A Comparative Application of Data Envelopment Analysis and Translog Methods: An Illustrative Study of Hospital Production

Abstract

This paper compares inferences about hospital cost and production correspondences from two different estimation models: (1) the econometric modeling of the translog cost function, and (2) the application of Data Envelopment Analysis (DEA). While there are numerous examples of the application of each approach to empirical data, this paper provides insights into the relative strengths of the estimation methods by applying both models to the same data. The translog results suggest that constant reruns are operant, whereas the DEA results suggest that both increasing and decreasing returns to scale may be observed in different segments of the production correspondence, in turn suggesting that the translog model may be `averaging' diametrically opposite behavior. On the other hand, by examining the rate of output transformation, both models agree that patient days devoted to care of children are more resource intensive than those devoted to adults or to the elderly. In addition, we compare estimates of technical efficiencies of individual hospitals obtained from the two methods. The DEA estimates are found to be highly related to the capacity utilization, but no such relationship was found for the translog estimates. This comparative application of different estimation models to the same data to obtain inferences about the nature of underlying cost and production correspondences sheds interesting light on the strengths of each approach, and suggests the need for additional research comparing estimation models using real as well as simulated data.organizational studies, data envelopment analysis, hospital costs, efficiency evaluation, production functions, translog estimation, returns to scale

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“…12 Relative advantage and disadvantage of parametric vs non parametric techniques have long been discussed. With direct references to public services, see, for instance Bardhan et al (1998), Banker et al (1986) and Newhouse (1994). 13 See for instance: Lovell and Schmidt (1988), Bauer (1990) and Kumbhakar and Lovell (2000).…”

Effects of an adapted physical activity program on the physical condition of elderly women: an analysis of efficiency

“…The operational research tool, Data Envelopment Analysis (DEA), was used to determine the relative efficiency of the participants, considered Decision-Making Units (DMUs) [44][45][46] . This analysis supports the hypothesis that the outputs do not need to be proportional to the inputs 47 .…”

Assessing the Queuing Process Using Data Envelopment Analysis: an Application in Health Centres

“…Table 1 shows the most commonly used inputs and outputs in previous DEA studies when comparing hospitals. Number of Outpatient visits (Bwana 2015 ;Flokou et al, 2011;Masiye et al, 2006 ;Ramanathan 2005;Zuckerman et al, 1994) Number of beds (Bwana 2015;Kose et al, 2014, Al-Shammari 1999Akazili et al, 2008;Harrison et al, 2004;Puig-Junoy 2000;Weng et al, 2009;Magnussen 1996) Number of Inpatient visits (Chuang et al, 2011;Butler and Li 2005;Tsai and Molinero 2002;Zere et al, 2001;Banker et al, 1986 ;Kawaguchi et al, 2014) Number of doctors (Grosskopf and Valdmanis 1987;Kirigia et al, 2008;Chang et al, 2004;Huang et al, 1989;Prior 2006;Kose et al, 2014;Dotoli et al, 2015) Number of major and minor surgeries (Al-Shammari 1999;Chang et al, 2004 ;Puig-Junoy 2000 ;Gerdtham et al, 1999 ;Dotoli et al, 2015) Number of nurses (Chuang et al, 2011;Masiye et al, 2006;Hollingsworth and Parkin 1997;Byrnes and Valdmanis 1993;Kawaguchi et al, 2014) Number of emergency visits (Butler andLi 2005 Grosskopf andValdmanis 1987;<...>…”