Using a sample of 21 OECD-countries we measure productivity in top-edge economic research by using data envelopment analysis (DEA). DEA is a tool for evaluating relative efficiency and is widely used when there are multiple inputs and outputs and one lacks a specific functional form of a production function. The publications in 10 economics journals with the highest average impact factor over the time period 1980-1998 are taken as research output. Inputs are measured by R&D expenditures, number of universities with economics departments and (as uncontrolled variable) total population. Under constant returns-to-scale the USA are in dominant position with remarkable distance to other countries. Under variable returns-to-scale the efficiency frontier is created by the USA with most productive scale size (MPSS), and by Ireland and New Zealand, which are technical efficient but scale inefficient. All countries - except the USA - display increasing returns-to-scale, which shows that they have a possibility to improve their efficiency by scaling up their research activities. (author's abstract) / Series: Department of Economics Working Paper Series

Data Envelopment Analysis (DEA) assigns efficiency values to decision making units (DMU) in a given period by comparing the outputs with the inputs. In many applications, inputs and outputs of DMUs are monitored over time. There might be a time lag between the consumption of inputs and production of outputs. We develop approaches that aim to capture the time lag between the outputs and the inputs in assigning the efficiency values to DMUs. We present computational results on randomly generated problems as well as on an application to R&amp / D institutes of the Scientific and Technical Research Council of Turkey (T&Uuml / BiTAK).

H ετερογένεια που χαρακτηρίζει τις τεχνολογίες των επιχειρήσεων που εντάσσονται σε διαφορετικά σύνολα και ενσωματώνουν στο τεχνολογικό τους σύνολο μια καινοτομία, δημιουργεί προβλήματα στην εκτίμηση της συνολικής παραγωγικότητας των εισροών (TFP). Σε αυτή την περίπτωση οι όποιες μεταβολές της παραγωγικής αποτελεσματικότητας, τεχνικής και κλίμακας, συναρτώνται άμεσα με τους ρυθμούς τεχνολογικής αλλαγής και ταυτόχρονα εξαρτώνται από τις διαφορές παραγωγικότητας και αποτελεσματικότητας των κλάδων που ανήκουν οι επιχειρήσεις. Στην διεθνή βιβλιογραφία το παραπάνω ζήτημα έχει αντιμετωπιστεί από αρκετούς ερευνητές (Battese et al., 2002; 2004, Orea and Kumbhakar, 2004; Caudill, 2003). Ωστόσο σε μια πρόσφατη έρευνα (Kounetas, Mourtos and Tsekouras, 2009) παρουσιάζεται ένα αναλυτικό μεθοδολογικό πλαίσιο που επιτρέπει, καταρχάς την εκτίμηση της διαφοράς των τεχνολογιών στις οποίες εντάσσονται οι επιχειρήσεις και στη συνέχεια αποτυπώνει τις όποιες μεταβολές μπορεί να επιφέρει η ενσωμάτωση των καινοτομιών, νέων τεχνολογιών κ.λ.π. στα επιμέρους συστατικά της παραγωγικότητας. Σκοπός αυτής της διπλωματικής εργασίας είναι η ανάπτυξη ενός αλγορίθμου που θα βασίζεται στο μεθοδολογικό αυτό πλαίσιο και θα εκτιμά την αποτελεσματικότητα επιχειρήσεων που λειτουργούν υπό διαφορετικά τεχνολογικά καθεστώτα και θα υπολογίζει εφόσον υπάρχουν τα τεχνολογικά χάσματα σε οποιοδήποτε από τα εξεταζόμενα επίπεδα τεχνολογικής ετερογένειας. / Ηeterogeneity that characterizes the technologies of enterprises that are included in different totals and incorporate in their technology a innovation, creates problems in the estimation of total productivity of inputs (TFP). In this case any changes of technical and scale productivity, are associated immediately with the rate of technological change and simultaneously depend from the differences of productivity and effectiveness of sectors that belongs the enterprises. In the international bibliography the above question has been faced by enough researchers (Battese et al., 2002;.2004, Orea and Kumbhakar, 2004 Caudill, 2003). However in a recent research (Kounetas, Mourtos and Tsekouras, 2009) is presented an analytic methodological frame that allows, firstly the estimation of the difference of technologies that belong the enterprises and then impress any changes that can effect the incorporation of innovations, new technologies etc in the individual components of productivity. Aim of this is the development of an algorithm that will be based on this methodological frame and will appreciate the effectiveness of enterprises that functions under different technological arrangements and will calculate, provided that exist, the technological gaps in anyone from the examined levels of technological heterogeneity.

