Innovation and growth: Evidence from Latin American countries

Avila-Lopez, Luis Alfredo; Lyu, Chaofeng; López Leyva, Santos Article Innovation and growth: Evidence from Latin American countries Journal of Applied Economics Provided in Cooperation with: University of CEMA, Buenos Aires Suggested Citation: Avila-Lopez, Luis Alfredo; Lyu, Chaofeng; López Leyva, Santos (2019) : Innovation and growth: Evidence from Latin American countries, Journal of Applied Economics, ISSN 1667-6726, Taylor & Francis, Abingdon, Vol. 22, Iss. 1, pp. 287-303, https://doi.org/10.1080/02102412.2019.1610624 This Version is available at: https://hdl.handle.net/10419/314062 Standard-Nutzungsbedingungen: Die Dokumente auf EconStor dürfen zu eigenen wissenschaftlichen Zwecken und zum Privatgebrauch gespeichert und kopiert werden. Sie dürfen die Dokumente nicht für öffentliche oder kommerzielle Zwecke vervielfältigen, öffentlich ausstellen, öffentlich zugänglich machen, vertreiben oder anderweitig nutzen. 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If the documents have been made available under an Open Content Licence (especially Creative Commons Licences), you may exercise further usage rights as specified in the indicated licence. https://creativecommons.org/licenses/by/4.0/ Journal of Applied Economics ISSN: 1514-0326 (Print) 1667-6726 (Online) Journal homepage: www.tandfonline.com/journals/recs20 Innovation and growth: evidence from Latin American countries Luis Alfredo Avila-Lopez, Chaofeng Lyu & Santos Lopez-Leyva To cite this article: Luis Alfredo Avila-Lopez, Chaofeng Lyu & Santos Lopez-Leyva (2019) Innovation and growth: evidence from Latin American countries, Journal of Applied Economics, 22:1, 287-303, DOI: 10.1080/02102412.2019.1610624 To link to this article: https://doi.org/10.1080/02102412.2019.1610624 © 2019 The Author(s). Published by Informa UK Limited, trading as Taylor & Francis Group. Published online: 09 Jun 2019. Submit your article to this journal Article views: 3425 View related articles View Crossmark data Citing articles: 16 View citing articles Full Terms & Conditions of access and use can be found at https://www.tandfonline.com/action/journalInformation?journalCode=recs20 Innovation and growth: evidence from Latin American countries Luis Alfredo Avila-Lopez a , Chaofeng Lyu b and Santos Lopez-Leyva c a Faculty of Accountancy and Administration, Universidad Autónoma de Baja California, Tijuana, Mexico; b Department of Economics, Southwestern University of Finance and Economics, Chengdu, China; c Faculty of Economics and International Relations, Universidad Autónoma de Baja California, Tijuana, Mexico ABSTRACT This paper aims to analyze the relationship between innovation and per capita economic growth over the long-run for 12 Latin American countries for the period 1996–2015. This study uses six different indicators of innovation. Using Granger causality test, the study finds the presence of unidirectional and bidirectional causalities between innovation and per capita economic growth. These results vary. Latin America is a diverse region, depending upon the types of innovation indicators that we use in the empirical investigation process. It is important to note that all these innovation indicators are considerably linked with per capita economic growth. ARTICLE HISTORY Received 11 November 2017 Accepted 28 March 2019 KEYWORDS Innovation; economic growth; Latin America; development 1. Introduction There is an extensive and long-standing support that innovation leads to economic growth (Beneki, Giannias, & Moustakas, 2012; Segerstrom, 1991; Verspagen, 2005; Wong, Ho, & Autio, 2005). This study is based on Maradana et al. (2017), and uses six different indicators of innovation and examines their relation to long-run economic growth: (1) Patents-residents (2) Patents-non-residents (3) Research and development expenditure (4) Researchers in research and development activities (5) High-technology exports (6) Scientific and technical journal articles Latin America lags in term of economic growth and innovation: Despite the recent, rapid economic growth experienced by several Latin American countries during the commodity boom, the fall in commodity export prices, including oil, coal, other minerals and agricultural products, has underscored the many competitiveness CONTACT Luis Alfredo Avila-Lopez [email protected] Faculty of Accountancy and Administration, Universidad Autónoma de Baja California, Calzada Universidad 14418 Parque Industrial Internacional Tijuana, Tijuana 22427, Mexico JOURNAL OF APPLIED ECONOMICS 2019, VOL. 22, NO. 1, 287–303 https://doi.org/10.1080/02102412.2019.1610624 © 2019 The Author(s). Published by Informa UK Limited, trading as Taylor & Francis Group. This is an Open Access article distributed under the terms of the Creative Commons Attribution License (http://creativecommons.org/licenses/ by/4.0/), which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited. challenges required for new growth sectors to emerge. Improvements could be made in many areas, and the skills and innovation gap ranks high on the list. Other areas for improvement include education, on-the-job training, scientific and technological investments by both government and business, and enhancing the innovation environment. (World Economic Forum, 2008) Some other regions, such as Eastern Europe or Asia, have experienced a recent economic growth, especially in the East Asian region (Hu, 2015); some authors attribute this growth to the process of turning imitation to innovation (Hobday, 1995; Mathews, 1995). According to Hu (2015), The economies that have made the leap from imitation to innovation in recent history are Japan, Korea, and Taiwan. Certain sectors and regions of mainland China are also in the process of accomplishing the same. A number of lessons emerge from this literature. (P.21) 2. Related literature We based the relationship between innovation and growth in the theory of endogenous growth addressed by Grossman and Helpman (1994). Literature specifies that innovation and new technology have emerged as one of the primary driving force of growth. Robert Solow described this perspective in his so-called “neoclassical growth theory”(Solow, 1979), Solow based his theory in standard neoclassical assumptions, such as perfect competition, maximizing behavior, no externalities, positive and decreasing marginal products and absence of scale economies. After this work, Solow added and exogenous term, labeled “technological progress”to allow for long-run growth in GDP per capita. Several works such as Aghion (1992) and Maradana et al. (2017) have also found technological innovation to be the main determinant of growth. In a research made by Mendoza (2017), the Mexican states that had a level of technological innovation in 1995, are those states that had faster growth. This result highlights not only the role of technological innovation as a growth factor economic but also it implies the fact that those regions that carried out greater activity of technological innovation are the regions that had a greater economic growth. That is, technological innovation has encouraged the economic divergence. Where does innovation come from? Traditionally scholars supported the idea of public sector. In this regard, Schumpeter (1961) suggests that technological advance is the result of a never-ending cycle of incoming innovative firms. However, some years later, Schumpeter (1975) moves his position from only firms to public policies. Martin and Scott (2000)imply the need to establish a long-term institutional framework for the support of basic research, generic-enabling research, and commercialization. The extent to which support should be directed to each area will vary with the sources of sectoral innovation market failure. Rudimentary efforts are also made to identify the dimensions