Measuring Inefficiency Via Potential Improvements

In a recent paper Bogetoft and Hougaard (1999) suggest the use of a new potential improvements approach to efficiency evaluation which has the advantage of separating the issue of benchmark selection from the issue of efficiency measurement. In the present paper the potential improvements inefficiency index is reexamined and a DEA-like approach for its determination is suggested. The approach is called Multi-directional Efficiency Analysis (MEA). An empirical example on Danish dairy farms is used for illustrative purposes and comparisons with various versions of DEA.     

Ranking journal quality by harmonic mean of ranks: an application to ISI statistics & probability

As the preponderance of journal rankings becomes increasingly more frequent and prominent in academic decision making, such rankings in broad discipline categories is taking on an increasingly important role. The paper focuses on the robustness of rankings of academic journal quality and research impact using the widely‐used Thomson Reuters ISI Web of Science citations database (ISI) for the Statistics & Probability category. The paper analyses 110 ISI international journals in Statistics & Probability using quantifiable research assessment measures (RAMs), and highlights the similarities and differences in various RAMs, which are based on alternative transformations of citations and influence. Alternative RAMs may be calculated annually or updated daily to determine when, where and how (frequently) published papers are cited (see Chang, McAleer and Oxley (2011a, b, c) , Chang, Maasoumi and McAleer (2012) ). The RAMs are grouped in four distinct classes that include impact factor, mean citations and non‐citations, journal policy, number of high quality papers, and journal influence and article influence. These classes include the most widely used RAMs, namely the classic 2‐year impact factor including journal self citations (2YIF), 2‐year impact factor excluding journal self citations (2YIF^*), 5‐year impact factor including journal self citations (5YIF), Eigenfactor (or Journal Influence), Article Influence, h‐index, PI‐BETA (Papers Ignored – By Even The Authors), 5YD2 (= 5YIF/2YIF) as a measure of citations longevity, and escalating self citations as a measure of increasing journal self citations. The paper highlights robust rankings based on the harmonic mean of the ranks of RAMs across the 4 classes. It is shown that focusing solely on the 2YIF of a journal, which partly answers the question as to when published papers are cited, to the exclusion of other informative RAMs, which answer where and how (frequently) published papers are cited, can lead to a distorted evaluation of journal quality, impact and influence relative to the more robust harmonic mean of the ranks.     

Efficiency Rankings of MBA Programs in Indian Top Public Colleges

This study examines the relative efficiency of the top 20 Indian public colleges that offer MBAs. These colleges were chosen from a list provided by Careers 360, a magazine in India known for its university rankings. The purpose of this study was to evaluate the colleges on an efficiency basis rather than on a total score ranking scale as is the common practice of most publications that rank universities or programs. The ranking method used in this study is based on data envelopment analysis （DEA）, a nonparametric procedure for evaluating entities based upon examining inputs in relation to outputs achieved. The rankings using DEA were somewhat different than those given by Careers 360. The results of the DEA analysis of this study rank the universities that are the most efficient at getting students the best salaries and return on investment （ROI） based on the inputs of diversity, work experience, and residency. The authors conclude, as previous studies have shown, that DEA analysis is a useful and non-biased method of comparing university programs.     

Firm exit and technical inefficiency

The purpose of the paper is to examine formally the fundamental implication that technical inefficiency (TI) is related to firm exit. Traditional stochastic frontier models allow for the measurement of TI but do not allow for a direct effect of TI on exit. We propose a model which allows for such effects and consists of a stochastic frontier model, and an additional equation that describes the probability of exit as a function of covariates and TI. Since TI is unobserved, econometric complications arise, and obtaining consistent estimates is non-trivial due to the presence of integrals in the likelihood function. We propose and implement maximum likelihood estimation one step, employing data for 3,404 manufacturing firms in Greece. We find significant positive effects from TI on the probability of exit. We also propose and provide measures of TI that respect the fact that unobserved TI affects the probability of exit and compare them to TI measures from the traditional stochastic frontier model.

从公私物品供应方式差异角度看我国地方政府机构改革的目标模式

通过对公共物品与私人物品生产、供应方式的差异角度进行的分析 ,得出了“较之私人物品供应 ,公共物品供应一般效率较低”的结论 ,从而肯定了建立公共管理体制的必要性与必然性。在此基础上 ,分析了我国在这方面存在的问题 ,并提出了应采取的建立公共管理体制的六条基本措施。     

Analysing inefficiency in a non-parametric spatial-dynamic by-production framework: A k-nearest neighbour proposal

This paper accounts for spatial effects by benchmarking farms against their k-nearest neighbours (KNN) and measuring their inefficiency in a non-parametric dynamic by-production setting. The optimal number of neighbours $k$ against which farms are compared corresponds to the value of $k$ that maximises the Moran I test for spatial autocorrelation of the good and the bad output of the farms' two sub-technologies. The inefficiency scores for farms' good output, variable inputs, investments and bad outputs are then computed and compared with those calculated based on a global technology, which benchmarks all farms together. The application focuses on an unbalanced panel of specialised Dutch dairy farms over the period 2009–2016 that contains information on their exact geographical locations. The results suggest that the inefficiency scores exhibit statistically significant differences between the KNN and the global model. Specifically, the inefficiencies are generally deflated when a KNN technology is considered, suggesting that ignoring spatial effects can overestimate inefficiency.