IT Educational Policies Boost National Competitiveness

Department of Economics
University of Warwick
EC331: Research in Applied Economics
April 2015
How can ‘IT’ improve National Competitiveness?
Student ID: 1203596
Research Supervisor: Dr. Piotr Jelonek1
Word Count: 4985
Abstract
The rise of Information and Technology (‘IT’) over recent decades has become to be considered a
defining characteristic of the age we live in. This paper is targeted towards policymakers who identify
IT related strategies as a tool to improve national competitiveness. A novel and objective framework
for optimally measuring the multi-dimensional concept of national competitiveness is first developed.
Through the estimation of a Dynamic Panel Data model, the paper then provides evidence that
policymakers should focus their efforts towards IT educational policies. These educational policies
should take priority over and above policies targeting a country’s national IT communication
infrastructure and the diffusion of IT throughout an economy. It is argued that IT educational policy
reforms will significantly and sustainably boost a country’s national competitiveness.
1
The author is grateful to Dr. Piotr Jelonek for his support and guidance throughout the project and
particularly in the development of the measure of national competitiveness derived in this paper. Any errors
remain the author’s own.

EC331 Research in Applied Economics 1203596
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Table of Contents
Section 1 Introduction 3
Section 2 Literature Review 4
2.1 What is National Competitiveness? 4
2.2 IT & National Competitiveness 4
Section 3 Data & Methodology 6
3.1 Data Overview 6
3.2 Measuring National Competitiveness 6
3.3 IT Variables & Research Hypotheses 8
3.4 Model Specification 9
Section 4 Results 10
4.1 Choice of Dependent Variable 10
4.2 Empirical Estimations 11
Section 5 Discussion 12
Section 6 Conclusions 13
6.1 Conclusions & Policy Recommendations 13
6.2 Limitations & Directions for Further Research 14
Appendices 16
Appendix A 16
Appendix B 19
Appendix C 21
Bibliography 24

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1. Introduction
The object of this thesis is to identify the most effective direction for Information & Technology (‘IT’)
related policies to take, in order to improve a typical country’s national competitiveness. Policymakers
across the world are increasingly considering the use of IT as a key tool to achieve policy objectives
due to the abundance of opportunities which it presents (e.g. European Commission, 2004).
The development of IT has driven a revolution in the modern world, with ever improving technology
rapidly changing our consumption, investment and behavioural patterns. IT now concerns a broader
spectrum than the desktop computers which simply performed repetitive calculations when first
introduced. This evolution and has prompted new industries, such as the smart mobile phone and
tablet markets, as well as the development of software applications which they use. Moreover the
majority of pre-existing industries in the modern economy are now shaped to some extent by IT. The
improvements in speed and effectiveness of communication methods is often at the heart of this
influence, particularly for developed nations of knowledge-based economies. The influence of IT
throughout our everyday lives is clearly evident and current trends suggest that the use of IT is only
likely to increase in the future.
As the world moves through the iterative development of newer, faster and better technologies,
educational policy has started to adapt to accommodate these patterns. IT has given rise to a body of
topics within its own right. Additionally schools use the products of IT to enhance learning in other
disciplines. The internet is perhaps the best example to give, as this allows for research in an abundant
library of knowledge. This broader effect of IT shows the potential of the knock-on effects that
technologies may develop, further evidencing the use of IT beyond its own borders.
The ease of communication is considered to be one of the key triggers of modern globalization, which
in turn has led policymakers to consider the national competitiveness of countries. This abstract
concept that has emerged offers many perspectives which form the topic matter of Section 2.1 below.
In the context of this thesis, national competitiveness is defined as the ability of a nation to compete
within itself and with other nations in global markets.
The complex and multidimensional nature of national competitiveness results in a non-trivial answer
to the title of this thesis. After deriving a measure of national competitiveness in line with the above
definition, three hypotheses are tested pertaining to the effects of IT communication infrastructure,
the diffusion of IT and of IT educational policy.
The empirical findings of this thesis suggest that IT related educational polices have a significant and
positive impact on national competitiveness. It is thus argued that policymakers intending to focus on
the development of IT in an economy need to direct efforts towards the upskilling of its labour force
through effective educational reform policies.
Section 2 presents a review of the relevant literature, briefly reviewing the concept of national
competitiveness before detailing the current understanding of the impact of IT. Section 3 outlines the
data and the methodology used in the formulation of the proposed measure of national
competitiveness and the dynamic panel model which is employed to test the research hypotheses.
Section 4 reports the empirical findings of the analysis which are thoroughly discussed in Section 5.
Finally, Section 6 offers conclusions of the thesis and suggest possibilities for further research.

