Evaluating Contextual Variables Affecting Productivity Using Data Envelopment Analysis
A DEA-based stochastic frontier estimation framework is presented to evaluate contextual variables affecting productivity that allows for both one-sided inefficiency deviations as well as two-sided random noise. Conditions are identified under which a two-stage procedure consisting of DEA followed b...
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| Published in | Operations research Vol. 56; no. 1; pp. 48 - 58 |
|---|---|
| Main Authors | , |
| Format | Journal Article |
| Language | English |
| Published |
Linthicum, MD
INFORMS
01.01.2008
Institute for Operations Research and the Management Sciences |
| Subjects | |
| Online Access | Get full text |
| ISSN | 0030-364X 1526-5463 |
| DOI | 10.1287/opre.1070.0460 |
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| Abstract | A DEA-based stochastic frontier estimation framework is presented to evaluate contextual variables affecting productivity that allows for both one-sided inefficiency deviations as well as two-sided random noise. Conditions are identified under which a two-stage procedure consisting of DEA followed by ordinary least squares (OLS) regression analysis yields consistent estimators of the impact of contextual variables. Conditions are also identified under which DEA in the first stage followed by maximum likelihood estimation (MLE) in the second stage yields consistent estimators of the impact of contextual variables. This requires the contextual variables to be independent of the input variables, but the contextual variables may be correlated with each other. Monte Carlo simulations are carried out to compare the performance of our two-stage approach with one-stage and two-stage parametric approaches. Simulation results indicate that DEA-based procedures with OLS, maximum likelihood, or even Tobit estimation in the second stage perform as well as the best of the parametric methods in the estimation of the impact of contextual variables on productivity. Simulation results also indicate that DEA-based procedures perform better than parametric methods in the estimation of individual decision-making unit (DMU) productivity. Overall, the results establish DEA as a nonparametric stochastic frontier estimation (SFE) methodology. |
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| AbstractList | A DEA-based stochastic frontier estimation framework is presented to evaluate contextual variables affecting productivity that allows for both one-sided inefficiency deviations as well as two-sided random noise. Conditions are identified under which a two-stage procedure consisting of DEA followed by ordinary least squares (OLS) regression analysis yields consistent estimators of the impact of contextual variables. Conditions are also identified under which DEA in the first stage followed by maximum likelihood estimation (MLE) in the second stage yields consistent estimators of the impact of contextual variables. This requires the contextual variables to be independent of the input variables, but the contextual variables may be correlated with each other. Monte Carlo simulations are carried out to compare the performance of our two-stage approach with one-stage and two-stage parametric approaches. Simulation results indicate that DEA-based procedures with OLS, maximum likelihood, or even Tobit estimation in the second stage perform as well as the best of the parametric methods in the estimation of the impact of contextual variables on productivity. Simulation results also indicate that DEA-based procedures perform better than parametric methods in the estimation of individual decision-making unit (DMU) productivity. Overall, the results establish DEA as a nonparametric stochastic frontier estimation (SFE) methodology. A DEA-based stochastic frontier estimation framework is presented to evaluate contextual variables affecting productivity that allows for both one-sided inefficiency deviations as well as two-sided random noise. Conditions are identified under which a two-stage procedure consisting of DEA followed by ordinary least squares (OLS) regression analysis yields consistent estimators of the impact of contextual variables. Conditions are also identified under which DEA in the first stage followed by maximum likelihood estimation (MLE) in the second stage yields consistent estimators of the impact of contextual variables. This requires the contextual variables to be independent of the input variables, but the contextual variables may be correlated with each other. Monte Carlo simulations are carried out to compare the performance of our two-stage approach with one-stage and two-stage parametric approaches. Simulation results indicate that DEA-based procedures with OLS, maximum likelihood, or even Tobit estimation in the second stage perform as well as the best of the parametric methods in the estimation of the impact of contextual variables on productivity. Simulation results also indicate that DEA-based procedures perform better than parametric methods in the estimation of individual decisionmaking unit (DMU) productivity. Overall, the results establish DEA as a nonparametric stochastic frontier estimation (SFE) methodology. A DEA-based stochastic frontier estimation framework is presented to evaluate contextual variables affecting productivity that allows for both one-sided inefficiency