tai lieu

The impact of human capitalon economic growth: a review

Rob A. Wilson, Geoff Briscoe

In:

Descy, P.; Tessaring, M. (eds)

Impact of education and trainingThird report on vocational training research in Europe: background report.

Luxembourg: Office for Official Publications of the European Communities, 2004(Cedefop Reference series, 54)

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• the background report to the third report on vocational training research in Europe contains originalcontributions from researchers. They are regrouped in three volumes published separately in English only.A list of contents is on the next page.

• A synthesis report based on these contributions and with additional research findings is being published inEnglish, French and German.

Bibliographical reference of the English version:Descy, P.; Tessaring, M. Evaluation and impact of education and training: the value of learning. Thirdreport on vocational training research in Europe: synthesis report. Luxembourg: Office for OfficialPublications of the European Communities (Cedefop Reference series)

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Contributions to the background report of the third research report

Impact of education and training

Preface

The impact of human capital on economic growth: areviewRob A. Wilson, Geoff Briscoe

Empirical analysis of human capital development andeconomic growth in European regionsHiro Izushi, Robert Huggins

Non-material benefits of education, training and skillsat a macro levelAndy Green, John Preston, Lars-Erik Malmberg

Macroeconometric evaluation of active labour-marketpolicy – a case study for GermanyReinhard Hujer, Marco Caliendo, Christopher Zeiss

Active policies and measures: impact on integrationand reintegration in the labour market and social lifeKenneth Walsh and David J. Parsons

The impact of human capital and human capitalinvestments on company performance Evidence fromliterature and European survey resultsBo Hansson, Ulf Johanson, Karl-Heinz Leitner

The benefits of education, training and skills from anindividual life-course perspective with a particularfocus on life-course and biographical researchMaren Heise, Wolfgang Meyer

The foundations of evaluation andimpact research

Preface

Philosophies and types of evaluation researchElliot Stern

Developing standards to evaluate vocational educationand training programmesWolfgang Beywl; Sandra Speer

Methods and limitations of evaluation and impactresearchReinhard Hujer, Marco Caliendo, Dubravko Radic

From project to policy evaluation in vocationaleducation and training – possible concepts and tools.Evidence from countries in transition.Evelyn Viertel, Søren P. Nielsen, David L. Parkes,Søren Poulsen

Look, listen and learn: an international evaluation ofadult learningBeatriz Pont and Patrick Werquin

Measurement and evaluation of competenceGerald A. Straka

An overarching conceptual framework for assessingkey competences. Lessons from an interdisciplinaryand policy-oriented approachDominique Simone Rychen

Evaluation of systems andprogrammes

Preface

Evaluating the impact of reforms of vocationaleducation and training: examples of practiceMike Coles

Evaluating systems’ reform in vocational educationand training. Learning from Danish and Dutch casesLoek Nieuwenhuis, Hanne Shapiro

Evaluation of EU and international programmes andinitiatives promoting mobility – selected case studiesWolfgang Hellwig, Uwe Lauterbach,Hermann-Günter Hesse, Sabine Fabriz

Consultancy for free? Evaluation practice in theEuropean Union and central and eastern EuropeFindings from selected EU programmesBernd Baumgartl, Olga Strietska-Ilina,Gerhard Schaumberger

Quasi-market reforms in employment and trainingservices: first experiences and evaluation resultsLudo Struyven, Geert Steurs

Evaluation activities in the European CommissionJosep Molsosa

The impact of human capitalon economic growth: a review

Rob A. Wilson, Geoff Briscoe

AbstractThis review provides an in-depth appraisal of a wide body of international research that examines thelinks between education and training in a country and its macroeconomic growth. An initial analysis ofbroad statistics for all EU Member States suggests a loose correlation between investment in humanresources and growth in gross national product (GNP), but clear causal relationships are difficult toestablish. Increased investment in education is shown to lead to higher productivity and earnings for theindividual and similarly, such investment results in significant social rates of return. The returns on invest-ment in vocational training are more difficult to demonstrate. This study reviews a large number of growthmodels that attempt to specify and quantify the GNP and human resource relationship. Wide differencesare found in the model specifications, the quality of the data inputs and the results obtained. Other linksbetween investment in human capital and economic performance are reviewed using diverse literaturesources on human resource management, corporate market value, company size and industry structure.The indirect impact of education on non-economic benefits is also examined in the context of the tech-nological, spatial and environmental gains to society. It is concluded that, overall, the impact of invest-ment in education and training on national economic growth is positive and significant. Some policyconclusions are drawn and directions for future research in this area are suggested.

1. Executive summary: key findings 13

1.1. Background 13

1.2. Rates of return on investment in human capital 13

1.3. Skills and organisational performance 14

1.4. Macroeconomic performance and education 14

1.5. Other links between investment in human capital and performance 14

1.6. Spill-over, external effects and non-economic benefits 15

2. Introduction 16

2.1. Aims of the study 16

2.2. Content of the study 16

2.3. Approach 16

2.4. Limitations of the study 17

2.5. Structure of the present report 18

3. Statistics of growth, education and training 19

3.1. Economic growth 19

3.2. Employment and unemployment 20

3.3. Educational participation 22

3.4. Educational qualifications of the workforce 24

3.5. Vocational training 26

4. Rates of return on education and training 29

4.1. General literature strands 29

4.2. Rates of return on investment in human capital 29

4.3. Private versus social rates of return 31

5. Human resources and performance 33

5.1. Links between human resources and performance at company level 33

5.2. Management skills and organisational performance 33

5.3. Empirical studies at company level 34

5.4. Empirical studies at the industry and sector level 35

6. The role of education and training in economic growth 37

6.1. General literature strands 37

6.2. Growth accounting literature 38

6.3. Neoclassical growth models 39

6.4. Endogenous growth models 40

6.5. Empirical growth regressions 41

6.6. A recent example of growth modelling 44

6.7. The influence of data quality on results 46

6.8. Human capital externalities 47

6.9. Social capital and economic growth 47

6.10.Possible endogeneity and simultaneity bias 48

Table of contents

7. Market value and related literature 50

7.1. Literature strands 50

7.2. Some empirical results on market value models 50

7.3. Knowledge, intellectual capital and intangible asset literature 51

8. Further links between education and training and company performance 53


8.2. Company size and the problems of small firms 53

8.3. Small and medium size enterprises and the qualifications of managers 54

8.4. Structure-conduct-performance models 54

9. Spill-over effects and externalities 56


9.2. Technological spill-overs 56

9.3. Spatial and city dynamics 56

9.4. Environmental externalities 57

10. Conclusions 59

10.1.Summary of key points 59

10.2.Some policy implications 60

10.3.Possible areas of future research 61

List of abbreviations 63

Annex 64

References 65

The impact of human capital on economic growth: a review 11

TablesTable 1: Estimated growth rates of GDP per capita 19

Table 2: Growth in total employment – average annual percentage changes 20

Table 3: Unemployment rates – average percentages 21

Table 4: Total expenditure on education as percentage of GNP 22

Table 5: Percentage of population aged 18-22 entering tertiary education 23

Table 6: Educational attainment of the adult population 24

Table 7: Average schooling and no schooling rates 1970-2000 26

Table 8: Comparisons of vocational training based on European labour force survey 28

Table 9: Barro growth regressions 45

FiguresFigure 1: Average growth and employment rates in EU 21

Figure 2: Highest educational attainment of the adult population 25

Figure 3: Average years of schooling 25

Figure 4: Schooling attainment 1970-2000: average years of schooling 27

Figure 5: Schooling attainment 1970-2000: percentage of population 15+ with no schooling 27

List of tables and figures

1.1. Background

Various conceptual approaches have been usedto explore the links between education andtraining and economic performance. The aim ofthe present review is to provide a critical assess-ment of this work, focusing primarily on themacroeconomic level. In order to reach a macrooverview, it is necessary to consider many otherbranches of literature than those just concernedwith macroeconomic growth models.

The study, therefore, presents an extensiveinternational review into a number of strands ofliterature that focus on the link between invest-ment in education, training and skills andeconomic performance at the macro level. Thisincludes some micro level research, where trainingand education conducted by firms and industriesis deemed to be an important contributor to overalleconomic growth. While a majority of the publica-tions examined are in English, much Europeanresearch is also incorporated in the form of empir-ical studies using European data and Europeanauthors publishing in UK and US publications.

All of the European Union (EU) 15 MemberStates have experienced significant growth overthe past 30-year period. However, there is varia-tion between countries, especially in the shortterm. Educational expenditure as a percentage ofgross national product (GNP) has been main-tained at roughly constant levels across all theMember States over the long term. This hasensured increased investment in human resourcesthroughout the EU. Other data show a stronggrowth trend in the percentage of the eligiblepopulation entering tertiary education over thethree decades. Consistent statistics on trends inEU vocational training prove more difficult toobtain, but there is evidence of significant voca-tional training inputs in a number of EU countries.A simple reading of these statistics indicates thatincreases in economic growth across the EU areassociated with increases in both education andtraining. However, more detailed comparisonsalso illustrate the difficulties in establishing acausal link between educational and training

inputs on the one hand and economic outputs (inform of growth in gross domestic product – GDP)on the other. Similar patterns are observed inother developed economies.

1.2. Rates of return oninvestment in human capital

A key body of literature relates to the rates ofreturn for the individual (and for society ingeneral) of investment in human capital, in theform of education and training. Much of thisresearch draws on the seminal work by Becker(1964), Mincer (1974) and many others. This bodyof work is founded on a microeconomicapproach. Nevertheless the results have impor-tant macroeconomic implications. They highlightthe strong links observed between education,productivity and output levels. Although somehave questioned the direction of causality andargued that much education simply acts as ascreening device to help employers to identifymore able individuals, the general consensusseems to be that education does result in higherindividual productivity and earnings. On balance,the results suggest a strong and positive causallink between investment in education and trainingand earnings. This applies both at the level of theindividual and also to the broader social returnson such investments, the evidence suggestingsubstantial social as well as private benefits. Theimplication is that what is good for the individualis also good for society at large at a macro level.

Evidence on social rates of return concentrateson the benefits to the economy arising fromincreased education by calculating all the costsof schooling and education compared to the rela-tive pre-tax earnings of individuals in receipt ofsuch education. Many qualifying assumptionsneed to be made in arriving at a quantifiable esti-mate, but several studies suggest a social rate ofreturn in the range 6 to 12 %. This compares wellwith rates of return on other capital. Comparablestudies to establish the social rate of return for

1. Executive summary: key findings

Impact of education and training14

vocational training are hard to find, although thereis some evidence that such training has a signifi-cant positive impact on productivity.

1.3. Skills and organisationalperformance

Another key focus of recent research has been theuse and deployment of human resources and skillsat an organisational level and how this is linked tothe economic performance of organisations andeconomies more broadly. While much of thisresearch lies outside main economic literature,research on the management of human resourcessuggests a range of results concerning variousaspects of human capital and firm performance.This is not really surprising, as there is a widely heldbelief that people are a key source of an organisa-tion’s competitive advantage. It is the quality of thehuman resources that determines organisationalperformance. There are a number of differentemphases in this research, including the role playedby human resource management (HRM) inpromoting such resources, and the role of manage-ment itself – particularly top managers. One impor-tant theme is the role of leadership, with a focus onthe role of managers, especially top executives, ininfluencing company performance. Their skills,education and training are important in what areoften very complex processes.

1.4. Macroeconomic performanceand education

Literature on economic growth models exploresdirectly the quantitative relationship betweeninvestments in education and training and thelevel and growth of per capita GDP. There are alarge number of studies, beginning with the clas-sical growth models first developed in the 1950s,through to the so-called endogenous growthmodels that are still widely applied in manycurrent empirical studies. Both data sets andeconometric modelling techniques have devel-oped extensively over recent years and manydifferent model specifications have beenproposed and empirically tested. Typically, thesemodels use data drawn from a cross-section of

countries, sometimes only for developed coun-tries but often for a wider set. Data difficultiescommonly mean that consistent series of educa-tional variables are hard to obtain over sufficientlylong periods to facilitate econometric time seriesanalyses. For developed countries over morerecent years, cross-sectional and time series dataare combined into panel sets to enable a morecomprehensive testing of the relationshipbetween human resources and economic growth.

This body of research can be divided up into anumber of subsections. The so-called ‘growthaccounting’ literature emphasises the importanceof measuring changes in the quality of labour, asindicated by improved qualifications and higherskills, when trying to account for economicgrowth over the long term. The impact of theaccumulation of knowledge though the under-taking of research and development (R&D) hasalso been a key feature of this body of research.

Other subsections focus on technical researchinto production and related functions, which areconcerned with the relationships between factorinputs (both tangible and intangible, such asknowledge) and economic outputs. The so-called‘new growth theories’ are also very relevant here.These highlight the determinants of economicgrowth in the broadest sense, concentrating onhuman capital inputs. There are a number ofdistinguishing features of the new growth models,but, essentially, they extend the existing modelsby endogenising technological change (hence,they are also sometimes referred to as endoge-nous growth theories). This contrasts with tradi-tional neoclassical models, where economicgrowth is driven by the increase in factor inputs(i.e. population growth) and by the exogenousrate of (labour augmenting) technological change.The newer models often allow for increasingreturns to scale, where growth is unbounded andin which growth rates can continue to increaseindefinitely over time.

1.5. Other links betweeninvestment in human capitaland performance

There are a number of other areas of researchthat have a bearing on economic growth issues.Further important evidence comes from market


value literature which is concerned with howintangible assets, such as technical knowledge,influence potential performance and net worth.

While macro growth models have developedthe production function approach to explainmacroeconomic growth, market valuation hasdeveloped the same approach to explain thegrowth of individual companies. This also hasimportant macroeconomic consequences.Research studies in market value are concernedwith how intangible assets, such as technicalknowledge, influence potential performance andnet worth. These are intimately tied up withinvestment in education and training as well asskills, which may often be necessary, if not suffi-cient, to improve such intangible assets.Research and development, patents and intellec-tual capital are identified as key determinants ofcorporate market value. These same variableswere also found to be important contributors tomacroeconomic growth in the new growth theoryliterature.

Other research has emphasised the impor-tance of knowledge, intellectual capital and otherintangible assets, in influencing the performanceof companies at a micro level and economies at amacro level. The role of firm size, includinginsights from the structure-conduct-performanceliterature (which highlights the importance of sizeand monopoly power in exploiting new knowl-edge) and also the role of qualifications andquality of management in the performance ofsmaller firms, need to be considered. Work in thisarea has been carried out for some time andmany of the studies emphasise the role of thesmall firm in economic growth. Such small firmsare an extremely heterogeneous group, that havemarkedly different training needs from those oftheir larger counterparts. There is found to be farless emphasis on educational and training qualifi-cations in small firm environments. Empiricalstudies suggest that conventional theories ofcompany behaviour are far too simplistic forexplaining the goals and aspirations of thesesmaller organisations.

1.6. Spill-over, external effectsand non-economic benefits

The final strands of literature considered here detailsome of the indirect effects of investment in humanresources that are not normally captured inmeasured national economic growth. Suchspill-overs and externalities can be technological,spatial or environmental and economic andnon-economic. Each contributes significant socialgains. Many of these effects are related to otherareas of the literature covered in this review. Thespatial externalities, that embrace city dynamics,demonstrate how geographical clustering of busi-nesses employing highly qualified workersproduces high productivity and strong localeconomic growth. Other significant spill-oversrelate to health and life expectancy, which typicallyincreases as a consequence of higher levels ofeducation.

Such spill-over and related external effects arepotentially very important. In making a traininginvestment, an individual firm takes into account theimpact of the training on its performance, given thecurrent total stock of human capital in the economy.The higher the human capital in the economy, thegreater is the firm’s own performance. However, itdoes not take into account the effect of its owninvestment on the total human capital stock. As faras the individual firm is concerned, the impact ofthat investment is minuscule and need not beconsidered. However, from the perspective of theeconomy as a whole, the totality of training invest-ments by firms can further increase economicoutput and economy-wide performance. Theseexternal effects can add considerably to themacroeconomic consequences of any initial invest-ment in human capital.

External benefits are often not economic.They also include improvements to the environ-ment, better health and reduced crime rates.Serious attempts are now being made to quan-tify these in economic terms. These are majorareas of study in their own right and are onlytouched on here.

2.1. Aims of the study

The main objective of this study is to provide acomprehensive and critical overview of the impactof education and training on economic perfor-mance, and, by implication, employment opportu-nities, at the macro level. In this context the term‘macro’ is defined quite widely to incorporate firmsand industries, as well as the general economy. Itis argued that there is a wide range of differentstudies that have a bearing on the link betweeninvestment in human capital and economicgrowth. These studies need to be synthesised inorder to obtain a comprehensive view of the manyways in which education, training and the accumu-lation and appropriate deployment of skills influ-ence economic performance.

The review considers the implications forpolicy and practice, drawing general lessons forfurther improvement in the economies of membercountries. While attention is given to the role ofvocational education and training, it is muchmore difficult to find relevant evidence in this areathan for more general education.

2.2. Content of the study

The expansion of formal education and training indeveloped economies in recent years has hadsubstantial and easily observed implications forthe skill levels and skill structures of the popula-tions and employed workforces of these coun-tries. However, the contribution of education andtraining to economic growth and other measuresof performance is less clear.

Simple statistics can be produced to demon-strate a well-established correlation betweeninvestment in human capital and economic indi-cators such as GDP. However, the direction ofcausality remains the subject of heated debate.The implications for future education and trainingpolicies are the subject of continuing researchand are addressed by this study.

A number of key areas can be identified, each

of which has an important bearing on the issuesconsidered here. The main ones include:(a) rates of return on investment in human capital

and the link between private rates of returnfrom education and training and social returnsfor society at large;

(b) links between the use and deployment ofhuman resources and economic performance,including literature on management skills andorganisational performance and general litera-ture on HRM, policy and performance;

(c) literature on the causes of economic growth,including the growth accounting studies,production and knowledge functions relatingGDP to various determinants and the newgrowth theories which endogenise thesources of economic growth;

(d) literature on market valuation of companiesand the role of R&D (a proxy for educationand training in higher level skills) and intellec-tual capital;

(e) the role of firm size and structure-conduct-perfor-mance theories of growth, with an implied role foreducation and training;

(f) the spill-over effects and externalities that resultfrom higher investments in human capital.