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2. Literature Review
2.1 What is National Competitiveness?
The intricate nature of national competitiveness has resulted in an array of differing approaches used
by researchers and institutions. In this subsection, some of the key contributions necessary to gain an
understanding for the context of this thesis are discussed.
Porter (1990) argues that several individual approaches to competitiveness contain ‘some degree of
truth’ but fail to achieve a general approval among academics. For example, the availability of cheap
labour in order to ‘compete’ in global markets, the availability of natural resources, ‘competitive’
exchange rates and the view that government policy is the key driver of national competitiveness all
provide some insight in a given context, but fail in others. Through his Diamond Model, Porter
proposed that productivity was the key underlying factor in evaluating national competitiveness.
Delgado et al (2012) present a comprehensive review of the development of national competitiveness,
suggesting that the debate centres around three core themes: costs, market share and productivity.
The researchers proceed to derive a complex measure of national competitiveness stemming from
what they define as ‘foundational competitiveness,’ a microeconomic measure of productivity.
Mittal et al (2013) frame three dimensions of competitiveness: (i) at the firm or company level, (ii) at
the sector or industry level, and (iii) at the national level. This framework is appealing, although due
to the interconnected nature of the three dimensions, these distinctions provide minimal structure to
shape our understanding. Momaya (2008) provides one such example of this, highlighting interplay
between industry and national levels.
The World Economic Forum’s Global Competitiveness Reports (‘GCRs’) and the IMD World
Competitiveness Yearbook each use a holistic approach to national competitiveness, but there are
differences between each. In the most recent GCR, national competitiveness is defined as the ‘set of
institutions, policies and factors that determine the level of production of a country’. This far reaching
definition covers many concepts and indicators. Neither however consider causal relationships, but
construct their own competitive indices based on a vast volume of information and factors.
National Competitiveness is increasingly being viewed as a dynamic concept. Kharlamova and
Vertelieva (2013) stress that the competitiveness of today will affect the competitiveness of
tomorrow. This logic is convincing, therefore it is important that future research on national
competitiveness employs time dependent models to at allow for this possibility.
2.2 IT & National Competitiveness
Numerous studies have directly and indirectly linked the impact of IT to competitiveness. Kumar and
Charles (2009), for example, identify technological change as the causal force of productivity
improvements in the modern economy, which under Porter’s approach would equate to national
competitiveness. Furthermore within the GCR, Technological Readiness is one of the 12 pillars of
competitiveness assessed. Löfgren (2008) argues a multidimensional approach is needed to assess
the effect of IT on an economy to account for its far reaching direct and indirect influences.

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In parallel to the rise of IT in the modern world has been the development of the knowledge economy,
which has influenced a large body of literature. Chen & Dahlman (2004) assess the contribution of
knowledge to growth. Among other findings, IT infrastructure is identified as an important channel for
communication, with the researchers reporting a 0.11 percentage point increase in economic growth
caused by a 20% increase of the number of phones per 1000 people in the population.
There is a rich body of literature which concerns the diffusion of IT and the benefits which it can bring
to national competitiveness, particularly for developing nations. Kubielas and Olender-Skorek (2014)
for example presents evidence that Central Eastern European countries have greatly benefited from
this rapid technology transfer.
Further attention however has been paid to the absorptive capacity of a nation, on the logic that a
nation must be well positioned in order to take advantage of these technology transfers. Dahlman &
Brimble (1990) talk about National Absorptive Capacity. Similarly, Cohen & Levinthal (1989, 1990)
stress some importance of an absorptive capacity.
Mowery and Oxley (1995) assess the contributions of IT to Total Factor Productivity Growth (TFP) and
conclude that there are enabling conditions necessary for the effective transfer of technology,
pointing directly to the need for adequate human capital. Achieving a sufficiently educated labour
force to meet the demand for modern-day jobs is not as simple as it would first seem. The problem is
due to the rapid development rates of IT which means that ‘the hardware doesn’t yet exist, the
software hasn’t yet been written, and some of the key companies don’t yet exist,’ (Yapp, 2001 in Riley
2004). Riley (2004) proposes reforms to the education system in line with many other researchers that
schools need to teach problem solving skills and facilitate creative thinking in the classroom in order
to allow the next generation entering the labour force to meet the demand for new (at this point
unknown) jobs and therefore encourage a competitive economy.
With regards to addressing which IT related factors are of the greatest importance, there is some
debate in the literature. Zamora-Torres (2014) performs principal component analysis on a set of
thirteen indicators sourced from the World Bank in 2012. He shows that the factors of (i) ‘High
development of technology and innovation’ (including exports of high technology products and patent
applications) and (ii) ‘Global investment in innovation and development’ (including spending on R&D
as a percentage of GDP) have the greatest impact towards being technologically competitive. This
paper contributes to the literature by testing three distinct IT related factors against national
competitiveness, which allows for the inclusion of the indirect effects of IT throughout the economy
to be considered.

