How economic competitiveness indexes view human capital?1
Competitiveness: just a fashionable concept?
Competitiveness is at the top of political agendas all over the world. Several countries and
regions have established policies and institutions devoted to competitiveness, including in
developing and transition countries.
Despite its popularity in economic literature and public policy, the concept of national
economic competitiveness remains unclear and the object of criticism. The lack of clarity was
recognised early by Michael Porter, whose name more than any other is associated with the
concept of national competitiveness. In 1990 he wrote: “we must abandon the whole notion of
a ‘competitive nation’ as a term having much meaning for economic prosperity” (Porter,
1990, p.6) and elsewhere he noted that “while the notion of a competitive company is clear,
the notion of a competitive nation is not” ((Porter, 1990a) reprinted in (Porter, 1998, p.158).
Critics such as Paul Krugman went further. For him, the concept of competitiveness is not
only based on wrong premises (i.e. the wealth of a country is based on its success in
international markets, where nations compete with each other like companies) but it is also
dangerous: it may lead to wrong allocation of resources and even to protectionism (Krugman,
1994).
It is thus essential to define national economic competitiveness and, as a logical
consequence, critically assess how to measure it. The issue of measurement is particularly
important because public authorities increasingly tend to follow the so-called “evidence-based
policy”, where policies are guided by indicators and benchmarks, typically in the form of hard
statistical data. A number of indexes, data collections and rankings have been developed to
present evidence in an aggregated form for policy makers on a range of topics like human
development (UNDP), student achievement (PISA, PIRLS, TIMMS), and, of course, national
economic competitiveness. Indeed, one of the criticisms formulated by Paul Krugman to the
competitiveness literature was the lack of evidence and what he called the “careless
arithmetics” – or incorrect use of data to justify policies or theories (Krugman, 1994).
However, defining critically national economic competitiveness lies outside the scope
of this article. For the purposes of this article it is sufficient to recall that institutions like the
1
We are grateful to the World Economic Forum, and particularly Irene Mia, Ciara Browne and
Margareta Drzeniek Hanouz, for having given us access to WEF data and for their valuable explanations and
inputs. We are also grateful to Ilze Gabrane for her support handling WEF´s data.
World Economic Forum (WEF) or the Institute for Management Development (IMD) think of
national competitiveness in terms of potentially increased productivity.
The intention behind this article is to shed some light on the arithmetic regarding the
human capital components in the dominant competitiveness indexes and to discuss whether
the mechanics behind the arithmetic has taken centre stage instead of the dimensions of
human capital, which national economic competitiveness is based on. In a global
interdependent world, the need for greater economic competitiveness is used to justify
educational reforms. The intense debate in Germany following relatively poor results in the
PISA assessments of student achievements was not only a question of wounded national
pride, but also reflected a fear that Germany would be lacking the skills needed to keep up
with other countries in terms of economic prosperity2 (Siebert, 2005). The article will
therefore examine how human capital is measured in the competitiveness indexes. How
important are the human capital components within the competitiveness indexes? What is the
relative weight given to human capital? And do the indicators used adequately capture the
elements of human capital which has an impact on national economic competitiveness?
Understanding how human capital affects the level of national economic competitiveness will
allow countries to better target their sparse resources to improve their national economic
competitiveness.
How is national economic competitiveness measured?
The WEF and the IMD publish the two major international indexes which purports to
measure (and compare) national economic competitiveness. WEF has published its Global
Competitiveness Reports since 1979, while IMF celebrated the 20th anniversary of its World
Competitiveness Yearbooks in 2008. As their definitions of national economic
competitiveness are similar, but not identical - for the WEF, national economic
competitiveness is “the set of institutions, policies and factors that determine the level of
productivity of a country” (Sala-i-Martín et al., 2008, p.3), whereas for the IMD, national
economic competitiveness is “how a nation manages the totality of its resources and
competencies to increase the prosperity of its people” (IMD, 2008, p.32) -.it will be no
2
This fear is also reflected in the attitude of the German Federal Ministry of Education, which states on its
website that : “In an increasingly globalised world the competition for Germany’s future chances has also
become an international competition about the quality of education systems” (accessed 24.7.2009 at
www.bmbf.de/de/6549.php, translated by the authors).
surprise that the two organisations apply similar but not identical methodologies to the
process of calculating competitiveness. These methodologies have naturally evolved over
time. Evolution is essential to adapt the methodology to the changes in the World. What 2030 years ago was an important factor to increase productivity and prosperity is not necessarily
so crucial now, while critical technologies, such as the browsers which opened up the internet
to the general public and more recently the web 2.0 and its applications, simply did not exist
at the time of the first publication of the two competitiveness reports.