Η παρούσα μελέτη εκπονήθηκε στα πλαίσια του Προγράμματος Μεταπτυχιακών Σπουδών «Νέες Αρχές Διοίκησης Επιχειρήσεων, ΜΒΑ». Σκοπός είναι η ανάλυση της αποδοτικότητας των Ελληνικών Εμπορικών Τραπεζικών Οργανισμών που είναι εισηγμένες στο Χρηματιστήριο Αθηνών, για το διάστημα 2006 – 2010, ουσιαστικά 2 χρόνια πριν και κατά τη διάρκεια της οικονομικής κρίσης, κι επιπλέον ο προσδιορισμός τρόπων βελτίωσης του βαθμού αξιοποίησης των διατιθέμενων πόρων από τις μη αποδοτικές μονάδες. Χρησιμοποιήθηκαν δυο μέθοδοι ανάλυσης, μέσω Αριθμοδεικτών αποδοτικότητας και η μέθοδος της Περιβάλλουσας Ανάλυσης Δεδομένων (DEA) και ειδικότερα στη δεύτερη περίπτωση επιλύθηκε σε δύο στάδια το μοντέλο CCR, CRS, input oriented. Αναλύθηκαν δύο περιπτώσεις, α) μόνο ενδογενείς μεταβλητές των εταιρειών και β) συμπεριελήφθησαν και εξωγενείς μακροοικονομικές μεταβλητές όπως Πληθωρισμός και ΑΕΠ, ώστε να περιγραφεί και η γενικότερη Οικονομική Κατάσταση. Τα αποτελέσματα της πρώτης μεθόδου, δείχνουν αρκετές τράπεζες να έχουν χαμηλή αποδοτικότητα Efficiency Ratio. Ωστόσο, οι επιμέρους αριθμοδείκτες ROA, ROE και NPM, καταδεικνύουν μη αποτελεσματική τη Geniki Bank κυρίως σε όλα τα έτη και τράπεζες όπως Ταχυδρομικό Ταμιευτήριο, T Bank, Proton Bank, Eurobank, σε συγκεκριμένα έτη κυρίως το 2008. Αξιοσημείωτη είναι η πολύ μεγάλη πτώση στις τιμές όλων των αριθμοδεικτών που αναλύθηκαν για την Αγροτική Τράπεζα της Ελλάδος, το 2010. Στην ανάλυση μέσω της DEA, τα αποτελέσματα δείχνουν ότι όταν υπολογίζεται συνολική αποδοτικότητα, λαμβάνοντας υπόψη πολλαπλές εισροές και εκροές, οι τράπεζες σε γενικές γραμμές σε λειτουργικό επίπεδο είναι αποτελεσματικές. Στην πρώτη περίπτωση βγήκαν αναποτελεσματικές οι τράπεζες σε ποσοστό 20%. Από αυτές κυρίως αναποτελεσματική είναι και πάλι η Geniki Bank. Στην δεύτερη περίπτωση, λαμβάνοντας υπόψην τη γενικότερη Οικονομική κατάσταση, το ποσοστό των αναποτελεσματικών Τραπεζών μειώνεται σε μόλις 5%. Σε γενικές γραμμές, ο μεγάλος αριθμός των αποδοτικών μονάδων συνάδει και με τη διαίσθηση που είχαμε γενικότερα, αλλά και απ’ όσα ακούμε σχετικά με την οικονομική κρίση ότι αφενός οι τράπεζες δεν αποτελούν παράγοντα που συντελεί στην οικονομική κρίση και επιπλέον έχουν διαμορφώσει λειτουργικό πλαίσιο που μπορεί να αναπροσαρμόζεται σε όλες τις συνθήκες και να είναι αποδοτικό. / Τhis study was conducted as part of the Postgraduate Program "New Principles of Business Administration, MBA." The aim is to analyze the efficiency of Greek Commercial Bank Institutions listed on the Athens Stock Exchange for the period 2005 - 2010, basically 2 years before and during the financial crisis. There were used two methods of analysis through Efficiency Ratios and the method of Data Envelopment Analysis in the form of two stages model CCR, CRS, input oriented. We analyzed two cases with DEA: a) only discretionary variables and b) with the aid of non discretionary (macroeconomic) variables such as inflation and GDP, in order to describe the overall economic situation. The results of the first method show that several banks have low Efficiency Ratio. However, the ratios ROA, ROE and NPM, demonstrate mainly ineffective Geniki Bank in all years and Banks like TT, T Bank, Proton Bank and Eurobank, in particular years, especially in 2008. It is worth noting that there is very large decline of all ratios for the Agricultural Bank of Greece, in 2010. DEA results indicate that in the banking sector the operational level is effective. In the first case we found inefficient banks up to 20%. Of these most inefficient is the National Bank of Greece (50% in the study period), followed by Geniki Bank and Proton Bank (with a rate of 33.3% inefficiency in the study period). In the second case, the proportion of inefficient banks is reduced to only 11.67%. The difference lies mainly in the National Bank of Greece which in the second model is effective throughout the whole period under study. Generally, the large number of efficient units is consistent with the general sense, about the economic crisis that banks are not a contributing factor to the financial crisis and have developed an operational framework that can be adjusted in all situations and be effective.

