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The role of secondary education inexplaining competitivenessChris Baumanna & Hume Winzara

a Department of Marketing & Management, Macquarie University,Sydney, AustraliaPublished online: 02 Jul 2014.

To cite this article: Chris Baumann & Hume Winzar (2014): The role of secondary education inexplaining competitiveness, Asia Pacific Journal of Education, DOI: 10.1080/02188791.2014.924387

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The role of secondary education in explaining competitiveness

Chris Baumann*1 and Hume Winzar

Department of Marketing & Management, Macquarie University, Sydney, Australia

(Received 20 August 2012; final version received 12 November 2013)

The literature establishes that education drives economic performance, but the extentthat education is associated with a country’s competitiveness is empirically untested.Our study analyses Programme for International Student Assessment (PISA) data from63 countries to ascertain education’s role in explaining the competitiveness of acountry. Strong correlations were found for reading and science (each 53% sharedvariance) and mathematics (50%). Educational achievement explains 54% ofCompetitiveness. Regional differences were found with East Asia performing stronglyboth academically and in competitiveness, ahead of Europe, the rest of Asia, and South/Central America. Anglo-Saxon countries rank second academically behind East Asia,but in terms of competitiveness, the Anglo-Saxon cluster ranks first. We show thatAnglo-Saxon countries’ leadership in education and competitiveness have beenchallenged by East Asia. Our diachronic analysis shows that together with CompetitiveIndustrial Performance, the cultural dimensions of Uncertainty Avoidance, Long-termOrientation and Indulgence Restraint, Education contributes to improvements inCompetitiveness. The strength of East Asia in educational achievement will haveimplications for the region’s future competitiveness compared to Anglo-Saxon andEuropean countries. Our empirical findings support theoretical arguments foreducation’s role in driving competitiveness. For education policy, the study emphasizesthe importance of investments in reading, science and mathematics education.

Keywords: PISA; competitiveness; East Asia; China; educational achievement; WEF

Introduction

The effect of education on a nation’s economic performance has long been an area of

investigation, particularly years of schooling (Barro & Lee, 1993), and quality of

schooling on growth in Gross Domestic Product (Hanushek & Woßmann, 2007a). Others

found that expenditures toward primary-school education contribute significantly to

economic growth, with enrolment rates in secondary education playing a key role (Keller,

2006a). While that finding holds true for growth in Asia, Keller (2006b) also investigated

these effects globally with a novel finding that public expenditures per student impact

growth the most. Keller concluded that enrolments at the secondary-school level promote

openness and decrease inflation, overall pointing towards an important contribution of

secondary-school education to economic performance. Despite these findings, how

education relates to a country’s competitiveness remains unclear. For educators, such a

gap in the literature is particularly perplexing given that competitiveness has long driven

educational policy and funding reforms (Massey, 1989; Mayhew & Keep, 1999).

q 2014 National Institute of Education, Singapore

*Corresponding author. Email: chris.baumann@mq.edu.auThe corresponding author is also a Visiting Professor at Seoul National University (SNU), SouthKorea

Asia Pacific Journal of Education, 2014

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In light of the recent debate on education in Europe sparked by the strong

Programme for International Student Assessment (PISA) results of East Asian students,

it has become ever more important to understand the impact of education on a nation’s

overall economic performance, including their level of competitiveness. South Korea’s

school system has earned attention and praise from American President Barack Obama

in his 2011 State of the Union address, in which he described the South Korean school

system as a role model, where teachers garner respect and are viewed as nation builders.

Subsequently, Korea has established itself as most successful in raising education and

productivity levels (Keller, 2006a). Even the popular press has picked up this topic,

from the tabloids to the mainstream newspapers. In the case of Britain, the popular

press has reported extensively on the country’s declining PISA ranking (e.g.,

Anonymous, 2007; Shephard, 2010).

The purpose of this study is threefold. Firstly we explore how well educational

achievement, specifically in reading, science, and mathematics, explains a country’s

competitiveness. Secondly the study investigates regional differences in education and

competitiveness. Thirdly and finally, the study offers a diachronic perspective to better

understand how education drives competitiveness. Given that different school policies and

priorities have resulted in divergent educational achievements around the world, this study

offers a wider lens through which to view the relationship between educational

achievement and competitiveness.

Educational achievement can globally be compared using PISA data. The

Organization for Economic Cooperation and Development (OECD) conducts the PISA

study every three years on 15-year-olds in, as of 2009, 75 countries (PISA, Organisation

for Economic Cooperation and Development, 2009). Extensive research on PISA and its

wider applicability has been conducted, and established, by and large, its validity as a

measure of a country’s educational performance (Smith, 2008). The economic

implications of improvements in educational achievement have been demonstrated with

projected growth in Gross Domestic Product (GDP) when education is improved

(Hanushek & Woßmann, 2007a, 2012). Our study is an attempt to quantify the extent to

which education affects country-level competitiveness as opposed to the previously

explored association of education with economic performance (see Sahlberg, 2006, where

competitiveness was defined as specifically economic competitiveness).

Economic output is a product of a variety of factors including education and, as

established in a recent study, is also related to competitiveness (Baumann &Hamin, 2011).

While the role of education in explaining economic performance is well documented in the

literature (Hanushek & Woßmann, 2012) the likely association between education and

competitiveness has received little, if any, attention. Our study fills this gap by testing the

explanatory power of education in a nation’s level of global competitiveness.

