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Linking National Systems of Innovation and Economic Growth under the Knowledge Economy Framework


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This dissertation applies the Knowledge Economy Framework developed by the World Bank as a means to assess the effect of National System of Innovation performance on economic growth.

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Linking National Systems of Innovation and Economic Growth under the Knowledge Economy Framework

  1. 1. Linking National Systems of Innovation and Economic Growth under the Knowledge Economy Framework With an overview of the Colombian case-study by Andrés Barreneche García B.Sc., University of the Andes (Colombia), 2008 A Dissertation Submitted in Partial Ful llment of the Requirements for the Degree of M A E Main Supervisor: Prof. Dr. Yoichi Koike Second Supervisor: Prof. Dr. Yongjin Park Ritsumeikan University Graduate School of Economics July 2010
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  3. 3. Linking National Systems of Innovation and Economic Growth under the Knowledge Economy Framework With an overview of the Colombian case-study by Andrés Barreneche García∗ B.Sc., University of the Andes (Colombia), 2008 Main Supervisor: Prof. Dr. Yoichi Koike Second Supervisor: Prof. Dr. Yongjin Park Abstract This dissertation applies the Knowledge Economy (KE) Framework developed by the W B as a means to assess the e ect of National System of Inno- vation (NSI) performance on economic growth. The KE approach integrates the NSI concept as one of the four factors deemed to enhance economic output in terms of knowledge creation, di usion and adaptation; these are: the Economic Regime, the Innovation System, Education and Information and Communica- tion Technologies. This framework is employed for an empirical study about the connection between KE variables and economic growth with a sample of 75 coun- tries (developed and developing) in the [1998, 2007] period. This work concludes that higher NSI performance, as a function of foreign technology transfer (man- ufactures imports and FDI) and knowledge appropriation (R&D expenditure and high-technology exports) variables, is conducive to superior increments of GDP. Furthermore, this dissertation advances the discussion of the Colombian case- study and diagnoses that the country has failed to harness opportunities of foreign technology transfer as a consequence of the disjunction between NSI actors.∗ E-mail: iii
  4. 4. “We have the good fortune to live in democracies, in which individuals can ght for theirperception of what a better world might be like. We as academics have the good fortune to be further protected by our academic freedom. With freedom comes responsibility: theresponsibility to use that freedom to do what we can to ensure that the world of the future be one in which there is not only greater economic prosperity, but also more social justice.” Joseph Stiglitz. Nobel Prize Lecture, December 8th, 2001. iv
  5. 5. AcknowledgementsI would like to thank:Professor Yoichi Koike, for his guidance, support, helpful comments, patience, and for giving me the opportunity to cultivate my ideas while keeping me on track.Ritsumeikan University: professors, sta members and colleagues, for contribut- ing towards a gratifying academic experience in Japan.The Government of Japan (MEXT), for funding me with a scholarship.Alejandro Hoyos Suárez, for his friendship and for providing me with thought- ful advice in the writing of this dissertation and through my studies in the master’s program.My family: my parents Juan José Barreneche Silva and Maria Cristina García de Barreneche and my brother Alejandro Barreneche García, for the uncondi- tional love they have provided me in spite of the thousands of kilometers that have separated us.Sebastián Perez Saaibi, for his encouragement and unquestionable companionship as a fellow Colombian expatriate. v
  6. 6. ContentsAbstract iiiAcknowledgements vTable of Contents viList of Tables viiiList of Figures ix1 Introduction 12 Background 3 2.1 Endogenous Growth and Innovation . . . . . . . . . . . . . . . . . . 3 2.2 The ‘National System of Innovation’ Approach . . . . . . . . . . . . 5 2.2.1 Empirical Studies of NSI . . . . . . . . . . . . . . . . . . . . . 8 2.3 Leveraging on the Knowledge Economy Framework . . . . . . . . . 9 2.4 Research Hypothesis . . . . . . . . . . . . . . . . . . . . . . . . . . . 123 Data and Methodology for Analysis 14 3.1 Data . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 14 3.1.1 Rationale for Variable Selection . . . . . . . . . . . . . . . . . 15 3.2 The Construction of KE Pillar Indices . . . . . . . . . . . . . . . . . . 19 3.3 Speci cation of Models . . . . . . . . . . . . . . . . . . . . . . . . . . 224 Results and Evaluation 25 4.1 KE Pillar Indices vs GDP per Capita . . . . . . . . . . . . . . . . . . . 25 4.2 Econometric Analysis . . . . . . . . . . . . . . . . . . . . . . . . . . . 295 An Overview of the Colombian National System of Innovation 35 vi
  7. 7. 5.1 Background . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 35 5.2 Current Status . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 38 5.3 Prospects . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 40 5.4 Recommendations . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 436 Conclusions 47A Constructed KE Pillar Indices 50B Statistical Tests 54 B.1 Endogeneity: Durbin-Wu Hausman Test . . . . . . . . . . . . . . . . 54 B.2 Heteroskedasticity: White Test . . . . . . . . . . . . . . . . . . . . . . 55C Estimations with GDP per Capita as the Explained Variable 56 C.1 Speci cation of Models . . . . . . . . . . . . . . . . . . . . . . . . . . 56 C.2 Results . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 57Bibliography 58 vii
  8. 8. List of Tables Table 3.1 Summary Statistics for the Selected KE Variables . . . . . . . . 16 Table 3.2 The Constitution of the KE Pillar Indices . . . . . . . . . . . . . 21 Table 3.3 Summary Statistics for the Regression Variables . . . . . . . . . 22 Table 3.4 Econometric Analysis: Model De nitions . . . . . . . . . . . . 23 Table 4.1 OLS Regression Results for GDP Growth. . . . . . . . . . . . . 30 Table 5.1 Detailed Innovation System Indicators for Colombia . . . . . . 43 Table 5.2 Retrospective Estimates for Colombia’s GDP Growth (Model 4) 45 Table A.1 KE Pillar Indices for [1998, 2002] . . . . . . . . . . . . . . . . . 50 Table A.2 KE Pillar Indices for [2003, 2007] . . . . . . . . . . . . . . . . . 52 Table B.1 GDP Growth OLS Regressions with added Proxy Residuals . . 54 Table B.2 White Test Summary Results . . . . . . . . . . . . . . . . . . . . 55 Table C.1 Model De nitions for GDP per Capita . . . . . . . . . . . . . . 56 Table C.2 OLS Regression Results for GDP per Capita . . . . . . . . . . . 57 viii
  9. 9. List of Figures Figure 4.1 GDP per Capita and Innovation System . . . . . . . . . . . . . 26 Figure 4.2 GDP per Capita and Economic Incentives . . . . . . . . . . . . 26 Figure 4.3 GDP per Capita and Governance . . . . . . . . . . . . . . . . . 27 Figure 4.4 GDP per Capita and Education . . . . . . . . . . . . . . . . . . 27 Figure 4.5 GDP per Capita and ICT . . . . . . . . . . . . . . . . . . . . . . 28 Figure 4.6 Marginal E ect of the Innovation System Scores (Model 4) . . 31 Figure 4.7 Regression’s Residuals vs Innovation System Scores (Model 4) 32 ix
  10. 10. Chapter 1Introduction has become understood to emerge through the interactions of a vari-I ety of agents such as rms, universities and governmental bodies. These actorsare considered to have particular roles in processes where knowledge is created,adapted, di used and incorporated into a speci c good or service. The synergiestaking place in a given country have been notably identi ed and studied by re-searchers using the National System of Innovation (NSI) concept. This comprehen-sion has provided tools for science, technology and innovation (STI) policy design.As follows, these instruments have been widely adopted by public administratorsfrom a diversity of countries, ranging from OECD founding members such as Franceand Finland, to developing countries like Korea and Brazil. The development of anNSI theory has been, however, hampered by the inherent di culties for empiricalanalysis. In contrast to other elds such as nancial economics, the interactionsinvolved in innovation are di cult to parametrize and thus analyze quantitatively.As a consequence, the impact of policies derived from the NSI concept has yet to befully understood. This dissertation leverages the Knowledge Economy (KE) Framework developedby the W B as a means to assess the e ect of NSI performance on eco-nomic growth. The framework centers on the following idea: the manner in whichapplicable knowledge is produced and owing within society is crucial for increas-ing economic output. The KE approach integrates the NSI concept as one of thefour components, referred as KE Pillars, deemed to enhance growth in terms ofknowledge creation, di usion and adaptation; these are: the Economic Regime, theInnovation System, Education and Information and Communication Technologies.This framework allows an empirical study about the connection between KE vari- 1
  11. 11. ables and economic growth for a sample of 75 countries (developed and developing)in the [1998, 2007] period. This analysis yields a signi cantly positive impact com-ing from the level of NSI performance, as a function of foreign technology transfer(manufactures imports and FDI) and knowledge appropriation (R&D expenditureand high-technology exports) variables, on increments of GDP. Furthermore, thisdissertation advances the discussion of the Colombian case-study and diagnosesthat the country has failed to harness opportunities of foreign technology trans-fer as a consequence of the disjunction between NSI actors. The organization ofdocument is described below.Chapter 2 elaborates on the main problem of concern: to establish a quantitative link between the NSI concept and economic growth. A hypothesized solution is proposed as an application of the KE Framework. The main concepts and the previous studies, in which this dissertation intends to build upon, are revised.Chapter 3 procures to describe the data and methodology selected to approach the hypothesis. The W B data for representative variables of the KE Framework is presented. It then explains how this information is prepared for an empirical study.Chapter 4 is where the the empirical analysis takes place. The gathered evidence is fully described and evaluated in detail.Chapter 5 reviews the Colombian case-study considering the current approach and the gathered evidence. It seeks to place the exposed link between the NSI approach and economic growth at the service of policy-making in the context of this particular country.Chapter 6 synthesizes the claims, the process for their validation and the ndings of this dissertation. It also mentions the recognized research opportunities for further development of the NSI concept and its potential applications. 2
