Receipt of workplace accommodations does not vary by race/ethnicity but does vary by organization size for older workers in the U.S

Abstract

Background:

Prior research has found evidence that persons who are nonwhite are less likely to receive workplace accommodations than other persons.

Objective:

To test whether the receipt of workplace accommodations by adults aged 50 and older who had work limitations varies by race/ethnicity in the U.S., controlling for age, gender, education, organizational size, and the physical nature of the occupation.

Methods:

Bivariate and logistic regressions using 2002 to 2018 Health and Retirement Study data on adults aged 50 and older with a work limitation (n = 6,711).

Results:

Receipt of workplace accommodations does not vary by race/ethnicity for U.S. adults aged 50 and older who had a work limitation, with an estimated 34% to 37% of all older adults receiving accommodations across all racial categories. Receipt of accommodations was more likely for persons working at organizations that employed 100 or more people, holding all else constant.

Conclusion:

Smaller employers may benefit from training or other supports to increase the availability of workplace accommodations. Workers with disabilities might also benefit from increased education offered by vocational rehabilitation agencies, workforce development programs, and other similar organizations on how to make requests for and implement reasonable accommodations.

Keywords

disability ethnic/racial disparities health and retirement study work limitation workplace accommodation

Introduction

This study examines whether people aged 50 and older who have disabilities and who are nonwhite are less likely to receive workplace accommodations than other people aged 50 and older who have disabilities in the United States (U.S.), controlling for individual and employment characteristics. Workplace accommodations, adjustments to jobs or work environments that make it possible for persons with disabilities to perform their job duties, include time-related accommodations (allowing more breaks, allowing different arrival or departure times, or shortening the workday), assistance-related accommodations (getting someone to help, getting special equipment, arranging special transportation), and job modification accommodations (changing the job, helping to learn new job skills) (U.S. Department of Labor, 2023a, 2023b). Estimates of workplace accommodation receipt range from 12% to 65% (Hill et al., 2016; Maestas et al., 2019; Schur et al., 2014; Wong et al., 2021) and vary by worker characteristics, including race and type of disability. Employee-related factors, workplace-related factors, job-related factors, and accommodation-related factors can serve as facilitators and barriers that influence the provision of workplace accommodations (Wong et al., 2021). Distinct types of occupations may offer different opportunities for accommodation. People who work in office settings, for example, can perhaps more easily adjust work schedules and use assistive technology to address accommodation needs. People who work in positions that require manual labor and/or offer more stringent work schedules may have fewer opportunities to use accommodations. Receipt of accommodation varies by type of work, with workers who have nonstandard and precarious types of jobs being more likely to have unmet accommodation needs (Shuey & Jovic, 2013).

While two studies have noted that the probability of receiving workplace accommodations is 6.5 percentage points higher for older white workers than for older nonwhite workers (Hill et al., 2016; Kofi Charles, 2004), these studies did not concurrently consider the association of occupation type or characteristics with accommodation receipt. To fill this gap, this study investigates the association of race and ethnicity in the receipt of workplace accommodations, hypothesizing that workers with disabilities who are nonwhite will have lower rates of receiving workplace accommodations, controlling for individual and employment characteristics.

Methods

Data

The primary data used for this project comes from public-use data files from the Health and Retirement Study (HRS). Funded by the National Institute on Aging (grant number NIA U01AG009740) and the Social Security Administration, the HRS is a nationally representative longitudinal study of people 51 and older who live in the U.S. Data collection for the HRS began in 1992. The HRS is conducted every two years and collects detailed information on assets, employment, finances, and health. Among nationally representative U.S. surveys, the HRS collects the most comprehensive information on workplace accommodations. Its focus on older Americans is particularly useful when examining the provision of workplace accommodations because of the heightened onset of disability at older ages (Schimmel Hyde et al., 2022). We focus our HRS analysis on data collected in waves 2002 through 2018.

Sample

We restricted the HRS analytical sample to employed people who experienced a work-limiting health condition while employed at least once during their survey participation. Excluding people who were self-employed (as they would have their own ability to use accommodations) and across waves of data from 2002 to 2018, we identified 6,444 individuals to include in our analytic sample, which is about one-quarter of all adults in this sample.

