# Introduction he term competitiveness is related to productivity and quality gains resulting from an interaction of factors, internal and external to the company, that make economic production more efficient, such as infrastructure, education, health, innovation and macroeconomic policy. Thus, competitiveness can be seen as the sum of productivity and quality gains related to important factors for building companies competitive advantages and, consequently, contributing to the countries own development. For the World Economic Forum (WEF, 2017, p. 11), competitiveness "is the set of institutions, policies and factors that determine a country's level of productivity". Productivity is the element that sustains the economic prosperity of nations. Porter (1990Porter ( , 2003) ) argues that a country's competitiveness depends on its industry's ability to innovate, keep up to date, and achieve continuous productivity and quality gains. Thus, the wealth of nations and the quality of life of populations depend on the ability of companies to innovate and increase productivity gains permanently. For Krugman (1996aKrugman ( , 1996b)), competitiveness is defined as the ability to produce goods and services that meet the test of international markets, while maintaining high and sustainable income levels or, more generally, the ability to generate, being exposed to external competition, relatively high levels of income and employment. Esser et al (1994) argue that the concept of competitiveness involves four levels of variables that affect the competitive capacity of companies and countries, calling it systemic competitiveness, namely: micro level, which considers the ability of companies to increase revenues; meso level, which deals with industrial and regional competitiveness related to infrastructure and the ability to network and make improvements to innovation systems; macro level, related to national macroeconomic factors that affect companies' competitiveness, such as interest and exchange rates, trade and payment balance and public debt; and target level, related to the cultural factors of the country, such as the ability of society to reach consensus to achieve the jointly defined objectives. In addition, the authors consider it important for countries to be competitive so that they can acquire more markets and consequently higher income levels. Given the preponderant role of competitiveness in the economic performance of countries, it was necessary to understand the factors that determine the level of competitiveness of nations. In the meantime, since 2004, the World Economic Forum (WEF) has developed a methodology for measuring the level of competitiveness of countries. The assessment is based on a nation's level of competitiveness, using the Global Competitiveness Index (GCI), which is published annually and contains a ranking among countries, as a parameter. The purpose of the report is to identify the factors that determine a nation's economic growth and development by trying to explain why some countries can grow more than others. With a focus on long-term economic performance, the Global Competitiveness Index combines a set of variables that are relevant to determining a country's prosperity. These variables are grouped into twelve pillars and divided into three nonindependent sub-indices. They are: basic requirements (institutions; infrastructure; macroeconomic stability; health and primary education); efficiency enhancers labor market efficiency; financial market sophistication; technological readiness; market size); and innovation and sophistication factors (business sophistication; innovation) (note 1) . According to WEF (2017), this division is important because it allows specifying in which areas a particular country needs to improve. In this context, the objective of this paper is to verify the relationship between competitiveness and economic growth from the Global Competitiveness Index. Therefore, a model is used in which it is assumed that efficiency enhancers and factors of innovation and sophistication depend on the evolution of basic requirements in previous periods. An application of the model is developed using the Pooled Ordinary Least Squares (POLS) and Fixed Effects (FE) methods. Evidence suggests that GCI competitiveness indicators are positively correlated with countries' economic performance. Moreover, they point out that the growth rate of the level of economic activity is a function of the current and lagged growth rate of two basic requirements. In addition to this introduction, the paper is further subdivided into four sections. The second section presents the concept of competitiveness of the World Economic Forum from the 12 pillars, as well as relates each pillar to the countries' economic growth. The third section develops the model. The fourth presents the database. The fifth exposes and discusses an application of the model. And the sixth section brings the final remarks. # II. # Economic Growth Driven by the Global Competitiveness Index: Theoretical aspects According to WEF (2017), a country's competitiveness is a set of 12 pillars, structured in three groups. The first group is related to the basic requirements of (i) institutions, (ii) infrastructure, (ii) macroeconomic stability, (iv) health and (v) primary education. The second group represents the sources of efficiency -(vi) higher