  12. 12. Chapter 2Background chapter surveys the theoretical preliminaries and previous works requiredT to de ne the research hypothesis which aims to connect the NSI approachwith economic growth. On this account, three main subjects are covered. First, aconcise background of economic theory is presented regarding endogenous growth,in order to understand the role of innovation in the development of markets. Theconcept of a NSI is the second topic of discussion. The trajectory of this approach isdescribed through the exposure of representative studies, in which this dissertationis based on. Lastly, the KE Framework is introduced in order to lay the grounds forthe quantitative analysis that will be undertaken in the forthcoming chapters.2.1 Endogenous Growth and InnovationThe Solow model is signi cant for economic growth theory, not only because of itsrevealed ndings, but also due to the shortcomings that can be derived from it. Thismodel has brought a general understanding on how savings, population growth andtechnological progress a ect the level of an economy’s output and its growth overtime [Mankiw, 2006]. However, the standard implementation of this model cannotexplain the di erences between per capita production in high-income countries andthat of the least developed countries, or why the average growth of GDP per capitais much higher in the present time than 200 years ago [Jones and Manuelli, 2005].Total Factor Productivity has come as a way to recognize these di erences, althoughthe reasons behind them are still a matter of debate under this approach. Theselimitations of the Solow model have been the main inspiration for the subsequent 3
  13. 13. development of Endogenous Growth Theory (EGT). According to [Jones and Manuelli, 2005], EGT models have focused on the pro-duction and dissemination of knowledge, whether by inclusion under the assump-tions or directly in the system’s speci cation. Studies of this branch have arrived toa common conclusion: asymmetries in development rely on the di erences betweensocial institutions across time and countries (e.g. countries with inadequate protec-tion of property rights will grow at a slower pace). In the case of the basic Solowmodel, the role of knowledge (technology) was considered along with labor andcapital, but left unexplained and assumed exogenous. There are two main criticismsfor considering knowledge as an externality, which endogenous growth centeredin resolving: the mentioned di culty to explain observed long run di erences ineconomic growth and the fact that changes of productivity are a result of consciousdecisions made by socio-economic agents. Innovation1 is a process in which new products and services are developedthrough R&D activities that originate in market competition. This permanently on-going process results in technological progress which is, in turn, the centerpiece ofendogenous growth theory. Most economists accept technological change and inno-vation as the principal constituents of economic growth [Aghion and Howitt, 2005].In other words, growth can di cultly be sustainable in the absence of steady tech-nological improvements [Barro and Sala-i-Martin, 2003]. This idea is supported bythe fact that innovation has been integrated to many contemporary growth models.Paradoxically, innovation has only received scholarly attention until recent years.Furthermore, economic studies of innovation have been centered in microeconomics[Fagerberg, 2006]. However, a particular approach regarding the procurement of in-novation under the macroeconomic perspective has been developed based on theconcept of a NSI. 1 This work implicitly uses the following de nition of innovation. It is a product or servicewhich ful lls the following conditions: i. it contains a technological novelty either by new devel-opment, combination or application of one or more technologies; ii. it addresses a speci c mar-ket need; and iii. it generates pro ts, meaning the investment involved yielded positive returns[Escorsa, 1998]. 4
  14. 14. 2.2 The ‘National System of Innovation’ ApproachIn the eld of economics, the NSI concept gained attention with the arrival of the-ories which highlighted the role of technology, such as the EGT. The NSI approachhas received researchers’ interest due to its focus on the endogenous building of ca-pabilities for development and also because it provides a speci c role for governmentpolicy towards the technological catch-up process [Gancia and Zilibotti, 2005]. The connection between the NSI approach and economic growth is still ratherunexplored. This is mainly explained by two reasons. First of all, the measure-ment of innovation systems for practical purposes has been a matter of debate[Holbrook, 2006]. This issue is important because this dissertation aims to focusin quantitative rather than qualitative analysis; the measurement problem, alongwith its present resolution, will be discussed and illustrated with empirical studieslater on. Secondly, the NSI approach has been found insu cient to explain growthby itself, which is why this concept has been mainly used to study industrial de-velopment dynamics qualitatively [Lundvall, 2007]. For this limitation, it will bedescribed below how the KE Framework provides a robust platform in order to linkthe NSI concept to economic growth. The concept of a NSI began to be developed more than 20 years ago. Sincethen, it has been employed to analyze industrially advanced countries such as Nor-way, Sweden and Australia; to explain successful development case-studies in Asia:Japan, South Korea, Taiwan, China and India; and likewise in Latin America: Braziland Chile [Feinson, 2003]. Among this diversity of studies, the de nition of a NSIpresented below is commonly seen. It corresponds to the rst introduction of theterm in print by Christopher Freeman2 , in his book about the favorable outcome ofJapanese technological and economic policy in the 1960s and 1970s [Edquist, 1997]. De nition of National System of Innovation: “The network of institutions in the public and private sectors whose activities and interactions initiate, import, modify and di use new tech- nologies.” Source: [Freeman, 1987]. 2 According to Freeman, the rst person he heard use the expression of ‘National System ofInnovation’ was Bengt-Åke Lundvall. 5
  15. 15. Although being increasingly popular within scholars and policymakers, the NSIapproach has experienced complications since its birth. Generally, theories comewithin a speci c eld of science i.e. addressed and built by academics and scien-tists from a particular discipline which, to a certain extent, share similar interests,methodologies, terminology, etc. This has certainly not been the case for the NSIapproach. Attempts to de ne, describe and explain this concept have ascended froma variety of elds, such as engineering, economics and management. Furthermore,the study of NSI is not only decentralized in terms of discipline, but also geograph-ically: pertaining books and papers are being published worldwide. However, the exibility of the term can be seen as an advantage, as it allowsscholars to adapt the analytical tool for studying di erent contexts. The rst studyby Freeman mentioned above, which began popularizing the concept, was con-ducted to understand the successful development experience of Japan. Ever since,the NSI approach has been widely used to understand how knowledge is producedand applied in industrially advanced countries and how developing economies catch-up in this process. Some exemplary studies are depicted below. [Freeman, 1995] reports how, since the 1970s, empirical evidence has begun tobe gathered regarding R&D investment and innovation, particularly in in Japan, theUnited States and Europe. The data permitted to demonstrate how the success ofinnovation depends on R&D expenditure. Furthermore, not only the links between rms were found to have a critical importance, but also the external relationshipswith other types of institutions such as universities. In the 1950s and 1960s, theJapanese success was simplistically endorsed to product imitation and the importa-tion of foreign technology. However, when Japan’s exports started outperformingthose of the United States, this explanation turned inappropriate. This overcom-ing is associated with relatively higher levels of industrial R&D spending in Japan.Nevertheless, this factor does not o er a su cient explanation. The Soviet Unionand other Eastern European countries proved that dedicating R&D resources with-out any elaboration did not guarantee innovation, di usion and productivity gains.This elaboration refers to the linkages within the innovation system i.e. the impactof technical innovations in society depended on how suitable these are for domesticbusiness, combined with the e orts devoted by rms to adopt them. A more recent NSI case-study is Korea. [Feinson, 2003] states that this country’sexperience displays the bene ts of dynamic, responsive science policies towards the 6
  16. 16. technological catch-up process. By articulating the NSI agents, the Korean govern-ment was able to drive the transition from a subsistence farming economy to onein which technology is acquired, di used throughout the nation and employed infavor of innovation. Korea’s rst stage was to promote technology in ows. For thispurpose, the traditional path of promoting FDI and licensing was not followed. Al-ternatively, policy at this stage concentrated on establishing turnkey businesses.The steel, paper, chemical and cement industries were all founded in the formof turnkey factories, which were domestically expanded afterwards. Rather thanfostering licensing, policymakers opted for the promotion of import capital goodswhich embody technology; the importation of this kind of goods may have been themost productive method of technology transfer. At that time, Korea probably reliedmore on this channel than any other newly industrialized country. The second stagewas to assimilate the imported technology into its domestic production lines. Thiswas addressed by public funding and a series of incentives towards R&D, includingtax breaks and exemption from military services for key personnel. The third stageconsisted in an outward orientation in the form of export promotion policies. Theseincluded the liberalization of access to imported intermediate products, the facili-tation of banking loans for working capital destined to export-related investmentsand the elimination of restrictions to foreign capital. The NSI approach has also been used to evaluate STI policies in countries of theLatin American region. For example, [Holm-Nielsen and Agapitova, 2002] studiedhow Chile has increased its competitiveness due to a favorable macroeconomic en-vironment for STI. However, in this country, research institutions have remainedrather disjointed from the productive sector, which wastes potential for continu-ous product innovation and hinders increments of living standards. The analysissuggests that Chile needs to make its NSI more e ective in two main ways: bystrengthening the venture capital market and introducing more measures for pro-moting networking and cooperation between science and industry. This achieve-ments are deemed to increase the returns to R&D investments. The unconstrained aspect of the de nition for NSI has allowed a wide varietyof analyses. However, this exibility brings complications because just as a NSIdiverges between countries and regions, so does the approach from their analysts[Lundvall, 2007]. In this ambivalence and lack of consensus relies the criticismsof the NSI concept. In natural sciences, agreeing in strict de nitions is seemed 7