Measures

In the HRS, accommodation receipt at the onset of the disability is measured using question M028 which asks: “At the time your health started to limit your ability to work, did your employer do anything special to help you out so that you could stay at work?” Possible responses include: Yes, No help needed, No, Left Immediately, Self-employed, Don’t know, Refused. Current accommodation receipt is measured using question M029, which asks: “Does your employer currently do anything special to make it easier for you to stay at work?” Possible responses include: Yes, No help needed, No, Don’t know, Refused. We created a new variable (received accommodation) from these two variables, counting anyone who reported “yes” to either of these items as receiving an accommodation and all others in our work disability sample as not receiving an accommodation. We also created a “needed accommodation” variable from these two variables, where people who reported Yes, No, or Left Immediately were categorized as needing accommodation. People reporting No help needed were categorized as not needing accommodations.

For people who responded affirmatively to any of these accommodation questions, the HRS captures more detailed information about the types of accommodations received. Following Hill et al. (2016), we grouped these accommodation types into three categories: time-related accommodations (allowing more breaks, allowing different arrival or departure times, or shortening the work day), assistance-related accommodations (getting someone to help, getting special equipment, arranging special transportation), and job modification accommodations (changing the job, helping to learn new job skills).

Independent variables

Our key focal independent variable for this analysis is race/ethnicity. We measured race/ethnicity as white, non-Hispanic; Black, non-Hispanic; Other, non-Hispanic; and Hispanic. Occupation was captured using three different U.S. Census Bureau coding schemes in the HRS and was collapsed into the following 10 categories: managerial, business, finance, and sales; professional occupations in computers, science, engineering, legal, and community; personal care; clerical, office, and administrative support; service (food, protective, building, and grounds); healthcare; farming, fishing, construction; entertainment and arts; military; and transport/handlers/material movers. We took the additional step of rating each of these occupations for physical demands by identifying the percentage of the day that people in these occupations spend sitting, on average, as reported in the Occupational Requirements Survey in 2020 (U.S. Department of Labor, 2023a, 2023b). These estimates ranged from a low of 2.4% in food service occupations to a high of 87% in computer and math occupations. We coded anyone working in an occupation that spent less than one-quarter of the day sitting as working in a “physical job.” About 25 percent of all workers were employed in physically demanding occupations under this definition.

As control variables, we chose a parsimonious set of variables that have been shown to be associated with accommodation receipt and occupation: age (at onset of condition), sex (male/female), and educational attainment (less than high school/GED, high school diploma, some college, or bachelor's or more).

Analytical approach

We first ran bivariate analyses to compare need for and receipt of accommodations by demographic variables, organizational size, and occupation type. We used t-tests to test for differences in age and Chi square to test for differences among our other variables, which were all nominal. We next used logistic regressions to estimate the odds of receiving a workplace accommodation, controlling for demographic variables, organizational size, and our measure of the physical nature of the occupation. Using our final logistic model, we computed marginal effects to estimate the predicted probability of receipt of workplace accommodations among our sample of people with work limitations for each value of our race/ethnicity variable, holding all else constant. We also examine differences in receipt of the types of accommodations received (time-related, assistance-related, or job modification) by individual and employment characteristics, using t-tests and Chi square.

Results

Table 1 shows differences in the need for accommodation by weighted demographic and work characteristics for people who reported a work-limiting condition between 2002 and 2018. The racial distribution of those with a work disability was similar to the U.S. population: 76.6 percent were white, non-Hispanic; 11.0 percent were Black, non-Hispanic; 8.3 percent were Hispanic, and 4.1 percent were other races, non-Hispanic.

Table 1.

Need for accommodation by demographic and work characteristics, health and retirement study, 2002-2018 (n = 6,444).

