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Country Performance Evaluation: The DEA Model Approach

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  • Po-Chin Wu
  • Tzu-Hsien Huang
  • Sheng-Chieh Pan

Abstract

This study employs four data envelopment analysis (DEA) models to evaluate the performance efficiency of 21 OECD countries and assess whether the undesirable outputs are over-produced relative to desirable outputs. In evaluating the performance of OECD countries via super-efficiency models, this study focuses on two aspects. First, employing the concept of the Sharpe ratio, we propose another method to deal with undesirable outputs (the unemployment rate, inflation, and air pollution) in DEA. This approach can reveal the relative importance of desirable outputs and undesirable outputs, detect whether undesirable outputs are over-produced, and obtain more accurate efficiency scores. Second, we examine whether knowledge capital can improve a country’s efficiency scores. Our empirical results support the above arguments. In addition, research and development (R&D) expenditures, the proxy variable for knowledge capital, can indeed improve countries’ efficiency scores, implying that the endogenous growth theory is supported in OECD countries. Evidently, whether the undesirable outputs are included in the DEA models and are properly treated is crucial in the evaluation of efficiency values. Copyright Springer Science+Business Media Dordrecht 2014

Suggested Citation

  • Po-Chin Wu & Tzu-Hsien Huang & Sheng-Chieh Pan, 2014. "Country Performance Evaluation: The DEA Model Approach," Social Indicators Research: An International and Interdisciplinary Journal for Quality-of-Life Measurement, Springer, vol. 118(2), pages 835-849, September.
  • Handle: RePEc:spr:soinre:v:118:y:2014:i:2:p:835-849
    DOI: 10.1007/s11205-013-0443-3
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    3. Lukáš Melecký & Michaela Staníčková & Jana Hančlová, 2019. "Nonparametric Approach to Evaluation of Economic and Social Development in the EU28 Member States by DEA Efficiency," JRFM, MDPI, vol. 12(2), pages 1-34, April.
    4. Pooja Bansal & Aparna Mehra, 2018. "Multi-period additive efficiency measurement in data envelopment analysis with non-positive and undesirable data," OPSEARCH, Springer;Operational Research Society of India, vol. 55(3), pages 642-661, November.
    5. Mohammad Nourani & VGR Chandran & Qian Long Kweh & Wen-Min Lu, 2018. "Measuring Human, Physical and Structural Capital Efficiency Performance of Insurance Companies," Social Indicators Research: An International and Interdisciplinary Journal for Quality-of-Life Measurement, Springer, vol. 137(1), pages 281-315, May.
    6. Danijela Despotovic & Slobodan Cvetanovic & Vladimir Nedic & Milan Despotovic, 2019. "Social Aspects of Sustainable Competitiveness in the Selected European Countries in the Period 2012–2015," Social Indicators Research: An International and Interdisciplinary Journal for Quality-of-Life Measurement, Springer, vol. 141(2), pages 841-860, January.
    7. Shih-Heng Yu, 2019. "Benchmarking and Performance Evaluation Towards the Sustainable Development of Regions in Taiwan: A Minimum Distance-Based Measure with Undesirable Outputs in Additive DEA," Social Indicators Research: An International and Interdisciplinary Journal for Quality-of-Life Measurement, Springer, vol. 144(3), pages 1323-1348, August.
    8. Jorge Antunes & Goodness C. Aye & Rangan Gupta & Peter Wanke & Yong Tan, 2020. "Endogenous Long-Term Productivity Performance in Advanced Countries: A Novel Two-Dimensional Fuzzy-Monte Carlo Approach," Working Papers 2020111, University of Pretoria, Department of Economics.
    9. Yun Hao & Degang Yang & Jingjing Yin & Xi Chen & Anming Bao & Miao Wu & Xiaoyun Zhang, 2019. "The Effects of Ecological Policy of Kyrgyzstan Based on Data Envelope Analysis," Sustainability, MDPI, vol. 11(7), pages 1-18, March.

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