  17. 17. as crucial to allow scienti c progress. Modern economics is characterized for theemulation of this rigidity. This might be the reason why the NSI approach hasslowly penetrated economic theory. The components, attributes and relationshipsthat compose a NSI are tremendously di cult to quantify because the level of anal-ysis corresponds to that of an entire nation. To overcome for this limitation, thequanti cation of National Systems of Innovation has been focused not in its com-ponents but in the overall performance of the system [Carlsson et al., 2002]. Datarecording has recently begun in this respect, across a wide range of countries. Asfollows, concrete indicators regarding manufactures imports and exports, FDI, R&Dexpenditure can be used to measure how a given NSI is performing. This quantita-tive approach is undertaken in the KE Framework and employed here, as explainedin the forthcoming Section 2.3. However, it is pertinent to revise before how previ-ous works have pursued to identify statistical evidence for a relationship betweenNSI performance and economic growth. These studies re ect various importantlessons which are recurrently taken into account in this dissertation.2.2.1 Empirical Studies of NSI[Freeman, 2002] discusses the relevance of innovation systems for economic growthover the last two centuries. Based on this timespan, the study uses gathered indi-cators which describe NSI performance and aims to identify to which extent theirvariations resulted in faster or slower rates of growth. While labor and capital pro-ductivity are employed for the rst century, the second is analyzed using more spe-cialized indicators such as manufactures exports, information and communicationtechnologies expenditure and R&D personnel, among others. The analysis is ratherlimited by data availability. However, it argues that these indicators show a clearpattern of how, after the industrial revolution, the NSI approach can be employedquantitatively to understand the divergence in paths of economic growth. [Rodríguez-Pose and Crescenzi, 2006] analyzes the link between R&D invest-ment and patents with economic growth. The study focuses on the connection be-tween the e ciency of innovation systems and the geographical di usion of knowl-edge spillovers. With an econometric analysis, that includes only European coun-tries, the work highlights two main results. First, the interaction between researchand socio-economic institutions determined the potential for maximizing the capac- 8
  18. 18. ity to innovate. Secondly, proximity had an important role in allowing the di usionof economically productive knowledge and its impact in overall growth. [Krammer, 2008] executes a cross-country analysis for Eastern Europe to explorewhat enables countries to innovate more than others at a national level. As a proxyfor innovation, Krammer uses the number of international patents granted by theUS patent o ce. His results suggest R&D commitments and the ‘innovative tradi-tion’ were key for increasing the knowledge stock. Openness and the protection ofintellectual property rights determined higher international patenting, while struc-tural industrial distortions had a negative in uence in the quantity of patents. Lastly, [Fagerberg and Srholec, 2007] uses a broader set of data for studying therole of innovation within a set of capabilities for development. The work bases itsmethodology on a factor analysis that compromises 25 indicators and 115 countriesfrom 1992 until 2004. By this method, four types of capabilities were identi ed: thedevelopment of the innovation system, the quality of governance, the character ofthe political system and the degree of openness. Of these, the innovation systemand governance were found to be of noteworthy pertinence for economic growth.As it will be shown later in Chapter 3, this dissertation will particularly build uponthis study. In contrast, however, di erent indicators are selected, arranged andstatistically analyzed here, using the KE Framework. This method of analysis pointsto similar conclusions to those of [Fagerberg and Srholec, 2007]. In synthesis, the trajectory of the NSI approach has been more qualitative thanquantitative. This approach aims to explain the dynamics of industrialization, tech-nological catch-up and development. Empirical analysis has been made di cult dueto the problems for measuring innovation systems. However, researchers have pro-posed performance as a plausible scale of reference, which has allowed some studiesto emerge. Still, there is a need for more quantitative research to validate the NSIconcept. This dissertation adds to these e orts by turning to the KE Framework,described in the following section.2.3 Leveraging on the Knowledge Economy FrameworkThis section describes how the KE Framework integrates the NSI approach, comple-menting it in order to allow a robust analysis of its role towards economic growth. 9
  19. 19. For this purpose, this section includes relevant excerpts from the book BuildingKnowledge Economies: Advanced Strategies for Development [World Bank, 2009a];in this publication, the Wold Bank compiled its work on the KE Framework. The W B has addressed the KE Framework through its Knowledge forDevelopment (K4D) Program. This program contributes to the framework by pro-ducing publications and distributing those of third-party specialists in this eld ofstudy. Its aim is to promote the framework’s awareness among policymakers world-wide. Through the KE literature and the e orts for data compilation from the K4DProgram, the W B has constructed the KE Framework to analyze, studyand devise policy recommendations for knowledge-driven economic growth. The Knowledge Economy Framework... “...describes how an economy relies on knowledge as the key engine for growth. It is an economy in which knowledge is acquired, created, disseminated and applied to enhance economic development.” Source: [World Bank, 2009a]. The KE Framework, as depicted in its name, highlights the increasingly protag-onist role knowledge has in an economy. Countries worldwide, both industriallyadvanced and developing, have been recognizing know-how and expertise as criti-cal as other economic resources. Industrial production requires appropriate policiesthat re ect the current interconnected and globalized economic context. According to [World Bank, 2009a], the KE Framework rests on four pillars: theEconomic Regime, the Innovation System, Education and Information and Com-munication Technologies. These have been previously supported as foremost foreconomic development by an ample literature and empirical works. Their de nitionand pertinence are described below.Economic Regime. It is de ned as the set of economic and institutional incentives designed to promote an environment that permits knowledge creation, assim- ilation and di usion. This pillar covers a broad set of macroeconomic issues and policies, such as trade, nance and banking and governance. Due to this broadness, this pillar is sometimes divided into two sub-pillars, Economic In- 10
  20. 20. centives and Governance, to facilitate analysis3 . The former is related to how resources can mobilize within an economy, while the later deals with how the political circumstances and its stability provide an appropriate business climate. A favorable Economic Regime is required to obtain better policy results from the other, more functional pillars. Industrially advanced countries generally have solid institutions based on democracy and free markets. Governments promote the development of their institutional regimes by improving labor and nancial markets, and by strengthening governance (e.g. increasing the enforcement of contracts and controlling corruption).Innovation System. It consists of rms, research centers, universities, think tanks and other institutions within a given country, that import or produce knowl- edge and adapt technologies to the local context4 . STI activities require pub- lic support in an ample range of ways such as the funding of basic research and the facilitation of knowledge di usion. The latter is of particular impor- tance for developing countries, where knowledge and technology, the inputs for innovation, arrive from abroad in the form of FDI and manufactures im- ports, among other channels. Indigenous knowledge capabilities should also receive attention. The importance of this pillar relies on the empowerment for achieving desired social and economic outcomes through the application of knowledge.Education. This pillar is related to the human skills required for the acquisition and exercise of knowledge. The preparation of the labor force includes the primary, secondary and tertiary levels of education, vocational training and continuous learning. The focus on a given level of education depends on the country’s stage in economic development. A member from the Least Devel- oped Countries group should give more attention to primary education, as literacy and arithmetic skills are required before more advanced competences are gained. As the country’s economy grows, the relevancy of continuous 3 This sub-pillar distinction will be taken into account in this dissertation. In Chapter 4 the re-sults will show that it is important to analyze both Economic Incentives and Governance separatelythan as a whole. 4 W B ’s nomenclature omits the word ‘national.’ However, by comparing the de ni-tions of ‘Innovation System’ and NSI, it can be a rmed that both terms are concurrent. 11