Educational achievement in a global context

While each country has long had their own way of measuring the academic ability and

achievement of students “within” their country, it is problematic, by the localized nature of

these measurements, to use them in international comparisons. Even prior to the

establishment of PISA testing by the OECD, there were attempts to create internationally

comparable measurements of educational achievement. The International Association for

the Evaluation of Educational Achievement has administered the Trends in International

Mathematics and Science Study (TIMMS) to 4th and 8th grade students from 1995, as well

as a study focused on reading skills, the Progress in International Reading Literacy Study

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(PIRLS) to 9-year-olds from 2001. These two studies continue to be run, and international

tests such as these have previously been used by Hanushek and Kimko (2000) to better

understand the effect of education on economic outcomes, through the collation and

adjustment of different studies spanning many years.

Traditionally education has been split into three main subject areas – mathematics,

science and reading – and this trio has been the focus of international studies such as

TIMMS and PIRLS. The problem with using these international studies is their

comparability (or lack thereof) across subjects; whilst test results from a study such as

TIMMS can be compared “within”, for example comparing different years of the same

study, they are not comparable “between” studies due to, for example, different

examination questions and different age groups tested.

PISA tests, in contrast, are administered to 15-year-olds in 75 countries at one time on

a global scale. PISA does not test the curriculum of a particular country, but the ability of

students to apply skills to situations outside of school, placing “an emphasis on functional

knowledge and skills that allow one to participate actively in society” (Schleicher, 1999).

PISA data ascertains the abilities of students in an internationally comparable fashion

making the data set ideal for our study. For full country data, please refer to Panel A in

Appendix 1.

Since its introduction PISA data has been used as the internationally comparable

measure of the achievement of students in participating countries. PISA produces three

kinds of information: student performance, socio-economic background, and school

characteristics. Since its inception, this data has been used in a variety of ways by

researchers, for example: to study the efficiency of resource allocation (Afonso & St.

Aubyn, 2006; Wilson, 2005), to better understand what factors contribute to the success of

students (Deutsch, Dumas, & Silber, 2013), and to assess factors contributing to concern

over teacher turnover and shortages (White & Smith, 2005).

Because of the notion that education drives a nation’s economic output, the

association between PISA and a nation’s GDP has been tested by Hanushek and

Woßmann (2007b, 2011, 2012). Their work can be categorized into three main areas of

focus: school improvement on economic development; looking at this relationship

specifically in the OECD context; and quantifying the improvement in GDP from school

achievement. The relationship between education and economic development has thus

been explored by Hanushek and Woßmann, yet a firm understanding of the relationship

between education and competitiveness has not been established. The unclear nature of

this relationship is puzzling, considering that international efforts for global

competitiveness have directed educational reform and policy in recent years.

Global competitiveness and education

The concept of global competitiveness refers to the ability of a nation to effectively use its

resources – whether natural, human or capital (Porter, 1990). The most prominent

measurement of this concept is the Global Competitiveness Index (GCI), produced by the

World Economic Forum (WEF).

In this index competitiveness is defined as “the set of institutions, policies, and factors

that determine the level of productivity of a country” (Schwab& Sala-i-Martin, 2011, p. 4).

The WEF produces the GCI scores based on 12 indicators of competitiveness, broken

down into three groups of requirements (Sala-i-Martin, Blanke, Hanouz, Geiger, & Mia,

2010). The first group are basic requirements, including factors such as institutions, health

and infrastructure. The second are efficiency enhancers including technological readiness

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and labour market efficiency. The final group are innovation and sophistication factors

such as business sophistication and innovation.2 This multidimensionality assures a

comprehensive and objective measurement of a country’s competitiveness and has

subsequently been used in our study to capture global competitiveness (country level

details in Panel B in Appendix 1).

The association between education and competitiveness has been the focus of a

recent study, which found that culture has a positive mediating effect on academic

performance, and also that national competitiveness explains 36% of academic

performance (Baumann & Hamin, 2011). Although the effect of competitiveness was

modelled on academic performance in that study, quite possibly, the association could

be conceptualized in the reverse order, a view hypothesized in this study. In fact, later

in this paper we will statistically test the directionality of that association and as such

contribute to the literature since to date, there is conflicting evidence.3 While, for

example, Baumann and Hamin (2011) in their study linked competitiveness to

performance, a study in Taiwan focused on, perhaps, the reverse relationship. Aldridge,

Fraser, Taylor, and Chen (2000) found that good examination results are of paramount

importance in Taiwan. If students attain good results, it increases the likelihood of

being allocated a position in a “star” school, that is, a school with outstanding results as

measured by the number of students who enter university. Interviews with teachers in

Taiwan highlighted the competitive nature of teaching, for example, test scores of the

different classes are displayed for other teachers to read, putting teachers under a

considerable amount of pressure from the principal and home-room teachers to ensure

that grades “do not slip” and that students improve their performance if their results are

lower than those of other teachers. In contrast, teachers in Australia did not report

feeling that the education system in Australia at the junior high school level was as

competitive and therefore teachers and students are not under such a great deal of

pressure; possibly then, performance is lower and the resulting level of competitiveness

inferior as well. While there could therefore be less pressure on students in Western

education systems, other highly competitive societies beyond Taiwan such as South

Korea, Singapore, Hong Kong and Mainland China have similarly competitive

education systems, that is, students have to study very hard, including private tutoring

beyond the formal school hours, to make it into one of their country’s top universities.