			Needed an accommodation
	Overall		Yes		No
	Percentage/Mean	(Std. Error)	Percentage/Mean	(Std. Error)	Percentage/Mean	(Std. Error)	p
Overall (%)	–	–	88.5%	(0.5)	11.5%	(0.5)
Age at onset (mean)	55.65	(0.2)	55.12	(0.2)	60.09	(0.5)	***
Gender (%)							***
Female	53.1%	(0.7)	89.8%	(0.5)	10.2%	(0.5)
Male	46.9%	(0.7)	87.1%	(0.7)	12.9%	(0.7)
Race/ethnicity (%)							**
White, non-Hispanic	76.6%	(1.2)	87.6%	(0.6)	12.4%	(0.6)
Black, non-Hispanic	11.0%	(0.7)	92.8%	(1.1)	7.2%	(1.1)
Other, non-Hispanic	4.1%	(0.5)	88.9%	(2.2)	11.1%	(2.2)
Hispanic	8.3%	(0.9)	91.1%	(1.4)	8.9%	(1.4)
Education (%)							*
Less than HS	14.9%	(0.6)	88.6%	(1.3)	11.4%	(1.3)
GED	6.7%	(0.5)	92.7%	(1.5)	7.3%	(1.5)
High school diploma	28.4%	(0.9)	88.9%	(0.9)	11.1%	(0.9)
Some college	27.9%	(0.7)	89.5%	(0.9)	10.5%	(0.9)
College	22.2%	(1.0)	85.7%	(1.2)	14.3%	(1.2)
Number of employees (%)							**
Fewer than 15	5.9%	(0.4)	93.6%	(1.6)	6.4%	(1.6)
15–24	2.1%	(0.4)	98.5%	(1.5)	1.5%	(1.5)
25–49	4.3%	(0.4)	98.5%	(0.7)	1.5%	(0.7)
50–99	7.4%	(0.5)	93.9%	(2.0)	6.1%	(2.0)
100–499	78.7%	(0.9)	92.0%	(0.6)	8.0%	(0.6)
500 or more	1.7%	(0.3)	93.6%	(4.7)	6.4%	(4.7)

Occupation (%)							***
Management, etc.	16.7%	(0.7)	92.5%	(1.3)	7.5%	(1.3)
Professional, etc.	12.3%	(0.6)	90.7%	(1.5)	9.3%	(1.5)
Personal care	4.5%	(0.4)	94.7%	(1.7)	5.3%	(1.7)
Clerical, etc.	12.5%	(0.7)	94.5%	(1.0)	5.6%	(1.0)
Service, etc.	12.8%	(0.6)	96.3%	(0.9)	3.8%	(0.9)
Health care	7.2%	(0.5)	94.6%	(1.9)	5.4%	(1.9)
Farming, fishing, construction	20.7%	(0.8)	97.7%	(0.8)	2.3%	(0.8)
Entertainment and arts	2.9%	(0.4)	90.9%	(3.7)	9.1%	(3.7)
Military	3.8%	(0.4)	93.7%	(2.7)	6.3%	(2.7)
Transportation, etc.	6.7%	(0.4)	99.6%	(0.2)	0.4%	(0.2)
Physical job (%)
Yes	25.8%	(0.8)	96.7%	(0.6)	3.3%	(0.6)	***
No	74.2%	(0.8)	85.7%	(0.6)	14.3%	(0.6)

Note: *p < 0.05, **p < 0.01, ***p < 0.001.

Eighty-nine percent of people with work limitations identified a need for accommodation, Significant differences in need for accommodations were noted by age, gender, and educational attainment (all p < .05). Significant differences in need for workplace accommodations were noted by race/ethnicity, as 93 percent of people who were Black, 91 percent of people who were Hispanic, 89 percent of people of other races, and 88 percent of people who were white needed accommodations (p < 0.01).

Table 2 shows differences in receipt of accommodation by weighted demographic and work characteristics. Only 33 percent of workers with limitations received accommodations. Significant differences in receipt of accommodations were noted by age, gender, educational attainment, and the physical nature of the job (all p < 0.05). Differences in receipt of accommodations by race/ethnicity, organization size, or occupation type were not significant.

Table 2.

Receipt of accommodation by demographic and work characteristics health and retirement study, 2002–2018 (n = 6,711).

