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MEASURING THE NEW ECONOMY STATISTICS BETWEEN HARD-BOILED INDICATORS AND INTANGIBLE PHENOMENA

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MEASURING THE NEW ECONOMY STATISTICS BETWEEN HARD-BOILED INDICATORS AND INTANGIBLE PHENOMENA

Edited by

Teun Wolters Statistics Netherlands Voorburg, The Netherlands

Statistics Netherlands

Amsterdam • Boston • Heidelberg • London • New York • Oxford Paris • San Diego • San Francisco • Singapore • Sydney • Tokyo

Elsevier Radarweg 29, PO Box 211, 1000 AE Amsterdam, The Netherlands The Boulevard, Langford Lane, Kidlington, Oxford OX5 1GB, UK First edition 2007 Copyright © 2007 Elsevier B.V. All rights reserved No part of this publication may be reproduced, stored in a retrieval system or transmitted in any form or by any means electronic, mechanical, photocopying, recording or otherwise without the prior written permission of the publisher Permissions may be sought directly from Elsevier’s Science & Technology Rights Department in Oxford, UK: phone (+44) (0) 1865 843830; fax (+44) (0) 1865 853333; email: [email protected] Alternatively you can submit your request online by visiting the Elsevier web site at http://elsevier.com/locate/permissions, and selecting Obtaining permission to use Elsevier material Notice No responsibility is assumed by the publisher for any injury and/or damage to persons or property as a matter of products liability, negligence or otherwise, or from any use or operation of any methods, products, instructions or ideas contained in the material herein. Because of rapid advances in the medical sciences, in particular, independent verification of diagnoses and drug dosages should be made Library of Congress Cataloging-in-Publication Data A catalog record for this book is available from the Library of Congress British Library Cataloguing in Publication Data A catalogue record for this book is available from the British Library ISBN-13: 978-0-444-52804-9 ISBN-10: 0-444-52804-0 For information on all Elsevier publications visit our website at books.elsevier.com Printed and bound in The Netherlands 07 08 09 10 11

10 9 8 7 6 5 4 3 2 1

CONTENTS

Preface

vii

Statistics Netherlands

ix

About the Authors

xi

Measuring the New Economy: Introduction to the Book Teun Wolters

1

1 Benchmarking the New Economy: The Lisbon Process as Policy Context for Statistical Indicators Graham Room

13

2 Key Indicators for the New Economy Andrea de Panizza and Mauro Visaggio

29

3 Enhancing Productivity Requires More than ICT Alone Bart van Ark

53

4 The National Accounts of Knowledge-Based Economies Mark de Haan and Myriam van Rooijen-Horsten

63

5 The Intangible Economy: Key Indicators of the “Hidden” Productive Capacities Clark Eustace

83

6 IT Investment, ICT Use and UK Firm Productivity Tony Clayton, Raffaella Sadun and Shikeb Farooqui

103

7 Travel Agents and ICT Technologies Cees van Beers and Harry Bouwman

127

vi

8 ICT Maturity and Firm Productivity Xander de Graaf

143

9 Innovation in Services: A Search for New Indicators Jeroen de Jong, Gerhard Meinen and Patrick Vermeulen

177

10 The Stability of the New Economy: An Alternative Approach Teodoro Dario Togati 11 The New Information Economy and the Measurement of Economic Growth Adrian Winnett

195

213

12 The Measurement of Progress in e-Government Teun Wolters

229

Index

247

PREFACE

Although the daily work of producing statistics is a demanding job, Statistics Netherlands manages to seize various opportunities to be engaged in statistical renewal. For a considerable part, this is a matter of being involved in international initiatives to modernise and improve the measurement of what happens in today’s society. Moreover, Statistics Netherlands aims to collaborate with universities when initiating its own research. This is exemplified by Statistics Netherlands’ strategic programme New Economy, which was launched in 2002. Then the Internet (dot-com companies) hype was over, causing the New Economy to fall into disrepute. However, in line with the Lisbon strategy towards a competitive European economy, the New Economy is not just about a small frontier in the economy that fires one’s imagination. On the contrary, it concerns the emergence of ICT as a modern, widely usable technology that, if combined with appropriate organisational change and suitable business concepts, can be a powerful means to improve the economy at large and create sustainable economic growth. Considering this, the strategic programme’s focus on innovation in this context does not come as a surprise. This book is a spin-off of this work. From a statistical point of view, this book is primarily about a search for indicators which capture the impact of ICT on the economy, both at micro and macro levels. It shows a balance between on the one hand a feel for practical solutions, making use of existing indicators and available research methods, and on the other hand a critical mind focusing on (possible) shortcomings and showing new ways of defining indicators. The book rightly pays quite some attention to the measurement of productivity change. For Statistics Netherlands, this is a major topic deserving to be further developed by additional research. This is why Statistics Netherlands has launched a special research programme on it. I hope, and also expect, that also in this new project, international cooperation will have a prominent place, wherever possible, leading to fruitful collaborative research. I thank all the authors and the editor for the work done to complete this book. It is a significant milestone in the process of continuous improvement. The strategic programme New Economy has come to an end. The economy goes on and so do our statistical endeavours between “hardboiled indicators and intangible phenomena”. Gosse van der Veen Director-General Statistics Netherlands

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STATISTICS NETHERLANDS

What does it stand for? Statistics Netherlands (in Dutch: Centraal Bureau voor de Statistiek or CBS) is responsible for collecting, processing and publishing statistics in the Netherlands which are used by policy makers and scientific researchers. In addition to its responsibility for (official) statistics, Statistics Netherlands also has the task of producing European (community) statistics. The information that Statistics Netherlands publishes incorporates a multitude of societal aspects, such as indicators on economic growth, consumer prices, demography, health care, education, ecology and the digital economy. Statistical figures are available on http://statline.cbs.nl The legal basis for Statistics Netherlands and its work is the Act of 20 November 2003 governing the central bureau of statistics. On 3 January 2004, Statistics Netherlands became an autonomous agency with legal personality. There is no longer a hierarchical relationship with the Minister of Economic Affairs. However, the Minister is responsible for legislation and budgets, and for the creation of conditions for an independent and public production of highquality statistics. Publications For detailed information on the publications of Statistics Netherlands, please visit www.cbs.nl (English). Press releases on the first results of statistics by Statistics Netherlands are published in the media on a daily basis. The Book section on the site makes paper publications available electronically. Other sections are: Web publications (not published elsewhere), Publication series and Publications by theme. The Calendar section gives the weekly planning of press releases and the web magazine. Centre for Policy Statistics In 2002, Statistics Netherlands established the Centre for Policy Statistics to cope with a growing demand for statistical information by ministries, economic research agencies and MPs.

x The Centre provides information and advice inter alia on data research possibilities. Moreover, it is possible to order analyses and statistical overviews at a charge. The Centre also makes available databases for external researchers if they work with a research institution which has been authorised by law to make use of the service or admitted to the service by the Central Statistics Commission. For this service, a considerable number of databases are available; albeit under strict security conditions (all data is and needs to remain anonymous). The data can be accessed in four different ways (considering, however, that in all cases specific restrictions may apply): on CD-ROM, onsite at Statistics Netherlands, remote execution and, most recently, remote access. The Digital Economy Statistics Netherlands has a number of theme-based annual publications. One of them is about the digital economy. It highlights a broad spectrum of New Economy topics, providing ample international benchmark indicators. The 2005 issue has become available in English (ISBN-357-1636-1; ISBN 978-90-357-1636-0; ISSN 1871-9759). Additional information on publications can be obtained via e-mail: [email protected]

ABOUT THE AUTHORS

Dr Bart van Ark is a Professor in Economic Development, Technological Change and Growth at the University of Groningen (The Netherlands). He is also Director of the Groningen Growth and Development Centre, a research group working on long-term economic growth and productivity, and Director of international economic research of The Conference Board (a global business organisation). He has widely published on global productivity issues. Dr Cees van Beers is an Associate Professor of (Innovation) Economics at the Faculty of Technology, Policy and Management of Delft University of Technology. He has amply published on topics in the fields of innovation economics and environmental economics. He has done research for Statistics Netherlands on ICT and complex value-adding networks. Dr Harry Bouwman is an Associate Professor at the Faculty of Technology, Policy and Management of Delft University of Technology. He specialised in research methods and techniques, statistics and communication science. Tony Clayton is the Director of Economic Analysis at the UK Office for National Statistics. He has run the ONS programme of work on ICT impacts in recent years, and is Chair of the OECD Information Society Indicators Group. He holds degrees in physics and economics. Before working in the UK statistics office, he was Director of PIMS Associates, an evidence-based strategy consulting and benchmarking firm in the City of London. Clark Eustace is a management consultant specialising in corporate strategy, governance and public policy affairs. His current special interests lie in the economic and regulatory impact of the emerging intangible economy and their implications for performance-measurement. A former senior partner with Price Waterhouse, he has directed research studies for the Brookings Institution, Washington, D.C., Cass Business School, London, and the Centre for European Policy Studies, Brussels. Shikeb A. Farooqui holds an MSc in Economics and Finance. As an Assistant Economist at the ONS, he has been closely involved with microdata analysis of the New Economy and issues concerning data linking, data aggregation and survey design. Shikeb’s research interests lie in industrial organisation, productivity and innovation. He is currently pursuing a PhD in Economics.

xii Xander de Graaf was a PhD student at the Free University of Amsterdam. His research on ICT investments, e-business and productivity was done in collaboration with Statistics Netherlands. He specialised in internal organisational change enabling successful investments in ICT. We deeply regret to announce that Xander passed away on 1 August 2006 at the early age of 29. Dr Mark de Haan works with Statistics Netherlands (macroeconomic statistics). He has done extensive work on satellite accounts, both in the area of environment and the knowledge-based economy. Other fields of work are capital and productivity measurement. He was a researcher within the New Economy Statistical Information System (NESIS) project and is currently Chair of the London Group on Environmental Accounting. Jeroen de Jong is a researcher and project leader at EIM Business and Policy Research (The Netherlands). He holds an MA in management studies at the Erasmus University of Rotterdam. His current research interests include innovation in small firms, and innovation and entrepreneurship in technology-based firms. Gerhard Meinen is a project manager for microdata services at Statistics Netherlands (CBS). Until recently he was working as a project manager for R&D and innovation statistics, and as such was responsible for the annual CBS publication (in Dutch) on the knowledge-based economy. He also worked within the NESIS project on the final statistical publication. Dr Andrea de Panizza is currently working as a researcher at the Italian National Institute of Statistics (Istat). He has also worked at the Italian National Research Council. He is an economist with strong interests in applied research, including international and public economics, business cycles and short-term analysis, competitiveness and the role of technology. He coordinated the contribution by Istat on indicators of productivity and competitiveness to the NESIS project. Dr Myriam van Rooijen-Horsten works with Statistics Netherlands (macroeconomic statistics). She has inter alia done work on the measurement of the knowledge-based economy as well as on capital and productivity measurement. She was a researcher within the NESIS project. Dr Graham Room is a Professor of European Social Policy at the University of Bath. He is author, co-author or editor of eleven books and numerous scholarly articles. He was Founding Editor of the Journal of European Social Policy. In 2004 he was elected Academician of the Social Sciences (AcSS). He coordinated the contribution by the University of Bath to the NESIS project, concerned with statistical indicators for the New Economy and funded by the EU (DG Information Society and Eurostat) under Framework Programme 5. He is also undertaking work on socio-economic dynamics and the scope for using non-linear models. Raffaella Sadun is a Research Economist at the Centre for Economic Performance, London School of Economics. Her research focuses on the impact of Information

xiii and Communication Technologies on firm level performance and on the productivity dynamics of the UK retailing sector. Raffaella holds an MSc in Economics from Universitat Pompeu Fabra (Barcelona) and is currently reading for a PhD in Economics at the LSE. Dr Teodoro Dario Togati is an Associate Professor of Economics at the Faculty of Economics and Business, University of Torino (Italy). His current research is on the link between the New Economy and macroeconomic stability. Dr Patrick Vermeulen is an Associate Professor of Organization Studies, Department of Organization Studies, Faculty of Social Sciences, University of Tilburg. He received his PhD from the University of Nijmegen and has also worked at the Rotterdam School of Management. His main research interests focus upon processes of institutional change, and innovation in institutionalised settings. Dr Adrian Winnett studied at the London School of Economics, the University of East Anglia, UK. He is currently a Senior Lecturer in Economics at the University of Bath, where he also jointly directs the International Centre for the Environment. He has also worked in Thailand and Slovakia. His research interests are in the economics of natural resources and in economic growth, in both areas with a particular emphasis on the role of technical innovation. He was a researcher within the NESIS project. Dr Mauro Visaggio is an Associate Professor of Economics at the University of Perugia (Italy). Formerly, he was member of the Council of Economic Advisors of the Italian Treasury Ministry. His areas of interest include public economics, pensions, economic growth, productivity and the role of ICT. Dr Teun Wolters works with Statistics Netherlands. He headed the strategic project New Economy, whose tail end is represented by this book. He was also researcher within the NESIS project, in particular editing its final statistical publication.

We deeply mourn the loss of Xander de Graaf, author of Chapter 8 of this book. As a PhD student at the Free University of Amsterdam, he was a passionate researcher dedicated to the subject of ICT and its impact on the economy. He died on 1 August 2006 at the early age of 29. Through the strength-sapping treatments he was enduring, he continued to show a great interest in his subject and in possibilities for analysing the survey that Statistics Netherlands had carried out, based on the model he had developed. He saw the chapter he wrote for this book as an opportunity to produce a full-fledged exposition of his model and its relation to the academic literature. It is so sad that in the end he had to give up. Despite our sorrow over the loss of Xander, some consolation may be found in the fact that Statistics Netherlands has used his work to upgrade some of its regular surveys. Because of this, his work continues to have a visible impact on the measurement of the New Economy in the Netherlands.

Measuring the New Economy Edited by Teun Wolters © 2007 Elsevier B.V. All rights reserved.

Measuring the New Economy: Introduction to the Book Teun Wolters (Statistics Netherlands)

New Economy and the need for benchmark indicators This book is about the intriguing subject of measuring the New Economy (NE), which in brief means ICT and its impact on society. It brings to the surface various old and new statistical issues which call for a solution, not in the least because politicians in the EU wish to be reliably enlightened on how the EU is performing vis-à-vis countries such as the US and Japan. Moreover, globalisation increases its priority as the burgeoning economic force of countries like China and India intensify international competition. The EU’s Lisbon strategy for the EU to “become the most competitive and dynamic knowledge-based economy in the world, capable of sustaining economic growth with more and better jobs and greater social cohesion” (European Council, 2000) has increased the need for benchmark indicators. This has unleashed a fervent drive to improve the statistical basis for such indicators. In the beginning, the policy makers tended to emphasise timeliness. For their annual reporting on the Lisbon strategy, they wanted readily available up-to-date statistical indicators and for that matter initiated a number of quick surveys. However, the National Statistical Institutes (NSIs) protested against this procedure, pointing out that results from these quick surveys were below any reasonable quality standard. The NSIs made it a point of honour to do a better job. They indeed were given an opportunity to contribute and produce a greater proportion of the needed benchmark indicators. This, in particular, applies to the benchmark indicators relating to the eEurope action plans. These indicators focus on the levels of connection to the internet by consumers and business and the degree to which e-commerce, e-government, e-health and e-education have developed. Other existing sources of benchmark indicators in the field of the NE and the knowledge-based economy are

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the European Innovation Score Board and the science and technology indicators of the European Research Area.1 These show that the NE is seen within a broad context of economic development. Therefore, it is also justified to add to these sets of indicators the EU’s Structural Performance Indicators. Along with these developments, there have been a considerable number of research projects which critically reflected on existing indicators and developed new ones where deemed necessary. On the EU level, the SINE2 programme has given rise to various research projects which focus on the NE. One of these projects was the NESIS3 project, which Statistical Netherlands participated in, leading to the publication “The EU-15’s New Economy: A Statistical Portrait”,4 presenting approximately 50 indicators on the NE. Besides this publication, other reports on different topics were issued. This book contains various chapters written by authors who in one way or another contributed to the NESIS project; they could draw on this work when writing their chapter.

From readiness to impact Statistics Netherlands also launched its own research on the NE in collaboration with a couple of universities and other knowledge institutions. This research has made efforts to discover the organisational variables which determine whether and to what extent ICT investments could make a positive contribution to process quality and productivity. It was mainly focused on the business sectors but also included e-government. Moreover, research has been done on innovations in the services sector, the role of ICT being part of it. Many indicators in the field refer to what is called readiness and intensity. That is, many indicators are informative as to the availability and use of ICT equipment and related devices but do not reveal their impact on the lives of people and the way businesses operate. Moreover, the emergence of ICT is going together with an intensified debate on labour productivity. The political aspect of this debate is the longer-term importance of productivity growth for prosperity especially under conditions of an aging population and growing international competition. The EU’s economic strength vis-à-vis the other economic blocks in the world is dependent on how it compares to them in terms of productivity growth. These interests induce the national statistical bureaus to give priority to productivity issues. Also Statistics Netherlands responded to this by putting extra efforts in measuring and explaining productivity.

1 These

reports parallel similar publications issued by the OECD. stands for Statistical Indicators for the New Economy. 3 NESIS stands for New Economy Statistical Information System. Web site: http://website.jrc.it. 4 This publication is available on the websites of Eurostat and Statistics Netherlands. A printed version can be obtained from Statistics Netherlands: [email protected] 2 SINE

Measuring the New Economy: Introduction to the Book

3

Innovation has a major place in it, and as ICT is a dominant emerging technology, innovation and ICT cannot be separated. The research done helps to identify rather intangible aspects of ICT investments and ICT innovations. ICT hardware and software are just a moderate part of all the costs that have to be incurred before these assets can perform to their full potential. A few chapters of this book go into this, indicating what kind of new indicators may help to unravel the complexities of successful ICT implementation.

How to consider the New Economy? The NE is difficult to capture by identifying a single or a few features. Rowlett et al. (2002) bring forward that there have always been “new economies” – the concept is not tied to time or technology. They continue: “For centuries there have been periods when changes in technology or social organisation brought: – Radical changes to market boundaries, expanding scope to exploit intellectual capital; – Access to new products and services for major sections of society and new consumers; – Significant changes in the interactions and operating processes of enterprises; – Redefinition of the relationship between customers and suppliers”. In the past, different technologies ranging from printing, to steam engines, canals and railroads, or mass media had that capacity. At last developments in ICT, which have the potential to make global information, entertainment and access to products available on an individual and interactive basis is the technology that leaves its mark on the latest NE. The literature on technical change invites one to see the NE as a pervasive innovation process based on a new techno-economic paradigm. As Freeman and Perez (1988) describe it, some changes in technological systems are so far reaching in their effects that they have a major influence on the behaviour of the entire economy. A change of this kind carries with it many clusters of radical and incremental innovations, and may eventually embody a number of new technological systems. A vital characteristic of this fourth type of technical change is that it has pervasive effects throughout the economy, i.e. not only does it lead to the emergence if a new range of products, services, systems and industries in its own right; it also effects directly or indirectly almost every other branch of the economy. The changes involved go beyond engineering trajectories for specific product or process technologies and affect the input cost structure and conditions of production and distribution throughout the system. They also relate to other phenomena, such as destabilising market forces and the turbulent relationship between financial capital and the upsurge of new technologies (Perez, 2002).

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ICT: Hype and undercurrent When talking about measuring the NE, the question arises whether one should focus on innovations in general rather than on ICT innovations. The notion of ICT as a change in techno-economic paradigm suggests that both are important. Basic hightech ICT innovations and advanced ICT applications by the ICT sector as well as other high-tech sectors are of crucial importance as these, to a great extent, determine which countries will be in a leading position. However, in the course of time, innovations at large, whether in high-tech or low-tech sectors, condition the further diffusion of available technology and therefore are a decisive factor in the relative economic success that the NE is supposed to generate. This also applies to the services sector, where new ICT plays a part in new ways of serving the client and ensuring higher productivity. In this context, hypes as occurred at the end of the nineties of the previous century are part of the instabilities that new technologies may bring about. At that time, there were unchecked expectations about the potential successes of dot-com companies and sky-rocketing share prices of technology-driven companies. The burst of this Internet-oriented bubble knocked the optimistic investors on the head, discrediting any reference to the NE (Figure 1). However, in the meanwhile ICT and its concomitant business opportunities have continued to change the economy at local, national and international levels. At the

Figure 1. Share price indices Monthly ultimo (1995=100) 700 600 500 400 300 200 100 0 1995

1996

1997

Dow Jones

1998

1999

2000 Nasdaq

2001

2002

2003

2004

FTSE100

Measuring the New Economy: Introduction to the Book

5

macroeconomic level, its effects tend to remain moderate. However, the NE in different ways operates like an undercurrent in the economy whose effects may not seem dominant according to traditional economic indicators; nonetheless, they are reshaping the competitive forces in the world. It is important to realise that the NE is not only a matter of new products and new industries; it also permeates the conventional sectors, even those which are known to be moderately thriving on technology.

ICT and hidden productive capacities There is a realisation that in many business cases it is not the ICT per se which generates competitive advantage, but the organisational and commercial capacities which take advantage of them. Here, we touch on the discussion about intangible or hidden productive capacities, both in terms of human and organisational capital, which are conjectured to be of vital interest even though they are not visible in regular statistics or conventional balance sheets. Networks play a crucial role here. Survival in the information age is reserved to those who have the social ability to work together on the basis of trust. Companies will confront crucial choices about the networks they want to be part of. The networks that facilitate incremental change are attractive as these changes can be controlled and integrated in existing executive-power networks. New breakthroughs, however, require unprecedented ways of communication with clients and employees involving processes of co-creation. This calls for adaptive management systems, which in fact are typically based on soft-power networks. Together, this tends to the formation of non-hierarchical networks which serve to cope with continuous uncertainties and change.5 To some extent, such networks are already emerging. Measuring the NE can also serve as a stepping stone towards measuring the newly maturing institutional patterns that will shape the future. In terms of measurement and analysis, the message is that the economy is too complex a phenomenon to be studied monolithically. An institutional approach seems to be justified allowing different levels (micro, meso and macro), different perspectives (such as innovation, social change and networking) and different disciplines.

The chapters of this book The thoughts presented earlier comprise the background of this book. Its chapters vary in scope and emphasis but have in common a consideration of the importance of ICT for the entire economy. Even though the book discusses a broad range of NErelated measurement and research issues, it does not claim to cover all relevant topics. 5 This

paragraph is based on De Ridder (2005).

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For instance, the issue of social inclusion is not dealt with here, although it has been addressed in the NESIS project.6 Moreover, various aspects of the human factor, in particular occupational profiling in the information society, have been studied in the STILE project, which are reported elsewhere.7 Other aspects such as social inclusion, e-learning, e-health and sustainability8 have not been dealt with. This book’s major angle is innovation and productivity as main vehicles and yard sticks of economic success in a world where ICT is the dominant technology. Innovation here, however, is understood to strongly involve new organisational and business concepts which are needed to make new technology, i.e. ICT, an effective and efficient instrument. Along with the conventional growth-accounting approach, various chapters plead for other angles and approaches, which would require serious pieces of basic research before they could become operational. Therefore, this book is of equal interest to those we look for short-term practical solutions (there are already plenty materials available) and to those who wish to contribute to new ways of measurement and economic analysis. Chapter 1, written by Graham Room (University of Bath) discusses the Lisbon process as policy context for benchmark indicators. It pleads for policy learning which shows the trade-offs among alternative outcomes and therefore, a variety of possible futures. Chapter 2, written by Andrea de Panizza (Istat) and Mauro Vissagio, discusses the conceptual and empirical basis of a set of widely used NE indicators. They present an analysis by which a list of indicators is selected as a key set of NE indicators. Chapter 3, written by Bart van Ark (University of Groningen), explains why productivity growth is important. It stresses that the strategic advantage relating to ICT is predominantly realised through complementary non-technological innovations. The Netherlands will have to take its chances in the services. A clear innovation strategy which combines ICT with non-technological innovation is the key to success. Chapter 4, written by Mark de Haan and Myriam van Rooijen-Horsten (both Statistics Netherlands), explores new indicators on the knowledge-based economy in the context of the upcoming upgrading of the System of National Accounts (SNA), which is scheduled for 2008. These indicators could be either fully integrated in the system or incorporated in a special satellite account on knowledge. Incorporating human capital seems hard to handle as it would require fundamental changes of the system. However, there is a case for capitalising (private) expenditure on R&D. ICT capital could be separately distinguished as part of gross capital formation. Educational expenditure – both public and corporate – reflects a country’s capacity

6 See

e.g. Room (2005), Chapter 7. on the results of the STILE project Ramioul et al. (2005). 8 See however, Sahota et al. (2004).

