Measuring efficiencies in U.S. hospital mergers
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Using non-parametric approaches, this study investigates the technical efficiencies of U.S. hospitals that have undergone horizontal mergers. Past studies have shown that hospital prices, hospital costs, quality of care provided, and consumer welfare are affected by these mergers. Theoretical studies on mergers propose potential gains from mergers. The results from these studies provide conflicting reports on the efficiencies of the merged hospitals. Hospitals merge in the anticipation of increasing market power by reducing operational expenses and expansion of services. However, many of these merged hospitals have filed for bankruptcy and have shut down in the years following the merger. This points to the importance of analyzing the post-merger performance of hospitals.The research question is to examine the impact of mergers on the hospitals using a two-stage Data Envelopment Analysis (DEA) and Free Disposal Hull (FDH) method. In the second stage, a Tobit regression model is used to determine the impact of hospital size, ownership, and urbanization levels on bootstrapped efficiency scores using the panel dataset for the years 2001-2011. While existing studies analyze the merged entity that comprises both the acquirer hospitals and the target hospitals, this paper focuses primarily on the target hospital in the merger. Also, a larger pre/post-merger time span is analyzed to fully capture the technical efficiency changes due to the mergers. Further analyses include efficiency comparisons of merged hospitals of various sizes, ownership types, and urbanization levels with a control group of unmerged hospitals.Data Envelopment Analysis and Free Disposal Hull are non-parametric performance benchmarking tools that are used to evaluate the performance of multi-input/output organizations such as hospitals where the market prices of inputs/outputs are unavailable. The data for this study are obtained from the American Hospital Association and Irving Levin Associates for the years 2001 to 2011. This research uses panel data on hospitals covering three years before and seven years after the merger (2001-2011).The main conclusion drawn from the study is that we cannot justify all hospital mergers on grounds of efficiency gains. Although hospitals merge with the post-merger expectations of higher efficiency, both cross-sectional and panel data analysis of the hospitals suggest that some hospitals had a decrease in efficiency scores in the years following the merger. It was also observed that the control group and the merger group had similar trends in the pre/post-merger periods. Additional analysis using Difference-in-Difference methods and non-parametric ANOVA tests also confirmed these findings. Under the assumption that hospitals have more control over their outputs than inputs, larger sized for-profit and urban hospitals showed higher efficiencies than their counterparts. Another significant finding from the study is that the merger effect is more pronounced in Micropolitan areas.Overall, this study contributes to the DEA and FDH literature by demonstrating the efficiency calculations for hospitals that undergo mergers. Hospital management can use these methods as a performance benchmarking tool to identify the efficiency of their organization and re-allocate resources to improve efficiency. The two-stage DEA analysis can be specifically used when hospital administrators are contemplating an upcoming merger with another hospital, keeping in mind the size, ownership, and the location of the participating hospital.