  21. 21. learning increases, as this type of education is necessary for innovation re- sulting from the constant adaptation of knowledge. Education creates jobs, reduces poverty levels and increases empowerment. It is a fundamental pillar for the KE.Information and Communication Technologies (ICT). ICT encompass the types of technologies that enable the di usion of knowledge. ICT, bearing tele- phone, television, radio and Internet networks, are critical for the economies of today, based on globalization and information. These reduce transaction costs signi cantly by providing accessibility to knowledge. A strong ICT pillar allows rapid and reliable exchange of information within a country and across its borders. Recent advances are a ecting how knowledge is acquired, created, shared and applied, which has positively impacted manufacturing, trade, gov- ernance and education activities, among others. Regarding this pillar, policies consider telecommunication legislation, along with the investment required for building and capitalizing ICT through the socio-economic dimension.2.4 Research HypothesisThe problem for the measurement of innovation systems and the lack of a robustframework, mentioned in Section 2.2, must be solved for studying the connectionbetween NSI theory and economic growth. These issues are both addressed by theKE Framework i.e. by adopting the accountability of performance, as the achieve-ments of a given NSI are analyzed using indicators such as R&D expenditure andhigh-technology exports. The KE Framework also integrates NSI theory with theother three5 pillars mentioned above, which allows to study economic developmentfrom a knowledge-based perspective. With the concepts that have been revised up to this point, this dissertation’sresearch hypothesis is structured below. 5 Four (a total of ve KE Pillars), if Governance and Economic Incentives are considered asseparate pillars, as it would be the case later on in this dissertation. 12
  22. 22. Research hypothesis: “A positive connection between NSI performance indicators and eco- nomic growth can be quantitatively found under the KE Framework. This connection reveals challenges and opportunities for a developing economy such as Colombia, along with STI policy recommendations.” The relevancy of this hypothesis lies in three main aspects. First of all, innova-tion has been considered to be a central issue for EGT and in Economics in general.Exploring the dynamics of technological progress using new metrics represents anattractive contribution to the eld. Second, it would contribute to the e orts for theempirical veri cation of the NSI approach reviewed above, by the means of a newmethodology. Thirdly, the validation of the hypothesis statement would favor theposition of the NSI concept as one of the centerpieces in the development process.Understanding the role of National Systems of Innovation would nurture policy-makers in the areas of STI. The subsequent pages aim to address this hypothesis. Inparticular, the next chapter describes the gathering of data and its analysis, takinginto account the literature revised until this point. 13
  23. 23. Chapter 3Data and Methodology for Analysis , it was discussed how the KE provides the required framework forP understanding the role of a given NSI in its economy. Upon this backgroundand seeking to contribute towards a deeper apprehension of the NSI concept and itsvalidity, a research hypothesis was de ned. Aiming to link NSI performance witheconomic growth, this chapter states and thoroughly describes the employed data,and then declares the methodology for the respective statistical analysis. With this purpose in mind, the subject of the KE data is discussed at rst. Thesource and the process of selection and recollection are concisely portrayed. After,the issue of a dataset with an excessive number of variables is exposed. This isresolved via a Principal-Component Factor Analysis, which groups the variablesof a given pillar in the construction of an associated index. With the constructedindices, the OLS regression models are stated for the hypothesis’s testing.3.1 DataTo investigate any e ect of NSI performance in economic growth, data was gatheredseeking to satisfy the following two criteria: a diversity that covers all the featuresof the KE and the availability of observations for signi cant amount of time. Through the K4D Program, mentioned in Section 2.3, the W B classi esa variety of pertinent statistics from the World Development Indicators (WDI) underthe four main KE Pillars [World Bank, 2010b]. The program’s dataset makes refer-ence to more than 100 WDI variables. Data recollection began with a depurationof this source, aiming to comply with the two criteria stated above. In particular, 14
  24. 24. the process took into account the fact that several of the referred KE variables havestarted being recorded, across a signi cant amount of countries, only until recently.These variables were discarded, for the limited observations would not allow a sig-ni cant timespan for analysis. The selection process yielded a total of 19 variables for 75 countries in the theperiod [1998, 2007]. To balance the dataset and compensate for missing gures, thetimespan was divided into two 5-year intervals. These are: [1998, 2002] and [2003,2007]. Observations were de ned as the average values of the variables for each ofthese two periods. The summary statistics of these variables are displayed in Table3. Rationale for Variable SelectionAlthough most of the variables are already classi ed under the KE Framework bythe K4D Program, it is necessary to discuss why each is signi cant for the pillar itrepresents. Starting with the Economic Regime Pillar, there is market capitalizationof listed companies, domestic credit provided by the banking sector and domesticcredit to the private sector, all measured in % of GDP. The rst variable is de ned asthe sum of the product between share price and the number of shares outstanding,for all companies listed in the country’s stock exchange. The second variable refersto the totality of credits conceded to various sectors on a gross basis, excluding thoseprovided to the central government. The third includes nancial resources providedto the private sector (e.g. loans, non-equity securities and trade credits). These threevariables have been employed in studies concerning nancial market developmentand economic growth; although the importance of the former in the latter has beena matter of debate, several studies have evidenced on a signi cantly positive e ect[Levine, 1997];[Levine and Zervos, 1998]. There are six variables for the Governance Pillar. Their de nitions are presentedas stipulated in [Kaufmann et al., 2009]. Voice and Accountability captures the per-ceptions to which citizens from a given country are able to participate in elections,along with freedom of expression, freedom of association, and free media. PoliticalStability re ects perceptions of the probability that the government will be destabi-lized or overthrown by unconstitutional or violent ways, including political violenceand terrorism. Government E ectiveness captures the perception of the quality of 15
  25. 25. Table 3.1: Summary Statistics for the Selected KE Variables Obs [1998,2002] Obs Mean Std. Dev. Min Max Obs [2003,2007] ∗ 100Economic Incentives (Values in % of GDP)Market capitalization of 225 53.21 60.05 0.07 434.31 49.78listed companiesDomestic credit provided by 362 57.66 53.63 -57.35 304.29 50.00the banking sectorDomestic credit to the 362 46.6 44.58 0.72 220.73 50.00private sectorGovernance (Indices)Voice and Accountability 396 -0.03 1 -2.19 1.66 49.24Political Stability 389 -0.06 0.98 -2.78 1.64 48.07Government E ectiveness 397 -0.02 1 -2.15 2.26 49.12Regulatory Quality 391 -0.04 1 -2.46 1.96 49.10Rule of Law 394 -0.05 0.99 -2.33 2.07 48.73Control of Corruption 391 -0.02 1 -1.79 2.49 49.10Innovation SystemManufactures imports 336 66.67 11.04 21.16 90.89 50.60(% of merchandise imports)High-technology exports 328 9.76 12.59 0 73.09 50.91(% of manufactures exports)Foreign direct investment, 347 4.96 5.58 -6.58 39.35 49.86net in ows (% of GDP)Research and development 200 0.87 0.92 0.01 4.47 52.50expenditure (% of GDP)EducationPublic spending on educa- 305 4.69 2.09 0.6 15.57 52.79tion, total (% of GDP)School enrollment, 355 72.54 31.06 5.93 156.48 49.86secondary (% gross)School enrollment, tertiary 318 26.67 23.32 0.14 91.35 50.31(% gross)ICT (Values per 100 people)Personal computers 366 11.9 17.3 0.01 84.69 49.45Mobile phones and landlines 398 51.03 49.19 0.17 186.37 50.25Internet users 393 14.67 18.98 0 81.21 49.62 Source: calculations based on [World Bank, 2009b]. 16
  26. 26. public services, the civil service and the extent of its independence from politicalpressures, along with the credibility towards the government’s formulation and im-plementation of policies. The Regulatory Quality indicator perceives the ability ofthe government to devise and carry out robust policies and regulations that allowand foster the development of the private sector. Rule of Law captures the impres-sion on how socio-economic agents have con dence in and abide to the rules ofsociety i.e. speci cally, the quality of contract enforcement, property rights, the po-lice and the courts, as well as the protection from crime and violence. Lastly, theControl of Corruption captures perceptions of the extent to which public power issafeguarded from rent-seekers, considering all levels of corruption, and the seclu-sion of the State from private interests. Over the last decade, governance has been a central topic of growth promotionpolicies, especially in developing countries. According to [Gray, 2007], the mostprevalent approach in governance policy-making is known as the ‘good governance’agenda, which contains the six variables previously mentioned. Representatives ofthis agenda highlight its importance not only in the satisfaction of citizens’ aspira-tions regarding public institutions, but also as a means to foster economic growthand as a sustainable mechanism to reduce poverty. While the link between institu-tions and growth was a central matter of classical economics, the notion of ‘goodgovernance’ had its grounds laid only until the 1970s and 1980s. The creation ofquantitative measurements has been key to structure a consensus of the positiverelationship between governance and economic growth. Regarding the (National) Innovation System Pillar, manufactures imports (%of merchandise imports, foreign direct investment (net in ows, % of GDP), high-technology exports (% of merchandise exports) and research and development ex-penditure (% of GDP) were selected as representative variables under the NSI per-formance approach. The case-studies included in Section 2.2 show that the rsttwo variables are pertinent channels of foreign technology transfer for the catch-up process. Manufactures imports incorporate foreign technology and representintermediate capital goods necessary for producing value added exports. FDI is rel-evant as a source of capital for export promotion albeit does not necessarily owinto sectors intensive in technology, which is why public policy is sometimes em-ployed to foster investments that imply technology transfer. The cited case-studiesalso show that other two variables of the Innovation System Pillar measure the in- 17