7 See

Measuring the New Economy: Introduction to the Book

7

to build up the knowledge that innovation requires, deserving a more explicit and detailed presentation. Chapter 5, written by Clark Eustace (Mantos Associates, UK), goes into the hidden productive factors and the key indicators needed to come to grips with them. It indicates areas where new indicators are needed, both at the macro and micro level. These should lead to richer clusters of leading indicators and information repositories that allow users to construct their own analytical models. At the corporate level, new accounting standards lead to the incorporation of business valuation (as opposed to cost accounting) into the financial reporting framework. This is expected to stimulate a wider interest in the management of intangibles. Chapter 6, written by Tony Clayton (ONS, UK), Raffaella Sadum (LSE) and Shikeb Farooqui (ONS and University of Pompeu Fabre), presents the results of research on the impact of IT investment and ICT use on firm level productivity across the UK economy. As well as providing an evidence base for policy, this chapter sets out to identify some of the measurement issues which need resolution to improve the support of policy makers. Chapter 7, written by Cees van Beers and Harry Bouwman (both from Delft Technical University), examines the relationship between emerging Internet (-based) technologies and the performance of travel agents in the Netherlands. It deals with organisational change induced by ICT technologies and how this works on business performance. It seems that travel agents are well prepared to take advantage of the sales through the Internet, as long as they succeed in making optimal use of web site functionality (which to a degree depends of the functionality of the tour operators’ web site). Chapter 8, written by Xander de Graaf (Free University of Amsterdam) builds on the work of Brynjolffsson and other researchers, presenting a model that explains firm productivity by adding to the equation variables about internal organisational change, such as business-ICT alignment, customer focus and the standardisation of ICT management. First empirical results of a survey based on the model show that higher levels of ICT standardisation correlate with higher labour productivity. Chapter 9, written by Jeroen de Jong (EIM Business and Policy Research), Gerhard Meinen (Statistics Netherlands) and Patrick A.M. Vermeulen (Tilburg University) – presents the results of a joint project by EIM and Statistics Netherlands to empirically validate six new output indicators for innovation in the services sector. These indicators were derived from a literature review and qualitative screening in various service industries. Four indicators were related to the dimensions of innovations in services (innovation in the service concept, client interface, supply system and technological options) and two indicators dealt with particular sources of innovation (supplier-driven and customer-driven). Chapter 10, written by Teodoro Dario Togati (University of Torino), addresses the implications of the NE for macroeconomic stability, which is taken in a broad sense as encompassing both cyclical and growth issues. The chapter presents a broad definition of the NE, namely one that regards it as the product of a number of interrelated

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features such as globalisation, weightlessness and technology, which are increasingly influenced by institutions and cultural factors, showing a tendency to accelerate. These lead to changes in the key behavioural functions of the economy, such as investment and consumption. Standard methods do not allow a full-blown global stability analysis. Therefore, new classification criteria and empirical evidence are required. Chapter 11, written by Adrian Winnett (University of Bath) also takes a critical view of the standard methods, in particular growth accounting, when examining the NE (which he called the New Information Economy) and the measurement of economic growth. The chapter recommends a stages-of-innovation framework so as to recognise the key characteristics of innovation (through which also ICT finds its way to implementation and usage). Chapter 12, written by Teun Wolters (Statistics Netherlands), discusses the measurement of e-Government, in particular ways to go beyond web-based surveys so as to gauge internal organisational change, service quality and customer satisfaction. A first step could be the measurement of the extent to which information processes are standardised and integrated. The various stages are based on a quality-management model which is well known within the public sector. A first pilot showed that this approach makes it possible to define statistical indicators which can show “distanceto-target” both for individual municipalities and groups of municipalities (e.g. based on number of inhabitants).

Main issues The main issues of the book and how these relate to the totality of NE issues can be explained with the aid of the following Exhibit 1.9 This picture sees ICT and globalisation as a major force presenting threats and opportunities to the national and EU economy. These have an influence on the economy (box A) through different channels: Through government policies and facilities (box F) and through the capacities and strategies of corporation and household (box E) to build up a response. Depending on the innovation systems (coherence, connections) in place, these will lead to innovations (box B), in addition to innovations which emanate directly from new ICT. The innovations will have an impact on the features and performance of the economy at different levels, first at the micro and meso level (box C) and later at the macro level (box D). Exhibit 2 gives a brief overview over the various subjects discussed in the chapters of this book. Considering the different chapters of this book, it can be concluded that the available statistics makes it possible to make analyses of the economy

9 This

exhibit is based on the NESIS project’s final statistical publication, Chapter 1. See footnote 4.

9

Measuring the New Economy: Introduction to the Book

Exhibit 1:

New Economy framework

Fa Public Domain and Fb Government: Enabling policies and facilities (education etc.)

A. ICT and globalisation: Threats and opportunities

B. Innovation

C. Features & Performance: Micro/meso Private/public

D. Aggregate Effects: Econ. growth Stability Sustainability Social inclusion

Ea Corporations and Eb Households: Capacities and strategies

and the impact of ICT and ICT-related innovations which are quite informative to policymakers. For the coming years there is ample room for research based on this material. However, to better understand the very impact of ICT and ICT-related investments and innovations, there is a need for additional conceptual models and surveys based on these models. This book gives interesting examples of how this can be developed. Coordination at the EU level can be extremely important here, as this allows research whose results can inform policies in different countries. Finally, it is important to prevent monolithic approaches to come to grips with the relationships between ICT investment and productivity growth. Along with the approaches such as growth accounting it is highly desirable to also develop new theoretical models such as indicated in Chapters 9 and 10 of this book. This requires pieces of basic research which could be organised by a collaborative research programme involving both universities and NSIs.

Exhibit 2: Public domain; enabling policies and facilities

Concise overview of the chapters and the issues they discuss Corporations and households: capacities and strategies

Innovation

Chapter 1

Policy background: Lisbon goals and benchmarking

Chapter 2

Educational expenditure; educational attainment; governance

Available ICT and infrastructure;

R&D and patents; innovation score board indicators

Chapter 3

Policy focus on services

Increase in workers’ skills; focus on cost reduction

Innovation in manufacturing and services

Chapter 4

Educational expenditure

Chapter 5

Liberalisation of product markets

Economy at micro and meso levels

Economy at macro level

e-Europe indicators

National economic growth; productivity growth; FDI

Contribution of ICT producing and using sectors to productivity

R&D

GDP, hours worked, labour productivity

R&D investment, ICT investment Categories of intangible capital; mark-to-book ratio

Intangibles

Chapter 6

Relative strength US multinationals

Productivity in manufacturing and services; e-commerce; e-procurement; employee use of ICT in manufacturing and services

Chapter 7

Alignment of information systems

Organisational change

ICT and productivity in travel sector

Chapter 8

Business-ICT alignment; customer focus; integration of business functions

Organisational change

ICT and productivity in larger firms

Chapter 9

Supplier- and client-based innovations

Innovation in services

Chapter 10

No predetermined causal links

Chapter 11 Chapter 12

Stages of innovation e-government

Productivity; ICT investments; employee use of ICT

Economic growth and stability; behavioural factors Economic growth and ICT

e-services to companies and public

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References European Council (2000), Lisbon European Council, Presidency Conclusions, 23–24 March 2000, Lisbon: European Council. De Ridder, J. (2005), “Koers 2020, Nieuwe toekomst, nieuwe leiders”, Den Haag: Stichting Maatschappij en Onderneming. Freeman, C. and Perez, C. (1988), “Structural crisis of adjustment: business cycles and investment behaviour”, in: G. Dosi, C. Freeman, R. Nelson, G. Silverberg and L. Soete (eds.), “Technical Change and Economic Theory”, London/New York: Pinter Publishers, pp. 38–67. Perez, C. (2002), Technological Revolutions and Financial Capital. The Dynamics of Bubbles and Golden Ages, Cheltenham UK: Elgar. Ramioul, M.; Huws, S. and Bollen, A. (eds.) (2005), Measuring the Information Society, Leuven: HIVA. Room, G. (2005), The European Challenge. Innovation, Policy Learning and Social Cohesion in the New Knowledge Economy, Bristol: The Policy Press. Rowlett, A.; Clayton, T. and Vase, P. (2002), “Where and how to look for the New Economy”, Economic Trends, No. 580, ONS, UK. Sahota, P.S.; Jeffrey, P. and Lemon, M. (2004), “Improving Decision Making and Embracing the Sustainability Agenda”, in: The NESIS Summative Conference, Athens 11–14 October 2004, Pre-proceedings Vol. I, pp. 311–331.

Measuring the New Economy Edited by Teun Wolters © 2007 Elsevier B.V. All rights reserved.

CHAPTER 1

Benchmarking the New Economy: The Lisbon Process as Policy Context for Statistical Indicators1 Graham Room (University of Bath)

1.1. Introduction The European Union has long been preoccupied with the fear of falling ever further behind the economies of the United States and East Asia. During the 1980s, the main barrier to European economic development was seen as being the fragmentation of different national markets: the response was to create a Single Market, a project which was in principle at least to be completed by 1992 (Cecchini, 1988). With a single home market, European enterprises would, it was hoped, be able to operate on a scale to match their American and Japanese rivals. During the 1990s, economic and monetary union consolidated the project. During the late 1990s, the focus of attention shifted to the new information technologies (ITs) and their associated economic transformations. The fear now was that the US would run away with the knowledge-based industries of the new economy, while East Asia – China in particular – would capture the manufacturing industries associated with the old economy. This would leave Europe with a bleak future. Moreover, the globalisation of the economy – the result in part of political initiatives, notably the rise of the WTO, and in part of the rapid communications and 24/7 working which the new technologies have enabled – meant that these challenges from North America and Asia would become more and more pressing. Europe had nowhere to hide. Even the notion that China would concentrate on the old economy, and leave the “triad” of Europe, North America and Japan to divide out the new economy, began to look forlorn (Schaaper, 2004). Recognising these dangers but also the opportunities, the Lisbon European Summit in March 2000 set a new strategic goal for the Union for the new decade: to become the most competitive and dynamic knowledge-based economy in the world, capable of sustaining economic growth with more and better jobs and greater social cohesion 1 The

argument in this chapter is developed at greater length in Room (2005a).

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(Presidency Conclusions: European Council, 2000b, para 5). This not only asserted the ambition to play a central role in the development of the new knowledge-based industries: it also re-affirmed a long-standing goal of European political economy and – implicitly at least – a critique of the American: to temper the flexibility and insecurity of the market with high quality social protection and an active public policy. The assumption here – common across the countries of the EU – is that public policy-makers can intervene in order to promote the dynamism of the economy, or at least to address its socially negative side-effects. There is, admittedly, a contrary view, and one that seeks support from the outstanding performance of the US. This is that interventions by public policy-makers are likely only to cripple the creative energies unleashed by the new economy. It would be better to leave these energies to produce new waves of Schumpeterian “creative destruction”: there may be victims of such change, but matters will only be made worse by public intervention: ultimately many more will suffer from those interventions than will benefit. (This view overlooks, however, the important role of US Government contracts – defence in particular – in supporting the development of high-tech innovation: Nelson, 2000). Be that as it may, the Lisbon Summit established a new approach to policy development, with the aim of promoting a concerted European drive towards the knowledge-based new economy, and encouraging imitation of the best performers (both within the EU and outside). Central to this was the notion of policy benchmarking, using appropriate statistical indicators which would compare national performances, both within the EU and by reference to the USA in particular. This chapter examines this approach to policy benchmarking and initial efforts to apply it to the knowledge-based economy. Our assessment of the Lisbon process – in particular policy benchmarking – in relation to the knowledge-based economy covers the initial five-year period. It therefore broadly coincides with the mid-term review which the European authorities have themselves undertaken. This review process started in 2004, with the publication of the so-called Kok Report (European Commission, 2004). Here is not the place to review and evaluate this report. Suffice to say that the report, while it endorses and reaffirms the broad thrust of the Lisbon strategy, concludes that by and large the strategy has had only limited success. The mid-term review has continued through a series of public documents emanating from the European institutions, which broadly endorse the Kok conclusions, and seek then to re-launch the Lisbon process in a streamlined and more coherent form (European Commission, 2005a–c).

1.2. The Lisbon process The Lisbon strategy for a concerted European drive towards the knowledge-based new economy included three distinctive elements (De la Porte et al., 2001): the coordination of a wide range of policy instruments within a framework collectively agreed among the member states; the promotion of benchmarking, exchange of good practice

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and peer review, in the so-called “open method of coordination”; the involvement of a wide range of actors in new modes of governance. 1.2.1. A coordinated policy approach The Lisbon Summit anticipated that a wide range of economic, technological, social and employment policies would be needed in order to achieve its strategic goals. Previous EU Summits had made no less sweeping reference to a wide range of policies: this was the first to set in motion a process by which all these policies would be “joined up”. Across a broad array of policies, all intended to promote the drive for a socially cohesive knowledge-based economy, a number of central preoccupation are evident. First, the Lisbon Summit was concerned with the macroeconomic health of the EU. As the Summit noted, the Union was experiencing its best macroeconomic outlook for a generation: the Single Market was a reality, public finances were in good order and the Euro had been introduced. Economic growth and job creation had resumed (European Council, 2000b, para 3). However, this European economy was being transformed by the information revolution, to an extent perhaps as great as the first industrial revolution: the consequences were difficult to understand, let alone predict. Since the Lisbon Summit, the rise of the new economy, seemingly unstoppable, has faltered but then, at least in the United States, has resumed. Meanwhile, in the European heartland, economies stagnate and show little sign of catching up with the US. Macroeconomic growth and stability are in question. Second, as the Lisbon Summit recognised, the new economy would not emerge from the womb of the old, unless there were further structural reforms at the micro-level, to improve productivity and competitiveness (European Council, 2000b, para 5). This would need to include concerted R&D policies and improved communication infrastructures, which could themselves benefit from the new information and communication technologies. The 2000 Action Plan eEurope: An Information Society for All (European Council and European Commission, 2000) therefore made reference to the role of information and communication technologies (ICTs) in developing a faster internet for researchers on the one hand, improving communication and transport infrastructures on the other. What would also be important was to identify and support the organisational architectures that favour dynamic innovation. Third, the Lisbon Summit looked to a knowledge-based economy which gave a central place to skills and training. Policies for human investment were therefore of central interest. Meanwhile, if new organisational architectures in enterprises are a key to success in the new economy, the same organisational strategies were being tested among education and training institutions. What remained unclear was precisely how human investment policies could drive the new economy and the ways in which education and training systems could best deliver these. Fourth, the Lisbon Summit affirmed its confidence that the new economy would promote employment and social inclusion: a European trajectory distinct from the

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liberal insecurity of the American. However, it also recognised that these fruits could not be taken for granted. In the new economy as in the old, the accelerating pace of change, while it opened up new employment opportunities, also rendered precarious traditional patterns of employment and placed a heavy burden on systems of social protection. It also thereby imposed new strains on the European social model, which would have to be reconfigured if it was to play a positive role in the information society. Here again, the delivery of such policies could benefit from the new ICTs: the Action Plan eEurope: An Information Society for All (European Council and European Commission, 2000) made reference to the role of ICTs in improving access to public services, including health care. 1.2.2. Benchmarking and the open method of coordination The Lisbon Summit established a new form of policy coordination among the Member States (European Council, 2000b). The “open method of coordination” (OMC) involves: – fixing guidelines for the EU as a whole, combined with specific timetables for achieving the goals which they set in the short, medium and long terms; – translating these European guidelines into national and regional policies, by setting specific targets and adopting appropriate measures, taking into account national and regional differences; – establishing, where appropriate, quantitative and qualitative indicators and benchmarks against the best in the world and tailored to the needs of different Member States and sectors, as a means of comparing best practice; – periodic monitoring, evaluation and peer review, organised as mutual learning processes. The OMC is distinctive in a number of respects. It involves “soft law” rather than Treaty-based legislation. National responsibility for the policy areas to which the OMC applies is not put in question: subsidiarity is respected: nevertheless, member states commit themselves to collective goals and disciplines. The overall aim is policy coordination, policy learning and performance improvement, so that Europe and its constituent nations can be at the leading edge of global economic performance. The OMC was applied first to the areas of employment policy and social inclusion. In the employment field, where even before Lisbon the OMC had been pioneered through the Luxembourg employment process, the cycle of annual national reporting has bedded down, and the peer review orchestrated by the Commission has not proved as anodyne as some predicted (Barbier et al., 2001). In the field of social inclusion, the first round of national reports were submitted in 2001 and the second round during Summer 2003. Other policy domains where elements of the OMC have been introduced include education (European Commission, 2001d), immigration (European Commission, 2001a) and pensions (European Commission, 2001c). Benchmarking is

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also proceeding in other policy domains, albeit not couched in terms of the OMC: in respect of competitiveness, for example, championed during the 1990s by the European Round Table of Industrialists and now taken forward by DG Enterprise (European Round Table of Industrialists, 2001). The backcloth for all of these is provided by the Commission’s Structural Performance Indicators, dealing with broader changes ranging from financial markets, R&D and productivity, through employment and poverty, to environmental indicators such as greenhouse gas emissions and energy efficiency (European Commission, 2000; 2003b). The OMC has prompted a variety of wide-ranging academic and policy debates. Our focus will be on the benchmarking processes and indicators that are being used in relation to the new knowledge-based economy. We will, in addition, consider some of the implications of these for processes for EU governance. 1.2.3. New modes of governance The Lisbon Summit moved beyond the traditional demarcation of national and Community competence and responsibility. Areas of national competence were now brought together in a common and cooperative endeavour: a process that involved “soft” policy-making rather than “hard” legal process, but which was not self-evidently inconsequential. The potential implications for EU governance have attracted widespread comment: on the one hand the new relations which the OMC establishes between national governments, the EU institutions and civil society, on the other the struggles between national social and economic ministries and directoratesgeneral of the Commission for control of the OMC process (De la Porte and Pochet, 2002, Ch. 1; Borras and Jacobsson, 2004). These questions will however be left in the background as far as the present discussion is concerned. The OMC is distinctive also in the role which Lisbon gave to a wide range of actors beyond national governments. This seems to have been driven in part by fears about the democratic deficit of EU policy-making: a deficit which the OMC threatens to exacerbate, separate as it is from formal scrutiny by the Parliament. It also, however, betrays recognition that in many of the policy fields with which the OMC deals, there is no strong commitment to a common European destiny, but rather a diversity of national policy preoccupations, and that even at the national level, policy goals are sharply contested. Policy coordination must therefore, it is argued, reach out to embrace a wide range of actors and secure their involvement in the process of policy reform (Lebessis and Paterson, 2001). In considering what benchmarking indicators may be appropriate for monitoring the development of the new economy, it will be necessary to consider their utility for policy actors. However, under the Lisbon process these policy actors go far beyond the EU institutions and their statistical services, to include all those who are involved in shaping or debating public policies to steer the new economy across the EU. This includes enterprises working at European level and making use of EU indicators, in order to plan their own activities and to engage in discussion of EU competition policy. It also, indeed, includes citizens more generally, insofar as they

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engage actively in debate on the direction of EU policies concerned with the new economy and are in need of transparent, clearly understandable indicators, in order to take a meaningful part in such debate. To these issues of EU governance we return later.

1.3. First efforts at benchmarking the knowledge-based economy The EU has developed a number of indicator sets for monitoring and benchmarking the development of the new knowledge-based economy. These include three in particular: the eEurope action plans and indicators for 2002 and 2005 (European Council, 2000a; European Commission, 2001b; 2002b); the European Innovation Scoreboard (EIS) (European Commission, 2002a); the science and technology indicators of the European Research Area (ERA) (European Commission, 2002c). These indicators are being used in regular publications, reporting the progress of the EU member states, judged against each other but also by reference to the USA and Japan. This progress is presented along with an assessment of existing policies and the identification of policy guidelines for the future, to support the move towards a knowledge-based new economy. These reports parallel similar exercises that are proceeding under the auspices of the OECD (OECD, 2001; 2002; 2003). The eEurope action plan was an early initiative in pursuit of the Lisbon goals. The initial plan, eEurope: An Information Society for All (European Council and European Commission, 2000) covered the period until 2002 and included a series of action points for benchmarking progress in relation to e-Europe. The statistical indicators for this benchmarking were the concern of a Council document in November 2000 (European Council, 2000a) and a further Commission document eEurope: Impact and Priorities in March 2001 (European Commission, 2001b). An updated plan for e-Europe 2005, with a revised set of benchmarking indicators, was published in 2002 (European Commission, 2002b). The indicators focus on the levels of connection to the internet by consumers and business and the degree to which e-commerce, e-government, e-health and e-education have developed (Table 1). The science and technology indicators are supposed to capture research capacity and activity levels in different member states (Table 2). They refer for example to R&D expenditure (both public and private), the human resources devoted to research and technological development (such as the numbers of science graduates) and the volume of venture capital investment in the early stages of innovation. They also refer to the impact of this research activity, for example in terms of the number of patents registered and the weight of high-tech industries within the national economy concerned. The European Innovation Scoreboard covers much of the same ground (Table 3). It also includes a number of additional indicators of innovation, including innovation activities by small and medium-sized enterprises (SMEs), which are particularly important as sources of new employment but which are rather limited in their capacity to generate and absorb innovation in technology and processes.

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Table 1. e-Europe 2005 benchmarking indicators A: Citizens’ access to and use of the Internet A.1

Percentage of households/individuals having access to the Internet at home

A.2

Percentage of individuals regularly using the Internet

A.3

Percentage of households with access to the Internet broken down by device for accessing via digital TV, mobile device (include all forms of mobile access; handheld computer, mobile phone, identifying 3G (UMTS) separately when available)

A.4

No. of individuals with access to the Internet broken down by place of access (home, workplace, place of education, Internet café, PIAP, etc.)

