Linking National Systems of Innovation and EconomicGrowth 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 FulVllment of the

Requirements for the Degree of

Master of Arts in Economics

Main Supervisor: Prof. Dr. Yoichi Koike

Second Supervisor: Prof. Dr. Yongjin Park

Ritsumeikan University

Graduate School of Economics

July 2010

ii

Linking National Systems of Innovation and EconomicGrowth 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 World Bank as a means to assess the eUect 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, diUusion 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: barreneche@gmail.com

iii

“We have the good fortune to live in democracies, in which individuals can Vght for their

perception 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: the

responsibility 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

Acknowledgements

I 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, staU 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

Contents

Abstract iii

Acknowledgements v

Table of Contents vi

List of Tables viii

List of Figures ix

1 Introduction 1

2 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 . . . . . . . . . . . . . . . . . . . . . . . . . . . 12

3 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 SpeciVcation of Models . . . . . . . . . . . . . . . . . . . . . . . . . . 22

4 Results and Evaluation 25

4.1 KE Pillar Indices vs GDP per Capita . . . . . . . . . . . . . . . . . . . 25

4.2 Econometric Analysis . . . . . . . . . . . . . . . . . . . . . . . . . . . 29

5 An Overview of the Colombian National System of Innovation 35

vi

5.1 Background . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 35

5.2 Current Status . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 38

5.3 Prospects . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 40

5.4 Recommendations . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 43

6 Conclusions 47

A Constructed KE Pillar Indices 50

B Statistical Tests 54

B.1 Endogeneity: Durbin-Wu Hausman Test . . . . . . . . . . . . . . . . 54

B.2 Heteroskedasticity: White Test . . . . . . . . . . . . . . . . . . . . . . 55

C Estimations with GDP per Capita as the Explained Variable 56

C.1 SpeciVcation of Models . . . . . . . . . . . . . . . . . . . . . . . . . . 56

C.2 Results . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 57

Bibliography 58

vii

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 DeVnitions . . . . . . . . . . . . 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 DeVnitions for GDP per Capita . . . . . . . . . . . . . . 56

Table C.2 OLS Regression Results for GDP per Capita . . . . . . . . . . . 57

viii

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 EUect of the Innovation System Scores (Model 4) . . 31

Figure 4.7 Regression’s Residuals vs Innovation System Scores (Model 4) 32

ix

Chapter 1

Introduction

Innovation has become understood to emerge through the interactions of a vari-

ety of agents such as Vrms, universities and governmental bodies. These actors

are considered to have particular roles in processes where knowledge is created,

adapted, diUused and incorporated into a speciVc good or service. The synergies

taking place in a given country have been notably identiVed 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 administrators

from a diversity of countries, ranging from OECD founding members such as France

and Finland, to developing countries like Korea and Brazil. The development of an

NSI theory has been, however, hampered by the inherent diXculties for empirical

analysis. In contrast to other Velds such as Vnancial economics, the interactions

involved in innovation are diXcult to parametrize and thus analyze quantitatively.

As a consequence, the impact of policies derived from the NSI concept has yet to be

fully understood.

This dissertation leverages the Knowledge Economy (KE) Framework developed

by the World Bank as a means to assess the eUect of NSI performance on eco-

nomic growth. The framework centers on the following idea: the manner in which

applicable knowledge is produced and Wowing within society is crucial for increas-

ing economic output. The KE approach integrates the NSI concept as one of the

four components, referred as KE Pillars, deemed to enhance growth in terms of

knowledge creation, diUusion and adaptation; these are: the Economic Regime, the

Innovation System, Education and Information and Communication Technologies.

This framework allows an empirical study about the connection between KE vari-

1

ables and economic growth for a sample of 75 countries (developed and developing)

in the [1998, 2007] period. This analysis yields a signiVcantly 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 expenditure

and high-technology exports) variables, on 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 trans-

fer as a consequence of the disjunction between NSI actors. The organization of

document 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 World Bank 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 Vndings

of this dissertation. It also mentions the recognized research opportunities for

further development of the NSI concept and its potential applications.

2

Chapter 2

Background

This chapter surveys the theoretical preliminaries and previous works required

to deVne the research hypothesis which aims to connect the NSI approach

with economic growth. On this account, three main subjects are covered. First, a

concise background of economic theory is presented regarding endogenous growth,

in order to understand the role of innovation in the development of markets. The

concept of a NSI is the second topic of discussion. The trajectory of this approach is

described through the exposure of representative studies, in which this dissertation

is based on. Lastly, the KE Framework is introduced in order to lay the grounds for

the quantitative analysis that will be undertaken in the forthcoming chapters.

2.1 Endogenous Growth and Innovation

The Solow model is signiVcant for economic growth theory, not only because of its

revealed Vndings, but also due to the shortcomings that can be derived from it. This

model has brought a general understanding on how savings, population growth and

technological progress aUect the level of an economy’s output and its growth over

time [Mankiw, 2006]. However, the standard implementation of this model cannot

explain the diUerences between per capita production in high-income countries and

that of the least developed countries, or why the average growth of GDP per capita

is 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 diUerences, although

the reasons behind them are still a matter of debate under this approach. These

limitations of the Solow model have been the main inspiration for the subsequent

3

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 speciVcation. Studies of this branch have arrived to

a common conclusion: asymmetries in development rely on the diUerences between

social 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 Solow

model, the role of knowledge (technology) was considered along with labor and

capital, but left unexplained and assumed exogenous. There are two main criticisms

for considering knowledge as an externality, which endogenous growth centered

in resolving: the mentioned diXculty to explain observed long run diUerences in

economic growth and the fact that changes of productivity are a result of conscious

decisions made by socio-economic agents.

Innovation1 is a process in which new products and services are developed

through R&D activities that originate in market competition. This permanently on-

going process results in technological progress which is, in turn, the centerpiece of

endogenous 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 diXcultly be sustainable in the absence of steady tech-

nological improvements [Barro and Sala-i-Martin, 2003]. This idea is supported by

the 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 the

concept of a NSI.

1This work implicitly uses the following deVnition of innovation. It is a product or servicewhich fulVlls 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 speciVc mar-ket need; and iii. it generates proVts, meaning the investment involved yielded positive returns[Escorsa, 1998].

4

2.2 The ‘National System of Innovation’ Approach

In the Veld 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 approach

has received researchers’ interest due to its focus on the endogenous building of ca-

pabilities for development and also because it provides a speciVc role for government

policy towards the technological catch-up process [Gancia and Zilibotti, 2005].

The connection between the NSI approach and economic growth is still rather

unexplored. 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 focus

in quantitative rather than qualitative analysis; the measurement problem, along

with its present resolution, will be discussed and illustrated with empirical studies

later on. Secondly, the NSI approach has been found insuXcient to explain growth

by itself, which is why this concept has been mainly used to study industrial de-

velopment dynamics qualitatively [Lundvall, 2007]. For this limitation, it will be

described below how the KE Framework provides a robust platform in order to link

the NSI concept to economic growth.

The concept of a NSI began to be developed more than 20 years ago. Since

then, 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: Brazil

and Chile [Feinson, 2003]. Among this diversity of studies, the deVnition of a NSI

presented below is commonly seen. It corresponds to the Vrst introduction of the

term in print by Christopher Freeman2, in his book about the favorable outcome of

Japanese technological and economic policy in the 1960s and 1970s [Edquist, 1997].

DeVnition of National System of Innovation:

“The network of institutions in the public and private sectors whose

activities and interactions initiate, import, modify and diUuse new tech-

nologies.”

Source: [Freeman, 1987].

2According to Freeman, the Vrst person he heard use the expression of ‘National System ofInnovation’ was Bengt-Åke Lundvall.

5

Although being increasingly popular within scholars and policymakers, the NSI

approach has experienced complications since its birth. Generally, theories come

within a speciVc Veld 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 NSI

approach. Attempts to deVne, describe and explain this concept have ascended from

a variety of Velds, 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 Wexibility of the term can be seen as an advantage, as it allows

scholars to adapt the analytical tool for studying diUerent contexts. The Vrst study

by 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 produced

and 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 to

be gathered regarding R&D investment and innovation, particularly in in Japan, the

United States and Europe. The data permitted to demonstrate how the success of

innovation depends on R&D expenditure. Furthermore, not only the links between

Vrms were found to have a critical importance, but also the external relationships

with other types of institutions such as universities. In the 1950s and 1960s, the

Japanese success was simplistically endorsed to product imitation and the importa-

tion of foreign technology. However, when Japan’s exports started outperforming

those 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 oUer a suXcient explanation. The Soviet Union

and other Eastern European countries proved that dedicating R&D resources with-

out any elaboration did not guarantee innovation, diUusion and productivity gains.

This elaboration refers to the linkages within the innovation system i.e. the impact

of technical innovations in society depended on how suitable these are for domestic

business, combined with the eUorts devoted by Vrms to adopt them.

A more recent NSI case-study is Korea. [Feinson, 2003] states that this country’s

experience displays the beneVts of dynamic, responsive science policies towards the

6

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 one

in which technology is acquired, diUused throughout the nation and employed in

favor of innovation. Korea’s Vrst stage was to promote technology inWows. For this

purpose, 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 form

of turnkey factories, which were domestically expanded afterwards. Rather than

fostering licensing, policymakers opted for the promotion of import capital goods

which embody technology; the importation of this kind of goods may have been the

most productive method of technology transfer. At that time, Korea probably relied

more on this channel than any other newly industrialized country. The second stage

was to assimilate the imported technology into its domestic production lines. This

was addressed by public funding and a series of incentives towards R&D, including

tax breaks and exemption from military services for key personnel. The third stage

consisted in an outward orientation in the form of export promotion policies. These

included the liberalization of access to imported intermediate products, the facili-

tation of banking loans for working capital destined to export-related investments

and the elimination of restrictions to foreign capital.

