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Intangible Capital and Growth: Essays on Labor Productivity, Monetary Economics, and Political Economy, Vol. 1

January 2022
Contributions to Economics

DOI:10.1007/978-3-030-86186-5

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Contributions to Economics

FelixRoth

Intangible

Capital and

Growth

Essays on Labor Productivity, Monetary

Economics, and Political Economy, Vol. 1

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Felix Roth

Intangible Capital

and Growth

Essays on Labor Productivity, Monetary

Economics, and Political Economy,

Vol. 1

Felix Roth

Department of Economics

Faculty of Business, Economics and

Social Sciences

University of Hamburg

Hamburg, Germany

ISSN 1431-1933 ISSN 2197-7178 (electronic)

Contributions to Economics

ISBN 978-3-030-86185-8 ISBN 978-3-030-86186-5 (eBook)

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Preface

For several decades now, advanced economies across the globe have been undergo-

ing a process of rapid transformation towards becoming knowledge economies. It is

now widely recognized that intangible capital has been a crucial element in the

growth performance of these economies and their ﬁrms. The term serves as a useful

device for capturing those dimensions of capital that are not tangible in nature but are

nevertheless fundamentally important for growth. It encompasses investments in

education (human capital) and in informal (social capital) and formal (rule of law)

institutions by the public sector and households, as well as investments by busi-

nesses aimed at enhancing their knowledge base, such as software, innovative

property, and economic competencies.

This book is the ﬁrst of two open-access volumes presenting a selection of my

essays on Labor Productivity, Monetary Economics, and Political Economy. They

are drawn from the ﬁrst part of my habilitation in economics on the topic of

Intangible Capital and Labor Productivity Growth and Determinants of Public

Support for the Euro, which I completed in June 2020, at the department of

economics at the faculty of Business, Economics, and Social Sciences at the

University of Hamburg. This ﬁrst volume contains 8 chapters, which follow a

reverse chronological order starting with my most recent research output in chapter

one. The essays in the individual chapters were selected with the aim of providing an

overview of my research to date on intangible capital and growth.

Half of the contributions, namely chapters 2,5,6, and 7, draw upon works of

mine previously published in the Journal of Intellectual Capital,Review of Income

and Wealth,Intereconomics, and Kyklos respectively. The other four essays, found

in chapters 1,3,4,and8, constitute unpublished research based on original project

reports prepared for the European Commission and translations of contributions

published in edited volumes produced by the Metropolis Publisher and Hamburg

University Press.

This volume would not have been possible without the thoughtful mentoring and

strong support generously given by Thomas Straubhaar, to whom I am deeply

grateful. He acted as a reporting reviewer in my Habilitation Committee and

encouraged me to publish the selected essays in this book. In addition, I would like to

thank Mary O’Mahony and Erich Gundlach for acting as reporting reviewers in my

Habilitation Committee, Katharina Manderscheid for chairing the Committee, and

Elisabeth Allgoewer and Ulrich Fritsche for their participation in its proceedings. I

would also like to express gratitude to Marianne Paasi, advisor to the

GLOBALINTO project, for her constant support. I gratefully acknowledge the

European Commission and German Science Foundation for funding the research

that led to the essays presented in this volume. I would also like to thank Aisada

Most, Anne Harrington, and Lorraine Klimowich for excellent assistance and

support in helping me organize and design the layout of this volume. Finally, I

would like to extend warm thanks to my family for their kind and generous

encouragement.

Felix Roth

Hamburg, Germany

January 2022

vi Preface

About the Author

Since 2017, Felix Roth has been a Senior Research Fellow and Senior Lecturer with

the Chair for International Economics in the Department of Economics at the

University of Hamburg. In June 2020, he successfully completed his German

Habilitation in economics on the topic of Intangible Capital and Labour Productiv-

ity Growth and Determinants of Public Support for the Euro. Prior to his appoint-

ment at the University of Hamburg, he worked six years as a Research Fellow in the

macroeconomic policy unit and as editor of the journal Intereconomics at the Centre

for European Policy Studies (CEPS) in Brussels. In addition to his ongoing research

association with the department of economics at the University of Göttingen, he

worked as a Research Fellow, Scientiﬁc Expert, and Economic Policy Advisor for

the European Commission in Brussels for over three years. He pursued his doctorate

in economics on the topic Social Capital, Trust and Economic Growth—A Cross-

Sectional and Panel Analysis at the University of Göttingen in the framework of a

post-graduate program funded by the Deutsche Forschungsgemeinschaft (DFG) and

jointly supervised by the University of Göttingen and the London School of Eco-

nomics and Political Science. He studied economics, sociology and European law at

the University of Munich where he received his Diploma in Social Sciences in 2003.

Dr. Roth has published his research in monographs and collective volumes produced

by internationally renowned academic publishing houses, such as Springer,

Routledge and Edward Elgar; in leading international journals in his ﬁeld, such as

Review of Income and Wealth and Journal of Common Market Studies; and in a wide

range of policy contributions, e.g., VoxEU and Intereconomics. Visit his personal

homepage at: https://www.felixroth.net.

vii

Contents
The Productivity Puzzle: A Critical Assessment and an Outlook
on the COVID-19 Crisis .................................. 1
Introduction ........................................ 2
Determinants of Labor Productivity Growth ................. 3
Labor Productivity Growth, 1950–2006 . . . . . . . . . . . . . . . . . . . . 4
The Productivity Puzzle, 2007–2015 . . . . . . . . . . . . . . . . . . . . . . . 4
Intangible Capital and the Productivity Puzzle . . . . . . . . . . . . . . . . 7
An Outlook on the COVID-19 Crisis and Labor Productivity
Growth . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 12
Conclusion ......................................... 13
References ............................................. 14
Revisiting Intangible Capital and Labor Productivity Growth,
2000–2015: Accounting for the Crisis and Economic Recovery
in the EU .............................................. 17
Introduction ........................................ 18
Theoretical Linkages between Intangible Capital and Labor
Productivity Growth . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 19
Estimates on Intangible Capital . . . ........................ 20
Previous Empirical Results .............................. 21
Model Speciﬁcation, Research Design and Data .............. 23
1 Model Speciﬁcation ............................... 23
2 Research Design . . ............................... 24
3 Data Sources .................................... 25
4 A Note on the Construction of Intangible Capital Stocks . . . . 25
5 A Note on the Construction of Intangible and Tangible
Capital Services . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 26
Descriptive Analysis .................................. 26
Econometric Estimation ................................ 31
Conclusions . ....................................... 35
ix

Appendix 1 Construction of Intangible and Tangible Capital
Services Growth . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 36
Appendix 2 Descriptive Statistics . . ........................... 38
Appendix 3 A Comparison of INNODRIVE and INTAN-Invest
datasets . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 39
References ............................................. 40
3 The Rule of Law and Labor Productivity Growth
by Businesses: Evidence for the EU, 1998–2005 ................ 43
1 Introduction ........................................ 43
2 The Rule of Law and Labor Productivity Growth
by Businesses: Theoretical Links in an EU Context ............ 45
2.1 Direct Inﬂuence of the Rule of Law on Labor
Productivity Growth by Businesses . ................... 46
2.2 Indirect Inﬂuence of the Rule of Law on Labor
Productivity Growth by Businesses . ................... 46
3 Model Speciﬁcations, Research Design, Operationalization,
and Data . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 48
3.1 Model Speciﬁcations .............................. 48
3.2 Research Design . . ............................... 51
3.3 Operationalization and Measurement of the Data . . ........ 51
4 Empirical Description of the Rule of Law within the EU . . ...... 53
5 Econometric Results . . . ............................... 57
5.1 Econometric Results between the Rule of Law and Labor
Productivity Growth . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 57
5.2 Determinants of Intangible and Tangible Business
Capital Investment ................................ 59
6 Empirical Conclusion, Discussion, and Policy Conclusion . . . . . . . 61
6.1 Empirical Conclusion . . ........................... 61
6.2 Discussion of the Results Considering the Underlying
Theoretical Literature . ............................ 62
6.3 Policy Conclusions ............................... 63
Appendices . ........................................... 64
Appendix 1 Operationalization of the Rule of Law ............. 64
Appendix 2 An External Instrument for Measuring the Rule
of Law in the Context of Development Economics . . . . . . . . . . . . 66
Appendix 3 Selected Statistics . . . . . . . . . . . . . . . . . . . . ....... 67
References ............................................. 69
4 Organizational Trust, Fear of Job Loss, and TFP Growth:
A Sectoral Analysis for the EU ............................. 73
1 Introduction ........................................ 74
2 Theoretical Links . .................................... 74
2.1 Organizational Capital and Economic Performance . . . . . . . . 74
2.2 Organizational Trust and Economic Performance .......... 75
x Contents

2.3 Fear of Job Loss and Economic Performance ............ 76
3 Model Speciﬁcation, Research Design, and Data . . . . . . . . . . . . . . 78
3.1 Model Speciﬁcation ............................... 78
3.2 Research Design . . ............................... 79
3.3 Data.......................................... 79
4 Descriptive Statistics . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 81
5 Econometric Analysis . ................................ 86
5.1 Sensitivity of Results . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 89
5.2 How Do these Results Fit in with Other Existing
Empirical Results? ................................ 91
5.3 Objective Forces Driving Job Insecurity at the Individual
Level......................................... 91
6 Conclusion ......................................... 93
Appendix . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 95
References ............................................. 96
5 Intangible Capital and Labor Productivity Growth: Panel
Evidence for the EU from 1998–2005 ........................ 101
1 Introduction ........................................ 102
2 Theoretical Links between Business Intangible Capital
and Labor Productivity Growth . . . . . . . . . . . . . . . . . . . . . . . . . . 103
2.1 Theoretical Relationship between Intangible Capital
and Labor Productivity Growth . . . . . . . . . . . . . . . . . . . . . . 103
2.2 The Treatment of Intangible Expenditures ............... 104
3 Estimates of Intangible Capital Investment . . . . . . . . . . . . . . . . . . 105
4 Previous Empirical Results .............................. 107
5 Model Speciﬁcation, Research Design, and Data . . . . . . . . . . . . . . 110
5.1 Model Speciﬁcation ............................... 110
5.2 Research Design . . ............................... 112
5.3 Data.......................................... 112
5.4 A Note on the Construction of Intangible Capital Stocks . . . . 113
5.5 A Note on the Construction of Intangible
and Tangible Capital Services . . . . . . ................. 114
6 Descriptive Analysis .................................. 114
7 Econometric Analysis . ................................ 118
7.1 Sensitivity Analysis . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 120
8 Conclusion ......................................... 123
Appendices . ........................................... 124
Appendix 1 Descriptive Statistics . . ....................... 124
Appendix 2 Construction of Intangible and Tangible
Capital Service . . . ................................... 124
References ............................................. 126
Contents xi

Measuring Innovation: Intangible Capital Investment in the EU . . . 129
Innovation and EU 2020: Is R&D the Sole Factor
for Measureing Innovativeness? .......................... 130
How Does R&D Investment by Businesses Compare
to Investment in Intangibles in the EU? . . . . . . . . . . . . . . . . . . . . . 132
Comparison between Tangible and Intangible Capital
Investment in the EU . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 135
Conclusion ......................................... 137
References ............................................. 138
Does Too Much Trust Hamper Economic Growth? ............. 141
Theoretical Links Between Social Capital, Trust,
and Economic Growth ................................. 142
1 Social Capital and Trust . . . ......................... 142
2 Relationship Between Social Capital, Trust,
and Economic Growth . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 143
Previous Findings . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 145
Data and Measurement . . . ............................. 147
1 Operationalization ................................ 147
2 Model Speciﬁcation ............................... 148
3 Measurement of Data .............................. 148
Descriptive Statistics . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 149
Econometric Analysis . ................................ 152
1 Cross-Sectional Analysis . . . . . . . . . . . . . . . . . . . . . . . . . . . 152
2 Pooled Panel Analysis ............................. 152
3 Panel Analysis . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 155
Conclusion ......................................... 161
Appendix . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 162
References ............................................. 163
Social Capital, Trust, and Economic Growth .................. 167
Introduction ........................................ 167
Extension of the Neoclassical Model Assumption . . . . . . . . . . . . . 168
Criticism of the Concept of Social Capital or Why Is There
Capital in Social Capital? . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 169
The State of the Art and Deﬁnition of Social Capital ........... 170
1 Coleman’sDeﬁnition of Social Capital ................. 170
2 Putnam’sDeﬁnition of Social Capital .................. 171
Interpersonal Trust .................................... 172
Positive Correlation between Social Capital, Trust, and Growth . . . 173
xii Contents

7 Negative Relationship Between Social Capital, Trust,
and Growth ......................................... 174
7.1 Mancur Olson . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 174
7.2 Mistrust, Fear, and Economic Growth . . . . . . . . . . . . . . . . . . 175
8 Operationalization of Social Capital ....................... 176
9 Social Capital, Trust, and Economic Growth: Empirical
Findings ........................................... 179
10 Concluding Remarks .................................. 183
References ............................................. 183
Contents xiii

Chapter 1

The Productivity Puzzle: A Critical

Assessment and an Outlook

on the COVID-19 Crisis

Felix Roth

Abstract This contribution critically assesses the productivity puzzle and gives an

outlook on the COVID-19 crisis. It offers two main conclusions. First, it posits that a

large fraction of the productivity puzzle can be solved by incorporating intangible

capital into the asset boundary of the national accounts. Thus, the productivity

puzzle is largely explained as a consequence of fundamental structural changes

that are underway, transforming industrial economies into knowledge economies.

Secondly, the contribution foresees a post-COVID-19 scenario that is likely to lead

to a pronounced increase in labor productivity growth. This depends, however, on

whether the current push for digitization will be backed by actual investments in

digitization and the necessary complementary investments in (business and public)

intangible capital.

Keywords Productivity puzzle · Intangible capital · Labor productivity growth ·

Structural change · COVID-19 crisis · Remeasurement of GDP

JEL Classiﬁcations E22 · F45 · O32 · O34 · O47 · O52

Originally published in: Thomas Straubhaar (ed.). Neuvermessung der Datenökonomie. Hamburg

University Press, Hamburg, 2021, pp. 61–82.

This contribution is based on the author’s Habilitation colloquium lecture entitled “The Productivity

Puzzle: A Critical Assessment”, which he delivered before the Habilitation Committee of the

faculty of Business, Economics, and Social Sciences at the University of Hamburg on June

29, 2020 (Roth, 2020a). A German translation of the contribution appeared as Chap. 3 of an

edited volume published by Hamburg University Press in April 2021 (Roth, 2021).

Felix Roth (*)

Department of Economics, University of Hamburg, Hamburg, Germany

e-mail: felix.roth@uni-hamburg.de

©The Author(s) 2022

F. Roth, Intangible Capital and Growth, Contributions to Economics,

https://doi.org/10.1007/978-3-030-86186-5_1

1 Introduction

Labor productivity growth is a central contributor to an economy’s competitiveness

(Krugman, 1994) and rising prosperity (Heil, 2018). In most advanced economies, it

is of key importance in maintaining the standard of living in societies experiencing

population aging (Posen & Zettelmeyer, 2019). Despite its importance, it is widely

acknowledged that advanced economies, such as the US and the EU, have suffered a

pronounced decline in labor productivity growth rates since the start of the Great

Recession in 2007 (Oulton, 2018; Van Ark & O’Mahony, 2016; Van Ark, 2016; Van

Ark & Jäger, 2017; Van Ark et al., 2018). In the aftermath of the ﬁnancial crisis

(2008–15/16), these rates have been more than halved compared to the pre-crisis

period (1995–2004/07) (Remes et al., 2018; Brynjolfsson et al., 2019; Van Ark et al.,

2018).

Although a steady decline in labor productivity growth can be observed in these

economies from the 1970s onward (Gordon, 2018; Bergeaud et al., 2016;

Brynjolfsson et al., 2019)—despite the exceptional experience of the US in the

mid-to-late 1990s—the magnitude of the decline since the start of the Great Reces-

sion (2008–2013/16) has posed a conundrum to many scholars (Oulton, 2018;

Remes et al., 2018; Van Ark & Jäger, 2017)—principally for two reasons.

First, the decline was puzzling given that real interest rates were close to or below

zero (Teulings & Baldwin, 2014; Summers, 2015; Haskel & Westlake, 2018a).

Second, the decline was puzzling as it occurred in the midst of ongoing revolutions

in both information and communications technology (ICT) and in artiﬁcial intelli-

gence (AI) (OECD, 2015). Economists have attempted to capture this conundrum

under several multifaceted labels, such as “the Secular Stagnation Puzzle”(Sum-

mers, 2014,2015; Teulings & Baldwin, 2014), “the Modern Productivity Paradox”

(Brynjolfsson et al., 2019), or simply “the Productivity Puzzle”(Haskel & Westlake,

2018a). This contribution critically discusses this conundrum by exploring the key

role of intangibles in labor productivity growth. It also explores current issues arising

from the COVID-19 crisis.

This contribution is organized as follows: it opens by offering some brief

introductory remarks and a summary of its main ﬁndings and their implications.

Second, it sketches an intangible capital-augmented model for labor productivity

growth as developed by Roth and Thum in 2013. Third, the contribution reviews

salient trends in labor productivity growth from 1950 until 2006. Fourth, it elabo-

rates upon the pronounced decline in productivity experienced from 2007 to 2015,

which ﬁrst prompted the ongoing discussion among economists over this so-called

“productivity puzzle”. Fifth, the contribution critically discusses this perplexing

“puzzle”by elaborating upon the key role of intangibles in labor productivity

growth. Sixth, it explores current issues arising from the COVID-19 crisis. And

ﬁnally, the contribution offers two main conclusions.

2 Felix Roth

2 Determinants of Labor Productivity Growth

This section elaborates the determinants of labor productivity growth by presenting

an intangible capital-augmented model speciﬁcation. This model was ﬁrst developed

by Roth and Thum in 2013 in the context of a European Commission-funded project

entitled Intangible Capital and Innovations: Drivers of Growth and Location in the

EU (INNODRIVE) (INNODRIVE, 2011; Roth & Thum, 2013). It is currently being

used in a subsequent project called GLOBALINTO (GLOBALINTO, 2020), which

is devoted to capturing the value of intangible assets in microdata to promote the

EU’s growth and competitiveness (Roth, 2020b). The model speciﬁcation follows an

approach developed by Benhabib and Spiegel in 1994, which is coined “cross-

country growth accounting”. The approach differs from the framework of traditional

single growth accounting methodology in two ways. First, the output elasticities are

estimated rather than imposed. Second, part of the model can be used to explain the

international variance in total factor productivity (TFP) growth. Following the

theoretical framework of Corrado et al., 2009, Benhabib and Spiegel’s model

speciﬁcations from 1994 are expanded by intangibles. The starting point for the

estimation is then an augmented Cobb–Douglas production function. Assuming

constant returns to scale, the Cobb–Douglas production function is ﬁrst rewritten

in intensive form. Second, differences in natural logarithms are taken and the TFP

term is estimated. This provides the following baseline for the econometric ﬁndings

to be displayed at a later point in this contribution:

ln qi,tln qi,t1



¼cþgHi,tþmHi,t

qmax,tqi,t



qi,t

þn1uri,t

ðÞ

þpX

j¼1

Xj,i,tþydi,tþαln ki,tln ki,t1

ðÞ

þβln ri,tln ri,t1

ðÞþui,tð1:1Þ

where labor productivity growth (lnq

i,t

lnq

i,t1

) [gross value added for the non-

farm business sectors, expanded by the investment ﬂows of business intangible

capital in country iand period t] can be essentially decomposed into a TFP term

and two capital terms: tangible and intangible capital. TFP is represented by a

constant term c, which represents exogenous technological progress. The level of

human capital (H

i,t

)reﬂects the capacity of a country to innovate domestically. The

term Hi,t

qmax,tqi,t

ðÞ

qi,tproxies a catch-up process. The term (1 ur

i,t

) takes into

account the business cycle effect. The term pPk

j¼1Xj;i;tis the sum of kextra policy

variables which could possibly explain TFP growth. This includes public intangi-

bles, e.g. formal and informal institutions such as the rule of law and trust. They are

of central importance for growth. yd

i,t

are year dummies to control among others for

The Productivity Puzzle 3

the economic downturns in 2001 and 2008. Next comes the term for tangible capital

services growth (lnk

i,t

lnk

i,t1

). Followed by the term intangible capital services

growth (lnr

i,t

lnr

i,t1

) and the error term. In Sect. 5we will elaborate upon the

β-coefﬁcient for intangibles capital services growth later within this contribution.

3 Labor Productivity Growth, 1950–2006

This section brieﬂy describes the trends in labor productivity growth in the EU and

the US from 1950 to 2006. Table 1 from Van Ark et al. (2008), depicts, inter alia, the

average annual growth rates of GDP per hour worked in the EU-15 and the US from

1950 to 2006. The empirical evidence demonstrates that the labor productivity

growth in the EU from 1950 to 1973, at 5.3%, was twice as high as that in the US,

at 2.5%. The same pattern—although with lower numbers—holds for the period

1973–1995, with values of 2.4% for the EU-15 and 1.2% for the US. The literature

clearly attributes the labor productivity growth increase in the EU vis-à-vis the US to

a catching-up process. This process is built on a strong skill base instilled in upper

secondary education and a production process based upon imitation. It is interesting

to note that the pattern changes when analyzing the period 1995–2006, with US

labor productivity growth increasing to 2.3%, compared to 1.5% in the EU-15.

In analyzing the underlying contributions to labor productivity growth in Table 4

from their article, Van Ark et al. (2008)ﬁnd that this decline in labor productivity

growth in the EU is largely due to a scant contribution from the knowledge economy.

A further sectoral decomposition by the authors demonstrates a pronounced decline

in TFP growth in the market economy of the EU-15 vis-à-vis the US, particularly in

market services. They link the productivity gap in EU market services to deﬁciencies

in ICT and complementary investment in intangible capital as well as rigidities in the

EU single market concerning product, labor and services markets.

Similar results in line with this overall argument are presented by a group of

economists working with Sapir and Aghion et al., who stress the importance of

public intangibles, namely the quantity and quality of higher education for

explaining the gap in labor productivity growth (Aghion, 2008; Aghion & Howitt,

2006; Aghion et al., 2007,2008,2010; Sapir et al., 2004). Brynjolfsson et al. (2019)

stress investment in ICT and AI and lagged complementary intangible capital

investments.

4 The Productivity Puzzle, 2007–2015

This brings us directly to the period starting from the Great Recession of 2007 and

running up to 2015. Table 1 in Van Ark et al. (2018) illustrates a pronounced decline

in labor productivity growth since the start of the Great Recession in 2008. Labor

productivity growth rates dropped by half in the euro area (EA) from 1.4% to 0.6%

and in the US from 2.5% to 1.3%. As pointed out by Oulton (2018), this decline is

4 Felix Roth

exceptional in its magnitude and not just a continuation of past historical trends, as

suggested by the American economic historians Gordon (2018) and Cowen (2011).

But what triggered this stark decline in labor productivity growth?

Two channels have been identiﬁed in this ﬁeld of research. First, the decline in

labor productivity growth has been linked to a pronounced fall in total factor

productivity growth. The long-term evidence produced by Bergeaud et al. (2016)

and illustrated with time series ﬁndings on labor productivity growth and total factor

productivity from 1890 to 2010 support such an assertion.

Second, the decline in labor productivity growth has been attributed to a drop in

investment. Such claims are supported by analyses of investments in tangible capital,

which have signiﬁcantly declined over the period 2008–2013. The decline

in tangible investment across EU economies is displayed in Fig. 1.1 as illustrated

in the work by Roth (2020b). In particular, one detects the most pronounced decline

in tangible capital investment in the periphery countries of the EA that implemented

intensive austerity measures.

This decline in labor productivity growth and investment has puzzled many

scholars for several reasons (Oulton, 2018; Remes et al., 2018; Van Ark & Jäger,

2017). In the ﬁrst instance, the decline was puzzling, given that real interest rates

were close to or below zero (Teulings & Baldwin, 2014; Summers, 2015; Haskel &

Westlake, 2018a).

Secondly, the decline was puzzling as it occurred in the midst of ongoing

revolutions in ICT and AI (OECD, 2015). As pointed out by Nakamura (2019),

the intensity of technological innovations since the beginning of the 1990s points to

a“dramatically dynamic economy!”As can be discerned from Fig. 5.4 in Haskel and

Westlake (2018a, p. 95), frontier ﬁrms actually saw a huge increase in their labor

productivity growth. Furthermore, the available empirical evidence points to the

increasing importance of intangibles among the S&P 500 companies and notes the

fact that the ten leading ﬁrms are almost entirely based on intangibles (Ross, 2020).

They all give evidence in support of Nakamura’s claim from 2019.

Several scholars, such as Lawrence Summers, identiﬁed a lack of aggregate

demand as the main culprit behind declining labor productivity growth and invest-

ment (Draghi, 2014; Krugman, 2014; Summers, 2014,2015). Applying their rec-

ommendations for stimulating aggregate demand to the EA implied two sets of

strategies. First, on the condition that member states would adopt a structural reform

agenda aimed at laying the basis for pro-growth support, the European Central Bank

committed to implement a quantitative easing (QE) program. Secondly, the

European Commission undertook to initiate an EU-wide European Investment

Plan (Fichtner et al., 2014). However, a ﬁscal stimulus package proposed on behalf

of the core economies, such as Germany, and favored by some prominent econo-

mists such as De Grauwe (2015) and Fratzscher (2014), was never launched.

Nevertheless, the policies initiated at the EU level have already been successful in

stimulating demand support. They have thereby succeeded in initiating an economic

recovery since 2014 and initiating investment in the EA, as shown in Fig. 1.1.

Triggering aggregate demand support, however, is only the ﬁrst step towards solving

the productivity puzzle. Another essential step is linked to the incorporation of

intangible capital investments into the asset boundary of the national accounts.

The Productivity Puzzle 5

Fig. 1.1 Intangible and tangible capital and Labor productivity growth, EU-16, 2000–2015

Notes: Investment in Intangibles, Tangibles, and Labor Productivity are given in millions of national currencies and are standardized to 1 in the year 2008. The

continuous line indicates the start of the ﬁnancial crisis in September 2008. The dashed line indicates the start of the economic recovery at the end of 2013.

Adopted y-scales are applied to Greece, Ireland, and Slovakia.

Data source: INTAN-Invest (NACE2) data (Corrado et al., 2018).

Source: Fig. 4 in Roth, 2020b, p. 680.

6 Felix Roth

5 Intangible Capital and the Productivity Puzzle

But which investments in intangible capital should be incorporated into the asset

boundary of national accounts? In their seminal paper published in 2005 and as

shown in Table 1.1, Corrado, Hulten, and Sichel (CHS) categorize three dimensions

of intangible assets (Corrado et al., 2005).

First, computerized information, which CHS deﬁne as “knowledge embedded in

computer programs and computerized databases.”Second, innovative property,

which CHS deﬁne as “scientiﬁc knowledge embedded in patents, licenses and

general know-how”. Third, economic competencies, which CHS deﬁne as “the

value of brand names and other knowledge embedded in ﬁrm-speciﬁc human and

structural resources.”

To what extent are these assets relevant for stimulating labor productivity

growth? Let us consider two examples drawn from a chain of arguments developed

by Brynjolfsson and Hitt (2000) and Brynjolfsson et al. (2002) over the last two

decades. He and his team ﬁnd that for every euro invested in software, a ﬁrm needs to

spend an additional 10 euros in developing economic competencies if they want to

reap the full potential of labor productivity growth. This includes the retraining of

staff to use the software effectively, along with the necessary restructuring of

organizational procedures. Similar results have been found for investments in AI.

And what economic contributions can be expected once these intangibles are

incorporated into the asset boundary of national accounts? Table 1.2, taken from the

Table 1.1 Overview of business intangible assets employed in CHS (2005)

Category of

intangible

assets Deﬁnition by CHS (2005)

Business intangible

item

Included

in NA

Computerized

information

“Knowledge embedded in computer

programs and computerized databases”

(p.23)

Computer software Yes

Computerized database Yes

Innovative

property

“Not only the scientiﬁc knowledge

embedded in patents, licenses and gen-

eral know-how (not patented) but also

the innovative and artistic content in

commercial copyrights, licenses and

designs”(p.26)

Science and engineer-

ing R&D

Yes

Mineral exploration Yes

costs

Yes

Other product develop-

ment, design, and

research expenses

No (new

intangible)

Economic

competencies

“The value of brand names and other

knowledge embedded in ﬁrm-speciﬁc

human and structural resources”(p.28)

Brand equity No (new

intangible)

Firm-speciﬁc human

capital

No (new

intangible)

Organizational

structure

No (new

intangible)

Note: NA ¼national account.

Source: Own adaption of CHS (2005) as published in Table 1 in Roth, 2019,p.6.

The Productivity Puzzle 7

Table 1.2 Contributions to the economy from incorporating intangibles into the asset boundary of national accounts: Overview of the empirical literature,

2009–2018

Authors Country

Investment (in GDP)

in % Contribution to LPG in %

Growth

acceleration in % Article

Harmonized cross-

country dataset Methodology

Corrado et al.

(2009)

US ~ 13

(03)

(95–03)

11.2

(95–03)

RoIW –GA

Fukao et al.

(2009)

JAP 11.1

(00–05)

27; 16

(95–00); (00–05)

17.3, 1.4

(95–00), (00–05)

RoIW –GA

Marrano et al.

(2009)

UK 13

(04)

(95–03)

13.1

(95–03)

RoIW –GA

Nakamura

(2010)

US Intangible ¼Tangible

(00–07)

/ / RoIW –GA

Edquist (2011) SE 10/~16

***

(04)

41; 24

(95–00); (00–06)

16, 2.3

(95–00), (00–06)

RoIW –GA

Roth and Thum

(2013)

EU-13 9.9

****

(98–05)

4.4

(98–05)

RoIW INNODRIVE CCGA

Corrado et al.

(2013)

EU-15 6.6

(95–09)

(95–07)

/ OREP INTAN-invest

(NACE1)

Corrado et al.

(2018)

EU-14,

NMS-4

7.2, 6.4

(00–13)

30, 10; 19, 8; 43{,17

(00–13);(00–07); (07–13)

/ JIPD INTAN-invest

(NACE2)

Notes:

LPG ¼labor productivty growth. *The measure here is non-farm business output. **The measure here is adjusted market sector gross value added

(MGVA). ***The measure here is gross value added (GVA). ****The measure is GVA (c-k+o excluding k70). {Capital share. US ¼United States,

UK ¼United Kingdom, JAP ¼Japan, SE ¼Sweden, EU ¼European Union, NMS ¼New Member States, RoIW ¼Review of Income and Wealth,

OREP ¼Oxford Review of Economic Policy, JIPD ¼Journal of Infrastructure, Policy and Development, GA ¼Growth Accounting, CCGA ¼Cross Country

Growth Accounting. The numbers in brackets refer to the relevant time periods.

Source: Table 1 in Roth (2020b, p. 675).

8 Felix Roth

work by Roth (2020b), summarizes three sets of main ﬁndings as reported in the

literature.

First, investment as a percentage of GDP increases signiﬁcantly and approaches

levels comparable to those of tangible capital once intangibles are incorporated.

Second, intangibles constitute a signiﬁcant contribution to labor productivity

growth. For example, the work by Roth and Thum (2013) shows that growth in

intangible capital services is able to explain 50% of the international variance in

labor productivity growth in the EU. It becomes, in fact, its dominant driver. Third,

the rate of labor productivity growth accelerates. As reported by Edquist (2011), e.g.,

once accounting for intangibles, labor productivity growth accelerates by 16% in

Sweden.

What are the implications of these ﬁndings for the productivity puzzle? Four

points can be elaborated. First, the “puzzling”decline in investment is largely due to

a mismeasurement in most advanced economies of the actual ongoing investment

rates by ﬁrms. Contemporary national accounting classiﬁcations have not yet been

fully revised to account for the ongoing transition towards the knowledge economy

of the twenty-ﬁrst century. Although selective elements of intangibles have already

been accounted for, such as software and scientiﬁc R&D, investments in economic

competencies, such as ﬁrm-speciﬁc human and organizational capital, are still

excluded.

Figure 1.2, taken from the work by Roth (2020b, p. 680), illustrates that once

intangibles are included in the national accounts, overall business investments in an

EU-16 country sample are almost twice as high and represent 25% of the total sum.

Moreover, it is interesting to observe that in seven out of the 16 countries surveyed,

business investments in intangible capital are already larger than those in tangible

capital.

Figure 1.1 also shows that despite a steady decline in tangible investments,

particularly in the aftermath of the ﬁnancial crisis, investments in intangible capital

have swiftly recovered and are on a steadily upward trend. These results are

consistent with the latest evidence from the INNODRIVE follow-up INTAN-Invest

dataset, referenced in a speech given early in 2020 by Jonathan Haskel, British

economist and Member of the Monetary Policy Committee at the Bank of England.

This evidence illustrates a steady decline in tangible capital and a solid increase in

intangible capital in the post-ﬁnancial crisis era in advanced economies. The above

ﬁndings demonstrate that the use of tangible investment ﬂows as the sole basis of

analysis leads to erroneous empirics and ultimately to the design of misguided policy

measures.

Second, incorporating intangibles into the asset boundary of national accounts

leads to an increase in labor productivity growth. This has already been shown by

Edquist (2011) for the case of Sweden. His results differ from claims published by

Haskel and Westlake (2018a) and Syverson (2017). The results by Roth (2020b)

from the GLOBALINTO project in 2020 support Edquist’s(2011)ﬁndings. Other

analyses of economic recovery show that labor productivity growth has accelerated

by 0.4% points (or 22%), from 1.8% to 2.2%. In this context, Nakamura (2019) even

suggests that the mismeasurement of labor productivity growth will most likely give

an annual growth rate of 2%.

The Productivity Puzzle 9

Third, several prominent contributions have highlighted the role of a decline in

TFP in relation to the level of business investments in intangibles. Van Ark (2016),

Van Ark and O’Mahony (2016), Van Ark and Jäger (2017), as well as Bounfour

and Miyagawa (2015) attribute the decline in labor productivity and TFP growth

primarily to a slower diffusion of technology and innovation, which is due to low

growth rates of investments in ICT and complementary intangibles. Haskel and

Westlake (2018b) also highlight a reduction in the spill-over effects of intangibles on

TFP due to the widening gap of intangible investment between leader and laggard

ﬁrms. Moreover, Brynjolfsson et al. (2019) argue that more investment in comple-

mentary intangibles is necessary to reap the full beneﬁts of AI to labor productivity

growth.

Fourth, as can be seen in Table 1.3 as taken from Roth (2020b), the econometric

results point towards the importance of intangible capital services growth for labor

productivity growth at the macro-level. The work by Roth from 2020 uses a cross-

country growth accounting estimation approach for an EU-16 country sample over

the period 2000–2015. It is based on the intangible augmented model speciﬁcation as

Fig. 1.2 Business tangible and intangible capital investments (as a percentage of GVA), EU16,

2000–2015

Notes: CT ¼communications technology; IT ¼information technology; OCon ¼total

nonresidential capital investment; OMach ¼other machinery and equipment; TraEq ¼transport

equipment; Cult ¼cultivated assets; IC ¼intangible capital. Residential Structure has been

excluded. Values on top of the bars depict the intangible/tangible capital investment ratio.

Data sources: INTAN-Invest (NACE2) data (Corrado et al., 2018) and EUKLEMS data (Jäger,

2017).

Source: Fig. 3 in Roth, 2020b, p. 680.

10 Felix Roth

introduced in the beginning of this contribution. It provides evidence that growth in

intangible capital services can explain the largest share of labor productivity

growth—up to 66%. This is demonstrated by the size of the beta coefﬁcient of

0.38. Equally signiﬁcant, but less pronounced results are found at the meso and

micro-levels (Niebel et al., 2017; Marrocu et al., 2011).

Table 1.3 Intangibles and labor productivity growth, 2000–2015, PP-PCSE estimation

Estimation method

PP-

PCSE

PP-

PCSE

PP-

PCSE

PP-

PCSE

PP-

PCSE 2SLS

Time sample

2000–

2015

2000–

2015

2000–

2015

2008–

2015

2000–

2015

2000–

2015

Equation (1) (2) (3) (4) (5) (6)

Tangible services

growth

0.31*** 0.19** 0.28*** 0.13 0.18** 0.58

(0.08) (0.08) (0.08) (0.15) (0.07) (0.42)

Tangible services

growth*crisis

–– 0.32** –– –

(0.13)

Tangible services

growth*recovery

–– – 0.47 ––

(0.30)

Intangible services

growth

–0.38*** 0.48*** 0.32*** –0.50***

(0.07) (0.09) (0.11) (0.16)

Intangible services

growth*crisis

–– 0.28** –– –

(0.13)

Intangible services

growth*recovery

–– – 0.42* ––

(0.23)

Innovative property

services growth

––––0.37*** –

(0.07)

Computerized informa-

tion services growth

––––0.01 –

(0.04)

Economic competencies

services growth

––––0.02 –

(0.06)

Upper secondary

education 15+

0.07*** 0.05*** 0.05*** 0.02 0.06*** 0.07***

(0.02) (0.01) (0.01) (0.02) (0.01) (0.02)

Catch-up 0.02** 0.02*** 0.02*** 0.01 0.02** 0.02*

(0.01) (0.01) (0.01) (0.01) (0.01) (0.01)

Business cycle 0.11* 0.12* 0.13** 0.13* 0.12* 0.11**

(0.06) (0.06) (0.06) (0.07) (0.06) (0.05)

R-squared 0.40 0.50 0.54 0.63 0.54 0.46

Observations 256 256 256 128 256 208

Number of countries 16 16 16 16 16 16

Notes: PP-PCSE ¼Pooled Panel - Panel-Corrected Standard Error. In regression (1), tangible

services growth, labor productivity growth, and the catch-up term exclude software, R&D, and

entertainment, artistic and literary originals, and mineral exploration. In regressions, (2–6) labor

productivity growth and the catch-up term are expanded with intangible capital. Tangible capital

excludes residential capital. Labor productivity growth was calculated based on the GVA of the

non-farm business sectors b n+rs (excluding real estate activities). ***p < 0.01, **p < 0.05,

*p < 0.1.

Source: Table 2 in Roth, 2020b, p. 682.

The Productivity Puzzle 11

6 An Outlook on the COVID-19 Crisis and Labor

Productivity Growth

How will the present COVID-19 crisis affect labor productivity growth? In order to

answer this question, we should distinguish between a short-term and a medium- to

long-term perspective.

To understand the short-term impact, it helps to examine the pattern that emerged

in the aftermath of the 2008 ﬁnancial crisis. Table 6, taken from Mas (2012), presents

evidence from the EU and the US in the period 2007–2010, which shows that

whereas the US saw an actual increase in labor productivity growth from 1.93% to

2.02%, the EU-15 experienced a pronounced decline in labor productivity growth

from 1.41% to 0.07%. This difference can be attributed to differences in labor market

arrangements between the two economies. Whereas EU welfare states have inten-

sively utilized short-term working schemes to dampen the threat of large layoffs in

the aftermath of the ﬁnancial crisis, the US refrained from such policies.

And indeed, as can be observed in the data from the spring 2020 projections by

DG ECFIN, labor productivity growth in the EA will decline by 3.2% points, with a

peak in Germany of 5.6% points (European Commission, 2020a). Conversely, the

decline in US labor productivity growth will be marginal, estimated at only 0.2%

points. Much like the experience following the ﬁnancial crisis in 2009, the short-

term working schemes adopted to dampen the threat of large layoffs will lead to a

pronounced decline in labor productivity growth in the euro area and in Germany

vis-á-vis the US. But how large is the economic impact caused by the COVID-19

pandemic from a historical perspective?

Recent evidence generated from empirical time series performed by Bergeaud

et al. (2020) over the period 1875–2025 shows that, although the impact on GDP

growth is more pronounced than that from the ﬁnancial crisis in 2008, it is only a

fraction of the decline suffered during the Great Depression in 1929. Furthermore,

there will be a swift recovery in 2021 beyond the previous level. Also, a similar

decline in investment in 2020 due to the COVID-19 crisis, with a strong recovery in

2021, is projected by ECFIN (European Commission, 2020a). Whether this holds

also for intangible capital investment remains an open question. We hope to arrive at

an answer by means of a customized COVID-19 survey to be administered by

the GLOBALINTO project (GLOBALINTO, 2020) on business intangible capital

investment in seven EU countries.

To understand the mid- and long-term impact, we must ﬁrst analyze the policy

measures adopted to address the COVID-19 crisis. In response to the pandemic,

historically large stimulus packages of up to 200 billion euro were agreed at the

member state level among selective core countries of the EA (Greive, 2020). At the

federal level of the EU, the agreed overall ﬁscal capacity is 750 billion euro

(European Commission, 2020b). These ﬁscal policies are ﬂanked by the ECB’s

Pandemic Emergency Purchase Programme (PEPP), with a total volume of 1350

billion euro. The novelty of PEPP is the role being assumed by the ECB to act as

lender of last resort in the government bond market, with no restrictions placed on

12 Felix Roth

single-country purchases (Schnabel, 2020). For the European Commission to bor-

row 750 billion euro in its capacity as a multinational actor within its multiannual

ﬁnancial framework is equally historic. This is most likely a signiﬁcant step forward

towards establishing a stronger ﬁscal union. As pointed out in my latest work, given

the large public support shown for the euro during its ﬁrst two decades, it is likely

that the presidents of both the ECB and the European Commission enjoy the

necessary political legitimacy to enact these decisive measures (Roth, 2020c).

But will these investment plans help to stimulate a recovery in the EA? As we

learned from the arguments presented above, these stimuli will surely help the euro

area to recover in the short-term, especially given that it is a three-fold program this

time round: ﬁscal stimuli at the member state and EU levels, paired with monetary

stimuli.

In a medium- to long-term perspective, two issues are relevant for a full recovery

of labor productivity growth. First, the current push for digitization needs to be

backed by investments from the recovery packages into digitization and the neces-

sary complementary (business and public) intangible capital. If the funds are used in

such a manner, we can expect to see labor productivity growth accelerate in the post-

COVID-19 era. Second, the ongoing investments in ICT and in intangibles must be

ﬂanked by pro-growth supply-side reforms within the labor, product, and services

markets in the larger EA economies, such as Italy. This should achieve the necessary

convergence in unit labor costs vis-á-vis Germany.

A post-COVID-19-scenario will likely lead to a pronounced increase in labor

productivity growth. This depends, however, on whether the current push for

digitization will be backed by actual investments in digitization and the necessary

complementary investments in (business and public) intangible capital.

7 Conclusion

We now come to the main conclusion of this contribution, which has attempted to

critically assess the productivity puzzle and give an outlook on the COVID-19 crisis.

It offers two main conclusions.

First, it posits that a large fraction of the productivity puzzle can be solved by

incorporating intangible capital into the asset boundary of the national accounts.

Thus, the productivity puzzle is largely explained as a consequence of fundamental

structural changes that are underway, transforming industrial economies into knowl-

edge economies. And it is precisely this radical transformation that yet needs to be

statistically validated by the national accounts.

Secondly, the contribution foresees a post-COVID-19 scenario that will likely

lead to a pronounced increase in labor productivity growth. This depends, however,

on whether the current push for digitization will be backed by actual investments in

digitization and the necessary complementary investments in (business and public)

intangible capital.

The Productivity Puzzle 13

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16 Felix Roth

Chapter 2

Revisiting Intangible Capital and Labor

Productivity Growth, 2000–2015:

Accounting for the Crisis and Economic

Recovery in the EU

Felix Roth

Abstract

Purpose—This contribution aims to revisit the relationship between intangible

capital and labor productivity growth using the largest, up-to-date macro database

(2000–2015) available to corroborate the econometric ﬁndings of earlier work and to

generate novel econometric evidence by accounting for times of crisis (2008–2013)

and economic recovery (2014–2015).

Design/methodology/approach—To achieve these aims, the study employs a

cross-country growth-accounting econometric estimation approach using the largest,

up-to-date database available encompassing 16 EU countries over the period

2000–2015. It accounts for times of crisis (2008–2013) and of economic recovery

(2014–2015). It separately estimates the contribution of three distinct dimensions of

intangible capital: 1) computerized information, 2) innovative property, and 3) eco-

nomic competencies.

Originally published in: Felix Roth. Revisiting Intangible Capital and Labour Productivity Growth,

2000–2015: Accounting for the crisis and economic recovery in the EU. Journal of Intellectual

Capital, Vol. 21, No. 5, 2020, pp. 671–690.

The author wishes to thank the participants at the 15th World Conference on Intellectual Capital for

Communities in Paris (July 2019), the GLOBALINTOmeeting in Athens (September 2019) and the

GLOBALINTO workshop on Advancing the Measurement of Intangibles for European economies

in Brussels (January 2020) for constructive comments. He would also like to thank Simone Calió

for excellent research assistance. Moreover, Dr. Roth is grateful for a grant received from the

European Commission under the Horizon 2020 programme for the GLOBALINTO project

(Capturing the value of intangible assets in microdata to promote the EU’s growth and

competitiveness, contract number 822259). And ﬁnally, he expresses his gratitude to Ahmed

Bounfour, Hannu Piekkola, Felicitas Nowak-Lehmann, Thomas Straubhaar, Iulia Siedschlag,

Robert Stehrer and Josh Martin for their valuable comments.

Felix Roth (*)

Department of Economics, University of Hamburg, Hamburg, Germany

e-mail: felix.roth@uni-hamburg.de

©The Author(s) 2022

F. Roth, Intangible Capital and Growth, Contributions to Economics,

https://doi.org/10.1007/978-3-030-86186-5_2

Findings—First, when accounting for intangibles, this contribution ﬁnds that these

intangibles have become the dominant source of labor productivity growth in the

EU, explaining up to 66% of growth. Second, when accounting for times of crisis

(2008–2013), in contrast to tangible capital, it detects a solid positive relationship

between intangibles and labor productivity growth. Third, when accounting for the

economic recovery (2014–2015), it ﬁnds a highly signiﬁcant and remarkably strong

relationship between intangible capital and labor productivity growth.

Originality/value—The study corroborates the importance of intangibles for labor

productivity growth and thereby underlines the necessity to incorporate intangibles

into today’s national accounting frameworks in order to correctly depict the levels of

capital investment being made in European economies. These levels are signiﬁcantly

higher than those currently reﬂected in the ofﬁcial statistics.

Keywords Intangible capital · Labor productivity growth · Crisis · Recovery ·

European Union

1 Introduction

Recent research has reported a disappointing performance in labor productivity

growth among European Union (EU) and euro area (EA) countries since the start

of the crisis from 2008 to 2015 (Van Ark & Jäger, 2017). According to this literature,

this performance stems largely from a slower diffusion of technology and innovation

due to low growth rates of information and communication technology (ICT) and

complementary intangible capital investment (Van Ark & Jäger, 2017, p. 15; Van

Ark, 2016, pp. 37–41; Van Ark & O’Mahony, 2016, pp. 132–138).

Indeed, a recent growth-accounting study at the macro-level over the period

2000–2013 identiﬁes the deepening of intangible capital as a main driver of labor

productivity growth (Corrado et al., 2018, p. 11). Such ﬁndings are in line with

existing growth-accounting studies for the US (Corrado et al., 2009), the UK

(Marrano et al., 2009), Japan (Fukao et al., 2009), Sweden (Edquist, 2011), and

the EU-15 (Corrado et al., 2013).

Within this substantial body of growth-accounting evidence, however, there

exists only scarce econometric evidence at the macro-level of the impact of intan-

gible capital investment on labor productivity growth. The only existing econometric

study analyses an EU-13 country sample for pre-crisis times from 1998 to 2005

(Roth & Thum, 2013). This scarcity of growth econometric studies is remarkable in

light of their general advantages in comparison to growth-accounting studies (Tem-

ple, 1999, pp. 120–121). To help close this gap in the research, this study conducts

an econometric analysis using a cross-country growth-accounting approach covering

16 EU countries over the period 2000–2015. This approach goes beyond earlier

18 Felix Roth

work in two ways. First, it enables us to corroborate earlier econometric ﬁndings

(Roth & Thum, 2013) with the help of a greatly extended dataset containing more

than two and half times the number of overall observations (256 versus 98). Second,

by covering a period until 2015, we are able to generate novel econometric ﬁndings

on the impact of intangible capital deepening on labor productivity growth by

accounting for times of crisis (2008–2013) and times of economic recovery

(2014–2015).

By matching the most recent release of the INTAN-Invest (NACE2)

dataset

(Corrado et al., 2018) with the latest ﬁgures from the EU KLEMS

dataset (Jäger,

2017), in combination with a wide range of growth-relevant policy variables from

Eurostat, the OECD and the World Bank, this contribution provides the largest up-

to-date intangible capital panel dataset at the macro-level containing an overall

number of 256 country observations. Estimating a slightly modiﬁed model speciﬁ-

cation as developed within the existing literature (Roth & Thum, 2013, p. 495), with

the help of a cross-country growth-accounting econometric approach, this contribu-

tion reaches three major ﬁndings. First, in line with the previous growth econometric

literature (Roth & Thum, 2013), it conﬁrms that once intangibles are accounted for,

they become the dominant source of labor productivity growth in the EU, explaining

up to 66% of this growth. Second, when accounting for times of crisis (2008–2013),

it ﬁnds that even when the relationship between tangible capital and labor produc-

tivity turned negative, the impact of intangibles on growth remained solidly positive.

Third, when accounting for the economic recovery (2014–2015), it reports a highly

signiﬁcant and remarkably strong relationship between intangible capital and labor

productivity growth.

