Soushi Suzuki

A distance friction minimization approach in data envelopment analysis: A comparative study on airport efficiency

This paper aims to present a newly developed distance friction minimization (DFM) method in the context of data envelopment analysis (DEA) in order to generate an appropriate (non-radial) efficiency-improving projection model, for both input reduction and output increase. In this approach, a generalized distance function, based on a Euclidean distance metric in weighted spaces, is proposed to assist a decision making unit (DMU) to improve its performance by an appropriate movement towards the efficiency frontier surface. A suitable form of multidimensional projection function for efficiency improvement is given by a Multiple Objective Quadratic Programming (MOQP) model. The paper describes the various steps involved in a systematic manner. The above-mentioned DFM model is illustrated empirically by using a data set on 30 European airports, where the aim is to present a comparative analysis of the efficiency of operational management in these airports. In addition, the comparative analysis of these airports is able to assess both input slacks and output slacks (or a combination of input reduction and output rise).

Regional Efficiency Improvement by Means of Data Envelopment Analysis through Euclidean Distance Minimization including Fixed Input Factors - An Application to Tourist Regions in Italy

Standard Data Envelopment Analysis (DEA) is characterized by uniform proportional input reduction or output augmentation in calculating improvement projections. This paper develops a new Euclidean Distance Minimization model in the context of DEA in order to derive a more appropriate efficiency-improving projection model by means of a weighted projection function. The model is extended to the situation where some factor inputs are fixed, for instance, due to lumpiness or natural constraints. The extended DEA model is illustrated in the context of regional planning by using a data set on Italian tourist destination regions.

Contrasts and similarities in economic performance of migrant entrepreneurs

In recent years, migrant entrepreneurs have come to occupy a prominent place in the SME sector in many cities in developed countries, with varying degrees of success. The concept of migrant entrepreneurship suggests a homogeneous set of actors, but it remains to be seen whether differences in cultural and ethnic backgrounds, in education, in age and gender, and in motivational profiles lead to contrasting business outcomes. The present paper aims to identify and compare differences in the economic performance of individual migrant business firms on the basis of a quantitative assessment of the drivers of their efficiency profiles. In this context, we will address in particular the drivers and barriers for the heterogeneous business strategies of specific classes of migrant entrepreneurs. After the use of a multivariate statistical analysis, a modern operational approach–originating from organizational theory–that aims to make a comparative study of quantitative efficiency differences between individual decision-making units (DMUs), viz. Data Envelopment Analysis (DEA) is employed. DEA is used here to assess relative performance differences between distinct categories of migrant entrepreneurs in the city of Amsterdam. A wealth of relevant data has been collected by systematic, personally-supervised interviews and questionnaires, and these contain a variety of efficiency-oriented indicators, on both the input and the output side. Several additional analyses ─ using multivariate cross-analysis methods ─ are also carried out to test the robustness of our findings by, inter alia, investigating the influence of specific socio-cultural ethnic groups, levels of education, first-and second-generation migrants, and age. Finally, the paper offers some lessons on entrepreneurship strategies.Jel codesR10, O15, L26.

An Evaluation of Energy-Environment-Economic Efficiency for Asian Countries: A Proposal for a Time-Series Target-Oriented DFM Model in Data Envelopment Analysis

This chapter aims to provide an advanced assessment methodology for sustainable national energy-environment-economic efficiency strategies, based on an extended Data Envelopment Analysis (DEA). The use of novel efficiency-improving approaches based on DEA originates from the so-called Distance Friction Minimisation (DFM) method. To design a feasible improvement strategy for low-efficiency DMUs, we develop a Target-Oriented (TO) DFM model. However, the TO-DFM model does not incorporate a time-series concept in setting the target improvement level. In this chapter, we develop a new model from a blend of the TO-DFM and a Time-Series (TS) approach that incorporates the time-series concept and a stepwise target score to achieve a final target-efficiency score in order to generate a more realistic efficiency-improving projection. In the empirical analysis, Super-Efficiency DEA is used in our comparative study on the efficiency assessment of energy-environment-economic targets for the Asian countries, for which we employ appropriate data sets from the years 2003 to 2012. Hence, the above-mentioned TS-TO-DFM model is able to analyse realistic circumstances and determine the requirements for an operational sustainability strategy for efficiency improvement in inefficient Asian countries.

Bright stars in the urban galaxy – the efficiency of ethnic entrepreneurs in the urban economy

An issue of continuous debate over recent decades has been the impact of migration on the development of both destination and origin countries of migrants. Migration is a form of optimal allocation of production factors in both sending and receiving countries. In the sending countries some positive effects could be the economic growth attributed to the remittances and to the return migrants, who are regarded as engines of change and innovation. On the other hand, migration could be a cause of increasing disparities in origin and host countries. Some negative effects in the origin countries could be the amplification of consumerist, nonproductive and remittance-dependent behavior. The goal of this paper is to assess the importance of migration in the currently globalizing world, with special attention being paid to the entrepreneurial behavior and performance of immigrants. In line with the main purpose of this paper, we provide some theoretical insight into the impact of ethnic diversity on the economic performance of receiving and origin countries, this being further narrowed down to the entrepreneurial behavior of the migrants. The empirical part consists of a migrant impact analysis of ethnic entrepreneurs, and presents the results of a cross-correlation and Data Envelopment Analysis.