The NSI approach has also been used to evaluate STI policies in countries of the

Latin American region. For example, [Holm-Nielsen and Agapitova, 2002] studied

how Chile has increased its competitiveness due to a favorable macroeconomic en-

vironment for STI. However, in this country, research institutions have remained

rather disjointed from the productive sector, which wastes potential for continu-

ous product innovation and hinders increments of living standards. The analysis

suggests that Chile needs to make its NSI more eUective in two main ways: by

strengthening 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 deVnition for NSI has allowed a wide variety

of analyses. However, this Wexibility brings complications because just as a NSI

diverges between countries and regions, so does the approach from their analysts

[Lundvall, 2007]. In this ambivalence and lack of consensus relies the criticisms

of the NSI concept. In natural sciences, agreeing in strict deVnitions is seemed

7

as crucial to allow scientiVc progress. Modern economics is characterized for the

emulation of this rigidity. This might be the reason why the NSI approach has

slowly penetrated economic theory. The components, attributes and relationships

that compose a NSI are tremendously diXcult to quantify because the level of anal-

ysis corresponds to that of an entire nation. To overcome for this limitation, the

quantiVcation 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]. Data

recording has recently begun in this respect, across a wide range of countries. As

follows, concrete indicators regarding manufactures imports and exports, FDI, R&D

expenditure 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 explained

in 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 between

NSI performance and economic growth. These studies reWect various important

lessons 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 growth

over 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 their

variations resulted in faster or slower rates of growth. While labor and capital pro-

ductivity are employed for the Vrst century, the second is analyzed using more spe-

cialized indicators such as manufactures exports, information and communication

technologies expenditure and R&D personnel, among others. The analysis is rather

limited by data availability. However, it argues that these indicators show a clear

pattern of how, after the industrial revolution, the NSI approach can be employed

quantitatively 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 eXciency of innovation systems and the geographical diUusion of knowl-

edge spillovers. With an econometric analysis, that includes only European coun-

tries, the work highlights two main results. First, the interaction between research

and socio-economic institutions determined the potential for maximizing the capac-

8

ity to innovate. Secondly, proximity had an important role in allowing the diUusion

of economically productive knowledge and its impact in overall growth.

[Krammer, 2008] executes a cross-country analysis for Eastern Europe to explore

what enables countries to innovate more than others at a national level. As a proxy

for innovation, Krammer uses the number of international patents granted by the

US patent oXce. His results suggest R&D commitments and the ‘innovative tradi-

tion’ were key for increasing the knowledge stock. Openness and the protection of

intellectual property rights determined higher international patenting, while struc-

tural industrial distortions had a negative inWuence in the quantity of patents.

Lastly, [Fagerberg and Srholec, 2007] uses a broader set of data for studying the

role of innovation within a set of capabilities for development. The work bases its

methodology on a factor analysis that compromises 25 indicators and 115 countries

from 1992 until 2004. By this method, four types of capabilities were identiVed: the

development of the innovation system, the quality of governance, the character of

the political system and the degree of openness. Of these, the innovation system

and 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 upon

this study. In contrast, however, diUerent indicators are selected, arranged and

statistically analyzed here, using the KE Framework. This method of analysis points

to similar conclusions to those of [Fagerberg and Srholec, 2007].

In synthesis, the trajectory of the NSI approach has been more qualitative than

quantitative. This approach aims to explain the dynamics of industrialization, tech-

nological catch-up and development. Empirical analysis has been made diXcult due

to the problems for measuring innovation systems. However, researchers have pro-

posed performance as a plausible scale of reference, which has allowed some studies

to emerge. Still, there is a need for more quantitative research to validate the NSI

concept. This dissertation adds to these eUorts by turning to the KE Framework,

described in the following section.

2.3 Leveraging on the Knowledge Economy Framework

This 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

For this purpose, this section includes relevant excerpts from the book Building

Knowledge Economies: Advanced Strategies for Development [World Bank, 2009a];

in this publication, the Wold Bank compiled its work on the KE Framework.

The World Bank has addressed the KE Framework through its Knowledge for

Development (K4D) Program. This program contributes to the framework by pro-

ducing publications and distributing those of third-party specialists in this Veld of

study. Its aim is to promote the framework’s awareness among policymakers world-

wide. Through the KE literature and the eUorts for data compilation from the K4D

Program, the World Bank has constructed the KE Framework to analyze, study

and 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 industrially

advanced and developing, have been recognizing know-how and expertise as criti-

cal as other economic resources. Industrial production requires appropriate policies

that reWect the current interconnected and globalized economic context.

According to [World Bank, 2009a], the KE Framework rests on four pillars: the

Economic Regime, the Innovation System, Education and Information and Com-

munication Technologies. These have been previously supported as foremost for

economic development by an ample literature and empirical works. Their deVnition

and pertinence are described below.

Economic Regime. It is deVned as the set of economic and institutional incentives

designed to promote an environment that permits knowledge creation, assim-

ilation and diUusion. This pillar covers a broad set of macroeconomic issues

and policies, such as trade, Vnance and banking and governance. Due to this

broadness, this pillar is sometimes divided into two sub-pillars, Economic In-

10

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 Vnancial markets, and by strengthening governance (e.g. increasing the

enforcement of contracts and controlling corruption).

Innovation System. It consists of Vrms, 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 diUusion. 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 continuous3This 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.

4World Bank’s nomenclature omits the word ‘national.’ However, by comparing the deVni-tions of ‘Innovation System’ and NSI, it can be aXrmed that both terms are concurrent.

11

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 diUusion 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 signiVcantly 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 aUecting 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 Hypothesis

The problem for the measurement of innovation systems and the lack of a robust

framework, mentioned in Section 2.2, must be solved for studying the connection

between NSI theory and economic growth. These issues are both addressed by the

KE 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 and

high-technology exports. The KE Framework also integrates NSI theory with the

other three5 pillars mentioned above, which allows to study economic development

from a knowledge-based perspective.

With the concepts that have been revised up to this point, this dissertation’s

research hypothesis is structured below.

5Four (a total of Vve KE Pillars), if Governance and Economic Incentives are considered asseparate pillars, as it would be the case later on in this dissertation.

12

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 an

attractive contribution to the Veld. Second, it would contribute to the eUorts for the

empirical veriVcation of the NSI approach reviewed above, by the means of a new

methodology. Thirdly, the validation of the hypothesis statement would favor the

position 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. In

particular, the next chapter describes the gathering of data and its analysis, taking

into account the literature revised until this point.

13

Chapter 3

Data and Methodology for Analysis

Previously, it was discussed how the KE provides the required framework for

understanding the role of a given NSI in its economy. Upon this background

and seeking to contribute towards a deeper apprehension of the NSI concept and its

validity, a research hypothesis was deVned. Aiming to link NSI performance with

economic 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 Vrst. The

source 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 is

resolved via a Principal-Component Factor Analysis, which groups the variables

of a given pillar in the construction of an associated index. With the constructed

indices, the OLS regression models are stated for the hypothesis’s testing.

3.1 Data

To investigate any eUect of NSI performance in economic growth, data was gathered

seeking to satisfy the following two criteria: a diversity that covers all the features

of the KE and the availability of observations for signiVcant amount of time.

Through the K4D Program, mentioned in Section 2.3, the World Bank classiVes

a variety of pertinent statistics from theWorld Development Indicators (WDI) under

the 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 depuration

of this source, aiming to comply with the two criteria stated above. In particular,

14

the process took into account the fact that several of the referred KE variables have

started being recorded, across a signiVcant amount of countries, only until recently.

These variables were discarded, for the limited observations would not allow a sig-

niVcant timespan for analysis.

The selection process yielded a total of 19 variables for 75 countries in the the

period [1998, 2007]. To balance the dataset and compensate for missing Vgures, the

timespan was divided into two 5-year intervals. These are: [1998, 2002] and [2003,

2007]. Observations were deVned as the average values of the variables for each of

these two periods. The summary statistics of these variables are displayed in Table

3.1.

3.1.1 Rationale for Variable Selection

Although most of the variables are already classiVed under the KE Framework by

the K4D Program, it is necessary to discuss why each is signiVcant for the pillar it

represents. Starting with the Economic Regime Pillar, there ismarket capitalization

of listed companies, domestic credit provided by the banking sector and domestic

credit to the private sector, all measured in % of GDP. The Vrst variable is deVned as

the 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 refers

to the totality of credits conceded to various sectors on a gross basis, excluding those

provided to the central government. The third includes Vnancial resources provided

to the private sector (e.g. loans, non-equity securities and trade credits). These three

variables have been employed in studies concerning Vnancial market development

and economic growth; although the importance of the former in the latter has been

a matter of debate, several studies have evidenced on a signiVcantly positive eUect

[Levine, 1997];[Levine and Zervos, 1998].