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3. Data & Methodology
3.1 Data Overview
The model developed in this thesis uses data from the World Economic Forum’s GCR which is
published annually. A panel of 116 countries over 8 waves from 2006 to 2013 inclusive, with the
selection of the countries determined by availability of the data below in the vast majority of waves.2
Where data is not available, it is for a variety of reasons, but these can include war and natural disaster
which hinder efforts to gather the required information. This in itself poses an initial problem as these
countries are typically characterized among the least competitive of nations, which implies downward
bias for the reporting of the results. Alternatively, the sample may be considered representative of
countries who are in a position to address economic policy objectives, due to the stability of their
country.
The GCR provides a rich and comprehensive dataset which is ideal for the analysis of national
competitiveness, including a range of microeconomic and macroeconomic factors that makes it ideal
for national competitiveness research. The dataset also includes the World Economic Forum’s own
Global Competitiveness Index (‘GCI’) which is constructed from the data its analysts gather and then
used to rank countries by competitiveness. These rankings are based on what the GCR terms the ‘12
Pillars of Competitiveness,’ and are outlined in Appendix C.
This thesis also uses GDP and export data from the World Bank and IMF databanks, in both absolute
levels and annual percentage increase. GDP data in absolute levels is expressed in 2005 USD and
exports are given in volumes indexed to 100 in the base year 2000.
3.2 Measuring National Competitiveness3
Existing measures on National Competitiveness may be divided between those focusing on an intra-
national competitiveness, that is the competition within a nation, and an inter-national
competitiveness, the competition between nations. The former is particularly true for larger nations,
such as the US. It is noted however that the two may not be mutually exclusive, as it is difficult to be
inter-nationally competitive without being intra-nationally competitive, and similarly for the converse.
Any indicator of competitiveness will align better to one of these perspectives and will critically shape
the direction the researcher’s analysis. To address this divide, the national competitiveness of a given
country i in year t is expressed as a general function its intra-national and inter-national
competitiveness:
, , ,( , )Intra Inter
i t i t i tNC f NC NC (1)
To proxy for each of these factors, GDP and exports are considered respectively, and expressed a
simple linear functional form of a weighted average of the two, which are normalized to the unit
interval, as detailed in Equation (2). Each proxy aligns well with the theoretical motivation. GDP
measures will favour intra-national competitiveness, but may also be sensitive to the inter-national
2
A complete list of these countries and further note son the data can be found in Appendix A.
3
Supporting notes on this methodology can be found in Appendix B.

7
competitiveness of a nation. Similarly, export measures will better reflect an inter-national
competitiveness, but also will be influenced by a nation’s intra-national competitiveness.
, , ,(1 )i t i t i tNC Y X    (2)
Where
,i tY represents the GDP of country i in year t, normalized to [0,1]
,i tX represents the exports of country i in year t, normalized to [0,1]
 0,1 is the weight of GDP
Given this functional form, two key questions emerge. Firstly, what value should the weight take?
To answer this, a framework is developed whereby the rankings produced by the GCI are compared
to rankings of nations produced by ranking the construct of national competitiveness. The problem is
set to minimize the square difference in ranking after summing over all countries in all waves, as
detailed in Equation (3). The GCI is chosen as reference point as a good collective understanding of
what it means to be nationally competitive.
 
2
, ,
[0,1] 1 1
arg min
T N
i t i t
t i
GCI NC
  
 
   
  
 
 (3)
Where
 ,i tGCI is the in sample ranking of country i in the start year t of the World Economic
Forum’s Global Competitive Index.
 ,i tNC is the ranking of country i in year t for a given weight  computed by
equation (2) .
N is the number of countries in sample, equal to 116.
T is the number of waves in sample, equal to 8.
The second question that arises is what units of measurement should be use to express GDP and
exports? Equation (3) is analysed through four cases of GDP and export data outlined in Table 1 below,
which also outlines the motivation for each.
The weight  and the optimal combination of units of measurement above is then chosen to
compute the measure of national competitiveness of country, using Equation (2). Whilst no case
appears to be clearly favourable to another, it is considered that Case 3 offers most promise as it
provides a logical middle ground between Case 1 and Case 2. This case may therefore be most suited
to a general measure of national competitiveness.

