Strengthening the Rural Home Care Workforce: How State Policies Impact Capacity and Quality

As growth in demand for home care accelerates, states face mounting challenges in building and sustaining a direct care workforce capable of meeting that need, particularly in rural communities. Geographic isolation, limited transportation infrastructure, and smaller labor pools make it increasingly difficult to recruit and retain home care workers in these settings, compromising access to essential services for older adults and people with disabilities.

In response, PHI partnered with the Research and Training Center on Disability in Rural Communities (RTC:Rural) at the University of Montana to examine how state policy environments shape the capacity of the home care workforce—and how that capacity affects care outcomes.

The two research analyses produced through this partnership provide a compelling initial evidence base that supports a core premise of PHI’s policy approach: strategic state investments in direct care jobs lead to a stronger workforce, and, ultimately, to better care.

Personal Assistance Workforce Capacity and Quality Outcomes

Our first PHI-RTC Rural analysis investigates the relationship between the number of personal care aides (PCAs) relative to potential consumers and state-level quality measures for long-term services and supports (LTSS). We compared each state’s workforce capacity—defined as the ratio of PCAs per 1,000 individuals reporting self-care disabilities (using 2018–2022 American Community Survey (ACS) data)—with its performance across five quality domains on the AARP LTSS State Scorecard.

Key findings include:

• A Strong Link: Higher PCA-to-consumer ratios were significantly associated with better state scores on four out of five LTSS dimensions: Choice of Setting and Provider, Safety and Quality, Support for Family Caregivers, and Community Integration.
• Quantifiable Impact: Statistically significant improvements in Choice, Safety/Quality, Family Caregiver Support, and Community Integration.
• Significant Influence: The connection was strongest for Support for Family Caregivers, with PCA ratios explaining over 36 percent of the variation in scores on that indicator across states. Overall, PCA capacity accounted for roughly 33 percent of the variance in the total LTSS Scorecard score.

Interestingly, there was no significant link found between PCA ratios and the Affordability and Access dimension. However, the overall results suggest that a sufficient supply of PCAs is strongly tied to improved quality outcomes and greater consumer choice.

Building Home Care Worker Capacity

The second analysis focuses on identifying policy differences between states with high versus low home care worker capacity. The brief explores how these states differ regarding median home care wages and the adoption of worker-supportive policies. Specifically, the brief compares the 15 states with the highest home care worker capacity ratio with the 15 states with the lowest capacity, examining median hourly wages (again using 2018-2022 ACS data) and the prevalence of home care worker-specific policies (namely, wage pass-throughs and training requirements) and universal labor policies (paid sick leave, union-supportive laws, Medicaid expansion, and state Earned Income Tax Credits, or EITCs).

Key findings include:

• Higher Wages: High-capacity states had significantly higher median hourly wages for home care workers ($14.67) compared to low-capacity states ($12.86).
• More Supportive Policies: High-capacity states were significantly more likely to have implemented:
  • Wage pass-through policies (47 percent vs. 7 percent)
  • Paid sick leave laws (67 percent vs. 0 percent)
  • Union-supportive legal environments (87 percent vs. 7 percent)
  • State EITCs (73 percent vs. 40 percent)

• Other Differences: Enhanced home care worker training requirements and Medicaid expansion were also more common in high-capacity states, though the differences between high- and low-capacity states were less pronounced and statistically insignificant.
• Overall Policy Adoption: On average, high-capacity states had adopted significantly more worker-supportive policies (with an average count of 4.1 policies) than low-capacity states (with an average of 1.4 policies).

These findings strongly indicate that higher wages and supportive state policies (both targeted and universal) are associated with greater home care workforce capacity.

Implications for Research, Policy, and Practice

Taken together, these analyses highlight two critical connections: state policy decisions influence home care worker and personal care aide capacity, and workforce capacity impacts care outcomes. This evidence supports PHI’s strong message to policymakers—investments in the direct care workforce are investments in the quality of care.

The PHI and RTC:Rural partnership underscores the value of accounting for geographic variation in LTSS analyses. While our analyses represent an important starting point, future research should explore causal relationships, examine local variations, and delve deeper into implementation challenges.

As the nation grapples with escalating challenges to workforce recruitment, especially in underserved rural areas, this research offers timely, evidence-based support for strengthening the workforce that forms the foundation of long-term care.

The research reported here was supported by funding from the National Institute on Disability, Independent Living, and Rehabilitation Research (NIDILRR grant numbers 90RTCP0002 and 90RTCP0007). NIDILRR is a Center within the Administration for Community Living (ACL), Department of Health and Human Services (HHS). The contents do not necessarily represent the policy of NIDILRR, ACL, or HHS, and you should not assume endorsement by the Federal Government.