There are six variables for the Governance Pillar. Their deVnitions are presented

as 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. Political

Stability reWects perceptions of the probability that the government will be destabi-

lized or overthrown by unconstitutional or violent ways, including political violence

and terrorism. Government EUectiveness captures the perception of the quality of

15

Table 3.1: Summary Statistics for the Selected KE Variables

Obs Mean Std. Dev. Min Max Obs [1998,2002]Obs [2003,2007] ∗ 100

Economic Incentives (Values in % of GDP)

Market capitalization oflisted companies

225 53.21 60.05 0.07 434.31 49.78

Domestic credit provided bythe banking sector

362 57.66 53.63 -57.35 304.29 50.00

Domestic credit to theprivate sector

362 46.6 44.58 0.72 220.73 50.00

Governance (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 EUectiveness 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 49.10

Innovation System

Manufactures imports(% of merchandise imports)

336 66.67 11.04 21.16 90.89 50.60

High-technology exports(% of manufactures exports)

328 9.76 12.59 0 73.09 50.91

Foreign direct investment,net inWows (% of GDP)

347 4.96 5.58 -6.58 39.35 49.86

Research and developmentexpenditure (% of GDP)

200 0.87 0.92 0.01 4.47 52.50

Education

Public spending on educa-tion, total (% of GDP)

305 4.69 2.09 0.6 15.57 52.79

School enrollment,secondary (% gross)

355 72.54 31.06 5.93 156.48 49.86

School enrollment, tertiary(% gross)

318 26.67 23.32 0.14 91.35 50.31

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

public services, the civil service and the extent of its independence from political

pressures, along with the credibility towards the government’s formulation and im-

plementation of policies. The Regulatory Quality indicator perceives the ability of

the government to devise and carry out robust policies and regulations that allow

and foster the development of the private sector. Rule of Law captures the impres-

sion on how socio-economic agents have conVdence in and abide to the rules of

society i.e. speciVcally, the quality of contract enforcement, property rights, the po-

lice and the courts, as well as the protection from crime and violence. Lastly, the

Control of Corruption captures perceptions of the extent to which public power is

safeguarded 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 promotion

policies, especially in developing countries. According to [Gray, 2007], the most

prevalent approach in governance policy-making is known as the ‘good governance’

agenda, which contains the six variables previously mentioned. Representatives of

this agenda highlight its importance not only in the satisfaction of citizens’ aspira-

tions regarding public institutions, but also as a means to foster economic growth

and 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 ‘good

governance’ had its grounds laid only until the 1970s and 1980s. The creation of

quantitative measurements has been key to structure a consensus of the positive

relationship between governance and economic growth.

Regarding the (National) Innovation System Pillar, manufactures imports (%

of merchandise imports, foreign direct investment (net inWows, % 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 Vrst

two variables are pertinent channels of foreign technology transfer for the catch-

up process. Manufactures imports incorporate foreign technology and represent

intermediate capital goods necessary for producing value added exports. FDI is rel-

evant as a source of capital for export promotion albeit does not necessarily Wow

into sectors intensive in technology, which is why public policy is sometimes em-

ployed to foster investments that imply technology transfer. The cited case-studies

also show that other two variables of the Innovation System Pillar measure the in-

17

digenous appropriation of knowledge i.e. these are related to how the mentioned

technology transfer channels are being utilized in the economy for producing inno-

vations and towards the promotion of technological capabilities. High-technology

exports 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 of

foreign technology to the local context. By understanding the signiVcance of these

four indicators, this dissertation seeks to elaborate on previous empirical research

and explore the link between NSI performance and economic growth.

Before proceeding with the remaining KE Pillars, it is important to acknowledge

that, similar to other quantitative studies, the variables selected here emphasize

formal modes of learning and innovation based in science and technology activi-

ties [Lundvall, 2007]. This emphasis is reWected here in the selection of indicators

of R&D and capital-embedded industrial goods. However, innovation strategies

based on experience and the “doing, using and interacting” learning mode are rather

overlooked. This is explained by the lack of standardized variables to represent

experience-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 of

public authorities at all levels, along with the subsidies to private education at the

primary, secondary, and tertiary levels. Secondary school enrollment (% gross) and

tertiary school enrollment (% gross) are the ratio of total enrollment, regardless of

age, to the population of the age group that oXcially corresponds to the level of

education. According to [World Bank, 2009a], secondary education completes the

provision 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 an

important amount of human capital required for countries’ economic growth. The

role of tertiary education is crucial. Universities and research institutions have to

address 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 evidence

has been asymmetrical it mainly points towards a positive causal relationship. The

same study identiVed a positive relation between public spending on education and

economic growth. The reported signiVcance of the relationship, however, varied

18

depending on the composition of governmental spending between basic and higher

education i.e. it lost its signiVcance 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 reported

by [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 increases

productivity. There is less agreement, however, on how much a priority it should

be to promote the increase of ICT infrastructure. These technologies are increas-

ingly being seen as means to other development requirements rather than as an end

themselves. Policies associated with this KE Pillar have been focused on alleviating

the wide disparities in access; the poor is the part of society most out of reach from

ICT.

3.2 The Construction of KE Pillar Indices

Even after depuration, the dataset from Table 3.1 is still composed of too many

variables for an econometric analysis. [Fagerberg and Srholec, 2007] faced a sim-

ilar problem in its attempt to explore the relationship between NSI and economic

growth. To face a dataset with numerous variables, the work employed a Principal-

Component Factor (PCF) Analysis. As described in the study, this process is based

on the idea that variables from the same category are likely to be signiVcantly cor-

related and thus can be reduced into a smaller number of indicators, which reWect

the variance dimension of the data. The PCF Analysis assigns speciVc “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 coeXcients, which are derived from the loadings.

As it was mentioned in Chapter 2, [Fagerberg and Srholec, 2007] identiVed four

factors from a set of variables: the innovation system, governance, the political sys-

tem, and openness. In contrast, this dissertation uses a diUerent set of representative

variables for the KE Pillars. Using the K4D Program’s classiVcation, individual PCF

Analyses 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

the indicators for each of the pillars contain information only from their respective

variables. The Economic Regime Pillar is divided into two sub-pillars, as suggested

by the World Bank: Economic Incentives and Governance [World Bank, 2009a].

The PCF Analysis executed here successfully identiVed an underlying structure

for 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 Indices

can be viewed in Appendix A.

The PCF Analysis maximizes the amount of overall variance (accounting by all

the variables as a group) that is to be captured by the index. The correlations in

Table 3.2 indicate the “relevance” each variable has in its corresponding index. For

example, in the case of Education, both secondary and tertiary school enrollment

have a higher weigh in the calculation of the associated index values, compared with

public spending variable. The constructed Education Index is more correlated with

the Vrst two variables because the two enrollment indicators are more correlated

with each other in comparison to the Vscal indicator1.

The variance explained by the Innovation System Index is 40.18%. While this

value is rather low, it is still signiVcant on what is considered best practices for PCF

Analysis, as stated in [Costello and Osborn, 2005]. Furthermore, correlations in this

index are all above the 32% recommended borderline. The values show that, in this

constructed index, the more relevant variable is high-technology exports, followed

by manufactures imports, R&D expenditure and, lastly, FDI inWows.

It is critical to recognize that this particular Innovation System Index is designed

to 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 comparative

analysis 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 as

these consider a series of factors that are better classiVed in other KE Pillars e.g.

in the case of the NIC index: venture capital availability (Economic Incentives),

the quality of institutions (Governance), human capital (Education), and the social

penetration of information and communications infrastructure (ICT). The KE ap-

proach allows the separation of these factors and thus an independent analysis of

1For more details on PCF Analysis, please refer to [Smith, 2002].

20

Table 3.2: The Constitution of the KE Pillar Indices

Economic Incentives Index Education IndexVariance Explained: 81.12% Correlation Variance Explained: 63.38% Correlation

Market capitalization of listedcompanies

0.79Public spending on education,total

0.45

Domestic credit provided bythe banking sector

0.94 School enrollment, secondary 0.93

Domestic credit to the privatesector

0.97 School enrollment, tertiary 0.91

Governance Index ICT IndexVariance Explained: 87.84% Correlation Variance Explained: 91.09% Correlation

Voice and Accountability 0.89 Personal computers 0.95Political Stability 0.86 Mobile phones and landlines 0.94Government EUectiveness 0.97 Internet users 0.97Regulatory Quality 0.95Rule of Law 0.98Control of Corruption 0.96

Innovation System IndexVariance Explained: 40.18% Correlation

Manufactures imports 0.69High-technology exports 0.76Foreign direct investment,net inWows

0.35

Research and developmentexpenditure

0.65

NSI performance.

To compare the NIC Index with the Innovation System Index, developed in this

dissertation, the correlation was calculated between the 2001 value of the former2

and 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 the

NIC dataset, while some of the remaining percentage is likely to be balanced with

the information included in the other pillars. As mentioned earlier when elaborat-

ing on its variables, the Innovation System Pillar Index should be interpreted as an

attempt to represent the general impression of how a given NSI performs through

its development. The indicator intends to reWect the several case-studies presented

in Section 2.2.

2Based on data from [Porter and Stern, 2002].3Using the values of Appendix A.

21

3.3 SpeciVcation of Models

The 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 included

in 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 2.85Governance 134 0 1 -1.88 1.65Innovation System 134 0 1 -2.05 2.26Education 134 0 1 -2.95 2.35ICT 134 0 1 -1.27 2.48

Control Variables

GDP per Capita134 8.60 1.35 5.53 10.61

(constant 2000 US $)

Dummies (Binary Variables)[1998, 2002] Observation 134 0.52 0.50 0 1Sub-Saharan Africa 134 0.07 0.26 0 1Latin America & the Caribbean 134 0.15 0.36 0 1East Asia & PaciVc 134 0.06 0.24 0 1Middle East & North Africa 134 0.05 0.22 0 1South Asia 134 0.01 0.12 0 1Europe & Central Asia 134 0.17 0.38 0 1

The logarithm of GDP growth is selected as the explained variable and the Vve

KE Pillar Indices are included as explanatory proxy variables. Furthermore, eight

control variables are included in the analysis. Six of them are regional dummies

(binary variables), which group developing countries according to World Bank’s

geographic classiVcation. These are: Sub-Saharan Africa, Latin America & the

Caribbean, East Asia & PaciVc, Middle East & North Africa, South Asia and Eu-

rope & Central Asia [World Bank, 2010a]. The null case of the regional dummy

22

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 are

from Latin America & the Caribbean). Adding all the means result in the propor-

tion of developing countries in the [1998, 2007] sample: 51%. Thus, the remaining

49% of observations conform the sampled high-income countries. Another control

variable 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 eUects i.e. temporal variations that are not

captured by the KE Pillars and the other control variables.

The econometric analysis is undertaken in a set of four models. These models are

represented in Table 3.4. All of these have the logarithm of GDP growth lngdpgi as

the explained variable and have the KE Pillar Indices as proxy variables, a constant

and an error term, represented as Pki, C and εi, respectively. The basic model

includes only the Vve proxies, while the subsequent models incorporate the control

variables progressively.

