A productivity analysis of Iranian industries using an additive data envelopment analysis

Abstract: Monitoring productivity of economic sections of a country would be an important step towards a reliable planning. Developmental decisions based on weaknesses and strengths will guarantee effectiveness, since it will lead to an effective allocation of resources. Among performance measurement approaches, the Data envelopment analysis (DEA), is a model that measures and reports excesses and deficits via analyzing input and output aspects. Aid of this exact and dis-criminating measurement, a proper DEA model applied in this study, can be an efficient instru-ment in fields which need scrutinizing analyses. Industrial productivity analysis of a country is one of such fields. This study applies an instrument developed based on the DEA approach for measuring the industrial productivity of the country. The results obtained, may pave the path for policy-making for economic growth in such a way that enables an effective resources allocation. The applied instrument is a weighted additive model, for which a sufficient number of yearly pe-riods are considered as decision making units (DMUs). The weights included in the model are driven by executing an analytical hierarchy process. After running the model the results demon-strate excesses and deficits in each DMU which can illuminate not only the past performance but also help to plan for the future policies.

How to cite this paper

 Rahmani, M. (2017). A productivity analysis of Iranian industries using an additive data envelopment analysis.Management Science Letters , 7(4), 197-204.

Refrences

Amann, E. (2002). Globalisation, industrial efficiency and technological sovereignty: Evidence from Brazil. The Quarterly Review of Economics and Finance, 42(5), 875-888.
Charnes, A., Cooper, W. W., Lewin, A. Y., & Seiford, L. M. (Eds.). (2013). Data envelopment analy-sis: Theory, methodology, and applications. Springer Science & Business Media.
Coelli, T. J., Rao, D. S. P., O'Donnell, C. J., & Battese, G. E. (2005). An introduction to efficiency and productivity analysis. Springer Science & Business Media.
Fuentes, R., & Lillo-Bañuls, A. (2015). Smoothed bootstrap Malmquist index based on DEA model to compute productivity of tax offices. Expert Systems with Applications, 42(5), 2442-2450.
Hitomi, K. (2004). Efficiency analysis of Japan’s industry and manufacturing. Technovation, 24(9), 741-748.
Jefferson, G. H., Rawski, T. G., & Zheng, Y. (1996). Chinese industrial productivity: trends, meas-urement issues, and recent developments. Journal of Comparative Economics, 23(2), 146-180.
Karagiannis, G., & Lovell, C. K. (2016). Productivity measurement in radial DEA models with a sin-gle constant input. European Journal of Operational Research, 251(1), 323-328.
Li, K., & Lin, B. (2016). Impact of energy conservation policies on the green productivity in China’s manufacturing sector: Evidence from a three-stage DEA model. Applied Energy, 168, 351-363.
Norsworthy, J. R., & Jang, S. L. (1992). Empirical Analysis of Technology and Productivity in High Technology and Service Industries.
Saaty, T. L. (1990). How to make a decision: the analytic hierarchy process. European journal of op-erational research, 48(1), 9-26.
Statistical Center of Iran, Statistical Yearbook, 1980-2000.
Wu, Y. (2001). Is China's economic growth sustainable? A productivity analysis. China Economic Review, 11(3), 278-296.

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Management Science Letters

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