JOURNAL OF SUSTAINABLE GROWTH AND DEVELOPMENT (THE ECONOMIC RESEARCH)
Year:2007 | Volume:7 | Issue:3|Papers Count:8

Financial markets play a vital role in supplying and facilitating the flow of funds into production and industry sectors of economy and can result into the acceleration of economic growth. Indeed, many experts believe that the development of financial markets acts as the engine for growth. The main objective of this paper is analyzing the relationships between financial market development and economic growth through focusing on Iranian economy and thirteen other countries for the period 1988-2003. In this regard in addition to the Beck and Levin model (2003), we have used three versions of Granger-Casualty approach, Cointegartion test and panel data estimation procedure. Casualty test shows that in Iran, bank and stock market size have no strong effect on economic growth despite the fact that the effect of economic growth on stock market is positive and meaningful. The results of panel data estimation revealed that in real terms, investment and labor force, positively and strongly affect economic growth. In the money sector, the effects of banking system are statistically acceptable, although the positive effect of stock market is not statistically acceptable. The absence of Long-run co integration relation between financial markets and economic growth for the period 1976-2003 is the result of Auto-Regressive Distributed Lag Estimation. In sum, the long- run relation between money market and economic grow this negative and this true for the Iranian economy.

One of the main concerns that many countries of the world are encountering, is some economic activities which are usually hidden from official view. Activities such as exchanging stolen goods, drug trafficking, corruption, gambling, smuggling, are among illegal activities, and others like refusing to report the incomes, fringe benefits, and cash discounts for the staff are among the legal activities of shadow economy. A considerable part of economic literature during the past decade focused on the research findings concering the ways of measuring, defining, and determing the extense of shadow economy in the world. Using the "Structural Equation Modeling" and some literature-specified causes and indicators we aim to reach the case study of Iran. Estimation of size and evolution of Iranian shadow economy is analyzed through "Dynamic Multiple Indicators-Multiple Causes" which is one of the most important indirect techniques available. The advantage of this model is evaluation of the simultaneous impacts of all variables on each other, and has fewer restrictions compered to other models. The research findings reveal the increasing trend of shadow economy in Iran and acts as an alarm for policy makers and authorities.

Competitiveness is considered as a key criterion for appraising the degree of success for countries, industries and enterprises in the political, economic and commercial competition areas. Research findings show that competitiveness has been discussed in three levels of national, industry and enterprise (organization or company). Among all these levels, the enterprise level seems to be more considerable. In this study enterprise competitiveness has been viewed from two main perspectives: construct and behavioral. According to construct perspective, competitiveness includes two groups of factors which are composing and affecting factors. Based on behavioral perspective an enterprise faces two types of factors in its decisions and actions which are strategic and operational ones. As a result of literature review, summarization and complementary edition, totally 28 factors have been identified as competitiveness factors ({16 composing f. and 12 affecting f.} and {15 strategic f. and 13 operational f.}). A questionnaire has also been developed based on these factors. The findings show that in Iranian business context, all competitiveness factors of enterprises are highly important but very weak.

Global experience shows that rural agro-based industries can result into rural production, productivity, access to job opportunities, promotion of intersectoral relationships and reduction of regional disparities. However, success of rural agro based industries depends heavily on selecting the best location for the establishment of these industrial activities. Although regional planners have used various methods for prioritizing industrial location, but it seems that TOPSIS (technique for order preference by similarity to an ideal solution), is one of the most practical methods for classification of places based on priority. In fact TOPSIS, known as one of the classical MCDM methods, is based on the .idea that the chosen alternative should have the shortest distance from the positive ideal solution and the furthest distance from the negative ideal solution on the other side.

Total Factor Productivity (TFP) is a measure for assessing the productivity of a firm or organization. The level of productivity score of a firm shows that how the firm succeeds in optimizing the usage of sources and producing more outputs by certain level of inputs. Nowadays the productivity growth has a crucially important role in economics and firms competition. The industrial countries increase their outcomes not necessarily through more inputs but, by making growth in productivity. Some of the duties of executive organizations of Iran which is explained in the 4thprogram of development is to determine the productivity growth 'rate of the related products, organizing the programs, making some solutions for increasing the productivity such as that of GDP growth to be at least 31.3% and average growth of labor, capital and TFP to be at least 3.5%, 1%, 2.5% respectively. For achieving these targets, firstly, the productivity score should be calculated' for every organization during previous periods of their activities. Then the effective factors of productivity growth should be determined and forecasted for the next period, to increase the productivity at least by 2.5%. In this paper we present a method based on productivity growth indexes and generalize inverse DEA. Using the proposed method, the productivity score of previous periods are calculated. Then the value of input and output changes for the next period is determined. This method is applied for a case study at National Iranian Oil Company.

In Iran, oil products are the most valuable export, 30% of which (Crude Oil) is used domestically every year. Countries like Iran depend heavily on oil revenues. One of the main sectors for consumption of crude oil in Iran is the transportation Industry. This paper aims to measure and estimate fuel productivity in land transportation in Iran using available data from 1973-2003. The function of fuel productivity is estimated using time series analysis, and the co integration with stationary variables have been accounted and analyzed. At this stage, initially the co-integration variables of the model are known, and then, the structure of the model and the number of optimal orders are identified. The next step however, determines the number of co-integration vectors of the model which eventually with some restrictions estimate the fuel productivity function within the land transportation sectors e.g. rail and road.

Due to the lack of General Practitioners (GP) in the past two decades in Iran, increasing the number of General Practitioners has been on the strategic agenda for health sector. However, this was an appropriate action for the time but, these augments unfortunately continued without scientific considerations, while these were based on the needs of society in that time. This led to some problems for all sectors in the health system. Unemployment, misemployment, underemployment were the results of these policies. Government suffered from heavy cost of educating General Practitioners. the system faced with inequality in their performance as well. Because of the importance of the subject, this research is done for avoiding such problems. It uses mathematical and economic models and techniques to estimate the number of GP from 2006 to 2011, which is believed to be essential for the health system. In this research, Cob-Douglas production function and partial adjustment model have been used for estimating GP labor demand function, then using growth rates of variables and growth mean of the period for each variable, the needed number of GP has been estimated. The future need of GP for years of 2006, 2007, 2008,2009,2010,2011 is respectively, 3864, 4507, 5282, 6224, 7384, and 9011. The elasticity is also calculated for the variables: (RInv), (RVA), (L). Point elasticities for the above variables are respectively0.035, 0.041, and 0.01.