This thesis examines banking efficiency and the productivity of thirteen transition Central and Eastern European banking systems during 1998-2003 using Data Envelopment Analysis (DEA). It proposes a non-parametric methodology for non-radial Russell output efficiency measure of banking firms, incorporating risk as an undesirable output. In addition, the proposed efficiency measure handles unrestricted data, i. e. both positive and negative. The Luenberger productivity index is suggested, which is applicable to technology where the desirable and undesirable outputs are jointly produced, and are possibly negative. Furthermore, the thesis addresses the main issue in the literature on banking performance measurement, which concerns the lack of consistency in the conceptual and theoretical considerations in describing the banking production process. Consequently, a metaanalysis tool, to examine the choice of inputs and outputs definitions in the banking efficiency literature, is suggested. In addition, the performance measures are estimated using three alternative definitions of the banking production process focusing on the risk and environmental dimensions of bank efficiency and productivity, with further comparative analysis using bootstrapping and kernel density techniques. Overall, the empirical results suggest that in Central and Eastern Europe Czech, Hungarian and Polish banks were the most technical efficient banks and the banking risk was mainly affected by external environmental factors during the analyzed period. Productivity analysis implies that the main driver of productivity change in the Central and Eastern European banks is the technological improvement. As meta-analysis revealed, the choice of particular approach of describing the banking production process is determined not by the availability of particular input or output variable information but the concepts of researcher's theoretical considerations. Statistical tests and density analysis indicate that efficiency scores, returns parameters and productivity indexes are sensitive to the choice of particular approaches.

Yes / Undesirable outputs can be challenging to avoid in the production of goods and services, often overlooked. Pollution is generally regarded as a negative externality and is taken into account during the production process. The novelty of this study lies in introducing CO2 as an economic “bad” in the energy sector's efficiency measure through a stochastic data envelopment analysis (DEA) cross-efficiency model. Unlike pollution and economic goods, where increased production leads to more pollution, CO2 is weakly disposable, meaning that higher CO2 values lead to a decrease in the number of good outputs produced. The study proposes a new stochastic model based on an extension of the cross-efficiency model and applies it to measure the energy efficiency of 32 thermal power plants in Angola in the presence of undesirable outputs. This will help promote better environmental management. The study's findings offer vital policy insights for the energy sector. The introduction of new stochastic models enables more accurate efficiency measurement under uncertain conditions, aiding policymakers in resource allocation decisions. Additionally, the adoption of stochastic cross-efficiency methods enhances performance assessments, facilitating targeted interventions for underperforming units. These findings contribute to evidence-based policymaking, promoting sustainability and competitiveness within the energy sector.

This study implements radial and non-radial Data Envelopment Analysis (DEA) models to assess eco-efficiency and eco-productivity of the 50 states in the United States in 2018. The models are based on three inputs (capital stock, employment, and energy consumption), a single desirable output (real gross domestic product) and a single undesirable output variable (CO2 emissions). The radial DEA models reveal that at least 32 states are operated efficiently. Five states perform at the most optimal scale size, whereas 17 states have considerable potential to boost their productive efficiencies by enlarging available resources, and 28 states are overinvested in their input variables given their current output levels. The non-radial DEA models show that, overall, the states’ capital efficiency is very high, whereas energy and emission efficiencies are very low. The states’ eco-productivity is relatively higher than the eco-efficiency levels. In the second stage of the analysis, non-parametric statistical tests and Tobit regressions are conducted for further investigation. According to the non-parametric statistical test, high capital stock, labor force, and energy usage do not affect the states’ productive efficiency. However, states with low carbon dioxide emissions have significantly higher eco-efficiency and eco-productivity levels. The Tobit regression results illustrate that nuclear power and renewable energy consumption significantly affect the states’ relative efficiencies.

