the impact of human capital and human capital investments ... · the impact of human capital and...

The impact of human capital and human capital investments on company performance. Evidence from literature and European survey results

Bo Hansson, Ulf Johanson, Karl-Heinz Leitner

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 transitionEvelyn 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 capital andhuman capital investments on company performance

Evidence from literature andEuropean survey results

Bo Hansson, Ulf Johanson, Karl-Heinz Leitner

AbstractThis study consists of a literature review and an analysis of an existing database on human resourcemanagement (HRM) (the Cranet survey). It focuses on research that connects human capital with the firmand asks whether education, skills/competence and training have any impact on company performance.The main results may be summarised as follows.It appears that training provided by firms for employees is not characterised by being general or specificbut by what is needed to stay ahead of competitors. There is a growing body of literature suggesting thatfirms are financing both types of training.More recent research also suggests that investments in training generate substantial gains for firms evenif employees can use this training in other firms. The evidence that employers profit from training invest-ments comes from different countries including Britain, France, Ireland, the Netherlands, Sweden, andthe US. Most of these studies indicate that training affects performance and not the other way around.The effects of education and skills/competence on aspects such as productivity and innovations aregenerally found to be positive and significant, though the connection with profitability might be lessexpected. Firms can also extract profit from prior education as they do from general training investments.Supporting employee development through training policies and methods for analysing training needs isimportant to explain the provision of training and training outcomes. Similarly, innovative (and compre-hensive) HRM practices tend to be associated with positive company performance.Innovation and information technology (IT) both result in greater investment in training and also dependon education and skills in generating profits. Other findings suggest that training and comprehensiveHRM practices are closely related to firms’ innovative capacity.The lack of studies connecting small and medium enterprises (SMEs), labour market conditions(systems), and social partners with company training policies and performance measures such asproductivity or profitability, makes it difficult to draw conclusions on these aspects. This suggests anincentive to research such matters more thoroughly in the future.

1. Introduction 264

2. Method and research problem 265

2.1. Inference and causality problems 265

3. Overview of research and findings 267

3.1. Labour economics 267

3.1.1. Recent advances on the effect of training for firms 268

3.2. HRM and HPWS literature 274

3.3. National European surveys and cross-national comparisons 279

3.4. Small and medium enterprise (SME) surveys 282

3.5. Other training and impact studies 283

4. Cranet survey results 287

4.1. Selected Cranet survey questions 287

4.2. Cranet survey results 288

4.2.1. The scope of training 288

4.2.2. Correlation between variables 290

4.2.3. What determines training? 291

4.2.4. What factors are indicative for top (10 %) performers? 293

4.2.5. Main findings 295

5. Results 297

5.1. The effects of training on company performance 297

5.1.1. The impact of training 297

5.1.2. Formal/informal and general/specific training 298

5.1.3. Timing of the effects of training 298

5.1.4. Timing of training investments 298

5.2. The effects of education, skills and competences on company performance 299

5.3. How firms profit from general human capital 300

5.4. Training and HRM practices 301

5.5. Innovation, technological change and training 302

5.6. Specifics of SMEs 303

5.7. The influence of labour markets and social partners 304

6. Summary and discussion 306

6.1. Implication of firm financed general human capital investments 306

6.2. Information on training and intangibles to capital markets 307

6.3. Strength and weakness of data and methods 309

6.4. Policy implications and future research 310

List of selected abbreviations 312

Annex – Selected Cranet questions 313

References 315

Table of contents

TablesTable 1: Labour economics 272

Table 2: HRM (HPWS) studies 276

Table 3: National and cross national studies 280

Table 4: SMEs and other training studies 284

Table 5: Cranet survey, descriptive statistics 289

Table 6: Correlations between main explanatory variables (private sector) 290

Table 7: Training regression (private sector) 293

Table 8: Mean difference between top 10 % and lower half of firms in sector (private sector) 295

List of tables

Human capital is a major factor in generatingfuture growth and prosperity. Human capitalinvestments such as education and training are,therefore, a main concern for individuals, firmsand governments. According to Becker (1993),human capital is the key determinant inexplaining the rise and fall of nations as well as amain factor in determining individual income. Theimpact of human capital on enterprises is lessclear. This is because the attributes of humancapital and human capital investments areascribed to the individual and not the firm. Thisstudy concerns research that connects humancapital with the firm. The question is whethereducation, skills/competence and training haveany impact on company performance. The focusis on continuous vocational training that takesplace inside companies and is paid for, in part orwhole, by the employer.

A considerable volume of employer sponsoredor company training takes place each year. TheEuropean Union continuing vocational trainingsurvey 1994 suggests that more than half of allfirms with 10 or more employees provided sometraining during 1993 and that about 1.6 % oflabour costs are spent on training (EuropeanCommission, 1999). More recent figures fromSweden suggest that company training plays anincreasingly important role in creating new knowl-edge and skills in society. Working time spent oncompany training in Sweden has increasedconsiderably in recent years, from about 2.5 % in1999 to roughly 3.5 % in 2001. A Norwegianstudy estimates the time spent on formal andinformal training as high as 4-6 % of working time(Hagen et al., 2001).

Although these investments amount to consid-erable sums, until recently little has been knownabout the return for firms (1). The impact ofeducation and training on company performanceis an important issue not only because of thelarge amount invested each year in knowledgeand skills, but also because it is pertinent toknow who benefits from these investments. Thelatter question has a bearing on who should carrythe costs of training investment, to what extentwe have under-investment in training, whetherthere is a need for policies to improve the currentsituation in regard to company training, etc.

The aim of this study is to provide an overviewof research that connects education, training orskills/competence with the impact of these activ-ities on productivity, profitability or other variablesof firm performance. Besides reviewing associ-ated literature, the study also involves an analysisof an existing database (Cranet survey) withregard to education and training.

The remainder of the paper is organised asfollows. The next chapter introduces the methodused in gathering studies for this review andprovides a short introduction to some statisticalproblems encountered in this line of research.Chapter 3 gives an overview of findings indifferent research disciplines. Chapter 4 takes acloser look at a European human resourcemanagement (HRM) survey (Cranet survey) in rela-tion to employee development issues. Chapter 5gives the combined findings of this paper andpresents the major results in regard to the impactof training on company performance. Chapter 6suggests some policy and future research impli-cations based on the main findings of this study.

1. Introduction

(1) An estimate by SCB (Statistics Sweden) considers as much as 3.8 % of GDP was spent on company training in 2001 whichis roughly SEK 80 billion or in the order of EUR 9 billion. The amount spent on company training is close to what is spent oncompulsory and secondary school in Sweden 2001 (SEK 88 billion). Source: The National Agency for Education. The amountspent on formal training in the US was close to USD 59 billion in 1997 (Bartel, 2000).

Literature on the impact of education, training, andskills/competence on firm performance has beenreviewed from several different sources, includingpublished material. We have scanned publishedliterature mainly through channels such asABI/Inform and other university library databases. Incollecting the most recent research papers we havealso surveyed different web based databases, thebest known being the social science researchnetwork (SSRN). This is a major source of informa-tion with over 3 million downloaded working paperssince the start. Another important source is theIDEAS (UQÀM – Université du Québec à Montréal)database with over 100000 working papers and arti-cles from 1 000 universities and research centresaround the world. It includes papers from IZA (Insti-tute for the study of labor), NBER (National bureau ofeconomic research), CESifo (Center for economicstudies and institute for economic research), andseveral other important institutions. Other sourcesof information include the Cedefop bibliography onimpact research. We have also collected researchpapers directly from different universities andresearch centres. These efforts have been made toprovide the latest working papers on the matter.

The focus of the survey lies on more recentstudies and on European based research. Whilethis is an area that clearly needs much additionalresearch, we have included all papers that wehave come across that connect education, skills,and training with measures of company perfor-mance. In addition to labour economics literature,we also include findings in areas such as HRM,high performance work systems (HPWS), innova-tion studies, accounting and finance basedstudies, SME based research, as well as otherreviews and meta-analyses. The focus in thereview has been on quantitative research from theeconomic perspective of training. For instance,we have not surveyed literature on psychologicalaspects of training nor with a more ad hocapproach to training issues. A prerequisite ofinclusion is that the paper uses a statisticalapproach and is concerned with economicaspects of human capital and human capitalinvestments. We have also made an effort to find

research published in other languages, thoughmost research papers included in this review arein English.

The review of the literature is divided intodifferent sections. Labour economics forms amain segment since human capital and humancapital investment has been a major researchquestion for a considerable time. The contributionof HRM literature in regard to education andtraining is also given a section of its own. In thissection, the literature on HPWS plays an impor-tant role because of the ability to connect HRMpractices with company performance measures.We also decided to give national, cross-national,and SME based research their own sections.Since the focus is on empirical findings, theoret-ical considerations are (briefly) dealt with inconnection with empirical findings or in relation totheir respective research area.

A brief description of previous work in eachresearch area is provided, followed by in-depthcoverage of more recent papers with an analysisof methods used, data, and results. The tablesthat summarise each section include only themore recent papers and those that we regard ascontributing substantially to the existing literature.

A short description of statistical problemstypical of this type of impact research follows toprovide an understanding of the problems dealtwith in the review.

2.1. Inference and causalityproblems

Heterogeneity problem poses statistical difficultiesin estimating the impact of training on any outcomevariable. Differences between those who receiveand those who do not receive training make it diffi-cult to maintain that training alone causes the entireeffect on a dependent variable. In laboureconomics, heterogeneity among workers is typi-cally controlled for by including proxies for differ-ences in human capital accumulation and othervariables (age, tenure, education, occupation, etc.).

2. Method and research problem

In addition to factors that we normally can controlfor in empirical work, we have a number of factorsfor which it is hard to attain estimates (unobservablefactors). The main concern in statistical analysis isunobservable factors that are correlated with theregressors of the estimated equation. There areseveral remedies for this problem. One is to findsome broad approximation for the unobservablefactor, as in firm-level data which typically includesindustry dummies to control for differences inproductivity, profitability, market valuation, etc.,between firms in different industries.

If the effect on the uncontrolled (unobserved)variable is considered fixed over time, it is normalto use changes in the variable, instead of theoriginal, level data. This procedure needs dataover time (panel data). An example of afixed-effect problem that can be solved by usingchanges in the variables (first difference) is that ofunobserved ability among individuals. Forinstance, if more capable individuals are morelikely to receive training, the return on pasttraining will be upward biased, measuring, in part,ability instead of human capital investments. Aslong as the effects of the unobserved variable (inthis case ability) are stable over time (timeinvariant) taking the first-difference (the change inthe variables) will mitigate the problem.

Another problem is that not all explanatoryvariables in an equation can be considered tohave a one-way relationship with the dependentvariable. Explanatory factors that also are deter-mined by the dependent variable (the variable wetry to explain) are called endogenous and pose aproblem in estimating the returns on training. Thefollowing example, given in Dearden et al. (2000),details the problem with mutually dependent vari-ables (endogenous variables) when trying toassess the impact of training on aggregatedindustry productivity.

‘Transitory shocks could raise productivity andinduce changes in training activity (and of courseother inputs, labour and capital). For example,faced with a downturn in demand in its industry,a firm may reallocate idle labour to training activ-ities (the pit stop theory). This would then meanthat we underestimate the productivity effects oftraining because human capital accumulation willbe high when demand and production is low. Iffirms train when production and demand is highthen the opposite applies.’ (p. 25).

The remedy here is usually to estimate theequation in a system that considers the two-wayrelationship between the dependent and explana-tory variables. Typically this procedure includes asearch for instrumental variable(s) that are corre-lated with the explanatory variable but not withthe dependent variable. Another way to mitigatethe problem is to include lagged variables of theendogenous variable, as the lagged variable canpartly alleviate the problem of simultaneity.

Few studies have been able to explore the effectof mutual dependence between training and produc-tivity or profitability. While this is a potential problemin most impact research, the results of Dearden et al.(2000) are particularly interesting, assessing theimpact of training on productivity from different esti-mation procedures. In their study, the impact fromincreasing the proportion of workers trained by 5 %would result in a 31 % increase in productivity usingonly the raw correlation between training andproductivity. ‘We account for an overwhelmingproportion of this correlation, however, by our controlvariables. The 31 % effect in model A (no controls)falls to 8.5 % in model B (some controls) and 2.6 %in model C (include controls for occupation). Dealingwith endogeneity through general method ofmoments (GMM) (model D) increases the effect to4.1 %.’ The results in Dearden et al. (2000) suggestthat a well-specified regression model with adequatecontrols works quite well even in the presence ofsimultaneity problems. Given the data set used intheir study, the main issue in a well specified regres-sion model is not whether there is an overestimationof the impact of training but to what extent the modelunderestimates the impact (due to the assumptionthat training is determined exogenously).

It is important that studies address the ques-tion of heterogeneity by including adequatecontrol variables in statistical models. Usingchanges in variables instead of level datanormally gives a better base for conclusion.Lagging the effect of the impact of training furtherstrengthens the basis for cause and effect rela-tionships. The results of Dearden et al. (2000)suggest that the problem of endogeneity intraining might be of a lesser concern, at least in awell-specified regression model. In our review ofliterature we have made an attempt to addressthese issues by examining the regression modelsused and by examining the variables included inthe estimates.

Impact of education and training266

3.1. Labour economics

There has been continuing debate in laboureconomics literature on the subject of whetherfirms can profit from training investments. BeforeBecker’s (1962) theory on company training, mosteconomists saw education and training as theinvestment decisions of individuals. From acompany perspective, investments in humancapital (on-the-job training) differ from invest-ments in other assets because the employee hasan option to leave the firm, engage in wagebargaining and, in other ways, influence theoutcome of the investment decision. Becker(1962) advanced a theory on investment inhuman capital, explaining levels of investmentand predicting who should pay for, and who willbenefit from, the completed training.

Becker divided on-the-job training into generaland specific. General training is useful not only tothe firm providing the training but to other firmsas well. Because of this, employers are lessinclined to invest in this type of training. In acompetitive labour market, general training wouldlead to a wage increase for the employee andwould offset the profit for the firm providing thetraining. In other words, general trainingincreases the market value of the employee,suggesting that the employee should pay for thistype of training, for example, by receiving wagesbelow his or her productivity. Specific training, onthe other hand, does not benefit other firms and,subsequently, the trainee’s market value is notaffected. Because specific training does not influ-ence wages, the employee is not willing to payfor it. The firm pays for specific, on-the-jobtraining and increased productivity is accrued bythe firm providing the training. The employer mayshare some of the increased productivity with the

employee to prevent the trainee from leaving thefirm before the specific training investment isrecouped (2).

The main idea of Becker’s theory is that theparty that is most likely to benefit from the invest-ment also pays for it. The basic reasoning thatemployers are unable to benefit from generalhuman capital such as schooling, apprenticeshipprogrammes, and company training that are alsouseful to other firms, might be mitigated by thefunction of different labour markets or the degreeof competition for the skills in the market (3).

Theoretically, specific training poses noproblem for firms as these investments are nottransferable to other firms. However, most of thetraining provided by companies is to be consid-ered general in nature. About 60-70 % of allcompany training is classified as general training(e.g. Barron et al., 1999; Loewenstein andSpletzer, 1999). The study by Loewenstein andSpletzer (1999) also indicates that the generalityof training increases with more complex jobs,which suggests that most of the trainingcompleted in human capital-intensive firms isuseful to other companies. While research indi-cates that most company training has a value toother employers, the question is who is actuallypaying for this type of training?

Because most training has a value to otheremployers, theory predicts that the individualshould pay directly (by bearing the full costs) orindirectly by accepting a wage below his or herproductivity. So even if firms pay for all explicitcosts such as trainers, course fees, allowances,the individual still has the potential to pay throughwages below productivity. Testing the theorydirectly requires not only data about wages butalso, more importantly, information about produc-tivity.

3. Overview of research and findings

(2) If one introduces turnover into the equation, this will result in joint investments in firm specific human capital. This is becausethe higher wage for employees receiving specific training leads to an excess supply of workers willing to be trained. To bringsupply more in line with demand some of the costs for specific training are shifted onto the workers.

(3) The division into general and specific training may be too rigid. Company training might better be viewed as training withdiffering degrees of generality (marketability), from benefiting only the current employer, to benefiting competitors, industriesand companies in general. Company training that has varying potential to suit other employers is of interest when consideringthe ability of employers to benefit from general (marketable) training investments.

The absence of measurement of individualproductivity guides labour economics studies tousing data on wages to examine the question ofpayment for company training. Asking who paysfor company training also implies an answer tothe question of who will benefit from the training,i.e., if firms pay for general company training italso suggests that firms are able to capture thereturns from such investment. By this reasoning,a first step in establishing whether training hasany impact on performance is to establishwhether firms fund such investments.

Empirical studies that focus on wage profilesappear to confirm the general human capitalprediction (Neumark and Taubman, 1995;Reilly, 1995), as well as the specific humancapital prediction (Topel, 1991). In addition to thedivision into general and specific human capital,Neal (1995) suggests that an industry-specificfactor constitutes an important component of thehuman capital stock. Neal (1995) investigateddisplaced workers and found that wages partlyreflect compensation for industry-specific skills.

Observations made on wage profiles seem tosupport predictions of human capital theory. Theproblem with inferences from wage profiles is that anumber of other theories and explanations alsopredict an upward sloping wage curve. Forinstance, wage growth is produced in job-matchingmodels because of imperfect information about theemployee’s productivity (Jovanovic, 1979).Self-selection models use back-loaded compensa-tion to discourage ‘movers’ from applying for jobs(Salop J. and Salop S., 1976). Implicit contractingmodels explain the firm’s future wage commitment(rigidity) as a consequence of an income insuranceagreement between the employer and theemployee (e.g. Azariadis and Stiglitz, 1983;Marcus, 1984). The forced savings explanationjustifies an upward sloping wage curve by workers’preferences (Loewenstein and Sicherman, 1991).

Apart from the problem of alternative theories ofwage growth, empirical studies that used a moredirect test by utilising training data have failed tosupport the predictions of human capital theory.Indications that firms invest in general training are

sometimes indirectly revealed in studies on theimpact of training on wages. For instance Lenger-mann (1996) found that recipients of what appearsto be general company training benefited fromincreased earnings during the training period. Veum(1995), after studying more recent data from thenational longitudinal survey of youth (NLSY), cameto the conclusion that firms pay for general training.Loewenstein and Spletzer (1998) conclude thatemployers pay for general training and contend thatfirms are able to obtain some return from generaltraining investments (4). More recent studies(Lowenstein and Spletzer, 1999; Barron et al., 1999)argue convincingly that firms pay for generaltraining and that firms are also able to benefit fromthese investments. Other studies that also suggestthat employers pay for general training includeAcemoglu and Pischke (1998; 1999a) on appren-ticeship programmes and Autor (2001) on tempo-rary help firms. Literature connecting training withwage effects indicates that firms pays for generaltraining. This is an important finding because itsuggests that firms benefit from all types of traininginvestments (specific as well as general training).