A.5

No. of individuals using the Internet for specific purposes (broken down by purposes: sending– receiving emails, finding information about goods and services, reading/downloading on-line newspapers, playing/downloading games and music, internet banking)

A.6

Percentage of households connected in Objective 1 regions

B: Enterprises’ access to and use of the Internet B.1

Share of total no. of persons employed using computers connected to the Internet, in their normal routine

B.2

Percentage of enterprises having access to the Internet

B.3

Percentage of enterprises having a web site/homepage

B.4

Percentage of enterprises using Intranet

B.5

Percentage of enterprises using Extranet

B.6

Share of total no. of persons employed regularly working part of their time away from enterprise premises and accessing the enterprise’s IT systems from there

C: Internet access costs C.1

Costs of Internet access broken down by different frequency of use: 20, 30, 40 hours/week, unmetered rates

C.2

Identification of cheapest access in each Member State in addition to overall basket

D: e-government D.1

No. of basic public services fully available on-line

D.2

No. of available basic public on-line services with integrated digital back-office processes

D.3

Percentage of individuals using the Internet for interacting with public authorities broken down by purpose (purposes: obtaining information, obtaining forms, returning filled in forms)

D.4

Percentage of enterprises using the Internet for interacting with public authorities broken down by purpose (purposes: obtaining information, obtaining forms, returning filled in forms, full electronic case handling)

D.5

Public procurement processes that are fully carried out on-line (electronically integrated) in % (of value) of overall public procurement

E: e-learning E.1

Total bandwidth divided by the number of users/PCs in place of education

E.2

Percentage of universities offering on-line facilities to their students (e.g. information about passed exams, marks obtained in exams)

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Table 1 (continued ) E.3

Percentage of non-teaching computers sharing the bandwidth

E.4

Percentage of universities/other places of educating offering courses via Internet

E.5

Percentage of individuals having used the Internet related to training and educational purposes

E.6

Percentage of enterprises using e-learning applications for training and education of persons employed

F: e-health F.1

Percentage of population using Internet to seek health information whether or not for themselves or others. Health information to include injury, disease and nutrition

F.2

Percentage of general practitioners using electronic patient records (easily included in existing general practitioners survey)

F.3

Percentage of prescriptions transmitted electronically between general practitioners and pharmacies

G: Buying and selling over the Internet G.1

Percentage of enterprises’ total turnover from e-commerce

G.2

No. of individuals having ordered/bought goods or services for private use over the Internet in the last 3 months

G.3

No. of enterprises having received orders via the Internet

G.4

No. of enterprises having received on-line payments for Internet sales

H: e-business readiness H.1

Average e-business readiness index value (composite indicator)

I: Internet-users’ experience and usage regarding ICT-security I.1

Percentage of individuals with Internet access having encountered security problems

I.2

Percentage of enterprises with Internet access having encountered security problems

I.3

Percentage of individuals having used digital signature within the last 3 months

I.4

Percentage of enterprises using authentication (e.g. digital signature) in relations with customers

I.5

Percentage of individuals not having access to the Internet at home due to privacy or security concerns

I.6

Percentage of individuals/enterprises that have installed security devices on their PCs

J: Broadband penetration J.1

Percentage of enterprises with broadband access

J.2

Percentage of households with broadband access

J.3

Percentage of public administrations with broadband access

J.4

Difference between availability and take-up of high-speed Internet access

J.5

Broken down by type of access

Source: European Commission (2002b)

Table 2. EU science and technology 2002 benchmarking indicators Percentage of GDP spent on R&D Government Budget allocated to R&D Industry-financed R&D as a percentage of industrial output SME share of publicly funded R&D executed by the business sector Volume of venture capital investment in early stages (seed and start-up) Number of researchers per thousand labour force New PhDs per thousand population aged 25–34 Number of scientific publications and number of highly cited papers per capita Number of patents at the EPO per million population Number of patents at the US Patent and Trademark Office per million population World market share of exports of high-tech products Technology balance of payments receipts as a % age of GDP Labour productivity – GDP per hour worked Value added of high-tech and medium high-tech industries Employment in high tech and medium high-tech industries Value added of knowledge intensive services Employment in knowledge intensive services Source: European Commission (2002c)

Table 3. European innovation scoreboard indicators Indicator Human resources S&E graduates (% of 20–29 years age class) Population with tertiary education (% of 25–64 years age class) Participation in life-long learning (% of 25–64 years age class) Employment in medium-high and high-tech manufacturing (% of total workforce) Employment in high-tech services (% of total workforce) Knowledge creation Public R&D expenditures (GERD–BERD) (% of GDP) Business expenditures on R&D (BERD) (% of GDP) EPO high-tech patent applications (per million population) USPTO high-tech patent applications (per million population) EPO patent applications (per million population) USPTO patents granted (per million population) Transmission and application of knowledge SMEs innovating in-house (% of manufacturing SMEs and % of services SMEs) SMEs involved in innovation co-operation (% of manuf. SMEs and % of services SMEs) Innovation expenditures (% of all turnover in manufacturing and % of all turnover in services) Innovation finance, output and markets Share of high-tech venture capital investment Share of early stage venture capital in GDP SMEs sales of “new to market” products (% of all turnover in manufacturing SMEs and % of all turnover in services SMEs) SME sales of “new to the firm but not new to the market” products (% of all turnover in manufacturing SMEs and % of all turnover in services SMEs) Internet access/use ICT expenditures (% of GDP) Share of manufacturing value-added in high-tech sectors Volatility-rates of SMEs (% of manufacturing SMEs and % of services SMEs) Source: European Commission (2003d)

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Taken individually, each of these domains and indicator sets is of some interest. Nevertheless, what may be needed are indicators which capture, at the micro-level, the intersections and interrelationships among the various elements of the new economy – ICT investment, human skills, organisational change and entrepreneurship – rather than their simple summation. To this extent, the main EU indicator sets look insufficient. However, if we are to appraise critically the Lisbon indicators of the knowledge-based economy, what is also necessary is to re-examine the purposes of the Lisbon process as such, and some of the ambiguities which lie at its heart. It is to these ambiguities that we now turn.

1.4. The Lisbon ambiguity The Lisbon process is in part the offspring of the Maastricht process of monetary union and the Luxembourg employment strategy (De la Porte et al., 2001; Room, 2005b). Both involved closer coordination of national economic and employment strategies, with member states reporting their performance within a clear and rule-based system, using quantitative indicators. The Lisbon process extended this approach to a broader range of policy areas, in particular those important for the attainment of a competitive and dynamic but socially inclusive knowledge-based economy. This extension was driven in part by the recognition that these other policy areas were significant for the attainment of economic and employment goals: they would therefore need similar top-down disciplines to encourage their convergence. Benchmarking is intended to provide these disciplines and secure policy coordination. At the same time, however, Lisbon recognised that in order to develop a knowledge-based economy, the member states of the EU would need to pool best practice and accelerate the transfer of technological and organisational know-how from the best performers to the rest of the Community. Benchmarking for purposes of policy learning and innovation is therefore another element of the Lisbon agenda (European Commission, 2003a). Here, however, benchmarking serves not as a tool of collective discipline, more as a means of coordinating intelligence about different national experiences and enriching national debates. This would seem to require more of a bottom–up logic, allowing political and economic actors on the ground to drive the process of comparison and policy learning, depending on their specific needs and interests. This does not necessarily sit easily with the top-down logic of policy coordination (De la Porte et al., 2001, pp. 131–132; for a not dissimilar discussion of alternative models of benchmarking, see Arrowsmith et al., 2004). This ambiguity in the Lisbon process is not mere untidiness in the debates at the Summit: it reflects competing policy agendas at national and EU levels. It is, arguably, because of the failure to address this ambiguity that the Lisbon process has proved to some extent a disappointment, as judged for example by the Kok report (European Commission, 2004) and the debates it set in motion during 2005. At a time when the whole Lisbon process is under review, the challenge is to re-cast it in terms which promote dynamic change and which also enhance political choice and open governance.

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1.4.1. Benchmarking for dynamic change Statistical indicators enable benchmarking, within a process of policy learning and the emulation of best practice. However, policy learning is not to be promoted through benchmarking alone. The Lisbon process is intended to bring together benchmarking, the identification and exchange of best practice, and peer review. The open method of coordination depends on a subtle and creative process involving these different elements. This approach is evident in some of the EU initiatives related to the new knowledge-based economy. Thus, for example, in reference to the European Innovation Scoreboard, the European Commission foresees that as well as the indicators and benchmarks of the scoreboard, there will be a database of comparable information on national policy measures and workshops for sharing best practices in innovation policy: these three instruments are to provide the tools for “intelligent” policy benchmarking. Benchmarking thus needs to be accompanied by “benchlearning”, involving the exchange of narratives, case studies and “stories”, which integrate these indicators into coherent accounts of how change practically occurs (European Commission, 2001d, Part III; 2002a, pp. 5–6; 2003c, para 2.4). These narratives are in part intuitive; they embody a range of tacit knowledge; they recognise complexity and unpredictability; they tap into the specificities of national context and the path dependencies these involve, as well as the strategic choices being made by different actors (Lundvall and Tomlinson, 2002, pp. 203, 207). This is, in turn, consistent with broader recent debates on innovation and policy learning, which offer a more adequate theory of policy learning than the mere comparison of national performances by reference to a series of indicators (Senge, 1990; Bennett, 1991; Dolowitz and March, 1996; Evans and Davies, 1999). It is, moreover, important to recognise that what counts as the “best” performance is not unproblematic: there may be a variety of possible trajectories of new economy development, embodying different trade-offs among its various outcomes and requiring different sorts of policy intervention. Naïve imitation of the supposed best performers may involve an abdication of political choice, as well as a failure to recognise the institutional specificities of the “best” performer and the imitator. It may even be detrimental to high rates of innovation: diversity rather than imitation is likely to be productive of future innovation; and convergence of practices may create greater instability (Lundvall and Tomlinson, 2002). An approach to benchmarking and indicators which stresses alternative futures and political choice may therefore not only avoid the unthinking embrace of a single future, it may also promote the dynamism and innovation on which the new economy depends.

1.4.2. Benchmarking for political choice Benchmarking also raises questions about political choice. Are these comparisons of national performance, by reference to indicators and benchmarks, intended to track the progress of member states towards a single common future, and one which can be defined by reference to common economic and technological requirements? In this

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case the role of political leaders is limited to ensuring as rapid and comfortable an adjustment as possible to that future. Alternatively, are these comparisons intended to provide national policy-makers with an array of different scenarios of potential development, to enrich national political debates about these alternative futures, and to provide guidance as to the policy interventions that might be made, in order to achieve one future rather than another? This suggests a quite different role for political leaders, making real political choices and trade-offs, on the basis of coordinated intelligence about different national experiences (Room, 2005b). EU reports which benchmark the new knowledge-based economy give little sense of alternative patterns of socio-economic development and trade-offs. The language used is that of laggards catching up with leaders, with the implication that those leaders – and in particular, the United States – hold out the future to which the laggards must adjust. Nevertheless, notwithstanding the success of the US in relation to the new economy, it would be wrong to assume that the EU has no alternative but to copy that experience. On the contrary, some of the smaller national economies within the EU demonstrate the scope for a very different type of “new economy”, involving positive policies of social inclusion, which draws “its strength from giving citizens security in [times of ] change, … building social capital [and] sharing the costs of change” (Lundvall and Tomlinson, 2002, p. 227). There would therefore seem to be no reason why, in principle, the selected benchmarking indicators should not be used to reveal the trade-offs among alternative outcomes and, therefore, a variety of possible futures. Do higher rates of competitiveness through technological innovation have to be traded against higher risks of social exclusion? Do high rates of social inclusion in the new economy presuppose high rates of investment in human capital? This type of exercise is consistent with studies such as that by Ferrera et al. (2000), undertaken in the lead-up to Lisbon, analysing the policy trade-offs of precisely this sort that have been made by different EU member states. It is also, of course, common in comparative policy studies, where outcome indicators are examined by reference to a variety of antecedent factors (see, for example, Wilensky, 1975). At present, however, this seems to be somewhat peripheral to the Lisbon process.

1.5. Conclusion When the Lisbon process was launched in 2000, it envisaged a 10-year programme of dynamic reform. With the half-way point approaching, the EU at the end of 2004 began a review of the process with the Kok Report (European Commission, 2004). The Kok report endorses and reaffirms the broad thrust of the Lisbon strategy, including the OMC, but concludes that by and large the strategy has had only limited success. One of the main reasons, according to Kok, is that the Lisbon strategy has addressed too many policy areas, thereby lacking coherence and a clear sense of priorities. Much of the subsequent debate has therefore focussed on bringing the different

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OMC processes together, streamlining them into a single process of national reporting (European Commission, 2005b). In regards more specifically to benchmarking, the report calls for a smaller number of simple indicators, more tightly applied, and with greater political costs – in terms of “naming and shaming” – imposed on poorly performing countries. The imposition of these political costs is intended to strengthen the role of benchmarking as far as policy convergence and performance improvement are concerned; it is also intended to increase the accountability of governments to the collectively agreed EU process. In light of the foregoing discussion, it is not at all clear that the Kok recommendations are well judged. A few simple indicators, imposed top-down as a set of goals to which all are committed, may be appropriate in the monetary field, or for the removal of the remaining barriers to the single market (European Commission, 2004, p. 24). They are less applicable elsewhere, if policy learning is the goal. Intelligent benchmarking is more likely to require bottom-up benchmarking, political choice by local and national actors among alternative socio-economic trajectories, and the selection by those actors of rather more sophisticated indicators of dynamic transformation.

References Arrowsmith, J.; Sissons, K. and Marginson, P. (2004), “What Can ‘Benchmarking’ Offer the Open Method of Coordination?”, Journal of European Public Policy, 11(2), pp. 311–328. Barbier, C.; De la Porte, C. and Pochet, P. (2001), “Digest: Employment and Social Policy”, Journal of European Social Policy, 11(1), pp. 67–77. Bennett, C. (1991), “How States Utilise Foreign Evidence”, Journal of Public Policy, 33(4), pp. 31–54. Borras, S. and Jacobsson, K. (2004), “The Open Method of Coordination and New Governance Patterns in the EU”, Journal of European Public Policy, 11(2), pp. 185–208. Cecchini, P. (1988), The European Challenge: 1992: The Benefits of a Single Market. Wildwood House, Aldershot. De la Porte, C. and Pochet, P. (eds.) (2002), Building Social Europe through the Open Method of Coordination. Presses Interuniversitaires Europeennes, Brussels. De la Porte, C.; Pochet, P. and Room, G. (2001), “Social Benchmarking, PolicyMaking and New Governance in the EU”, Journal of European Social Policy, 11(4), pp. 291–307. Dolowitz, D. and March, D. (1996), “Who Learns What from Whom: A Review of the Policy Transfer Literature”, Political Studies, 44, pp. 343–357. European Commission (2000), “Structural Performance Indicators”, ECOFIN/330/00. Brussels. European Commission (2001a), “Communication on an Open Method of Coordination for the Community Immigration Policy”, COM(2001)387 Final, 11 July 2001, Brussels.

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European Commission (2001b), “Communication: eEurope: Impact and Priorities”, COM(2001)140, Brussels. European Commission (2001c), “Supporting National Strategies for Safe and Sustainable Pensions through an Integrated Approach”, COM(2001)362, 3 July 2001, Brussels. European Commission (2001d), “Work Programme for the Follow-Up of the Report on the Concrete Objectives of Education and Training Systems”, COM(2001)501 Final, Brussels. European Commission (2002a), “Commission Staff Working Paper: 2002 European Innovation Scoreboard”, SEC(2002)1349, Brussels. European Commission (2002b), “Communication: eEurope 2005: Benchmarking Indicators”, COM(2002)655 Final, Brussels. European Commission (2002c), “Towards a European Research Area: Science, Technology and Innovation: Key Figures 2002”, Brussels. European Commission (2003a), “Communication: Innovation Policy: Updating the Union’s approach in the context of the Lisbon Strategy”, COM(2003)112 Final, Brussels. European Commission (2003b), “Communication: Structural Indicators”, COM(2003)585 Final, Brussels. European Commission (2003c), “eLearning: Designing Tomorrow’s Education: A Mid-Term Report”, SEC(2003)905, Brussels. European Commission (2003d), “European Innovation Scoreboard 2003: Technical Paper No 1: Indicators and Definitions”, Brussels: DG Enterprise. European Commission (2004), “Facing the Challenge: The Lisbon Strategy for Growth and Employment”: Report of the High Level Group chaired by Wim Kok, Brussels. European Commission (2005a), “Commission Work Programme for 2005”, COM(2005)15 final, 26 January 2005, Brussels. European Commission (2005b), “Communication to the Spring European Council: Working Together for Growth and Jobs: A New Start for the Lisbon Strategy”, COM(2005)24, 2 February 2005, Brussels. European Commission (2005c), “Strategic Objectives 2005-2009: Europe 2010: A Partnership for European Renewal: Prosperity, Solidarity and Security”, COM(2005) 12 Final, 26 January 2005, Brussels. European Council (2000a), “eEurope Benchmarking Indicators, 20 November 2000”, Brussels. European Council (2000b), “Lisbon European Council, Presidency Conclusions, 23–24 March 2000”, Lisbon: European Council. European Council and European Commission, (2000). Action Plan, eEurope: An Information Society for All, 14 June 2000, Brussels. European Round Table of Industrialists (2001), “Actions for Competitiveness through the Knowledge Economy of Europe, Report to Stockholm European Council”, European Round Table of Industrialists, Brussels.

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Evans, M. and Davies, J. (1999), “Understanding Policy Transfer: A Multi-Level, Multi-Disciplinary Perspective”, Public Administration, 77(2), pp. 361–383. Ferrera, M.; Hemerick, A. and Rhodes, M. (2000), “The Future of the European Welfare States, Report for the Portuguese Presidency of the European Union”, European Council, Lisbon. Lebessis, N. and Paterson, J. (2001), “Developing New Modes of Governance”, in O. de Schutter, N. Lebessis and J. Paterson, (eds.), Governance in the European Union, Luxembourg. Lundvall, B.-A. and Tomlinson, M. (2002), “International Benchmarking as a Policy Learning Tool”, in M. J. Rodrigues, (ed.), The New Knowledge Economy in Europe. Edward Elgar, Cheltenham. Nelson, R. (2000), The Sources of Economic Growth. Harvard University Press, Cambridge, Mass. OECD (2001), “The New Economy: Beyond the Hype”, Paris. OECD (2002), “Measuring the Information Economy”, Paris. OECD (2003), “2003 Scoreboard of Science, Technology and Industry Indicators”, Paris. Room, G. (2005a), The European Challenge: Innovation, Policy Learning and Social Cohesion in the New Knowledge Economy. The Policy Press, Bristol. Room, G. (2005b), “Policy Benchmarking in the European Union: Indicators and Ambiguities”. Policy Studies, 26(2), pp. 117–32. Schaaper, M. (2004), “An Emerging Knowledge-Based Economy in China?”, STI Working Paper 2004/4, Paris: OECD. Senge, P. (1990), The Fifth Discipline. Random Century, London. Wilensky, H. (1975), The Welfare State and Equality. University of California Press, Berkeley.

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Measuring the New Economy Edited by Teun Wolters © 2007 Elsevier B.V. All rights reserved.

CHAPTER 2

Key Indicators for the New Economy Andrea de Panizza (Istat, Italian NSI) Mauro Visaggio (University of Perugia)1

2.1. Introduction This chapter aims to give guidance through the growing wealth of indicators available for measuring the New Economy (NE), with special attention to the indicators’ analytical strength. The study combines statistical and economic perspectives. It examines the conceptual and empirical scope of the most popular and promising indicators. It discusses their content, mutual interactions and relationships with the different aspects of performance. The data considered refer to the EU-15 countries, the US and Japan, over the period 1996–2004. This chapter consists of an introduction and three sections. Section 2.2 discusses and defines the problem area. As happens with most complex non-technical phenomena, the essence and boundaries of the NE remain vague, notwithstanding the great number of theoretical and empirical publications on the subject. We apply a working definition of the NE based on stylised facts and issues discussed in the literature. This has served to identify “thematic domains” or conceptual variables which are to be captured by indicators. Section 2.3.1 presents the criteria for selecting and assessing indicators, resulting into a short-list of relevant NE indicators. The two subsequent sections present an empirical analysis for the period 1996–2004, as well as for the periods 1996–2000 and 2001–2004, which correspond to the up- and down-going phases of the business cycle. Section 2.3.2 examines short-listed indicators with respect to their information content and (time/spatial) coverage. The number of indicators to work with could also be reduced without a loss of essential information with the help of bivariate, non-parametric and partial correlation analysis. Section 2.3.3 presents a complementary assessment carried out by means of an iterated exercise of factor analysis. This gives rise to a further creaming off in the number of indicators. Moreover, by means

1 Their

e-mail addresses are respectively: [email protected]; [email protected]

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Andrea de Panizza and Mauro Visaggio

of a cluster analysis the economies involved could be grouped. Section 2.4 summarises the main findings and makes suggestions for further research.

2.2. Which are the phenomena that we need to track? During the last 15 years all advanced economies underwent fast and dramatic changes which affected their competitive positions. A key aspect of this change is the globalisation of production, finance and trade, which was supported by political decisions,2 and reinforced by advances in Information and Communication Technology (ICT) and transport technologies. High technology products – notably ICT – have in return acquired a growing importance in world demand (Figure 1). To track how the NE impacts a national economy, it is necessary to monitor the degree of specialisation in high technology and knowledge-intensive products (especially in ICT goods and services), in connection with Foreign Direct

Figure 1. World: Foreign Direct Investments Stocks and International trade flows of goods and services (average of imports and exports) as a percentage of GDP; ICT Product as a percentage of goods exports. Period 1980–2004 FDI Stock (% of GDP) 20

30

ICT Products (% of Goods exports) Imp-Exp avg. (% GDP, rhs)

15

25

10

20

5 1980

1982

1984

1986

1988

1990

1992

1994

1996

1998

2000

2002

15 2004

Source: de Panizza (2005), based on IMF World Economic Outlook Database and UNCTAD Yearbook of Trade Statistics data; International trade = imports and exports average

2 Such

as liberalisation policies in advanced economies, or the entrance of China and of other previously centrally planned and emerging economies in the “trading league”.

Key Indicators for the New Economy

31

Investments (FDI). Indeed, these variables have changed the international division of labour. Some countries have been advantaged by this development while others suffer, at least in the short and medium term. As a matter of fact, (i) the US economy has experienced a recovery in productivity growth after a 20-year slowdown, whereas productivity growth in the EU-15 has continued to decrease, and (ii) the historical process of convergence in per capita GDP levels between the USA and the EU has come to an end. A new pattern characterised by divergence has emerged, with the USA taking the lead (Figure 2a). Similar differences are there with respect to the production, consumption and usage of ICT goods and services throughout the economy, considering the dynamics of ICT shares in business sector value added and investment (Figure 2b). In the economic literature, these two phenomena (as reflected by Figure 2) have often been correlated. ICT has been considered to have a pervasive effect on the way firms are organised and do business. Theoretical and empirical analyses on the differences in productivity growth between the USA and the EU show that GDP growth in innovative countries – according to our preliminary assumption, both ICT producers and users – was higher than in other countries. The former were able to exploit new technologies in terms of higher productivity outside the ICT production sector itself.3 Besides, it appeared that countries that primarily are ICT users without much own ICT production (“imitators”) find it often hard to translate the usage of new technologies into superior macroeconomic performance. Indeed, such differences in performance are due not only to a delay in ICT adoption,4 but also to difficulties in embedding new technologies into the EU’s economies (absorption delay view). Moreover, there is a wide range of rather intangible factors which are deemed relevant

3 Growth

accounting divides labour productivity into the contribution of different inputs (Gordon 2002; Oliner and Sichel, 2002; Jorgenson et al., 2002a,b, 2003) and by different industries (Stiroh, 2002; van Ark et al., 2003; van Ark, 2005). This exercise allows highlighting the crucial role of ICT spread in explaining the speed up of American productivity by ICT capital deepening, and by the increase in Total Factor Productivity (TFP) both in the ICT producing sector and, significantly, in the rest of the economy. Hence, the US performance success appears to rest on its being at the same time a large ICT producer and user. 4 The ICT adoption delay view maintains that the EU started to invest in ICT later than the US so that, according to the American experience, it has been proved that the adoption of new technologies in the ICT-using industries required a long period of gestation before resulting in higher productivity growth rate (van Ark et al., 2002, 2003; Daveri, 2003, 2004; van Ark, 2005). The latter view rests on the role of ICT as a new General Purpose Technology (GPT), like electricity and steam engine before, because ICT developments have spread their effects throughout the economy, as testified by the increase in TFP in the ICT using sector (David, 1990, 1991; Bresnahan and Trajtenberg, 1995). In this line of thought, some authors pointed out that the initial impact of GPTs on productivity growth is typically restrained by factors such as substitution costs between old and new technologies (Greenwood and Yorukoglu, 1996), poor quality of human capital in managing new technologies (Helpman and Rangel, 1999), a low organizational capital (Brynjolfsson, 2003) and so on, hence highlighting the role of complementary factors and at the same time justifying the delay with which investment translates into higher productivity growth.