	Received accommodation
	Yes		No
	Percentage/ Mean (Std. Error)		Percentage/ Mean (Std. Error)		p
Overall (%)	32.9%	(0.8)	67.1%	(0.8)
Age at onset (mean)	53.1	(0.3)	57.0	(0.2)	***
Gender (%)					*
Female	34.6%	(0.9)	65.4%	(0.9)
Male	30.9%	(1.2)	69.1%	(1.2)
Race/ethnicity (%)
White, Non-Hispanic	33.5%	(1.0)	66.5%	(1.0)
Black, Non-Hispanic	30.7%	(1.8)	69.3%	(1.8)
Other, Non-Hispanic	33.9%	(3.9)	66.1%	(3.9)
Hispanic	29.8%	(1.9)	70.3%	(1.9)
Education (%)					***
Less than HS	24.9%	(1.4)	75.1%	(1.4)
GED	31.6%	(2.9)	68.4%	(2.9)
High school diploma	31.9%	(1.5)	68.1%	(1.5)
Some college	36.0%	(1.7)	64.0%	(1.7)
College	36.0%	(1.9)	64.0%	(1.9)
Number of employees (%)
Fewer than 15	29.6%	(3.6)	70.4%	(3.4)
15–24	34.7%	(7.1)	65.3%	(7.1)
25–49	34.5%	(4.9)	65.5%	(4.9)
50–99	34.0%	(3.4)	66.0%	(3.4)
100–499	36.5%	(1.0)	63.5%	(1.0)
500 or more	50.4%	(7.2)	49.6%	(7.2)
Occupation (%)
Management, etc.	40.8%	(2.6)	59.2%	(2.6)
Professional, etc.	37.3%	(2.6)	62.7%	(2.6)
Personal care	47.4%	(3.9)	52.6%	(3.9)
Clerical, etc.	39.3%	(2.6)	60.7%	(2.6)
Service, etc.	33.1%	(3.0)	66.9%	(3.0)
Health care	40.4%	(3.7)	59.6%	(3.7)
Farming, fishing, construction	37.4%	(2.1)	62.6%	(2.1)
Entertainment and arts	31.3%	(4.9)	68.8%	(5.1)
Military	40.7%	(4.9)	59.3%	(4.9)
Transportation, etc.	30.8%	(2.7)	69.2%	(2.7)
Physical job (%)
Yes	36.7	(1.8)	63.3%	(1.8)	*
No	31.6%	(1.0)	68.4%	(1.0)

Note: *p < 0.05, **p < 0.01, ***p < 0.001.

Table 3 shows the results of the logistic regressions predicting receipt of accommodations. In Model 1, which includes only age, gender, and race/ethnicity as predictor variables, persons who were Hispanic had significantly lower odds of receiving accommodations (OR: 0.77, p < .01). Model 2 adds in education, Model 3 adds in organizational size, and Model 4 adds our measure of the physical nature of jobs. Organizational size was significant in Models 3 and 4, which race/ethnicity was not. We also ran an additional model similar to Model 4 (not shown) using occupation type in place of physical nature of the job, just to check the utility of this approach even though we did not note any significant differences by occupation in our bivariate analyses. In this model, using fishing, farming, construction as the reference group, we found that people working in personal care occupations had significantly higher odds of receiving accommodations, but no other significant differences among occupation types.

Table 3.

Logistic regression models predicting odds of having received an accommodation among those with a work-limiting condition, health and retirement study, 2002–2018.

	Model 1			Model 2			Model 3			Model 4
	OR	se	p > \|t\|	OR	se	p > \|t\|	OR	se	p > \|t\|	OR	se	p > \|t\|
Age at condition onset (in years)	0.96	0.00	***	0.96	0.00	***	0.96	0.00	***	0.96	0.00	***
Female	1.11	0.09		1.11	0.09		1.10	0.09		1.11	0.10
Race (ref = White, non-Hispanic)
Black, non-Hispanic	0.83	0.09		0.87	0.09		0.90	0.10		0.90	0.10
Other, non-Hispanic	0.88	0.17		0.89	0.18		0.87	0.19		0.87	0.19
Hispanic	0.77	0.08	**	0.87	0.09		0.97	0.10		0.97	0.10
Education (ref = college degree or more)
Less than high school	–	–		0.68	0.08	**	0.81	0.11		0.79	0.11
GED	–	–		0.79	0.12		0.82	0.14		0.80	0.14
High school graduate	–	–		0.88	0.10		0.91	0.11		0.89	0.11
Some college	–	–		1.02	0.11		1.02	0.12		1.01	0.12
Number of employees (ref = fewer than 15)
15–24	–	–		–	–		1.42	0.50		1.42	0.50
25–49	–	–		–	–		1.40	0.39		1.41	0.39
50–99	–	–		–	–		1.11	0.28		1.11	0.28
100–499	–	–		–	–		1.54	0.30	*	1.55	0.31	*
500+	–	–		–	–		2.49	0.87	*	2.48	0.87	*
Physical occupation	–	–		–	–		–	–		1.07	0.11
Constant	5.47	1.47	***	5.76	1.68	***	4.78	1.63	***	4.60	1.60	***
Number of cases	5,780			5,780			4,853			4,853

Note: *p < 0.05, **p < 0.01, ***p < 0.001; OR = odds ratio; se = standard error.