  27. 27. digenous appropriation of knowledge i.e. these are related to how the mentionedtechnology transfer channels are being utilized in the economy for producing inno-vations and towards the promotion of technological capabilities. High-technologyexports account for this explicitly, as it refers to domestic production which em-ploys indigenously developed or adapted technology. Regarding R&D expenditure,the referenced authors of NSI studies recognize it as decisive in the adaptation offoreign technology to the local context. By understanding the signi cance of thesefour indicators, this dissertation seeks to elaborate on previous empirical researchand explore the link between NSI performance and economic growth. Before proceeding with the remaining KE Pillars, it is important to acknowledgethat, similar to other quantitative studies, the variables selected here emphasizeformal modes of learning and innovation based in science and technology activi-ties [Lundvall, 2007]. This emphasis is re ected here in the selection of indicatorsof R&D and capital-embedded industrial goods. However, innovation strategiesbased on experience and the “doing, using and interacting” learning mode are ratheroverlooked. This is explained by the lack of standardized variables to representexperience-based innovations. In the case of the Education Pillar, three representative variables were selected.Public spending on education (% of GDP) adds up expenditure on education ofpublic authorities at all levels, along with the subsidies to private education at theprimary, secondary, and tertiary levels. Secondary school enrollment (% gross) andtertiary school enrollment (% gross) are the ratio of total enrollment, regardless ofage, to the population of the age group that o cially corresponds to the level ofeducation. According to [World Bank, 2009a], secondary education completes theprovision of basic education that began at the primary level and lays the founda-tion for future learning. It yields both individual and social returns and provides animportant amount of human capital required for countries’ economic growth. Therole of tertiary education is crucial. Universities and research institutions have toaddress the call for creating a pool of experts capable of acquiring science and tech-nology and adapting it to the domestic context. Regarding the link between educa-tion and economic growth, [Teles and Andrade, 2004] states that while the evidencehas been asymmetrical it mainly points towards a positive causal relationship. Thesame study identi ed a positive relation between public spending on education andeconomic growth. The reported signi cance of the relationship, however, varied 18
  28. 28. depending on the composition of governmental spending between basic and highereducation i.e. it lost its signi cance when the latter was not promoted. For the remaining Pillar, ICT, the variables measured per 100 people are: per-sonal computers, mobile phones and landlines and Internet users. As reportedby [Batchelor et al., 2005], previous studies agree on how ICT can help develop-ing countries address a wide range of socioeconomic activities: the use of ITC en-hances the production of goods and the provision of services and thus increasesproductivity. There is less agreement, however, on how much a priority it shouldbe to promote the increase of ICT infrastructure. These technologies are increas-ingly being seen as means to other development requirements rather than as an endthemselves. Policies associated with this KE Pillar have been focused on alleviatingthe wide disparities in access; the poor is the part of society most out of reach fromICT.3.2 The Construction of KE Pillar IndicesEven after depuration, the dataset from Table 3.1 is still composed of too manyvariables for an econometric analysis. [Fagerberg and Srholec, 2007] faced a sim-ilar problem in its attempt to explore the relationship between NSI and economicgrowth. To face a dataset with numerous variables, the work employed a Principal-Component Factor (PCF) Analysis. As described in the study, this process is basedon the idea that variables from the same category are likely to be signi cantly cor-related and thus can be reduced into a smaller number of indicators, which re ectthe variance dimension of the data. The PCF Analysis assigns speci c “loadings”which weigh in the calculation of the factor score for each country. Countries re-ceive scores for each of its KE Pillars by adding the product of the pillar variables’values and the corresponding coe cients, which are derived from the loadings. As it was mentioned in Chapter 2, [Fagerberg and Srholec, 2007] identi ed fourfactors from a set of variables: the innovation system, governance, the political sys-tem, and openness. In contrast, this dissertation uses a di erent set of representativevariables for the KE Pillars. Using the K4D Program’s classi cation, individual PCFAnalyses were carried out for each of the pillars. This ensured that the result-ing indicators kept the structure suggested by the KE Framework. Consequently, 19
  29. 29. the indicators for each of the pillars contain information only from their respectivevariables. The Economic Regime Pillar is divided into two sub-pillars, as suggestedby the W B : Economic Incentives and Governance [World Bank, 2009a]. The PCF Analysis executed here successfully identi ed an underlying structurefor each of the KE Pillars and generated proxy indices. For every sampled country,an associated index (value) was calculated. The resulting set of KE Pillar Indicescan be viewed in Appendix A. The PCF Analysis maximizes the amount of overall variance (accounting by allthe variables as a group) that is to be captured by the index. The correlations inTable 3.2 indicate the “relevance” each variable has in its corresponding index. Forexample, in the case of Education, both secondary and tertiary school enrollmenthave a higher weigh in the calculation of the associated index values, compared withpublic spending variable. The constructed Education Index is more correlated withthe rst two variables because the two enrollment indicators are more correlatedwith each other in comparison to the scal indicator1 . The variance explained by the Innovation System Index is 40.18%. While thisvalue is rather low, it is still signi cant on what is considered best practices for PCFAnalysis, as stated in [Costello and Osborn, 2005]. Furthermore, correlations in thisindex are all above the 32% recommended borderline. The values show that, in thisconstructed index, the more relevant variable is high-technology exports, followedby manufactures imports, R&D expenditure and, lastly, FDI in ows. It is critical to recognize that this particular Innovation System Index is designedto be functional only in the context of the KE and thus should not be employed in-dependently. There are other innovation indices more suitable for a comparativeanalysis or ranking purposes; a prominent one is the National Innovative Capac-ity (NIC) Index used in the Global Competitiveness Report [Porter and Stern, 2002].Stand-alone innovation indices are, however, not suitable for this dissertation asthese consider a series of factors that are better classi ed in other KE Pillars the case of the NIC index: venture capital availability (Economic Incentives),the quality of institutions (Governance), human capital (Education), and the socialpenetration of information and communications infrastructure (ICT). The KE ap-proach allows the separation of these factors and thus an independent analysis of 1 For more details on PCF Analysis, please refer to [Smith, 2002]. 20
  30. 30. Table 3.2: The Constitution of the KE Pillar Indices Economic Incentives Index Education Index Variance Explained: 81.12% Correlation Variance Explained: 63.38% Correlation Market capitalization of listed Public spending on education, 0.79 0.45 companies total Domestic credit provided by 0.94 School enrollment, secondary 0.93 the banking sector Domestic credit to the private 0.97 School enrollment, tertiary 0.91 sector Governance Index ICT Index Variance Explained: 87.84% Correlation Variance Explained: 91.09% Correlation Voice and Accountability 0.89 Personal computers 0.95 Political Stability 0.86 Mobile phones and landlines 0.94 Government E ectiveness 0.97 Internet users 0.97 Regulatory Quality 0.95 Rule of Law 0.98 Control of Corruption 0.96 Innovation System Index Variance Explained: 40.18% Correlation Manufactures imports 0.69 High-technology exports 0.76 Foreign direct investment, 0.35 net in ows Research and development 0.65 expenditureNSI performance. To compare the NIC Index with the Innovation System Index, developed in thisdissertation, the correlation was calculated between the 2001 value of the former2and the average for the years [1998, 2002] of the latter3 . A correlation of 65% sug-gests that the native Innovation System Index captures a considerable portion of theNIC dataset, while some of the remaining percentage is likely to be balanced withthe information included in the other pillars. As mentioned earlier when elaborat-ing on its variables, the Innovation System Pillar Index should be interpreted as anattempt to represent the general impression of how a given NSI performs throughits development. The indicator intends to re ect the several case-studies presentedin Section 2.2. 2 Based on data from [Porter and Stern, 2002]. 3 Using the values of Appendix A. 21
  31. 31. 3.3 Speci cation of ModelsThe KE Pillar Indices, which contain the information of the variables in Table 3.1,can now be used as proxy variables in an econometric analysis for testing the hy-pothesis devised in Chapter 2. The summary statistics of the variables to be includedin the upcoming regressions are presented in the following Table 3.3. Table 3.3: Summary Statistics for the Regression Variables Obs Mean Std. Dev. Min Max Explained Variable Annual GDP Growth (%) 134 1.27 0.67 -1.90 2.57 Proxy Variables (KE Pillar Indices) Economic Incentives 134 0 1 -1.25 2.85 Governance 134 0 1 -1.88 1.65 Innovation System 134 0 1 -2.05 2.26 Education 134 0 1 -2.95 2.35 ICT 134 0 1 -1.27 2.48 Control Variables GDP per Capita 134 8.60 1.35 5.53 10.61 (constant 2000 US $) Dummies (Binary Variables) [1998, 2002] Observation 134 0.52 0.50 0 1 Sub-Saharan Africa 134 0.07 0.26 0 1 Latin America & the Caribbean 134 0.15 0.36 0 1 East Asia & Paci c 134 0.06 0.24 0 1 Middle East & North Africa 134 0.05 0.22 0 1 South Asia 134 0.01 0.12 0 1 Europe & Central Asia 134 0.17 0.38 0 1 The logarithm of GDP growth is selected as the explained variable and the veKE Pillar Indices are included as explanatory proxy variables. Furthermore, eightcontrol variables are included in the analysis. Six of them are regional dummies(binary variables), which group developing countries according to W B ’sgeographic classi cation. These are: Sub-Saharan Africa, Latin America & theCaribbean, East Asia & Paci c, Middle East & North Africa, South Asia and Eu-rope & Central Asia [World Bank, 2010a]. The null case of the regional dummy 22