National economic competitiveness is measured by the WEF (Sala-i-Martín et al.,
2008) and IMD (2008) through composite indexes that include a set of indicators on different
relevant factors. In 2008, the WEF covered and ranked 134 economies, through 123 variables.
The same year, the IMD analysed 55 countries, using more than 300 criteria. Such data were
organised in different ways (Table I).
Table I: WEF and IMD main categories of indicators
WEF: “Twelve pillars of economic
IMD: Four categories with several sub-
competitiveness”
categories
“Basic requirements”
Economic performance:
1- Institutions
-
Domestic economy
2- Infrastructure
-
International trade
3- Macroeconomic stability
-
International investment
4- Health and primary education
-
Employment
-
Prices
“Efficiency enhancers”
Government efficiency:
5- Higher education and training
-
Public Finance
6- Goods market efficiency
-
Fiscal policy
7- Labour market efficiency
-
Institutional framework
8- Financial market sophistication
-
Business legislation
9- Technological readiness
-
Societal framework
10- Market size
Business efficiency:
-
Productivity and efficiency
“Innovation and sophistication factors”
-
Labour market
11- Business sophistication
-
Finance
12- Innovation
-
Management practices
-
Attitudes and values
Infrastructure:
-
Basic infrastructure
-
Technological infrastructure
-
Scientific infrastructure
-
Health and environment
-
Education
A quick overview of the main pillars/categories and sub-categories shows that the
IMD and the WEF follow the same theoretical premises. Most factors appear in both indexes,
even if presented and weighted in different ways. Another similarity concerns the source of
the data. Both competitiveness indexes are based on a combination of hard data, coming from
international databases (the World Bank's World Development Indicators which in itself
draws on many international data sources as well as World Bank estimates; the OECD; the
World Trade Organisation Statistics Database; and the UNESCO Institute for Statistics being
the principal providers of data), and the results of an annual Executive Opinion Survey, which
collects responses to long questionnaires (in 2008, there were 144 questions broken down in
thirteen sections in WEF´s survey and 123 items in the IMD questionnaire), from a small
sample of managers of companies in each country covered by the reports. The sample sizes
vary from country to country for both the WEF and the IMD, but the average size of the
country samples is 92 and 72 respectively for the WEF and the IMD.
These surveys permit the WEF and the IMD to cover data gaps. The surveys also
allow executives to provide their opinions about the economic, social and political
environment where they operate. However, the questionnaires also include areas where the
executives may not have any personal or professional insights, such as the quality of primary
education to give but one example.
In the case of the WEF index, the very vast majority of the indicators come from the
Executive Opinion Survey (81, out of a total of 110 in the 2008-2009 edition).
The main difference between the WEF and the IMD indexes concern the classification
of countries and, as a consequence, how indicators are weighted. The IMD applies exactly the
same calculations to each country in order to establish their competitiveness scores and ranks.
The WEF distinguishes between three kinds of economies and classify countries according to
their stage of development: factor-driven economies (stage 1), efficiency-driven economies
(stage 2) and innovation-driven economies (stage 3).
The rationale behind such classification and weighting is that, according to their level
of development (based on GDP per capita and by the share of exports of minerals in total
exports), countries face different paths to improve their competitiveness. Infrastructure,
institutions, macroeconomic stability or health and primary education, the so-called “basic
requirements”, are according to this rationale particularly relevant for factor-driven
economies, while business sophistication and innovation variables (pillars 11 and 12) are
more important for the most advanced economies. According to the stage of development of a
country, the WEF applies greater weights to the most relevant indicators at that stage. Basic
requirements represent 60% of the score for factor-driven economies, and only 20% for
innovation-driven countries.