Figure 1 shows the predicted probability of receiving workplace accommodations by race, using Model 4 as the basis for estimation. Note that the sample used in Model 4 includes fewer people (n = 4,853) than the number of people included in our bivariate analysis (n = 6,444) that estimated that 32 percent of people with work limitations received accommodations. The predicted probabilities here are slightly higher: approximately 37 percent for people who are White or Hispanic, 35 percent for people who are Black, and 34 percent for people of other races.

Figure 1.

Predicted probability of receiving accommodations among people with work limitations by race, controlling for demographic and work characteristics, health and retirement survey, 2002–2018.

Figure 2 shows the predicted probability of receiving accommodations by organizational size, controlling for demographic and work characteristics. Nearly half (48.8 percent) of employees with a work limitation who worked at large employers (organizations employing 500 + people) received accommodations. In contrast, approximately 29 percent of people working at organizations employing less than 15 people received accommodations.

Figure 2.

Predicted probability of receiving accommodations by number of employees, controlling for demographic and work characteristics, Health and Retirement Survey, 2002–2018.

Table 4 shows differences in type of accommodation received by key demographic and work characteristics. Notably, significant variation in receipt of time-related accommodations and job modification accommodations are found by race. No differences were found by race in terms of receiving assistance types of accommodations. A significantly higher proportion of people with a physically demanding job (51 percent) received job modification accommodations than people with less physically demanding jobs (35 percent) (p < 0.001).

Table 4.

Accommodation type received by demographic and work characteristics, health and retirement study, 2002–2018 (n = 1,954 who received accommodations).

	Time related		Assistance related		Job modification accommodations
	%	p	%	p	%	p
Overall	52.7%		47.1%		39.8%
Gender (%)						*
Female	52.5%		45.6%		37.0%
Male	53.1%		49.1%		43.3%
Race/ethnicity (%)		*				**
White, non-Hispanic	51.5%		46.0%		37.2%
Black, non-Hispanic	59.4%		52.3%		48.7%
Other, non-Hispanic	65.2%		50.9%		44.6%
Hispanic	50.7%		49.6%		51.9%
Education (%)						***
Less than HS	54.3%		43.8%		41.9%
GED	47.5%		41.0%		50.4%
High school diploma	48.6%		48.0%		43.6%
Some college	53.8%		46.3%		42.3%
College	56.8%		50.5%		28.4%
Number of employees (%)
Fewer than 15	57.0%		43.2%		38.5%
15–24	50.6%		47.1%		31.2%
25–49	56.5%		47.2%		29.6%
50–99	60.7%		55.1%		44.6%
100–499	54.6%		49.2%		42.2%
500 or more	54.6%		50.2%		56.3%
Occupation (%)
Management, etc.	60.2%		50.5%		31.9%
Professional, etc.	50.0%		50.1%		35.9%
Personal care	49.6%		42.8%		43.4%
Clerical, etc.	54.6%		49.1%		38.0%
Service, etc.	53.2%		46.0%		50.8%
Health care	62.6%		46.2%		37.7%
Farming, fishing, construction	58.2%		52.6%		51.8%
Entertainment arts	51.4%		66.9%		22.3%
Military	49.7%		57.8%		63.4%
Transportation, etc.	45.7%		44.4%		44.3%
Physical job (%)						***
Yes	56.7%		50.5%		50.6%
No	51.1%		45.8%		35.4%

Note: *p < 0.05, **p < 0.01, ***p < 0.001.

Discussion

The results from this study do not confirm that people who are nonwhite are less likely to need or receive workplace accommodations when controlling for age, gender, education, organizational size, and the physical nature of a particular job. Specifically, our fully adjusted models find no significant variation in receipt of workplace accommodations by race, estimating that 37 percent of people with work limitations who are white, 35 percent of people who are Black, 34 percent of people who are of other races, and 37 percent of people who are Hispanic received accommodations. This finding counters other research which has found that the probability of receiving workplace accommodations is 6.5 percentage points higher for white workers than for nonwhite workers (Hill et al., 2016; Kofi Charles, 2004), perhaps because we adjusted for a different set of variables than these other studies. Our estimates are also lower than those of Maestas et al. (2019) who reported that 42% to 53% of people ages 18 to 70 who have a work-limiting condition received an accommodation at work, although their estimates were based on data from an Internet panel and included a wider age range.