of firms in terms of their resources rather than in terms of their products that can be sources of advantage, and to explain how combinations of competences and resources can be developed, deployed, and protected. Teece, Pisano, and Shuen (1997) refer to this as the “dynamic capabilities”approach in order to stress exploiting existing internal and external firm specific competences to address changing environments. Bureaucracy and lack of investment might be an obstacle to accomplish the dynamic capabilities approach, in particular, Fuentes and Mendoza (2003) attributed to public investment in 288 L. A. AVILA-LOPEZ ET AL. infrastructure, an important role as a brake on regional inequality. They find that in the period of convergence 1980–1985, the infrastructure Social status represents an important factor in reducing regional differences, not so in the case of the economic infrastructure. In the period of 1985–1998 divergence, find that the infrastructure variables are not significant, which is consistent with the change in the functions of the State, which now it encourages more the element of private investment. Scientific and technological knowledge and the ability to innovate are elements that contribute to increase the productivity and the standard of living of nations. International experience shows that developing countries rely increasingly on their ability to generate, absorb and transfer knowledge because that way goods and services with a higher added value enrich their development capabilities and position the nation in a global environment that is increasingly interconnected and competitive (CONACyT, 2014). The government’s role in economic development is decisive; Guisan (2017) appoints that R + D are important for economic development and quality of life, both in Humanities and Social Sciences (H + S) and in Natural Sciences and Engineering (NSE). In the Latin American region, some countries have not achieved a global integration, for example, Mexico’s exports dependency on the United States market and the relative importance of such exports in Mexico’s overall economic performance makes it highly susceptible to U.S. economy fluctuations (Villareal, 2010). 2.1. A. An outline of innovation in the Latin American countries Today, Latin America regions face important challenges to foster sustained economic growth, reduce poverty and improve the living standards of their population. In this context, promoting progress of innovation in the regions becomes a key priority (Olavarrieta & Villena, 2014). The Latin American economic integration process has been developed throughout the twentieth century making use of the following strategies: regional economic research, financing agencies, and trade agreements. It is important to note that research on regional economic issues and financing agencies are mainly funded by the United States, and that even though most of the countries are part of a trade agreement, the intraregional trade is not significant in any case (Olarte, 2016). According to Olavarrieta and Villena (2014), Latin America lags behind the more advanced economies in terms of innovative activities. This is not only at the output level, patent applications, high-technology exports (percentage of manufactured exports) and scientific and technical journal articles, including business research, but also at the input level, R&D expenditure (as percentage of the GDP) and researchers in R&D (per million people). Hence, it is not expected that this scenario will dramatically change at least in the short run. Previous works have addressed innovation–growth issues in two ways: The Regional disparities of innovation activities and economic growth in countries and the causality between both issues (see Maradana 2017; Pradhan, Arvin, Hall, & Nair, 2016). The six variables of innovation we use are as follows: (1) PAR: Number of patents filed by residents measured per thousand of