education, (vii) commodity market efficiency, (viii) labor market efficiency, (ix) financial market development, (x) technological readiness, size and sophistication of the financial market. The third group includes factors of (xi) innovation and (xii) business sophistication. Pillars are important for all economies; however, due to the different stages of development of countries, they affect them in different ways. Basic requirements are crucial for countries that are still in the factor-oriented stage, and efficiency enhancers are important for countries that have progressed in the efficiency-oriented stage. The factors of innovation and sophistication affect countries at the innovation stage. All countries between two of the three stages can be considered in transition. For each of the 12 pillars of a country's competitiveness, there is empirical evidence of its impact on economic growth. The quality of a country's institutions (i), which can be determined by the legal framework in which individuals, businesses and governments interact to generate wealth, has been proven to be a factor in economic growth in several studies (eg, Acemoglu et al (2002); North 1989;Rodrik et al (2002). According to Miller et al (2014), public institutions can impose significant economic costs on companies and slow down the process of economic development (eg, excessive bureaucracy, over-regulation, corruption, dishonesty in dealing with public procurement, lack of transparency, inability to provide appropriate business services, inadequate management of public finances, and political dependence on the judiciary.) In addition to public institutions, good governance of private institutions and maintaining the trust of investors and consumers are also important elements of the process of generating wealth (ZINGALES, 1998). The quality and breadth of infrastructure networks (ii) that integrate the domestic market and connect it at low cost to markets in other countries, allow companies to market their products and services securely and timely, enable a fast and cheap flow of information, determine the location of economic activities, facilitate the movement of workers, prevent interruptions and shortages of energy supply, among others. It's positive impact on economic growth has been identified, for example, by Canning and Pedroni (1999) and Calderon and Serven (2004). Although Fischer 1993 found only weak effects of macroeconomic stability (iii) on productivity and growth, there is evidence of its impact on short-term economic activity. For example, the positive impacts of low and moderate inflation levels are studied by Goodfriend (2007) and Temple (2000), the impacts of government debt levels are examined by Reinhart and Rogoff (2010) and the tax structure and the way the government spends money are analyzed by Johansson et al (2008), among others. Healthy workers are vital to a country's productivity. Thus, investment in the provision of health services (iv) is a factor of economic development and growth (SACHS, 2001). The amount and quality of basic education (v) received by the population increases worker efficiency and contributes to the creation or execution of innovations. Secondary and tertiary enrollment rates, as well as the quality of higher education (vi), are also key factors for economies wishing to move up the value chain (KRUEGER AND LINDAHL, 2001). Commodity market efficiency (vii) is related to producing the right mix of products and services, given a country's specific supply and demand conditions, as well as the effectiveness of trade with these products (WEF, 2017). The best environment for commodity exchange requires a high level of competition in the market and a minimum of government intervention that hinders commercial activities (BRANSTETTER ET AL, 2010). Opening up to international competition via trade and investment allows a country to improve productivity, expand its most productive local industries, and access more advanced knowledge and technologies from abroad (DELGADO ET AL, 2012). A positive relationship between openness and prosperity was found by Alesina et al (2005); Baldwin (2003); Dollar and Kraay (2003) among others, as well as the positive influence of trade on knowledge transfer and innovation in a country (BRANSTETTER, 2006). Market efficiency also depends on demand conditions, such as customer orientation and buyer sophistication (PORTER, 1998). More demanding customers force companies to be more innovative and customer-oriented and thus impose the discipline necessary for market efficiency. To achieve labor market efficiency (viii), workers must be allocated to their most effective use in the economy and given incentives to invest their best efforts in their jobs. Thus, the labor market creates support for economic growth if it is flexible enough to move workers from one economic activity to another quickly and at low cost, and to allow wage fluctuations without much social disruption (KAPLAN, 2009). Efficient access to capital (ix) is important for companies to make the long-term investments needed to increase productivity levels (LEVINE, 2005). Financial market development is reflected in the allocation of financial resources to business or investment projects with the highest expected rates of return rather than politically connected ones. To fulfill these functions, the financial market needs appropriate