This thesis is concerned with measuring and explaining the productive efficiency of firms or organizations. In particular, the study compares three alternative methods of measuring efficiency, namely, the deterministic frontier method, the stochastic frontier method, and the data envelopment analysis method (DEA). The dissertation consists of two parts. In Part I, the relative merits of the three methods are discussed and evaluated through a Monte Carlo study under certain known conditions. The study focuses on the effects of exogenous variables on efficiency estimates. The results show that the stochastic frontier method generally produces better efficiency estimates than the other two methods. The DEA, however, has a slight advantage in cases where there are weak input substitution and large variations in input variables. In Part II, the three methods are examined empirically through their applications to a panel of 19 railways in OECD countries and a panel of 36 international airlines. Comparison of the three sets of efficiency estimates confirms that on average the stochastic frontier method yields higher efficiency estimates than the other two methods, as indicated by the Monte Carlo results. The efficiency estimates by the two parametric methods are highly correlated, whereas there are considerable differences between the DEA estimates and those from the parametric methods. This is also consistent with the Monte Carlo results. By comparing the alternative efficiency estimates in the two applications, it is found that there is less discrepancy among the three sets of efficiency estimates in the airline case than in the railway case. This can be partly attributed to the fact that there are fewer variations in the operating environments in the airline case than in the railway case. The simulation results in Part I provide some general guidelines regarding the relative merits of the three alternative methods under certain known conditions. The two applications of the three methods in Part II serve as examples of how these three methods can be applied to practical problems where no a priori knowledge of either the production technology nor the efficiency profile exists. They illustrate some of the problems that may be encountered in empirical applications.

This thesis is concerned with measuring and explaining the productive efficiency of firms or organizations. In particular, the study compares three alternative methods of measuring efficiency, namely, the deterministic frontier method, the stochastic frontier method, and the data envelopment analysis method (DEA). The dissertation consists of two parts. In Part I, the relative merits of the three methods are discussed and evaluated through a Monte Carlo study under certain known conditions. The study focuses on the effects of exogenous variables on efficiency estimates. The results show that the stochastic frontier method generally produces better efficiency estimates than the other two methods. The DEA, however, has a slight advantage in cases where there are weak input substitution and large variations in input variables. In Part II, the three methods are examined empirically through their applications to a panel of 19 railways in OECD countries and a panel of 36 international airlines. Comparison of the three sets of efficiency estimates confirms that on average the stochastic frontier method yields higher efficiency estimates than the other two methods, as indicated by the Monte Carlo results. The efficiency estimates by the two parametric methods are highly correlated, whereas there are considerable differences between the DEA estimates and those from the parametric methods. This is also consistent with the Monte Carlo results. By comparing the alternative efficiency estimates in the two applications, it is found that there is less discrepancy among the three sets of efficiency estimates in the airline case than in the railway case. This can be partly attributed to the fact that there are fewer variations in the operating environments in the airline case than in the railway case. The simulation results in Part I provide some general guidelines regarding the relative merits of the three alternative methods under certain known conditions. The two applications of the three methods in Part II serve as examples of how these three methods can be applied to practical problems where no a priori knowledge of either the production technology nor the efficiency profile exists. They illustrate some of the problems that may be encountered in empirical applications. / Business, Sauder School of / Graduate

In this work I integrate some of my recent research developments in the theory and practice of Productivity and Efficiency Analysis of Economic Systems. In particular, I present some new theoretical relationships between various measures of efficiency and productivity, propose new solutions to some aggregation problems in efficiency analysis and apply the existing theory and the new findings to empirical analysis in Industrial Organization. / Graduation date: 2003

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