Classifying countries according to their developmental stage and weighting the
indicators allows for a more dynamic analysis of the data, in the sense that one can observe
how, as an economy develops, the needs of its companies to be competitive change: having a
low-cost labour force or rich natural resources used to be essential at the lowest stages of
development, while advanced technologies and methods are generally crucial for innovationdriven economies (Porter, Ketels and Delgado, 2007). Nevertheless, it can be argued that a
bias is introduced by using different weights for different countries introduced, which has an
effect on the comparability of final scores and ranking.
Does human capital matter? Analysis of the importance of education in competitiveness
indices
Macro-economic evidence shows that human capital leads to economic growth
(Mankiw, Romer and Weil, 1992; Barro, 1997; Barro, 2000; Krueger and Lindhal, 2003).
Social and economic returns of human capital (generally measured by education attainment
levels) are at least as important as those of physical capital (De la Fuente, 2003).
It is therefore not surprising that both the IMD and the WEF indexes include a set of
data related to education, training and skills. Concretely, IMD included a sub-category of 19
indicators devoted to education in the 2008 edition of its report, as well as other variables
linked to human capital: skilled labour force, finance skills, foreign highly-skilled people,
competent senior managers, etc. Something similar can be said about the WEF, where the 4th
pillar includes indicators on primary education and the 5th pillar is completely oriented to
measure higher education and training assets. Other indicators on innovation are also directly
or indirectly linked with human capital (see below).
The theoretical and political quorum about the importance of human capital for
competitiveness leads to another question: how important is human capital for the
competitiveness models, relative to other factors?
This question will be answered using WEF data. There were several reasons to focus
on data from WEF. The model developed by the IMD gives the same weight to all variables3,
thus it deliberately does not give any special emphasis to any given aspect. Furthermore the
WEF covers many more countries than the IMD which helps explain why its index is more
widely referred to globally. With the greater visibility and the deliberate choice of weights
comes greater responsibility for the weight assigned to any given element, whether that be
human capital, physical infrastructure or a third component. And finally it was because the
IMD’s definition of prosperity (the goal behind increasing competitiveness) is so vague that it
can not be pinned down what competitiveness will achieve. Having increased productivity as
the aim for the competitiveness measured by the WEF ensures that variables should be
included to the extent that the variables have a positive effect on productivity.
This section analyses data used by the WEF for the 2008-2009 edition of its Global
Competitiveness Report. The objective is to understand to what extent education and training
matter in competitiveness, according to the WEF index. This can be done in two different
ways:
1- Summing-up the weight of each indicator on education and training within the total,
according to the stage of development.
2- Through simulations of the effect of changes to the human capital variables on
countries’ ranking of competitiveness.
We will first examine and discuss the weight of human capital in the WEF index
before moving onto simulations. The first step is to determine which indicators cover human
capital. Some are quite obvious, like those directly linked to education included in pillar 5
and 6. Other choices need further justification. They refer more largely to human capital
stock, attitudes or even outputs, also in the field of research. Table II presents the WEF
human capital indicators.
Table II: Human capital indicators in the WEF Global Competitiveness Index 2008
“Directly“ linked with education and
3
“Largely“ linked human capital (indicator
An examination would therefore have to determine how the IMD variables are chosen. If the choice is based on
availability rather than theoretical justifications, the index risks becoming a random list of variables and then a
country may find itself propelled to the top of the rankings simply because additional indicators are added for a
domain where the country does relatively well.
training (indicator number)
number)
- Quality of primary education (4.09)
- Brain-drain (7.09)
- Primary enrolment (4.10)
- Capacity for innovation (12.01)
- Education expenditure (4.11)
- Quality of research institutions (12.02)
- Secondary enrolment (5.01)
- Company spending on research and
- Tertiary enrolment (5.02)
development (12.03)
- Quality of the educational system (5.03)
- University-industry research collaboration
- Quality of science and mathematics (12.04)
education (5.04)
- Availability of researchers and engineers
- Quality of management schools (5.05)
(12.06)
- Internet access in schools (5.06)
- Utility patents (12.07)
- Local availability of research and training
services (5.07)
- Extent of staff training (5.08).
