The Journal of Economic Studies and Policies

The Journal of Economic Studies and Policies

Modeling Undesirable Factors (Environmental Pollutants)in Efficiency Evaluation:Using DEA, Case Study of IRAN’S Manufacturing Industries

Document Type : Original Article

Authors
Zahra Nasrollahi 1
Zahra Sadeghi Aarani 2
Marzieh Ghafari Goolak 3
1 Assistant Professor, Faculty of Economics, Management and Accounting, Yazd University
2 PhD student in Management, University of Tehran
3 PhD student in Economics, Tarbiat Modares University
10.22096/esp.2012.26174
Abstract
Efficiency measurement is one of the most important methods for evaluation of organization performance. Data envelopment analysis (DEA), as a nonparametric method, that measures the relative efficiency of decision making units (DMUs) with multiple performance factors which are grouped into outputs and inputs, is one of the important methods that are highly used in this field. However, both desirable (good) and undesirable (bad) factors may be present and we must consider both of them. For example, if inefficiency exists in production processes where final products are manufactured with a production of wastes and pollutants, the outputs of wastes and pollutants are undesirable and should be reduced to improve the performance.
This paper uses industry-level dataset of IRAN manufacturing industries during the period 2000-2006 to measure the efficiency of industrial sectors. The inputs are labor force, energy and raw material, and value added is desirable output (good) and pollutant emission for three atmospheric pollutants (CO2, SO2 and SPM) are undesirable output (bad) .The results show that during this period only industry with code of 32 (Radio and Television) were efficient and also during this period from 21 industries more than 17 of them were inefficient. In fact, during this period, only 20% of manufacturing industries were efficient.
Keywords
Efficiency
Data Envelopment Analysis
IRAN’S Manufacturing Industries
Undesirable Factors
Environmental Pollutants
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