Advancing efficiency analysis using data envelopment analysis: the case of German health care and higher education sectors

The main goal of this dissertation is to investigate the advancement of efficiency analysis through DEA. This is practically followed by the case of German health care and higher education organizations. Towards achieving the goal, this dissertation is driven by the following research questions: 1.How the quality of the different DEA models can be evaluated? 2.How can hospitals’ efficiency be reliably measured in light of the pitfalls of DEA applications? 3.In measuring teaching hospital efficiency, what should be considered? 4.At the crossroads of internationalization, how can we analyze university efficiency? Both the higher education and the health care industries are characterized by similar missions, organizational structures, and resource requirements. There has been increasing pressure on universities and health care delivery systems around the world to improve their performance during the past decade. That is, to bring costs under control while ensuring high-quality services and better public accessibility. Achieving superior performance in higher education and health care is a challenging and intractable issue. Although many statistical methods have been used, DEA is increasingly used by researchers to find best practices and evaluate inefficiencies in productivity. By comparing DMU behavior to actual behavior, DEA produces best practices frontier rather than central tendencies, that is, the best attainable results in practice. The dissertation primarily focuses on the advancement of DEA models primarily for use in hospitals and universities. In Section 1 of this dissertation, the significance of hospital and university efficiency measurement, as well as the fundamentals of DEA models, are thoroughly described. The main research questions that drive this dissertation are then outlined after a brief review of the considerations that must be taken into account when employing DEA. Section 2 consists of a summary of the four contributions. Each contribution is presented in its entirety in the appendices. According to these contributions, Section 3 answers and critically discusses the research questions posed. Using the Translog production function, a sophisticated data generation process is developed in the first contribution based on a Monte Carlo simulation. Thus, we can generate a wide range of diverse scenarios that behave under VRS. Using the artificially generated DMUs, different DEA models are used to calculate the DEA efficiency scores. The quality of efficiency estimates derived from DEA models is measured based on five performance indicators, which are then aggregated into two benchmark-value and benchmark-rank indicators. Several hypothesis tests are also conducted to analyze the distributions of the efficiency scores of each scenario. In this way, it is possible to make a general statement regarding the parameters that negatively or positively affect the quality of DEA estimations. In comparison with the most commonly used BCC model, AR and SBM DEA models perform much better under VRS. All DEA applications will be affected by this finding. In fact, the relevance of these results for university and health care DEA applications is evident in the answers to research questions 2 and 4, where the importance of using sophisticated models is stressed. To be able to handle violations of the assumptions in DEA, we need some complementary approaches when units operate in different environments. By combining complementary modeling techniques, Contribution 2 aims to develop and evaluate a framework for analyzing hospital performance. Machin learning techniques are developed to perform cluster analysis, heterogeneity, and best practice analyses. A large dataset consisting of more than 1,100 hospitals in Germany illustrates the applicability of the integrated framework. In addition to predicting the best performance, the framework can be used to determine whether differences in relative efficiency scores are due to heterogeneity in inputs and outputs. In this contribution, an approach to enhancing the reliability of DEA performance analyses of hospital markets is presented as part of the answer to research question 2. In real-world situations, integer-valued amounts and flexible measures pose two principal challenges. The traditional DEA models do not address either challenge. Contribution 3 proposes an extended SBM DEA model that accommodates such data irregularities and complexity. Further, an alternative DEA model is presented that calculates efficiency by directly addressing slacks. The proposed models are further applied to 28 universities hospitals in Germany. The majority of inefficiencies can be attributed to “third-party funding income” received by university hospitals from research-granting agencies. In light of the fact that most research-granting organizations prefer to support university hospitals with the greatest impact, it seems reasonable to conclude that targeting research missions may enhance the efficiency of German university hospitals. This finding contributes to answering research question 3. University missions are heavily influenced by internationalization, but the efficacy of this strategy and its relationship to overall university efficiency are largely unknown. Contribution 4 fills this gap by implementing a three-stage mathematical method to explore university internationalization and university business models. The approach is based on SBM DEA methods and regression/correlation analyses and is designed to determine the relative internationalization and relative efficiency of German universities and analyze the influence of environmental factors on them. The key question 4 posed can now be answered. It has been found that German universities are relatively efficient at both levels of analysis, but there is no direct correlation between them. In addition, the results show that certain locational factors do not significantly affect the university’s efficiency. For policymakers, it is important to point out that efficiency modeling methodology is highly contested and in its infancy. DEA efficiency results are affected by many technical judgments for which there is little guidance on best practices. In many cases, these judgments have more to do with political than technical aspects (such as output choices). This suggests a need for a discussion between analysts and policymakers. In a nutshell, there is no doubt that DEA models can contribute to any health care or university mission. Despite the limitations we have discussed previously to ensure that they are used appropriately, these methods still offer powerful insights into organizational performance. Even though these techniques are widely popular, they are seldom used in real clinical (rather than academic) settings. The only purpose of analytical tools such as DEA is to inform rather than determine regulatory judgments. They, therefore, have to be an essential part of any competent regulator’s analytical arsenal

