Macroeconomics
Hadi Keshavarz; Ramezan Hosseinzadeh
Abstract
1- INTRODUCTION
Investigation of the importance and the impact of various factors on the economic growth of countries is crucial in short-term and long-term planning in various countries. Traditional theories and models of economic development only consider capital and labor as economic growth ...
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1- INTRODUCTION
Investigation of the importance and the impact of various factors on the economic growth of countries is crucial in short-term and long-term planning in various countries. Traditional theories and models of economic development only consider capital and labor as economic growth factors for nations and regions. Today, economists consider innovation, along with knowledge and technology, to be one of the fundamental variables in the economic development and development of countries.
Knowledge and innovation can generate social welfare in diverse regions and countries and contribute to achieving sustainable economic growth. In this regard, it is crucial to note that the path of innovation development varies across regions and countries, and that a distinct innovative geography is created based on these differences. The issue of inter-regional or inter-country spillover effects of various variables, such as innovation spillovers, is a second crucial aspect of economic growth and development planning in different regions or countries. Thus, innovation can impact both the economic development of the innovating country and the economic growth of neighboring countries with trade linkages to that country. Examination the spatial dimension of the problem will be crucial for determining how spillovers occur and their effectiveness in the innovation process as well as economic growth and development, whereas excluding inter-regional (inter-country) effects will bias the results and misleading results. On the other hand, considering the inter-regional (inter-country) effects of innovation and other variables in the model can help in the planning of regional development in different countries.
2- THEORETICAL FRAMEWORK
According to new theories, there are four distinct categories of innovation: product innovation, process innovation, organizational innovation, and marketing innovation. There is substantial evidence that various categories of innovation have distinct economic effects in countries. These differences are primarily attributable to variations in the level of pertinent externalities (spillovers) and the capacity of innovators to internalize the public benefits of these activities (fit). Thus, innovative knowledge penetrates the production process in two different ways. The first instance is when a company utilizes new technical knowledge developed during the production process. The second consequence is the spillovers of such knowledge. However, knowledge diffusion in other innovation institutions can only be observed once innovation and technology have reached a certain level.
The concept of knowledge spillover is closely associated with the correlation effect, where in the recipient of an innovation assimilates it to facilitate economic advancement. The spillover effect has the potential to yield beneficial outcomes by fostering innovation and facilitating economic progress, but it can also have negative consequences. The adverse impact of knowledge spillover primarily arises from external circumstances, as well as the inherent uncertainties and risks associated with research and development endeavors. Consequently, the inability of spillovers to fully realize the benefits of their research and development endeavors diminishes enterprises' motivation to allocate resources towards innovation. The positive impact of knowledge spillover is directed towards individuals or organizations that possess absorptive potential, enabling them to effectively assimilate and utilize sophisticated information and technology.
3- METHODOLOGY
The primary objective of the present study is to examine the direct and spillover effects of innovation on economic growth within the D8 group of countries during 2012 -2021. This investigation will be conducted through the utilization of a spatial econometric model. Spatial econometrics is widely regarded as a major development in the field of estimation, having emerged alongside the introduction of the "New Economic Geography (NEG)" theory. This technique is associated with the research conducted by Krugman (1991), Fujita, Krugman, and Venable (2001), as well as Venables and Puga (1998). The econometric models under consideration has the capability to incorporate both spillover and indirect impacts of variables, in addition to the direct effects that are typically addressed in classic econometrics.
4- RESULTS & DISCUSSION
Based on the results of the model, the direct effect of innovation index on economic growth has been positive and significant. Also, the indirect effects of this variable have been positive and significant. Therefore, it can be said that the amount of innovation in the studied countries has both domestic and international spillover effects (through the establishment of trade relations) on the economic growth of the countries.
5- CONCLUSIONS & SUGGESTIONS
Based on this, it is suggested that the studied countries pay special attention to the issue of innovation. Provide the necessary incentives to strengthen innovation in these countries, such as paying special attention to patents. Because having a patent is one of the motivating factors for innovation and further to achieve new technologies. This can be the basis for creating new processes in production, inventing new methods in countries. Paying attention to the spillover and indirect effects of innovation can also be very important. Based on this, it can be suggested that countries should pay attention to the fact that they prioritize the trade of goods with more knowledge (accumulation of knowledge and its transfer) in order to benefit more from the spillover effects of innovation. The higher the trade and especially the import of goods with knowledge and innovation, the countries can use the knowledge and innovation stored in these goods to strengthen knowledge and innovation within the country and economic growth will be strengthened.