Table 3.4: Econometric Analysis: Model DeVnitions

lngdpgi =5∑

k=1

βkiPki + β6ifirsti + β7ilngdpli +6∑

k=1

β(k+7)iRki + β14C + εi

Model 1 3 3 3

Model 2 3 3 3 3

Model 3 3 3 3 3 3

Model 4 3 3 3 3 3 3

The second model adds the firsti dummy variable, which equals to one when

the observation corresponds to the [1998, 2002] period and zero otherwise, thus tak-

ing into account time eUects. The third augments the analysis with the variable

lngdpli (logarithm of GDP per capita) to consider the inWuence of conditional con-

vergence. Finally, the fourth model adds the six regional dummy variables, written

as Rki, to check for the geographical particularities that might aUect growth and are

not captured by the other variables.

4The 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 eUects of diminishing returns are not as consolidated as in higherincome countries.

23

To recapitulate, this chapter described how data was extracted using World

Bank guidelines and databases, in order to robustly represent the KE Pillars of

75 countries worldwide for a ten-year period of analysis: [1998, 2007]. This time

interval was divided into two consecutive quinquennial periods: [1998, 2002] and

[2003, 2007]. For each of these, averages of available data were calculated. The

resulting variables were then employed for the construction of the associated KE

Pillar Indices using PCF Analysis. These indices are to be used as explanatory proxy

variables for GDP growth, along with eight control variables. The coeXcients from

the models stipulated above are to be estimated through regressions. In the next

chapter, these results are displayed and analyzed in detail.

24

Chapter 4

Results and Evaluation

Based on the data and the methodology for its analysis, both presented in the

last chapter, this part of the dissertation seeks to exhibit the relevant outcomes

in the validation of the hypothesis: the existence of a statistical link between NSI

performance and economic growth under the KE Framework. This chapter is di-

vided into two parts. First, scatter plots are included for each of the constructed

KE Pillar Indices and GDP per capita. These give a Vrst view on how each index

is relevant within the economic activity. The second part focuses on giving out

information from the econometric analysis of the previously deVned models.

4.1 KE Pillar Indices vs GDP per Capita

Before revising the econometric analysis, it is appropriate to get an initial sense of

the 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. These

show 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 the

y-axis. To check for diUerences between the two time periods, the dots are clas-

siVed accordingly. A Vtted line is included to illustrate the general trend of the

relationship between the index’s scores and the production levels.

Regarding the main Pillar Index of concern, the Innovation System, Figure 4.1

displays a positive relationship with GDP per capita, with a correlation equivalent

to 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

Figure 4.1: GDP per Capita and Innovation System

higher levels of income. The Vtted lines suggest that the relationship strengthened

between the Vrst 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 System

index: 0.6926. The Vgure depicts that countries which scored high in this index,

with an environment suitable for a better allocation of resources represented by

superior 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

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

Vtted value lines are steepest. It shows that countries with better institutions si-

multaneously experienced superior positions of GDP per capita. This relationship

appears 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 Vtted value lines remained

practically the same. The data from the analyzed time interval supports the idea

that richer nations have a more skilled labor force, which eXciently generates and

Figure 4.4: GDP per Capita and Education

27

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. The

slopes of the Vtted value lines, however, presented the most enunciated decrease

between [1998, 2002] and [2003, 2007]. This change might have aUected the econo-

metric analysis, as mentioned later on. Still, it can be said that better infrastructure

for the communication and diUusion 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 display

a positive correlation with economic growth. The fact that the Innovation Systems

Index scores account for the lowest correlation with GDP per capita (albeit still

high), is noteworthy. In a broader perspective, these graphs support the notions of

the authors mentioned in Chapter 2: the NSI concept and the KE framework are

relevant towards economic output. The Innovation System’s performance, along

with the set of Economic Incentives, the level of Governance, Education and the

expansion of ICT are playing signiVcant roles in the economies of today. For a

deeper understanding on how these economies grow in relation to the KE Pillars,

the following section presents and discusses the results of the econometric analysis

based on the models proposed in Chapter 3.

28

4.2 Econometric Analysis

The KE Pillar Indices constructed in Section 3.2 are now employed for regressions

with economic growth as the explained variable, using the models deVned in Section

3.3. Table 4.1 contains the results from the regressions against the logarithm of GDP

growth. The table points out the estimated coeXcients for each of the models. All

the 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 standard

OLS regressions, while (5) and (6) use a stepwise estimation to identify the speciVca-

tion with the best statistical properties2. Column (5) begins the estimation process

with 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 necessary

to acknowledge that the stepwise regressions are included speciVcally to provide

secondary evidence for the relationship between the Innovation System Index and

GDP growth. The resulting estimations (5) and (6), unlike (1) through (4), are not

intended to be representative for the KE Framework as the stepwise regressions

discarded 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 Vrst one, each potentially

endogenous proxy variable was regressed on all exogenous variables (the other

proxies), along with the variables that were used in the construction of the regressed

index. The resulting residuals are, in the second step, added to the new regression

of the original model [Wooldridge, 2002]. If any residual coeXcient was to come

as signiVcant in one of these latter regressions, endogeneity of the corresponding

proxy variable needs to be accepted and the associated model should be estimated

by two-stage least squares in order to achieve consistent results. The resulting co-

1The results of White’s heteroskedasticity tests are included in Appendix B.2.2The stepwise estimation seeks to dismiss variables that do not provide explanatory power

to the model given a particular signiVcance 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 signiVcant.

3Endogeneity occurs when there is a causality loop between the explained and the explanatoryvariables.

29

Table 4.1: OLS Regression Results for GDP Growth.

CoefficientsVariables 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)

Governance-0.14 0.17 0.271** 0.280** 0.219*(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)

Education-0.02 0.01 0.05 0.01

(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& PaciVc (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)

Constant1.279*** 1.684*** 3.554*** 3.518*** 4.703*** 1.404***(0.0568) (0.0734) (0.793) (1.0950) (0.999) (0.0793)

Observations 134 134 134 134 100 134R-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

eXcients of this test are included in Appendix B. In this occasion, no evidence of

endogeneity was found.

Straightforwardly, the most notable result is the recurrent signiVcance of the

Innovation System proxy variable through the regressions, suggesting a positive

eUect from this particular Pillar Index upon GDP growth. Out of all the KE Pillars,

it displays the most signiVcant evidence: in columns (2), (3), (4) (5) and (6) with

p-values lower than 10%, 5%, 1%, 5% and 5%, respectively. These results indicate

that countries with better NSI performance experienced greater GDP growth in the

period of analysis.

This positive relationship is depicted in the following Figure 4.6. The graph

shows the marginal eUect of the Innovation System Index regressor on GDP growth,

after taking into account the associations between the other variables included in

the regression (column (4); Model 4). The slope of the Vtted line corresponds to the

Innovation System Index’s β calculated in the regression. As a support for the va-

lidity of this model’s particular speciVcation, another plot is included as Figure 4.7.

The regression’s residuals do not display any apparent pattern with the Innovation

System Index, supporting the absence of endogeneity discussed earlier.

Figure 4.6: Marginal EUect of the Innovation System Scores (Model 4)

To analyze the relationship between Innovation System’s performance and GDP

growth, it is necessary to recall Table 3.2 (p. 21), which shows that the index is more

correlated to high-technology exports, manufactures imports and R&D expenditure

(in that order) than FDI inWows. Although this structure should be considered as

31

Figure 4.7: Regression’s Residuals vs Innovation System Scores (Model 4)

it provides insights about its constitution, the Innovation System Index, due to its

nature (calculated by PCF Analysis), must be appreciated as a whole. Determining

which of the indicators that belong to this index is more critical for growth falls out

of the scope of this approach.

The Governance Pillar Index returned signiVcant coeXcients less consistently

as with the IS index: in columns (3), (4) and (5) with respective p-values lower

than 5%, 5% and 1%. Still, the unchanging positive sign supports the theory; ‘good

governance’ has been relevant for higher growth.

The outcome of the coeXcients for the ICT and Economic Incentives indicators

are more paradoxical, being both signiVcantly negative in some iterations: (2) and

(4) for the former and the latter in all but (4). This is explained, to an extent, due

to the eUect of conditional convergence. The regression table portrays how intro-

ducing the variable log(GDP per capita) in (3) reduces the signiVcance of both ICT

and Economic Incentives indicators, the latter losing all explanatory power. Coher-

ently, these two pillars are strongly correlated with GDP per capita, 0.84 and 0.69

respectively.

Particularly for the ICT Index, it is worth to revisit Figure 4.5. The scatter plot

shows how the slope of the Vtted line became Watter over time. This adjustment

is most likely explained by the characteristic of the ICT variables employed here.

‘Personal computers,’ ‘Internet access’ and ‘mobile phones and landlines’ are all

technologies that mature and continue to fall in price, thus become more accessible

32

to poorer countries. As follows, a more dynamic ICT Index that contains this eUect

might be more suitable for the present approach.

Education did not yield a signiVcant result. There are two main possible expla-

nations for this. First, the calculation of the variance inWation factors4 for each of

the explanatory variables suggested the presence of multicollinearity. It does not

appear to be so severe, as the signs of the coeXcients in Table 4.1 seldom changed.

However, it might have deterred the signiVcance levels. The second issue is that

the Education Pillar Index variables represent current investment and enrollment,

which do not reWect so strongly in present growth, but rather have a more important

eUect in future increments of GDP.

Regarding the control variables, the signiVcance of the first dummy variable’s

negative coeXcient indicates a strong and generalized trend of greater growth for

the years [2003, 2007] in comparison to [1998, 2002]. Also, although in some iter-

ations more so than others, there were meaningful negative coeXcients 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 yielding

signiVcant coeXcients, they eliminated the explanatory power of the Economic In-

centives proxy variable and reduced that of lngdpl. Thus, the standard regression of

Model 4 suggests that a portion of the negative eUect from the Economic Incentives

Pillar and the ‘conditional convergence’ observed in the previous columns is related

to regional particularities.