A key challenge facing dairy farming is to meet the increasing demand for dairy products from a growing and more affluent global population in a period of unprecedented socio-economic and environmental change. In order to address this challenge, policies are currently placing emphasis on ‘sustainable intensification’ (SI), i.e. producing ‘more’ outputs and services with ‘less’ resources and environmental impacts. Determining whether or not SI can deliver greater yet sustainable dairy production requires understanding of the relationships between sustainability pillars (environmental; economic; and social) and farm aspects (e.g. on-farm management; and animal productivity) under particular farming systems and circumstances (e.g. regional bio-physical conditions). Trade-offs between pillars and aspects is inevitable within a farming system. Many widely-used assessment methods that aim to measure, scale and weight these pillars and aspects are unable to fully capture trade-offs between them. The objectives of this thesis are: 1) to identify key trade-offs in dairy farming systems to inform greater yet sustainable food production; and 2) to introduce models and methodologies aiming at a more holistic measurement and better understanding of dairy farm sustainability. This thesis assesses the sustainability of French and UK dairy farming systems via a farm efficiency benchmarking modelling framework coupled with statistical analyses. It explores the relationships between pillars, aspects and technical, economic and environmental performance; and identifies important drivers/differentials in dairy farm efficiency. Importantly, it also suggests ways in which farm inputs and outputs can be adjusted so that improvements in environmental, technical and economic performance become feasible. Efficiency benchmarking was performed with the multiple-input – multiple-output productive efficiency method Data Envelopment Analysis (DEA). DEA calculates single aggregated efficiency indices per farm by accounting for several farm inputs and outputs which the DEA model endogenously scales and weights. In this work, the notion of farm inputs and outputs was extended to also include ‘undesirable’ outputs (greenhouse gas emissions) and environmental impacts (e.g. eutrophication, acidification etc.) of dairy farming. The DEA models employed belong to the family of ‘additive’ models, which have several advantages over ‘traditional’ DEA models. These include their ability (i) to simultaneously increase outputs and reduce inputs, undesirable outputs and environmental impacts; (ii) to identify specific sources of inefficiency. These ‘sources’ represent a farm’s shortfalls in output production and its excesses in input use and/or in undesirable outputs and environmental impacts, relatively to the other farms; (iii) to position undesirable outputs in the output set rather than consider them as inputs or ‘inverse’ outputs; and (iv) to rank farms by efficiency performance. Importantly, this thesis also proposes a new additive model with a ranking property and high discriminatory power. In a second stage, DEA was coupled with partial least squares structural equation modelling (SEM) so as to develop and relate latent variables for environmental performance, animal productivity and on-farm management practices. The results suggested that the efficacy of SI may be compromised by several on-farm trade-offs between pillars, aspects and farm inputs and outputs. Moreover, trade-offs depended on particular farming systems and circumstances. Increasing animal productivity did not always improve farm environmental performance at whole farm-level. Intensifying production at animal and farm-levels, coupled with high reliance on external inputs, reduced farm environmental performance in the French case, i.e. a significant negative relationship was found between intensification and environmental performance (SEM path coefficients ranged between -0.31 and -0.57, p < 0.05). Conversely, in the UK case, systems representing animal-level intensification (via genetic selection) for increased milk fat plus protein production performed better, on average, than controls of UK average genetic merit for milk fat plus protein production in terms of technical efficiency (DEA scores between 0.91– 0.92 versus 0.78–0.79) and environmental efficiency (scores between 0.92–0.93 versus 0.80), regardless of whether on a low-forage or high-forage diet. The levels of inefficiency in (undesirable) outputs, inputs and environmental impacts varied among farming systems and depended on the regional and managerial characteristics of each system. For instance, in France, West farms had higher eutrophication inefficiencies than East