Whether this is the case is the main question ofthis review of literature. A prerequisite for a directtest of Becker’s theory is performance data suchas productivity or profitability, though dataconnecting training with productivity or profitabilityis hard to come by. The absence of company datais striking and few studies have had access toperformance data until very recently. These studieswill be examined in greater detail below.

3.1.1. Recent advances on the effect oftraining for firms

In a review of the literature on effects of companytraining for employers, Bartel (2000) concludesthat econometric analysis of a large sample offirms provides little guidance on the question ofthe employer’s rate of return on training. Thereason given by the author is that few data setsinclude the cost of training, that few studies havebeen able to control for heterogeneity amongfirms or addressed the question regarding theendogeneity of training.


(4) In the shared investment model of Loewenstein and Spletzer (1998), the employer shares general training investments with theemployee as a consequence of the employer’s inability to commit credibly to future wages. The employer, instead, commits toa minimum guaranteed wage and shares the investment in general training and realises the returns if the minimum wage guar-antee is binding. The wage floor is only binding for less productive workers. If the wage guarantee is not enforced, the workerthen realises all returns and incurs all the costs of the training.

One reason for this lack of research results is thedifficulty in estimating the amount invested intraining. The definition of what to include in estimatesof the time spent on training is unclear (e.g.informal/formal training). Similarly, the costs to beincluded in calculating training investments (e.g.direct/indirect costs) are not standardised. The lackof a coherent definition of training and of a standard-ised way of measuring training investments hampersefforts to address the question of the actual benefitof training. In many cases training is measured as theproportion of employees being trained in a yearinstead of the actual amount invested.

The review by Bartel (2000) is concerned withthe return on investment (ROI) in training. Thepresent overview of training literature is focusedon whether there is an impact on performanceand is less concerned with estimating the rate ofreturn on these investments. However, the issueof the return on training investments is still verymuch an open question as few of the studiescited include the cost of training.

European research has made some importantadvances by incorporating measures of produc-tivity in the statistical models. The major studiesare summarised in Table 1. The results of an Irishstudy by Barrett and O’Connell (1999) suggest thatthe amount of general training has a significantpositive impact on productivity, in contrast withspecific company training. These results comefrom a first-difference approach that cancels outtime-invariant effects. Barrett and O’Connell arguethat a plausible explanation why only generaltraining is significant is that general trainingprovides a greater incentive for employees tospend more effort in the learning process.

It is worth noting that these results are realisedin a model with controls for changes in corporateinnovations and the introduction of newpersonnel policies. These two control variablesshow no significant relationship to changes inproductivity. Because few HRM studies haveused the change in HRM policies, this study alsocontributes to discussion of whether personnelpractices or human capital investments are themain factor in generating the effects on companyperformance. Other interesting findings in thestudy by Barrett et al. (1998) include:(a) that training and the change in productivity is

significant whereas training and the level ofproductivity is not;

(b) that the correlation between training andtangible investments is relatively low (Rs 0.13)which suggests that tangible investments canonly explain a small part of the impact oftraining in a first-difference model;

(c) an interaction term between tangible invest-ments and general training renders thetangible investment coefficient insignificantwhile the general training variable remainssignificant.

The combined result of the study by Barrett andO’Connell suggests that training has a major influ-ence on productivity and that other plausible andnormally uncontrolled factors have little or no influ-ence on productivity effects ascribed to training.

A study by Dearden et al. (2000) suggests thatcompany-sponsored training generates substan-tial gains for employers in terms of increasedproductivity. Different methods are used tocontrol for unobserved heterogeneity and poten-tial endogeneity of training, including GMMsystem estimation. Their estimates consistentlyshow that the impact of training on productivity isabout twice as large as the impact on wages.Their results also suggest that formal training hasa larger impact on productivity than informaltraining. These results are obtained by examiningthe direct impact of training on industrial produc-tion. They also argue that treating training asexogenous leads to an underestimation of thereturns on training for employers. This is animportant observation since few studies havecontrolled for the possibility of two-way relation-ships between training and company outcomevariables such as productivity and profitability.

The results from Groot (1999) suggest that thereis a rather weak connection between whocontributes to training investment and who benefitsfrom it. The study by Groot is based on telephoneinterviews with 479 Dutch firms. In about 43 % of allcases the workers either contributed through use oftheir leisure time and did not receive benefits or didnot contribute to the training but reaped some of thebenefits. Only 5 % of the workers contributed finan-cially but more than 75 % of the workers contributedwith leisure time. Groot concludes that the benefit ofenterprise-related training is high, both in terms ofproductivity and wage effects. Average productivitygrowth following training was found to be 16% whileaverage wage growth was 3.3 %. The difference inproductivity between trained and non-trained

The impact of human capital and human capital investments on company performance 269

workers was 8 %. These effects are based on esti-mates (0-100 % scale) of productivity growth bycompany personnel. The average length of trainingwas found to be close to six months.

In a study of programming consultants inSweden, Hansson (2001) found strong evidencethat the employer paid for all programming trainingeven though the resultant skills were highly attrac-tive to other firms. This study is unique in thesense that it had access to employee measuressuch as profitability, amount of training, wages,and each employee’s acquired human capitalstock (approximated by the individual’s compe-tence profile). The results indicate that theemployer not only paid for all direct costs associ-ated with the training (course fees, travelexpenses, etc.) but also lost a considerableamount of profit during the training. Hanssonfound no evidence that the individual contributedto the training investment by receiving a wagebelow his or her productivity. The findings alsosuggest that the employer recovered the invest-ment in programming training in the long run, asindividual programming skills (competence) weresignificantly associated with profitability. Theseresults were realised in an environment with similarworking conditions such as type of job, customerbase, etc., and with a number of control variables(including a control for differences in ability amongemployees). Hansson argues that the investmentin general human capital largely looks like anyother investment scheme that firms normallyundertake in their business operations (with aninitial investment and a payoff in the future).

The work of Gunnarsson et al. (2001) suggeststhat the increased educational level of theSwedish workforce between 1986 and 1995 is animportant factor in explaining IT-related produc-tivity growth during these years. Gunnarssonet al. examined the IT productivity paradox byincluding measures of interaction between IT andeducational level in 14 industries (manufacturingsector). The IT productivity paradox relates to thefact that massive investments in IT in the 1980sdid not have any positive effects on productivityuntil the beginning of the 1990s. The interactionbetween IT and educational level is significantand contributes significantly in explaining produc-tivity growth during this period. As the inclusion

of the human capital measures increases theexplanatory power substantially, the authorsconclude that human capital is a key in explainingthe IT productivity paradox. Other interestingfindings in Gunnarsson et al. are that a marginalskill upgrading has the same effect acrossdifferent levels of education and that IT-relatedproductivity growth occurs in several industriesoutside the IT sector.

Other European based studies include those ofKazamaki Ottersten et al. (1996) who studied theimpact of training in the Swedish machine toolindustry, using an evaluation drawn upon costfunctions and productivity estimations. Their anal-ysis is based on a formal model that was appliedto the panel data of eight Swedish machine toolfirms between 1975 and 1993. Their results implythat training expenditures result in net decrease intotal costs. The estimates of productivity effectsare also positive, but smaller in magnitude (alsoKazamaki Ottersten et al., 1999).

US studies that have had access to perfor-mance data on either employees or firms aremainly from the mid 1990s (5). Some results aregiven next.

Krueger and Rouse (1998) investigated work-place education programmes in two Americancompanies in the service and manufacturingsectors respectively. The programmes includedlearning of generic skills such as reading, writing,and mathematics as well as more occupationalskills such as blueprint maths and blueprintreading. The results indicated that participating ingeneric training classes had no significant impacton employee wage growth. Occupational trainingclasses, on the other hand, yielded a positiveimpact. Training influence on the available perfor-mance measures was generally weak. In theservice company, classes had no significantimpact on whether employees received perfor-mance awards or not. The effect on absenteeismduring the training period was positive in bothcompanies but not statistically robust. Theauthors also conducted a survey of the personnelat both companies. With two exceptions, the vari-ables showed no difference between employeesparticipating in the programme and thenon-participants. Participants were more likely toreport that they would take additional training


(5) Since we have not been able to find more recent papers we are uncertain whether this is caused by a lack of research orwhether we have missed out on important publications or working papers.

classes in the future and they were also morelikely to report that their supervisor would saythat they were doing better than a year ago. Thelatter result might be interpreted as an improve-ment of self-reported job performance.

Another study focused on basic skills trainingis the investigation by Bassi and Ludwig (2000) ofdifferent school-to-work (STW) programmes inthe US. The purpose of the study is to analysewhether those programmes providing generaltraining also can be cost-effective for firms spon-soring them. Data comes from case studies ofseven STW programmes representing a diverseset of industries and regions. Data was collectedthrough interviews. The authors found that mostof the STW firms studied were willing to pay forgeneral training, though it is less clear whetherfirms would be able to recoup the full costs ofthis training, given labour market institutions andpublic policies in the US. Contrary to the predic-tions of the classic Becker model, the authorsfound that in all but one case the firm paid forsome or all of the costs of general training. Theresults show a substantial variation incost/benefit ratios across the STW programmes.The discounted cost/benefit ratios variedbetween 0.69 and 1.81. One explanation for thisvariation is that firms with relatively high ratiosmay be the ones that provide little training. Otherexplanations for the large variation in ratios arethe ability of students to pay for training and theability of firms to extract profits from trainedworkers. The findings also suggest that Americanlabour markets are imperfect enough to motivatefirms to participate in STW programmes.

The findings of Black and Lynch (1996) indicatea somewhat mixed result with regard to humancapital and productivity. This study used leveldata in estimating the impact of human capitalinvestments on (log) sales and a regressionmodel with a number of control variablesincluded in the regression. The results indicatethat human capital in the form of education had asubstantial impact on productivity. Formaltraining conducted outside the company had asignificant impact on productivity for manufac-turing firms whereas computer training had asignificant impact for non-manufacturing firms.The proportion trained did not yield any signifi-cant relationship. Their results also indicated thattraining appears to have a lagged impact on

productivity. Black and Lynch (1997) reworkedtheir regression model with access to longitudinaldata on productivity. The results in this estimationprocedure indicated no significant relationshipbetween training and productivity. The authorsattributed this insignificant impact to increasedmeasurement errors.

The results of Bartel (1995) indicate thatreceiving training increased the probability of apositive change in performance the following year.Bartel investigated 1 487 professional employeesin a manufacturing firm. Different types of trainingand the amount of training (days) showed nosignificant impact. The author attributed the ratherweak impact on employee performance to thesample and scale used in this specific case. Thesample consisted only of employees whoremained in the same job (e.g. employees whogot promoted were excluded from the sample).The performance rating was executed on a singleitem (7-point scale). Because of these twoconstraints the author argues that training effectsprobably underestimate the real impact thattraining has on employee performance.

Another study by Bartel (1994) suggests thatimplementing training programmes generatesconsiderable productivity effects measured as thechange in log sales. This finding is robust fordifferent personnel categories (professionals,clerical staff, etc.) and changes in personnel poli-cies (the results are not caused by a Hawthorneeffect). In addition, the results are robust to meanreversion of productivity between firms and showthat low productivity firms were more likely toimplement training programmes. That lowproductivity firms implement training programmesto a larger extent than other firms can producedownward biased or insignificant effects oftraining programmes in cross-sectional regres-sions. The effect of using cross-sectional dataappears to underestimate the impact of trainingon productivity growth. It is important to note thatthe training effect on productivity is achieved inexcess of changes in personnel policies, indi-cating once again that training is a major factor toconsider in HPWS literature.

The main findings in labour economics withregard to the impact of human capital oncompany performance can be summarised asfollows. Previous research on training and wageeffects has established that firms pay for all type



Study Database/ Data Sample Aim/ Methodsurvey and size subject

Dearden et al. British labour Incidence of 94 industries, Impact of First difference,(2000) force survey training, informal maximum 12 training on fixed effect,UK (LFS), COP and formal years, 818-970 productivity GMM system

consensus of training, industry years and wages equationproduction longitudinal

–industry level data

Barrett and EU survey In company 215 firms, Impact of First differenceO’Connell and a follow up training two points training on regression(1999) survey of Irish (continuous in time productivity, IRL business vocational impact of

training), general general and and specific specific trainingtraining, panel data

Groot Telephone and Duration of 479 firms with Impact of Frequency/(1999) questionnaire formal training 10 or more training on ordinary least NL survey employees wages and square (OLS)

productivity difference growth approach

Hansson Company Type of training, 132 Impact of OLS(2001) database training days, programming training and S competence, consultants competence

education (skills) onprofitability and wages

Gunnarsson Labour force Proportion with 14 industries Impact of Industry weighted et al. survey, different over 10 years human capital least square (2001) employment educational and IT regression, S register, levels on productivity interaction

investment growth education and ITsurvey, etc.

Black EQW national Company 1346 Impact of OLSand Lynch employers’ training. manufacturing training on(1996) survey Number trained, and non- productivity, US type of training, manufacturing impact of types

level data establishments of training

Bartel Columbia Implementation 180 firms in Impact of Level and(1994) Business of formal training manufacturing training first difference US School survey programmes sector over programmes regression

and compustat (manag., profes., three years on productivityclerical, produc. workers) paneldata

Krueger Company Type of training, 800 (of which Impact of OLS, Probit, and Rouse personnel basic skills 480 workers training on random and(1998) record education, attending wages fixedUS (manufacturing occupational training) and employee effect models

and service courses, performance

Bartel Company Incidence of 1 478 Impact of First difference.(1995) personnel formal training professional training on Two–stepUS records and days employees productivity multinomal

(manufacturing in formal training, and wages logit modelfirm) type of training

Table 1: Labour economics


Control Outcome Strength/ Findingsvariables measures weakness

Tenure, age, Productivity Extensive Training has a positive impact on productivityeducation, measured as robustness tests and wages, with a twice as large effect on occupation change in log and econometric productivity. Formal training has larger industry, R&D, real value added modelling/data impact on productivity than informal capital intensity, per employee on incidence training.firm size, etc. of training

(not days in training)

Change in Log sales Days in training, General training has a positive impact on personnel policy, growth panel data, productivity, specific training has no impact.corporate important restructuring/ control variablesorganisation, assets, employees, etc.

Tenure, age, Estimates Direct estimates Average productivity growth about 4-5 timestime since (100 % scale) of differences larger than wage growth. Weak connection training, of productivity in productivity/ between who contributes to training education, growth before estimates of investment and who benefits from the trainingmobility, etc. and after, trained productivity

and not-trainedTenure, age, Profitability, Direct measure The concurrent impact of training on profitgross (revenues net of profitability is negative and impact on wages positive.contribution, of wage and and human The skill/competence of the individual is ability, education, overhead costs) capital significantly related to profitabilitygender, etc. stock/level data,

single employerBusiness cycle, Total factor Lagged IT and Interaction term IT and educational levelnon-computer productivity human capital is highly significant indicating that theequipment, IT effects increase in productivity growth is largelygrowth, gender, tied to increase in higher educational leveletc.

R&D, capital, Log sales A number of Education positively related to productivity, TQM, multiple growth control variables, formal training off working hours and establishment, type of other training variables computer training

training/level significant with productivity, not (number data trained)

Capital assets, Log sales Controls for Implementing training programmes positively no. employees, growth personnel related tochange in productivity, not due unions, raw policy, relation to mean reversion orchange in personnel policy.material, age between training Low productivity firms more likely to implementof firm, industry, programme training programmeschange in and personnel policy productivity/weak

training measureEducation, Performance Homogenous The work place education programme hadtenure age, awards workers, generally a weak effect on the employee gender, absenteeism, compare performancemeasures, except for company) self-reported results at perceived performance. The effect of the type of performance two companies/ training was positive but not significant in department, weak many cases.type of work, etc. performance dataOccupational Change in Controls for Individuals receiving training significantly dummies, performance determinants associated with probability of increased education, rating of training/weak performance score. Days in training and type tenure, etc. outcome variable of training not significantly associated

(rating by managers) increased performance.

of training no matter whether it is general orspecific. More recent findings also indicate thatfirms benefit from such training investments.There are even signs that general (formal) trainingproduces greater benefits than specific training.There are also some indications that the educa-tion and skills of the individual are associated withproductivity and, in some cases, with profitability.

3.2. HRM and HPWS literature

The impact of human resource management(HRM) practices on company performance hasattracted considerable attention. Many specialissues of management literature are devoted toHRM practice and company performance, forinstance the Academy of Management Journal(Vol. 39, No 4, 1996), International Journal ofHuman Resources (Vol. 8, No 3, 1997), HumanResource Management (Vol. 36, No 3, 1997),Human Resource Management Journal (Vol. 9,Issue 4, 1999). The argument put forward in thisline of research is that advanced HRM practicesproduce a higher level of productivity. The find-ings suggest that there is a connection betweenHRM practices or what is often referred to ashigh performance work systems (HPWS), andcompany performance indicators such as sales,market values, market-to-book values, prof-itability, productivity, etc. (6).

Generally, this research area has good access tocompany based performance measures. Thedisadvantage is often that the statistical methodsare based on level data, which makes it difficult toestablish causality. That most of the research isbased on level data is largely a consequence of thefact that firms seldom make any large changes totheir HRM policies. Measuring changes in HRMpractices, therefore, requires extensive measure-ment periods (longitudinal data). Much of the infer-ence about the impact on firm performance is thusconfined to cross-sectional data. However, manypapers use a research design that accounts for theheterogeneity among firms, which makes the statis-tical models more robust.

In HPWS literature, education and training ispart of a larger package of the activities of a

human resource function. The areas that are typi-cally covered in these studies are screening andemployee selection, compensation systems,employee communication, teamwork practices,etc. In many cases studies also examine howaligned or integrated these practices are with theobjectives or strategy of the company. Much ofthe current debate centres on whether bundles ofhuman resource practices are the source of valuecreation in firms or whether certain practicescontribute more than others. There is also thequestion of whether there is a HRM practice thatis generally applicable to most enterprises orwhether HRM practices are firm-specific orcountry-specific.

For instance, a Dutch study by Boselie et al.(2001) argues that the institutional setting inEurope affects the potential to create high perfor-mance work practices because of the presenceof strong labour regulations and the interaction ofsocial partners. They maintain that to applyresearch on high performance work practices weneed to adjust the theoretical framework to suitthe European situation. Boselie et al. (2001) alsoprovide an overview of the findings that HRMresearch has produced in the last decade. Theresults with regard to the effects of training oncompany performance are reproduced below.Some of these papers will be examined in greaterdetail in this section:(a) training has a positive impact on the different

dimensions of the performance of the firm:product quality, product development, marketshare and growth in sales (Kalleberg andMoody, 1994);

(b) higher investment in training results in higherprofits (Kalleberg and Moody, 1994; d’Arci-moles, 1997);

(c) higher investment in training results in a lowerdegree of staff turnover (Arthur, 1994);

(d) training has a positive impact on the relation-ship between management and the otheremployees (Kalleberg and Moody, 1994);

(e) training has a positive impact upon perceivedorganisational performance (Delaney andHuselid, 1996);

(f) management development is positivelyrelated to profit (Leget, 1997);


(6) These human resource management practices are sometimes referred to as human capital enhancing systems, or highcommitment policies (systems).