regulation to protect investors and other actors in the economy. For an economy to thrive, it is important to be agile in adopting technologies to increase the productivity of its industries (BARRO AND SALA-IMARTIN, 2003). Thus, contemporary technological readiness (x a ) is reflected in the access and use of information and communication technology (ICT). Market size (x b ) affects productivity through opportunities to achieve economies of scale. In the age of globalization, international markets have become a substitute for domestic markets, especially for small countries. Thus, exports and participation in regional integration (which allows cheaper and simpler access to other markets) can be a substitute for domestic demand in determining the size of the market for companies in a country. The effects of international markets on the economic growth of countries are shown by Parteka and Wolszczak-Derlacz (2013). The positive impact of technological innovation (xi) (including innovation support institutions and policies) on productivity has been empirically proven by Grossman and Helpman (1991) and Furman et al (2002). According to Romer (1990), technological innovation is particularly important for economies that can no longer improve their productivity simply by integrating and adapting exogenous technologies. Business sophistication (xii) is concerned with the quantity and quality of local suppliers, service providers and institutions and the extent of their interactions. The companies' advanced operations and strategies (brands, marketing, distribution, advanced production processes and unique and sophisticated product production) spread throughout the economy and lead to sophisticated and modern business processes in the country's business sectors, which contributes to higher productivity (WEF, 2013). Bloom and Van Reenen (2007) confirm the importance of business operations and productivity strategies. # III. The Model ?? ?? = ?? ?? ?? (1) Where ?? is the elasticity of the output rate relative to the competitiveness growth rate. However, according to the Global Competitiveness Report, a country's competitiveness can be expressed in twelve pillars, divided into three sub-indices: basic requirements (B) efficiency enhancers (E) innovation and sophistication factors (I). Therefore, it is possible to represent the degree of competitiveness of a country in a given period of time t from equation 2: ?? ?? = ?? ?? ?? ?? ?? ? ?? ?? ??(2) According to WEF (2017), the twelve pillars of competitiveness are not independent, but sequentially interdependent, forming three stages/steps that countries must go through to become competitive and consequently achieve higher growth. The country starts at the first stage driven by its endowment of factorsmainly unskilled labor and natural resources. However, as the country develops the basic requirements indicators, it becomes competitive, allowing it to reach the developmental stages towards efficiency and innovation, respectively. Thus, there is a relationship of dependence of one stage on the other. For a country to enter stage 2, there is a need for deep development of the stage 1 pillars. For a country to enter stage 3, there is a need for improvements to the stage 1 and 2 pillars. Stage 1, being the most basic, is the one that contains the most relevant pillars, which will provide the evolution of the other pillars contained in stages 2 and 3. Therefore, based on WEF (2017), it appears that the performance of a country in efficiency enhancers and factors of innovation and sophistication in a given period t, is a function of the growth rate of lagged basic requirements, ð??"ð??" ?? ????? and ð??"ð??" ?? ????? . ?? ?? = ???? ????ð??"ð??" ?? ?????(3) ?? ?? = ???? ???? ð??"ð??" ?? ????? (4) ?? ?? = ð??"ð??" ?? = ?? ???? = ??ð??"ð??" ?? ?????(5)???? = ð??"ð??" ?? = ???? = ??ð??"ð??" ?? ?????(6) Substituting equation ( 2) into equation ( 1), one can rewrite the product as a function of the basic requirements, efficiency enhancers, and innovation and sophistication factors (7): ?? ?? = ??? ?? ?? ?? ?? ? ?? ?? ?? ? ??(7) Log linearizing (7) and deriving from time, we have the product growth rate, as a function of the growth rates of the basic requirements, the efficiency enhancers and the innovation and sophistication factors (8): ð??"ð??" ???? = (????)ð??"ð??" ???? + (???)ð??"ð??" ???? + (????)ð??"ð??" ????(8) Substituting ( 5) and ( 6) for ( 8), the product growth rate is a function of the current and lagged growth rate of the basic requirements (9): ð??"ð??" ???? = (????)ð??"ð??" ???? + (?????)ð??"ð??" ??????? + (??????)ð??"ð??" ???????