本文以資料包絡分析法衡量2004年第一季至2008年第三季台灣商業銀行之效率與生產力，並以其代表銀行之經營績效。不同於過去文獻之處在於本文以前瞻之觀點（沈中華，2005）求出放款預期損失以衡量放款風險，作為放款過程中的非意欲產出。本文並利用Ray and Desli（1997）架構拆解Malmquist總要素生產力指數，探討銀行績效變動之來源，此外，亦將該拆解結果與Färe et al.（1994）拆解方式比較，發現在技術變動部分有明顯不同。本文並探討影響銀行效率之因素以及檢定不同類型銀行之生產力表現是否有顯著差異。 / The purpose of this paper is to examine the efficiency and productivity change of Taiwan’s banks over 2004Q1-2008Q3 using data envelopment analysis. Unlike the literature of the past, this paper uses the forward-looking viewpoint to derive expected losses from loans to measure the credit risk. In addition, the loan expected loss is thought of as an undesirable output in the estimation of efficiency and productivity. On the other hand, this paper uses a VRS frontier benchmark (Ray and Desli, 1997) to analyze the sources of productivity change. This paper also compares this result with that of Färe et al.（1994）decomposition, and finds that the estimated technical changes of two approaches are significantly different. Further, this paper analyzes the factors influencing banks’ efficiency, and investigates the productivity differences between different ownerships.

本文採取隨機方向距離函數方法，探討制度變革前、後(第一次金融改革)對台灣銀行業技術效率的影響。資料期間涵蓋1999年至2012年。相較傳統Shephard距離函數，隨機方向距離函數方法最大優點係可同時考量增加意欲產出、減少投入與非意欲產出。本文依循Koutsomanoli-Filippaki et al. (2009a) 模型，並納入考慮非意欲產出。本文採取隨機邊界法進行實證估計，以最大概似法估計方向距離函數，依據Battese and Coelli (1995)的模型將環境變數納入實證模型 (主要模型)，並考量未包含環境變數之模型，與主要模型比較。此外，為凸顯非意欲產出之重要性，本文亦估計未考慮非意欲產出之模型以及傳統距離函數，以茲比較。 實證結果顯示，考慮非意欲產出與環境變數的主要模型，其估計結果相較其他模型之無效率明顯高估。2002年以前，技術無效率逐漸攀升。一次金改期間，技術無效率明顯下降，證明制度變革下，銀行效率獲得改善。惟2004年後反轉向上，尤其在雙卡風暴與次貸風暴期間，技術無效率明顯惡化。此外，本文將資料分群進行分析，發現公營銀行或是金控銀行較有效率。 / This paper aims to gain further insights into whether the policy of First Financial Restructuring (FFR) does improve the technical efficiency of banks in Taiwan during the period 1999-2012 by using the directional technology distance function (DDF). Compared to the conventional distance function, DDF simultaneously allows for the expansion of the desirables and the contraction of the undesirables. We follow Koutsomanoli-Filippaki et al. (2009a), and differing from them, we include undesirable outputs in DDF to depict a bank’s true production activities. We find on average that the banks have a lower technical inefficiency with the main model compared to the other models. However, prior to 2002, the technical inefficiency exhibits a gradual upward trend and then posts a downward trend during the FFR period. These results suggest that the improved efficiency in the FFR period is possibly due to enhanced banking and benefits obtained from compliance with FFR. After the FFR period, the inefficiency scores deteriorate sharply, especially during the “credit card and cash card crisis” in 2006 and “the subprime mortgage crisis” in 2008. Public banks are more efficient than private banks. Banks belonging to a financial holding company (FHC) may operate more efficiently than those belonging to a non-FHC.