All of this shows that the interaction – including the directionality – between education

and competitiveness is rather complex, but in one way or another, also points towards

an undeniable association between education and competitiveness, or as Avis (2003,

p. 317) puts it: “It is now taken for granted that education at whatever level has an

important role to play in contributing to national competitiveness”. To make things

more complex, it is also possible, if not likely, that the association between education

and competitiveness is also linked to another set of socio-economic variables, but our

study assumes a ceteris paribus assumption, acknowledging that other factors cannot

altogether be ruled out, but would be beyond the scope of our research.

An early exploratory study conceptualizing an effect of PISA results on economic

strength found that academic results were more important in less competitive nations

than more competitive nations (Tienken, 2008). While Tienken’s study was limited

by the use of only one PISA test (i.e., the 2003 data set), having excluded reading

results from his analysis and also tested his hypothesis on the basis of GCI rankings

rather than scores, our study utilizes three periods of data (2003, 2006, 2009) and all

three dimensions of PISA results (mathematics, science and reading scores), and also

measures GCI scores rather than rankings.

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Results

Explanatory power of education on competitiveness

In order to test the overarching hypothesis of our study – that education explains

competitiveness – bivariate regressions were undertaken. These regressions demonstrated

that, of the three skills we are testing, reading and science are best able to explain

competitiveness, each with an explanatory power of 54% (from Table 1, in year 2009).

This was closely followed by mathematics (50%). In this study we tested three separate

years (2003, 2006 and 2009) and the explanatory power of the three PISA elements was

consistent over the last two measurement points (around 50%), and to a lesser extent also

the first year of our study (2003).

Throughout the 2003 to 2009 period, reading consistently had the strongest

explanatory power, ranging from 52% to 54% of competitiveness. Surprisingly, reading

has often been excluded from previous research and policy development aimed at

increasing economic outcome through education. Science and mathematics have

historically been viewed as key factors in driving technological innovations (Massey,

1989; Steen, 1987) and have motivated the recent drive to increase enrolments in Science,

Technology, Engineering and Mathematics (STEM) fields through to higher educaton,

following the 1998 Competitiveness White Paper in the UK (Smith, 2010). Previous

significant research has excluded reading as a unit of analysis for two main reasons.

Firstly, reading has not been considered an important factor in driving innovation, and

secondly, concerns over the translation of the PISA reading component have meant it has

not been incorporated into studies.

Our results may well reflect the shift in society and economic activities towards

services industries. Approximately three quarters of economic output in Western

countries, for example 76.7% in the United States (CIA World Factbook, 2012), is

generated in services such as education, health, finance/banking, tourism and retailing. In

such industries, reflection, creative and independent thinking and soft skills such as

communication are key, more so than mathematics and science skills. Reading reflects this

soft skill set, which must form an integral part of school curricula in order to ensure

competitiveness in economies increasingly defined by service industries.

Moving on from the previously discussed individual effects of the PISA components

on competitiveness, we next calculated the composite value to summarize educational

achievement into one factor score. The resulting educational achievement was tested for

its association with competitiveness. The emerging simple regression equation’s adjusted

R-squared equals 0.539; in other words, educational achievement “explains” 54% of a

country’s competitiveness. This high adjusted R-squared is evidence of the strength of the

Table 1. Reading, science and mathematics explaining competitiveness.

Competitiveness

2003 2006 2009

StandardizedBeta t

Adj. RSquare

StandardizedBeta t

Adj. RSquare

StandardizedBeta t

Adj. RSquare

Reading 0.729 6.474 0.518 0.740 7.930 0.539 0.736 8.480 0.534Science 0.596 4.516 0.338 0.733 7.834 0.528 0.732 8.385 0.528Maths 0.614 4.738 0.361 0.699 7.112 0.479 0.711 7.901 0.498

p , 0.001.

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resulting equation since it demonstrates how adequately the model captures the overall

association between the two variables, as is also illustrated in Figure 1. The equation used

to determine competitiveness is as follows:

YComp ¼ 1:105þ 0:008EA

where the notation signifies:

YComp ¼ Competitiveness

EA ¼ Educational Achievement (EA)

Looking at Australia as a case study, where realistically resources could be

reallocated to the education sector, an increase in the PISA score of 50 marks would

result in an increase in Australia’s competitiveness from the current 5.20 to 5.60 (i.e., for

each 100 additional educational achievement units, competitiveness increases by 0.8

units). Such an increase would bring Australia on a par with Switzerland (ranked second

in the world), which has a GCI, or Global Competitiveness Index, of 5.61. Such an

Figure 1. Association between education and competitiveness.

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evaluation of course ignores the considerable estimation error that is likely in a relatively

small sample and with 46% unexplained variance, but it gives us a ball-park guideline

for making some comparisons amongst different countries’ competitiveness and

educational levels.

Regional comparison of education and competitiveness

Clearly there are differences between countries in their approach to education and their

competitiveness, and these differences are plotted in Figure 1. While overall the

association between education and competitiveness has been found to be of a linear nature

in this study, we hypothesized regional divergence from linearity. Hanushek and

Woßmann (2007b, 2012) have established the ability of education to create strong

economic outcomes. At the same time, the effect of education on economic outcomes has

also been established to have substantial variation.