(9) IV. # Data Base For the application of equation 9, data were considered for 105 countries in the period from 2006 to 2017. As a proxy for economic performance, we used the Gross Domestic Product at constant 2010 prices, present in the World Bank database. For information on basic requirements, efficiency enhancers, and innovation and sophistication factors, we used the scores of the respective indices presented in the Global Competitiveness Reports of the World Economic Forum. From table 1, it is observed the impact that the increase of the score in the basic requirements sub-index would have in the two other sub-indices. Overall, it is noted that a positive 0.36 change in the average of the basic requirements sub-index score over a 2-year average yields an average increase of 0.15 point in the efficiency stimulant sub-index over a medium range 3.86 years, and a 0.18 point increase in the sub-index of innovation and sophistication factors over an average range of 4 years. In addition, the data show that 81 countries have improved in either or both sub-indices (efficiency drivers and innovation and sophistication factors) following the prior development of the basic requirements sub-index. Stage 1 countries had an average increase of 0.1 point in 4 years in the second sub-index and an average increase of 0.2 point in the third sub-index in 4.25 years, with emphasis on four countries -Cameroon, Nepal, Tajikistan and Zambia -, which had the most significant evolution. For countries in the transition from stage 1 to stage 2, they had an average improvement of 0.18 points over 4.1 years in the efficiency stimulators subindex, and an average evolution of 0.22 points over 3.2 years in the sub-index of innovation and sophistication factors. From this group of countries, the most evolving in the period were: Algeria, Azerbaijan, Kazakhstan, Mongolia and the Philippines. Stage 2 countries had an average improvement of 0.17 point over 4.2 years in the second sub-index and a high average of 0.15 point over 5 years in the third sub-index. Noteworthy are Ecuador and Russia. Countries transitioning from stage 2 to stage 3 had an average increase of 0.17 points over 4 years in the efficiency drivers sub-index and an average evolution of 0.04 points over 4 years in the innovation and sophistication factors sub-index. The best performing nations were Oman, Panama and Turkey. Stage 3 countries had an average improvement of 0.14 points in 3.3 years on efficiency enhancers, and a high average of 0.22 points in 3.6 years on innovation and sophistication factors. Highlighting the development of Estonia, Qatar, the United Arab Emirates and the United States. Considering the stages of development, it can be said that the transition countries from stage 1 to stage 2 had the highest average evolution in the subindices of efficiency enhancers and factors of innovation and sophistication, after a previous increase in the basic requirements sub-index. This confirms the arguments of the World Economic Forum that nations in this rating range already have improvements in the pillars of institutions, infrastructure, the macroeconomic environment, and health and primary education (pillars of the first basic requirements sub-index), enabling the subsequent pillars, that are responsible for the performance of the second and third sub-indices, to be developed. Therefore, there is a dependence on innovation, business sophistication, the goods, labor and financial markets, technological capacity, higher education, training and market size in relation to institutions, infrastructure, the macro environment, health and primary education. From the improvement of these last four pillars, there will be greater evolution of the pillars contained in the subsequent stages (subindices). V. # Application and Discussions To estimate the parameters of equation 9, we used Pooled Ordinary Least Squares and Fixed Effects methods for panel data. The use of the methods is justified by the fact that the first one works with the unfiltered variables, allowing a purer analysis of the relations, and second because it controls the bias of omitted variables, making the analysis more robust (note 2) The second column of table 2 presents the estimated coefficients by POLS for the ratios of equation 9 -Mod 1. Considering a two-year lag for the growth rate of the basic factors (note 3) , the estimates indicate that the activity level is positively related to current and time-lagged basic requirements, corroborating the implications of the model. That is, a positive 1% change in the growth rate of basic requirements in the current period, ð??"ð??" ???? , and the same two-phase lag, ð??"ð??" ?????2 , increases the growth rate of the domestic product, ð??"ð??" ???? , by 0.274% and 0.162%, respectively. As a comparison, two more exercises using the POLS method were implemented -Mod2 and Mod3. In Mod2, we estimate the effect of the growth rate of the basic requirements, ð??"ð??" ?????? , efficiency enhancers, ð??"ð??" ???? , and innovation and sophistication factors, ð??"ð??" ???? , on the GDP rate, ð??"ð??" ???? . The objective is to testthe current relationship between economic performance level and WEF subindices (2018). In the third exercise, Mod3, the growth rate of the two-phase lagged requirements, ð??"ð??" ?????2 , is added to Mod2. The purpose of this application is to verify how the significance and magnitude of the coefficients related to the current variables -ð??"ð??" ???? , ð??"ð??" ???? and ð??"ð??" ???? -change by adding the conclusion of the model -the basic requirements are the main determinants of the efficiency enhancers and the power innovation factors and sophistication of countries in subsequent periods. In the third column of Table 2, the estimated coefficients for Mod2 indicate that the growth rate of the basic requirements, ð??"ð??" ???? , and the efficiency enhancers, ð??"ð??" ???? , are significant at 99% confidence and present signal as proposed by the Forum. Economic growth, that is a positive 1% change in the growth rate of basic requirements and efficiency enhancers, raises the product growth rate by 0.124% and 0.163%, respectively. On the other hand, the growth rate of innovation and sophistication factors, ð??"