A key objective is to draw together thesedisparate strands and to identify the commonmessages about the links between education,training and skills and various indicators ofperformance.

2.3. Approach

The approach adopted is to conduct a series ofextensive international literature reviews ofresearch in each of these areas, coveringeconomic, econometric, business organisationand other related disciplines. These each focuson different aspects of the relationship betweeninvestment in education, training and skills andeconomic performance. It is argued that it is alsonecessary to consider some micro level research,which has a bearing on outcomes at a broadermacro level. In particular, research into firms and

2. Introduction


industries is considered very important in under-standing the contribution of education andtraining to overall economic growth. The paperbuilds upon and extends the analysis presentedin the two previous Cedefop reports edited byTessaring (1998) and Descy and Tessaring (2001).

The aim was to include as much material aspossible from non-English speaking countries, aswell as to provide a greater focus on VET asopposed to more general education. However,this proved much easier said than done and thepresent draft is heavily reliant on Englishlanguage material (particularly American research)and the effects of more general education. Thisreflects the balance of studies currently available.In the country comparative analyses that arereported in the section on economic growthmodelling, much of the analysis focuses on inter-national comparisons, often based on OECDstatistics. Although many of the authors emanatefrom non-English speaking countries, theypublish in English.

2.4. Limitations of the study

The research explores the many diverse studieswhich touch upon the relationship betweeneducation, training and skills and performance inall its aspects. A variety of variables are used asindicators of investment in skills and investmentin human capital. Measures of performance at amacroeconomic level are equally varied. Thediversity of skills-performance literature iscaused, in part, by the range of both performanceand input measures used. These include differ-ences in the level of aggregation and whether thefocus of the research is concerned with private orsocial returns. The latter are potentially very wide,encompassing all kinds of spill-over and externaleffects, including impacts on the general environ-ment. Many of these effects are especially diffi-cult to quantify. The associated study by Greenet al. (2003) into the non-material benefits ofeducation, training and skills focuses moredirectly on these issues.

In investigating the relationships betweeneconomic performance and education,economists have focused on a range of measuressuch as the rate of economic growth, the level andrate of growth in income and wealth, the level and

change in unemployment, export performance,and the like. At an individual level more emphasisis usually placed on the link with earnings. Empir-ical investigations have been undertaken at bothmicro and macro levels, including individual,establishment, firm, industry and economy-widelevel studies. The present study covers all ofthese.

Education and training are key contributors tothe development of skills and knowledge. Typi-cally, but not always, the latter are reflected in theaward of some form of qualification or credential.It is these measured qualifications that arenormally used to capture the human resourceinput into the production and growth process.Where such measures are not available, years ofcompleted schooling are often used instead.

At the organisational level, more complex indica-tors are often deployed. Higher levels of compe-tences should result in superior performance by theindividual and, other things being equal, by theorganisation in which the individual works. Whensuch organisations are aggregated together, thehigher competences should be reflected in highernational growth. Campbell (2001) has recentlyargued, for example, that, for the UK, long-termstudies show a considerable contribution byeducation to economic growth at a local level.

Most studies of economic performance at thelevel of individual establishments have been ofprivate sector companies. While there are exam-ples of economic studies of public organisationsand institutions, these are relatively rare and it isusually necessary to go to other disciplines, such asHRM, to find more information. There are also other,more holistic, approaches to performance thatfocus mainly on geographical areas. For example,city dynamics forms a very large and growing areaof research undertaken by economic geographers.This is given brief coverage in the present review,but the associated study by Izushi and Huggins(2003) analyses the impact of investment in humancapital on regional growth across the EU.

It is important to emphasise that the presentreview does not attempt to assess the precisevalues of the parameters reported, although it isconcerned with the direction and strength of therelationships. The focus is more on the nature ofthe key indicators and the variables used instudies and the general nature of the relation-ships established.


2.5. Structure of the present report

The remainder of this paper is divided into sevenmain sections, each dealing with a particular areaof literature.

Chapter 3 presents a brief overview of statis-tics on economic growth, employment, unem-ployment and various measures of investment ineducation and training. While this highlights thestrong correlation between some of these indica-tors, it also emphasises the difficulties in identi-fying and establishing causal relationshipsbetween the two.

Chapter 4 examines evidence of such links,focussing on the conventional rate of return oneducation and training. Although much of thisliterature is now based on the use of micro level,individual data sets on earnings, its conclusionshave very important macroeconomic conse-quences. This includes the aggregate effects ofinvestments by individuals as well as the widersocial returns.

Chapter 5 concentrates on the links betweeninvestment in skills and performance at the levelof the organisation or firm. This has focussed onthe use and deployment of human resources inthe broadest sense. While much of this researchhas a micro focus, in terms of the units of obser-vation examined, it has crucial macro implicationsfor the performance of economies at muchbroader sectoral and national levels.

Chapter 6 focuses more directly on macroeco-

nomic literature. It highlights the increasing rangeof evidence of the links between investment ineducation and training and economic growth.This covers a range of different approaches fromthe classical growth models of the 1950s and1960s, through growth accounting methods tothe new endogenous growth theories, whichplace education and training and other forms ofinvestment in human capital at the centre ofthings.

There are a number of other areas of researchwhich have focussed on the link between invest-ment in human capital and economic perfor-mance. These are covered in Chapter 7, whichhighlights, in particular, some of the insights frommarket value literature. It also covers otherstudies concerned with the value of intangibleassets, much of which is tied up in some form ofhuman capital.

Chapter 8 covers a further series of researchareas, all of which have some bearing on thistopic, including structure conduct performanceliterature. Chapter 9 provides a round-up of workthat has emphasised the externalities andspill-over effects of investment in human capital.These encompass a large range of both economicand non-economic benefits. In total, these consti-tute very significant macro effects of many indi-vidual investments in education and training byindividuals, companies and other organisations.

Chapter 10 gives an overall summary of thefindings, draws some implications for policy andmakes suggestions for possible future work.

Many international agencies collate data on GDP,employment and unemployment. Others providea wide array of statistics on educational spendingand qualifications within different countries.Generally there is good availability of this type ofdata for the Member States of the EU, coveringthe period after 1970. Some problems arise withconsistency and comparability of the data setsthrough time. Quantitative measures of vocationaltraining in Member States are much more difficultto obtain and, most commonly, such data areonly available for a limited number of countries,and then only for more recent years.

3.1. Economic growth

Table 1 presents estimated annual GDP per capitagrowth rates (1) for the 15 Member States for various5-year periods between 1970 and the year 2000.These data are based on the main economic indi-cators series published by OECD; the growth ratesare based on GDP per capita estimates, allexpressed in 1995 constant prices and using USDexchange rates for the same year. The averageannual growth rate for the 30-year period is given inthe right-hand column of Table 1. This shows howall countries have experienced significant

(1) GDP per capita is the most widely adopted measure of economic growth, as it standardises for the size of country. Alternativegrowth measures, such as the growth of GDP without any adjustment for population size, may well show a different picture.

EU Member 1970-75 1975-80 1980-85 1985-90 1990-95 1995-2000 1970-2000 States

Belgium 3.46 2.91 0.08 2.99 0.90 2.81 2.18

Denmark 2.19 2.50 0.47 1.41 1.63 2.85 1.84

Germany* 2.25 3.34 0.68 2.91 -3.28 1.91 1.28

Greece 5.07 4.41 0.07 1.72 0.59 3.42 2.53

Spain 5.52 1.96 1.08 4.49 0.91 3.80 2.95

France 4.00 3.27 0.35 3.20 0.38 2.68 2.30

Ireland 4.93 4.48 0.27 4.48 3.56 9.90 4.56

Italy 2.41 3.85 0.81 3.03 1.08 0.11 1.87

Luxembourg 3.23 2.61 1.18 4.69 1.59 6.33 3.26

Netherlands 3.22 2.61 -0.54 3.11 0.93 3.68 2.16

Austria 3.94 3.41 1.05 3.00 1.50 2.57 2.58

Portugal 4.73 5.27 0.75 5.11 0.98 3.82 3.43

Finland 4.09 3.20 1.87 3.39 -1.51 2.07 2.17

Sweden 2.57 1.33 1.01 2.29 -0.54 2.96 1.60

United Kingdom 2.17 1.64 1.30 3.28 0.85 2.85 2.01

EU-15 (average) 3.28 2.93 0.73 3.18 0.48 3.51 2.34

* Discontinuity in German series between 1990 and 1995 is attributable to Unification.Source: OECD main economic indicators

3. Statistics of growth, education and training

Table 1: Estimated growth rates of GDP per capita


economic growth in recent decades, with an EUannual average growth rate of about 2.3 %. Theaverage for Germany turns out to be artificially lowas a result of much slower growth (decline) in theperiod after unification. Ireland, a relatively smallcountry, has the highest growth rate of all the15 Member States. There is significant variationaround the EU long-term average. Sweden showsone of the lowest growth rates over this period.

Over time, economic growth rates differ acrossthe respective five-year periods shown in Table 1.For most of these EU countries growth was rela-tively strong in the 1970s, but the period from 1980to 1985 produced a marked slowdown for all coun-tries, with average growth rates below 1 %. Thesecond half of the 1980s resulted in much strongereconomic growth, but the early 1990s producedrelative stagnation for most Member States, withsome countries experiencing GDP per capitadeclines. There was a return to significant growthin the most recent five-year period, although whileIreland averaged annual growth rates approaching10 %, the comparable figure for Italy was onlyabout 1 %.

3.2. Employment and unemployment

Associated with the growth in GDP are changes innational employment levels. Table 2, based onstatistics provided by Eurostat, shows longer-termtrends in average annual rates of growth in totalnational employment. Most of the Member Stateshave experienced positive growth in employmentover each of the past four decades, despite the vari-ation in economic growth rates. Across all theMember States employment has grown over thelonger term, although individual rates of growthshow some variability. Table 3, using consistentEurostat definitions, presents estimates of unem-ployment rates also averaged over decades. Theseshow a significant upward trend, especially in theperiod after 1980. Lower rates of economic growthhave brought about higher unemployment levelsacross the EU, as job creation has failed to keeppace with the numbers in the labour market. Thedifference in unemployment rates between MemberStates is very marked, especially in the 1990s.

Country 1961-70 1971-80 1981-90 1991-2000

Belgium 0.5 0.2 0.1 0.5

Denmark 1.1 0.3 0.3 0.4

Germany 0.2 0.2 0.5 0.3

Greece -0.8 0.7 1.0 0.4

Spain 0.6 -0.6 0.8 1.2

France 0.6 0.5 0.3 0.5

Ireland 0.0 0.9 -0.2 3.7

Italy -0.5 0.7 0.6 0.2

Luxembourg 0.6 1.2 1.7 3.4

Netherlands 1.9 0.7 1.1 1.9

Austria -0.4 0.7 0.1 0.3

Portugal 0.2 0.1 0.2 -0.1

Finland 0.4 0.3 0.5 -0.8

Sweden 0.7 0.8 0.7 -0.7

United Kingdom 0.3 0.3 0.5 0.2

EU-15 (average) 0.3 0.3 0.5 0.4

Source: Eurostat - European economy: 2001 review

Table 2: Growth in total employment – average annual percentage changes


Country 1961-70 1971-80 1981-90 1991-2000

Belgium 1.9 4.6 9.7 8.7

Denmark 1.0 3.7 8.4 7.1

Germany 0.7 2.2 6.0 8.1

Greece 5.0 2.2 6.4 9.5

Spain 2.5 5.4 18.5 19.6

France 1.8 4.1 9.2 11.3

Ireland 5.4 7.7 14.7 11.1

Italy 4.8 6.1 8.7 10.7

Luxembourg 0.0 0.6 2.5 2.6

Netherlands 0.9 4.4 8.5 5.4

Austria 2.5 1.8 3.3 3.9

Portugal 2.5 5.1 7.3 5.6

Finland 2.3 4.0 4.7 12.5

Sweden 1.7 2.1 2.6 7.7

United Kingdom 1.7 3.8 9.8 8.1

EU-15 (average) 2.2 4.0 9.0 9.9

Source: Eurostat - European economy: 2001 review

Figure 1: Average growth and employment rates in EU

Source: Eurostat statistics

-2

0

2

4

6

8

10

12

1970-1975 1975-1980 1980-1985 1985-1990 1990-1995 1995-2000

GDP p.c. growthAverage unemployment Employment growth

Table 3: Unemployment rates – average percentages

Figure 1 provides a simple comparison of GDPper capita growth rates, total employment averageannual growth rates and unemployment averagepercentages for the 15 Member States across sixtime intervals over the period 1970-2000. The graphshows how GDP and employment growth moveclosely together. Unemployment has risen steadilyover the longer term.

3.3. Educational participation

International education statistics tend to be lessreadily comparable than economic growth dataand employment data, but organisations such asUnesco and OECD have long published figuresmeasuring educational variables. More recently,results from various European labour surveyshave supplemented these sources of information.Unfortunately, in this area, different surveys oftenseem to produce statistics that can be signifi-cantly at odds with one another; see for examplethe discussion in Barro (2000). Table 4 repro-duces data measuring total expenditure oneducation as a percentage of GNP; this is mainly

derived from various Unesco Statistical Year-books. Since the statistics in Table 4 are onlyconcerned with the 15 Member States, thesedata are considered to be relatively comparable.

Table 4 shows how, overall, EU countries havemaintained educational expenditure as a more orless constant percentage of GNP. Given that theseeconomies have been growing steadily over theperiod since 1970, educational investment hasalso been increasing in line with economic growth.Some Member States significantly increased theireducational expenditures (as percentage of GNP)over the 30-year period; Greece, Spain, Irelandand Portugal are the best examples. The averagepercentage levels were consistently higher inDenmark, Finland and Sweden than in the otherMember States. The data in Table 4 suggest somevariability in education budgets over the five-yearobservation periods. This may be as a result ofpolitical policy changes or it could simply be areflection of changing measurement practices inthe different countries. International time series ofeducation variables can sometimes be ratherinaccurate and most studies, such as thosedescribed in Chapter 6 below, use broad average

Country 1970 1975 1980 1985 1990 1995 1998*

Belgium 6.0 6.2 6.0 6.0 5.0 5.0 4.7

Denmark 6.7 7.6 6.7 7.0 7.1 7.7 7.4

Germany 4.8 5.0

Greece 1.7 1.7 1.8 2.4 2.5 2.9 3.0

Spain 2.0 1.8 2.6 3.3 4.4 4.9 4.8

France 4.8 5.2 5.0 5.8 5.4 6.1 6.0

Ireland 4.8 5.8 6.3 6.4 5.6 6.0 5.8

Italy 3.7 4.1 4.4 5.0 5.2 4.7 4.3

Luxembourg 3.6 4.7 5.7 3.8 3.6 4.1 4.3

Netherlands 7.2 8.1 7.6 6.4 6.0 5.2 5.0

Austria 4.5 5.6 5.5 5.8 5.4 5.6 5.5

Portugal 1.5 3.5 3.8 4.0 4.2 5.3 5.7

Finland 5.9 6.4 5.3 5.4 5.7 7.5 7.9

Sweden 7.6 7.0 9.0 7.7 7.7 8.1 8.5

United Kingdom 5.3 6.6 5.6 4.9 4.9 5.3 5.3

EU-15 (average %) 4.7 5.3 5.4 5.3 5.2 5.5 5.5

NB: * Estimated percentage based on OECD Education at a glance, 2001Source: Unesco statistical yearbooks


Table 4: Total expenditure on education as percentage of GNP

observations in cross-sectional comparisons,rather than time series analyses.

Comparison of the data in Tables 1 and 4 illus-trates immediately some of the difficulties inestablishing links between indicators of educa-tion and economic growth. There are no obviouspatterns. Sweden, which has one of the highestpercentages of expenditure on education, hasone of the lowest growth rates. Ireland, thefastest growing country, cannot attribute this to ahigher than average proportion of spending oneducation or growth in the percentage. The linksbetween these variables are often complex anddifficult to identify.

Educational expenditure provides only a verybroad measure of investment in humanresources. Other statistics are available on thepercentage of those of eligible age in the popula-tion enrolled in various categories of education.For most Member States, primary and secondaryeducation participation is virtually 100 % andtrends over time have very little significance.However, the percentage of the eligible popula-tion in tertiary education shows marked variabilitybetween EU countries and changes over time are

very apparent. Table 5 summarises this data, asderived from various Unesco statistical year-books. All the Member States show very signifi-cant increases in the percentages enteringtertiary education over the 25-year periodcovered by the data. By 1995, the EU averagewas approaching 50 %, showing a strong trendgrowth, especially in the period since 1985.Again, there is no obvious link between theseindicators and performance in terms of GDPgrowth.

Other indicators of educational investment arealso available, such as statistics on the averagenumber of years of education completed bymembers of the working population in differentcountries. Some detailed studies are availablecomparing qualification rates in particular areasof study, such as mathematics, sciences andforeign languages; see, for example, some of thedata reproduced in Prais (1995). Further statisticsthat try to provide some measure of the quality ofeducational input detail the qualifications of thosewho carry out the teaching in schools and relatededucational establishments.

Virtually all published education statistics show

Country 1970 1975 1980 1985 1990 1995

Belgium 17 22 26 32 40 56

Denmark 19 30 28 29 36 48

Germany 34 46

Greece 13 18 17 24 36 42

Spain 9 20 23 29 37 48

France 19 25 25 30 40 51

Ireland 12 17 18 22 29 40

Italy 17 26 27 25 32 42

Luxembourg

Netherlands 20 25 29 32 40 48

Austria 12 18 22 26 35 48

Portugal 7 10 11 12 23 39

Finland 13 28 32 34 49 55

Sweden 22 30 31 31 32 47

United Kingdom 14 19 19 22 30 50

EU-15 (average %) 15 22 24 25 35 47

NB: * Basic group is ages 18-22, but this varies slightly for different countriesSource: Unesco statistical yearbooks


Table 5: Percentage of population aged 18-22* entering tertiary education

increasing levels of investment over time acrossall the Member States. These increasing trendsare correlated with economic growth statistics.However, the direction of causality remains to beestablished. Is increased educational investmenta prime factor determining growth in GDP percapita or is this observed increase in education aresult of continuing economic growth? The litera-ture on economic growth reviewed in Chapter 6of this paper suggests that there is a growingweight of evidence that it is the former, although

there is also important feedback in the otherdirection.