(g) focus on training is positively related toperceived profit, market share and investmentin the near future (Verburg, 1998);

(h) training practices affect perceived organisa-tional performance positively (Harel andTzafrir, 1999).

(Boselie et al., 2001, p. 1112)

Besides the issue of whether there is a genericHRM practice, there is continuing debate aboutwhether employee development is the key factorin the HRM bundles (e.g. Barnard andRodgers, 2000). The results presented in theprevious section of this review suggest thattraining is a main factor in generating productivityeffects, as training yields a significant impactwhile controlling for changes in personnel prac-tices (Bartel, 1994; Barrett, 2001). Other studiesmaintain that the impact on company perfor-mance is caused by the combined effect of HRMpractices (Becker and Huselid, 1997;Huselid, 1995; Becker and Gerhart, 1996). Thiscontroversy is also reflected in the selectedstudies of our review of this research area.

HPWS might appear to be connected mainlywith the knowledge intensive sector, but humanresource policies to enhance efficiency andworker commitment can work equally well inmore mature sectors of the economy. Ichniowskiet al. (1995) investigated HRM policies in the USsteel industry. Their findings indicate that innova-tive human resource practices have a significanteffect on productivity. Ichniowski et al. (1995) wasone of the few studies to examine the produc-tivity effect of firms changing their humanresource practices. Interestingly, the impact ofthe first-difference (change) approach supportedthe results of original estimates on level data. Thebenefits, in the form of increased revenues, farout-weighted the costs associated with thesehuman resource programmes. Ichniowski et al.(1995) also argue that complementarity betweendifferent human resource practices has a signifi-cant effect on worker performance, whilechanges in individual employment practices havelittle or no effect (training by itself is not enough).

D’Arcimoles (1997) utilises the disclosure ofinformation in French company personnelreports. These reports are sanctioned by law andinclude unmatched firm-based information on themain aspects of HRM such as compensation,training, recruitment, dismissal, and general

working conditions. Apart from having access todata on variables that researchers normally havea great difficulty in obtaining through surveys, thisstudy also has access to data over time (paneldata). The panel with training and HRM measuresincludes six two-year periods. The main resultswith regard to training are that the level of traininginvestment is consistently associated with boththe level of, and changes in, current and futureproductivity and profitability. Profitability isapproximated by the return on capital employedand productivity by value added per employee.The impact from the change in training on thechange in productivity seems to appear with aconsiderable lag. The results presented by d’Arci-moles suggest that the effect of training invest-ments might take as long as two to three yearsbefore they emerge in form of increasing produc-tivity. The results between the change in trainingand change in profitability are less precise. Still,these findings indicate that there exists a causallink between training and firm performance in thesense that firms invest in the current period andharvest the benefits in future periods. One mightadd that these results are achieved while control-ling for absenteeism, hiring/dismissal, work acci-dents, and total rate of resignation (all controlvariables are considered proxies for working andsocial climate at the firm).

Laursen and Foss (2000) studied the relation-ship between HRM practices and innovationperformance based on the data of the DISKOproject, a large survey on innovation behaviour in1 900 Danish firms, cofunded by the OECD. Thesample includes nine sectors in manufacturingand service industries. Laursen and Foss alsopropose some theoretical explanation as to whyHRM practices influence innovation performance,e.g. new HRM practices often increase decentral-isation, in the sense that problem-solving rightsare delegated to the shop floor, which might facil-itate the discovery and utilisation of local knowl-edge and thus enhance innovation. Due to thecomplementarities between HRM practices, theyalso state that systems of HRM will be signifi-cantly more conducive to innovation than indi-vidual practices.

Laursen and Foss used principal componentanalysis based on nine HRM factors and identi-fied two different HRM systems that areconducive to innovation. In the first system all


nine HRM variables are relevant; in the secondonly performance-related pay and internaltraining are dominant. In the latter system onlythese two factors out of nine have individualimpact, but when all factors are combined into asingle variable this is highly significant. Thisfinding supports their thesis of the complemen-tarities of HRM practices. In addition, they alsoidentified some sector-specific patterns, such asthe fact that firms in wholesale trades tend tobelong to the second system. They conclude thatthe application of HRM practices is important tothe likelihood of a firm being an innovator.

Table 2 presents the studies by Ichniowskiet al. (1995), d’Arcimoles (1997), and Laursen andFoss (2000). These studies have made an effortto disentangle training effects from other HRMpractices and two of them have also been able toexamine the impact on company performance ofchanges in HRM and training.

Other HRM studies also measuring the effectof training separately from HRM practices includeDelaney and Huselid (1996) based on US data.The training measurement in their study shows aconsistent and significant relationship withperceived organisational performance (irrespec-tive of the statistical model). The results forperceived market performance are less clear butsuggest a significant influence of at least 10 %.

These training effects are demonstrated in thepresence of other HRM aspects such as staffing,compensation, degree of internal labour market,etc. Delaney and Huselid used cross-sectionaldata on 590 firms to estimate organisationalperformance and 373 firms to estimate stockmarket performance. Because this study is basedon cross-sectional data it is difficult to establishthe relationship between organisational perfor-mance and training measurement.

Michie and Sheehan (1999) studied the data ofthe UK’s workplace industrial relations survey(WIRS) with regard to the impact of HRM practiceson innovation. They separated three different typesof HRM practice using variables such as payment,worker involvement in teams, incentives, informa-tion sharing, etc. However, they did not explicitlyintegrate the degree of training. Innovation ismeasured by research and development (R&D)expenditure and the introduction of new micro-elec-tronic technologies. The workplace industrial rela-tions survey contains information on 2061 firms withmore than 25 employees in various sectors. Michieand Sheehan were able to use 274 data sets thatcontained information on HRM as well as innova-tion. Based on an econometric model, whichexplained the probability of innovating, they wereable to identify some significant HRM factors. Theirresults suggest that low road HRM practices – short



Ichniowski Interviews Proportion Longitudinal Impact Level andet al. (1995) and al. employees data on 36 of HRM first difference US company received steel practices (fixed effect)

specific off-the-job production on regressiondata training lines, productivity

(dummy 2 190 monthly variable) observations

d’Arcimoles French Formal 61 firms Impact Level (1997) company training level data, of HRM, regressionFR personnel expenses, 42 firms wage growth, and first

report level and panel data and training differencechange (7 years) on firm OLS data performance regressions(panel data)

Laursen Data Classification 1 900 firms HRM Probit and Foss exaggerated of two HRM Practices model(2000) from the practices and their to explain theDK DISKO impact probability

project on innovation to be an (Database) performance innovator

Table 2: HRM (HPWS) studies

term contracts, etc. – are negatively correlated withinvestment in R&D and new technology, whereashigh road practices are positively correlated withR&D investment and the introduction of new tech-nology. This study also shows that skill-shortage isa serious obstacle for innovation and for movementtowards differential and higher-priced products.Their findings also deliver evidence that the strategyto increase employment flexibility by short-termcontracts, weakening trade unions, etc., does notenhance the innovation performance of firms.

MacDuffie (1995) tested the impact of humanresource activities in automotive production. Thesample consisted of 62 car assembly plants inthe US, Asia, Europe and Australia. The studywas influenced by the organisational contingencytheory and the hypothesis that the internal fitbetween different organisational strategies andcharacteristics affects performance (7). MacDuffieseparated two production systems, massproduction and flexible production. Disruptions tothe mass production process prevent the realisa-tion of economies of scale with the use of buffersas an indicator for the prevalence of this system,

whereas under flexible production buffers areseen as costly.

Within flexible production, the link betweenminimisation of buffers and the development ofhuman capabilities is driven by the philosophy ofcontinuous improvement. MacDuffie separatedproduction organisation, work systems and HRMpolicies as three interrelated independent variablesand creates corresponding indices to capturesystemic differences in organisational logicbetween mass production and flexible production.He used different human resource variables suchas job rotation, recruitment policy, training of newemployees, etc., based on a cluster analysis, toclassify human resource practices characterisedthrough a consistent bundle. Performance hasmeasured by labour productivity and quality,expressed as defects per 100 vehicles.

Regression analysis indicated that indices ofmass production, flexible production, transition,and intermediate stage, were statistically signifi-cant predictors of productivity and quality.High-commitment human resource practices,such as contingent compensation and extensivetraining, in flexible production plants, were char-


(7) The contingency theory stresses the importance of the link between human resource practices and other factors such asorganisational strategy. In contrast, the universal approach maintains that human resource management practices have a posi-tive effect on performance in general (independent of other factors).


HRM controls, Productivity A number of Adopting a coherent system of HRMcontrols for measured robustness tests, practices produces significant production line by uptime of same production productivity effects. Adopting (maintenance, production line process, panel individual work practice in age, raw data/weak isolation has no effect on material, training data productivity (training).etc.)Wages, social Productivity Impact of Level of training is consistently climate (change in change correlated with level and change (absenteeism, value added), in training in productivity and profitability,work accident, profitability measured with change in training is associated social (return on lag, HRM with change in performance expenditures), capital controls/ weak with a two to three year lagemployment employed) firm and (less stable result)variables, etc. industry controlsSector, firm Innovative Only innovation The application of HRM practices size, performance performance is does matter for the likelihood cooperation the dependent of a firm being an innovator

variable


acterised with low inventories and repair buffers,and consistently outperformed mass productionplants. MacDuffie found empirical evidence thatbundles of interrelated and internally consistenthuman resource practices, rather than individualpractices, are better predictors of performance.Overall, evidence supports the thesis thatassembly plants using flexible productionsystems, which bundle human resource practicesinto a system that is integrated with productionstrategy, outperformed plants using more tradi-tional mass production in both productivity andquality. While other academics believe that eithermass or flexible production will perform well ifthere is a good fit between their human resourceand production strategies, MacDuffie found thatflexible production leads to better performancefor automotive plants.

A similar matter was raised by Arthur (1994) instudying the impact of human resource systemsacross steel mini-mills in the US. His main hypoth-esis was that specific combinations of humanresource policies and practices are useful inpredicting differences in performance andturnover. In the tenor of the contingency theory, hestated that congruent human resource and organi-sational policies are more significant than separatehuman resource practices. He separated twohuman resource systems, control and commit-ment, which stress the importance of cost reduc-tion and commitment maximisation respectively,and shape employee behaviour and attitudes atwork. Despite the contingency view of humanresource systems, he stated that, in general,commitment human resource systems will beassociated with higher performance, especiallybecause of the high control and monitor cost inthe control system. In addition, he was interestedin the question of the impact on turnover.

Arthur expects higher turnover in firms withcontrol systems because of the lower cost forwages and training. Empirical data were based ona questionnaire from human resource managersof 30 US steel mini-mills. Based on a cluster anal-ysis, he separated the two human resourcesystems, using labour efficiency, scrap rate(number of tons of raw steel to melt one ton offinished product) and turnover as performancemeasures. Regression analysis indicated that thepresence of commitment human resourcesystems was significantly related to fewer labour

hours per ton and lower scrape rate, whereasturnover was higher in control systems. Arthuralso found, that human resource systemsmoderate the relationship between turnover andperformance, since there is a negative relationshipbetween turnover and performance in commit-ment systems. However, the results have to betreated with caution due to the small sample size.

Baldwin and Johnson (1996) studied businessstrategies in Canadian small and medium-sizedfirms, with less than 500 employees, demon-strating varying degrees of innovation. Besidesmarketing, finance, and production strategiesthey also investigated whether innovative firmsalso followed specific human resource strategies.The sample size was 850, including all majorindustrial sectors. Innovation classification wasbased on the traditional question of R&D intensityand additional variables regarding innovationbehaviour (patenting, source of innovation, etc.).

Baldwin and Johnson were able to verify theirthesis that greater innovation accompaniesgreater emphasis on human resources, stressingtraining. They found statistical evidence that moreinnovative firms offered formal and informaltraining more often and with greater continuity,accompanied by innovative compensation pack-ages. While almost three-quarters of the group ofmore innovative firms offered some form oftraining, just over half of the group of less innova-tive ones engaged in training. The authors alsoused quantitative data to estimate the amount oftraining and found that the more innovative onesspent CAD 922 on average per employee, signifi-cantly more than the CAD 789 spent by less inno-vative firms. Moreover, the former firms usedproduction employees more often as a source ofinnovation.

In addition to linear correlation analysis, theauthors used multivariate models to establishwhether certain combinations of factors or allfactors combined contributed to a given humanresource strategy. A probit model and principalcomponent analysis indicated that the firms thatfollowed the most comprehensive humanresource strategy (stressing all factors simultane-ously) are most significant. Considering all factorsstudied (human resources, marketing, etc.) theyfound that all areas are important for innovationsuccess and that more innovative firms take abalanced approach to their business by striving


for excellence in a number of different areas.However, Baldwin and Johnson did not carry outanalysis of how far specific human resourcestrategies are related to other business strategies.Finally, they analysed the relationship betweeninnovation behaviour and company performance,based on administrative data sources. Variousperformance criteria and indices (sales, prof-itability, market share, employment, and assets)suggested that more innovative firms performedbetter. Thus, the study delivers evidence for arelationship between sophisticated humanresource strategies and company performance.

In conclusion, HRM literature stresses theimportance of comprehensive HRM practices ingenerating effects on company performance(training by itself is not enough). However, fewstudies measure training separately and fewstudies have had the opportunity to examinechanges in HRM practice and in performance.Nevertheless, there are indications that training ismore efficient or generates larger effects inconnection with other HRM practices.

3.3. National European surveysand cross-nationalcomparisons

Many surveys and studies conducted by nationalinstitutions attempt to answer questions such aswhat generates innovation and what createsgrowth in their respective countries. Similarly,many cross-national comparisons are aimed atunderstanding the reasons for differences ingrowth and innovation between countries.Measurements of training and education are typi-cally included in these types of study as part of thefirm’s innovative capacity. Besides human capital,studies typically include variables that areassumed to generate innovations and growth,such as investments in IT, R&D, technology, capitalintensity, etc., and a number of control variables.The aim in many instances is to understand theinnovative capacity of firms and not human capitalper se, though this is an advantage as the resultsfor education and training are generally morerobust (as the inclusion of other variables controlsfor the influence of these factors).

A study by a Swedish business developmentagency (NUTEK, 2000) on different learning

strategies shows that competence developmentactivities are associated with both productivityand profitability. Training is measured in a broadsense by three establishment level activities(planning, learning at the job, and proportion ofemployees trained). In this study the effects oftraining activities are observed after holding otherlearning strategies constant (e.g. R&D, innova-tions, cooperation, etc.). Other findings indicatethat the effect is more pronounced for larger firmsand that higher educated personnel is associatedwith both productivity and profitability. This studyuses level data, which render causality difficult toachieve. However, since both profitability (whichis net of investment costs) and productivity aresignificantly influenced by training activities, itstrengthens the interpretation that trainingincreases productivity and that employers areable to capture some of the returns generated.

In a study of Finnish companies, Leiponen(1996a) finds a significant association betweeneducational level and profitability. Other findingssuggest that strong complementarities existbetween different educational factors. It appearsthat a sufficient number of more highly educatedemployees is a prerequisite for the profitability ofdoctoral level researchers. The results alsosuggest that innovative firms are more dependenton educational competence in generating profit.The sample used in this study consists of paneldata for 209 firms. The author deals with theendogeneity problem (caused by the effect ofprevious economic performance on the explana-tory variables) by applying a two-stage regressionprocedure. An interesting finding is that withoutaddressing the problem of endogeneity betweenprofitability and other human capital variables,the results are largely insignificant. Leiponenconcludes that general competences acquired ineducation, notably higher and post-graduateeducation, are beneficial for the profitability of thefirm. In another study of Finnish companies,Leiponen (1996b) comes to the conclusion thatinnovative firms have a more educated workforceand that they are more profitable than non-inno-vating firms.

The German Institute for employment research(IAB) has, since 1990, conducted a large establish-ment survey with over 9 200 participating establish-ments. This is one of the most comprehensive estab-lishment panel surveys in Europe (Bellmann, 2001).



NUTEK Flex-2 survey Learning 911 The impact OLS level(2000) (telephone strategies, establishments of learning regressionsS and competence strategies

questionnaire development on firm’ssurvey) activities competitiveness

(measured 0-3 scale), level data

Leiponen Survey data, Educational 209 firms, Impact of First difference(1996a) compiled by level, type of time-series education on GMM, two-stageFIN Statistics of education data profitability weighted least

Finland (15 (technical, (1985-93) and innovations square regressionmanufacturing natural industries) science)

Bellmann IAB Amount 3 400 Impact of OLS level and Büchel 1997 invested cross-sectional training regressions, two (2000) Survey in training observations on productivity stage least D squareDoucouliagos Case Cost and 7 cases Application Design of a et al. study benefits of a four-step cost-effective (2000) of training evaluation training evaluationAU programmes process of model

training investments

Blandy Questionnaire Training 41 Effects of the Regressionset al. similar to UK quantity on-the-job ‘matched plant’(2000) CEP survey in hours training on methodology AU by the LSE productivity between hotel and

and earnings kitchen furniture manufacturers

Maglen Case study Training 30 case studies Evaluation Comparativeet al. based on expenditure in four sectors of the return case studies(1999) interviews with on training AU managers and depending on

employees other factors

Table 3: National and cross national studies

However, the research that has come out of the IABsurvey so far is less focused on the effects oftraining investments for firms and more on theeffects for individuals. An exception is the study byBellmann and Büchel (2000) examining the effectsof continuous vocational training on productivity.Their result is based on 3 400 cross-sectionalobservations from the 1997 IAB survey. The authorsapply different models in estimating the impact oftraining, including an OLS and a two-stage regres-sion model as well as including controls for industry,size, and employee characteristics. The initialregression results for both parts of reunitedGermany indicate a significant relationshipbetween how much is invested in training and

productivity (log annual sales per employee).However, the authors argue that this finding islargely a consequence of a selection problem, suchas the individuals receiving training having moreability and that certain firms are more capableproviding adequate training for their employees.Bellmann and Büchel stress the importance ofstrategic HRM practices in generating productivityeffects from training investment.

