The appreciative democratic voice of DEA: A case of faculty academic performance evaluation

Data envelopment analysis (DEA) is in fact more than just being an instrument for measuring the relative efficiencies of a group of decision making units (DMU). DEA models are also means of expressing appreciative democratic voices of DMUs. This paper proposes a methodology for allocating premium points to a group of professors using three models sequentially: (1) a DEA model for appreciative academic self-evaluation, (2) a DEA model for appreciative academic cross-evaluation, and (3) a Non-DEA model for academic rating of professors for the purpose of premium allocations. The premium results, called DEA results, are then compared with the premium points “nurtured” by the Dean, called N bonus points. After comparing DEA results and N bonus points, the Dean reassessed his initial bonus points and provided new ones – called DEA-N decisions. The experience indicates that judgmental decisions (Dean's evaluations) can be enhanced by making use of formal models (DEA and Non-DEA models). Moreover, the appreciative and democratic voices of professors are virtually embedded in the DEA models.     

Identifying “best” applicants in recruiting using data envelopment analysis

Selecting the most promising candidates to fill an open position can be a difficult task when there are many applicants. Each applicant achieves certain performance levels in various categories and the resulting information can be overwhelming. We demonstrate how data envelopment analysis (DEA) can be used as a fair screening and sorting tool to support the candidate selection and decision-making process. Each applicant is viewed as an entity with multiple achievements. Without any a priori preference or information on the multiple achievements, DEA identifies the non-dominated solutions, which, in our case, represent the “best” candidates. A DEA-aided recruiting process was developed that (1) determines the performance levels of the “best” candidates relative to other applicants; (2) evaluates the degree of excellence of “best” candidates’ performance; (3) forms consistent tradeoff information on multiple recruiting criteria among search committee members, and, then, (4) clusters the applicants.     

A model of diffusion and implementation: An exploratory study of managerial innovation in Colombia

The focus of this paper is characterized by (1) an examination of the factors related to the “anticipation” of potential innovations in any organizational setting and (2) the identification of strategies for the diffusion and implementation of operations research/management science (OR/MS) techniques in a particular developing region. Based on the methodology used in studying change (innovation) in health care systems, a managerial innovation model incorporating four main components [the executive, the organization, the task environment of the organization and change agent(s), including the OR/MS manager and outside consultants] is developed and examined in terms of data obtained from top executives and other managers in forty industrial firms in Cali, Colombia. In the model developed the process of innovation is decomposed into the levels of; (a) attitudes and motivations of the executive, (b) “readiness” to take action, (c) action characteristics, (d) triggering cues and (e) actions taken and evaluation (feedback loop). The model was found useful for providing predictions indicating areas to which intervention and “marketing” of OR/MS strategies should be devoted. Overall, the study provides a base for comparative and longitudinal studies.     

Measuring and Evaluating Efficiency of Hospitals Through Total Quality Management

ABSTRACT

The purpose of this study is to measure and evaluate the efficiency of 12 hospitals in Turkey using a multi-criteria Data Envelopment Analysis (DEA) methodology. Number of beds, number of physicians and the critical factors of total quality management in the health care sector were used as inputs of the model. The outputs used in this analysis incorporated financial and non-financial performance of hospitals, number of outpatients and number of patient days. Performance of the hospitals was measured using subjective measures based on executive's perception of how their organization performed relative to the competition. Results provide management with information regarding the relatively best practice hospitals in the observation sets and locate the relatively inefficient hospitals by comparison with the best practice ones. At last some suggestions are made for the least efficient hospital.     

A credit scoring approach for the commercial banking sector

For managing credit risk, commercial banks use various scoring methodologies to evaluate the financial performance of client firms. This paper upgrades the quantitative analysis used in the financial performance modules of state-of-the-art credit scoring methodologies. This innovation should help lending officers in branch levels filter out the poor risk applicants. The Data Envelopment Analysis-based methodology was applied to current data for 82 industrial/manufacturing firms comprising the credit portfolio of one of Turkey's largest commercial banks. Using financial ratios, the DEA synthesizes a firm's overall performance into a single financial efficiency score—the “credibility score”. Results were validated by various supporting (regression and discriminant) analyses and, most importantly, by expert judgments based on data or on current knowledge of the firms.     

