The objective of this paper is to identify the economic importance of Iranian industries by multivariate analysis. This study is among the first in the world to examine the industrial sectors by two robustapproaches, namely, Principal Component Analysis (PCA) and Data Envelopment Analysis (DEA). DEA is a mathematical programming approach and PCA is a multivariate statistical technique. Furthermore, the results of PCA is verified and validated by DEA through a nonparametric statistical test. The two methodologies are used to rank and analyze the Decision-Making Units (DMUs) which in our case are the industrial sectors. To achieve the above objective, first the
industrial sectors are classified according to two-digit International Standard for Industrial Classification for all economics activities. ISIC is the most well known industrial classification format in the world. It is used by such international organization as UNIDO, UN and World Bank. ISIC classification ranges between two to four
digit formats. This study considers the twodigit format of industrial classification and further analysis (three and four digit) is left
for a future research paper. Second, a
comprehensive study was conducted to locate the most important economic shaping factors in the world, which were identified as eight economic indicators such as value added per employee. The eight indicators are obtained (for PCA) by two output and four input measures (for DEA). The findings of PCA and DEA show the
importance and influence of each of the eight indicators used in this study. Also, the industrial sectors (DMUs) are ranked to reveal the best and worst industrial sectors
with respect to the selected indicators. In addition, the weak and strong points of each industrial sector are identified. This paper presents an integrated standard approach
for economic assessment and ranking of
industrial sectors in Iran and the world. Furthennore, for the first time the Iranian industrial sectors (and in the world) are analyzed and ranked by multivariate analyses according to international
standards. Also, the results and rankings of PCA and DEA are verified and validated through Spearman test of correlation. The test shows a direct relationship between
DEA and PCA, with Spearman test statistic rs = 0.873 which is very close to unity at
? = 0.05 level of significance. In summary, this paper presents a unique standard
methodology for assessment and ranking of industrial sectors based on economic
indicators. The structure and approach of this paper could be applied for other sectors in particular and other countries in general. The results of this study would help policy makers and top managers to have better understanding of their sectors with respect to economic factors. Also, designers and engineers could identify weak and strong points in regard to economic factors.
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