Evidence map›Paper›PMID 42453282›Full record

ArticleResearch in social stratification and mobility2026

Estimating the Intersecting Labor Market Disparities for Justice-Impacted Individuals with Disabilities: Evidence from Two United States National Data Sets.

Jennifer Brooks, Matthew Saleh, Star Yanxin Gao

Abstract read
In one paragraph

Article in Research in social stratification and mobility, 2026. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Not yet cited in PubMed.

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0cells of the map it votes in
0citing papers in PubMed
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1 · What the graph read from it

What it found

Each row is one number read from the abstract, on the scale the paper reported it, with its interval. Left of the dashed line favours the treatment, right favours the comparator. Under each row is the sentence it came from. New to these charts? A ten-minute tutorial.

The abstract states no effect estimate the extractor could read, or names no intervention and outcome on the map, so this paper lights no cell and moves no belief. It is still indexed, cited and linked below.

2 · The registry

The trial behind it

Trials whose registry record cites this paper, or whose number appears in the abstract. A trial that started after this paper was published is citing it as background, not reporting it.

Neither the registry nor the abstract names a trial number. If this is a trial report, that itself is worth knowing.

3 · Its place in the literature

Who cites it

0 citing papers in PubMed.

No citing paper in PubMed yet.

4 · The record

Corrections and comments

PubMed lists nothing against this paper. Absence here is not a guarantee, only a check that was made.

5 · Who and what money

Authors and funding

3 authors.

Jennifer BrooksILR School, Cornell University, Ithaca, NY, USA, Yang-Tan Institute on Employment and Disability.
Matthew SalehILR School, Cornell University, Criminal Justice and Employment Initiative.
Star Yanxin GaoILR School, Cornell University, Ithaca, NY, USA, Yang-Tan Institute on Employment and Disability.

Funding

ACL HHS 90IFRE0106
6 · The paper itself

Abstract

Individuals with disabilities and people with a history of justice interaction each face substantial barriers to employment. These populations also significantly overlap, with two-thirds of individuals in federal or state prisons reporting at least one disability. Studies qualitatively explore how justice-impacted individuals with disabilities (JIID) face compounded labor market disadvantages, often shaped by race and gender. However, there has been little effort to quantify this disadvantage robustly. Our study addresses this gap by analyzing data from two national datasets, the Rehabilitation Services Administration Case Service Report (RSA-911) and the National Survey on Drug Use and Health (NSDUH), to examine employment outcomes of JIID and non-JIID individuals, stratified by race and gender. Consistent with prior research, our findings suggest that over one fourth (26%) of people with disabilities experience some type of justice interaction. In terms of employment, using the NSDUH data, we found that 43% of justice-impacted individuals with disabilities were employed, compared with 51% of non-justice-impacted individuals with disabilities. Analysis from both the RSA-911 and the NSDUH indicate that JIID employment rates vary by gender and race/ethnicity, with JIID women having lower rates than men, and Black JIID having the lowest employment rates of any racial/ethnic subgroup. Overall, our findings underscore that JIID are far from a homogeneous group, particularly with respect to employment, highlighting the need for more intersectional research and targeted policies that account for subgroup differences.

Indexed as

DisabilityEmploymentEthnicityGenderJustice-ImpactedRace

Identifiers

PMID42453282
PMCPMC13366510

What OpenQuestion holds

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Read under generation 80e0d062 · epoch 390. Bibliography from PubMed, PubMed Central and OpenAlex; grants from NIH RePORTER; trial links from ClinicalTrials.gov; estimates, votes and beliefs from the OpenQuestion graph.