Stochastic Frontier Estimation of Cost Models Within the Hospital

Bradford, W. David; Kleit, Andrew N.; Krousel‐Wood, Marie; Ré, Richard N.

doi:10.1162/00346530151143833

Cited by 42 publications

(31 citation statements)

References 19 publications

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“…In the US, Chirikos and Sear (2000) calculated mean efficiencies of 80 to 97 percent and 82 to 84 percent for DEA and SFA respectively, while Rosko (2001) found mean SFA efficiencies of 85 percent. In nonhospital studies, Rollins et al (2001) measured inefficiencies of between 19 and 42 percent in health maintenance organizations (HMOs) and Bradford et al (2001) estimated inefficiencies of between 9 and 27 percent in the treatment of cardiac revascularization patients. This divergence in results has, of course, awakened interest in the consistency of frontier-based measures of efficiency, both with alternative frontier approaches and with the earlier least squares production and cost functions.…”

Section: Specification Of Inputs and Outputsmentioning

confidence: 99%

Frontier Efficiency Measurement in Health Care: A Review of Empirical Techniques and Selected Applications

2004

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Health care institutions worldwide are increasingly the subject of analyses aimed at defining, measuring, and improving organizational efficiency. However, despite the importance of efficiency measurement in health care services, it is only relatively recently that the more advanced econometric and mathematical programming frontier techniques have been applied to hospitals, nursing homes, health management organizations, and physician practices, among others. This article provides a synoptic survey of the comparatively few empirical analyses of frontier efficiency measurement in health care services. Both the measurement of efficiency in a range of health care services and the posited determinants of health care efficiency are examined.

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Section: Specification Of Inputs and Outputsmentioning

confidence: 99%

Frontier Efficiency Measurement in Health Care: A Review of Empirical Techniques and Selected Applications

2004

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“…The formal extension of the method to nonlinear models, however, is of more recent vintage. The familiar approach [i.e., add "lambda" to the equation -e.g., Bradford et al (2000) in a stochastic frontier model and the earliest attempt to extend to nonlinear models, Wynand and van Praag (1981) in a probit model] is not appropriate for nonlinear models such as the stochastic frontier. A method of incorporating "selectivity" in some nonlinear models, notably the Poisson and other loglinear models, was proposed in Terza (1995Terza ( , 1998 and applied in Greene (1995Greene ( , 1997.…”

Section: Introductionmentioning

confidence: 99%

A stochastic frontier model with correction for sample selection

2009

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Heckman's (1979) sample selection model has been employed in three decades of applications of linear regression studies. The formal extension of the method to nonlinear models, however, is of more recent vintage. A generic solution for nonlinear models is proposed in Terza (1998). We have developed simulation based approach in Greene (2006). This paper builds on this framework to obtain a sample selection correction for the stochastic frontier model. We first show a surprisingly simple way to estimate the familiar normal-half normal stochastic frontier model (which has a closed form log likelihood) using maximum simulated likelihood. The next step is to extend the technique to a stochastic frontier model with sample selection. Here, the log likelihood does not exist in closed form, and has not previously been analyzed. We develop a simulation based estimation method for the stochastic frontier model. In an application that seems superficially obvious, the method is used to revisit the World Health Organization data [WHO (2000), Tandon et al. (2000)] where the sample partitioning is based on OECD membership. The original study pooled all 191 countries. The OECD members appear to be discretely different from the rest of the sample. We examine the difference in a sample selection framework.JEL classification: C14; C23; C25

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“…Many of the added theoretical contributions that are beyond the scope of the present study are comprehensively available in Kumbhakar and Lovell (2003); Coelli (2005) and Fried et al (2008). In addition, one can easily access the numerous industrial applications, including, for example, empirical studies of U.S. dairies , U.S. airlines (Kumbhakar, 1991), India paddy farms (Battese and Coelli, 1992;1995), the U.S. insurance industry (Cummins and Weiss, 1993), international airlines (Coelli et al, 1999), U.S. hospital care (Bradford et al, 2001) Japanese hospitals (Fujii, 2001), Taiwan banking (Huang and Wang, 2001), U.S. electricity (Knittel, 2002), Switzerland nursing homes (Farsi and Filippini, 2004), British railways (Mulatu and Crafts, 2005), Lisbon crime prevention (Barros and Alves, 2005) and English football (Barros and Leach, 2007), among many others.…”

Section: Methodsmentioning

confidence: 99%

Cost Efficiencies and Rankings of Flagship Universities

Sav¹

2011

American Journal of Economics and Business Administration

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Problem statement: Each state in the U.S. touts a premier university as the flagship of its publicly funded higher education system. With decreased government budgets and increased interest in public management reforms, these institutions are being pressured to provide evidence of and set examples for ever greater improvements in operating cost efficiencies. The problem, however, is that empirical measures of their efficiencies or inefficiencies can be sensitive and, therefore, vary widely depending upon the underlying model specification. Approach: The study used stochastic frontier analysis to estimate university cost inefficiencies over the 2005-09 academic years. Transom and Cobb-Douglas specifications were combined with two inefficiency models that treated university environmental factors as inefficiency determinants and as cost determinants. University inefficiency rankings were provided on the basis of mean scores and compared to the rankings obtained under the alternative models. Results: University mean inefficiency scores were estimated to vary between 1.19 and 1.32, indicating that costs were on average between 19 and 32% above the minimum frontier costs. At the individual university level, inefficiencies ranged from 1.015-2.43. Two specifications indicate that efficiency improvements occurred in the 2008-09 academic year. A university ranking of mean scores indicated an 87% difference between the most and least inefficient university. There was substantial sensitivity of rankings to model choice, but university rank status was found to be most unstable in the middle of the ranking distribution. Conclusion: The findings offer caution in proceeding to stochastic frontier estimates of inefficiencies without careful consideration and investigation of the sensitivities of such estimates to model choices. This study showed that advice is applicable, at least, to inefficiency estimates of U.S. flagship universities. Whether or not it is applies equally to other groups of universities is a matter of consideration for future studies.

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Stochastic Frontier Estimation of Cost Models Within the Hospital

Cited by 42 publications

References 19 publications

Frontier Efficiency Measurement in Health Care: A Review of Empirical Techniques and Selected Applications

Frontier Efficiency Measurement in Health Care: A Review of Empirical Techniques and Selected Applications

A stochastic frontier model with correction for sample selection

Cost Efficiencies and Rankings of Flagship Universities

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