In order to test our hypothesis of regional divergence of the association between

education and competitiveness we have clustered the 63 countries into five categories.

Four of these are geographic: East Asia (e.g., China, Japan, South Korea), Rest of Asia

(e.g., Indonesia), Europe (e.g., Denmark and Switzerland) and South and Central America

(e.g., Brazil and Chile). We have identified a fifth category, “Anglo-Saxon” countries,

which have a common British history, resulting in a shared language and common cultural

heritage, and have had similar patterns of economic development, for example rapid post-

war growth (e.g., Australia, Canada, UK, USA).

Figure 1 with the previously mentioned adjusted R-squared of 0.539 showcases the clear

linearity between educational achievement and competitiveness as all countries scatter

relatively close to the regression line.We can see in Figure 1 that some regional areas seem to

cluster within the graph. East Asian and Anglo-Saxon countries have both high levels of

education and higher competitiveness. Other Asian countries have lower education but

somewhat higher than “expected” levels of competitiveness, while South and Central

American countries also have lower levels of educational attainment but lower than expected

competitiveness. The purpose of this analysis is to illustrate countries’ (categorized into five

regions) position in relation to the overall equation line. The problem for countries below the

line is that they fail to transform their level of educational achievement into the expected

degree of competitiveness according to the overall equation. This effect is strongest for

South/Central America and the majority of countries from the European cluster. The

converse situation is true for East Asia, the Rest of Asia, and Anglo-Saxon countries where

our study finds an Uber-performance. Indeed, their education systems seem to be able to

capitalize on their educational achievement as a catalyst for higher rates of competitiveness

than would be expected according to the regression line. It is expected that countries that are

moving up in PISA rank from developing nations may lie below the line, whereas nations

with falling ranks in PISAmay still enjoy a higher competitiveness as lead-lag characteristics

are not properly captured. At the same time, it is noted that it does not explain this for East

Asia, specifically for China, being above the curve (i.e., China is quite different in 2003, 2006

and 2009 from Japan and South Korea as South Korea and Japan are more comparable in

2009 than in 2003).4

As previously suggested, there is, no doubt, a strong association between

competitiveness and socio-economic development levels of nations. Our findings may,

as our diachronic perspective explained next will further explore, be due to a lead-lag

nature of the association between education, especially so for basic/secondary education

as in our study, with economic performance, socio-economic development and

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competitiveness. This may be more prominent in developing nations. As East Asia and

China comprise part of the scope of our study, it can be argued that the association of

present education level with these nations’ present competitiveness may not get the full

impact of their recent improvements in PISA ranks/quality. For developed nations, this

impact may be less, but for developing nations – it is likely to be higher.

Our results point towards the importance of instilling a competitive attitude in schools

beyond simply teaching knowledge and skills, since clearly not all countries are transforming

educational achievement into the creation of a competitive economy and society. Hanushek

andWoßmann have found that “evidence from both within and across countries points to the

positive impact of competition among schools, of accountability and student testing, and of

local school autonomy in decision making. Research on these policies, separately and in

combination, indicates some continuing uncertainty about the magnitude of any effects but

does support more aggressive attention to these in setting school policies” (2011, p. 479).

Perhaps our findings here also relate back to what Baumann, Tung, and Hamin (2012)

found: despite the pedagogic literature in the West favouring a less authoritarian approach,

Caucasians sampled in Australia and Canada are in fact in favour of stricter schools. They

perceive schools as too soft, and their views are in line with how overseas Chinese – that

have often experienced the performance-driven Chinese system during their childhood –

perceive Western schools. Such findings are in line with what has been reported on

Western schools out of Holland where parents demand that schools should be more strict:

“Only with a combination of more discipline and a more individual approach could

undisciplined or unmotivated children be brought back on the right track, achieve in an

expected way, and finally pass their examinations” (Regt & Weenink, 2005, p. 75).

Diachronic perspective

The third perspective of our study approaches the relationship between education and

competitiveness from a temporal perspective, and is illustrated in Figure 2.

Our longitudinal analysis utilizes three-year intervals based around the PISA cycle.

As we would expect, competitiveness from year to year is highly correlated, as are

education levels. Moreover, education is highly correlated with competitiveness (Table 2).

Any causal linkages amongst these variables will be confounded by a very high level of

multicollinearity. The appropriate solution in a case such as this is the application of the

Ridge Regression (Hoerl & Kennard, 1970; Marquardt & Snee, 1975). Multicollinearity

causes high variances in coefficient estimates, making them unreliable. Put simply, if two

variables are highly correlated, then information in one is also contained in the other. If

both are used in a regression model, then any estimate for one variable is also partly an

estimate for the other variable. If weight is given to one variable, then less weight must be

given to the other, or if a lot of weight is given to one variable, then possibly a lot of weight

in the opposite direction is needed to counterbalance the first, so estimates can be

overstated, appearing significant when they are not, or understated, appearing not

significant when they are, or even statistically significant with the wrong sign.