Interestingly, the stepwise regression in column (6) traded lngdpl for the regional

dummy variables; this trade-oU, however, did not produce a big change the signiV-

cance and values of the other coeXcients. For Latin America & the Caribbean and

East Asia & PaciVc no signiVcant coeXcients were produced, this suggests that the

particularities of these regions are expressed by the proxy variables of the Inno-

vation System and the Economic Incentives pillars, the latter containing the eUect

of conditional convergence. Excluding the poorest 25% of countries in column (5)

reduced the explanatory power of the Governance Index, suggesting its importance

4vif(B̂i) = 11−R2

i, where R2

i corresponds to the R-squared of the OLS regression in which the

explanatory variable associated with B̂i becomes the explained variable, as a function of all theother explanatory variables of the original model. A large R-squared suggests a high goodness ofVt 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 Vve,multicollinearity is present.

33

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 of

regressions is included in Appendix C. First of all, it is necessary to highlight the

well documented likelihood for the results with respect to GDP per capita to suUer

from endogeneity. With this setup, it is diXcult to know whether the KE Pillar In-

dices aUect the levels of GDP per capita, or if the relationship is opposite e.g. richer

countries can aUord better public education. In the previous regressions no evidence

of endogeneity was found i.e. the tests did not show that fast growth rendered better

performance of the pillars. Even though a causal relationship cannot be identiVed

in the regressions with GDP per capita as the dependent variable, these illustrate a

positive connection between all KE Pillars.

In this chapter, the coeXcients of models derived from the KE Framework were

estimated using the Pillar Indices constructed in Chapter 3. The Innovation System

Index, as a function of inward FDI, manufactured goods imports, high-technology

exports and R&D expenditure, was found to have promoted economic growth dur-

ing [1998, 2007] in the 75 sampled countries. This relationship reached conVdence

levels lower than 1% when taking into account time eUects, conditional conver-

gence and regional particularities. Furthermore, under this approach the ‘good gov-

ernance’ index displayed a similar positive eUect, albeit less prominently. These

results are concurrent to those of [Fagerberg and Srholec, 2007] which also calcu-

lated innovation system and governance indices, albeit using diUerent variables,

complementary factors for economic growth (i.e. the political system and openness)

and other methodological diUerences as to this dissertation. For the remaining KE

Pillars, 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 an

developing country like Colombia, whose policymakers have traditionally focused

KE eUorts towards the Economic Incentives Pillar and neglected the consolidation

of the Innovation System Pillar. The following chapter explores the signiVcance of

the exposed link, between NSI performance and GDP growth, for this particular

country.

34

Chapter 5

An Overview of the Colombian National

System of Innovation

Results point towards a positive eUect of NSI performance on economic growth.

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 speciVc country is chosen as a case-study.

Colombia’s NSI is to be analyzed using the considered theoretical background and

the gathered evidence. First of all, the general circumstances in which the system

began to be recognized as a public institution are described. Secondly, attention

is given to various studies which characterize the present state of Colombia’s NSI.

Thirdly, the current policy framework and instruments are described, in order to

grasp 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 performance

and economic growth.

5.1 Background

Since 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 Wedged liberalization was

gradually undertaken. In 1999 the country experienced a recession, caused by the

generalized capital outWows experienced across the developing world at that time,

which was aggravated in Colombia by an internal mortgage crisis. Although the

downturn worsened the country’s poverty Vgures, it was followed by a recovery

35

stage i.e. the economy experienced accelerated growth between 2002 and 2007; for

this period, the increase of real GDP averaged 5.32%. This was mainly due to

more favorable economic conditions abroad and a series of policies that enhanced

the Colombian business climate, perhaps the most signiVcant one being the sus-

tained progress in domestic security. Nevertheless, with the current global economic

downturn, the most recent Vgures are rather timid. Colombia’s economy grew only

2.53% in 2008 and the following year GDP growth was practically nonexistent.

To enter the globalized economy, the Colombian State tore down tariU barriers

and other protectionist measures. This new approach to international trade, while

rightfully seeking to improve domestic productivity levels, aUected many Colom-

bian Vrms which could not compete with the foreign companies that entered the

country. Policymakers, aware of this, have appointed export promotion mecha-

nisms, which mainly seek to improve the country’s level of competitiveness. The

current 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 Política

Económica y Social (Conpes; National Council for Socio-economic Policy), a gov-

ernmental institution which provides the framework for Colombia’s development

policies. 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 theimprovement of the physical infrastructure or the training of the laborforce, to the reorganization of institutions or the eliminations of [bureau-cratic] procedures. All these projects [...] seek to eliminate the obstaclesfaced by the productive sector during its operation...”

Source: translated from [Mincomercio and DNP, 2004].

“A nation’s competitiveness is deVned as the degree to which a coun-try can produce goods and services capable of competing successfully inglobalized markets and, at the same time, improve the population’s in-come conditions and the quality of life. Competitiveness is the resultof the interaction of multiple factors related to the conditions faced bybusiness which aUect 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

These two extracts from the policy documents show that, in Colombia, there

has been a bias towards exogenous mechanisms for increasing competitiveness i.e.

by infrastructure improvements (e.g. highways, access to utilities and airports) and

through the reVnement of institutions (e.g. a more eXcient bureaucracy, decreasing

corruption and a more eUective enforcement of legal contracts). In both documents,

the role of innovation is seldom mentioned and the respective policy guidelines are

practically 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 Innovation

System 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 mechanisms

that favors competitiveness. A domestically developed innovative product or ser-

vice will (by deVnition) outperform its competition, while implicitly contributing

towards technological learning inside the Vrm and thus enhancing its productivity1.

The fact that innovation has been belatedly adopted as a State policy has brought

upon several weaknesses in the Colombian NSI, as reported by the several studies

included in the following section.

In Colombia, the institutional framework for a NSI was introduced in 1995. It

was preceded by the Sistema Nacional de Ciencia y Tecnología (SNCYT; National

Science and Technology System), conceived in 1990. The SNCYT aimed to integrate

a diversity of institutions which shared a common vision, mission and objectives. It

included Vrms, universities, public research institutions, research centers, and tech-

nological institutions, among other actors. The innovation system was born as a

sub-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 de

Ciencia, Tecnología e Innovación (SNCTI; National Science, Technology and In-

novation System)2 [Monroy Varela, 2006]. To conserve the terminology employed

in this dissertation, the SNCTI will be referred as the Colombian NSI, henceforth

CNSI. The public organization in charge of devising and executing policies that aim

to structure and strengthen the system is Colciencias, an institution that seeks to

1For a detailed description of this process see [Kim, 1997], pp. 88-90.2This is due to the fact that both systems are fundamentally composed by the same actors,

share common concepts, basic strategies and challenges.

37

lead the generation an utilization of knowledge in favor of socio-economic develop-

ment in Colombia. Colciencias deVnes the CNSI as follows.

“The National System of Science, Technology and Innovation is anopen system composed by policies, strategies, programs, methodologiesand mechanisms in favor of the management, promotion, Vnancing, pro-tection and diUusion of scientiVc investigation and technological inno-vation, as well as the public, private or mixed organizations which carryout or promote the development of scientiVc, technology and innovationactivities.”

Source: translated from [Colciencias, 2010].

According to [Monroy Varela, 2006];[Forero, 2000], the CNSI has faced diXcul-

ties since its conception. The most conspicuous have been: an unstable and weak

budget, inadequate policy formulation (e.g. shortsightedness and vertical decision-

making), a lack of public directives, the social apathy towards the socio-economic

value of scientiVc research, a stagnant scientiVc 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 Status

Several 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. The

Encuesta 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 eUorts of Colombia’s Departamento

Nacional de Planeación (National Planning Department) and with the support of

Colciencias. The introduction of an institutional NSI framework matched with

an increased interest in the measurement and tracking of the industry’s techno-

logical development. For this purpose, the Vrst EDT was conducted in 1996 and

these eUorts were continued with iterations of the survey in 2003, 2005 and 2006.

However, the methodology has been constantly amended. While the survey meets

higher standards today and has a considerably larger sample size (6080 Vrms in 2006

[DANE, 2010] as to 885 in 1996 [DNP, 1997]), its more recent results are hard to

38

be compared with those of 1996 [Vargas Pérez and Malaver Rodríguez, 2004]. One

telling indicator which has remained practically unchanged is the percentage of in-

novative Vrms3: 11.3% (1996) and 11.8% (2003)4.

[Monroy Varela, 2006] is another survey-based study, which queried about the

articulation of the CNSI. It found that while the knowledge-producing agents of

the system knew about the existence of a CNSI framework, business owners were

mostly 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 into

Colombian products or services. The survey shows that this bias is reWected on the

disjunction of the system: agents primarily interact with others of the same type.

The third survey reported in [Malaver Rodríguez and Vargas Pérez, 2006] was

carried 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 (one

of Colombia’s thirty-two administrative divisions). It found that less than a third of

the 400 sampled Vrms in the aforementioned region interacted with other agents of

the CNSI in order to complement their eUorts in producing innovations and improve

their technological capabilities. Furthermore, less than 10% of the companies turned

to the public institutions of the CNSI. However, the low share of Vrms that engaged

the system found few diXculties in doing so, meaning that the problem lies in the

absence of associations rather in than the links themselves. Once again the cultural

problem is brought to focus, as 44% of the sampled Vrms considered unnecessary to

reach the CNSI, while 19% did not innovate because it was not considered proVtable

or signiVcant to do so.

[Torres et al., 2007] gathered data through the Premio Innova (Innova Award), a

technological innovation competition for Colombian small and medium enterprises

(SMEs), created in 2004 as a mechanism to promote innovation in technological

start-ups5. The work analyzes how the participating companies are seeking to in-

novate and typiVes the interactions between the CNSI agents. The study is based

3The survey classiVes a Vrm as innovative if, at the corresponding period of inquiry, ithas launched at least one good or service signiVcantly improved by international standards[DANE, 2010].

