فصلنامه پژوهش های اقتصادی ایران
پیاپی 17 (زمستان 1382)

Economic theory suggests a causal and positive relationship between human capital and economic growth. Empirical findings, however, report an insignificant and even negative relationship between the two, for many cases. This has been explained by some researchers as a result of neglecting the educational distribution of labour force. In this paper, the significance of this explanation has been examined for the case of Iran. We have shown that, while the relationship between human capital and economic growth is significant and positive, it is statistically improved when the educational distribution of labour force is added to the model. According to our findings, policy should give more priority to primary and secondary education as opposed to higher education.

The issue of Iran’s Accession to WTO has been widely discussed in academic, business and political cycles of the nation Significant questions have been raised from industrialists and policy makers, as to the efficiency and competitiveness of new Iranian firms, and their comparative advantages in the world market. The role of government policies for promotion of competitive industries has also been discussed. So, this paper presents a method which draws on economic theory to measure cost competitiveness and its sources at the firm and industry levels. The concept of competitiveness that we use in the present study is one of cost competitiveness as measured by unit cost ratio. In other words, a firm or an industry is deemed to be competitive if its unit costs are less than or equal to the unit costs of its domestic or international rivals before or after joining to WTO. Also this study focuses on the sources of competitive advantage. Two types of them are distinguished: first, the real sources of competitiveness such as factor productivity and factor abundance, which many lead to comparative advantage, and second, various price distortions of products and factors of productions, which may either enhance or diminish competitiveness. To do so we measure and analyze the cost competitiveness of “Mobarake Steel Complex”. We will measure the cost competitiveness of this company in aggregate level at first step, and for every product at the second step.

Due to negative effects of financial crises on the real sectors of the economy, and the consequeut reccessionary impacts of them, the idea of forecasting feinancial crises via an early warning system has become a subject of interest to researchers. Financial crises of 1997 in South East Aisa, and the fear of similar crises in the process of globalization has brought the subject to the attention of economists to a greater extent. This paper introduces an early warning system for detection and forecasting of financial crisis as an impirical diagnostic tool of macroeconomic policy in Iran. The sysytem is developed with reference to literature review in other countries, and on the basis of “Signal Extraction” and “Financial Crises Probability Forecasting Methods”. In a simulation test our system was effective in detecting the crisis of 1993 two years ahead. This system may be further supplemented by using alternative econometric methods for future forecastings.

One of the classical applications of operation research in investment decision making is the portfolio selection problem. In this problem a fixed sum of money is to be spread among different investments and there is a risk associated with the rate of return on each investment. The object of the portfolio selection problem is to determine how much money should be allocated to each investment to maximize the total expected return and minimize the portfolio’s risk. Since there is no specific algorithm to find an optimal feasible solution for large scale portfolio problems, in this paper two genetic algorithms are developed to find a near optimal solution. In the first algorithm the selection of investments is determined and in the second one the weight of each investment in the portfolio is calculated. Finally, the two algorithms have been applied successfully to the portfolio of stocks of the Tehran Stock Exchange with more than 200 stocks.