Competition in the public school sector: Evidence on strategic interaction among US school districts

This paper provides evidence on strategic interaction among local school districts. The analysis makes use of a significant change in the institutional environment for school districts in Michigan in 1996, when the state established a voluntary inter-district choice program. The school districts' participation decisions are modelled as discrete choice decisions using a spatial latent variable model. Strong effects are found saying that lagged adoptions of neighbors positively affect the current probability of participation. A simple test exploiting limitations of student mobility in inter-district transfers suggests that the driving force for interdependencies among adoption decisions was competition for students.     

Evaluation of public HEI on teaching,research, and knowledge dissemination by Data Envelopment Analysis

Governmental policies, such as educational ones, require money from taxes to implement them. Therefore, a government must spend its resources in such a way to maximise the benefits. However, to evaluate the Public Higher Education Institutions (P–HEI) is a very complex task since many factors that can be assessed are involved. In this study, we focus on the performance of P–HEI in three activities: teaching, research, and knowledge dissemination. To accomplish this end, we develop a Data Envelopment Analysis Model to evaluate the efficiency of each activity, separately. Using an official database called ExECUM, we compute the efficiency of 40 Mexican P–HEI from year 2008–2016. The funds allocated for the government are an input in our study.According to our results, most P-HEIs are efficient in only one activity, while few are efficient in two activities. Only one P–HEI reached efficiency of 100% in the three models. On the other hand, 37.5% of the P–HEI do not reach 100% efficiency in any model. Our study provides the set of references for each P–HEI and the increments/decrements in the inputs and outputs to increase its efficiency.     

A hybrid DEA-MOLP model for public school assessment and closure decision in the City of Philadelphia

Data envelopment analysis (DEA) is generally used to evaluate past performance and multi objective linear programming (MOLP) is often used to plan for future performance goals. In this study, we establish an equivalence relationship between MOLP problems and combined-oriented DEA models using a direction distance function designed to account for desirable and undesirable inputs and outputs together with uncontrollable variables. This equivalence model can be effectively used to support interactive processes and performance measures designed to establish future performance goals while taking into account the preferences of decision makers (DMs). In particular, it allows DMs to consider different efficiency improvement strategies when subject to budgetary restrictions. The applicability of the proposed method and the efficacy of the procedures and algorithms are demonstrated using a case study where the performance of high schools in the City of Philadelphia is evaluated.     

Data envelopment analysis and Malmquist indices for measuring group performance

This paper develops measures, based on the Malmquist index, that enable the decision making units’ internal inefficiencies to be distinguished from those associated with their group (or program) characteristics. The applicability of these measures is illustrated with the assessment of bank branches’ performance. The analysis involves the construction of an index reflecting the relative performance of branches in four different regions, which can be decomposed into an index for the comparison of within-group efficiency spread, evaluating internal managerial efficiencies, and an index for the comparison of frontier productivity, reflecting the impact of environmental factors and regional managerial policies on branches’ productivity.     

Environmental performance evaluation: A state-level DEA analysis

In recent years, the continuous development of every country's economic activities has generated undesirable impacts on the environment. Common problems are high water and energy consumption rates, jointly with harmful pollution levels. This situation has gained the research community's interest in exploring and analyzing the extent to which initiatives to reduce such environmental problems have succeeded. Therefore, it is relevant to have measures that encompass information on the results obtained by such initiatives. Using the data envelopment analysis (DEA) methodology, it is possible to measure the efficiency of an entity under evaluation, such as an industry, state, or country. DEA also allows one to compare the performance measures of entities operating in similar circumstances and identify which entities are performing best, given the inputs they use and the outputs they produce. This study evaluates different states in Mexico in terms of their environmental performance and provides a perspective on how environmental initiatives can contribute to protecting and preserving the environment. By addressing this problem, best-performers and practices are identified, and valuable insights are gained regarding how each state carries out such initiatives.     

Research performance evaluation of Chinese university: A non-homogeneous network DEA approach

Performance evaluation for universities or research institutions has become a hot topic in recent years. However, the previous works rarely investigate the multiple departments’ performance of a university, and especially, none of them consider the non-homogeneity among the universities’ departments. In this paper, we develop data envelopment analysis (DEA) models to evaluate the performance of general non-homogeneous decision making units (DMUs) with two-stage network structures and then apply them to a university in China. Specifically, the first stage is faculty research process, and the second stage is student research process. We first spit each DMU (i.e. department) into a combination of several mutually exclusive maximal input subgroups and output subgroups in terms of their homogeneity in both stages. Then an additive DEA model is proposed to evaluate the performance of the overall efficiency of the non-homogeneous DMUs with two-stage network structure. By analyzing the empirical results, some implications are provided to support the university to promote the research performance of each department as well as the whole university.     