In Ordinary Least Squares Regression, coefficient estimates can vary enormously with

small changes in the data, and even change sign with the addition or removal of other

correlated predictor variables. At the expense of incurring some bias, the Ridge

Regression method reduces the high variances of coefficients by augmenting the main

diagonal elements of the correlation matrix by small positive quantities. The possible bias

in the estimates rarely is a problem, especially when the predictor variables have the same

sign and similar magnitude (Mason & Brown, 1975). In this case we used the traditional

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Ridge method (Phillips, 1962) to search for the level of ridge adjustment that minimizes

the Mean Squared Error in the estimates contained within the CatReg algorithms of the

Statistical Package for the Social Sciences (SPSS). Experimentation with different

“penalty” loadings, and with bootstrapping of data observations, produced consistent

coefficient estimates, so we are confident that these results are sound. That is, the Ridge

Regression procedure gives reliable estimates of the different levels of influence of each of

the predictor variables.

Education is not likely to be the only driver, possibly not even the most important

driver of a country’s competitiveness, so we should include other likely predictors in the

model to avoid omitted variables bias (Heckman, 1979). Even though it is never clear how

much or in what direction omitted variables may cause bias (Clarke, 2005), it is usually

preferable to include them with time-series and relatively small-sample data (Clarke,

2012; Vance & Ritter, 2012). Education has been shown to impact such variables as

economic growth, investment, inflation, and trade (e.g., Keller, 2006a, 2006b) that also in

turn are associated with economic performance, it is highly likely that excluding the many

other variables impacting competitiveness will create an omitted variables bias. To at least

partially account for such variables as investment, inflation and trade we include

Competitive Industrial Performance (UNIDO, 2013); hereafter CIP. The CIP is a

composite score, created by the United Nations Industrial Development Organization

(UNIDO), comprised of measures for countries’ capacity to produce and export

Alternative Causal Directions

Competitiveness-Driven Model Education-Driven Model

Ridge Regression R2=.143 Ridge Regression Adjusted R2=.094 Beta (Competitiveness 2003) = 0.189α=.006 N=37

Ridge Regression R2=.601 Ridge Regression Adjusted R2=.534 Beta (CIP Index) = 0.192α=.006 Beta (Education 2003) = 0.130α=.020 Beta (Uncertainty Avoidance) = –.235α<.001Beta (Long-term orientation) = .158α=.003 Beta (Indulgence Restraint) = 0.102α=.064 N=35

Figure 2. Longitudinal effect of education on competitiveness and vice versa.

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manufactured items, countries’ level of technological deepening and upgrading, and

countries’ impact on world manufacturing, both in terms of their value added share in

world manufacturing and trade. We are taking the CIP measure as a proxy for the level of

industrial development of a country. Baumann and Hamin (2011) showed that culture is

associated with the competitiveness of a country, suggesting that cultural dimensions

could increase explanatory power, and overcome some of the likely bias from an

education-only model. We took the six current measures of the Hofstede cultural

dimensions (Hofstede, 2011; Hofstede, Hofstede, & Minkov, 2010):

(1) Power Distance, related to the different solutions to the basic problem of human

inequality;

(2) Uncertainty Avoidance, related to the level of stress in a society in the face of an

unknown future;

(3) Individualism versus Collectivism, related to the integration of individuals into

primary groups;

(4) Masculinity versus Femininity, related to the division of emotional roles between

women and men;

(5) Long Term versus Short Term Orientation, related to the choice of focus for

people’s efforts: the future or the present and past.

(6) Indulgence versus Restraint, related to the gratification versus control of basic

human desires related to enjoying life.

Logically, education is unlikely to affect competitiveness for some time. Here we use

available data and posit that it would take at least six years for education to influence

competitiveness. Competitiveness in 2009 is our dependent variable and education in

2003 is the predictor variable. We allow for serial correlation in the dependent variable by

including competitiveness in the preceding competitiveness measure, that is,

competitiveness in 2003. Competitiveness for 2009 was regressed against Competitive-

ness for 2003, and the residuals stored to give a measure of difference over time in

expected level of Competitiveness. This Change in Competitiveness was then regressed

against Education and other measures of culture and level of industrial development.

Results for this analysis are summarized in Table 3 and Figure 2. In Table 3 we see the

results of three levels of analysis where we first look for the effect of Education alone on

the change in Competiveness, then the effect of Education and current levels of

industrialization, and finally all of Education, Industrialization and Cultural measures. As

we should expect, industrial performance (CIP) has a moderate and highly significant

positive contribution to changes in competitiveness. We see in the final model the

Table 2. Correlation table of education and competitiveness.

PISA2003

PISA2006

PISA2009

Competitiveness2003

Competitiveness2006

Competitiveness2009

PISA 2003 1PISA 2006 0.983 1PISA 2009 0.974 0.984 1Competitiveness2003

0.661 0.718 0.704 1

Competitiveness2006

0.670 0.739 0.742 0.929 1

Competitiveness2009

0.653 0.710 0.721 0.896 0.965 1

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standardized coefficient for Education (2003) is 0.13. It has been shown that Ridge

Regression coefficients, in the presence of even moderately correlated other predictor

variables, are almost always understated from the true population values (Hastie,

Tibshirani, & Friedman, 2009; Tibshirani, 1996). We can therefore comfortably conclude

that education within a country accounts for at least 1.7% of the change in

Competitiveness of that country within a few years (partial correlation squared:

.13*.13 ¼ .017). The inclusion also of measures of industrialization added 12% more

explained variance (Table 3, Model 2), and the cultural dimensions added a further 23%

of explained variance in changes in Competitiveness (Table 3, Model 3). Not all of the

cultural dimensions were statistically significant. Indeed the three dimensions of Power-

Distance, Individualism and Masculinity had no effect, with significance values greater

than 0.8. These were sequentially removed from the Ridge Regression model (backward

deletion method) to arrive at the most parsimonious model.