population; (2) PAN: Number of patents filed by non-residents measured per thousand of population; JOURNAL OF APPLIED ECONOMICS 289 (3) RDE: Research and development expenditure measured as a percentage of real GDP; (4) RRD: Researchers in research and development activities measured per thousand population; (5) HTE: High-technology exports measured as percentage of real domestic product; and (6) STJ: Scientific and technical journals articles measured per thousand population. Table 1 shows the definition of the variables. Tables 2 and 3provide a general status of innovation indicators in the Latin American countries. The status of innovation regarding each indicator (PAR, PAN, RDE, RRD, HTE, and STJ) in the Latin American countries are examined in three different time periods from 1996 to 2015. These three periods are P1: 1996–2007, P2: 2007–2013 and P3: 1996–2013. In order to have an initial understanding of the variable, in Table 4 we present the descriptive statistics of these innovation variables, data forPAR,PAN,RDE,RRD,HTE,andSTJfromWorldDevelopmentIndicatorsofWorld Bank 2016. 4. Methods of study The methodology chosen in this study is based on Granger (1969). Other causality testing methods reported in the literature include the test proposed by Sim (1972) and the procedure suggested by Pierce and Haugh (1977). We empirically test the Table 1. Definition of variables. Variable code Variable definition GDP Per capita economic growth expansion of a country’s economy, expressed in per capita gross domestic product. PAR Patents filed by residents: expressed in numbers per thousand population. PAN Patents filed by non-residents: expressed in numbers per thousand population. RDE Research and development activities: expressed as a percentage of real gross domestic product. RRD Research and development expenditure expressed as a percentage of real gross domestic product. HTE High-technology exports: expressed as percentage of real gross domestic product. STJ Scientific and technical journal articles: expressed in numbers per thousand population. Source: Own elaboration based on variables defined in the World Development Indicators of World Bank. Table 2. Trend of innovation (per thousand population) in Latin American countries. PAR PAN RDE Countries P1 P2 P3 P1 P2 P3 P1 P2 P3 Argentina 0.029 0.027 0.028 0.107 0.097 0.103 0.409 0.517 0.451 Brazil 0.006 0.006 0.006 0.022 0.020 0.022 1.005 1.146 1.070 Chile 0.071 0.064 0.069 0.260 0.236 0.252 0.417 0.354 0.396 Colombia 0.027 0.024 0.026 0.097 0.087 0.094 0.173 0.205 0.186 Costa Rica 0.275 0.242 0.263 1.004 0.884 0.962 0.343 0.485 0.408 Ecuador 0.085 0.074 0.081 0.311 0.269 0.296 0.078 0.299 0.166 Guatemala 0.091 0.075 0.085 0.332 0.273 0.311 0.050 0.053 0.051 Mexico 0.010 0.009 0.010 0.038 0.034 0.037 0.349 0.431 0.380 Panama 0.354 0.303 0.336 1.293 1.109 1.229 0.300 0.141 0.245 Paraguay 0.202 0.177 0.193 0.740 0.646 0.707 0.076 0.067 0.072 Peru 0.042 0.037 0.040 0.152 0.137 0.147 0.111 0.111 0.111 Uruguay 0.331 0.325 0.329 1.211 1.189 1.203 0.284 0.365 0.314 Latin American panel* 0.127 0.114 0.122 0.464 0.415 0.447 0.300 0.348 0.321 PAR is the number of patents filed by residents, PAN is the number of patents filed by non-residents, and RDE is research and development expenditure. P1. Is 1996–2007, P2 is 2007–2013, P3 is 1996–2013. 290 L. A. AVILA-LOPEZ ET AL. relationship between innovation and per capita economic growth. Specifically, the causality between innovation and per capita economic growth can be addressed in four different ways: supply-leading hypothesis of innovation–growth nexus, demandfollowing hypothesis of innovation–growth nexus, feedback hypothesis of innovation– growth nexus, and neutrality hypothesis of innovation–growth nexus. We intend to test the following hypotheses: H°₁A: Innovation activities do not Granger-cause per capita economic growth. H1₁A: Innovation activities Granger-cause per capita economic growth. H°₁B: Per