3.4. Educational qualifications of the workforce

The OECD has adopted the international stan-dard classification of education (ISCED) systemto facilitate comparisons of educational attain-

Highest completed level of education

Upper secondary Tertiary (%)Average years

or above (%)of schooling

Australia 53 24 11.9

Austria 69 8 11.9

Belgium 53 25 11.7

Canada 75 47 13.2

Czech Republic 83 11 12.4

Denmark 62 20 12.4

Finland 65 21 11.6

France 68 19 11.2

Germany 84 23 13.4

Greece 43 17 10.9

Ireland 47 20 10.8

Italy 35 8 10.0

Netherlands 61 22 12.7

New Zealand 59 25 11.4

Norway 81 29 12.4

Portugal 20 11 10.0

Spain 28 16 11.2

Sweden 75 28 12.1

Switzerland 82 21 12.6

United Kingdom 76 21 12.1

United States 86 33 13.5

OECD average 62 21 11.9

NB: The estimates of average years of schooling relate to total cumulative time spent in formal education over all ISCED levelsfrom the beginning of primary level (ISCED 1) to tertiary level. These estimates are obtained by using data on educational attain-ment of each age group from the labour force survey and applying an estimated average cumulative duration for each level ofeducation. Where there are programmes of different duration at the same ISCED level, a weighted average is taken based onweights corresponding to the number of persons in each broad educational programme.Source: Education at a glance – OECD indicators (OECD, 2001a), indicator A2.1, p. 38 (using data on educational attainment ofindividuals from labour force survey sources, or in the case of Denmark, the register of educational attainment of the popula-tion).


Table 6: Educational attainment of the adult population.

(2) OECD has adopted ISCED, but Unesco remains responsible. Unesco revised ISCED in 1997 with the aim of reflecting evolu-tion in education-training systems and increasing comparability of data. The new ISCED distinguishes between programmeorientation (general education, pre- and vocational education and training) and programme destination (further studies, labourmarket). This means that there exist consistent and comparable data on education and training (but the potential of new ISCEDis still unexplored).

ment between countries (2). ISCED was originallydesigned to serve as an instrument suitable forassembling, compiling and presenting statisticsof education both within individual countries andinternationally. The ISCED typology is described

in further detail in Annex 1. This classificationsystem forms the basis for much of the compara-tive evidence emanating from the OECD oneducation systems (e.g. OECD, 2001a; Steedmanand McIntosh, 2001). International comparisons

Figure 2: Highest educational attainment of the adult population

Source: OECD (1998)

Tertiary

0

25

50

75

100

AS A B CA CZ DK FIN F D EL IRL I NL NO NZ P E S CH UK US

Upper secondary or above

Figure 3: Average years of schooling

Source: OECD (1998)

0

5

10

15

AS A B CA CZ DK FIN F D EL IRL I NL NO NZ P E S CH UK US

Average years of schooling


of education are typically based on years ofschooling, and/or the highest educational levelachieved.

A comparison between OECD countries ofeducational attainment is provided in Table 6 andFigures 2 and 3. Two measures are provided – theproportions of the working age population whosehighest attainment level is upper-secondary ortertiary (primary level attainment is now almostuniversal in the OECD) and the average numberof years of schooling. As can be seen, there areonly relatively small differences between coun-tries in terms of average years of schoolingacross the OECD.

Recent work by Barro and Lee (1993, 1996,2001) has also focused on comparisons of educa-tional attainment between countries. However,given the wider set of countries they consider –including many developing and transition coun-tries – and the paucity of detailed data (such ashousehold surveys), especially for their historicalseries, they simply focus on average years ofschooling and a broad measure of education levelattained. Table 7 and Figures 4 and 5 providesome details for a range of country groups.

In general, there have been increasing averagelevels of participation in education over the lastthree decades, in all regions, but growth hasbeen particularly strong in the Middle East andNorth Africa. Mean school years have increasedin developed countries too, with the net resultthat there has been a substantial increase in theworld stock of educated people, although littleconvergence between regions. South Asia and

Sub-Saharan Africa still have substantial propor-tions of their populations with very little or noeducation.

A simple comparison of these data with thoseon economic growth rates does not reveal anyobvious and immediate links. Teasing out thecausal links between the two is a complexproblem, as the discussion in subsequentsections makes clear.

3.5. Vocational training

Consistent statistics detailing trends in vocationaltraining across EU countries are often more diffi-cult to obtain, although more recent OECD statis-tics and European labour force surveys (ELFS)provide much useful comparable information inthis area. Table 8 shows some comparative datafor selected Member States for one particularsurvey period. While consistent definitions wereagreed for the ELFS, exactly what sorts of trainingare included and which are excluded by thesurvey questions is not at all obvious. McIntosh(1999) has detailed a whole range of difficulties intrying to use these data to draw quantitativecomparisons about levels of vocational trainingacross the EU. Questionnaires used to collect thisinformation are often revised and modifiedbetween successive surveys, so that discontinu-ities in the data sets over time are commonplace.

Table 8 appears to show some marked discrep-ancies across countries between the percentagesof individuals receiving training in the month prior

Mean school years % with no education (age 15+)1970 1980 1990 2000 1970 1980 1990 2000

Middle East/North Africa 2.07 3.29 4.38 5.44 69.8 55.5 42.8 32.0

South Asia 2.05 2.97 3.85 4.57 69.3 66.9 55.2 45.2

Sub-Saharan Africa 2.07 2.39 3.14 3.52 63.8 56.8 45.9 42.8

East Asia and Pacific 3.80 5.10 5.84 6.71 35.4 22.6 26.4 19.8

Latin American and Caribbean 3.82 4.43 5.32 6.06 31.2 23.8 17.2 14.6

Advanced countries 7.56 8.86 9.19 9.76 5.1 4.8 4.5 3.7

Transitional 8.47 8.90 9.97 9.68 3.1 2.8 1.7 2.2

World 5.16 5.92 6.43 6.66 31.4 29.5 26.4 24.2

Source: Barro and Lee (2001)


Table 7: Average schooling and no schooling rates 1970-2000

to the survey; while Sweden has just over 10 %,France and Portugal have less than 1 %.However, much of this variation is explainable interms of how the training question is interpreted indifferent countries, rather than real observable

differences. Much of Table 8 is concerned withhow the incidence of training is distributed acrossthe characteristics of individuals, their job tenuresand their places of work. In most Member States,training is concentrated on those in the younger

Figure 4: Schooling attainment 1970-2000: average years of schooling


0

2

4

6

8

10

Advanced East Asia Latin America Middle East/North Africa

Sub-SaharanAfrica

South Asia Transitional

years - 1970 years - 1980 years - 1990 years - 2000

Figure 5: Schooling attainment 1970-2000: percentage of population 15+ with no schooling


no education - 1970 no education - 1980 no education - 1990 no education - 2000

0

20

40

60

80

Advanced East Asia LatinAmerica

Middle East/North Africa

Sub-SaharanAfrica

South Asia Transitional


age groups, although Sweden is a significantexception. While in the Sweden and the UK voca-tional training is aimed more towards thoseemployees with higher prior education levels(ISCEDs), in Germany the opposite is the case,with the least qualified employees being thosemost likely to receive training. Workers who havebeen in the job for the shortest period of timereceive more training in France and the UK thanlonger-term employees, whereas in Sweden thepattern is reversed with training increasing withlength of job tenure. Table 8 also shows how inGermany, France and the UK training incidence ishighest in smaller enterprises that employ fewerworkers, while in the Netherlands training ishighest in the largest enterprises.

It is clear that these training statistics arecomplex and difficult to interpret, given the signif-icant differences that underlie the national voca-

tional training systems. There is good reason tosuppose that vocational training, like its educa-tional counterpart, has been on an increasingtrend in all Member States over recent decades.However, measurement problems mean that voca-tional training data are unlikely to be suitable forinclusion in models of the kind described later inthis study. In an earlier Cedefop report, Luttringer(1998) has detailed some of the difficulties indefining precisely investment in continuing voca-tional training and also in relating it to productivitygains, both at the micro and macro levels.

A proxy measure of skill levels in populations isprovided by the OECD’s international adultliteracy survey (IALS). However this survey onlycovers a limited number of EU countries over theperiod 1994 to 1998. It is very likely thatincreases in adult literacy are associated witheconomic growth.

Category Germany France Netherlands Portugal Sweden UK

All employees 4.9 0.5 5.3 0.1 10.5 7.3

Females 4.8 0.3 4.4 0.1 10.8 6.7

Males 5.0 0.6 5.8 0.1 10.2 7.8

Age 15-20 65.1 26.5 8.0 0.2 3.4 25.6

Age 21-30 3.5 0.5 6.7 0.1 9.0 6.6

Age 31-40 0.8 0.1 5.4 0.1 12.1 6.2

ISCED high 1.0 0.2 3.7 0.3 14.7 10.5

ISCED medium 1.9 0.2 5.8 0.2 10.1 6.0

ISCED low 24.6 1.1 5.1 0.1 6.0 6.5

Tenure +6 years 0.5 0.1 4.6 0.2 11.7 5.6

Tenure 1-5 years 9.8 0.1 8.5 0.1 10.0 6.7

Tenure <1 year 6.2 2.5 3.4 0.1 7.7 8.1

< 11 employees 5.5 1.4 3.5 0.1 - 9.3

11-19 employees 5.5 1.3 4.4 0.2 - 6.6

20-49 employees 4.4 0 3.9 0 - 6.9

50 > employees 3.4 0 5.9 0.4 - 7.0


Table 8: Comparisons of vocational training based on European labour force survey

4.1. General literature strands

There is extensive and detailed literature on ratesof return on education and training, based onhuman capital theory. Much of this literaturerelates to the rates of return for the individual andsociety in general of investment in human capital,in the form of education and training. Most of thismicroeconomic research draws on work byBecker (1964), Mincer (1974) and many others.Measuring the benefits of education and trainingis complex and can be considered at variouslevels and from differing perspectives:(a) at an individual level, examining the impact of

education or training on the chances of beingemployed or unemployed or, most commonly,earnings;

(b) from an organisational perspective, examiningthe impact of education and training onorganisational performance. The extent towhich employers engage in training their staffand provide employees with transferableskills in the labour market is critical (Stevens,1999);

(c) at a macro level, examining the role of educa-tion and training, typically using qualificationsas a measure of activity, and their impact onproductivity, output growth and employment.

The discussion in this section briefly focuseson certain aspects of the first of these, while thesecond and third form the subject matter ofChapter 5 and 6 respectively.

4.2. Rates of return oninvestment in human capital

A number of reviews on the rates of return ongeneral education and training have been under-taken; most recently both Harmon and Walker(2001) and Blundell et al. (2001). A review of themost significant literature on rates of return oncontinuous vocational training in companies hasbeen carried out by Barrett (2001), in an earlierCedefop report. Sianesi and Van Reenen (2000)

have carried out a review of the macroeconomicreturns on education, but this largely deals withresearch considered in subsequent sections ofthe present document.

General literature on the subject demonstratesmost directly the links between education andtraining and performance at an individual level.However, it is important to appreciate how someof the microeconomic theory and analysis carriesimplications for the macroeconomic level which,(subject to certain concerns about the extent towhich qualifications act as a signalling devicerather than actually enhancing productivity), canbe regarded as the sum of the individual parts.While much of the evidence relates to the bene-fits to the individual, it is clear that this has directimplications at a more macro level.

The main emphasis of most of this research ison the returns on higher education (e.g. a firstdegree), although there is fairly extensive litera-ture on the returns on training, particularly in theUS. The prime focus of most of this literature isalso upon the benefit to individuals, althoughsome explicit efforts have also been made toassess wider benefits to society as a whole thateffectively represents a macro effect, which is theprinciple concern of this study.

The key concept here is the notion of the rateof return on the investment in human capital thateducation and training represents. Rates of returnare typically estimated in two ways:(a) early studies used a discounted cash flow,

accounting framework;(b) more recently researchers have adopted an

earnings function approach.The effects on employment, productivity and

earnings can be expressed in terms of the rate ofreturn on education and training.

Typically, rates of return are computed forvarious qualifications or for the length of timespent in education or on training courses. Therate of return expresses the value of an additionalyear of education (or the value of a particularqualification) in terms of the associated increasein earnings or income. The issues addressed inthe wide variety of studies that have estimated

4. Rates of return on education and training

rates of return on education and training includevariations in rates of return according to type ofqualification, gender, age and ability; thescreening impact of additional education to filterindividuals into well-paid jobs; the effects ofover-education and the direct impact of educa-tion and training (including government trainingschemes) on the probability of employment.

The discounted cash flow accounting approachcompares income or earnings streams of individ-uals with and without the education or trainingprogramme (i.e. they compare graduate incomestreams with those whose highest qualification is Alevel (3)). The calculation generates an internal rateof return on the investment that can be compared tothe going rate of interest. It is possible to distinguishbetween two substrands: ex post rates of return(which generally compare actual incomes of indi-viduals of different ages, with and without theeducation or training) and ex ante rates of return(which generally compare an individual’s percep-tions of future income streams over future yearswith and without the education or training).

The earnings function approach, involves esti-mating an earnings function based on data on theearnings of large samples of individual workers,together with information about their personaland other characteristics, including their invest-ments in education and training. Estimates ofrates of return on years of schooling or on indi-vidual qualifications can be obtained from suchan analysis using a method pioneered by Mincer(1974). Although it has been refined in variousrespects, this basic approach has become theaccepted standard, allowing for problems ofendogeneity and spurious correlation to bedirectly addressed. Although the discounted cashflow approach still has some adherents and is stilluseful in situations where the largecross-Sectional data sets on individual earningsare not available, the earnings function model isnow by far the most popular.

In effect, both approaches attribute the differ-ence in income between individuals or groups ofindividuals to the education or trainingprogramme. This is only true if the individualsbeing compared (i.e. those with and without theeducation or training) are identical in every impor-tant respect. Finally, this approach makes no

allowance for selection biases (i.e. that the indi-viduals most likely to benefit select themselves/are selected onto the programme). Someresearchers have tried to address the issue ofselection as well as testing the biases resultingfrom ability. Their results suggest that the majorpart of the benefits that accrue to individuals as aresult of investment in education or trainingreflect enhanced productivity. Hujer et al. (2003),in an associated study, discuss some of themethods and limitations of evaluation and impactresearch.

Despite this, there have been continuingconcerns about over-education, qualificationinflation and graduate unemployment, see forexample, Büchel (2001). In the UK, two papers byBrynin, (2002a and b) focus on these concerns.They examine first, the impact of graduate densi-ties on wages, and second, the impact of overqualification on returns on first and second jobs.Graduate density is defined as the proportion ofgraduates in employment defined by both occu-pation and industry. Together with other variables,including average education, these measures arethen used as potential explanations of wageoutcomes.

The results suggest some evidence of grad-uate overcrowding but also gender-based jobsegregation that appears to operate in favour ofwomen. For men, there are diminishing returnsfrom expansion in graduate numbers overall.Brynin also examines the extent and impact ofover qualification for a job. Normally this isassessed by asking about the level of educationrequired for the job and comparing this to actualqualifications. This often indicates that a largeproportion of people have some excess educa-tion, although typically they earn more comparedto other people doing the same kind of work.Normally such individuals earn less than thosedoing the kind of job for which they consider theyare appropriately educated. The results indicatethat for younger cohorts, having excess educa-tion brings diminishing returns in their first job,but this does not apply to the older cohorts.These points were originally raised in theCedefop second research report, Part 4.

Brynin reports that, as average qualificationlevels have increased over time (as measured by


(3) The General Certificate of Education (GCE) Advanced (A) level is normally taken at the end of secondary school in the UK.A levels are the main standard for entrance to higher education.

average O-level/GCSE (4) results), the status ofsecond as well as first jobs (as measured by the‘Hope-Goldthorpe scale’ of occupational status)has been declining. This does not necessarilyimply the need to put the brakes on the expan-sion of education but does demonstrate that noteveryone will benefit uniformly from furtherexpansion of education.

4.3. Private versus social rates of return

An important feature of this micro literature,carrying implications for macro studies, is the keydistinction between private and social rates ofreturn. In effect, the private rates relate only tothe costs and benefits experienced by the indi-vidual undertaking the education or training. Inparticular, the costs of education would onlyinvolve those elements paid for by the individual(i.e. they would not include fees if these werepaid by the government) and higher futureincomes would be assessed net of tax. The socialrate of return, on the other hand, takes intoaccount all costs and benefits. Private and socialreturns are generally always different for the typesof reasons set out above. An Austrian empiricalstudy by Fersterer and Winter-Ebmer (1999)demonstrates some of the differences in the ratesof return on various levels of education forAustria.

In addition, there is the issue of spill-overeffects and externalities from education andtraining, which may also have a distinct spatialaspect. An example relates to poaching. If, forexample, few firms train, then the trained workerscan be offered higher salaries by firms that do notundertake training. The firms which train lose inthis process and the firms that do not trainbenefit, although, increasingly, firms ask for arepayment of training costs if workers leave afirm. This undermines the incentive to train. If,however, all firms train (to the same standard),then, ignoring differences in types of skills, thereis no incentive to poach and, when individuals domove, those lost can be replaced by others of

equal skill level. There are other aspects of socialreturns, however, linked to new growth theories,which are developed in Chapter 9 below. Inparticular, the bridging concept is that educationspills-over from the educated to thenon-educated group. The original idea wasproposed many years ago by Blaug (1972).