4As reported by [Vargas Pérez and Malaver Rodríguez, 2004] and [DANE, 2010].5The award aims to bring attention to Colombian innovations, benchmark technological capa-

bilities among diUerent sectors, and to encourage an innovation culture in SMEs.

39

on 225 Vrms which participated in the third version of the competition in 2006. The

gathered data gave pertinent insights to the articulation of the innovation system.

From the total of participating Vrms, around 80% developed innovative products

or services exclusively in-doors, while the remaining 20% cooperated with external

actors (e.g. suppliers, technological development centers and universities). Further-

more, Vrms seldom turned to Vnancial institutions to fund their developments and,

when they did, the Vrm’s share of capital was procured to be higher than that of the

third party. The study highlights a consequence of this low degree of connectivity:

a shallow participation of domestic Vrms in the provision of technological support

for the economic transformation of the country’s natural resources. In other words,

this separation hinders domestic Vrms from evolving economically and in techno-

logical capability together with primary sector industries, which captures the largest

fraction of Colombia’s inward FDI. The work also found the following industries to

be strongly inclined towards increasing eUorts in innovation: agro-industry, man-

ufacture, machinery and services. Moreover, [Torres et al., 2007] makes a special

mention of the cultural factor i.e. actors within the CNSI have diUerent notions of

what constitutes innovation. The study recommends the deVnition of a common

framework for understanding innovation, in order to promote interrelationships in

the system.

5.3 Prospects

The expansion of the NSI concept among scholars and the growing literature have

been far from ignored by Colombia’s policymakers. Researchers have devoted ef-

forts to produce signiVcant studies, which in turn have allowed the assessment of

the CNSI’s challenges and opportunities. Public oXcers have, to a certain extent,

listened to these arguments: the investment budget of Colciencias has grown by

98.15% from 2002 to 2007 (the second period of analysis in this research) and the

following two years (2007-2009) by an additional 50.28%67. A law passed in 2009

granted Colciencias the status of governing institution of the CNSI, bestowing it

with additional responsibilities and independence in designing policies towards the

6Calculations based on [Daza and Lucio, 2007] and [Salazar et al., 2009].7However, Colciencias’s investment budget’s share in the total national government has Wuc-

tuated without any identiVable tendency around 0.75% between 2002-2009 [Salazar et al., 2009].

40

system.

Furthermore, the Conpes public institution has recognized the issues presented

in the survey-studies mentioned above. In the most updated framework paper

for STI, six policy directives are devised to face the main challenges of the CNSI

[Colciencias, 2009]. These are presented below, along with the corresponding sub-

set of strategies.

i. To promote innovation in Vrms. OUer a portfolio of incentives towards in-

novation: public Vnancing instruments, a scheme for technological consulting,

support for technology transfer, and stimulate venture capital; create and fortify

applied research units to identify technology gaps and close them by carrying

out pertinent projects; foster innovative entrepreneurship; and reinforce the le-

gal and institutional framework of intellectual property rights.

ii. To strengthen public CNSI institutions. Continuously drive the regulation of

the legal framework for the CNSI; attain higher budget allocations for R&D and

innovation; create mechanisms for articulation of the system by advancing pro-

grams at the national level and through the participation of public and private

institutions in the formulation of policies; develop the market of science and

technology services; and reinforce the already existing institutions that support

innovation in Colombia, including the relevant data gathering and information

systems.

iii. To expand the base of human capital for R&D and innovation activities.

To develop scientiVc capabilities since primary and secondary education; for-

tify research competences in institutions of tertiary education; continue sup-

porting technical education programs; standardize evaluation schemes; seek the

instruction of professors and researchers in strategic specialties; and to promote

networks for techno-scientiVc exchange between research centers.

iv. To encourage the appreciation of science and technology in the Colom-

bian society. DiUuse R&D and innovation processes and positive outcomes;

distribute the historical, current and future perspectives of STI in Colombia

and Latin America; inform the framework for editorial communications among

CNSI agents (e.g. terminology); increase citizen’s participation in the genera-

41

tion and adoption of knowledge; implement best practices in research; monitor

and evaluate the advance of the social appreciation of STI.

v. To increase the long-term focus towards strategic sectors8. Start a long-

term program that periodically analyzes Colombia’s strategic areas; promote

the CNSI as a platform for increasing their competitiveness; support related

R&D activities of any level of technological complexity; identify personnel and

infrastructure requirements; establish a policy for clusters, value chains, tech-

nological parks and other types of agglomerations encompassing private initia-

tives for innovation and opportunities of collaboration with public institutions.

vi. To decrease regional inequalities in scientiVc and technological capabili-

ties. Increase regional capabilities in favor of knowledge generation, allocation

and management; support research in education institutions; advance the inter-

nationalization of Colombian STI activities.

The execution of the previous CNSI framework of public policy has already been

set in motion. The Inter-American Development Bank (IDB) and the World

Bank approved a multilateral loan amounting to USD 500 million9, to provide the

Vnancial means required to carry out the mentioned guidelines in the following

nine years [2010, 2019]10. The funds are planned to be delivered in two phases: the

Vrst with USD 50 million in [2010, 2013] and the second with the remaining USD

450 million. The funds will be entirely managed by Colciencias. The portion of

the resources from the IDB are focused towards items i., ii., iv., and v. presented

earlier. SpeciVc lines of actions are deVned for each of these. Particularly notewor-

thy are the components for increasing the level of R&D investment in Colombia,

the strategic investments in speciVc sectors, and eUorts for the socialization of in-

novation [Navarro, 2010]. The credit from the World Bank centers in items i., ii.,

8Lessons provided by recent development experiences in developing countries suggest the im-portance of correcting the dispersion of R&D resources, by means of identifying strategic ac-tivities which combine a high socio-economic potential and maximize the beneVt of currentlyavailable resources through the intensive use of knowledge [Navarro, 2010]. The selected sectorsare: biodiversity, water resources, bio-fuels, sustainable energy, materials, electronics and forestry[Colciencias, 2009].

9Each agency having a participation of 50% in the funds provided.10The signiVcance of the USD 500 million loan can be understood by considering that this

amount, divided by the number of years for its implementation (10), accounts for around 51%of Colciencias’s investment budget in 2009 (calculations based on [Salazar et al., 2009]).

42

iii., and v. including speciVc goals for i.: to strengthen Colciencias’s capacity to

promote R&D and innovation; for ii.: to enhance the institution’s operational and

policy-making capabilities; for iii. to fortify Colciencias’s capacity for promoting

human capital; and for iv.: promote social dissemination of STI [Caballero, 2010]. In

2013, the decision of continuing with the second phase of the multilateral loan will

depend on the results attained by the allocated resources and the disposition of the

Colombian government.

5.4 Recommendations

The evidence presented in Chapter 4 is meaningful for the case of Colombia, as it

highlights the country’s troubles for seizing the potential growth relying in inno-

vation and technological progress. The calculated Innovation System Index, which

showed to have a positive eUect on economic growth, increased from -0.2 to -0.13,

since the deVned [1998, 2002] initial period until [2003, 2007]. This change is ex-

plained in Table 5.1, which breaks down the indicators employed in the construction

of the index. The table shows the average values for Colombia for the two periods

of analysis, along with their variation. As discussed in Section 3.1.1 (p. 17), the Vrst

two variables are important inputs in the technological catch-up process and thus

have a more signiVcant role in the earlier stages of the NSI. The other two indicators

show whether these inputs are being eUectively utilized for producing innovations

and the promotion of technological capabilities; hence, these are representative of a

more advanced stage in the development of the NSI.

Table 5.1: Detailed Innovation System Indicators for Colombia

[1998, 2002] [2003, 2007] % variation

Manufactures imports(% of merchandise imports)

80.80 82.72 2.38

Foreign direct investment,net inWows (% of GDP)

2.44 4.02 64.75

High-technology exports(% of manufactured exports)

7.80 4.78 -38.72

Research and developmentexpenditure (% of GDP)

0.19 0.18 -5.26

Source: calculations based on [World Bank, 2009b].

Colombia displays favorable opportunities in the considered channels of tech-

43

nology transfer. Capital with embedded technologies is Wowing to the country.

The conditions towards FDI have particularly augmented, as the country recovered

from the aforementioned crisis and improved its macroeconomic environment. On

the other hand, the other two indicators indicate a poor generation, diUusion and

appropriation of knowledge in Colombia. Despite the increased resources devoted

to Colciencias and the CNSI institutional framework since 1995, both Vgures have

contracted over time.

Evidence from the quantitative analysis supports policies aimed to increase in

NSI performance, in order to achieve considerably higher levels of economic growth.

This result backs the continuation of Colombia’s loan program with the World

Bank and the IDB. Fiscal eUorts assigned to increase Colciencias’s investment

budget are necessary for providing the policy instruments required to structure the

CNSI. This claim makes part of the justiVcation of IDB’s participation in the loan,

as shown below.

“The Colombian economy grew 7% in 2007, culminating seven con-secutive years of economic expansion. Private investment has been aleading source of growth, accounting for 74% of total investment and20% of GDP. Still, studies show that the sustainability of this growth,based on exports of traditional primary and industrial products, hingeson developing a technological foundation whereby such products wouldhave technical speciVcations that would be acceptable to the countriesof destination, and on diversifying exports toward products and servicesin which the intensive incorporation of knowledge plays a fundamentalrole.”

Source: [Navarro, 2010].

The loan program is opportune as it not only proposes to increase public invest-

ment in R&D and the promotion of high-technology exports in speciVc and strategic

sectors, but also because it intends to spark private initiative and encourage the cre-

ation of linkages within the system. Evidence gathered in this dissertation shows

that these policies, provided successful for increasing NSI performance, will increase

Colombia’s economic growth. Can this additional growth be quantiVed? A simple

approximation is included in Table 5.2.