farms (average normalized eutrophication inefficiencies were, respectively 0.141 and 0.107), perhaps because of their more intensive production practices. However, West farms were more DEA-efficient than East farms as the former benefited from bio-physical conditions more favourable to dairy farming (mean DEA score ranks were 97 for West and 83 for East). Such findings can guide policy incentives for SI in different regions or dairy systems. The proposed modelling framework significantly contributes to current knowledge and the search for the best pathways to SI, improves widely-used modelling approaches, and challenges earlier findings based on less holistic exercises.

Este estudo tem como objetivo apresentar as alterações que ocorrem nos perfis das escolas públicas municipais do ensino fundamental classificadas como eficientes por meio da aplicação da técnica de Análise Envoltória de Dados (DEA), conforme se modificam as proporções dos valores percentuais da nota média na Prova Brasil e do indicador de rendimento no cálculo do Índice de Desenvolvimento da Educação Básica (IDEB). Para isso, foram coletados dados sobre as notas na Prova Brasil e sobre os indicadores de rendimento de 2011 a partir da base de dados do Instituto Nacional de Estudos e Pesquisas Educacionais (INEP), o número de alunos por escola pública municipal por meio da base de dados do Censo Escolar de 2011, que também é disponibilizado pelo INEP e finalmente, os dados sobre os recursos públicos destinados para a educação nos municípios em 2011 obtidos no Finanças do Brasil (FINBRA). Desta maneira, identificou-se as escolas mais eficientes por meio da técnica DEA que tem a finalidade de mensurar a eficiência relativa de unidades consideradas homogêneas e comparáveis, em um universo de pesquisa composto por 17.124 escolas públicas municipais do ensino fundamental. Em seguida, foram estabelecidos cinco padrões de ponderação em relação à nota média da Prova Brasil e à média do indicador de rendimento, por meio dos quais evidenciou-se que as escolas públicas municipais classificadas como eficientes se localizam principalmente em municípios pequenos e que conforme se aumenta o valor percentual da nota média da Prova Brasil ocorre uma mudança de eixo de eficiência saindo da região nordeste em direção as regiões sudeste e sul. Portanto, o presente estudo fornece subsídios e suporte para a discussão, alteração, criação e desenvolvimento de sistemas avaliativos padronizados que melhor transpareçam a realidade educacional brasileira, de tal modo, que os seus resultados possibilitem a proposição de soluções mais efetivas para combater os baixos indicadores educacionais do país. / This study aims to present the changes that occur in the profiles of local public elementary schools classified as efficient by applying the technique of Data Envelopment Analysis (DEA), as modify the proportions of the percentages of the average grade in the Prova Brasil and the performance indicator in calculating the Índice de Desenvolvimento da Educação Básica (IDEB). For this, data on the grades in Prova Brasil on performance indicators from 2011 from the database of the Instituto Nacional de Estudos e Pesquisas Educacionais (INEP) were collected, the number of students per public school through the base data from the Censo Escolar 2011, which is also available by INEP and finally, data on public resources for education in the municipalities in 2011 obtained the Finanças do Brasil (FINBRA). Thus, we identified the most effective schools through the DEA technique that aims to measure the relative efficiency of units considered homogeneous and comparable, in a research universe consists of 17.124 municipal public elementary schools. Then, five patterns were established weighting relative to the average grade of Prova Brasil and the average performance indicator, by means of which became evident that public schools classified as efficient lie mostly in small towns and as if increases the percentage of the average grade of Prova Brasil a change of axis of efficiency out of the northeast toward the southeast and south occurs. Therefore, the present study provides grants and support for discussion, modification, creation and development of standardized assessment systems that better showing through and the Brazilian, so educational reality, their results allow to propose more effective solutions to combat the low indicators education in the country.