2 Theoretical Linkages between Intangible Capital

and Labor Productivity Growth

The earliest work highlighting the importance of intangible capital for labor produc-

tivity dates back to the 1960s (Haskel & Westlake, 2018, p. 38). Based on research

by Brynjolfsson et al. (2002) and Nakamura (2001), among others, Corrado et al.

(2005) developed a methodological framework for the US of how to account for

business intangibles in the “new economy”. The authors used an intertemporal

framework for investment and grouped the various business intangibles into three

broad dimensions: 1) computerized information, namely software, 2) innovative

property, namely research & development (R&D) and 3) economic competencies,

namely brand names, ﬁrm-speciﬁc human capital and organizational capital.

Conducting a growth-accounting analysis alongside their methodological frame-

work, Corrado et al. (2009) showed that business intangibles were able to explain

Accessible at www.intaninvest.net (Corrado et al., 2018).

Accessible at www.euklems.net (Jäger, 2017).

Revisiting Intangible Capital and Labor Productivity Growth, 2000–2015 19

a signiﬁcant share of labor productivity growth. Using growth-accounting studies,

similar results were found for the UK (Marrano et al., 2009), Japan (Fukao et al.,

2009), Sweden (Edquist, 2011) and the EU (Corrado et al., 2013,2018). Econo-

metric cross-country growth-accounting studies for the EU (Roth & Thum, 2013)

ﬁnd an even stronger impact of intangibles on labor productivity growth. In addition,

the positive relationship between intangible capital and labor productivity was

prominently discussed and established in the work of Bounfour (Bounfour &

Miyagawa, 2015; Delbecque et al., 2015); Piekkola (2016,2018) and Miyagawa

(Miyagawa & Hisa, 2013; Bounfour & Miyagawa, 2015).

The positive relationship between computerized information and labor produc-

tivity growth—particularly the interaction between software and organizational

capital (Brynjolfsson et al., 2002)—and R&D and labor productivity growth

(Guellec & van Pottelsberghe de la Potterie, 2001) has already been well established

in the literature. Consequently, the intangible assets—software, R&D and entertain-

ment, artistic and literary originals, and mineral exploration—were already included

in the asset boundary of the national accounts. Given that economic competencies, in

particular, were not yet included in the national accounts, it seems necessary to once

more elaborate their positive role in labor productivity growth. Concerning brand

names, Cañibano et al. (2000) argue that the ownership of an attractive brand permits

a seller to retain a higher margin for goods or services compared to his competitors.

Since the consumer is driven by his perceptions in choosing among the products of

competing ﬁrms, the development of an appealing image or brand is crucial in

producing future beneﬁts. Concerning training or ﬁrm-speciﬁc human capital, the

same authors stress that a ﬁrm with higher-skilled employees is likely to attain higher

proﬁts than competitors whose workers are less competent. This observation is in

line with Abowd et al. (2005), who argue that the value of a ﬁrm will increase if the

quality of its ﬁrm-speciﬁc human capital resources improves. Concerning organiza-

tional capital, Lev and Radhakrishnan (2005, p. 75) deﬁne organizational capital as

“an agglomeration of technologies (...) business practices, processes and designs

and incentive and compensation systems—that together enable some ﬁrms to con-

sistently and efﬁciently extract from a given level of physical and human resources a

higher value of product than other ﬁrms ﬁnd possible to attain.”The authors classify

this as the only competitive asset truly possessed by a ﬁrm, whereas the others are

exchangeable and thus can be obtained by any company prepared to make the

necessary investment.

3 Estimates on Intangible Capital

A methodological framework originally developed by Corrado et al. (2005) for

measuring business intangibles in the US has become widely used internationally.

The framework was adopted in individual country-case studies for the UK (Marrano

20 Felix Roth

et al., 2009), Japan (Fukao et al., 2009), and Sweden (Edquist, 2011). Adapting this

methodological framework to the EU, the FP7 INNODRIVE project

constructed the

ﬁrst harmonized dataset for an EU-27 country sample (plus Norway), alongside the

three dimensions mentioned above. It contained two “old”national account intan-

gibles and eight “new”intangibles over the time period 1980–2005 (INNODRIVE,

2011; Jona-Lasinio et al., 2011; Gros & Roth, 2012; Roth & Thum, 2013). The

INNODRIVE macro database was used as the base for the EU-27 countries within

the ﬁrst version of the INTAN-Invest (NACE1) dataset

—a harmonized and updated

intangible dataset covering the EU and the US over the time period 1980–2010

(Corrado et al., 2013). In developing the second version of the INTAN-Invest

(NACE2) dataset, Corrado et al. (2016,2018) signiﬁcantly altered their methodol-

ogy to provide information on intangible capital on single-digit NACE2 economic

sectors and updated their dataset in the latest January 2019 release until the

year 2015.

The INTAN-Invest (NACE2) covers 19 EU countries plus the US over the

period 1995–2015. The dataset measures three “old”national account intangibles

and ﬁve “new”intangibles. The dataset groups business intangibles under three

dimensions: 1) computerized information, 2) innovative property and 3) economic

competencies. The ﬁrst dimension, i.e. computerized information, contains com-

puter software and databases. The second dimension, i.e. innovative property,

contains 1) entertainment, artistic and literary originals, and mineral exploration,

2) R&D, 3) design and 4) new product development in the ﬁnancial industry. The

third dimension, i.e. economic competencies, contains 1) brand, 2) ﬁrm-speciﬁc

human capital and 3) organizational capital. A detailed explanation of the altered

methodology of the INTAN-Invest (NACE2) dataset is provided in Corrado et al.

(2016), pp. 42–47.

4 Previous Empirical Results

Table 2.1 gives an overview of the existing empirical results of the growth-

accounting and cross-country growth econometric literature analyzing the relation-

ship between business intangible capital and labor productivity growth by businesses

at the macro-level. The table displays three distinct effects once intangible capital

has been incorporated into the asset boundary of the national accounts.

In the ﬁrst instance, the table clariﬁes that investments in intangible capital reach

signiﬁcant levels, once they are fully accounted for. Analyzing the business invest-

ment level for the US in pre-crisis times, Corrado et al. (2009)ﬁnd a business

investment level of 13% of non-farm business output, whereas Nakamura (2010)

ﬁnds equal shares of intangible and tangible capital investments. Similar investment

Accessible at https://cordis.europa.eu/project/id/214576/reporting/de (INNODRIVE, 2011).

Accessible at www.intaninvest.net (Corrado et al., 2013).

Revisiting Intangible Capital and Labor Productivity Growth, 2000–2015 21

Table 2.1 Overview of existing empirical studies, 2009–2018

Authors Country

Investment (in GDP) in

Contribution to LPG

in %{

Growth

acceleration in % Article

Harmonized cross-

country dataset Methodology

Corrado et al.

(2009)

US ~ 13*

(03)

(95–03)

11.2

(95–03)

RoIW –GA

Fukao et al.

(2009)

JAP 11.1

(00–05)

27; 16

(95–00); (00–05)

17.3, 1.4

(95–00), (00–05)

RoIW –GA

Marrano et al.

(2009)

UK 13**

(04)

(95–03)

13.1

(95–03)

RoIW –GA

Nakamura (2010) US Intangible ¼Tangible

(00–07)

/ / RoIW –GA

Edquist (2011) SE 10/~16***

(04)

41; 24

(95–00); (00–06)

16, 2.3

(95–00), (00–06)

RoIW –GA

Roth and Thum

(2013)

EU-13 9.9****

(98–05)

4.4

(98–05)

RoIW INNODRIVE CCGA

Corrado et al.

(2013)

EU-15 6.6

(95–09)

(95–07)

/ OEP INTAN-invest

(NACE1)

Corrado et al.

(2018)

EU-14,

NMS-4

7.2, 6.4

(00–13)

30, 10; 19, 8; 43{,17

(00–13); (00–07); (07–13)

/ JIPD INTAN-invest

(NACE2)

Notes: {LPG ¼labor productivty growth. *The measure here is non-farm business output. **The measure here is adjusted market sector gross value added

(MGVA). ***The measure here is gross value added (GVA). ****The measure is GVA (c-k+o excluding k70). {Capital share. US ¼United States, UK ¼

United Kingdom, JAP ¼Japan, SE ¼Sweden, EU ¼European Union, NMS ¼New Member States, RoIW ¼Review of Income and Wealth, OREP ¼Oxford

Review of Economic Policy, JIPD ¼Journal of Infrastructure, Policy and Development, GA ¼Growth Accounting, CCGA ¼Cross Country Growth

Accounting. The numbers in brackets refer to the relevant time periods.

22 Felix Roth

rates for precrisis times are found for Japan (Fukao et al., 2009) and the UK

(Marrano et al., 2009) with 11.1% of GDP and 13% of adjusted MGVA (market

sector gross value added), respectively. With a value of 16% of GVA(gross value

added), higher business investment rates are found in Sweden (Edquist, 2011).

Utilizing INNODRIVE data, Roth and Thum (2013)ﬁnd an average business

investment rate for precrisis times (1998–2005) for 13 EU countries of 9.9% of

GVA. Utilizing the ﬁrst version (NACE1) of the INTAN-Invest dataset, Corrado

et al. (2013)ﬁnd an average business investment rate of 6.6% of GDP for an EU-15

country sample from 1995 to 2009. Utilizing the second version of the INTAN-

Invest (NACE Rev.2) dataset, Corrado et al. (2018)ﬁnd an average investment rate

for business intangibles for the EU-14 and NMS-4 of 7.2% and 6.4% of GDP,

respectively, from 2000 to 2013.

Second, the contribution from intangible capital services to labor productivity

growth is signiﬁcant. Once business intangible capital is accounted for, 27% and

20% of labor productivity growth were explained in the US and the UK, respec-

tively. The same and higher values of up to 41% hold for Japan and Sweden (Fukao

et al., 2009; Edquist, 2011). Utilizing INNODRIVE data and analyzing 13 EU

countries with the help of an econometric cross-country growth-accounting meth-

odological approach, Roth and Thum (2013)ﬁnd that 50% of labor productivity can

be explained. Using INTAN-Invest (NACE1) data for an EU-15 country sample

over the time period 1995–2009, Corrado et al. (2013)ﬁnd a value of 24%. In their

most recent study, using INTAN-Invest data (NACE2), Corrado et al. (2018)

differentiate between a precrisis and a crisis period. They ﬁnd that intangible capital

contributes 30% over the time period 2000–2013, and 19% and 43% in times of

precrisis and crisis respectively, for an EU-14 country sample.

Third, the capitalization of intangibles accelerates productivity growth.

5 Model Speciﬁcation, Research Design and Data

5.1 Model Speciﬁcation

We estimate a slightly revised model speciﬁcation as developed in the existing

econometric literature (Roth & Thum, 2013, p. 495). Following this literature, the

slightly revised model speciﬁcation takes the following form:

ln qi,tln qi,t1



¼cþgHi,tþmHi,t

qmax,tqi,t



qi,t

þn1uri,t

ðÞ

þpX

j¼1

Xj,i,tþydi,tþαln ki,tln ki,t1

ðÞ

þβln ri,tln ri,t1

ðÞþui,tð2:1Þ

Revisiting Intangible Capital and Labor Productivity Growth, 2000–2015 23

where (lnq

i,t

ln q

i,t1

) is labor productivity growth (GVA expanded by intangibles

and divided by total hours worked) for the non-farm business sectors bn+rs

excluding real estate activities expanded by the investment ﬂows of business intan-

gible capital in country iand period t. The constant term crepresents exogenous

technological progress; the level of human capital (H

i,t

)reﬂects the capacity of a

country to innovate domestically; and the term H

i,t

max,t

q

i,t

)/q

i,t

proxies a catch-

up process, with q

max,t

using a purchasing power parity-weighted GVA measure

divided by total hours worked and representing the country with the highest level of

labor productivity at period t. The term (1 ur

i,t

) takes into account the business

cycle effect proxied by 1 minus the unemployment rate (ur); the term P

j¼1

Xj,i,tis the

sum of kextra policy variables, which could possibly explain TFP (total factor

productivity) growth and yd

i,t

are year dummies to control among others for the

economic downturn in 2001, in the wake of the bursting of the information technol-

ogy bubble in the previous year and the 9/11 attack in 2001, as well as the

pronounced economic downturn since 2008. (lnk

i,t

ln k

i,t1

) and (lnr

i,t

ln r

i,t1

)

represent the growth of tangible and intangible capital services, and u

i,t

represents the

error term.

5.2 Research Design

The econometric analysis covers 16 out of 27 EU countries from 2000 to 2015. The

countries included are Austria, Czech Republic, Denmark, Finland, France, Ger-

many, Greece, Ireland, Italy, the Netherlands, Portugal, Spain, Slovakia, Slovenia,

Sweden, and the United Kingdom.

With 16 EU countries and 16 time periods from

2000 to 2015, this leaves the econometric analysis with an overall number of

256 observations. Following the approach by Roth and Thum (2013, p. 496), annual

growth rates from 2000 to 2015 were estimated. The econometric analysis was

restricted to a period of 2000–2015, due to the valid calculation of capital stock

data. Equation (2.1) is estimated with the help of an econometric cross-country

growth accounting approach. This approach differs from traditional single-growth

accounting in two ways. First, the output elasticities are estimated rather than

imposed. And second, the model can be designed to explain the international

variance in TFP (total factor productivity) growth. The whole research design

applies to non-farm business sectors bn+rsexcluding real estate activities.

For Greece, Ireland, and Portugal, measures for the total economy were adjusted to

the non-farm business sectors. For Greece, disproportionately high levels of organi-

zational capital investment were adjusted to an average EU-16 level. Measurement

errors and missing values in the latest releases of the EU KLEMS (Jäger, 2017) and

The cases for Belgium and Hungary were excluded due to missing data in the EU KLEMS dataset.

Luxembourg was excluded due to signiﬁcant inconsistencies in the intangible capital data.

24 Felix Roth

the INTAN-Invest (NACE2) dataset (Corrado et al., 2018) were dealt with when

necessary.

5.3 Data Sources

The data were retrieved from the sources speciﬁed below:

1. Data on the single components of intangible capital were taken from the INTAN-

Invest (NACE2) dataset (Corrado et al., 2018), which provides information on

gross ﬁxed capital formation (GFCF) and intangibles adjusted GVA. The data

cover 19 EU countries + the US over the period 1995–2015, for 21 NACE2

economic sectors. The INTAN-Invest (NACE2) dataset does not provide intan-

gible capital stocks.

2. Data on the single components of tangible capital were taken from the EU

KLEMS database (Jäger, 2017). The database provides data on GFCF, tangible

capital stocks, GVA, labor compensation, capital compensation, and number of

hours worked per employee. The data cover the EU-28 countries and the US, over

the period 1995–2015, for 21 NACE2 economic sectors.

3. Human capital is measured as the percentage of the population aged 15+ that has

attained at least upper-secondary education, which is taken as a proxy for the

stock of human capital. The data were obtained from Eurostat.

4. Data on unemployment, power purchasing parity (PPP), inﬂation (HICP), gov-

ernment expenditures on education (percent of GDP), total government expendi-

tures (percent of GDP), social expenditure (percent of GDP), and stock of foreign

direct investment (FDI) (percent of GDP) were obtained from Eurostat.

5. Data on income tax (as a percent of GDP) were obtained from the OECD. The

variables rule of law (Kaufmann et al., 2010), data on market capitalization

(percent of GDP), and openness to trade were retrieved from the World Bank.

5.4 A Note on the Construction of Intangible Capital Stocks

In line with the literature (Niebel et al., 2017, p. 55; Roth & Thum, 2013, p. 497;

Timmer et al., 2007, pp. 32 and 39), intangible capital stocks for the selected

16 EU-27 countries for the time period 2000–2015 were constructed by applying

the perpetual inventory method (PIM) to a series of intangible capital investment

going back to 1995 and using the depreciation rates (δ

) as suggested by Corrado

et al. (2009): 20% for R&D, design, and new product development in the ﬁnancial

services industry; 35% for software; 40% for organizational capital and ﬁrm-speciﬁc

Details on the exact procedure followed for each country and asset type can be obtained from the

author upon request.

Revisiting Intangible Capital and Labor Productivity Growth, 2000–2015 25

human capital; 60% for brand names; and 13.75% for entertainment, artistic and

literary originals, and mineral exploration. For the calculation of the intangible

capital stock R

, the PIM takes the following form:

Rt¼Ntþ1δR

ðÞRt1ð2:2Þ

which assumes that (1) geometric depreciation, (2) constant depreciation rates over

time, and (3) depreciation rates for each type of asset are the same for all countries.

The real investment series for (N

) uses a GVA price deﬂator which is the same for all

intangibles.

5.5 A Note on the Construction of Intangible and Tangible

Capital Services

Data on intangible capital service services were generated according to the work by

Oulton and Srinivasan (2003) and Marrano et al. (2009) and are consistent with the

EU KLEMS approach (Timmer et al., 2007). This work contends that rather than

using a wealth measure (such as the capital stock), it is vital to ascertain the ﬂow of

services a capital stock can provide to production. The technical steps of the

construction of intangible and tangible capital services are in line with Roth and

Thum (2013) and are explained in detail in Appendix 1.

6 Descriptive Analysis

Table 2.A1 in Appendix 2shows the descriptive statistics of the analyzed dataset.

Labor productivity growth increased by 0.1% points (from 1.5 to 1.6), or by 6.7%, a

slightly higher value than the value of 4.4% detected in previous work (Roth & Thum,

2013, p. 498). Figure 2.1 shows the business intangible capital investment over GVA

for the eight intangible capital indicators for the 16 EU countries over the 16-year

average time period 2000–2015. The ﬁgure shows that overall business intangible

capital investments vary considerably across the 16 EU countries. Sweden ranks ﬁrst

with an investment of 17.1%. This is similar to the ﬁndings by Edquist (2011), who

reports an investment rate of 16, but higher than the ﬁndings by Roth and Thum

(2013), who report an investment rate of 13.6% over business GVA. Sweden is

followed by Finland, France, Denmark, and Ireland with investment rates of 15.6%,

14.5%, 13.4%, and 13.4% over GVA, respectively. Such values are again higher than

those found by Roth and Thum (2013). In particular, the Irish case seems noteworthy,

given its low values in the literature (Roth & Thum, 2013, p. 498). Most countries’

investment rates are positioned between 9% and 12%, and therefore fall near the

EU-16 average investment rate of 11%. This is in the range of the value of 9.9%, as

26 Felix Roth

reported in earlier econometric work (Roth & Thum, 2013,p.498).Thelowest

investment levels can be detected in Spain, Slovakia, and Greece, with values of

7.0, 6.8, and 4.5, respectively. Overall, it is noteworthy that the equal investment levels

for Germany and Italy—with values of 9.3% and 9.2%—as well as the pronounced

difference between Germany and France by 5.2% points, were not detected in the

earlier literature using INNODRIVE data (Roth & Thum, 2013,p.498).

In order to analyze the distribution of the three intangible dimensions, Fig. 2.2

displays a scatterplot between the innovative property and economic competencies.

The ﬁve countries located in the upper-right corner—Sweden, Ireland, Finland,

Denmark, and France—can be classiﬁed as highly innovative and strong investors

in economic competencies. In addition, four out of these ﬁve countries score high on

computerized information. There are some economies, however, that are highly

Fig. 2.1 Business intangible investment (as a percentage of GVA) in 16 EU countries, 2000–2015

Notes: Investments are compared to GVA (non-farm business sector b-n + r-s excluding real estate

activity). Softdb ¼software and databases; Minart ¼entertainment, artistic and literary originals,

and mineral exploration; NFP ¼new product development costs in the ﬁnancial industry; Design ¼

design; R&D ¼research and development; Brand ¼brand names; Org.Cap. ¼organizational

capital; FSHC ¼ﬁrm-speciﬁc human capital.

Sources: INTAN-Invest (NACE2) data (Corrado et al., 2018).

Aﬁrst comparison of the time series patterns of the INNODRIVE and INTAN-Invest (NACE2

rev.) in Fig. 2.A1 in Appendix 3reveals that total intangible capital investment has strongly

increased in the case of Italy, moderately increased in the case of France, and has not increased at

all in the case of Germany, compared to the original INNODRIVE data. Future research should

analyze these differences in more detail, by country and asset type.

Revisiting Intangible Capital and Labor Productivity Growth, 2000–2015 27

innovative, but which invest less in economic competencies and computerized

information, such as Germany.

The third category includes countries that score

low on innovative property but high on economic competencies, namely the UK, the

Netherlands, and Portugal, of which only the Netherlands scores high on promoting

computerized information. The fourth category contains countries that score low on

both dimensions: Italy, Spain, Slovakia, and Greece. Three out of these four coun-

tries also score low on computerized information.

Figure 2.3 compares business investments in intangible and tangible capital as

used in the econometric estimation. Once intangibles are included in the asset

boundary of the national accounts, the average level of investment of the 16 EU

countries is 25.1%. This value is signiﬁcantly higher than the value produced if one

only considers tangible capital investment, which would be at 14.1%. Among the

16 EU countries, seven countries (Finland, France, Sweden, the Netherlands, the

United Kingdom, Ireland, and Denmark) invest more in intangibles than in tangi-

bles—their share of intangible/tangible investment is already greater than 1%. This

is in line with the ﬁnding by Nakamura (2010), who detected this pattern for the US

as early as the year 2000, but contrasts with an earlier analysis for the time period

Fig. 2.2 Scatterplot between innovative property and economic competencies (as a percentage of

GVA), 2000–2015

Notes: The dashed lines indicate the EU16 average values. AT ¼Austria; CZ ¼Czech Republic;

DE ¼Germany; DK ¼Denmark; EL ¼Greece; ES ¼Spain; FI ¼Finland; FR ¼France; IE ¼

Ireland; IT ¼Italy; NL ¼the Netherlands; PT ¼Portugal; SE ¼Sweden; SI¼Slovenia; SK ¼

Slovakia; UK ¼United Kingdom.

Sources: INTAN-Invest (NACE2) data (Corrado et al., 2018).

Germany’s position might be related to the altered methodology in the INTAN-Invest (NACE2)

dataset (Corrado et al., 2018) (see Fig. 2.A1 in Appendix 3).

28 Felix Roth

1998–2005 (Roth & Thum, 2013, p. 500), which did not ﬁnd such a pronounced

pattern.

Figure 2.4 shows the time series pattern for intangible and tangible capital

investment and labor productivity growth for the 16 individual EU countries and

the average EU-16 pattern. Three ﬁndings are especially noteworthy. First, in line

with earlier literature (Corrado et al., 2018), when analyzing an average EU-16 time

series pattern, the crisis has led to a slight decline in intangible capital investment but

a more pronounced decline in tangible capital. Whereas intangible capital invest-

ments have swiftly recovered, tangible capital investments have not yet recovered to

pre-crisis levels. Second, the decline in investment in tangible capital has been

pronounced in EA countries due to the sovereign debt crisis, particularly in Greece,

Spain, Italy, Portugal, and Slovenia. Conversely, with the exception of Greece,

intangible capital investment has even increased in these countries in times of crisis

and economic recovery. Third, the Irish case is exceptional. In times of economic

recovery, Ireland has managed to more than double its intangible capital invest-

ments—largely due to signiﬁcant investments in R&D.

Fig. 2.3 Business tangible and intangible capital investments (as a percentage of GVA), EU16,

2000–2015

Notes: CT ¼communications technology; IT ¼information technology; OCon ¼total non-

residential capital investment; OMach ¼other machinery and equipment; TraEq ¼transport

equipment; Cult ¼cultivated assets; IC ¼intangible capital. Residential Structure has been

excluded. Values on top of the bars depict the intangible/tangible capital investment ratio.

Sources: INTAN-Invest (NACE2) data (Corrado et al., 2018) and EUKLEMS data (Jäger, 2017).

Such contrasting ﬁndings might be related to the overall increase in total intangible capital

investment in the INTAN-Invest dataset (NACE2), as displayed in Fig. 2.A1 in Appendix 3.

Revisiting Intangible Capital and Labor Productivity Growth, 2000–2015 29

Fig. 2.4 Investments in intangibles and tangibles and labor productivity in the 16 EU countries (2000–2015)

Notes: Investment in intangibles, tangibles, and labor productivity are given in millions of national currencies and are standardized to 1 in the year 2008. The

continuous line indicates the start of the ﬁnancial crisis in September 2008. The dashed line indicates the start of the economic recovery at the end of 2013.

Adapted y-scales are applied to Greece, Ireland, and Slovakia. EU-16 average is based on PPP-adjusted values.

Sources: INTAN-Invest (NACE2) data (Corrado et al., 2018).

30 Felix Roth

7 Econometric Estimation

We estimate eq. (2.1) with the help of a pooled panel (PP) estimation approach.

control for panel heteroscedasticity, a panel-corrected standard error estimation

procedure (PCSE) was used.

It should be noted that the PP-PCSE estimation yields

the same coefﬁcients as a random-effects estimator (see row 27 in Table 2.3). This

property permits us to compare our results directly with the econometric ﬁndings of

the existing literature (Roth & Thum, 2013, pp. 501–505). Regression 2.1 in

Table 2.2 shows the results when estimating a traditional production function

without the inclusion of intangibles (excluding software, R&D, and entertainment,

artistic and literary originals, and mineral exploration from the tangible capital

investment). Growth in tangible capital services is positively associated with labor

productivity growth and has a coefﬁcient of 0.31, which explains a 64% share of

labor productivity growth.

Regression 2.2 includes intangibles. Growth in intan-

gible capital services positively relates to labor productivity growth with a coefﬁ-

cient of a magnitude of 0.38, explaining 66% of labor productivity growth. As can be

inferred from Table 2.1, this value is higher than the ﬁgure of 50% reported in earlier

work (Roth & Thum, 2013, p. 502). Once intangibles are included, the impact of

tangible capital diminishes to 34%, which is a slightly lower value than previously

reported in the literature (Roth & Thum, 2013, p. 503).

This ﬁnding clariﬁes that

intangible capital investments have become the dominant source of growth in EU

countries.

Regression 3 in Table 2.2 analyzes the relationship between intangible capital and

labor productivity during times of crisis by adding a crisis (2008–2013) interaction

effect to the speciﬁcation of regression 2. Regression 3 clariﬁes that while the

relationship between tangible capital services growth and labor productivity growth

actually turns negative in times of crisis, with a coefﬁcient of 0.04 (0.28–0.32), the

relationship between intangible capital services growth and labor productivity

growth remains positive with a coefﬁcient of 0.20 (0.48–0.28). To analyze this

novel ﬁnding in more detail, regression 4 adds a recovery interaction effect

Without a lagged initial income term on the left-hand side, the baseline model speciﬁcation in

eq. (2.1) may be estimated without the complexities of a dynamic panel analysis. When replicating

the random-effects estimation by Roth and Thum (2013, pp. 501–505), a Breusch and Pagan LM

test for random effects was performed via the post-estimation command “xttest0”(Stata Corpora-

tion, 2017). With a χ

value of 0, the rejection of the null hypothesis fails. This validates the usage of

a pooled panel estimation approach.

The PCSE calculation was performed via the “xtpcse”command (Stata Corporation, 2017).

Taking eq. (2.1) as our reference, with the mean value of (lnq

i,t

ln q

i,t1

) being 1.5, the mean

value of (lnk

i,t

ln k

i,t1

) being 3.1, and αbeing 0.31, the calculation can be set up as follows:

(0.31*3.1)/1.5 ¼0.64.

When controlling for Ireland in 2015 (see row 2 in Table 2.3 and Fig. 2.4), intangible capital

services explain 46% of labor productivity growth. This value is closer to the 50% ﬁnding by Roth

and Thum (2013, p. 502). Growth in tangible capital services and TFP then explains 31% and 23%,

respectively.

Revisiting Intangible Capital and Labor Productivity Growth, 2000–2015 31

(2014–2015) to a crisis-recovery sub-sample (2008–2015). Regression 4 clariﬁes

that in times of economic recovery, intangible capital services growth has a strong

positive relationship to labor productivity growth. This ﬁnding is particularly evident

in Ireland in 2015, where a large intangible service growth (20%) is related to a large

labor productivity growth of (25.8%) (see rows 2 and 3 in Table 2.3 and Fig. 2.4).

Table 2.2 Intangibles and labor productivity growth, 2000–2015, PP-PCSE estimation

Estimation method

PP-

PCSE

PP-

PCSE

PP-

PCSE

PP-

PCSE

PP-

PCSE 2SLS

Time sample

2000–

2015

2000–

2015

2000–

2015

2008–

2015

2000–

2015

2000–

2015

Equation (1) (2) (3) (4) (5) (6)

Tangible services

growth

0.31*** 0.19** 0.28*** 0.13 0.18** 0.58

(0.08) (0.08) (0.08) (0.15) (0.07) (0.42)

Tangible services

growth*crisis

–– 0.32** –– –

(0.13)

Tangible services

growth*recovery

–– – 0.47 ––

(0.30)

Intangible services

growth

–0.38*** 0.48*** 0.32*** –0.50***

(0.07) (0.09) (0.11) (0.16)

Intangible services

growth*crisis

–– 0.28** –– –

(0.13)

Intangible services

growth*recovery

–– – 0.42* ––

(0.23)

Innovative property

services growth

––––0.37*** –

(0.07)

Computerized informa-

tion services growth

––––0.01 –

(0.04)

Economic competencies

services growth

––––0.02 –

(0.06)

Upper secondary

education 15+

0.07*** 0.05*** 0.05*** 0.02 0.06*** 0.07***

(0.02) (0.01) (0.01) (0.02) (0.01) (0.02)

Catch-up 0.02** 0.02*** 0.02*** 0.01 0.02** 0.02*

(0.01) (0.01) (0.01) (0.01) (0.01) (0.01)

Business cycle 0.11* 0.12* 0.13** 0.13* 0.12* 0.11**

(0.06) (0.06) (0.06) (0.07) (0.06) (0.05)

R-squared 0.40 0.50 0.54 0.63 0.54 0.46

Observations 256 256 256 128 256 208

Number of countries 16 16 16 16 16 16

Notes: In regression (2.1), tangible services growth, labor productivity growth, and the catch-up

term exclude software, R&D, and entertainment, artistic and literary originals, and mineral explo-

ration. In regressions, (2–6) labor productivity growth and the catch-up term are expanded with

intangible capital. Tangible capital excludes residential capital. Labor productivity growth was

calculated based on the GVA of the non-farm business sectors bn+rs(excluding real estate

activities). ***p < 0.01, **p < 0,05, *p < 0.1.

32 Felix Roth

Table 2.3 Sensitivity analysis for the baseline PP-PCSE estimator

Row Speciﬁcation change

Coefﬁcient on

intangibles Countries Obs. R-squared

Baseline regression

(1) Baseline—regression 0.38*** 16 256 0.50

Inﬂuential cases

(2) Including Irish 2015 Dummy 0.26*** 16 256 0.59

(3) Excluding Ireland 0.24*** 15 240 0.48

(4) Excluding Greece 0.44*** 15 240 0.56

(5) Excluding Greece and Ireland 0.28*** 14 224 0.56

(6) Excluding New Member States 0.37*** 13 208 0.53

Restructuring of country sample

(7) 13 EU countries, 2000–2015 0.52*** 13 208 0.65

(8) Dummy for coordinated

economies

0.37*** 16 256 0.51

(9) Dummy for Mediterranean

countries

0.37*** 16 256 0.50

(10) Dummy for New Member States 0.33*** 16 256 0.53

(11) Dummy for Scandinavian

countries

0.37*** 16 256 0.50

(12) Dummy for liberal economies 0.37*** 16 256 0.50

Speciﬁcations

(13) Rule of Law 0.37*** 16 240 0.51

(14) Openness to Trade 0.33*** 16 256 0.53

(15) FDI 0.39*** 16 241 0.54

(16) Government Expenditures 0.35*** 16 256 0.52

(17) Social Expenditures 0.31*** 16 256 0.54

(18) Education Expenditures 0.41*** 16 241 0.57

(19) Inﬂation 0.38*** 16 256 0.53

(20) Income Tax 0.36*** 16 256 0.50

(21) Stock Market Capitalization 0.38*** 16 204 0.50

(22) Alternative Business cycle 0.38*** 16 256 0.50

Other independent variables

(23) Without Ireland (Inno. Prop.) 0.11 16 240 0.50

(24) Without Ireland (Computerized

Information)

0.01

(25) Without Ireland (Economic

Competencies)

0.17***

Methods

(26) Panel Autocorrelation—Order 1 0.40*** 16 256 0.58

(27) Random-Effects 0.38*** 16 256 0.51

Notes: The random-effects estimator depicts an overall R-Square value. ***p < 0.01, **p < 0,05,

*p < 0.1. Obs. ¼Observations.

Revisiting Intangible Capital and Labor Productivity Growth, 2000–2015 33

Regression 5 assesses which dimensions of intangible capital services are the key

drivers for the positive relationship between intangible capital and labor productivity

growth. It includes 1) computerized information, 2) innovative property, and 3) eco-

nomic competencies. In contrast to earlier work (Roth & Thum, 2013, p. 503), which

ﬁnds economic competencies to be the main driver, we now ﬁnd innovative property

to be a strong driver (0.37) of labor productivity growth. This relationship describes

the evidence in the Irish case in 2015, in which a large share of innovative property

services growth is related to a large labor productivity growth. Excluding Ireland in

rows 23–25 in Table 2.3 renders innovative property insigniﬁcant and re-establishes

economic competencies with a coefﬁcient of 0.17 as the main driver. In order to

control for potential endogeneity, regression 6 estimates eq. (2.1) with the help of a

2SLS estimation approach and 208 overall observations. Following earlier econo-

metric work by Roth and Thum (2013, p. 503), lagged levels of intangible and

tangible capital as instruments were chosen.

The results clarify that while the

relationship between tangible capital and labor productivity growth is rendered

insigniﬁcant after controlling for endogeneity, the coefﬁcient for intangible capital

services growth remains highly signiﬁcant, yielding a further increase in magnitude

(0.50). The sensitivity analysis in Table 2.3 further explores the robustness of the

coefﬁcient of intangible capital on labor productivity growth, from regression

2, permitting us to conduct an analysis with a maximum of 256 observations.

Table 2.3 displays a sensitivity analysis of regression 2 in Table 2.2. The ﬁrst row

shows the coefﬁcient for the Baseline regression, regression 2 in Table 2.2. Rows

2–6 analyze the sensitivity due to inﬂuential cases.

When controlling for Ireland in

2015, as expected, the intangible capital coefﬁcient declines (0.26), explaining a

46% share of labor productivity growth. A similar decline in magnitude (0.24 and

0.28) is found when excluding Ireland or Ireland and Greece from the country

sample in rows 3 and 5. Excluding Greece in row 4 yields a higher coefﬁcient

(0.44). Excluding the three new member states in row 6 yields a slight reduction of

the coefﬁcient (0.37). Rows 7–12 restructure the country sample and analyze ﬁve

distinct European regime dummies. When analyzing the 13 EU countries from 2000

to 2015 from earlier work (Roth & Thum, 2013), the relationship remains highly

signiﬁcant and reveals an increase in magnitude (0.52). Neither controlling for the

ﬁve European regime dummies in rows 8–12, nor altering the model speciﬁcations in

rows 13–22, nor using alternative estimation approaches in rows 26–27 alters the

signiﬁcance of the relationship between intangible capital and labor productivity in

any appreciable manner, although the magnitude of the relationship varies slightly.

To be precise, the ﬁrst two lagged levels were used. A Wooldridge robust score test of

overidentifying restrictions was performed via the 2SLS post-estimation command “estat overid”

(Stata Corporation, 2017). With a χ

(2) value of 0.4, the rejection of the null hypothesis fails. This

indicates that the instruments used are valid.

The inﬂuential cases of Ireland and Greece have been detected via the “avplot”command (Stata

Corporation, 2017), as well as from Fig. 2.4.

34 Felix Roth

8 Conclusions

This contribution analyzes the relationship between intangible capital investment by

businesses and labor productivity growth by analyzing an EU-16 country sample

over the time period 2000–2015, with the help of a cross-country growth-accounting

estimation approach. By matching the most recent release of the INTAN-Invest

(NACE2) dataset (Corrado et al., 2018) with the latest data available from the EU

KLEMS dataset (Jäger, 2017) alongside a wide range of growth-relevant policy

variables from Eurostat, the OECD, and the World Bank, this contribution generates

the largest and most up-to-date panel dataset developed on intangible capital at the

macro-level, based on a total of 256 country observations.

This contribution reaches three major ﬁndings. First, in line with previous growth

econometric literature (Roth & Thum, 2013), this contribution conﬁrms that once

intangibles are factored into the calculations, they become the dominant source—up

to 66%—of labor productivity growth in the EU. Second, when focussing on times

of crisis (2008–2013), this contribution ﬁnds that whereas the relationship between

tangible capital and labor productivity turned negative, the impact of intangibles on

growth remained solidly positive throughout this period. Thirdly, when accounting

for the economic recovery (2014–2015), this contribution establishes a highly

signiﬁcant and remarkably strong relationship between intangible capital and labor

productivity growth.

In light of these novel empirical results, four main policy conclusions can be

drawn from our analysis of European economies. First, given the paucity of econo-

metric ﬁndings in the literature analyzing the relationship between intangible capital

and labor productivity growth at the macro-level, additional research should be

devoted in the future to further econometrically corroborate the positive relationship

between intangible capital and labor productivity. This future research should

examine in more detail the evolutionary changes in existing cross-country intangible

capital datasets, by country and by asset type. Second, as developed economies

transition into knowledge societies, it is essential to incorporate a complete set of

intangibles—including branding, ﬁrm-speciﬁc human capital, and organizational

capital—into today’s national accounting framework in order to acknowledge the

pronounced shift in investment patterns from tangible to intangible investment in

contemporary national accounting frameworks. The current frameworks are inade-

quate, as they under-represent actual levels of capital investment in European

economies. Their reported levels of capital investment would undoubtedly be greater

once the full range of investment in intangible capital is incorporated into the

accounting framework.

Third, the incorporation of a broader dimension of innovation investment seems

to be an important ﬁrst step in revising today’s national accounting framework,

particularly when focusing on the business sector. Moreover, a follow-up step

consists of broadly adapting the national accounting framework to reﬂect environ-

mental, health, and public intangible capital investment. Fourth, government policies

that actively support the accumulation of business intangibles should be designed

Revisiting Intangible Capital and Labor Productivity Growth, 2000–2015 35

and implemented as soon as possible. This will foremost require government

investment in public intangibles, such as enhancing the quantity and quality of a

highly skilled labor force, well-functioning formal and informal institutions, and a

well-designed policy framework that includes credible ﬁnancial conditions and an

effective scheme offering intangible tax incentives at the member state and EU

level.

Appendix 1 Construction of Intangible and Tangible Capital

Services Growth

Following Oulton and Srinivasan (2003), Marrano et al. (2009) and the EU KLEMS

approach (Timmer et al., 2007) and consistent with Roth and Thum (2013), tangible

and intangible capital services growth (lnk

i,t

ln k

i,t1

) and (lnr

i,t

ln r

i,t1

)or

respectively Δln k

i,t

and Δln r

i,t

are deﬁned as:

Δln ki,t¼X

i¼1

νi,tΔln ai,tð2:A1Þ

Δln ri,t¼X

i¼mþ1

νi,tΔln ai,tð2:A2Þ

where a

i,t

is the asset-speciﬁc capital stock, as calculated with the PIM, assets from

1tomare tangible assets, and assets from m+1tonare intangible. Lower case k

i,t

and a

i,t

indicate that the variables are scaled on hours worked. νi,tis a 2-year

average weighting term deﬁned as:

νi,t¼1

2νi,tþνi,t1

½ ð2:A3Þ

The term ν

i,t

is computed as:

νi,t¼pa

i,tai,t

i¼1

i,tai,t

ð2:A4Þ

See here Gros and Roth (2012); Haskel and Westlake (2018); Roth (2019); and Thum-Thysen

et al. (2019).

36 Felix Roth

From (2.A4), a

i,t

is the asset-speciﬁc capital stock and pa

i,tis the asset-speciﬁc

(tangible or intangible) user cost. The latter user cost is deﬁned as:

i,t¼pI

i,t1itþδi,tpI

i,tpI

i,t1

 ð2:A5Þ

where pI

i,t1is the investment price, constructed from the price index of the GFCF

series at chained prices, i

is the time-speciﬁc rate of return (common to all tangible

and intangible assets) and δ

i,t

is the time variant and asset-speciﬁc depreciation rate.

The depreciation rate that varies over time reﬂects the varying contribution over time

of industries to the total non-farm business sector (b-n + r-s excluding real estate

activities). The time-varying depreciation rate used here is deﬁned as:

δi,t¼Ai,t1þIi,tAi,t

Ai,t1

ð2:A6Þ

The last term in (A5) is the capital gain term pI

i,tpI

i,t1



; following Niebel and

Saam (2011), it is computed considering the price indices of three consecutive

periods using the formula:

i,tpI

i,t1



¼1

2ln pi,t



ln pi,t2



pi,t1ð2:A7Þ

The rate of return i

is common to all the tangible and intangible assets and

represents the overall return on the investment under the proﬁt maximization

assumption, as explained in Oulton and Srinivasan (2003). Following Timmer

et al. (2007), the common rate of return is computed here using an ex-post approach

that accounts for the rental payments of each asset:

it¼

tatþP

i,tpI

i,t1



ai,tP

i,tδi,tai,t

i,t1ai,t

ð2:A8Þ

where pa

tatis the total nominal capital compensation, obtained by subtracting the

labor compensation from the GVA.

Revisiting Intangible Capital and Labor Productivity Growth, 2000–2015 37

Appendix 2 Descriptive Statistics

Table 2.A1 Descriptive statistics, EU16, 2000–2015

Obs. Mean

Standard

deviation Min. Max.

LPG—expanded by intangibles (in %) 256 1.6 3.1 7.6 25.8

LPG–excluding all intangibles (in %) 256 1.5 3.0 8.7 16.7

Intangible services growth (in %) 256 2.8 3.4 7.9 20

Tangible services growth (in %) 256 3.1 2.9 4.4 13.8

Tangible services growth-expanded by intan-

gibles (in %)

256 2.9 3.0 5.8 13.3

Innovative property services growth (in %) 256 4.0 4.3 8.2 33.5

Economic competencies services growth

(in %)

256 1.4 3.8 13.0 17

Computerized information services growth

(in %)

256 3.9 6.4 18.4 40.1

Upper-secondary education 15+ (in %) 256 67.8 14.2 21 87.6

Interaction education and catch-up—expanded

by intangibles

256 34.7 35.0 0 197.2

Interaction education and catch-up—excluding

all intangibles

256 31.8 30.4 0 158.1

Business cycle (in %) 256 91.2 4.5 72.5 96.9

Rule of law 240 1.4 0.5 0.3 2.1

Openness (in %) 256 92.3 39 45.6 215.4

FDI (main balance of payments as a % of

GDP)

241 0.4 5.2 15.2 10.2

Government expenditure (as a % of GDP) 256 47.1 5.9 29 65.1

Social expenditure (as a % of GDP) 256 25.3 4.7 14.8 34.5

Education expenditure (as a % of GDP) 241 5.3 1.2 3 8.8

Inﬂation (in %) 256 2.2 1.7 1.7 12.2

Income tax (as a % of GDP) 256 8.9 5.0 2.6 26.3

Stock market capitalization (as a % of GDP) 204 52.8 36.2 1.5 233.9

Notes: LPG ¼Labor Productivity Growth; Obs. ¼Observations; Min. ¼Minimum; Max. ¼

Maximum.

38 Felix Roth

Appendix 3 A Comparison of INNODRIVE and INTAN-Invest datasets

Fig. 2.A1 Intangible investments in 13 EU countries, 1995–2015: a comparison of INNODRIVE and INTAN-Invest (NACE2) datasets

Notes: PPP-Adjusted time series were used. The 13 EU countries are: Austria, Czech Republic, Denmark, Finland, France, Germany, Ireland, Italy, the

Netherlands, Slovenia, Spain, Sweden, and the United Kingdom. “Total Intangible Investments”is the sum of Computerized Information, Innovative Properties,

and Economic Competencies. Economic sectors for INNODRIVE (NACE1) dataset include c-k + o (excluding k70) and for INTAN-Invest (NACE2) dataset

include b-n + r-s (excluding l).

Sources: INNODRIVE data (INNODRIVE, 2011) and INTAN-Invest (NACE2) dataset (Corrado et al., 2018).

Revisiting Intangible Capital and Labor Productivity Growth, 2000–2015 39

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42 Felix Roth

Chapter 3

The Rule of Law and Labor Productivity

Growth by Businesses: Evidence for the EU,

1998–2005

Felix Roth

Abstract This contribution analyses the relationship between the rule of law and

labor productivity growth by businesses within an EU country sample over the

period 1998–2005. It ﬁnds that the rule of law affects labor productivity growth

(LPG) by businesses within the EU via two distinct channels. First, the rule of law

positively affects labor productivity growth by stimulating total factor productivity

(TFP) growth. Second, the rule of law positively inﬂuences business investments in

intangible capital. The author concludes that the rule of law is beneﬁcial in facili-

tating an economy’s transformation towards becoming a knowledge economy.

JEL Classiﬁcations E02 · E22 · O34 · O43 · O52 · P14

Keywords Rule of law · Labor productivity growth · Intangible capital investment ·

1 Introduction

The academic literature (Barro, 2001, p. 13; Knack & Keefer, 1995, p. 210), as well

as policymaking institutes (European Commission, 2013a, p. 1, World Bank, 2006,

pp. 87–99) and non-proﬁt organizations (Agrast et al., 2013, p. 8) have stressed that

respect for the rule of law constitutes a prerequisite for a nation’s economic

Originally published in: Felix Roth. The Rule of Law and Labor Productivity Growth by

Businesses: Evidence for the EU, 1998–2005. European Commission, DG Joint Research Centre,

Project Report, No. 258747, 2014.

The author wishes to thank Sjoerd Hagemann, Raf van Gestel, Leandro Elia, Michaela Saisana,

and Andrea Saltelli for their valuable comments. The paper received funding from the European

Commission’s DG Joint Research Centre.

Felix Roth (*)

Department of Economics, University of Hamburg, Hamburg, Germany

e-mail: felix.roth@uni-hamburg.de

©The Author(s) 2022

F. Roth, Intangible Capital and Growth, Contributions to Economics,

https://doi.org/10.1007/978-3-030-86186-5_3

performance. In this context, the European Commission recently claimed that in the

EU “the quality (...) of national justice systems plays a key role in restoring (...) the

return to growth”(European Commission, 2013a, p. 1). Given this prominent claim

by the European Commission and the fact that economic empirical literature focuses

strongly on the dichotomy between developed and developing economies (see

among others Acemoglu et al., 2001; Barro, 2001; Easterly & Levine, 2003; Hall

& Jones, 1999; Kaufmann & Kraay, 2002; Knack & Keefer, 1995; Rigobon &

Rodrik, 2005; Rodrik et al., 2004; c.f. World Bank 2006, p. 4, pp. 96–99), this

contribution’s aim is to assess the relationship between the rule of law and a

country’s economic performance in an EU context.

In this respect, it can be claimed, that the economies of most EU member states,

similar to those of the US (Corrado et al., 2005,2009; Nakamura, 2010) and other

highly developed economies, such as Japan (Fukao et al., 2009), are on the verge of

evolving into knowledge economies (Piekkola 2011). This claim is substantiated by

empirical evidence, which highlights that in some EU countries such as France,

Sweden, and the United Kingdom, business intangible capital investments already

represent two-thirds made in tangible capital investment. Similarly, when accounting

for labor productivity growth of businesses in the EU, the growth of intangible

capital services explains a larger share (50%) than the growth of tangible capital

services (40%) (Roth & Thum, 2013). Given these and other most recent empirical

ﬁndings, which underline the importance of intangible capital investment for labor

productivity growth in the EU (Marrano et al., 2009; Edquist, 2011), this contribu-

tion utilizes an intangible capital-enhanced production model as introduced by Roth

and Thum (2013) to explore the relationship between the rule of law and labor

productivity growth by businesses in the EU. Following the existing literature

(Benhabib & Spiegel, 1994; Knack & Keefer, 1995), this contribution identiﬁes

two main theoretical channels of how the rule of law might affect labor productivity

growth by businesses. First, the rule of law might directly inﬂuence labor produc-

tivity growth by businesses via its TFP component. Second, the rule of law might

inﬂuence labor productivity growth indirectly via its impact on business investments

in tangible and intangible capital.

This study is structured in the following manner: The second section explores the

theoretical links between the rule of law and labor productivity growth and identiﬁes

two transmission channels through which the rule of law affects a country’s labor

productivity growth by businesses. The third section introduces the model speciﬁ-

cations for the two main theoretical channels and elaborates upon the research

design, the operationalization of the rule of law and the data sources used. The

fourth section carries out a descriptive analysis of the distribution of the rule of law

across an EU-27 country sample and the bivariate relationship between the rule of

law and intangible and tangible capital investments. The ﬁfth section undertakes an

econometric analysis of the relationship between the rule of law and labor produc-

tivity growth by businesses, as well as the rule of law and business investments in

tangible and intangible capital. The last section presents the main ﬁndings, discusses

the empirical ﬁndings in light of the theoretical discussion and offers policy

conclusions.