TECHNICAL EFFICIENCY IN RUSSIAN AGRICULTURE

For decades, Russian agriculture had had little technological progress and virtually no foreign investment, which resulted in a stable production possibilities frontier and made the sector ideally suited to production function analysis. The production function estimations reported in Chapters 10-13 add to a series of previous studies of the input/output relationship in Russian agriculture (e.g., Clayton, 1980, 1984; Gray, 1981; Johnson and Brooks, 1983), which generally followed the same methodology. In the late 1970s and the 1980s, however, the average response production functions gave way in the economics literature to more sophisticated production analysis techniques that measured not only productivity but technical efficiency as well (Aigner, et al., 1977; Bauer, 1990). Some of the major methodological advances in applying technical efficiency analysis to individual firms were made by a joint Russian-American team in Moscow in the early 1980s (Jondrow, et al., 1982; Danlin et al., 1985), but lack of data for many sectors of the Russian economy precluded the application of this technique until the end of the decade. When the Soviet Union collapsed, the initial optimistic expectation was that many sectors of the new Russian economy could rapidly achieve both higher productivity and higher technical efficiency once market forces prevailed. Our research attempts to understand why this has not happened in Russian agriculture in terms of technical efficiency.Research and Development/Tech Change/Emerging Technologies,

Productive efficiency and regulatory reform: The case of vehicle inspection services.

Measuring productive efficiency provides information on the likely effects of regulatory reform. We present a Data Envelopment Analysis (DEA) of a sample of 38 vehicle inspection units under a concession regime, between the years 2000 and 2004. The differences in efficiency scores show the potential technical efficiency benefit of introducing some form of incentive regulation or of progressing towards liberalization. We also compute scale efficiency scores, showing that only units in territories with very low population density operate at a sub-optimal scale. Among those that operate at an optimal scale, there are significant differences in size; the largest ones operate in territories with the highest population density. This suggests that the introduction of new units in the most densely populated territories (a likely effect of some form of liberalization) would not be detrimental in terms of scale efficiency. We also find that inspection units belonging to a large, diversified firm show higher technical efficiency, reflecting economies of scale or scope at the firm level. Finally, we show that between 2002 and 2004, a period of high regulatory uncertainty in the sample’s region, technical change was almost zero. Regulatory reform should take due account of scale and diversification effects, while at the same time avoiding regulatory uncertainty.Productive Efficiency, Regulatory Reform, Vehicle Inspections.

Portuguese local government relative efficiency: a dea approach

It is widely accepted that performance measurement in the Public Sector is a difficult task, either in terms of efficiency or in terms of effectiveness. The most important reason for this relates to the lack of objective measures, given the market-aside operation of governmental activities. Therefore traditional methods of performance measurement, such as those based on the operating statement and the net income, tend fail. It is typical that public bodies’ outputs are multiple and qualitative and consequently do not have the physical characteristic of being countable or divisible. Portuguese Local Government has, in the last decade, undergone considerable management changes under the flag of efficiency improvement, namely additional competencies, supplementary and more diverse financial resources and a new accounting system. In this context, this paper assesses the efficiency of Portuguese Continental municipalities, using year 2004 data and following a data envelopment analysis (DEA) methodology in order to provide a relative efficiency indicator. The analysis compares the ratio between resources as inputs (“undertaken commitments”) and the activities accomplished as outputs, considering the functional classification used in municipalities’ accounting and financial system. This research adds to the knowledge of local authorities’ performance the possibility of establishing a functioning ranking, nowadays increasingly important in what concerns financing issues. The preliminary results show that larger municipalities tend to be more efficient.Fundação para a Ciência e a Tecnologia (FCT

Perfomance and Efficiency in Colombia's Power Distribution Sistem: Effects of the 1994 Reform

We asess evolution in perfomance, efficiency and productivity of Colombia's power distribution utilitiesbefore and after the 1994 regulatory reform that introduced electricity market activities for the power sector in 12 distribution companies from 1985 to 2001. Perfomance is evaluated contrasting changes in meanand median by Wilcoxon Rank Sum and Pearson test on financial and other perfomance indicators. Technical efficiency is measured by means of Data Envelopment Analysis (DEA). The nature of the dataset allows the the estimation of Malmquist productivity index and its evolution in time. Results show a recovery after thereform in the mains perfomance indicators of profitability, parcial input productivity, and output. Plantefficiency and productivity increased after de reform, mainly in the largest utilities used as benchmarks inthe DEA efficiency scores measures. Meanwhile, the less efficient power distribution companies did not improve after the reform and were not able to undertake plant restructuring to chatch up in plant efficiency with respectto the Pareto efficient input allocation. Econometric results on DEA efficiency scores suggest a positive effectof policy reform.Electricity distribution, productive efficiency, power sector Colombia, Malmquist productivity index