Wind power becomes one of the most promising energy sources in the electricity generation sector in Texas over the past decade by declining levelized cost of wind energy. However, recent studies show that the wind farms in Texas are relatively less productive. Hence, this study aims to find out reasons of inefficiencies by constructing a two-stage performance assessment of wind farms in Texas. In the first stage of analysis, comprehensive input- and output-oriented Data Envelopment Analysis (DEA) models are applied to evaluate productive efficiencies of the 95 large utility-scale wind farms by using pre-determined three input and two output variables. The sensitivity analysis is provided for the robustness of the DEA models with different combinations of input and output variables of the original model. The slack analysis and projection data are obtained for inefficient wind farms to find out optimal input-output variables. Tobit regression models are conducted for the second stage of the analysis to investigate the reasons of inefficiencies. DEA results indicate that half of the wind farms were operated efficiently in Texas during 2016. 13 wind farms were performed at the most productive scale size, ten wind farms should reduce their operational size to improve production efficiency, and 72 wind farms have the notable potential to increase their production efficiency by expanding operational sizes with modern wind turbine technologies. The sensitivity analysis shows the importance of each input-output variables. Tobit regression models indicate that elevation of the site, rotor diameter, hub height, and brand of the turbine have significant contributions to the relative efficiency scores of the wind farms, and the age of turbine has a negative impact on the productive efficiency of the wind farms.

Wind power is one of the most promising renewable energy sources that has gained enormous attention, especially in the electricity generation sector over the past decade in the United States. In this study Data Envelopment Analysis (DEA) is implemented to quantitatively evaluate the relative efficiencies of the 236 large utility-scale wind farms. Input- and output-oriented CCR (Charnes, Cooper, and Rhodes) and BCC (Banker, Charnes, and Cooper) models are applied to pre-determined three input and three output variables. The sensitivity analysis is conducted for the robustness of DEA by introducing seven new models with the various combinations of input and output variables of the original model. Tobit regression models are developed for the second stage of the analysis to investigate the effects of specifications of the wind turbine technologies. DEA results indicate that two-thirds of the wind farms are operated efficiently. On average, 70% of the wind farms have a considerable potential for further improvement in operational productivity by expanding these wind farm projects, 24% of them should reduce their operational size to increase their productivity level, and 6% of them are operating wind power at the most productive scale size. Nonparametric statistical tests show that the most efficient wind farms are located in Oklahoma because of the relatively high wind speed resources. Tobit regression model indicates the selection of the brand of the wind turbine has a significant contribution to the productive efficiency of the wind farms. The results of this study shed some light on the current efficiency assessments of the 236 large utility-scale wind farms in the United States and the future of wind energy for both energy practitioners and policy makers.