The included cultural dimensions of Uncertainty Avoidance, Long-term Orientation

and Indulgence Restraint all were significant beyond the 90% confidence level.

Interestingly those countries with higher uncertainty avoidance tend to have lower changes

in competitiveness. On the other hand, countries with higher levels of long-term

orientation and indulgence restraint had higher improvements in competitiveness.

As suggested in our theory discussion, the directionality of association between

education and competitiveness is not clear per se, and we therefore next tested a diachronic

alternative. In other words, of course it is possible that competitiveness could affect

education rather than the so far discussed education to competitiveness paradigm. We ran

the same Ridge Regression procedures model but with the variables reversed. That is,

education in 2009 became the dependent variable first regressed against education (2003)

and the residuals saved as the difference from expected education in 2009.

Competitiveness in 2003 then became the predictor variable. As we might expect, the

results are also statistically significant, and meaningful. The coefficient for competitive-

ness is 0.19, explaining something more than 3.6% (.19*.19 ¼ .036) of the variance in the

change in education. Importantly, none of the additional variables relating to Culture and

Industrial Development had any effect on the change in Education over time. This

coefficient of 0.19 for the Competitiveness-affects-Education model compares with a

standardized coefficient of 0.226 for the alternative Education-affects-Competitiveness

model when there is a single predictor.

Table 3. Hierarchical ridge regressions on change in competiveness 2003–2009.

Model 1:Educationaffects

D Competitiveness

Model 2:Education

and Industrialization affectD Competitiveness

Model 3:Education, Industrialization

and Culture affectD Competitiveness

Education2003

0.226 0.172 0.130

CIP Index 0.228 0.192UncertaintyAvoidance

20.235

Long-termorientation

0.158

IndulgenceRestraint

0.102

Adjusted R2 0.182 0.300 0.534

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What our results mean here is that the effect of educational achievement on

competitiveness becomes stronger as time elapses. Given that PISAmeasures the abilities of

15-year-olds, it is logical that the effect of their educational abilities on the economy will be

felt more as they enter and spend time in the workforce. Needless to mention that youth

entering theworkforce do not have an immediate direct effect on a country’s competitiveness,

Table 4. Regional comparison of education and competitiveness.

N MeanStd.

DeviationStd.Error Minimum Maximum

pvalue

PISA Reading2009

East Asia 6 521.67 26.546 10.837 487 556 ,0.001

Rest of Asia 10 405.50 57.023 18.032 314 526Europe 32 477.66 31.365 5.545 385 536Anglo-Saxon 6 508.33 13.246 5.408 494 524S. & CentralAmerica

9 408.89 25.751 8.584 370 449

Total 63 463.49 52.166 6.572 314 556PISA Mathematics2009

East Asia 6 549.67 27.038 11.038 525 600 ,0.001

Rest of Asia 10 409.80 64.183 20.297 331 562Europe 32 481.03 36.942 6.530 377 541Anglo-Saxon 6 504.33 17.750 7.246 487 527S. & CentralAmerica

9 389.67 37.961 12.654 306 427

Total 63 465.43 61.982 7.809 306 600PISA Science2009

East Asia 6 538.67 22.545 9.204 511 575 ,0.001

Rest of Asia 10 411.40 58.148 18.388 330 542Europe 32 485.59 35.369 6.252 391 554Anglo-Saxon 6 518.67 12.388 5.057 502 532S. & CentralAmerica

9 405.89 23.846 7.949 369 447

Total 63 470.63 56.605 7.132 330 575Competitiveness2003

East Asia 6 4.8683 0.56866 0.23216 4.19 5.58 ,0.001

Rest of Asia 5 4.5320 0.75271 0.33662 3.42 5.54Europe 30 4.6793 0.71663 0.13084 3.36 6.01Anglo-Saxon 6 5.2567 0.34425 0.14054 4.73 5.81S. & CentralAmerica

9 3.9789 0.40294 0.13431 3.35 4.86

Total 56 4.6357 0.70453 0.09415 3.35 6.01Competitiveness2006

East Asia 6 5.0133 0.61818 0.25237 4.24 5.60 ,0.001

Rest of Asia 10 4.4200 0.58854 0.18611 3.31 5.63Europe 31 4.8187 0.66794 0.11997 3.46 5.81Anglo-Saxon 6 5.3617 0.18247 0.07449 5.15 5.61S. & CentralAmerica

9 4.1356 0.28076 0.09359 3.94 4.85

Total 62 4.7266 0.65873 0.08366 3.31 5.81Competitiveness2009

East Asia 6 5.1017 0.31562 0.12885 4.70 5.38 ,0.001

Rest of Asia 10 4.4450 0.55676 0.17606 3.40 5.53Europe 32 4.6709 0.57775 0.10213 3.55 5.61Anglo-Saxon 6 5.2550 0.29304 0.11963 4.93 5.74S. & CentralAmerica

9 4.1200 0.26509 0.08836 3.85 4.72

Total 63 4.6530 0.58211 0.07334 3.40 5.74

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but longer term our results suggest a statistically signicant relationship with a not immaterial

(i.e., 2% fromFigure 2) explanatory power of educational achievement. This is especially true

given that year after year eventually additive effects can be expected from the influx of a

competitive youth into a country’s workforce. Two percent explanation of the variation in

changes to competitiveness is not a trivial amount when we consider that so many other

factors affect a nation’s competitiveness, and when we consider that, in a competitive “race”,

the differences between “winners” and “losers” in their ability can be very small.