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Table 1: Combinations of GDP and exports expressed in levels or annual percentage change
Case GDP Unit Measure Export Unit Measure Motivation
1
Annual Percentage
Change
Annual Percentage
Change
 Percentage changes may better
reflect the competitiveness of a
given year.
 Smaller and developing countries
are not penalized by these factors.
 Consistency in measure of
Percentage change.
2
Absolute Value
expressed in 2005
USD
Volume, with index
to year 2000 for
baseline score of 100
 Developed countries may have
low growth rates but still be
considered competitive.
 Consistency in measure of levels.
3
Absolute Value
expressed in 2005
USD
Annual Percentage
Change
 Competitive nations characterised
by high intra-national
competitiveness and inter-
national competitiveness may
both favour each unit respectively.
4
Annual Percentage
Change
Index volume
(2000=100)
 Completeness of analysis.
3.3 IT variables
Three variables are constructed to distinguish the contributions of IT on national competitiveness,
which are described in Table 2 below. Each is constructed of an equally weighted average of the
original variables reported in the GCRs, so the newly constructed variables follow the same
measurement units also.
It is worth noting here that the Quality of Maths & Science Education is preferred to a measure of IT
teaching. This is in line with Riley (2004) who argues that due to technological innovation, the jobs of
the future are unknown and uncertain. It is therefore necessary to consider the mathematical and
scientific educational policy as these subjects give the transferable skills and ideal background for
software and hardware engineering. The internet access in schools component may be viewed from
two angles: the potential use of IT to aid in learning in other subject areas and the teaching a basic
computer skills. The use of the internet in schools thus implies a basic understanding of computing
knowledge.
The involvement of IT communication infrastructure provides the basis for the knowledge economy,
as discussed in Chen and Dahlman (2004) and the IT diffusion measure is reflective of approaches
taken in Mowery and Oxley (1995) and Nelson (1990).

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Table 2: Construction of IT related variables
Variable
Name
Description Original Variables from GCR Units of Measurement
ITcom
IT communication
infrastructure in a nation
Mobile Phone Subscriptions Per 100 individuals
Fixed Broadband
Subscriptions
Per 100 individuals
Individuals using the Internet Per 100 individuals
ITdiff
Factors affecting the
diffusion of IT in a nation’s
economy
Availability of Latest
Technology
Normalized Scale: 1-7
Firm Level Technology
Absorption
FDI & Technology Transfer Normalized Scale: 1-7
ITeduc
Ability of the nation to
produce a labour force with
appropriate IT skills
Quality of Maths & Science
Education
Internet Access in Schools Normalized Scale: 1-7
Based on the above discussion, the following hypotheses are proposed:
Hypothesis 1
IT communication infrastructure has a significant effect on national competitiveness.
Hypothesis 2
The diffusion of IT has a significant effect on national competitiveness.
Hypothesis 3
IT educational policies have a significant effect on national competitiveness.
It is expected that all will have positive and significant effects, but no expectations are attributed to
the relative magnitude of the coefficient estimates.
3.4 Model Specification
Based on the above national competitiveness and IT variable derivations, an Arellano-Bond two-step
dynamic panel data (‘DPD’) model is specified by the Generalised Method of Moments (GMM):
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, 1 , 1 1 , 2 , 3 , ,
1
i t i t i t i t i t j j i t
j
NC NC ITcom ITdiff ITeduc X     

             (4)
Where
,i tNC Is the national competitiveness of country i in wave t

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, 1i tNC 
Is the lagged national competitiveness of country i in wave t
,i tITcom
,i tITdiff
,tiITeduc
Are the three IT related variable of interest, as defined in Table 2
jX Is a vector of controls comprising of the GCR Pillars of Competitiveness4
Each control is reported for country i in wave t
,i t Is the error term
All variables are given in first differences, as denoted by  , (Arellano and Bond, 1991).
4. Results
4.1 Choice of Dependent Variable
Figure 1 below shows the results of the four cases for determining the optimal weight and measure
of GDP and exports in order to construct the dependent variable of national competitiveness.
4
The ninth pillar of Technological Readiness has been excluded due to its similarity with the ITflow variable
of interest.
0
0.5
1
1.5
2
2.5
3
0 0.2 0.4 0.6 0.8 1
Sumofsquredifferenceinranking,
Overallcountriesandwaves,
Millions
Weighting, ω
Figure 1: Effectiveness of the constructed NC variables
As specified in Equation (3), by measurement unit comibations in Table 1
Case 1: GDP in % change & Exports in % change
Case 2: GDP in 2005 USD & Exports in volumes
Case 3: GDP in 2005 USD & Exports in % change
Case 4: GDP in % change & Exports in volumes