“Capacity for innovation” for instance, can be encouraged by innovative pedagogies,
as mentioned in another paper in this publication (Sahlberg and Oldroyd, 2010). The “quality
of research institutions” and the “availability of researchers and engineers” is a direct function
of the production of human capital or the ability to attract it (like in the USA). “Utility
patents”, along with “university-industry research collaboration”, can be considered as an
output of the national “knowledge triangle” (education, innovation and research). “Company
spending on research and development” is intended to generate new knowledge and thus has
clear implications for the level of human capital. Finally, brain drain may be viewed as a
proxy of the capacity to retain highly-educated citizens.
Accepting such choices, there are 18 indicators related to human capital or, in other
words, 14.6% of the total. Amongst these 18, only four are based on hard data, the rest rely on
the Executive Opinion Survey.
A more accurate picture of the importance of education and training indicators in the
WEF competitiveness index is provided by Table III, which presents the total weight of such
variables within the total, by stage of development. As the various stages of development
emphasise different pillars through the allocation of different weights to these pillars, a true
picture of the weight of human capital in the competitiveness index must weight the human
capital indicators with the weights allocated to their pillars and this must be done for each of
the three possible stages.
Table III: Weight of human capital indicators in WEF Global Competitiveness Index
2008
Primary
Higher
Innovatio
Total
Institutio
Infrastruc
Macroeco
education
education
n
human
ns (pillar
ture
nomy
indicator
indicator
indicator
capital
1)
(pillar 2)
(pillar 3)
s (pillar
s (pillar
s (pillar
4)
5) + brain
12)
drain
(7.09)
Factordriven
stage (1)
7.5 %
6.8 %
2%
16.3 %
15 %
15 %
15 %
5.0 %
9.7 %
4%
18.7 %
10 %
10 %
10 %
2.5 %
9.7 %
12 %
24.2 %
5%
5%
5%
Efficiencydriven
stage (2)
Innovation
-driven
stage (3)
These data indicate that, based on this simple calculation, human capital appears as a very
relevant driver of national economic competitiveness. The table shows that human capital
accounts for 16.3% of the score given to a country at the factor-driven stage. As a country
becomes more developed, the weight given to human capital increases due to the weight of
the innovation pillar that makes up for the declining influence of primary education. Countries
at the efficiency-driven stage rely on human capital variables for 18.7% of their score and
countries at the innovation-driven stage get as much as 24.2% of their final score for national
economic competitiveness from variables linked to human capital.
Hiding the role of human capital?
Education and training is increasingly important as a country develops and moves from a
lower to a higher developmental stage. This relevance is scarcely reflected in the
competitiveness model underlying WEF’s index. Looking back over the last reports, human
capital was acknowledged explicitly in the 2004-2005 report in the form of a separate pillar
out of the 12 which collectively make up the index. Already in this report, the difference
between basic human capital and advanced human capital is recognised, and the indicators
making up the 5th pillar were distributed between the sub-pillars of basic requirements and
efficiency enhancers respectively (Sala-i-Martín and Artadi, 2004, p.53 and 75).
By the 2008-2009 report (Sala-i-Martín, Blanke; Drzeniek Hanouz, Geiger, Mia and
Paua, 2008, pp.4-7) the human capital indicators are largely the same. Only two new
indicators have been added: quality of primary education (4.09) and education expenditure
(4.11). Most indicators have now been split up in two separate pillars (at the expense of the
security pillar present in the 2004-2005 report) explicitly linked with education and training,
called “health and primary education” (fourth pillar) and “higher education and training”
(fifth pillar). The changes concerning human capital over the years 2004 to 2008 can therefore
be said to be mainly technical adjustments, but the changes announced for the new global
competitiveness index are more fundamental.
Human capital would cease to be explicitly acknowledged in the new Global
Competitiveness Index (GCI) as it was announced in the 2008-2009 report (Porter, Delgado,
Ketels and Stern, 2008, pp.43-59), however it appears that the new GCI will not be introduced
in the imminent future after all4. This is a critical issue as the implicit downplaying of human
capital can lead to large policy implications. Even using the present model, policy makers are
guided by the explicit emphasis of other indicators to give less emphasis to education and
training than what is merited judging by the importance of human capital in the current
competitiveness index.