Synthesis of results

Our final approach to the analysis of the relationship between competitiveness and

education is to identify key trends for each region of this study. While over the timeframe

of this analysis the Anglo-Saxon cluster has dominated the competitive ranking as the

clear leader over the nine years that we are studying, it is apparent that the East Asian

cluster is “catching up” with values “converging” (Ralston, 2008) to the Anglo-Saxon

cluster. In 2003, East Asia had a GCI of 4.87 compared to 5.26 for the Anglo-Saxon

cluster, rising and remaining static in 2009 at 5.10 and 5.26 respectively (from Table 4).

Based on the diachronic pattern we have established, a plausible explanation for changes

to the GCI can be found in the latest PISA rankings, where the East Asian cluster ranks first

for all PISA components (Reading 521.67, Mathematics 549.67, Science 538.67).

Looking at the clusters in greater detail, alarmingly, the maximum score in

mathematics for the Anglo-Saxon cluster (i.e., Canada with 527) is only two points higher

than the lowest nation in the East Asian cluster. While the discrepancy in marks between

East Asia and Anglo-Saxon countries is largest for mathematics with 45 points, the picture

of comparatively poor performance for science (20 points difference) and reading

(13 points) is consistent. If this trend were to continue, Anglo-Saxon dominance in

competitiveness will soon be contested by East Asia.

In the case of the European cluster, we observe little variation over time, with both

education and competitiveness centring/converging towards/around the mean of all

countries. Europe consistently ranks third in terms of its competitiveness behind the

Anglo-Saxon cluster (ranked first) and East Asia (ranked second). This stagnation has to

be viewed in contrast to East Asia with its increase both in terms of competitiveness and

educational performance over the study’s timeframe. As such then, the European cluster

has been surpassed by 2009, and based on the now supported premise that education drives

competitiveness, European countries not managing to transform education achievement

into competitiveness face the prospect of falling far behind the more competitive East

Asian, and currently still more competitive Anglo-Saxon economies.

Conclusion

This study makes a contribution in three distinct ways. Firstly, we have demonstrated the

clear explanatory power of educational achievement in explaining a country’s

competitiveness. All three educational components of reading, science, and mathematics

contribute strongly to a country’s competitiveness, explaining roughly 50–54%. Secondly,

our study demonstrates significant regional differences in education and competitiveness.

An Uber-performance was established for East Asia, the Rest of Asia, and Anglo-Saxon

countries, in contrast to the underperforming South/Central American cluster and most

European countries, likely also due to externalities like manpower costs, highlighting the

characteristics of the “drivers” of competitiveness beyond education alone.

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Finally, a diachronic perspective was used to better understand how education links to

competitiveness. In essence, we demonstrate that the effect of educational achievement on

competitiveness becomes stronger over time. Further, our results point towards a paradigm

where education “drives” competitiveness. For education policy, our study points towards

the importance of investment in reading, science andmathematics education due to the now

demonstrated effects over time of these competencies on competitiveness. Our study also

suggests that education alone is not sufficient to create a competitive country: the effect of

education on competitiveness is ameliorated by culture and industrial development.

Cultural values can be passed on by parents, the education system (schools, universities) and

the media. To what extent cultural values passed on from these sources impact an

individual’s competiveness and performance is yet to be established in the literature.

As with most research, our study has limitations. We rely on secondary education data,

but great care was taken to match the data sets used. The scale and year of the data

correspond, however the response rate for the PISA studies is not always 100%, and this

has been flagged as a potential issue in the usability of the data (Smith, 2008). Our study

also offers input for future research, namely, when more PISA data becomes available, our

diachronic analysis can be extended to further validate the longitudinal association

between education and competitiveness. Future research should also aspire to explain the

regional variation we have found in our study, and as such provide valuable input for

education and school policy.

Acknowledgements

The authors wish to acknowledge the invaluable early involvement of Hannah Altern, Honoursgraduate at Macquarie University, in this project. We also thank the editor of the APJE and twoanonymous reviewers for their guidance and suggestions.

Notes

1. Visiting Professor: Seoul National University (SNU), Korea.2. For a detailed analysis of the GCI composition of human capital, please refer to Sabadie and

Johansen (2010).3. Credit must be given to both reviewers for raising this issue, prompting us to statistically check

the association in both directions (see the Diachronic perspective section of this paper).4. Weare grateful to the second reviewerof our paperwho contributed this and theproceeding thought.

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Appendix 1. Data tabulation

While we use secondary data that is publically available, we provide the compiled data in theappendix for the convenience of fellow researchers.

Panel A: PISA.