44 Felix Roth

2 The Rule of Law and Labor Productivity Growth by

Businesses: Theoretical Links in an EU Context

Although ﬁnal agreement on precisely what constitutes the rule of law has yet to be

established (Botero & Ponce, 2011, p. 2), the academic literature in the general social

sciences has identiﬁed four theoretical links through which the rule of law is

associated with economic growth: namely 1) the provision of the personal security

of individuals, 2) the security of property and enforcement of contracts, 3) institu-

tional checks on government, and 4) control of private capture and corruption

(Haggard & Tiede, 2011, pp. 674–75). Within these four theoretical links, the

economic literature has identiﬁed the security of property and enforcement of

contracts as one of the core theoretical links through which the rule of law affects

economic growth (Haggard & Tiede, 2011, p. 675). The preeminence of the security

of property rights and the enforcement of contracts within the discipline of econom-

ics has been theoretically elaborated upon by classical economic thinkers such as

Smith (1998, pp. 407–408, p. 459) and contemporary economists such as North

(1990, pp. 64–65). The fact that the rule of law is essential for a nation’s economic

performance, inter alia, by securing property rights and enforcing contracts, is also

used as a theoretical presumption by the extensive empirical economic literature

focusing on the relationship between the rule of law and economic performance

(Acemoglu et al., 2001, p. 1369; Barro, 2001, p. 13; Brunetti et al., 1998, p. 353;

Hall & Jones, 1999, p. 84; Knack & Keefer, 1995, p. 207; c.f. Li & Li, 2013; for a

general review, see Asoni, 2008).

In this respect, some authors even start their analysis of the relationship between

the rule of law and economic growth with the remark that “few would dispute that

the security of property and contractual rights (...) are signiﬁcant determinants of

the speed with which countries grow”(Knack & Keefer, 1995, p. 207). However,

although there is a clear understanding in the economic literature that the rule of law,

among others, by securing property rights and enforcing contracts, is important for

the economic performance of a nation, it seems worthwhile to once more identify the

theoretical transmission channels between the rule of law and a country’s labor

productivity growth by businesses. This is particularly the case considering the fact

that, within an EU context, it is necessary to differentiate business investments in

intangible capital from those in tangible capital. Following the argumentation of the

existing economic growth literature (Benhabib & Spiegel, 1994 on modelling

political instability; Knack & Keefer, 1995 on modelling the rule of law), one is

able to identify two transmission channels of how the rule of law might inﬂuence a

country’s labor productivity growth by businesses. First, the rule of law might

directly be related to labor productivity growth by stimulating TFP growth. Second,

the rule of law might stimulate business investments in intangible and tangible

capital.

The Rule of Law and Labor Productivity Growth by Businesses 45

2.1 Direct Inﬂuence of the Rule of Law on Labor Productivity

Growth by Businesses

The direct inﬂuence of the rule of law on labor productivity growth by businesses is

exerted through its inﬂuence on an economy’s TFP growth (or technological pro-

gress) by businesses. TFP accounts for all components, which facilitate the overall

production process and thus imply an amelioration of the general efﬁciency and

effectiveness with which the business sector utilizes its given level of intangible and

tangible capital. In this respect, an important channel through which the rule of law

might positively impact the business sector’s TFP growth is via the reduction of

transaction costs as proposed within the theoretical framework by North (1990, pp.

27–28 and pp. 64–65). Applying this theoretical framework, one can argue that a

lower level of the rule of law will lead to higher transaction costs, hampering those

activities that aim at raising productivity, for example, the invention and innovation

of new technologies, resulting in a lower level of technological progress within a

country’s business sector (North, 1990, pp. 64–65). This theoretical reasoning is

prominently picked up by Hall and Jones (1999) in their elaboration of the rule of

law as one central indicator within their concept of the term “social infrastructure”.

Hall and Jones (1999) argue that the rule of law, by protecting the output of

individual productive units and preventing predator behavior, is beneﬁcial for high

levels of output per worker by encouraging technological progress (Hall & Jones,

1999, pp. 95–97). The above argumentation is universal in nature and can be applied

in the context of developing as well as highly developed economies, such as the EU

member states.

2.2 Indirect Inﬂuence of the Rule of Law on Labor

Productivity Growth by Businesses

In the absence of a substantial level of the rule of law, one would expect that

entrepreneurs and businesses will in general invest less in intangible and tangible

capital as the economic environment is strongly prone to uncertainty (Brunetti et al.,

1998). Thus, a large part of the economic literature agrees on the fact that the rule of

law should positively inﬂuence economic performance via the indirect channel by

stimulating business investments in capital (c.f. Li & Li, 2013 who discuss the

Chinese case). In this context, the literature argues that it would be expected that a

business environment characterized by unclear property rights and uncertain contract

enforcement would be associated with lower investment by companies (Brunetti

et al., 1998, p. 353). Along these lines, it is argued that without secure property and

contractual rights, investment would be discouraged (Knack & Keefer, 1995,p.

207), which will lead to lower levels of investments in capital (Hall & Jones, 1999,p.

95; North, 1990, p. 65).

46 Felix Roth

However, before concluding that the rule of law would always be positively

related to business investments, in particular when analyzing a country sample of

advanced economies, such as the one from the EU, it seems necessary, for an

adequate discussion, to distinguish between investments in tangible and intangible

capital, given that the EU economies are on the verge of transforming themselves

into knowledge economies (Piekkola 2011). Whereas protecting the property rights

of tangible capital, such as nonresidential investments and machinery and equip-

ment, has long been internalized within the institutional framework of many highly

developed EU economies—with the rule of law’s roots being traceable inter alia to

the Roman Empire (Finley, 1976)—protectiing the property rights of intangible

capital, such as investments in enhancing the knowledge base of businesses includ-

ing computerized information, innovative property, and economic competencies

(Corrado et al., 2005) leading to patents, trademarks, copyrights, and design rights,

is a more recent phenomenon tht prominently emerge in the 19 century (Mayer-

Schönberger, 2010, p. 164).

In this respect, when considering advanced economies, such as those of the EU

member states, one can take for granted that investments in tangible capital are

well protected from theft and expropriation, even in countries with relatively lower

levels of the rule of law. Conversely, in those economies exhibiting a relatively

higher level of the rule of law it might even be that the rule of law, in its role as a

regulator (Mayer-Schönberger, 2010, pp. 155–160), hampers investments in tan-

gible capital. In this respect, a higher level of the rule of law might be associated

with a higher level of, e.g., labor market regulations. By making the factor input of

labor more costly, however, these labor market regulations might act as an

incentive to invest in an environment that is less prone to regulation and in

which the utilization of the labor factor is less costly (Nicoletti & Scarpetta,

2003). Thus, it would be plausible, for example, that within the transition countries

of the EU a lower level of the rule of law would be associated with higher business

investments in tangible capital. It might also potentially be that lower levels of the

rule of law are associated with higher levels of corruption which ultimately act as a

grease in fostering investment in tangible capital and growth (for a discussion of

the literature asserting a positive relationship between corruption and growth, see

Méon & Sekkat, 2005, p. 70).

Similar to the ambivalent relationship between the rule of law and tangible capital

investments, one is able to identify both sets of arguments for the relationship

between the rule of law and business investments in intangible capital. On the one

hand, the rule of law’s function as a protector and enforcer of intellectual property

rights will most likely be a key prerequisite for enhancing investment in intangible

capital, and thus the knowledge base of businesses, aimed at generating patents,

trademarks, copyrights, and design rights (Gould & Gruben, 1996, p. 323; pp.

326–327; for speciﬁc R&D activity see Park & Ginarte, 1997, p. 60). The fact that

adequate protection and enforcement of intellectual property rights might be indeed

a basic prerequisite for business investments in intangible capital in the EU context

The Rule of Law and Labor Productivity Growth by Businesses 47

is related to the fact that the given institutional structure has not yet fully adapted to

the new reality of a knowledge economy, thus making intangible capital investments

more prone to theft and property rights violation. Thus, in particular, in an EU

context, it can be argued that the rule of law, by securing intellectual property rights,

functions as a core incentive for entrepreneurial activity and business investments in

intangible capital (Baumol, 2002, p. 8; Mayer-Schönberger, 2010, pp. 164–65).

However, similar to the arguments presented in the discussion above on investment

in tangible capital investments, it should also be mentioned that an excessively strict

intellectual property regime might hamper innovation activity, as it might be pri-

marily used by large corporations to block new market entrants (Dosi et al., 2006;

Mayer-Schönberger, 2010, p. 166; Verspagen, 2006).

To conclude, in the context of the EU, the theory on the impact of the rule of law

on both types of business capital investments, tangible and intangible capital,

remains ambiguous and needs to be tested empirically.

3 Model Speciﬁcations, Research Design,

Operationalization, and Data

3.1 Model Speciﬁcations

As mentioned above, the literature on economic growth (see notably Benhabib &

Spiegel, 1994, on modelling political instability and Knack & Keefer, 1995,on

modelling the rule of law) has identiﬁed two distinct channels of how the rule of law

might affect labor productivity growth: 1) a direct channel by stimulating TFP

growth and 2) an indirect channel by stimulating business investments in intangible

and tangible capital.

3.1.1 A Model for the Direct Contribution of the Rule of Law to Labor

Productivity Growth

Following the theoretical framework by Roth and Thum (2013, pp. 494–95), who

combine a model speciﬁcation by Corrado et al. (2009) with one from Benhabib and

Spiegel (1994), the starting point for estimating the direct contribution of the rule of

law on labor productivity growth by businesses is the following intangible capital-

augmented Cobb-Douglas production function,

Qi,t¼Ai,tKαi,tLγi,tRβi,tεi,tð3:1Þ

where Q

i,t

is GVA (Gross Value Added for the non-farm business sectors c-k + o

excluding real estate activities) expanded by the investment ﬂows of business

48 Felix Roth

intangible capital in country iand period t,Ris the intangible capital stock by

businesses, Kis the tangible capital stock by businesses, Lis labor, and Ais TFP.

Following the authors and assuming constant returns to scale, rewriting the

Cobb-Douglas production function in intensive form and taking differences in

natural logarithms, the following equation is obtained:

ln qi,tln qi,t1



¼ln Ai,tln Ai,t1

ðÞþαln ki,tln ki,t1

ðÞ

þβln ri,tln ri,t1

ðÞþui,tð3:2Þ

where u

i,t

¼ln ε

i,t

ln ε

i,t1

, (lnq

i,t

ln q

i,t1

) is labor productivity growth (GVA

for the non-farm business sectors c-k + o excluding real estate activities expanded by

the investment ﬂows of business intangible capital in country iand period t,

(lnk

i,t

ln k

i,t1

) and (lnr

i,t

ln r

i,t1

) represents the growth of tangible and

intangible capital services and (lnA

i,t

ln A

i,t1

) represents the TFP growth.

As elaborated above, we believe that the rule of law should positively affect labor

productivity growth by businesses via its TFP term. Utilizing an extended approach

of Roth and Thum (2013, 495), a model for (lnA

i,t

ln A

i,t1

) is speciﬁed as follows:

ln Ai,tln Ai,t1

ðÞ¼cþnRoLi,tþgHi,tþmHi,t

Qmax ,tQi,t



Qi,t

þp1uri,t

ðÞþqX

j¼1

Xj,i,tþcdi,t¼2001 ð3:3Þ

where cis the constant term and represents exogenous technological progress, RoL

i,t

is the level of the rule of law of a country, H

i,t

is the level of human capital and

reﬂects the capacity of a country to innovate domestically, the term Hi;t

Qmax ,tQi,t

ðÞ

Qi,t

proxies a catch-up process, the term (1 ur

i,t

) takes into account the business cycle

effect (and is measured as 1–unemployment rate),

the term P

j¼1

Xj,i,tis a sum of

kextra policy variables that could possibly explain TFP growth and cd

i,t ¼2001

is a

crisis dummy to control for the economic downturn in 2001 after the bursting of the

Information Technology bubble in the year 2000 and the 9/11 attack in the United

For the detailed formula of the calculation of the tangible and intangible capital services growth,

see Roth and Thum (2013).

This approach was introduced by Guellec and van Pottelsberghe de la Potterie (2001, pp.

107–116).

The Rule of Law and Labor Productivity Growth by Businesses 49

States in 2001. Inserting Eq. (3.3) into Eq. (3.2) provides the baseline model to be

estimated within the econometric estimation in Sect. 5:

ln qi,tln qi,t1



¼cþnRoLi,tþgHi,tþmHi,t

Qmax ,tQi,t



Qi,t

þp1uri,t

ðÞþqX

j¼1

Xj,i,tþcdi,t¼2001

þαln ki,tln ki,t1

ðÞþβln ri,tln ri,t1

ðÞþui,tð3:4Þ

We now turn our attention to displaying the model speciﬁcation of the indirect

contribution of the rule of law to labor productivity growth.

3.1.2 The Indirect Contribution of the Rule of Law to Labor

Productivity Growth

Applying the logic of the model speciﬁcations by Knack and Keefer (1995, pp.

220–23) and Benhabib and Spiegel (1994, pp. 163–66) to the model speciﬁcation of

Eq. (3.4), the indirect impact of the rule of law on labor productivity via the two

investment channels, business investment in intangible and tangible capital can be

expressed as follows:

Ni,t¼cþnRoLi,tþm1Ri,tþgHi,tþp1uri,t

ðÞþqX

j¼1

Xj,i,tþcdi,t¼2001 þεi,t

ð3:5Þ

Ii,t¼cþnRoLi,tþm2Ki,tþgHi,tþp1uri,t

ðÞþqX

j¼1

Xj,i,tþcdi,t¼2001 þεi,t

ð3:6Þ

where N

i,t

represent the real investment rates for intangible and tangible capital

by businesses respectively, cdisplays the constant term, RoL

i,t

is the level of the rule

of law in country iand period t,R

i,t

,andK

i,t

are the intangible and tangible capital

stock by businesses, respectively, H

i,t

is the level of human capital, the term

(1 ur

i,t

) takes into account the business cycle effect, the term P

j¼1

Xj,i,tis a sum

of kextra policy variables which could possibly explain investment rates in tangible

and intangible capital, cd

i,t ¼2001

is a crisis dummy to control for the economic

downturn in 2001 following the bust of the Information Technology bubble in 2000

and the 9/11 attack in 2001 and ε

i,t

is the error term.

50 Felix Roth

3.2 Research Design

Whereas the description of the distribution of the rule of law indicator of the World

Bank’s Worldwide Governance Indicators Project (WGIP) (Kaufmann et al., 2010)

was conducted within an EU-27 country sample, the econometric analysis between

the rule of law and labor productivity growth as well as between the rule of law and

investments in intangible and tangible capital will be limited to an EU-13 country

sample. Similar to the analysis of Roth and Thum (2013, pp. 495–96), this is due to

limitations in the EUKLEMS data (O’Mahony & Timmer, 2009) concerning tangi-

ble capital data. The econometric exercise is thus limited to the following 13 EU

countries: Austria, Czech Republic, Denmark, Finland, France, Germany, Ireland,

Italy, the Netherlands, Slovenia, Spain, Sweden, and the United Kingdom. Due to

data availability concerning intangible stocks and the construction of intangible

capital services, the time period of the analysis is restricted to 1998 to 2005.

Following an approach by Bassanini and Scarpetta (2001) and utilizing yearly

data, an overall amount of 98 observations were retrieved.

Given that the two

transition countries—the Czech Republic and Slovenia—are following a different

pattern than the 11 EU-15 countries (see Fig. 3.4), a full country sample with all

13 EU countries will be compared to a sample of 11 EU-15 countries, excluding the

two transition countries. The whole research design applies to non-farm business

sectors c-k + o.

3.3 Operationalization and Measurement of the Data

Although data from the World Justice Project (WJP) (Agrast et al., 2013) would have

been based on an excellent operationalization of the rule of law (Agrast et al., 2013,

p. 11) and would have offered high-quality data by utilizing output measures, as well

as individually constructed and polled data (Agrast et al., 2013, pp. 185–90) (for a

detailed discussion, see Appendix 1), the data are incompatible with this contribu-

tion’s research design as the WJP only started to conduct its ﬁrst wave of polling in

2009. Similarly, data from the European Commission (CEPEJ, 2012; European

Commission, 2013a), offering a multitude of interesting input indicators concerning

the rule of law, are incompatible with the present study’s research design, as these

indicators are only measured from 2004 onward. Moreover, in contrast to data from

WGIP and WJP, the European Commission has not yet constructed an overall rule of

Due to shorter time series in intangible capital investment in the Czech Republic and Slovenia, we

were able to generate only ﬁve time observations for intangible capital services for these two

transition countries but eight for the other 11 countries.

The Rule of Law and Labor Productivity Growth by Businesses 51

law index, by combining the most relevant indicators to form an overall index. Given

the fact that the WGIP offers time series data from 1996 to 2012

and thus covers the

time from 1998 to 2005, data from the WGIP are utilized in the descriptive and

econometric sections of this contribution. In addition, although the WGI’s

operationalization of the rule of law is less conceptualized than the one from the

WJP (for a detailed elaboration, see again Appendix 1), it offers a wider set of

information by aggregating indicators concerning the rule of law from 23 different

data sources, including a total of 84 indicators.

All single indicators are then

aggregated to construct the rule of law indicator by using an unobserved components

model (Kaufmann et al., 2010, p. 9). The unit of the rule of law indicators applied to

the 214 countries are those of a standard normal random variable and range from

2.5 to 2.5 (Kaufmann et al., 2010, p. 9 and p. 15).

Beyond the rule of law indicators, the other data for the following econometric

analysis were taken from the various sources described below. Data on the

real investments rates and stock data of intangible capital were taken from

the INNODRIVE macro dataset ( INNODRIVE , 2011). In accordance with the

INNODRIVE data, intangible capital investment included investment in: 1) software,

2) R&D, 3) new architectural and engineering designs, 4) new product development

in the ﬁnancial services industry, 5) mineral exploration and copyright and licenses

costs, 6) organizational capital (own account component), 7) organizational capital

(purchased component), 8) ﬁrm-speciﬁc human capital, 9) advertising, and 10) mar-

ket research. The construction of intangible capital stocks and intangible capital

services follows the approach by Roth and Thum (2013, p. 497). Data on GVA

(nonfarm business sectors excluding real estate activities), tangible capital stocks,

capital compensation, gross ﬁxed tangible capital investments, tangible investment

price indices and labor input (number of hours worked), and depreciation rates were

taken from the EUKLEMS database (O’Mahony & Timmer, 2009). Tangible capital

included: 1) communications equipment, 2) computing equipment, 3) total

nonresidential investment, 4) other machinery and equipment, 5) transport equip-

ment, and 6) other assets, but excluded residential capital. Similar to intangible

capital services, tangible capital services were constructed following the approach by

Roth and Thum (2013, p. 497). Human capital is measured as the percentage of the

population who attained at least upper secondary education, which is taken as a

proxy for the inherent stock of human capital. These data are provided by Eurostat.

The unemployment rate is taken from Eurostat and is utilized to calculate the

business-cycle effect.

The data from 1996 to 2002 have only been collected on a 2-year basis in 1996, 1998, 2000, and

2002. Thus, for the econometric analysis at hand, the values for 1995, 1997, 1999, and 2001 have

been interpolated by linear interpolation. The data can be downloaded from http://info.worldbank.

org/governance/wgi/index.aspx#home.

A list of all 23 data sources and 84 indicators can be downloaded online from the World Bank’s

website (http://info.worldbank.org/governance/wgi/index.aspx#doc).

52 Felix Roth

4 Empirical Description of the Rule of Law within the EU

To analyze the cross-sectional variance of the rule of law in the European context,

Fig. 3.1 displays the distribution of the average value of the rule of law in an EU-27

country sample over the time period 1998–2005. The lower and upper bound values,

indicated by the symbols ●and ▲, respectively, report the 90% conﬁdence interval

associated with the rule of law estimate to identify potential measurement errors

(Kaufmann et al., 2010, p. 13).

Figure 3.1 clariﬁes three important issues. First, across the EU-27, there exists a

signiﬁcant variance concerning the rule of law. Whereas Finland leads the ranking

with an average value of 1.95, Bulgaria and Romania, which are positioned at the

end of the ranking, only display average values of 0.20 and 0.19. The fact that

there is sufﬁcient variation even in an EU-27 country sample is also highlighted by a

sizeable standard deviation of 0.61 by a given mean of 1.09 (see variable named

“Rule of law - EU27 - average 98-05”in Table 3.A1 in Appendix A3).

Second, the given variance in the rule of law is driven by certain country regime

typologies.

Whereas countries from the coordinated, liberal, and Scandinavian

regime typology are solely located at the upper third of the distribution, eight of

the nine countries in the lower third of the distribution are from transition and Baltic

countries.

Third, Italy, the fourth-largest EU economy, is the only EU-15 country that is

positioned in the lower third of the distribution, with a value of 0.66. Even if

considering Italy’s upper bound value of 0.95, it still ranks behind the lower

-1

-0.5

0.5

1.5

2.5

Rule of law Lower bound Upper bound

Fig. 3.1 The rule of law in the EU-27, 1998–2005

Notes: The data are based upon the WGIP (Kaufmann et al., 2010) and are averaged from 1998 to

2005. The 13 countries included in the econometric analysis are denoted with an asterisk.

For an introduction to the different regime typologies, see among others Hall and Soskice (2001).

The Rule of Law and Labor Productivity Growth by Businesses 53

bound values of France (1.07), Germany (1.33), and the UK (1.36). Thus, Italy’s

level of the rule of law is signiﬁcantly smaller in comparison to the three other

equally large EU economies.

The fact that the variance in the rule of law indicator within the EU is driven by

regime characteristics is more clearly highlighted in Fig. 3.2, which compares six

regime typologies within the EU-27. Whereas the Scandinavian (Finland, Den-

mark, and Sweden), Coordinated (Austria, Luxembourg, the Netherlands, Ger-

many, France, and Belgium), and Liberal (the United Kingdom and Ireland)

regime typologies are all positioned at values of 1.5 and above, the Mediterranean

(Italy, Spain, Greece, Portugal, Cyprus, and Malta) typology is positioned only at a

value of around 1.0. The transition (Bulgaria, Romania, Slovak Republic, Poland,

Czech Republic, Hungary, and Slovenia) and Baltic (Lithuania, Latvia, and Esto-

nia) regime typologies display signiﬁcantly lower levels of the rule of law than the

other four.

Having already shown that there is a substantial degree of variance between the

countries of the EU-27 and its various regime typologies, Fig. 3.3 explores whether

there is also sufﬁcient variance in the time trends within the EU-27. Figure 3.3

clariﬁes that, in contrast to the signiﬁcant between-variance, the within-variance is

less pronounced in the time period 1998 to 2005 (identiﬁed in Fig. 3.3 with the two

0.5

1.5

2.5

Rule of Law

Lower Bound

Upper Bound

Fig. 3.2 Regime typologies and rule of law in the EU-27, 1998–2005

Notes: Data are based on the World Governance Indicators by Kaufmann et al. (2010). Data are

averaged data from 1998 to 2005. The Baltic regime typology, which is marked with an asterisk, is

the only typology not present within the econometric analysis.

This signiﬁcant difference between Italy and the other large EU economies is also shown in the

analysis of four important subindicators of the rule of law, as presented in Fig. 3.A2 in Appendix

A3: 1) enforcement of patents and copyrights,2)property rights,3)stable laws, and 4) effective

enforcement of civil justice. Italy’s scores are signiﬁcantly lower than those of the UK, France, and

Germany in all four indicators.

54 Felix Roth

dashed lines). In most countries, time trends behave in a very stable fashion. In fact,

within the time period 1998 to 2005, there is no signiﬁcant increase or decline to be

observed when analyzing a 90% conﬁdence interval. However, the conclusion that

the rule of law indicator is a constant variable without any within-variance would

also be premature. In analyzing changes over time from 1996 to 2012 (as in addition

displayed in Fig. 3.3 outside the dashed lines) particularly in Estonia and Latvia, one

ﬁnds signiﬁcant increases when using the 90% conﬁdence interval (see here also

Kaufmann et al., 2010, p. 28). Relaxing the 90% conﬁdence interval as suggested by

Kaufmann et al. (2010, p. 14), over the same time period, one would, e.g., also detect

a signiﬁcant decrease in the level of the rule of law in Italy.

8,9

Overall, given the stable within-variation in most countries (utilizing the 90%

conﬁdence interval) and the signiﬁcant between-variation, a positive relationship

-1

1998 2005 1998 2005 1998 2005 1998 2005 19982005 1998 2005 19982005 1998 2005 1998 2005

Austria, * Belgium, - Bulgaria, - Cyprus, - Czech Rep., * Denmark, * Estonia, - Finland, * France, *

Germany, * Greece, - Hungary, - Irelan d, * Italy , * Latvia, - Lithuania, - Luxembourg, - Malta, -

Netherlands, * Poland, - Portugal, - Romania, - Slovak Rep., - Slovenia, * Spain, * Swed en, * UK, *

Year

Rule of law

Fig. 3.3 Trends of the rule of law in the EU-27, 1998–2005

Notes: Time trends display the period from 1996 to 2012. The two dashed lines represent the time

period for the econometric analysis from 1998 to 2005. The * next to the country name denotes

those countries that are included in the econometric analysis.

It should be noted that the indicators utilized were increased from 7 in 1996 to 13 in 2012.

However, those indicators which were utilized over the 17-year time period decreased steadily. The

country report for Italy can be downloaded at http://info.worldbank.org/governance/wgi/index.

aspx#countryReports.

The assumption that the rule of law is not a constant variable is also conﬁrmed by analyzing time

series data from the WJP for the case of Spain. From 2012 to 2013, in times of economic crisis, the

WJP data identify a decline in Spain in four out of eight rule of law indicators (World Justice

Project, 2014, p. 37).

The Rule of Law and Labor Productivity Growth by Businesses 55

between the rule of law and labor productivity growth and intangible and tangible

capital investment rates would foremost be based on the cross-sectoral or between-

variance.

Before shifting our attention to the econometric analysis, Fig. 3.4 depicts respec-

tively the bivariate relationship between the rule of law and business investment in

intangible and tangible capital for the 13 EU economies, as covered within the

following econometric exercise. Whereas one detects a positive bivariate relation-

ship between the rule of law and business investment in intangible capital, interest-

ingly the opposite is true for the relationship between the rule of law and business

investments in tangible capital. Figure 3.4 clariﬁes, however, that this negative

relationship between the rule of law and tangible capital investment is strongly

driven by the two transition countries Czech Republic and Slovenia. The same

does not hold for the relationship between the rule of law and intangible capital

investment. Once the transition countries are excluded, the positive relationship

between the rule of law and intangible capital investment remains robust (see here

also Fig. 3.A1 in Appendix A3, which depicts the relationship for an EU-25 country

sample).

cz de

0.5 11.5 2

Rule of law

0.5 11.5 2

Rule of law

Fig. 3.4 Scatter plot of the relationship between the rule of law and business investments in

intangible and tangible capital in the EU-13, 1998–2005

Notes: Data on intangible capital investments are taken from the INNODRIVE dataset (INNODR

IVE, 2011). Data on tangible capital investment are taken from the EUKLEMS dataset (O’Mahony

& Timmer, 2009). The rule of law indicator is taken from the WGIP. The long dashed line

represents the linear regression line for a sample considering all countries. The short dashed line

represents the linear regression line for a scenario excluding the two transition countries the Czech

Republic and Slovenia.

56 Felix Roth

5 Econometric Results

5.1 Econometric Results between the Rule of Law and Labor

Productivity Growth

When estimating Eq. (3.4), the standard methods for panel estimations are ﬁxed or

random. The ﬁxed effects are calculated from differences within each country across

time; the random-effects estimation, in contrast, incorporates information across

individual countries as well as across periods of time. The major drawback with

random-effects estimations, despite their being more efﬁcient, is that they are

consistent only if the country-speciﬁc effects are uncorrelated with the other explan-

atory variables (Forbes, 2000, c.f. Mundlak, 1978). A Hausman speciﬁcation test can

evaluate whether this independent assumption is satisﬁed (Hausman, 1978). The

Hausman test applied here indicates that a random-effects model can be utilized.

addition, to control for potential cross-sectional heteroskedasticity, a robust VCE

estimator was used.

As highlighted within the research design of this study, the

random-effects estimation uses 13 countries with 98 observations. It is an almost

balanced panel, with two countries (the Czech Republic and Slovenia) missing three

time observations from 1998 to 2000. Regression 1 in Table 3.1 shows the estima-

tion results when estimating Eq. (3.4) (Table 3.1).

In accordance with economic theory, with a coefﬁcient of 1.3, the rule of law

indicator is positively related to labor productivity growth. However, the effect is

weak (signiﬁcant only at the 90% conﬁdence level). Controlling for endogeneity,

by utilizing the lagged value of the rule of law indicator in regression 2, renders a

slightly higher coefﬁcient (1.6) and increases the signiﬁcance of the relationship

(95% level). Whereas utilizing a lagged value of the potential endogenous variable is

a common approach in the economic literature (see e.g. Clemens et al., 2012, p. 591),

utilizing an instrumental approach is argued to be preferable (Reed, 2013). However,

as the author was not able to retrieve a valid external instrument in the EU context

and as the utilization of internally generated instruments by using the lags of the

endogenous variables (Griliches & Hausman, 1986) would lead to weak instruments

(Mc Kinnish, 2000) and respectively strongly biased estimates (Murray, 2006; Stock

The test statistic is χ

(7) ¼2.70. This clearly fails to reject the null hypothesis of no systematic

differences in the coefﬁcients.

Using an xtoverid command (Schaffer & Stillman, 2010), the Sargan-Hansen test statistic is

(7) ¼5.4. This clearly fails to reject the null hypothesis of no systematic differences in the

coefﬁcients.

When running growth regressions, such as in Eq. (3.4), one must be aware of the possibility that

the left-hand side and the right-hand side variables will affect each other. More speciﬁcally, the rule

of law might be endogenous, affected by a common event such as an economic shock, or stand in a

bidirectional relationship with labor productivity. Thus, an increase in labor productivity growth

might, for example, increase spending in the judicial system and increase the level of the rule of law.

For a discussion of a valid instrument for the rule of law within the ﬁeld of development

economics, see Appendix A2.

The Rule of Law and Labor Productivity Growth by Businesses 57

& Watson, 2007), the above-mentioned lagged value approach was chosen.

In this

respect, one should highlight that in general within the social sciences causal

inference should be foremost theoretically driven and generally cannot be demon-

strated directly from the data (Frees, 2004, 205). Applying this study’s research

design and excluding the two transition countries Czech Republic and Slovenia in

regression 3, the coefﬁcient increases (1.8) and the relationship is rendered more

signiﬁcant.

How should one interpret the coefﬁcient of 1.8, as displayed in regression 3? The

rule of law indicator for the given 98 observations in the sample ranges from 0.47 in

Italy in the year 2005 to 1.97 in Finland in the year 2004 (for the summary statistics,

see also the variable named “Rule of Law –EU13”in Table 3.A1 in Appendix A3).

Given the fact that most of the variance of the rule of law indicator is cross-sectional

(see here Figs. 3.1 and 3.3), it seems reasonable to interpret the coefﬁcients as

follows: If Italy, with an average value of 0.66 (as displayed in Fig. 3.1) would

hypothetically be able to reach the same level of the rule of law as Finland, with an

average value of 1.95 (as displayed in Fig. 3.1). this increase would be associated

with an increase of its labor productivity growth by approximately 2.3%.

Table 3.1 Rule of law and labor productivity growth by businesses

Estimation method Random-effects Random-effects Random-effects

Equation 1 2 3

Rule of law 1.3* 1.6** 1.8**

(0.69) (0.81) (0.80)

Intangible services growth Yes Yes Yes

Tangible services growth Yes Yes Yes

Upper secondary education 15+ Yes Yes Yes

Catch-up Yes Yes Yes

Business cycle Yes Yes Yes

Crisis dummy 2001 Yes Yes Yes

Observations 98 98 88

Number of countries 13 13 11

R-square overall 0.52 0.52 0.51

R-square within 0.37 0.38 0.37

R-square between 0.73 0.72 0.75

Notes: Labor productivity growth was calculated with GVA of the non-farm business sectors

c-k + o excluding real estate activities expanded with intangible capital. Robust standard errors

are provided below coefﬁcient estimates between brackets.

*** p < 0.01, ** p < 0.05, * p < 0.1.

In addition, the utilization of internally generated instruments by using the lagged values only

holds if the error term would be serially uncorrelated (Griliches and Hausmann 1986, p. 94). A test

for serial correlation of the error term as introduced by Drukker (2003) indicates that this assump-

tion was violated. Detailed results can be obtained from the author if it were able on request.

The calculation is as follows: Given that the distance from the average value of Italy to the

average value of Finland is 1.29 (1.95–0.66), labor productivity growth gains for Italy would be

approximately 2.3% (1.29*1.8) if it managed to close the gap with Finland.

58 Felix Roth

However, as the overall effect of all TFP components within the utilized model

speciﬁcation and research design only accounts for 10% of the share of labor

productivity (Roth & Thum, 2013, p. 503), the analysis will now continue to explore

the indirect channels.

5.2 Determinants of Intangible and Tangible Business

Capital Investment

Regression 1 in Table 3.2 shows the results when estimating Eq. (3.5) with the help

of a random-effects estimation controlling for potential cross-sectional

heteroscedasticity.

When estimating the association between the rule of law and

investment in business intangible capital, one detects a positive (1.1) but weak

association (signiﬁcant at the 90% conﬁdence level). Applying a similar methodo-

logical logic as discussed above and controlling for endogeneity,

regressions 2–4

incorporate the ﬁrst, second, and third lag of the value of the rule of law. Whereas the

ﬁrst lag renders an insigniﬁcant association, the incorporation of both the second and

third lag yields similar coefﬁcients (1.4 and 1.0) and more signiﬁcant associations

between the rule of law and business investment in intangible capital (signiﬁcant at

the 95% conﬁdence level). If one excludes the two transition countries Czech

Republic and Slovenia (for their location within the bivariate relationship (see also

Fig. 3.4), the coefﬁcient remains robust (1.2) and signiﬁcant at the 95% conﬁdence

level). Similar to the above interpretation, the coefﬁcient of 1.2% could be

interpreted in the following manner: if Italy would be able to gain the same average

level of the rule of law as Finland, this increase would be associated with an increase

in intangible capital investment of approximately 1.5%.

Regressions 6–10 estimate Eq. (3.6), the association between the rule of law and

tangible capital investment by businesses, and apply the same methodological

procedure as in regressions 1–5. In alignment with the bivariate relationship

(Fig. 3.4), regressions 6–9 yield a negative coefﬁcient of 5.7 to 7.3. Utilizing

Using an xtoverid command (Schaffer & Stillman, 2010), the Sargan-Hansen test statistic is

(5) ¼4.8. This clearly fails to reject the null hypothesis of no systematic difference in the

coefﬁcients. Thus, the test applied here indicates that a random-effects model with a robust VCE

estimator can be used.

When running growth regressions, such as in Eqs. (5) and (6), one must be aware of the

possibility that the left-hand side and the right-hand side variables will affect each other. More

speciﬁcally, the rule of law might be endogenous, affected by a common event such as an economic

shock, or stand in a bidirectional relationship with investments in tangible and intangible capital.

Thus, an increase in the investment in intangible and tangible capital might, for example, be related

to an increase in spending in the judicial system and ultimately lead to a higher rule of law.

The calculation is as follows: Given that the distance from the average value of Italy to the

average value of Finland is 1.29 (1.95–0.66), investments in intangible capital by businesses would

be approximately 1.5% (1.29*1.2) higher if it managed to close the gap with Finland.

The Rule of Law and Labor Productivity Growth by Businesses 59

Table 3.2 Rule of law and investment in intangible and tangible capital by businesses

Dependent Variable IC IC IC IC IC TC TC TC TC TC

Estimation method RE RE RE RE RE RE RE RE RE RE

Equation 1 2 3 4 5 6 7 8 9 10

Rule of law 1.1* 0.9 1.4** 1.0** 1.2** 5.7* 6.3* 7.6** 7.3*** 2.8

(0.56) (0.63) (0.58) (0.46) (0.56) (3.03) (3.49) (3.01) (1.96) (2.34)

Intangible capital stock Yes Yes Yes Yes Yes No No No No No

Tangible capital stock No No No No No Yes Yes Yes Yes Yes

Upper secondary education 15+ Yes Yes Yes Yes Yes Yes Yes Yes Yes Yes

Business cycle Yes Yes Yes Yes Yes Yes Yes Yes Yes Yes

Crisis dummy 2001 Yes Yes Yes Yes Yes Yes Yes Yes Yes Yes

Observations 98 98 98 98 88 98 98 98 98 88

Number of countries 13 13 13 13 11 13 13 13 13 11

R-square overall 0.34 0.33 0.30 0.28 0.36 0.72 0.73 0.74 0.75 0.01

R-square within 0.18 0.17 0.20 0.19 0.22 0.11 0.11 0.13 0.14 0.18

R-square between 0.31 0.3 0.26 0.25 0.38 0.60 0.61 0.63 0.64 0.02

Notes: IC ¼Business intangible capital investment; TC ¼Business tangible capital investment; RE ¼Random-Effects. Robust standard errors are provided

below coefﬁcient estimates between brackets.

*** p < 0.01, ** p < 0.05, * p < 0.1.

60 Felix Roth

the third lag of the rule of law in regression 9 yields the most signiﬁcant result

(signiﬁcant at the 99% conﬁdence level). However, again in alignment with the

bivariate relationship in Fig. 3.4, once the two transition countries Czech Republic

and Slovenia are excluded in regression 10, the relationship between the rule of law

and investments in tangible capital loses signiﬁcance (and explanatory power with a

R-square between value as low as 0.02).

Thus, the signiﬁcant negative relationship between the rule of law and investment

in business tangible capital is entirely driven by the two transition countries. In those

two countries, a relatively low level of the rule of law is associated with a high

investment rate in tangible capital. As theoretically discussed, this might be because

a lower level of the rule of law is associated with less regulatory activity, thus

making it more attractive for enterprises to make tangible capital investments.

6 Empirical Conclusion, Discussion, and Policy Conclusion

6.1 Empirical Conclusion

This contribution has investigated the relationship between the rule of law and labor

productivity growth in an EU context. Seven ﬁndings emerge from the empirical

analysis.

First, there exists considerable variance concerning the rule of law within an

EU-27 country sample. The Baltic, transition, and Mediterranean countries incorpo-

rate signiﬁcantly lower levels of rule of law than the liberal, coordinated, and

Scandinavian countries. Countries such as Romania, Bulgaria, Italy, and Greece

have signiﬁcantly lower positions than the three largest economies in the EU, namely

France, Germany, and the UK.

Second, although one detects a signiﬁcant variance in some EU countries over

time, e.g. an increase in Latvia and Estonia, the main variance is cross-sectional in

nature. In countries such as Germany, Austria, and Finland, time trends behave in a

very stable fashion.

Third, using a random-effects estimation among an EU-13 country sample with

98 overall observations in an intangible capital-augmented production model and

controlling for endogeneity by using the ﬁrst lag, the rule of law is signiﬁcantly and

positively related to labor productivity growth by stimulating its TFP growth.

Fourth, in analyzing the relationship between the rule of law and business

intangible capital investment graphically within a bivariate relationship for an

EU-25 country sample and econometrically by using a random-effects estimation

across an EU-13 country sample with 98 observations and controlling for

endogeneity by using the second and third lag, one detects a positive association

between the level of the rule of law and business investment of intangible capital.

The Rule of Law and Labor Productivity Growth by Businesses 61

Fifth, in contrast to the positive and signiﬁcant association between the rule of law

and business investments in intangible capital, business investments in tangible

capital are negatively related to the level of the rule of law. This negative ﬁnding

is entirely driven by the two transition countries in which a low level of the rule of

law is associated with a high level of investment in tangible capital by businesses.

Sixth, overall, the results indicate that an improvement in the rule of law in those

countries with relatively low levels would be beneﬁcial in facilitating the transfor-

mation towards becoming knowledge economies. It seems that the rule of law, by

protecting and enforcing the intellectual property rights associated with patents,

copyrights, and trademarks, stimulates investments in intangible capital.

Seventh, it should be highlighted that more empirical research is needed to

corroborate these ﬁrst ﬁndings. It would be of particular interest in corroborating

the ﬁndings by utilizing an external instrumental variable to address potential

endogeneity issues.

6.2 Discussion of the Results Considering the Underlying

Theoretical Literature

The empirical ﬁndings in the EU context over the period 1998–2005 conﬁrm the

theoretical arguments that the rule of law, by lowering transaction costs, positively

contributes to economic performance (North, 1990; Hall & Jones, 1999).

Concerning the ambivalent theoretical reasoning on the relationship between both

the rule of law and investments in intangible and tangible capital by businesses, the

empirical ﬁndings support the theoretical argument that in the EU from 1998 to

2005, the rule of law, by securing intellectual property rights, was beneﬁcial to

business investments in intangible capital (Baumol, 2002, p. 8; Mayer-Schönberger,

2010, pp. 164–65). The empirical ﬁndings seem to reject concerns about an exces-

sively strict intellectual property regime hampering innovation activity (Dosi et al.,

2006; Mayer-Schönberger, 2010, p. 166; Verspagen, 2006). However, the rule of

law is either negatively (once accounting for the transition countries) or insigniﬁ-

cantly related to investment in tangible capital. In the EU context from 1998 to 2005,

the ﬁndings indicate that the rule of law in its role as regulator either hampers

business investment in tangible capital (Mayer-Schönberger, 2010, pp. 155–160;

Nicoletti & Scarpetta, 2003) or that the variance in the rule of law does not matter for

investment in tangible capital.

Overall, these ﬁrst empirical ﬁndings of this study tend to support the validity of

the European Commission’s claim that the rule of law is important for the economic

performance of EU economies (European Commission, 2013a, p. 1).

62 Felix Roth

6.3 Policy Conclusions

Six policy conclusions can be drawn from the foregoing analysis.

First, in order to enhance labor productivity growth—in line with the Europe

2020 strategy (European Commission, 2010)—it would be beneﬁcial to enhance the

level of the rule of law in those countries that perform relatively worse in an EU

context. In EU-15 countries such as Italy and Greece, and transition countries such as

Romania and Bulgaria, low levels of the rule of law hamper labor productivity

growth. In those countries, enhancing the level of the rule of law by reforming the

judiciary system seems to be essential. In this regard, it is favorable that the

European Commission is well aware of the necessity to improve the rule of law in

those countries. Among other ways, this awareness is made explicit by its country

recommendations within the European semester. In the Italian case, the European

Commission recommends to “simplify the administrative and regulatory framework

for citizens and businesses and reduce the duration of case-handling and the high

levels of litigation in civil justice, including by fostering out-of-court settlement

procedures (...) and strengthening the legal framework for the repression of corrup-

tion”(European Commission, 2013b, p. 7). In the case of Romania, the European

Commission recommends: “to strengthen the governance and the quality of institu-

tions and the public administration (...) and step up efforts to improve the quality,

independence and efﬁciency of the judicial system in resolving cases and ﬁght

corruption more effectively”(European Commission, 2013c, p. 7).

Second, the low level of the rule of law in the third-largest economy in the

eurozone, Italy, needs to be taken into consideration in particular in efforts to

improve the governance of the euro area. The signiﬁcant difference in the rule of

law in those three countries, with Italy performing signiﬁcantly worse compared to

France and Germany, leads, inter alia, to a continued divergence in labor produc-

tivity growth and time-lagged investments to business intangible capital. Thus, in the

long run, in order to smooth economic divergences between the three largest euro

area economies, Italy’s level of the rule of law would need to be increased.

Third, DG Justice should continue its effort to construct a scoreboard on the rule

of law. Similar to the methodological approaches taken by the WJP and WGIP, this

scoreboard should aim at building an index based on the rich data as presented by the

CEPEJ (2012) from 2004 onward. The operationalization of this index could be

based on the methodological approach of the WJP In contrast to the WJP, however,

the index could consist of a mix of the rule of law as measured de facto and as

measured based upon expenditure-based data. In this regard, it would be important

that the European Commission would be willing to contribute sufﬁcient resources to

allow for constructing time series data over the coming years, which would allow

researchers to compare potential changes in a rule of law index over time.

Fourth, in the medium to long run, such an index as constructed by DG Justice,

which takes into consideration the speciﬁc aspects of the EU economies, should be

incorporated as a benchmark indicator within the European semester. In particular,

an improvement in the various underlying indicators of the rule of law in some

The Rule of Law and Labor Productivity Growth by Businesses 63

Mediterranean and transition economies should be closely monitored by the

European semester. Most importantly, however, the progress of the third-largest

euro area economy and the fourth-largest EU economy, namely Italy, should be

closely monitored.

Five, DG Justice should ﬁnance research exploring the effects of the most recent

economic crisis in the euro-area periphery, particularly in Spain and Greece, on the

levels of the rule of law. First evidence from the WJP indicates that the rule of law in

Spain has dropped in four out of the eight dimensions measured (World Justice

Project, 2014, p. 37). In light of the systemic trust crisis in Spain triggered by the

economic crisis (Roth et al., 2013), the development of a decrease in the rule of law in

the fourth-largest eurozone economy, i.e., Spain, ought to be closely monitored.

Six, following the initial theoretical arguments advanced by the World Bank (2006,

p. 98), future research endeavors should evaluate how much of the expenditure in the

national judicial systems should be considered investment in intangible capital. As

these investments are most often undertaken by the public sector and not by the

business sector, they should be coined public investment in intangible capital. In this

regard, it can easily be concluded that a share of the public expenditure in the judicial

system represents an investment by its very nature, as the existence of an efﬁcient

judicial system is a prerequisite for the protection and enforcement of property and

contract rights, which are essential for the conduct of economic activities within

functioning market economies. Future research endeavors should thus try to estimate

how much of the expenditure should be considered investment in order to be able to

adequately revise the national accounting systems.

Appendices

Appendix 1 Operationalization of the Rule of Law

Given the theoretical character of the discussion in this contribution, a promising

operationalization of the concept of the rule of law has been offered by the World

Justice Project (WJP) (Agrast et al., 2013). The WJP’s working deﬁnition of the rule

of law is based on the following four universal principles: 1) governmental and

private actors are accountable under the law, 2) the laws protect the security of

persons and property, 3) the laws are efﬁciently enforced, and 4) the law is delivered

in a timely fashion by competent and independent representatives (Agrast et al.,

2013, p. 9). All four principles broadly cover the rule of law’s function of protecting

and enforcing property and contract rights, including the accountability of govern-

mental and private agents, the efﬁcient protection and enforcement of property, as

well as the timely delivery of justice. The four guiding principles are measured with

the help of eight factors: 1) limited government powers, 2) absence of corruption,

3) order and security, 4) fundamental rights, 5) open government, 6) regulatory

enforcement, 7) civil justice, and 8) criminal justice—all of which in turn are based

on a total of 48 sub-indicators. An aggregation of all eight factors is statistically

64 Felix Roth

sound (Saisana & Saltelli, 2013, p. 198) and leads to an index of the rule of law for

20 of the 27 EU countries.

In addition to an adequate conceptualization and

operationalization, a comparative advantage of the WJP data, in contrast to other

data sources (WGIP), is the fact that the database consists of new data collected from

independent original sources (Agrast et al., 2013, p. 19) and that the data have been

measured in de facto terms (Agrast et al., 2013, p. 17). Although all of the above-

mentioned arguments would indeed indicate the use of the WJP data for this study’s

research design, the WJP database has a severe disadvantage: it offers no observa-

tions for the time period 1998–2005, as the ﬁrst wave and pilot study of the WJP was

launched as recently as 2009, with only six countries included. Only from 2009

onward was the country sample expanded to cover over 100 countries in the

2012–13 wave (Fig. 3.A2).

In contrast to the WJP database, the WGIP database (Kaufmann et al., 2010)

offers times series data from 1996 to 2012. The World Bank’s WGIP deﬁnes the rule

of law as “capturing perceptions of the extent to which agents have conﬁdence in and

abide by the rules of society, and in particular the quality of contract enforcement,

property rights, the police, and the courts, as well as the likelihood of crime and

violence”. (Kaufmann et al., 2010, p. 4). The WGIP consists of 84 individual

indicators from 23 separate data sources.

The 84 individual questions consist of

indicators concerning the enforceability of contracts, the protection of (intellectual)

property rights, and the timeliness of judicial decisions, but they also cover rule of

law indexes and indicators concerning the respondents’trust in the justice system.

All single indicators are then aggregated to construct the rule of law indicator by

using an unobserved components model (Kaufmann et al., 2010, p. 9). The unit of

the rule of law indicator applied to the 214 countries is that of a standard normal

random variable and ranges from 2.5 to 2.5 (Kaufmann et al., 2010, p. 9 and p.15).

The WGIP began to systematically construct a rule of law indicator from 1996

onward. Starting from a 2-year base in 1998, 2000, 2002, from 2002 onward, the

Cyprus, Malta, Latvia, Lithuania, Luxembourg, the Slovak Republic, and Ireland are not included

due to missing data in the WJP data.

The 23 sources are: African Development Bank Country Policy and Institutional Assessments,

Afrobarometer Survey, Asian Development Bank Country Policy and Institutional Assessments,

Business Enterprise Environment Survey, Bertelsmann Transformation Index, Freedom House

Countries at the Crossroads, Economist Intelligence Unit Riskwire and Democracy Index, Freedom

House, World Economic Forum Global Competitiveness Report, Global Integrity Index, Gallup

World Poll, Heritage Foundation Index of Economic Freedom, Cingranelli-Richards’Human

Rights Database and Political Terror Scale, IFAD Rural Sector Performance Assessments, Institu-

tional Proﬁles Database, Latinobarometro, World Bank Country Policy and Institutional Assess-

ments, Political Risk Services International Country Risk Guide, US State Department Trafﬁcking

in People report, Vanderbilt University Americas Barometer, Institute for Management and Devel-

opment World Competitiveness Yearbook, World Justice Project Rule of Law Index, and Global

Insight Business Conditions and Risk Indicators. The list can be downloaded at http://info.

worldbank.org/governance/wgi/index.aspx#doc.