Some interesting studies have also been carriedout in Australia. Blandy et al. (2000) found a posi-tive relationship between a firm’s profitability andthe quantity and quality of training offered by thefirm, the latter also being correlated with otherforms of investment. A profitability index, based on



Other learning Productivity Includes a Competence development activities strategies, R&D, (value added), large number have a significant effect IT, innovations, profitability of control on productivity and profitability. cooperation (revenues to variables and Larger effects for larger firms. with partners, cost ratio) competing Education is associated with profitability.decentralisation, learning etc. variables/level

data, weak training data

Sales, market Net profit Use first Educational competence is significantly share, capital margin, difference, associated withprofitability. intensity, innovations control variables, Complementarities exist betweenindustry such as two-stage different general skills dummies patents, weighted least acquired in higher education.

improvements, square to handle Innovative firms are etc. simultaneity more dependent on educational

problems and level in generating profitability.GMM

Industry, size, Productivity Large number Initial results indicate a strong associationemployee measured by of observations/ between training and productivity. However,characteristics log annual level data the results are likely driven by ability.

sales Authors stress HRM practices as main factor.(case study) Calculations Quantitative Return on investment (%) calculated

of return on estimation of is between 70 and 7000investment returns on

training investments

Industry Productivity The study uses Profitability is directly related toand quantitative i) quantity and quality of training,profitability data on ii) firms paying above market wage rates,

training and iii) firms’ difficulties in finding suitable employeesperformance/ iv) no clear picture regarding the impact of small sample size training onthe productivity

Firm strategy Labour Control variables In most cases training investmentsand human productivity are only used had led to positive returns, though dependent resource policy for a qualitative on human resource practices; the bundling

interpretation of human resource policies is crucial; better of the differences performers also planned strategically

firms’ statements on profitability compared withtheir main competitors, was found to be positivelyassociated with indices of volume (expenditure ontraining relative to main competitors) and thequality of company training (existence of a formaltraining strategy, written commitments to trainingin workplace agreements, etc.). In addition, themore profitable firms pay above market wage ratesand operate in labour markets where suitablelabour is hard to find and keep. The authorsconclude that the principal reason why training isprofitable for firms is that it increases the produc-tivity of their employees more than it raises theiremployees’ wages.

In another Australian study, Maglen and Hopkins

(1999) integrated variables such as work organisa-tion, job design, employment practices and othercompany-specific variables to analyse returns ontraining. They compared enterprises that producedsimilar goods or services, and were similar in size.Their findings suggest that there is no key set of rela-tionships that could be translated into a series ofbest practice procedures, but that the main factorsin optimising business performance are linksbetween strategic objectives and practice within theenterprise itself. This means that the effectiveness oftraining is contingent upon the idiosyncratic circum-stances of the firm, a theory based on Becker et al.(1997) and known as ‘idiosyncratic contingency’. Acomparison between seven firms in four sectors

found that better performers, measured by labourproductivity, included training as an integral part ofstrategic planning. In contrast, the poorestperformers lacked these characteristics.

Doucouliagos and Sgro (2000) developed atraining evaluation model, which they tested onseven Australian firms with longitudinal firm-leveldata, aiming to calculate the return on investmentin training, both financially and non-financially.Collected data indicated that the return on invest-ment in training programmes varied between 30and 7.125 % for seven different trainingprogrammes. Performance and benefits wereestimated by indicators such as the saving ofenergy after training of train drivers or increase insales growth after the training of store managers.

Carr (1992) studied productivity differentials inthe automotive sector between Britain, Germany,the US and Japan and the impact of skills differ-ences. He based the investigation on 56 matchedvehicle component manufacturers in the four coun-tries in 1982. The study included interviews withchief executive officers and other personnel downto shop floor level. In 1990 Carr carried out 45 moreinterviews to gauge the effects of past differencesand to capture the impact of increased labour flex-ibility in Britain. Despite differences in product andtechnical characteristics between the productareas studied, the data clearly showed productivitydifferentials between Britain, Germany, the US, andJapan. In nearly all areas Britain lagged behind,measured by sales per employee. The studyprovides some evidence that the highly educatedand trained German workforce (craft apprentice-ship, the high qualification of the foremen, andvocational training organised by firms) explains theproductivity advantage of Germans firms. However,Carr was not able to carry out a statistical analysisregarding the relationship between specific trainingand its volume, measured by factors such asexpenditure, and its impact on productivity.Compared with the 1980s, the data showed the UKto be catching up slowly, which he ascribes to anincrease in labour flexibility in Britain, though unableto verify this statistically. Finally, he concludes thatthe UK has to place more emphasis on high stan-dards of basic education and programmes aimed atcontinuous employee development.

Mason et al. (1992) carried out a study similarto Carr (1992), where they compared the produc-tivity differences between Britain and the Nether-

lands and the impact of vocational education. Likeother studies, their investigation deals with lowerworkforce qualifications in Britain. Mason et al.compared the skills and productivity in a matchedsample of British and Dutch manufacturing plants.The study was conducted on 36 plants in twoindustries, engineering and food-processing. Theauthors also summarised the main specifics of theDutch vocational education and training system,which relies principally on full-time vocationalcolleges. This system is nearer to the Frenchschooling-based system than to the Germanapprenticeship system. They concluded that thehigher average level of skills and knowledge in theDutch workforce contributes to higher productivitythrough better maintenance of machinery, greaterconsistency of product-quality, greater workforceflexibility, and less learning-time on new jobs.These findings are not based on a statistical anal-ysis regarding the two samples, but rathercomparing the general profiles and characteristicsof workforces in the two countries. Substantiallyhigher proportions of vocationally qualifiedpersonnel were found at virtually all levels inDutch plants in both industries.

Concluding, the indications are that the educa-tional level appears to be a factor in profitabilityof firms. There are also some indications thatdifferences in educational level (system) betweenEuropean countries might explain productivitydifferentials among the countries. Most studiesstress the importance of training as an integralpart of strategy and other HRM practices.

3.4. Small and mediumenterprise (SME) surveys

Even though some of the above mentionedstudies include smaller firms in their sample anduse firm size as a (control) factor, few studiesexist which deal explicitly with the impact oftraining investment on SME performance.

Leitner (2001) carried out a study on AustrianSMEs (between 20 and 50 employees) with theaim of investigating the impact of differentstrategic investments on company performance.He also analysed training investments, measuredwith an ordinal variable, in the context of variousinternal strategic factors, endogenous factorsand their impact on performance. He found that


the amount of training was one of the few internalfactors that had a direct impact on companyearnings, this correlation being highly relevant forsmaller firms with less than 50 employees. Ingeneral, smaller firms invested less than biggerfirms. However, no positive relationship betweenthe different kinds of strategies pursued and theamount of training could be identified.

Furthermore, Leitner discovered a positive rela-tionship between training and the corporateculture and communication within the company.Given the impact of external (endogenous)factors, he found that training was an essentialaspect of profitability for firms in dynamic envi-ronments. He concluded that, in general, traininginvestments allow firms in different competitiveand rather hostile environments (mature productlife cycles, conjuncture-dependent life cycles,dynamic environment) to perform better thanother firms in similar environments. His findingspartly support the idea that the impact of trainingon company performance depends on endoge-nous and firm specific factors.

Romijn and Albaladejo (2000) investigated therole of various internal and external sources of inno-vation capability in SMEs in the UK. Besides factorssuch as R&D investment and interaction withresearch institutions, they found that a range ofinternal factors, including the owners’ technicaleducation and their prior work experience, the tech-nical skills of the workforce (measured by univer-sity-trained engineers as % of total workforce) andtraining (measured as training expenditure peremployee and % of sales) have a significant effecton innovation capability. Furthermore, a close linkto nearby training institutions also had a positiveimpact. Innovation capability was measured by thepresence of innovations during the preceding threeyears and their technological complexity. However,the authors did not report any specific analysisregarding firm size and its impact.

There is considerable literature on the importanceof human capital for the success of businessstart-ups (e.g. Trouvé, 2001). In these studies formaleducation, skills and experience and the talent of thefounder are integrated to explain business success.However, the role that the training of the workforceor teams plays for a company’s success is scarcelyinvestigated. Bosma et al. (2002) studied the valueof human capital for start-up companies inthe Netherlands. The study separated general,

industry-specific and entrepreneurship-specifichuman capital investments by the founder andmeasured performance according to survival, profitand employment generated. The main findings arethat investments in industry-specific human capital,such as former experience, and entrepreneur-ship-specific human capital, such as experience inbusiness ownership, contribute significantly to theperformance of small firm founders. General invest-ments, such as the level of higher education, play aminor role. A methodological problem of the study isthat investments are only operationalised by theexperience of the founder, without a direct analysisof training expenditure during the firm’s life.

The Irish study by Barrett and O’Connell (1999)cited in Section 3.1.1, also analysed whether afirm’s size had an impact on the relationshipbetween general or specific training and perfor-mance but they did not find any significant differ-ences based on the size of the firm. The study byMason et al. (1992), carried out in British andDutch SMEs with up to 400 employees, indicatedthat the Dutch productivity advantage wasgreatest in product areas where small- ormedium-sized batches were in demand by themarket. In engineering plants they found no vari-ation in productivity with firm size, but in thefood-processing industry (biscuits) they foundsome differences. While the largest British biscuitplants, which were highly automated, had thesame productivity as the Dutch firms, smallerDutch plants were almost twice as productive ascorresponding British plants. Mason et al. ascribethis difference in productivity to the lack of tech-nical competence of the workforce.

While few studies have examined the effects ofhuman capital and human capital investment on theperformance of smaller firms, there is nothing indi-cating that experience, skills and training shouldhave any less impact on company performance. Onthe contrary, many of the studies emphasise theimportance of these factors for smaller firms.

3.5. Other training and impactstudies

The research done at the American society oftraining and development (ASTD) is an importantsource of information. ASTD perform annualbenchmarking studies on employee development


and training. A clear advantage of the datacollected by ASTD is the quality of the informationon training. All companies subscribing to ASTD’sbenchmarking survey gather information on typesof training, amount spent on training, etc. Allmeasures of training are clearly defined andcompanies participating in this survey providetraining information that is collected in a similarway. Studies based on ASTD data thus have farless variance caused by measurement errors intraining than most other studies. The importanceof committing firms to a common definition andstandard of measuring training cannot be over-stated. The lack of a common definition of what toregard as training will be discussed in more detailin the last chapter of the present study.

The possibility of connecting training data withcompany outcome measures is also an importantadvantage of the ASTD database and offersadvantages to investigations of company training.The study by Bassi et al. (2002) shows that firmswith higher training investments also have higherstock returns the following year, higher grossprofit margins, higher return on assets, higherprice to book ratio, as well as higher income peremployee. The results are very similar whenexamining changes in performance measure-ments. An important aspect of being able toconnect training investment with profitability andstock market performance is that we aremeasuring the impact net of the cost of theinvestment. The study by Bassi and van Buren



SME

Leitner Empirical Importance 100 Intangibles, Correlation(2001) study of training SMEs strategy and Anova-A based on a and firm Analysis

questionnaire performance

Bosma Panel survey General, 1 100 Does human Regressionset al. among 1 100 industry-specific firms capital (2002) new business and investmentNL founders entrepreneurship- enhance

between specific entrepreneurial 1994-97 investment in performance?

human capital

Romijn and Survey of Skills of 50 information Internal and Correlation Albaladejo 50 SMEs workforce and computer external sources analysis(2000) (interviews) technologies of innovation UK (ICT) and capability in

electronic firms SMEs

Other studies

Bassi ASTD survey, Training 314 Impact of OLS, first et al. Compustat investment publicly training difference(2001) per employee listed firms investment regressionsUS on firm’s

(stock market) performance

Büchel German Educational 800-1 900 Impact of Logit,(2000) Socio-economic level individuals overeducation survivalD panel (GSOEP) on productivity model

Table 4: SMEs and other training studies

(1998) indicates a similar result, that training haspositive effects on company performance.

That training investments are associated withstock market performance is more rigidly demon-strated in Bassi et al. (2001). In a well-specifiedregression model the authors demonstrate thatthe level of training expenditure (investment) peremployee is associated with next years’ stockmarket return. This training effect is demonstratedin the presence of variables capturing stockmarket risk and known stock market anomaliessuch as momentum and the book-to-marketeffect. While controlling for momentum effect byincluding a lagged dependent variable, theauthors also account for (eliminate) the potentialeffect that training might have had on stock

returns during the investment period. It is alsoimportant to note that a first difference approach(change in training investment and stock marketreturn) gives substantially the same result. Theresults also indicate that the effects of trainingemerge with some lag and that training appears tohave long-term effects on profitability.

An important aspect of training and the impacton stock returns is that this information is valuerelevant and that investors are currently unable toget hold of this type of information. In a perfectworld, a well-informed investor would anticipatethe increased earnings and returns from suchhuman capital investments at the moment theywere made. Because of the absence of informa-tion on human capital investments in corporate



Industry, size, Earnings, sales No quantitative Firms with regular trainingstrategy and employment measurement of have higher returns

growth training investments

Various controls Survival, profit, Human capital Human capital (experience of the founder) such as industry, employment is measured influences the entire set gender, labour only through of performance measuresmarket histories experience

None with Innovation Captures various Skill of workforce (share of university-trained) respect to capability indicators to has a positive impact training and (index) explain on innovation performanceperformance innovation

performance, not possible to relate training directly to performance

Industry, R&D, Stock market Good quality Training investments are associated assets, returns, income-, training data, with next year’s stock market performance.investment, price Tobins Q-, and firm performance Same result for level and change data. to book, beta sales per measures, and Changes in training investment price/earnings, employee control variables can predict future stock returnsprevious performance, etc.

A number of Job satisfaction, Large number Overeducated employees are healthier, employee tenure, of observations/ have longer tenure and are characteristics, participation in self-reported more work and career minded.age, gender, on-the-job- variableshealth status, etc. training, health

reports it appears that investors are unable togather this type of information. However, ifinvestors had knowledge of these investmentsand anticipated the impact on the share price, wewould be unable to document any effect on shareperformance the following year.

Another study that connects human capital withstock market performance is Hansson (1997) onSwedish stock market information. Because of theunavailability of training data, the author createdthree stock market portfolios reflecting depen-dence on human resources and investment intraining. One portfolio mainly consisted of knowl-edge-intensive firms, a second reflected lessdependence on human resources and consistedlargely of capital-intensive firms. The third portfoliocontained a mix of firms from both knowledge-and capital-intensive sectors. The basic idea of thestudy was that these portfolios would mimic unob-served training expenditure, with relatively higherhuman capital investment in knowledge-basedfirms compared with capital-intensive firms. Thefindings indicated that knowledge-based firmsconsistently earned higher risk-adjusted returnsthan the more capital-intensive firms. The authorascribed the higher returns in the portfolio withknowledge-based firms to greater investment inunaccounted (unobserved) human capital.

In a German study on overeducation andemployee performance, Büchel (2000) comes tothe conclusion that overeducated employees inlow-skill jobs tend to be more productive than theircorrectly positioned colleagues. Büchel used ques-tions from the German socio-economic panel(GSOEP) and found that overeducated personnelhad better health, received more on-the-jobtraining, had longer tenure, and were not moredissatisfied with their job than personnel with theright educational level. This study usedself-reported questions on productivity. The resultsindicated that the risk of employing too highlyeducated personnel might be exaggerated sincethere are no indications that overeducatedpersonnel are more negative towards their work.These findings also suggest that we may possiblybe less concerned with the potential negative

effects that education can have on company perfor-mance, i.e., education might have an upside but aless pronounced downside effect.

The effects of web-based training are studiedby Schriver and Giles (1999) within a nuclearfacility, Oak Ridge, Tennessee, US. The organisa-tion has about 14 000 employees, with a trainingbudget that reflects their high qualificationrequirements. The training for Oak Ridge ismainly organised by the centre for continuouseducation, which serves as corporate university.In 1995 the management decided that significantsavings could be realised by using the Intranet todeliver selected courses and qualification tests.Schriver and Giles evaluated this new project,introduced in 1997. Calculating the investmentsin the new system such as technology, downloadcosts, etc., and savings such as travel costs,fewer instructors, physical copies, classrooms,etc., they found that the cost-benefit ratio was1:9.5. The return on investment was calculated at845 %. However, despite this convincing data,the analysis should be taken with caution, since itdid not assess the effectiveness of training andany intangible effects, such as networking oppor-tunities. Neither did it calculate how much timethe employees spent in front of the computer.

In summary, studies based on stock marketdata suggest that human capital and investmentin it are important factors in understanding stockreturns. Some of the studies based on the ASTDdata provide convincing evidence that traininggenerates substantial gains for firms. The studyby Büchel (2000) also suggests that we might beless concerned with the potential negative impactof overeducation on firm performance.

It is important to point out that additional refer-ences to impact research can be found in Barrett(1998, 2001). While these overviews also largelycover the impact that training and skills have onfirm performance, there are some overlaps withour study. We have, however, tried to distinguishour review not only by including other researchpapers but also by looking at some of the prob-lems associated with this kind of research from adifferent perspective.


The Cranet network was established in 1989 byfive founder countries (Germany, Spain, France,Sweden and United Kingdom). It is coordinatedby the Centre for European Human ResourceManagement at Cranfield School of Manage-ment. The Cranet survey is now the largest andmost representative independent survey of HRMpolicies and practices in the world. The networkitself is a collaboration between 34 universitiesand business schools, which carries out a regularinternational comparative survey of organisationalpolicies and practices in HRM across Europe.

Cranet has been running the survey since 1990using standardised questionnaires sent to privateand public organisations in different countries. Thestandardised questionnaire is translated into eachmember country’s language and adapted to thedifferent national contexts (taking into considera-tion such factors as legislation, labour markets andculture). During each round of the survey, amend-ments are made to capture new developments butthe questionnaire stays mainly unchanged in orderto observe developments over time.

The questionnaire is distributed by post, exceptin Greece where interviews are used to gather theinformation. The questionnaire is distributed toorganisations with 200 or more employees and isaddressed to the most senior human resource/personnel specialist in the organisation. The 1999survey was distributed to over 50 000 organisationsand 8 050 responses were received giving a totalresponse rate of 15 %. The willingness of compa-nies to respond was higher in Scandinavian coun-tries than in Southern European countries. Thecurrent version of the 1999-survey databaseincludes 8 487 observations from 27 countries, 17 ofwhich are European countries. The number ofobservations in the forthcoming tests variesbecause not all companies have answered all ques-tions. The survey is sent to both private and publicorganisations. The descriptive statistics in thepresent investigation include responses from bothpublic and private organisations. For the analysisthat includes performance variables, the sample isrestricted to private organisations.

It is also important to note that the provider of

the information in this survey is the firm and thefigures presented here can deviate from otherstudies. As noted by Barron et al. (1997)employer-based surveys typically report moretraining than individual-based surveys. Becausethe survey is focused on larger organisations it isexpected that the incidence and amount oftraining will be higher than in surveys conductedon a more distributed population.

4.1. Selected Cranet surveyquestions

A clear advantage of the Cranet survey is theaccess to two direct questions on companytraining. The first question concerns the amountspent on training in proportion to annual salaries.The second question is related to the proportionof employees that participated in training duringthe year. As with most training studies, we havevery little knowledge of what the respondentsregard as training. It can be anything from contin-uing vocational training, initial training, toin-company apprenticeship training. Other ques-tions related to employee development includewhether the organisation has a written policy fortraining and development, whether it analysesemployees’ training needs, and whether it moni-tors training effectiveness.

Questions related to company performance areweaker. Variables are perceptions of organisa-tional performance (fairly common in studies)typically measured at different levels. One ques-tion is related to the performance of the organisa-tion for the past three years. This variable ismeasured at five levels and could be included inthe analysis as a measurement of whether prof-itability affects the provision of training. The mostinteresting performance measurements are ques-tions on the rating of the organisation’s perfor-mance compared with other firms in the samesector. These industry-adjusted questionsconcern productivity, rate of innovation, servicequality, profitability, and stock market perfor-

4. Cranet survey results

mance. Variables are measured at three levels,according to whether the firm belongs to:(a) the top 10 % of the firms in the sector;(b) the upper half of the firms in the sector, or;(c) the lower half of the firms in the sector.