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Each point on each line represents a unique measure of national competitiveness. Computations are
made for incremental magnitudes of 0.01 for omega within the unit interval, giving a total of 400
measures of national competitiveness that are considered in this figure. Each measure is thus
characterized by the combination of:
(i) the weighting of omega (depicted on the abscissa);
(ii) the unit measure of GDP (depicted in the case combinations); and,
(iii) the unit measure of exports (depicted in the case combinations).
As the function f(omega) is to be minimized, the lower the score on the ordinate axis, the lower the
difference between the newly constructed ranking and the ranking determined by the GCI, and
therefore the more effective the combination is for a measure of national competitiveness.
The minimum point is identified at 1  , showing that the GDP expressed in levels (2005 USD)
outperforms all other combinations. Given this result, national competitiveness is expressed in line
with Equation (5) which reduces to Equation (6) for use in Section 4.2 below.
2005 2005 %
, , , ,, (1) (0) (1) (0)USD Vol USD
i t i t i t i ti tNC Y X Y X 
   (5)
2005
,,
USD
i ti tNC Y (6)
4.2 Empirical Estimations
Equation (7) reports the key results of the Arellano-Bond DPD model. Robust standard errors, adjusted
for clustering by country, are given in parentheses.
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, , 1 , , , ,
1
ˆ0.4228 0.0005 0.0038 0.0299
(0.1330) (0.0032) (0.0078) (0.0122)
i t i t i t i t i t j j i t
j
NC NC ITcom ITflow ITeduc X 

            
(7)
The results of the model give evidence which supports the third hypothesis that IT education policy
significantly impacts on national competitiveness, which is significant at the 5% level (p=0.014).
Conversely, both the first and second hypothesis, the use of IT in communication and that the diffusion
of IT significantly impact national competitiveness, are not supported (p=0.627 and p=0.871
respectively). Eight of the eleven controls are also shown to be significant. These results will be
discussed extensively in Section 5 below.5
5
See Appendix C for the full empirical estimations.

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5. Discussion
Given the lack of a consensus on the approaches to national competitiveness, the choice of dependent
variable is fundamental to research undertaken in this field. By construction of a new index for
national competitiveness a simple measure of the multi-dimensional concept has been developed.
The use of the Global Competitiveness Report’s rankings as a benchmark for comparison allows one
to make use of this rich understanding to develop an informed metric which is objectively defined.
It is interesting to note the existence of a corner solution for each of the four combinations of
measures of Figure 1, resulting in a strict preference ordering for the measures of GDP and exports
considered in this thesis. From the most to least effective measure:
1. GDP in levels (2005 US Dollars),
2. Exports in annual percentage change.
3. Exports in levels (indexed to 2000 volumes)
4. GDP in annual percentage changes.
This simplification is welcomed in what is often a complex and interconnecting field of study, however
further investigation would be needed to confirm and validate this finding. It may for example occur
as the result of the specific functional form employed in Section 3.2. Indeed, these results prompt the
call for further research to investigate this methodology involving linear forms of higher degree
polynomials.
A second and critical assumption implicit in the above analysis is that GDP and exports are good
proxies for intra-national and inter-national competitiveness respectively. The intuition behind is this
is reasonable, however one may criticism may be that they alone are overly simplistic. This criticism
could be levied against any single measure of competitiveness, as Porter (1990) observes. A critical
contribution of the analysis is the novel methodology in selecting the most appropriate measure to
use. It remains true that no single metric will fully encapsulate our broad and qualitative
understanding of national competitiveness without being constructed from all explanatory variables
– which of course makes it difficult to undertake causal analysis. Nevertheless, Figure 1 makes good
progress in clarifying the understanding of approach taken in this thesis.
Analysis of this approach is an important pretext for explaining the results of the three hypothesis.
The inability to reject the null of the first hypothesis regarding the communication of IT is perhaps the
most surprising, especially when contrasted with the results of Chen and Dahlman (2004). It is possible
that the knowledge economy is only significant for the most economically developed nations, which
could accommodate this result.
The lack of evidence to support the second hypothesis regarding the diffusion of IT is contradictory to
much of the existing literature, but it is argued that this may be reconciled alongside the importance
of education which is supported through the empirical estimations. Access to new technology may
lead to ambiguous effects on productivity depending on the skill of the labour force using it. On the
one hand, if a new technology is known or understood by an individual then productivity is likely to
improve. However, if a new technology is unknown or misunderstood by an individual, this will hinder
productivity in the short term until such a time when the individual learns to use it effectively. It should

