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Energy carrier’s subsidization has placed a significant pressure on government budget in Iran thus, energy price increase is performed in order to ameliorate this case. One of the main challenges that policymakers need to consider is the impact of energy prices increase on the labor market especially, when the national unemployment rate is high. This paper utilizes a computable general equilibrium model based on a Micro Consistent Matrix for 2006 in order to evaluate the impact of energy price increase on the Iranian labor market during 2006. The empirical results are based on two scenarios: Baseline and FOB price increase scenarios. They show that the activity level and demand for labor in “crude oil, natural gas, and coal” as well as “other services” sectors will increase in short-run while the energy carriers’ prices increase. However, in long-run, the labor increment will be lower. Furthermore, the model results indicate that in short-run, the activity level and demand for labor in the other sectors will decrease. On the other hand, the policy will result in a larger decrement in the activity level and demand for labor in these sectors in long-run.

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In many Developing Countries liberalization of international trade has been accompanied by demand for skilled labour and inequalityof wages.Thisphenomenon seems to be inconsistant with the Stopler- Samuelson Theorem.Studies in this respect show that imported high –tech capital andintermediate goods are skill-based, thus increasing the relative demand for skilled labour.
In such circumstances, identifying the impact of such goods upon the demand for skilled labour in Iran is of great importance.
In this paper, by using Translog cost function and the Method of Seemingly Unrelated Regression, short and long run demand function for the period of 1977- 2014 in Iran has been estimated. Althoug the short and long –run results arecompatible with the theortical expections, the investment on domestic research and development regarding the employment of skilled labour has been effective only in the long-run and not the short –run.

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Fiscal decentralization that is considered a transfer of responsibilities that associated with accountability to sub – national governments, increases efficiency and providing better access to public goods in the Economy. According to the five-year development plans of Iran creating and allocating structure for provincial budgeting, fiscal decentralization generally is moving in the costs of its Provinces in order to give more responsibility to the provincial development projects. The aim of this study is an investigation of effect for partial fiscal decentralization on regional economic growth of Iran. Fiscal decentralization index is proportion of provincial's capital assets to government's capital assets, So this researches the effects of decentralization on economic growth in the framework of Solow's growth model. That the results based on data from 30 provinces between 2000 and 2007 on the panel data estimation, shows partial fiscal decentralization which has a non-linear relationship with the growth (convex shape) and partial fiscal decentralization Indicts the Optimal degree in growth of regional economy in Iran.

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As one of the important energy forms, natural gas consumption has an upward trend in recent years. Therefore management and planning for provision of it requires prediction of the future consumption. But many of prediction procedures are inherently stochastic therefore it is important to have better knowledge about the robustness of prediction procedures. This paper compares robustness of two prediction procedures Artificial Neural Networks as a nonlinear and ARIMA as a linear model. using resampling method to predict the monthly consumption of natural gas in the household sector. Data spans from 2001-4 to 2012-3, to train the networks, we used genetic algorithms and Particle Swarming Optimization then results were compared using 10-fold method. According to the results, the particle swarm optimization (PSO) outperforms the genetic algorithm. Then we used data from 2001-4 to 2010-3, with resampling by 2000 to predict the  natural gas consumption for the 2001 -4 to 2012-3 and to form critical values. Results show that prediction by a mixed method using ANN and PSO is more robust than ARIMA method.