Hashem Dadashpoor; Sadegh Saeidi
Abstract
Transition of economy towards knowledge-based economy highlighted innovation as a requirement for metropolitan regions. Innovation which is understood in broad sense to include product, process and organizational innovation in the firms as well as social and institutional innovation at the level of an ...
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Transition of economy towards knowledge-based economy highlighted innovation as a requirement for metropolitan regions. Innovation which is understood in broad sense to include product, process and organizational innovation in the firms as well as social and institutional innovation at the level of an industry, region and nation has assumed to play ever more central role in theories of economic development. On this basis, improvement on different types of innovation at different levels is assumed as a strategy for the development of metropolitan areas. In spite of this, studies show that only investment in R & D units cannot solely improve innovation. In the context of learning regions, this gap which is called innovation gap is filled by regional characteristics of firms. Accordingly, this article seeks to study the impact of regional learning variablesof firms on their innovative capacities. For this aim, it studies regional learning features namely commercial relations volume (vertical relations), feedbacks, horizontal relations with other firms, firm’s size, inter-firm competition, employing local skilled labor and relation with academic institutional infrastructure. In this p, effect of these regional learning variables on different types of innovation evaluated including innovation in the production of new products, innovation in improving quality of current products, innovation in improving production processes, innovation in marketing and innovation in the organization. Two groups of industrial firms (food and auto parts industries) were chosen as case study, and features of regional learning were studied and compared with each other, then, the effect of these features on innovative capacities was studied in all firms.
Methodology
On this basis, prepositions of the research are:
1) There is meaningful relation between regional learning variables in industrial firms of food and auto parts industries in Toos Industrial Township;
2) Regional learning variables influence innovation of food and auto parts industries locating in Toos Industrial Township.
A quantitative method was used to test these prepositions. First, required data for regional learning features and firms’ innovation were collected through half-structured interview with mangers. Then,
the regional learning features of the two studied industries were described and regional learning variables of these firms were compared, then, the effect of these variables on the regional innovation was examined. Collected data were analyzed using t-test for Equality of Means for comparing of groups of firms and correlation and linear regression for examining effect of regional learning on innovation. Statistical analysis was done using SPSS software.
Results and Discussion
Results show that firms are different in some components of regional learning and are equal in the rest. T-test for equality of means confirms the inequality with 90 percent level of significance only in variables of feedback providing and size of firms. The preposition was rejected regarding variables of commercial relations, receiving feedback, horizontal relations, competition among firms, employing local skilled labor, and relation with academic institutional infrastructure. Considering the second preposition, significance of affecting of some regional learning variables such as size of firms, competition among firms, employing local skilled labor and relation with academic institutional infrastructure on the innovation of the firms was confirmed; it was calculated that 44% of varieties of firms innovation was the result of difference in their regional learning variables (correlation coefficient = 0.67).
Model Unstandardized Coefficients Standardized Coefficients t Sig.
B Std. Error Beta B Std. Error
1 (Constant) .175 .670 .261 .796
Vertical relations -.045 .076 -.085 -.595 .556
Horizontal relations .094 .084 .150 1.122 .270
Size of the firms (number of employees) -.175 .078 -.312 -2.255 .031
Number of Competitor) .235 .085 .396 2.765 .009
Ratio local labor in the total labor .133 .078 .237 1.708 .097
Relation with academic institutions .097 .052 .250 1.856 .072
Providing feedback -.102 .063 -.227 -1.608 .117
Receiving feedback .001 .032 .006 .045 .964
Conclusion
Given the fact that the impact of regional learning variables on innovations of firm was confirmed in the case study, thus, the reinforcement of these characteristics seems to be necessary. Accordingly, since relation with scientific institutes highly influences firms’ innovation in the studied cases, paying especial care on relations of firms and academic institutes and providing necessary institutional infrastructures to strengthen this relation can positively influence innovation of firms locating in Toos Industrial Township. In addition, academic institutions and facilitated relation between them and industrial firms must be taken into consideration in development of third phase of Toos Industrial Township. It was confirmed that smaller firms have more innovative employees; accordingly, firms can add to their products’ competitiveness. Out-sending can make firms more specialized and innovative. Taking into account that the third phase of the Township is under preparation, allocating facilities for small and medium firms can add to innovation in township level and promote its status.
Based on observations, firms locating in the studied Industrial Township (in both groups) had low level of non-zero horizontal relations, which explains meaningful influence of these relations on firms’ innovation. Cooperation among industrial firms – either within official or non-official frameworks – and formation of horizontal relations among them are known as the most influential factors in firms’ innovation. These relations among firms can help their becoming more innovative and consequently more competitive.
