John Ruggiero

On the measurement of technical efficiency in the public sector

Abstract Existing measures of technical inefficiency obtained through linear programming models in the public sector do not properly control for environmental variables that affect production. It will be shown that the consequences of not controlling for these fixed factors are biased estimates of technical efficiency. This paper extends the mathematical programming approach to frontier estimation known as Data Envelopment Analysis to allow for environmental variables. This modified model will be then contrasted with the existing model that purportedly controls for exogeneous factors to measure public sector efficiency with simulated data. The results provide evidence that the existing Data Envelopment Analysis model will overestimate the level of technical inefficiency and that the modified model developed in this paper does a better job controlling for exogenous factors. The modified model is also applied to analyze the technical efficiency of school districts.

Non-discretionary inputs in data envelopment analysis

The technique for efficiency measurement known as Data Envelopment Analysis (DEA) has been extended to allow non-discretionary inputs that affect production. Several methods exist for measuring efficiency while controlling for these fixed factors of production. This paper reviews these approaches, providing a discussion of strengths and weaknesses and highlighting potential limitations. In addition, a new approach is developed that overcomes existing weaknesses. To facilitate comparison, an analysis using simulated data is performed. The results show that the new approach improves existing models and performs relatively well.

Empirical Evaluation of Bureaucratic Models of Inefficiency

Two separate but related strands of literature exist regarding the efficiency of public sector service provision – the theoretical base developed in the bureaucratic models of supply and the methodological base developed in the operations research and economic literatures. Most analyses focus exclusively on either the measurement or causes of inefficiency. This paper seeks to empirically test bureaucratic models of supply by drawing on the measurement literature. In anticipation of the results, it is found that there does exist empirical evidence supporting some of the implications of these models.

Performance evaluation of National Football League teams

Most recent empirical analyses of production in the sports economic literature have focused on Major League Baseball. This paper extends that literature by analysing football production in the National Football League (NFL). Using the Poisson regression model, we measure the performance of NFL teams and head coaches. The measure is based on a production process where player skills are converted into games won. The evidence reveals that quality coaching is an important component in the production process. It appears that efficient coaching can account for an additional three to four victories in a given season. Copyright

Efficiency measurement in the stochastic frontier model

Abstract Deterministic models of technical efficiency assume that all deviations from the production frontier are due to inefficiency. Critics argue that no allowance is made for measurement error and other statistical noise so that the resulting efficiency measure will be contaminated. The stochastic frontier model is an alternative that allows both inefficiency and measurement error. Advocates argue that the stochastic frontier models should be used despite other potential limitations because of the superior conceptual treatment of noise. As will be demonstrated in this paper, however, the assumed shape of the error distributions is used to identify a key production function parameter. Therefore, the stochastic frontier models, like the deterministic models, cannot produce absolute measures of efficiency. Moreover, we show that rankings for firm-specific inefficiency estimates produced by traditional stochastic frontier models do not change from the rankings of the composed errors. As a result, the performance of the deterministic models is qualitatively similar to that of the stochastic frontier models.

Evaluating alternative DEA models used to control for non-discretionary inputs

Evaluation of performance using DEA requires models consistent with the underlying technology. There have been a number of models proposed for analyzing performance in the presence of non-discretionary inputs. Banker and Morey (Operations Research 34 (1986) 513-521) provided the first DEA model to measure technical efficiency. Other single- and multiple-stage models that incorporate DEA have been developed. This paper discusses the various approaches and provides a simulation analysis to compare the relative performance of each.

Performance evaluation when non-discretionary factors correlate with technical efficiency

Abstract The current data envelopment analysis (DEA) literature on non-discretionary inputs ignores the possibility that efficiency may be correlated with the non-discretionary factors. This paper extends the literature by analyzing the effects that such correlation has. It will be shown that if the true technical efficiency is negatively correlated with the non-discretionary inputs, the existing DEA efficiency estimates will be biased upward. Using simulated data, the performance of the existing model will be analyzed. In addition, a corrected model will be introduced to effectively handle the problem. The resulting model is able to disentangle the two effects that the non-discretionary factor has on production.

Efficiency estimation and error decomposition in the stochastic frontier model: A Monte Carlo analysis

Critics of the deterministic approach to efficiency measurement argue that no allowance is made for measurement error and other statistical noise. Without controlling for measurement error, the resulting measure of efficiency will be distorted due to the contamination of noise. The stochastic frontier models purportedly allow both inefficiency and measurement error. Some proponents argue that the stochastic frontier models should be used despite the limitations because of the superior conceptual treatment of noise. However, the ultimate value of the stochastic frontier depends on its ability to properly decompose noise and inefficiency. This paper tests the validity of the stochastic frontier cross-sectional models using a Monte Carlo analysis. The results suggest that the technique does not accurately decompose the total error into inefficiency and noise components. Further, the results suggest that at best, the stochastic frontier is only as good as the deterministic model.

A comparison of DEA and the stochastic frontier model using panel data

Two competing approaches for the measurement of efficiency are the stochastic frontier model and data envelopment analysis (DEA). Previous research has established that the two models applied to cross-sectional data are both adversely affected by measurement error. While the cross-sectional stochastic frontier model does not effectively handle statistical noise, panel data models do. This is true because additional information from multiple time periods is incorporated into the estimation. A panel data DEA model that uses averaged data has been shown to effectively smooth out measurement error. In this paper, we compare the panel data models using simulated data.

IMPACT ASSESSMENT OF INPUT OMISSION ON DEA

It is well known in the Data Envelopment Analysis literature that proper variable selection is necessary for the reliable measurement of efficiency. Omitting production relevant variables and/or including irrelevant variables will lead to biased measurement. It is also known that the sample size needs to be large relative to the number of inputs and outputs to prevent classification of efficiency by default. In some empirical settings the number of potential relevant variables is large. Careful selection of an appropriate set of variables is necessary for reliable efficiency measurement. This paper looks at the issue of input selection and uses simulation analysis to develop statistical procedures to provide guidelines for input selection.