13
be note that the technology need only to be considered ‘new’ in the eyes of the individual who uses
it, so this may apply to older technologies which are diffused throughout an economy as well.
The apparent importance of educational policy is however consistent with previous research literature
(Mowery and Oxley 1995, Dahlman and Brimble 1990, Cohen and Levinthal 1990). IT orientated
education policies will have a direct impact on the labour force supplying the growing and increasingly
diverse IT sector. In addition, the policies will have a broader effect of producing a technologically
capable labour force to supply efficient workers to other industries throughout the economy. It is
argued that IT educational policies should drive the development of the typical nation which attempts
to improve its national competitiveness. Critically this should be prioritised over policies addressing
the IT communication infrastructure or the diffusion of IT throughout an economy.
For a policymaker intent on acting on this argument, Hargreaves and Goodson (2006) analyse the
sustainability of educational reform, noting that changing economic conditions are one of the key
drivers of sustainable educational development. However, the importance of a skills based approach
over a syllabus of IT content must again be stressed. Therefore such IT orientated educational policies
should be broad: the focus needs to be on the mathematical and scientific topics rather than purely
computing. The integration of IT does however does bring potential for further engaging students,
which is another important consideration for educational reforms (Sahlberg, 2006).
This policy recommendation is consistent with Nelson (1992) who argues that national
competitiveness is built upon scientists and engineers. This thesis extends this argument, focusing on
the contributions that an education in mathematics and the sciences will produce the hardware and
software engineers of tomorrow. The development of IT engineers will strongly contribute to national
competitiveness and, all else being equal, will improve a nation’s national competitiveness.
The use of the Arellano-Bond dynamic panel data model is vindicated through the evaluation of the
lagged term value of national competitiveness. The coefficient is significant at the 1% level (p=0.001)
which confirms the importance of allowing for the partial endogeneity of the dependent variable. The
magnitude of the coefficient is far larger than any other (see Appendix C) which suggests that there
are strong momentum effects of national competitiveness. Further research may be needed to
validate this conclusion, if it is to be applied generally across all measures of the concept.
6. Conclusions
6.1 Conclusions & Policy Implications
It is argued that any policymaker who aims to target the abstract concept of national competitiveness
must consider a range of approaches and their respective findings, judging each on its own merits.
In this thesis, national competitiveness is defined as a general function of a nations’ intra-national
(‘within country’) and inter-national (‘between countries’) competitiveness. A simple proxy for each
was constructed in GDP and exports respectively, with a proposed specific functional form expressed
as a weighted average of the two.

14
The units of measurement for GDP and exports, alongside the weighting were determined objectively
by benchmarking the rankings produced from the 400 combinations considered against the rankings
of the World Economic Forum’s Global Competitiveness Report. The optimal measure of national
competitiveness was found to be GDP expressed in 2005 USD (with the weight omega=1 eliminating
the involvement of exports). Using this measure as the dependent variable, an Arellano-Bond DPD
model estimated the impact of IT on national competitiveness. Estimations focused on three distinct
channels: IT communication infrastructure, the diffusion of IT and IT educational efforts.
The derivation of this national competitiveness measure forms a novel way to assess a complex and
multidimensional concept, taking into account a rich volume of this information in a relatively
simplistic manner. The optimization problem of Equation (3) crucially gives objectivity in determining
the weighting and unit measurement of GDP and exports. Without this, the choice of omega is purely
arbitrary, and intuition leads to multiple combinations of measurement units.
In itself, the evaluation of this problem delivered interesting results contributing to our conceptual
understanding of national competitiveness. However more importantly, it provided a solid basis on
which to test hypotheses at the heart of this thesis.
Through analysis of the three channels, the results support the importance of educational policies to
give significant contributions to national competitiveness, in line with much of the existing literature
(Mowery and Oxley 1995, Riley 2004, Chen and Dahlman 2004). The diffusion of IT and IT
communication infrastructure were shown to have insignificant effects on national competitiveness,
both of which are inconsistent with previous research in the area. The second critical contribution of
this thesis suggests that for a typical country to utilise IT to improve its competitiveness, policymakers
should focus principally on IT educational policies over and above efforts that target IT communication
infrastructure and the diffusion of IT throughout its economy.
In consistency with the measure of the IT education variable, educational policies targeting IT should
focus on a skills and problem solving based approach, over syllabus orientated reforms. Taken
together, findings from the educational reform literature, and the sizeable estimated lagged effect of
national competitiveness in the model suggests that IT orientated reforms in the education systems
are likely to be sustainable as well as effective.
6.2 Limitations & Directions for Further Research
Whilst the simplicity of the measure of national competitiveness employed in the thesis in one light is
refreshing, in another it may be viewed as overly simplistic. One may ask if it is it reasonable that the
absolute value of GDP alone is sufficient to capture the intra-national and inter-national
competitiveness of a nation. The analysis argues that this is sufficient, however does not objectively
address this question. This is an important consideration, which is difficult to test as there is no
absolute metric for national competitiveness. Whilst one approach is to take each measure on its own
merits, a more rigorous analysis of this assumption would be favourable. Thus one direction of further
research would be the further investigation of specific functional forms derived from the theoretical
foundations which this thesis offers.