Preferred outcome variables in this survey aremeasurement of stock market performance andprofitability, as these are net of the investmentcost of training. It is important to note that we arenot working with actual performance but withperceptions of performance, which are both a‘noisier’ measurement and demonstrate less vari-ation than actual performance. Performancemeasurement in the survey has the advantage ofbeing relative to other firms in the same sector(industry). Controlling for heterogeneity betweenindustries or sectors has proved to be importantin most firm-based studies of company training.

Two variables related to internal market andunionisation are utilised in the survey. The vari-able on unionisation is measured at six levelsfrom 0 % to 100 %. A measure of internal marketcan be constructed from the question on howmanagerial vacancies are filled. The respondentsare asked how three different levels of managersare recruited: senior, middle, and juniormanagers. As with most proxies this variable isonly a rough measurement of the actualconstruct, which also includes other characteris-tics such as promotion, seniority, on-the-jobtraining, etc. The respondents can select fromfour different types of recruitment:(a) internally;(b) head hunters or recruitment consultancies;(c) advertising in newspapers;(d) word of mouth.

From this it is possible to construct a roughmeasurement of the extent the firm’s internalmarket. The number of ticked suggestions inoption (a) (maximum three) related to the totalticked suggestions (maximum twelve) can workas an approximation for the degree of internalmarket. It is arguably a coarse measurement butit indicates whether the recruitment of managersis focused on internal employees or whether it isfocused on attracting employees externally.

Other variables that can be used in explainingtraining are educational structure (% graduates),age structure (% above 45 years), proportion of

manual workers, number of employees, and theimportance of innovations. The questions used inthe present study involve mainly those from theemployee development section, organisationaldetails and some questions on unions, staffingpractices and the human resource function ingeneral. The Cranet database is a rich source ofinformation and only a small section is analysed inthis report. Please refer to Annex 1 for the exactwording of all variables included in this analysis.

4.2. Cranet survey results

4.2.1. The scope of trainingTable 5 provides descriptive statistics on thetraining variables used in this study. The tableshows the mean values for the percentage ofwage bill spent on training and the proportion ofemployees trained in each country. The averageamount of wage bill spent on training in 1995survey was 3.1 %; the figure was 2.94 % in the1999 survey. The proportion trained in 1999 issomewhat lower than the figures given by Euro-stat in the second continuous vocational trainingsurvey (8). The countries that deviate most fromthe Eurostat figures are Belgium (-14 %), Norway(-11 %), and Ireland (-10 %) while other countriesshow more or less the same result. The restrictedsample in the Eurostat survey is slightly differentbecause it contains enterprises with250 employees or more compared with 200 andabove in the Cranet survey. The difference in theamount of training between the original (Euro-pean) countries in 1995 and the new countries in1999 is also marginal. More striking is the factthat the proportion of employees trained hasincreased quite dramatically in all countries, theoverall increase being 11 % since 1995 to about45 % of the employees receiving training eachyear in 1999. Again there is no marked differencebetween European countries and other countriesin the proportion of employees trained.

The number of firms answering each questionis given in parenthesis. In the 1999 survey, (whichis the foundation for this investigation)5 463 public and private organisations answeredthe question on the amount spent on training and


(8) Eurostat: The second continuous vocational training survey, 1999 (Newcronos).

6 685 organisations answered the question onthe proportion trained during the year. For somecountries the number of observations is low. Thatsome countries have too few observations isevident when the sample is restricted to privatefirms in the analysis of training and performance.As can be seen in Table 5, the survey is mainly

focused on European countries with most of theanswers from countries within the EuropeanUnion.

If one compares these figures with the 1999ASTD survey it appears that European companiesspend considerably more on training than theirAmerican counterparts. In 1999 US firms spent


1995 1999 1995 1999Country Code % spent on % spent on Change Proportion Proportion Change

training training trained % trained %

United Kingdom UK 2.6 (813) 2.9 (644) + 0.3 42.1 (903) 52.9 (784) + 10.8

France F 4.8 (499) 4.2 (374) - 0.6 44.2 (471) 49.5 (355) + 5.3

Germany D 2.8 (321) 2.8 (311) UNCH 26.4 (357) 32.8 (415) + 6.4

Eastern Germany D (east) 3.2 (155) 2.5 (151) - 0.7 27.7 (189) 31.8 (218) + 4.1

Sweden S 4.3 (186) 3.7 (157) - 0.6 51.5 (266) 66.1 (239) + 14.6

Spain E 2.1 (213) 2.0 (241) + 0.1 37.6 (247) 51.1 (268) + 13.5

Denmark DK 2.8 (427) 2.8 (306) UNCH 37.2 (496) 49.6 (355) + 12.4

Netherlands NL 3.8 (238) 3.4 (202) - 0.4 34.0 (258) 42.2 (193) + 8.2

Italy I 1.9 (65) 2.2 (63) + 0.3 21.4 (86) 36.2 (71) + 14.8

Norway NO 2.7 (331) 3.3 (325) + 0.6 40.0 (317) 41.5 (344) + 1.5

Switzerland CH 2.9 (170) 2.6 (113) - 0.3 36.0 (189) 42.8 (128) + 6.8

Ireland IRL 3.7 (249) 3.2 (278) - 0.5 36.2 (308) 47.1 (381) + 10.9

Portugal P 3.0 (128) 36.9 (128)

Finland FIN 2.7 (262) 2.5 (215) - 0.2 45.2 (269) 61.1 (229) + 15.9

Greece EL 2.5 (81) 36.0 (111)

Austria A 2.2 (153) 36.4 (181)

Belgium B 2.4 (252) 3.3 (182) + 0.9 27.9 (311) 45.5 (223) + 17.6

Northern Ireland NI 3.1 (107) 54.4 (157)

Estonia EE 4.3 (304) 47.0 (388)

Bulgaria BG 2.9 (70) 17.4 (107)

Czech Republic CZ 2.3 (141) 45.3 (176)

Cyprus CY 1.4 (52) 34.1 (73)

Turkey TR 3.8 (96) 3.9 (128) + 0.1 27.9 (148) 49.4 (191) + 21.5

Tunisia TN 4.3 (45) 24.6 (54)

Israel IL 3.3 (89) 47.5 (148)

Japan JP 1.7 (423) 31.6 (582)

Australia AU 3.0 (180) 56.3 (186)

Average 3.10 (4277) 2.94 (5463) -0.08 35.7 (4815) 45.22 (6685) +11.0

NB: The table shows the average percentage of wage bills spent on training in each country (% spent on training) and theaverage proportion of employees trained during the year (Proportion trained %) in the 1995 and in the 1999 Cranet survey.Number of firms answering each question in parenthesis.

Table 5: Cranet survey, descriptive statistics

1.8 % of payrolls on training (van Buren andErskine, 2002) compared with 2.9 % for the firmsincluded in our sample. It is important to notethat both surveys are completed by companiesand that the question regarding the amountinvested in training is similar in both studies. Themeasurement errors in the Cranet survey areprobably larger because of less rigorous trainingdefinitions in the questionnaire. Whether thismeasurement problem inflates or deflates thereported figures in the Cranet survey comparedwith the ASTD survey is difficult to determine.

4.2.2. Correlation between variablesThe investigation that follows is based on thesample of private companies for which there areperformance measurements. The remainder of theanalysis in this section is restricted to 5 824 privatecompanies that answered the 1999 survey. Table 6shows the correlation between the main explana-tory variables used in the present study (number ofobservations in parenthesis). We have chosen vari-ables in an effort to reflect factors used in bothlabour economics and human resource literature.To increase readability, only significant correlationsare shown in the table. A number of interestinginitial observations can be made. First, the amount

spent on training is positively correlated withpersonnel turnover. This is an observation thatgoes against common knowledge that turnoverreduces training. The proportion of employees intrade unions is negatively correlated with amountof training and staff turnover. That training is less inmore unionised companies is possibly the result ofa greater proportion of manual workers. Moreinteresting is that turnover appears to be lower inmore unionised establishments (especially since% manual workers are positively correlated withturnover). This result is in line with the argumentthat unions reduce personnel turnover and therebypromote more training (e.g. Booth et al., 1999).

The internal labour market measurement isnegatively related to the amount of training andproportion trained, contradictory to expectationswhich assume that firms invest in trainingbecause internal labour markets reduce the riskof poaching. However, the prediction thatturnover is lower in firms with more focus oninternal promotion appears to be confirmed in thepresent material (negative correlation betweenturnover and internal market). That the internallabour market is positively correlated with theproportion of graduates and negatively correlatedwith the proportion of manual workers seem


% Turnover Absenteeism Unionisation Internal % %trained L.M. graduates manual Size

% spent 0.179 (a) 0.041 (b) -0.109 (a) -0.105 (a) 0.102 (a) -0.110 (a)

on training (3419) (2998) - (3217) (3645) (2726) (2774) -

% trained - - - -0.031 (b) 0.137 (a) -0.167 (a)

(4672) (3418) (3502) -

Staff turnover 0.089 (a) -0.179 (a) -0.131 (a) -0.066 (a) 0.053 (a)

(2550) (3757) (4258) (3314) (3273)

Absenteeism0.158 (a) -0.133 (a) -0.266 (a) 0.191 (a) 0.047*

(2586) (2885) (2336) (2261) (2829)

Unionisation0.059 (a) -0.232 0.245 (a)

(4908) (3451) (3635) -

Internal Labour 0.207 (a) -0.057 (a)

Market (3879) (4069) -

% graduates -0.468 (a)

(3072) -

% manual -

(a) denotes significance at 1 % level(b) denotes significance at 5 % level

Table 6: Correlations between main explanatory variables (private sector)

plausible. While our measure of internal labourmarket is at odds with the prediction related totraining, the remaining correlations appear to bein line with expectations. However, a more rigidanalysis of what determines training will beperformed in Section 4.2.3.

Other correlations that are also less consistentwith prior expectations relate to graduates andmanual workers. The size of the organisation,measured by the number of employees, is notcorrelated with the two training variables. Thisapparent inconsistency with prior findings mightbe due to restricting the sample to firms with200 or more employees. A somewhat surprisingresult is also the very low correlation between theproportion of wage bills spent on training and theproportion of employees trained. This resultsuggests that these two factors measure differentaspects of training. We will elaborate on thisargument in more detail in the next section. Inconclusion, the results of Table 6 indicate thatthere is also little or only moderate correlationbetween our explanatory variables.

4.2.3. What determines training?A number of questions in the Cranet survey offerinteresting perspectives on the amount or inci-dence of training. As well as including customaryfactors such as education, age, company size,unionisation, and manual labour, the Cranetquestionnaire includes variables that mightprovide a better understanding of what influencesthe decision to train people. First are questions ofwhether the company has a written trainingpolicy, analyses employee training needs, andhas an internal labour market. Other variablesinclude personnel turnover and whether innova-tions are important. Perhaps the most interestingvariable is the one that indicates prior profitability,as this describes the impact of prior performance(profitability) on the decision to train. This variablemay assist in understanding the causality oftraining, i.e., whether profitable firms can affordtraining or whether training generates profitability.Finally, two different training measurements(proportion trained and proportion of wage billsspent on training) might provide some idea ofwhether these two common estimates of training

measure the same thing. To account for whatdetermines the amount of training and proportiontrained in a company the following trainingregression is estimated (9):

TRAINING = POLICY + NEEDS + INTERNAL +UNION + AGE45 + MANUAL + GRADUATES +TURNOVER + SIZE + PRIORPROFIT +INNOVATION (1)

where:

TRAINING is the amount of training or propor-tioned trained (all subscripts suppressed);

POLICY is a dummy variable taking the value ofone if the firm has a written training policy;

NEEDS is a dummy variable equal to 1 if the firmanalyses employee training needs;

INTERNAL and UNION are internal labour marketand degree of unionisation;

AGE45 is the proportion of employees 45 yearsor older;

MANUAL, GRADUATES, TURNOVER, SIZE arepercentage of manual employees, percentage ofgraduates in the workforce, percentage ofpersonnel turnover and number of employees atthe firm;

PRIORPROFIT is the measurement of the perfor-mance over the past three years;

INNOVATION is a dummy variable taking thevalue of 1 if the firm considers innovation as veryimportant to the organisation.

The results for estimating equation 1 areshown in Table 7. We have chosen to analyse theresults of Table 7 in the light of the findings inliterature that employers pay for all types oftraining no matter whether the training is specific,industry-specific (occupational) or general innature. The first variable indicates that firms witha written training policy are more likely to providetraining to their employees (proportioned trained)but having a written training policy is not associ-ated with how much training is provided. Thesecond variable indicates that firms that analysetheir employees training needs also train their


(9) Because less than 1 % of the firms answered that no employees received any training and just over 1 % of the firms answeredthat 0 % of salaries is spent on training, an OLS regression is estimated in the training regression.

employees to a greater extent than firms notconducting this type of analysis.

The impact from the internal labour marketmeasurement is negative on both the amount oftraining and number of people trained, which indi-cates that firms focusing more on internal promo-tion provide less training. This is contrary to thefindings of Delaney and Huselid (1996) for the USmarket. The correlation between training and theirmeasure of internal labour market was high (0.56)indicating a that firms with a higher degree ofinternal labour market also provide more training.Whether this deviation in our result is caused bydifferent measures of internal labour market orwhether there exist differences between US andother (predominantly) European countries is diffi-cult to gauge. However, the negative impact of theinternal labour market measurement in our studymight also be interpreted along the lines thatthese types of internal structures do not provideenough incentive to train or to be trained. Internallabour markets are typically based on seniority inpromotion and pay levels are based on position(post occupied) rather than on competence andskills. Both the employee and the employer haveless incentive to invest in training. For a morethorough discussion on the subject see Hanchaneand Méhaut (2001).

The degree of unionisation at the firm has anegative impact on the proportion of the wage billspent on training whereas the impact on thenumber of employees trained is positive (but notsignificant at 5 % level). Though we have controlsfor both educational level and the proportion ofmanual workers at the firm, the negative associa-tion between unionisation and the amountinvested in training is probably caused by ourinability to control for industry differences in thetraining regression. The impact from having moreold employees in the organisation is negative onboth training measures but not significant. Thisresult might be taken as an indication thatcompany training persists throughout theemployee’s career. The proportion of manualworkers is associated with fewer workers being

trained but not with the amount of trainingprovided at the firm. The proportion of graduatesat the firm has a positive but not significantimpact on training measurements. The size of theorganisation is not associated with any of thetraining measurements, possibly explained by oursample of larger firms.

Personnel turnover appears not to be a factordetermining training since it is not significant inthe training measurements. This result is qualita-tively similar to those presented in Goux andMaurin (2000) for France and in Green et al.(2000) for Britain. Both studies indicate thattraining measured at an aggregated level hadlittle impact on mobility. Considering the impor-tance of staff turnover to the potential for compa-nies to benefit from training investments we alsoconducted a simple analysis of aggregatedcountry data and found a positive relationshipbetween average personnel turnover and averageproportion of wage bills spent on training. Thisoutcome is slightly contrary to what is expected,as turnover of personnel is normally consideredto lower firms’ willingness to invest in training.However, turnover might force firms to increasetheir spending on training newly hired employees.A division of what type of training is provided bythe firm might thus give a different result (10).

The variable that indicates past profitabilityshows an interesting division in the impact on thetwo training measurements. Prior profitability ispositively and significantly associated with theproportion of employees being trained but notassociated with the proportion of wage bills spenton training. This result indicates that the propor-tion trained in a firm is largely conditioned by pastperformance. It seems that measurements oftraining based upon proportion of employeesbeing trained contain an element of reward or, atleast, dependence on past performance. That thedecision regarding the number of employeesbeing trained is endogenous to, or mutuallydependent on, past profitability makes it impor-tant to address the problem of endogeneity instudies using this measure of training.


(10) The country comparison is based only on univariate regression and there could thus be several factors such as economicconditions, unemployment rate, etc., that might drive this outcome. However, Green et al. (2000) also come to the conclusionthat different types of training have different impacts on the individual’s decision to search for a new job. The large differencein personnel turnover between different countries might still indicate that this is an important variable to consider in cross-country comparison. There are considerable differences in personnel turnover between European countries, with the lowestturnover in the Netherlands (4.69) and Germany (5.70) and the highest turnover in the UK (15.06) and Portugal (13.24).

Because the proportion of wage bills spent ontraining is not associated with past performance,it might indicate that how much firms invest intraining their employees is not dependent onwhether they can afford training or not. Takingthis reasoning a bit further suggests that theinvestment volume is not a reward for pastperformance but more likely seen as an invest-ment with future benefits for the firm. That theamount spent on training is not associated withthe dependent variable (prior profitability) alsogives us a better basis for making interpretations


Dependent % spent Proportionvariable on training trained

INTERCEPT2.795 (a) 14.637 (a)

(5.53) (3.25)

POLICY0.233 9.434 (a)

(1.22) (5.60)

NEEDS0 844 (a) 17.575 (a)

(4.05) (9.51)

INTERNAL-1.501 (a) -6.883 (b)

(-4.92) (-2.51)

UNION-0.126 (a) 0.725

(-2.74) (1.75)

AGE45-0.004 -0.072

(-0.85) (-1.88)

MANUAL-0.003 -0.135 (a)

(-0.84) (-4.53)

GRADUATES0.008 0.070(1.89) (1.79)

TURNOVER0.005 0.090(0.70) (1.40)

SIZE-0.000 -0.000(-0.27) (-0.30)

PRIORPROFIT0.099 4.465 (a)

(1.21) (6.02)

INNOVATION0.169 0.637(1.03) (0.43)

F-statistics 7.827 29.556

R2 (adjusted) 0.05 0.167

N 1359 1566

T-values are in parentheses.(a) Denotes significance at the 1 % level.(b) Denotes significance at the 5 % level.

Table 7: Training regression (private sector) in, for instance, cross-sectional estimates. Inother words, examination of the amount investedin training might suggest that it is not profitablefirms that can afford training but it is training thatgenerates profitability.

The last variable, the importance of innova-tions, shows a positive but not significant impacton training measurements. Given the generallevel of analysis used in this study, the resultsshould be interpreted with some care, particularlybecause the regressions do not include importantvariables such as controls for industries orcontrols for countries. However, an impression ofthe results presented in Table 7 is that trainingappears to be discretionary within firms. Themeasurements that show the strongest influenceson training are largely determined by the firmitself. The difference between what determinesthe proportion being trained and what determinesthe amount of money spent on training is alsoworth noting. The large difference in impactamong the explanatory variables indicates thatthese two measurements indicate quite differentdimensions of training decisions. This finding is inline with the arguments in Orrje (2000) that thedeterminants of the probability of receivingtraining and the determinants of the amount oftraining are not the same.

