Does Nonprofit Ownership Matter for Firm Performance? Evidence from Ownership Conversions of Nursing Homes
39 Pages Posted: 20 Mar 2019 Last revised: 26 May 2021
Date Written: May 16, 2021
In the past two decades, many nursing homes converted their ownership status from nonprofit to for-profit (NP-to-FP). These conversions have drawn public scrutiny and triggered a debate about the implications of ownership conversions on nursing home performance. Exploring a nationwide panel dataset of U.S. nursing homes from 2006 to 2017, we observe that nursing homes with higher financial distress are associated with higher likelihood of NP-to-FP conversions. The post-conversion operating margins increased significantly. Converted nursing homes improved their financial performance by reducing operating costs while keeping net resident revenues unchanged. Both cutting registered nurse staffing and cutting overhead staffing contributed to reductions in operating costs; however, only the former cost-reduction measure had a negative impact on quality. On average, the post-conversion quality of care declined. The effects of NP-to-FP conversions on nursing homes were moderated by pre-conversion financial distress: High-distress nursing homes aggressively cut registered nurse staffing and experienced severe quality decline, whereas low-distress ones kept registered nurse staffing unchanged and largely avoided quality decline. These findings lead to both policy and managerial insights. To nursing home regulators, we recommend increased oversight on NP-to-FP conversions of nursing homes with high pre-conversion financial distress. To managers of nursing homes undergoing NP-to-FP conversions, our findings suggest that although cost reduction is an effective strategy to improve financial performance, they need to avoid the pitfall of cutting registered nurse staffing and instead focus on streamlining overhead operations in order to increase operating efficiency without compromising quality.
Keywords: nonprofit, for-profit, ownership conversion, financial performance, quality of care, patient selection, nurse staffing, wage cost, machine learning
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