  32. 32. variables corresponds to high-income countries. The mean of the regional dum-mies represent their share in the dataset (e.g. 15% of the considered countries arefrom Latin America & the Caribbean). Adding all the means result in the propor-tion of developing countries in the [1998, 2007] sample: 51%. Thus, the remaining49% of observations conform the sampled high-income countries. Another controlvariable is the logarithm of GDP per capita, to consider conditional convergence4 .Lastly, there is one more dummy that indicates if the data-point corresponds to a[1998, 2003] observation, to consider time e ects i.e. temporal variations that are notcaptured by the KE Pillars and the other control variables. The econometric analysis is undertaken in a set of four models. These models arerepresented in Table 3.4. All of these have the logarithm of GDP growth lngdpgi asthe explained variable and have the KE Pillar Indices as proxy variables, a constantand an error term, represented as Pki , C and i, respectively. The basic modelincludes only the ve proxies, while the subsequent models incorporate the controlvariables progressively. Table 3.4: Econometric Analysis: Model De nitions 5 6 lngdpgi = βki Pki + β6i f irsti + β7i lngdpli + β(k+7)i Rki + β14 C + i k=1 k=1 Model 1 Model 2 Model 3 Model 4 The second model adds the f irsti dummy variable, which equals to one whenthe observation corresponds to the [1998, 2002] period and zero otherwise, thus tak-ing into account time e ects. The third augments the analysis with the variablelngdpli (logarithm of GDP per capita) to consider the in uence of conditional con-vergence. Finally, the fourth model adds the six regional dummy variables, writtenas Rki , to check for the geographical particularities that might a ect growth and arenot captured by the other variables. 4 The theory of conditional convergence states that, given certain conditions, poorer countriesgrow faster than their richer counterparts, until all economies reach the same level of GDP percapita. Developing countries have the potential to increase their economic output levels at a fasterrate, due to the fact that the e ects of diminishing returns are not as consolidated as in higherincome countries. 23
  33. 33. To recapitulate, this chapter described how data was extracted using WB guidelines and databases, in order to robustly represent the KE Pillars of75 countries worldwide for a ten-year period of analysis: [1998, 2007]. This timeinterval was divided into two consecutive quinquennial periods: [1998, 2002] and[2003, 2007]. For each of these, averages of available data were calculated. Theresulting variables were then employed for the construction of the associated KEPillar Indices using PCF Analysis. These indices are to be used as explanatory proxyvariables for GDP growth, along with eight control variables. The coe cients fromthe models stipulated above are to be estimated through regressions. In the nextchapter, these results are displayed and analyzed in detail. 24
  34. 34. Chapter 4Results and Evaluation on the data and the methodology for its analysis, both presented in theB last chapter, this part of the dissertation seeks to exhibit the relevant outcomesin the validation of the hypothesis: the existence of a statistical link between NSIperformance and economic growth under the KE Framework. This chapter is di-vided into two parts. First, scatter plots are included for each of the constructedKE Pillar Indices and GDP per capita. These give a rst view on how each indexis relevant within the economic activity. The second part focuses on giving outinformation from the econometric analysis of the previously de ned models.4.1 KE Pillar Indices vs GDP per CapitaBefore revising the econometric analysis, it is appropriate to get an initial sense ofthe roles the Innovation System Index and the other KE Pillar Indices have on eco-nomic growth. For this purpose, the present section will use scatter plots. Theseshow a graph with the scores obtained by the 75 sampled countries on each pillar(see Appendix A) on the x-axis and their respective log of GDP per capita on they-axis. To check for di erences between the two time periods, the dots are clas-si ed accordingly. A tted line is included to illustrate the general trend of therelationship between the index’s scores and the production levels. Regarding the main Pillar Index of concern, the Innovation System, Figure 4.1displays a positive relationship with GDP per capita, with a correlation equivalentto 0.6373. Countries that scored a better NSI performance i.e. a better acquisition,production and absorption of applicable knowledge, at the same time experienced 25
  35. 35. Figure 4.1: GDP per Capita and Innovation Systemhigher levels of income. The tted lines suggest that the relationship strengthenedbetween the rst period and the second. For the Economic Incentives Index, Figure 4.2 shows an apparently similar as-sociation. The correlation is slightly stronger compared to the Innovation Systemindex: 0.6926. The gure depicts that countries which scored high in this index,with an environment suitable for a better allocation of resources represented bysuperior levels of domestic investment, had greater GDP per capita levels between[1998, 2007]. The slope decreased from the former quinquennial period to the latter,although not by much. Figure 4.2: GDP per Capita and Economic Incentives 26
  36. 36. Figure 4.3: GDP per Capita and Governance The case of the Governance Index, shown in Figure 4.3, exhibits the largest cor-relation among all pillars: 0.8714. Compared to the rest graphs, the Governance tted value lines are steepest. It shows that countries with better institutions si-multaneously experienced superior positions of GDP per capita. This relationshipappears to have slightly decreased over the time of analysis. Figure 4.4 shows the scatter plot for the Education Index. This index has a cor-relation of 0.7546 with GDP per capita. The slopes of the tted value lines remainedpractically the same. The data from the analyzed time interval supports the ideathat richer nations have a more skilled labor force, which e ciently generates and Figure 4.4: GDP per Capita and Education 27
  37. 37. applies knowledge. Lastly, Figure 4.5 is the respective graph for the ICT Index. As the other KE Pil-lars, it also displays a strong positive correlation with GDP per capita: 0.8392. Theslopes of the tted value lines, however, presented the most enunciated decreasebetween [1998, 2002] and [2003, 2007]. This change might have a ected the econo-metric analysis, as mentioned later on. Still, it can be said that better infrastructurefor the communication and di usion of information and knowledge is strongly re-lated to higher income per capita levels. Figure 4.5: GDP per Capita and ICT Although ones in greater measure than others, all the KE Pillar Indices displaya positive correlation with economic growth. The fact that the Innovation SystemsIndex scores account for the lowest correlation with GDP per capita (albeit stillhigh), is noteworthy. In a broader perspective, these graphs support the notions ofthe authors mentioned in Chapter 2: the NSI concept and the KE framework arerelevant towards economic output. The Innovation System’s performance, alongwith the set of Economic Incentives, the level of Governance, Education and theexpansion of ICT are playing signi cant roles in the economies of today. For adeeper understanding on how these economies grow in relation to the KE Pillars,the following section presents and discusses the results of the econometric analysisbased on the models proposed in Chapter 3. 28
  38. 38. 4.2 Econometric AnalysisThe KE Pillar Indices constructed in Section 3.2 are now employed for regressionswith economic growth as the explained variable, using the models de ned in Section3.3. Table 4.1 contains the results from the regressions against the logarithm of GDPgrowth. The table points out the estimated coe cients for each of the models. Allthe estimations are based on the Ordinary Least Squares (OLS) method. However,Model 1 and Model 3 were estimated using the ‘Huber-White Sandwich Estimator’of variance in order to calculate robust standard errors, as evidence of heteroskedas-ticity was found for these models1 . Columns (1) through (4) correspond to standardOLS regressions, while (5) and (6) use a stepwise estimation to identify the speci ca-tion with the best statistical properties2 . Column (5) begins the estimation processwith Model 3 and excludes the quartile of countries with lowest GDP per capita,while (6) starts with Model 4 without any variation to the sample. It is necessaryto acknowledge that the stepwise regressions are included speci cally to providesecondary evidence for the relationship between the Innovation System Index andGDP growth. The resulting estimations (5) and (6), unlike (1) through (4), are notintended to be representative for the KE Framework as the stepwise regressionsdiscarded some of the KE Pillar Indices. The possibility of endogeneity3 was addressed by the means of the Durbin-Wu-Hausman Test, which is conformed by two steps. In the rst one, each potentiallyendogenous proxy variable was regressed on all exogenous variables (the otherproxies), along with the variables that were used in the construction of the regressedindex. The resulting residuals are, in the second step, added to the new regressionof the original model [Wooldridge, 2002]. If any residual coe cient was to comeas signi cant in one of these latter regressions, endogeneity of the correspondingproxy variable needs to be accepted and the associated model should be estimatedby two-stage least squares in order to achieve consistent results. The resulting co- 1 The results of White’s heteroskedasticity tests are included in Appendix B.2. 2 The stepwise estimation seeks to dismiss variables that do not provide explanatory powerto the model given a particular signi cance level, in this case 10%. The process begins with thefull model and checks whether the calculated p-value of a variable falls farther from the selectedfrontier. It then excludes the most statistically meaningless variable and starts over. At each stepthe procedure also inspects if a variable that was discarded earlier has become signi cant. 3 Endogeneity occurs when there is a causality loop between the explained and the explanatoryvariables. 29