What effect does a change in human capital have on competitiveness?
De-emphasising human capital is acceptable if countries generally have similar scores. Even
if human capital can account for a quarter of the final competitiveness score, this hardly
matters if a country can not make sufficient changes to its education and training system to
impact its overall score. This section will therefore examine how much a country can change
its ranking by changing its human capital scores. What would happen if a country made such
drastic policy changes as to change its scores on human capital to be the best amongst all the
economies covered by the WEF competitiveness report, all other things remaining constant?
And what would happen if the education and training system collapsed to the point of the
4
Email exchange with WEF staff late July 2009.
worst country in the world? Simulations were run independently for each country by
substituting the values of the human capital indicators with the corresponding values of
respectively the best and the worst performer in the world for that variable. The resulting
scores were then used to assess where the country would be placed in the competitiveness
ranking. Table IV shows the results of such simulation for a selection of countries. Countries
mentioned in brackets in the last two columns indicate the country currently occupying the
ranking which a country might achieve as a result of the simulations.
The five countries that could increase their Global Competitiveness Index ranking the
most through improving their human capital are all African: Chad, Burundi, Mozambique,
Mali and Burkina Faso. In the case of Chad, ranked last in the world in the ranking published
by the WEF, the improvement in the score would be close to 25%, allowing the country to
jump to number 109, similar to the current rank of Albania. The other four African countries
mentioned could increase their scores by more than 19%.
Table IV: Simulations
Score
Country
in
Score,
GCI'07-
"Best"
08
simulation
Rank
Rank,
Rank,
Score,
%
GCI
"Best"
"worst"
%
"Worst"
decreas
07-
simulat
simulat
increase
simulation
e
08
ion
ion
109
Chad
Burundi
2.78
2.84
3.48
3.49
24.97
22.93
2.67
2.68
3.95
5.43
131
130
Mozambi
que
3.02
3.62
19.98
2.81
6.73
128
(Albani
131
a)
(Chad)
108
131
(Benin)
(Chad)
98
130
(Venez
(Burund
uela)
i)
126
Mali
3.37
4.03
19.37
3.22
4.53
115
Burkina
Faso
3.53
4.09
19.30
3.28
4.24
112
70 (Sri
(Guyan
Lanka)
a)
63
123
(Costa
(Ethiopi
Rica)
a)
76
128
(Botswa
(Mozam
Nepal
3.38
3.96
17.11
3.16
6.71
114
na)
bique)
Ecuador
3.57
4.17
16.70
3.28
8.03
103
60
123
Albania
3.48
4.05
16.23
3.17
8.86
109
Dominica
n
Republic
3.65
4.24
16.19
3.36
8.05
96
(Mauriti
(Ethiopi
us)
a)
67 (El
128
Salvado
(Mozam
r)
bique)
54
118
(Indone
(Madag
sia)
ascar)
63
126
(Costa
(Guyan
Rica)
a)
Bosnia
and
Herzegov
ina
3.55
4.12
16.16
3.25
8.53
106
32
Greece
Armenia
4.08
3.76
4.62
4.26
13.30
13.24
3.54
3.45
13.22
8.2
65
93
(Tunisia
108
)
(Benin)
52
111
(Mexico
(Nicara
)
gua)
44
Algeria
3.91
4.42
13.19
3.54
9.48
81
(South
108
Africa)
(Benin)
92
Italy
4.36
4.85
11.36
3.77
13.52
46
Russian
Federatio
n
4.19
4.59
9.50
3.70
11.75
58
26
(Pakista
(Chile)
n)
32
95
(Tunisia
(Nigeria
)
)
United
States
29
5.67
5.79
2.15
4.66
17.92
1
1 (USA)
(Spain)
2
Finland
Israel
5.49
5.20
5.57
5.37
1.54
3.23
4.44
4.23
19.02
18.56
6
17
(Switzer
42
land)
(Oman)
12
55
(Hong
(Cyprus
Kong)
)
36
(Puerto
Sweden
5.54
5.67
2.36
4.54
18.14
4
1 (USA)
Switzerla
nd
Rico)
32
5.62
5.75
2.39
4.61
17.99
2
1 (USA)
(Tunisia
)
These five African countries are followed closely by Nepal, which is the Asian
country that has the most to gain by improving its human capital, with a potential gain of
17.1%, Ecuador (16.7%) and Albania (16.3%). According to these simulations, they may
improve their ranks respectively from ranks 114, 103 and 109 to the 76th, 60th and 67th
positions.
