بررسي تاثير شاخص پيچيدگي اقتصادي بر رشد اقتصادي در 42 كشور برتر در توليد علم

An Examination of Economic Complexity Index Effect on Economic Growth in The top 42 countries producing science

رشد و توسعه اقتصادي همواره از اهداف اصلي سياست‌گذاران اقتصادي بوده است و آنها همواره به دنبال يافتن عواملي بوده‌اند كه موجب تسريع در رشد اقتصادي شود. در اين زمينه نظريه ها و تئوري‌هاي مختلفي وجود دارد كه هركدام به يكسري از عوامل موثر بر رشد اقتصادي مي‌پردازند. به‌طوريكه درنظريه‌هاي اوليه رشد، به دو عامل سرمايه فيزيكي و نيروي كار به عنوان عوامل بهبود دهنده رشد اقتصادي تاكيد داشتند. اما اين نظريات در توضيح تفاوت‌هاي سطح درآمد سرانه و ميزان رشد اقتصادي كشورها توفيق چنداني نداشته لذا موجب شد به برخي عوامل سرمايه اي موثر و ظاهرا غيرمحسوس در مقوله رشد، توجه شود. ظهور اقتصاد دانش محور در ادبيات اقتصادي، حاصل اين منظر جديد به مقوله رشد و توسعه است. به‌طوريكه در اقتصاد معاصر از دانش به‌عنوان عامل اصلي توليد ياد مي‌شود. براي اندازه‌گيري ميزان دانش به‌كار رفته در توليدات يك كشور شاخص‌هاي مختلفي وجود دارد كه يكي از اين شاخص‌ها، شاخص پيچيدگي اقتصادي مي‌باشد. اين مقاله در نظر دارد تاثير اين متغير را بر رشد اقتصادي مورد بررسي قرار دهد. براي اين منظور با به كارگيري اقتصاد سنجي داده هاي پانلي 42 كشور منتخب (42 كشور برتر در توليد علم )، در يك دوره 17 ساله (2012-1996)، اقدام به برآورد و آزمون مدل رشد ارايه شده در تحقيق نموده است. نتايج تحقيق نشان دهنده نامناسب بودن استفاده از داده هاي پانلي در برآورد مدل اين تحقيق، و البته تاثير معني دار و مثبت اين متغير بر رشد اقتصادي در برآورد مدل براساس داده هاي مقطعي كشورهاي مورد مطالعه است.

Extended Abstract Introduction Economic growth and development has always been being a major goal of policy makers. They have always been seeking factors to increase growth apos s speed. There are some different theories that each one, account of some kinds of factors related to the growth. In the earlier of growth literature theories, the focus was on two factors: physical capital and labor. But these theories couldn apos t exactly explain the persist gap between different countries ‘per capita income. This cause to more pay attentions to some other factors that are seemingly insensible in the first glance. The advent of knowledge oriented economics is the outcome of this new vision on growth field. In modern economics, knowledge is the main factor in production function. There are some indicators to measure the knowledge applied in a country apos s products. Economic complexity index is one of them. This article wants to examine the effect of this variable on economic growth. For this, the article examine growth model by using panel econometrics technique during 19962012 for 42 Chosen Countries (The top 42 countries producing science). The structure of this paper, in theoretical basis, is to examine the ideas of economists in the world and background research, furthermore, Economic Complexity Index and check out selected countries in this indicator, research and study models will be introduced. Final section will be devoted to analyzing the results and conclusions estimates. Theoretical Frame Work As seen in all of these theories, knowledge has always been considered the engine of economic growth in two ways on the production and consequently affects economic growth: as a new production or to increase the overall productivity of production factors. Since 2006, a group of authors began to explore the extensive economic growth based on the idea of quot the products quot and quot complexity economics quot were. The group apos s research led to the extraction of economic complexity Index (Hidalgo and other, 2007). Based on this thinking, the most important determinant of the level of development of each country, based on its knowledge of the country. Knowledge means of a stream of experiences, values, and attitudes expert information system is a framework to evaluate and take advantage of the experience and new information gives (APEC.2002). Methodology In this study, data from the first 42 countries in the production of knowledge is analyzed. Period of study 20121996 Ast years. Statistics variables above except the index of economic complexity, for all the countries in the study were collected from the site of the World Bank. In order to avoid bias said, be on the side of the complexities of economic and other factors affecting economic growth are properly specified model. According to the theoretical literature on the factors affecting economic growth, the most important variables affecting economic growth in the countries studied, per capita physical capital formation (CF), government size (GE), human capital (LE), the volume of trade (TRAD), in addition to the economic complexity index (ECI) growth models have been added. Econometric research process consists of three steps: 1. Crosssectional data unit root test 2. The accumulation of data on crossexamination, if not stationary variables In the case of cointegration vector accumulation estimate coefficients of the variables Results and Discussion As mentioned, according to the estimated model, the relationship between economic growth and negative economic complexity index, and this is unexpected. To investigate the cause of the issue, again a model for cross cutting (CrossSection) estimated that the estimated model, the relationship between these two variables is positive and significant for the years after 2006. For a certain period and between different countries is presented. The results show using panel data technique is not appropriate, however crosssection estimation of model shows positive effect of Economic complexity index on economic growth. Conclusions Suggestions In advanced economies contemporary knowledge in the development and growth of the country apos s annual production (GDP), played a key role stems. Economic Complexity Index, an indicator for measuring the amount of product used in a country. In this study, the relationship between these indicators and economic growth top 42 countries in the production of a 17year period (20121996) and related techniques using panel data consists of panel unit root, panel Hm Anbashtgy , Has been studied. The results of research panel data on the contrary the index suggests a negative relationship with economic growth of the countries that represent the inappropriate use of panel data in this research model. But the results of crosssectional data model represents a significant and positive relationship between these variables on economic growth. Economy reaching a middleincome country in the world, due to the unlimited supply of natural production factors (eg oil and abundant labor force) and rely on a certain level of production and industries heavily on its user.