ð??" ???? , is negative and not significant. The non-significance may be due to the joint dependence of this sub-index with the efficiency enhancers on time-lagged basic requirements, which makes ð??"ð??" ???? and ð??"ð??" ???? highly correlated (note 4) -Appendix 1. For Mod3 -fourth column of table 2 -, it is noted that both the growth rate of the current basic requirements, ð??"ð??" ???? , and lagged in two periods, ð??"ð??" ?????2 , are significant at 99% confidence and positive, that is , a positive change of 1% in ð??"ð??" ???? and ð??"ð??" ?????2 , raises the product growth rate by 0.263% and 0.163%, in due order. Otherwise, ð??"ð??" ???? and ð??"ð??" ???? are not significant to explain variations in product growth rate, ð??"ð??" ???? . The nonsignificance of the efficiency enhancer sub-indices and the innovation and sophistication factors have similar characteristics to the previous one, that is, since these indices are highly correlated with the time-lagged basic requirements, ð??"ð??" ?????2 , this may have captured the full effect, making ð??"ð??" ???? and ð??"ð??" ???? nonsignificant and reinforcing the outcome of the model that, at the limit, changes in the growth rate of countries are essentially explained by changes in the current and lagged rate of basic factors (institutions; infrastructure; macroeconomic stability; health and primary education). In order to make the predicted ratios of POLS estimates robust, the previous exercises are redone using the Fixed Effects method, with columns 5, 6 and 7 representing the modeling structures of columns 2, 3 and 4, respectively. The estimation of fixed effects equation 9 -Mod4 -confirms the results presented in column 2, that is, the positive values of the ð??"ð??" ???? and ð??"ð??" ?????2 coefficients indicate that the current and lagged rate of the basic requirements positively affects the activity level. Concerning columns 6 (Mod5) and 7 (Mod6), the evidence corroborates the estimates presented in columns 3 and 4, indicating that the basic requirements tend to attract the full effect on the activity level, making the coefficients of ð??"ð??" ???? and ð??"ð??" ???? . In general, both POLS and EF estimates corroborate the implications of the model, suggesting that in the limit the GDP growth rate is a function of the current and lagged rate of the basic requirements. # VI. # Conclusion This article sought to broaden the discussion between competitiveness and economic growth by focusing on the Global Competitiveness Report (GCR) developed by the World Economic Forum (WEF). A model was developed, in which the solution is that the growth rate of a country's level of economic activity over a given period of time is a function of the current and lagged growth rate of basic requirements. Using data for 105 countries from 2006 to 2017, six econometric exercises were performed to test the implications of the model. Based on the estimation coefficients, the evidence suggests that GDP growth rate and economic competitiveness growth are positively related, corroborating the studies by Canning and Pedroni (1999), Calderon and Serven (2004) Thus, for a country to achieve a satisfactory and sustainable level of economic performance, it is recommended that there be high investments in the basic requirements pillars, in order to allow the full performance of the other pillars referring to the most advanced stages of stimulators -efficiency, innovation and sophistication -in subsequent periods. 1Evolution -WEF (2006-2017) 2POLSEFExplanatory VariablesMod1Mod2Mod3Mod4Mod5Mod6\ModelCoefficient Coefficient Coefficient Coefficient Coefficient Coefficientð??"ð??" ????0.274***0.124***0.263***0.230***0.100***0.226***(0.000)(0.000)(0.000)(0.000)(0.001)(0.000)ð??"ð??" ????_20.162***-0.163***0.127***-0.127***(0.000)(0.000)(0.000)(0.000)ð??"ð??" ????-0.164***0.039-0.09***-0.005(0.000)(0.492)(0.018)(0.916)ð??"ð??" ????--0.0130.027--0.0140.033(0.691)(0.482)(0.667)(0.343)Constant0.015***0.018***0.015***0.016***0.019***0.015***(000)(0.000)(0.000)(000)(0.000)(0.000)Number of observations94511559459451155945Source: Own elaboration? p -value in parentheses with *** p <0.01, ** p <0.05, * p <0.1 Year 2020 © 2020 Global Journals Competitiveness and Economic Growth: A Model with Application to 105 Countries (2006 To 2017) ( E ) Note 3: Given the evidence that on average basic factors have an effect on innovation potential and efficiency over 2.5 years, it was decided to work across the application with the basic factor growth rate, ð??"ð??" ?? , lagged by 2 years. In addition, to corroborate the evidence of dependence on the Efficiency Stimulators (E) and Innovation and Sophistication Factors (I) in relation to the basic factors lagged in 2 periods, (B) a correlation test was applied (Appendix 1). The evidence is highly correlated. ## Note 4: In the process of parameter estimation, when there are highly correlated explanatory variables, usually one attracts every effect, rendering the others meaningless. * Reversal of fortune: Geography and institutions in the making of the modern world distribution of income DAcemoglu SJohnson Robinson J Quarterly Journal of Economics 117 4 2002 * Human Capital and Development DAcemoglu FGallego Robinson JA Annu. Rev. 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