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Andrea de Panizza and Mauro Visaggio

Figure 2. Long-run economic and technological trends in the EU15 and the US: (a) Labour productivity (YoY % change) and US–EU gap in per capita PPA GDP growth (% points), (b) Shares of the ICT sector in value added and investment (%) 6.0 5.0

US prod.

EU prod.

Trend US prod.

Trend EU prod.

Trend US-EU per Cap.Gdp growth difference (Poly)

4.0 3.0 2.0 1.0

12 11 10

V.A. EU

V.A. USA

Inv. EU (rhs)

Inv. USA (rhs)

2004

2001

1998

1995

1992

1989

1986

1983

1980

1977

1974

−1.0

1971

0.0

40 35 30 25

9 20 8 15 7 6

10 5

5 0 1980 1983 1986 1989 1992 1995 1998 2001 2004 Source: Authors’ calculations on OECD NA, STAN and productivity databases

Key Indicators for the New Economy

33

performance.5

in explaining economic The US is in a better position also with respect to these latter factors, which thus attracted the attention of EU policy makers as areas for improvement. They involve a range of issues such as knowledge, including human capital,6 organisational capital,7 and the functioning of goods, labour, and financial markets and other institutions.8 In terms of indicators, we can identify at least six areas relevant for analyzing the NE: (i) performance; (ii) technological specialisation (ICT and high-tech); (iii) ICT adoption; (iv) knowledge production; (v) human capital; (vi) quality of Institutions, including regulation, market functioning and other factors reflecting the overall attractiveness of a country for investment.

2.3. Tracking the NE: Available indicators and their features 2.3.1. The short-list of indicators, and selection and assessment criteria As outlined above, the definition of NE involves a large set of variables, some of them representing phenomena which have gained importance in the recent past, raising new statistical measurement issues, which are also discussed in other chapters of this book.9 Correspondingly, over the last ten years the production of NE-related indicators has rapidly increased, boosted by the fast change which the phenomena discussed here are subject to: while new indicators appear, others become quickly obsolete. In this chapter we could not discuss the features of the whole range of available indicators, but had to start already from a short-list.10 To this end, the thematic

5 The

ICT absorbing delay view – maintained for instance recently by Crafts (2003) – is rooted in the seminal work of Abramovitz (1986), and in Abramovitz and David (1996). 6 Recent endogenous growth models point to knowledge, human capital and innovation as growth drivers. The recognition of knowledge as a part of total capital not only provides a better insight into the role played by technological progress but assigns to it a fundamental role in the determination of long-run output growth rates. Similarly, human capital allows explaining growth differentials between countries. In fact, while knowledge is, to some extent, a public good that moves freely, human capital does not. As a consequence, since human and physical capital are complementary inputs, a scarce quality of human capital could prevent the conversion of the use of new technologies into productivity gains and could even be a cause of poor growth performance. 7 The role of organisational capital and investment in it as a necessary complement to ICT is stressed by Bresnahan and Greenstein (1995) and Brynjolfsson (2003). 8 Some authors have stressed the role of market rigidities, pointing to failure to (timely) reform goods, labour and financial markets (e.g. Nicoletti et al., 2000) with a view to successfully addressing the challenges of the NE (Phelps 2003; Blanchard, 2004; Gordon, 2004a,b). 9 For instance, given the growing importance of ICT products in advanced economies, national accountants have to decide on new accounting rules – such as hedonic pricing and chained indices – in order to deal with the special effects of ICT goods, in particular rapid price reductions, quick depreciation of investment and fast quality change. The NE impacts also on other measurement aspects: GDP, for instance, is increasingly influenced by the options of multinational corporations regarding favourable locations and tax regimes. 10 For a wider selection see de Panizza et al. (2004a,b).

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Andrea de Panizza and Mauro Visaggio

Table 1. NE thematic domains and related indicators

PERFORMANCE

TECHNOLOGY (IC)

HT/ICT production

– Value added of HT industry & ICT sector (Manufacturing & services) – % of Total Business Sector(b) – Exports of High Tech & ICT products (% of total) & normalised Balance(d) – Employment in High Tech & ICT industries (Manufacturing & services)(a,b)

Economic perspective

– ICT Expenditures (= Consumption + Investment)(a,b) – ICT Investment & ICT capital services % of total(a) – Capital service ICT & non ICT % of total(b) – Employment in ICT related occupations % of total (narrow & broad)(a,b)

Technological perspective

– Personal computers (Households & Enterprises)(a,e) – Internet Connections (Households & Enterprises → +intranet & extranet) & Broadband; Hosts & Websites; E-Business (% firms & % of turnover); E-Government (n. services & usage)(a,b)

ICT usage

Production and usage KNOWLEDGE

– GDP growth(a) – Labour productivity (Overall and disentangled by sector: ICT production & using & non-using industries)(a,b,c) – Employment growth(a)

– Research and Development Expenditure % of GDP (& by source of funds); Knowledge Expenditure % of GDP (R&D + SW + Higher Education) Researchers as a % of Labour Force & Working Age Population(a,b) – Human Resources in Science and Technology(b) – Employment in High Tech Manufacturing & Knowledge Intensive Services(a) – Patents (EPO/USPTO/JPO & TRIADIC) per million inhabitants & Labour Force(a) – Innovation innovators %; expenditure %; results (products % turnover), by type (product/process/ organisational, marketing. . .)(a)

Human capital

– – – –

Expenditure (Public & Total)(b) % of population with at least secondary education(a) Tertiary graduates in S&T % of population 20–29(a) Educational attainment (Years of Schooling)(b)

Key Indicators for the New Economy

35

INSTITUTIONS AND ENVIRONMENT

Table 1. continued – “Doing Business” indices of regulation: Hiring and firing; Getting Credit; Starting a Business; Closing a Business; Enforcing Contracts & COMPOSITE(f ) – Indices of regulation for professions(g) Regulation & governance

Finance-related

– Governance indicators: Voice and Accountability; Political Stability and Absence of Violence; Government Effectiveness; Regulatory Quality; Rule of Law; Control of Corruption (COMPOSITE)(f ) – FDI flows/stocks in % of I & GDP(a,d) – Market Capitalisation in % of GDP(a) – Business Angels (number per million inh. & Financial flows on GDP)(h) – Venture capital investments % GDP (by investment stage)(a)

Main Sources: (a) Eurostat, (b) OECD, (c) Groningen Growth and Development Centre (GGDC), (d) United Nations Conference on Trade and Development (UNCTAD), (e) International Telecommunications Union (ITU), (f ) World Bank, (g) European Commission – DG Competition, (h) European Commission – DG Enterprise.

domains identified in the previous section – which as such are more stable over time than individual indicators – served. In the following, we shall discuss the selected indicators with respect to their conceptual resonance, variability, stability, correlation with other indicators of the same domain and in other areas, and data availability. These criteria, and the practical solutions adopted, can be summarised as follows: – Conceptual resonance: The extent to which an indicator captures the specifics of the phenomenon intended to be measured.11 – Variability: As regards two indicators addressing the same phenomenon, preference should be given to the one which is superior in reflecting differences between countries.12 In general, indicators will become obsolete as country values are converging. In this case they need to be replaced by other indicators. However, this poses the crucial issue of how to link old and new ones.

11 This criterion has been proposed by the University of Bath and used extensively with respect to the NE in the framework of the NESIS project (Room et al., 2004). 12 For instance, country values for educational attainment (any indicator – output) show a wider variability than public expenditure on education (input) in percentage of GDP, and ought to be preferred as a key indicator. This obviously does not mean that the former is perfect, or that the latter is useless.

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Stability: Indicators prone to erratic year-by-year fluctuations and/or heavily influenced by the business cycle should be dropped in favour of more stable ones. As this option is not always available, in our analysis we considered average values for the whole period 1996–2004 and, separately, for the two sub-periods 1996–2000 and 2001–2004. This also proved helpful when annual figures for a particular indicator were very limited in supply. – Correlation: High correlation between two indicators belonging to the same domain (i.e. targeting the same phenomena) signals their proximity. One of the two might be preferred because of being correlated with other sets of NE indicators or general performance indicators. These relationships have to be considered with caution, and case by case. In this respect, a general warning regarding the actual meaning of the whole exercise of correlation or factor analysis is deployed in the next section. The countries in the panel involved have quite different structural features, showing a multiplicity of performance patterns, which complicates their interpretation. Moreover, while prima facie a variable that correlates well with various others in different domains seems fit to represent more than one dimension, a variable that does not seem to correlate with any other variable may represent a relevant NE dimension which is not addressed by any other variable. In addition, simple correlation analysis alone does not reveal much; it can be a highly misleading tool. In order to overcome some of its major shortcomings, (a) non-parametric correlation was also checked so to explore non-linear relationships, (b) partial correlation with per capita GDP as control variable was performed to relax the questionable assumption saying that key structural features are independent of income levels, (c) the stability of relationships was tested by building distinct correlation matrices for the whole period under examination and the two periods 1996–2000 and 2001–2004, and (d) the issue of weighting was not addressed, but Luxembourg and Ireland were excluded as being both outliers and very small, while all other observations were given unit weight. – Data availability: The attractiveness of indicators heavily relies on adequate time series and a coverage including all EU countries and at least also the US and Japan. This may create difficulties for brand new indicators measuring emerging phenomena, as well as indicators which rest on data which are not sufficiently harmonised across the Atlantic. 2.3.2. Conceptual and correlation analysis Performance The most obvious indicator of performance is the GDP growth rate. Furthermore, its decomposition into the factors contributing to economic growth is equally relevant,

Indeed, (especially overall) expenditure has anyway a relevance of its own, and its juxtaposition with attainment is useful for the assessment of efficiency. Furthermore, educational attainment indicators based on certifications (Eurostat) or mean years of schooling (OECD) suffer from the possible heterogeneity of their contents, as witnessed by the wide variability of results in the OECD PISA indicators.

Key Indicators for the New Economy

37

particularly within the framework of the NE. As outlined in the previous section, technological and organisational developments are supposed to have an impact on performance, particularly through productivity increases. Productivity, for its part, in the long run is the key source of growth in per capita income. Hence, huge efforts have been made in recent years to improve the availability of productivity-related data sets, especially with a view to disentangling ICT contributions.13 In the following we shall refer to changes in labour productivity only (defined as the percentage change of GDP at constant prices per worked hours). However, it is useful noting that, as Figure 3 shows, the relationship between GDP and productivity is not straightforward. Indeed, there is a clear difference between the periods 1996–2000 and 2001–2004 in terms of the overall strength and steepness of the relationship between productivity change and GDP growth. Moreover, during the whole 1996–2004 period some economies grew only because of an increase in labour input (notably, Spain and Italy, both with initially a very low participation rate), while others (notably, Japan and to a lesser extent Germany) combined slow growth with significant productivity increases. Technology production This sub-domain aims at capturing the extent and characteristics of technological specialisation. As stated before, economic survival of advanced countries relies heavily on their technological standing. Available indicators address the dimensions of employment, value added and (net) exports with reference specifically to ICT manufacturing and services and more generally to high-tech manufacturing and Knowledge Intensive Services (HTM-KIS). On conceptual grounds, being interested in having the widest coverage of the technological dimension, we ought to consider the high-tech sectors as a whole (i.e. including both manufacturing and services).14 However, attempts in this direction did not yet fully succeed, either because time and country coverage were insufficient or definitions are too broad (e.g. Eurostat statistics on high-tech manufacturing & knowledge-intensive services employment) or too strict (OECD), and in all cases there is not yet full agreement on the service component to be included. Anyway, we have to use in our empirical analysis what is available: high-tech exports and ICT value added. There is not much to choose between these two indicators; each of them has its drawbacks. High-tech exports have a relatively wide technological spectrum, but with a limited coverage (only goods). ICT added

13 It

is worth referring to the EU KLEMS (Capital–Labour–Energy–Materials) project, aimed at providing measures of economic growth, productivity, employment creation, capital formation and technological change at the industry level for all EU member states from 1970 onwards (see: http://www.euklems.net/). 14 In the previous section, ICTs are just a component of hi-tech products, just as international trade is only a fraction of value added, and the share of manufacturing industries in the economy is much smaller than that of services. On the other hand, there are good reasons to focus on ICTs and, specifically, on manufacturing. Indeed, since the early nineties the share of ICTs in international goods trade more than doubled (reaching nearly 20% of the total), and ICT manufacturing industries caused more than half of US productivity growth.

3.5 3.5

1996-00

1996-2004

pt

3.0

gr

2.5

gr

3.0

at pt

1.5

3.5

be dk

2.0

nl

1.5

it

Gdp % Change

3.0

5.0 gr

se ukus jp fr dk de atbe

fi es

1.0

y = 0.40x + 0.76 R2 = 0.21 2.0

4.0

2001-04

2.5 es

0.0 1.0

nl es

3.0

1.0

0.5

it

0.5 y = 0.22x +1.23 R2 =0.06 0.0 1.0 2.0 3.0

fr

jp de

Productivity % Change

1.0

fi

uk

dk

4.0

0.5

nl

0.0

pt it

−0.5 1.0

y = 0.68x + 0.38 R2 = 0.58 2.0

3.0

Source: OECD productivity database (July 2005) – data centred on Eu13 values (excludes Austria and Luxembourg)

4.0

5.0

Andrea de Panizza and Mauro Visaggio

2.0

us

fi

us

be

1.5 se

se fr uk at

de

2.0 jp 2.5

38

Figure 3. GDP and productivity growth in EU-15 Countries, the US and Japan; 1996–2004 period

Key Indicators for the New Economy

39

value is superior in terms of correlations with indicators from other domains. Besides the expected linkages with various ICT indicators, it appears also to be significantly correlated indicators of human capital and knowledge production, market capitalisation, productivity and overall GDP growth. This holds also, even in a reinforced way, when partial correlation analysis is performed, controlling for per capita income level. ICT usage The spread of ICT in the economy is regarded as the key to productivity improvements as well as to a wider range of reasonably priced goods and services (including public services and knowledge) available to end users. Usually, the use of ICT by households and enterprises are measured by means of separate surveys. The latter distinguish between different technological features, considering the two components of ICT, information technology (IT) and communication technology (CT). Finally, their measurement can be done either from an economic viewpoint (i.e. in terms of monetary expenditure) or straight away in terms of the diffusion of certain technologies. (a) The economic perspective Available indicators refer either to the total use of ICT by firms and consumers (IT/CT expenditure) or to their productive usage (IT/ICT investment). Expenditure indicators could be considered conceptually superior as they take into account the technology infrastructure of the whole economy. However, for this indicator, the available time series are limited. More importantly, we have observed that this expenditure-based indicator is misleading because countries spending heavily on ITs tend to have a lower CT expenditure and vice versa (essentially due to the consumption component). As for investment, longer time series are available, and here the two components show a similar pattern, so that in this case there is no reason for isolating the IT component. Correlation analysis shows that IT expenditure and ICT investment positively relate to performance and other blocks (while the opposite holds for CT expenditure). The relationship with most variables appears to be non-linear, and thus correlation coefficients are higher when non-parametric techniques are used. However, both indicators strongly correlate with GDP levels, especially in the first (1996–2000) period, in which the spread of ICTs was lower. Controlling for this variable, the relationship with performance weakens for both indicators, while overall, that of IT expenditure with other blocks is slightly stronger. Mainly because of data availability, in the following we shall refer to ICT investment. (b) The technological perspective PCs, internet usage, infrastructure (hosts) and connections Available indicators address IT both in terms of PCs and internet readiness and the intensity of their usage – overall, as reflected by number of hosts or websites, and with reference to areas such as e-business and e-government. Conceptually, tracking the effective usage of a technology (and its impact, for instance on firm organisation)

40

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would be far more interesting than just record availability (readiness), the more so as the latter is approaching its saturation point. Indeed, basic readiness indicators such as PCs and then internet access seem to become obsolete quickly. These should be replaced by more sophisticated information (type of access and, notably, broadband), and/or complemented by a broader target population (e.g. so as to also include firms with less than 10 persons engaged).15 For the time being, there are good reasons to work with proxies. Correlation analysis reveals that even for the limited period 2001–2004, e-business indicators strongly correlate with GDP levels while e-government indicators do not stand out highlighting essential differences. Basic indicators (i.e. PCs and internet access per 100 inhabitants) correlate more significantly with indicators in other domains notably, in the areas of knowledge and human capital. Among the two, data from the PCs are available for a longer time series than the internet, and so is to be preferred for the time being. Knowledge Knowledge indicators address distinct aspects of the process of knowledge production, and its transfer and usage. They reflect diverse views on knowledge management and innovation at large.16 The knowledge indicators considered here belong to different families – R&D and Patents, Innovation, Human capital – which correspond to the underlying data sources. 15 In

the case of firms, intranet is also to be retained as a key indicator for the degree of sophistication of its infrastructure and usage. However, common sense and empirical analyses show its strong correlation to firm size and sector, suggesting the development of a corrected indicator to net for these effects. 16 Following the taxonomy used by the OECD (2003), Working Party on S&T indicators for knowledge management, we can distinguish three generations of indicators with respect to the innovation process. Initially, assuming a linear relationship between science/technology and growth, statistical research focused on the visible inputs to innovation – such as expenditure on, and human resources devoted to, R&D – as well as the resulting patents and publications. Indicators of this group, already presented in the seminal work of Griliches (1957), and further developed by Mansfield (1968), considered knowledge-related activities as a driver of competitiveness which should be codified at the international level, although in some cases harmonisation of more detailed information occurred very recently or still in the pipeline. Indeed, the first edition of the OECD Frascati Manual on R&D dates back to 1962, and some information was already available from administrative sources (patents; education) or could easily be captured adding to established surveys (labour force). Indicators of the second and third generation are theoretically related to the interactive model of Innovation (as opposed to the traditional linear model). This was outlined by Kline and Rosenberg (1986) as a “chain-linked model” for the relationship between research, invention, innovation and production, introducing small and larger feedback loops, within each part of the chain and to preceding parts respectively. A broader approach, including organisation-oriented adaptation cycles (i.e. not only for technology) was developed by Leonard and Barton (1988). The presence of a variety of innovation areas and paths is recognised already in a “second generation” framework literature. At the EU level, the current wide approach to innovation is reflected in the Green Paper on Innovation (Eurostat, 1995), where innovation is defined as “the renewal and enlargement of the range of products and services and the associated markets; the establishment of new methods of production, supply and distribution; the introduction of changes in management, work organisation, and the working conditions and skills of the workforce”.

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The first set focuses on the investment in knowledge (in terms of financial and human resources) by private and public agents (R&D expenditure; knowledge expenditure; researchers, human resources in science and technology) and the output produced by knowledge as primary input (Patents).17 R&D activities and patenting are key factors in international competition and a crucial aspect of the NE, considering the steady rise of the demand for hi-tech and knowledge-intensive goods and services. However, as we shall see, they do not directly (or necessarily) result into a better economic performance – as not all research needs patenting, and not all patenting is fruitful – and do not fully capture the processes of innovation. Indicators on innovation, the second set we consider, are an attempt to capture the usage of knowledge from a broad perspective (i.e. besides innovation in new products and productive processes, they also involve new approaches in areas such as organisation and marketing). The main data source here is the EU’s Community Innovation Survey (CIS), which is undertaken every fourth year (plus a new “CIS light” every second year on a voluntary basis), providing information on innovators (innovative enterprises: percentage by size and sector), innovation effort (expenditure on innovation as a percentage of turnover) and results (innovative products as a percentage of turnover, etc.), overall and by type of innovation. Finally, knowledge can be looked at as a public good, by means of indicators capturing human capital from different perspectives: the extent of investment (educational expenditure); educational achievements, both high-level skills and basic skills (science and engineering graduates, tertiary education, educational attainment level of young people, average number of years of schooling) and finally the usage of highly educated human resources (Human Resource in Science and Technology). The indicators in the area of both R&D and patenting strongly correlate with GDP levels and with each other, while none of them presents any significant straightforward relation with GDP growth; although both appear to be mildly correlated with productivity growth, also after controlling for the level of GDP per capita (Figure 4). Concerning R&D, the key indicator is overall expenditure on R&D relative to GDP, as detailed information by source of funds is heavily conditioned by the dominant business component. Concerning patents, there is a strong regional bias. To account for this and identify the high-value segment, the OECD has also computed “triadic” patents statistics. In our empirical assessment we considered the latter and an average of US and EU PTO data. Within the correlation framework, these two did not behave

17 Indicators

on both phenomena are built as ratios to population variables – per inhabitant, employed population, etc. (in the case of R&D expenditure also to GDP) – or explore particular aspects, such as sectors of performance or funding (R&D). European patent data refer to applications filed, and do not include national patents; US to patents granted. Patenting systems & procedures are different (although always long), and the same is for patenting behaviours across countries. For instance, “the same” invention might require a single file in one country (eventually protecting also other associated inventions) and multiple files in other countries, or may even not be admitted to patenting, or finally might not be patented due to different (opportunity) costs.