PISA

2003 2006 2009

Reading Maths Science Reading Maths Science Reading Maths Science

Albania 385 377 391Argentina 374 381 391 398 388 401Australia 525 524 525 513 520 527 515 514 527Austria 491 506 491 490 505 511 470 496 494Azerbaijan 353 476 382 362 431 373Belgium 507 529 509 501 520 510 506 515 507Brazil 403 356 390 393 370 390 412 386 405Bulgaria 402 413 434 429 428 439Canada 528 532 519 527 527 534 524 527 529Chile 442 411 438 449 421 447Colombia 385 370 388 413 381 402Croatia 477 467 493 476 460 486Czech Rep. 489 516 523 483 510 513 478 493 500Denmark 492 514 475 494 513 496 495 503 499Estonia 501 515 531 501 512 528Finland 543 544 548 547 548 563 536 541 554France 496 511 511 488 496 495 496 497 498Germany 491 503 502 495 504 516 497 513 520

(Continued)

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(Continued )

PISA

2003 2006 2009

Reading Maths Science Reading Maths Science Reading Maths Science

Greece 472 445 481 460 459 473 483 466 470Hong Kong 510 550 539 536 547 542 533 555 549Hungary 482 590 503 482 491 504 494 490 503Iceland 492 515 495 484 506 491 500 507 496Indonesia 382 360 395 393 391 393 402 371 383Ireland 515 503 505 517 501 454 496 487 508Israel 439 442 508 474 447 455Italy 476 466 486 469 462 475 486 483 489Japan 498 534 548 498 523 531 520 529 539Jordan 401 384 422 405 387 415Kazakhstan 390 405 400Kyrgyz Republic 285 311 322 314 331 330Latvia 491 483 489 479 486 490 484 482 494Lithuania 470 486 488 468 477 491Luxembourg 479 493 483 479 490 486 472 489 484Macau 498 527 525 492 525 511 487 525 511Mexico 400 385 405 410 406 410 425 419 416Montenegro 392 399 412 408 403 401Netherlands 513 538 524 507 531 525 508 526 522New Zealand 522 523 521 521 522 530 521 519 532Norway 500 495 484 484 490 487 503 498 500Panama 371 306 376Peru 370 365 369Poland 497 490 498 508 495 498 500 495 508Portugal 478 466 468 472 466 474 489 487 493Qatar 312 318 349 372 368 379Romania 396 415 418 424 427 428Russia 442 468 489 440 476 476 459 468 478Serbia 412 437 436 401 435 436 442 442 443Shanghai 556 600 575Singapore 526 562 542Slovak Republic 469 498 495 466 492 488 477 497 490Slovenia 494 504 519 483 501 512South Korea 534 542 538 556 547 522 539 546 538Sweden 514 509 506 507 502 503 497 494 495Switzerland 499 527 513 499 530 512 501 534 517Taiwan 496 549 532 495 543 520Thailand 420 417 429 417 417 421 421 419 425Trinidad

and Tobago416 414 410

Tunisia 375 359 385 380 365 386 404 371 401Turkey 441 423 434 447 424 424 464 445 454UK 495 495 515 494 492 514United

Arab Emirates459 453 466

Uruguay 434 422 438 413 427 428 426 427 427USA 495 483 491 474 489 500 487 502

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Panel B: Competitiveness.

Competitiveness

2003 2006 2009 2003 2006 2009

Albania 3.46 3.55 Lithuania 4.39 4.53 4.45Argentina 3.35 4.01 3.87 Luxembourg 4.99 5.16 4.85Australia 5.33 5.29 5.20 Macau 4.19 4.24 4.70Austria 5.07 5.32 5.23 Mexico 4.12 4.18 4.23Azerbaijan 4.06 4.10 Montenegro 4.11Belgium 4.88 5.27 5.14 Netherlands 5.24 5.56 5.41Brazil 3.95 4.03 4.13 New Zealand 5.23 5.15 4.93Bulgaria 3.67 3.96 4.03 Norway 5.33 5.42 5.22Canada 5.21 5.37 5.37 Panama 3.81 4.18 4.24Chile 4.86 4.85 4.72 Peru 3.88 3.94 3.95Colombia 3.74 4.04 4.05 Poland 4.15 4.30 4.28Croatia 3.97 4.26 4.22 Portugal 4.92 4.60 4.47Czech Rep. 4.48 4.74 4.62 Qatar 4.55 4.83Denmark 5.61 5.70 5.58 Romania 3.38 4.02 4.10Estonia 4.96 5.12 4.67 Russia 3.46 4.08 4.31Finland 6.01 5.76 5.50 Serbia 3.36 3.69 3.90France 4.91 5.31 5.22 Shanghai 4.19 4.24 4.70Germany 5.24 5.58 5.46 Singapore 5.54 5.63 5.53Greece 4.58 4.33 4.11 Slovak Rep. 4.23 4.55 4.40Hong Kong 4.93 5.46 5.33 Slovenia 4.70 4.64 4.50Hungary 4.61 4.52 4.22 South Korea 5.07 5.13 5.28Iceland 5.34 5.40 5.05 Sweden 5.80 5.74 5.53Indonesia 3.42 4.26 4.25 Switzerland 5.51 5.81 5.61Ireland 4.73 5.21 4.99 Taiwan 5.58 5.41 5.22Israel 5.02 5.38 4.97 Thailand 4.63 4.58 4.60Italy 4.38 4.46 4.35 Trinidad and Tobago 4.07 4.03 3.85Japan 5.25 5.60 5.38 Tunisia 4.49 4.71 4.58Jordan 4.58 4.25 4.37 Turkey 3.65 4.14 4.15Kazakhstan 4.19 4.11 UK 5.23 5.54 5.30Kyrgyz Republic 3.31 3.40 United Arab Emirates 4.66 4.68Latvia 4.54 4.57 4.26 Uruguay 4.03 3.96 4.04

USA 5.81 5.61 5.74

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