15
A second limitation concerns the sample used. In its current form, the model applies to the ‘typical’
nation, but says nothing about how the policies could change depending on factors such as the stage
of development of a nation, or the strength of its national competitiveness. It is likely that policy
recommendations may differ between the least and the most competitive nations. Instead the
conclusions apply more directly to those around the average, and furthermore this range itself is not
objectively considered. Kharlamova and Vertelieva (2013), for example, makes some advances
identifying 5 country groups from 36 countries, and it would be interesting to extend his analysis, to a
larger sample and then apply those groups to this DPD model.
A final limitation to highlight concerns the data, as a multicollinearity issue prevents the estimation of
the lagged effects of the IT variables of interest. This again departs from the consensus in the literature
in that, as with national competitiveness itself, the IT related measures should be measured
dynamically throughout an economy. An analysis of these dynamic effects would be a final worthwhile
direction in which to take the analysis, potentially in a time series model to compute the long-run
effects.

16
Appendices
The Appendices are organised as follows.
Appendix A 16
A.1 List of Abbreviations
A.2 List of Countries and Regions
A.3 Data Sources
A.4 Cases of Missing Data
Appendix B 19
B.1 Notes on Section 3.2
B.2 Selected results of the NC computations
Appendix C 21
C.1 Control variables
C.2 Full Empirical Estimations
C.3 Test for Autocorrelaction
Appendix A
A.1 List of Abbreviations
Abbreviation In Full
DPD Dynamic Panel Data
GCI Global Competitiveness Indicator
GCR(s) Global Competitiveness Report(s)
IMD Institute for Management Development
IT Information and Technology
WEF World Economic Forum

17
A.2 List of Countries and Regions
Albania Algeria Argentina Armenia
Australia Austria Azerbaijan Bahrain
Bangladesh Barbados Belgium Benin
Bolivia Bosnia & Herzegovina Botswana Brazil
Bulgaria Burkina Faso Burundi Cambodia
Cameroon Canada Chad Chile
China Colombia Costa Rica Croatia
Cyprus Czech Republic Denmark Dominican Republic
Ecuador Egypt El Salvador Estonia
Ethiopia Finland France Gambia, The
Georgia Germany Greece Guatemala
Guyana Honduras Hong Kong SAR Hungary
Iceland India Indonesia Ireland
Israel Italy Jamaica Japan
Jordan Kazakhstan Kenya Korea, Rep.
Kuwait Kyrgyz Republic Latvia Lesotho
Lithuania Luxembourg Macedonia, FYR Madagascar
Malaysia Mali Malta Mauritania
Mauritius Mexico Mongolia Morocco
Mozambique Namibia Netherlands New Zealand
Nicaragua Nigeria Norway Pakistan
Panama Paraguay Peru Philippines
Poland Portugal Qatar Romania
Russian Federation Singapore Slovak Republic Slovenia
South Africa Spain Sri Lanka Sweden
Switzerland Tajikistan Tanzania Thailand
Trinidad and Tobago Tunisia Turkey Uganda
Ukraine United Arab Emirates United Kingdom United States
Uruguay Venezuela Vietnam Zambia

18
A.3 Data Sources
Data Source
Global Competitiveness Index
World Economic Forum’s
Global Competitiveness Reports
The 12 Pillars of Competitiveness
Mobile Phone Subscriptions
Fixed Broadband Subscriptions
Individuals using the Internet
Availability of Latest Technology
Firm Level Technology Absorption
FDI & Technology Transfer
Quality of Maths & Science Education
Internet Access in Schools
GDP in 2005 US Dollars
World Bank Databank
GDP in Annual Percentage change
Exports Volumes
(indexed to 100 in the year 2000)
Export Volume Annual Percentage change IMF Databank
A.4 Missing Data
Data is fully collected for all countries listed above, over the period 2006-2013 inclusive, with two
exceptions:
 GCR data for Tunisia in the year 2012
 GCR data for Tajikistan in the year 2013
The selection of countries for use in the analysis is based on the criteria that there is data in the vast
majority of waves. Data is recorded as 0 for all missing values. Given the size of the dataset used, this
is unlikely to significantly affect the results of the model, and the conclusions drawn.