R2 = 0.6965

R2 = 0.7888

R2 = 0.8169

R2 = 0.8646

200

R2 = 0.6871

100

R&D Exp % GDP

300 Patents/ Inhabitants

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300

200 100

50 70 90 110 130 150 Per Capita GDP (PPS; EU25=100) EPO USPTO Triadic Pow (EPO) Pow (USPTO) Pow (Triadic)

4.0

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R2 = 0.0227

2.5 2.0

R2 = 0.0906

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0

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2 3 4 R&D (GERD) Gdp Productivity Pow (GDP) Poli. (Productivity)

1 2 3 4 R&D expenditure % GDP (GERD) EPO Triadic Pow (USPTO)

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0

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0 50 100 150 200 Per Capita GDP (PPS; EU25=100) GERD Pow BERD

BERD Pow (EPO)

4.0 R2 = 0.0875

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3

R2 = 0.6403

USPTO Pow (EPO) Linear (Triadic)

0

20

40 60 80 100 120 Patents (Triadic)

Gdp Pow (GDP)

Productivity Log. (Productivity)

GDP & Productivity Growth

0

GDP & Productivity Growth

GDP & Productivity Growth

0

3.5 R2 = 0.571

R2 = 0.1083

3.5 3.0 2.5

R2 = 0.0566

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0

100 200 300 Patents (EPO-USPTO)

Gdp Pow (GDP)

400

Productivity Log. (Productivity)

Andrea de Panizza and Mauro Visaggio

R2 = 0.9258

4.0 3.5 3.0 2.5 2.0 1.5 1.0 0.5 0.0

42

Figure 4. Indicators of Patents/Population (EPO, USPTO, Triadic), GDP per capita (left) and R&D expenditure (GERD, right); EU15 countries, US and Japan – averages 1996–2003

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remarkably differently, but R&D reflects the pattern of both while having a slightly better overall behaviour with respect to other indicators.18 Innovation indicators have been tested within the European Innovation Scoreboard framework (EIS, 2003), finding a very weak relationship with performance. As regards human capital, there appears to be a strong correlation between expenditure indicators and GDP levels (less when non-linearity is assumed) as well as with IT production and usage indicators. Output indicators19 and in particular science & engineering graduates follow less GDP levels (which makes them preferable) and – as expected – are also more strictly associated with developments in knowledge and IT-related indicators. Institutional and other “context” factors (a) Financial structure and “attractiveness” The economic literature has since long highlighted the pivotal role of financial markets and foreign investments in generating economic growth. Their strategic relevance – together with NE-inspired international capital movements – led to an increase in the production of statistics and indicators in these areas. We have put them together even though they are conceptually distinct. All indicators as presented in Table 1 are useful to some extent, and complementary: FDI flows and stocks indicate how attractive it is to invest in a particular economy. The table relates them to different aspects, from its location (being a large market or a bridge towards it), to human capital and to regulatory conditions and taxation. The number of business angels indicates the possibilities for translating good ideas into concrete investment, while venture capital shows the financing of high-risks projects. Market capitalisation reflects in a sense a country’s financial structure (the market value of company shares). All financial indicators are dependent on the business cycle, which makes them structurally unstable. Volatility can be reinforced by different factors; for instance FDI time series may on the short run be shaken up by a single exceptional event. We addressed this issue by considering period averages. However, in certain cases this may be not be sufficient. Indeed, in the case of venture capital (be it in total or split into “initial” and “substitution”, as in the Eurostat figures), its importance relative to GDP is so small that country rankings can be changed by the slightest displacement of air. Moreover, as for business angels, data availability is too limited, so that we excluded both from further correlation analysis. This brings to light that FDIs and market capitalisation are significantly cross-related, while both scarcely correlate with GDP levels. Only market capitalisation has a significantly positive relationship with

18 It

is worth noting that both R&D and Patents reflect the productive structure of individual economies and that “normalised” indicators might be tested to net for this effect. 19 We chose educational flow instead of stock indicators because they can change faster, and are thus more suited to track NE developments. In all cases, quality is not considered (interesting, available indicators produced in the PISA framework by OECD are limited to high school level).

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GDP growth, for all sub-periods and even after controlling for GDP levels. Significant correlations are found for both with indicators in the technology (production and usage) domain; in the area of knowledge, again only market capitalisation presented a positive, significant, partial correlation with S&E graduates, while FDIs were negatively correlated with average schooling. (b) Regulation and Governance The importance of both Regulation and Governance – as well as of underlying factors – has grown enormously in the NE, as international competition gets tougher and is difficult to be compensated by national policies (be it state aids, exchange rate depreciation – at least for the EU countries – or other forms of advantages to national firms through protection or procurement). On these premises, we opted for the inclusion of some indicators for these two areas in the shortlist in Table 1, although empirical research and data production are still at an early stage in both domains. In our analysis we considered the sets developed by the World Bank,20 which take into account five key aspects of business dynamics for regulation (starting a business, hiring and firing, getting credit, enforcing contracts and closing a business, for each aspect providing estimates based primarily on time, money and permission requirements), and six environmental aspects in the case of Governance (voice and accountability; political stability and absence of violence; government effectiveness; regulatory quality; rule of law; control of corruption).21 For assessment purposes, individual indicators on regulation were first combined as composite thematic indicators (translating other aspects into “money equivalent”, often by discounting time); these were tested together with an overall composite indicator;22 in the case of governance, a simple average of thematic indices was performed. Results of correlation exercises highlight the need for additional research and refinements. Data for regulation had a major drawbacks: they were available only for 2004 (the same value was assumed for the three periods). However, nearly all regulation indicators had expected (negative) correlation with most other “behavioural” and performance variables, but this often proved to be not significant. However, a stronger linkage was found for both the aggregate regulation and governance indicators with performance and other key indicators in the areas of technology and knowledge; the relationship remained significant with IT usage variables (PC & internet) also when

20 For

governance-related methodologies, see Kaufmann et al. (2005). For the overall methodology followed in regulation indices, see the URL: http://www.doingbusiness.org/Methodology, while for: Starting a Business: Djankov et al. (2002); Hiring and Firing: Botero et al. (2004); Getting Credit: Djankov et al. (2005); Enforcing Contracts: see URL above; Closing Business: Djankov et al. (2005). 21Another ongoing exercise is proposed by the European Commission on Professions. However, these data are more limited in scope, and do not cover US and Japan. 22 The aggregation and weighting procedure implied (a) the attribution of market real long term average interest rates for converting time into money equivalent and (b) a robustness check both for sub-indices based for instance on “number of procedures” and for composite indicators. Further details can be provided on request.

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controlling for GDP levels sketching a “science and order” paradigm, that calls for further investigation.

2.3.3. Further empirical analyses: positioning indicators and countries in factor space Now we shall complement the conceptual assessment and empirical analysis of indicators by means of factor analysis (FA) and subsequent clustering.23 This exercise entails a refinement of the selection undertaken in the previous section, providing additional information on indicators and countries in what could be defined as the NE factor space. Although it was not intended to select indicators which “load” to one factor only (hence dropping the others), FA allowed dropping variables which had a lower explanatory power in terms of the overall variance explained and of each variable contributing to it. To overcome limitations because of a low observation/variables ratio, FA was performed iteratively, considering, within each domain, a single candidate at a time; the split of the reference period in two sub-periods allowed for a dynamic evaluation and a robustness check (performing FA for the 1996–2004 period and the two sub-periods separately), while permitting also a final overview by considering the two sub-periods as separate observations, and increasing correspondingly the number of variables. In general, two factors were enough to explain more than 70% of overall variability, and in about half the cases more than 80 per cent. In a few instances, in which not all domains were included, a single factor emerged, and only seldom a third factor would present a higher than unit eigenvalue.24 The two first factors can be best described as representing performance and technology, with performance being in nearly all runs the main factor, i.e. it explained most of the variability between countries. This partition can be easily understood, as for the panel of countries concerned the technological level was overall neutral with respect to GDP performance. As to individual indicators, the overall position of variables in the performance – technology factor space is exemplified in Figure 5 hereunder.25

23 This

part of the chapter builds on the methodology developed in de Panizza et al. (2005), extending the scope of the analysis to new indicators (in particular those referring to the areas of governance and regulation) and to a longer time span, as well as providing some technical refinements, such as the chronological articulation. 24 We had only 16 observations and almost as many candidate indicators, while rules of thumb suggest keeping the ratio at least at 3:1. FA was carried out adopting the principal components method of factor extraction, and subsequently rotating factors with the varimax (orthogonal) method. Factors were selected when presenting higher than unit eigenvalues and indicators on the basis of their contribution to overall variability. 25 The position of indicators in Factor Space is the result of a specific FA run, with all reported indicators and the 1996–2000 and 2001–2004 periods as distinct observations. Coordinates represent approximately the barycentre of effective positions of indicators along all the two factors’ analysis “normal” runs undertaken.

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Figure 5. Overall position of variables in the Performance – Technology (NE) factor space* 1 GDP Productivity 0.8 FDI 0.6

Governance 1996–00

Market capitalisation

Area spanned by productivity

Performance

0.4

ICT added value

S&E graduates ICT investment Governance 2001–04 R&D

0.2

PCs 0 0

0.2

0.4

0.6

0.8

1

Technology * Dots represent approximately the barycentre of reported indicators, throughout the 1996–2004 analysis and the two sub areas

The main findings in each domain can be summarised as follows: Performance: GDP, employment and productivity dynamics were positioned around the same (first) factor axis. Employment, which provided the smallest contribution, was soon excluded. Productivity always happened to also make a contribution to the other (secondary) factor, confirming its supposed linkages with technological developments. In the field of technological specialisation, FA confirmed that ICT value added had superior explanatory power with respect to HT exports and employment indicators. It is worth noting that ICT value added contributed to both factors throughout all FA runs. Considering ICT usage from an economic perspective, ICT investment confirmed its superiority vs. the IT component, but also vs. ICT employment and expenditure and vs. IT expenditure alone for all periods distinguished (hence, irrespective of data availability issues). Concerning technological infrastructure and usage variables, the PCs/inhabitants ratio provided the highest contribution, but for the 2001–2004 period internet users gave similar results. However, for the time being the former has to

Indications on effective movements (with factor spaces less populated of indicators) of variables along time and/or across FA runs is also reported.

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be preferred on grounds of data availability (the same applies to e-business), while broadband, website and hosts indicators all were comparatively weaker. A comparison of the “economic” and “technological” indicators (i.e. ICT investment and PCs) reveals a slight superiority of the latter in terms of overall contribution. However, this was almost exclusively concentrated on the secondary (technological) factor, while ICT investment contributed also to the primary (performance) one, as expected from theory. Among knowledge indicators, R&D and the artificial variable built by averaging USPTO and EPO patents gave similar results (always better than HRST, employment and innovation indicators), with a slight superiority of R&D for period 1996–2004, and of patents over the years 2001–2004. In line with the findings reported in the previous section, the contribution of both R&D and patents was almost entirely to the secondary (technological) factor, with no apparent influence on performance. In the sub-domain of human capital, Science and Engineering Graduates as a percentage of the population 20–29 years old, clearly outperformed expenditure and educational attainment indicators, and its contribution usually spanned both factors, although with a prevalence for the secondary (technology) one. In the field of quality of institutions, all indicators of regulation proved to be fairly significant. On the whole, the composite indicator of governance appeared quite relevant and, though not exactly “A class”, often resulted in the selection of a third factor representing, indeed, governance itself. Among finance related indicators, Market capitalisation appeared in general to be relevant, and its contribution applied to both factors, while FDI in isolation appeared to be more directly related to the first (performance) factor, and venture capital indicators confirmed their weakness. Summing up, throughout the FA runs, most indicators of technological sophistication (IT usage, R&D, Patents) go together (like what came out of the correlation analysis). Other aspects, instead, appeared to be positively linked with both the performance and technological dimension: indicators which contributed to both factors were especially IT-added value (representing technological specialisation in a hybrid way), Market capitalisation (financial deepening), to a lesser extent science & engineering graduates (human capital) and the composite indicator for governance (quality of institutions). These indications can be qualified by looking at the position of countries in factor space exemplified in Figure 6, where proximity circles report the results of hierarchical clustering of FA coefficients.26 Indeed, this allows a grouping of countries according to structural features, which encompass different domains, as well as to performance patterns.

26 Country

positions refer to FA for 1996–2004, considering GDP & productivity growth, R&D, Market Capitalisation and S&E graduates. Very similar results for clusters are obtained by substituting variables one by one. However, both some variables and some countries change their position in factors space along time.

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Figure 6. Countries in factor space* 2.5 Tech Leaders

2

gr

1.5

US

fi

UK

1 Olive belt catch-up 0.5

es

0

se pt

fr

nl EU15 be

−0.5 PERFORMANCE

at

Core Followers

dk

−1

de

it

jp

−1.5 Laggards

−2 −3

−2

−1

0

1

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TECHNOLOGY * Line colours report Euclidean distances between cluster centroids (black < 0.4; dark grey < 0.6; light grey < 0.8)

In brief, in most FA and clustering exercises, a few key groups appear. A first one is that of innovators comprising US, UK, Finland and Sweden, all of them occupying the quadrant North East, or of the NE, and characterised by leadership in both economic and NE performance. This seems to suggest that innovation activity matters, as countries in this group have a leading position in both production and effective usage of technologies, which is reflected in performance. However, the story of growth divergence is more complex, and more traditional macroeconomic constraints and dynamics appear to have played a role too. A large group of followers or imitators is made up of core EU countries: France, Belgium, Denmark and Austria, with a comparatively good economic and NE performance, although less pronounced than innovators. A third group is made up of countries which are still experiencing a catch-up process in income levels with respect to the EU, and benefit from EU-specific funding: Spain, Portugal, and Greece share an outperforming GDP growth although they lag behind in technological advancement. These three countries, however, present diverse patterns with respect to productivity and employment dynamics, as well as some

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important differences in other domains, with Spain and Portugal being considerably close to each other and to the group of imitators. Outside these groups there are the three large growth laggards: Germany, Japan and Italy: all of them in the period considered suffered in the trenches of globalisation the fact of having a comparatively higher share of manufacturing in employment and value added, and each of them had also a specific internal trouble to resolve.27 However, while Germany and Japan belong to the same cluster of science based losers, as for their technological endowment they are near to each other and to the groups of leaders, Italy falls somewhere in between the imitators and the catching up clusters. Finally, the key indicators offer a more detailed view on the characteristics of these groups in the different domains. The cluster of innovators appeared to excel in all fields, from technological specialisation to ICT usage, knowledge and human capital, and also for other institutional indicators (governance, market capitalisation, etc.). This is an important finding as for the inclusion of these areas into the NE setting. At the opposite, all three laggards appear to miss something in one or the other of the less “material” aspects of the economy, with respect not only to the group of imitators but even, although to a lesser extent, to the catching-up countries.

2.4. Concluding remarks This chapter addressed the issue of identifying key indicators for the NE by means of conceptual thinking as well as different statistical methodologies. This implied a preliminary outline of the NE framework, within which a few key thematic domains were identified, representing the ideal variables that indicators should represent. Candidate indicators within a short-list were then assessed. Indicators and domains were thus located with respect to each other in the NE framework, and the number of selected indicators was further reduced. This implied some loss of information, while results are inevitably dependent on the subjective analytical framework developed and followed, and on data availability issues, which are bound to change in time. These were unavoidable trade-offs to pay for collecting information in a systematic way on the basis of given data material used to find stable key indicators within each domain. This was done with the objective of providing effective assistance to macroeconomic and policy-relevant analyses. In the same line of thought, the analysis was extended to the domains of regulation and governance with encouraging results. The methodology was also a pilot in a process of developing a useful tool that can be repeatedly updated. Directions for future research include a more thorough analysis of emerging domains, and the further development of composite thematic indicators. 27 The financial sector crisis in Japan, the integration of former DDR Länder in Germany and public debt in Italy.

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de Panizza, A.; Fazio A.; Orsini, D. and Visaggio, M. (2005), “The EU Productivity and Growth Gap versus the US: ICT and other Drivers of Performance”, Proceedings of the Nesis Summative Conference, vol. 2. Djankov, S.; La Porta, R.; Lopez-de-Silanes, F. and Shleifer, A. (2002), “The Regulation of Entry”, Quarterly Journal of Economics, 117, pp. 1–37. Djankov, S.; Hart, O.; McLiesh, C. and Shleifer, A. (2005a), “Efficiency in Bankruptcy”, World Bank, in progress. Djankov, S.; McLiesh, C. and Shleifer, A. (2005b), “Private Credit in 129 Countries”, NBER Working Paper No. 11078. EIS (2003), “European Innovation Scoreboard 2003”, Technical Paper, No. 6: Methodology Report. European Commission (1995), “Green Paper on Innovation”, COM(1995)688, December. Gordon, R.J. (2002), “Technology and Economic Performances in the American Economy”, NBER Working Paper, No. 8771, Cambridge, Mass. Gordon, R.J. (2004a), “The Stability Why was Europe Left at the Station When America’s Productivity Locomotive Departed?”, mimeo CEPR. Gordon, R.J. (2004b), “Five Puzzle in the Behavior of Productivity, Investment, and Innovation”, NBER Working Paper, No. 10660. Greenwood, J. and Yorukoglu, M. (1996), “The Third Industrial Revolution: Technology, Productivity, and Income Inequality”, Federal Reserve Bank of Cleveland, Economic Review, 35(2), pp. 2–12. Griliches, Z. (1957) “Hybrid Corn: An Exploration in the Economics of Technological Change”, Econometrica, 25:6, pp. 501–522. Helpman, E. and Rangel, A.J. (1999), “Adjusting to a New Technology: Experience and Training”, Journal of Economic Growth, 4, 359–383. Jorgenson, D.W.; Ho, M.S. and Stiroh, K.J. (2002a), “Information Technology, Education and the Sources of Economic Growth across US industries”, paper presented at the Conference on Technology, Growth, and Labor Market, Federal Reserve Bank of Atlanta and Georgia State University, Atlanta, Georgia. Jorgenson, D.W.; Ho, M.S. and Stiroh, K.J. (2002b), “Raising the speed limit: U.S. economic growth in the information age”, Brooking Paper on Economic Activity, 125–211. Jorgenson, D.W.; Ho, M.S. and Stiroh, K.J. (2003), “Projecting Productivity Growth: Lessons from the US growth resurgence”, CESifo Economic Studies, 2. Kaufmann, D.; Kraay, A. and Mastruzzi, M. (2005), “Governance Matters IV: Governance Indicators for 1996–2004” (Draft, May 9), World Bank. Klenow, P.J. and Rodriguez-Clare, A. (2005), “Externalities and economic growth”, in: Aghion, Ph. and Durlauf, S. (eds.) Handbook of Economic Growth, edition 1, Vol. 1, Chapter 11, pp. 817–861. Kline, S.J. and Rosenberg, N. (1986), “An Overview of Innovation”, in Landau, R. and Rosenberg, N. (eds.), The Positive Sum Strategy: Harnessing Technology for Economic Growth. National Academy Press, Washington, DC.

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Leonard-Barton, D. (1988), “Implementation as Mutual Adaptation of Technology and Organization”, Research Policy, 17, pp. 251–267. Mansfield, E. (1968), Industrial Research and Technological Innovation. Norton, New York. Nicoletti, G.; Scarpetta, S. and Boylaud, O. (2000), “Summary Indicators of Product Market Regulation with an Extension to Employment Protection Legislation”, OECD Economics Department Working Paper, No. 226. OECD (2003), “Measuring Knowledge Management in the Business Sector: First Steps”, This publication reports chapters 1 and 8 of a book with this title still in the pipeline, otherwise to be referred as OECD-Statistics Canada (2004). Working Party of National Experts on Science and Technology Indicators [DSTI/EAS/STP/NESTI/RD(2003)7 - May]. Oliner, S.D. and Sichel, D.E. (2002), “Information Technology and Productivity: Where are We Now and Where Are We Going?”, Economic Review, 3, Federal Reserve Bank of Atlanta, pp. 15–44. Phelps, E. (2003), “Economic Underperformance in Continental Europe: A Prospering Economic Runs on the Dynamism from its Economic Institutions”, lecture, Royal Institute for International Affairs, London, March 18. Room, G. et al. (2004), “Final Report on Conceptualisation and Analysis of the New Information Economy”, Nesis Deliverable D5.3 (August). Stiroh, K.J. (2002), “Information Technology and the US Productivity Revival”, American Economic Review, December. Van Ark, B. (2005), “Does the European Union Need to Revive Productivity Growth?”, Research Memorandum GD-75. Groningen Growth and Development Centre. Van Ark, B.; Inklaar, R. and McGuckin, R.H. (2002), “Changing Gear. Productivity, ICT and Services Industries: Europe and the United States”, Research Memorandum GD-60. Groningen Growth and Development Centre. Van Ark, B.; Inklaar, R. and McGuckin, R.H. (2003), “ICT and Productivity in Europe and the United States. Where do the differences come from?”, CESifo Economic Studies, 49, 3/2003, pp. 295–318.

Measuring the New Economy Edited by Teun Wolters © 2007 Elsevier B.V. All rights reserved.

CHAPTER 3

Enhancing Productivity Requires More than ICT Alone Bart van Ark 3.1. Introduction The labour unrest of 2004 could be appeased by a typically Dutch social accord, after which it was possible for the representatives of employers and employees to negotiate over the 2005 collective labour agreements. Everything, therefore, seemed alright. But this was just appearance. The social accord has saddled corporate Holland with the almost impossible assignment to bear the increasing costs of aging (and other demographic problems such as integrating immigrants into the labour market) whilst being a good match for its international competitors. Not surprisingly, during the negotiations employers – and also the government – talked about longer working times and moderation of the wage claims. However understandable they may be, these statements testify to a rather shortsighted view and a lack of creativity. There is too much emphasis on cost reductions, which on the long run is harmful to the Netherlands. Such a policy tends to repeatedly decrease the economic value of Dutch products and services, which undermines the country’s competitive strength. Continuous cost savings change the country’s economic structure going from bad to worse: it will increasingly become vulnerable to price competition from abroad. Fortunately, entrepreneurs in the Netherlands recognise this harmful development. A way out has been pleaded time and again: the Netherlands (and Europe) should consider innovation so that high-quality products and services can compensate for high cost levels. Many industries have embarked on intensive collaboration within innovation networks; the government has launched a special innovation platform according to the Finnish model and has worked on a true innovation accord. However, for all that has been pleaded, reality appears hard to manage. In spite of various attempts to turn the tide and a few occasional successes, the economy remains ailing. Unfortunately, all the talking, the many plans and the meagre results make the Netherlands tired of innovation. What is needed now is a clear focus and concrete stepping-stones for action. At the same time, we have to be realistically aware of our limitations when assessing our chances and making choices. I plead

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for a focus on productivity, investing in Information and Communication Technology (ICT) and non-technological innovation while giving more attention to the productive and innovative role of the services.

3.2. Productivity is key to economic growth The slight recovery of the Dutch economy in 2004 did not lead to a recovery in the growth of employment. Preliminary figures indicate that in 2004 the total number of hours worked has continued to go down. In terms of productive employment, the Netherlands stays behind what has been achieved in Europe. It is no surprise, though, that labour productivity in 2004, as compared to 2003, has recovered fairly well. However, this effect is mostly a matter of the business cycle. As soon as employment growth improves – this is what we hope for – it is essential that productivity growth comes along. This is particularly so as productivity in the Netherlands compares unfavourably with what happened elsewhere in Europe over the last 15 years. Taking 1995 as a starting point, the average annual labour productivity growth in the Netherlands (0.6%) was less than half of that of the EU-15 countries (1.5%) and even a quarter of that of the US (2.5%) (Table 1).

Table 1. GDP, hours worked and labour productivity (average annual growth rates, 1987–2004) The Netherlands

EU-15

United States

Real GDP 1987–1995 1995–2004 2003 2004

2.7 2.3 −0.9 1.2

2.2 2.1 0.9 2.0

2.7 3.3 3.0 4.4

Total hours worked 1987–1995 1995–2004 2003 2004

1.1 1.7 0.4 −0.6

0.0 0.7 0.0 0.8

1.6 0.9 −0.5 1.4

Labour productivity 1987–1995 1995–2004 2003 2004

1.6 0.6 −1.3 1.8

2.2 1.5 0.9 1.3

1.1 2.5 3.6 3.1

Note: 2004 figures are provisional. Source: Groningen Growth and Development Centre (http://www.ggdc.net/dseries/totecon.shtml)

Enhancing Productivity Requires More than ICT Alone

55

But why is productivity growth that important? Did the Netherlands not show in the 1990s a spectacular improvement in employment? And that improvement was indispensable, wasn’t it? That is correct. However, the Netherlands are characterised by a strong trade-off between job growth and labour productivity. The exact causes of this phenomenon are debatable. Unfortunately, we still do not know the productivity profile of the jobs we lost (e.g. because of competition or outsourcing abroad). It is clear, though, that since the mid-1990s we have been harder hit in terms of less productivity growth than the EU as a whole, while also before 1995 our performance in this area was moderate. Some will argue that this is no big problem as in the Netherlands the level of productivity is relatively high. The latter is true, but this advantage will be lost soon. In the beginning of the 1990s, the Dutch productivity level was 15% points higher than that of the US. However, this lead has been evaporating since then and has practically disappeared in 2004. Some compensation for this may still lie in the possibility of increasing the overall labour participation – for instance, by more working years before retirement and return of disabled workers into the employment – but this source will be exhausted fairly soon. Given an aging population and a restrictive immigration policy, in the coming decades economic growth will depend on productivity. Therefore, new jobs in the Netherlands have to become productive jobs.