capita economic growth does not Granger-cause innovation activities. H1₁B: Per capita economic growth Granger-causes innovation activities. This study considers 12 of the largest Latin American economies according to the World Bank: Argentina, Brazil, Chile, Colombia, Costa Rica, Ecuador, Guatemala, Mexico, Panama, Paraguay, Peru, and Uruguay; we have omitted Venezuela due to the lack of data available. We use the GDP as a reference for our variables. The empirical investigation considers annual data over the period 1996 to 2015 which was obtained from the World Development Table 3. Trend of innovation (per thousand population) in Latin American countries. RRD HTE STJ Countries P1 P2 P3 P1 P2 P3 P1 P2 P3 Argentina 0.018 0.017 0.018 0.002 0.001 0.002 0.061 0.055 0.059 Brazil 0.004 0.003 0.004 0.001 0.000 0.000 0.013 0.012 0.012 Chile 0.045 0.041 0.043 0.005 0.002 0.004 0.148 0.134 0.143 Colombia 0.017 0.015 0.016 0.004 0.001 0.003 0.056 0.050 0.054 Costa Rica 0.173 0.152 0.166 0.025 0.011 0.020 0.572 0.504 0.549 Ecuador 0.054 0.046 0.051 0.014 0.006 0.011 0.177 0.153 0.169 Guatemala 0.057 0.047 0.054 0.019 0.009 0.016 0.189 0.155 0.178 Mexico 0.007 0.006 0.006 0.001 0.000 0.001 0.022 0.019 0.021 Panama 0.223 0.191 0.212 0.030 0.014 0.024 0.737 0.632 0.700 Paraguay 0.128 0.111 0.122 0.047 0.020 0.037 0.422 0.368 0.403 Peru 0.026 0.024 0.025 0.007 0.003 0.005 0.087 0.078 0.084 Uruguay 0.209 0.205 0.208 0.021 0.011 0.018 0.691 0.678 0.686 RRD is research and development activities, HTE are high-technology exports, and STJ is scientific and technical journal articles. P1. Is 1996–2007, P2 is 2007–2013, P3 is 1996–2013. Table 4. Descriptive statistics of the variables. Variables Countries PAR PAN RDE RRD HTE STJ Argentina 0.028/0.002 0.102/0.006 0.460/0.081 0.018/0.001 0.001/0.001 0.058/0.004 Brazil 0.021/0.001 0.006/0.001 1.070/0.086 0.004/0.001 0.001/0.000 0.012/0.001 Chile 0.068/0.005 0.249/0.017 0.395/0.043 0.043/0.003 0.003/0.002 0.142/0.010 Colombia 0.025/0.002 0.093/0.007 0.186/0.054 0.016/0.002 0.003/0.001 0.053/0.004 Costa Rica 0.260/0.023 0.950/0.085 0.407/0.099 0.164/0.015 0.019/0.009 0.541/0.048 Ecuador 0.080/0.008 0.292/0.029 0.166/0.139 0.050/0.005 0.010/0.006 0.166/0.016 Guatemala 0.083/0.011 0.305/0.041 0.050/0.010 0.053/0.007 0.015/0.006 0.174/0.024 Mexico 0.010/0.001 0.036/0.003 0.388/0.069 0.006/0.001 0.001/0.000 0.021/0.002 Panama 0.330/0.035 1.209/0.128 0.245/0.099 0.208/0.022 0.023/0.011 0.689/0.073 Paraguay 0.191/0.018 0.697/0.067 0.072/0.011 0.120/0.011 0.035/0.016 0.398/0.038 Peru 0.040/0.003 0.145/0.011 0.110/0.020 0.025/0.002 0.005/0.002 0.083/0.006 Uruguay 0.328/0.005 1.200/0.017 0.315/0.073 0.207/0.003 0.017/0.008 0.684/0.010 PAR is the number of patents filed by residents, PAN is the number of patents filed by non-residents, and RDE is research and development expenditure, RRD is research and development activities, HTE is high-technology exports, and STJ is scientific and technical journal articles, and GDP is per capita economic growth. Values reported here are natural logs of the variables. JOURNAL OF APPLIED ECONOMICS 291 Table 5. Results of unit root test. PAR PAN RDE RRD HTE STJ GDP Countries LD/FD LD/FD LD/FD LD/FD LD/FD LD/FD LD/FD Argentina −106.749***/-7.408*** −106.663***/-7.730*** 1.345/-3.807*** −106.086***/-7.246 *** −1.002/-4.625*** 1.345/-3.807*** −3.159 ***/-5.712*** Brazil −20.553***/-7.297*** −20.540***/-8.481*** 1.474/-3.439 *** −20.681 ***/-6.067*** −0.897/-2.971*** −20.593***/-8.191*** −2.598**/-6.841*** Chile −52.708***/-13.689*** −52.750***/-13.678*** 0.840/-6.345 *** −52.814***/-13.655 *** −52.708***/-13.689*** −52.708***/-13.689*** −2.270**/-5.073 *** Colombia −27.399***/-23.080*** −27.412 ***/-24.030 *** −0.007/-4.578 *** −27.440 ***/-20.578 *** −2.068 **/-2.553** −27.411***/-23.537 *** −1.773*/-5.206*** Costa Rica −16.622***/-11.389*** −16.623***/-11.384*** 0.837/-3.208*** −16.625***/-11.370*** −8.687***/-1.736* −52.753***/-13.662*** −2.270 **/-5.073*** Ecuador −49.121***/-8.542*** −49.103***/-8.558*** 0.630/-1.278*** −49.189***/-8.586*** −1.488/-3.080*** −49.095***/-8.555*** −2.668***/-5.78*** Guatemala −71.729***/-16.148*** −71.751***/-16.140 *** 0.008/-1.988*** −4.717***/-3.120*** −4.717 ***/-3.120*** −71.749***/-16.131*** −1.731*/-4.400*** Mexico −38.383***/-3.208*** −3.222***/-38.381*** 2.195/-3.000*** −37.975***/-3.146*** −4.360***/-3.792*** −38.412***/-3.241*** −3.276***/-6.322*** Panama −55.657***/-20.072*** −55.653***/-20.096*** −1.357/-3.963*** −55.644***/-20.161*** −7.894***/-1.591 −55.652***/-20.096*** −1.185/-3.829*** Paraguay −21.794***/-11.173*** −21.795***/-11.171*** 0.595/-3.659*** −21.793***/-11.169*** −1.081***/-2.598** −21.796***/-11.171*** −4.011***/-7.850*** Peru −57.323***/-4.883*** −57.277***/-4.895*** 1.331/-1.129 −57.202***/-4.867*** −2.590**/-1.896* −57.283***/-4.890*** −1.815*/-8.095*** Uruguay −6.032***/-2.212** −6.032***/-2.213** −0.980/-2.428** −0.980/-2.428** −6.032**/-2.213** −6.032***/-2.213** −1.725*/-4.164*** PAR is the number of patents filed by residents, PAN is the number of patents filed by non-residents, and RDE is research and development expenditure, RRD is research and development activities, HTE is high-technology exports, and STJ is scientific and technical journal articles, and GDP is per capita economic growth. The investigation is done at three levels: (1) no trend and intercept, (2) with intercept, and (3) with both intercept and trend. The results are more or less uniform; however, the reported statistics in the table present the ADF statistics at no trend and no intercept. *Statistical significance at 1% level; **statistical significance at 5% level; ***statistical significance at 100% level. 292 L. A. AVILA-LOPEZ ET AL. Table 6. Results of Johansen–Juselius co-integration test (Max test). Co-integration with GDP Countries PAR PAN RDE RRD HTE STJ Argentina 12.266/3.971* 15.198*/5.702* 11.886/3.683 10.813/6.803* 14.122*/3.832* 15.919*/6.102* Brazil 6.358/0.000 17.056*/0.067 22.284*/5.316* 9.649/0.019 7.460/2.585 10.906/.142 Chile 12.543/3.368 11.244/4.218* 15.028*/1.609 12.20,409/3.262 Colombia 20.286*/9.284* 49.092*/11.399* 13.044/9.441* 17.041*/11.595* 15.308*/1.024 42.039*/10.245* Costa Rica 47.130*/11.195* 49.092*/11.281* 46.614*/11.258* 20.436*/1.640 −52.753*/-13.662* Ecuador 41.284*/16.688* 42.595*/16.855* 47.069*/16.767,225* 6.125/0.436 42.521*/16.879* Guatemala 53.008*/8.984* 58.176*/9.156* 7.398/0.0 58.959*/9.113* 14.468*/0.261 56.275*/9.258* Mexico 27.770*/9.7163* 38.617,959*/10.084* 14.744*/8.167* 12.800/9.637* 17.374*/8.996* 37.447,046*/9.959* Panama 55.452*/8.626* 54.107*/8.562* 13.457/4.431* 56.168*/8.605* 14.060/0.294 −55.652*/-20.096* Paraguay 16.936*/10.668* 16.714*/10.709* 16.455*/10.726* 12.612/0.090 16.756*/10.720* Peru 20.929*/8.114* 25.540*/9.936* 130.775*/4.988* 19.872*/10.793* 15.940*/0.995 28.230*/10.126* Uruguay 8.807/.780 8.850/0.794 8.905/0.725 22.023*/2.593 8.838/0.797 Note: We observe statistical significance at 5% level * Indicates the statistical significance of the co-integrating vector and confirms the presence of co-integration between innovation and per capita economic growth. JOURNAL OF APPLIED ECONOMICS 293 Table 10. Results of test from error correction model. Ganger causality test between Countries RRD and GDP HTE and GDP STJ and GDP Short-run Long-run Short-run Long-run Short-run Long-run Argentina 0.80/4.06 NA/NA 1.61/8.21** NA/NA 0.59/3.02 −2.70**/-2.74** Brazil 1.07/5.47 −0.85/-2.98** 1.34/6.82* −2.51**/-3.44*** 1.34/6.86* 0.60/-3.19** Chile 1.03/5.26 NA/NA −2.91**/-1.5 1.67/8.54* 1.21/6.19 −2.34**/-3.05** Colombia 0.84/4.28 −3.42***/-5.49*** 0.76/3.87 −3.34***/-1.32 1.37/7.01* −3.08**/-4.13*** Costa Rica 2.00/10.21** −3.74***/-1.59 0.31/1.58 NA/NA 2.01/10.25** −3.74***/-1.68 Ecuador 4.64**/23.64 *** −1.89*/-2.27* 0.39/1.99 −3.63***/-2.13* 4.67*/23.81 *** −1.92*/-2.30* Guatemala 0.68/3.46 NA/NA 3.03*/15.44*** −1.97/-0.59 0.62/3.17 