4.2.4. What factors are indicative for top(10 %) performers?

This part of the analysis utilises the questions onhow the organisation is performing relative toother firms in the same sector. Table 8 shows themean difference between organisations belongingto the top 10 % of performers and organisationsconsidered performing below average in thesector. Five industry-adjusted performancemeasures are shown in Panel A (profitability,productivity, innovations, service quality andstock market performance). The minimum andmaximum number of observations used in theanalysis is given in brackets in the first column.We have chosen to examine whether topperformers show any differences with respect tovariables normally considered in studies. Themean difference between top performers andthose performing below average is shown in thetable together with t-values (in parenthesis).

Panel A gives the results for the whole sampleof private organisations and Panel B gives theresults on profitability for United Kingdom, France

and Germany. The lack of observations for otherEuropean countries rendered further divisionsunworkable. Since we are not working with actualperformance but perceptions of performance weneed to be cautious with interpretations. Oneproblem with the data is that around 30 % of thefirms responded that they belonged to the top10 % of the firms in their sector, suggesting thatwe either have a response bias or that we haveover-sampled firms performing well. To whatextent this caveat influences the resultspresented in Table 8 is difficult to gauge, but aresponse bias is likely, rendering the statisticsless significant.

The first column shows that top performingfirms spend more on training compared with firmsperforming below average. This is true for allperformance measurements except for servicequality where there is no significant difference.For instance, firms belonging to the top 10 %with regard to profitability on average spend0.6 % more of their wages on training than firmshaving a profitability below average in theirrespective industry. Considering that prior prof-itability did not have any significant impact on theamount spent on training (Table 7) it might beassumed that top performing firms are topperformers in part because of their investment intraining (11).

The difference between top performers and thebelow 50 % performers with regard to theproportion trained in a year is significant for allperformance measurements (including servicequality). That high performers in service qualitytrain significantly more of their employees eachyear most likely indicates that achieving topservice quality requires all staff to undergo regulartraining. Top performers train close to 10 % moreof their staff in a year compared with firmsperforming below average on service quality.

Having a written training policy and analysingemployee training needs appear to be indicativefor top performing firms regardless of perfor-mance measurement. These two variables areimportant determinants of training, training poli-cies and support functions to provide the righttype of training, and they are characteristic ofhigh performing firms whether measured by prof-

itability, productivity, innovations, service quality,or stock market performance.

The next column indicates that firms consideredas more innovative also employ more highlyeducated personnel (percentage of graduates inthe workforce). This result is reasonably expectedbecause of the complementarity between innova-tion and education (e.g. Leiponen, 1996b). Thatfirms with better profitability and stock marketperformance also employ more graduatescompared with firms performing below average ismore interesting. This result is in line with the find-ings presented earlier that the educational level ofemployees is positively associated with produc-tivity and profitability (Black and Lynch, 1996;NUTEK, 2000; Gunnarsson et al., 2001). That firmsare able to extract higher profitability from moreskilled or more educated workers is an argumentput forward by those who propose that wagecompression is a major reason for firms to invest ingeneral skills (Acemoglu and Pischke, 1998, 1999a;Booth and Zoega, 1999; Brunello, 2002).

The last columns indicate that staff turnover doesnot vary between high and low performers, but thathigh performing firms have significantly less absen-teeism among employees than low performingfirms. An exception occurs in the results for servicequality where there is no difference between the twogroups with regard to absenteeism.

Panel B indicates that some of the results inPanel A might be driven by country specificcircumstances since not all of the three countriesshows the same response on profitability.However, the lack of significance between prof-itability and some of the variables is possiblymore a consequence of fewer sample cases.France, with few cases shows a significant differ-ence only in the proportioned trained whereasthe UK and Germany, with more cases, also showmore significant results. It is interesting to notethat staff turnover is significant in Germany yetnot for the total sample or for France and the UK.Another observation is that absenteeism is onlysignificantly different in the UK. Because of thelow numbers it is difficult to draw any generalconclusions regarding differences between thethree countries. One lesson that can be drawnfrom the Cranet survey is that when the respon-


(11) Because it appears that the decision on how much to spend on training is not conditioned by how profitable the firm has been,it is more likely that training generates profitability and not the other way around that profitability generates training (since thereis no association between amount of wage bills spent on training and past three years profitability in Table 7).

dents are requested to provide actual figures on,for instance, training, staff turnover, absenteeism,etc., the response rate drops dramatically.

4.2.5. Main findingsThe investigation is based on a crude statisticalanalysis, compounded by the problem of percep-tions of organisational performance, which tendto increase measurement errors and decrease thetrue variation among firms. This analysis of theCranet data should, therefore, be seen as a firstrough attempt to explore this database. However,some interesting indications have emerged fromthe present analysis, as summarised below:(a) the proportion of the wage bill spent on training

and of employees being trained appears not to

measure the same thing because the determi-nants of these two variables are quite different(the correlation between the two trainingmeasures is also weak);

(b) the proportion of employees trained appearspartly dependent upon whether the firm canafford the training, as indicated by the annualnumber of employees being trained corre-lating with prior profitability;

(c) the proportion of the wage bill spent ontraining does not correlate with past prof-itability, which might indicate that it is not aprofitable firm that can afford training but it istraining that generates profit;

(d) analysis of employee training needs and exis-tence of a written training policy are two


% wages Proportion

Written Analyse % % staff Absenteeism spent on

trained %training training

graduates turnover daystraining policy needs

Panel AAll countries

Profitability 0.60 (a) 9.21 (a) 0.08 (a) 0.06 (a) 2.46 (b) -1.22 (a)

N (1498-2795) (4.29) (6.81) (4.26) (3.58) (2.23)–

(-3.21)

Productivity 0.43 (b) 7.46 (a) 0.05 (b) 0.06 (a) -1.84 (a)

N (1243-2389) (2.27) (4.36) (2.12) (2.65)– –

(-3.67)

Innovations 0.73 (a) 9.80 (a) 0.12 (a) 0.09 (a) 6.62 (a) -1.26 (a)

N (1253-2375) (4.52) (6.63) (6.00) (4.90) (5.31)–

(3.05)

Service Quality 9.92 (a) 0.10 (a) 0.11 (a)

N (1549-2983)–

(3.90) (3.16) (3.48)– – –

Stock Market 0.55 (a) 6.34 (a) 0.06 (b) 0.06 (b) 6.04 (a) -1.87 (a)

N (617-1174) (2.74) (3.04) (2.26) (2.23) (3.42)–

(-2.95)

Panel BSpecific countries

UK – Profitability 0.63 (c) 10.17 (a) 0.10 (b) -1.19 (b)

N (189-381) (1.79) (2.64)–

(2.52)– –

(-2.18)

F – Profitability 15.14 (a)

N (91-184)–

(3.79)– – – – –

D – Profitability 13.26 (a) 0.25 (a) 0.15 (b) -2.60 (b)

N (127-196)–

(3.39) (3.68) (2.36)–

(-2.40)–

T-statistics (parenthesis) whether the mean is different from zero.(a) denotes significance at 1 % level(b) denotes significance at 5 % level(c) denotes significance at 10 % level

Table 8: Mean difference between top 10 % and lower half of firms in sector (private sector)

important factors associated with the numberof employees trained at the firm and, for theformer, also the amount of training providedby the firm;

(e) most education and training-related measure-ments are significantly higher or morefrequent in high performing firms comparedwith firms performing below average in theirrespective sector.

An interesting aspect of these results is that thedecision to train is largely determined by companyspecific factors. Without taking the interpretation ofthe results too far, a general picture seems to

emerge where factors that one can consider asapproximations for good working conditions or the‘good employer’ are also largely connected withperformance measurements such as productivityand profitability. It is also important to note thatfirms with better profitability and stock marketperformance also provide more training, and trainmore of their employees, compared with firmsperforming below average in their respectiveindustry sector. Another notable observation is thatfirms with better sector-adjusted profitability andstock market performance also have moreeducated employees.


The amount of money spent by companies onwages (the rent for human capital) suggests thatmore efficient use of this production factor wouldhave a significant impact on most firm-basedperformance measurements. The flaw in thisreasoning is that firms do not own the labour oftheir employees but pay a rent for its hire. Thequestion is whether increased efficiency arisingfrom company training benefits the hirer of thelabour or the provider. That at least some of theincreases in efficiency are captured by firms is aprerequisite for any impact on company perfor-mance measurements such as profitability orstock market performance.

Research on the effects of education, training,and skills/competence on company performancehas made some important advances in recentyears. Increasing evidence that employers benefitfrom human capital, and investment in humancapital, has led to a substantial increase in thenumber of theoretical papers seeking to explainthese findings. In the following section weprovide information on what empirical researchhas achieved in different areas.

5.1. The effects of training oncompany performance

5.1.1. The impact of trainingA somewhat crude description of the researchagenda is that the general understanding hasmoved from regarding all investments in employeetraining as unprofitable for firms, to regardingspecific training as viable, and now to consideringall types of training as potentially profitable.Considering that 40 years have passed sinceBecker (1962) wrote his seminal paper on humancapital investment, research has moved slowly onthe question of whether firms can benefit fromtraining their employees. Only very recently havewe seen papers showing that employers profitfrom investment in all kinds of training.

The majority of the papers included in this

review point toward substantial gains foremployers from continuous vocational training.The absence of studies indicating that employersdo not profit from training investments maygenerate some concern over whether we have apotential bias in reporting only significant andpositive results for company training. However,because research on training has largelyaccepted that employers pay for companytraining (no matter how general) the finding thatemployers also benefit from such investmentseems progressively more plausible. Despiteacceptance that firms pay for (general) training,there is still a need for additional research on theeffects of training to understand better thecomplexities of company-provided training.

Increasingly, studies provide evidence thattraining generates substantial gains foremployers. The most compelling evidence ispresented in several recent papers connectingtraining investment with changes in productivity,profitability and stock market performance. Themajority of these studies also establish the direc-tion of the relationship, i.e., we can, with reason-able confidence, maintain that training generatesperformance effects and not the other wayaround. The studies that provide the strongestevidence of this are (dependent variable in paren-thesis):(a) Barrett and O’Connell (1999) based on

215 Irish firms (sales growth);(b) Dearden et al. (2000) based on 94 British

industries over 12 years (value added);(c) Groot (1999) based on 479 Dutch firms

(productivity estimates);(d) Hansson (2001) based on a Swedish case

study of programmers (net profitability);(e) d’Arcimoles (1997) based on French firm-level

data (value added, return on capitalemployed);

(f) Bassi et al. (2001) based on 314 US firms(stock market return, sales per employee,etc.).

There are also studies that have only been able todemonstrate a weak connection between companytraining and company performance (Black and

5. Results

Lynch, 1997; Bartel, 1995). However, the authors ofthese studies attribute weak or insignificant resultsto measurement problems. The main impressionfrom research is that firms profit from training theiremployees regardless of whether the trainingprovided is useful to other firms. Other tentativeconclusions that can be drawn from the review arebriefly presented below.

5.1.2. Formal/informal and general/specifictraining

Few studies have been able to examine theeffects of different types of training. Besides diffi-culties in acquiring more specific training infor-mation, the distinction between different types oftraining appears somewhat arbitrary since mostdefinitions are not mutually independent. Never-theless, the results concerning formal andinformal training suggest that formal trainingcourses have more impact on productivity thaninformal training (Dearden et al., 2000; Black andLynch, 1996). This finding is puzzling, because itis likely that formal training relates more togeneral training and informal training more tospecific training. This result would indicate thatgeneral training might be more profitable for firmsto invest in than specific training. The study byBarrett et al. (1998) suggests that this might bethe case. The results of Barrett et al. show thatgeneral training has a significant positive impacton productivity whereas specific trainingappeared to be insignificant. The authors explainthis result by reasoning that general trainingprovides greater incentive for employees tospend more effort in the learning process.

However, the results are not entirely consistent.Bosma et al. (2002) conclude that their findingssupport the thesis that specific investments aremore influential for company success in start-upsthan general investment. This difference in resultswith regard to general and specific training can beaffected by weak definitions and measurementproblems, or the difference between entrepreneurialperformance and employee performance.

Other studies that can be distinguished interms of type of training are the studies focusedon teaching basic skills to employees (work placeeducation programmes) or students (STWprogrammes). The impact of generic skillsprogrammes is ambiguous in that it is difficult toassess the real pay-off for employers from thistype of training investment. Though much

research is needed on this subject, there are indi-cations that basic skills training can influence firmperformance positively (Bassi and Ludwig, 2000;Krueger and Rouse, 1998).

5.1.3. Timing of the effects of trainingAn important issue related to the impact oftraining is when one can expect to see the effectsof the training investment. There is evidence thateffects of training emerge some time after it takesplace. The results and arguments forwarded inBlack and Lynch (1996) and Bassi et al. (2001,2002) based on US data, d’Arcimoles (1997)based on French data, and Hansson (2001)based on Swedish information, suggest that theeffects of training materialise one to two yearsafter the training period. The results presented inthese studies suggest that we should measurethe effects of training after at least one year fromthe point of the investment and possibly also overa longer time horizon.

However, typically the effects of training areregistered in cross-sectional estimates, whichimplies an instant effect of the training investment.The question is whether this impact is caused by animmediate effect from the training or whethercross-sectional estimates capture the return onpast training investments. This question is validbecause it is likely that the level of training iscontinual in firms. In other words, firms that investmore in training in one period (t-1) continue to investmore in training in the following period (t). Becauseof delayed training effects, we might measure theeffect of prior training investments incross-sectional estimates. The question of when toexpect the effects of training investment to materi-alise is by no means clear and it would be beneficialto have this matter determined in future research.

If the productivity effects from training lag, thishas implications for the conclusions we can drawon instant wage effects from training. It hasgenerally been accepted that wage increases inconnection with training arise from resultingincreased productivity. The results of the abovestudies imply that wage increases during trainingmight have some other basis. Recent access todata matching employees’ and firms’ characteris-tics will possibly shed more light on this question.

5.1.4. Timing of training investmentsThe amount of training firms undertake ispossibly affected by the economy. The general


understanding is that expansionary economicconditions, with firms hiring new employees, alsoare associated with an upsurge in firm-sponsoredtraining. The results of Dearden et al. (2000) andBartel (1994) imply the opposite – that firms trainwhen production is low (the pit stop theory). Onereason why favourable economic conditions donot produce more company training may be thatonly a portion of all company training is gearedtowards new employees. For instance, only 18 %of all training provided by publicly held compa-nies in Sweden was introductory training fornewly hired employees (12). Since firms train whenthey have slack time, we also typically underesti-mate the impact of training on productivity incross-sectional analyses (13).

Another important finding is that the timing oftraining appears not to depend on tangible invest-ments (Barrett and O’Connell, 1999). The finding thatinvestments in training and investments in tangibleassets are only weakly correlated suggest thattangible investments do not cause the trainingeffects observed at company level (industry level).Apart from tangible investments and training, theCranet survey results also indicate that the amountof training provided by firms is not dependent onprevious profitability. Both results indicate that thedecision on how much training to provide has little todo with whether the firm has done well in the past orwhether the firm increases its tangible investments.These findings have importance for what conclu-sions that can be drawn from statistical models,especially cross-sectional regression estimates.

5.2. The effects of education,skills and competences oncompany performance

The effects of education or skills/competence oncompany performance are generally more difficultto establish, as these factors are accumulatedmeasures of human capital stock. Compared withcompany training that normally varies from year toyear, educational levels are much more constant.Because of this we are typically restricted to leveldata (with some exceptions) in analysing the impact

that human capital stock might have on companyperformance. Educational or skills levels arenormally included as control variables in mostimpact research but less frequently used as a mainvariable (at least in firm-based research). Still, this ispossibly one of the more interesting areas inexplaining company performance as the studiesincluded in this review indicate that education andskills are important factors in understanding differ-ences in performance among firms.

There is research to connect the effect ofeducational level or skills/competence level withproductivity, with positive association in the workof Black and Lynch (1996) and NUTEK (2000).Indications that skills are an important factor inproductivity are presented in Carr (1992) andMason et al. (1992). A significant paper notrestricted to cross-sectional data is the study byGunnarsson et al. (2001) examining educationallevel and productivity growth over a ten-yearperiod. Their findings suggest that the increase inthe educational level between 1986 and 1995explains a large part of the IT-related productivitygrowth. Their results also suggest that a marginalskill upgrading has the same effect acrossdifferent levels of education. Gunnarsson et al.conclude by stating that ‘measures to promoteincreased use of IT should be followed up bymeasures promoting skill upgrading. Our resultsactually show that, in general, upgrading skills ata given level of IT (i.e. share of computers in totalcapital) has a much stronger growth-enhancingeffect than increasing IT investments at a givenhuman capital structure.’ (p. 44).

The findings of Leiponen (1996b) indicate thatinnovative firms have a more educated workforceand that innovative firms are more dependent oneducational competence in generating profit(Leiponen, 1996a). The study by Michie andSheehan (1999) also suggests that skill shortageis a severe obstacle to innovation. Similarly, thefindings of Romijn and Albaladejo (2000) in SMEspropose that the owners’ technical education andtheir prior working experiences, in addition to thetechnical skills of the workforce, have a signifi-cant effect on innovative capability.

Taken together, the results indicate that we havecomplementarities between different types of


(12) IPF – Institute of personnel and corporate development. The human capital survey 2002. Uppsala: IPF, Uppsala University(please contact the authors for results).

(13) However, as noted in the previous section this conclusion depends to some extent on when the training effects materialises.

education (Leiponen, 1996a) and between educa-tion and IT investments (Gunnarsson et al., 2001)that generate significant synergies or externalities.The evidence that the level of education or skills isrelated to innovation and productivity might not betoo surprising since education and skills are gener-ally considered as associated with more complexjobs and increased flexibility. What is moresurprising is that we start to see studies relatingeducation and skills to profitability.

The results of Leiponen (1996a) show thateducational level is associated with profitability(net profit margin). Leiponen uses panel data anda two-stage procedure to handle problems ofendogeneity, providing results that are morerobust than ordinary cross-sectional estimates,especially on arguments that profitability causesfirms to hire more highly educated personnel. Thestudy by NUTEK (2000) shows that the proportionof higher educated employees is significantlyassociated with both productivity (value added)and profitability (revenues to cost ratio). Becauseeducational level is associated with both produc-tivity and profitability it gives us a more solidbasis for inferring that higher education generateshigher productivity and that firms are able tocapture some of the benefits.

The idea that skills in the form of programmingcompetence are associated with how much theindividual produces in net contribution (profit) tothe firm is presented in Hansson (2001). Thisinvestigation is based on a single firm and it isthus difficult to draw any far-reaching conclu-sions. Nevertheless, because the examination isbased on differences in employees’ net contribu-tion this study avoids the argument that onlyprofitable firms can afford to hire more skilledworkers. Similarly, the results of the Cranetsurvey suggest that the more profitable firms andfirms with better stock market performance intheir respective industry sector also have morehighly educated personnel than firms performingbelow average in their respective industry sector.

From the above, it is possible to speculate onthe degree to which firms are able to capturereturns on human capital investments thatnormally are considered to belong to the indi-vidual. Because prior education is a function of

the individual it is assumed that the individualaccrues the benefit of these investments throughhigher wages. However, indications that investorsand beneficiaries are not always the same arepresented in Groot (1999) who points out the weakconnection between those who contribute totraining investment and those who benefit from it.

