Uncovering Racial Disparities in Local Welfare-to-Work Program Responses to the COVID-19 Pandemic in California

Background and Purpose

During the COVID-19 pandemic, mass unemployment or underemployment, school closures, and childcare facility shutdowns made it harder for Welfare-to-Work (WTW) program participants to meet their work requirements, putting them at risk of welfare sanction. To address these challenges, state and local social service agencies used the flexibility provided under the Temporary Assistance for Needy Families (TANF) to grant good-cause exemptions. Past research has shown that macroeconomic conditions, racial makeup, and political power can affect spatial inequalities in TANF WTW provisions (Fording et al., 2007, 2011; Chang et al., 2020). However, the pandemic's impact on existing structural inequities in the decentralized U.S. welfare system remains under-studied. This study aims to examine local WTW program responses to the pandemic in 58 California counties and analyze how the local racial composition contributes to county-level variations in WTW exemption, sanction, good cause, and non-compliance rates.

Methods

This study utilizes multiple monthly administrative datasets obtained from the California Department of Social Services, including Cal-OAR, CA237, WTW25, and CW115, spanning from January 2012 to June 2021. We employ multivariate regression models with time-fixed effects to examine the association between racial composition and WTW program indicators (i.e., WTW exemption rate, sanction rate, good causes rate, and non-compliance rate) both pre- and post-onset of the COVID-19 pandemic. Specifically, we include the pandemic status and the percentage of the TANF caseload consisting of Black, Hispanic, Asian, and other racial/ethnic groups respectively as our primary predictors. Our models also control for various local socio-economic-political characteristics, including child poverty rate, unemployment rate, political ideology, fiscal capacity, and cost-of-living.

Results

Preliminary findings reveal that an increased percentage of Black caseload is significantly associated with higher TANF application denial rates (β = 0.23, p < .05), higher WTW sanction rates (β = 0.26, p < .05), and higher WTW non-compliance rates (β = 0.41, p < .01) before the COVID-19 pandemic. However, during the pandemic, a higher percentage of Black caseloads were significantly associated with a decrease in WTW non-compliance rates (β = -0.4, p < .001) and an increase in good cause rates (β = 0.82, p < .05). We also found a higher percentage of Hispanic caseloads were significantly associated with higher WTW sanction rates (β = 0.18, p < .05) before the pandemic, but this association did not differ by pandemic status. Furthermore, a higher percentage of Asian caseloads were significantly associated with a decreased good cause rate (β = -4.3, p < .05) during the pandemic, indicating different racialized mechanisms in the WTW program responses between Asian and Black caseloads. Additionally, we performed a sensitivity analysis utilizing alternative racial composition variables that capture the overall population's racial composition.

Conclusions and Implications

This study offers a nuanced understanding of how the local racial compositions of caseload and population shape the responses of the WTW program during the COVID-19 pandemic. These findings have significant implications for the development of WTW policies and practices that promote social and racial justice, particularly in times of economic crises and pandemics.