3.3. Dutch ICT sector performs well It would be beyond the scope of this chapter to discuss the causes of slow productivity growth at length (van Ark, 2003). Some commentators say it is the Dutch arrears of innovation (if compared with other developed countries) that are to blame. The Netherlands are said to have insufficient R&D expenditure; consequently, its industrial base is slowly but surely being eroded. Others lay a link between the low innovation rate and a long-term policy of moderate wage increases, which is supposed to have discouraged entrepreneurs to invest in innovations. It is also argued that the Dutch government does not spend enough on education while the sluggish labour market and product market reforms have slowed down innovative activity. Finally, a number of prominent figures in politics and business call attention to the inadequate work, management and entrepreneurial culture. In particular, they criticise the traditional “polder” model (consultations always and everywhere) that has deeply penetrated the Dutch business community so as to slacken new initiatives and at times even abort them. Whatever it may be, it seems that the ICT sector is beyond criticism here. Like what happened in other European countries and the US, the ICT producing sector in the Netherlands has seen a rapid growth in productivity. The average productivity growth since 1995 in this sector (production of computers, semi-conductors, telecom equipment, etc.) amounted to 8% per annum. However, the ICT producing industry in the Netherlands is relatively small, and therefore its contribution to labour productivity growth at a macro-level is small as well.

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Contrary to this, the ICT supporting services do make a considerable contribution to the growth in labour productivity. This group, comprising the telecommunications and computer services sector, account for one-third of the total labour productivity growth (22% in the EU and 13% in the US).1 A productively operating ICT supporting service sector is of utmost importance when it comes to ICT implementation throughout the economy. Especially in those countries which depend on the hardware imports, an effective ICT supporting service sector is crucial to a productive application of ICT in the entire economy.

3.4. ICT use in Europe is less productive than in the US But what about the rest of the economy: how productive do investing companies apply ICTs (computers, software and the like) in their own production processes? From a macroeconomic perspective, the answer to this question is more important than the ICT producers’ productivity. After all, the ICT users have a far greater stake in the national economy as a whole. The most intensive investors in ICT operate in the services. In particular, the financial and other professional services, but also the wholesale and retail sectors, belong to the greatest users of ICT (van Ark et al., 2003). Figure 1 shows that the advanced position of the US in productivity growth is primarily determined by the size of the intensively ICT using sector. The difference in productivity growth between the US and the EU can be explained for more than 100% by this role together with the size of the ICT producing sector in the US. The macroeconomic difference in productivity growth between the US and the Netherlands can be explained for 66% from the ICT using sector. The contribution of industrial sectors to productivity growth at a macro-level consists of two parts: a sector’s actual productivity growth and its relative size in terms of labour force or production volume (which is a sector’s weight in the macroeconomic total). Because of its substantial stake in the national economy, the Dutch ICT using services’ share in productivity growth is substantial as well. However, productivity growth in the ICT using services in the Netherlands is much lower than in the US, exclusive of the knowledge-intensive services (Table 2). In particular, since the mid 1990s in the US, productivity growth appeared to have gained momentum in the retail, wholesale and financial services. Otherwise, Table 2 makes it clear that the Netherlands has not performed that badly when compared with the rest of Europe, especially due to the wholesale sector and some segments of the transport sector (including transportation by water).

1As

regards the computer services (hardware consultancy, software consultancy, data processing, database processing, data activities, and repairs & maintenance of ICT equipment), it is noted that despite rapid growth until 2001, productivity has developed changeably because of strong fluctuations in the prices of the services and labour volume.

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Enhancing Productivity Requires More than ICT Alone

Figure 1. Contribution of ICT producing, intensively ICT using and less intensively ICT using sectors to growth in labour productivity (GDP per worked hour), 1995–2002 ICT Using Manufacturing

ICT Producing Manufacturing

ICT Producing Services

13%

22%

32%

−0.50

0.00

0.50

Less Intensive ICT Users

ICT Using Services

U.S. Growth: 2.31%

53%

EU-15 Growth: 1.73%

23%

43%

1.00

NL Growth: 1.35%

1.50

2.00

2.50

3.00

Source: Groningen Growth and Development Centre, 60–Industry Database (http://www.ggdc.net/dseries/)

3.5. Immaterial investments and non-technological innovations Productive ICT use seems to be the key to the productivity miracle that took place in the US since the mid-1990s. Nonetheless, it is questionable as to whether this has been a matter of higher investments in ICT. It may not be a lack of ICT investments in the Netherlands, but a less productive use of these investments than in the US. To find this out, it is necessary to decompose productivity growth into the contributions of the growth of ICT capital (computer, telecommunication equipment), the growth of other capital goods, a better-educated labour force and more efficiency. The latter is also called “total factor productivity” (TFP): this is the efficiency of the implementation of all previously mentioned inputs. Table 3 makes clear that only to a limited degree can different levels of ICT investments account for the great difference in labour productivity growth between the US and the Netherlands. As far as the last period (1995–2001) is concerned, it appears that about 1.3% of the difference in labour productivity growth (2% points) between ICT producing sectors in the Netherlands (1.6%) and the US (3.6%) can be explained by a lower TFP in the Netherlands.

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Table 2. Labour productivity (annual growth rates, 1995–2002) 1995–2002

Total economya ICT producing sectors ICT producing industryb ICT producing services

EU-15

Netherlands

USA

1.7

1.4

2.3

9.4 10.2 5.9

6.0 8.0 4.3

10.7 23.9 4.0

ICT using sectorsc ICT using industry ICT using sectors Of which: Wholesale Retail Financial services ICT-intensive business services

1.6 1.9 1.5

2.0 1.8 1.9

4.5 1.8 5.0

1.6 1.6 2.0 0.4

3.8 1.2 0.1 1.2

7.5 5.4 5.7 1.0

Less ICT-intensive sectors Non-ICT industry Non-ICT servicesa Other non-ICT sectors

1.0 2.1 0.4 2.1

0.6 2.9 0.3 0.3

0.1 1.7 −0.1 0.6

a Exclusive

of real estate sector. hedonic price deflators for ICT production in the US (adapted to national inflation). These deflators reflect the fast price decreases in ICT goods resulting from strong technical progress better than the national deflators. c Exclusive of ICT producing. Notes: Source of definition of ICT producing sectors is the OECD; distinction between ICT using sectors and less ICT-intensive sectors is based on the number of ICT capital services in total of number of capital services; see van Ark et al. (2003). Source: Groningen Growth and Development Centre, 60-Industry Database. Intermediate Version, June 2005; http://www.ggdc.net b Using

TFP refers to the efficiency by which measured means of production (ICT capital, non-ICT capital and the skills of employees) are deployed. This efficiency is strongly dependent on investments in intangible capital such as knowledge, organisational change and marketing of new products and services. As intangible assets as such frequently remain unmeasured, the positive returns on investments that they bring about in practice are reflected in TFP growth. Intangible capital proves to be necessary especially when it comes to using ICT productively. A great deal of research has been done in this area, but one could also intuitively understand this: by just dumping a number – say 100 – of computers into the organisation, productivity will not increase. A recent study on the Netherlands demonstrates that companies which have a high ICT intensity purchase significantly

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Enhancing Productivity Requires More than ICT Alone

Table 3. Contributions of inputs and TFP to labour productivity growth in the intensively ICT using sector 1979–1989

1989–2001

1995–2001

The Netherlands Labour productivity growth (annual %) Contributions from: Increase in workers’ skills Increase in ICT capital per hour worked Increase in other capital per hour worked Total factor productivity growth

2.5

1.1

1.6

0.2 0.7 0.3 1.4

0.2 0.7 0.1 0.1

0.2 1.0 0.1 0.3

United States Labour productivity growth (annual %) Contributions from: Increase in workers’ skills Increase in ICT capital per hour worked Increase in other capital per hour worked Total factor productivity growth

1.3

2.3

3.6

0.4 0.9 0.4 −0.4

0.3 1.0 0.4 0.6

0.3 1.4 0.4 1.6

Note: ICT using sectors are industries with a relatively high share of ICT investments in the total of investments. Included are inter alia publishing houses, machine building industry, trade, financial and business services. Source: Inklaar et al. (2003)

more knowledge-intensive business services – such as consultancies, technical support and marketing services – than companies which have a low ICT intensity. More importantly, this combination of ICT and the involvement of business services has led to an increasing productivity growth, in particular after 1995 (Broersma and van Ark, 2004). This seems to be good news for the consultancy firms, but there is one condition to be considered: for this to happen it is necessary that a company is bound and determined to undertake innovative activities. A study by the Netherlands Bureau for Economic Policy Analysis (CPB) has shown that companies that have a distinct innovation strategy – in particular if focused on product innovation – have higher benefits from the ICT they have than companies which are not innovative (Hempell et al., 2004).

3.6. Strategy for non-technological innovation With this in mind, it is easier to understand Nicholas Carr (an Harvard Business Review journalist) when he stated that ICT as such is not relevant anymore (Carr, 2003). According to Carr, ICT is so widespread that it does not provide any competitive advantage whatsoever. ICT has become a “commodity input” which – like what happened with electricity a century ago – is just a necessary part of the infrastructure of almost every company.

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Bart van Ark

There is not much to be said against this, albeit that it ignores the fact that the strategic advantages which ICT is capable of generating are predominantly realised through complementary non-technological innovations. To bring about non-technological innovations, companies need an outspoken strategy. Research firm Dialogic has developed a useful framework for this, which is primarily focused on the multidimensional nature of innovations in services (Figure 2). Innovations in services normally have both technological and nontechnological dimensions, which tend to be interdependent. The technological dimension (Dimension 4) could involve a new computer or a new software package. The first non-technological dimension (Dimension 1) is the introduction of a new service concept. In the services, new concepts mostly refer to gradual innovations based on a combination of old and new applications (for instance, a call centre or supermarket having an online delivery service; a café run in a bookshop). The second non-technological dimension (Dimension 2) is the creation of a new client interface. This is typically a matter of the previously mentioned coincidence of the production and use of a service (for instance, the introduction of a system mediating the exchange of electronic data, such as EDI, for the purpose of e-commerce). The service delivery concept (Dimension 3) involves new ways of delivering services (for instance, home shopping, electronic banking or in-house

Figure 2. A four-dimensional model of innovation on services Characteristics of existing and competing services New Service concept (dimension 1)

organisational competences

Characteristics of existing and potential clients

Marketing & distribution competences

Technological options (dimension 4)

New Client interface (dimension 2)

HRM competences

New service delivery concept (dimension 3)

Source: Den Hertog (2000) Competences, skills and attitudes of in-company en competing service workers

Enhancing Productivity Requires More than ICT Alone

61

temporary services). For most non-technological innovations (but not for all of them) particular ICT applications are a major condition. The importance of each innovation dimension may differ by the type of service provision. For example, offering a totally new service lays a greater emphasis on the service concept and the delivery system, whereas if there is no new service as such involved, the innovation is likely to focus on a new way of linking with the client. The relative importance of the different dimensions of innovation may vary with time. The first step in a certain innovation trajectory tends to be dominated by just one dimension. However, this dimension will lead to involving the other dimensions. For example, for innovations in the wholesale and retail sectors to be successful, the commercial application of scanner and data storage technologies (that is, the technological dimension of Figure 2) was a conditio sine qua non. These technologies made it possible to strongly improve the efficiency of stock control as well as to develop detailed client profiles and customised offers of products and services. This kind of innovation cannot be implemented without specific choices concerning the retail formula (Dimension 1), communication with clients such as e-retail formats, loyalty and incentive programs etc. (Dimension 2) and the development of new worker competencies such as ICT skills (Dimension 3).

3.7. Opportunities and threats However great the importance for companies to go for innovation, still there is need for a government that opens the door to success. The need for larger budgets to boost an effective innovation policy is beyond doubt, but this route has limited potential to stimulate non-technological innovation. Likewise, the Innovation Platform cannot solve all the problems here. Equally important for the services is a consistent policy by the government to liberalise the labour and product markets. It is precisely the services where considerable structural change is taking place. For that matter, companies and employees need to be stimulated to focus their capacities on where their productive impact is greatest. It should, however, be noted that market liberalisation is not always the best strategy. In many sectors there is a need for some degree of regulation so as to uphold the quality of the means of production. “Smart reforms” require a measure of experimenting with regulations, research on their effects and a careful consideration of costs and benefits. The greatest threat to the Dutch economy lies in a continuation of the downward spiral whereby a one-sided focus on cost reduction – especially if this hampers innovation – plays into the hands of the international competitors. Such a process will further slow down the innovatory activities of enterprises and spoil the potential for the recovery of economic growth. For the Netherlands the choice to be made is clear. The Dutch economy has developed into a service economy, exceeding in this its neighbouring countries a substantial stake in the country’s exports. Services exports belong to the fastest growing categories in foreign trade. The Netherlands will have to take its chances in the services.

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A clear innovation strategy which combines ICT with non-technological innovation is the key to success.

References Broersma, L. and van Ark, B. (2004), “ICT, Organisation Innovation and Productivity Growth”, Groningen Growth and Development Centre, mimeographed. Carr, N.G. (2003), “IT Doesn’t Matter”, Harvard Business Review, May, pp. 41–49. Den Hertog, P. (2000), “Knowledge-intensive business services as co-producers of innovation”, International Journal of Innovation Management, 4(4), pp. 491–528. Hempell, Th.; van Leeuwen, G. and van der Wiel, H. (2004), “ICT, Innovation and Business Performance in Services: Evidence for Germany and the Netherlands”, in OECD (ed.), “The Economic Impact of ICT, Measurement, Evidence, and Implications”, Paris, pp. 131–152. Inklaar, R.; O’Mahony, M. and Timmer, M.P. (2003), “ICT and Europe’s Productivity Performance: Industry-level growth account comparisons with the United States”, Research Memorandum GD-68, Groningen Growth and Development Centre, December. van Ark, B. (2003), “The Productivity Problem of the Dutch Economy: Implications for Economic and Social Policies and Business Strategy”, Research Memorandum GD-66, Groningen Growth and Development Centre, September (downloadable from http://www.ggdc.net/pub/). van Ark, B.; Inklaar, R. and McGuckin, R.H. (2003), “Changing Gear: Productivity, ICT and Service Industries in Europe and the United States”, in: Christensen, J.F. and Maskell, P. (eds.), The Industrial Dynamics of the New Digital Economy, Edward Elgar, pp. 56–99.

Measuring the New Economy Edited by Teun Wolters © 2007 Elsevier B.V. All rights reserved.

CHAPTER 4

The National Accounts of Knowledge-Based Economies Mark de Haan and Myriam van Rooijen-Horsten1 (Statistics Netherlands)

4.1. Introduction The competitiveness of firms in post-industrialised economies is being increasingly determined by product and process innovations in which intangible capital such as human capital, knowledge obtained from research and development, patenting and brand building play a crucial role. Another important characteristic of postindustrialised economies is the rise of information and communication technology (ICT). Despite overwhelming evidence about the economy-wide adoption of ICT in the 1990s, its effect on economic performance, also in relation to knowledge creation, is still not fully understood. One fundamental part of this puzzle concerns measurement issues. An important precondition for analysing and understanding “new economy” features is undoubtedly their coherent representation in terms of statistics and indicator frameworks. Policy strategies may aim at enhancing the knowledge and ICT orientation of economies as a way to increase competitiveness, employment and productivity. A good example in this respect is the Lisbon strategy in Europe. At the 2000 Lisbon Summit, the EU formulated the ambition to transform itself in 10 years into “the most competitive and dynamic knowledge-based economy in the world capable of sustainable economic growth with more and better jobs and greater social cohesion”. Within this strategy, full employment and a rise in labour productivity are considered as important intermediate goals in abating social exclusion and sustaining pension provisions in an ageing society. The European Commission (2000) stated in the context of the Lisbon strategy that “the indicators selected should not be seen in isolation

1 The authors work with Statistics Netherlands. Their e-mail addresses are respectively: [email protected] and [email protected] The views expressed in this chapter are those of the authors and do not necessarily reflect the views of Statistics Netherlands.

64

Mark de Haan and Myriam van Rooijen-Horsten

but rather as different elements of the same picture”. In addition to general indicator qualities such as relevance, reliability, international comparability and timeliness, such a systems approach should have the following two characteristics. First, the selected indicators should be complementary in scope and mutually coherent. de Haan and van Rooijen-Horsten (2003) identified several overlaps and conceptual inconsistencies between the following three Lisbon indicators that are part of the innovation pillar: “Public expenditure on education”, “R&D expenditure” and “ICT expenditure”. The satellite account they propose provides a consistent picture of these indicators. Their results are summarised in the subsequent sections. Second, the indicator frameworks should have a well-defined conceptual structure. Indicator systems should be shaped in such a way that the impacts of knowledge creation and information technology on, for example, economic growth and labour productivity can be analysed. In this respect, the current Structural Indicator list is inadequate. The list lacks a conceptual sound analytical framework, restricting its use as a policy instrument. The national accounts play an important role in the structuring of indicators and enhancing their policy relevance. They provide the worldwide accepted macroeconomic statistics. The national accounts can be regarded as an information pyramid with a detailed information system at the bottom and a set of macroeconomic indicators – e.g. gross domestic income, net national income, capital formation – at the summit. In this way, the national accounts integrate the information requirements of different kinds of uses. The key macroeconomic aggregates provide the condensed set of indicators required for short-term reviews. The underlying accounts serve various analytical purposes such as productivity measurement and macroeconomic modelling. Both types of uses are unified by one set of macroeconomic entities. It is infeasible, and probably not helpful, to construct an accounting system covering all dimensions of the Lisbon strategy. However, with respect to knowledge and information, one may think of several potential improvements that will reinforce the relevance of the national accounts statistics. In the context of the imminent revision of the System of National Accounts (SNA, 1993), scheduled for 2008, this chapter explores several changes that enhance the System’s descriptive and analytical capacity in terms of indicators that are currently being used to measure the knowledge- and information-based economy. Strengthening this relationship is expected to increase the analytical rigour of both national accounting and the indicators which reflect the knowledge and information technology dimension of post-industrialised economies.

4.2. Indicators based on national accounts Probably the best-known indicator defined in the SNA is Gross Domestic Product (GDP). GDP is a measure of production. The quarterly or annual volume change of GDP is the internationally standard measure of economic growth. If for the sake of simplicity imports and exports are ignored, the level of GDP represents the value

65

The National Accounts of Knowledge-Based Economies

Table 1. Knowledge-related final expenditure (% shares of GDP) 1995

1996

1997

1998

1999

2000

2001

%-shares in (extended) GDP1 Gross fixed capital formation2 ICT capital Computers Software Telecomunication infrastructure R&D Enterprises Other intangible assets Other assets

21.3 2.2 0.9 0.9 0.4

22.0 2.5 1.0 1.0 0.4

22.5 2.9 1.0 1.3 0.5

22.6 3.4 1.0 1.7 0.6

23.7 4.0 1.1 1.9 1.0

23.3 4.0 1.0 1.9 1.0

22.2 4.0 0.9 2.0 1.1

1.1 0.1 17.8

1.1 0.2 18.3

1.1 0.2 18.3

1.1 0.2 18.0

1.2 0.1 18.4

1.1 0.1 18.1

1.1 0.0 17.1

4.3

4.2

4.1

4.1

4.2

4.1

4.2

4.2 3.2 0.9 0.2

4.0 3.1 0.9 0.2

3.9 3.1 0.9 0.2

3.9 3.1 0.9 0.2

4.0 3.1 0.9 0.2

3.9 3.0 0.9 0.2

4.0 3.1 0.9 0.2

R&D Government (final consumption)

0.9

0.9

0.8

0.8

0.8

0.7

0.7

Total knowledge related expenditure3

8.7

8.8

9.2

9.5

10.2

10.0

10.0

Total education (final consumption) Public education Level 1–2 Level 3 Other education

1 Extended

GDP (market prices), including R&D and software revision. total gross fixed capital formation, including R&D and software revision. 3 Includes gross fixed capital formation in ICT, R&D and other intangilbe assets and final consumption expenditure on education and R&D (government). 2 Extended

sum of goods and services produced for the purpose of consumption or investment. Goods and services instantly used up in production – also referred to as intermediate consumption – have, on balance, no impact on the level of GDP. Table 1 gives a number of knowledge-related expenditure categories that are part of GDP. These expenditure categories are also found in the following indicator lists: – Investment in knowledge, being the sum of expenditure on R&D, software and higher education as a percentage of GDP (STI scoreboard, OECD, 2001); – Structural indicators (Lisbon Strategy) – theme: Innovation and research: spending on human resources (i.e. public expenditure on education), R&D expenditure (i.e. GERD), ICT expenditure as percentage of GDP (Eurostat, 2003); – Knowledge Investment including computers, education, R&D, expenditure on royalties and licences, software and advertising (Statistics Netherlands, 2001). In these indicator sets GDP is often used as a scaling factor. In Table 1 all expenditures shown as percentages of GDP are actually parts of GDP. This means that the

66

Mark de Haan and Myriam van Rooijen-Horsten

table shows shares of GDP that are devoted to maintain or enhance the knowledge and information infrastructure of the economy. Most expenditure categories presented in the table refer to investment (gross fixed capital formation). Knowledge-related investment includes investment in information and communication equipment, R&D and other intangible assets (e.g. mineral exploration and artistic originals). Deviating from current national accounting practice, the company R&D expenditure in this table is recorded as capital formation. This change is likely to be part of the upcoming SNA revision. This is discussed in Section 4.4.2. In the SNA, education expenditure is not regarded as investment but as consumption; however, it can still be regarded as contributing to the knowledge infrastructure of the economy. Although most economists will consider education expenditure as investment, the update of the SNA will not take this option into consideration. Section 4.4.1 discusses this issue in greater detail. Structural Indicators on education, R&D expenditure and ICT (software) expenditure show a considerable overlap. A major advantage of an SNA-based representation of such indicators is that overlaps are excluded (with possible well-defined deviations). The identification and subsequent elimination of these overlaps are discussed in Section 4.4.3. Thanks to internal consistency, the indicators can be summed up to a total referred to in Table 1 as knowledge-related expenditure. This aggregate includes gross fixed capital formation from ICT, R&D and other intangible assets and all final consumption expenditure of households and government on education. The results in the table show that shares of knowledge-related final expenditure in GDP have increased between 1995 and 2001. These increasing shares are entirely caused by the rapidly expanding investments in ICT during late 1990s. Other knowledge-related expenditure categories such as R&D and education show rather stable shares in GDP between 1995 and 1999. These results indicate that the Netherlands are not rapidly changing into a knowledge-based economy. The subsequent sections provide further background information on the definition and measurement aspects of the indicators presented in Table 1.

4.3. ICT capital 4.3.1. Introduction The revision of the International Standard Industrial Classification (ISIC rev4) is due to be completed by 2007. A major change that is being considered is the introduction of the “Information and Communication” section (5) that unites under one heading various ICT-related activities from the current ISIC such as publishing, motion picture and sound recording activities, broadcasting and telecommunications. Together with the division “Manufacture of computers, communication equipment, and electronic components” (26), this newly introduced section allows for a full representation of the ICT industry in different economic statistics, including the national accounts.