Colombia’s Innovation System Index was recalculated by changing the values

of its knowledge appropriation variables. Then, using the coeXcients estimated by

the standard OLS regression of Model 4 (see column (4) of Table 4.1, p. 30) to predict

44

Table 5.2: Retrospective Estimates for Colombia’s GDP Growth (Model 4)

[2003, 2007]

Observed Scenario 1 Scenario 2 Scenario 3High-technology exports (% ofmanufactured exports)

4.78 4.78 15 15

Research and development ex-penditure (% of GDP)

0.18 0.4 0.18 0.4

Predicted GDP growth (%)∗ [2.29, 8.25]† [2.34, 8.41] [2.48, 8.94] [2.51, 9.03]GDP growth increment (inrelation with Observed)∗

- [0.05, 0.16] [0.19, 0.69] [0.22, 0.78]

∗95% conVdence intervals.†Actual growth was 5.90%.

in retrospective what the changes would have meant for Colombia’s GDP growth11.

The results suggest that if Colombia’s high-technology exports and R&D expen-

diture had doubled from [1998, 2002] levels, the country would have experienced

between 0.22 to 0.78 additional percentage points of average growth in [2003, 2007].

The case-studies presented in Chapter 2, whereas being attached to a particular

context, share a common denominator: the extent of the organization and perfor-

mance of a NSI is determined by the utilization of technology transfer channels

at the service of domestic businesses– or, in terms of the KE Framework, by the

generation and diUusion of applicable knowledge among the economic agents. As

follows, even though there is no doubt about the desirability of high R&D expendi-

ture levels, the eXciency of the NSI is conditioned by its degree of connectivity. In

this sense, the several studies based on Colombian surveys presented earlier show

that the agents of the CNSI are severely disjointed. The scrutinies revealed that

innovation in Colombia is hindered not only by Vnancial impediments, but also on

account of a considerable cultural problem i.e. some Vrms do not acknowledge the

need of interacting with the CNSI nor the proVtability of innovating. This ques-

tions, in general terms, both the applicability of knowledge produced by Colombian

research institutions and the interest of businesses to incorporate new indigenous

knowledge in their products or services as a means to innovate.

Consequently, it is pertinent to recognize that both the Conpes paper and the

derived IDB & World Bank loan program have a stronger emphasis towards im-

11The 95% conVdence intervals were calculated by using the root mean square error of therespective regression.

45

proving R&D expenditure than to increase the articulation among the CNSI. This

might be explained on the fact that the eUect of R&D investment is much more

documented than the relationship between the NSI’s degree of connectivity and in-

novation. Thus, the assessment of whether this issue is addressed to an suitable

extent is recommendable. There are opportunities for future research concerning

the structures of interrelationships within a NSI, their impact on the system’s per-

formance and the right policy instruments to promote them. This line of research

could provide valuable lessons for developing countries like Colombia, which are

currently seeking to structure a NSI.

46

Chapter 6

Conclusions

The performance of National Systems of Innovation, as a function of inward

FDI, manufactures imports, high-technology exports and R&D expenditure,

was found to have promoted economic growth during [1998, 2007]. This dissertation

applied the KE Framework, which compiles the Innovation System with Economic

Incentives, Governance, Education and ICT as the pillars which support knowledge

as the primal generator of economic growth. Under this framework, representative

data was recollected for each of these KE Pillars. The data was employed to con-

struct indices using PCF Analysis, which express the scores for the pillars in each

of the 75 sampled countries, for two consecutive Vve-year periods: [1998, 2002] and

[2003, 2007]. This setup allowed an econometric analysis which identiVed the afore-

mentioned relationship, reaching conVdence levels lower than 1% when taking into

account time eUects, conditional convergence and regional particularities. Under

this approach, the ‘good governance’ index displayed a similar positive eUect, albeit

less prominently. For the remaining pillars, the evidence was inconclusive.

To validate the reliability of these results, the Durbin-Wu-Hausman Test and the

White Test were conducted in order to prompt for the possibility for endogeneity

and heteroskedasticity, respectively. The former did not indicate the presence of a

causality loop between the explained and the explanatory variables. On the other

hand, the latter yielded signiVcant evidence in some iterations; the ‘Huber-White

Sandwich Estimator’ of variance was employed accordingly in order to calculate

robust standard errors.

Furthermore, the gathered evidence was capitalized for a revision of the Colom-

bian case. After providing the necessary background, several survey-studies were

presented which determined the diXculties experienced in the country regarding in-

47

novation and technological progress. The diagnoses emphasized the lack of integra-

tion of domestic Vrms with the economic activities expanded by inward FDI. This is

concurrent with the Innovation System Index developed in this work, which high-

lights how Colombia exhibits increasingly better channels of international technol-

ogy transfer and, at the same time, worsening indicators of knowledge appropriation

i.e. R&D expenditure and high-technology exports. Notwithstanding, policymakers

in Colombia have recognized these deVciencies and are seeking additional eUorts

for the strengthening of the Colombian NSI. A multilateral loan of 500 million USD

is planned to be contracted in conjunction between the IDB and the World Bank in

order to provide additional funds for Colciencias, the public institution responsible

for administering the innovation system in Colombia. The evidence gathered here

shows that the policy instruments provided by the loan, proven successful, will have

a positive impact upon Colombia’s economic growth. In this sense, the low level of

articulation in the system, paired with the rough mechanisms proposed in the loan

program to increase it, is an issue that deserves attention.

The application of the KE Framework utilized in this work can be improved prin-

cipally in two ways. First, the current approach to National Systems of Innovation

emphasizes formal modes of learning and technological innovation based in R&D.

However, innovation strategies based on experience and the corresponding “doing,

using and interacting” learning mode are rather overlooked. This is explained by

the lack of standardized variables to represent experience-based innovations. This

type of innovation is especially relevant for less industrialized countries, in which

the lack of resources limit the development of frontier technological breakthroughs.

Thus, incorporating learn-by-doing indicators into the KE Framework is deemed

conducive to an analysis of higher quality. Second, although tests did not identiVed

endogeneity in the econometric study, the new calculations of an Innovation System

Index could employ other instrumental variables. Using indicators representative of

innovation which are more distanced of economic output would allow an even more

canonical argument in favor of a causal relationship between NSI performance and

economic growth.

This dissertation identiVed further research opportunities centered in the study

of knowledge interactions between NSI agents, a recurrent subject in previous case-

studies and also found particularly pertinent for the Colombian experience. A com-

pelling requirement is to advance the comprehension regarding the role of the sys-

48

tem’s articulation in its performance. Promoting interactions within the system

has been recognized as key for its favorable operation. In Colombia, as expected

from a less developed economy, the NSI is severely disjointed. The impact of this

systemic feature and the methods to correct it are, however, not understood suX-

ciently. There are various strategies to increase the degree of connectivity, ranging

from direct State-administered coordination of institutions to industrial associations

of Vrms and organizations, both of which seek collaboration based on common in-

terests and technical requirements. These strategies could be assessed in terms of ef-

fectiveness towards the diUusion of applicable knowledge, in order to devise deeper

lessons in favor of STI policy-making.

49

Appendix A

Constructed KE Pillar Indices

Table A.1: KE Pillar Indices for [1998, 2002]

[1998, 2002]

Economic Incentives Governance Innovation System Education ICTArgentina -0.65 -0.79 0.25 -0.01 -0.83Armenia -1.23 -1.3 -1.71 -1.01 -1.13Australia 0.4 1.3 0.79 1.87 0.9Austria 0.24 1.32 0.74 0.63 0.58

Azerbaijan -1.25 -1.74 -0.18 -1.08 -1.15Belgium 0.38 1.03 2.21 1.8 0.2Bolivia -0.46 -0.84 1.02 -0.18 -1.14Brazil -0.5 -0.61 0.15 -0.45 -0.94

Bulgaria -1.11 -0.43 -1.01 -0.29 -0.8Canada 1.06 1.35 1.04 0.81 0.72Chile 0.1 0.69 -0.32 -0.55 -0.64China 0.42 -1.2 0.37 -1.94 -1.09

Colombia -0.81 -1.43 -0.2 -0.95 -1.02Costa Rica -0.89 0.29 1.18 -1.14 -0.72Croatia -0.74 -0.49 -0.01 -0.46 -0.53Cyprus 1.74 0.47 -0.64 -0.22 -0.13

Czech Republic -0.59 0.3 0.61 -0.46 -0.32Denmark 0.38 1.45 1.14 1.89 1.08El Salvador -0.69 -0.89 -1 -1.83 -1.08Estonia -0.72 0.4 0.42 0.58 -0.08Finland 0.34 1.58 1.36 1.74 0.89France 0.37 0.81 0.98 0.77 0.2Georgia -1.16 -1.71 -0.84 -0.96 -1.11Greece -0.01 0.31 -0.55 -0.25 -0.3Hungary -0.66 0.51 0.97 -0.01 -0.5Iceland 0.12 1.43 0.55 0.84 1.12India -0.66 -0.87 -2.04 -1.79 -1.25

Indonesia -0.67 -1.65 -1.27 -1.83 -1.22...

......

......

...

50

1998-2002

Economic Incentives Governance Innovation System Education ICT...

......

......