A list of all 23 data sources and 84 indicators can be downloaded online on the World Bank

website (http://info.worldbank.org/governance/wgi/index.aspx#doc).

The Rule of Law and Labor Productivity Growth by Businesses 65

aggregated data on the rule of law continued to be constructed on a yearly basis, with

the latest published data stemming from 2012. The WGI data are most often based

on experts, perceptions and, similar to the WJP, measures the rule of law in de facto

terms. Although the WGI indicators have been criticized on various accounts

(Kaufmann et al., 2007), the very thorough and transparent manner in which the

authors have set up their rebuttal to the various criticisms (Kaufmann et al., 2007), in

the author’s view, has secured conﬁdence in the general validity of the methodo-

logical approach and the data of the WGIP. Thus, overall the WGIP indicators fulﬁll

the methodological requirements for their use in the EU context, taking into consid-

eration the methodological background information (e.g., interpreting the data by

utilizing the provided conﬁdence intervals within the cross-sectoral and time series

data, controlling individual country cases for measurement changes in the time series

data, etc.) as pointed out by the authors (Kaufmann et al., 2010, pp. 9–12 and p. 29).

Thus, given the lack of time series data from the WJP, the WGI data represent a well-

designed alternative with which to measure the rule of law. In addition, it should be

mentioned that the WJP rule of law index correlates as high as 0.98 for the year 2012

in a sample of 20 EU-27 countries (see also Fig. 3.A3 in Appendix A3). It thus seems

appropriate to conclude that they both measure the same construct on an aggregated

level.

As this contribution focuses in particular on the EU, a third data source should not

be overlooked, namely data from the new EU Justice Scoreboard as published by

DG Justice and Home Affairs (European Commission, 2013a). Most of the data

contained within this scoreboard stem from a very detailed report by the European

Commission on the Efﬁciency of Justice (CEPEJ, 2012) and offer a range of cross-

sectional statistics on EU countries. However, since the data were only collected

from 2004 onward, similar to the WJP, no adequate time series data are available for

the period of interest (1998–2005).

Appendix 2 An External Instrument for Measuring the Rule

of Law in the Context of Development Economics

A prominent instrument to disentangle the causality-related issues between the rule

of law and economic performance (log of per capita income), as discussed in the

literature focusing on the dichotomy between developed vs. developing economies,

has been introduced by Acemoglu et al. (2001). In their seminal study, the authors

introduce the variable “settlers’mortality”to serve as an instrument for the institu-

tional differences (largely property rights) among countries colonized by Europeans.

The theory behind this instrument is as follows: depending upon the mortality rate in

the various colonies established by European powers, the European settlers would

decide to either settle for the long-term or simply to set up “extractive states”in order

to obtain a maximum quantity of resources. In countries with lower mortality rates,

such as the US, Canada, Australia, and New Zealand, settlers replicated the

66 Felix Roth

institutional design of former European countries, stressing the protection of prop-

erty rights and the introduction of checks and balances against government power. In

countries with a high mortality rate, European countries set up extractive states in

which little emphasis was placed on ensuring an effective and equitable property

rights regime. Given the assumption that the original institutional design persisted

even after independence, Acemoglu et al. (2001) utilize the settlers’mortality rate

during times of colonization for the current institutional design (and the rule of law)

within a country in order to causally explain a country’s current economic perfor-

mance. In developing their concept of social infrastructure, Hall and Jones (1999)

used a slightly different set of instruments, based on geographical, inguistic, and

trade-related variables.

Both sets of literature are focused on the dichotomy between developed and

developing economies. Unfortunately, neither of these prominent papers offers a

valid instrument for analyzing the impact of the variance of the rule of law on labor

productivity growth in an EU country sample. Future research endeavors in this ﬁeld

of research should be devoted to generating a valid and relevant instrumental

variable, avoiding the usual weak instrumental bias (Murray, 2006; Stock & Watson,

2007).

Appendix 3 Selected Statistics

Table 3.A1 Descriptive statistics

Mean St. Dev. Min. Max. Cou. Obs.

Rule of law—EU27—average

98–2005

1.09 0.61 0.20 1.95 27 27

Rule of law—EU13 1.50 0.40 0.47 1.97 13 98

IC investment in % 10 2 6 15 13 98

TC investment in % 21 6 14 44 13 98

IC stock 0.28 0.31 0 1 13 98

TC stock 0.32 0.31 0 1 13 98

Secondary education in % 66 12 38 83 13 98

Business cycle 0.93 0.03 0.85 0.98 13 98

Labor productivity growth in % 2.4 1.8 2.2 8.4 13 98

Intangibles services growth in % 4.1 2.4 2.8 9.2 13 98

Tangible services growth in % 3.3 1.8 0.3 9.9 13 98

Interaction education catch-up 0.16 0.26 0 1.16 13 98

Notes: WGIP data on the rule of law have been interpolated for the years 1999 and 2001.

IC ¼intangible capital; TC ¼tangible capital.

The Rule of Law and Labor Productivity Growth by Businesses 67

cz de

pl pt

0.5 11.5 2

Rule of law

Fig. 3.A1 Scatter plot of

the relationship between

rule of law and business

investment in intangible

capital (% of GVA) in the

EU-25, 1998–2005

Data sources: Data on

intangible capital

investments are taken from

the INNODRIVE dataset (I

NNODRIVE, 2011). Data

on the rule of law are taken

from the WGIP (Kaufmann

et al., 2010). Data on

intangible capital

investment are missing for

Romania and Bulgaria.

IT UK FR DE

Enforcement of patent & copyright protecti on

IT FR DE UK

Property rights

IT FR UK DE

Stable laws

IT UK FR DE

Civil justice eff ectively enforced

Fig. 3.A2 A comparison of four selected indicators of the rule of law—in the four largest EU

economies

Notes: The data on “Enforcement of patent & copyright protection”and “Property rights”are taken

from the WGIP (Kaufmann et al., 2010) based on the Institute for Management Development’s

“World Competitiveness Yearbook”and the Heritage Foundation’s Index of Economic Freedom,

respectively. The data stem from the year 2005. The data concerning “Stable laws”and “Civil

justice effectively enforced”are taken from the WJP (Agrast et al., 2013). The data stem from the

year 2012.

68 Felix Roth

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72 Felix Roth

Chapter 4

Organizational Trust, Fear of Job Loss,

and TFP Growth: A Sectoral Analysis

for the EU

Felix Roth

Abstract Analyzing the sectoral variance in growth rates of total factor productivity

in a European country sample from 1996 to 2006, this contribution detects no

signiﬁcant relationship between organizational trust and TFP growth. Yet, the

relationship between fear of job loss and TFP growth seems to be signiﬁcantly

associated, in an inverted U-shaped relationship. This relationship proves to be

robust to a range of alterations. The analysis concludes that depending on the speciﬁc

sector, to enhance productivity it might be beneﬁcial to liberalize or regulate

employment relations. When analyzing the non-farm market sectors C-K, the rela-

tionship takes the form of a signiﬁcant, negative linear relationship.

Keywords Organizational trust · Fear of job loss · TFP growth · Sectoral analysis ·

JEL Classiﬁcations D24 · J89 · L23 · L60 · L70 · L80 · O30 · O52 · Z13

Originally published in: Felix Roth. Organizational Trust, Organizational Fear and TFP Growth:

A sectoral analysis for the EU. European Commission, 7th Framework Programme, No. 258747,

2013. The Impact of Service Sector Innovation and Internationalisation on Growth and Productivity

SERVICEGAP Discussion Paper No. 20.

The author wishes to thank the participants at the INDICSER project during workshops in London

(February 2010) and Valencia (April 2011), those at the SERVICEGAPproject during workshops in

Birmingham (June 2010), Dublin (June 2011), and Mannheim (November 2012), and those at the

ZEW research seminar (June 2011) in Mannheim. In addition, the author would like to thank Mary

O’Mahony, Jonathan Haskel, Marcel Timmer, Irene Bertschek, and Stanley Siebert. The author is

grateful for a grant from the European Commission under the Seventh Framework Programme

(FP SSH 2009 1.2.1) for the SERVICEGAP project (“The Impact of Service Sector Innovation and

Internationalisation on Growth and Productivity,”contract number 214576). The author would also

like to thank Paola Trevisan, Anna Thum, and Raf van Gestel for valuable research assistance.

Finally, the author would like to thank Greet Vermeylen and Sylvie Jacquet at the European

Foundation for the Improvement of Living and Working Conditions (Eurofound) for valuable

help concerning the screening of all available datasets produced by Eurofound.

Felix Roth (*)

Department of Economics, University of Hamburg, Hamburg, Germany

e-mail: felix.roth@uni-hamburg.de

©The Author(s) 2022

F. Roth, Intangible Capital and Growth, Contributions to Economics,

https://doi.org/10.1007/978-3-030-86186-5_4

1 Introduction

In analyzing the productivity gap between the US and Europe over the period

1995–2006, Timmer et al. (2010, p. 32 and p. 36) and van Ark et al. (2008,

pp. 39-41) highlight that the gap largely results from slower growth of total factor

productivity (TFP) in European market services, particularly in distributive trade as

well as ﬁnancial and business services. The authors argue that among other factors it

will most likely be the missing input of intangible capital that is able to explain the

result of slower TFP growth in European market services (Timmer et al., 2010,

pp. 259–262; Van Ark et al., 2008, pp. 41–42). Concerning the conceptualization of

intangible capital, Corrado et al. (2005,2009) group intangible capital into three

categories: 1) software, 2) innovative properties, and 3) economic competencies.

The last category, economic competencies, is further subdivided into three indica-

tors: 1) brand names, 2) ﬁrm-speciﬁc human capital, and 3) organizational capital.

Within this wide range of indicators, this contribution concentrates primarily on the

concept of organizational capital—more concretely, it analyzes the relationship

between organizational trust, fear of job loss, and TFP growth over the period

1996–2006 at the sectoral level within a given sample of 15 European countries

and 10 economic sector clusters. In this instance, the manufacturing sectors (C-F) are

differentiated from the service sectors (G-K), the public sectors (L-N), and the non-

farm business sector (C-K).

2 Theoretical Links

2.1 Organizational Capital and Economic Performance

Organizational capital is seen by many authors as a key driver of economic perfor-

mance in industries or individual economic sectors (Lev & Radhakrishnan, 2005,

p. 73; Black & Lynch, 2005, p. 205; Corrado et al., 2005, p. 29 and p. 33). Van Ark

et al. (2008, p. 41), and Timmer et al. (2010, pp. 259–260) conclude that among

other intangible capital indicators, a lack of organizational capital might explain

slower TFP growth in European market services and thus the productivity gap

between the US and Europe. But how can organizational capital best be conceptu-

alized? An initial, suitable working deﬁnition of organizational capital is given by

Lev and Radhakrishnan (2005, p. 75), who deﬁne organizational capital as “an

74 Felix Roth

agglomeration of technologies”that enables some ﬁrms in contrast to others to

produce higher output by utilizing more efﬁciently their given level of physical

and human capital. These technologies include organizational processes and design

(Lev & Radhakrishnan, 2005), work design and employee voice (Black & Lynch,

2005), and corporate management practices (Bloom & van Reenen, 2007).

Whereas the above-mentioned factors could be classiﬁed as formal indicators of

organizational capital contributing to economic performance, another strand of the

literature from diverse scientiﬁcﬁelds (including psychological, organizational,

business, management, and economic studies) identiﬁes rather informal indicators

of organizational capital as being key to organizational performance. Alongside

general factors like organizational climate (Patterson et al., 2004) and employee

working conditions (Royuela & Surinach, 2009), these informal indicators include

more speciﬁc factors, such as organizational social capital (Nahapiet & Goshal,

1998; Leana & van Buren III, 1999,2000), and those connected to organizational

trust

(in some of the literature called workplace trust

or employees’trust

) and fear

of job loss (also identiﬁed with job insecurity).

2.2 Organizational Trust and Economic Performance

In its conceptualization of organizational trust, this contribution follows Leana and

van Buren III (1999,2000), who identify two types of organizational trust as being

particularly important to the competitiveness of an organization and thus its eco-

nomic performance: 1) employees’trust among colleagues and 2) employees’trust

towards their superiors/bosses.

But how does employees’trust affect economic

performance? The argumentation presented below sheds light on this question

without being caught up in the positive trust bias that is common in this ﬁeld.

Leana and van Buren (2000, pp. 221–25) identify three main factors explaining how

employees’trust fosters the competitiveness of an organization. If trust exists,

1) employees are more committed to their organizations than to the particular

On organizational trust, see among others Mayer et al. (1995), Dirks and Ferrin (2001), Dirks and

Skarlicki (2004), Harisalo and Stenvall (2004) and Gargiulo and Ertug (2006).

Concerning workplace trust, see Heliwell (2006), Heliwell et al. (2009) and Heliwell and

Huang (2011).

With regard to employees’trust, see Leana and van Buren III (1999,2000) and on workers’trust,

see Schotter (1996).

On fear of job loss, see Blanchﬂower (1991), Brockner et al. (1992), Probst (2002), Sverke et al.

(2002), Probst et al. (2007), Staufenbiel and König (2010).

On the importance of trust in leaders, see also Dirks and Skarlicki (2004).

Two books by prominent academics from the discipline of political science, Trust by Francis

Fukuyama (1996) and Bowling alone by Robert Putnam (2000), are biased towards the positive

effects of trust. Both books tend to neglect the “dark side of trust”(Gargiulo and Ertug, 2006).

Organizational Trust, Fear of Job Loss, and TFP Growth 75

work they do, 2) the goal of creating a more ﬂexible work organization will be easier

to achieve, and 3) collective action will be more easily managed within the ﬁrm.

According to the authors, all three factors add to the competitiveness of an

organization.

A similar but more speciﬁed discussion is given by Gargiulo and Ertug (2006)onthe

“dark side of trust”, in which the authors identify a theoretical curvilinear relationship

between trust and economic performance. Summarizing the literature on the conse-

quences of trust, the authors point to three theoretical channels through which trust might

be beneﬁcial for economic performance (p. 170). First, trust is related to lower levels of

monitoring, vigilance, and safeguards concerning the actions of the trusted party. This

argument is in accordance with Knack and Keefer (1997) and Whiteley (2000), who

stress among other things that an employer’s monitoring costs are lower in high-trust

societies. Second, trust is related to higher levels of commitment concerning the

interaction with the trusted party. Third, trust is related to an enlargement of the scale

of the exchange between parties. According to the authors, this will then lead to positive

economic performance by 1) lowering information processing costs, 2) increasing

satisfaction, and 3) reducing uncertainty. In the context of employees’trust towards

colleagues/bosses, the second and third arguments are more important than the ﬁrst, as

the ﬁrst argument would imply the employer’s trust of the employee.

Interestingly, alongside these positive outcomes, the authors also identify the

negative effects of excessive levels of trust on economic performance (growth):

1) blind faith, 2) complacency, and 3) unnecessary obligations. For a start, excessive

trust can produce blind faith, leading to a reduction of monitoring below an optimal

threshold, thereby increasing the risk of malfeasance. Furthermore, excessive trust can

turn commitment into complacency, which may prevent rapid intervention in declin-

ing performance. This argument is in accordance with Bidault and Castello (2009),

who assert that when there is a very high level of trust, actors might become too

complaisant, leading to diminished levels of task-oriented conﬂicts and thus lower

effectiveness (p. 267). In this instance, Hardin (2006) mentions the potential of

blocking social capital (p. 94). Dirks and Ferrin (2001) discuss empirical evidence

showing that positive attitudes, such as satisfaction, are not robustly linked to work

performance (p. 455). Finally, excessive trust can lead to a swift enhancement of a

relationship beyond the optimal level, thereby creating unneeded obligations that act

as constraints on the interaction.

2.3 Fear of Job Loss and Economic Performance

The above-derived theoretical, curvilinear relationship between organizational

trust and growth and the explicit reference to the paradigm of excessive trust

already points towards the potential importance of an opposite

but distinct

According to theoretical (Ashford et al., 1989, p. 808) and empirical studies (Sverke et al., 2002,

p. 253), the concept of trust and fear of losing one’s job are negatively related to one another.

76 Felix Roth

concept from trust, that of fear,

for explaining economic performance. To

conceptualize fear this contribution uses a prominent concept of fear, namely

employees’fear of losing their jobs (for a deﬁnition see De Witte, 2005,p.1;

Sverke et al., 2002, p. 243; and Greenhalg & Rosenblatt, 2010,pp.9–10).

But

how is employees’fear of losing their jobs related to economic performance?

Research from the disciplines of psychology and business studies depicts a

curvilinear relationship between job insecurity and economic performance. In

this context, the popular management literature points out that the relationship

between stress (being induced by, among other phenomena, job insecurity) and

economic performance takes an inverted U shape (Marks, 2003,p.42).Asimilar

argument from the academic literature, directly related to job insecurity, is made

by Brockner et al. (1992). The assumption of a curvilinear relationship seems to

be well embedded in the literature, which puts forward mixed theoretical argu-

ments highlighting both the positive and negative effect of unemployment fears

on economic performance.

Concerning the positive relationship, some scholars stress that job insecurity

creates a cognitive awareness on the part of employees that will consequently

increase their performance (Probst et al., 2007). In addition, heightened perceptions

of job insecurity may lead employees to engage in less counterproductive work

behavior out of the “fear of termination and the associated ﬁnancial ramiﬁcations

with potential job loss”(Probst et al., 2007, p. 483). Staufenbiel and König (2010)

argue that fear of losing one’s job might motivate employees to work harder in order

to safeguard against that loss (p. 103). Another argument is given in the economic

literature by Blanchﬂower (1991), speciﬁcally, that fear of unemployment among

employees depresses wages signiﬁcantly, thereby granting the hiring organization a

comparative advantage over its competitors.

Concerning the negative relationship, Sverke et al. (2002) conclude that job

insecurity lowers economic performance because less secure employees are 1) less

involved with the organization and 2) have incentives to withdraw from the organi-

zation. Probst et al. (2007) suggest that job insecurity might inﬂuence productivity

and performance negatively because of a drain of the working memory resources

owing to anxiety. Staufenbiel and König (2010) contend that job insecurity produces

stress, which in turn negatively affects an employee’s organizational commitment

(p. 102). Renzl (2008) holds that fear in the workplace leads to a disruption of

knowledge sharing, which is of crucial importance to the innovativeness and com-

petitiveness of a ﬁrm.

Concerning the generalized paradigm of fear, classical sociological thinking has long held that fear

is one of the main driving forces behind the evolution of advanced societies and economic

performance (see Elias, 1980, pp. 447–51; Marcuse, 1998).

The fear of losing one’s job is a more specialized conceptualization than, for example, the

organizational climate of fear (Ashkanasy & Nicholson, 2003).

Organizational Trust, Fear of Job Loss, and TFP Growth 77

The foregoing discussion clariﬁes that, similar to the theoretical arguments on

organizational trust, fear of job loss and economic performance are related in a

curvilinear manner.

3 Model Speciﬁcation, Research Design, and Data

3.1 Model Speciﬁcation

Following Nicoletti and Scarpetta (2003) and McMorrow et al. (2010), who use a

neo-Schumpeterian growth model, the baseline speciﬁcation takes the following

form:

TFPi,j,t¼β0þβ1d

TFPL,j,tþβ2ln TFPi,j,t1



ln TFPL,j,t1



þβ3OTFi,j,tþβ4OTFi,j,t



2þβ5Xi,j,tþγiþαjþεi,j,t,ð4:1Þ

where the sign “^”represents the growth rates of the depicted variables; d

TFPi,j,tis the

average TFP growth in country iand sector jfor the 11-year period 1996–2006;

TFPL,j,trepresents TFP growth at the frontier economy Land is supposed to capture

the degree to which countries have to do analogous innovation activity as lead

countries or acquire potential knowledge spillovers; ln(TFP

i,j,t1

)ln (TFP

L,j,t1

)

represents the productivity gap between a country and the frontier in order to proxy

the room for adoption of technologies from the frontier; OTF

i,j,t

and (OTF

i,j,t

)

represent the level and the squared level respectively of organizational trust and

fear of job loss in country i, sector jat time t

i,j,t

is a vector of supplementary

explanatory variables containing policy and control variables in country i, sector jat

time t

;γ

and α

represent dummy variables for country iand sector j; the residual

i,j,t

is, as always, assumed to follow a normal distribution where the mean equals

zero and a constant standard variance σ2

ε.β

depicts a constant term.

For pragmatic reasons it is assumed that organizational trust and fear of job loss remain stable

over time. This assumption was necessary to be able to utilize an organizational trust and fear of job

loss indicator from 2005 as an explanatory variable for TFP growth from 1996 to 2006. In this

instance, Blanchﬂower and Oswald (1999)ﬁnd out, when analyzing the trend in job insecurity

levels in the US for the period 1977–1998, that job insecurity remains stable. Yet, when comparing

the 2005 European Working Conditions Survey (EWCS) (Eurofound, 2005) with the 2010 EWCS

(Eurofound, 2010a) data from the INDICSER project (Saam et al., 2011), the fear values differ

signiﬁcantly, particularly in sectors that have been strongly exposed to the economic crisis, such as

construction. This ﬁnding is also conﬁrmed by Eurofound (2010b, p. 2). Still, the question arises of

whether a comparison between these two periods makes a valid counter case, as between 2005 and

2010 nothing less than the worst ﬁnancial and economic crisis since the 1930s hit most advanced

economies around the world.

The control and policy variables have been constructed as averages from 1996 to 2006.

78 Felix Roth

3.2 Research Design

The dependent variable is the average TFP growth from 1996 to 2006 in sector jand

country i. The country sample consists of 15 European countries: Austria, Belgium,

the Czech Republic, Denmark, Finland, France, Germany, Hungary, Ireland, Italy,

Portugal, Slovenia, Spain, Sweden, and the UK. Of these 15 countries, 12 are from

the EU-15 and 3 are transition countries. Overall, 10 sectors (A-B, CtD, F, G, H, I, J,

K, L, MtN) are included in the analysis.

Following the INDICSER

methodology,

only sectoral cells with more than 30 observations were utilized (Saam et al., 2011).

Given this limitation, with 10 sectors and 15 countries, it was possible overall to

retrieve 103 observations for organizational trust and 100 observations for fear of job

loss as depicted in Table 4.1 (in Sect. 4) and Table 4.A1 (in the Appendix).

3.3 Data

Following the approach of the INDICSER project (Saam et al., 2011), data on

organizational trust and fear of job loss were taken from the European Working

Conditions Survey (EWCS) conducted on behalf of the European Foundation for the

Improvement of Living and Working Conditions (Eurofound), as it is the only

publicly available dataset that enables employees’attitudes and working conditions

to be matched with speciﬁc economic sectors in a larger European country sam-

ple.

14,15

More concretely, data were taken from the fourth wave of the EWCS

The empirical design of the paper has been constructed around the built-in “NACE11-variable”,

which is given in the fourth wave of the EWCS (Eurofound, 2005). For reasons that seem to be arise

from an erroneous coding by Eurofound, the NACE11 variable has included sectors O-Q in the

missing category. This error should be corrected by the responsible persons at Eurofound. Being

based on the output of the NACE11 variable, the econometric analysis of this paper thus focuses

solely on sectors AtN. In contrast to this paper, the INDICSER project (Saam et al., 2011) also

depicts data on sectors O-Q by retrieving data from a variable within the fourth EWCS, which

provides information at a more disaggregated level than the 1-digit NACE classiﬁcation. Sector E

had to be dropped because the cells had fewer than 30 observations in all 15 countries.

The INDICSER (“Indicators for evaluating international performance in service sectors”)projectis

funded by the European Commission under its Seventh Framework Program (https://cordis.europa.

eu/project/id/244709/de).

According to Eurofound (2007) the statistical population of the EWCS includes all persons aged

15 or older whose usual place of residence is in the territory across31 European countries and who are

in employment during the reference period. A person is considered in employment if he or she did any

work for pay or proﬁt during the reference week for at least 1 hour. The EWCS draws a representative

sample by using multistage sampling; thus, in the ﬁrst stage, population sampling units were selected

using stratiﬁed random sampling. The target number of interviews was 1,000 in 14 of the 15 countries

in the sample, with the exception being Slovenia, for which the target was 600 interviews.

Although other data sources, such as the European Social Survey, have recently started to collect

data on the sectoral structure, these surveys do not include an equally rich range of survey questions

concerning working conditions.

Organizational Trust, Fear of Job Loss, and TFP Growth 79

(Eurofound, 2005) because it was the ﬁrst to include information on both of the

relevant items: organizational trust and fear of job loss.

To adequately measure the concept of employees’organizational trust and fear of

job loss, the raw population of the EWCS 2005 was ﬁltered by the following criteria:

ﬁrst, self-employed persons were dropped. Second, to analyze a sample of

employees who follow a regular work-engagement week, only those employees

were kept who worked at least 8 hours but less than 84 hours a week.

Third,

managers with supervisory tasks were dropped. Fourth, employees who worked

alone (without any other colleagues) were eliminated from the survey.

As direct measures of trust have not been included in the EWCS for ethical

reasons,

a trust proxy had to be devised. This proxy is based on the question of

whether an employee can get assistance from his or her colleagues and/or boss.

The survey item thus reads as follows: “For each of the following statements, please

select the response which best describes your work situation. You can get assistance

from colleagues if you ask for it. You can get assistance from your superiors/boss if

you ask for it.”The responses are based on a Likert scale, with the ﬁve answer

categories being “almost always,”“often,”“sometimes,”“rarely,”“almost never,”

and “don’t know”or “refusal.”Following the INDICSER methodological approach

(Saam et al., 2011), net measures have been formed by adding the categories “almost

always”and “often”and subtracting them from the sum of the two categories

“rarely”and “almost never.”To measure fear of job loss, there is a question in

line with the given literature in the ﬁeld (Sverke et al., 2002, p. 243): “How much do

you agree or disagree with the following statements describing some aspects of your

job? I might lose my job in the next 6 months.”This survey item also uses a Likert

scale, with the ﬁve responses being “strongly agree,”“agree,”“neither agree nor

disagree,”“disagree,”“strongly disagree,”“don’t know,”and “refuse to answer.”In

accordance with organizational trust, a net fear of job loss variable is calculated by

adding the responses “strongly agree”and “agree”and subtracting the sum of those

who “strongly disagree”and “disagree.”

It might theoretically be that, for example, employees who undertake shifts work more than

84 hours. To control for any potential outliers however the author believes that it is valid to exclude

any work arrangements of less than 8 hours (or one working day) or more than 84 hours.

In an e-mail communication with the author, an expert at Eurofound noted that the items on trust

in one’s colleagues and boss are not included in the EWCS on the grounds that they “are very

difﬁcult to handle and are easily abused.”An adequate survey item for measuring trust, such as “[e]

valuate how well or poorly the following descriptions apply to your own workplace: very well,

rather well, rather poorly or very poorly...[t]he relationships between the workers and the man-

agement are open and based on trust,”has been used by Eurofound only in the Finnish Quality of

Life Survey.

An expert from Eurofound conﬁrmed that Eurofound itself proxies an item like trust in colleagues

and the boss through the above-stated proxy on support from colleagues and the boss. Although this

item seems to be more strongly connected to the concept of organizational social capital than

organizational trust, to the author’s knowledge it is the best publically available proxy for organi-

zational trust.

80 Felix Roth

Other data have been gathered from the following sources:

•Sectoral TFP growth data were retrieved from the EUKLEMS

database. For the

dependent variable, the average annual growth rate over the period 1996–2006

was calculated. TFP growth for the aggregated sectors CtD and MtN were

calculated following formulas from Timmer et al. (2007, pp. 14–16).

•Sectoral TFP-level data were taken from the GGDC Productivity Level Database

(Inklaar & Timmer, 2008). Categories CtD and MtN had to be combined

according to the formula in Inklaar and Timmer (2008, pp. 36–38).

Being

based on 1997 PPP and the US as a benchmark, the data have been recalculated

for the base year 1995 and using Germany as the benchmark.

•The intangible capital variable for sectoral ﬁrm-speciﬁc human capital and a

sectoral indicator for employment protection legislation were taken from the

INDICSER (2013) project.

•Sectoral data on the size of ﬁrms and type of working contract for the instrumental

variable estimation were taken from the fourth wave of the EWCS (Eurofound,

2005).

•Sectoral data on R&D intensity and product market regulation were taken from

the OECD.

•The data for the micro-analysis were taken from the fourth wave of the EWCS

(Eurofound, 2005).

4 Descriptive Statistics

Table 4.1 shows all 100 aggregated values (subdivided into 15 countries and

10 sectors) of the fear of job loss that are used in the econometric analysis of this

contribution. With a value of 95.1%, Austrian employees who work in the public

administration and defense sector (L) have the lowest fear of job loss, whereas Czech

employees who work in the construction sector (F) have the highest fear of job loss,

with a value of 30.4%. As can be seen from Table 4.A1, with a mean value of 55%

and a standard deviation of 25%, on average only a minority of employees are afraid

of losing their jobs.

The sectors with the highest fear of job loss are 1) agriculture and ﬁsheries (AtB)

at 16.7%, 2) hotels and restaurants (H) at 33% (with 39.6% being the highest

value in the EU-15 country sample), and 3) construction (F) at 40.6%. The sectors

EUKLEMS refers to the research project “Productivity in the European Union: A Comparative

Industry Approach”and involves EU-level analysis of capital (K), labor (L), energy (E), materials

(M), and service (S) inputs. The data can be downloaded from the EUKLEMS website (http://www.

euklems.net/).

The author wishes to thank Robert Inklaar for providing the ex-ante capital compensation data

required to perform the valid calculation to combine sectors C with D and sectors M with N.

The author is grateful to Anna Rinkow for providing the data.

Organizational Trust, Fear of Job Loss, and TFP Growth 81

Table 4.1 Levels of fear of job loss in different economic sectors and countries

AtB CtD F G H I J K L MtN Avg.

Austria –61.7 53.5 42.9 47.5 50.0 –55.9 295.1 88.5 61.9

Belgium –45.7 –49.0 –66.7 83.8 50.0 82.5 82.7 65.8

Czech Republic –11.9 30.4 19.7 13.3 3.1 ––20.0 27.9 20.3

Germany –43.4 34.7 40.4 –46.9 –––66.0 46.3

Denmark –77.9 77.5 76.5 –72.3 –85.4 87.2 80.8 279.7

Spain –60.8 28.9 45.3 12.5 56.7 –36.5 70.3 78.3 48.7

Finland –54.8 35.0 55.6 –63.6 –60.7 61.9 75.3 58.1

France –66.9 –72.3 –60.9 –44.7 91.7 87.7 70.7

Hungary 16.7 28.3 19.6 7.2 –50.9 ––38.4 46.7 29.7

Ireland –68.2 70.7 65.5 58.8 66.7 –65.1 79.6 77.2 69.0

Italy –46.6 –58.2 –65.6 –49.3 82.5 82.8 64.2

Netherlands –56.2 –57.0 –47.4 66.7 37.8 63.6 71.9 57.2

Sweden –51.1 –56.8 –37.5 –43.3 57.7 66.9 52.2

Slovenia –6.0 –20.8 –––15.0 –61.3 25.7

UK –69.8 75.6 78.9 –77.3 –68.8 90.2 85.5 78.0

Observations, all 1 15 9 15 4 14 2 12 13 15 –

Average, all 216.7 48.4 40.6 47.1 33.0 54.7 275.2 51.0 70.8 72.0 –

Observations, EU-15 –12 7 12 3 12 2 11 11 12 –

Average, EU-15 –58.6 53.7 58.2 239.6 59.3 75.2 54.3 78.4 278.6 –

Notes: As the ﬁgure depicts net fear of job loss, values can range from a potential of +100 (complete fear of job loss) to 100 (no fear of job loss at all). In

addition, all values above 0 indicate that a majority of the respondents are afraid of losing their jobs. Minimum and maximum values for the speciﬁc country and

sector sample are depicted in bold. Cells are empty where there were fewer than 30 observations (and thus sector E has been dropped). To facilitate interpretation

of the table, net fear of job loss measures have been multiplied by 100.

Source: EWCS 2005 (Eurofound, 2005).

82 Felix Roth

with the lowest fear of job loss are ﬁnancial intermediation (J) at 75.2%, education

and health (MtN) at 72.0% (with 78.6% being the lowest value in the EU-15

country sample), and public administration and defense (L) at 70.8%. When

differentiating the non-agricultural market sectors (CtK) from the public sectors

(LtN), with the exception of sector J, one ﬁnds overall lower levels of job insecurity

in the public sector.

When analyzing the average level of fear of job loss from a country perspective, the

distribution of fear of job loss is more pronounced compared with the sectoral analysis

(16% to 75.2%). It ranges from 0.3% in the Czech Republic to 79.7% in

Denmark. Most notably, there exists a signiﬁcant difference between fear of job loss

levels in the three new member states, the Czech Republic, Hungary, and Slovenia

(with levels of 0.3%, 29.7%, and 25.7%, respectively) and the other EU-15

countries (which range in values from around 50% to 80%). The only exceptions

among the EU-15 countries are Germany and Spain, with an average fear of job loss

level of 46.3% and 48.7%, respectively, among employees.

Workers in similar

large economies, such as France, the UK, and Italy, have markedly lower levels of fear

of job loss, with 70.7%, 78.0%, and 64.2%, respectively.

In contrast to fear of job loss, which was depicted for all 100 individual obser-

vations, Fig. 4.1 shows the net levels of the proxies for trust in colleagues and the

boss within the sectoral and country aggregations, while Table 4.A1 shows the

summary statistics of net trust in colleagues and the boss. Figure 4.1 reveals that

in the transport and communication sector (I), employees’trust in colleagues ranges

from 7% in France to 96% in Denmark. With a mean value of 69% and a standard

deviation of 19%, levels of net trust in colleagues are in general relatively high and

quite evenly distributed around the mean. As depicted in Fig. 4.1, from a sectoral

point of view (left side of Fig. 4.1), net trust in colleagues varies from 54% in the

transport and communication sector to 78% in the ﬁnancial intermediation sector (J).

The picture looks somewhat more differentiated when analyzing net trust in col-

leagues from a country standpoint. France and Italy, with values of 33% and 39%,

respectively, have signiﬁcantly lower levels of net trust in colleagues than the other

European countries and the mean of 69%.

Concerning net trust in the boss, the mean value of 52% is 17% points lower than

net trust in colleagues. In addition, the variation is more pronounced, with trust

ranging from 11% in the Italian education and health sector (MtN) to 83% in the

Irish wholesale and retail sector (G) and a standard deviation of 0.23 compared with

0.19. Concerning sectors, it is quite evenly distributed, ranging from 41% in

transportation and communication (I) to 65% in ﬁnancial intermediation (J).

Looking at individual countries, a startling ﬁnding can be detected. Although net

trust is relatively evenly distributed among 13 EU countries, Italy and France are

clearly outliers. On average net trust in the boss in France is only 2.8% and in Italy it

is only 4.3%. This is an astonishing difference compared with the high levels in

Sweden (59%) and Denmark (77%), for example, and a mean of 52%.

It has to be pointed out, however, that data for sector L in Germany is missing. As the values for

sector L are on average higher than in other sectors, this fact partially contributes to a lower average

value in Germany.

Organizational Trust, Fear of Job Loss, and TFP Growth 83

Figure 4.2 shows a partial regression plot between fear of job loss and TFP

growth, which reveals the regression results from regression 4 in Table 4.2 (see

Sect. 5). When controlling for an economic sector dummy variable in an EU-27

country sample (including the 15 countries under study), the signiﬁcant curvilinear

relationship between fear of job loss and TFP growth is strongly driven by the three

transition countries, the Czech Republic, Slovenia, and Hungary.

In the Czech

Republic, in particular, high levels of fear of job loss in almost all sectors are

associated with low levels of TFP growth.

Figure 4.3 shows a partial regression plot between fear of job loss and TFP

growth based on regression 6 in Table 4.2. When analyzing an EU-15 country

sample without the Czech Republic, Slovenia, and Hungary and controlling for

country effects but not for sectoral effects (in order to fully attribute the full sectoral

variance), a signiﬁcant curvilinear relationship between fear of job loss and TFP

growth is detected. Speciﬁc sectors that exhibit levels of fear of job loss that are too

low and too high with respect to TFP growth rates are identiﬁed by country and

sector in Fig. 4.3.

Those exhibiting fear of job loss levels that are too low are public

administration (L) in Austria, Spain, and France, education and health (MtN) in

Austria and Spain, and ﬁnancial intermediation (J) in Belgium. Sectors exhibiting

100

I H G K C tD Mt N AtB L F J FR I T DE BE ES AT UK C Z H U SI I E FI NL SE DK

Trust boss Trust colleagues

Fig. 4.1 Organizational trust within the different economic sectors and countries

Notes: As the ﬁgure depicts net trust, values can range from a potential +100 (complete trust) to

100 (complete mistrust). In addition, all values above 0 indicate that a majority of the respondents

have trust. The left-hand side of the ﬁgure shows the variation by sector. The right-hand side of the

ﬁgure shows the variation by country.

Source: EWCS 2005 (Eurofound, 2005).

As depicted in regression 3 in Table 4.2, this relationship turns out to be insigniﬁcant when

controlling for country effects.

The calculation is based on the distance of these cases to the mean. If the distance is larger than

one standard deviation, they are displayed in Fig. 4.3 with the country and sector identiﬁcation.

84 Felix Roth

hu hu

-.1

-.05

.05

-.5 0 .5 1

Fear of job loss

Fig. 4.2 Partial regression plot between fear of job loss and TFP growth—all countries

Notes: For country abbreviations, cz ¼Czech Republic; si ¼Slovenia; hu ¼Hungary.

be CtD

be G

be J

es F

es H

es L

es MtN

fr 71t74

fr L it CtD

nl 71t74

at G

at H

at L at MtN fi F

-.04

-.02

.02

.04

-.4 -.2 0 .2 .4

Fear of job loss

Fig. 4.3 Partial regression plot between fear of job loss and TFP growth—sectors in EU-15

countries

Notes: On sector abbreviations, CtD ¼manufacturing; F ¼construction; G ¼wholesale and retail

trade; H ¼hotels and restaurants; J ¼ﬁnancial intermediation; 71–74 ¼K71–74 ¼business

activities, excluding real estate activities; MtN ¼education and health; L ¼public administration.

For countries, be ¼Belgium; fr ¼France; nl ¼the Netherlands; ﬁ¼Finland; es ¼Spain, it ¼Italy;

at ¼Austria.

Organizational Trust, Fear of Job Loss, and TFP Growth 85

high levels of fear of job loss include wholesale and retail trade (G) in Austria and

Belgium, hotels and restaurants (H) in Spain and Austria, manufacturing (CtD) in

Belgium and Italy, construction in Spain and Finland, and business in the Nether-

lands and France (K71–74). In this instance, the high levels of fear of job loss in the

knowledge-intensive production of business activities (K71–74) in the Netherlands

and France seem worrying for the competitiveness of their ﬁrms.

5 Econometric Analysis

When estimating Eq. (4.1) in Sect. 3, a least square dummy variable (LSDV)

approach is applied to take into account the interdependence of observations in

sectors and countries.

2526

In addition, to control for potential cross-sectional

heteroscedasticity, a robust-VCE estimator has been utilized.

Regressions 1 and

2 depict the relationship between the trust proxies and TFP growth.

Both trust

proxies turn out to be insigniﬁcant. The results hold no matter which dummies are

included in the regressions.

Regressions 3–7 analyze the relationship between fear of job loss and TFP

growth. When analyzing all countries in the given sample and controlling for

country and industry-speciﬁc effects (regression 3), the squared term of the fear of

job loss exhibits no signiﬁcant relationship. Once excluding the country dummy in

regression 4, however, the relationship of the squared term becomes signiﬁcant

(at the 90% level) with a coefﬁcient of 0.04. As depicted earlier in Fig. 4.2, this

curvilinear relationship seems to be strongly driven by the three transition countries,

the Czech Republic, Slovenia, and Hungary. As can be observed from Table 4.1

above, in those three countries the levels of fear of job loss are signiﬁcantly higher in

almost all sectors compared with those of EU-15 countries, especially in the Czech

Republic (with values close to 0). Given that these three countries act as outliers with

respect to the EU-15 country sample, regressions 5–7 focus on the remaining

The assumptions on the residuals are met and allow the estimation through LSDV. The residuals

are symmetrically distributed around 0 and follow a normal distribution. The underlying graphical

results from the rvfplot, qnorm, and pnorm Stata command can be obtained from the author on

request.

McMorrow et al. (2010) have also utilized sectoral and country dummies in their analysis.

The Stata robust command is based on the Huber-Sandwich Estimator.

To reduce the degree of multicollinearity, when constructing the squared terms of each respective

variable, the underlying variables were ﬁrst centered. This means that the mean of the variable was

subtracted from its real value (see here Kutner et al., 2004, p. 295). It turns out that the correlation

between these two variables effectively goes down from 0.92 to 0.57.

In addition, both proxies turn out to be insigniﬁcant when modeling them linearly. The results can

be obtained from the author. The insigniﬁcant result might also stem from the fact that the proxy

used misspeciﬁed the concept of trust.

86 Felix Roth

12 countries of the EU-15 country sample. When controlling for country and

industry effects in regression 5, no signiﬁcant relationship between fear of job loss

and TFP growth appears in the EU-15 country sample. However, as this contribution

is foremost interested in a cross-sectoral approach rather than a cross-country

approach, it seems reasonable to exclude the industry dummy in order to obtain

the full variance of the industrial sector observations. When excluding the industry

dummy in regression 6, a signiﬁcant (at the 95% level) negative effect (with a

coefﬁcient of 0.111) between the squared term of fear of job loss and TFP growth

appears. The speciﬁc pattern of this curvilinear relationship is shown in detail in

Fig. 4.3. The coefﬁcient of fear of job loss in regression 6 is 0.001 for the linear

Table 4.2 Organizational trust, fear of job loss, and TFP growth, LSDV estimation

Estimation

method

LSDV,

robust

LSDV,

robust

LSDV,

robust

LSDV,

robust

LSDV,

robust

LSDV,

robust

2SLS,

robust

Country

sample

All All All All EU-15 EU-15 EU-15

Equation 1 2 3 4 5 6 7

Trust

colleagues,

squared

0.013

(0.024)

–

Trust

colleagues,

squared

0.026

(0.042)

–

Trust boss –

–

0.003

(0.027)

–

Trust boss,

squared

–

0.020

(0.043)

–

Fear of job loss ––0.000

(0.026)

0.008

(0.009)

0.031

(0.019)

0.001

(0.013)

0.005

(0.020)

Fear of job

loss, squared

––0.006

(0.035)

0.040*

(0.024)

0.066

(0.046)

0.111**

(0.045)

0.138*

(0.082)

Catch-up term

Yes Yes Yes Yes Yes Yes Yes

Growth at the

frontier

No No No No No Yes Yes

Country

dummies

Yes Yes Yes No Yes Yes Yes

Industry

dummies

Yes Yes Yes Yes Yes No No

No. of

observations

103 103 100 100 82 82 82

No. of

countries

15 15 15 15 12 12 12

R-square 0.57 0.56 0.57 0.42 0.57 0.35 0.35

Notes: Organizational trust and fear of job loss variables are centered to reduce the degree of

multicollinearity (Kutner et al., 2004, pp. 295–300).

*** p < 0.01, ** p < 0.05, * p < 0.1.

For the EU-15 sample, these variables differ slightly from the larger sample, as technological

leaders might change. Instrumental variables in regression 7 include the type of working contract

and size of organization. The numbers in parentheses are robust standard errors.

Organizational Trust, Fear of Job Loss, and TFP Growth 87

term and 0.111 for the squared term. With a coefﬁcient of this size, the optimal

level of fear of job loss is 64% in the EU-15 country sample.

Thus, from a

productivity point of view, the optimal proportions of fear of job loss at the

aggregated level should equal approximately four-ﬁfths (82%) of employees who

do not fear job loss and one-ﬁfth (18%) who do. With a current mean of 63%,

aggregate fear of job loss is at its optimal point. Comparing the optimal level of fear

of job loss with the average sectoral levels in Table 4.1, it becomes apparent that

there are sectors with fear of job loss levels that are too high, optimal, and too low.

The sectors with fear of job loss levels that are excessively high are hotels and

restaurants (H) (24.4), construction (F) (10.3), and business activities (K) (9.7). The

sectors with an optimal fear of job loss level are wholesale and retail trade (G) (5.8),

manufacturing and mining (CtD) (5.4), and transport and communication (I) (4.7).

The sectors that on average have fear of job loss levels that are too low are ﬁnancial

intermediation (J) (11.2), public administration (L) (14.4), and education and

health (MtN) (14.6). Analyzing these results from a country perspective, one could

conclude that employees report too much fear of job loss in Germany (17.7) and

Spain (15.3), an optimal level in Austria (2.1), Belgium (1.8), and Italy (0.2), and

too little in the UK (14) and Denmark (15.7).

When running growth regressions, such as in eq. (4.1) and regression 6, one must

be aware of the possibility that the left-hand side and the right-hand side variables

will affect one another. More speciﬁcally, the independent variable fear of job loss

might be endogenous, affected by a common event ,such as an economic shock, or

stand in a bi-directional relationship with TFP growth; thus, lower levels of TFP

growth might, for example, inﬂuence an agent’s fear of job loss. As there is no

information on fear of job loss for the period t-1, the only possible solution is to

retrieve valid external instruments

for fear of job loss. To address the possibility of

endogeneity, a two-stage least squares (2SLS) estimation has been applied in

regression 7. The set of instruments utilized include among others the ﬁrm size

(medium and large) and type of working contract (indeﬁnite and ﬁxed). The

The calculation to determine the optimal level of fear of job loss in an EU-15 country sample has

been derived in the following manner: ﬁrst, one differentiates Eq. (4.1) with respect to OTF

i,j,t

. With

¼0.001 and β

¼0.111 one arrives at ∂c

TFPi,j,t

∂OTFi,j,t¼-0.001 0.222 OTF

i,j,t

. Second, to determine

the optimal OTF

i,j,t

value, one solves the following equation: 0.001 -0.222 OTF

i,j,t

¼0 with

respect to OTF

i,j,t

and we obtain OTF

i,j,t

¼0.001/0.222 ¼0.0045. As this value is still centered,

it still has to be demeaned by adding the mean value of 0.6294 to the optimal point of 0.0045 to

obtain a demeaned optimal point of 0.6339 0.63. For reasons of comparability with Table 4.1,

this value is multiplied by 100 to derive an optimal value of 64%. Utilizing the same methodology

for regression 4, the optimal level of fear of job loss is then 45%.

In the context of curvilinear relationships (as depicted in regression 6), Woolridge (2002,

pp. 230–237) advises the direct application of the 2SLS method to both endogenous regressors

(the linear and quadratic effect) with the supplementary nonlinear transformations (quadratic terms)

of exogenous variables appearing somewhere in the system. A second option is to predict the linear

term with the exogenous variables and to square this prediction. Subsequently, the predicted b

ðÞ

then added to the instrumental variables regression. Regression 7 depicts the coefﬁcient for the

linear and squared term of fear of job loss when utilizing the second option.

88 Felix Roth

underlying speciﬁcation tests show that the instrument set is valid

and relevant.

Utilizing this set of instruments yields a signiﬁcant coefﬁcient for the squared term at

the 90% level. The size of the coefﬁcient becomes slightly smaller with a coefﬁcient

of 0.005/0.138 and an optimal point of fear of job loss of 65%.

5.1 Sensitivity of Results

To control whether the empirical result between fear of job loss and TFP growth in

regression 6 in Table 4.2 can be considered robust, Table 4.3 presents the results of a

sensitivity analysis on the coefﬁcient of fear of job loss in regression 6 in Table 4.2.

The ﬁrst row, under the heading “Baseline Regression”thus depicts the coefﬁcient of

regression 6.

The second row excludes obvious outliers that might drive the curvilinear

relationship as identiﬁed in Fig. 4.3. After excluding Spain’s hotel and restaurant

sector (es H), the coefﬁcient remains signiﬁcant at the 95% level. After additionally

excluding Austria’s public administration and defense sector (at L) in row 3, the

coefﬁcient remains signiﬁcant at the 95% level.

Rows 4–7 analyze the robustness among the various sectors. Figure 4.3 has

already highlighted that cases from the public sector seem to be more oriented

towards the left-hand side effect of the curvilinear relationship (positive relationship)

and those from the service and manufacturing sectors towards the right-hand side

(negative relationship). With this observation taken for granted, rows 4–7 differen-

tiate speciﬁc sectoral clusters by modeling them in a linear relationship. When

analyzing the service (GtK) and manufacturing (C-F) sectors in rows 4 and 5, one

detects a non-signiﬁcant negative relationship (although for the service sector the

90% level of signiﬁcance is only slightly missed). As each of the two sectors only

has a small number of observations, row 6 pools the observations of the service and

manufacturing sectors into a non-farm market sector classiﬁcation (C-K). When

analyzing the 59 observations from the non-farm market sector (C-K), a negative

linear relationship (at the 95% level) is notable. This result gives some initial

empirical evidence of the assumption that the left-hand side effect of the curvilinear

relationship (positive relationship) is driven by the public sector while the right-hand

side effect (negative relationship) is driven by the non-farm market sector. The

obtained β

-coefﬁcient of 0.06 should be interpreted in the following manner: on

average a sector with a fear of job loss level that is 10% higher is associated with

0.6% lower TFP growth. In accordance with this argument, when analyzing the

An overidentiﬁcation test (Hansen J statistic) of the validity of the instruments was automatically

performed within the utilized ivreg2 Stata command (Baum et al., 2010). With a χ(3) value of 3.67,

the rejection of the null hypothesis fails. This indicates that the instruments used are valid.