Mingat and Tan (1996) produced estimates ofsocial returns on education, to incorporate exter-nalities and non-economic effects, for a widerange of countries over the period 1960-85. Theyused the overall economic performance of thevarious countries to capture these externalities.Their results confirm the social profitability ofinvesting in education, but they suggest that theresults are sensitive to the stage of economicdevelopment achieved by the individual country.The best returns for low-income countries camefrom investing in primary education, in secondaryeducation for middle-income countries, and forthe highest-income countries the optimal socialreturn was from higher education. Despite this,there is evidence from some countries, includingthe UK, that there are still significant benefits tobe obtained by investing in basic literacy andnumeracy among those segments of the popula-tion that have, for one reason or another, missedout on education, Dearden et al. (2002)

Harmon and Walker (2001), in their recentreview of evidence and issues relating to thereturns on an individual’s education, indicate aconsiderable degree of consensus on estimatesof the rate of return for an additional year’seducation. Basic specifications suggest a returnfor a year of schooling of between 7 and 9 %.Most studies suggest that women gain more fromadditional education than men, that those in thetop part of the income distribution gain higherreturns per year of education than those in thebottom part , and that the effect of underlyingability on earnings is small compared with theeffect of education. The impact of such educationon overall national output and productivity is notconsidered by such studies.

Much of the analytical debate regarding theneed for greater public investment in educationand training has rested on the social rate of returnrelative to the private one. Evidence on social rates


(4) The Ordinary (O) level is the main examination taken by secondary school pupils in the UK leading to the General Certificateof Secondary Education (GCSE). They are usually taken after four or five years of secondary schooling and can lead to moreadvanced education and training.

of return looks at the benefits to the economy fromincreased years of education, typically by calcu-lating the costs of education/schooling comparedto pre-tax earnings. From a methodologicalperspective this is contentious due to the need tomake many assumptions, but available evidenceindicates returns are around 6-12 % (Chevalieret al., 2001) (Steel and Sausmann, 1997). A newstudy by Trostel et al. (2002), uses broadlycomparable data covering a number of countriesto show that recent rates of return on schoolingvary significantly across the 28 countries in thesample set but are still generally positive andlarge.

The present study is as much concerned withvocational training as with general education. Themost obvious and direct effect of training byemployers is to provide additional skills and toraise productivity. This is the central notion of thehuman capital model of training. Barrett (2001)reviewed a number of empirical studies acrossseveral countries, examining the links between

returns on continuing vocational training andcompany performance. The main results suggestthat strict rates of return, equivalent to thoseused in the higher education studies, have rarelybeen used in the evaluation of continuing voca-tional training. Such training is usually found tohave a positive effect on both productivity andwages, but there have been few attempts todemonstrate whether these are sufficient to guar-antee a good rate of return. Specific training hasthe greatest impact by raising the productivity ofthe employee within the firm providing thetraining. General training, which can be used byall companies, is still beneficial, but it tends tohave less impact on company performance,although it may have important benefits for theindividual and for society at large. There remainimportant differences in the perception of thevalue of training between the individual, thecompany investing in the training and society asa whole. These issues are more fully investigatedin the associated study by Hansson et al. (2003).


5.1. Links between humanresources and performanceat company level

Most of the studies on rates of return discussedin the previous section are concerned with issuesof appropriate measurement and analysis of thefactors that lead to variation in the measuredrates for the individual. Generally, there is noattempt to relate such rates of return specificallyto more general corporate performance. It can beassumed from human capital literature thatgreater education leads, ceteris paribus, to higherproductivity, which will be reflected in economicgrowth or other kinds of improved performancefor the individual company or organisation.

Outside mainstream economic literature, studiesin the management of human resources report arange of results concerning the impact of humancapital on firm and industry performance. Suchliterature bridges the gap between rate of returnanalysis and macro level studies. It suggests thatcompanies employing managers, professional andother staff with higher qualifications can expect toachieve better commercial performance. This liter-ature is briefly reviewed here.

There has long existed a widely held belief thatpeople are a key source of an organisation’scompetitive advantage (Prahalad, 1983) (Pfeffer,1994) (Wright et al., 1994). By implication, it may beargued that it is the quality of the management ofhuman resources that determines organisationalperformance (Adler, 1988) (Reich, 1991) (Youndtet al., 1996). There appear to be a number ofdifferent emphases in this literature, including therole played by managers, as well as the extent andnature of human resources themselves.

5.2. Management skills andorganisational performance

A key theme in the economics and broadermanagement literature has been the importantrole that managers (and administrators) play in

determining organisational performance.Amongst trading companies, for example, it hasgenerally been assumed that the capacity of thechief executive officer (CEO) or the managementteam forms the ultimate limit to the rate ofsustainable growth (Bosworth and Jacobs, 1989).

The way in which this line of thinking developedwas to look at the goals of the largeowner-managed firms and the manner in whichthese impacted upon company behaviour. Theempirical counterpart to this was structure-conduct-performance literature, where firm sizeand market power were key components. Thissection focuses on more recent literatureconcerned with the level and discipline of qualifi-cations of the workforce and, in particular, theCEO or management team. (Bosworth, 1999).

There is no real underpinning theoretical litera-ture, although there are clearly links with thetheories relating to individual rate of return,human capital and the value of intangible assets.Empirical modelling usually attempts to relate P,some measure of company performance to Q, avector of qualification and skill-related variables,and X, a vector of other variables influencingcompany performance.

A further issue is the use of qualifications as ameasure of the quality of workers or managers.Certainly education and training impart a varietyof knowledge and skills that may raise theproductivity of the individual in the job in whichthey are employed. However, these are input andnot output measures. There is no indication as tohow good or relevant the education or training isor, indeed, the capacity of the individual toabsorb the knowledge. In some ways this can beseen as an empirical issue. It is possible simply toestimate an equation where P is determined by Qand X, to see whether qualifications are signifi-cant or not, in the presence of other determinants.It should be noted that measurable qualificationsare not the same as actual skills or quality ofcompetence.

The role of leadership has been defined inseveral ways. The first is ‘stewardship’, which istypically represented as a dummy variable for a

5. Human resources and performance

specific CEO in a particular company (Liebersonand O’Connor, 1972) (Weiner and Mahoney,1981). A dummy variable is defined Dijt as takinga value of 1 when the ith CEO is headingcompany j at time t. An alternative way is toattempt to quantify CEO ‘strategy’ by observingkey variables over which the individual hascontrol, such as capital structure and retainedearnings (i.e. two financial strategy variables). Itcan be seen that the first variable is a crudeproxy, and the second echoes the Tobin’s Q typespecifications discussed in Chapter 7 below.

HRM literature is mixed in its views about theimpact of leadership. One strand argues that theevolution of formal and informal organisationalstructures in large corporations means that theylargely run themselves, so effectively limiting theinfluence of any single individual, even the CEO.Hall (1977) argues that leadership is important intimes of growth, development and crisis, but notin times when the organisation is roughly main-taining a status quo. Weiner and Mahoney (1981)point out that while the presence of a leaderremains essential, the particular leader that ischosen may be of little importance. Liebersonand O’Connor (1972) found that situational andorganisational factors were of greater importancethan leadership.

5.3. Empirical studies at company level

Issues of HRM at company level were discussedearlier in Cedefop’s second research report,Part 3 and they are also reviewed in the parallelstudy by Hansson et al. (2003). A key theme ofHRM literature is the link between HRM strategyemployed by the organisation and performance.Youndt et al. (1996), for example, distinguishbetween universal and contingency strategies (5).The authors report that their empirical studylargely supports the contingency approach. Moreimportantly, from the perspective of the presentstudy, the authors conclude that HRM systems,which focus on human capital enhancement, aredirectly related to multiple dimensions of organi-

sational performance, in particular to employeeproductivity, machine efficiency, and consumeralignment. However, in line with their conclusionsabout the contingency approach, Youndt et al.report that fuller analysis reveals that enhancedperformance is predominantly the result of linkinghuman-capital-enhancing HRM systems withquality manufacturing strategies. There is a linkbetween HRM strategy and the quality ofmanagement decisions.

An earlier survey of over 700 UK productioncompanies revealed the relatively low levels ofqualifications of both the boards of directors andtheir CEOs (Bosworth et al., 1992). However therewas strong confirmation of a link between theexistence of a R&D culture and the ability andwillingness to innovate, particularly when gradu-ates were employed to carry out R&D. Regres-sion analysis of the extent of the introduction ofnew technologies by companies in the sample (aperformance indicator) revealed a positive corre-lation between the probability of innovation andthe presence of graduates in the workforce. Firmsthat utilised these advanced technologies tendedto outperform non-users in a number of respects.In particular they worked closer to full capacityand had higher growth in turnover and marketshare. However, one consequence of workingcloser to full capacity and being more dynamicwas that such companies tended to experiencemore acute skill shortages. The study reportedboth direct and indirect (i.e. via innovation) linksbetween qualifications and performance. Two ofthe main conclusions drawn from the study, were‘[…] the presence of graduates […] improvesgeneral economic performance’ and ‘[…] thedifferential impact of graduates (generally) oncompany performance […] is much moreclear-cut than the differential performance thatcould be attributed to the employment of profes-sional scientists and engineers vis-à-vis gradu-ates from other discipline areas.’

Barry et al. (1997) update and extend the earlierresults of Bosworth et al. (1992) and compare themwith a study by Wood (1992), who linked aprofit-based measure of performance to qualifica-tions. Wood found that manufacturing companiesrun by CEOs possessing any kind of degree/profes-


(5) ‘The universal, or “best practices” perspective implies a direct relationship between particular approaches to human resourcesand performance, and the contingency perspective posits that an organisation’s strategic posture either augments or dimin-ishes the impact of HR practice on performance.’ (op cit. p. 837).

sional qualifications were more likely to achievesuperior levels of profitability. However, there wasno evidence that the precise type of degree/profes-sional qualification held by the CEO made a greatdeal of difference to the level of profitability.

The Barry et al. (1997) study focused moresimply on the top executives of the companies,rather than the earlier approach that had incorpo-rated both non-executive and executive directors.A key conclusion on the role of qualifications wasthat companies headed by qualified top execu-tives tended to outperform those with unqualifiedtop executives and many of the latter companiesexhibited significant under-performance in themarket-place. A further conclusion concerned thecontribution of the discipline background of thetop executive, whereby companies headed bytop executives with non-technical qualifications –particularly accounting – outperformed compa-nies with top executives who were qualified engi-neers or scientists.

A key question relates to what the measure ofcompany performance should be. Earlier concep-tual literature suggested that growth might be a keyperformance indicator, but there is little in empiricalliterature that has tested such a relationship. Thereare some models in structure-conduct-perfor-mance literature that have explored the influenceson growth, but these have not usually incorporatedthe human capital variables. There are, however,some studies that test alternative goals, such asprofits. Here, however, it is not possible to use, forexample, private rates of return theory as an under-pinning, as this is based upon the idea that individ-uals are paid the value of their marginal product (orsome equivalent dynamic concept). However, it isfairly easy to think of a rationale from the more insti-tutional/contracting literature that suggests, forexample, more qualified individuals are offeredcontracts that induce them to generate a surplus.This surplus is then shared between shareholdersand qualified workers in the form of both higherprofits and higher wages.

A study by Ballot et al. (2001) examined theimpact of human and technological capital onproductivity in a panel sample of large Frenchand Swedish firms. Specifically, measures of afirm’s human capital stock were constructed onthe basis of past and present training expendi-tures. The results confirm that such firm-spon-sored training, together with R&D, are key inputs

to the market performance of firms in both coun-tries. Another study by Blechinger and Pfeiffer(1998) used survey data for the German manufac-turing sector to explore the links betweenemployment growth, technological change andlabour force skill structures. Innovative firmsexperienced the highest growth rates and suchfirms tended to employ more highly skilledworkers. An HRM study by Papalexandris andNikandrou (2000) into Greek firms found that,where training was treated as a continuous, life-long learning process, it had considerable impacton the growth of firms.

5.4. Empirical studies at theindustry and sector level

A different approach to exploring the links betweeninvestment in human resources and productivity inorganisations is that developed over the last20 years by researchers at the national institute ofeconomic and social research (NIESR) in the UK.Much of this work is summarised in Prais (1995) andall the relevant papers have been published in full invarious editions of the National Institute EconomicReview. The research approach consists of carryingout empirical international comparisons of produc-tivity and associated schooling and vocationaltraining investments between Germany, France,the Netherlands, the UK and other selected coun-tries. Often matched samples of plants and firmsfrom specific industrial sectors have been used toexplore the productivity and human capital rela-tionship.

It should be noted that this body of researchdraws on statistics and information from severalEU countries. Moreover, this research is note-worthy in utilising vocational training data, as wellas the more commonly adopted years ofschooling measures. The concern is not so muchwith the statistical impact of an extra year oftraining or schooling on productivity, but ratherwith the results of different kinds and qualities ofhuman resource investment.

Prais (1995) summarises broad statistical esti-mates from various national labour force surveyscarried out in the period 1988 to 1991. This datashows marked differences in intermediate voca-tional training qualifications between selected EUcountries. While in the UK only 25 % of all


economically active persons achieved such qual-ifications, the comparable figure for France was40 % and for Germany it was 63 %. The maindifference was found in craft training rather thantechnician training. Conversely, some 64 % ofthe UK workforce were found to have no voca-tional qualifications of any sort, whereas thecomparable figure for France was 53 % and forGermany only 26 %. Prais concluded that the UKwas anomalous in the relatively low proportion ofits workforce that has received systematicallyorganised vocational preparation and attainedformally examined vocational qualifications.During the ensuing decade, investments in voca-tional training across the Member States havechanged these percentages but the differentialsbetween countries still persist and this has impli-cations for comparative productivity.

Various studies carried out by researchers atthe NIESR have sought to demonstrate the linkbetween a better educated and better trainedworkforce and realised higher output per worker.Investigations were made into manufacturingplants in the UK and their counterparts in otherEU countries. The studies looked into the metal-working, woodworking, clothing and food manu-facturing industries, as well as selected servicesector trades. It was found that across all theseindustries the acquisition of skills amongst theworkforce was a critical factor in raising produc-tivity, as measured by output per worker. Recent

studies of this kind have been carried out byMason et al. (1999) into banking services and byJarvis et al. (2002) into the ceramic tablewareindustries. Commonly, plants in Germany, Franceand the Netherlands, where the percentage ofvocational qualifications was much higher, returnsignificantly higher output per worker than theirUK counterparts.

Steedman (2001) has recently comparedsystems of apprenticeship training across variousEuropean countries (from a UK perspective) anddrawn implications for productivity. This studyidentified significant shortcomings in the UKapproach that have resulted in an inferior qualityof vocational training for young workers.

Another study by O’Mahony and de Boer(2002) confirms that the UK continues to lagbehind both Germany and France in terms oflabour productivity, and this gap is primarilyexplained by differential rates of investment inboth human and physical capital. This predomi-nantly statistical study compared labour produc-tivity not only across the aggregate economy butalso over some 10 broad industrial sectors. Itapplied education and training statistics, dividedinto higher, intermediate and lower level qualifica-tions, to quantify comparative workforce skills inthe different countries. It identified a significantassociation between labour productivity andmeasured workforce skills across the differentindustrial sectors of the comparator countries.


6.1. General literature strands

Over the last three decades a large body of liter-ature has been produced examining the role ofhuman capital in determining the level and growthof GDP per capita. Much of the earlier work ismainly theoretical and deals with different growthmodel specifications and their associatedeconomic properties; for a summary, see Aghionand Howitt (1998). More recent work seeks totest empirically the different model specifications,most commonly using cross sectional data for alarge number of countries. Some researchershave attempted time series analyses for smallergroups of countries, such as those in the OECDarea, where the quality of educational data areboth more frequent and perceived to be morereliable. A few studies have combined crosssectional data with time series information toproduce panel data sets in which allowances canbe made for country-specific effects. Two recentpapers by Sianesi and Van Reenen (2000) andTemple (2000) provide useful overviews of thevarious empirical studies. The former paper looksat the links between formal education andeconomic growth across all countries, while thelatter is concerned with the impact on growth inOECD countries of both education and morewidely defined social capital. The present paperdraws on both these studies.

From a comparative international perspective,attempts to identify the extent to which educationand qualifications affect relative economic perfor-mance, including employment levels, have begunto reveal the importance of human capital.Macroeconomic models incorporate humancapital into growth specifications either throughextensions of the Solow neoclassical growthmodel (Solow, 1957) or through endogenousgrowth equations, as developed by Romer (1986),Lucas (1988) and others. A current application ofsuch a model is shown in the associated study byIzushi and Huggins (2003). Fundamentally,neoclassical models imply that a one-off increase

in the stock of human capital leads to an associ-ated one-off increase in productivity growth,whereas endogenous growth models suggestthat the same one-off increase in human capitalcan lead to a permanent increase in productivitygrowth. In the short term, both models producebroadly similar results, each dependent on theprecise specification, but, over the longer term,the newer growth models imply significantlyhigher returns on investments in human capital.

Regardless of the precise model that isadopted, there is strong evidence that highereducational inputs increase productivity and soproduce higher levels of national growth. Aftersurveying the empirical results from a wide rangeof model specifications, Sianesi and Van Reenen(2000) concluded that an overall 1 % increase inschool enrolment rates leads to an increase inGDP per capita growth of between 1 and 3 %. Anadditional year of secondary education whichincreases the stock of human capital, rather thanjust the flow into education, leads to more than a1 % increase in economic growth each year. Theresults vary dependent on the model specifica-tions and the data sets in use. Sylwester (2000)has suggested that current educational expendi-ture leads to future economic growth, so there isa significant time lag in the causal relationship.

Aside from concerns relating to the preferredmodel specification, other methodological issuesconcentrate on the most appropriate variables forinclusion and their methods of measurement. Avery important strand in the literature concernsthe definition of human capital and how farhuman capital can be practically measured. Thisissue was originally addressed in an earlierCedefop report (Westphalen, 2001), where humancapital was widely defined to include knowledge,skills, competences and similar attributes. For themost part, empirical growth models adopt amuch narrower interpretation of human capitaland usually some measure of flow or stock ofsecondary education is used to proxy humancapital. Typically, no attempt is made to incorpo-

6. The role of education and training in economic growth

rate vocational training data into growth models.Koch and Reuling (1998) pointed out some of thedifficulties of measuring training investments withany degree of consistency across different coun-tries. Recently, other authors have suggested thatit is social capital as well as human capital whichcontributes significantly to growth (Woolcock,2000), but given the measurement difficulties,social capital type variables have not, to date,been much used in growth models.