A framework to measure the relative socio-economic performance of developing countries

This research develops a framework to estimate the relative efficiency of developing countries in utilizing both their domestic and external resources to achieve the Millennium Development Goals. The analysis highlights distinct efficiency differences across lending groups and geographic regions e.g. between Sub-Saharan Africa and Latin America. The performance of regions exposed to natural disasters and political violence need to be analyzed individually and risk management ought to be an integrated part of development policy. The gap between the Millennium Development Goals and human rights approaches is then examined. Finally, we qualify a country's performance level as potentially sustainable in terms of human, social and environmental welfare, in turn searching for realistic benchmarks and intermediate targets for the relatively inefficient countries. Principal component analysis in combination with data envelopment analysis was applied to solve the problem of efficiency overestimation with multi-dimensional scaling used to present the issue graphically. In summary, the aim of this work is not to rank countries in a league table rather to provide a framework that combines economic, environmental and social issues in order to search for sustainable, pragmatic benchmarks, pushing the boundaries of the Human Development Index.     

Exploring the performances and determinants of public service provision in 35 major cities in China from the perspectives of efficiency and effectiveness

The measurement of public service provision (PSP) performance and identification of its influencing variables are of great importance for the government to efficiently utilize public resources and provide more effective public services. However, existing studies have mainly focused on the efficiency to provide public services and ignored the effectiveness to improve public subjective well-being. To fill this gap, this study constructs a two-stage PSP conceptual model from the perspective of service production and consumption and develops a corresponding PSP performance evaluation indicator system to evaluate PSP efficiency, effectiveness, and comprehensive performance. The PSP performances of 35 major cities in China during 2011–2018 are calculated using the super efficiency network slack-based measure model. Moreover, the influencing factors of these performances are identified through the truncated regression model. Results indicate that the comprehensive performance of these cities is relatively low and shows a trend of decrease that is predominantly caused by provision efficiency. Owing to the absolute advantage in effectiveness and the catch-up effect in efficiency, cities in the western region perform the best in comprehensive performance, followed by the eastern region and then the central region. In addition, only seven cities achieve a balance between efficiency and effectiveness. Factors from economy, society, government, and public aspects have different impacts on PSP performances. On the basis of these findings, several policy suggestions are proposed. This study proposes a relatively comprehensive and accurate measurement framework of PSP performance that considers both efficiency and effectiveness and can help policymakers implement appropriate strategies promoting PSP in an efficient, effective, and sustainable manner.     

Allocating additional resources to public universities. A DEA bargaining approach

This paper deals with the problem of allocating additional resources in a public university system. Instead of a solution imposed from above, the proposed bargaining approach lets the universities voluntarily agree on a solution that benefits them all. To be realistic the allocation assumes that the universities will keep their current technical efficiency. A bargaining problem is formulated, defining the corresponding feasible set and disagreement point. Four different bargaining solutions, namely Nash, Kalai-Smorodinsky, Egalitarian and Utilitarian, are considered and discussed. The proposed approach is applied to a real-world dataset involving the nine public universities of the Andalusian region. The results of the different bargaining solutions are very consistent as regards the output and utility improvements to be achieved by each university.     

Congestion in the Chinese automobile and textile industries revisited

This paper re-examines a problem of congested inputs in the Chinese automobile and textile industries, which was identified by Cooper et al. [Cooper WW, Deng H, Gu B, Li S, Thrall RM. Using DEA to improve the management of congestion in Chinese industries (1981-1997). Socio-Economic Planning Sciences 2001;35:227-242]. Since these authors employed a single approach in measuring congestion, it is worth exploring whether alternative procedures would yield very different outcomes. Indeed, the measurement of congestion is an area where there has been much theoretical debate but relatively little empirical work. After examining the theoretical properties of the two main approaches currently available, those of Färe et al. [Färe R, Grosskopf S, Lovell CAK. The measurement of efficiency of production. Boston: Kluwer-Nijhoff; 1985] and Cooper et al., we use the data set assembled by Cooper et al. for the period 1981-1997 to compare and contrast the measurements of congestion generated by these alternative approaches. We find that the results are strikingly different, especially in terms of the amount of congestion identified. Finally, we discuss the new approach to measuring congestion proposed by Tone and Sahoo [Tone K, Sahoo BK. Degree of scale economies and congestion: a unified DEA approach. European Journal of Operational Research 2004;158:755-772].     

