Measuring technical efficiency of different industries plays an important role on making managerial decisions. In this paper, we present an empirical study to measure technical efficiencies of various industries based on two-digit ISIC classification method in Iran. The proposed model uses stochastic frontier analysis (SFA) and implements maximum likelihood estimation (MLE) to estimate the parameters. The proposed study gathers the necessary data from year 2001 to year 2008 and implemented two methods where the second method is an extended model by using energy as part of efficiency estimation. The results of the survey indicate that auto industry was the most productive sector followed by equipments and the paper industry was among inefficient sectors.

One of the most important issues on educational systems is to measure the relative efficiency of similar units based on non-financial factors. Data envelopment analysis (DEA) has become popular among many who wish to rank educational systems using different factors such as the rate of educational success or the number of employees, etc. However, one of the main concerns on implementing DEA methods is the uncertainty involved in input/output parameters. In this paper, a robust data envelopment analysis (RDEA) is developed to measure the efficiency of high schools considering uncertainty on output parameters. We present an empirical study on a set of high schools located in Tehran, which is the capital city of Iran. The study uses uncertain data for input/output information and the results are compared with an existing parametric stochastic frontier analysis (SFA). The preliminary results indicate that the robust DEA approach is relatively a reliable method for efficiency estimating.