Detecting Outliers in Frontier Models: A Simple Approach

In frontier analysis, most of the nonparametric approaches (DEA, FDH) are based on envelopment ideas which suppose that with probability one, all the observed units belong to the attainable set. In these deterministic frontier models, statistical theory is now mostly available (Simar and Wilson, 2000a). In the presence of super-efficient outliers, envelopment estimators could behave dramatically since they are very sensitive to extreme observations. Some recent results from Cazals et al. (2002) on robust nonparametric frontier estimators may be used in order to detect outliers by defining a new DEA/FDH deterministic type estimator which does not envelop all the data points and so is more robust to extreme data points. In this paper, we summarize the main results of Cazals et al. (2002) and we show how this tool can be used for detecting outliers when using the classical DEA/FDH estimators or any parametric techniques. We propose a methodology implementing the tool and we illustrate through some numerical examples with simulated and real data. The method should be used in a first step, as an exploratory data analysis, before using any frontier estimation.     

Exploring the operating efficiency of Technology Development Programs by an intellectual capital perspective—A case study of Taiwan

Government supported technological research and development can help the private sector to compete globally. A more accurate evaluation system considering multi-factor performance is highly desired. This study offers an alternative perspective and characterization of the performance of Technology Development Programs (TDPs) via a two-stage process that emphasizes research and development (R&D) and technology diffusion. This study shall employ a sequential data envelopment analysis (DEA) with a non-parametric statistical analysis to analyze differences in intellectual capital variables among various TDPs. The results reveal that R&D performance is better than technology diffusion performance for the TDPs. In addition, the “Mechanical, Mechatronic, and Transportation field” is more efficient than the other fields in both R&D and technology diffusion performance models. The findings of this study point to the importance of intellectual capital in achieving high levels of TDP efficiency. The potential applications and strengths of DEA and intellectual capital in assessing the performance of TDP are also highlighted.     

An Efficiency Comparison of European Banking Systems Operating under Different Environmental Conditions

The paper investigates the operating efficiency differences of a sample of commercial banks across 10 European countries. First, the paper analyzes the technical efficiency of each country sample following the basic Data Envelopment Analysis (DEA) model incorporating only banking variables. Then, a complete DEA model is introduced, incorporating environmental factors together with the banking variables of the basic model. The comparison between the two models shows that country-specific environmental conditions exercise a strong influence over the behavior of each country's banking industry.     

Combining DEA Window Analysis with the Malmquist Index Approach in a Study of the Canadian Banking Industry

The banking industry in Canada is essentially an oligopoly with five large participants controlling about 90% of the market. To evaluate the industry's performance over time, we need to deal with the problem of a small number of DMU's compared to the number of relevant inputs and outputs. To overcome this problem we use data envelopment analysis (DEA) window analysis, whereby efficiency scores for the 20 year period 1981–2000 are obtained. To measure productivity changes over time, Malmquist indices can be calculated from DEA scores. Using DEA window analysis scores, however, raise the question of how to define the same period frontier in a DEA window analysis. We show that for both the adjacent and the base period Malmquist index and for all suggested definitions of same period frontier, the standard decomposition into frontier shift and catching up effects gives inappropriate results when Malmquist indices are based on DEA window analysis scores.     

A PCA–DEA approach to measure the quality of life in Estonian counties

This paper investigates the application of a PCA–DEA model to assess the quality of life (QOL) scores in Estonian counties and analyses the model's results. The dataset is a balanced panel of 15 Estonian counties covering the period from 2000 to 2011. We consider a PCA–DEA model as an alternative method to estimate and predict QOL scores and rankings of Estonian counties. The method consists of a two-stage analysis that begins with a principal component analysis. In the second stage, the standard DEA is used. The results from the conventional DEA model and the PCA–DEA model are compared and discussed. A comparison of the methodologies demonstrates that a PCA–DEA model provides a powerful tool for performance ranking. The rankings of Estonian counties using QOL scores for different model specifications are presented. Finally, the QOL ranking of Estonian counties is revised using PCA–DEA.     

