Financial benchmarking of transportation companies in the New York Stock Exchange (NYSE) through data envelopment analysis (DEA) and visualization

In this paper, we present a benchmarking study of industrial transportation companies traded in the New York Stock Exchange (NYSE). There are two distinguishing aspects of our study: First, instead of using operational data for the input and the output items of the developed Data Envelopment Analysis (DEA) model, we use financial data of the companies that are readily available on the Internet. Secondly, we visualize the efficiency scores of the companies in relation to the subsectors and the number of employees. These visualizations enable us to discover interesting insights about the companies within each subsector, and about subsectors in comparison to each other. The visualization approach that we employ can be used in any DEA study that contains subgroups within a group. Thus, our paper also contains a methodological contribution

Identifying and Measuring Technical Inefficiency Factors:Evidence from Unbalanced Panel Data for Thai Listed Manufacturing Enterprises

This study employs stochastic frontier analysis (SFA) and two-stage DEA approaches to predict firm technical efficiency and analyse an inefficiency effects model. Aggregate translog stochastic frontier production functions are estimated under the SFA approach using an unbalanced panel data of 178 Thai manufacturing enterprises listed in the Stock Exchange of Thailand (SET), covering the period 2000 to 2008. The maximum-likelihood Tobit model is used to conduct the second-stage of the two-stage DEA model to investigate the relationship between technical inefficiency and environmental variables. Both parametric and nonparametric approaches are found to produce consistent results. The empirical evidence from both approaches highlight that Thai listed manufacturing firms had been operating under decreasing returns to scale over the period 2000 to 2008. The SFA approach reports that technical progress decreased over time, and relied on labour input. Both estimation approaches suggest that leverage (financial constraints), executive remuneration, managerial ownership, exports, some types of listed firms (i.e., family-owned firm and foreign-owned firm), and firm size have a negative (positive) and significant effect on technical inefficiency (technical efficiency). The empirical results obtained from both approaches also suggest that liquidity, external financing, and research & development (R&D) have a significantly positive (negative) effect on technical inefficiency (technical efficiency)Stochastic Frontier Analysis (SFA); Data Envelopment Analysis (DEA);Technical Efficiency; Manufacturing; Thailand

Do Mergers Improve Efficiency? Evidence from Restructuring the U.S. Electric Power Sector

This paper analyses the performance impact of the merger wave which took place in the US electricity industry during the period 1994-2003. It does so by analyzing the impact on operating and total cost in electricity distribution. While there are past studies of efficiency and productivity effects, as well as of prices, profits, and other outcomes, this study differs in several ways. First, the database consists of many merging and non-merging firms, rather than only a few on which to base inferences. Second, all of these mergers arise in a single industry, greatly facilitating controlled comparison. Third, we have data on the several years of pre-merger and post-merger efficiency of the specific merging units, unlike virtually all past studies. And finally, we employ a powerful nonparametric technique - data envelopment analysis - to measure the efficiency of each operating unit. The results indicate that electricity mergers are not consistent with improved cost performance

Efficiency Analysis for Peruvian electricity distribution sector: Inefficiency’s explicative factors. A study for 2000 – 2008

"This paper estimates the inefficiency of Peruvian electricity distribution companies to determine if the expected results from the 90’s reform were met. To do this, we used data for 19 distribution companies for the period 2000 – 2008 using a Cost Stochastic Frontier approach and estimating inefficiency in a one-step procedure. The analysis suggests that private utilities are less inefficient than public Utilities because of better management practices. In other words, private management and investments had been favorable in terms of efficiency for the period studied. Also, regulation changes, especially in 2005, have increased distribution costs and geographical characteristics impact negatively on efficiency, especially in public Utilities. This can be explained by the fact that State investment in difficult areas, with public Utilities operating, imposes additional costs (management and operational) that makes them inefficient. Future investigations should focus in a Region Analysis, for the sake of a bigger Panel sample."Distribution sector, Utilities, Inefficiency, Ranking, Stochastic Frontier, Reform

Performance Measurement in the Australian Water Supply Industry

Various government-owned businesses provide water supply services to Australian residents. With the advent of recent competition and regulatory reforms in infrastructure industries in Australia, more and more of these businesses are now facing new types of incentive-based regulatory regimes. This has led to a desire for more information on the performance of these businesses, both relative to each other and over time. In this study we use panel data on the 18 largest Australian water services businesses, observed over an eight-year period from 1995/6 to 2002/3, to measure the relative efficiency and productivity growth of these businesses. Data envelopment analysis (DEA) methods are used to obtain estimates of the multi-input, multi-output production technology. The potential use of these performance measures in price-cap regulation is discussed, where the effects of variable selection and data quality upon empirical results is emphasised.

