Measuring Capital Productivity of Companies Affiliated with the Armed Forces, using the Data Envelopment Analysis

Emami Meibodi, Ali; Vahabi, Meysam

Measuring Capital Productivity of Companies Affiliated with the Armed Forces, using the Data Envelopment Analysis

Document Type : Original Article

Authors

Ali Emami Meibodi ¹
Meysam Vahabi ²

¹ Professor, Department of Energy Economics, Faculty of Economics, Allameh Tabatabai University, Tehran, Iran.

² M.A in Financial Engineering, Faculty of Industrial Engineering and Systems, Tarbiat Modares University, Tehran, Iran.

Abstract

Being aware of the criticality of paying attention to productivity levels and their fluctuations over time, the objective of the present study is to measure capital efficiency of companies affiliated with the Armed Forces of Iran. For this purpose, this paper employs a model of Auxiliary Variable Measure related to the Data Coverage Analysis method to calculate capital efficiencies. Thereafter, it compares the results obtained, with the capital efficiency results obtained through other commonly used methods. Additionally, the Malmquist index was used to calculate productivity growth.
Considering that the results obtained from the data envelopment analysis are highly dependent on the sample size used, the bootstrap method was used to correct the skewness of the results. Finally, using our model of auxiliary variables measures related to the data coverage analysis method, we rank the companies understudy in terms of capital efficiency, and furthermore, calculate the exact amount of modification of each of the input and output variables. Moreover, using a larger sample we evaluate the performance of the companies understudy in relation to their competitors. The results obtained show that between 2015 and 2017, Ansar Bank and Hekmat Saba Insurance Company, had the highest and lowest capital and labor productivities, respectively. On the other hand, in terms of total productivity of the surveyed companies, the results show that productivity in 2019 compared to 2017 has either remained constant or has decreased.

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