The average global surface temperature increased by 0.85 °C since 1850 because of irrepressible increase of the concentration of greenhouse gases (GHG). Electricity generation is the primary source of GHG emissions in the United States. Hence, renewable energy sources, which produce a negligible amount of GHG emissions, have gained enormous attention, especially in the electricity generation sector over the past decade. Wind power is the second largest renewable energy source to generate electricity in the United States. Therefore, in this study, a two-stage Data Envelopment Analysis (DEA) is developed to quantitatively evaluate the relative efficiencies of the 39 state's wind power performances for the electricity generation. Both input- and output-oriented CCR (Charnes, Cooper, and Rhodes (1978)) and BCC (Banker, Charnes, and Cooper (1984)) models are applied to pre-determined four input and six output variables. The sensitivity analysis is conducted to test the robustness of the DEA models. Tobit regression models are conducted by using the DEA results for the second stage analysis. The DEA results indicate that more than half of the states operate wind power efficiently. Tobit regression indicates that early installed wind power was more expensive and less productive relative the currently installed wind power. Findings of this study shed some light on the current efficiency assessments of the states and the future of wind energy for both energy practitioners and policy makers.

The implementation of lean manufacturing concepts has shown significant impacts on various industries. Numerous tools and techniques have been developed to tackle specific problems in order to eliminate wastes and carry out lean concepts. With the focus on "how to make a system leaner," little effort has been made on determining "how lean the system is." Lean assessment surveys evaluate the current status of a system qualitatively against predefined lean indicators. Lean metrics are developed to quantify performance of improvement initiatives, but each metric only focuses on one specific area. Value Stream Maps demonstrate the current and future states graphically with the emphasis on time-based performance only. A truly quantitative and synthesized measure for overall leanness has not been established. In some circumstances, being lean may not be the only goal for manufacturers. In order to compete in the rapidly changing marketplace, manufacturing systems should also be agile to respond quickly to uncertain demands. Nevertheless, being extremely agile may increase the cost of regular operations and reduce the leanness of the system. Similarly, being extremely lean may reduce flexibility and lower the agility level. Therefore, a manufacturing system should be agile enough to handle the uncertainty of demands and meanwhile be lean enough to deliver goods with competitive prices and lead time. In order to achieve the appropriate leanness level, a leanness measure is needed to address not only "how lean the system is" but also "how lean it should be." In this research, a methodology is proposed to quantitatively measure leanness level of manufacturing systems using the Data Envelopment Analysis (DEA) technique. The production process of each work piece is defined as a Decision Making Unit (DMU) that transforms inputs of Cost and Time into output Value. Using a Slacks-Based Measure (SBM) model, the DEA-Leanness Measure is developed to quantify the leanness level of each DMU by comparing the DMU against the frontier of leanness. A Cost-Time-Value analysis is developed to create virtual DMUs to push the frontier towards ideal leanness so that an effective benchmark can be established. The DEA-Leanness Measure provides a unit-invariant leanness score valued between 0 and 1, which is an indication of "how lean the system is" and also "how much leaner the system can be." With the help of Cost-Time Profiling technique, directions of potential improvement can be identified by comparing the profiles of DMUs with different leanness scores. The leanness measure can also be weighted between Cost, Time and Value variables. The weighted DEA-Leanness Measure provides a way to evaluate the impacts of improvement initiatives with an emphasis on the company's strategic focus. Performing the DEA-Leanness measurement requires detailed cost and time data. A Web-Based Kanban is developed to facilitate automated data collection and real-time performance analysis. In some circumstances where detailed data is not readily available but a Value Stream Maps (VSM) has been constructed, the applications of DEA-Leanness Measure based on existing VSM are explored. Besides pursuing leanness, satisfying a customer's demand pattern requires certain level of agility. Based on the DEA-Leanness Measure, appropriate leanness targets can be identified for manufacturing systems considering sufficient agility level. The Online-Delay and Offline-Delay Targets are determined to represent the minimum acceptable delays considering inevitable waste within and beyond a manufacturing system. Combining the two targets, a Lean-Agile Performance Index can then be derived to evaluate if the system has achieved an appropriate level of leanness with sufficient agility for meeting the customers' demand. Hypothetical cases mimicking real manufacturing systems are developed to verify the proposed methodologies. An Excel-based DEA-Leanness Solver and a Web-Kanban System have been developed to solve the mathematical models and to substantiate potential applications of the leanness measure in real world. Finally, future research directions are suggested to further enhance the results of this research. / Ph. D.