A nonparametric analysis of the Greek renewable energy sector

This paper applies a bootstrapped Data Envelopment Analysis (DEA) formulation aiming to evaluate the financial performance of the firms operating in the Greek renewable energy sector. With the use of financial ratios in a DEA setting, efficiency ratios are constructed in order to analyse firms’ financial performance. The results reveal that firms’ performances are positively influenced by the high levels of return on assets and equity and by lower levels of debt to equity. In addition it appears that there are not significant differences of firms’ efficiency levels indicating high competitiveness between firms. Finally, firms producing wind energy appear to perform better than firms producing hydropower energy. It emerges that the majority of firms are operating in the wind and hydropower energy production making the Greek market of solar energy production being an emerging segment of the Greek renewable energy sector.Renewable energy market; Data Envelopment Analysis; Financial ratios; Greece

The equity theory: A quantitative perspective using data envelopment analysis

Equity theory (ET) is an organizational theory investigating how fairly people feel they have been treated. The literature on ET does not address two essential questions: what is the magnitude of the equity that one may perceive compared to other members in an organization?, and how much should be the resources (outcomes) of an underpaid member reduced (increased) to feel equal? The group members may respond to these questions emotionally, and their answers could be biased based on their personalities. This paper proposes a novel method using data envelopment analysis (DEA) to quantify the ET and answer these questions more logically. DEA is a mathematical model that is conceptually similar to ET. We will show how DEA can estimate the degree of equity perceived by members of a group with different personalities, including optimistic, pessimistic, benevolent, and entitled characters

Robust optimization in data envelopment analysis: extended theory and applications.

Performance evaluation of decision-making units (DMUs) via the data envelopment analysis (DEA) is confronted with multi-conflicting objectives, complex alternatives and significant uncertainties. Visualizing the risk of uncertainties in the data used in the evaluation process is crucial to understanding the need for cutting edge solution techniques to organizational decisions. A greater management concern is to have techniques and practical models that can evaluate their operations and make decisions that are not only optimal but also consistent with the changing environment. Motivated by the myriad need to mitigate the risk of uncertainties in performance evaluations, this thesis focuses on finding robust and flexible evaluation strategies to the ranking and classification of DMUs. It studies performance measurement with the DEA tool and addresses the uncertainties in data via the robust optimization technique. The thesis develops new models in robust data envelopment analysis with applications to management science, which are pursued in four research thrust. In the first thrust, a robust counterpart optimization with nonnegative decision variables is proposed which is then used to formulate new budget of uncertainty-based robust DEA models. The proposed model is shown to save the computational cost for robust optimization solutions to operations research problems involving only positive decision variables. The second research thrust studies the duality relations of models within the worst-case and best-case approach in the input \u2013 output orientation framework. A key contribution is the design of a classification scheme that utilizes the conservativeness and the risk preference of the decision maker. In the third thrust, a new robust DEA model based on ellipsoidal uncertainty sets is proposed which is further extended to the additive model and compared with imprecise additive models. The final thrust study the modelling techniques including goal programming, robust optimization and data envelopment to a transportation problem where the concern is on the efficiency of the transport network, uncertainties in the demand and supply of goods and a compromising solution to multiple conflicting objectives of the decision maker. Several numerical examples and real-world applications are made to explore and demonstrate the applicability of the developed models and their essence to management decisions. Applications such as the robust evaluation of banking efficiency in Europe and in particular Germany and Italy are made. Considering the proposed models and their applications, efficiency analysis explored in this research will correspond to the practical framework of industrial and organizational decision making and will further advance the course of robust management decisions

Benchmarking and Regulation in the Electricity Distribution Sector

In the last two decades electricity distribution sector have witnessed a wave of regulatory reforms aimed at improving efficiency through incentive regulation. Most of these regulation schemes use benchmarking namely measuring a company’s efficiency and rewarding them accordingly. The reliability of efficiency estimates is crucial for an effective implementation of those incentive mechanisms. A main problem faced by the regulators is the choice among several legitimate benchmarking models that usually produce different results. After a brief overview of the benchmarking methodologies, this paper summarizes the methods used in the regulation practice in several OECD countries, in which the benchmarking practice is relatively widespread. Repeated observation of similar companies over time namely panel data, allows a better understanding of unobserved firm-specific factors and disentangling them from efficiency estimates. Focusing on parametric cost frontier models, this paper presents two alternative approaches that could be used to improve the reliability of benchmarking methods, and based on recent empirical evidence, draws some recommendations for regulatory practice in power distribution networks.