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In economic accounting, the economy wide dissemination of ICT can be traced down with the help of a proper delineation of ICT investment and capital. To that effect, Schreyer (2000) distinguishes the following three ICT capital categories: Computer software, Computer hardware and Network equipment. Most, if not all, of these investment categories are already regarded as gross fixed capital formation in the current SNA. However, the classification of assets in the SNA at present does not always make the ICT-related assets explicit. The upcoming revision of the SNA (the SNA93rev1) is an opportunity to distinguish ICT capital in the main classification of assets. This following section discusses how this can be accomplished. 4.3.2. Computer software In the current SNA asset classification (AN.1122) computer software (including large databases) is headed separately. The SNA93 records expenditure on computer software, i.e. purchases of copies and costs of in-house developed software, as gross fixed capital formation. This requirement represents one of the major changes compared to the former 1968 SNA. In recent years, countries have included estimates for software as gross fixed capital formation in their national accounts, adding directly to GDP. It became evident, though, that the applied estimation methods differed substantially between countries. In 2002, a Eurostat-OECD task force finalised its recommendations on how to improve the international comparability of software investment estimates. One outstanding issue is the coverage of databases. The SNA recommends large databases to be included in gross fixed capital formation although it is not clear what is meant by “large”. One conceptual issue here is to determine whether a database’s value differs fundamentally from software in general. Is a database mainly a device to access data (i.e. a piece of software) or should a value be assigned to the data content as well? Another conceptual issue is whether only commercially exploitable databases such as client records should be considered an asset or should all databases be involved, including, for example, business administrations? Most countries find it hard to trace expenditure on databases. Computer software purchases may include databases which cannot be valued separately. Also, as estimates of the output value of own-account software is usually based on the involvement of software development staff, one may expect that the output of own-account software will cover most of the costs related to own-account developed databases. This is why it was recently agreed to propose for the SNA93 revision to capitalise expenditure on databases together with expenditure on software. For databases, a subdivision will be provided for those users that are able to make a sensible breakdown. 4.3.3. Computer hardware ICT hardware may include servers, personal computers, workstations and peripherals. In the current SNA asset classification, computer hardware is headed under “Other machinery and equipment” (AN.11132; 45 office accounting and computing

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equipment). Except for household consumption, expenditure on computer hardware qualifies as gross fixed capital formation and thus as part of the ICT capital stock. Nowadays, many types of assets like motor cars and all kinds of machinery are equipped with microchips and corresponding software. ICT capital should mainly address the information and communication infrastructure of an economy and not the automation of regular production systems. Therefore, the application of microelectronic devices in general machinery and equipment should not be considered part of ICT capital. 4.3.4. Network equipment Today there is no internationally acknowledged definition of network (or communications) equipment. Attempts have been made to define network equipment along the lines of product classifications (e.g. radio, audio and communication equipment). However, one may argue that network equipment should represent more generally the complete ICT network infrastructure, including, for example, cable and mobile telecommunication networks. After all, telecommunication equipment does not function without a wider infrastructure. A part of this infrastructure coincides with traditional fixed asset types such as “Other machinery and equipment” (AN.11132) and “Other structures” (AN.11122). One way to identify the ICT utilisation of these assets is by adding further subcategories: AN.11132; (CPC-47) audio, video communication equipment. This subcategorisation – which is based on the Central Product Classification – is already present in the SNA93 (p. 307). For ICT Infrastructure specifically tied to the Information and Communication industry the following subcategories could be distinguished: AN.11132; (..) e.g. Transmission equipment; AN.11122; (..) e.g. Communication lines, Radio–TV towers, Mobile phone networks, etc. Its growing importance in most economies justifies that ICT capital should be represented as a main category in the asset classification of the SNA93rev.1. The demarcation of ICT capital from other asset types can be established along the lines presented here. With respect to network equipment, this subheading should, in addition to radio, audio and communication equipment, more broadly include the complete telecommunication infrastructure.

4.4. Knowledge capital When identifying the knowledge orientation of economies, it is relevant to distinguish human skills, which to a large extent are tacit, from codified knowledge such as scientific and artistic originals. Tacit knowledge is the result of human

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capabilities, education and working experience. Although dissemination of knowledge via education and training may enhance human skills, its level is equally determined by non-exchangeable virtues such as intelligence and imaginative powers. Tacit knowledge is inseparable from human beings (or a particular group of human beings in an organisation) and therefore principally not exchangeable. Codified knowledge refers to the output of an artistic or scientific nature, registered by way of written text, images, sound, computer software code, etc. Codified knowledge may exist independently from its creator and is therefore principally exchangeable between individuals. Codified and tacit knowledge are complementary to each other. The creation of codified knowledge highly depends on the availability of human capital. Similarly, the productive use of codified knowledge is only possible in combination with people endowed with the required knowledge and skills. The representation of tacit and codified knowledge in the SNA is discussed below. 4.4.1. Human capital Knowledge embodied in persons may have a market value and this value is usually referred to as human capital. Human capital is currently not an asset defined in the SNA. Human capital is inseparable from individuals, and for that reason it cannot be regarded as a freely exchangeable entity. The exclusive rights of use coincide with the individual endowed with human capital. Education is generally acknowledged as a key source of economic growth. This is why most economists will consider education as investment in human capital, which reinforces the knowledge base of the labour force over longer periods of time. However, the representation of human capital in the SNA as a fixed asset would be a revolutionary change of the system, which is beyond the scope of the upcoming SNA update. Several authors such as Bos (1996), and more recently Aulin-Ahmavaara (2004), show by way of a human capital satellite account several consequences of recording education as gross fixed capital formation in human capital. Then, expenditure on education, either by households, government or enterprises, is no longer considered as final or intermediate consumption, but instead recorded as capital formation. Following the approach of Bos, formal education, provided by the government or nonprofit institutions that render services to households, is recorded as capital formation by households while company education is considered as fixed capital formation by enterprises. Education expenditure is recorded as work-in-progress when pertaining to persons who have not yet entered the labour force. Fixed capital formation takes place at the moment a student finalises his or her education and enters the labour force. This accumulated value of education expenditure is counterbalanced by a concomitant “work-in-progress” inventory withdrawal. The recording of education and training expenditure as gross fixed capital formation implies that compensation of employees must now be regarded as a payment of (human) capital services provided by households. In other words, the employee

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has become an entrepreneur selling the services derived from the exploitation of his human capital. He can decide to increase the quality and value of his labour services by additional investments in education. Also, a part of the income generated from human capital consists of the depreciation of human capital. The value of human capital services encompass the full compensation of employees: wages and social contributions paid by employers. One could argue that this way of recording reflects the more flexible and dynamic relationship between employers and employees experienced in the modern economy. However, the implications of this recording for the SNA are substantial. For example, value added at the industry level will no longer include the compensation of employees. Instead, a corresponding amount of value added is generated by the household sector in newly introduced production activities, which output mainly consists of human capital services. These newly introduced human capital services industries would generate together more than 50% of GDP at market prices in most countries. GDP itself is likely to change as well because work-related consumption expenditure, such as commuter traffic expenses, must now be recorded as intermediate consumption. GDP will also increase because education expenditure by enterprises is no longer part of intermediate consumption but instead is included in gross fixed capital formation. Furthermore, this approach may substantially widen the production boundary. If the process of learning leads to capital formation, it seems almost unavoidable to also incorporate in this capital the opportunity costs of time spent on education by those who have enjoyed it. In other words, the described implications of recording education expenditure as gross fixed capital formation will rigorously change the SNA. Beforehand, it has been decided that the upcoming SNA revision will not adopt such fundamental changes. The representation of human capital in the System will not be taken into consideration. Therefore, for the foreseeable future accounting for human capital will remain the realm of satellite accounting. Satellite accounts will be instrumental in further developing the concepts needed to properly incorporate human capital in the System. These satellite accounts also show the magnitude of changes in the System, if alternative conceptions of the recording of human capital are applied. In the national accounts the separation of gross fixed capital formation from current expenditure is required to show how fixed assets contribute to production over longer periods of time. Capital services are being approximated to quantify the capital inputs of production. In the case of human capital the recording of compensation of employees provides an almost complete picture of the human capital inputs of production. Only the labour component of mixed income is hard to separate.

4.4.2. Codified capital Conceptual issues The current SNA93 considers the creation of books, recordings, films and software as the production of intangible fixed assets. One important exception, however, is the

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knowledge obtained from R&D. Expenditure on R&D is currently not recorded as gross fixed capital formation and scientific knowledge is not considered a fixed asset in the System. Only patented entities are regarded as non-produced assets in the SNA93. However, many characteristics of codified knowledge conform to the SNA definition fixed assets. In many cases, the creation of codified knowledge is the outcome of a production activity as defined in the SNA. Codified knowledge may be used repeatedly or continuously in production processes over longer periods of time. Knowledge assets may be subject to exclusive ownership or the right to use them, which rights can be transferred to other parties. One may wonder whether all R&D expenditure is conducive to the creation of an asset in the SNA sense. Some advocate a generic capitalisation of R&D, including both privately and publicly performed R&D (basic research).2 R&D may lead to the creation of an asset as the owner exerts a certain level of market power. The service provided by a knowledge asset once will pass its peak reflecting the inevitable loss of the owner’s monopolistic power. Also the sharing of knowledge is not without opportunity costs as it dissolves the monopolistic power of the initial owner. Exclusive ownership of scientific knowledge is not necessarily obtained by way of patenting only. Other possibilities are secrecy or by complementary tacit knowledge which cannot easily leak. This opportunity cost is not present in the use of freely accessible knowledge. Knowledge created in the public domain misses any form of ownership. Although governments can be identified as the financer and performer of R&D, it is not necessarily true that they also own this public knowledge. In other words, exclusive ownership (which creates scarcity) remains a decisive precondition for knowledge to be accepted as an asset in the SNA sense. Therefore, public R&D should be excluded from capitalisation unless the resulting knowledge is either being patented or explicitly tied to government production. For example, R&D carried out for defence purposes does not usually lead to publicly accessible knowledge and therefore qualifies for being capitalised in a meaningful way.

Measurement issues related to R&D trade Many countries compile R&D statistics according to the international Frascati guidelines (cf. OECD, 2002). Gross Expenditure on Research and Development (GERD) is probably the most well-known indicator defined in the Frascati system. de Haan and van Rooijen-Horsten (2003) discuss in detail how these R&D statistics can be translated into the national accounts based recording of the supply and use of R&D services. The supply and use framework is particularly helpful in determining the destination of R&D and the subsequent accumulation of knowledge capital.

2 This issue was extensively discussed in the Canberra II Group. This group prepared SNA revision proposals regarding the measurement of non-financial capital in the System.

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The Dutch R&D supply and use table is constructed with the help of R&D import and export data derived from the annual R&D survey. However, it should be kept in mind that this survey does not explicitly ask for R&D exports and imports, i.e. foreign sales and purchases. A distinction between sales and purchases on the one hand, and donations and other transfers on the other, is currently not being made. Data from the Dutch R&D survey indicates that the Netherlands is a net R&D exporter. A positive R&D trade balance indicates that the economy has a competitive advantage here. However, this also implies that knowledge capital created by domestic R&D will be deployed outside the Netherlands. The services derived from this R&D, therefore, are likely to be consumed abroad as well. Conversely, a negative R&D balance of trade may imply higher R&D related spill-overs in the domestic economy than one would expect based on GERD. In other words, import/export data provide an important role in determining the destination of knowledge capital, especially in small and open countries like the Netherlands. Especially, larger companies may transfer R&D to different company divisions without the presence of opposite money flows. Clearly, this makes efforts to determine the destination of knowledge capital created in these companies complex. For internationally operating companies, this may also lead to problems in describing R&D import–export relationships. An attempt has been made to investigate whether data from the R&D survey in the Netherlands correctly reflect the international R&D flows of multinationals. In the Netherlands, R&D is highly concentrated in a limited number of multinationals. The investigation included eight multinationals, whose R&D expenditure together comprises approximately fifty percent of the Dutch GERD. The number of employees working in the Netherlands as a percentage of total corporate employment was taken as an indication of the proportion of worldwide production that is performed in the Netherlands. When the latter percentage is compared with the number of R&D employees in the Netherlands as a proportion of worldwide R&D employees, it appears that a relatively large part of the R&D personnel of most of the multinationals involved is concentrated in the Netherlands. Aggregating the data of eight multinationals, 11% of total personnel worldwide and 44% of R&D personnel worldwide is employed in the Netherlands. Figures on R&D expenditure suggest a similar kind of concentration. This indicates that these multinationals are likely to transfer certain amounts of R&D carried out in the Netherlands to foreign company divisions. It is striking, however, that only two out of eight multinationals reported substantial amounts of R&D export (more than 80% of their GERD), whereas the other six report zero R&D export (five multinationals) or insignificant shares (3% of GERD by one multinational). Of those six multinationals who report (almost) zero R&D export, five multinationals carried out relatively large parts of their R&D in the Netherlands (for one of the six multinationals missing data on the number of R&D employees worldwide prevented a conclusion). For these five multinationals the difference between the proportion of worldwide R&D personnel working in the Netherlands and the proportion of worldwide total personnel employed in the Netherlands ranges from 12 to 42%. These results point to a substantial amount of R&D that crosses borders wilst remaining unobserved by the R&D survey.

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The analysis above is restricted to the measurement of R&D export only. The size of a possible underestimation of R&D import has not yet been investigated. In case import figures are less affected by the unobserved international R&D transfers of multinationals, the figures derived from the R&D survey lead to an overestimation of R&D use and the subsequent accumulation of knowledge capital accumulation in the Netherlands. The present Frascati Manual (2002) takes the globalisation process into account by suggesting further breakdowns of sources of funds for R&D and extramural R&D for transactions with units abroad. In addition, the Frascati Manual mentions that “As the R&D activities of multinational groups of enterprises are usually organised, managed and financed at group level or group division level, it is sometimes very difficult, if not impossible, to identify R&D performed in units of the group in different countries and to obtain information on R&D flows between these units”. The analysis carried out for the Netherlands suggests this is indeed the case. To make better estimates of R&D export and import in the future, it is recommended for the R&D survey to include explicit questions about the precise allocation of R&D. As R&D may be transferred to different company divisions without the presence of observable opposite money flows, the R&D survey should not be restricted to capturing flows of funds. Instead, the survey should include questions about on whose behalf (domestic versus foreign) R&D is undertaken. Similarly, questions should be added as to whether access to knowledge has been obtained from R&D carried out by other (foreign) company divisions. For the time being the results presented below are based on the assumption that the underreporting of R&D export and import on balance does not affect the domestic formation and use of knowledge capital.

Results of R&D capitalisation Generally, there are substantial differences in how (intangible) capital is administrated in company records. A complete economy-wide recording of knowledge capital stock is often possible on the basis of time series R&D expenditure data only. In general, the recommended method to estimate capital stock in the SNA is the so-called Perpetual Inventory Method, by which capital stock measures are constructed on the basis of time series investment data. As it is expected that the capitalisation of R&D is going to be introduced in the upcoming SNA revision, preliminary findings for the Netherlands are summarised here. For a detailed review, see de Haan and van Rooijen-Horsten (2004). First, price information is required to adjust investment time series for annual price changes. Preferably, constant price R&D investment series should be estimated with the help of an output-related price index adjusted for quality improvements. However, the uniqueness of R&D makes output price comparisons of R&D over time by definition impossible. When output prices are not available, input-based price measures addressing the costs of R&D production are the only alternative. For this

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pilot study a cost, or input-based, price index with a reasonable breakdown including a specific subprice index for R&D labour input was made. Second, one may wonder whether unsuccessful R&D does contribute to the R&D capital stock. If one accepts that unsuccessful R&D is an indispensable part of knowledge creation, then the R&D capital stock should be valued on the basis of expenditure on both successful and unsuccessful R&D. In a similar way, the SNA93 recommends that all expenditure on mineral exploration should be treated as gross fixed capital formation (§166). Third, so-called gestation periods cause a time lag between R&D production and the emergence of productive knowledge capital stock (cf. Pakes and Schankerman, 1984). As a preliminary assumption, we use a time lag of 1 year. The presence of such a time lag implies that R&D output is at first instance recorded as work in progress, i.e. changes in inventories. The complete project expenditure is recorded as gross fixed capital formation in the year of finalisation. Expenditures in former years on projects that are finalised in the current year are counterbalanced by corresponding withdrawals from inventories. Material from Australia (ABS, 2004) indicates that the median life for patents is around 9 years. In our own estimates we adopt the same average service life for R&D capital with a declining age–efficiency profile. We assume that on average the competitive advantage obtained from the knowledge assets resulting from R&D is subject to progressive decline. Table 2 summarises the results of R&D capitalisation. As a rule, all government performed R&D ends up as government consumption while all other R&D is accounted for as work in progress, gross fixed capital formation or export. Due to the assumed gestation of 1 year, initially all domestic private R&D use is recorded as work in progress. Gross fixed capital formation corresponds to the domestic expenditure on market and own-account R&D at time (t–1) recorded at current prices (and not at t–1 prices). This gross fixed capital formation goes together with a similar but negative recording of work in progress. Table 2 summarises the effect of R&D capitalisation on the major aggregates of the national accounts. When market and own-account R&D are capitalised according to the above specified method, gross fixed capital formation is adjusted upwards by approximately 5% for all years. This share seems to decline in more recent years, indicating that total investment in the Netherlands is not increasingly directed towards knowledge capital. The effects of R&D capitalisation on GDP are rather modest. Total GDP is adjusted upwards by 1.1–1.2%. Equally, economic growth, measured by the volume increase of GDP, is hardly affected. Likewise, changes in the net national income are also quite modest as upward adjustments of gross fixed capital formation are counterbalanced by downward adjustments from consumption of fixed capital. In addition to the results presented in Table 2, the effects on capital stocks were also approximated. The upward adjustments in net wealth stocks as a result of R&D capitalisation are rather modest (around 1.2%). However, the increase in coverage of intangible fixed assets is substantial. This specifically results from the relatively

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Table 2. Provisional estimates of changes in the main national accounts aggregates as a result of R&D capitalisation, the Netherlands, 1995–1999 1995 Gross fixed capital formation in R&D

1996

1998

1999

3 657

3 824

4 035

mln

E

3 250

E E

139

89

3 086

3 171

E E

61 347

66 381

71 680

76 230

84 186

64 597

69 825

75 337

80 054

88 221

5.1

5.0

4.8

Changes in inventories

mln

Consumption of fixed capital

mln

Gross fixed capital formation

mln

Adjusted gross fixed capital formation

mln

Adustment in %

%-share

Gross domestic product, market prices

mln

Adjusted gross domestic product, market prices

mln

Adustment in %

3 444

1997

92

152

550

3 329

3 445

3 552

5.3

5.2

E

302 233

315 059

333 725

354 194

374 070

E

305 622

318 592

337 474

358 170

378 655

%-share

1.1

1.1

1.1

1.1

1.2

Gross domestic product, market prices

%-volume change

3.0

3.0

3.8

4.3

4.0

Adjusted Gross domestic product, market prices

%-volume change

3.1

3.0

3.8

4.3

4.1

Net national income, market prices

mln

E

260 178

269 064

287 624

295 441

318 239

Adjusted net national income, market prices

mln

E

260 481

269 426

288 044

295 972

319 272

Adustment in %

%-share

0.1

0.1

0.1

0.2

0.3

longer service lives assumed for R&D as compared to for example software, which in the Dutch national accounts is written off on average over a 3-year period of time. Other intellectual property The Frascati handbook (OECD, 2002) carefully demarcates intramural expenditure on research and experimental development. These Frascati guidelines are equally useful to define in the SNA93rev1 (codified) knowledge capital as a fixed asset. This will certainly be an improvement of the System’s coverage of the knowledge economy. However, one may argue that this narrowly defined fixed asset, i.e. knowledge capital, still represents only a part of all intangible capital associated with innovation processes in companies. It should be acknowledged that a broader range of creative activities (and related expenditure) may lead to the blueprints of new modes of production and new products that do not meet the definition of R&D. In several ways, these

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creative processes may enhance the intangible assets of companies. In many cases, such intangibles are legally protected by copyrights, brands or trademarks. Innovation-related expenditures, other than those on research, seem to be hard to measure. In many cases companies find it difficult to provide reasonable estimates (cf. Statistics Netherlands, 2003, p. 104). More over, their contribution to asset values is perphaps even more difficult to quantify. One innovation expenditure category discussed in this context by van de Ven (2000) is expenditure on advertising. van de Ven argues that so-called long-term marketing expenditure such as brand names, trademarks and franchise formulas, add to the value of assets. This expenditure could therefore be regarded as part of gross fixed capital formation. However, a major problem relating to capitalising marketing expenditure is the uncertainty about the extent to which related expenditure genuinely and exclusively contributes to the value of brand names or trademarks. One expects that brands or trademarks are a matter of production to a limited extent only. Consequently, the service lives of advertising expenditure cannot be determined easily. In conclusion, the capitalisation of non-public R&D is certainly a major improvement. Despite measurement problems, the revision of the SNA93 should at least acknowledge that creative activities – other than scientific work – may equally contribute to the value of companies and for that matter should be subject to future development activities. 4.4.3. The demarcation of individual intangible asset categories Introduction A clear delineation of intangible fixed assets in the SNA is complicated by the fact that intangible capital is frequently used to produce other intangible capital. This may give rise to double-counting problems in macroeconomic entities such as gross fixed capital formation and GDP. A frequently debated example is the use of software, literary or artistic originals for the purpose of selling copies. Another example is the use of fixed intangible assets in the creation of new intellectual property. Both examples are briefly discussed below. Originals and copies Concerning originals and copies, the double counting issue is well documented in the OECD’s software taskforce report (Ahmad, 2003, A.2.6). One crucial element in the discussion of originals and copies is whether or not the copying of originals encompasses a production activity. Another way of putting this is asking whether intellectual property can be copied at all or can be shared only. The current SNA93 (6.143) considers the production of books, recordings, films, software, tapes, disks, etc. as a two-stage process of which the first stage is the production of the original and the second stage the production and use of copies of the original. The output of the first stage is the original itself over which legal or de facto ownership can

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be established by copyright, patent or secrecy. The value of the original depends on the actual or expected receipts from the sale or use of copies at the second stage. Alternative options are to record the sale of a reproduction as either a partial sale of the original asset or as a rent payment. In both alternatives, which differ from the present SNA93 guidelines, the reproduction of originals is not recorded as production. One may argue that intangible fixed assets are not very different from other assets in a sense that capital goods are more generally used for producing capital goods. As explained in the OECD software taskforce report, for software this analogy is refused by some because the actual costs of reproduction may be negligible. Also a copy may not differ in any way from the original. However, although the intermediate costs of physical reproduction may be relatively small, other intermediate costs such as marketing, distribution and packaging expenses may be significant. Also from the perspective of the users of copies, it makes sense to capitalise the concomitant payments on licensees and other types of payments when these are expected to be used for more than one year.

Delineation of R&D and software The SNA93 (§6.163) considers by convention all expenditure on R&D as intermediate inputs. However, at the same time the current System recommends that expenditure on R&D should be explicitly recorded as output. “Research and development is not an ancillary activity, and a separate establishment should be distinguished for it, when possible” (SNA93, §6.142). Concerning the delineation between computer software and R&D, the European System of Accounts (ESA95) indicates that “expenditure on R&D does not include the costs of developing software as a principal or secondary activity” (§3.64). In the Frascati Manual, according to which a majority of countries compile R&D statistics, R&D related to software development is principally included. It is not useful to single out R&D output that is fully devoted to the development of a new software original. In this case, R&D and software development will normally constitute an inseparable part of the production process with one identifiable output, being the software code that defines the original. All R&D with the specific goal of developing a software original should be identified as software and not as R&D, which is in line with the present recording of software according to ESA95. In the event that the R&D concerns basic or applied research of a more general nature, so that it can used in several software development projects, it is meaningful to distinguish this R&D output (and the resulting knowledge asset) separately from software. Similarly, when the development of software is an inseparable part of an R&D project (not resulting to the development of a software original), this software should not be identified as a separate asset. The costs of this software development should be an integral part of the R&D project. When software is developed as a supplementary multi-purpose tool, it should be identified as a distinct computer software asset, in which case the consumption of fixed capital consisting of this software should be part of the production costs of the R&D output.