  39. 39. Table 4.1: OLS Regression Results for GDP Growth. C V Standard Regressions Stepwise Regressions (1) (2) (3) (4) (5) (6) Model 1 Model 2† Model 3 Model 4† Model 3‡ Model 4† Economic -0.213** -0.194** -0.150* -0.125 -0.151** -0.220*** Incentives (0.0852) (0.0860) (0.0781) (0.0942) (0.0700) (0.0737) -0.14 0.17 0.271** 0.280** 0.219* Governance (0.119) (0.127) (0.127) (0.141) (0.113) Innovation 0.04 0.119* 0.139** 0.243*** 0.178** 0.201** System (0.0747) (0.0683) (0.0686) (0.0847) (0.0749) (0.0876) -0.02 0.01 0.05 0.01 Education (0.0100) (0.0802) (0.0911) (0.0959) ICT 0.190 -0.306** -0.20 -0.329* (0.119) (0.148) (0.147) (0.174) [1998, 2002] -0.771*** -0.717*** -0.819*** -0.500*** -0.544*** Observation (0.165) (0.143) (0.173) (0.108) (0.105) log(GDP -0.221** -0.207* -0.361*** per capita) (0.0933) (0.119) (0.112) Sub Saharan -0.08 0.440*** Africa (0.252) (0.148) Latin America -0.27 the Caribbean (0.212) East Asia -0.65 Paci c (0.491) Middle East 0.21 0.552*** North Africa (0.219) (0.162) South 0.42 1.100*** Asia (0.309) (0.236) Europe 0.19 0.467** Central Asia (0.209) (0.181) 1.279*** 1.684*** 3.554*** 3.518*** 4.703*** 1.404*** Constant (0.0568) (0.0734) (0.793) (1.0950) (0.999) (0.0793) Observations 134 134 134 134 100 134 R-squared 0.09 0.25 0.29 0.37 0.30 0.31 Note: Standard errors in parentheses *** p 0.01, ** p 0.05, * p 0.1 † Using the Huber-White Sandwich Estimator of variance. ‡ Excluding the quartile of poorest countries. 30
  40. 40. e cients of this test are included in Appendix B. In this occasion, no evidence ofendogeneity was found. Straightforwardly, the most notable result is the recurrent signi cance of theInnovation System proxy variable through the regressions, suggesting a positivee ect from this particular Pillar Index upon GDP growth. Out of all the KE Pillars,it displays the most signi cant evidence: in columns (2), (3), (4) (5) and (6) withp-values lower than 10%, 5%, 1%, 5% and 5%, respectively. These results indicatethat countries with better NSI performance experienced greater GDP growth in theperiod of analysis. This positive relationship is depicted in the following Figure 4.6. The graphshows the marginal e ect of the Innovation System Index regressor on GDP growth,after taking into account the associations between the other variables included inthe regression (column (4); Model 4). The slope of the tted line corresponds to theInnovation System Index’s β calculated in the regression. As a support for the va-lidity of this model’s particular speci cation, another plot is included as Figure 4.7.The regression’s residuals do not display any apparent pattern with the InnovationSystem Index, supporting the absence of endogeneity discussed earlier. Figure 4.6: Marginal E ect of the Innovation System Scores (Model 4) To analyze the relationship between Innovation System’s performance and GDPgrowth, it is necessary to recall Table 3.2 (p. 21), which shows that the index is morecorrelated to high-technology exports, manufactures imports and RD expenditure(in that order) than FDI in ows. Although this structure should be considered as 31
  41. 41. Figure 4.7: Regression’s Residuals vs Innovation System Scores (Model 4)it provides insights about its constitution, the Innovation System Index, due to itsnature (calculated by PCF Analysis), must be appreciated as a whole. Determiningwhich of the indicators that belong to this index is more critical for growth falls outof the scope of this approach. The Governance Pillar Index returned signi cant coe cients less consistentlyas with the IS index: in columns (3), (4) and (5) with respective p-values lowerthan 5%, 5% and 1%. Still, the unchanging positive sign supports the theory; ‘goodgovernance’ has been relevant for higher growth. The outcome of the coe cients for the ICT and Economic Incentives indicatorsare more paradoxical, being both signi cantly negative in some iterations: (2) and(4) for the former and the latter in all but (4). This is explained, to an extent, dueto the e ect of conditional convergence. The regression table portrays how intro-ducing the variable log (GDP per capita) in (3) reduces the signi cance of both ICTand Economic Incentives indicators, the latter losing all explanatory power. Coher-ently, these two pillars are strongly correlated with GDP per capita, 0.84 and 0.69respectively. Particularly for the ICT Index, it is worth to revisit Figure 4.5. The scatter plotshows how the slope of the tted line became atter over time. This adjustmentis most likely explained by the characteristic of the ICT variables employed here.‘Personal computers,’ ‘Internet access’ and ‘mobile phones and landlines’ are alltechnologies that mature and continue to fall in price, thus become more accessible 32
  42. 42. to poorer countries. As follows, a more dynamic ICT Index that contains this e ectmight be more suitable for the present approach. Education did not yield a signi cant result. There are two main possible expla-nations for this. First, the calculation of the variance in ation factors4 for each ofthe explanatory variables suggested the presence of multicollinearity. It does notappear to be so severe, as the signs of the coe cients in Table 4.1 seldom changed.However, it might have deterred the signi cance levels. The second issue is thatthe Education Pillar Index variables represent current investment and enrollment,which do not re ect so strongly in present growth, but rather have a more importante ect in future increments of GDP. Regarding the control variables, the signi cance of the f irst dummy variable’snegative coe cient indicates a strong and generalized trend of greater growth forthe years [2003, 2007] in comparison to [1998, 2002]. Also, although in some iter-ations more so than others, there were meaningful negative coe cients for lngdpl,which support the theory of conditional convergence. Finally, the regional dum-mies had a more secondary role on the model. In column (4), although not yieldingsigni cant coe cients, they eliminated the explanatory power of the Economic In-centives proxy variable and reduced that of lngdpl. Thus, the standard regression ofModel 4 suggests that a portion of the negative e ect from the Economic IncentivesPillar and the ‘conditional convergence’ observed in the previous columns is relatedto regional particularities. Interestingly, the stepwise regression in column (6) traded lngdpl for the regionaldummy variables; this trade-o , however, did not produce a big change the signi -cance and values of the other coe cients. For Latin America the Caribbean andEast Asia Paci c no signi cant coe cients were produced, this suggests that theparticularities of these regions are expressed by the proxy variables of the Inno-vation System and the Economic Incentives pillars, the latter containing the e ectof conditional convergence. Excluding the poorest 25% of countries in column (5)reduced the explanatory power of the Governance Index, suggesting its importance 4 ˆ vif (Bi ) = 1 2 where Ri corresponds to the R-squared of the OLS regression in which the 1−Ri , 2 ˆexplanatory variable associated with Bi becomes the explained variable, as a function of all theother explanatory variables of the original model. A large R-squared suggests a high goodness of t and so, in this case, multicollinearity in the original model. As the R-squared increases, so doesvif . The “rule of thumb” states that if the vif for a particular explanatory variable exceeds ve,multicollinearity is present. 33
  43. 43. in the excluded countries. To check for the consistency of the constructed KE Pillar Indices, the economet-ric analysis is replicated with GDP per capita as the explained variable. The set ofregressions is included in Appendix C. First of all, it is necessary to highlight thewell documented likelihood for the results with respect to GDP per capita to su erfrom endogeneity. With this setup, it is di cult to know whether the KE Pillar In-dices a ect the levels of GDP per capita, or if the relationship is opposite e.g. richercountries can a ord better public education. In the previous regressions no evidenceof endogeneity was found i.e. the tests did not show that fast growth rendered betterperformance of the pillars. Even though a causal relationship cannot be identi edin the regressions with GDP per capita as the dependent variable, these illustrate apositive connection between all KE Pillars. In this chapter, the coe cients of models derived from the KE Framework wereestimated using the Pillar Indices constructed in Chapter 3. The Innovation SystemIndex, as a function of inward FDI, manufactured goods imports, high-technologyexports and RD expenditure, was found to have promoted economic growth dur-ing [1998, 2007] in the 75 sampled countries. This relationship reached con dencelevels lower than 1% when taking into account time e ects, conditional conver-gence and regional particularities. Furthermore, under this approach the ‘good gov-ernance’ index displayed a similar positive e ect, albeit less prominently. Theseresults are concurrent to those of [Fagerberg and Srholec, 2007] which also calcu-lated innovation system and governance indices, albeit using di erent variables,complementary factors for economic growth (i.e. the political system and openness)and other methodological di erences as to this dissertation. For the remaining KEPillars, the evidence was inconclusive. The constructed NSI performance index is an insightful proxy variable for ex-plaining how economies have grown between. This evidence is pertinent for andeveloping country like Colombia, whose policymakers have traditionally focusedKE e orts towards the Economic Incentives Pillar and neglected the consolidationof the Innovation System Pillar. The following chapter explores the signi cance ofthe exposed link, between NSI performance and GDP growth, for this particularcountry. 34