The construction of empirical credit scoring rules based on maximization principles

We examine the econometric implications of the decision problem faced by a profit/utility-maximizing lender operating in a simple “double-binary” environment, where the two actions available are “approve” or “reject”, and the two states of the world are “pay back” or “default”. In practice, such decisions are often made by applying a fixed cutoff to the maximum likelihood estimate of a parametric model of the default probability. Following (Elliott and Lieli, 2007), we argue that this practice might contradict the lender’s economic objective and, using German loan data, we illustrate the use of “context-specific” cutoffs and an estimation method derived directly from the lender’s problem. We also provide a brief discussion of how to incorporate legal constraints, such as the prohibition of disparate treatment of potential borrowers, into the lender’s problem.     

DEA and Activity Planning under Asymmetric Information

The productivity analysis literature has traditionally focused on the evaluation of past performances. In this paper, we consider the post productivity analysis problem of deciding which production plans to choose in the future given information from a productivity analysis. In particular, we demonstrate that Data Envelopment Analysis (DEA) has an important role to play in the reallocation game following a normal productivity analysis. DEA estimates reduce the information rents an agent can extract by claiming high costs for the least reduced or most expanded activity. We also examine how to optimally combine DEA estimates with other information in a planning context, including preference information, ex-ante cost reports and ex-post cost data.     

A new approach to assess the performance of the Brazilian National Immunization Program (NIP)

The present study develops an alternative measure of efficiency to assess the Brazilian National Immunization Program (NIP) using Data Envelopment Analysis (DEA), an output-oriented variable returns to scale (VRS) model, to combine different homogeneity indicators within a unique composite index and evaluate the socio-demographic differences among states. The new DEA index allows homogeneity indicators to be measured for various vaccines in the same model, which enables the development of a composite index for “the first year of life” immunization cycle. In Brazil 2010, the mean efficiency score for the 26 states was 0.89 (0.14 SD). Eleven states were considered efficient, and eight of them were located in regions with a high Human Development Index (HDI) and small rural population, which reinforces the concept that regional and socioeconomic differences must be considered during immunization planning. To reach the frontier of best practice, each state should have an individual and attainable goal for vaccine homogeneity.     

Efficiency aggregation with enhanced Russell measures in data envelopment analysis

In aggregation for data envelopment analysis (DEA), a jointly determined aggregate measure of output and input efficiency is desired that is consistent with the individual decision making unit measures. An impasse has been reached in the current state of the literature, however, where only separate measures of input and output efficiency have resulted from attempts to aggregate technical efficiency with the radial measure models commonly employed in DEA. The latter measures are “incomplete” in that they omit the non-zero input and output slacks, and thus fail to account for all inefficiencies that the model can identify. The Russell measure eliminates the latter deficiency but is difficult to solve in standard formulations. A new approach has become available, however, which utilizes a ratio measure in place of the standard formulations. Referred to as an enhanced Russell graph measure (ERM), the resulting model is in the form of a fractional program. Hence, it can be transformed into an ordinary linear programming structure that can generate an optimal solution for the corresponding ERM model. As shown in this paper, an aggregate ERM can then be formed with all the properties considered to be desirable in an aggregate measure—including jointly determined input and output efficiency measures that represent separate estimates of input and output efficiency. Much of this paper is concerned with technical efficiency in both individual and system-wide efficiency measures. Weighting systems are introduced that extend to efficiency-based measures of cost, revenue, and profit, as well as derivatives such as rates of return over cost. The penultimate section shows how the solution to one model also generates optimal solutions to models with other objectives that include rates of return over cost and total profit. This is accomplished in the form of efficiency-adjusted versions of these commonly used measures of performance.     

