Journal of rehabilitation · 2025
A National Analysis of Self-Employment Among People with Disabilities Across Demographic and Geographic Factors.
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Peterson Gemarco J, Webb Kelsey R, Moore Corey L, Wang Penghua, Manyibe Edward O, Dentleegrand Kacie R, Webster Fanee
Аннотация
BACKGROUND: A scarcity of research has examined self-employment status in the United States (U.S.) among people with disabilities across multiple social identities to understand in-depth variations.
OBJECTIVE: The purpose of this analysis was to identify predictors of self-employment status among disabled individuals across select social identities (i.e., race/ethnicity, sex, sexual orientation, poverty status, and geography).
METHOD: Data from four combined waves (years 2020, 2021, 2022, and 2023) of the National Survey on Health and Disability (NSHD) (N = 762) on self-employment status were extracted and analyzed using Machine Learning algorithms (Random Forest, Logistic Regression, and XGBoost) to identify significant predictors of self-employment.
RESULTS: Key findings indicated that disabled African Americans/Blacks had lower self-employment rates compared to all other racial/ethnic cohorts; disabled African Americans/Blacks living in poverty or in urban locales had significantly lower odds of self-employment compared to Asians, Native Hawaiians, Other Pacific Islanders, and American Indian and Alaska Natives (A/NH/OPI/AIAN) not living in poverty or living in rural areas, respectively. Additionally, disabled A/NH/OPI/AIAN individuals living in poverty showed significantly lower self-employment odds compared to their non-poverty status counterparts.
CONCLUSIONS: Recommendations are presented for employment service interventions and entrepreneurial policy modifications aimed at catalyzing self-employment opportunities for people with disabilities that could help accelerate local and national economic growth.
OBJECTIVE: The purpose of this analysis was to identify predictors of self-employment status among disabled individuals across select social identities (i.e., race/ethnicity, sex, sexual orientation, poverty status, and geography).
METHOD: Data from four combined waves (years 2020, 2021, 2022, and 2023) of the National Survey on Health and Disability (NSHD) (N = 762) on self-employment status were extracted and analyzed using Machine Learning algorithms (Random Forest, Logistic Regression, and XGBoost) to identify significant predictors of self-employment.
RESULTS: Key findings indicated that disabled African Americans/Blacks had lower self-employment rates compared to all other racial/ethnic cohorts; disabled African Americans/Blacks living in poverty or in urban locales had significantly lower odds of self-employment compared to Asians, Native Hawaiians, Other Pacific Islanders, and American Indian and Alaska Natives (A/NH/OPI/AIAN) not living in poverty or living in rural areas, respectively. Additionally, disabled A/NH/OPI/AIAN individuals living in poverty showed significantly lower self-employment odds compared to their non-poverty status counterparts.
CONCLUSIONS: Recommendations are presented for employment service interventions and entrepreneurial policy modifications aimed at catalyzing self-employment opportunities for people with disabilities that could help accelerate local and national economic growth.