An underidentiﬁcation test (Kleibergen-Paap rk LM statistic) was automatically performed within

the utilized ivreg2 Stata command (Baum et al., 2010). With a χ(4) value of 20.3, the rejection of the

null hypothesis fails. This indicates that the instruments used are relevant.

Organizational Trust, Fear of Job Loss, and TFP Growth 89

public sectors (L-N) in row 7, one detects a positive but insigniﬁcant relationship

(which might stem from the small sample size).

To control for the effect of potential control and policy variables, rows 8–11

include R&D (McMorrow et al., 2010), ﬁrm-speciﬁc human capital, labor market

regulation (Nicoletti & Scarpetta, 2003; Timmer et al., 2010), and product market

regulation (McMorrow et al., 2010; Nicoletti & Scarpetta, 2003; Timmer et al.,

2010). As the variables for R&D and labor market regulation are only available for

the non-farm business sector, the coefﬁcient of the linear relationship as depicted in

row 6 is used as the basis of the sensitivity analysis in rows 8 and 9. After including

the control variable, the coefﬁcient remains negative and signiﬁcant at the 90% and

95% levels. Rows 10 and 11 control for the inclusion of ﬁrm-speciﬁc human capital

and product market regulation. The signiﬁcance of the curvilinear relationship is not

altered.

Table 4.3 Fear of job loss and TFP growth—sensitivity analysis

Row Speciﬁcation change

Fear of job loss/fear of

job loss squared

Standard

error

No. of

observations R-squared

Baseline regression

1 No change 0.001/0.111** (0.013)/

(0.045)

82 0.35

Outliers

2 Spain sector H 0.003/0.150** (0.01)/

(0.06)

81 0.34

3 Austria sector L and

Spain sector H

0.003/0.150** (0.01)/

(0.07)

80 0.34

Restructuring—different sectors

4 Service sectors G-K 0.05 (0.03) 40 0.47

5 Manufacturing sector 0.03 (0.08) 19 0.89

6 Nonfarm business

sector C-K

0.06** (0.02) 59 0.45

7 Public sector 0.03 (0.02) 23 0.83

Control and policy variables—LR

8 R&D intensity 0.04* (0.03) 46 0.68

9 Employment

protection

0.06** (0.02) 59 0.46

Control and policy variables—CLR

10 Product market

regulation

0.005/0.12*** (0.01)/

(0.04)

82 0.37

11 Firm-speciﬁc human

capital

0.02/0.07** (0.01)/

(0.04)

71 0.54

Notes: On abbreviations, LR ¼linear relationship and CLR ¼curvilinear relationship. The fear of

job loss variable is centered to reduce the degree of multicollinearity (Kutner et al., 2004, pp. 295–

300). All regressions include country dummies; numbers in parentheses are robust standard errors.

*** p < 0.01, ** p < 0.05, * p < 0.1.

90 Felix Roth

5.2 How Do these Results Fit in with Other Existing

Empirical Results?

To the author’s knowledge, this is the ﬁrst empirical analysis of the relationship

between organizational trust, fear of job loss and TFP growth at a sectoral level.

This section discusses the empirical evidence, using different research designs for

interpreting the results presented above.

The non-signiﬁcant empirical results between organizational trust and economic

performance contrast with the positive ﬁnding by Frenkel and Orlitzky (2005) that

higher workplace trust leads to higher workplace labor productivity. The results also

contrast with the negative ﬁnding by Langfred (2004), that an excessively high trust

level among autonomous, self-managing work teams leads to diminished monitoring

activity and lower overall team performance, as well as the ﬁnding by Zaheer et al.

(1998) of a positive relationship between interorganizational trust and economic

performance. Likewise, the empirical results differ from the existing curvilinear

evidence between interpersonal trust at the nation-state level (Roth, 2009) and

individual level (Butler et al., 2009), and between interorganizational trust (Bidault

& Castello, 2009) and economic performance. A ﬁnal interpretation of the results,

however, should be treated with caution, as the invalidity of the proxy may be

affecting the empirical results.

The signiﬁcant curvilinear relationship between job insecurity and economic

performance may be related to empirical results by Brockner et al. (1992), who

ﬁnd a curvilinear relationship between job insecurity and employees’work efforts.

The left-hand side of the curvilinear relationship (positive relationship) is in accor-

dance with empirical work by Probst (2002, p. 211), who ﬁnds that job insecurity is

associated with higher productivity but lower quality output, and by Probst et al.

(2007), who ﬁnd that job insecurity is associated with higher productivity but lower

creative problem-solving. The right-hand side of the curvilinear relationship (nega-

tive relationship) can be linked to empirical results by Reisel et al. (2007), who report

a negative relationship between job insecurity and organizational performance, and

by D’Souza et al. (2006), who focus on sickness absence as a key indicator of labor

productivity and ﬁnd a negative relationship between job insecurity and organiza-

tional performance. The results are in contrast to the claim by Sverke et al. (2002)

who ﬁnd, when conducting a meta-analysis, that job insecurity and work perfor-

mance are not signiﬁcantly related to one another.

5.3 Objective Forces Driving Job Insecurity

at the Individual Level

For a policy-relevant conclusion, one might turn to the question of which objective

factors potentially shaped by policy-makers are the driving forces behind

employees’fears of job loss. To answer this question, the results of a generalized

Organizational Trust, Fear of Job Loss, and TFP Growth 91

ordered probit model

estimating individual data from the EWCS fourth wave

(Eurofound, 2005) with a total of 6, 744 employee observations (drawn from 12 of

the EU-15 countries) are presented in Table 4.4. Based on the given theoretical and

empirical literature (Leana & van Buren III, 2000, p. 230; Eurofound, 2010b;

Campbell et al., 2007, p. 552), the analysis focuses primarily on one objective factor

regarded by the author as realistically inﬂuenced by policy-makers: the impact of the

type of working contract on an employee’s fear of job loss.

Table 4.4 shows the

results (marginal effects) for the two response categories “Strongly disagree”and

“Strongly agree”of a generalized ordered probit model with job insecurity as the

dependent variable, and indeﬁnite and ﬁxed working contracts as independent vari-

ables (under the control of various, theoretically important variables). When

interpreting the marginal effects of the two response categories “Strongly agree”

and “Strongly disagree,”it can be concluded that concerning indeﬁnite contracts, in

all sectors the probability of responding “Strongly disagree”is around 12% higher

for employees who have an indeﬁnite contract and 14% lower for employees who

have a ﬁxed contract. Concerning ﬁxed contracts, in all sectors, the probability of

Table 4.4 Type of work

contract and employees’fear

of job loss, EU-15, 2005

Marginal effects

Strongly disagree Strongly agree

Indeﬁnite contract 0.12*** 0.03***

(0.02) (0.01)

Fixed contract 0.14*** 0.07***

(0.03) (0.02)

Notes: Estimation by a generalized ordered probit model. The

displayed values depict the marginal effects. The control variables

include gender, age, size of household, tenure, living with partner,

at least one other income in the household, education dummies for

ISCED 3–4 and ISCED 5–6, gender of boss, regional unemploy-

ment rate, ISCO dummies, country dummies, and sectoral dummies

(Table 4.A2). The excluded categories are other contracts, ISCED

0–2, female, female boss, no other income, and no partner. Similar,

but distinct control variables have been used by Campbell et al.

(2007). The numbers in parentheses are robust standard errors. The

number of observations is 6, 774 employees from 12 EU-15 coun-

tries. Filters have been utilized as described above.

*** p < 0.01, ** p < 0.05, * p < 0.1.

Source: EWCS 2005 (Eurofound, 2005).

Because the measurement of job insecurity in the EWCS of 2005 (Eurofound, 2005)isonaﬁve-

point scale, an ordered probit analysis has been utilized for this purpose. As the underlying

assumptions for estimating a proportional odds model are violated, the likelihood-ratio test of the

equality of coefﬁcients clearly rejects the null hypothesis of no violation, a generalized ordered

probit model is estimated in which coefﬁcients are allowed to vary over outcome categories (Boes,

2006).

In contrast to Campbell et al. (2007), who ﬁnd that 77% of workers who have a high fear of job

loss have indeﬁnite contracts, among the given population of 6,744 employees within the fourth

wave of the EWCS (Eurofound, 2005), only 57% of the workers who have a high fear of job loss

(agree + strongly agree) have indeﬁnite contracts.

92 Felix Roth

responding “Strongly agree”is around 7% higher for employees who have a ﬁxed

contract and 3% lower who have an indeﬁnite contract.

6 Conclusion

This contribution has analyzed the relationship between organizational trust, fear of

job loss, and TFP growth at a sectoral level in a European country sample. Six

empirical conclusions are noteworthy.

First, there is no signiﬁcant relationship between organizational trust and TFP

growth at the sectoral level. This ﬁnding is in contrast to most other ﬁndings in the

general literature on trust and economic performance. As the non-signiﬁcant result

might, however, be due to the invalidity of the proxy used, this result should be

interpreted with caution. Taking the theoretical literature and the most recent empir-

ical work on the relationship between trust and economic performance seriously, a

valid measure of trust should most likely yield a curvilinear relationship between

trust and growth.

Second, there is a curvilinear relationship between fear of losing one’s job and TFP

growth at the sectoral level in a European country sample of 100 observations

consisting of 15 EU-27 countries. This curvilinear relationship is driven by the three

transition countries, the Czech Republic, Slovenia, and Hungary, indicating that in all

three countries fear of job loss levels are above optimality with respect to TFP growth.

Third, given an EU-15 country sample with a total of 82 observations, a curvi-

linear relationship is detected. This curvilinear relationship proves to be robust

1) when controlling for potential endogeneity, 2) excluding outliers, and 3) including

relevant policy variables. The curvilinear relationship is driven by the sectoral

variance within the given sample. The sectors with levels of fear of job loss that

are excessively high are hotels and restaurants, construction, and business activities.

The sectors with an optimal level of fear of job loss are wholesale and retail trade,

manufacturing and mining, and transport and communication. The sectors that on

average have fear of job loss levels that are too low are ﬁnancial intermediation,

public administration, and education and health.

Fourth, when analyzing the curvilinear relationship between fear of job loss and

TFP growth in the EU-15 country sample from a sectoral perspective, the individual

sectors exhibiting levels of fear of job loss that are too low are public administration

in Austria, Spain, and France, education and health in Austria and Spain, and

ﬁnancial intermediation in Belgium. Those exhibiting fear of job loss levels that

are too high are wholesale and retail trade in Austria and Belgium, hotels and

restaurants in Spain and Austria, manufacturing in Belgium and Italy, construction

in Spain and Finland, and business in the Netherlands and France.

Fifth, when analyzing the curvilinear relationship between fear of job loss and

TFP growth within the EU-15 country sample from a country perspective,

employees exhibit excessive fear of job loss across the sectors in Germany and

Spain, an optimal level in Austria, Belgium, and Italy, and too little in the UK and

Denmark.

Organizational Trust, Fear of Job Loss, and TFP Growth 93

Sixth, on an individual basis, an objective factor that is associated with fear of job

loss is the type of contract an employee possesses. As expected, employees with

indeﬁnite contracts are signiﬁcantly less afraid of losing their jobs than those with

ﬁxed contracts.

The following four policy conclusions can be drawn. The ﬁrst two are general

policy conclusions and the latter two seek to improve lagging TFP growth.

First, to address ethical concerns, Eurofound should consider incorporating a

valid measure of organizational trust in the next design of the EWCS (most likely to

be conducted in 2015). The concept of organizational trust is theoretically too

important not to be validly measured in publicly available surveys.

Second, as job insecurity theoretically and empirically seems to have a signiﬁcant

and curvilinear relationship with TFP growth, academic research in economics that

interlinks such psychological concepts as trust to economic performance should also

extend awareness of the importance of the concept of fear (of job loss). In addition,

the concept of the “dark side of trust”(Gargiulo & Ertug, 2006) has been neglected

by current academic research focusing on the relationship between trust and eco-

nomic performance. Being aware of this “dark side of trust”, academic research

should incorporate models of trust and economic performance in a curvilinear

manner.

Third, from a policy point of view, most sectoral fear of job loss levels in the three

transition countries are well above the optimal degree of fear of job loss with respect

to TFP growth. In all three countries, employment relations seem to be too

deregulated and liberalized with respect to the high levels of job insecurity found

in most sectors. From a fear of job loss perspective, these countries should imple-

ment regulations enhancing job security. Such regulations should be implemented

by governmental actors in consultation with labor unions and employer

organizations.

Fourth, from a policy point of view, there are some sectors in the EU-15 in which

employment relations need to be further deregulated and liberalized to improve the

EU’s productivity performance, especially public administration, health and educa-

tion, and ﬁnancial intermediation. In other sectors, notably construction on the

manufacturing side, and hotels and restaurants and business services on the services

side, fear of job loss has already reached levels that are counterproductive to

Europe’s productivity performance. Here, foremost, the excessive levels of fear of

job loss in the business services seem particularly worrying, as this sector is deeply

engaged in knowledge production—a key asset for the future competitiveness of

European economies. In these sectors, liberalization has already been taken too far,

triggering growth-hampering effects, as discussed in the theoretical part of this

contribution. These sectors should be regulated again towards more job security.

Private consultancy ﬁrms that are aware of the importance of trust do measure it, but their results

are not made publicly available to academic researchers.

94 Felix Roth

Appendix

Table 4.A1 Summary statistics for the aggregate data analysis

Variable Observations Mean Std. Dev. Min. Max.

TFP growth 165 0.007 0.023 0.091 0.074

Catch-up term 165 0.63 0.58 4.79 0

Growth at the frontier 165 0.009 0.027 0.029 0.07

Trust colleagues 103 0.69 0.19 0.075 0.96

Trust boss 103 0.52 0.23 0.11 0.83

Fear of job loss 100 0.55 0.25 0.95 0.30

Contract ﬁxed 82 0.11 0.072 0.0 0.42

Indeﬁnite contract 82 0.77 0.14 0.25 0.98

Medium-size ﬁrm 82 0.45 0.10 0.22 0.69

Large-size ﬁrm 82 0.30 0.16 0.02 0.73

R&D intensity (in %) 46 1.92 3.11 0 12.95

Employment protection 59 2.30 1.28 0.46 5.61

Product market regulation 82 0.15 0.13 0.019 0.43

Firm-speciﬁc human capital 71 0.11 0.079 0.018 0.35

Table 4.A2 Summary statistics for the individual analysis

Variable Observations Mean Std. Dev. Min. Max.

Job insecurity 6774 1.90 1.17 1 5

Male 6774 0.47 0.50 0 1

Age 6774 40.6 11.6 15 72

Tenure 6774 20.6 12.5 0 64

Size of household 6774 2.8 1.4 1 13

Indeﬁnite contract 6774 0.79 0.41 0 1

Fixed contract 6774 0.11 0.31 0 1

Living with a partner 6774 0.64 0.49 0 4

At least one other income 6774 0.61 0.49 0 1

ISCED 3–4 6774 0.50 0.50 0 1

ISCED 5–6 6774 0.30 0.46 0 1

Male boss 6774 0.68 0.47 0 1

Regional unemployment rate 6774 7.12 3.35 2.6 21.3

Organizational Trust, Fear of Job Loss, and TFP Growth 95

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Organizational Trust, Fear of Job Loss, and TFP Growth 99

Chapter 5

Intangible Capital and Labor Productivity

Growth: Panel Evidence for the EU from

1998–2005

Felix Roth and Anna-Elisabeth Thum

Abstract Using new international comparable data on intangible capital investment by

business within a panel analysis between 1998–2005 in an EU country sample, a positive

and signiﬁcant relationship between intangible capital investment and labor productivity

growth is detected. This relationship proves to be robust to a range of alterations. The

empirical analysis conﬁrms previous ﬁndings that the inclusion of business intangible

capital investment in the asset boundary of the national accounting framework increases

the rate of change of output per hour worked more rapidly. In addition, intangible capital

is able to explain a signiﬁcant portion of the unexplained international variance in labor

productivity growth and becomes a dominant source of growth.

JEL Codes C23 · O47 · O52

Keywords Intangible capital · Labor productivity growth · Panel analysis · EU

Originally published in: Felix Roth and Anna-Elisabeth Thum. Intangible Capital and Labor

Productivity Growth: Panel evidence for the EU from 1998–2005. Review of Income and Wealth,

Vol. 59, No. 3, 2013, pp. 486–508.

Felix Roth wishes to thank the participants of the INNODRIVE project during workshops in Brussels

(2008), Vaasa (2008), Prague (2009), Rome (2009), Ljubljana (2010), and Berlin (2010), as well as the

participants at the ﬁnal INNODRIVE conference in Brussels (February 2011), the SEEK conference at

the ZEW in Mannheim (March 2011), the CEGE research colloquium at the University of Göttingen

(June 2011), and the EWEPA in Verona (June 2011) for valuable comments. In addition, he wants to

thank Bart van Ark, Mary O’Mahony, Jonathan Haskel, Marcel Timmer, Felicitas Nowak Lehmann,

Marianne Saam, Thomas Niebel, and two anonymous referees for valuable comments. He is grateful for

a grant from the European Commission under the Seventh Framework Programme (FP SSH 2007 1) for

the INNODRIVE project (Intangible Capital and Innovations: Drivers of Growth and Location in the

EU, contract number 214576). He would also like to thank Massimiliano Iommi, Cecilia Jona-Lasinio,

and Stefano Manzocchi for their contribution of the variables on intangible capital, and Raf van Gestel

for valuable research assistance. Preliminary versions of this paper were published as a CEPS Working

Document335inSeptember2010andINNODRIVEWorkingPaper3inSeptember2010.

Felix Roth (*)

Department of Economics, University of Hamburg, Hamburg, Germany

e-mail: felix.roth@uni-hamburg.de

A.-E. Thum

Directorate-General Economic and Financial Affairs, European Commission, Brussels, Belgium

©The Author(s) 2022

F. Roth, Intangible Capital and Growth, Contributions to Economics,

https://doi.org/10.1007/978-3-030-86186-5_5

101

1 Introduction

As highly developed economies transform more and more into knowledge econo-

mies, the input of intangible capital becomes vital for the future competitiveness of

their economies (Corrado et al., 2005; World Bank, 2006), as well as the compet-

itiveness of their ﬁrms (Teece, 1998, p. 76; Eustace, 2000, p. 6; Lev &

Radhakrishnan, 2003,2005). Although further reﬁnement of the concept of intan-

gible capital is still clearly needed, the overall measurement of the different dimen-

sions of intangible capital has largely improved, and past assessments, which have

called into question the possibility of measuring certain dimensions of intangible

capital, seem to have been too pessimistic.

Nevertheless, it remains an open

question as to which range of intangible capital indicators should be incorporated

into the asset boundary (Hill, 2009; Stiglitz et al., 2009) and which dimensions

should be included in a deﬁnition of intangible capital (World Bank, 2006).

This contribution focuses on intangible capital investment by businesses.Using

international comparable data on business intangible capital investment generated

within the INNODRIVE project

(INNODRIVE, 2011; Jona-Lasinio et al., 2011),

this contribution aims to present new econometric evidence about the impact of

investments in business intangible capital on labor productivity growth in the

business sector. As envisaged in the INNODRIVE framework (Jona-Lasinio

et al., 2011), the dimensions of business intangible capital were generated along

the framework originally proposed by Corrado et al. (2005,2009). However, as the

As recently as 1999, Robert Solow criticized the introduction of the term “social capital”into the

discipline of economics, by highlighting that “the term capital stands for a stock of produced or

natural factors of production that can be expected to yield productive services for some time.”He

continues to state the view that: “Originally, anyone who talked about capital had in mind a stock of

tangible, solid, often durable things such as buildings, machinery, and inventories”(Solow, 1999,

p. 6; emphasis added). Ten years later, the concept of intangible capital (including social and human

capital) seems to have found its way into the economic discipline. Other than the notion of social

capital, intangible capital deﬁnes itself exactly as not being tangible. Hence, the term intangible

capital seemsto offer an umbrella term for all those forms ofcapital that are theoretically important for

productivity but are not tangible in nature. A very similar deﬁnition is used in the World Bank (2006)

book entitled Where is the Wealth of Nations?, in which the authors use intangible capital as an

umbrella term for human capital,the skills and know-how of the workforce, social capital, the level of

generalized trust among citizens, and an economy’s institutional framework, such as an efﬁcient

judicial system and clear property rights, which exert a positive inﬂuence on the overall economy.

The INNODRIVE (Intangible Capital and Innovation: Drivers of Growth and Location in the EU)

project consists of the National Intangibles Database and the Company Intangibles Database. The

INNODRIVE National Intangibles Database provides time series of the Gross Fixed Capital Formation

for different intangible capital components for the EU-27 countries and Norway. This datasetis utilized in

the following empirical analysis and is cited as INNODRIVE, 2011. A detailed description of the dataset

is given in Jona-Lasinio et al. (2011). The goal of the INNODRIVE macro approach was to replicate the

intangible capital measures which were produced by CHS (2005 and 2009) for the US for the EU.

102 Roth and Thum

author wholly shares the view of the World Bank (2006), namely that the dimen-

sions of human and social capital should also be classiﬁed as intangible capital, the

dimensions of human and social capital have been included in the Total Factor

Productivity (TFP) term of the utilized model speciﬁcation.

2 Theoretical Links between Business Intangible Capital

and Labor Productivity Growth

2.1 Theoretical Relationship between Intangible Capital

and Labor Productivity Growth

The importance of Business Enterprise Research & Development (BERD) and

innovation was explicitly recognized in the “Lisbon process”and has been adopted

by the European 2020 strategy for smart, sustainable, and inclusive growth

(European Commission, 2010). Although the importance of business investment in

Research & Development has already been widely acknowledged—by policy-

makers and in economic theory—our knowledge of the contribution of business

intangibles to labor productivity growth is still incomplete. Generating a wider

concept of innovation and focusing on the issue of a possible revision of the national

accounting framework, Corrado et al. (2005) have grouped various items that

constitute the knowledge of the ﬁrm into three basic categories: 1) computerized

information, 2) innovative property, and 3) economic competencies.

Whereas computerized information includes knowledge that is contained in com-

puter programs and computerized databases, innovative property includes the scien-

tiﬁcknowledge embedded in patents, licenses, and general know-how, as well as “the

innovative and artistic content in commercial copyrights, licences and designs”

(Corrado et al., 2005,pp.23–26). Corrado et al. (2005,p.28)deﬁne the economic

competencies category of intangibles as “the value of brand names and other knowl-

edge embedded in ﬁrm-speciﬁc human and structural resources”.Itincludesexpendi-

tures on advertising, market research, ﬁrm-speciﬁc human capital, and organizational

capital. These measures indicate that the potential of intangible capital for stimulating

productivity growth lies in the provision of knowledge, an increase in the selling

potential of a product, and the development of processes and a productive environment

for the actual physical production of a good, or as van Ark et al. (2009, p. 63) stress,

that products and services are becoming more knowledge-intensive.

While the positive relationship between computerized information, here in par-

ticular via an interaction effect with organizational capital (Brynjolfsson et al.,

2002), and certain dimensions of innovative property (scientiﬁc R&D) (Lichtenberg,

1993; Coe & Helpman, 1995; Park, 1995; Guellec & van Pottelsberghe de la

Potterie, 2001) on labor productivity growth have already been extensively

discussed it seems important to stress once more the theoretical importance of the

Intangible Capital and Labor Productivity Growth 103

single dimensions of economic competencies, namely brand names, ﬁrm-speciﬁc

human capital, and organizational capital.

In theory, brand names should positively affect labor productivity growth since

an important aspect of today’s products is the “image”attached to them. Cañibano

et al. (2000) argue that the ownership of a brand that is appealing to customers

permits a seller to acquire a higher margin for goods or services that are like those

offered by competitors. As the consumer’s choice among the products of competing

ﬁrms is often motivated by a perception of reliability and trustworthiness, the

development of this image or brand has to be considered pivotal in the yield of

future beneﬁts. Expenditure on market research comprises, next to expenditure on

advertising, an important part of the investment in brand equity.

Firm-speciﬁc human capital is another important asset of a ﬁrm. Cañibano et al.

(2000) stress that a ﬁrm with more competent employees is likely to earn higher

proﬁts than competitors whose workers are less skilled. In this regard, Abowd et al.

(2005) argue that the value of companies will increase if the quality of their human

resources increases.

In addition to the “image”projected by a ﬁrm or a product and the quality of the

training of workers, the management of a production process involving highly

technological physical capital has also become important. As goods become more

and more sophisticated, production processes become more complex and the

organizational capital of a ﬁrm becomes crucial. Lev and Radhakrishnan (2005,

p. 75) deﬁne organizational capital as “an agglomeration of technologies –busi-

ness practices, processes and designs and incentive and compensation systems –

that together enable some ﬁrmstoconsistentlyandefﬁciently extract from a given

level of physical and human resources a higher value of product than other ﬁrms

ﬁnd possible to attain.”Organizational capital is seen by them (Lev &

Radhakrishnan, 2003,2005) as the only competitive asset truly owned by a ﬁrm,

while the others are tradable and thus available to any ﬁrm that wants to invest

in them.

2.2 The Treatment of Intangible Expenditures

Although, as argued above, the existing literature widely recognizes the importance

of the various dimensions of business intangible capital for the enhancement of

growth, in contemporary accounting practice, intangibles are treated as intermediate

expenditures and are not classiﬁed as investments in Gross Fixed Capital Formation

(GFCF). This situation has improved with the inclusion of software, mineral explo-

ration, and entertainment, literary, and artistic originals within the asset boundary of

the national accounts. Moreover, for innovative properties, such as scientiﬁc R&D

investment, national accounts have started to set up satellite accounts.

In the economic literature, this situation has gradually improved as a result of

Corrado et al.’s(2005) approach to capitalize the above-mentioned intangibles.

Utilizing standard intertemporal capital theory, they deﬁne investments as “any

104 Roth and Thum

use of resources that reduces current consumption in order to increase it in the

future”(Corrado et al., 2005, pp. 17–19) and treat intangibles, in contrast to the

national accounting framework, as investments rather than as intermediate goods;

thus, including it in the asset boundary rather than netting it out. Corrado et al. (2009,

pp. 663–66) model the impact of capitalizing intangible assets on the sources-of-

growth model as follows:

gQtðÞ¼sLtðÞgLtðÞþsKtðÞgKtðÞþsRtðÞgRtðÞþgAtðÞ ð5:1Þ

where Qis GDP expanded by the ﬂow of new intangibles, g

(t) denotes the rate of

growth of the respective variables, and s

(t) represents the input shares. Lis labor,

Kis the tangible capital stock, Ris the intangible capital stock, and Ais the TFP term.

3 Estimates of Intangible Capital Investment

Following Corrado et al. (2005,2009), the INNODRIVE macro approach

(INNODRIVE, 2011; Jona-Lasinio et al., 2011) has classiﬁed business intangible

capital investment into three groups: 1) computerized information, 2) innovative

property, and 3) economic competencies. Moreover, it differentiates two “old”

intangible capital variables

—1) software and 2) mineral exploration and copyright

and license costs

—in to eight new intangible capital variables: 3) scientiﬁc R&D,

4) new product development in the ﬁnancial services industry, 5) new architectural

and engineering designs, 6) advertising, 7) market research, 8) ﬁrm-speciﬁc human

capital, 9) own-account, and 10) purchased component of organizational capital.

The ﬁrst group, computerized information, contains: 1) computer software.

Computer software was measured by using data from the EUKLEMS

project, as

well as ofﬁcial national account data and the use table from the supply and use

framework (Jona-Lasinio et al., 2011, pp. 35–36).

The second group, innovative property, contains the following variables: 2) sci-

entiﬁc R&D, 3) new product development in the ﬁnancial services industry, 4) new

architectural and engineering designs, and 5) mineral exploration and copyright and

license costs.

In the INNODRIVE macro approach (INNODRIVE, 2011), software and mineral exploration, and

considered national account intangibles (i.e., they have already been included in the national

accounts), whereas the other intangibles are considered as new intangibles.

In contrast to Corrado et al. (2005,2009), the INNODRIVE macro approach (INNODRIVE, 2011)

has merged these two variables when presenting the ﬁnal investment data.

EUKLEMS refers to the research project “Productivity in the European Union: A Comparative

Industry Approach”and stands for EU level analysis of capital (K), labor (L), energy (E), materials

(M), and service (S) inputs. The data can be downloaded at: http://www.euklems.net/.

Intangible Capital and Labor Productivity Growth 105

To measure investment in scientiﬁc R&D, data on expenditure on R&D by

businesses were retrieved from Eurostat. To avoid double-counting of software

investment and investment in the development of new products within the ﬁnancial

services industry, data for the subsector K72 (computer and related activities) and

sector J (ﬁnancial intermediation) were subtracted from the R&D expenditure. In

accordance with Corrado et al. (2005), expenditure in scientiﬁc R&D was considered

a 100% investment in intangible capital (Jona-Lasinio et al., 2011, pp. 37–39).

Mineral exploration and copyright and license costs were measured with the help

of data from the national accounts and the use tables from the supply and use

frameworks (Jona-Lasinio et al., 2011, p. 39).

Investment in new architectural and engineering designs has been measured

using data from the national accounts (Jona-Lasinio et al., 2011, pp. 40–41).

Investment in new product development in the ﬁnancial services industry was

measured, according to Corrado et al. (2005), on the basis of 20% of total interme-

diate spending for intermediate inputs by the ﬁnancial intermediation industry,

which is deﬁned as excluding insurance and pension funding (NACE J65) (Jona-

Lasinio et al., 2011, pp. 41–42).

The third group, economic competencies, contains the following variables:

6) advertising expenditure, 7) expenditure on market research, 8) ﬁrm-speciﬁc

human capital, 9) own-account development of organizational structure, and 10) pur-

chased organizational structure.

To measure investment in advertising, a private data source (Zenith Optimedia)

was used.

Zenith Optimedia data report the expenditure on advertising in newspa-

pers and other media which should capture the purchased and own-account expen-

diture. Following Corrado et al. (2005), who followed Landes and Rosenﬁeld

(1994), only 60% of the actual expenditure was considered investment (Jona-Lasinio

et al., 2011, pp. 42–44). In order to measure the investment in market research, data

on the turnover of subsector K7413 (Market Research and Public Opinion Polling)

from Eurostat’s Structural Business Statistics on Business Services were taken.

Following the approach of Corrado et al. (2005), the prevalence of own-account

market and consumer research was estimated by doubling the estimate of the data on

market research (Jona-Lasinio et al., 2011, pp. 44–46).

Data on ﬁrm-speciﬁc human capital were taken from Eurostat’s Continued

Vocational Training Survey. This variable is a measure of the training expenditure

by enterprises and is computed as the cost of continued vocational training courses

as a percentage of total labor costs multiplied by employee compensation. This

training expenditure was considered a 100% investment in intangible capital. The

estimation method is applied at the industry level to guarantee that the compositional

changes of industries are taken into account. The measures are then aggregated to

obtain data on the national level (Jona-Lasinio et al., 2011, pp. 46–49).

Organizational capital is measured by the own-account and purchased investment

in the organizational structure of a ﬁrm. Data on the own-account component of

Felix Roth is grateful to Zenith Optimedia for making the data available to him.

106 Roth and Thum

organizational capital are taken from the EU Structure of Earnings Survey (in 2002)

and the EU Labor Force Survey. Own-account organizational capital is represented

by the compensation of the management. Manager compensation is computed as

the manager compensation share multiplied by the compensation of all employees.

The manager compensation share is the share of gross earnings of managers over the

gross earnings of all employees. Following Corrado et al. (2005), it was assumed that

20% of manager compensation is spent on investment in the organizational structure

of a ﬁrm. Data on the purchased component of organizational capital are taken from

Eurostat’s Structural Business Statistics on Business Services and the FEACO

Survey of the European Management Consultancy Market. Purchased organizational

capital is represented by management consultancy fees. In order to compute the

purchased component of organizational capital, the nominal gross output or turnover

of NACE 7414 (business and management consultancy activities) was used. It was

assumed that 80% of business sector expenditure is considered an investment (Jona-

Lasinio et al., 2011, pp. 49–54, 61–62).

As can be inferred by the INNODRIVE macro dataset (INNODRIVE, 2011), all

investment rates were constructed for the non-farm business sector and thus for

NACE sectors c–k+o.

4 Previous Empirical Results

Several empirical studies have tried to estimate the importance of business intangible

assets for labor productivity growth. Up to now, all existing studies have utilized a

growth accounting methodology.

There is an extensive body of literature studying

intangible capital investment both at the micro (ﬁrm) level (e.g., Brynjolfsson &

Yang, 1999; Webster, 2000; Brynjolfsson et al., 2002; Cummins, 2005; Lev &

Radhakrishnan, 2005) and at the macro (national) level. A detailed summary of

the microeconomic studies, as mentioned above, is not undertaken here as this

analysis focuses solely on the macroeconomic level.

FEACO stands for Fédération Européene des Associations de Conseils en Organisation.

NACE stands for Nomenclature Générale des Activités Economiques dans I0Union Européenne

and covers sectors from a to q. According to NACE rev. 1.1, sectors c to k plus o cover the non-farm

(a + b) market sectors: mining and quarrying (c), manufacturing (d), electricity, water, and gas

supply (e), construction (f), wholesale and retail trade (g), hotel and restaurants (h), transport,

storage, and communication (i), ﬁnancial intermediation (j), real estate, renting, and business

activities (k), and other community, social, and personal service activities (o). They exclude: public

administration, defense, and compulsory social security (l), education (m), health and social work

(n), activities of households (p), and extra-territorial organizations and bodies (q).

For a more detailed discussion, see Barro and Sala-i-Martin (2004, pp. 433–60) or Temple (1999,

pp. 120–21).

Intangible Capital and Labor Productivity Growth 107

Recent literature on the macroeconomic level has highlighted three important

lines of results once intangibles are capitalized: 1) the share of intangible capital

investments as a percentage of GDP or market sector gross value added (MGVA),

2) the contributions of intangible capital on output growth within an accounting

framework, and 3) the growth acceleration. Table 5.1 provides an overview of these

three dimensions in the literature most recently published in this ﬁeld. Corrado et al.

(2005)ﬁnd for the United States that the investment in business intangibles

represented 10%–12% of existing GDP between 1998 and 2000 and approximately

13% of non-farm business output in 2003 (see Fig. 2 of Corrado et al., 2009, p. 673).

In line with Corrado et al., Nakamura (2010, p. S138), analyzing a timeframe from

1959 to 2007, ﬁnds that investments in intangibles become as important as invest-

ment in tangibles in the US around 2000. Marrano et al. (2009) show that in the

United Kingdom the private sector spent a sum equivalent to 13% of adjusted

MGVA on business investment in intangibles in 2004. A working paper by Jalava

et al. (2007)ﬁnds that the Finnish investment in non-ﬁnancial business intangibles

was 9.1% of unrevised GDP in 2005. Fukao et al. (2009) estimate 11.1% of GDP

was invested in intangible capital in Japan in 2000–2005. According to Hao et al.

(2009), Germany and France invested 7.1% and 8.8%, respectively, and Italy and

Spain invested 5.2% in intangibles in the market sector over GDP in 2004. A

working paper by Van Rooijen-Horsten et al. (2008)ﬁnds an investment share for

intangibles of 8.3% of GDP when general government industry is excluded for the

Netherlands in 2001–2004. Van Ark et al. (2009)ﬁnd intangible investment shares

in the market sector of 6.5% in Austria, 6.5% in the Czech Republic, and 7.9% of

GDP in Denmark in 2006.

Investments for the UK, Germany, France, Italy, Spain,

and the US are similar to those in the other papers. Edquist (2011)ﬁnds that in

Sweden, total business investment in intangibles was equivalent to 10% of GDP, or

approximately 16% of the business sector gross value added (GVA) in 2006.

Second, when looking at the contribution of intangible capital to output growth,

Corrado et al. (2009)ﬁnd for the US that 27% of labor productivity growth in

1995–2003 is explained by intangible capital. Marrano et al. (2009)ﬁnd that 20% of

labor productivity growth is accounted for by intangible capital deepening in

1995–2003 in the United Kingdom. Jalava et al. (2007)ﬁnd that intangible capital

accounts for 16% of labor productivity growth in 1995–2000 and for 30% in

2000–2005 in the Finnish case. Fukao et al. (2009) show that intangible capital

explains 27% of the Japanese growth rate in 1995–2000 and 16% in 2000–2005.

Hao et al. (2009)ﬁnd that intangibles account for 31% of labor productivity growth

in Germany, 37% in France, 59% in Italy, and 64% in Spain. Van Ark et al. (2009)

ﬁnd that in Germany, France, Italy, Spain, Austria, the Czech Republic, and Den-

mark, intangible capital accounts for, respectively, 21%, 24%, 41%, 26%, 23%,

The authors also provide estimates for Greece and Slovakia. These are not reported here as they

are not part of the country sample in this paper.

108 Roth and Thum

15%, and 34% of labor productivity growth. Finally, Edquist (2011)ﬁnds for

Sweden that the contribution of intangible capital drops from 41% in the period

1995–2000 to 24% from 2000 to 2006.

Third, overall the capitalization of intangible capital accelerates productivity

growth. Detailed results of the growth acceleration values will be discussed in

comparison with the INNODRIVE data in Sect. 6.

Table 5.1 Summary of existing empirical studies

Article Country

Investment

(as a % of GDP)

Contribution to

LPG in %

Growth acceleration

in %

Corrado et al.

(2005)

US 10–12

(98–00)

Nakamura

(2010)

US Intangible ¼Tangible

(00–07)

Corrado et al.

(2009)

US ~13

(03)

(95-03)

11.2

(95–03)

Marrano et al.

(2009)

UK 13

(04)

(95-03)

13.1

(95–03)

Fukao et al.

(2009)

JAP 11.1

(00–05)

27, 16

(95–00), (00–05)

17.3, 1.4

(95–00), (00–05)

Jalava et al.

(2007)

FI 9.1

(05)

16, 30

(95–00), (00–05)

13.2, 2.1

(95–00), (00–05)

Van Rooijen

et al. (2008)

NL 8.3

(01–04)

Hao et al.

(2009)

DE, FR, IT, ES 7.1, 8.8, 5.2, 5.2

(04)

31, 37, 59, 64

(95–03)

10.5, 13.8, 37.2, 40

(95–03)

Van Ark et al.

(2009)

DE, FR, IT, ES,

AT, CZ, DK

7.2, 7.9, 5.0, 5.5,

6.5, 6.5, 7.9

(06)

21, 24, 41, 26,

23, 15, 34

(95–06)

11.2, 9.3, 11.5, 30.6,

18.6, 2.2, 37.0

(95–06)

Edquist (2011) SE 10/~16

(04)

41, 24

(95–00), (00–06)

16, 2.3

(95–00), (00–06)

LPG ¼Labor Productivity Growth.

Measure here is adjusted MGVA.

Only for Czech the period ranges from 1997 to 2006.

Measure here is intangible capital spending excluding general government industry.

Measure here is GVA.

Measure here is non-farm business output.

Notes: AT ¼Austria; CZ ¼Czech Republic; DE ¼Germany; DK ¼Denmark; ES ¼Spain; FI ¼Finland; FR ¼

France; IT ¼Italy; JAP ¼Japan; NL ¼the Netherlands; SE ¼Sweden; UK ¼the United Kingdom; US ¼the

United States.

The numbers in parentheses refer to the relevant time periods.

Intangible Capital and Labor Productivity Growth 109

5 Model Speciﬁcation, Research Design, and Data

5.1 Model Speciﬁcation

The model speciﬁcation within this contribution follows an approach by Benhabib

and Spiegel (1994), which Temple (1999) coined “cross-country growth account-

ing”or “growth accounting with externalities”(p. 124). The model by Benhabib and

Spiegel (1994) differs from the framework of a traditional single growth accounting

methodology as depicted by Eq. (5.1) by two components. First, the output elastic-

ities are estimated, rather than imposed. Second, part of the model can be designed to

explain the international variance in TFP growth.

One advantage of the utilization of stock data for tangible and intangible capital,

in contrast to other econometric growth estimations (e.g., Mankiw et al., 1992), is

that one is able to estimate the production function without the term for initial

efﬁciency and thus without “the complexities of dynamic panel data models, pro-

vided that TFP growth is unrelated to initial income”(Temple, 1999, p. 125). Like

the approach by Fleisher et al. (2010), the empirical model by Benhabib and Spiegel

(1994) is applied in a panel context.

Following the theoretical framework of Corrado et al. (2009) as depicted in

Eq. (5.1), Benhabib and Spiegel’s(1994) model speciﬁcations are expanded by

intangibles. The starting point for the estimation is then an augmented Cobb-

Douglas production function,

Qi,t¼Ai,tKα

i,tLγ

i,tRβ

i,tεi,t,ð5:2Þ

where intangible capital Ris added to the conventional production function because

it is treated as investments rather than intermediate expenses. Q

i,t

is GVA (non-farm

business sectors c–k + o excluding real estate activities) expanded by the investment

ﬂows in intangible capital in country iand period t. Similar to Eq. (5.1), Kis the

tangible capital stock, Lis labor, and Ais TFP. Assuming constant returns to scale

and rewriting the Cobb-Douglas production function in intensive form, the follow-

ing equation is obtained: with lower case letters indicating variables in terms of total

hours worked. If differences in natural logarithms are taken, the annual growth

relationship can be expressed as follows:

qi,t¼Ai,tkα

i,trβ

i,tεi,tð5:3Þ

with lower-case letters indicating variables in terms of total hours worked. If

differences in natural logarithms are taken, the annual growth relationship can be

expressed as follows:

110 Roth and Thum

ln qi,tln qi,t1



¼ln Ai,tln Ai,t1

ðÞþαln ki,tln ki,t1

ðÞ

þβln ri,tln ri,t1

ðÞþui,tð5:4Þ

where

ui,t¼ln εi,tln εi,t1:ð5:5Þ

Unless the TFP growth term is estimated, the estimation of this equation will be

biased. (Benhabib & Spiegel, 1994; Temple, 1999). Therefore, using a similar but

extended approach to Benhabib and Spiegel (1994), a model for (lnA

i,t

ln A

i,t 1

)

is speciﬁed as follows:

ln Ai,tln Ai,t1

ðÞ¼cþgHi,tþmHi,t

Qmax,tQi,t



Qi,t

þn1uri,t

ðÞ

þpX

j¼1

Xj,i,tþcdi,t¼2001 ð5:6Þ

where the constant term crepresents exogenous technological progress, the level of

human capital (gH

i,t

)reﬂects the capacity of a country to innovate domestically, the

term mHi;t

Qmax,tQi,t

ðÞ

Qi,tproxies a catch-up process, the term n(1 ur

i,t

) takes into

account the business cycle effect,

the term pP

j¼1

Xj;i;tis a sum of kextra policy

variables which could possibly explain TFP growth, and cd

i,t¼2001

is a crisis

dummy to control for the economic downturn in 2001 after the IT (information

technology) bubble burst in the year 2000 and the 9/11 attack in 2001. Inserting

Eq. (5.6) into Eq. (5.4) provides the baseline model to be estimated within the

econometric estimation in Sect. 7:

ln qi,tln qi,t1



¼cþgHi,tþmHi,t

Qmax,tQi,t



Qi,t

þn1uri,t

ðÞ

þpX

j¼1

Xj,i,tþcdi,t¼2001 þαln ki,tln ki,t1

ðÞ

þβln ri,tln ri,t1

ðÞþui,tð5:7Þ

Similar to Guellec and van Pottelsbergh (2001, pp. 107–116), as the research design of this

contribution uses yearly growth data, a control for the business cycle is speciﬁed as (1 ur

i,t

). The

analysis by de la Fuente (2002, p. 580) uses only uses ur

i,t

Intangible Capital and Labor Productivity Growth 111

5.2 Research Design

The econometric analysis covers 13 EU-27 countries for a time period from 1998 to

2005. The countries included are Germany, France, Italy, the United Kingdom, Spain,

Sweden, Denmark, Finland, Austria, Ireland, the Netherlands, Slovenia, and the Czech

Republic.

Although the INNODRIVE Macro approach (INNODRIVE, 2011)has

managed to produce a complete set of intangible capital variables for all 27 EU

countries plus Norway, the following econometric analysis had to be restricted to a

maximum of 13 EU countries, due to a lack of sectoral tangible capital input data

within the EUKLEMS database. With the 13 countries and the given timeframe, this

leaves the analysis with 98 overall observations (the Czech Republic and Slovenia,

each miss three time observations from 1998 to 2000). Following existing empirical

studies (e.g., Bassanini & Scarpetta, 2001), annual growth rates over the time period

1998–2005, rather than long-term growth rates from 1998 to 2005, have been chosen

to be able to apply a panel data analysis.

With intangible capital stocks having been

calculated for the period 1995–2005, the econometric analysis was restricted to a time-

frame of 1998–2005 as the calculation of capital services (as can be depicted in

Supplementary Appendix A2) needed intangible capital stock information from

1995 to 1997 to produce intangible capital service growth for the year 1998. The

whole research design applies to non-farm business sectors c–k + o. The output

measure is GVA for the non-farm business sectors c–k + o excluding real estate

activities. Tangible and intangible are non-farm business investments c–k+o.Tangi-

ble capital investments excluded residential capital.

5.3 Data

The data for the following econometric analysis were taken from various different

data sources, as described below.

•Data on the single components of intangible capital were taken from the

INNODRIVE macro dataset ( INNODRIVE , 2011). The INNODRIVE macro

data to a large extent conforms to EUKLEMS data, and GFCF data are provided

for all intangible assets and all EU-27 countries plus Norway.

•Data on GVA (non-farm business sectors excluding real estate activities), tangi-

ble capital stocks, capital compensation, gross ﬁxed tangible capital investments,

tangible investment price indices, labor input (number of hours worked per

persons engaged), and depreciation rates for tangible capital were calculated

Felix Roth wishes to thank Mary O’Mahony for making available the capital input data for France

and Ireland.

By using annual data this contribution assumes that intangible capital stocks have an immediate

effect on labor productivity growth that speciﬁc year. By contrast, the growth accounting

approaches, as described before, take into account the long-term effects of capital services.

112 Roth and Thum

from the EUKLEMS database. Tangible capital included communications equip-

ment, computing equipment, total non-residential investment, other machinery

and equipment, transport equipment, and other assets, but excluded residential

capital.

•Human capital is measured as the “percentage of population who attained at least

upper secondary education”, which is taken as a proxy for the inherent stock of

human capital. These data are provided by Eurostat.

•The variable rule of law is taken from the Worldwide Governance Indicators

project (Kaufmann et al., 2010). The World Bank (2006) uses this indicator as a

proxy for generalized trust, an important indicator of social capital (Roth, 2009).

•The data on openness to trade are retrieved from the Penn World Table Version

6.2 (Heston et al., 2006).

•The data on unemployment rates, the stocks of foreign direct investment (FDI)

(as a % of GDP), total government expenditure (as a % of GDP), total expenditure

on social protection (as a % of GDP), total general government expenditure on

education (as a % of GDP), inﬂation rates (annual average rate of change in

Harmonized Indices of Consumer Prices), taxes on income (as a % of GDP), and

the stock market capitalization (as a % of GDP) are taken from Eurostat.

5.4 A Note on the Construction of Intangible Capital Stocks

Intangible capital stocks for the 11 EU-15 countries for the time period 1995–2005

were constructed by applying the perpetual inventory method (PIM) to series of

intangible capital investments going back to 1980 and using the depreciation rates,

, suggested by Corrado et al. (2009): 20% for scientiﬁc R&D, new architectural

and engineering designs, and new product development in the ﬁnancial services

industry; 40% for own and purchased organizational capital and ﬁrm-speciﬁc human

capital; and 60% for advertising and market research. In accordance with

EUKLEMS for software, a depreciation rate of 31.5 was used.

Intangible capital

stocks for the two transition countries, Slovenia and the Czech Republic, were

calculated by applying the PIM to investment ﬂows from 1995 to 1999, constructing

stocks for the 6-year period 2000–2005. For the calculation of the intangible capital

stock R

the PIM takes the following form:

Rt¼Ntþ1δR

ðÞRt1ð5:8Þ

which assumes: 1) geometric depreciation, 2) constant depreciation rates over time,

and 3) depreciation rates for each type of asset are the same for all countries. The real

Intangible capital stocks on mineral exploration and copyright and license costs had to remain in

the tangible capital stock as they could not be distinguished from tangible assets in the remaining

category “other”in the EUKLEMS dataset.

Intangible Capital and Labor Productivity Growth 113

investment series for N

use a GVA price deﬂator which is the same for all intangible

assets.

5.5 A Note on the Construction of Intangible and Tangible

Capital Services

Data on intangible capital services were generated according to the literature by

Oulton and Srinivasan (2003) and Marrano et al. (2009), and are consistent with the

EUKLEMS approach (Timmer et al., 2007). This literature argues that rather than

using a wealth measure for capital (like the capital stock), it is crucial to derive the

ﬂow of services a capital stock can provide to production. An overview of the

technical steps in how intangible and tangible capital services were constructed is

given in Appendix A2.

