Efficiency Measurement in the Local Public Sector: Econometric and Mathematical Programming Frontier Techniques

Local government in advanced economies is undergoing a period of rapid reform aimed at enhancing its efficiency and effectiveness. Accordingly, the definition, measurement and improvement of organisational performance is crucial. Despite the importance of efficiency measurement in local government it is only relatively recently that econometric and mathematical frontier techniques have been applied to local public services. This paper attempts to provide a synoptic survey of the comparatively few empirical analyses of efficiency measurement in local government. We examine both the measurement of inefficiency in local public services and the determinants of local public sector efficiency. The implications of efficiency measurement for practitioners in local government are examined by way of conclusion.

Transit Agencies Performance Assessment and Implications

Although most transit systems operate in small urban and rural areas in the United States, these systems have rarely received the same attention as their urban counterparts, both in terms of ensuring the efficiency and effectiveness of their operations and understanding the factors that affect their performance. This thesis\u27s main goals are to assess the performance of rural and small urban public transit agencies and help them evaluate adopting a ridehailing program, thereby improving their performance. We applied operations research and decision-making tools to two public transit projects in small urban and rural areas. The first project focuses on three models developed to evaluate the efficiency, effectiveness, and combined efficiency-effectiveness of rural transit agencies using data envelopment analysis. The models were estimated for the case study of transit systems in rural Appalachia and measured the agencies\u27 performance relative to their peers. Besides, the returns to scale were explored in the context of rural transit management. The second project focused on employing ridehailing programs in small urban and rural areas to improve agencies’ performance and reach. The most relevant criteria were identified to evaluate the performance of different ridehailing programs using multi-criteria decision analysis methodology. To perform a set of MCDA methods, we used the perceived rating of each ridehailing program according to the stakeholders\u27 opinions with respect to each criterion. The framework was estimated for the case study of Mountain Line Transit Authority in Morgantown, WV

Regional effectiveness of innovation – leaders and followers of the EU NUTS 0 and NUTS 2 regions

Innovation constitutes an important factor for growth in all EU countries. Regions of the EU play a principle role in shaping new innovation trajectories and in bringing out the hidden potential for national growth. However, it is not only the level of innovation that diversifies regions, but also the innovative potential and the level of its realization. Therefore, the aim of this paper is to assess the realization of innovative potential, defined as effectiveness, in EU NUTS 0 and, if possible, NUTS 2 regions. To accomplish this goal a relative effectiveness method in used. The DEA (Data Envelopment Analysis) makes it possible to analyse the relative technical effectiveness based on regional inputs and outputs, without incorporating the legal and technological specifications of innovations, thus treating it like a production process. The inputs of the process are employment in technology and knowledge-intensive sectors and R&D expenditure, while the outputs include the number of patents and GDP. All variables are standardized by the size of the economically active population. DEA results divide regions in to two groups – effective, being the leaders; and ineffective, or followers. The DEA approach was combined and extended by ESDA (Exploratory Spatial Data Analysis) in order to pinpoint spatial patterns of innovation efficiency across NUTS 2 regions. Defining the best practices and implementing the learning-from-the-best policy is important in the process of regional development and specialization

Performance Analysis of Urban Public Transport Service Enterprise in Addis Ababa: Data Envelopment Analysis

Measuring the public transit service enterprise's performance is a powerful tool for decision-making and managerial control to assess the utilization level of various inputs to obtain the desired outputs. Thus, this study aims to measure the performance of public bus transport enterprises of Addis Ababa using the Data Envelopment Analysis method during the year 2016/17 to 2017/18. There is an absence of studies in the country examining public transport sector efficiency using the DEA approach, which makes this research a chance. The study employed an input-oriented DEA model to measure bus transit efficiency. Thus, fleet size and a total number of employees are used as inputs, while covered vehicle km and total passengers transported per year are used as an output to measure performance. Then, the enterprises' technical efficiency and operational effectiveness are analyzed based on secondary data collected from each enterprise. The overall results show Anbessa and Sheger city buses are technically efficient and operationally effective in utilizing their inputs to deliver the desired output compared to others in the city. However, outcomes for Alliance city bus and Public Service Employees Transport Service Enterprise indicate that they utilize their inputs inefficiently and consumed their services ineffectively. Hence, these inefficient enterprises need significant improvements in using their resources to enhance their performance and deliver services incompetent with other operators in the city. Besides, the Government should encourage privately owned public transport operators in the city and provide subsidies and other incentives to all based on their existing performance. Keywords: efficiency, public transport, Data Envelopment Analysis, Addis Ababa, Ethiopia DOI: 10.7176/PPAR/11-7-05 Publication date:August 31st 202