R&D performance assessment of industrial enterprises in China: A two-stage DEA approach

With the growing expenditure on the R&D activities of industrial enterprises above designated size (IEDSs) in China, it is important to evaluate the R&D efficiency of the Chinese IEDSs. However, few studies about R&D efficiency measurement of Chinese IEDSs have considered the internal structure of the R&D production process. To fill this gap, this paper investigates the R&D performance of IEDSs of 30 sample provinces on China's mainland from 2009 to 2014, based on a two-stage data envelopment analysis (DEA) model. The major findings from the empirical results are shown as follows: (i) serious imbalance exists in the R&D resources among Chinese IEDSs of 30 provinces; (ii) there is a decline in the average overall efficiencies after 2012; (iii) there are great differences regarding the performances of R&D activities among the Chinese IEDSs of 30 provinces; (iv) high attention to the R&D activities or strong scientific research atmosphere may promote the R&D efficiency of Chinese IEDSs; and (v) the IEDSs with the relatively high profitability or high government support in terms of R&D activities have relatively poor performance. Based on these findings, several policy suggestions are proposed for the R&D activities of Chinese IEDSs.     

Classification in automobile insurance using a DEA and discriminant analysis hybrid

In this article, we adopt an efficiency approach to the two-group linear programming method of discriminant analysis (DA), using principles taken from data envelopment analysis (DEA), to predict group membership in an insurance underwriting scheme. Using an empirical insurance data base we illustrate the effectiveness of our model as a decision-making tool to distinguish among automobile insurance applicants by contrasting our hybrid model with both statistical and LP methods of discriminant analysis. We find for this insurance application that our hybrid model significantly outperforms the more traditional methods in separation and misclassification outcomes.     

Private demands for public capital: evidence from school bond referenda

This paper develops a framework for estimating demand for school infrastructure investment that is financed through local bond referenda. Our framework takes explicit account of the irregular and discrete nature of local capital investment and the objective functions of local school boards. Our empirical model consists of a three-equation system composed of a proposed spending equation, a vote equation, and a selection equation. Estimated income and price elasticities of demand for school infrastructure are similar to those found in studies of current school spending. We also find that school boards act like risk-averse, budget-maximizing agenda-setters.     

Projections Onto Efficient Frontiers: Theoretical and Computational Extensions to DEA

Data Envelopment Analysis (DEA) has been widely studied in the literature since its inception in 1978. The methodology behind the classical DEA, the oriented method, is to hold inputs (outputs) constant and to determine how much of an improvement in the output (input) dimensions is necessary in order to become efficient. This paper extends this methodology in two substantive ways. First, a method is developed that determines the least-norm projection from an inefficient DMU to the efficient frontier in both the input and output space simultaneously, and second, introduces the notion of the observable frontier and its subsequent projection. The observable frontier is the portion of the frontier that has been experienced by other DMUs (or convex combinations of such) and thus, the projection onto this portion of the frontier guarantees a recommendation that has already been demonstrated by an existing DMU or a convex combination of existing DMUs. A numerical example is used to illustrate the importance of these two methodological extensions.     

Target and technical efficiency in DEA: controlling for environmental characteristics

In this paper we propose a target efficiency DEA model that allows for the inclusion of environmental variables in a one stage model while maintaining a high degree of discrimination power. The model estimates the impact of managerial and environmental factors on efficiency simultaneously. A decomposition of the overall technical efficiency into two components, target efficiency and environmental efficiency, is derived. Estimation of target efficiency scores requires the solution of a single large non-linear optimization problem and provides both a joint estimation of target efficiency scores from all DMUs and an estimation of a common scalar expressing the environmental impact on efficiency for each environmental factor. We argue that if the indices on environmental conditions are constructed as the percentage of output with certain attributes present, then it is reasonable to let all reference DMUs characterized by a composed fraction lower than the fraction of output possessing the attribute of the evaluated DMU enter as potential dominators. It is shown that this requirement transforms the cone-ratio constraints on intensity variables in the BM-model (Banker and Morey 1986) into endogenous handicap functions on outputs. Furthermore, a priori information or general agreements on allowable handicap values can be incorporated into the model along the same lines as specifications of assurance regions in standard DEA.