6 Descriptive Analysis

Table 5.A1 in Appendix A1 shows the descriptive statistics of the data utilized over

the 13 EU countries and over the time period 1998–2005. Annual labor productivity

growth increases by 0.10 (from 2.3 to 2.4) percentage points, or by 4.4%, when

taking into account the contribution of intangibles in the measure of GVA. This

value is smaller than most values reported by the existing literature as depicted in

Table 5.1, which ﬁnd that productivity growth increased by 11% in Germany, 9%–

14% in France, 11%–37% in Italy, 31%–40% in Spain, 19% in Austria, 37% in

Denmark, and 13% in the UK when adding intangible capital to the asset boundary.

The mean value of 4.4% however is larger than the value for Sweden of 2.3%, for

Finland of 2.1%, and the Czech Republic with 2.2%. The descriptive results in

Table 5.A1 also show that the services of the intangible capital stock (4.1%) grow on

average faster than the services of the tangible capital stock (3.3%).

Figure 5.1 shows the business intangible capital investments over GVA for the

ten single intangible assets and the three core dimensions as described in Sect. 3.

Overall business intangible capital investments differ considerably across the 13 EU

countries used in the econometric estimation.

Sweden ranks ﬁrst, with an overall

investment of 13.6% of GVA. As can be inferred from Table 5.1, this is within the

range of the values pointed out by Edquist (2011), who ﬁnds investment rates of

10% over GDP and 16% over business GVA. Sweden is followed by the UK, which

has an investment rate of intangible capital of 12.4%. As depicted in Table 5.1, this is

quite similar to the value for the UK, as measured by Marrano et al. (2009), of 13%.

The UK is followed by the second largest economy, France, with an investment rate

For a comparison of the intangible capital investment in the EU-25, see Gros and Roth (2012).

114 Roth and Thum

of 11.6%. France’s investment rate is in the range of the results of Hao et al. (2009),

who report that it invests 8.8% of GDP. The two large Mediterranean economies of

Italy and Spain are situated within the two last positions in the distribution. Spain

invests 6.3% on intangibles and Italy around 7.0% on intangible capital. This again

ﬁts with the reported investment rate by Hao et al. (2009) of 5.2% and 5.2%, and van

Ark et al. (2009), with investment rates of 5.5% and 5.0% over GDP. The largest

European economy—Germany—is positioned in the middle of the distribution, with

an overall investment of 9.6%.

This is in accordance with van Ark et al. (2009) and Hao et al. (2009), who ﬁnd

investment rates of 7.2% and 7.1%, respectively. On average the included 13 EU

countries invest 9.9% in intangibles over GVA.

The right-hand bar charts in Fig. 5.1 make clear that overall the largest shares of

intangibles are in either economic competencies or innovative properties, and only a

small part of the investment consists of investment in software. In order to identify

the distribution between the three dimensions in each country more clearly, Fig. 5.2

shows a scatterplot between the two larger dimensions, innovative property and

10%

12%

14%

16%

So Arch R&D

So

Arch

R&D

NFP OKP OKO FSHK ADV MKTR CompInf InnoProp EconComp

Fig. 5.1 Business intangible investment (as a percentage of GVA) in 13 EU countries, 1998–2005

Notes: The left bar chart for each country shows all ten single intangible capital items and the right

bar chart indicates the three dimensions: Computerized Information, Innovative Property, and

Economic Competencies. All variables in the graph are compared to GVA (non-farm business

sector c–k + o excluding real estate activity). Soft ¼Software; Arch ¼New architectural and

engineering designs; NFP ¼New product development in the ﬁnancial services industry; R&D ¼

Scientiﬁc research and development; Other NA ¼Other national account intangibles (mineral

exploration and copyright and license costs), OKP ¼Organizational capital (purchased compo-

nent); OKO ¼Organizational capital (own-account component); FSHK ¼Firm-speciﬁc human

capital; ADV ¼Advertising; MKTR ¼Market research; CompInf ¼Computerized Information;

InnoProp ¼Innovative Property and EconComp ¼Economic Competencies.

Source: INNODRIVE data (INNODRIVE, 2011).

Intangible Capital and Labor Productivity Growth 115

economic competencies. These are the countries that can be classiﬁed as being

highly innovative and investing strongly into economic competencies, and which

can be detected in the upper right corner, namely Sweden, Slovenia, and France. In

addition, France and Sweden score high on computerized information. On the other

hand, these are the economies that invest more in innovative properties than in

economic competencies, such as Denmark, Finland, and Germany. The third cate-

gory includes those countries that score low on innovative property and high on

economic competencies: the UK, the Netherlands, and the Czech Republic.

The

fourth category includes those countries that score low on both dimensions: Austria,

Ireland, Italy, and Spain. Overall, Fig. 5.2 clariﬁes that the sole focus of the Europe

2020 strategy (European Commission, 2010) on R&D investment seems to be too

narrow in view of the signiﬁcant investments in economic competencies.

EU-13

1% 2% 3% 4% 5% 6% 7%

Economic Competencies

Innovative Property

Fig. 5.2 Scatterplot between innovative property and economic competencies (as a percentage of

GVA), 1998–2005

Notes: EU-13 mean value of all 13 countries; es ¼Spain, it ¼Italy; fr ¼France; ﬁ¼Finland; de ¼

Germany; dk ¼Denmark; at ¼Austria; ie ¼Ireland; cz ¼Czech Republic; nl ¼the Netherlands; si

¼Slovenia; uk ¼United Kingdom; ♦Low Computerized Information; ●High Computerized

Information.

Source: INNODRIVE data (INNODRIVE, 2011).

One reason for this signiﬁcant difference might be the fact that those economies that invest higher

proportions in economic competencies are more specialized in the services sector. However, this

argument will be more consistent for the Netherlands and the United Kingdom than for the Czech

Republic.

116 Roth and Thum

Figure 5.3 shows a comparison between business investments in intangible

capital and tangible capital as it is used in the econometric estimation. Interestingly,

one detects that when including intangible capital investments, the average invest-

ment for the 13 EU countries is over 30% of GVA. This value is signiﬁcantly higher

than when solely considering tangible investments. Values on top of the bar charts

depict the ratio of intangible/tangible capital investment. Ratios close to but still less

than 1 indicate that intangible capital investment is almost as large as tangible

capital. France, Sweden, the UK, the Netherlands, and Finland have reached ratios

of larger than 0.6, with France already having reached a value of 0.78. It thus seems

sound to conclude that some EU countries have started to converge towards the US,

for which Nakamura points out that investment in intangible capital has become as

large as investment in tangible capital. In the transition countries Slovenia and the

Czech Republic, and the Mediterranean countries Spain and Italy, tangible capital

still dominates investments, with ratios below 0.4 (ratios of 0.37, 0.23, 0.29, and

0.32, respectively).

0.63 0.57

0.78 0.36

0.29

0.32 0.63

0.42

0.65

0.45 0.71

0.44

0.37

0.23

10%

20%

30%

40%

50%

60%

CT IT OCon OMach Other TraEq IC

Fig. 5.3 Business tangible and intangible investment (as a percentage of GVA) in 13 EU countries,

1998–2005

Notes: CT ¼Communications equipment; IT ¼Computing equipment; OCon ¼Total

nonresidential capital investment; OMach ¼Other machinery and equipment; Other ¼Other

assets; TraEq ¼Transport equipment; IC ¼Intangible capital. Residential capital has been

excluded.

Source: INNODRIVE data (INNODRIVE, 2011) and EUKLEMS data.

Intangible Capital and Labor Productivity Growth 117

7 Econometric Analysis

Without a lagged initial income term on the left-hand side, the baseline model in

Eq. (5.7) may be estimated without the complexities of a dynamic panel analysis.

Thus, when estimating Eq. (5.7), the standard methods of panel estimation are ﬁxed

effects or random-effects. The ﬁxed effects are calculated from differences within

each country across time; the random-effects estimation, in contrast, incorporates

information across individual countries as well as across periods.

The major

drawback with random-effects is, although being more efﬁcient, they are consistent

only if the country-speciﬁc effects are uncorrelated with the other explanatory

variables. A Hausman speciﬁcation test can evaluate whether this independent

assumption is satisﬁed (Hausman, 1978; Forbes, 2000, p. 874). The Hausman test

applied here indicates that a random-effects model can be used.

In addition, to

control for potential cross-sectional heteroskedasticity, a robust VCE estimator has

been utilized.

As highlighted in Sect. 5.2, the random-effects estimation uses

13 countries with a total of 98 observations. It is a balanced panel, with two countries

(the Czech Republic and Slovenia) missing three time observations from 1998 to

2000. Regression 1 in Table 5.2 shows the estimation results when estimating the

traditional production function (without the inclusion of intangible capital and

speciﬁcally excluding software from the tangible capital investment).

The overall

R-square value is 0.40, with a within R-square value of 0.20 and a between R-square

value of 0.63. The growth of tangible capital services is positively associated with

labor productivity growth and has a coefﬁcient of 0.47, explaining a 65% share of

labor productivity growth.

Regression 2 shows the same model speciﬁcation when

including intangible capital investment. When including intangible capital invest-

ment in the asset boundary, the overall R-square value increases by 11% points to

0.51, the within R-square value increases by 16% points to 0.36, and the between

R-square value increases by 9% points to 0.72. Growth of intangible capital services

For the complexities of modeling the lagged income term within the growth econometric

equation, see, for example, Bond et al. (2001) and Roodman (2009a,2009b).

More precisely, a random-effects estimator uses a GLS estimator which produces a matrix

weighted average of the between and within results.

The test statistic is χ

(6) ¼3.45. This clearly fails to reject the null hypothesis of no systematic

differences in the coefﬁcients.

Using a xtoverid command (Schaffer & Stillman, 2010) the Sargan–Hansen test statistic is

(6) ¼5.5. This clearly fails to reject the null hypothesis of no systematic difference in the

coefﬁcients.

It was not possible to exclude mineral exploration and copyright and license costs from the

tangible assets as the EUKLEMS category “other”is a rest category and separate elements cannot

be ﬁltered out. Thus, tangible capital services and GVA in regression 1 include mineral exploration

and copyright and license costs, but exclude software.

Considering Eq. (5.7), with the mean value of (lnq

i,t

ln q

i,t1

) being 0.23, the mean value of

(lnk

i,t

ln k

i,t1

) being 0.32, and a being 0.47, the calculation can be set up as follows: (0.47 

0.32)/ 0.23 ¼0.65.

118 Roth and Thum

is positively related to labor productivity growth, with a coefﬁcient of magnitude

0.29. With this magnitude, intangible capital is able to explain around 50% of labor

productivity growth. As can be inferred from Table 5.1, a value of 50% is in close

range to the results of the growth accounting results for the relevant country cases

presented in this contribution; in particular, the results from Hao et al. (2009), who

ﬁnd 59% for France, 59% for Italy, and 64% for Spain. It is larger than the value of

Marrano et al. (2009) of 20%, however. Once including intangible capital, the

impact of tangible capital diminishes to 40%. TFP then changes from 35% to 10%

and is thus diminished by 25%. As intangible and tangible capital are able to explain

90% of labor productivity, the ﬁnding by Corrado et al. (2009) that capital deepening

becomes the dominant source is sustained.

In order to assess which dimensions of intangible capital services are the main

drivers of the positive relationship between intangible capital and labor productivity

growth, regression 3 includes the three dimensions of 1) computerized information,

Table 5.2 Intangibles and labor productivity growth; random-effects estimations

Estimation method

Random-

effects

Random-

effects

Random-

effects G2SLS

Equation 1 2 3 4

Intangible services growth –0.29*** –0.25*

(0.09) (0.13)

Tangible services growth

0.47*** 0.29** 0.24** 0.30*

(0.13) (0.11) (0.12) (0.18)

Computerized information services

growth

––0.03 –

(0.03)

Innovative property services growth ––0.09 –

(0.09)

Economic comp. services growth ––0.2*** –

(0.05)

Upper secondary education 15+ Yes Yes Yes Yes

Catch-Up

Yes Yes Yes Yes

Business cycle Yes Yes Yes Yes

Crisis dummy 2001 Yes Yes Yes Yes

Observations 98 98 98 72

Number of countries 13 13 13 13

R-square overall 0.40 0.51 0.56 0.53

R-square within 0.20 0.36 0.41 0.35

R-square between 0.63 0.72 0.77 0.81

Notes: Labor Productivity Growth was calculated with GVA of the non-farm business sectors c–

k + o excluding real estate activities. Labor Productivity Growth is in all regressions, except in RE1,

expanded with intangible capital. Robust standard errors are provided below coefﬁcient estimates in

parentheses. Tangible capital excludes residential capital. Intangible and tangible depict business

(sectors c–k + o) services growth.

***p < 0.01, **p < 0.05, *p < 0.1.

For equation 1, this variable is without software but includes mineral exploration and copyright and

license costs.

Intangible Capital and Labor Productivity Growth 119

2) innovative property, and 3) economic competencies, instead of the overall

intangible capital index. Interestingly, the main driver is not innovative property as

expected from the guidelines of the Europe 2020 strategy (European Commission,

2010), but rather economic competencies.

When running growth regressions, such as in Eq. (5.7), one must be aware of the

possibility that the left-hand side and the right-hand side variables will affect each

other. More speciﬁcally, the growth of the factor inputs intangible and tangible

capital deepening might be endogenous, affected by a common event such as an

economic shock (Temple, 1999, p. 125), or stand in a bi-directional relationship with

labor productivity; thus, an increase in labor productivity growth might, for example,

inﬂuence the agent’s decision to invest in tangible and intangible capital. Following

Temple (1999, p. 125), as the authors have not been able to retrieve valid external

instruments,

for example, for intangible capital, lagged levels of intangible and

tangible capital as instruments were chosen. Regression 4 shows the estimation

results when instrumenting with lagged levels of intangible and tangible capital.

A Sargan–Hansen test of overidentiﬁcation conﬁrms the validity of the utilized

instruments.

After controlling for endogeneity, the relationship between intangible

capital and labor productivity remains signiﬁcant (90% level) and the coefﬁcient is

only slightly reduced to 0.25. Therefore, it seems valid to conclude that the estima-

tion results from regression 2 were indeed unbiased and not affected by uncontrolled

endogeneity. Thus, the following sensitivity analysis will further explore the robust-

ness of the coefﬁcient of intangible capital on labor productivity growth, from

regression 2, permitting us to conduct an analysis with a maximum of

98 observations.

7.1 Sensitivity Analysis

Table 5.3 shows a sensitivity analysis of regression 2 in Table 5.2. The ﬁrst row,

under the title Baseline regression, depicts the coefﬁcient of regression 2 in

Which is quite common in such cases and normally leads to a weak instrument problem (Stock &

Watson, 2007).

To be precise, the ﬁrst three lagged levels of tangible and intangible services growth have been

utilized as instruments. Next to the lagged levels, the estimation has used education, the catch-up

term, the business cycle control, and the crisis dummy as instruments adding up to a total of

10 instruments. With a rule of thumb being that the total amount of instruments used should be

below the country cases (Roodman, 2009a, p. 128), the total usage of 10 instruments thus seems

adequate. The use of too large an instrument collection tends to overﬁt endogenous variables as it

weakens the Sargan–Hansen test (Roodman, 2009b). This is why typically difference and system

gmm estimator should be applied in cases of large n and small t, as within the gmm methodology

instruments tend to explode with increasing t (Roodman, 2009a, p. 99).

A Sargan–Hansen test of the validity of the instruments was performed via the command xtoverid

cluster-robust (Schaffer & Stillman, 2010) after the G2SLS estimation. With χ

(4) a value of 7.3,

the rejection of the null hypothesis fails. This indicates that the used instruments are valid.

120 Roth and Thum

Table 5.3 Sensitivity analysis for the baseline random-effects model

Row

Speciﬁcation

change

Coefﬁcient on

intangibles

Standard

error Countries Observations R-square

Baseline regression

(1) Baseline—RE2 0.29*** (0.09) 13 98 0.51

Inﬂuential cases

(2) Ireland and Italy 0.24** (0.10) 11 82 0.47

(3) Sweden 0.35*** (0.09) 12 90 0.53

Restructuring of data

(4) 1998–2001 0.32*** (0.08) 13 46 0.46

(5) 2002–2005 0.30* (0.18) 13 52 0.57

Restructuring of country sample

(6) Without

transition

0.26*** (0.09) 11 88 0.47

(7) Mediterranean 0.60*** (0.14) 2 16 0.91

(8) Liberal 0.14 (0.18) 2 16 0.58

(9) Coordinated 0.33*** (0.13) 4 32 0.58

(10) Scandinavian 0.25 (0.26) 3 24 0.55

Speciﬁcations

(11) Rule of law 0.28*** (0.08) 13 98 0.52

(12) Openness 0.29*** (0.08) 13 98 0.57

(13) FDI 0.31*** (0.08) 13 97 0.58

(14) Government

expenditure

0.29*** (0.08) 13 98 0.52

(15) Social

expenditure

0.28*** (0.08) 13 98 0.54

(16) Educational

expenditure

0.29*** (0.09) 13 98 0.50

(17) Inﬂation 0.29*** (0.09) 13 98 0.51

(18) Income tax 0.29*** (0.09) 13 98 0.51

(19) Stock market

capitalization

0.30*** (0.10) 13 91 0.58

(20) Forward BC 0.28*** (0.09) 13 98 0.50

(21) Dummies for all

years

0.22** (0.10) 13 98 0.56

(22) Dummy for 2000 0.22** (0.09) 13 98 0.48

Methods

(23) Jackknife 0.22*** (0.08) 13 98 0.56

Notes: The R-Squared values represent the overall R-Squared in a random-effects regression.

Robust standard errors are provided in parentheses.

*** p < 0.01, ** p < 0.05, * p < 0.1.

Intangible Capital and Labor Productivity Growth 121

Table 5.2. The second and third row exclude potential inﬂuential cases from the

country sample.

Thus, in row 2 of Table 5.3, Ireland and Italy are excluded from

the country sample. After the exclusion, the intangible capital coefﬁcient is reduced

(0.24) and remains signiﬁcant at the 95% level. With Sweden being an outlier in

opposition to the positive relationship, when excluding Sweden in row 3 the rela-

tionship gets signiﬁcantly larger, with a coefﬁcient of 0.35. When restructuring the

data in rows 4 and 5 into the two time periods 1998–2001 and 2002–2005, we detect

that the relationship seems to be slightly stronger in the time period 1998–2001

(0.32) than in 2002–2005 (0.30). In addition, the relationship is highly signiﬁcant in

the 1998–2001 time period and only signiﬁcant at the 90% level in the 2002–2005

time period. Since the EU-13 country sample analyzed is very heterogeneous

considering its economic structure, rows 6–10 explore the different regime typolo-

gies.

Excluding the two transition countries, Czech Republic and Slovenia, from

the country sample does not alter the coefﬁcient in a signiﬁcant manner. Whereas the

four coordinated (Germany, Austria, the Netherlands, and France) and two Medi-

terranean cases (Spain and Italy) remain highly signiﬁcant, the two liberal cases

(Ireland and the UK) and the three Scandinavian cases (Sweden, Denmark, and

Finland) lose signiﬁcance. Moreover, the coefﬁcient increases signiﬁcantly for the

Mediterranean case.

Since labor productivity growth might be related to many other determinants of

labor productivity growth, in particular, characteristics of the institutional settings

within the 13 EU economies, rows 11–19 include a range of policy variables. The

magnitude of the coefﬁcient of intangible capital remains robust after their inclusion,

and thus none of the included policy variables is able to alter the relationship. Rows

20–22 alter the included Business Cycle (as after a downturn in the economy,

unemployment usually starts to rise with a lagged effect), incorporate 8-year

dummies, and add an additional crisis dummy for the year 2000. Using a forward

lagged business cycle in row 20 does not alter the coefﬁcient. The 8-year dummies or

an additional crisis dummy for the year 2000 are only able to alter the signiﬁcance of

the coefﬁcient partially (to the 95% level) but tend to reduce its size to 0.22. When

utilizing a jackknife post-estimation command (Stata Corporation, 2007, p. 22) in

row 23, the coefﬁcient is also around 0.22. A coefﬁcient of 0.22 would still represent

an impact of 39% on labor productivity growth.

These cases have been detected by the usage of the command xtdata (Stata Corporation, 2007,

pp. 59–64). Results can be obtained from the authors on request.

For the classiﬁcation of the different regime typologies, see Hall and Soskice (2001). In contrast

to Hall and Soskice, France was included in the coordinated cases, and Scandinavian and transition

countries were grouped into individual regime typologies. As the number of observations reach

numbers as small as 16, which are below standard statistical reasoning, the results for the regime

typologies should be considered as economically signiﬁcant (McCloskey & Ziliak, 1996).

122 Roth and Thum

8 Conclusion

Using new international comparable panel data on business intangible capital

investment within a panel analysis from 1998 to 2005 in an EU country sample,

this contribution detects a positive and signiﬁcant relationship between business

investments in intangible capital and labor productivity growth within the business

sector. Five ﬁndings emerge. First, the empirical analysis conﬁrms the view that

intangible capital investment is able to explain a signiﬁcant portion of the

unexplained international variance in labor productivity growth, and becomes the

dominant source of growth of labor productivity. Second, this result is robust to a

range of alterations and holds when controlling for endogeneity. The result is

stronger in Mediterranean and Coordinated countries and within the time period

1998–2001. Third, the empirical analysis conﬁrms the ﬁnding that the inclusion of

intangible capital investment in the asset boundary of the national accounting

framework implies that the rate of change of output per worker increases more

rapidly. Fourth, the empirical analysis demonstrates that when incorporating

intangibles into the national accounting framework, tangible and intangible capital

become the unambiguously dominant source of growth. Fifth, the most important

intangible capital dimension seems to be the dimension of economic competencies.

Innovative property and software do not seem to have an impact on labor produc-

tivity growth within the given research design of this contribution.

In light of these ﬁve points, three main policy conclusions can be drawn from our

analysis of European economies. First, measuring innovation by solely focusing on

R&D, as is currently proposed in the European 2020 agenda, seems to be problem-

atic, and the R&D benchmark measure should be substituted by a wider intangible

capital benchmark. Second, incorporating intangible capital into today’s national

accounting framework seems to be necessary as developed economies transition into

knowledge societies and thus the signiﬁcant change of investment from tangible to

intangible investment is not acknowledged in today’s national accounting frame-

work. The current accounting framework seems to be inaccurate as it incorrectly

depicts levels of capital investment within European economies that are too low. In

reality, European economies’levels of capital investment are signiﬁcantly greater

once incorporating investment in intangible capital. Third, incorporating a wider

dimension of innovation investments seems to be a ﬁrst important step in revising

today’s national accounting framework, in particular when focusing on the business

sector. In addition, a next step seems to involve the wider adaptation of the national

accounting framework by environmental, educational, health, and social capital.

Moreover, wider reform of the national accounting framework should be envisaged

to achieve a more accurate signaling of real economic performance, to allow

developed and emerging countries to strive for sustainable economic growth.

See the report by Stiglitz et al. (2009), for example.

Intangible Capital and Labor Productivity Growth 123

Appendices

Appendix 1 Descriptive Statistics

Appendix 2 Construction of Intangible and Tangible Capital

Services

The consecutive steps required to construct the services are brieﬂy depicted in this

section. They broadly follow the literature by Oulton and Srinivasan (2003) and

Table 5.A1 Descriptive statistics, EU13, 1998–2005*

Observations Mean

Standard

deviation Minimum Maximum

LPG (%) 98 2.3 1.9 2.1 8.9

LPG—Expanded by intangibles

(%)

98 2.4 1.8 2.2 8.4

Intangible services growth (%) 98 4.1 2.4 2.8 9.2

Tangible services growth—

Expanded by intangibles (%)

98 3.3 1.8 0.3 9.9

Tangible services growth (%) 98 3.2 1.8 0.3 9.9

Computerized information Ser.

Gr. (%)

98 7.6 5.7 4.3 25.5

Economic competencies Ser.

Gr. (%)

98 2.8 3.2 6.4 11.2

Innovative property Ser. Gr. (%) 98 4.0 2.4 1.5 11.4

Education 15+ (%)** 98 65.7 11.8 38.3 83.2

Interaction education and catch-

98 16.4 26.4 0.0 116.2

Business cycle (%) 98 92.9 2.6 85.0 97.5

Rule of law*** 98 1.5 0.4 0.5 2.0

Openness (%) 98 82.3 30.8 45.2 157.5

Stock of FDI (% of GDP)**** 97 36.8 28.3 8.3 133.9

Government expenditure (% of

GDP)

98 47.2 6.4 31.5 58.8

Social expenditure (% of GDP) 98 26.1 4.7 12.0 33.2

Education expenditure (% of

GDP)

98 5.6 1.2 4.0 8.2

Inﬂation (%) 98 2.2 1.4 0.1 8.6

Income tax (% of GDP) 98 13.9 5.4 7.3 28.9

Stock market capitaliz. (% of

GDP)

91 76.5 51.6 13.3 284.0

Notes: LPG ¼Labor productivity growth. *For Slovenia and the Czech Republic the included

values range from 2001 to 2005. **Three missing values were interpolated by linear interpolation.

***Missing data were extra and interpolated. ****One value is missing for Austria in 2004.

124 Roth and Thum

Marrano et al. (2009) and the EUKLEMS approach (Timmer et al., 2007). In

Eq. (5.7), the growth of tangible and intangible capital services (lnk

i,t

ln k

i,t1

)

and (lnr

i,t

ln r

i,t1

), or respectively Δln k

i,t

and Δln r

i,t

are speciﬁed as:

Δln ki,t¼Xm

i¼1vi,tΔln ai,tð5:A1Þ

Δln ri,t¼Xn

i¼mþ1vi,tΔln ai,tð5:A2Þ

where small letters for k,r, and aare reﬂecting variables divided by hours worked in

the entire industry and for all assets, and where capital services from each asset iare

assumed to be proportional to the capital stocks. More concretely, in the equation,

i,t

is the asset-speciﬁc capital stock in hours worked obtained by applying the PIM

on the asset investments and dividing the stock by the hours worked. Assets 1 to

mrefer to tangible capital and assets from m+1tonrefer to intangible capital. The

capital stocks are weighted by a two period average weight vi,tgiven as:

vi,t¼1

2vi,tþvi,t1

½and vi,t¼pA

i,tAi,t

i¼1pA

i,tAi,t

!ð5:A3Þ

The weight consists of the user cost of a speciﬁc asset (tangible or intangible) pA

i,t

and the capital stocks A

i,t

for each asset i. The user cost equation can be displayed as:

i,t¼pI

i,t1itþδi,tpI

i,tpI

i,t1

 ð5:A4Þ

with the capital gain term pI

i,tpI

i,t1



, following Niebel and Saam (2011), being

expressed as

i,tpI

i,t1



¼1

2ln pi,t



ln pi,t2



pi,t1ð5:A5Þ

where pI

i,t1is the investment price derived from the price index for gross ﬁxed

capital investments, i

is the overall time-speciﬁc rate of return, and δ

i,t

is the asset-

speciﬁc, time-variant depreciation rate. As relative contributions of industries to the

total business sector (ck+o) change over time, depreciation rates are allowed to

vary over time and are calculated from the EU KLEMS dataset as:

δi,t¼Ai,t1þIi,tAi,t

Ai,t1

ð5:A6Þ

Intangible Capital and Labor Productivity Growth 125

where A

i,t

is the asset-speciﬁc capital stock and I

i,t

the asset-speciﬁc investment. The

rate of return i

is common to all assets (tangible and intangible) under assumption of

proﬁt maximization (Oulton & Srinivasan, 2003) and it is the only unknown element

in the user cost equation (Eq. 12). Following Timmer et al. (2007) it can be estimated

using an ex post approach where it is assumed that total capital compensation is

equal to the sum of all rental payments. It can as such be calculated using the

following equation:

it¼pA

tAtþPipI

i,tpI

i,t1



Ai,tPipI

i,tδi,tAi,t

PipI

i,t1Ai:t

ð5:A7Þ

Following Timmer et al. (2007), the ﬁrst term at the right-hand side is the total

nominal capital compensation and has been obtained by subtracting the labor

compensation from the value added.

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128 Roth and Thum

Chapter 6

Measuring Innovation: Intangible Capital

Investment in the EU

Felix Roth

Abstract This contribution is drawn from the discussions of a forum that examined

the strategies adopted by the European Commission and the member states for the

intensiﬁcation of innovative activities. It analyses business investment in intangible

capital using a new internationally comparable dataset in the EU27 created within

the FP7 project INNODRIVE. First, it ﬁnds that the EU’s innovation agenda would

be well advised to switch its benchmark criteria from a sole focus on R&D to a focus

on overall investment in intangible capital, in particular, on investments in economic

competencies. Second, it ﬁnds that today’s national accounting framework seems to

be ill-suited for identifying the ongoing transition of European economies towards

knowledge economies. Without identifying the full range of intangibles as an

investment in Gross Fixed Capital Formation, the overall levels of capital investment

of European economies are too low.

Keywords Innovation · Intangible capital · R&D · EU · Manufacturing sector ·

Service sector

Originally published in: Felix Roth. Measuring Innovation—Intangible Capital Investment in the

EU. Intereconomics, Vol. 45, No. 5, 2010, pp. 273–277.

The author is grateful for a grant from the European Commission under the Seventh Framework

Programme for the INNODRIVE project (Intangible Capital and Innovations: Drivers of Growth

and Location in the EU, contract number 214576). The ﬁnal construction and merging of all

intangible capital components, as well as the construction of the stock of intangible capital, were

performed by the INNODRIVE project, where LUISS and CEPS contributed to the macro data. The

INNODRIVE team is especially thankful for the contribution by LUISS team members

Massimiliano Iommi and Cecilia Jona-Lasino in their efforts to ensure the validity of the macro

data. Although the ﬁnal dataset will not be released until the end of the project in March 2011 and

minor changes might still be applied, any possible alteration will be slight and will not have the

potential to inﬂuence the ﬁndings and conclusions of this contribution.

Felix Roth (*)

Department of Economics, University of Hamburg, Hamburg, Germany

e-mail: felix.roth@uni-hamburg.de

F. Roth, Intangible Capital and Growth, Contributions to Economics,

https://doi.org/10.1007/978-3-030-86186-5_6

129

The European Commission’s 2020 strategy has put forward ﬁve EU targets for the

year 2020: focusing on 1) employment, 2) research and innovation, 3) climate

change and energy, 4) education, and 5) poverty reduction. The following contribu-

tion focuses on the target of research and innovation and is structured as follows.

First, the EU-2020 target on Research and Development is brieﬂy discussed and the

most recent criticism of the sole measure of R&D to capture innovativeness is

highlighted. Second, R&D investment in the EU-25

is compared to the wider

investments in intangible capital using a new internationally comparable dataset

on intangibles for the EU-27 created within the FP7 project INNODRIVE. Third, the

comparison of investments in tangible and intangible capital in 11 selected European

countries is discussed. This contribution concludes by putting forward policy

conclusions.

1 Innovation and EU 2020: Is R&D the Sole Factor

for Measuring Innovativeness?

In measuring innovation, most contemporary research identiﬁes investments in

Research and Development (R&D) as a percentage of GDP as one of the classical

benchmarks. In this sense, many empirical papers on the relationship between

innovation and productivity growth focus on a set of R&D indicators.

This focus

on R&D is prominently emphasized in the European 2020 strategy

for smart,

sustainable, and inclusive growth, which proposes as one of its headline targets to

foster innovation via a 3% benchmark for investment by the individual member

states in R&D as a share of GDP. This target of investing 3% of GDP in R&D had

already been formulated in the Lisbon strategy in 2000 and seems to be the only

The cases of Bulgaria and Romania were not analyzed, as the data from the INNODRIVE project

does not include values for Gross Value Added at current basic prices.

A. Bassanini, S. Scarpetta: Does human capital matter for growth in OECD countries? A pooled

mean-group approach, in: Economic Letters, Vol. 74, No. 3, 2002, pp. 399–405; M. Khan,

K. Luintel: Sources of Knowledge and Productivity: How Robust is the Relationship?, OECD

Science, Technology and Industry Working Paper No. 2006/6; F.R. Lichtenberg: R&D Investment

and International Productivity Differences, 1993, In H. Siebert (Ed.), Economic growth in the world

economy, J. C. B. Mohr, pp. 89–110.; D. Coe, E. Helpman: International R&D Spillovers,

European Economic Review, 39, 859-887. 1995; W.G. Park: International R&D Spillovers and

OECD Economic Growth, in: Economic Inquiry, Vol. 33, No. 4, 1995, pp. 571–591; D. Guellec,

B. van Pottelsberghe: R&D and productivity growth: panel data analysis of 16 OECD countries, in:

OECD Economic Studies No. 33, 2001/II; D. Coe, E. Helpman, A.W. Hoffmaister: International

R&D Spillovers and Institutions, NBER Working Paper 14,069, 2008.

European Commission: Europe 2020 –A European strategy for smart, sustainable and inclusive

growth, 2010.

130 Felix Roth

benchmark criterion to have been carried over from the original Lisbon strategy.

However, initial criticism of exclusively applying the 3% benchmark can already be

heard.

This criticism is strongly based on the fact that R&D investment does not

seem to be a valid indicator of a country’s innovativeness. It is rightly claimed that

R&D measures are of the utmost concern for those countries with a strong

manufacturing sector, e.g. Germany, but can more easily be neglected in those

countries with a strong services sector, e.g. the UK.

This is one of the reasons

why the most recent research ﬁnanced within the FP7 research program of the

European Commission has developed an internationally comparable dataset to

measure innovation by including a wider range of innovational dimensions, identi-

fying these dimensions as knowledge or intangible capital.

Early research results

suggest that an innovation indicator focusing solely on R&D might not take all

dimensions of innovation into proper consideration and thus might overlook impor-

tant information on how to strengthen Europe’s competitiveness.

This view of treating innovation as general knowledge capital has been promi-

nently developed by Corrado, Hulten, and Sichel,

who have grouped the various

items that constitute a ﬁrm’s knowledge into three basic categories: 1) computerized

information, 2) innovative property, and 3) economic competencies. Their approach

is currently under consideration by national statistical agencies

and think tanks

such as the OECD

and several research projects ﬁnanced under the European

Commission’s 7th Framework Program, as indicated above.

It should be noted that the Europe 2020 strategy does indicate that it is necessary to develop an

indicator that would reﬂect “R&D and innovation intensity”(p. 9); thus the European Commission

seems to be aware of the weakness of putting forward spending on R&D as the sole indicator to

measure innovativeness.

S. Tilford, P. Whyte, The Lisbon Scorecard X The road to 2020, Centre for European Reform,

London 2010.

S. Tilford, P. Whyte, ibid., p. 23; OECD: The OECD Innovation strategy –Getting a head start on

tomorrow, OECD, Paris 2010.

C. Jona-Lasinio, M. Iommi, F. Roth: Report on data gathering and estimations for the INNODR

IVE project –Macro approach (Deliverable No. 15, WP9), 2009; F. Roth, A.E. Thum: Does

intangible capital affect economic growth?, CEPS Working Document 335, 2010, http://www.ceps.

eu/book/does-intangible-capital-affect-economic-growth.

F. Roth, A.E. Thum: Does intangible capital affect economic growth?, op. cit.

C. Corrado, C. Hulten, D. Sichel: Measuring Capital and Technology: An expanded framework, in

C. Corrado, J. Haltiwanger, D. Sichel (eds.): Measuring Capital in the New Economy, National

Bureau of Economic Research, Studies in Income and Wealth, Vol. 65, Chicago 2005, University

Chicago Press, pp. 11–45; C. Corrado, C. Hulten, D. Sichel: Intangible Capital and Economic

Growth, Review of Income and Wealth, 55, 661–685, 2009.

J. Kestenbaum: New approaches to measuring innovation, in: S. Tilford, P. Whyte, op. cit, p. 26.

OECD, op. cit.

Two projects measuring a wider set of innovation indicators have been ﬁnanced under the 7th

Framework Program of the European Commission: COINVEST and INNODRIVE . Whereas the

COINVEST project has focused on a more detailed measurement for six European countries, the

INNODRIVE project has developed an intangible capital dataset for the EU-27.

Measuring Innovation: Intangible Capital Investment in the EU 131

In particular economic competencies—which include the three dimensions of

brand names, workforce training (or ﬁrm-speciﬁc human capital), and organizational

design (or organizational capital) of a ﬁrm—seem to be essential prerequisites for

innovative processes in the manufacturing and service sectors. In the manufacturing

sector, these investments should be regarded as crucial complementary investments

alongside classical R&D investment. In the services sector, investments in economic

competencies seem to play a key role in enhancing labor productivity.

2 How Does R&D Investment by Businesses Compare

to Investment in Intangibles in the EU?

Using newly developed internationally comparable data on intangible capital,

Fig. 6.1 shows the overall investment in intangible capital by businesses

when

including scientiﬁc R&D and the three dimensions of economic competencies:

gr es lt lv cy pt it pl mt ie sk lu cz at ee si de hu dk fi fr nl uk be se

R&D/VA Economic Competencies/VA

Fig. 6.1 Investment in intangible capital by businesses in the EU25 compared to R&D

Source: INNODRIVE Project (F. Roth, A.E. Thum: Does intangible capital affect economic

growth?, op. cit).

Currently the two FP7 projects INDICSER and SERVICEGAP try to identify, among other

things, the role of intangible capital on labor productivity within the service sector.

As the Europe 2020 strategy identiﬁes in particular business investment in R&D as signiﬁcantly

lower compared to levels in the US and Japan, it seems crucial to focus on businesses’investments

of intangible capital. Concrete reasons why the included intangible indicators should be classiﬁed as

investment in Gross Fixed Capital Formation are given in C. Jona-Lasinio, M. Iommi, F. Roth,

op. cit. and F. Roth, A.E. Thum: Does intangible capital affect economic growth?, op. cit.

132 Felix Roth

1) brand names (advertising and market research investment), 2) ﬁrm-speciﬁc human

capital, and 3) organizational capital investment.

Interestingly, closer analysis of intangible capital investment indicates that the

3% benchmark for total R&D spending is quite low in comparison to intangible

capital investments of up to 9% by businesses in Sweden. In addition, the innovation

ranking has changed signiﬁcantly. When focusing solely on business R&D spend-

ing, Sweden is followed by Finland, Germany, and France (see R&D share in

Fig. 6.1). Furthermore, the UK is positioned at the lower end of the distribution.

However, when focusing on a wider range of innovation indicators, Sweden is

followed by Belgium and the United Kingdom, both of which have investment

rates of approximately 8%. These two countries are then followed by the Nether-

lands and France. Germany and Austria are positioned in the middle of the distri-

bution, while the two Mediterranean countries Greece and Spain are positioned at the

bottom of the distribution. With an investment rate of more than 4%, Italy performs

similarly compared to the analysis with a focus solely on R&D. It is the poorest

performer among the four big European economies. This ﬁnding in combination

with Italy’s poor achievement when it comes to human capital indicates that the

country seems to be ill-equipped for future economic competition.

It also under-

lines once more the deep structural imbalances existing within the Eurozone, with

Mediterranean countries lagging behind in terms of innovativeness. Figure 6.2 once

more clariﬁes the signiﬁcant differences between R&D investments and investment

in economic competencies within an EU-15 country sample.

Investment in R&D seems to be positively (although weakly) related to invest-

ments in economic competencies. In Sweden and Finland, high investment in R&D

As opposed to the original CHS framework, the author has not included software and entertain-

ment, mineral exploration and literary or artistic originals, as those indicators have already been

included in the asset boundary of national accounts (see here F. Roth, A.E. Thum: Does intangible

capital affect economic growth?, op. cit.). Furthermore, the following intangible index will not

include the indicator “development in the ﬁnancial service industry,”as the inclusion of this

indicator creates a clear outlier in the EU-15 in the case of Luxembourg, distracting from the

overall importance of the ﬁndings for policymaking. Furthermore, taking the ﬁnancial crisis into

consideration, the author feels that the indicator should be handled quite cautiously when measuring

intangible capital in future approaches. Focusing on economic competencies in addition to R&D

already highlights the inadequacy of an innovation indicator focusing solely on R&D.However, the

indicator “development in the ﬁnancial service industry”will be included in intangible capital

measure later in this paper. In 2005, it represented around one-tenth of intangible capital in the

EU-25 on average.

D. Gros, F. Roth, The Post-2010 Lisbon Process—The Key Role of Education in Employment

and Competitiveness, in: Bundesministerium für Wirtschaft und Arbeit: Die Zukunft der

Wirtschaftspolitik der EU—Beiträge zum Diskussionsprozess “Lissabon Post 2010,”Vienna

2008, Bundesministerium für Wirtschaft und Arbeit, pp. 179–195; F. Roth, A.E. Thum: The Key

Role of Education in the Europe 2020 strategy, CEPS Working Document 338. Centre for European

Policy Studies. 2010.

Measuring Innovation: Intangible Capital Investment in the EU 133

by businesses is associated with moderate investment in the economic competencies

of their ﬁrms. The same is true for the three economies Denmark, Austria and

Germany, as well as for Luxembourg, Ireland, Portugal, and Italy, in which the

investments in business R&D are also closely matched to their investments in

economic competencies. However, the scatterplot also identiﬁes four interesting

cases in which R&D investment seems to be not so closely linked to investment in

economic competencies. These countries are the Netherlands, Belgium, the United

Kingdom, and Greece. Whereas Greek investment in economic competencies seems

to be relatively small compared to its investment in R&D, investments by the

Netherlands, the UK, and Belgium are particularly higher than their R&D invest-

ment. This ﬁnding implies that especially for the UK, the Netherlands, and Belgium,

an innovation indicator focusing solely on R&D investment poorly measures these

countries’competitiveness if focusing on their innovative potential. In the UK this is

due to the fact that its economic structure is more heavily dependent on the services

sector as opposed to the manufacturing sector, which tends to be more important in

other European member states.

2 3 4 5 67

0 1 2 3 4

R&D

Economic Competencies

Fig. 6.2 Relationship between investment in R&D and economic competencies

Source: INNODRIVE Project (F. Roth, A.E. Thum: Does intangible capital affect economic

growth?, op. cit).

134 Felix Roth

3 Comparison between Tangible and Intangible Capital

Investment in the EU

Efforts have been made to stop the steady decline of investment in traditional

tangible capital in most advanced economies. However, the efforts to increase

investment in tangible capital do not seem to have taken into account the fact that

the most advanced economies have simply undergone a structural transformation

towards becoming knowledge societies. But since the traditional national accounting

framework has not taken these processes into consideration, the accounts were (and

still are) not able to identify the actual investments made by businesses in recent

decades. Figure 6.3 compares the levels of investment in traditional tangible capital

with the new investments made in ICT and intangible capital for an EU11 country

sample

for the time period 1995–2005. Whereas traditional tangible capital invest-

ments have remained at a 16% level, the investments in ICT and intangible capital

1995 1996 1997 1998 1999 2000 2001 2002 2003 2004 2005

Percentage of investments in traditional assets (non ICT) in new GVA

Percentage of new forms of investment (ICT + new intangibles) in new GVA

Percentage of total capital investment in new GVA

Fig. 6.3 Comparison of business investment in traditional tangible capital and new ICT and

intangible capital in an EU11 country sample

Sources: INNODRIVE Project (F. Roth, A.E. Thum: Does intangible capital affect economic

growth?, op. cit) and EUKLEMS database (EUKLEMS: EU KLEMS Growth and Productivity

Accounts, March 2008 Release, http://www.euklems.net/).

The following 11 countries in the EUKLEMS dataset (EUKLEMS: EUKLEMS Growth and

Productivity Accounts, March 2008 Release, http://www.euklems.net/) are included in the aggre-

gated EU-11 trend: Austria, Czech Republic, Germany, Denmark, Finland, Italy, the Netherlands,

Portugal, Slovenia, Sweden, and the United Kingdom. ICT includes computing equipment and

communications equipment. New intangibles include scientiﬁc R&D, economic competencies,

software and—as Luxembourg is not included in the country sample—“new development in the

ﬁnancial service industry.”The share of investment in “new development in the ﬁnancial service

industry”in 2005 was, as stated above, on average one-tenth of total investment in intangible capital

in the EU25 countries.

Measuring Innovation: Intangible Capital Investment in the EU 135

have risen continuously and in 2005 reached a higher investment ratio than tradi-

tional tangible capital investment. Furthermore, if one accounts for both invest-

ments, the overall capital investments in the 11 EU member states were as high as

approximately 32% in 2005 and have steadily risen (due to ICT investment) from

1995 to 2001 and beyond. Due to the burst of the dot-com bubble, the investment

rate in 2005 remained at the same level as in 2001.

Figure 6.3 shows aggregated trends of 11 European countries. But to what extent

do the trends differ in the individual EU member states? Figure 6.4 shows the three

trends for the United Kingdom. Most interestingly, new investments in ICT and

intangibles were already higher than investments in traditional capital investment in

1996, and were equal in 1997 for the last time. From 1997 onward, there has been a

steady increase in investment in ICT and intangibles coupled with a steady decrease

in traditional tangible capital. Whereas business investment in traditional capital, e.g.,

machinery, equipment, buildings, etc., reached a level as low as 10% in 2004,

investments in new ICT and intangibles doubled that amount. Focusing on the total

capital investment shows a steady increase in capital investment in the UK (with a

minimal decline from 2002 to 2003 due to the burst of the dot-com bubble), reaching

a level of approximately 32% in 2005.

We now turn to Europe’s largest economy. Figure 6.5 shows the comparison of

business investments in traditional capital investment and new ICT and intangible

capital investment in Germany. As in the UK, investments in ICT and intangible

capital are diametrically related to each other. Whereas investment in traditional

capital has decreased slowly but steadily, investments in ICT and intangibles have

gradually grown. In 2001, investments in ICT and intangible capital were already

higher than in traditional capital. Furthermore, Germany’s overall capital investment

1995 1996 1997 1998 1999 2000 2001 2002 2003 2004 2005

Percentage of investments in traditional assets (non ICT) in new GVA

Percentage of new forms of investment (ICT + new intangibles) in new GVA

Percentage of total capital investment in new GVA

Fig. 6.4 Comparison of business investment in traditional tangible capital and new ICT and

intangible capital in the UK

Source: INNODRIVE Project (F. Roth, A.E. Thum: Does intangible capital affect economic

growth?, op. cit) and EUKLEMS database (EUKLEMS: EU KLEMS Growth and Productivity

Accounts, March 2008 Release, http://www.euklems.net/).

136 Felix Roth

in 2005 was near the 26% benchmark and increased steadily over the time period

1995–1999 and again from 2002 to 2005 after the bursting of the dot-com bubble.

4 Conclusion

This contribution has analyzed business investment using a new internationally

comparable dataset comparing the rate of business investment in intangible

capital in the EU27. Two main policy conclusions can be drawn.

First, the European 2020 agenda should switch its benchmark criteria from a sole

focus on R&D to a focus on overall investment in intangible capital, in particular, on

investments in economic competencies. The R&D indicator seems to be particularly

inappropriate for European economies with stronger services sectors, e.g. the United

Kingdom, and to overestimate the innovation potential for those countries that rely

heavily on manufacturing, e.g. Germany. Thus, including a wider range of intangible

capital variables in measuring innovative potential would give a more accurate

picture to European policymakers.

Second, today’s national accounting framework seems to be ill-suited to correctly

identify the ongoing transition of European economies to becoming knowledge

economies. Failing to identify intangibles as an investment in Gross Fixed Capital

Formation has the effect of strongly mismeasuring the levels of capital investment by

European economies. Any policy conclusion based purely on an analysis of “brick

1995 1996 1997 1998 1999 2000 2001 2002 2003 2004 2005

Percentage of investments in traditional assets (non ICT) in new GVA

Percentage of new forms of investment (ICT + new intangibles) in new GVA

Percentage of total capital investment in new GVA

Fig. 6.5 Comparison of business investment in traditional tangible capital and new ICT and

intangible capital in Germany

Sources: INNODRIVE Project (F. Roth, A.E. Thum: Does intangible capital affect economic

growth?, op. cit) and EUKLEMS database (EUKLEMS: EU KLEMS Growth and Productivity

Accounts, March 2008 Release, http://www.euklems.net/).

Measuring Innovation: Intangible Capital Investment in the EU 137

and mortar”investment without accounting for intangible capital variables seems to

be highly problematic. The frequently heard lament of falling capital investment

levels in the European Union seems to be unsubstantiated once ICT and intangible

investments are taken into account. The apparent decline in traditional ﬁxed capital

formation is in fact in most European economies more than fully compensated for by

an increase of ICT and intangible capital formation. European policymakers should

therefore ﬁnd new ways of promoting investment in intangible capital and stop

subsidizing traditional forms of tangible capital, e.g., via the European structural

funds.

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Measuring Innovation: Intangible Capital Investment in the EU 139

Chapter 7

Does Too Much Trust Hamper Economic

Growth?

Felix Roth

Abstract This contribution examines the relationship between trust and economic

growth. Taking panel data and using a ﬁxed-effects estimation for a 41-country

sample over the time period from1980 to 2004 and with a total of 129 observations,

tis points out that economic growth is negatively related to an increase in trust. This

negative ﬁnding is in contrast to most empirical ﬁndings using a cross-sectional

design. The common knowledge which has governed the nature of discussions in the

social sciences and economics for the last 10 years, namely that trust is generally

positively related to economic performance, must be seriously questioned. From a

policy point of view, an increase in trust is crucial for countries with low levels of

trust, but can likely be neglected by countries with sufﬁcient levels of trust and may

even hamper economic performance in countries with high levels of trust. The

relationship is tested in the context of EU countries, OECD countries, and develop-

ing countries. Interpersonal trust and systemic trust are differentiated.