Another strand in the literature is concernedwith the selection of appropriate data formeasuring economic growth across countries andthrough time, on a consistent basis. A majority ofstudies make use of the Penn World Table seriesproduced by Summers and Heston (1991) andthis comprehensive data set has been updatedand extended by Barro and Lee (1996). Someauthors, such as Osberg and Sharpe (2000), haveargued that GDP per capita is an inadequate indi-cator of the overall economic well-being of anation and they maintain that the link betweenhuman or social capital and economic well-beingis much stronger than is often implied whensimple GDP measures are used in growth models.Such analyses lead towards the literature thatlinks investments in human capital, and educationin particular, to externalities in economic growth.Higher levels of education are typically associatedwith better environment, higher levels of publichealth and greater social cohesion, all of whichwould be expected to feed back into fastereconomic growth measured in the wider sense.This strand of literature has recently beensurveyed in OECD (1998).

Most of the literature produced on this topichas appeared in English language publications. Amajority of the studies are by UK and US authors;European authors have produced some of theOECD publications. However, the empiricalstudies use international data sets and EU andOECD countries are widely analysed within muchof the published research.

6.2. Growth accounting literature

Growth accounting literature is deemed to besufficiently well known to warrant only brief treat-ment here. Griliches (1997) has provided a usefulsurvey of literature on applied growth accounting

exercises. This has much in common with generalrates of return on education and training,although it focuses much more explicitly onmacroeconomic issues with the measured effectsof education and training in the form of higheroutput or sales rather than earnings. Growthaccounting literature has a long pedigree, whichcan be traced back to Solow (1957). In essence,this assumes a Cobb-Douglas production func-tion:

Yt = At Kta Lt

b Mtc (1)

Where Y is gross output (e.g. sales turnover), K isphysical capital, L is labour stock (employment)and M is materials and intermediate inputs. At

reflects efficiency, so that the higher At, for anygiven values of the inputs, the higher is output.The units of measurement of the inputs affect thelevel of At. This can be avoided by writing theequation in growth form (i.e. differentiating withrespect to time),

∆Yt = ∆At + a ∆Kt + b ∆Lt + c∆ Mt (2)

where the ∆ associated with each variablereflects the rate of growth. It can be seen, byrearranging equation (2), that ∆At is the rate ofchange in total factor productivity.

∆At = ∆Yt - a ∆Kt - b ∆Lt - c∆ Mt (3)

In other words, it represents the output growththat cannot be explained by changes in thegrowth of physical inputs. Hence At was chris-tened the ‘residual factor’. In the original Solowstudy this residual component accounted forsome 85 % of output growth. A follow-up studyby Jorgenson and Griliches (1967) attempted toexplain this residual factor.

Further extensions of the framework to allowexplicitly for quality change and control over inputand output prices are reported in Bosworth andGharneh (1996), using a pure accountingtautology in a dynamic context. This literature isshown to have evolved through many phases,which can be categorised as three different gener-ations. First generation models are of the formdescribed in equation (1), using highly aggregatemeasures of the inputs (i.e. all physical capital, allemployees and the total real expenditure onmaterials and intermediate inputs). Second gener-


ation models are basically the same functionalform, but utilise a much more disaggregated setof variables, many different types of capital and alarge variety of different types of hours worked.Third generation models not only disaggregate theinputs in detail, but also begin to disaggregate thesectors of the economy, separately distinguishingbetween the production sectors of the economyand the educational sector.

Jorgenson and Fraumeni (1992) use growthaccounting methodology in a third generationframework to demonstrate that investment inhuman and physical capital accounts for a veryhigh proportion of growth in both the educationand production sectors of the US economy overthe post-war period. Specifically, growth in labourinput is held to account for just over 60 % ofoverall economic growth and increases in labourquality (education and training) explain some42 % of this labour contribution. As usual in thegrowth accounting approach, the contributions ofthe various inputs to output growth are obtainedby weighting the input growth rates by theirshares in the sector’s value added. The contribu-tions of capital and labour inputs are thendecomposed into separate components ofcapital stock and quality, and of hours workedand labour quality.

Hall and Jones (1999) adopt a growthaccounting framework to explore differences inoutput per worker across different countries. Theyuse the Summers and Heston (1991) database,augmented by educational attainment statistics asproduced by Barro and Lee (1996). Their findingssuggest that differences in physical capital andeducational attainment explain only a smallamount of the differences in output per worker.International output differences are predominantlyaccounted for by differences in productivity anddifferences in growth rates derive almost entirelyfrom differences in the growth of A in equation (2).This result is at odds with the results of Jorgensonand Fraumeni (1992).

It is important not to read too much into theempirical estimates based upon the growthaccounting type of model because this approachentails imposing many assumptions rather thantesting them empirically. The methodology typi-

cally involves constructing the estimates using anaccounting tautology. The rate of change of eachinput is weighted by the share of that input withinthe total. The validity of this depends crucially onthe assumption that each factor is paid the valueof its marginal physical product. The growthaccounting approach only reveals the sources ofeconomic growth, if, amongst other things, thereare competitive factor markets. The apportion-ment of output growth to measured and residualinputs provides no real insight into the processthat determines such contributions. While humancapital has a significant role to play in explainingoutput growth in this type of model, the measure-ment of this human capital is usually a complexmix of educational, demographic and labourmarket variables.

6.3. Neoclassical growth models

Although growth accounting exercises are usefulfor exploring the links between human capital andeconomic growth, they are limited by their basicassumptions. As Griliches (1997) recognised inhis survey, the true test of the importance ofeducation to growth is to include such a variablein an estimated production function. The startingpoint for such an approach is the Solow (1957) orneoclassical model. This growth model has previ-ously been described in detail in an earlierCedefop report (Barrett, 2001), so only an outlineis provided here.

The production function can be written in theform where some measure of output is related tothe relevant inputs (as in equation (1) above) (6).Using the Cobb-Douglas form:

Yt = A egt Kta Lt

b Mtc (4)

where Y is gross output or sales, K is the physicalcapital stock, L denotes labour and M is thematerials and intermediate input variable (all vari-ables are in real or volume measures) (7). This issupposedly a technical relationship betweeninputs and outputs. The link with the growthaccounting approach is obvious, as (4) is almost


(6) Gross output is preferred. This is related to capital, labour, materials and fuels (often called the KLEM models). Net output (orvalue added) is sometimes used, which is related only to capital and labour.

(7) The A term is now a constant.

identical to (1). There are two main differences.First there is an extra term, egt, which attempts topick up the effects of technological change (i.e.an exponential time trend in times series estima-tion). Second, the function is now estimateddirectly using regression techniques, rather thanimposing accounting values. The parameters a, band c are still interpreted as factor shares.

This basic model can be augmented, in approx-imation to second generation growth accountingmodels, by the inclusion of different types (orvintages) of capital, K1t

a1 … Kntan, and similarly,

different types of labour (i.e. different lengths ofschooling, qualifications, occupations, etc.).Assuming that the homogeneous nature of the vari-ables, as in equation (4) is maintained, but repre-senting their changing quality by some measure orvector of measures, H, a model is derived:

Yt = A Kta Lt

b Mtc Ht

d (5)

where H is an indicator of the changing quality ofinputs and represents some weighted average ofeducation or qualifications (8).

It is now fairly easy to develop a link to theliterature of knowledge production functions. Thebasic idea of this approach is to regress somemeasure of output, preferably gross output, ontangible and intangible inputs. Thus:

Yt = A Kta Lt

b Mtc Rt

d … Rt-ne (6)

where the R variables denote current and pastinvestments in knowledge and are virtually iden-tical to H in equation (5). One way of looking atthis relationship is that current and past knowl-edge generates current output (or sales), holdingtangible inputs constant. An alternative interpre-tation is to note that {Yt/AKt

aLtbMt

c} is a measureof total factor productivity, and that current andpast knowledge is driving total factor productivity.

Although their endogenous counterparts have,to some extent, superseded these neoclassicalmodels, they continue to be empirically tested andSianesi and Van Reenen (2000) were led toconclude that the neoclassical approach producesresults that are more consistent with the estab-lished microeconomic evidence. One of the betterknown and most influential contributions to growth

literature is the study by Mankiw et al. (1992), whouse an augmented Solow model to explain crosscountry differences in income levels from 1960 to1985. This model of equation type (5), but withoutthe materials input variable, explains over 70 % ofthe variation in income per capita across a largesample of countries. Human capital, as proxied bysecondary school enrolment ratios, accounts foralmost half the difference in per capita incomes. Fornon-oil countries, a 1 % increase in the averagepercentage of the working-age population insecondary school is estimated to lead to a 0.66 %increase in long-term income per capita.

Koman and Marin (1999) applied an augmentedSolow model, in a time series framework, toexplain recent economic growth trends inGermany and Austria. The results obtained didnot support this type of specification and theincorporation of a variable measuring the accu-mulation of broadly-defined human capital led toinsignificant estimates. Temple (1998) has shownthat the estimated parameters and convergencerates in this type of model are highly sensitive tomeasurement errors, and the obtained results arenot always statistically robust.

Neoclassical models remain restricted by theirunderlying assumptions of perfect competition andconstant returns to scale. A critical property of thebasic production function, equation (4), is that thereare diminishing returns on the accumulation ofcapital. In the absence of any technological change,diminishing returns will eventually choke off anyeconomic growth. The inclusion of human capitalaccumulation in the model, equation (5), increasesthe impact of physical investment on the steadystate level of output and it can then account for avery slow rate of convergence in income levelsacross countries. However the problem of exoge-nous technological change remains and Romer(1994), amongst others, has stressed the need tomake technological advances explainable withinthe model framework.

6.4. Endogenous growth models

In contrast to neoclassical models, endogenousgrowth models explicitly incorporate technology


(8) Some models use stock or levels of education and training, while others use flow or accumulation of the same variable. Thepreferred specification is usually empirically determined.

(the A term in equations (4) through (6)) andattempt to recognise that technological changedepends on economic decisions in the same wayas capital accumulation. In particular, technolog-ical change is most commonly related to the stockof human capital, which is explicitly modelled interms of educational investments in theseendogenous specifications (9). The inclusion oftechnological change and knowledge dissemina-tion into the neoclassical framework is rendereddifficult because of the underlying competitiveassumptions, which do not allow for the possi-bility of increasing returns to scale. In the endoge-nous models, economic growth can continueindefinitely because the returns on investment in abroad class of both physical and human capitalgoods do not necessarily diminish through time.Spill-overs of knowledge across producers andexternal benefits from improvements in humancapital are part of this process because theyoffset tendencies to diminishing returns. Growthframeworks have also incorporated R&Dconcepts, as well as imperfect competition(Romer, 1986) (Barro and Sala-I-Martin, 1995).

A large number of endogenous growth specifi-cations have been put forward. A typical specification for analysing growth across severalcountries follows Barro (1997):

∆y = f ( y, y* ) (7a)

y* = f ( Z ) (7b)

where ∆y is the growth rate of per capita output,y is the current level of per capita output and y* isthe long-term or steady state level of per capitaoutput. For a given value of y, the growth raterises with y*, which is determined by a wide set ofeconomic, policy and environmental variables.These variables differ between studies, but typically Z in equation (7b) contains variablesmeasuring population (fertility and lifeexpectancy), labour supply, government expendi-ture and investment, terms of trade, inflation and,most significant for present purposes, educationalvariables. Measurement issues associated withthe educational variables are discussed below.

Barro (2000) maintains that in this model any

increase in the steady-state level y* will raise theper capita growth rate, y, over a transitioninterval. So if, for example, government improvesthe business climate by increasing its expenditureor if they decide to increase their investment ineducation by increasing enrolment rates insecondary education, this will increase the targetlevel y* and raise ∆y. As actual per capita outputincreases, diminishing returns will eventuallyrestore the growth rate to a level determined bythe long-term rate of technological progress. Inthe very long term, the impact of improved policyis on the level of per capita output rather than justits growth rate. However, transitions to the longterm tend to be lengthy and the growth effectsfrom shifts in government policy tend to persistfor a significantly long period.

6.5. Empirical growth regressions

Following the early work of Barro and his collabo-rators (Barro, 1991) (Barro and Sala-I-Martin,1995) (Barro and Lee, 1996), a large number ofgrowth regressions containing human capitalvariables in the set of regressors have appeared.Temple (2000) has distinguished two main groupsof model specification, those that link outputgrowth to some initial level or stock of educa-tional attainment, such as secondary schoolenrolment rates, and those that relate growth tothe flow of educational attainment rather than itslevel. The first group assume that the stock ofhuman capital is the engine of economic growthwhereas the second group attribute such growthto the accumulation of education and training in agiven period. This distinction is more fully devel-oped in the associated paper by Izushi andHuggins (2003), where empirical tests are carriedout on both model forms.

Most empirical models combine data from alarge group of countries, both developed anddeveloping, often using dummy variables todistinguish geographical or economic groupingsof countries. The aim of these studies is to iden-tify statistically significant and robust relation-ships between the various factors (the Z variablesin equation (7a) above) and economic growth


(9) Applied examples of such models are provided in Sections 6.5 and 6.6 below. The methods of incorporating technologicalchange and human resource investments into the models are discussed in these parts of the paper.

across the full sample of countries. The estimatesseek to determine causal relationships and soestablish the sources of economic growth.

Barro’s original 1991 study used data for98 countries from 1960 to 1985 and related thereal growth rate of GDP per capita to initial humancapital, as proxied by school enrolment rates for1960, and a large set of other potential deter-mining variables. It found that output growth wassignificantly positively determined by both primaryand secondary school enrolment, in the presenceof other determinants. A one percentage pointincrease in primary school enrolment was associ-ated with a 2.5 % increase in GDP growth and asimilar increase in secondary school enrolmentproduced 3 % growth. When attempts were madeto control for measurement error, the resultsbecame weaker in magnitude but still significant.When 1950 school enrolment rates were added tothe model they become insignificant, as became avariable measuring the student-teacher ratio. Thisstudy also revealed how human capital variableswere significantly correlated with lower levels ofnet fertility and larger levels of physical capitalinvestment.

There followed several papers in the Barrotradition; see Sianesi and Van Reenen (2000) for amore complete overview. Different data sets andmeasured variables were applied in an attempt toreplicate and establish the robustness of theBarro results. Englander and Gurney (1994)re-estimated some of the early growth regres-sions using only OECD data, which are regardedas a more homogeneous and superior qualitydata set. Secondary school enrolment rates wereconfirmed as one of only three significant deter-minants of labour productivity growth, alongsidegrowth in the labour force and increases in thecapital to labour ratio. However, this study foundthat these significant regressors provided muchless explanatory power when estimated for thenarrower group of OECD countries than for theoriginal wide sample. Englander and Gurneyacknowledged difficulties with the educationproxy variables, measurement reliability issuesand problems in ensuring strict internationalcomparability: all these issues cast doubt on therobustness of the results.

Gemmell (1996) also worked on OECD datasets and his original contribution involved theconstruction of some alternative measures of

human capital based on both stocks and annualaverage growth rates at primary, secondary andtertiary education levels. He found that a 1 %increase in initial tertiary human stock was asso-ciated with a 1.1 % increase in per capita GDPgrowth, while a 1 % increase in subsequentgrowth in tertiary education (flow) was associatedwith almost 6 % output growth. While the directgrowth effects come through tertiary education,secondary education was found to have an indi-rect impact through its positive significant associ-ation with physical investment. When theseOECD results were compared to a wider sampleof countries, it was found that primary humancapital had the most impact in the poorest groupof the less developed countries and secondaryhuman capital was the most significant variablefor the intermediate group of less developedcountries.

Benhabib and Spiegel (1994) use a new set ofcountry comparative data on human capital stockinitially to test the augmented neoclassical model,given as equation (5) above and later to modifythe specification to allow human capital to enterdirectly into productivity in an endogenousgrowth specification. The neoclassical modelyields insignificant and generally negative coeffi-cients on the human capital stock variables, aresult which holds when other regressors areadded into the model and alternative proxies forhuman capital are applied. In contrast, theendogenous specification produces humancapital levels that are positive, but not alwayssignificant, determinants of per capita incomegrowth. These results suggest a distinct role forhuman capital in enabling foreign technology indeveloping countries and the creation of newdomestic technologies in more highly developedcountries, rather than entering the model on itsown as a conventional factor of production.

Barro and Lee (1993) add comprehensive neweducational data sets to the Summers and Hestondata and test endogenous specifications of equa-tion type (7) using a much wider set of potentialregressors. In particular, the average years ofsecondary schooling of the adult population at thebeginning of the data period are introduced as akey explanatory variable. Barro and Lee’s resultssuggest that an extra year of male secondaryschooling is associated with a 1.4 % increase inGDP growth per worker. In comparison, an addi-


tional year of female schooling seemingly has anegative impact on a country’s growth rate.Female education is significant in reducingnational fertility and hence population growth.Barro (1997), using modified data in panel formatand applying more sophisticated estimating tech-niques, produces a similar set of findings to theearlier paper. An extra year of male upper-levelschooling is associated with a 1.2 % increase inper capita GDP growth rate. Male primaryschooling is found to have no significant impacton growth and, again, female schooling at variouslevels have negative but insignificant coefficientsin the various equations that are reported.

Krueger and Lindahl (1998) criticise the find-ings of both Benhabib and Spiegel (1994) andBarro (1997). In particular, they focus on theresults which identify only the initial level or stockof educational attainment as the determinant ofGDP growth and the failure to find growth in suchattainment (the flow of educational investment) asa key determinant. Krueger and Lindahl show thespurious results of the earlier two papers to beattributable to the extremely high and unaccount-able measurement error in first-differencedcountry comparative education data. In a simpli-fied version of equation (7), with only levels ofschooling and changes in schooling variables onthe right hand side (10), the increase in educationas the key determining variable of GDP growth isshown. The initial level of education is not posi-tively related to future economic growth for theaverage country when assumptions on linearityand homogeneity of parameters for education arerelaxed. Unfortunately, the estimates of theimpact of changes in educational attainment onincome growth turn out to be implausibly largewhen set against findings from microeconomicstudies into private returns and the authors iden-tify endogeneity bias (richer countries tend toexpand their educational infrastructure) as thelikely cause of the problem.

