration of adverse employment conditions.

Conclusion

Our study identified harmful effects of the COVID-19 pandemic on employment conditions among older adults, which were associated with respondents’ financial well-being and self-reported health and which were inequitably distributed across the population. This suggests the need for policies to improve employment quality and stability and permanently expand safety-net provisions for older adults. One recent study found state policy—including Medicaid expansion, greater maximum unemployment insurance benefits, and freezes on utility shutoffs—buffered the mental health consequences of COVID-19-related income shocks (Donnelly & Farina, 2021). Another found that expanded unemployment insurance and the $600 per week COVID-19 federal supplement were associated with reductions in food insecurity (Raifman et al., 2021). As COVID-19 continues to affect our daily lives, further research is needed to evaluate its long-term impact on employment conditions and how subsequent policy decisions influence those conditions. Given the potential for forced and permanent labor force exits for older workers, efforts to expand the eligibility and increase the generosity of Social Security benefits to reduce growing inequities in financial security and well-being later in the life course may be warranted (Truesdale, 2020).

Funding

None declared.

Author Contributions

S.B.A. acquired the data, conducted the analyses, interpreted the results, and drafted the initial version of the manuscript. All authors advised S.B.A. on study conceptualization, design, and results interpretation and provided feedback on subsequent drafts of the manuscript. All authors approved the final version of the manuscript and agree to be accountable for all aspects of the work.

Conflict of Interest

None declared.