In our models, organizational size is the primary predictor of receipt of workplace accommodations. As other research has found that most organizations in the U.S. that employ more than 25 people have formal processes in place to allow employees to request accommodations (Houtenville et al., 2022), the lack of accommodation provision noted in this study is concerning. Employees working for larger firms had significantly higher odds of receiving accommodations, suggesting that organizational size, rather than employee characteristics, is one of the most important factors driving the receipt of any workplace accommodation. Our results suggest a 20 percentage point difference in receipt of accommodations between people working at small organizations (29 percent in organizations employing less than 15 people) and large organizations (49 percent in organizations employing 500 or more people). Vocational rehabilitation professionals can work with individuals and organizations to ensure more equitable provision of workplace accommodations across all sizes of organizations.

We did not find any differences by organizational size in the types of accommodations provided. Organizational size might serve as a proxy for job quality in that larger organizations are more likely to offer better protections for employees, provide more standard work schedules, and have other benefits that make the provision of accommodations more likely. As Shuey and Jovic (2013) note, workers who have nonstandard and precarious types of jobs are more likely to have unmet accommodation needs.

Our preliminary analyses did highlight the large gap between need for and receipt of accommodations overall, with 85 percent of people with a work limitation identifying a need for accommodation, yet only 32 percent receiving accommodations. The HRS does not collect any information about whether employees requested accommodations, however. Without such information, we are not able to fully understand the reasons for this gap. For example, we cannot determine whether people who had limitations simply did not disclose their limitations to their employers and we cannot estimate the percentage of people who might have requested but not received an accommodation. This type of information is particularly important in understanding employee and employer behavior.

Our bivariate analysis did not indicate any significant differences in need for nor receipt of workplace accommodations by occupation type. While occupation type may perhaps have utility for other studies, measures of the actual job functions and the nature of the job can provide more meaningful context when studying workplace accommodations. Although our proxy for the physical nature of jobs was not significantly related to the odds of accommodation receipt in Model 4, perhaps other measures that more closely examine the mismatch between job duties and functional ability could be helpful in better understanding situations in which employees need to receive accommodations. Our measure of the physical nature of a job also did not capture other job duty characteristics important in the context of accommodations, such as mental functioning (Henly et al., 2022, 2023).

Limitations

Several limitations of our study must be noted. First, our data spans a broad period of time (2002 to 2018). We would expect that improvements have been made in recent decades which would increase the use of accommodations across the board, as well as for certain subpopulations. A recent survey of supervisors found substantial increases from 2017 to 2022, for example, in the percent of supervisors reporting that their organizations had dedicated accommodation funds (Houtenville et al., 2022). Second, our data is focused on older adults who are reporting retrospectively, excluding younger workers who might also require accommodations. In addition, the COVID-19 pandemic has increased the percentage of the general population that is working remotely (Parker et al., 2022) or with flexible schedules (Houtenville et al., 2022), two work options that are commonly used as accommodations for people with disabilities. Additional analyses, with more recent data, can examine whether disparities exist in workplace accommodations in the post-COVID-19 era by race/ethnicity.

Conclusion

Our analysis of data from the HRS did not find evidence of significant differences in the proportion of U.S. adults age 50 and older who had work limitations and who received workplace accommodations by race, controlling for other factors. Our findings do highlight, however, the gap between the large proportion of people aged 50 and older who stated that they needed a workplace accommodation and the small proportion who actually received accommodations. This points to a continued need for vocational rehabilitation professionals and others to work closely with employers and employees to ensure that accommodations are provided when needed.

Footnotes

Ethics statement

Publicly available data was used in this study. Institutional Review Board approval was not required.

Informed consent

Publicly available data was used in this study. Informed consent was not required.

Funding

The authors disclosed receipt of the following financial support for the research, authorship, and/or publication of this article: The research reported herein was performed pursuant to a grant from the U.S. Social Security Administration (SSA) funded as part of the Retirement and Disability Research Consortium through the Michigan Retirement and Disability Research Center Award RDR18000002. The opinions and conclusions expressed are solely those of the author(s) and do not represent the opinions or policy of SSA or any agency of the Federal Government. 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.