Based on the present research, numerous competitors for a firm is an influential factor for its innovation. Thus, homogenizing industrial sites – in a way that variety of producers exist for each stage of production chain – is a useful strategy to improve firms’ innovation.
sadegh bafandeh imandoust; ali mofidi
Abstract
The GCI combines 113 indicators. These indicators are grouped into 12 pillars: institutions, infrastructure, macroeconomic environment, health and primary education, higher education and training, good market efficiency, labor market efficiency, financial market development, technological readiness, ...
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The GCI combines 113 indicators. These indicators are grouped into 12 pillars: institutions, infrastructure, macroeconomic environment, health and primary education, higher education and training, good market efficiency, labor market efficiency, financial market development, technological readiness, market size, business sophistication and innovation. These are in turn organized into three sub-indexes, in line with three main stages of development; basic requirements, efficiency enhancers and innovation and sophistication factors. In this paper, the effect of global competitiveness index (GCI) on economic growth has been studied. To this end, panel data of 42 countries collected in the period 2010 to 2014, and the model is estimated. The model estimation results show that the GCI score has positive and significant effects on GDP per capita growth among selected developed and developing countries.
Methodology
Competitiveness is defined as the set of institutions, policies, and factors that determine the level of productivity of a country. Many determinants drive productivity and competitiveness. Understanding the factors behind this process has occupied the minds of economists for hundreds of years, engendering theories ranging from Adam Smith’s focus on specialization and the division of labor to neoclassical economists’ emphasis on investment in physical capital and infrastructure and, more recently, to interest in other mechanisms such as education and training, technological progress, macroeconomic stability, good governance, firm sophistication, and market efficiency among others. While all of these factors are likely to be important for competitiveness and growth. In economic theory of stages of development, the GCI assumes that, in the first stage, the economy is factor-driven and countries compete based on their factor endowments— primarily unskilled labor and natural resources. Companies compete on the basis of price and sell basic products or commodities, with their low productivity reflected in low wages. Maintaining competitiveness at this stage of development hinges primarily on well-functioning public and private institutions (pillar 1), a well-developed infrastructure (pillar 2), a stable macroeconomic environment (pillar 3), and a healthy workforce that has received at least a basic education (pillar 4). As a country becomes more competitive, productivity will increase and wages will rise with advancing development. Countries will then move into the efficiency-driven stage of development, when they must begin to develop more efficient production processes and increase product quality because wages have risen and they cannot increase prices. At this point, competitiveness is increasingly driven by higher education and training (pillar 5), efficient goods markets (pillar 6), well-functioning labor markets (pillar 7), developed financial markets (pillar 8), the ability to harness the benefits of existing technologies (pillar 9), and a large domestic or foreign market (pillar 10). Finally, as countries move into the innovation-driven stage, wages will have risen by so much that they are able to sustain those higher wages and the associated standard of living only if their businesses are able to compete with new and unique products. At this stage, companies must compete by producing new and different goods using the most sophisticated production processes (pillar 11) and by innovating new ones (pillar12). The Global Competitiveness Index (GCI) has been used by the World Economic Forum to assess the level of productivity of an economy. Hall and Jones (1996) have shown that around 89 percent of the variation in GDP per capita is due to variation in the level of productivity. As a result, GDP per capita can be used as a proxy for the level of productivity of a country. The regress of the log level of GDP per capita on the GCI score reveals that about two-thirds of the variation in GDP per capita can be explained by the GCI. However, estimating a bivariate relation between the growth rate and the GCI would be a mistake. The reason for that lies in what economists call the “conditional convergence effect”, which posits that, all other things being equal, there is a natural tendency for poor economies to grow faster—a phenomenon known as conditional convergence. In other words, if all countries had the same investment and population growth rates and the same levels of productivity, then we should observe poor countries growing faster than rich ones. Conversely, if all countries had the same level of income, then those that were more competitive would experience higher rates of long-term economic growth. In reality, however, countries differ both in their levels of income and their levels of productivity, and therefore it is very hard to predict the relationship between the growth rate and the level of productivity with a bivariate correlation analysis that includes the initial level of income. Formally, in a growth convergence equation, the growth rate of GDP per capita of country is a positive function of the GCI score and a negative function of GDP per capita.
Results and Discussion
The model estimation results show that the GCI score has positive and significant effects on GDP per capita growth among selected developed countries and a %10 increase in a country’s GCI score would lead to an increase in the economic growth by 17.32588 percentage points. This amount is 15.49522 for selected developing and emerging countries. Results of this paper show that “net growth rate” against the GCI score, revealing a positive and strong correlation, which is consistent with the view that the GCI is a good proxy for the level of productivity or competitiveness of an economy.