Multi-Dimensional Assessment of Transit System Efficiency and Incentive-based Subsidy Allocation

Over the past several decades, contending with traffic congestion and air pollution has emerged as one of the imperative issues across the world. Development of a transit-oriented urban transport system has been realized by an increasing number of countries and administrations as one of the most effective strategies for mitigating congestion and pollution problems. Despite the rapid development of public transportation system, doubts regarding the efficiency of the system and financing sustainability have arisen. Significant amount of public resources have been invested into public transport; however complaints about low service quality and unreliable transit system performance have increasingly arisen from all walks of life. Evaluating transit operational efficiency from various levels and designing incentive-based mechanisms to allocate limited subsidies/resources have become one of the most imperative challenges faced by responsible authorities to sustain the public transport system development and improve its performance and levels of service. After a comprehensive review of existing literature, this dissertation aims to develop a multi-dimensional framework composed of a series of robust multi-criteria evaluation models to assess the operational and financial performance of transit systems at various levels of application (i.e. region/city level, operator level, and route level). It further contributes to bridging the gap between transit efficiency evaluation and the subsequent subsidy allocation by developing a set of incentive-based resource allocation models taking various levels of operational and financial efficiencies into consideration. Case studies using real-world transit data will be performed to validate the performance and applicability of the proposed models

INTERNAL BENCHMARKING IN RETAILING WITH DEA AND GIS: THE CASE OF A LOYALTY-ORIENTED SUPERMARKET CHAIN

[EN] Data Envelopment Analysis (DEA) is a relative measure of efficiency applied to a set of decision units and is being used more and more frequently in the supermarket sector. Nonetheless, given how strongly the sector's financials depend on demand, companies need to combine this measurement with trade area information to best manage corporate efficiency. In this paper, the proposal consists of integrating DEA with a clearly articulated, structural typology so that supermarkets, based on their particular characteristics, can determine which variables are most critical for improving their efficiency. This methodology has been validated in the case of one of Spain's five largest supermarket chains. A principal component analysis and a classification analysis were carried out on a series of internal management variables from 61 locations for which DEA had been used to calculate efficiency and to which multiple trade area variables were added using GIS. Some of them are related to the loyalty scheme membership programme. These latter variables described the implantation of the loyalty scheme member programme and were revealed as key elements for the efficiency of the supermarket. This methodology provides marketing profiles that are more adapted to local circumstances, thus allowing companies to set better internal benchmarking objectives.The authors would like to thank the Consum-Universitat Politècnica de València Chair (Cátedra) for its collaboration in this study.Baviera-Puig, A.; Baviera, T.; Buitrago Vera, JM.; Escribá Pérez, C. (2020). INTERNAL BENCHMARKING IN RETAILING WITH DEA AND GIS: THE CASE OF A LOYALTY-ORIENTED SUPERMARKET CHAIN. Journal of Business Economics and Management. 21(4):1035-1057. https://doi.org/10.3846/jbem.2020.12393S10351057214Álvarez-Rodríguez, C., Martín-Gamboa, M., & Iribarren, D. (2019). 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Transit productivity analysis in heterogeneous conditions using data envelopment analysis with an application to rail transit

This dissertation extends transit productivity analysis by developing a new method of Data Envelopment Analysis (DEA), the linear programming approach to productivity analysis. The new model analyzes productivity of transit working under heterogeneous operating conditions. It is named Two-Farrell DEA for it applies DEA in two stages, DEA (1), that calculates the productivity frontiers at given operating conditions and DEA (2), that uses inputs adjusted by multipliers calculated in DEA (l). The model Two Farrell DEA calculated productivity benchmarks for each rail transit agency and estimated its potential for higher revenue or lower expense improvement. Additionally, the results identify two production techniques of rail transit, the sources of increasing returns to scale, the degree of flexibility to changes in the shadow prices of the inputs, and a method to prioritize investment for expansion of operations. Its indirect contribution to transit operations planning consists of checking the consistency and feasibility of new rail projects. Moreover, this dissertation includes the first correlation analysis made between productivity and operating conditions related to network form, factor analysis of transit operating conditions, the comparison of results between the new model to four other methods, and the evaluation of the empirical accuracy of methods with cluster analysis