Keywords Trust · Economic growth · Panel analysis · EU · OECD · Developing

countries

Originally published in: Felix Roth. Does too much trust hamper economic growth? Kyklos, Vol.

62, No. 1, 2009, pp. 103–128.

Felix Roth wishes to thank Stephan Klasen, Fran Tonkiss, seminar participants of the research

seminar for Ph.D. candidates at the chair of Stephan Klasen, the participants in the summer school

of the postgraduate program The Future of the European Social Model, the participants in the 2006

Ratio Colloquium for Young Social Scientists: Trust, Reciprocity and Social Capital, the

participants in the workshop and summer school program Social Capital, Corporate Social

Responsibility, and Sustainable Economic Development, the participants in the Post Graduate

Summer School Civil Society, Social Capital and Democracy, and the participants in the

Symposium on Social Capital in European Regions for valuable comments and suggestions.

Felix Roth (*)

Department of Economics, University of Hamburg, Hamburg, Germany

e-mail: felix.roth@uni-hamburg.de

F. Roth, Intangible Capital and Growth, Contributions to Economics,

https://doi.org/10.1007/978-3-030-86186-5_7

141

Introduction

Recent years have seen interest in the theoretical and empirical relationship between

social capital and economic growth. Social capital is said to be “the glue that holds

societies together”and it is emphasized that “without it no economic growth or

human well-being is possible”(Serageldin 1999, p. iii). Empirical research shows

that there is a positive relationship between interpersonal trust and economic growth

(Knack & Keefer, 1997; La Porta et al., 1999; Whiteley, 2000; Zak & Knack, 2001;

Beugelsdijk et al., 2004). In contrast to existing works, which examine the relation-

ship between social capital and economic growth using a cross-section research

design, this contribution uses a panel research design.

1 Theoretical Links Between Social Capital, Trust,

and Economic Growth

1.1 Social Capital and Trust

Many economists focus on the concept of trust when talking about social capital

(Knack & Keefer, 1997; Solow, 1999; Whiteley, 2000; Berggren & Jordahl, 2006;

c.f. Bjørnskov, 2003; Sabatini, 2008). Tonkiss (2000) comments that “trust regularly

features—together with norms and networks—within deﬁnitions of social capital”

(p. 78). But how is trust related to social capital? Although there are various

deﬁnitions of social capital (Bourdieu, 1983; Coleman, 1988,1990; Putnam, 1993;

Fukuyama, 1996, p. 26; Temple 2001 in OECD, 2001, p. 39; Ostrom, 1999, p. 176;

Newton, 1997, p. 576; for a wide range of deﬁnitions see Woolcock, 1998, p. 189),

trust is considered to be the most important dimension of social capital (Coleman,

1990; Fukuyama, 1996; Newton, 1997, p. 576; Ostrom, 1998; Uslaner, 1999, p. 122;

Tonkiss, 2000; Zak & Knack, 2001).

Therefore, this contribution focuses primarily on the dimension of trust in the

concept of social capital in the following empirical application.

Although there is a variety of deﬁnitions of trust (Fukuyama, 1996, p. 26;

Misztal, 1996, p. 16; Delhey & Newton, 2005, p. 311; Dasgupta 1997, p. 5 in

Ostrom, 1998, p. 12; Luhmann, 2000, pp. 1, 27), recent literature distinguishes

between three different forms: 1) thick trust, 2) interpersonal or generalized trust,

and 3) systemic or institutional trust (Putnam, 2000, p. 137; Newton, 1997, p. 578,

ff.; Luhmann, 2000).

Newton (1997) and Williams (1988) classify trust that is generated by family

networks as thick trust. In contrast, interpersonal or generalized trust is deﬁned as

trust that is generated by looser, secondary relations in modern societies, based on

everyday interaction between people who do not otherwise know each other. Most

scientists focus on interpersonal trust when examining the relationship between

economic growth and trust, as it should facilitate cooperation and lower transaction

costs in economic systems. Economic systems tend to be characterized by a sub-

stantial degree of differentiation, and exchange activity frequently depends upon

142 Felix Roth

trust in strangers. Interpersonal trust can be regarded as a good indicator of the levels

of solidarity in society, as well as a good indicator of the overall level of social

cohesion in society. This survey item, which is used in several international surveys,

is likewise used in this contribution when discussing trust.

The third category of trust, systemic or institutional trust, refers to the conﬁdence

people have in certain institutions. When discussing systemic trust here, the focus is

on trust in the parliament, the police, the armed forces, and major companies.

1.2 Relationship Between Social Capital, Trust,

and Economic Growth

Arrow (1972) argues that the presence of virtues such as trust plays a signiﬁcant role

in the operation of economic systems (p. 345). He builds his assumption upon the

paradigm of exchange and elaborates that the process of exchange requires or is

greatly facilitated by virtues such as trust (p. 345). For Fukuyama (1996), a nation’s

well-being and its ability to compete depend upon the level of trust inherent in a

society (p. 7). This argument is built upon his belief that economic activity itself is

part of the social life and constitutes itself according to the norms, rules, and moral

obligations of a society (p. 7). Robert Putnam (1993) comes to the conclusion that

high stocks of social capital in an economic region “bolster the performance of the

polity and the economy, rather than the reverse”(p. 176). He puts forward four

arguments why social capital has a positive effect on the economy: 1) it facilitates

coordination and cooperation for mutual beneﬁt, 2) it solves dilemmas of collective

action, 3) it reduces the incentives for opportunism, and 4) it reduces egoism (1995,

p. 76). In line with this argument, Sen (1999) argues that “the development and use

of trust in one another’s words and promises can be a very important ingredient of

market success”(p. 262) and that “no society would be viable without some norms

and rules of conduct”(Sen, 1977, p. 332).

According to Whiteley (2000), interpersonal trust has three direct channels

through which it might stimulate economic growth (p. 451).

Firstly, trust has a direct effect on economic performance by reducing transaction

costs. Transaction costs evolve during the economic process of exchange and

specialization and are deﬁned as the costs associated with banking, insurance,

ﬁnance, wholesale, and retail trade, or in terms of dealing with lawyers and accoun-

tants, etc. (North, 1990, p. 28). For North, the transaction costs are a part of the costs

of production. Taking this new production function into consideration, high-trust

societies should produce a higher output than low-trust societies as the cost for

transactions like monitoring, enforcing, and protecting contracts is smaller. People

who trust each other do not spend as much time or money protecting their property

rights. They might be able to solve their problems without lawyers or lawsuits.

Secondly, trust has a direct inﬂuence on growth because it enables actors to solve

collective action problems (Whiteley, 2000, p. 451). These arguments are in line

with Hardin (1982) and Ostrom (1990). In high-trust societies, it should theoretically

Does Too Much Trust Hamper Economic Growth? 143

be easier to cope with free rider problems that evolve, for example, with smog

problems, CO

emissions, and clean neighborhoods (Hardin, 1982, p. 9), as well as,

for example, the problem of overﬁshing (Ostrom, 1990, p. 3). Generally, in high-

trust societies, people will not so readily take advantage of the public infrastructure.

The third direct effect is that principal-agent problems might be much less

signiﬁcant in high-trust societies (North, 1990, pp. 32, 33). According to Knack

and Keefer (1997), two arguments can be mentioned in this context: 1) if entrepre-

neurs devote more time to monitoring possible malfeasance by partners, employees,

and suppliers, they will have less time to devote to innovation in new products or

processes: and 2) employment contracts in which managers rely on employees to

accomplish tasks can be difﬁcult to monitor. Fukuyama (1996) argues that high-trust

communities are not as dependent on extensive contracts and legal regulations

(p. 26) and that cooperation in high-trust societies will not have to be enforced by

coercive means (p. 27). He concludes that “if people who have to work together in an

enterprise trust one another, ... doing business costs less”(p. 27).

It has been argued thus far that trust, and therefore the facilitation of collective

action, leads to economic development and growth. But is this necessarily or always

the case?

One starting point for a possible negative relationship between trust and eco-

nomic growth can be found in the literature on collective action by Mancur Olson

(1982). This literature admittedly deals with the dimension of networks rather than

the dimension of trust, but the discussion proves quite relevant for these purposes.

Olson analyses the relationship between collective action and economic perfor-

mance in quite a contrary way. For example, collective action can undermine the

state’s power to implement necessary reforms or agendas to maintain high economic

growth rates. Olson argues that stable societies are in danger of accumulating

“collusions”and “organizations of collective action”over time (p. 41). If a society

accumulates too many organizations that function as special interest groups, eco-

nomic growth is harmed by reduced efﬁciency, by income being aggregated in the

societies in which they operate, and by political life being made more divisive

(p. 47). To give one example, if a state desires to implement labor market reform

in which, for example, employee rights are reduced, a sector with cheap labor is

implemented, working hours are extended, and social spending on unemployment

beneﬁts and support is decreased to reduce the costs of the labor factor, a highly

trusting and solidaristic society would more likely oppose the state’s efforts at

reform and will, via the mobilization of collective action, stop the reform agenda,

and therefore limit the potential of higher economic growth rates. This argument is

built upon Putnam’s empirical ﬁndings that a vibrant civil society is crucial for high

levels of trust (Putnam, 1993,1995). In fact, it could be actors within civil society

such as church groups, professional groups, and Social Movements Organizations

(SMOs) that oppose the state’s will to implement reforms. Similarly, the number of

workers being members of labor unions might be a critical factor for the existence of

high levels of trust (Putnam, 1993,1995,2000). For Putnam himself, civic associ-

ations and stocks of interpersonal trust are clearly interlinked. As such, the negative

relationship between trust and economic growth could be driven by associational

activity. Groups with strong bonding ties may produce, on an aggregated scale, a

144 Felix Roth

high interpersonal trust stock, while reducing economic outcomes, as described

above. Although being aware of various negative outputs that can evolve from a

strong civil society, Putnam never really clariﬁed the extent to which civic engage-

ment and high stocks of trust may hamper economic performance.

2 Previous Findings

Using a cross-sectional analysis with 29 market economies as units of observations,

Knack and Keefer (1997) discover that trust, in particular, as well as norms, matter for

economic growth, but that associations do not. Their social capital variable is measured

taking 21 observations from the ﬁrst wave of the World Value Survey (1981–1984) and

eight observations from the second wave of the WVS (1990–1993). Thus, the authors

utilize trust values from 1990 to 1993 to explain the economic growth rate from 1980 to

1992. The authors were aware of the endogeneity problem and argue that reverse

causation is not problematic due to the fact that the correlation between countries

from the ﬁrst and second wave of the WVS is very high (0.91).

In 2001, Zak and Knack reinvestigated the empirical results from Knack and

Keefer were published in 1997. They used a cross-sectional analysis and observa-

tions from 41 market economies. They used all three waves from the WVSs of

1981–1984, 1990–1993, and 1995–1997, the Eurobarometer and a government-

sponsored survey for the case of New Zealand. Their dependent variables were

investment share as a percentage of GDP, averaged over the period from 1970 to

1992, and average annual growth in per capita income over the same period.

Depicting the relationship between trust and economic growth, the authors came

to the conclusion that a positive relationship exists between trust and growth. They

determined that growth rises by nearly 1% point on average for each 15% point

increase in trust (p. 309).

Beugelsdijk et al. (2004) analyzed the statistical robustness of the results of

Knack and Keefer and Zak and Knack along four dimensions of robustness. They

concentrated on the statistical signiﬁcance and explored the inﬂuence of changing

sets of conditioning variables on the estimated effect of trust. Moreover, they

analyzed the sensitivity of the results for using different proxies or speciﬁcations

for basic variables like human capital. Finally, they investigated the effects on the

signiﬁcance and effect size when the 29-country sample by Knack and Keefer was

extended by 12 in the Zak and Knack paper. They conclude that the empirical

literature on trust and economic growth seems to be plagued more by data limitations

than by econometric problems such as omitted variable biases. The authors come to

the conclusion that “their extensive robustness analysis further adds to the empirical

evidence that trust matters for explaining variation in economic performance”

(p. 132) (Table 7.1).

Berggren et al. (2007) conducted an extensive robustness analysis of the rela-

tionship between trust and growth by investigating a latter time period and a larger

sample size. The authors worked with 63 countries using data on trust from the

fourth version of the WVS and from the Latinobarometro, as well as new data on

Does Too Much Trust Hamper Economic Growth? 145

growth, to separate time and sample effects. They investigated whether previous

results on the trust-growth relationship for the period of 1970–1992, studied by Zak

and Knack and Beugelsdijk et al., also hold for the 1990s. They learned that when

outliers are removed (here they mention China, speciﬁcally) the trust-growth rela-

tionship is only statistically signiﬁcant (with signiﬁcance at the 95% level) in 10% of

their 1140 regressions and that it is half as large compared to the results that had been

previously reported. The authors emphasize however that their results do “not

necessarily mean that trust is unimportant for growth, but its importance seems to

be more limited and uncertain than previously claimed”(p. 1).

La Porta et al. (1999), using an OLS regression on 39 countries and a cross-

section design with a dependent variable, per capita GDP growth rate from 1970 to

1993, found a signiﬁcant positive relationship between trust and economic growth.

They concluded that “in sum trust enhances economic performances across coun-

tries”(p. 317) and that “despite economist’s skepticism ... theories of trust hold up

remarkably well when tested on a cross-section of countries”(p. 320).

Whiteley (2000) examined the relationship between trust and economic growth in

the framework of a modiﬁed neoclassical model of economic growth. Using cross-

section designs in a 34-country sample, and using the timeframe of 1970–1992, he

came to the conclusion that an index of three trust indicators from the World Value

Survey (1990–1993) has a positive effect on economic growth, with an impact as

great as the variable human capital and conditional convergence. His ﬁndings

support the idea that “values play a key role in explaining cross-national variations

in economic performance and that they cannot be ignored in any properly speciﬁed

model of economic growth”(p. 460).

In contrast to these ﬁndings, Heliwell (1996), taking an OECD country sample

(17 OECD countries), found a negative relationship between trust and productivity

growth from 1960 to 1992 (associations and social capital, an equally weighted

combination between trust and associations, are also negatively related to

Table 7.1 Previous empirical results between trust and economic growth

Dependent variable Growth of GDP per capita

Equation 1 2 3

Article Knack and Keefer

(1997)

Zak and Knack

(2001)

Berggren et al.

(2007)

Growth per capita 80–92 70–92 90–00

Interpersonal trust 0.082

0.063

0.062

Income Yes Yes Yes

Primary schooling Yes No No

Secondary

schooling

Yes No No

Schooling No Yes Yes

PPP Yes Yes Yes

N29 41 63

Notes: Yes ¼variable is included in the growth model; No ¼variable is not included in the growth

model.

Signiﬁcance at the 90% level and higher (one-tailed test).

146 Felix Roth

productivity growth). His results seem to be the only cross-country indication of a

negative effect between trust and economic performance.

These empirical studies involve a critical and important step in focusing on the

concept of trust when reﬂecting upon economic growth. Their cross-section design

strongly supports the hypothesis that trust is relevant to economic growth. Never-

theless, they all neglect to examine how changes in trust affect economic growth. For

policy decision-making, however, it might be more relevant to analyze the effect of

changes in trust on economic performance. Furthermore, using a ﬁxed-effects model

provides two advantages. Firstly, unobserved heterogeneity can be controlled for.

Secondly, the problem that the interpretation of the trust items differs across

countries can be addressed.

3 Data and Measurement

3.1 Operationalization

The World Value Survey presents only limited data on trust. The trust variable is

constructed, as it is usually agreed upon by scholars from various disciplines

(Inglehart, 1990,1999; Knack & Keefer, 1997; Paxton, 1999,2002; Uslaner,

1999; Alesina & La Ferrara, 2000; Putnam, 2000; Whiteley, 2000; Zak & Knack,

2001; Van Oorschot & Arts, 2005; Delhey & Newton, 2005; Berggren & Jordahl,

2006), by aggregating the answer, “Most people can be trusted.”

(after deleting the

“Don’t know.”answers) to the item, “Generally speaking, would you say that most

people can be trusted or that you need to be very careful in dealing with people?”

(WVS 1999–2002).

It is thereby possible to compare the stock of trust in different

nations, from developed to developing, including transition states. The stock of trust

varies from 2.6% in Brazil 1995–1997 (Inglehart 2000) to 66.5% in Denmark

1999–2002 (European Values Study Group and World Values Survey Association,

2004). There are various critiques of this operationalization.

In the Eurobarometer 25, the answer is “Most people could be trusted.”

The ending of the question is slightly different in the ﬁrst three waves of the WVS and the

Eurobarometer 25: “[One] can’t be too careful in dealing with people.”(WVS 1981–84; WVS

1990–93; WVS 1995–97) and “[One] could not be too careful in dealing with people.”

Eurobarometer 25 (Rabier et al., 1988).

This approach is criticized by referring to the non-comparability of the different cultural back-

grounds of the countries that participate in the WVS. Researchers question whether data from China

can be compared to data from Germany, when the etymological meaning of the term trust differs in

the languages. Although correct, this criticism must be disregarded when comparing different

cultures, in so far as intercultural comparison would otherwise be made impossible. One must

therefore be pragmatic in using the data are available. Furthermore, recent research provides

evidence that individuals from the different countries did interpret the question from the WVS in

similar ways (Paxton, 2002, p. 261) and that the trust data are valid and of high quality as they

correlate highly to a natural experiment done by the Readers Digest (Knack & Keefer, 1997,

Does Too Much Trust Hamper Economic Growth? 147

3.2 Model Speciﬁcation

To be able to compare these results with previous empirical work conducted on the

relationship between trust and economic growth, a version of the economic growth

model used by Knack and Keefer (1997), Zak and Knack (2001), Beugelsdijk et al.

(2004), and Berggren et al. (2007) was used. Furthermore, a version of this type of

growth model was used by Forbes (2000) when analyzing the relationship between

inequality and economic growth in a panel setting from 1965 to 1995.

In the baseline model, economic growth is estimated as a function of the natural

logarithm of initial income, the price level of investment, human capital, and

interpersonal and systemic trust. An estimate of an unbalanced panel was made.

The baseline growth model for the ﬁxed-effects estimation is modelled as follows:

Growthi,t¼αiþβ1Trusti,t1

þβ2Incomei,t1

þβ3Human Capitali,t1

þβ4PPPIi,t1

þwi,t,

where irepresents each country and trepresents each time period (with t¼1–5);

Growth

i,t

is the average annual growth for country iat period t; Trust

i,t-1

, Income

i,t-1

Human Capital

i,t-1

, PPPI

i,t-1

, and are respectively trust, income, human capital, and

price level of investment for country iduring period t–1; α

represents a group-

speciﬁc constant term and w

i,t

is the error term.

3.3 Measurement of Data

Data on income and growth are based on per capita income between 1980 and 2004,

adjusted for purchasing power parity (PPP, expressed in constant 2000 US Dollars),

are drawn from the World Development Indicator Database, 2006. Since yearly

growth rates incorporate short-run disturbances, growth is averaged over 5-year

periods. The dependent variable here is an average growth rate per capita for the

periods 1980–1984, 1985–1989, 1990–1994, 1995–1999, and 2000–2004.

•The data on the price level of investment, population growth as a proxy for the

factor, Labor, the investment share of GDP at constant prices, and openness at

p. 1257). Glaeser et al. (2000) doubts that the item measures trusting behavior, and believes that it

measures the overall level of trustworthiness in a society. Jagodzinski and Manabe (2005) state that

the item does not measure trust but misanthropy, instead, and it was taken as an index of

misanthropy by Rosenberg. Sobel (2002, p. 151), Portes (2000, pp. 4 ff.), and Durlauf and

Fafchamps (2005) criticise the method of aggregation. For them social trust should more accurately

be measured on a micro- and meso-level.

148 Felix Roth

constant prices, are drawn from the Penn World Table 6.1 (Heston et al., 2002).

The variables were constructed by using lagged variables (1979, 1984, 1989,

1994, and 1999) to reduce the problem of endogeneity.

•The data on interpersonal trust and systemic trust are drawn from four waves of the

WVS 1981–1984, 1990–1993, 1995–1997 (Inglehart et al., 2000), and 1999–2002

(European Values Study Group and World Values Survey Association, 2004)and

the Eurobarometer 25 (Rabier et al., 1988) providing data for 1986.

•The data on human capital are based on Barro and Lee (2000) and refer to the total

years of schooling of the total population aged 25 and over. Data were taken for

1980, 1985, 1990, 1995, and 2000.

4 Descriptive Statistics

The country sample consists of 41 countries. Table 7.2 lists all interpersonal trust

values for the included country observations in my dataset. Twenty-seven out of

30 OECD

countries and 14 out of 15 EU15

countries are included. The observa-

tions were made over the time period from 1980 to 2004 providing ﬁve time periods

with a total of 129 cases for the analysis.

In contrast to the consensus that interpersonal trust is a constant variable, formed

by the cultural background of a nation (Knack & Keefer, 1997; Zak & Knack, 2001;

Knowles, 2005; Delhey & Newton, 2005, p. 314; c.f. Inglehart, 1997, p. 224;

Inglehart, 1999, p. 95; Noelle, 2005, p. 5), a closer look at Table 7.2 highlights the

existing variance in trust, with a strong decline in trust between the years 1990 and

1995.

Only Germany, Japan, and India have increased their levels of trust. On the other

end of the scale, the two liberal economies, the UK and the US, face a severe decline.

The US loses 14.4% of interpersonal trust and the UK, 12.2%. Poland and Finland

face the most severe losses; Poland loses 16.6%, Finland loses 15.1%, South Africa

loses 10.1%, China loses 7.8%, and Sweden loses 6.4%. Argentina and Mexico lose

around 5%. Only Chile and Norway behave in a more stable manner.

Luxembourg, New Zealand, and the Czech Republic had to be excluded due to data restrictions.

Only Luxembourg had to be excluded.

Although trust values intercorrelate strongly (comparing every combination of two waves gives

values from 0.75 to 0.93), there are still very important changes over time. If the wealthiest nation in

the world, the United States, and the United Kingdom lose nearly one-third of their original trust

level, trust cannot be treated as a constant variable. These changes in trust must be highlighted and

examined. Taking the case of Germany for instance clariﬁes that over the timespan from1950 to

2005, there is steady increase of the level of interpersonal trust (Noelle, 2005). To emphasize the US

case once more: Inglehart (1999, p. 95) and Uslaner (1999, p. 132) show that there is a decline in

interpersonal trust from 58%in 1960 to 36%in 1994. Paldam (2007), who has worked independently

on the analysis of the variance in interpersonal trust, discovers that there exists a great variance in

the interpersonal trust data over time.

Does Too Much Trust Hamper Economic Growth? 149

Table 7.2 Levels of interpersonal trust

Country Trust 81 Trust 86

Trust 90 Trust 95 Trust 99

Argentina 27 –23.3 17.5 15.4

Australia 47.8 ––39.9 –

Austria

–– 31.8 –33.9

Bangladesh –– –20.9 23.5

Belgium

30.2 29.5 33.2 –30.7

Brazil –– 6.7 2.8 –

Britain

44.4 39.7 43.6 31 29.7

Bulgaria –– –28.6 26.9

Canada 49.6 –52.4 –38.8

Chile –– 22.7 21.9 22.8

China –– 60.1 52.3 54.5

Denmark

56 63.5 57.7 –66.5

Finland

57.2 –62.7 47.6 58

France

24.8 21.3 22.8 –22.2

Germany

29.8 43.4 37.8 41.8 34.8

Greece

–50 ––23.7

Hungary 33.1 –24.6 –21.8

Iceland 41.6 –43.6 –41.1

India –– 34.3 37.9 41

Ireland

40.2 33.3 47.4 –35.2

Italy

26.3 30.3 35.3 –32.6

Japan 40.8 –41.7 46 43.1

Mexico 17.7 –33.5 28 21.3

The Netherlands

46.2 50.2 55.8 –59.8

Norway 61.2 –65.1 65.3 –

Pakistan –– –20.6 30.8

Peru ––5.0 10.7

Philippines –– –5.5 8.4

Poland –– 34.5 17.9 18.9

Portugal

–28.4 21.4 –10

Romania –– 16.1 –10.1

Slovak Rep. –– 23 –15.7

Slovenia –– –15.5 21.7

South Africa 29 –28.3 18.2 11.8

South Korea 38 –34.2 30.3 27.3

Spain

34.5 35.3 33.8 29.7 36.2

Sweden

57.1 –66.1 59.7 66.3

Switzerland –– 43.2 40.9 –

Turkey –– 10 6.5 15.7

The United States 45.4 –50 35.6 35.8

Venezuela –– –13.7 15.9

(continued)

150 Felix Roth

Figure 7.1 shows the relationship between the changes in trust for the period

[1995–1990] and the changes in growth in the period [9599–9094] for all countries

(“Before and After”Comparison). The change in the trust level in the US of 14.4%

is associated with a change in the annual growth for that period of 1.2%. In the US, a

decline in trust went hand in hand with a rise in annual growth. In the UK, the same

picture is replicated. The change in the trust level of 12.2% is associated with a

change in the annual growth rate of 2.08%. The Scandinavian countries Finland and

Sweden support the ﬁndings on the US and the UK. The decline in trust of 15.1

and 6.4% corresponds to an increase in the growth rate of 5.8% and 2.9%. The

5.000.00-5.00-10.00-15.00-20.00

Delta Trust (1995-1990)

5.00

2.50

0.00

-2.50

-5.00

Delta Growth

South Africa

Chile

Brazil

Argentina

India

China

Bulgaria

Slovenia

Poland

Turkey

Switzerland

Sweden

Spain

Norway

Mexico

South Korea

Finland

United Kingdom

Germany

Japan

USA

R Sq Linear = 0.173

Fig. 7.1 Scatter plot between Δtrust [1995–1990] and Δgrowth [9599–9094]

Table 7.2 (continued)

Country Trust 81 Trust 86

Trust 90 Trust 95 Trust 99

Observations 22 11 32 27 37

Average 39.9 38.6 37.4 28.9 30.1

Note: Countries in italics represent OECD Countries.

Countries from the EU-15.

The trust data from 1986 were taken from the Eurobarometer 25.

Trust data for Germany were taken from West Germany in 1981, 1986, 1990, and 1995. The data

from 1999 were taken from uniﬁed Germany.

Does Too Much Trust Hamper Economic Growth? 151

transition countries Poland and Bulgaria behave in the same manner. In Poland the

decline in the trust level of 16.6% is related to the increase of 5.2% in annual growth.

This relationship changes when observing Argentina and India. In Argentina, a

decline in the level of trust of 5.8% corresponds to a decline in the annual growth

rate of 4.3%. In India, an increase in the level of trust of 3.4% is followed by an

increase in the annual growth rate of 1.7%. In the cases of Argentina and India, there

seems to be a positive relationship between trust and economic growth. Taking all

countries into consideration, a weak negative relationship exists between delta Trust

and delta Growth with an R-Square value of 0.173. Considering only OECD

countries, the R-Square rises to 0.461.

5 Econometric Analysis

5.1 Cross-Sectional Analysis

First of all, using a cross-section design, an OLS model is estimated with robust

estimators of standard errors for the dataset. For the dependent variable, the average

growth rate of GDP per capita for the 15-year period from 1990 to 2004 is used. The

country sample consists of 32 countries due to data limitations from the interpersonal

trust value in the 1990s. All variables used here are stock variables. Interpersonal

trust values are all taken from the second wave of the WVS which was conducted

from 1990 to 1993. The variable Human Capital is applied for the 1990s and the

price level of investment is taken from 1989.

Regression 1 in Table 7.3 indicates that all variables have the expected signs

except the human capital variable. A negative signiﬁcant coefﬁcient for the income

variable (conditional convergence) is produced; likewise, a negative signiﬁcant

coefﬁcient for the price level of investment is produced and the positive signiﬁcant

relationship between interpersonal trust and economic growth is replicated. This

result, the positive relationship between Interpersonal Trust and Economic Growth,

is in accordance with most empirical ﬁndings using a cross-section design (see here

particularly Knack & Keefer, 1997; Zak & Knack, 2001).

5.2 Pooled Panel Analysis

Secondly, an estimate for the model using a pooled panel analysis is made. A pooled

panel analysis is similar to the method of a standard ordinary least-square estimation,

but in order to obtain more reliable estimates of the parameters, a pooled panel

estimation widens the database by pooling the time series of the country sample.

Hence, the pooled panel consists of 129 observations with 41 individual cases. Using

a pooled panel regression and examining all 129 observations, Regression 2 in

Table 7.3 replicates the result from the cross-section design and the results of most

152 Felix Roth

empirical research. A signiﬁcant positive coefﬁcient for the trust variable is obtained.

However, the proxy for the human capital variable “average years of schooling”

shows no signiﬁcant relationship to economic growth. Furthermore, conditional

convergence shows no signiﬁcant relationship to economic growth. Overall the

model does a poor job of describing the variance in the short-term growth rates

utilized. Only 22% of the variance of economic growth can be explained by the

model. As transition countries follow an economic growth pattern that is quite

different from the rest of the countries in the sample, Regression 3 uses a country

sample excluding the six transition countries. This country sample still has

115 observations. All variables have the expected signs and are signiﬁcant. This

yields conditional convergence, a positive relationship between human capital and

economic growth, a positive relationship between interpersonal trust and economic

growth, and a negative coefﬁcient for price levels of investment. Some 35% of the

variance in international growth can be explained. Regression 4, taking a country

sample without transition countries, modulates trust as a curvilinear relationship to

economic growth by including the squared term of interpersonal trust into the

Table 7.3 Interpersonal trust and economic growth—a pooled panel analysis

Dependent

variable Growth of GDP per capita 1980–2004

Estimation

method

OLS,

robust

OLS,

robust

OLS,

robust

OLS,

robust

OLS,

robust

Country sample All All

All without

transition

All without

transition OECD-23

Equation 1 2 3 4 5

Trust 0.072*** 0.05*** 0.05*** 0.16*** 0.17***

(3.81) (2.77) (3.07) (4.42) (3.47)

Trust, squared ––– 0.0015*** 0.002***

(3.24) (3.47)

Income 1.13** 0.69 0.9** 1.19*** 1.58***

(2.68) (1.40) (2.12) (2.73) (2.74)

Education 0.03 0.15 0.26** 0.31*** 0.23*

(0.33) (1.10) (2.36) (2.86) (1.93)

PPP 0.03*** 0.03*** 0.04*** 0.03*** 0.02***

(2.88) (3.30) (4.27) (4.18) (3.18)

Constant 12.8*** 8.3** 10.0*** 10.3*** 14.11***

(3.76) (2.25) (3.00) (3.09) (2.85)

R-squared 0.63 0.22 0.35 0.39 0.34

Countries 32 41 35 35 23

Observations 32 129 115 115 83

Period 90–04 80–04 80–04 80–04 80–04

Note: Numbers in parentheses are heteroskedasticity-adjusted t-ratios.

* Signiﬁcance at the 90% level (one-tailed test).

** Signiﬁcance at the 95% level (one-tailed test).

*** Signiﬁcance at the 99% level (one-tailed test).

Does Too Much Trust Hamper Economic Growth? 153

regression. Astonishingly, the curvilinear relationship is highly signiﬁcant. All vari-

ables in the regression have the expected signs and are highly signiﬁcant (99% level

of signiﬁcance). The linear and squared terms of interpersonal trust are each statis-

tically signiﬁcant: 0.16 (4.42) and 0.0015 (3.24). These estimates imply that

starting from a low-trust country (where the interpersonal trust value is for instance

2.8, as in Brazil), increases in interpersonal trust tend to stimulate economic growth.

However, the positive inﬂuence attenuates as the level of trust rises and reaches zero

when the indicator takes on a mid-range of 53.3. Therefore, an increase in the level

of trust appears to enhance economic growth in countries that have initial low levels

of trust but to retard economic growth for countries that have already achieved a

substantial level of trust. The model is able to explain 39% of variance in interna-

tional growth rates (4% more than the linear modulation).

Regression 5 examines an OECD-23 countries sample.

A signiﬁcant curvilinear

relationship exists between trust and economic growth. All other variables have the

expected signs and behave signiﬁcantly. Conditional convergence, a positive rela-

tionship between human capital and growth and a negative relationship between

price level of investments and economic growth, exists. Figure 7.2 shows the partial

uk ca

au fi

es se

0 1 2 3 4 5

Growth

0 20 40 60 80

Trust

Fig. 7.2 Partial regression plot for 23 OECD countries—trust and economic growth (1980–2004)

The OECD country sample, which includes the three transition countries Slovak Republic, Poland,

and Hungary as well as Iceland, has to be differentiated to an OECD23 country sample as the three

transition countries are hard to interpret. Iceland is often excluded in cross-country investigations

due to the size of its economy.

154 Felix Roth

regression plot between trust and economic growth for the OECD-23 sample. The

positive inﬂuence attenuates as the level of trust rises and reaches zero when the

indicator takes on a mid-range of 42.5.

5.3 Panel Analysis

In order to explore how changes in trust levels affect economic growth, the model is

estimated using a panel analysis. The standard methods of panel estimation are ﬁxed-

effects or random-effects. The ﬁxed-effects estimates are calculated from differences

within each country; the random-effects estimation, in contrast, incorporates infor-

mation across individual countries as well as across periods. The major drawback

with the random-effects analysis is that it is consistent only if the country-speciﬁc

effects are not correlated with the other explanatory variables. A Hausmann speci-

ﬁcation test can evaluate whether this independence assumption is satisﬁed

(Hausman, 1978; Forbes, 2000, p. 874). The Hausmann test applied here indicates

that the ﬁxed-effects model should be used.

Regressions 1 through 4 in Table 7.4 consider the case of linear regression with

panel data. As there has been no research conducted on panel data of which the

author is aware, it seems most appropriate to begin the estimation of the panel data

using the linear regression method. As there is the possibility of cross-sectional

heteroskedasticity, a robust estimation technique is used. The coefﬁcients are the

same with and without the robust estimation technique; however, the robust estima-

tor produces larger standard errors. The ﬁxed-effects estimations use 41 countries

with a total of 129 observations. It is an unbalanced panel. Regression 1 in Table 7.4

contradicts the results of all previous empirical works (Knack & Keefer, 1997;La

Porta et al., 1999; Whiteley, 2000; Zak & Knack, 2001, Beugelsdijk et al., 2004;

cf. Heliwell, 1996), as well as these results from the cross-section design and the

pooled panel analysis, a negative (0.08) and signiﬁcant (2.52) coefﬁcient for the

interpersonal trust variable is obtained, indicating that changes in trust and economic

growth are negatively related to each other. All other variables in the model have the

expected signs. Signiﬁcant conditional convergence, a positive relationship between

human capital and economic growth, and a signiﬁcant negative coefﬁcient for the

variable price level of investment all appear. Some 28% of the within-variance can

be explained. Regression 2 presents the random-effects model. As expected when

employing a random-effects model, the positive result from the cross-sectional and

the pooled panel analysis is replicated. It indicates a positive (0.04) and signiﬁcant

result (signiﬁcance at the 90% level). Regression 3 shows the results for the growth

model when the six transition countries are omitted from the country sample.

Interestingly, the relationship between interpersonal trust and economic growth

can also be modeled curvilinearly in the 115-country sample when trying to explain

The test statistic is χ

(4) ¼1129.17. This rejects the null hypothesis at any standard of signiﬁcance.

Does Too Much Trust Hamper Economic Growth? 155

the within-variation with a ﬁxed-effects model. In country observations with lower

levels of trust, an increase in trust seems to have a positive effect on economic

growth, whereas in country observations with high levels of trust, a decrease in trust

seems to have a positive effect on economic growth. Regression 4 estimates the

115-country sample with a random-effects model. The results from Regression 4 in

Table 7.3 are replicated.

5.3.1 Sensitivity Analysis

Since the negative relationship between interpersonal trust and economic growth in

Regression 1 in Table 7.4 challenges econometric work using a cross-sectional

design, the robustness of the results must be tested. To test the sensitivity of the

results, Table 7.5 shows several speciﬁcation tests including the exclusion of

inﬂuential observations, the alteration of case speciﬁcations, the inclusion of addi-

tional regressors, the restructuring of the data, resampling techniques, and clustering

for human capital. The ﬁrst row of Table 7.5 (labelled “None”) reports the results,

Table 7.4 Trust and economic growth—ﬁxed and random-effects estimation

Estimation

Method

Fixed-Effects

Robust

Estimation

Random-Effects

Robust

Estimation

Fixed-Effects

Robust

Estimation

Random-Effects

Robust

Estimation

Country sample All All

All without

Transition

All without

Transition

Equation 1 2 3 4

Trust 0.08** 0.04** 0.18** 0.17***

(2.52) (2.15) (2.35) (3.88)

Trust, squared 0.003*** 0.002***

(3.03) (3.26)

Income 4.81*** 0.81 4.78*** 1.81***

(3.67) (1.38) (3.73) (3.05)

Education 0.87*** 0.20 1.0*** 0.50***

(3.49) (1.19) (4.05) (3.14)

PPP 0.04*** 0.03*** 0.03*** 0.03***

(3.36) (3.00) (3.03) (3.19)

Constant 46.2*** 9.1** 39.9*** 14.2***

(4.12) (2.09) (3.58) (3.09)

R-Squared 0.28 0.32 0.45 0.38

Countries 41 41 35 35

N 129 129 115 115

Period 80–04 80–04 80–04 80–04

Note: Numbers in parentheses are heteroskedasticity-adjusted t-ratios. R-Squared is the within-R-

Squared for ﬁxed-effects and the between-R-Squared for random-effects.

* Signiﬁcance at the 90% level (one-tailed test).

** Signiﬁcance at the 95% level (one-tailed test).

*** Signiﬁcance at the 99% level (one-tailed test).

156 Felix Roth

Table 7.5 Sensitivity analysis—ﬁxed-effects estimation

Row

Speciﬁcation

change

Coefﬁcient

on trust

Standard

Error Countries Observations R-square

Inﬂuential cases

1 None 0.08** (2.52) 41 129 0.28

2 1 (Poland) 0.06* (2.06) 40 126 0.27

3 2 (Poland + Greece) 0.05 (1.60) 39 124 0.27

Country samples

4 OECD 0.08** (2.45) 27 94 0.21

5 OECD-23 0.05* (1.68) 23 83 0.32

6 OECD-23 0.26***/

0.004***

(3.05 /

3.76)

23 83 0.48

7 EU-15 0.08* (1.91) 14 54 0.34

8 EU-15 0.28***/

0.004***

(2.31 /

3.13)

14 54 0.52

9 Liberal 0.09*** (3.58) 5 18 0.60

10 Scandinavian 0.21* (2.17) 5 15 0.74

11 Developing 0.13* (1.99) 11 29 0.71

12 Latin America 0.27** (3.50) 5 13 0.96

Speciﬁcations

13 Open 0.05* (1.68) 41 129 0.46

14 KI 0.08** (2.59) 41 129 0.29

15 Pop. growth 0.07** (2.48) 41 129 0.29

16 Conf. parliament 0.1*** (2.64) 41 114 0.26

17 Conf. forces 0.1*** (2.95) 41 114 0.26

18 Conf. police 0.11*** (3.01) 41 114 0.27

19 Conf. company 0.04 (1.35) 41 102 0.46

20 Social expend. 0.065** (2.14) 27 84 0.32

21 Inequality 0.09** (2.27) 20 62 0.42

Restructuring of data

22 3 Waves (unbal.) 0.11** (2.21) 41 96 0.28

23 3 Waves (bal.) 0.09* (1.81) 15 45 0.60

24 5 Waves (bal.) 0.08 (1.30) 3 15 0.50

Methods

25 Clustering for

human capital

0.08*** (2.62) 41 129 0.28

26 Boot 0.08* (1.91) 41 129 0.28

27 Jack 0.08* (1.86) 41 129 0.28

Note: Numbers in parentheses are heteroskedasticity-adjusted t-ratios. R-Squared is the within-R-

squared.

* Signiﬁcance at the 90% level (one-tailed test).

** Signiﬁcance at the 95% level (one-tailed test).

*** Signiﬁcance at the 99% level (one-tailed test).

Does Too Much Trust Hamper Economic Growth? 157

standard errors, and regression coefﬁcient, taken from Regression 1 in Table 7.4.

Successive rows reﬂect the effects of interpersonal trust on economic growth when

the indicated change is made.

The second row of Table 7.5 reports the results after omitting the case of Poland

from the country sample. As can be inferred from Fig. 7.1, the case of Poland

exhibits the strongest negative relationship between changes in trust and changes in

economic growth (speciﬁcally, a decrease in interpersonal trust of 16.6% is associ-

ated with an increase in economic growth of 5.2%). As suspected, Poland plays an

important part in explaining the relationship between trust and economic growth.

Although the relationship between trust and economic growth remains signiﬁcant

(signiﬁcance at the 90% level) the coefﬁcient decreases from 0.08 to 0.06.

In the third row, the case of Greece is omitted. As can be inferred from Table 7.2,

Greece’s level of trust decreases by 26.7%, whereas its economic growth rate increases

by 2.91%. After deleting Greece from the country sample, the relationship between

changes in trust and changes in economic growth loses statistical signiﬁcance.

Rows 4 through 12 examine the different country samples. When analyzing an

OECD country sample, changes in trust and changes in economic growth are

negatively related (which is strongly inﬂuenced by the data on Poland). In the

OECD 23-country sample, the relationship can be either linearly modulated or

curvilinear. In the linear modulation, a signiﬁcant negative result appears; however,

the curvilinear relationship explains 16% more of the variance in international

growth rates. As with the sample of the OECD-23 countries, the EU-15 countries

sample can be modulated in both relationships, either linear or curvilinear. In the

linear modulation, a signiﬁcant negative coefﬁcient (strongly inﬂuenced by the data

on Finland and the United Kingdom) appears; the curvilinear model, however, is

able to explain 52% of the within-variation (18% more than the linear model). Apart

from Poland and Greece, the negative relationship between trust and economic

growth seems to be driven by the highly developed countries from the sample of

liberal countries

(signiﬁcance at the 99% level) and the Scandinavian countries

sample. As already seen in Fig. 7.1, in the United Kingdom and the United States, a

strong decrease in trust is associated with an increase in economic growth. Row

11 examines the sample of developing countries sample.

An increase in interper-

sonal trust is associated with an increase in economic growth (as the author is

currently investigating the changes within particular cases, it is not problematic at

this time to include China in the sample). After excluding the case of China, the

relationship is still signiﬁcant (90% level) and positive (0.16)). Countries from Latin

America (Row 12) face a positive relationship between changes in trust and eco-

nomic growth. The theoretical claim that, considering developing countries, trust

level changes should have a positive effect on economic growth is hereby veriﬁed.

Following Hall and Soskice (2001) Liberal Market Economies include the following ﬁve coun-

tries: the United States, the United Kingdom, Canada, Australia, and Ireland.

The developing country sample includes the 11 countries South Africa, Bangladesh, Pakistan,

Philippines, China, India, Argentina, Venezuela, Brazil, Peru, and Chile.

158 Felix Roth

Figure 7.3 illustrates the ﬁndings between trust and economic growth from Regres-

sion3inTable7.4. In a country with a low level of trust, an increase in trust is associated

with an increase in economic growth if the increase in trust takes place on the left side of

the distribution (the maximum value of the graph is 30). Once a threshold of 30% of trust

is exceeded, the increase in trust will hamper economic growth.

Row 13 includes the variable Openness. The trust coefﬁcient stays statistically

signiﬁcant. The model now explains 46% of the within-variation of economic

growth (18% more than the original result from Regression 1 in Table 7.4). Open-

ness seems to be a very important variable when trying to explain the within-

variation of economic growth. Rows 14 and 15 include the two Solow parameters,

Investment Share of GDP and Population Growth. The trust coefﬁcient remains

statistically signiﬁcant.

Rows 16 through 19 include four indicators of systemic trust variables: 1) conﬁ-

dence in the parliament, 2) conﬁdence in the forces, 3) conﬁdence in the police, and

4) conﬁdence in major companies. None of the four systemic trust variables is

statistically signiﬁcantly related to economic growth. However, conﬁdence in compa-

nies is related to interpersonal trust as this variable loses statistical signiﬁcance when

the item is included in the regression. Furthermore, when examining an OECD or

EU-15-country sample, the variables Conﬁdence in the Parliament and Conﬁdence in

70.0060.0050.0040.0030.0020.0010.000.00

Interpersonal Trust

3.00

2.00

1.00

0.00

-1.00

-2.00

-3.00

Economic Growth

Fig. 7.3 Predicted relationship between trust and economic growth—ﬁxed-effects estimation

Does Too Much Trust Hamper Economic Growth? 159

major companies are both negatively related to economic growth. Particularly in the

Liberal Market Economies (LMEs), a decline in Conﬁdence in the Parliament is

associated with an increase in economic growth (signiﬁcance at the 99% level).

Row 20 includes social expenditure in the regression (OECD, 2004). If the

welfare state creates high levels of interpersonal trust and negatively affects eco-

nomic growth (see Atkinson, 1999 for a detailed discussion of the relationship

between the welfare state and economic growth), an increase in welfare state activity

would go hand in hand with an increase in levels of interpersonal trust and a decrease

in economic growth. However, the trust coefﬁcient is not altered by the inclusion of

social expenditure. The hypothesis, that social expenditure could explain the nega-

tive relationship between trust and economic growth, must be rejected. (However,

due to data restrictions, the hypothesis was only tested in 27 OECD countries with a

total of 84 observations).

Row 21 includes the Gini-Coefﬁcient.

On the one hand, taking the empirical

results from Forbes (2000) for granted, an increase in social inequality is related to

an increase in economic growth. On the other hand, an increase in social inequality

seems to be strongly related to a decrease in interpersonal trust. Knack and Keefer

(1997), Zak and Knack (2001), Knack and Zak (2002), in particular, as well as

Delhey and Newton (2005) and Rothstein and Uslaner (2005), have given ﬁrst

empirical proof that trust is stronger in nations with more equal income among

their citizens. However, the trust coefﬁcient is again not altered. The hypothesis that

social inequality could explain the negative relationship between trust and economic

growth has to be rejected. (Here, also due to data restrictions, the hypothesis was

only tested in 20 OECD countries with a total of 62 observations.)

Row 22 examines an unbalanced panel for the time period, 1990–2004. This

procedure allows the exclusion of data derived from the Eurobarometer 25. After

excluding the ﬁrst two periods (1980–89), trust is still negatively and signiﬁcantly

related to economic growth. Row 23 considers a balanced panel with 15 countries

and 45 country observations examining economic growth from 1990 to 2004 using

data from the second, third, and fourth waves of the WVS. Trust is negatively related

to economic growth. When using a balanced panel from 1980 to 2004 (Row 24)

taking ﬁve countries with 15 observations into consideration, trust loses statistical

signiﬁcance (primarily due to the small number of observations).

Row 25 shows the result when clustering for the Human Capital variable.

(Clustering for the other variables does not change the results.) This procedure

produces an estimator “that is robust to cross-sectional hereroskedasticity and

within-panel serial correlation which is asymptotically equivalent to that proposed

by Arellano (1987)”(Stata Corporation, 2005, p. 293).

Rows 26 and 27 introduce resampling techniques. Either when using Bootstrap

Estimation or Jackknife Estimation, the coefﬁcient remains statistically signiﬁcant

(however only at the 90% level).

Data on income inequality are based on the UN-database, WIDER. Only data originally drawn

from the Luxembourg Income Study (LIS) are taken.

160 Felix Roth

6 Conclusion

This contribution examined the relationship between trust and economic growth.

Two ﬁndings are especially important.

First, taking panel data and using a ﬁxed-effects estimation for a 41-country sample

over the time period from 1980 to 2004 and with a total of 129 observations, this

contribution points out that economic growth is negatively related to an increase in

trust. This negative ﬁndingisincontrasttomostempiricalﬁndings using a cross-

sectional design. The negative relationship seems to be mainly driven by developed

countries from the OECD (here speciﬁcally Poland, Greece, and the United States),

and the EU-15 (here particularly the United Kingdom and Finland), and very strongly

by LMEs and Scandinavian countries. However, when considering a country sample

which excludes the six transition countries, a curvilinear relationship appears. In

countries with low initial levels of trust, an increase in trust leads to an increase in

economic growth (samples for developing countries and Latin American countries). In

countries with high initial levels of trust, an increase in interpersonal trust leads to a

decrease in economic growth (especially in the samples of LMEs and Scandinavian

countries). The curvilinear relationship can be replicated in a sample of OECD-23

countries, as well as in an EU-15-country sample, meaning that in those countries in

the OECD and EU-15 which have low initial stocks of trust, as for instance Portugal,

an increase in trust is associated with an increase in economic growth.

Second, when analyzing the relationship between interpersonal trust and eco-

nomic growth in a cross-section of countries using either a cross-section, pooled

panel, or random-effects design, the positive results from previous empirical

research were replicated. However, when examining a country sample which

excluded the six transition countries, a curvilinear relationship between interpersonal

trust and economic growth was detected. In countries with low initial levels of trust,

an increase in trust is associated with an increase in economic growth. But once a

threshold of trust is surpassed, an increase in trust harms economic growth.