A study by Judson (1998) attempts not only tosubstantiate the role of increasing investment ineducation in promoting growth, but also toexamine the importance of the allocation of thisinvestment in the growth context. In addition to

the familiar Summers and Heston data, and theBarro and Lee human capital stock statistics,Judson uses Unesco data on educational enrol-ments and spending to estimate the efficiency ofexisting educational allocations within countries.Overall, a 1 % increase in human capital growthis found to be associated with an 11 % increasein GDP growth rate (11). Judson applies a countrycomparative growth decomposition regression toshow that the correlation of human capital accu-mulation is not significant in countries with poorallocations but it is strongly significant and posi-tive in countries with better allocations (predomi-nantly richer countries). This finding that thecontribution to growth of human capital dependson the efficiency with which it is accumulated hasimportant policy implications in terms of theexact allocation of educational and trainingresources.

Graff (1995) examined the role of humancapital in explaining economic growth in some114 countries from 1965 to 1985. Generally, theresults showed the accumulation of humancapital, physical capital and technologicalprogress all to be significant determinants of thegrowth process. In a related study, Graff (1996)investigated the importance of higher levels ofeducation for a subset of poorer countries. Thisvariable was found to be important for growth, solong as the investment in higher education didnot lead to imbalances elsewhere in educationprovision, especially to the detriment of elemen-tary levels of education.

While most empirical growth studies usecountry comparative data, either averaged acrossa sample of years or taken over several years inpanel data format, a few studies attempt timeseries analysis within an individual country. Themajor problem with the time series approach isobtaining adequately long series on consistentbases; this is a particular problem for educationand training variables. One example of the timeseries approach is provided by Jenkins (1995) forthe UK. This paper uses annual data from 1971 to1992 and it proxies the stock of human capital bythree series measuring workforce qualifications.These series are used as key determinants of


(10) Some of these variables are lagged and both time and geographic-regional dummies are also included.(11) This implausibly large elasticity impact is a common finding in studies of this kind and it has led Sianesi and Van Reenen (2000)

to conclude that the effect is seriously overstated due to a number of methodological problems.

aggregate output, alongside physical capital,total workforce, capacity utilisation and a timetrend. The overall result confirms the finding thatinvestment in human capital increases produc-tivity. Highly-qualified workers are found tocontribute almost twice as much to productiveefficiency as those with no qualifications at all.The relatively small number of observations (12)mean that the unrestricted estimates are impre-cisely determined and such results cannot beregarded as robust.

Another example of time series modelling isAsteriou and Agiomirgianakis (2001), who usecointegrated regressions to explore the long-termrelationship between formal education and GDPin the Greek economy. This study finds a signifi-cant relationship between primary, secondary andhigher education enrolments and GDP per capita.The main direction of causality runs through theeducation variables to economic growth, but inthe case of higher education, there exists reversecausality. This problem is more fully discussed inSection 6.10 below.

6.6. A recent example of growth modelling

One of the more recent papers in the Barro tradi-tion reports empirical results for the countrycomparative data set updated to 1995 (Barro,2000). The paper shows the results of using paneldata for more than 80 countries over 3 decades;the sample is disaggregated into three 10-yeartime periods and the countries are groupedaccording to various development criteria. Thegrowth rate of real per capita GDP is regressedagainst a large group of potential determinants,as outlined in equation (7) above. Several of thesedetermining variables are only available, at best,at five-year intervals and some less frequently, soeither average values are used for each decadeor, in the case of the key education variables,their level at the beginning of each period isapplied. The main education variable is onefavoured by Barro from earlier empirical exer-cises. It measures the average years of schoolattainment at the upper (secondary and tertiary)levels for males aged 25 and over. Subsequent

analysis introduces several alternative educa-tional measures into the model: primary schoolattainment, attainment by females and results oninternationally comparable examinations. Itshould be noted that due to lack of data avail-ability at the country comparative level, nomeasures of growth in education (flows) areincorporated, only stock levels at the beginningof each period.

The model is estimated by three-stage leastsquares, using instrumental variables. Somebasic results are reproduced as Table 9. In theoverall sample, the education variable turns outsignificantly positive, in the presence of manyother policy variables. The estimated coefficientimplies that an additional year of schooling raisesthe growth rate on impact by 0.44 % per year.Barro maintains that a possible interpretation ofthis effect is that a work force educated atsecondary and tertiary levels facilitates theabsorption of technologies from more advancedcountries. In this model, the effect from an addi-tional year of schooling impacts on the growthrate of GDP and only feeds back onto the level ofGDP slowly over time (higher levels of GDP havea negative impact on the growth rate). Usingfurther assumptions on national convergencerates and the average costs of providing an addi-tional year of schooling, Barro suggests that thecoefficient value of 0.0044 implies a real socialrate of return on schooling of the order of 7 % peryear; a figure within the range of typical microe-conomic estimates of returns on education.

Column 2 in Table 9 shows how results change,using a slightly more restricted version of themodel, when the sample is based only on OECDcountries. The result for this group of countries ismuch less satisfactory in terms of the signifi-cance of the regressors, very few of which aresignificant. In particular, the key education vari-able takes a value of zero. When other wealthier,non-OECD, countries are added to the OECD setto form the rich country sample (column 4), thereis some improvement in the results and the addi-tional year of schooling impact is now significant,but its effect on economic growth is only abouthalf as strong as was the case for the full sample.Results for the poor country sample are better interms of significant regressors, although the


(12) This is the main restraint on time series analysis in growth studies.

overall coefficients of determination are relativelymuch lower for this group (below 50 %). Theimpact of the additional year of schooling ongrowth turns out to be almost four times greaterfor the poor countries compared to the richgroup.

Barro (2000) considers additional dimensionsto the years of schooling, beyond those reportedin Table 9. Female attainment in secondary andhigher levels of education become insignificantwhen added to the basic model for the overallsample. The same insignificant results apply tovariables measuring average years of primaryschooling for males and females separately,although it is clear that primary schooling is crit-ical as a prerequisite for secondary education.Problems of colinearity are present betweensome education variables and other potentialgrowth determinants; most obviously betweenfemale education and fertility rates.

Barro also attempts within the model frame-work to introduce measures of quality of educa-tion as represented by international examinationscores, where available. Such indicators of thequality of schooling capital have been claimed byHanushek and Kimko (2000) to be more impor-

tant for subsequent economic growth than yearsof educational attainment. Predominantly, dataare only available for richer countries and only forrecent years. Results suggest that science scores(as a measure of quality of education) are signifi-cantly positive for growth, mathematics scoresare also positive, but of less significant impactthan science, while reading scores apparentlyhave no significant impact. Given the dubiousquality of some of these data, it would be wrongto read very much into the results.

It is clear that modelling exercises in the Barrotradition do not produce results that can beregarded as robust. Modifications in specifica-tions and changes in data sets can lead to largedifferences in estimated coefficient values, asexemplified in Table 9. It might have beenexpected that the superior quality data availablefor the more homogeneous group of countrieswithin the OECD would have yielded betterresults than those for the full sample, but this wasnot the case. Such findings have prompted someresearchers to carry out tests of robustness onthe model results (Temple, 1998). Others haveintroduced more complex model specifications toaccommodate full sets of panel data (Islam,


Independent variable Overall OECD Rich-country Poor-country sample sample sample sample

Log GDP 0.107* -0.034* -0.0343* -0.0190*

Log (GDP)squared -0.0084*

Years of upper school for males over 25 0.0044* 0.000 0.0023* 0.0084*

Govt consumption/GDP -0.157* 0.015 -0.014 -0.167*

Rule of law index 0.0138* 0.0115 0.0116* 0.0196*

Exports + imports/GDP (Openness ratio) 0.0133* 0.0148* 0.0112* 0.0361*

(Openness ratio)* Log GDP -0.0142*

Inflation rate -0.0137* -0.0228 -0.0051 0.0033

Log fertility rate -0.0275* -0.0209* -0.0174* -0.0212*

Investment/GDP 0.033 0.045* 0.029 0.053

Growth rate terms of trade 0.110 -0.010 -0.008 0.0134*

Observations & R squared

1965-75 81, 0.62 23, 0.85 32, 0.77 49, 0.48

1975-85 84, 0.50 23, 0.65 32, 0.62 52, 0.39

1985-95 81, 0.47 23, 0.50 31, 0.52 50, 0.44

NB: * indicates coefficient is statistically significant at 5 % levelSource: Adapted from Barro (2000, Table 1, p. 19)

Table 9: Barro growth regressions

1995), while a further group have explored thedifferences that data quality makes to the results(De La Fuente and Doménech, 2000). Some ofthe results obtained from growth modelling implysome improbably large impacts of initial humanstock (education) on growth and these results aresometimes at odds with findings from microeco-nomic studies (Topel, 1999). One of the issueswhich has not been resolved is the extent towhich some of the results based on the broadergroups of countries reflect greater variability inthe data (which helps to establish the parametersof the relationships being tested) or whether it isa consequence of including extreme outlierobservations, which bias the outcomes andcause spurious results based on poor qualitydata.

6.7. The influence of data quality on results

The issue of data quality is a common theme inmany of the studies reported above; both Sianesiand Reenen (2000) and Temple (2000) raise prob-lems with data quality and suggest that empiricalrelationships between human capital and growthmay be compromised by measurement errors.De La Fuente and Doménech (2000) suggest thatsome of the insignificant relationships areattributable to some of the educational data thatare commonly used in growth regressions.Results that rely on time series variation in thedata are especially prone to error. When dataadjustments are applied in a preferred growthspecification, a much more significant relation-ship between educational attainment andeconomic growth is identified.

De La Fuente and Doménech review the variousdata sets on educational attainment that have beenconstructed on the basis of Unesco enrolmentstatistics; the best known of these is the Barro andLee (1996) series. Enrolment data are transformedinto attainment stock figures through a perpetualinventory method and interpolations betweencensus observations. The various authors applydifferent assumptions and estimating proceduresto produce series that exhibit inconsistencies, oneto another, across countries. Beyond this problem,there remain accuracy and consistency concernsabout the base Unesco enrolment data. Some

schooling levels reported for some countries do notappear to be very plausible, while others displayextremely large changes in attainment levels overshort time periods or extremely unlikely trends; seethe discussion in Behrman and Rosenzweig (1994).

While De La Fuente and Doménech (2000)conclude that the Barro and Lee series are prob-ably the most accurate source of data on humancapital stocks, these data still contain a largeamount of noise that can be traced largely toinconsistencies in the underlying primary statis-tics. De La Fuente and Doménech revise theeducational attainment data for 21 OECD coun-tries during the period 1960-90, using detailedstatistics from national sources within eachcountry. In general, the resulting series are held tobe much smoother through time and, as a result,much more plausible. The new data are thencompared to the original in various growth speci-fications (variants of equation (5)), using averageyears of schooling of the adult population as themeasure of human capital stock. The size andsignificance of the coefficient on this newlyconstructed human capital variable shows anappreciable improvement over the original Barroand Lee variable. When the estimates arerepeated with data in first differences, the gain iseven more noticeable and only the revised dataproduce a significant coefficient. Further testsusing other growth specifications confirm thisresult and the equations with the revised datameet the appropriate tests for robustness.

Bassanini and Scarpetta (2001) apply theDe La Fuente and Doménech human capital seriesin panel regressions over the period 1971-98. Thisstudy follows Islam (1995) in making full use of boththe time series and cross-sectional dimensions ofthe data. Country comparative studies that fail touse the time dimension produce results which arean average for heterogeneous countries and there-fore hard to interpret in the context of a singlecountry policy (Lee et al., 1997). The estimationmethod used by Bassanini and Scarpetta employspooled mean group techniques that allowshort-term coefficients, speed of adjustment anderror variances to differ across countries, butimposes homogeneity on long-term coefficients.Various specifications are estimated, but the empir-ical results favour an endogenous growth modelwith broad constant returns on human and physicalcapital. The results indicate a significant and posi-


tive impact of human capital accumulation on percapita output growth. One additional year of educa-tion produces 6 % growth in output in the long term,a result held to be consistent with microeconomicevidence. The findings survived a number of checksfor robustness using modified specifications andcountry comparative samples. The empiricalresults were sensitive to the use of more reliabledata and the choice of estimating approach.

6.8. Human capital externalities

It has long been recognised that the benefits ofinvesting in human capital are not restricted to thedirect recipient but spill over to others in society.A better-educated and trained workforce may wellimprove the productivity of their less educatedwork colleagues. Acemoglu (1996) argues that anincrease in the average level of human capital willoften cause firms to anticipate growth and makegreater investments in physical capital andresearch and development. Given an imperfectmatching process, firms that invest more will notnecessarily be associated with the workers whohave invested more in education. However, otherworkers will benefit, as firms using more physicalcapital than before employ them. Increases in theaverage level of human capital clearly createexternal benefits, but growth models may not becapable of capturing all these external effects.

McMahon (2000) has argued that the directeffect of education on economic growth is sepa-rable from the indirect effect or externalities. Ofthese externalities, probably about 75 % arenon-market outcomes which feed back intoeconomic growth, but are not readily measurablein the same way as GNP. The main non-marketexternalities are: health, including longevity, infantmortality and fertility; environmental impact,including various forms of pollution and defor-estation; crime, including rule of law, crimesagainst the person as well as property crime;better income distribution and the issue ofpoverty; and democratisation, including humanrights and political stability. These issues aredealt with more fully in Chapter 9 of this paperand in the fuller treatment in the associated paperby Green et al. (2003). It will be recalled howBarro regressions, such as those in Table 9,attempt to capture some of these impacts on

growth, but measurement limitations preventmodels capturing most of these influences. Thereis also likely to be much interaction between thedifferent non-market social outcomes.

It is clear that human capital externalities aremuch more than just a spill-over effect fromeducation in the economy. They are a wholeseries of net outcomes, most of which are onlypartially realised after initial impact and many takefull effect over a very long time span. These exter-nalities, along with investment in human capital,are important in offsetting diminishing returns onphysical capital and so determining positive futureper capita growth rates. Investment in educationand training is important, not only for its directcontribution to growth but also for the indirectexternalities that it creates, as these eventuallyfeed back into the growth process.

6.9. Social capital and economic growth

Most of the literature reviewed in this section isconcerned with the link between human capitaland measured per capita output or productivitygrowth. As such, human capital is narrowlydefined to be some measure of education,usually average years of schooling at primary,secondary or tertiary levels. Temple (2000) recog-nises that the failure to incorporate measures ofvocational training is a serious omission from thedefinition of human capital. Broadberry andWagner (1996) have shown that vocationaltraining is closely connected with corporateproduction strategies and also national outputgrowth. However, the extreme variability in voca-tional training approaches across countriesmakes it difficult to measure and quantify to facil-itate inclusion in country comparative growthmodelling. McIntosh (1999) demonstrates someof the problems involved in making comparisonsof vocational training between six EU countries. Awide spectrum of training exists in most countriesand often a large amount of training is informal,on-the-job training that may not be recorded andso cannot be systematically measured. It isunlikely in the near future that country compara-tive growth models, which constitute the core ofthis growth literature, will be able to incorporatemeasured training variables.


Despite measurement difficulties, a number ofresearch papers have recently appeared linkingeconomic growth to social capital (e.g. Temple,2000; Osberg and Sharpe, 2000; Woolcock, 2000and the literature reviews in these papers). Socialcapital is a difficult concept to define precisely,but, most simply, it refers to the social norms andnetworks that facilitate collective action. Itembraces the nature of social ties within commu-nities, the relationship between civil society andthe state and the quality of governing institutions.The development of social capital is likely toprove highly significant for improvements ineconomic well-being; a wider concept thansimply growth in per capita GDP.

While literature on social capital lies mostlyoutside mainstream economics, there has, in recentyears, been an increasing body of material on socialcapital published by organisations such as OECD.The majority of this literature is still to be foundpredominantly in social science and political publi-cations. Two studies have attempted to constructquantitative indicators of social capital and relatethem to other economic variables, includingproductivity. La Porta et al. (1997), Knack andKeefer (1997) and, most recently, Green et al. (2003)used an index of trust, derived from a World valuessurvey, carried out across some 28 marketeconomies. La Porta et al. found the trust index tobe weakly associated with country comparativegrowth from 1970 to 1993, but the explanatorypower was low and measurement concerns serveto limit the usefulness of these findings. Knack andKeefer report a strong correlation between trust andaverage years of schooling. Education is argued asstrengthening trust and civic norms, thus producinganother external effect from investment in humancapital. Literature on social capital and growthremains at a relatively early stage and until bettermethods of measuring the social variables can bedevised, social capital, together with vocationaltraining variables, are likely to have a only marginalrole in quantitative growth analyses.

6.10. Possible endogeneity andsimultaneity bias

Sianesi and Van Reenen (2000) have identifiedpossible problems of reverse causality (i.e. growthstimulates education and training) in the links

between investment in human capital and economicgrowth. As per capita income increases, so educa-tional inputs, both in terms of quantity and quality,also grow, but it is not obvious that this economicgrowth is caused by the rising educational stan-dards. Income growth is likely to induce a higherdemand for education and training and much of thisdemand will be income elastic. In countries experi-encing significant economic growth, governmentsare more able to increase public spending on educa-tion and training, and to ensure better access tosuch education for more of the population.

A review of international literature on the demandfor higher education shows that one of the keydeterminants is real income per capita (Briscoe andWilson, 1998). In an empirical study of entrants intoUK higher educational institutions, real income wasidentified as one of the more consistently significantdeterminants, drawn from a wide list of potentialexplanatory variables. Literature on education alsosuggests a similar link between demand for bothprimary and secondary education and the level of,or growth in, national income.

For the growth models discussed in thepresent paper, the issue is whether growth in theeconomy is brought about by the accumulation ofhuman capital or whether the stimulus of growthinduces the workforce to seek after higher educa-tional and training standards. Bils and Klenow(2000) have challenged the findings of Barro andothers (Section 6.6) on the grounds of reversecausality, whereby it is shown that fastereconomic growth induces greater schoolingopportunities by raising private returns.

