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Investigating the impact of endogeneity on inefficiency estimates in the application of stochastic frontier analysis to nursing homes |
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| Authors: | Ryan L. Mutter William H. Greene William Spector Michael D. Rosko Dana B. Mukamel |
| Affiliation: | 1. Center for Delivery, Organization and Markets, Agency for Healthcare Research and Quality, 540 Gaither Road, Rockville, MD, 20850, USA 2. Stern School of Business, New York University, 44 West 4th St., New York, NY, 10012, USA 3. School of Business Administration, Widener University, One University Place, Chester, PA, 19013, USA 4. Department of Medicine, Health Policy Research Institute, University of California, Irvine, Irvine, CA, 92697, USA |
| Abstract: | This paper examines the impact of an endogenous cost function variable on the inefficiency estimates generated by stochastic frontier analysis (SFA). The specific variable of interest in this application is endogenous quality in nursing homes. We simulate a dataset based on the characteristics of for-profit nursing homes in California, which we use to assess the impact on SFA-generated inefficiency estimates of an endogenous regressor under a variety of scenarios, including variations in the strength and direction of the endogeneity and whether the correlation is with the random noise or the inefficiency residual component of the error term. We compare each of these cases when quality is included and excluded from the cost equation. We provide evidence of the impact of endogeneity on inefficiency estimates yielded by SFA under these various scenarios and when the endogenous regressor is included and excluded from the model. |
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