Yield curve in an estimated nonlinear macro model

This paper estimates a sticky price macro model with US macro and term structure data using Bayesian methods. The model is solved by a nonlinear method. The posterior distribution of the parameters in the model is found to be bi-modal. The degree of nominal rigidity is high at one mode (“sticky price mode”) but is low at the other mode (“flexible price mode”). I find that the degree of nominal rigidity is important for identifying macro shocks that affect the yield curve. When prices are more flexible, a slowly varying inflation target of the central bank is the main driver of the overall level of the yield curve by changing long-run inflation expectations. In contrast, when prices are more sticky, a highly persistent markup shock is the main driver. The posterior probability of each mode is sensitive to the use of observed proxies for inflation expectations. Ignoring additional information from survey data on inflation expectations significantly reduces the posterior probability of the flexible price mode. Incorporating this additional information suggests that yield curve fluctuations can be better understood by focusing on the flexible price mode. Considering nonlinearities of the model solution also increases the posterior probability of the flexible price mode, although to a lesser degree than using survey data information.     

Productivity drivers and market dynamics in the Spanish first division football league

This paper analyses efficiency drivers of a representative sample of Spanish football clubs by means of the two-stage data envelopment analysis (DEA) procedure proposed by Simar and Wilson (J Econ, 136:31–64, 2007). In the first stage, the technical efficiency of football clubs is estimated using a bootstrapped DEA model in order to establish which of them are the most efficient; the ranking is based on total productivity in the period 1996–2004. In the second stage, the Simar and Wilson (J Econ, 136:31–64, 2007) procedure is used to bootstrap the DEA scores with a truncated bootstrapped regression. Policy implications of the main findings are also considered.     

Supply structure: The pendulum in action

Using an in-depth longitudinal case study based on an “historical retrospective”, this article focuses on how some factors (“drivers”) set into motion the pendulum of change in supply structures, a research theme neglected in the supply field. It examines, over a 20-year period, the changes made to the supply structure of a well-known organization. The data comes mainly from: (a) semi-structured interviews with 21 managers and professionals within eight operating units, and (b) documentary analyses of the archives. This article shows how to map structural changes of the supply function, while explaining the nature and the causes of these changes. Understanding the pendulum movement from one supply structure to another can be very valuable, as well for academics as for managers.     

Foreign direct investment: A genetic algorithm approach

This paper proposes a genetic algorithm (GA) approach as an analytical tool with a carefully defined fitness function for variable selection. Discriminant analysis will be used as a parameter evaluation method for the analysis of inward foreign direct investment (FDI) in China. Results indicate that the proposed GA method is more efficient in classifying “successful or unsuccessful” inward FDI by providing higher accuracy rates while using fewer variables than previous efforts. An implication of this result is that, given a scarcity of resources and the need to promote FDI, the proposed GA may provide a more efficient way to concentrate on those fewer variables found to be important determinants of “successful” FDI inflow.     

DEA, DFA and SFA: A comparison

The nonparametric data envelopment analysis (DEA) model has become increasingly popular in the analysis of productive efficiency, and the number of empirical applications is now very large. Recent theoretical and mathematical research has also contributed to a deeper understanding of the seemingly simple but inherently complex DEA model. Less effort has, however, been directed toward comparisons between DEA and other competing efficiency analysis models. This paper undertakes a comparison of the DEA, the deterministic parametric (DFA), and the stochastic frontier (SFA) models. Efficiency comparisons across models in the above categories are done based on 15 Colombian cement plants observed during 1968–1988.     

Using data envelopment analysis to quantify ILO objectives and identify policies conducive to decent work in a globalizing world

The International Labor Office, an arm of the UN based in Geneva, has as its goal the promotion of opportunities for women and men to obtain decent and productive work, in conditions of freedom, equity, security and human dignity. Since 1999, the ILO has conducted a series of studies of decent work. In 2001, the organization posed the global challenge of reducing the decent work deficit as measured by an employment gap, rights gap, a social protection gap, and a social dialogue gap. Using standard economic terms, “decent work” may be seen as an efficiency point along a generalized input-output function, dependent upon variables of both economic performance and economic and social policy. The decent work deficit of a given country (if any) is then obtained as the difference between an observed point and its projection on the efficiency frontier. Using data envelopment analysis (DEA), we fit a piecewise linear frontier to observations for 61 countries from all continents. Importantly, 27 of these countries lie on the decent work frontier; the remaining ones reveal conditions of decent work deficit. The possibilities of reducing such deficits by appropriate control of policy variables are discussed.