...Iran, Islamic Rep. -0.77 -1.55 -0.69 -0.82 -1.04

Ireland 0.42 1.21 2.27 0.31 0.3Israel 0 0.07 1.91 0.72 0.14Italy 0.05 0.4 -0.5 0.18 0.15

Jamaica -0.67 -0.64 -1.28 -0.37 -0.85Japan 2.45 0.63 0.17 0.01 0.41Jordan 0.1 -0.63 -1.15 -0.26 -1.02

Korea, Rep. 0.11 0.02 0.36 0.57 0.62Kuwait 0.04 -0.26 -0.71 0.01 -0.66

Kyrgyz Republic -1.23 -1.38 -1.28 -0.43 -1.19Latvia -1 -0.02 -0.58 0.47 -0.59

Lithuania -1.09 0.08 -0.85 0.6 -0.68Macedonia, FYR -1.11 -1.25 -2.05 -0.79 -0.91

Malaysia 1.89 -0.23 1.81 -0.38 -0.51Malta 0.51 0.74 1.99 -0.47 -0.13

Mauritius -0.25 0.1 -0.92 -1.25 -0.73Mexico -0.87 -0.63 0.66 -0.84 -0.91Mongolia -1.17 -0.51 -1.17 -0.43 -1.18Morocco -0.37 -0.73 -0.92 -1.56 -1.16

Netherlands 1.27 1.54 1.34 0.82 0.85New Zealand 0.36 1.44 0.21 1.37 0.68

Norway -0.03 1.38 0.72 1.57 0.92Panama 0.06 -0.4 -0.76 -0.52 -0.96Paraguay -0.91 -1.71 -1.05 -1.08 -1.1Peru -0.89 -0.94 -0.88 -0.73 -1.07

Philippines -0.34 -0.86 1.91 -0.87 -1.13Poland -0.86 0.19 -0.21 0.44 -0.69Portugal 0.58 0.87 -0.35 0.62 -0.15Romania -1.16 -0.66 -0.48 -0.88 -0.95

Slovak Republic -0.62 -0.02 -0.21 -0.57 -0.45Slovenia -0.76 0.54 0.05 0.83 0.09

South Africa 1.38 -0.23 -0.57 -0.48 -0.89Spain 0.45 0.9 -0.17 0.62 -0.15Sweden 0.49 1.44 1.81 2.35 1.28

Switzerland 2.22 1.52 1.37 0.1 1.3Thailand 0.6 -0.34 0.51 -0.56 -1.06

Trinidad and Tobago -0.4 -0.19 -1.01 -1.29 -0.84Tunisia -0.35 -0.61 -0.42 -0.33 -1.12Turkey -0.84 -0.95 -0.81 -1.01 -0.82Uganda -1.24 -1.57 -0.8 -2.96 -1.28Ukraine -1.09 -1.45 -1.97 0.21 -1.08

United Kingdom 1.27 1.33 1.23 0.53 0.59United States 1.95 1.15 1.5 0.8 1.04Uruguay -0.59 0.22 -0.91 -0.55 -0.68Zambia -0.89 -1.32 -0.66 -2.87 -1.27

51

Table A.2: KE Pillar Indices for [2003, 2007]

2003-2007

Economic Incentives Governance Innovation System Education ICTArgentina -0.85 -0.96 0.16 0.11 -0.28Armenia -1.23 -1.05 -1.46 -0.91 -0.92Australia 0.95 1.31 0.59 1.84 1.6Austria 0.45 1.28 0.9 0.47 1.6Belgium 0.31 1.04 0.65 1.25 1.2Botswana -1.1 0.26 -1.02 -0.09 -0.84Brazil -0.26 -0.64 -0.38 -0.19 -0.24

Bulgaria -0.63 -0.33 -0.31 0.05 -0.03Canada 1.78 1.34 0.68 0.61 1.85Chile 0.42 0.78 -0.67 -0.27 -0.09

Colombia -0.74 -1.23 -0.13 -0.54 -0.52Costa Rica -0.77 0.09 1.02 -0.62 -0.12Croatia -0.3 -0.23 0.06 -0.06 0.43Cyprus 2.14 0.5 -0.09 0.39 0.76

Czech Republic -0.61 0.38 0.59 0.07 0.7Denmark 1.41 1.56 0.83 2.15 2.17Estonia -0.19 0.61 0.53 0.65 1.4Finland 0.28 1.65 0.96 1.8 1.68France 0.5 0.83 0.72 0.9 1.24Georgia -1.05 -1.27 -0.36 -0.71 -0.81Greece 0.19 0.24 -0.73 0.68 0.28

Hong Kong, China 2.86 0.98 1.62 -0.54 1.89Hungary -0.42 0.46 1.09 0.7 0.47Iceland 2.38 1.66 1.76 1.59 1.87India -0.24 -0.83 -1.94 -1.77 -1.08

Iran, Islamic Rep. -0.64 -1.82 -1.44 -0.54 -0.56Ireland 1.11 1.2 0.76 0.69 1.3Israel 0.3 -0.01 1.62 0.73 0.57Italy 0.26 0.17 -0.69 0.56 1.17Japan 2.34 0.77 -0.06 0.16 1.24

Korea, Rep. 0.38 0.22 0.53 0.99 1.65Kyrgyz Republic -1.2 -1.65 -1.68 -0.08 -0.94

Latvia -0.36 0.21 -0.56 0.81 0.53Lithuania -0.65 0.28 -0.69 0.86 0.5Malaysia 1.23 -0.17 1.5 -0.28 0.4Mauritius 0.17 0.13 -1.04 -0.73 -0.09Mexico -0.86 -0.69 0.41 -0.32 -0.35Mongolia -0.9 -0.73 -1.16 -0.04 -0.73Morocco -0.15 -0.95 -0.8 -1.29 -0.68

Netherlands 1.51 1.38 0.63 0.98 2.17New Zealand 0.47 1.52 0.1 1.71 1.43

Norway 0.22 1.43 0.51 1.71 1.99Panama 0 -0.52 -0.7 -0.56 -0.51Paraguay -1.08 -1.55 -0.6 -1 -0.75

......

......

......

52

2003-2007

Economic Incentives Governance Innovation System Education ICT...

......

......

...Peru -0.82 -1.05 -1.14 -0.66 -0.63

Philippines -0.54 -1.15 1.28 -0.92 -0.78Poland -0.69 0.02 -0.32 0.74 0.26Portugal 0.94 0.71 -0.59 0.55 0.52Romania -0.94 -0.54 -0.24 -0.38 -0.23

Russian Federation -0.58 -1.43 -0.2 0.16 -0.13Slovak Republic -0.75 0.25 -0.21 -0.15 0.78

Slovenia -0.34 0.52 -0.07 1.09 0.96South Africa 2.07 -0.12 -0.79 -0.36 -0.51

Spain 1.23 0.65 -0.34 0.86 0.9Sweden 0.81 1.48 1.17 1.71 2.48

Switzerland 2.36 1.51 1.57 0.32 2.35Thailand 0.61 -0.71 0.05 -0.42 -0.47Tunisia -0.37 -0.63 -0.33 0.14 -0.57Turkey -0.74 -0.72 -1.05 -0.7 -0.3Uganda -1.23 -1.4 -0.89 -2.17 -1.2Ukraine -0.64 -1.23 -1.13 0.8 -0.36

United Kingdom 1.62 1.19 0.74 0.7 1.98United States 2.24 0.93 1.01 1.04 2.02Venezuela, RB -1.12 -1.88 -0.69 -0.6 -0.48

53

Appendix B

Statistical Tests

B.1 Endogeneity: Durbin-Wu Hausman Test

Table B.1: GDP Growth OLS Regressions with added Proxy Residuals

Coefficients

Standard RegressionsVariables

Model 1 Model 2 Model 3 Model 4...

......

......

Econ. Incen. 1.27 0.625 0.176 1.15Residuals (2.83) (2.57) (2.54) (2.52)

Governance -1.61 -1.14 -1.93 -1.81Residuals (2.68) (2.43) (2.27) (2.36)Inn. System -0.798 -0.534 -1.39 -1.74Residuals (2.74) (2.48) (2.46) (2.42)Education -0.086 -2.11 -1.61 -1.08Residuals (2.83) (2.59) (2.21) (2.55)

ICT -0.517 -0.649 0.0008 1.15Residuals (2.76) (2.50) (2.45) (2.43)R-squared 0.09 0.26 0.30 0.38

Note: Standard errors in parentheses*** p ≤ 0.01, ** p ≤ 0.05, * p ≤ 0.1

Values in the order of 10−6

54

B.2 Heteroskedasticity: White Test

Table B.2: White Test Summary Results

Model 1 Model 2 Model 3 Model 4

GDP Growth

p-value 0.2919 0.0331 0.1683 0.0141

GDP per Capita

p-value 0.1313 0.1360 0.3659

55

Appendix C

Estimations with GDP per Capita as the

Explained Variable

C.1 SpeciVcation of Models

Table C.1: Model DeVnitions for GDP per Capita

lngdpli =5∑

k=1

βkiPki + β6ifirst +6∑

k=1

β(k+6)iRki + β14C + εi

Model 1 3 3 3

Model 2 3 3 3 3

Model 3 3 3 3 3 3

56

C.2 Results

Table C.2: OLS Regression Results for GDP per Capita

Coefficients

Variables Standard RegressionsStepwise

Regressions

Model 1 Model 2 Model 3 Model 3Economic 0.202*** 0.195*** 0.273*** 0.285***Incentives (0.0735) (0.0728) (0.0627) (0.0616)

Governance0.541*** 0.436*** 0.391*** 0.442***(0.110) (0.112) (0.0983) (0.0856)

Innovation 0.119* 0.09 0.05System (0.0641) (0.0649) (0.0571)

Education0.223*** 0.214** 0.186** 0.189**(0.0858) (0.0850) (0.0751) (0.0746)

ICT 0.296*** 0.469*** 0.209* 0.175**(0.103) (0.133) (0.125) (0.0839)

[1998, 2002] 0.268** 0.07Observation (0.0786) (0.120)Sub Saharan -0.926*** -0.937***

Africa (0.210) (0.171)Latin America -0.04& the Caribbean (0.165)

East Asia -1.225*** -1.190***& PaciVc (0.227) (0.182)

Middle East -0.591** -0.595***& North Africa (0.232) (0.194)

South -1.529*** -1.606***Asia (0.379) (0.340)

Europe & -0.723*** -0.727***Central Asia (0.169) (0.127)Constant 8.859*** 8.447*** 8.872*** 8.903***

(0.0485) (0.0859) (0.124) (0.0536)Observations 134 134 134 134R-squared 0.83 0.83 0.89 0.89

Note: Standard errors in parentheses*** p < 0.01, ** p < 0.05, * p < 0.1

57

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