Greece, Italy and the Russian Federation were ranked 65th, 46th and 68th respectively
by the WEF Global Competitiveness index. Our simulations show that improvements in
education and training in these countries could lead to significant improvements in their
measured competitiveness. Greece and Russia could move up to rank 32 (currently occupied
by Tunisia), and Italy could jump 20 positions to the 26th position (equal to Chile). These
examples provide a clear message for these countries’ authorities. Their current human capital
make them weak in terms of competitiveness, so by focusing on improving the skills of their
populations, these countries can make up a large part of the productivity gap and enhance
their overall prosperity.
Obviously, for some countries like Finland, Sweden, Switzerland or the United Stated,
the margin of potential improvement through human capital is narrow. But our simulations, as
shown in Table IV, demonstrate that the opposite might be true. If their education and training
systems collapsed, they would loose an important part of their national competitiveness. Table
IV shows what happens when we replace their human capital scores by the worst amongst the
countries covered by the WEF. Under such simulations, their competitiveness scores would
decrease by more than 17%. The United States, ranked first in the WEF report published in
2008, would become the 29th (which is the current level of Spain). Finland, ranked 6th, would
appear to be as competitive as Oman.
Of course, these simulations are based on unrealistic premises. First, because such
increases or decreases in education and training variables are not possible in the short or
medium term. Secondly, because the main assumption of the simulations is that other
variables (and other countries) would remain constant. This is obviously false: indicators
correlate. Improving human capital is at the same time a cause and a consequence of many
other factors, included or not in the Global Competitiveness Index. And countries do not
operate in a vacuum. Other countries will continue to make changes to their own education
and training systems as well.
Regardless, this analysis has two advantages:
-
It provides strong messages to the countries about the relevance of human capital to
improve their national competitiveness. Human capital improvements offer great
potential for improving overall economic competitiveness for many countries, and for
the countries that are very competitive; the simulations serve to underline the
importance of maintaining well functioning education and training systems.
-
Simulations can be done at a greater level of detail for a more sophisticated analysis.
Even if it is in practice impossible for a given country to become the best (or the
worst) in education and training in the short or medium term, incremental
improvements are very often part of government programmes and agendas, and the
intended improvements often include tangible targets with clear benchmarks. More
detailed simulations can take such targets into account to estimate how the country’s
position on the competitiveness index would be affected. Trends and policy
indications can be provided5, even if accurate forecasts cannot be done under the
premise of all other variables remaining constant. The authors of this article have
developed a tool that simulates how the final Competitiveness scores evolve when a
user introduces a change of X percent in human capital variables for a country. For
example, if Italy was able to increase its human capital scores by 10%, the final score
would increase by 1.76%, improving its rank by four positions. If the Finnish human
capital decreased by 10%, its final competitiveness score would be reduced by 2.38%,
taking Finland from the 6th position in the 2007-2008 ranking to the 12th.
Concluding remarks and a proposal on how to include the intangible elements of
education and training
Human capital is an essential component of national economic competitiveness and, as
demonstrated in our analysis, it is increasingly important the more developed a country
becomes. More than 24% of the 2007-2008 WEF competitiveness score of the most advanced
countries and more than 16% of the score of the least developed countries relate to human
capital indicators. Our simulations show that countries which are not ranked highly can
5
The incapacity to make forecasts is one of the criticisms raised against competitiveness indexes. See
for instance Ståhle and Ståhle, 2006: “The overall rankings factually correlate rather weakly with the national
economic performance: they correlate strongly with the present and past few years, rather weakly with the
longer period of past development, and not necessarily at all with the future success”.
achieve large gains in competitiveness if they implement reforms aiming at improving their
human capital and conversely countries which are currently ranked highly must maintain and
continue to upgrade their human capital if they wish to retain their competitive edge. By
analysing the scores for each human capital indicator in detail countries may develop blue
prints for future reform efforts.