Sianesi and Van Reenen (2000) maintain thatthe most plausible answer to the causalityproblem is that both processes are at work simul-taneously, so that there is a bi-directionalcausality between investment in human capitaland economic growth. Such considerationssuggest that growth models ideally should recog-nise the endogenous nature of human capital andcontrol for the simultaneity bias. Unfortunately,endogeneity problems apply to other right-handside variables in the growth equations, such asphysical capital accumulation. There would seemto be a shortage of plausible instruments toensure robust empirical estimating.

Aghion and Howitt (1998) have pointed outsome of the potential difficulties in relatingeconomic growth to the initial value of an


explanatory variable, such as school enrolmentsat the start of the data period (Section 6.5 above).There exists the possibility that both the growthand the enrolment variable are jointly determinedby some external factor, such as the prevailingpolitical regime, that plays no specific part in themodel. There is also a strong likelihood thatexpectations of future economic growth may spurmany workers to invest in education and training

now, in anticipation of future economic opportu-nities. Where time series data are available on asufficiently long basis, lagged values of theseendogenous variables may be used as instru-ments in the estimating equations. However, eventhen, the exogeneity of such lagged estimatorsmay be questioned for, as Temple (1999) hasshown, there may be long and delayed impactsof human capital accumulation on growth.


7.1. Literature strands

Market valuation literature has much in commonwith the production function approach on whichthe growth models discussed above are based.However, much of it is concerned with individualcompanies, although their effects can haveimportant macroeconomic consequences. Thereare two main market valuation literaturesubstrands. The archetypal market value modelsexamine the role of the stocks of intangibleassets, including human capital investments, indetermining market value (13). The estimatedcoefficients effectively provide a measure of themarket’s valuation of these intangibles. A secondsubstrand examines the different impacts of theexpected and unexpected changes in intangibleassets, for example, an unanticipated increase inthe company’s R&D expenditure or a new inven-tion that is reflected in a patent grant. A furtheremerging theme concerns the distribution ofvalues of intangibles, for example, linked to therisky nature of R&D investment.

The market value of a company is taken to be ameasure of the firm’s (potential) dynamic perfor-mance. In essence, the rationale for the model isthat the market value of the firm is higher the greaterthe future profit flows of the firm and, thereby, thehigher the future dividend payment to shareholders.On the assumption that intangible assets are themain source of abnormal profits, then market valueswill reflect the perceived returns on R&D and otherintangible capital, such as goodwill built up throughadvertising. Market value is, therefore, aforward-looking indicator of company perfor-mance, in other words, it is a dynamic measure ofperformance. In the long run, the market value isequal to the discounted sum of expected profits(although it may be higher than this, given that theholding of any share is an option).

The so-called Tobin’s Q form of this relation-ship is set out in a number of places (e.g. Hall,1999), which also describes the potential bias of

the linear approximation that is usually adoptedprior to estimating. This can be written as,

log MV = log Q + a log K + a log (1 + c {R/K}) (8)

where K denotes (the replacement value of)tangible assets and R refers to intangible assets;MV/Q is analogous to Tobin’s Q (and identical toTobin’s Q where a=1 and there is only one type ofasset, a tangible asset, K); the coefficient ‘a’describes the overall scale effect and should beequal to unity under constant returns to scale.The specification may disaggregate K and/or Rinto various components.

The link to the models outlined in Section 6.3 isfairly easy to establish. Clearly, the higher thecontribution of current and past R&D to futuretotal factor productivity, other things being equal,the higher profits and growth are likely to be. Ithas been demonstrated elsewhere that themarket value approach and the production func-tion when applied to individual companies arebroadly consistent from a theoretical perspective.Under (fairly heroic) assumptions regardingperfect information and efficient operation ofcapital markets, in principle, they will yield iden-tical results (Bosworth and Gharneh, 1996).Certainly, the empirical results from the twoapproaches available for the US look broadlyconsistent. A key distinctive feature of marketvaluation literature is that it has, to date, onlybeen investigated for quoted companies, usingfinancial accounting and stock market price data.

7.2. Some empirical results on market value models

This work has recently been reviewed by Toivanenet al. (1998) and others. Studies are usually basedon large-scale panel data sets of individualcompanies. Most of the literature focuses on therole of R&D, rather than human capital per se, and

7. Market value and related literature

(13) A minor offshoot of this literature is the work that attempts to look at the explanation of share prices. For the purposes of thepresent paper, we treat these as broadly equivalent approaches.

the principal result is that the R&D has a signifi-cant positive effect on market value. In general, itmakes little difference whether the R&D flow orstock is used, as the flow is relatively constant foreach company. However, in those studies whichdistinguish between R&D anticipated, e.g. byinvestors, and unanticipated R&D, it is normallythe latter that plays a significant role.

The second variable that has been extensivelyinvestigated is the number of patents, althoughthere is a small amount of work emerging now onthe quality of patents as measured by citationcounts. Market valuation literature suggests astrong positive relationship between patents andmarket value, especially when patents are used inplace of R&D.

Patents represent an outcome of the R&Dprocess. Carrying out R&D and obtaining patentsrequires the deployment of high level skills.However, such activity involves a certain level of risk,so for any particular project there is no definite linkbetween employment of skilled labour and suchoutcomes. Nevertheless, the evidence suggeststhat, on average, they are important. When the twovariables are entered together, they tend to dobroadly the same job, although patents are some-what more noisy and the R&D variable tends todominate. Patents also have the disadvantage thatthey are less relevant outside of certain key manu-facturing sectors, and also in the case of smallcompanies. New intellectual property variables haverecently been tested, including trademark data.Anecdotal evidence suggests that, on average,trademarks are at least as valuable as patents.

7.3. Knowledge, intellectualcapital and intangible assetliterature

The growing interest in knowledge and intellec-tual capital forms one of the most exciting devel-opments in recent economics and managementstudies. It builds upon a number of the earlierdiscussions described above, including the roleof R&D knowledge, intellectual property andintangible assets. However, it should be notedthat work in this area, particularly empiricaltesting of the emerging models, is in its infancyand research seeking to identify various indica-tors or proxies is somewhat speculative.

The literature points to important distinctionsbetween information, knowledge, intellectualcapital and the broader range of intangible assets(Nonaka and Takeuchi, 1995). These distinctionsare important insofar as information and knowl-edge are only inputs into the production of intel-lectual capital, where the latter can be defined asall of the knowledge that has produced or iscapable of producing value for the company. Inother words, intellectual capital is intellectualmaterial that has been formalised, captured andleveraged to produce a higher value asset. Theprincipal focus is the efficiency with whichcompanies access information and transform itinto intellectual capital and other valuable intan-gible assets, thereby determining competitiveadvantage and improvements in performance.

The growth in the importance of intellectualcapital is a result of the shift towards knowl-edge-based, rather than manufacturing-basedproduction, with the implications that this has forthe focus on intangible rather than tangibleassets. Lynn (1998) has claimed that there hasbeen a metamorphosis from a resource andmanufacturing-based economy to one in whichknowledge and services are the key drivers ofeconomic growth. Evidence of the importance ofthe broader range of intangible assets abounds.For example, a number of pharmaceuticalcompanies are sold at many times the book valueof their tangible assets. Similarly, the market valu-ation of many companies is significantly higherthan their balance sheet valuations. In addition, itis argued that organisations have restructured inorder to cope, increasing their agility, by elimi-nating hierarchies and decentralising, in somecases creating ‘spider web’ or ‘fish net’ organisa-tions (Bartlett and Ghoshal, 1993) (Hedlund,1994).

Applying a process perspective, it is possibleto distinguish at least eight categories of knowl-edge focused activities:(a) generating new knowledge;(b) accessing valuable knowledge from outside

sources;(c) using accessible knowledge in decision

making;(d) embedding knowledge in processes, prod-

ucts and services;(e) representing knowledge in documents, data-

bases and software;


(f) facilitating knowledge growth through cultureand incentives;

(g) transferring existing knowledge into otherparts of the organisation;

(h) measuring the value of knowledge assetsand/or impact of knowledge management(Ruggles, 1998).

Much of this knowledge is created throughinvestment in human capital, with high levels ofeducation and training inputs.

The importance of the management of intellec-tual capital has been pointed out by a number ofcommentators. For example, it has been arguedthat intellectual capital is the invisible skeleton ofthe corporation; that it is, in fact, an economicoperating system, widely viewed as thecompany’s single most valuable asset (Darling,1996). It is only by formal systems of human

capital reporting that intellectual capital can bemade visible; see the discussion in Cedefopsecond research report, Part 3. At the presenttime, the management of intellectual capital is stillan experimental exercise for most organisations,although a number of key companies (e.g.Hewlett-Packard and Dow Chemicals) are recog-nised to be leading the process. The Society ofManagement Accountants of Canada is at theforefront of the promotion of measurement andreporting of intellectual capital. Currently, nocompany is likely to have a comprehensive intel-lectual capital management system in place;many will actively manage parts of their intellec-tual capital, such as their patent portfolios orbrands, or parts of their human capital throughtheir HRM systems. There is a strong link here tothe discussion in Chapter 5.



This section draws together a number of moredisparate areas of literature that have some bearingon the links between education, training, skills andeconomic performance. The concern here is mainlywith company performance because, whencompanies are aggregated together, the macroe-conomy emerges. The key issues are to do withcompany size, including the problems of smallfirms, and with work on structure conduct andperformance. These are considered in turn. As withsome of the other areas of research previouslydiscussed, the importance of the links betweeninvestments in human capital (in various forms) andeconomic success are highlighted.

8.2. Company size and theproblems of small firms

There is a significant amount of literature onvarious aspects of company size and aspects ofperformance, such as growth, with importantlinks to the structure-conduct-performancehypotheses. The material can be traced back atleast to Gibrat’s law of proportionate effect.However, this literature has become increasinglyless mechanistic and more behavioural as it hasevolved over time, branching off in many direc-tions, some of which have already beendiscussed in other sections of this report. In thissection, the focus is wholly on the rather mecha-nistic Gibrat’s law. Structure-conduct-perfor-mance and the small firm elements are examinedin the following subsection.

In essence, Gibrat’s law is concerned withwhether company growth is related to size. Thus,if the following relationship is estimated:

(St+1/S t ) = aS tb-1 (9)

using data on firm size, S (where t denotes theyear), it is possible to test whether large firms

grow faster than small firms (b>1) or whethersmall firms grow faster than large firms (b<1).Simple tests of the law involve no other variables,but more recent tests have included a variety ofother right-hand side variables to control for otherinfluences on growth, including human capital.

There are a number of reviews reporting esti-mates of Gibrat’s relationship (Hay and Morris,1979). The results indicate that the simple formula-tion of Gibrat’s law does not generally hold,although it is accepted that there are some indus-tries in which company growth is significantlyrelated to size (both positively and negatively). Ageneral finding is that variance in growth rate isnegatively related to size; in other words, there is agreater diversity of performance among small firms.For example, in the small firm sector in the UK,approximately half the growth in the sector comesfrom a very small percentage of firms. One early, yetstill valid observation, from Singh and Whittington(1968) was that of the serial correlation of growthrates; in other words, firms that tended to grow fastover one period would grow faster in other periods.This links to the company-fixed effects literature tobe found, for example, in the market value basedstudies of performance.

A recent paper in this tradition, but focusing ongrowth in company size in the service sector, isJohnson et al. (1999). It estimates an equation ongrowth in employment, using existing size as one ofthe explanatory variables. It finds a non-linear rela-tionship between growth and size, with growthdecreasing with size up to five employees, thenrising with size from five to 15 employees, beforefalling again. They note that the higher profitmaximising scale of around 17 employees is in themiddle of the range at which salaried managerialappointments tend to be made by small firms. Theother variables included, however, give counterin-tuitive results, with both the education level of theowner and the number of professionals employedhaving a negative impact on growth rates. There areclearly important differences between small andlarge firms in a number of respects.

8. Further links between education and training and company performance

8.3. Small and medium sizeenterprises and thequalifications of managers

Bosworth (1999) has shown how there has been amajor growth in interest in the performance ofsmall and medium sized enterprises in recentyears. Interest arises for a number of reasons,including the large proportion of total employmentaccounted for by the small firms’ sector and alsothe high failure rate among small firms, particularlythose that are newly established. Two potentiallyrelevant areas of analysis are identifiable in litera-ture on small firms’, the role of qualifications andthe quality of management within small firms. Thishas tended to be a more qualitative literature,often descriptive or case study in nature.

There are several important problems in comingto any firm conclusions about small businesses,including the general paucity of the data, and, inparticular, the coverage of the very smallest, oftenowner-managed firms (i.e. micro-firms). A secondproblem is that both the data and case studyevidence indicate the extreme heterogeneity offirms in the small business group.

While there is little dispute about the hypoth-esis that small firms would perform better ifmanagement quality were to be increased, it isclear that this may not carry the same implica-tions for the role of qualifications leading toimproved performance. It appears to be the casethat the distinction between qualifications andskills/quality is the greatest for small firms. Smallfirms have quite different management needsand, with the exception of certain professionaland related services, qualifications per se are notseen as important, at least for theowner-manager. Qualifications may be moreimportant for non-owner managers and otheremployees of small firms, in order to gain flexi-bility in the job market.

Studies suggests that a number of qualifica-tions available in the UK, such as MBAs, do notappear to be at all relevant to the small firm. Eventhose which are potentially relevant, such asNVQs and GVNQs, do not appear to be whollyuseful for many small firms. Other countries’experiences suggest that one possible way

forward is to link the training provided to thedevelopment of the business, for example, toprovide training implicitly or explicitly whenproviding help to the business to overcome prob-lems. Demonstrating the usefulness of training tothe development of the business can encouragesmall firms to take further education and trainingsubsequently. This is often best taken forward ona regional or local basis (14).

8.4. Structure-conduct-performancemodels

The bulk of structure-conduct-performance litera-ture is now somewhat dated, although therecontinue to be articles published in this area. Anoverview of this literature suggests that there areessentially two main strands of investigation:(a) static models, which focus on the impact of

company size and market power on profits (orother current performance measures);

(b) dynamic models, which focus on the impactof company size and market power on inven-tion and innovation and, thereby, by implica-tion, on company growth and performance.

The key importance of the dynamic models isthe role that they ascribe to managers (includingtheir skills and qualifications) and the importancethey attach to intangible assets (including knowl-edge and skills) in determining company perfor-mance.

Managerial theories, first seen in the 1960s and1970s, were primarily concerned with companygoals in large, professionally managed compa-nies, as opposed to the small owner-managedcompanies. In general, the former were argued tobe sales revenue, utility or growth maximisersand the latter were depicted as profit maximisers.This strand of the literature involves some form ofoptimisation and the rationale for this literaturewas that professional managers would act in theirown best interests. The main theoretical predic-tions are that, in the main, managerial modelssuggest higher levels of labour demand than theprofit maximising case. In addition, a number ofthese models suggest a skewing of demandtowards higher-level skills and occupations.


(14) The examples of Emilia Romagna in Italy and the small firm hubs in India and Pakistan, in particular, are examples of localsmall firm synergies.

Behavioural theories, which developed a fewyears after their managerial counterparts,discussed firms as ‘satisfiers’, rather than opti-mising units, operating under uncertainty andimperfect information. These behavioural theoriessaid little about labour demands per se, butsuggest that all firms operate with some degreeof inefficiency (i.e. X-inefficiency), only part ofwhich it is economic to remove.

In practice, most empirical literature is basedupon fairly ad hoc tests of relationships betweenperformance, company size and market struc-ture. Bosworth (1983) has argued that there arevarious categories of model that are potentiallyrelevant. In those which have the longest pedi-gree, causality runs from company size to perfor-mance:

P = f (S, C, X) (10)

where P is a measure of performance, such asprofitability or sales growth, S refers to size, Cdenotes the degree of concentration in themarket and X is a vector of other explanatoryvariables, which often have included factors suchas advertising expenditure. This is of someinterest, as size is purported to affect perfor-mance, and size is almost certainly a proxy forother things such as skills, knowledge and R&D.

If different sizes of companies had differentgoals, this would have direct implications for theirperformance and for their labour demands,including the demand for skills.

In the case of dynamic structure-conduct-performance models, while it is true to say thatincreases in size give rise to greater R&D, it isless clear whether the increase in R&D is justproportionate, more than proportionate or lessthan proportionate with size. In addition, thefinding that formal R&D only begins above acertain size threshold is muddied by the fact thatsmall firms may still undertake R&D, but this isnot a separate function and tends to be moreinformal in nature than in larger firms. This ismade even less clear by the fact that there isfairly strong evidence that many major inventionshave been made by individuals rather than by

large corporations. In addition, as noted above,changes in R&D expenditure (an input measure ofinvention and innovation) with company size saysnothing about the relationship between R&Doutputs and size. This issue does not appear tohave been resolved. However, the empiricalevidence seems to support the hypothesis thatlarger firms are better innovators than small ones,and that R&D expenditures and associatedinvestments in human capital help in the scaleand success of innovation activities.

The stylised views of management behaviourset out in managerial and behavioural theorieshave proved to be far too simplistic. While profitmaximisation seems a reasonable first approxi-mation of the likely goal of an owner-manager, inpractice, even here there appears to be a muchmore complex picture. Bosworth and Jacobs(1989) reviewed the various literatures aboutmanagement goals and behaviour. They found awide range of goals, including the maximisationof profits. Equally, however, they included themaximisation of the owner’s utility (which wasinfluenced by time spent working, by the rewardsgained from extra work and by the status andpower conferred by self-employment and thecontrol of others). Other managers purported tominimise their risk, growth often being associatednot only with high risk, but also with personal andsocial (family) costs.

There were some characteristics that helped todetermine which goal would be set. It was oftenthe older small business, rather than the newer,that tended to adopt more conservative and lessdynamic goals. Owner-managers themselvestended to be divided between the artisan and theentrepreneurial, an almost cultural differencebetween individuals. In addition, psychologicalmake-up of the individual was also important;often the individual’s need to maintain controlconstrained the willingness to grow. Wherecontrol was important, the manager often exhib-ited a reluctance to employ more highly qualifiedindividuals, as the latter might deploy reasoningand knowledge that the managers would notunderstand, thereby leading to a loss of control.