La prise en compte des problèmes environnementaux dans la responsabilité sociale des entreprises a généré en économie de nombreuses propositions. Parmi elles, le cadre d’analyse basé sur l’évaluation de la performance en utilisant notamment les techniques d’enveloppement des données (DEA) s’est très vite répandu dans la littérature théorique comme empirique. Ce travail de thèse s’inscrit dans cette logique en mettant l’accent sur la modélisation des technologies polluantes. Par ailleurs, la question des changements climatiques et de la forte contribution de l’agriculture et en particulier de l’élevage dans les émissions de gaz à effet de serre (GES) impose à ce secteur de relever aujourd’hui en plus du défi économique celui de l’amélioration de sa performance environnementale. L’objectif général de cette recherche doctorale est donc de fournir un nouveau cadre d’analyse théorique et empirique dans la modélisation des technologies polluantes afin d’évaluer l’éco-efficience des systèmes productifs, en particulier le cas des émissions de GES en élevage extensif de ruminants. Dans un premier temps, nous montrons les limites théoriques et méthodologiques des modèles existants. Néanmoins, nous insistons sur le fait que les approches basées sur l’estimation de plusieurs sous-technologies indépendantes pour prendre en compte les différents processus présents dans les systèmes productifs sont très prometteuses. Dès lors dans un deuxième temps, nous proposons une nouvelle extension de la méthode « by-production » qui repose sur l’introduction d’interconnections entre les différentes sous-technologies impliquées afin de construire un système plus unifié. Dans un troisième temps, une comparaison empirique utilisant des données d’exploitations de viande ovine de notre extension avec les approches existantes a révélé certaines incohérences de ces dernières. Enfin pour aller plus loin, nous élargissons dans un quatrième temps notre approche afin de prendre en compte les aspects dynamiques et notamment la présence de coûts d’ajustement. Les résultats de l’analyse empirique entreprise avec des données d’exploitations bovines allaitantes (viande) ont révélé la nécessité de prendre en compte ces aspects, mais ont aussi révélé la forte hétérogénéité existante dans les stratégies d’investissements des éleveurs. / The growing importance of environmental matters in social responsibility of firms has generated many frameworks of analysis in the economic literature. Among those frameworks, performance evaluation and benchmarking using the non-parametric Data Envelopment Analysis (DEA) have increased at a very fast rate. This PhD research focuses on models that include undesirable outputs such as pollution in the overall production system, to appraise eco-efficiency of decision making units (DMUs). Besides, the recent awareness on the large contribution of agriculture and particularly livestock farming to global warming, has highlighted for this sector the challenge of reaching both economic and environmental performances. In this line, the overall objective of this dissertation is to provide a theoretical and empirical background in modelling pollution-generating technologies and to suggest theoretical improvements that are consistent with the particular case of greenhouse gas emissions in extensive livestock systems. Firstly, we showed that all existing approaches that deal with undesirable outputs in the non-parametric analysis (i.e. DEA) have some strong drawbacks. However, the models grounded on the estimation of multiple independent sub-technologies offer interesting opportunities. Secondly, I developed a new framework that extends the by-production approach through the introduction of some explicit dependence constraints that link the sub-technologies in order to build a unified system. Thirdly, an empirical comparison, using a sample of French sheep meat farms, of this by-production modelling extension with the existing approaches, revealed some inconsistencies of these latter. Finally, we expanded this new by-production formulation to account for dynamic aspects related to the presence of adjustment costs. The application to the case of French suckler cow farms underlined the necessity of accounting for dynamic aspects and also showed high heterogeneity in investment strategies of these farmers.

本研究發展新的計量方法，運用隨機邊界法將單調性和曲度條件等性質納入迴歸模型，使用聯立迴歸模型進行估計，藉此讓係數估計值不易出現違反經濟理論的情況。 採用台灣2002年至2015年，51家商業銀行進行實證分析，發現本研究方法估計產出方向距離函數時，僅有5%以下的樣本點不符合單調和曲度等性質，用於估計產出面距離函數時，僅有2%以下的樣本點不符合。進一步探討台灣銀行業之非意欲產出--逾期放款--對估計技術效率的影響，顯示不考慮此非意欲產出造成整體銀行業、非金控本國銀行與外商銀行的技術效率被高估，而金控本國銀行的技術效率則被低估，此外，分析2007年金融風暴前後銀行業經營效率變化，顯示考慮非意欲產出銀行業經營效率顯著提升，然而，未考慮非意欲產出銀行業經營效率卻下降，兩者結果有著極大的差異。 / The aim of the paper is to develop a new approach, which is stochastic frontier analysis imposing monotonicity and curvature conditions, then using simultaneous regression model to estimate. By the approach, it can solve the problem of most of the coefficient estimates violating the economic theory. The study uses the data of 51 commercial banks in Taiwan from 2002 to 2015 to conduct the empirical analysis. It indicates that by output directional distance function, less than 5% sample points violate the monotonicity and curvature conditions; by output distance function, less than 2% sample points don’t obey the restricted conditions. Further, the paper discusses the effect of commercial banks’ undesirable output- non-performing loan- on estimating technical efficiency. The results show that ignoring the undesirable output cause the technical efficiency of overall banks, non-finance holding banks and foreign banks are overvalued, and the technical efficiency of finance holding banks are undervalued. Furthermore, analyze the change of business efficiency after financial crisis in 2007. It points out that using the model consider the undesirable output, the banks’ efficiency rises. However, using the model no consider the undesirable output, the banks’ efficiency decreases. There is an extremely conflict between two approach.