19
Appendix B
B.1 Notes on Section 3.2
The optimization problem of Equation (3) may be equivalently expressed as:
    , , ,
2
[0,1] 1 1
(1 )argmin in sample
i t i t i t
T N
t i
Rank GCI Rank Y X

 
  
 
   
  
 (8)
Where
,i tY represents the GDP of country i in year t, normalized to [0,1]
,i tX represents the exports of country i in year t, normalized to [0,1]
,i tGCI is the (absolute) score on the Global Competitiveness Index of country i in year
t.
 0,1 is the weight of GDP
It should be noted that the in-sample rank of the GCI is computed. Put differently, countries are ranked
only against the other countries which are considered in this thesis. Therefore this ranking will differ
slightly from the rankings produced by the Global Competitiveness Reports. The difference is likely to
be greater towards the lower end of the rankings.
As the GCR is published for overlapping years, the start year is taken as the base. Therefore the data
from the 2010-2011 is taken as the year 2010 for use in the analysis.
Furthermore, note that the variables ,i tY and ,i tX are taken as exogenous for each of the four cases
combination of unit measures in Table 1. This allows the problem to be solved systematically.
Combining the notion in Equations (3) and (8) asserts that
(i)   , ,
in sample
i t i tGCI Rank GCI
 , and
(ii) 
,i tNC   , ,(1 )i t i tRank Y X  
B.2 Selected Results of the NC optimization computation
The table below reports a selection of the results for the National Competitiveness measures derived
above. Combinations using the continuous variable  were computed in increments of 0.01 from 0
to 1 (inclusive). Therefore 101 measures for each of the four cases are computed however four corner
computations are repeated. Thus, the total number of combinations considered is equal to
(4 101) 4 400x   .

20
Combination Sum of Square difference in ranking,
over all nations and waves GDP Units Export Units
0 --
Absolute
Levels
2, 480, 620
0 -- % Change 2, 181, 965
0.2
Absolute
Levels
% Change 2, 256, 292
0.4
Absolute
Levels
% Change 2, 039, 576
0.6
Absolute
Levels
% Change 1, 785, 282
0.8
Absolute
Levels
% Change 1, 428, 954
0.9
Absolute
Levels
% Change 1, 129, 588
1*
Absolute
Levels*
--* 620, 002*
1 % Change -- 2, 755, 961
* Denotes the optimal combination.

21
Appendix C
C.1 Control Variables
Eleven of the twelve Pillars of Competitiveness are employed as controlling variables. A full
description can be found in the WEF Global Competitiveness Report.
Control Variable Associated Pillar
in the GCR
Institutions 1
Infrastructure 2
Macroeconomic Environment 3
Health & Primary Education 4
Higher Education & Training 5
Goods Market Efficiency 6
Labour Market Efficiency 7
Financial Market Development 8
Market Size 10
Business Sophistication 11
Innovation 12
The 9th
Pillar in the GCR, Technological Readiness, is not included in the model employed in this thesis
as it is strongly influenced by the combination of:
(i) the availability of the latest technology;
(ii) the firm level of technology absorption;
(iii) the FDI and technology transfer variables; and
(iv) The number of fixed broadband subscriptions per 100 citizens
Other factors also play a role, but the pillar is too similar to two of the constructed variables of interest
in this thesis, namely the IT communication infrastructure and the diffusion of IT variables when
contrasted with Table 2 in Section 3.3.

22
C.2 Full Empirical Estimation
This subsection reports the full estimations of the two-step Arellano-Bond DPD Model specified in
Equation (4). All variables are in first differences.
Variable
Coefficient Estimate
(Robust Standard Errors*)
P value
Lag of National
Competitiveness
0.4228
(0.1330)
0.001
IT Communication
Infrastructure
-0.0005
(0.0032)
0.871
Diffusion of IT
0.0038
(0.0078)
0.627
IT Education
0.0299
(0.0122)
0.014
Institutions
-0.0042
(0.0091)
0.644
Infrastructure
0.0168
(0.0087)
0.052
Macroeconomic
Environment
0.0124
(0.0051)
0.015
Health & Primary
Education
0.0297
(0.0085)
0.000
Higher Education &
Training
-0.0042
(0.0139)
0.763
Goods Market Efficiency
0.0053
(0.0069)
0.441
Labour Market Efficiency
-0.0176
(0.0072)
0.014
Financial Market
Development
0.0125
(0.0073)
0.086
Market Size
0.0975
(0.0373)
0.009
Business Sophistication
0.0208
(0.0090)
0.020
Innovation
-0.0194
(0.0089)
0.031
Number of Instruments: 35
Number of Observations: 696
Number of Groups: 116
Wald 2
15 statistic: 249.44
* Robust Standard Errors are adjusted for clustering by country.
Dependent Variable: National Competitiveness.

23
C.3 Test for Autocorrelation
Arellano-Bond test for zero autocorrelation in first differenced errors.
Null Hypothesis: No Autocorrelation.
Order z P value
1 -1.2654 0.2057
2 -1.1512 0.2497
The Null hypothesis of No Autocorrelation is not rejected.

24
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