Declaration of conflicting interests

The authors declared no potential conflicts of interest with respect to the research, authorship, and/or publication of this article.

Acknowledgements

None to report.

Appendix

Appendix.

Crosswalk of occupations across 1980–2000 census coding typologies.

	2010 codes	2000 codes	1980 codes
Management, Business, Finance and Sales	Management occs	Management occupations	Managerial specialty
	Business + financial oper	Business Operations Special	Sales
	Sales + Related Occs	Financial specialists
		Sales Occups
Professional specialties, Computer, Science, Engineering, Legal, Comm/Social Services, Librarians	Computer + Mathematical Oc	Computer + Math Occs	Prof specialty opr/tech
	Architecture + Engineering	Architecture + Engineering
	Life/Physical/Social Sci	Life/Physical/SocailSci
	Community + Social Service	Community + Social Svcs Oc
	Legal Occs	Legal Occups
	Education/Training/Libra	Education/Training/Libra
Personal care	Personal Care + Service Oc	Personal Care + Service Oc	Personal svc
Clerical, office, administrative support	Office + Administrative Su	Office + Admin Support Occ	Clerical/admin supp
Service, including food, protective, building, grounds	Protective Service Occs	Protective Service Occs	Svc: prv hhld/clean/bldg
	Food Prep + Serving Relate	Food Prep + Service Oc	Svc: protection
	Building/Grounds Cleaning	Bldg/Grounds/Clean/Mai	Svc: food prep
Health care	Healthcare Practitioners	Hlthcare Practition/Tech	Health svc
Health care	Healthcare Support Occs	Healthcare Support Occs
Farming, fishing, construction	Farming/Fishing/Forestry	Farm/Fish/Forestry Occup	Farming/forestry/fishing
	Construction + Extraction	Construction Trades	Mechanics/repair
	Installation/Maintenance	Extraction Workers	Constr trade/extractors
	Production Occs	Install/Maint/Repair Wor	Precision production
		Production Occups	Operators: machine
Entertainment and arts	Arts/Design/Entertainment	Arts/Design/Entertnmt Oc
Military	Military Specific Occs	Military specific occups	Member of Armed Forces
Transportation, handlers, material movers	Transportation + Material	Transport/Material Moving	Operators: transport, etc
Transportation, handlers, material movers			Operators: handlers, etc.

References

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D. L.

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Hill

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K. J.

(2016). Employer Accommodation and Labor Supply of Disabled Workers (SSRN Scholarly Paper ID 3183827). Social Science Research Network. https://papers.ssrn.com/abstract=3183827

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A. J.

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D. L

Jablonski

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(2022). Kessler Foundation 2022 National Employment and Disability Survey: Supervisor Practices. https://kesslerfoundation.org/employment-disability-survey-supervisor-perspectives-KFSurvey2022

Kofi Charles

(2004). The extent and effect of employer compliance with the accommodations mandates of the Americans with disabilities act. Journal of Disability Policy Studies, 15(2), 86–96. https://doi.org/10.1177/10442073040150020301

Maestas

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K. J.

Rennane

(2019). Unmet need for workplace accommodation. Journal of Policy Analysis and Management, 38(4), 1004–1027. https://doi.org/10.1002/pam.22148

Parker

Horowitz

J. M.

Minkin

(2022, February 16). COVID-19 Pandemic Continues To Reshape Work in America. Pew Research Center’s Social & Demographic Trends Project. https://www.pewresearch.org/social-trends/2022/02/16/covid-19-pandemic-continues-to-reshape-work-in-america/

Schimmel Hyde

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Livermore

(2022). Responding to disability onset in the late working years: What do older workers do? Research on Aging, 01640275221074634. https://doi.org/10.1177/01640275221074634

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Blanck

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K. M.

Jovic

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U.S. Department of Labor. (2023a). Job Accommodations. https://www.dol.gov/general/topic/disability/jobaccommodations

12.

U.S. Department of Labor. (2023b). ORS Home Page: U.S. Bureau of Labor Statistics. Occupational Requirements Survey. https://www.bls.gov/ors/

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Wong

Kallish

Crown

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Q. E.

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Tamayo

Heinemann

A. W.

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