P2-13: Modeling spatial patterns in stochastic efficiency frontiers of health care services
PhD student: Katharina Schley
Thesis Committee: Helmut Herwartz, Thomas Kneib, Stephan v. Cramon-Taubadel
Two alternative approaches to efficiency estimation are wildly used in academia. On the one hand, the stochastic frontier analysis (SFA) is a regression based approach and the data envelopment analysis (DEA), on the other hand, is a linear programming approach. Until recently the focus of health economic research lay on the analysis of (in)efficiencies of particular health care providers.
This project considers the quality of health care provision at the level of counties in Germany. The counties are regarded as decision making unit for the provision of health care. Due to the correlation in the data, the regional analysis needs to include spatial effects in the stochastic frontier model using adequate estimation methods like Generalized Additive Models for Location, Scale and Shape (GAMLSS).
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