Performance Measurement of Public Transport Operation in Japan: Combination of Stepwise Improvement Model with CCR Model

Tightening public expenditure budgets prompts the need for a careful analysis of the performance of public bodies in terms of an efficient execution of their tasks. A standard tool to judge the efficiency of such organizations is data envelopment analysis (DEA). In the past years, much progress has been made to extend this approach into various directions. Examples are the distance friction minimization (DFM) model and the context-dependent (CD) model. The DFM model is based on a generalized distance friction function and serves to improve the performance of a decision making unit (DMU) by identifying the most appropriate movement toward the efficiency frontier surface. Likewise, the CD model yields efficient frontiers in different levels, while it is based on a level-by-level improvement projection. The present chapter will offer an integrated DEA tool – emerging from a blend of the DFM and CD model – in order to design a balanced stepwise efficiency-improvement projection model for a conventional DEA. The abovementioned stepwise-projection model is illustrated on the basis of an application to the efficiency analysis of public transport operations in Japan.

Performance Measurement of Global Cities: Combination of a Stepwise Improvement Model with an SE Model

This chapter aims to provide an empirical contribution to the rising literature on the relative performance and benchmarking of large cities in a competitive world. On the basis of a recent detailed database on many achievement criteria of 35 major cities in the world, it seeks to arrive at a relative performance ranking of these cities by using Data Envelopment Analysis (DEA). A novel element is the use of a new type of “Super-Efficiency DEA” to identify unambiguously the high performers in the group of world cities investigated. We also introduce an adjusted DEA model, emerging from a blend of a DFM and a context-dependent (CD) model, namely, a Stepwise Improvement model. This model can provide a stepwise efficiency-improvement projection to provide more practical and feasible solutions for realistic circumstances and requirements in an efficiency-improvement projection. In this chapter, we also make a new contribution to DEA analysis by combining a Super-Efficiency (SE) DEA with the Stepwise Improvement model. The above mentioned stepwise-projection model is next applied to a performance analysis in the context of an efficiency-improvement plan for inefficient global cities.

The Need for a New Energy Performance Balance in Japan: Combination of an Adjusted-Improvement Model with a CCR Model

This chapter aims to present an application result of the Adjusted-Improvement (AI) approach to DEA for generating an appropriate efficiency-improvement projection model. We propose a Target-Oriented (TO) DFM model that allows reference points that remain below the efficiency frontier. Our TO-DFM model specifies a Target Efficiency Score (TES) for inefficient DMUs. This model is able to compute an improvement projection based on an input reduction value and an output increase value in order to achieve a TES. However, in reality, these values may represent an infeasible case; for example, a Networking Rate may be required of more than 100% in the improvement projection, but this would exceed a physical limit. Therefore, we propose an Adjusted-Improvement (AI) approach based on the TO-DFM model. The AI approach specifies an adjustment in input/output items based on the absence or presence of a DMU’s improvement limit. This approach can compute an input reduction value and an output increase value in order to achieve a TES that maintains an improvement limit condition. This chapter evaluates the efficiency of new energy in Japan based on DEA and the abovementioned Adjusted-Improvement TO-DFM model to produce a realistic efficiency-improvement projection. The focus will be on three input cost criteria (cost of power generation, energy payback time, and CO2 emissions) and one output performance criterion (Networking Rate). Based on the results of the performance analysis and the efficiency-improvement projection of new energy performance needs in Japan, we offer a quantitative contribution to efficiency rise in energy-environment policy in Japan.

Extended DFM Models in DEA

This chapter aims to present a newly developed and extended distance friction minimization (DFM) model in the context of Data Envelopment Analysis (DEA), in order to comply with plausible and real-world circumstances. The DFM model is generally able to calculate either an optimal input reduction value or an output increase value in order to reach an efficiency score of 1.000, even though in reality this might be hard to achieve for low-efficiency DMUs. Most DEA models and also the DFM model have intrinsic limitations or weaknesses. Therefore, we need a method that allows for the presence of reference points that remain below the efficiency frontier. In this chapter we propose successively a Goals-Achievement model, a Stepwise Improvement model, and a Target-Oriented model based on the DFM framework. These models are categorized as “Target approaches.” On the other side, in many cases, input or output factors may not be directly flexible or adjustable due to the indivisible nature or inertia in some input or output factors. Usually, the original DEA model and the DFM model do not allow for such a non-controllable or a fixed input factor. Therefore, we need a method that may take into account a flexible or adjustable factor in a DFM model. In this chapter, we propose an Adjusted-Improvement model and a Fixed-Factor model based on the DFM framework. These models are categorized as “Adjustment approaches.”

Overview of DEA and Its Improvements

The aim of this chapter is to provide a very concise overview of Data Envelopment Analysis (DEA) and its subsequent improvements. DEA proposed by Charnes et al. (Eur J Oper Res, 2:429–444, 1978) and based on the seminal article by Farrell (J Roy Stat Soc 120:253–290, 1957) aims to develop a comparative measure for production efficiency. We present first a brief history of the development of DEA. Next, we make a comparison of DEA and stochastic frontier analysis (SFA). DEA is a nonparametric and deterministic approach, whereas SFA is a parametric and stochastic approach. We also focus on the history of the development of the efficiency-improvement projection model in DEA. The existence of many possible efficiency-improvement solutions has in recent years prompted a rich literature on the methodological integration of multiple objective quadratic programming (MOQP) and DEA models. The first contribution was made by Golany (J Oper Res Soc 39:725–734, 1988), and we introduce here a concise overview of the history of the development of efficiency-improvement projection models in DEA. Based on these backgrounds, we present advantages and features of our DFM (distance friction minimization) model.