That firms are able to extract higher profitabilityfrom more highly skilled or educated workers isan argument put forward by those proposing thatwage compression is a major reason for firms toinvest in general skills (e.g. Acemoglu andPischke, 1998, 1999a). The basic reasoning isthat individuals are not paid their marginalproduct and that firms are able to extract higherprofits from more skilled workers who are notpaid what they are worth for the company. Wagecompression is not only a European phenomenonbut can also be found in the US (Bewley, 1998).The findings of Bewley suggest that the internalequity (fairness and moral) in firms’ pay structurerestrains managers from paying employees thefull value of their contribution. Consequently, highperforming employees are more valuable to thefirm. Bewley takes this reasoning one step furtherby arguing that low performing employees areseldom fired and, if they are, it is for grossmisconduct rather than for under perfor-mance (14). Wage compression is one of severalrecent theories that try to reconcile the empiricalfindings that firms both invest in, and extractprofit from, general human capital. The nextsection provides some further explanation.

5.3. How firms profit fromgeneral human capital

Explanations of why firms invest in, and are able toprofit from, marketable human capital are abundant.Based on differences in bargaining power, Glick andFeuer (1984) propose that general training is superiorto straight money payment as an insurance againstpersonnel turnover and that firms should invest ingeneral training to safeguard joint investments inspecific training. In the shared investment model ofLoewenstein and Spletzer (1998), the employer


(14) However, low performers are the first to be laid off when firms are reducing their workforce and the pay for low-performingemployees is often allowed to fall behind the rate of inflation. Bewley interviewed over 270 executives and managers of USbased firms about wages and layoffs.

shares general training investment with theemployee as a consequence of the employer’sinability to commit credibly to future wages. Theemployer, instead, commits to a minimum guaran-teed wage, shares the investment in general trainingand realises the returns on the training if the minimumwage guarantee is binding. Autor (2001) proposes amodel in which firms offer general training to induceself-selection and perform screening of workerability. In this model general training and ability arecomplementary and it is assumed that more ableworkers self-select to receive general training to agreater extent than low ability workers. In the modelof Acemoglu and Pischke (1999a) firm-financedgeneral training is a result of compressed wagestructure. Wage compression makes employersmore willing to invest in general training as firmsextract higher profits from more skilled workers withincreased human capital.

Another response to rent extraction fromgeneral human capital investments is thatmobility thresholds reduce the ability of the indi-vidual to capitalise on such investments. Argu-ments against turnover (mobility) include:(a) the loss of firm-specific investments for the

individual when changing work (Glick andFeuer, 1984). Or that a recruiting (raiding) firmneeds to make additional investments infirm-specific knowledge;

(b) firms use back-loaded compensation schemesthat induce costs for individuals who changeemployer (Salop J. and Salop S., 1976).Back-loaded compensations are typicallydetected or defined as increasing wages withseniority over and above productivityincreases;

(c) workers have incomplete information aboutpay elsewhere (Polachek and Robst, 1998;Bewley, 1998);

(d) individuals are constrained by liquidity oraversion to risks, forcing firms to carry theseinvestments (Bishop, 1994);

(e) firms have superior information about theprofitability (payoff) of training investments(Green and Kahn, 1983).

If it is possible for firms to redistribute invest-ment risk through capital markets, this mightcause employers of larger firms to be more willingto invest in general human capital. However, Katz

and Ziderman (1990) (15), have attracted attentionwith the argument that information asymmetrybetween the training firm and a recruiting (raiding)firm about training received reduces the potentialbenefits that a worker with general training canobtain by moving to another firm. Consequently,information asymmetries render general trainingspecific in the sense that the investment is notobservable (verifiable) to other firms.

Less attention has been given to findings thatboth employers and employees benefit from theseinvestments and both parties would be worse offif they did not take place. It is tempting to assumethat employers increase wages for individualsreceiving marketable training sufficiently to offsetthe increased probability of turnover. That boththe employer and the employee benefit from theseinvestments also implies that individualsemployed in firms that provide training receivehigher wages in the long run compared withemployees of firms that do not provide training.Higher growth in wages provides a strong incen-tive to stay with an employer who continuouslyupgrades human capital instead of possiblymoving to a new employer with an unknownhuman capital investment strategy.

Also, the employer-employee relationship iscomplex and it might be myopic to focus only onmonetary gains. Part of the rent extractionconsideration might be that these investmentsrepresent good working conditions and that theemployers are committed to their employees. Inthis sense, training, no matter how general, is ameasure of employer commitment, which is likelyto reduce the probability (threat) of changingemployers.

5.4. Training and HRM practices

The basic question of whether the combinedeffect of human resource management (HRM)practices produces good performance or whethercertain practices, such as employee develop-ment, generate effects on company performanceis not easy to answer. In general, there isevidence that training has a greater impact whenundertaken in connection with supporting HRMpractices, in particular existence of a formal


(15) Schlicht (1996) has also covered asymmetric information and its impact on a firm’s training decisions.

training strategy, written commitments to training,methods for analysing training needs, linkingtraining and strategic objectives (e.g. Maglen andHopkins, 1999; Blandy et al., 2000; Baldwin andJohnson, 1996). The Cranet data also indicatesthat support functions are important to training,with the variables on training needs and writtentraining policy significantly associated withtraining provision and industry-adjusted perfor-mance measurements (profitability and stockmarket return) (16).

Whether training has an additional effect overand above high performance work practices isdifficult to determine. In high performance worksystem (practice) literature, training is generallyincorporated as a factor in the larger construct ofHPWS (with some exceptions). The bundling ofdifferent human resource practices is typicallybased upon factor analysis or analysis of theinternal consistency of the total HPWS measure-ment. Because training is generally part of alarger construct it is difficult to find studies thatmeasure the additional effect of each individualvariable. However, some studies account for theimpact of different variables. The study byLaursen and Foss (2000) highlighted training andperformance-related pay as important factors forinnovation. However, the combined effect of allnine factors proved highly significant, indicatingthe existence of complementary HRM practices.

Other studies also concluded that internallyconsistent or congruent HRM practices are betterpredictors of performance than individual prac-tices, for example MacDuffie (1995) and Arthur(1994). These findings do not mean that training byitself does not have a predictive ability for perfor-mance, but only that the whole set of practicescombined were more informative. The study byDelaney and Huselid (1996) illustrates the problemof focusing solely on the effect of total HRM prac-tices since the training measurements in their studyconstantly appeared to be related to performancemeasurements, even with a significant volume ofvariables capturing other HRM practices.

High performance work system (practice) litera-ture is normally restricted to level data, which makesit difficult to establish the direction of the relation-ship. There are some exceptions to this rule. The

studies by Barrett and O’Connell (1999), d’Arcimoles(1997), Ichniowski et al. (1995), and Bartel (1994) allhave measurements of HRM practices and trainingover time. The results do not agree on whethertraining or HRM generates the effects on companyperformance. The study by Barrett and O’Connellsuggests that training causes productivity effectswhereas introduction of new personnel policies didnot show any significant impact on productivity. Theresults of Bartel propose that training programmesgenerate considerable productivity effects, inexcess of changes in personnel policies. The mainresults of d’Arcimoles indicate that trainingproduces substantial effects on both productivityand profitability. The study by d’Arcimoles includedcontrols for working and social climate at the firmsinvestigated. A contradictory view is presented inIchniowski et al. where innovative HRM practiceshave a large effect on productivity while individualemployment practices had little or no effect. Thisresult suggests that training by itself is not enough.

The common denominator of these studies isthat we can, with reasonable confidence, main-tain that a cause and effect relationship existsbetween the variables studied (training and HRM)and performance measurements. The line ofresearch or the tradition within which the studywas performed might explain the somewhatcontradictory results. For instance, researchers inlabour economics are possibly more used tomodelling training compared with HRM variables,the converse being true for researchers in theHRM tradition. In conclusion, it seems appro-priate to reason that both training and other HRMpractices are important factors in explaining whysome firms do better than others.

5.5. Innovation, technologicalchange and training

There is a multiple relationship between innova-tion behaviour, innovation performance andtraining investment. The consequences of tech-nological change and the introduction of processand product innovation, and the relationship withtraining investment, are important issues. Theinternal organisation and human resources were


(16) With one exception, the variable ‘written training policy’ was not significantly associated with the amount of wage bills spenton training.

neglected for a long time in traditional,economic-oriented innovation literature. In morerecent literature attention to the role of HRM andits impact has increased. In the knowledge-basedeconomy, training investment and HRM practicesare prerequisites of innovation and are necessaryto realise the productivity potential of new infor-mation or advanced manufacturing technologies.In order to use the potential of new technologies,complementary investments in training arecrucial. There is a growing awareness of the roleof internal or organisational factors that mediatethe relationship between innovation and companyperformance. Some authors, such as Laursenand Foss (2000), stress the importance of thecomplementarities between technology andlearning.

A similar view is presented by Baldwin andJohnson (1996). Their findings suggest that moreinnovative firms place greater emphasis on humanresources. Innovation requires a human-resourcestrategy that stresses training. They found statis-tical evidence that more innovative firms offeredformal and informal training more frequently, thatthe training was more often continuous in char-acter, and that these firms had a greater tendencyto innovative compensation packages. The studyby Michie and Sheehan (1999) also stresses theimportance of HRM in generating an innovativeenvironment.

That both training and skills are importantdeterminants of innovative capability is offered inRomijn and Albaladejo (2000). The owners’ tech-nical education and their prior working experi-ence, the technical skills of the workforce, and theamount of training provided proved to be impor-tant aspects of innovation capability. Their find-ings also suggest that interaction with researchinstitutions and a close link to nearby traininginstitutions enhance innovation capacity. As notedearlier, the composition of the workforce is impor-tant for innovating firms (Leiponen, 1996a). Thefindings of Leiponen indicate that innovative firmsare more dependent on educational competencein generating profit.

Training associated with the introduction ofnew technologies or new work practices is likelyto have high productivity effects. Blandy et al.(2000) found evidence for this relationship withinthe case studies they carried out in addition tothe questionnaire survey. That IT generates a

substantial amount of training is clear. In the Insti-tute of personnel and corporate development’s(IPF, Uppsala University) human capital survey2002 (see footnote 12) about 41 % of allcompany-provided training was considered to berelated to information technology.

In addition, innovation is frequently used as aperformance measurement for companies. Inno-vation itself is related to various financial returnsbut there is no definite association between inno-vation performance and the financial performanceof firms. However, there is broad empiricalevidence that innovation is associated with thegrowth of firms and that, in specific industries,more innovative firms yield higher financialreturns. The results of the Cranet survey alsoindicate a connection between innovation and anumber of personnel related variables. The top(10 %) performing firms in their sector had moretraining, trained more of their employees, hadgreater supporting HRM policies for employeedevelopment, and employed more graduatesthan low performing firms.

5.6. Specifics of SMEs

Small and medium sized enterprises (SMEs) areusually defined as firms with less than250 employees. They represent more than 95 %of all firms and employ around 65 % of allemployees in the EU. Given their importance tothe economy, there are surprisingly few empiricalstudies dealing explicitly with the impact oftraining on SME performance. Despite greatheterogeneity across Europe and the diversity ofindustries, most surveys in different Europeancountries show that SMEs make fewer invest-ments in vocational training and do not useformalised forms of training. While SMEsgenerate many jobs and attract young people,most do not provide them with skills and improvetheir long-term employability. Apprenticeship isan important form of SME initial vocationaltraining of the workforce. In general, most SMEshave difficulty in appropriating codified forms oftraining (Trouvé, 2001), which, in turn, leads toadditional methodological problems in measuringinformal training within SMEs. The organisationalstructure of SMEs is an important reason for thelack of vocational training by them.


The survey on continuing vocational training inenterprises, carried out by the EU in 12 MemberStates, shows that larger firms offer continuingvocational training more often (EuropeanCommission, 1999). Small firms, especially thosewith 10-49 employees, seldom offer continuingvocational training; only half the firms of this sizeprovide it, compared with more than 90 % offirms with more than 250 employees. Corre-spondingly, expenditure on vocational training inSMEs is lower than in larger firms, though this ishighly dependent on country, size and sector.Similarly, SMEs rarely have a clear humanresource strategy, training plan, or advancedHRM practices.

The importance of human capital investment isalso of interest in relation to other forms of intan-gible and tangible investments in explainingcompetitiveness and performance. Flexibility,entrepreneurship, close relations with partnersand customers, motivation of the workforce, andthe realisation of niche strategies are importantSME strengths in comparison with larger firms(for more details see Descy and Tessaring, 2001).In addition, there is empirical evidence thatsmaller firms use their R&D investments moreefficiently. Nevertheless, there are no theoreticalor empirical facts stating that this holds fortraining investments. Yet, it is probable that addi-tional training investment could yield higherreturns in SMEs than in larger firms given the lowlevel of training of the workforce.

It is difficult to deal with return on traininginvestments in more detail, leaving us with thequestion why SMEs do not invest more intraining. We conclude by stating that there isnothing in the current review suggesting thatskills or training in SMEs have any less impact oncompany performance. The study by Leitner(2001) indicates that training is one of the fewvariables associated with company earnings. Thefindings of Bosma et al. (2002) suggest thatcertain skills, such as experience in businessownership and industry experience, contribute tothe success of start-up companies. The study byRomijn and Albaladejo (2000) indicates that bothskills and training are associated with the innova-tion capability of SMEs.

5.7. The influence of labourmarkets and social partners

Research that connects education, training, orskills/competence with different labour marketconditions and the impact that they may have ontraining strategies and company performance isnot very common. We have not seen any paperexamining the effect of different labour marketconditions on, for instance, the ability of firms toprofit from training investments. However, somegeneral remarks can be made on existing trainingliterature and the influence of labour markets. Ithas been argued that differences between the USlabour market and labour market in Germany another European countries in regard to mobilityand wage structure would indicate that traininginvestment is more likely in Europe (e.g.Acemoglu and Pischke, 1998; 1999a). Thesearguments have been supported by differentempirical reasoning based mainly upon observa-tions that firms invest in general training, such asin the German apprenticeship system (17). Never-theless, there are no clear empirical results indi-cating that less efficient labour markets maketraining investments more profitable for firms,though mobility has the ability to reduce thispotential. Similarly, it is likely that more equality inwages (compressed wage structure) has a posi-tive influence on the ability to extract profits fromtraining investments (and prior education). Still,we have not been able to locate research veri-fying differences in return on company trainingbetween different labour market systems.

There are some indications that differences inproductivity between countries can be explainedby differences in national education and trainingsystems. For instance, Mason et al. (1992) foundsubstantially higher proportions of vocationallyqualified personnel on all job levels in the Nether-lands compared with Britain. They also argue thatthe higher average level of skills and knowledgein the Dutch workforce contributes to higherproductivity through better maintenance ofmachinery, greater consistency of product quality,greater workforce flexibility, and less learningtime on new jobs. Similarly, Carr (1992) found that


(17) Other features of the German apprenticeship system are that firms screen potential employees and that the apprentice systemprovides a better matching of employee and employer. According to Euwals and Winkelmann (2001) apprentices staying withintheir firm after graduation have higher wages and longer first job duration than apprentices leaving the training firm.

Britain had substantially lower productivity(measured by sales per employee) than, forinstance, Germany. Carr maintains that the highlyeducated and trained German workforce, thehigh qualification of the foremen, and extensivevocational training by firms, explains the produc-tivity advantage of German firms. Carr alsoascribed improved productivity growth in Britainduring the 1980s to increased labour flexibilityduring this period. However, it is important tonote that neither study is based on any (signifi-cant) statistical test but more on reasoning basedupon gathered information.

A somewhat contradictory argument on theeffect of increased labour flexibility is put forwardin Michie and Sheehan (1999). Their findingssuggest that strategies to increase employmentflexibility by short-term contracts, weakeningtrade unions, etc., do not enhance innovationperformance. This study leads us to the effectsthat social partners may have on trainingoutcomes. Again we have not come across anystudy that connects the influence of social part-ners on the decision to train employees and whateffect this may have on company performance.The role of the social partners is not explicitlytreated in any of the analysed studies.

Nevertheless, there are some general observa-tions that can be made. The role of social partnersis strongly connected with the different nationalfunding systems for education and training. Asargued by Mason et al. (1992) and Carr (1992),education and vocational training may, in turn, havean effect on productivity. Other observations thatcan be made are, for instance, that higher wagesand lower mobility in unionised establishmentstypically promote training (Booth et al., 1999).

The Cranet survey results also cast some lighton the question of whether different labourmarket systems or the social partners have anyeffect on the decision to train. The trainingregression is based on about 1 400 companyresponses mainly from European countries. The

results indicated that unions might have a nega-tive impact on the amount of training providedbut a positive (not significant) impact on thenumber of employees being trained in a year. Asmentioned earlier, the negative associationbetween unionisation and the amount invested intraining is probably caused by our inability tocontrol for industry differences in the trainingregression. The indication that unions mightaffect the training decision by providing moreemployees with company training is more inter-esting. However, this is not statistically verified inthe analysis of the Cranet survey. The correlationanalysis also indicated a lower personnelturnover in more unionised establishments, whichis in line with the findings of Booth et al. (1999).

Our measurement of the degree of internallabour market appears to confirm that these typesof structures do not promote training and learning.In spite of the indication of lower personnelturnover in firms with more internal promotion,training is less provided in these types of estab-lishments. The result of the training regressionalso revealed that personnel turnover itself doesnot determine training. This finding might be inter-preted as an indication that turnover does notreduce the incentive to train employees or thatpersonnel turnover induces training by forcingfirms to train newly hired employees. We also seelarge differences in turnover among different Euro-pean countries but this seems to affect theamount of training provided to a minor degree.

It is important to note that these findings onlyconcern the provision of training and not thepotential effect that different labour marketssystems may have on training outcomes. This isclearly an area that needs much additional work.We conclude this section by merely stating thatthere are indications that labour market condi-tions and the role of, for instance, unions mayhave an effect on company training outcomes bytheir effect on mobility, wages, and the incentivesto train and be trained.


A growing number of papers focus on the effects ofhuman capital and human capital investments oncompany performance. Previously, this subject waslargely disconnected from company based impactresearch as human capital (investments) are notowned or controlled by firms. However, morestudies are needed to understand how education,training and skills/competence affect firms, in aneffort to comprehend fully what generates profitsand growth. The main findings of this review of liter-ature on the impact of education, training, andskills/competence may be summarised as follows.(a) The type of training firms provide to their

employees is not so much a question ofwhether the training is general or specific butpossibly more a question of what is needed tostay ahead of competitors. A growing body ofliterature suggests that firms are financing alltypes of training (general as well as specific).

(b) More recent research findings also suggestthat investments in training generate substan-tial gains for firms irrespective of whether thetraining is useful to other firms. The evidencethat employers profit from training investmentcomes from different countries includingIreland, Britain, the Netherlands, Sweden,France, as well as the US. In most of thesestudies we can, with reasonable confidence,maintain that training generates performanceeffects and not the other way around.