  44. 44. Chapter 5An Overview of the Colombian NationalSystem of Innovation point towards a positive e ect of NSI performance on economic growth.R What can can be inferred from this particular link, in favor of policy-making?This chapter focuses on answering this question. Due to the fact that each NSI re-lies heavily upon a particular context, a speci c country is chosen as a case-study.Colombia’s NSI is to be analyzed using the considered theoretical background andthe gathered evidence. First of all, the general circumstances in which the systembegan to be recognized as a public institution are described. Secondly, attentionis given to various studies which characterize the present state of Colombia’s NSI.Thirdly, the current policy framework and instruments are described, in order tograsp the system’s outlook. Finally, based on the revision of this case-study, recom-mendations are provided in relation to the observed link between NSI performanceand economic growth.5.1 BackgroundSince 1991, Colombia has decidedly shifted into a full market-oriented economic ap-proach. The Import Substitution Industrialization model, commonly seen through-out the region, was discarded in the mid-1970s and full edged liberalization wasgradually undertaken. In 1999 the country experienced a recession, caused by thegeneralized capital out ows experienced across the developing world at that time,which was aggravated in Colombia by an internal mortgage crisis. Although thedownturn worsened the country’s poverty gures, it was followed by a recovery 35
  45. 45. stage i.e. the economy experienced accelerated growth between 2002 and 2007; forthis period, the increase of real GDP averaged 5.32%. This was mainly due tomore favorable economic conditions abroad and a series of policies that enhancedthe Colombian business climate, perhaps the most signi cant one being the sus-tained progress in domestic security. Nevertheless, with the current global economicdownturn, the most recent gures are rather timid. Colombia’s economy grew only2.53% in 2008 and the following year GDP growth was practically nonexistent. To enter the globalized economy, the Colombian State tore down tari barriersand other protectionist measures. This new approach to international trade, whilerightfully seeking to improve domestic productivity levels, a ected many Colom-bian rms which could not compete with the foreign companies that entered thecountry. Policymakers, aware of this, have appointed export promotion mecha-nisms, which mainly seek to improve the country’s level of competitiveness. Thecurrent policy approach to competitiveness can be roughly understood by two doc-uments written in the second half of the 2000s by the Consejo Nacional de PolíticaEconómica y Social (C ; National Council for Socio-economic Policy), a gov-ernmental institution which provides the framework for Colombia’s developmentpolicies. The following extracts are representative of their respective documents. “...[in Colombia] a series of measures and projects must be estab- lished and carried out in order to advance competitiveness in interna- tional markets. These measures may go from the construction and the improvement of the physical infrastructure or the training of the labor force, to the reorganization of institutions or the eliminations of [bureau- cratic] procedures. All these projects [...] seek to eliminate the obstacles faced by the productive sector during its operation...” Source: translated from [Mincomercio and DNP, 2004]. “A nation’s competitiveness is de ned as the degree to which a coun- try can produce goods and services capable of competing successfully in globalized markets and, at the same time, improve the population’s in- come conditions and the quality of life. Competitiveness is the result of the interaction of multiple factors related to the conditions faced by business which a ect their performance e.g. infrastructure, human re- sources, science and technology, institutions, the macroeconomic envi- ronment, and productivity.” Source: translated from [Mincomercio and DNP, 2006]. 36
  46. 46. These two extracts from the policy documents show that, in Colombia, therehas been a bias towards exogenous mechanisms for increasing competitiveness infrastructure improvements (e.g. highways, access to utilities and airports) andthrough the re nement of institutions (e.g. a more e cient bureaucracy, decreasingcorruption and a more e ective enforcement of legal contracts). In both documents,the role of innovation is seldom mentioned and the respective policy guidelines arepractically absent. As follows, the background of Colombia’s approach to compet-itiveness is, in terms of the KE Framework, focused in improvements of the Eco-nomic Incentives, Governance, ICT and Education Pillars, leaving the InnovationSystem Pillar as secondary at best. Contrary to other countries under the export-promotion scheme, Colombian pol-icy has lagged in addressing the role innovation has as an endogenous mechanismsthat favors competitiveness. A domestically developed innovative product or ser-vice will (by de nition) outperform its competition, while implicitly contributingtowards technological learning inside the rm and thus enhancing its productivity1 .The fact that innovation has been belatedly adopted as a State policy has broughtupon several weaknesses in the Colombian NSI, as reported by the several studiesincluded in the following section. In Colombia, the institutional framework for a NSI was introduced in 1995. Itwas preceded by the Sistema Nacional de Ciencia y Tecnología (SNCYT; NationalScience and Technology System), conceived in 1990. The SNCYT aimed to integratea diversity of institutions which shared a common vision, mission and objectives. Itincluded rms, universities, public research institutions, research centers, and tech-nological institutions, among other actors. The innovation system was born as asub-system of the SNCYT, at a similar time as in other Latin American countries.Both systems are, in practical terms, considered as one: the Sistema Nacional deCiencia, Tecnología e Innovación (SNCTI; National Science, Technology and In-novation System)2 [Monroy Varela, 2006]. To conserve the terminology employedin this dissertation, the SNCTI will be referred as the Colombian NSI, henceforthCNSI. The public organization in charge of devising and executing policies that aimto structure and strengthen the system is C , an institution that seeks to 1 For a detailed description of this process see [Kim, 1997], pp. 88-90. 2 This is due to the fact that both systems are fundamentally composed by the same actors,share common concepts, basic strategies and challenges. 37
  47. 47. lead the generation an utilization of knowledge in favor of socio-economic develop-ment in Colombia. C de nes the CNSI as follows. “The National System of Science, Technology and Innovation is an open system composed by policies, strategies, programs, methodologies and mechanisms in favor of the management, promotion, nancing, pro- tection and di usion of scienti c investigation and technological inno- vation, as well as the public, private or mixed organizations which carry out or promote the development of scienti c, technology and innovation activities.” Source: translated from [Colciencias, 2010]. According to [Monroy Varela, 2006];[Forero, 2000], the CNSI has faced di cul-ties since its conception. The most conspicuous have been: an unstable and weakbudget, inadequate policy formulation (e.g. shortsightedness and vertical decision-making), a lack of public directives, the social apathy towards the socio-economicvalue of scienti c research, a stagnant scienti c community, the phenomenon re-ferred as “brain drain,” and the disjunction among the system’s actors. The follow-ing sub-section presents representative studies which diagnose the CNSI’s weak-nesses.5.2 Current StatusSeveral studies have faced the task of diagnosing the CNSI. Three in-depth sur-veys are worth mentioning, as these reveal important aspects of the system. TheEncuesta de Desarrollo Tecnológico en el establecimiento industrial colombiano(EDT; Technological Development Survey on the Colombian industrial settlement)began to be recorded since 1996 through the e orts of Colombia’s DepartamentoNacional de Planeación (National Planning Department) and with the support ofC . The introduction of an institutional NSI framework matched withan increased interest in the measurement and tracking of the industry’s techno-logical development. For this purpose, the rst EDT was conducted in 1996 andthese e orts were continued with iterations of the survey in 2003, 2005 and 2006.However, the methodology has been constantly amended. While the survey meetshigher standards today and has a considerably larger sample size (6080 rms in 2006[DANE, 2010] as to 885 in 1996 [DNP, 1997]), its more recent results are hard to 38
  48. 48. be compared with those of 1996 [Vargas Pérez and Malaver Rodríguez, 2004]. Onetelling indicator which has remained practically unchanged is the percentage of in-novative rms3 : 11.3% (1996) and 11.8% (2003)4 . [Monroy Varela, 2006] is another survey-based study, which queried about thearticulation of the CNSI. It found that while the knowledge-producing agents ofthe system knew about the existence of a CNSI framework, business owners weremostly unaware of it. This feature suggests the system’s bias towards the supply-side of knowledge, which is particularly problematic for the development of inno-vations i.e. indigenous knowledge and technologies are not being incorporated intoColombian products or services. The survey shows that this bias is re ected on thedisjunction of the system: agents primarily interact with others of the same type. The third survey reported in [Malaver Rodríguez and Vargas Pérez, 2006] wascarried out in 2005, aiming to advance the results of the EDT albeit delimited to Bo-gotá (the capital city district) and its circumvent department of Cundinamarca (oneof Colombia’s thirty-two administrative divisions). It found that less than a third ofthe 400 sampled rms in the aforementioned region interacted with other agents ofthe CNSI in order to complement their e orts in producing innovations and improvetheir technological capabilities. Furthermore, less than 10% of the companies turnedto the public institutions of the CNSI. However, the low share of rms that engagedthe system found few di culties in doing so, meaning that the problem lies in theabsence of associations rather in than the links themselves. Once again the culturalproblem is brought to focus, as 44% of the sampled rms considered unnecessary toreach the CNSI, while 19% did not innovate because it was not considered pro tableor signi cant to do so. [Torres et al., 2007] gathered data through the Premio Innova (Innova Award), atechnological innovation competition for Colombian small and medium enterprises(SMEs), created in 2004 as a mechanism to promote innovation in technologicalstart-ups5 . The work analyzes how the participating companies are seeking to in-novate and typi es the interactions between the CNSI agents. The study is based 3 The survey classi es a rm as innovative if, at the corresponding period of inquiry, ithas launched at least one good or service signi cantly improved by international standards[DANE, 2010]. 4 As reported by [Vargas Pérez and Malaver Rodríguez, 2004] and [DANE, 2010]. 5 The award aims to bring attention to Colombian innovations, benchmark technological capa-bilities among di erent sectors, and to encourage an innovation culture in SMEs. 39