Transit Costs and Cost Efficiency: Bootstrapping Nonparametric Frontiers.

This paper explores a selection of recently proposed bootstrapping techniques to estimate non-parametric convex (DEA) cost frontiers and efficiency scores for transit firms. Using a sample of Norwegian bus operators, the key results can be summarised as follows: (i) the bias implied by uncorrected cost efficiency measures is numerically important (close to 25%), (ii) the bootstrapped-based test rejects the constant returns to scale hypothesis (iii) explaining patterns of efficiency scores using a two-stage bootstrapping approach detects only one significant covariate, in contrast to earlier results highlighting, e.g., the positive impact of high-powered contract types. Finally, comparing the average inefficiency obtained for the Norwegian data set with an analogous estimate for a smaller French sample illustrates how the estimated differences in average efficiency almost disappear once sample size differences are accounted for.

Efficiency and Productivity Changes in the Indian Food Processing Industry: Determinants and Policy Implications

This paper analyses efficiency and productivity changes in 12 broad segments of food manufacturing industries during pre and post liberalisation periods, covering a period of two decades, from 1980-1981 to 2001-2002. The nonparametric Data Envelopment Analysis (DEA) approach is used to compute the Malmquist Total Factor Productivity (TFP) change, which has been further decomposed into efficiency and technical change. This paper also evaluates the performance of major inputs used in the food processing industry and identifies the causes of inefficiency across various segments. Based on the findings, the paper gives suggestions that can be used by policy makers and food processors in making decisions regarding various technical and managerial aspects to improve productivity and efficiency.Technical Efficiency, Total Factor Productivity (TFP), Food Processing, Data Envelopment Analysis (DEA), India, Productivity Analysis, Research Methods/ Statistical Methods, Q10, Q11, Q13,

Research on Financing Efficiencies of Strategic Emerging Listed Companies by Six-Stage DEA Model

Accounting for the information of input slack variables, as well as the effects of external environment and stochastic factors, a six-stage DEA model was proposed based on four-stage DEA model. It was employed to assess the financing efficiencies of 689 strategic emerging listed companies in 2015. By isolating the environmental and stochastic factors, the final efficiencies can reflect the actual financing level of these companies. The empirical results show that most financing efficiencies are still at a low level relatively. The scales of these strategic emerging companies are the main constraint on their development. And the special technical level also has an impact on these efficiencies. In addition, the efficiency difference among provinces in China gives another support to environmental influence on the strategic emerging industry. Therefore, a strategic emerging company should pay attention to expanding its scale of production and heighten its special technical level and it should improve its financing efficiencies with the help of local government power

Estimating Total Factor Productivity and Its Components: Evidence from Major Manufacturing Industries of Pakistan

This paper estimates the trend in total factor productivity growth for eleven major manufacturing sub-sectors/industries listed on Karachi Stock Exchange. 1998 to 2007 Malmquist total factor productivity growth indices have been calculated using nonparametric Data Envelopment Analysis which also shows TFP growth sources including efficiency change and technical change. The results of this study are showing a mixed trend for all manufacturing sub-sectors/industries in terms of TFP, technical efficiency change and technological change. Cement and Oil and Gas marketing sectors depict a relatively stable position. Most of the manufacturing industries have gained, in terms of technical efficiency but the technical change is putting a negative affect on the productivity growth except for a few industries.Data Envelopment Analysis, Manufacturing Sector, Malmquist Productivity Index, Total Factor Productivity, Technical Efficiency Change, Technical or Technological Change, Pakistan