Evidence of the positive influence of education,training and skills on performance is mostapparent at the level of the individual. Commenthas a tendency to focus on private rather thansocial performance. Some, although by no meansall, of these benefits can also be expected toaccrue at a macro level. Some of the benefitsmay, of course, be dissipated in competitionbetween individuals or companies, with no overallsocial gain.

Several literature strands indicate that theremay be important additional spill-over effects andexternalities. Spill-overs include technological,spatial and environmental aspects. Some refer-ences have already been made to externalities inSection 6.8 above.

9.2. Technological spill-overs

It is neither necessary nor appropriate to dwell ontechnological spill-overs, as they are a naturalextension of both endogenous growth theories(Chapter 6) and the market valuation approach, inwhich the performance of a particular company isinfluenced by the R&D or patents of othercompanies (Chapter 7). In the case of patents, forexample, the publication of the patent specifica-tion discloses information that may be of use tocompetitors or to companies in other productareas which use (or might use) related technolo-gies. In practice, many of the market valuestudies that have investigated the role ofspill-overs have found that they appear to bestatistically more important than the firm’s ownR&D or patents. There are a number of existingreviews (Griliches, 1981 and 1992). The key vari-ables are R&D and/or patent flows and/or stocks,although, in this case, the focus is on the generalpool of intellectual property generated by othercompanies. There are clearly potentially impor-tant links with the knowledge management area,including the sources and management of infor-

mation, and the most appropriate organisationalstructure to maximise the benefits to thecompany of the intellectual capital pool.

9.3. Spatial and city dynamics

Spatial literature considers the synergies thatstem from a particular location. One particulartheme has been the dynamics of small firm clus-ters, stimulated originally by the dynamics ofsmall firms in the Emilia Romagna region of Italy(Goodman and Bamford, 1989). However, therehas been similar work in developing countries,including India and Pakistan (Nadvi, 1998). In thecase of the small firm surgical instruments clusterin Sialkot, Pakistan, for example, ‘Clustering hasallowed small businesses to become highlyspecialised, and to benefit from externaleconomies and joint action.’ The evidencesuggests that such clusters benefit from bothvertical and horizontal synergies, from coopera-tion and other forms of spill-overs, as well asfrom intense competition between rivals. Suchclusters also benefit from the presence ofexternal institutions, such as the Metal IndustriesDevelopment Centre, in Pakistan, which is widelyused by local small and medium size enterprises.External agents form the principal sources ofinformation and technical know-how.

Spatial literature resides primarily witheconomic geographers and a comprehensivesurvey of UK urban labour markets was carriedout by Turok (1999). Some 20 cities with a popu-lation size above 250 000 are identifiable from the1991 census and of these, 8 are deemed to beconurbations, while 12 are free-standing. These20 cities accounted for more than 40 % of allnational jobs and so they exhibit a high concen-tration of both skills and companies.

During the 1980s and 1990s these citiescontinued to suffer from a relative decline in jobavailability, especially for full-time males in thelocal population. In consequence, there was aslow outward migration of skills from the urbancentres and reduction in male activity rates

9. Spill-over effects and externalities

(Turok, 1999). The long-term trend out of manu-facturing and into service sector activitycontinued and its impact was most strongly felt inthe cities. Although government provided a rangeof training schemes for the unemployed, somekey skills were eroded, as the jobs were lost,especially in the recession of the early 1990s.When the economy began to recover later in thedecade, companies found problems with skillshortages and deficiencies in the cities.

For the highly qualified workforce, the city hasincreasingly become a magnet for job opportuni-ties. Warf (1995) has noted the geographic reper-cussions of the growth of telecommunications onthe development of world cities, of which Londonis one of the foremost. There has been anagglomeration of professional jobs on offer astelecommunications offices have mushroomedacross the city. Graham (1999) has noted how ‘ahost of value-added and consultancy firms arealso concentrated in the city, supportingstate-of-the-art innovation, financial telecomsservices and corporate telematics applications’. Itis clear that synergies of all kinds stem from citylocation.

As the knowledge economy gathers strength,so cities become ever more important. It hasbeen claimed that trends towards decentralisa-tion, which were observable from 1960 to 1980,are now in the process of being reversed (Aminand Graham, 1997), although in some parts of theEU there remains statistical evidence of with-drawal of firms and people from large agglomera-tions to rural areas. Large cities at the end of thetwentieth century have become the nationaleconomic motor, acting as powerhouses for thepersonal interaction which drives the teamworkand creativity that is essential for good corporateperformance. Skill training which concentrates oncity professional workers is likely to produce thehighest social benefits, even where labourturnover is significantly high for individual compa-nies. A key feature of much of the literature in thisarea is the emphasis on professionalisation ofemployment in many cities and at the same timethe polarisation of employment structure withmany low skilled and low paying jobs beingcreated (e.g. Hamnett,1996).

A recent study by OECD (2001b) providesevidence of links between skills acquisition andeconomic growth in 180 regions across

15 Member States. Correlations were madebetween primary, secondary and tertiary educationlevels and regional GDP per capita. Whilst tertiaryeducation was important, the strongest significantassociation was with secondary education. Theformer level of education is critical for R&D andrelated innovations, but it is secondary educationthat provides the intermediate skills that are criticalto industrial know-how and learning-by-doing.Correlations at the regional level are blurred by themobility of labour between regions.

Moretti (1999) used US census data to try toestimate the external returns on education bycomparing wages for otherwise similar individualswho work in cities with differential levels ofeducation. Estimating problems arose in trying toisolate the causal impact on individual wages ofthe average level of city-specific education. Themain finding was that a one-year increase inhigher education in a city raised average wagesby between 8 and 15 %, after controlling for theprivate return on education. However, this resultdoes not necessarily point to an external effect,as it may simply be caused by complementaritiesbetween well and poorly educated workers.There are significant econometric difficulties inachieving robust results in this type of study.

9.4. Environmental externalities

Investment in education produces environmentalspill-overs in many different ways. One obviousarea is through health and life-expectancy. Higherlevels of investment in human capital creategreater awareness of the potential causes ofillness and promote increasingly healthierlife-styles. A more highly educated workforcechoose safer occupations and societies withhigher relative education bring in health andsafety laws to protect the employees. Grossmanand Kaestner (1997), after controlling for percapita income, found that people with moreeducation simply live longer. One particular mani-festation of the external impact of education onhealth is in the reduction in infant mortality ratesin developing countries. Women with higherlevels of education are more aware of thedangers to their children’s health and they aremore likely to take the steps that lead to lowerinfant mortality. Wolfe and Zuvekas (1997)


demonstrate that better education also promptslower fertility rates and smaller family sizes givethe children better health and survival prospects.

McMahon (2000) has argued that higher levelsof education will lead to the creation of moresustainable environments, as greater awarenessof physical environmental damage becomesapparent. Initially, faster economic growth isassociated with global warming, deforestation

and many forms of pollution, but educationenables populations to take steps to rectify someof this damage without sacrificing the growthgains. Most developed societies, with relativelyhigher human capital investments, have environ-mental protection laws. The spill-over of educa-tion on the physical environment is an expandingarea of specialist study that lies beyond thereaches of the present paper.


This section contains an overview of the key find-ings in the paper. It also attempts to drawtogether the implications for policy and to identifypossible directions for future research.

Some of the material presented in this sectionalso appears in the executive summary.

10.1. Summary of key points

This paper has presented an extensive interna-tional review into the various strands of literaturethat bear on the links between investment ineducation, training and skills and economicperformance at the level of the macroeconomy.Where training and education conducted by firmsand industries was identified as contributing tooverall economic growth, this was also reviewed.It was not possible to segregate macro frommicro studies, as the amount of overlap wasfound to be considerable.

An initial examination of recent EU statisticsmeasuring GNP, employment, unemployment andvarious educational and vocational traininginvestments suggested that economic growthacross the 15 Member States is associated withincreases in both education and training.However, a fuller appraisal of the data revealedthe difficulties in demonstrating significant causallinks between growth and investment in humanresources. The amount of literature in this areaunderscored the complexity of this relationship.

Studies on the general rate of return werefound to demonstrate strong links betweeneducation and training and economic perfor-mance, not only for the individual, but for societyas a whole. Evidence on social rates of return hasconcentrated on the benefits to the economyarising from increased education, calculating allthe costs of schooling and education comparedto the relative pre-tax earnings of individuals inreceipt of such education. Many qualifyingassumptions needed to be made in arriving at aquantifiable estimate, but several studies havesuggested a social rate of return in the range 6 to12 %. Comparable studies to establish the social

rate of return for vocational training proved hardto find, although there was some evidence thatsuch training has a significant positive impact onproductivity.

Literature on the management of humanresources at both company and industry levelexists in both micro and macro level studies. Thisresearch stresses the belief that individualworkers are a key source of an organisation’scompetitive advantage. It is the quality of thesehuman resources that determines organisationalperformance. There appeared to be a number ofdifferent emphases, including the role played byHRM in promoting such resources, and the roleof management itself, particularly top managers.One important theme was the role of leadership.Another theme stressed the importance of work-force skill qualifications in contributing to higherlevels of productivity across different industrialsectors. This literature emphasised the impor-tance of education and training in often verycomplex industrial processes.

Economic growth models directly explore thequantitative relationship between investments ineducation and training and the level and growthof per capita GDP at national level. This covers alarge number of studies, beginning with thegrowth accounting models, through to theendogenous growth models that are widelyapplied in many recent empirical studies. Bothdata sets and econometric modelling techniqueshave developed extensively over recent yearsand many different model specifications havebeen proposed and empirically tested. Typically,these models use data drawn from across-section of different countries, sometimesonly for developed countries but often for a widerset of nations. Data difficulties commonly meanthat consistent series of educational variables arehard to obtain over sufficiently long time periodsto facilitate econometric time series analyses. Fordeveloped countries over more recent years,cross-sectional and time series data have beencombined into panel sets to enable a morecomprehensive testing of the relationshipbetween human resources and economic growth.

10. Conclusions

Overall, these growth models demonstrate thathigher educational investments have had a signif-icant impact on national economic growth.Broadly, the weight of evidence suggests that a1 % increase in school enrolment rates has leadto an increase in GDP per capita growth ofbetween 1 and 3 %. An additional year ofsecondary education, which increases the stockof human capital, rather than simply the flow intoeducation, has lead to a more than 1 % increasein economic growth each year. The results varyconsiderably, depending upon details of modelspecification and the precise data sets in use.Issues dealing with the robustness of such resultsare widely discussed. Almost all these growthmodels use only a limited set of educational vari-ables to proxy human resource input and they donot incorporate any vocational training or socialcapital variables.

Market valuation literature is an areaconcerned with the factors that determine thegrowth of individual companies and so, indirectly,the overall economy. Research studies in thisarea are concerned with how intangible assets,such as technical knowledge, influence potentialperformance and net worth. These are intimatelytied to investment in education and training, aswell as skills, which may often be a necessary, ifnot sufficient, condition for improving such intan-gible assets. Research and development, patentsand intellectual capital are key determinants ofcorporate market value.

Further studies relate to the links betweeneducation, training, skills and economic perfor-mance. This is concerned with company size andthe structure, conduct and performance. Manystudies have emphasised the role of the smallfirm in economic growth. Such small firms are anextremely heterogeneous group that havemarkedly different training needs from those oftheir larger counterparts. There is often far lessemphasis on educational and training qualifica-tions in small firm environments. Empiricalresearch studies suggest that conventional theo-ries of company behaviour ware far too simplisticfor explaining the goals and aspirations of thesesmaller organisations.

The final strand of literature deals with some ofthe indirect effects of investment in humanresources that are not normally captured inmeasured national economic growth. Such

spill-overs and externalities can be technological,spatial or environmental. Each can contributesignificant social gain. Many of these effects arefound to be related to other areas covered in thisreview. The spatial externalities, that embrace citydynamics, demonstrate how geographical clus-tering of businesses employing highly qualifiedworkers can produce high productivity and stronglocal economic growth. Environmental spill-oversare often related to health and life expectancy,which typically increase in consequence of higherlevels of education.

10.2. Some policy implications

The overall conclusion from this body of work isthat the impact of investment in education andtraining on national economic growth is positiveand significant. However, its total effect is difficultto measure with any precision, as many of theinfluencing mechanisms are indirect andcomplex. Education is certainly a key determinantof economic growth for both developed anddeveloping countries: its impact is probably moremarked in developing nations. The link betweenvocational training and economic growth is moredifficult to demonstrate at the macro level, mainlybecause of measurement difficulties associatedwith training investments. It is easier to see apositive impact from vocational training atcompany and industry levels, although againthese are difficult to quantify precisely.

Despite the qualifying assumptions and compu-tational difficulties, evidence suggests a very signif-icant return for both the individual and society as awhole from investments in higher education indeveloped countries. Computed social rates ofreturn vary between countries and over differenttime periods, but most are positive and they providejustification for government continuing investmentin higher education. However, there are emergingconcerns of graduate overcrowding, as thepercentage of graduates in the labour marketcontinues to increase, and this trend needs carefulmonitoring to ensure that social returns are notdiminished through overqualification.

The computation of social rates of return onvocational training, equivalent to that for highereducation, is very difficult to achieve because of theinconsistent data usually available on such training


investments. Research supports the claim thatvocational training improves the performance of theindividual in the workplace and so raises theproductivity of the employing organisation. Studieson HRM argue that the skill of the individual workeris often a critical factor in an organisation’s compet-itive advantage. Comparative international studiesemphasise the key importance of skills training inraising productivity. Despite this, employers inmany countries have a rather negative attitudetowards investment in skills and training. Unless theinstitutional and legal infrastructure encouragesemployers to invest (as in Germany) there is atendency to rely on others to carry out the invest-ment (as is often the case in the UK). It seems clearthat governments should provide every incentive tocompanies, either through tax allowances or directgrants, as well as through reforms to institutionalstructures, etc., to encourage them to increase theirinvestment in training. Such training can providestrong spill-over effects for the whole of society.

The weight of research evidence suggests thatwhere governments manage to increase the stockof the educated workforce, through additionalyears of secondary or tertiary education, rather thansimply enhancing the flow into education in anygiven year, the impact on economic growth is moresignificant. Such a finding directs governmentpolicy towards changes to improve the quality ofeducation rather than just the numbers passingthrough the system in the short term. For mostMember States the emphasis is on access totertiary education and the quality of the graduatesproduced by universities. For less developed coun-tries the critical factor is secondary school enrol-ment rates and the need to extend the number ofyears in such education. It is difficult to measure therelative quality of education in different countries,but this would appear to be a key issue for govern-ments to address if educational quality is to besignificantly improved.

The role of R&D in the growth of firms isstrongly underlined in various studies and it isclear that successful R&D depends on skilledhuman resources. Governments need to provideincentives to firms to continue to make invest-ments in the education and training thatenhances their innovative capabilities. In partic-ular, attention needs to be paid to the role of thesmall firm in contributing to R&D and so to overalleconomic growth. Often small firms may not have

sufficient resources to invest in training theirworkforce, but they can be expected to benefitfrom training carried out by larger firms when thetrained workers subsequently move to new jobsin the small firm sector.

There is no guarantee that any investment inhuman capital will result in a positive return,whether it be investment in training, deploymentof highly skilled labour, R&D or in some otherform. There will always be some risk and uncer-tainty. In general, the evidence suggests thatsuch investments pay off but each case needs tobe considered on its merits.

10.3. Possible areas of futureresearch

This review has drawn attention to difficulties withpublished data related to the measurement ofeducation and training provision. Many of thegrowth model studies raised problems with dataquality. In particular, consistent time series ofeducational statistics were often lacking or thetime-span coverage was too short for meaningfulanalysis. Empirical results often proved sensitive tothe exact data set in use. The compilation of accu-rate educational statistics measuring both quantityand quality of provision is an important area ofresearch. Information on the quality of teaching wasfrequently unavailable to researchers and thisundoubtedly limited the rigour of the analysis.

Most of the reviewed research studies wereconcerned only with educational variables anddid not extend to vocational training. This limita-tion arises from the restricted availability of suit-able data on training provision at industry or theeconomy level. Individual companies oftenproduce such data but they are less frequentlyavailable at higher levels of aggregation. There isa strong need to continue to produce consistentstatistics on all types of training provision and toensure that these statistics are broadly compa-rable across EU countries.

This paper touched upon insights into linksbetween skills, education and training andproductivity growth at corporate level. However,given that the prime emphasis was the link tomacroeconomic growth, it was not possible hereto explore this area fully. Specifically, researchstudies in HRM and the market valuation of


companies could be examined in greater depth toreveal the mechanisms by which education andtraining investments lead to improved corporateperformance.

Relatively recent studies on social capital andits contribution to economic growth was only

touched upon in this paper but it would certainlywarrant a much fuller exploration. Several papershave now been published on social capital but itsrole as a determinant of economic growth needsto be tested empirically. Again, some importantdata difficulties will first need to be overcome.


List of abbreviations

CEO Chief executive officer

ELFS European labour force surveys

EU European Union

GDP Gross domestic product

GNP Gross national product

HRM Human resources management

IALS International adult literacy survey

ISCED International standard classification of education

NIESR National institute of economic and social research

R&D Research and development

Annex

International standard classification of education (ISCED)

Educational level Details/age range Common terminology Classification

Early childhood education Introduction of pre-school Pre-primary, nursery, ISCED 0children to a school-type kindergarten, pre-schoolenvironment, from age 3

Primary education First stage of Elementary school ISCED 1basic schooling,up to age 11 or 12

Lower secondary education Second stage of Junior high school ISCED 2basic schooling,up to age 14 or 15

Upper secondary education Stage leading to final Senior high school, Lycee, ISCED 3secondary qualification, Gymnasium, Sixth form, ISCED 4typically age 18 or 19 Further education

Tertiary education Programmes significantly Higher education, ISCED 5Amore advanced than upper College education ISCED 5Bsecondary studies ISCED 6

University level education Studies leading to a first ISCED 5Adegree, at least bachelor’s ISCED 6level or equivalent

Source: OECD (2001a).

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