NA/NA Mexico 0.05/0.23 −1.98*/-3.90*** 1.17/5.99 NA/NA 0.05/0.25 −2.05*/-2.21* Panama 5.24**/26.71*** −0.49/-1.57 1.46/13.31* NA/NA 4.85**/24.73*** −5.03***/-1.43 Paraguay 3.53*/18.00*** −0.22/-0.25 0.15/0.77 −0.74/-3.34*** 3.51*/17.91*** −4.06***/-0.03 Peru 2.60/13.27*** NA/NA 3.41*/17.39*** −3.10**/-4.61*** 2.92*/14.89*** −3.15**/-0.68 Uruguay 3.37*/17.17*** −0.43/-2.88** 2.56/13.03*** −2.16*/-4.27*** 3.39*/17.28*** −0.44/-2.95** The short-run causality is detected through the Wald statistics, while long-run causality is detected through the statistical significance of error correction term. For both terms (PAR/PAN/RDE) innovation is the dependent variable. *Indicates the statistical significance at 5% level; ** indicates the statistical significance at 10% level. 300 L. A. AVILA-LOPEZ ET AL. Table 11. Summary of granger casualty test. Nature of Granger Causality between Countries Case 1 Case 2 Case 3 Case 4 Case 5 Case 6 PAR and GDP PAN and GDP RDE ad GDP RRD and GDP HTE and GDP STJ and GDP Argentina NEH FBH NEH NEH NEH FBH Brazil DFH NEH SLH DFH FBH DFH Chile NEH DFH DFH NEH FBH FBH Colombia FBH NEH FBH FBH SLH FBH Costa Rica FBH FBH NEH FBH NEH FBH Ecuador FBH FBH NEH FBH FBH FBH Guatemala NEH FBH NEH NEH FBH NEH Mexico FBH FBH NEH FBH NEH FBH Panama FBH FBH NEH FBH NEH FBH Paraguay FBH FBH NEH FBH DFH FBH Peru NEH FBH NEH NEH FBH FBH Uruguay FBH FBH NEH FBH FBH FBH Table 12. Summary of granger causality test results. Supply-leading hypothesis of innovation growth Demand-following hypothesis innovation–growth nexus Case 1 Case 2 Case 3 Case 1 Case 2 Case 3 Brazil Brazil Chile Chile Feedback hypothesis of innovation–growth nexus Neutrality hypothesis of innovation–growth nexus Case 1 Case 2 Case 3 Case 1 Case 2 Case 3 Argentina Argentina Argentina Brazil Chile Colombia Colombia Colombia Costa Rica Costa Rica Costa Rica Ecuador Ecuador Ecuador Guatemala Guatemala Guatemala Mexico Mexico Mexico Panama Panama Panama Paraguay Paraguay Paraguay Peru Peru Peru Uruguay Uruguay Uruguay Table 13. Summary of Granger causality test results. Supply-leading hypothesis of innovation growth Demand-following hypothesis innovation–growth nexus Case 4 Case 5 Case 6 Case 4 Case 5 Case 6 Brazil Brazil Colombia Paraguay Feedback hypothesis of innovation–growth nexus Neutrality hypothesis of innovation–growth nexus Case 4 Case 5 Case 6 Case 4 Case 5 Case 6 Argentina Argentina Argentina Brazil Chile Chile Chile Colombia Colombia Costa Rica Costa Rica Costa Rica Ecuador Ecuador Ecuador Guatemala Guatemala Guatemala Mexico Mexico Mexico Panama Panama Panama Paraguay Paraguay Peru Peru Peru Uruguay Uruguay Uruguay JOURNAL OF APPLIED ECONOMICS 301 According to previous studies mostly focus on indicating the supply-driven approach of innovation-growth nexus, in Table 13 we show that only Colombia have this nexus and particularly in case 5. In the Demand-following hypothesis innovation–growth nexus we found only Brazil and in particular in cases 4 and 6. In order to achieve innovation, it is necessary to implement regular evaluations regarding policy design and financing needs; imitate practices that in other countries are increasingly implemented to promote innovations; the governmental agencies responsible for the funding of S&T (Science and Technology) and innovation projects should develop monitoring and assessment systems based on qualitative and quantitative information and indicators; support programs as well as the expected outputs and outcomes should be highlighted at the outset. Since enterprises also foster and develop innovations, it is important for the government to promote effective loans among them. In order to create interest and facilitate loans, governments need to: (1) Promote low interest loans (2) Reduce bureaucracy and times (3) Constant evaluation of loans Government also has an important role in attracting capital from foreign firms. 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