(c) The effects of education and skills/compe-tence on productivity and innovation aregenerally positive and significant. That wealso start to see studies that connect educa-tion and skills with profitability might besomewhat more unexpected. That firmsextract profit from prior education is alsorelated to the ability of firms to capturereturns from general training investments.

(d) Employee development practices, such astraining policies and methods for analysingtraining needs, appear to be importantelements in explaining the provision oftraining and training outcomes. Similarly,innovative or advanced HRM practices aregenerally associated with firm performance.

(e) Innovation and IT not only cause firms toinvest more in training but are also highlydependent on education, skills and training ingenerating profit from these investments.Other findings suggest that training, togetherwith comprehensive HRM practices, is closelyrelated to firms’ innovative capacity.

(f) The lack of studies connecting SMEs, labourmarket characteristics, and social partnerswith training strategies and company perfor-mance measurements such as productivity orprofitability makes it difficult to draw anyconclusions. This research gap provides animportant incentive to investigate these typesof question more thoroughly in the future.

In conclusion, research concerning the effects ofeducation and training on firm performance is gath-ering momentum. Much more research on thissubject will appear in the near future. However, thefindings thus far raise questions and issues that wewill discuss in more detail in the next section.

6.1. Implication of firm financedgeneral human capitalinvestments

That firms invest in general training implies that theremight be a market failure in vocational training.Several authors, (Acemoglu and Pischke, 1999a;Bassi and Ludwig, 2000; Booth et al., 1999), see inrejection of Becker’s theory on company trainingevidence of under-investment in vocational training.In a perfect labour market, individuals pay for theirgeneral training by accepting a lower wage than theirproductivity during the training. The individual thencaptures all benefits from the training by an increasedwage after the training. In this case it is likely that theprovision of training is close to the social optimal levelas the investment decision is made by the individual.Acemoglu and Pischke (1999a) noted that in aperfectly competitive labour market, insufficientinvestments in skills could only arise becauseworkers are severely credit constrained. But in thiscase, the solution may be better loan markets ratherthan direct subsidies to training.

6. Summary and discussion

When the firm makes the training decision it ismost likely that too little training is provided. Or,as Booth et al. (1999) put it, when training isgeneral to an industry, firms will choose a subop-timal level of such training, since they realise thatworkers would take these skills with them whenthey leave for other firms in the same industry.However, human capital is not lost to society so amarket failure arises. The findings that firms areactive in, and profit from, general human capitalinvestments might thus warrant governmentregulations and subsidies for training, as thesefindings suggest under-investment in vocationaltraining. However, such a definite statement isnot warranted by the present state of research oncompany training. More research is required to becertain that company-based decision-makingconcerning the provision of general training leadsto fewer investments in training.

That firms profit from all types of traininginvestments indicates that we have underesti-mated the benefits from company training.Because most (formal) training is general innature, research on the impact of training hasbeen largely focused on the effects for the indi-vidual (wages); the benefits for employers havebeen considered to a lesser extent. As noted byDearden et al. (2000) by only examining the effectof education and training on wages, economistsmay have underestimated the importance oftraining for modern economies. The authors’conclude that it is time to start casting the netwider than wages in seeking the impact oftraining on corporate and national economicperformance.

It appears that it is not only researchers who haveunderestimated the effects of training investmentsbut, perhaps more severely, also the owners of thecompanies and investors. The findings of Bassi et al.(2001) indicate that firms investing more in traininghave a better stock market performance. This resultsuggests that investors are not aware of the invest-ments in training and that this type of information hasrelevance for investors. The lack of information abouttraining investment and the consequent benefitsleads to a suboptimal allocation of resources totraining in the capital market. It is conceivable thatthe lack of information about company training leads

to under-investment in profitable training projects(training projects with a positive net present value).So another implication of the evidence that trainingpredicts stock market returns is that investorspossibly need more information about these invest-ments in order to make better decisions about whereto allocate their financial resources. The issue aboutinformation to the capital markets will be discussedin more detail in the next section.

The problem of allocating enough resources is,however, complex as information asymmetries isone of the more prominent reasons given for theexistence of firm financed general human capitalinvestments. According to Katz and Ziderman(1990) information asymmetries between the firmcarrying out the training and other firms makefirms more willing to invest in general training.This is because the lack of information about thetraining investment reduces the potential benefitsthat a worker with general training can obtain bymoving to another firm. If Katz and Ziderman areright, providing more information about traininginvestments to capital markets might have anegative effect on the provision of training.

However, another information-based argumentimplies the opposite effect. Acemoglu and Pischke(1999b) argue that firms train their employeesbecause they have sufficient monopsony powerover their employees due to information asymme-tries. While asymmetric information encouragesfirms to invest in training, it reduces the workers’incentive to invest in their skills, as most of thereturns on training will be appropriated by the firm.This means, in contrast to Katz and Zidermann’sargument, that asymmetric information in labourmarkets might undermine the existence of trainingby not giving enough incentives to workers. Moreinformation about the training investment in thiscase leads to more investment in training (18).

6.2. Information on training andintangibles to capitalmarkets

We proposed earlier that investors possibly needmore information about training investments.


(18) The divergent views of the outcome of more information about training investments between Katz and Ziderman (1990) andAcemoglu and Pischke (1999b) appear largely a consequence of different opinions about how inefficient the labour marketreally is.

Johanson (2003) proposes that capital marketactors are hesitant regarding recent knowledgegathered from research on the importance ofhuman capital investments because of thefollowing five reasons.

First, capital market actors might be ambiva-lent because they fail to understand the impor-tance of a certain human capital investment. Theyprobably are not aware of recent research on theprofitability of human capital investments. Theylack the necessary understanding of the potentialof human capital investments in a specific firm.They have little or no appreciation of how humancapital contributes to the value creation process.This inability to comprehend the meaning ofhuman capital could be conceptualised as a‘knowledge’ problem.

Second, even if capital market actors dounderstand the connection between indicatorsand the vision of the firm, they are probably hesi-tant about human capital investments becausethey do not know if they can rely on the indica-tors. Do indicators of human capital transformadequate information? Are they valid? And arethe methods of measurement reliable? Theseissues of validity and reliability could be referredto as the ‘uncertainty’ problem.

Third, this reluctance might be connected tothe lack of ownership of intangibles related topeople. For example, because an organisationcannot own individual competence, the risk ofloosing this competence might be overly exag-gerated. This condition could be known as theproblem of ‘ownership’.

A fourth problem could be that capital marketactors are ultimately hesitant and indecisivebecause they do not know if the measurementsmatter in the management control processes ofthe firm. Is information taken care of? Doesmanagement take the necessary action on data,i.e. a ‘management’ problem?

The final problem suggested by Johanson(2003) concerns the ‘mentality’ of different capitalmarket actors, who are neither used to consid-ering human capital investments as importantfactors that drive firm performance nor encour-aged to do so.

These five barriers are probably relevant notonly for capital market actors but also forcompany management and policy makers.

There is a need to develop a new way of

measuring and reporting internally as well asexternally on training investments with the poten-tial to increase understanding of the financialimpact of education and training. Our proposal atthis point relates to the debate about humanresource costing and accounting, intellectualcapital (IC), balanced scorecard, etc. (herereferred to as the IC-movement).

During the last decade numerous initiatives havebeen taken to encourage the development of a newglobal framework for the measurement, manage-ment and reporting on intangibles. Major initiativeshave been taken by the OECD and the EuropeanCommission. In 1998 the Commission decided tosupport a six-nation (Denmark, Finland, France,Norway, Spain and Sweden) research projectnamed Measuring and reporting intangibles tounderstand and improve innovation management(Meritum, 2002). The Meritum work, which wasperformed between the years 1998 and 2001 wasorganised in four different activities:(a) definition and classification of concepts, e.g.

intangibles and intellectual capital;(b) investigation of how management control of

intangibles was performed at company level;(c) capital market implications of the poor infor-

mation from firms on intangibles;(d) development of guidelines for the reporting

and management of intangibles.The guideline was subject to a Delphi test at

the end of the project.The Meritum work is presently subject to a

follow-up project E*KNOW-NET which is alsofinanced by the European Commission. The aimof the follow-up project is to spread the findingsfrom the Meritum work, to improve guidelines andto propose a research and education agendaregarding intellectual capital.

The Meritum and E*KNOW-NET works are basedupon the belief that firms are facing a major trans-formation in the value creation process. Intangibles,including, more specifically, knowledge, areincreasingly becoming the major driver of firms.These changes pose a great challenge to firmsbecause intangible resources are not easily identi-fied, not measured, and not reported internally orexternally. Another basic assumption is that there isa need to develop a common framework, whichinvolves definitions and classifications of intangi-bles and a guideline for measuring, managing, andreporting intangibles.


The mismeasurement of knowledge may leadto inefficient allocation of financial and humanresources. As the European Commission states inits report Towards a European research area, ‘theEuropean financial market has not yet sufficientlydiscovered the economic value to investment inknowledge’ (European Commission, 2000, p. 7).This is partly due to the fact that the informationprovided by companies to the financial markets isprimarily based on traditional tangible invest-ments, whereas value increasingly relies oninvestment in intangibles.

Efforts are needed both to provide informationon how knowledge is produced and accumu-lated, and on the way knowledge can be trans-formed into profits. The generalisation of goodpractice in the management of intangibles alsoneeds to be encouraged. New common proce-dures, documents, rules, etc., should be providedin order to improve the informative capacity of thefirm’s financial statements. This is precisely themain purpose of the Guidelines for managing andreporting on intangibles (Meritum, 2002), here-after referred to as Guideline.

The Guideline document attempts to supportfirms in the process of developing their ability toidentify, manage and value their intangible assets.To start with, a set of definitions on intangibleresources and intangible activities is provided; itis integrated with a classification used for theproposed intangible management system (humancapital, structural capital and relational capital).Based on the experience of best practice firms, amodel for the measurement and management ofintangibles is suggested, which covers threedifferent phases: identification, measurement andmonitoring of intangibles.

The Guideline also contains information on thestructure and contents of an external documentcalled the Intellectual capital statements. Threedifferent parts are considered for inclusion in thatdocument:(a) vision of the firm,(b) a summary of intangible resources and activi-

ties, and(c) a system of indicators.

To overcome the barriers proposed byJohanson (2003) it is important, as well as chal-lenging, to develop understandable indicators onissues related to training investments. The indica-tors have to be measurable and valid. Because

the very idea behind the development of trainingindicators is to increase the understanding of theimportance of knowledge, the indicators have tobe clearly related to the vision of the firm or thevalue creation process. It is probable that thisnew kind of standardised indicator also needs tobe subject to independent audit.

6.3. Strength and weakness ofdata and methods

The lack of information on training investmentsalso poses a problem for researchers, as the datahas to be gathered from different sources.Depending on how one defines training, the esti-mate of working time spent on training variesconsiderably. The IPF at the Uppsala Universityregularly carries out surveys of companies listedon the Stockholm stock exchange. The humancapital survey of 2002 included questions onwhat firms defined as company training. Somecompanies report only training conducted outsidethe firm (12 %), some report internal and externaltraining sessions with a defined curriculum(39 %), and others report anything from formaltraining sessions to such informal training aslearning by doing and self studies (45 %).

The lack of a coherent definition of training thatis used and reported consistently by companiesis one of the more important issues for researchon company training. The problem of varyingmeasurements of training is not likely to besolved by defining what is regarded as training indifferent surveys. Firms are unlikely to changetheir data collecting methods regarding trainingfor each new survey. It seems likely that whatcompanies report as training is what they havedata for, no matter what is defined as training ineach specific survey. At best one can expect theprovider of the information to make some profes-sional judgement or correction of their data fordifferent surveys. Some straightforward guide-lines or general agreement among researchersand companies on what to define as trainingseems be warranted.

Apart from a common definition and standard oftraining, another problem with the data concernsagreement on what type of costs should beincluded in training measures. The variety ofmeasurements of training costs in different studies


and in different databases hinders the opportunityto make comparisons across countries and acrossdifferent studies. A comprehensive measurementof training investments in companies will not onlywork as a foundation for comparison and acrosscountries and studies but also facilitate compar-ison with other types of tangible investments. Ifinvestments in human capital can be comparedwith, and have the same credibility as, tangibleinvestments we would advance understanding ofwhat drives firms and, ultimately, what generateswealth for firms and society.

However, varying views of what is to beconsidered as company training do not mean thatinferences drawn from, for example, currentcross-sectional data are not valid. If there is atrue relationship between training and companyperformance, vague definitions of training typi-cally make estimates less precise and less signif-icant. In other words, as long as it cannot beshown that profitable firms are constantly using abroader definition of training, and also accountfor more training investment, than less profitablefirms, the consequence of vaguely definedtraining measures is a downward bias in theimpact of training. Increased variance due tomeasurement problems (errors) leads normally toless significant results or, in the case of severemeasurement problems, insignificant results.Definition problems might partly explain the lowor insignificant impact of training in some of thestudies reviewed in the present paper.

In the case of studies based on panel data thedefinition problem is less important since we arelargely concerned with changes in variables overtime in this type of investigation. If we follow thesame company at different time intervals thismeans that we cancel out all time-invarianteffects that can bias the results. As long as theunit of analysis (e.g. the company) does notchange its definition over time the differencesbetween how companies measure training is ofless importance. The fact that we can drawstronger conclusions from panel data studies alsomitigates problems with vague definitions.

In general, the weakness of data and the weak-ness of methods used in the reviewed studies donot exaggerate the results but, on the contrary,work against finding positive responses to humancapital investments and thus tend to underestimatetheir impact on company performance.

6.4. Policy implications andfuture research

This review of research has highlighted a numberof questions that possibly need more attention inan effort to establish a common understanding ofhow human capital and human capital invest-ments influence growth and performance onfirms. The general impression of the research thathas been reviewed in this study is encouraging inthat the economic effects of education, skills andtraining can be observed in company data. Thatfirms appear to benefit from all types of training(no matter how general) is an important findingthat also generates a number of other trainingrelated questions and implications. We will high-light some of the implications and research ques-tions we feel should be pursued in the future.(a) What are different aggregated measures of

training actually measuring?We have seen in the Cranet survey that twocommon ways of measuring training (theproportion of employees being trained andpercentage of wage bills spent on training)are determined by quite different factors.Whether this is the case in other samples andwhether it has any meaningful interpretationin regard to company performance would beuseful to determine in future research.

(b) What influence has firm performance on theprovision of training?We have also discussed the timing of training.We have seen a number of studies indicatingthat training is more likely to be carried out intimes of weak productivity; we have also seenin the Cranet survey that how much firmsinvest in training is not conditioned by pastperformance. Clarification of any mutualdependence between training and economicconditions is important as the answer wouldfacilitate interpretation of cross-sectionalresults.

(c) When do the effects of training materialise?Some of the studies reviewed suggest that ittakes some time before the effects of trainingare seen in company performance measure-ments. Whether this is the case is an impor-tant research question as it influences inter-pretations made in cross-sectional estimates(are we measuring the effects of past trainingor the effects of current training efforts?) and


it also influences the time horizon withinwhich we can expect benefits from training.

(d) Are certain types of training or certain ways ofconducting training more efficient?The current state of research typically usesvery coarse and aggregated measurements.A more precise division of human capital andhuman capital investments might give us abetter understanding of the type of educationand training that generates profit and growthfor firms.

(e) Do firms benefit from employing moreeducated and skilled workers?More research is warranted that connectsskills, competence and education withcompany measurements such as profitabilityand stock market return.

(f) How important are supportive HRM practicesfor generating training and performanceeffects?There is a need to understand how and in whatway HRM practices influence training decisionsand to what extent are they increase efficiencyin training and training outcomes.

(g) More research is needed on the impact oftraining for SMEs, and on the influence ofsocial partners and the labour market.While smaller firms undertake less training,more research is needed to explain thereasons for this. Research is also warrantedon the influence that social partners anddifferent labour market systems might have

on the ability of firms to benefit from traininginvestment. We see quite large differencesbetween different countries in companytraining and an explanation for these differ-ences might be found in the effect that thesetwo factors have on the provision of trainingand training outcomes.

(h) Another important issue is a common stan-dard for defining and measuring training andtraining costs.There is a significant difference betweenasking individuals about training and askingfirms about their training. Gathering informa-tion on training in large organisationsconsumes considerable resources andtraining data is not easily changed in accor-dance with different surveys. A common defi-nition of what to regard as training and acommon definition of costs in training invest-ments would be beneficial not only forresearchers but to compare companies,industries, and countries on company training.

(i) More information for capital markets abouttraining investments appears to be warranted.A way of providing information on how muchfirms invest in training appears to be animportant issue in establishing a more effi-cient allocation of resources to firms withgood investment opportunities. Morecompany-based information on training mightalso lead to a better allocation in the humancapital (labour) market.


GMM General method of moments

HPWS High performance work systems

HRM Human resource management

IC Intellectual capital

IT Information technology

LFS Labour force survey

OLS Ordinary least square

R&D Research and development

SME Small and medium enterprise

STW School-to-work

TQM Total quality management

List of selected abbreviations

Training related questions

3:1 a) Approximately what proportion of the annual salaries and wages bill is currently spent ontraining?

——————————————— % 1� don’t knowb) Approximately what proportion of employees have been on internal or external training activi-

ties within the last year?

——————————————— % 1� don’t know

3:3 Do you systematically analyse employee training needs?

1� Yes 2� No 3� Don’t know

3:5 Do you monitor the effectiveness of your training?

1� Yes 2� No 3� Don’t know

3:6 Does your organisation have a policy for the following personnel/human resource managementareas:

Yes, Yes, No Don’twritten unwritten know

C. Training and development �1 �2 �3 �4

Performance related questions

7. If you are a private organisation would you say the gross revenue over the past 3 years hasbeen:A. Well in excess of costs �1B. Sufficient to make a small profit �2C. Enough to break even �3D. Insufficient to cover costs �4E. So low as to produce large losses �5

9. Compared to other organisations in your sector, where would you rate the performance of yourorganisation in relation to the following?

Top 10 % Upper half Lower half Not applicableA. Service quality �1 �2 �3 �4B. Level of productivity �1 �2 �3 �4C. Profitability �1 �2 �3 �4E. Rate of innovation �1 �2 �3 �4F. Stock market performance �1 �2 �3 �4

Annex — Selected Cranet questions

Questions related to internal job market and unionisation

2:5 How are managerial vacancies generally filled? (Please tick as many as applicable for eachmanagement level).

Senior Middle JuniorManagement Management Management

A. Internally �1 �1 �1B. Recruitment by head hunters/consultancies �1 �1 �1C. Advertise in newspapers �1 �1 �1D. Word of mouth �1 �1 �1

5:1 What proportion of the total number of employees in your organisation are members of a tradeunion?1� 0 % 2� 1-10 % 3� 11-25 % 4� 26-50 %5� 51-75 % 6� 76-100 % 7� Don’t know

Employee related questions

6:3 Please provide the following information about your workforce:

A. Annual staff turnover ____________% turnover per year �1 don’t know

B. Age structure ____________% of employees over 45 years �1 don’t know

C. Absenteeism ____________average days per year �1 don’t know

D. Education structure ____________% of graduates �1 don’t know


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