On Testing Equality of Distributions of Technical Efficiency Scores

The challenge of the econometric problem in production efficiency analysis is that the efficiency scores to be analyzed are unobserved. Statistical properties have recently been discovered for a type of estimator popular in the literature, known as data envelopment analysis (DEA). This opens up a wide range of possibilities for well-grounded statistical inference about the true efficiency scores from their DEA estimates. In this paper we investigate the possibility of using existing tests for the equality of two distributions in such a context. Considering the statistical complications pertinent to our context, we consider several approaches to adapting the Li test to the context and explore their performance in terms of the size and power of the test in various Monte Carlo experiments. One of these approaches shows good performance for both the size and the power of the test, thus encouraging its use in empirical studies. We also present an empirical illustration analyzing the efficiency distributions of countries in the world, following up a recent study by Kumar and Russell (2002), and report very interesting results.

[1]  Valentin Zelenyuk,et al.  Testing for Catching-up: Statistical Analysis of DEA Efficiency Estimates , 2004 .

[2]  M. Farrell The Measurement of Productive Efficiency , 1957 .

[3]  P. Hall Central limit theorem for integrated square error of multivariate nonparametric density estimators , 1984 .

[4]  P. W. Wilson,et al.  Asymptotics for DEA estimators in non-parametric frontier models , 2003 .

[5]  R. Robert Russell,et al.  Continuity of measures of technical efficiency , 1990 .

[6]  Qi Li,et al.  Nonparametric testing the similarity of two unknown density functions: local power and bootstrap analysis , 1999 .

[7]  P. Hall The Bootstrap and Edgeworth Expansion , 1992 .

[8]  G. Debreu The Coefficient of Resource Utilization , 1951 .

[9]  R. Russell,et al.  Human Capital and Convergence: A Production-Frontier Approach , 2005 .

[10]  B. Park,et al.  A NOTE ON THE CONVERGENCE OF NONPARAMETRIC DEA ESTIMATORS FOR PRODUCTION EFFICIENCY SCORES , 1998, Econometric Theory.

[11]  N. H. Anderson,et al.  Two-sample test statistics for measuring discrepancies between two multivariate probability density functions using kernel-based density estimates , 1994 .

[12]  B. Efron Bootstrap Methods: Another Look at the Jackknife , 1979 .

[13]  B. Park,et al.  THE FDH ESTIMATOR FOR PRODUCTIVITY EFFICIENCY SCORES , 2000, Econometric Theory.

[14]  E. Mammen When Does Bootstrap Work?: Asymptotic Results and Simulations , 1992 .

[15]  Léopold Simar,et al.  A General Methodology for Bootstraping in Nonparametric Frontier Models , 1998 .

[16]  Leâ Opold Simar,et al.  A general methodology for bootstrapping in non-parametric frontier models , 2000 .

[17]  Eugene F. Schuster,et al.  Incorporating support constraints into nonparametric estimators of densities , 1985 .

[18]  Bernard W. Silverman,et al.  Density Estimation for Statistics and Data Analysis , 1987 .

[19]  R. Färe,et al.  Intertemporal Production Frontiers: With Dynamic DEA , 1996 .

[20]  A. Ullah,et al.  Nonparametric Econometrics , 1999 .

[21]  Valentin Zelenyuk,et al.  Corporate Governance and Firm’s Efficiency: The Case of a Transitional Country, Ukraine , 2006 .

[22]  P. W. Wilson,et al.  Sensitivity Analysis of Efficiency Scores: How to Bootstrap in Nonparametric Frontier Models , 1998 .

[23]  Hurvey Leibenstein Allocative efficiency vs. X-Efficiency , 1966 .

[24]  D. Freedman,et al.  On the consistency of Bayes estimates , 1986 .

[25]  P. W. Wilson,et al.  Statistical Inference in Nonparametric Frontier Models: The State of the Art , 1999 .

[26]  Yanqin Fan,et al.  On goodness-of-fit tests for weakly dependent processes using kernel method , 1999 .

[27]  H. Leibenstein,et al.  Empirical Estimation and Partitioning of X-Inefficiency: A Data-Envelopment Approach , 1992 .

[28]  Léopold Simar,et al.  Estimating efficiencies from frontier models with panel data: A comparison of parametric, non-parametric and semi-parametric methods with bootstrapping , 1992 .

[29]  P. J. Green,et al.  Density Estimation for Statistics and Data Analysis , 1987 .

[30]  E. Mammen,et al.  On estimation of monotone and concave frontier functions , 1999 .

[31]  R. Shepherd Theory of cost and production functions , 1970 .

[32]  Léopold Simar,et al.  On estimation of monotone and convex boundaries , 1995 .

[33]  R. R. Russell,et al.  Technological Change, Technological Catch-up, and Capital Deepening: Relative Contributions to Growth and Convergence , 2002 .

[34]  M. C. Jones,et al.  A reliable data-based bandwidth selection method for kernel density estimation , 1991 .