deviations as well as two-sided random noise. Conditions are identified under which a two-stage procedure consisting of DEA followed by ordinary least squares (OLS) regression analysis yields consistent estimators of the impact of contextual variables. Conditions are also identified under which DEA in the first stage followed by maximum likelihood estimation (MLE) in the second stage yields consistent estimators of the impact of contextual variables. This requires the contextual variables to be independent of the input variables, but the contextual variables may be correlated with each other. Monte Carlo simulations are carried out to compare the performance of our two-stage approach with one-stage and two-stage parametric approaches. Simulation results indicate that DEA-based procedures with OLS, maximum likelihood, or even Tobit estimation in the second stage perform as well as the best of the parametric methods in the estimation of the impact of contextual variables on productivity. Simulation results also indicate that DEA-based procedures perform better than parametric methods in the estimation of individual decision-making unit (DMU) productivity. Overall, the results establish DEA as a nonparametric stochastic frontier estimation (SFE) methodology. [PUBLICATION ABSTRACT] A DEA-based stochastic frontier estimation framework is presented to evaluate contextual variables affecting productivity that allows for both one-sided inefficiency deviations as well as two-sided random noise. Conditions are identified under which a two-stage procedure consisting of DEA followed by ordinary least squares (OLS) regression analysis yields consistent estimators of the impact of contextual variables. Conditions are also identified under which DEA in the first stage followed by maximum likelihood estimation (MLE) in the second stage yields consistent estimators of the impact of contextual variables. This requires the contextual variables to be independent of the input variables, but the contextual variables may be correlated with each other. Monte Carlo simulations are carried out to compare the performance of our two-stage approach with one-stage and two-stage parametric approaches. Simulation results indicate that DEA-based procedures with OLS, maximum likelihood, or even Tobit estimation in the second stage perform as well as the best of the parametric methods in the estimation of the impact of contextual variables on productivity. Simulation results also indicate that DEA-based procedures perform better than parametric methods in the estimation of individual decisionmaking unit (DMU) productivity. Overall, the results establish DEA as a nonparametric stochastic frontier estimation (SFE) methodology. Subject classifications: organizational studies: productivity, effectiveness/performance; statistics: nonparametric; probability: stochastic model applications; simulation: applications. Area of review: Decision Analysis. |
| Audience | Trade |
| Author | Banker, Rajiv D Natarajan, Ram |
| Author_xml | – sequence: 1 fullname: Banker, Rajiv D – sequence: 2 fullname: Natarajan, Ram |
| BackLink | http://pascal-francis.inist.fr/vibad/index.php?action=getRecordDetail&idt=20192534$$DView record in Pascal Francis |
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| CODEN | OPREAI |
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| Keywords | Performance evaluation Productivity Stochastic model Monte Carlo method Random noise organizational studies: productivity Statistical analysis Probabilistic approach Decision support system Decision making Non parametric estimation Regression analysis Modeling Independent variable Parametric method Consistent estimator Least squares method effectiveness/performance; statistics: nonparametric; probability: stochastic model applications; simulation: applications Maximum likelihood Data envelopment analysis |
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| Snippet | A DEA-based stochastic frontier estimation framework is presented to evaluate contextual variables affecting productivity that allows for both one-sided... |
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| SubjectTerms | applications Applied sciences Consistent estimators Context effects (Psychology) Cubic polynomials Data envelopment analysis Decision making Decision theory. Utility theory effectiveness/performance Efficiency Error rates Estimation methods Estimation theory Estimators Evaluation Exact sciences and technology Firm modelling Influence Inventory control, production control. Distribution Mathematics Maximum likelihood estimation Methods Monte Carlo simulation Noise nonparametric Nonparametric inference Operational research and scientific management Operational research. Management science Operations research organizational studies Performance evaluation probability Probability and statistics Production estimates Production functions Productivity Regression analysis Sciences and techniques of general use Simulation Social accounting Socioeconomic factors Statistics stochastic model applications Stochastic models Studies Variables |
| Title | Evaluating Contextual Variables Affecting Productivity Using Data Envelopment Analysis |
| URI | http://or.journal.informs.org/cgi/content/abstract/56/1/48 https://www.jstor.org/stable/25147166 https://www.proquest.com/docview/219164356 |
| Volume | 56 |
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