Nowadays, comparative statistics like those elaborated by the WEF or the IMD on
competitiveness play a crucial role in policy-making. This “evidence-based policy”
phenomenon has scathingly been called “governing by numbers” (Grek, 2009). Messages
provided by publications like the Global Competitiveness Report (amongst others, alongside
the OECD's PISA reports or the World Bank's Doing Business surveys) severely impact
policy making. This is particularly true in transition and developing countries, which are
highly dependent on donors' funding. For this reason, an accurate and critical analysis of
methodologies and contents is important, particularly for developing and transition countries
with limited resources to be allocated efficiently.
Despite the importance of human capital indicators on the final competitiveness
scores, the possible new breakdown of indicators announced by the WEF for future editions
of its Global Competitiveness Report implies a very limited emphasis on human capital. This
shift in focus away from human capital in the model risks diverting country attention towards
other areas with a lesser potential impact on competitiveness, with critical consequences in
developing and transition countries.
All the same, it must be remembered that the current list of indicators are limited.
Partly due a lack of hard data, which mean that efforts to gather more hard data for more
countries for a greater variety of variables must continue at the international level, and partly
because the human capital indicators do not cover all aspects of education and training.
Creative solutions are needed to make up for the fact that the existing indicators do not
touch upon a number of the elements which can be expected to lead to improved human
capital. Pedagogy, for example, as demonstrated by Sahlberg and Oldroyd (2010), has a huge
impact on the quality of education taught in schools, but there are no variables to measure
this. Creativity, innovation, social capital and methods to foster risk taking merit being
included in competitiveness indexes.
Many (Sahlberg and Oldroyd, 2010; Sharan, 2010; Villalba, 2010) argue that these
more intangible elements of education have large effects on the final outcomes of education
and training systems. As they have a large impact on the learning outcomes, they directly
affect the country’s human capital and thereby the national economic competitiveness.
There is not any indicator in the national competitiveness indexes that cover creativity,
even if attempts of constructing composite indicators exist (Villalba, 2008). The same can be
said about social capital, or "the values and beliefs that citizens share in the everyday dealings
and which give meaning and provide design to all sort of rules", with substantial implications
for innovation and economic performance (Maskell, 2000). Such social capital can hardly be
measured, but a qualitative assessment could be provided by survey respondents.
Similarly, the intangible aspects mentioned above could be judged by survey
respondents. But there is no reason to limit the survey respondents to business men, as is
currently done by both the WEF and the IMD. Groups of international education and training
experts could be consulted on how to rank countries on how well their education and training
systems do on a range of human capital variables, which can then be used to give a more
comprehensive picture of how well a country performs in terms of human capital. Each expert
group do not need to cover all countries in the world as there are statistical techniques for
merging results from different groups as long as there is a minimum of overlap in coverage.
Such expert groups could be asked to assess how well countries do to foster creativity in
students, nurture innovation, promote entrepreneurship and risk taking, and build social
capital for example. The expert groups could also assess other aspects, which contribute to
future competitiveness, by gauging the reform will of ministries of education, openness to
innovation in teacher training and pedagogical innovation. And of course the expert groups
can also assess the same questions business men are currently being asked on the quality of
the education system. Correlating the responses of education experts with responses to the
same questions from business men and also correlating the expert responses regarding
education quality with PISA results will provide a much needed basis for estimating the
reliability of the various surveys.
A full list of the issues relating to human capital that has an impact on competitiveness
remains to be derived. Many elements have already been referred to in this article, but the
links between these elements and national economic competitiveness needs to be spelled out.
This could be a very fruitful line of future research. Educationalists can thus help refocus
policy discussions on the deeper issues of how to improve education and training systems and
in that process steer policy makers from governing by numbers to governing by substance.
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