Taking these results into consideration, theoretical implications and empirical

ﬁndings between trust and economic growth must be reevaluated. More theoretical

and empirical research is necessary to clarify the relationship. From a policy point of

view, it is important to differentiate between countries with high and low initial

levels of trust. An increase in trust is crucial for countries with low levels of trust, but

can likely be neglected by countries with sufﬁcient levels of trust and may even

hamper economic performance in countries with high levels of trust. The common

knowledge which has governed the nature of discussions in social science and

economics for the last 10 years, that trust is positively related to economic perfor-

mance, must be seriously questioned. The relationship depends on the level of trust

already existing in a country, thus determining whether it is important to invest in

trust-building policies or not.

Still one has to bear in mind that the marked difference across time and across

countries, and particularly the difference between a cross-section analysis using

long-term growth, could have to do with the fact that a 5-year average of growth

could be more sensitive to business cycle inﬂuences than, for example, a 10- or

15-year average. Although 5-year growth averages are commonly used for analyzing

Does Too Much Trust Hamper Economic Growth? 161

short- or medium-term growth dynamics, it is not yet fully clear if business cycle

considerations can be neglected without caution.

Furthermore, despite the fact that these results appear to be statistically robust and

in line with theoretical assumptions, it is possible that the ﬁndings are partly due to

the omission of some variable not considered, or that measurement error affected the

results, or that the model is misspeciﬁed in other ways. Further investigations are

necessary to corroborate the ﬁndings to be able to answer relevant policy questions.

Appendix

Table 7.A1 Summary statistics

Variable Year Observations Mean

Standard

deviation Minimum Maximum

Growth 1980 22 1.6 1.6 1.64 6.65

1985 11 3.0 1.3 1.17 5.12

1990 32 1.13 3.21 5.07 11.38

1995 27 2.15 2.1 2.24 7.52

2000 37 2.29 2.04 0.58 8.37

Interpersonal

Trust

1980 22 39.9 12 17.7 61.2

1985 11 38.6 12.3 21.3 63.5

1990 32 37.4 15.8 6.7 66.1

1995 27 28.9 16.7 2.8 65.3

2000 37 30.1 15.7 8.4 66.5

Income 1980 22 9.62 0.38 8.49 10.03

1985 11 9.73 0.23 9.32 10.03

1990 32 9.50 0.76 7.38 10.33

1995 27 9.15 0.92 7.19 10.31

2000 37 9.45 0.87 7.3 10.43

Education 1980 22 7.80 1.85 4.49 11.91

1985 11 7.28 1.76 3.57 9.42

1990 32 7.94 2.20 3.68 12

1995 27 7.76 2.74 2.32 12.18

2000 37 8.14 2.27 2.45 12.25

PPP 1980 22 101.4 24.6 58.6 143.2

1985 11 62.6 8.13 47.5 73.9

1990 32 82.5 24.7 39.8 128.5

1995 27 75.6 31.3 29.6 154.5

2000 37 75.3 27.0 31.97 126.8

162 Felix Roth

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Does Too Much Trust Hamper Economic Growth? 165

Chapter 8

Social Capital, Trust, and Economic

Growth

Felix Roth

Abstract This contribution revisits the existing research in the ﬁeld of social

capital, trust, and economic growth, with the aim of elaborating a possible extension

of the neo-classical model by incorporating social capital into its assumptions. It

describes the state of the art and deﬁnition of social capital and interpersonal trust

and discusses the positive and negative relationships between social capital, trust,

and growth. It offers a brief discussion of the operationalization of social capital and

provides an overview of the empirical ﬁndings to date with respect to social capital,

trust, and growth. In its conclusions, this contribution calls for further research on the

relationship between trust and economic growth.

Keywords Social capital · Trust · Economic growth

1 Introduction

In recent years, the concept of “social capital”has been ﬁrmly established within the

academic discipline of economics under the JEL classiﬁcation Z13. The World Bank

in particular helped to operationalize the concept of social capital by recognizing its

contribution to sustainable development and to combatting global poverty. In his

Foreword to the 24-volume series on social capital, Ismail Serageldin argues that the

Originally published in: Wenzel Matiaske and Gerd Grözinger (eds.). Sozialkapital: eine

(un)bequeme Kategorie—Ökonomie und Gesellschaft Jahrbuch 20. Metropolis-Verlag, Marburg,

2008, pp. 111–138.

Felix Roth (*)

Department of Economics, University of Hamburg, Hamburg, Germany

e-mail: felix.roth@uni-hamburg.de

F. Roth, Intangible Capital and Growth, Contributions to Economics,

https://doi.org/10.1007/978-3-030-86186-5_8

167

“traditional composition of natural capital, physical or produced capital, and human

capital needs to be broadened to include social capital.”(Serageldin, 1999, p. iii). He

continues: “Social capital is the glue that holds societies together and without it there

can be no economic growth or human well-being.”But the World Bank is not the

only institution to stress the importance of social capital. The Organization for

Co-operation and Economic Development (OECD) has intertwined the paradigm

of social capital with that of human capital and analyses possible interaction between

social capital, human capital, human well-being, and economic growth (OECD,

2001). In addition, the European Union, while not explicitly emphasizing the

paradigm of social capital, promotes in its Lisbon Strategy the idea that, in addition

to economic growth and employment, special attention must be paid to increasing

social cohesion within the European Community.

2 Extension of the Neoclassical Model Assumption

The logic of social capital has mostly been negotiated in economics as a black-box

concept. Social contexts do not play a role in the neoclassical production function

(Solow, 1956). One thing is certain: alongside the classic factors of production—

capital, labor, and human capital—one ﬁnds an equally important factor, namely

social infrastructure. In contemporary research, it is referred to under the collective

term “social capital”. Temple identiﬁes with social capital all those social phenomena

that decisively inﬂuence long-term growth (Temple, 2001 in OECD, 2001,p.39).A

clear delineation of which cultural and social factors should ultimately be included in

the concept of social capital has not yet been made in contemporary research.

Nevertheless, research in the ﬁeld of social capital and economic growth is based

primarily on the paradigm of trust (Inglehart, 1990; Putnam, 1993; Fukuyama, 1996;

Heliwell, 1996; Knack & Keefer, 1997; Whiteley, 2000; Zak & Knack, 2001;

Beugelsdijk et al., 2004; Berggren et al., 2008; Roth, 2007,2009), the concept of

civic engagement (Heliwell, 1996; Inglehart, 1997; Putnam, 1993; Putnam &

Helliwell, 1999), and the concept of norms of reciprocity (Knack & Keefer, 1997).

Whereas economists ﬁrst extended the neoclassical production function in the early

1990s to include the human capital paradigm, so that the concept of conditional

convergence and international growth rates could be better explained empirically

(see Barro, 1991; Mankiw et al., 1992; Barro & Sala-i-Martin, 2004, p. 60), in recent

years, the neoclassical model is being extended by the social capital factor

(Dasgupta, 1999; Serageldin, 1999; Serageldin & Grootaert, 1999; Whiteley,

2000). In the scientiﬁc debate, however, it has not yet been clariﬁed whether social

capital should be included in the production model as a simple scale factor (Knack &

Keefer, 1997; Zak & Knack, 2001; Whiteley, 2000) or whether it should be included

168 Felix Roth

in total factor productivity as the basis of the entire production process (Dasgupta,

1999, p. 390 ff.). If it is included as a simple scale factor, the true potential and cost

of social capital are likely to be underestimated.

3 Criticism of the Concept of Social Capital or Why Is

There Capital in Social Capital?

Robert Solow discusses the concept of social capital controversially. On the one

hand, he considers research on social capital to be an important but difﬁcult task; on

the other hand, he considers the term social capital to be poorly chosen (Solow,

1999, pp. 6–10). He criticizes the term social capital by pointing out that capital is

usually a stock of produced and natural production factors that support production.

Moreover, he argues, social capital does not correspond to the conventional deﬁni-

tion of capital, i.e., a stock of tangible, solid, and enduring things such as buildings,

machinery, and inventory. Kenneth Arrow argues similarly (Arrow, 1999, pp. 3–5).

He criticizes the use of the term “capital”in social capital. According to Arrow, the

concept of capital involves three aspects: 1) extension in time, 2) deliberate sacriﬁce

in the present for future beneﬁt, and 3) alienability. Arrow argues that especially the

second point does not apply to social capital. Social networks are not linked for the

purpose of economic beneﬁt.

Neither contribution really gets to the heart of the discussion. They suggest,

however, that the concept of social capital is controversial. This can certainly be

inferred from the fact that the capitalization of social phenomena, such as interper-

sonal trust as a proxy for civic solidarity and networks of civic engagement, is an

indication that these phenomena can no longer be considered as natural, but rather

that they are part of a social infrastructure that must be supported by the state and

kept alive by civil society. They are not a collective good with unlimited resources,

but always run the risk of being written off in the market-economy based production

process. This is precisely why politicians are concerned every day about whether the

social glue that holds society together is not eroding. Criticizing social capital

theorists of poor conceptualization does not help scientiﬁc theorizing and prevents

formulating questions to describe problems that currently exist. Or as Habisch puts

it: “In a certain way, social capital theory is itself a consequence of a changed

reality: for something that is self-evident must ﬁrst become non-self-evident before it

can even be the subject of explicit scientiﬁc research, before it can be conceptual-

ized”(English translation of Habisch, 1999, p. 497).

Social Capital, Trust, and Economic Growth 169

4 The State of the Art and Deﬁnition of Social Capital

In recent years, the literature on the topic of social capital has grown exponentially.

Woolcock (1998, pp. 193–196) differentiates six areas of research.

And Portes

(2000) differentiates between two levels of analysis.

This contribution deals with

the research on the interface between social capital and economic growth. Although

there are various deﬁnitions of social capital (Fukuyama, 1996, p. 26; Temple, 2001

in OECD, 2001, p. 39; Ostrom, 1999, p. 176; Newton, 1997, p. 576; Woolcock,

1998, p. 189),

the “classical”deﬁnitions of James Coleman and Robert Putnam will

be used here to clarify the relationship between social capital and trust.

4.1 Coleman’sDeﬁnition of Social Capital

Coleman clariﬁes the paradigm of social capital in his treatises Social Capital in the

Creation of Human Capital and Foundations of Social Theory (Coleman, 1988,

1990). According to Coleman’sdeﬁnition, social capital is intended to be a resource

from the social structure of actors within society. This resource represents capital for

the actors. All social structures favor certain actions by actors who are within the

structure, some more effectively and others less so. The concept of social capital

Research has found that only 20 international research papers were published on social capital

before 1983, p. 109 between 1991 and 1995, and 1,003 between 1996 and March 1999 (Winter

2000, p. 17 in Putnam, 2001, p. 18). As of June 2006, the number has increased to 1,429. For an

historical overview, see Putnam (2001) and Woolcock (1998). For a detailed review of the literature

on social capital, see Habisch (1999).

The research areas can be divided into six categories: 1) family and youth, 2) education, 3) com-

munity, 4) labor and organizations, 5) democracy, and 6) general cases of collective action

problems. In current research, more than six research areas have been established. The research

area between social capital and growth and between welfare state mechanisms and social capital

should be mentioned here.

The concept of social capital can be differentiated between two levels of analysis. On the one hand,

the analysis can take place at the micro-level. In the center of this research are the so-called

“networks of an actor”. With the help of this research design, relationships between income,

human capital, and the networks of a person can be analyzed. This type of research was initiated

by Pierre Bourdieu (1983) and James Coleman (1988). Esser (2000) calls this form of social capital

“relational capital”. Relational capital is a private good. On the other hand, social capital can also be

used as a concept at both the meso- and macro-levels. In this kind of relationship, social capital is

seen as a stock, which is available for communities, regions, or nations. The analysis then does not

focus on the individual actor but on the nation with its particular characteristics. These character-

istics include aggregated entities, for example, the yearly change of stock of the Gross Domestic

Product, the stock of the labor force, or the stock of human capital. The stock of social capital is, as

well, a characteristic of a nation. Esser calls this kind of social capital “system capital”. System

capital is characterized by its quality as a collective good.

For a detailed list of deﬁnitions of social capital, see especially Woolcock (1998, p. 189). For

relevant deﬁnitions in the ﬁeld of social capital and growth, see Durlauf and Fafchamps (2005).

170 Felix Roth

offers the possibility of embedding the extremely individualistic homo oeconomicus,

who acts solely out of the motive to maximize his utility function, in his environ-

ment, thereby creating a relationship between the action of an actor and the action of

his environment. The actor acts according to the social norms and rules he has

learned from his environment (see also Sen, 1977). Coleman regards the socializa-

tion paradigm as a crucial explanation for actions, but he misses the importance of

the actor’s initiative. As this he understands the Rational-Choice paradigm of utility

maximization (Coleman, 1988, p. 95). Coleman intends to introduce a new form of

capital, alongside the existing forms of capital, such as physical and human capital,

in the process of building scientiﬁc theories. Just as physical capital is created by

changes in materials to create tools that facilitate production, and human capital is

created by changes in people that bring skills and capabilities that allow them to act

in new ways, social capital is generated through changes in the relations among

people that facilitate action. Social capital facilitates productive activity just as much

as physical and human capital (Coleman, 1988, pp. 100–101).

Unlike other forms of capital, social capital seems to be embedded in the

relationship between two or more people. But what exactly characterizes this

relationship, which creates social capital? Coleman names three forms of relation-

ships involving social capital: 1) obligations, expectations, and trustworthiness of

structures (the less the exchange of interactions between actors A and B is accounted

for in the short run, the more social capital is produced in the relationship between

the actors), 2) information channels, and 3) norms and sanctions. Coleman considers

the trustworthiness of the social environment as the most important form of social

capital (Coleman, 1990 in Whiteley, 2000, p. 448).

4.2 Putnam’sDeﬁnition of Social Capital

Putnam is one of the ﬁrst authors to apply the term social capital, which is used by

Coleman and Bourdieu at the micro-level, as a concept for the meso-level. Social

capital in Putnam’s work refers to stocks of social capital that are available to a

region (state) (for the change of level within the paradigm of social capital, see

Portes, 2000). High stocks of social capital promote the economic development of a

region and support state administration (Putnam, 1993, p. 176). Putnam associates

certain features of social organizations, such as networks, norms, and trust, with

social capital (Putnam, 1993, p. 167). He relates the term social capital automatically

with the concept of civic engagement and the existence of a strong civil society. This

is also emphasized in his later deﬁnition of social capital, in which he links social

capital with the concept of civic virtues (Putnam, 2000).

The inclusion of psychological factors (trust and norms) and behavioral structures

(networks) into one deﬁnition has been criticized. Newton (1997) argues that from

an empirical point of view, the concept of social capital should be separated into its

component parts. Whether civic engagement and trust are associated must be tested

empirically before they can be combined into a common deﬁnition. Other

Social Capital, Trust, and Economic Growth 171

researchers also distance themselves from combining all three indicators in one

deﬁnition. Knack and Keefer (1997) identify social inequality as a more important

determinant of trust than civic engagement, whereas other lines of research focus on

the performance of the welfare state as a producer of trust and norms (van Oorschot

& Arts, 2005). Therefore, it seems appropriate for future empirical research to

examine the individual components of social capital separately, given the number

of researchers who attach great importance to the dimension of trust in the context of

social capital (Newton, 1997; Fukuyama, 1996; Uslaner, 1999; Tonkiss, 2000; Zak

& Knack, 2001; Roth, 2007,2009). This view is also salient in microeconomics

research dealing exclusively with trust and norms (Ostrom, 1998). Even Putnam

attaches a high priority to the dimension of trust when he writes that norms and

networks are the prerequisites for trust. Thus, trust can be seen as the output of the

two other dimensions of social capital. The next section focuses on the paradigm of

trust and the relationship between trust and growth.

5 Interpersonal Trust

Luhmann (2000, p. 1) states that “trust, in the broadest sense of trusting one’s own

expectations, is an elementary fact of social life”[English translation]. It is “the

generalized expectation that the other will manage his freedom, the uncanny poten-

tial of his possibilities of action, in the sense of his personality –or, more precisely,

in the sense of the personality he has presented as his and made socially visible”

[English translation] Luhmann (2000, p. 48). Fukuyama describes trust as the

“expectation that arises within a community of regular, honest, and cooperative

behavior, based on commonly shared norms”(Fukuyama, 1996, p. 26). Although

there is a variety of deﬁnitions of trust (Dasgupta, 1999, p. 5 in Ostrom, 1998, p. 12;

Misztal, 1996, p. 16; Delhey & Newton, 2005, p. 311), the current research distin-

guishes between three different forms: interpersonal or generalized trust, thick trust,

and systemic or institutional trust (Putnam, 2000, p. 137; Newton, 1997, p. 558 ff.;

Luhmann, 2000).

Newton (1997) and Williams (1988) categorize trust that is generated by family

networks as “thick trust”.

In contrast, generalized or interpersonal trust is deﬁned as

trust that is generated by looser, secondary relations in modern societies, based on

everyday interaction between people who do not otherwise know each other. Most

scientists rely on generalized trust when examining the relationship between eco-

nomic growth and trust, as it should facilitate cooperation and reduce transaction

costs in economic systems. Economic systems are characterized by a substantial

degree of differentiation, and exchange activity frequently depends upon trust

Thick trust is usually measured by asking whether the respondent trusts his or her own family

members. This question is asked, for example, in the second wave of the World Value Survey

(1990–93).

172 Felix Roth

between strangers. The third category of trust, systemic or institutional trust, refers to

the respondent population’s trust in certain institutions. These include, for example,

trust in parliament, the police, the army, and large corporations. When trust is

referred to in this study, interpersonal trust is meant.

6 Positive Correlation between Social Capital, Trust,

and Growth

Arrow (1972, p. 345) argues that the presence of virtues such as trust plays a

signiﬁcant role in the operation of economic systems. Theses virtues represent the

basis for or at least facilitate the process of exchange, which is essential for any

economy. For Fukuyama (1996), trust is an essential factor of economic perfor-

mance. A nation’s well-being and its ability to compete depend upon the level of

trust within the society (Ibid, 7). This argument arises from his general assumption

that economic activity is part of the social life and constitutes itself according to the

norms, rules, and moral obligations of a society. Sen states that the “development

and use of trust in one another’s words and promises can be a very important

ingredient of market success”(Sen, 1999, p. 262) and that “no society would be

viable without some norms and rules of conduct”(Sen, 1977, p. 332). Robert

Putnam concludes that “norms and networks have fostered economic growth, not

inhibited it”(Putnam, 1993, p. 176).

The foregoing authors argue for a positive relationship between trust and eco-

nomic beneﬁt. But how is trust related to growth?

Whiteley (2000, p. 451) distinguishes three direct and indirect channels through

which interpersonal trust might stimulate economic growth.

First, trust has a direct effect on economic performance through reducing trans-

action costs. These are deﬁned as costs incurred in the economic processes of

exchange and specialization and are typically associated with banking, insurance,

ﬁnance, wholesale, and retail trade or securing professional services from lawyers

and accountants, etc. (North, 1990, p. 28). North therefore advocates the develop-

ment of a new production function that takes transaction costs into account. In high-

trust societies, transaction costs should be lower. Fewer lawyers, fewer police to

enforce property rights, and fewer insurance policies to protect against possible risks

are needed.

Second, high levels of trust enable actors to solve collective action problems

(or “prisoner’s dilemma”). Putnam (1995) puts forward four arguments why social

capital, including interpersonal trust, has a positive effect on the economy: 1) it

facilitates coordination and cooperation, 2) it allows dilemmas of collective action to

be resolved and reduced, 3) it reduces incentives for opportunism, and 4) it reduces

human egoism. “Making the I into the we”is the technical term in the language of

“rational choice”theorists. In high-trust societies, it should theoretically be easier to

cope with such problems. Hardin (1982) cites problems that can arise, for example,

Social Capital, Trust, and Economic Growth 173

with smog and CO

emissions, and Ostrom, as well, cites the problem of overﬁshing

(Ostrom, 1990).

The third direct effect is that principal-agent problems might be much less

signiﬁcant in high-trust societies than in low-trust societies. Entrepreneurs who

devote more time to monitoring employees, suppliers, and trading partners have

less time to devote to innovation in new products or processes. In addition, they

might rely on simpler contractual arrangements to retain their managers and spe-

cialists. Entrepreneurs with high levels of trust therefore theoretically pay fewer

costs to monitor production.

Whiteley (2000) identiﬁes three indirect channels. Trust affects economic growth

through its interactions with 1) physical investment, 2) human capital, and 3) condi-

tional convergence. In high-trust societies, on the one hand, the risk appetite of

entrepreneurs should be greater to invest in physical capital (see also Keynes, 2000,

p. 125); whereas on the other hand, the risk appetite of employees to invest in human

capital should be greater. Finally, the diffusion of innovations and the implementa-

tion of new technologies should be greater.

It has been argued thus far that trust and the facilitation of collective action have a

positive impact on economic growth. But is this necessarily or always the case? The

following section presents counter-arguments to this thesis.

7 Negative Relationship Between Social Capital, Trust,

and Growth

7.1 Mancur Olson

Let us ﬁrst consider the argumentation of the American economist and political

scientist Mancur Olson (1982). Although his analysis deals primarily with the

dimension of networks within the concept of social capital, it is nevertheless

appropriate for the present purposes. Olson analyses the relationship between col-

lective action and economic growth in quite a different way from Putnam, arguing in

fact that collective action can undermine the state’s power to implement necessary

reforms aimed at maintaining high economic growth rates. Olson argues that stable

societies in highly developed states are in danger of encouraging the formation of

cartels and collective action organizations over time. Organizations that function as

special-interest groups harm economic growth by reducing economic efﬁciency, by

aggregating income in the societies in which they operate, and by making political

life more divisive. Putnam’s approach thus seems to be limited.

A high level of solidarity, i.e., high stocks of interpersonal trust, need not

automatically promote economic performance if the collective action is aimed at

blocking government reform policies and thereby harming the economy. To give

one example: If a state desires to implement labor market reform in which employee

rights are reduced, a low-wage sector is implemented, working hours are extended,

174 Felix Roth

and social spending on unemployment beneﬁts and support is decreased to reduce

the costs of the labor factor, a trusting and solidaristic society would more likely

oppose the state’s efforts at reform. In response, the mobilization of collective action

would stop the reform agenda, thereby limiting the potential of higher economic

growth rates. This argument is built upon Putnam’sﬁndings that a strong civil

society is crucial for high levels of trust to emerge. In fact, it could be civil society

actors, such as church groups, professional groups, and social movements organi-

zations (SMOs), that oppose the state’s will to implement reforms. Similarly, the

number of workers who are (voluntary) labor union members may be a critical factor

for the existence of high levels of trust. Thus, higher levels of trust do not necessarily

lead to more economic growth.

A synthesis of both Putnam and Olson’s approaches seems the most plausible. In

low-trust societies, an increase in trust should theoretically have a positive effect on

economic performance, given that a certain level of trust is essential for an economic

system to function smoothly. A further increase above a certain level of trust,

however, might have a negative impact on economic performance, which could

subsequently be used to fuel opposition to a government’s efforts at reform. Thus,

the relationship between trust and economic growth can be expected to be curvilin-

ear (inverted U-shaped).

This relationship should apply both within a country and in a cross-country

comparative study design. In Scandinavian states, which are prototypes of highly

developed economies with high levels of trust, a decrease in the level of trust should

lead to an increase in growth, according to the arguments above. These states already

have large stocks of interpersonal trust and actors of collective action. From the point

of view of promoting growth, these countries would theoretically have to reduce

parts of their solidarity levels. In contrast, in Latin American countries, such as

Brazil, where interpersonal trust levels are very low, an increase in trust levels should

support economic development. The same applies to Mediterranean countries, such

as Turkey, where very low-trust levels are observed. This assumption of a curvilinear

relationship between trust and growth is conﬁrmed by the empirical results between

democracy and economic growth. Barro and Sala-i-Martin (2004) determine a

curvilinear relationship between democracy and growth, i.e. in states with weak

democratic structures, democratization appears to enhance growth; but in countries

with a highly developed level of democracy, the relationship is reversed and an

increase in democracy retards growth.

7.2 Mistrust, Fear, and Economic Growth

The second explanation could be that mistrust or even fear is a key explanatory

variable for productivity. A society with high levels of fear will less easily oppose

state reform processes. Let us consider an example from organizational theory. It

may be part of a company’s strategy to create an atmosphere of fear among its

employees. This non-solidaristic working atmosphere mobilizes the employees to

Social Capital, Trust, and Economic Growth 175

monitor themselves, to work harder, and to increase the overall productivity of the

company. Another example of the positive relationship between fear and productiv-

ity is a high national unemployment rate and the associated fear of losing one’s job.

Employees who fear losing their jobs work harder, attach less importance to their

legal employment rights, take less sick leave, and are less demanding overall. This

fear also affects the actions of trade unions. Employers’associations have more

leverage to push through wage reductions and extend working hours if trade unions

give top priority to the preservation of jobs. The extension of working hours, in turn,

has a direct positive impact on economic growth.

8 Operationalization of Social Capital

Based on Putnam’s theoretical work, most empirical work in the area of social

capital has relied on the three dimensions: trust, norms of reciprocity, and networks.

But how can these three dimensions be measured, i.e., operationalized?

1. Interpersonal trust is measured by means of survey respondents’replies to the

question: “Generally speaking, would you say that most people can be trusted or

that you need to be very careful in dealing with people?”The respondent has the

option to answer with “Most people can be trusted,”“[One] can’t be too careful

in dealing with people,”and third “Don’t know.”To capture the aggregate trust

level of a society, the total valid responses of the surveyed population are ﬁrst

calibrated by removing the “Don’t know”responses. In the next step, the “Most

people can be trusted”responses are aggregated.

These steps yield trust levels

ranging between 2.8% (28 out of 1,000 respondents answering “Most people can

be trusted”) in Brazil (WVS 1995–97

) and 66.5% (665 out of 1,000 respondents

answering “Most people can be trusted”) in Denmark (WVS 1999–2002).

But what is the validity of such a measurement? The informative value of trust

levels is widely recognized among researchers (see Knack & Keefer, 1997;

Paxton, 1999; Whiteley, 2000; Alesina et al., 2000; Gabriel et al., 2002; Delhey

& Newton, 2005; van Oorschot & Arts, 2005). Most notable is the dispersion of

the aggregate variable, the discrepancy between Scandinavian countries, which

have very high-trust levels, in contrast to countries in Latin America such as

Colombia, Brazil, and Peru. But there are also large differences within OECD

countries. Countries in Southern Europe and the Mediterranean (for the country

The aggregation of interpersonal trust has been criticized from several sides. Portes (2000), Sobel

(2002), Durlauf and Fafchamps (2005) advise working with the concept of trust on the micro-level.

Work on the aggregate level would have no theoretical social science foundation (Durlauf &

Fafchamps, 2005) or should at least be scaled back from the macro-level (nation) to the meso-

level (state, region, or federal state).

WVS refers to the World Value Survey. For a description, see: https://www.worldvaluessurvey.

org/wvs.jsp.

176 Felix Roth

typology “Mediterranean-,”“coordinated-,”and “liberal-”countries, see Hall &

Soskice, 2001), such as Portugal, France, and Turkey, are endowed with lower

trust levels than countries with coordinated market economies, liberal market

economies, and Scandinavian countries.

In addition to interpersonal trust, systemic trust is measured by means of the

following question: “I am going to name a number of organizations. For each one,

could you tell me how much conﬁdence you have in them: Is it a great deal of

conﬁdence, quite a lot of conﬁdence, not very much conﬁdence or none at all?”For

example, the WVS 1999–2002 lists 15 organizations (4 of the 15 have already been

mentioned as examples in Section 5of this contribution). The answers are ﬁrst

processed by removing the “Don’tknow”answers, and then recoded and aggre-

gated. This results in values of 1–4 for each individual organization, with high

values representing high systemic trust. It is now worth considering whether an

index construction of the individual institutions is appropriate.

Figure 8.1 shows an example of the trust levels of the countries of the former

15 EU member states

and the two largest economies, Japan and the US, in a

cross-section of countries (WVS 1999–2002). The three Scandinavian countries

and the Netherlands have very high-trust levels of over 55%. The countries Great

Britain and Japan have average trust levels between 29.7% and 43.1%. The three

Mediterranean countries Portugal, France, and Greece have levels between 10%

and 23.7%. If countries from Latin America, such as Brazil with a trust level of

2.8% (WVS 1995–97), were now added to the country sample, the range of

variance would increase further. But even without the addition of countries

Fig. 8.1 Trust levels in selected countries (in %)

No data were available for Luxembourg.

Social Capital, Trust, and Economic Growth 177

outside the EU-15, the discrepancy between Portugal with 10% and Denmark

with 66.5% is very large.

2. Reciprocity norms are operationalized using questions from the “norms item

battery”from the World Value Survey (WVS). The introductory question

reads: “Please tell me for each of the following statements whether you think it

can always be justiﬁed, never be justiﬁed, or something in between.”The

respondent has the option to answer using a Likert scale ranging from 1 to

10 with 1 representing “Never justiﬁed”and 10 representing “Always justiﬁed.”

In the WVS 1999–2002, the item battery consists of 24 individual items. In the

panel design, it is possible to rely on four items: “Claiming government beneﬁts

which you are not entitled to,”“Cheating on taxes,”“Someone accepting a bribe

in the course of their duties,”and “Avoiding a fare on public transport”. (For the

cross-sectional procedure, see Knack & Keefer, 1997. The authors use ﬁve items

of the item battery). The items are aggregated by their mean value and recorded.

The result is a reciprocity norm index, which theoretically takes values between

4 and 40 (in Knack & Keefer, 1997,5–50), where high values mean that there is a

high stock of norms in society and low values mean that there are few reciprocity

norms. While the measurement of interpersonal trust is very common, the

construction of the norm index is less widespread (while Knack & Keefer,

1997 still worked with this index, Zak & Knack, 2001 were already working

with only aggregated trust). One reason for this is that the dispersion of the index

does not have the same variance as the aggregated variable trust. Furthermore, the

validity of the index is more questionable than that of the trust variable. Further

research would be needed to examine the variable in more detail. Knack and

Keefer (1997) point to a high correlation between trust stocks and norm stocks.

3. The operationalization of networks has not yet been fully clariﬁed theoretically. It

remains worth discussing which types of civic engagement should be included as

a basis for social capital. For example, unlike Putnam, Minkoff (1997) argues for

including social movement organizations in a conceptualization of social capital.

Most common is the measurement of associations using the following item from

the WVS: “Please look carefully at the following list of voluntary organizations

and activities and say a) which, if any, do you belong to, and b) for which, if any,

you are currently doing unpaid voluntary work?”Respondents are asked about

the following organizations; (1) groups for social welfare services for elderly,

handicapped, or deprived people, (2) religious or church organizations, (3) edu-

cation, arts, music, or cultural activity groups, (4) labor unions, (5) political

parties or groups, (6) local community action groups on issues like poverty,

employment, housing, or racial equality, (7) third-world development or human

rights advocacy groups, (8) environmental groups, (9) professional associations,

(10) youth work groups, (11) sports or recreation associations, (12) women’s

groups, (13) peace movement, (14) voluntary organizations concerned with

health, (15) Other groups, and (16) None. The “Not-mentioned”are removed

and the answers “Belong”and “Do voluntary work”are aggregated and added

together.

178 Felix Roth

4. As discussed earlier, current social capital research does not agree on whether or

not dimensions of social capital should be summarized as an index. Putnam’s

approaches, for example, are mostly driven by indexing. In his book Making

Democracy Work (Putnam, 1993), he works with a civic index; in his paper

Economic Growth and Social Capital in Italy, he works with an index measuring

civic engagement, one measuring institutional performance, and one measuring

citizen satisfaction. In the empirical section of his book Bowling Alone (Putnam,

2000), Putnam works with a social capital index that consists of ﬁve dimensions

with 14 individual items. These items are indicators of civic engagement, but also

of voting behavior and interpersonal trust. Pamela Paxton, for example, works

with a social capital index consisting of 14 items (Paxton, 1999) and a social

capital index consisting of two dimensions associations and trust (Paxton, 2002).

9 Social Capital, Trust, and Economic Growth: Empirical

Findings

Most economists addressing the relationship between social capital and economic

growth refer to the concept of trust (Knack & Keefer, 1997; La Porta et al., 1999;

Whiteley, 2000; Zak & Knack, 2001; Beugelsdijk et al., 2004; Berggren et al., 2008;

Roth, 2007,2009). The paper in this area that has received the most attention is

probably the 1997 article Does social capital have a payoff? by authors Knack and

Keefer, whose article takes Robert Solow’s(1995) harsh critique of Fukuyama’s

book Trust - The social virtues and the creation of prosperity as a starting point for

an empirical examination of Fukuyama and Putnam’s theses. Thus, in his book

review But Verify?, Solow writes: if trust really is to be an important indicator of a

nation’s economic development, then trust should be able to explain some of the

residual growth that remains unexplained, after previously controlling for the factors

of labor, share of investment rate in GDP, and human capital. He writes:

A standard exercise in economics is to decompose the observed growth of a national

economy into its sources. How much can be attributed to the growth of the labour force?

How much to the improved quality of labour? How much to the accumulation of the capital

in the form of factories, machines, computers and so on? After all this is done, almost always

there is a “residual”left over, some part of observed growth that cannot be credited to a

measured factor of production. One would expect the contribution of “trust”or the

perceived growth of “social capital”to be captured in this residual. If Fukuyama is right,

they should be an important contribution to it (1995, p. 37).

Knack and Keefer examine the relationship between social capital and economic

growth in a cross-country comparative research design considering 29 market econ-

omies. They use the common method of empirical growth regressions. Relying on

Barro’s(1991) results, Knack and Keefer apply a widely used production function

that includes the basic factors of natural logarithm of initial per capita income (to test

for conditional convergence), stock of human capital (primary and secondary

Social Capital, Trust, and Economic Growth 179

education levels), and initial price level for investment, as well as the dimensions of

social capital. The authors examine as the dependent variable the growth of per

capita income between 1980 and 1992. They did not include the classic “Solow”

variable, i.e. the share of investment in GNP, in their production model because they

assume that trust stimulates growth via the impact on investment. The social capital

variable is operationalized by the authors into the dimensions of trust, norms of

reciprocity, and membership in voluntary associations, as described above. The

dimensions of social capital are measured using 21 observations from the ﬁrst

wave of the WVS (1981–84) and 8 observations from the second wave of the

WVS (1990–93). The authors conclude that trust and norms of reciprocity have a

positive effect on long-term economic growth, but civic associations do not. The

authors interpret their results as follows: 1) a 10% rise in trust is associated with an

increase in growth of 0.8% and 2) each four-point rise in the reciprocity norms index

targeted at a maximum of 50 points is associated with an increase in growth of more

than 1% point. To check the robustness of their results, the authors use instrumental

variables for trust, include additional regressors in their production function, and

examine their results for potential outliers. The signiﬁcant positive relationship

between trust and growth is robust to all these speciﬁcation changes. It should be

noted with respect to the authors’approach that they base their results on the analysis

of only 29 cases. Similarly, it is difﬁcult to test for causality because they work with

a cross-sectional design and with “ﬂow variables”instead of “stock variables”at the

beginning of the growth period to be explained. Data from 1990 to 1993 are used to

explain growth in the 1980–1992 period. The authors are themselves aware of the

endogeneity problem at hand and argue as follows: Since the trust scores of the ﬁrst

and second waves of the WVS are highly correlated, trust is interpreted as a constant

cultural factor of a nation that does not change over time. Therefore, it is possible for

the authors to use 1990–1993 trust scores as a proxy for 1980 trust scores. The

authors’case selection is based on the assumption that social capital operates only in

market economies. Therefore, the authors do not include transition countries or

China in their country sample. The latter case, however, is particularly interesting

because China, as a socialist and totalitarian state, registers not low but high-trust

scores associated with high growth.

Building upon this article, Zak and Knack (2001) examine solely the relationship

between trust and economic growth. The authors expand the country sample by nine

developing countries and three OECD countries, to reach a total of 41 market

economies, taking observations from the third wave of the WVS and including

data from the Eurobarometer and an independent survey for the case of

New Zealand. The values of the underlying data range from 5.5% for Peru to

61.2% for Norway. Their dependent variable is the average growth in per capita

income between 1970 and 1992. The endogeneity problem is more prevalent in this

study than in Knack and Keefer’s study. The authors use trust variables from 1995 to

1997 to explain growth between 1970 and 1992. The independent variable trust is

even lagged behind the dependent variable (growth 1970–1992). The authors are

aware of this fact and rely on Knack and Keefer’s argument that trust levels behave

consistently over time and trust values from 1996 can be used as a proxy for trust

180 Felix Roth

values around 1970. The authors again use a growth model with the control variables

price level of investment, human capital, and initial national income. The authors

concluded that a positive and signiﬁcant relationship exists between trust and

economic growth. They determined that growth rises by nearly one percentage

point on average for every 15% point increase in trust.

Beugelsdijk et al. (2004) reevaluate the results of Knack and Keefer and Zak and

Knack using robust regression techniques. They analyzed the results of the two

studies along four dimensions of robustness: 1) statistical signiﬁcance, 2) the inﬂu-

ence of changing sets of conditioning variables on the estimated effect of trust, 3) the

sensitivity of the results for using different proxies or speciﬁcations for basic vari-

ables like human capital, and 4) the effects on the signiﬁcance and size when

expanding the country sample to 41 countries. The authors conclude that their

study provides further empirical evidence of an important relationship between

trust and economic growth.

Whiteley (2000) examined the relationship between trust and growth in the

framework of a modiﬁed neoclassical model of economic growth. He uses a

34-country sample with growth in per capita income between 1970 and 1992 as

the dependent variable. As a social capital variable, he uses a trust index consisting

of three different items (trust in one’s own family, trust in one’s own compatriots,

and interpersonal trust) from the WVS 1990–1993. He concludes that the trust index

of the three indicators has a positive effect on economic growth, with an impact as

great as the variable conditional convergence and human capital. His results support

the idea that attitudinal values are indispensable for the correct speciﬁcation of

growth regressions.

La Porta et al. (1999) work with trust data from the second wave of the WVS.

They operate on a 39-country sample with the dependent variable growth in per

capita income between 1970 and 1993, generated from World Development Report

data. A 10% rise in trust is associated with a 0.3% rise in per capita income. They

concluded that trust enhances economic performance and is remarkably robust in the

cross-section country design.

In contrast to these results, Heliwell (1996) found a negative relationship between

trust and productivity growth and between associations and economic productivity

growth, in a sample of 17 OECD countries. He works with the dependent variable

productivity growth between 1960 and 1992. His negative result in the cross-section

country design is the only one known to the present author.

Except for Heliwell’s negative result in 1996, all empirical studies conducted to

date have found a positive relationship between trust and economic growth. Many

social scientists who study the concept rely on the positive research results and

mostly associate social capital with a positive relationship between social capital and

economic growth. Social capital therefore enjoys a positive image.

Recent research has questioned the signiﬁcant positive relationship between trust

and economic growth. Berggren et al. (2008) test the robustness of the results of

Knack and Keefer (1997), Zak and Knack (2001), and Beugelsdijk et al. (2004).

They expand the country sample to 63 countries using data from the fourth wave of

the WVS and from the Latinobarómetro. They investigated whether previous studies

Social Capital, Trust, and Economic Growth 181

on the relationship between trust and growth, which produced signiﬁcant results

between 1970 and 1992, also hold for the 1990–2000 period. They learned that when

outliers are removed, speciﬁcally in China, the relationship between trust and growth

is only statistically signiﬁcant (with signiﬁcance level of 5%) in 10% of the cases out

of 1140 regressions. The authors emphasize that their results “show that the trust-

growth relationship is less robust than claimed earlier”(Berggren et al., 2008, 252).

Roth (2007,2009) even ﬁnds a signiﬁcant negative correlation between trust and

economic growth. His research also points to the downside of the social capital

paradigm. Willingness to cooperate and high levels of interpersonal trust within a

society can, in Olson’s sense, turn against state reform processes. Unlike the studies

mentioned so far, he works with a panel design. Roth assumes that trust cannot be

readily understood as a constant cultural variable and points out that especially

countries with a liberal-country regime, such as the US, the UK, Ireland, Canada,

and Australia, have experienced strong declines in trust over time. For example, the

trust level of the US dropped from 50% to 35.6% between 1990 and 1995. The UK’s

trust level dropped from 43.6% to 31% between 1990 and 1998. Even though the

correlation of all countries between the periods is high, a loss of trust by the world’s

largest economy, the US, of almost one-third of its trust stocks in a period of only

ﬁve years, is enough to question the constancy of interpersonal trust.

To Roth, it therefore seems reasonable to examine what impact this loss of trust

has on economic performance. The assessment that trust should not be treated as a

constant variable is based, among other things, on studies by Inglehart (Inglehart,

1997, 224; Inglehart, 1999, 95) and Noelle (2005). Using Germany as a case study,

Noelle shows that interpersonal trust in Germany increased from 15% to 45%

between 1950 and 2005. Inglehart (1999) and Uslaner (1999) use the US as a case

study to demonstrate that interpersonal trust fell from 58% in 1960 to 36% in 1994.

Roth’s study (Roth, 2009) examines 41 countries with 129 observation points over

the period 1980–2004. The dependent variable is the growth rate of per capita

income for the ﬁve growth periods 1980–1984, 1985–1989, 1990–1994,

1995–1999, and 2000–2004. Trust data are generated from all four waves of the

WVS 1981–1984, 1990–1993, 1995–1997, and 1999–2002, as well as one wave of

the Eurobarometer (Eurobarometer 25 from 1986). To avoid endogeneity problems,

trust as a stock variable is used as a lagged variable vis-à-vis the growth periods to be

explained. The study uses the same growth model as used in the studies by Knack

and Keefer (1997), Zak and Knack (2001), Beugelsdijk et al. (2004), and Berggren

et al. (2008) for better comparability of results. Using this research design and a

ﬁxed-effects model, Roth (2009)ﬁnds a signiﬁcant negative relationship between

interpersonal trust and economic growth. A decrease in the level of interpersonal

trust within a country is associated with an increase in the growth rate. This negative

relationship is at odds with a positive relationship in the cross-section of countries.

182 Felix Roth

10 Concluding Remarks

The ﬁnding that interpersonal trust has a positive effect on economic growth has

matured into a certainty in the international scientiﬁc community in recent years.

This positive result is most often associated with the academic work of Knack and

Keefer (1997) and Zak and Knack (2001). In particular, the Knack and Keefer article

is used to paraphrase the relationship between trust and economic development.

More recent research challenges the signiﬁcant positive relationship found by Knack

and Keefer and Zak and Knack (Berggren et al., 2008) and even ﬁnds a signiﬁcant

negative relationship between trust and growth (Roth, 2007,2009). Further research,

as well as networking efforts among scientists currently researching the relationship

between trust and growth, are necessary. It should be noted that research on the

relationship between trust and economic growth remains relevant, as the market-

economy based production process embedded in democratic structures depends on a

basic level of social trust.

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Social Capital, Trust, and Economic Growth 185

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Mercedes Gumbau-Albert

In the last decade, many studies discussed the impact of intangible investment on firms' performance. However, comparing the effect of different types of intangible investments at regional and sectoral level is not well explored. The paper aims to fill this gap by assessing the impact of several intangible capital on labor productivity using for the first‐time updated data for the period 2000–2019. Adopting panel data techniques and system GMM, the results show that regions with higher endowments of intangible capital get higher levels of labor productivity. The study results also highlight the importance of differentiating between different sectors and types of intangible capital as they have different impact on productivity labor. In the primary sector only investments in R&D result in additional productivity gains. In the manufacturing industry, investments in R&D and software result in additional productivity gains while the service sector also increases labor productivity through other intangibles assets with innovative property such as mineral exploration, entertainment, and artistic originals.

Digital economy and total factor productivity of tourism enterprises in China: the perspective of market competition theory

Article

Jan 2023

Rui Tang

The digital economy drives the increase in total factor productivity (TFP) of tourism enterprises. The Spatial Durbin Model is used to analyse the influence of digital economy on TFP of tourism enterprises based on the market competition theory. The findings suggest that the digital economy promotes the TFP of tourism enterprises with spatial diffusion effect in China. Heterogeneity tests indicate that the digital economy has contributed to the growth of TFP of travel agency and the TFP of tourism enterprises in the eastern and central regions. Additionally, the spatial diffusion effect of the digital economy lags behind its direct effect. The digital economy promotes TFP growth of tourism enterprises by improving the market environment. The convergence effect of digital economy and market competition on TFP becomes significant with the growth in TFP of tourism enterprises. These findings are an extension and complement to the effective competition theory and optimal competitive intensity theory.

Intangible Capital and Labour Productivity Growth: A Review of the Literature

Conference Paper

Full-text available

Nov 2019

Felix Roth

This paper surveys a wide range of studies on the impact of capital investment in intangible assets on labour productivity growth and highlights their main findings on. Surveying the literature at the country, industry and firm level, this paper finds evidence of the increasing importance of business investment in intangible assets in explaining the dynamics of labour productivity growth. Moreover, the findings reported in the literature surveyed suggest that in order to fully reap the benefits of investment in information and communication technology (ICT) and artificial intelligence (AI), it is essential for businesses to make complementary investment in intangible assets. In addition, the literature on the drivers of business capital investment in intangibles highlights the importance of having in place a well-endowed infrastructure of public intangibles. Judging from the wide range of economic literature surveyed, this paper finds that the contemporary economic debate now broadly acknowledges the importance of intangibles for the transformation of developed economies towards becoming fully-fledged knowledge economies.

National Measures of Intangible Capital in the EU-27

Chapter

Full-text available

Jan 2011

Felix Roth

Democracy and social capital

Chapter

Oct 1999

Eric Uslaner

Surveys suggest an erosion of trust in government, among individuals, and between groups. Although these trends are often thought to be bad for democracy, the relationship between democracy and trust is paradoxical. Trust can develop where interests converge, but in politics interests conflict. Democracy recognizes that politics does not provide a natural terrain for robust trust relations, and so includes a healthy distrust of the interests of others, especially the powerful. Democratic systems institutionalize distrust by providing many opportunities for citizens to oversee those empowered with the public trust. At the same time, trust is a generic social building block of collective action, and for this reason alone democracy cannot do without trust. At a minimum, democratic institutions depend on a trust among citizens sufficient for representation, resistance, and alternative forms of governance. Bringing together social science and political theory, this book provides a valuable exploration of these central issues.

How to do Xtabond2: An Introduction to Difference and System GMM in Stata

Article

Mar 2009

David Roodman

The difference and system generalized method-of-moments estimators, developed by Holtz-Eakin, Newey, and Rosen (1988, Econometrica 56: 1371–1395); Arellano and Bond (1991, Review of Economic Studies 58: 277–297); Arellano and Bover (1995, Journal of Econometrics 68: 29–51); and Blundell and Bond (1998, Journal of Econometrics 87: 115–143), are increasingly popular. Both are general estimators designed for situations with “small T, large N″ panels, meaning few time periods and many individuals; independent variables that are not strictly exogenous, meaning they are correlated with past and possibly current realizations of the error; fixed effects; and heteroskedasticity and autocorrelation within individuals. This pedagogic article first introduces linear generalized method of moments. Then it describes how limited time span and potential for fixed effects and endogenous regressors drive the design of the estimators of interest, offering Stata-based examples along the way. Next it describes how to apply these estimators with xtabond2. It also explains how to perform the Arellano–Bond test for autocorrelation in a panel after other Stata commands, using abar. The article concludes with some tips for proper use.

Intangible Capital and Economic Growth

Article

Jun 2006

Published macroeconomic data traditionally exclude most intangible investment from measured GDP. This situation is beginning to change, but our estimates suggest that as much as

800 billion is still excluded from U.S. published data (as of 2003), and that this leads to the exclusion of more than

3 trillion of business intangible capital stock. To assess the importance of this omission, we add intangible capital to the standard sources-of-growth framework used by the BLS, and find that the inclusion of our list of intangible assets makes a significant difference in the observed patterns of U.S. economic growth. The rate of change of output per worker increases more rapidly when intangibles are counted as capital, and capital deepening becomes the unambiguously dominant source of growth in labor productivity. The role of multifactor productivity is correspondingly diminished, and labor's income share is found to have decreased significantly over the last 50 years.

Measuring Capital and Technology : An Expanded Framework

Article

Aug 2004

Business outlays on intangible assets are usually expensed in economic and financial accounts. Following Hulten (1979), this paper develops an intertemporal framework for measuring capital in which consumer utility maximization governs the expenditures that are current consumption versus those that are capital investment. This framework suggests that any business outlay that is intended to increase future rather than current consumption should be treated as capital investment. Applying this principle to newly developed estimates of business spending on intangibles, we find that, by about the mid-1990s, business investment in intangible capital was as large as business investment in traditional, tangible capital. Relative to official measures, our framework portrays the U.S. economy as having had higher gross private saving and, under plausible assumptions, fractionally higher average annual rates of change in real output and labor productivity from 1995 to 2002.

Productivity growth in Europe before and since the 2008/2009 economic and financial crisis

Chapter

Aug 2016

The balance of the world economy is shifting away from the established economies of Europe, Japan, and the USA, towards the emerging economies of Asia, especially India and China. With contributions from some of the world's leading growth theorists, this book analyses the long-term process of structural change and productivity growth across the world from a unique comparative perspective. Ongoing research from the World KLEMS Initiative is used to comparatively study new sources of growth - including the role of investment in intangible assets, human capital, technology catch-up, and trade in global value chains. This book provides comparisons of industries and economies that are key to analysing the impacts of international trade and investment. This makes it an ideal read for academics and students interested in understanding current patterns of economic growth. It will also be of value to professionals with an interest in the drivers of economic growth and crisis.

Content, Cause, and Consequences of Job Insecurity: A Theory-Based Measure and Substantive Test

Article