Financial liberalization and banking efficiency: evidence from Turkey

This paper examines the banking efficiency in a pre- and post-liberalization environment by drawing on the Turkish experience by using DEA. The paper also investigates the scale effect on efficiency. Our findings suggest that liberalization programs were followed by an observable decline in efficiency. Another finding of the study is that the Turkish banking system had a serious scale problem during the study period. The second part of our analysis relied on econometric methods and found that one major reason for such system-wide efficiency decline has been the growing macroeconomic instability of the Turkish economy in general and financial sector in particular.

Comparison of dimension reduction methods for DEA under big data via Monte Carlo simulation

Data with large dimensions will bring various problems to the application of data envelopment analysis (DEA). In this study, we focus on a “big data” problem related to the considerably large dimensions of the input-output data. The four most widely used approaches to guide dimension reduction in DEA are compared via Monte Carlo simulation, including principal component analysis (PCA-DEA), which is based on the idea of aggregating input and output, efficiency contribution measurement (ECM), average efficiency measure (AEC), and regression-based detection (RB), which is based on the idea of variable selection. We compare the performance of these methods under different scenarios and a brand-new comparison benchmark for the simulation test. In addition, we discuss the effect of initial variable selection in RB for the first time. Based on the results, we offer guidelines that are more reliable on how to choose an appropriate method.     

Nonparametric Methods for Evaluating Economic Efficiency and Imperfect Competition

This article unifies and extends ideas from nonparametric production analysis and DEA for testing organizational efficiency. We show how the admissible price set can be restricted to account for prior information on prices. These restrictions may relate prices to input and output quantities in order to test noncompetitive behavior of the evaluated decision making unit. While the resulting efficiency tests cannot always be cast into linear programming problems, we discuss various solution strategies for the tests. Thereby we consider the question when does local optimality of the result guarantee global optimality. We also show how the decision maker's preferences, for example ranking information, can be adopted into DEA models in a simple manner. Finally, the approach with price restrictions is illustrated with an application to test noncompetitive behavior of the pulp and paper industries in Finland.     

An index aggregation approach to comparing the overall performance of emerging and developed countries

An index aggregation approach is proposed to carry out comparisons of BRICSAM, a populous rapidly growing economic group consisting of Brazil, Russia, India, China, South Africa, Association of South-East Asian Nations (ASEAN), and Mexico with Group of Seven (G7), the most developed country club including Canada, France, Italy, Japan, Germany, United Kingdom and the United States. It is estimated that by 2050 the accelerated economic activity of BRICSAM could have significant impact on investment flows, legal and regulatory frameworks, the stability of political institutions, human capital and migration flows, competition policy, intellectual property rights, and social and environmental policies. The comparison analyses of BRICSAM and G7 countries could assist people to better understand the status quo of these countries in the global economy and international system, particularly in the areas of economics and responsible activities such as sustainable development, global commitments and transparent practices. Many country-ranking indices, such as the indices given in the global competitiveness report by the World Economic Forum, and the environmental sustainability index by Yale University, constitute evaluations of countries from different perspectives. This paper proposes a data envelopment analysis-based approach to aggregate different ranking indices for BRICSAM and the G7 countries. The approach can provide a fair overall assessment of a country's standing by maximizing its possibility of obtaining the best possible result.     

Measuring efficiency in Australian Schools: A preliminary analysis

In this paper, we apply a public sector Data Envelopment Analysis model to estimate the efficiency of Australian primary and secondary schools. Standard microeconomic production theory showing the transformation of inputs into outputs is extended to allow nondiscretionary environmental variables characteristic of educational production. Failure to properly control for the socioeconomic environment leads to inappropriate comparisons and biased efficiency estimates. We employ a conditional estimator that does not allow a school with a better environment to serve as a benchmark for a school with a worse environment. The results suggest that Australian schools are moderately inefficient and that efficiency increases for the quintile of schools with the most favorable environment. Further, efficiency gains are realized with increasing enrollment.