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A plea for net measures. As regards the delineation of software and R&D, the SNA revision does not imply major changes. Additional guidance with respect to a sound delineation of the recording of these fixed intangible assets in the System will be sufficient. Another concern is the use of GDP as the leading production indicator in most countries. This gross indicator is inevitably distorted by double counts since investment is included and consumption of fixed capital is not excluded. This distortion is amplified by the capitalisation of software and R&D. The recent debate among national accountants about double-counting issues showed the confusion that results from putting gross measures in the forefront. As a consequence of expanding the boundary of fixed assets, the use of net indicators should be advocated more strongly, for example in the introductory chapter of the revised SNA93. In this context, it is also highly desirable to underline the importance of internationally harmonised estimates for consumption of fixed capital. As argued by Bos (1990, p. 6) “both concepts (i.e. capital formation and capital consumption) must be regarded as inseparable twins, because accounting for capital formation without accounting for capital consumption is like making a pudding without eating it”.

4.5. Income-based indicators In Section 2 of this chapter it was mentioned that GDP roughly represents the value of goods and services either consumed or transformed into capital. By definition, GDP also entails the total sum of generated income in an economy. So, GDP can either be measured on the expenditure side, or on the income generation side. Both measures lead conceptually to the same GDP figure. Generated income (or valueadded) includes the compensation of employees, gross operating surplus (or mixed income in the case of unincorporated enterprises) and taxes minus subsidies on production. Table 1 of this chapter highlights a part of the total final expenditure that is particularly devoted to the knowledge and information infrastructure of the economy. Complementary to this picture, an income-based representation of knowledge-related expenditure indicates which part of generated income originates from knowledgebased production factor inputs. This section discusses the income-based presentation of knowledge indicators. One of the main goals of national accounting is the measurement of economic growth. The national accounts can be used inter alia as a data source for growth accounting. These growth accounts are subsequently used for measuring (multifactor) productivity change. For any production unit (be it an enterprise or an industry) (multifactor) productivity change is generically defined as output quantity change relative to input quantity change. Expressing change by index numbers, a (multifactor) productivity index is defined as an output quantity index divided by an input quantity index. Multifactor productivity is driven by technological change. This is why productivity analysis should also take into consideration knowledge-related inputs of

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production such as highly educated labour and the services of ICT and knowledge capital. Multifactor productivity analysis requires a meticulous identification in the national accounts of the labour and capital inputs of production. Value added as currently presented in the income generation account of the national accounts is generally understood as the remuneration of these production factors. Yet, this account does not fully differentiate between individual factor incomes. For example, the compensation of employees does not include the value of self-employed labour. Also, the value of most capital services is included in the balancing item of the income generation account: operating surplus or, in the case of unincorporated enterprises, mixed income. The latter includes rewards both for capital and for labour. It is expected that the upcoming update of the SNA will lead to guidelines about an explicit presentation of capital services and labour income in the income generation account. Such a presentation requires, among other things, a proper allocation of mixed income to capital and labour inputs. The problem is that capital services are generally not directly observable. Their value can be approximated with the help of the so-called user cost of capital model. This model explains that the value of capital services roughly includes capital depreciation and a return on capital. The measurement of capital stock and derived concepts such as depreciation and capital services are by now well understood and explained in the OECD (2001a, b) manuals for measuring capital and productivity. The OECD manuals pay attention to the measurement of capital services, and some national statistical offices even regularly publish estimates. Schreyer et al. (2003) and Diewert (2003) discuss the measurement of capital services in detail. If pure profits are assumed not to exist, i.e. markets are fully competitive, mixed income entirely represents the reward for capital or labour inputs. Similarly, the gross operating surplus of incorporated enterprises is entirely the reward of capital. Statistics on hours worked of self-employed persons can be used to impute a labour income based on the assumption that the average income per hour worked corresponds to that of a wage earner. However, the capital part of mixed income could also be estimated independently by compiling capital services cross-classified by industry branches and institutional sectors. Estimates for the Netherlands show that in several industries such estimates for labour and capital income together substantially exceed mixed income, even when the lowest rates of return are assumed, and over longer ranges of years. Therefore, this outcome is highly implausible. It could be that the imputed wage rates for the self-employed are overstated but there is no evidence that this is actually the case. Since the capital and labour constituents of mixed income are not directly observable, an arbitrary (pro-rata?) adjustment seems required to reconcile both estimates. Similarly, the assumption of zero real profits implies that the gross operating surplus precisely represents the reward for all capital inputs. As a result, returns on capital can be determined endogenously. However, one may wonder whether this assumption is always realistic. If one accepts the existence of real profits, the valuation of capital services must instead be based on exogenous rates of return.

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These assumptions underlying the valuation of capital services complicate the formulation of straightforward guidelines as to how the SNA should be adapted. As a result, the capital services in the next SNA will be given an experimental status: they will not be part of the core framework. Nevertheless, the introduction of capital services in the national accounts is a requisite for their use as growth accounts. As mentioned, growth accounts may, and probably should, take into consideration production factors that are knowledge- and information-oriented. Several productivity studies have linked them to the expected productivity gains of ICT capital (cf. Colecchia and Schreyer, 2001). Similarly, growth accounts should take into consideration other knowledge-oriented capital categories such as R&D and the services of other intangible assets. Above all, growth accounts should take into consideration changes in the mix of different labour types. It may be desirable to add more detail to wages and labour inputs, for example by means of a subdivision by educational attainment or occupation. For this purpose, reference can be made to the Canberra Manual (OECD, 1995) and to the sub-categorisation of labour presented in the Human Resources in Science and Technology (HRST) concept found in this manual. This approach is also followed in the “knowledge” satellite account recently developed for the Netherlands by de Haan and van Rooijen-Horsten (2003).

4.6. Conclusions This chapter addresses several changes of the SNA that will enhance the System’s relationship to the knowledge and information economy. Most of these changes are being discussed as part of the upcoming SNA revision scheduled for 2008. Work carried out at Statistics Netherlands on satellite accounting within the framework of the NESIS project brings us to the following conclusions. In national accounting, the economy-wide dissemination of ICT is most straightforwardly measured by the gross fixed capital formation in ICT equipment. In our opinion, the upcoming revision of the SNA should be taken as an opportunity to distinguish ICT capital in the main classification of assets. Section 4.3 of this chapter shows how this can be accomplished. We argue that network and telecommunication equipment should include the complete telecommunication infrastructure such as cable networks and mobile telecommunication networks. Human capital is undoubtedly a crucial production factor, especially with regard to the innovative capacity of companies. Most economists will consider expenditure on education as investment in human capital. However, the SNA does not currently support this view and the upcoming revision of the System will also not take the capitalisation of education expenditure into consideration. It must be acknowledged that the inclusion of human capital into the asset boundary would lead to fundamental changes in the system. One of these changes is that being educated or being trained becomes a production activity leading to the creation of human capital. This opens the door to including in the production boundary all sorts of unpaid productive

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activities, e.g. cleaning, caring, preparing meals. The measurement and valuation of these activities are not straightforward and probably beyond the scope of statistical observation. This does not alter the fact that satellite accounts can be very helpful in introducing the concept of human capital into economic analysis. R&D may lead to codified knowledge with properties that are in conformity with the general SNA definition of an asset. Therefore, there is a strong case for capitalising expenditure on R&D. As the current SNA93 recommends R&D to be recorded as current expenditure, the upcoming update should be taken as an opportunity for improvement. However, freely accessible knowledge created in the public domain cannot be recognised as an asset in the SNA sense because it is void of any form of ownership. This implies that most university research is not eligible for capitalisation. The capitalisation of R&D increases the need to establish in the SNA a sound delineation of intangible assets covered in the System. We argue that it is not meaningful to regard software as an independent asset when it is an inseparable part of an R&D project. Likewise, the reverse holds for R&D that is totally devoted to the development of a software original. Especially in Europe, there is a rising policy interest in productivity statistics. National accounts statistics should facilitate as much as possible the compilation of productivity statistics at the industry and macro-level. For the compilation of multifactor productivity measures, it seems helpful to take on board the concept of capital services when revising the SNA93. Due to several measurement problems, we argue that capital services should be introduced within a satellite accounting context only. Finally, a wider coverage of intangible capital in the SNA increases the importance of “net” against “gross” balance items. Or to put it in another way, the expansion of the asset boundary decreases the meaning of indicators such as gross domestic product and gross national income. This has come to light particularly in the recording of originals and copies. The use of net balancing items such as net domestic product and net national income should be advocated more strongly. Moreover, the international “national accounting community” should make a real effort to further harmonise the measurement of the consumption of fixed capital estimates.

References Ahmad, N. (2003), “Measuring investment in software”, OECD (Paris). Aulin-Ahmavaara, P. (2004), “Moving human capital inside the production boundary”, Review of Income and Wealth, 50, pp. 213–228. Australian Bureau of Statistics (ABS) (2004), Capitalising research and development, Paper presented at the third Canberra II Group meeting, 17–19 March, Washington DC. Bos, F. (1996), “Human capital and economic growth; a national accounting approach”, Paper presented at the 24th General Conference of the International Society of Research in Income and Wealth (Lillehammer, 18–24 August).

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Colecchia, A. and Schreyer, P. (2001), “ICT investment and economic growth in the 1990s: Is the United States a unique case? A comparative study of nine OECD countries”, OECD STI Working Paper, 2001/17, OECD (Paris). Commission of the European Communities, International Monetary Fund, Organisation for Economic Co-operation and Development, United Nations and World Bank (1993), “System of National Accounts 1993”, Series F, No. 2, Rev. 4, United Nations (New York). de Haan, M. and van Rooijen-Horsten M. (2003), Knowledge indicators based on satellite accounts – Final report for NESIS – work package 5.1, BPA-nr: 2097-03MOO, Statistics Netherlands (Voorburg/Heerlen). de Haan, M. and van Rooijen-Horsten, M. (2004), “Measuring R&D Output and Knowledge Capital Formation in Open Economies”, Paper prepared for the 28th General Conference of the International Association for Research in Income and Wealth (Cork, August 22–28). Diewert, W.E. (2003), “Measuring capital”, NBER Working Paper 9526, National Bureau of Economic Research, Cambridge (Mass.). European Commission (2000), “Communication from the Commission on Structural indicators”, COM (2000) 594 Final. Eurostat (2003), “Structural Indicators – Short Methodological Overview”, Eurostat (Luxembourg). OECD (1995), “Canberra Manual”, Paris. OECD (2001), “STI Scoreboard; Creation and Diffusion of Knowledge”, Paris. OECD (2001a), “Productivity Manual”, Paris. OECD (2001b), “Measuring Capital”, Paris. OECD (2002), “Frascati Manual 2002”, Paris. Pakes, A. and Schankerman, M. (1984), “The Rate of Obsolescence of Patents, Research Gestation Lags, and the Private Rate of Return to Research Resources”, in: Griliches, Z. (ed.) R&D, Patents and Productivity, The University of Chicago Press, Chicago. Schreyer, P. (2000), “The Contribution of Information and Communication Technology to Output Growth: A Study of the G7 Countries”, OECD STI Working Papers 2000/2, OECD, Paris. Schreyer, P.; Bignon P.E. and Dupont, J. (2003), “OECD Capital Services Estimates: Methodology and a First Set of Results”, OECD Statistics Working Paper 2003/6, OECD, Paris. Statistics Netherlands (2001), “Kennis en economie 2001”, Voorburg/Heerlen. van de Ven, P. (2000), “Intangibles: Invaluable? – Should the asset boundary in the 1993 SNA be extended?”, Paper presented at the 26th General Conference of the International Association for Research on Income and Wealth (Krakow).

Measuring the New Economy Edited by Teun Wolters © 2007 Elsevier B.V. All rights reserved.

CHAPTER 5

The Intangible Economy: Key Indicators of the “Hidden” Productive Capacities Clark Eustace (Mantos Associates, UK)1

5.1. Introduction This chapter sets out a rationale and a framework for a range of leading performance indicators as proxies for the “hidden” productive capacities that are embedded in the modern knowledge economy.2 For ease of reference, it is organised in three sections: Overview: a perspective on how today’s business economy differs from the “old” economy, including a synthesis of some of the key drivers and enablers. Preliminary ideas on the measurement gaps for corporate reporting and national statistics. Proposals relating to some key areas where new indicators are required. The emphasis is principally – but not exclusively – on business intangibles. A number of new indicator clusters are pinpointed and these are structured around a series of headings for conceptual clarity.

5.2. Transformations at work in the modern business economy The economic hype that accompanied the dot-com boom of the 1990s contained a great deal of rhetoric about a new economic paradigm. The debate surrounding the disconnect has largely fallen silent with the downward business cycle that followed. One of the PRISM group’s main conclusions is that there really isn’t a new economy,

1 Chairman

of the European Commission’s PRISM Research Group (2001–03): e-mail [email protected] 2 It draws extensively on a paper the author presented at the NESIS project’s IDWG meeting on 13–14 May 2004 in Athens.

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but a soft revolution in number of areas of including the asset base, the speed of markets and the nature of the value chain, which is resulting in deeper transformations with significant implications for the productivity of knowledge-intensive services and the creation of intangible goods. This revolution has gone largely undetected over a number of years because our systems of measurement aren’t able to pick it up. The disconnect, as this chapter will show, lies in our economic and business measurement systems, which are tracking – with ever increasing efficiency – a smaller and smaller proportion of the real economy. Far from being new topics, knowledge and intangibles have been important throughout history. The difference is that, today, a firm’s intangible assets are often the key element in its competitiveness. Increasingly, the capacity to combine external and internal sources of knowledge to exploit commercial opportunities has become a distinctive competency. Firms possess many different types of knowledge, which may be codified or tacit – codified knowledge can be bought, sold, stocked and valued, tacit cannot. A number of transformations are at work in the modern economy. The primary drivers centre on the rapid pace of improvements in computer power and connectivity, and “global contestability” – the speed with which leading-edge practices migrate around the world. Their impact is not limited to service industries and internet enterprises. Many traditional sectors are also profoundly affected. The effects, and the extent of the challenge to our corporate accounting and statistical measurement systems, may be characterised as follows: – The clarity of 20th century markets was characterised by a system of fixed boundaries, with one-to-one trading relationships, linear value chains and balance-sheet accounting concepts. – The economy today operates increasingly without fixed boundaries, and this has far reaching implications for companies, financial markets, public institutions and regulators. By the mid-1970s, structural forces such as globalisation and deregulation were already opening up the major world economies and creating profound changes in the behaviour of global markets. This paved the way for new modes of competition (and a re-definition of acceptable competitive norms) at a time when the build-up of latent cost and automation pressures in the mature mass-production industries was reaching critical mass. History will show that this volatile crucible was fuelled and given a massive, unprecedented impetus by the global revolution in digital technology – in the widest sense of the term, not just corporate ICT – and the arrival of the World Wide Web for commercial use. These changes did not occur suddenly or from a common cause, but occurred progressively and can be attributed to different causes which, taken together, have induced important changes in the architecture of the corporate value chain. Value chains always had a limited life in competitive markets, but they are now eroding at a much faster rate than in the past.

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By the mid-1990s, a new generation of business models had arrived, whereby sustainable value-creation is geared less to economies of scale than to nimbleness and speed of execution in exploiting innovation, arbitrage and market-scope effects. The economic effects can be seen most clearly in terms of their impact in four areas: (i) We are in economies of surplus, in the sense that consumers’ basic needs have been met and product markets have become increasingly commoditised. This, in turn, forces firms to intensify their search for new strategic assets to eliminate effective competition as well as unique factors of differentiation and market leverage. (ii) Successful players in today’s open, hyper-competitive markets must have access to a nexus of unique, or at least difficult-to-replicate, capabilities, competences and quasi-assets in order to stay ahead of the game. The star performers of the modern business world are those who can create, maintain or invade monopolies founded on intangibles. (iii) Cumulatively over the last two to three decades, the major world economies have experienced a fundamental shift in the corporate asset base. Recent surveys on both sides of the Atlantic show significant levels of “hidden” capital formation, with material country-to-country variations across the EU. This is not being routinely picked up by our existing measurement systems. The data is not there, we simply missed it. (iv) In the financial markets, as elsewhere, Information and Communication Technology (ICT) is both a codifier and a migrator. Time and space vanish at the touch of a button. The dramatic reduction in interaction time and costs (and, hence, system inertia) has led to increased instability in the financial markets. Concurrently, the fallout from the hi-tech bubble, Enron & Parmalat, etc. has accelerated the spread of an Anglo-Saxon market control ethos at a much faster pace than expected. In these days of squeaky clean accounting, the need for transparency is paramount. We just can’t afford huge black holes in the global financial system – the risks are far too great.3 Looking beyond the corporate world, it is clear that the impact of successive waves of new digital technology has not only transformed the economic landscape, but is also acting as an economic and social catalyst to promote changes in work and leisure patterns. This latter transformation is only just beginning and, as such, the diffusion of ICT will continue to act as an engine of change in the fabric of society for some considerable time to come.

3As

observed in January 2002 by Howard Davies, Chairman of the UK Financial Services Authority, in a speech to senior finance industry figures in London.

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5.3. Key information gaps Probably the most enigmatic challenge presented by the modern economy is that faced by macroeconomists, statisticians and accountants. Since the 1950s, economic theorists have offered a succession of views as to why the macroeconomic indicators have for many years failed to reflect the cumulative investment in technological, management, logistical, educational and other improvements aimed at raising efficiency. As a result, we have seen a succession of new growth theories, each claiming unique insights into the mechanisms of technical progress and innovation. A key question – which has fuelled a heated 40-year economic debate – is the failure of the massive expansion of intangible investment to show up explicitly in productivity performance statistics.4 The debate has been heightened since the mid-1990s when, after several decades of unexplained productivity slowdown in the global economies, the US trend has exhibited a sharp, sustained reversal. That we are living in a transitional economy and need to move quickly to modernise our concepts is a central recurring theme voiced by many experts. The main problem is that, as yet, no unifying theory or empirical model exists to provide a satisfactory explanation of the workings of the modern economy. Nor is there a cogent explanation for the (recent) disparities in the Gross Domestic Product (GDP) and productivity trends exhibited by the leading economies. In these circumstances, we have little alternative but to step back and let the scientific due process run its course, as each new theory compromises and displaces its predecessors. Accordingly, priority should be given to the following information deficiencies: – Business services: major gaps exist in our baseline of routine statistics. – Tracking of “macro” intangibles (leading indicators of the potential of the economic environment to foster rapid enterprise growth and comparative advantage). – Flushing out intangible goods (scientific, artistic or literary rights that can be bought, sold, stocked and readily traded in disembodied form, and (generally) protected). – Financial markets: there is a major black hole in our statistics on their behaviour and dynamics. – Returns to intangible investment: a major focus for academic research in applied econometrics. – Corporation Tax. Some re-orientation of the measurement process is also required. The old business measurement system is rooted in a classical world of deterministic maths, linear

4A detailed

treatment of the theoretical and experimental work on the “productivity paradox” since Robert Solow’s 1987 call to arms “you can see computers everywhere except in the productivity statistics”, is beyond the remit of this report. OECD (2000d) offers a comprehensive treatment of the theoretical and statistical problems, together with comprehensive references to the on-going work of researchers such as Brynjolfsson, Hitt, Yang and Strassmann.

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stock-flow theory and discrete, additive and serially coherent data points. Its main purpose in life is to build, with ever increasing accuracy,5 a historical picture of costs, prices and events geared to a family of business concepts and models that originated in the manufacturing era and are today becoming increasingly obsolete. In its essence, it is retrospective – it provides a rear-mirror view. In terms of statistical measurement, the future lies in richer clusters of leading indicators and the application of probabilistic maths and econometrics. In respect of official filings, the emergence of XML-enabled online third-party hosts – information repositories that allow users to construct their own analytical models – will have a profound impact over the next decade. As yet, there are few causal models to explain the workings of the modern economy – the first generation of “new economy” models was almost exclusively heuristic. The focus of statistical and measurement research now needs to shift to enriching the base data and experimentation with forward-looking causal simulation models.

5.4. Priority areas where indicator development is required The following is a broad synthesis of six areas where features of the NIE need to be brought firmly on to the radar screen. It does not set out to be exhaustive or Cartesian, rather it is intended to stimulate thinking and debate.6 5.4.1. A richer breakout of data on the service sector At the macro-level, we urgently need to capture a much richer base of information on the weight and contribution of services in the modern economy. The starting point should be the market service sectors, with leading indicators geared to growth of value-added, investment, employment and workforce skills. Also balance of trade, royalty flows and tax yield. Here, initially at any rate, the accuracy of the aggregate numbers is less important than tracking and providing insights into what is happening in the critical growth sectors – in particular, business services and financial services – as a basis for meaningful analysis and policy decisions.

5As

long ago as 1946, Peter Drucker observed that “… we are measuring with ever increasing accuracy an ever decreasing proportion of the real economy”. 6 Given the history of the NESIS project, I have deliberately avoided any attempt to impose, in a formulaic sense, yet another framework or taxonomy. I have also concentrated exclusively on the business statistics domain. I leave it to others much better qualified to expound on indicators appropriate to domains such as social, environmental and educational behaviour. Finally, I have tried to avoid any unnecessary repetition of indicators recommended by other NESIS contributors.

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5.4.2. Intangible investment and economic performance Macro intangibles Leading indicators of the potential of the economic environment and framework conditions to foster rapid, flexible enterprise growth and hence comparative advantage. Recent research in the US and Europe on the influence of macro intangibles on GDP and corporate performance thresholds has shown some encouraging signs of progress, especially the econometric work carried out under the auspices of the OECD Growth Project. This has demonstrated a more-or-less robust correlation between intangible investment, GDP and productivity growth. According to the OECD.7 the following intangibles have been shown to Correlate positively with GDP and/or productivity growth: (i) Business-funded R&D investment. There is a strong body of empirical evidence to correlate the intensity of R&D expenditure with economic performance as measured by productivity gains. This is illustrated by the indicators in Figure 1, which show a clear, positive correlation between changes in the intensity of R&D expenditure and the productivity growth indicators for 17 OECD countries over the period 1980–98.8 The correlation holds good for three indicators of R&D expenditure: – Business Enterprise Expenditure on R&D (BERD) – Gross Expenditure on R&D (GERD) – The ratio of BERD to GERD Although there are indications that the overall R&D spend (public and private) also acts as a driver of productivity growth, the correlation is strongest when business-funded R&D (BERD) is used as the explanatory variable. Evidence from other studies also suggests that basic research has higher returns than applied R&D (Griliches, 1986) and that process R&D has higher returns than

7 OECD,

2000d. Although the correlations are robust, this work is subject to a range of caveats concerning its premises and boundary conditions in common with all econometric modelling. 8 In addition to limitations in the econometric models themselves, the basic R&D data captured from firms also has to be treated with caution since they follow an obsolescent laboratory-to-factory data model, and even this is not always applied consistently between countries. Corporate R&D budgets now invariably encompass a labyrinth of complex innovation processes, and a growing proportion of “D” expenditure relates to kaizen – continuous process improvement – and soft development activities both within and beyond the legal boundaries of the organisation making the R&D investment. Thus, an essential, material part of total innovation spend now lies outside the traditional R&D domain, in areas such as quality control, training and IT&C systems for process codification, managing the market franchise, alliances, etc. These are excluded under the OECD’s “Frascati” R&D definitions, which are generally used as the basis for firm-level data capture and statistical analysis. The definitions need to be expanded to cover activities such as these that are essential to the development process, but currently excluded.

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Figure 1. Productivity growth vs. BERD, 1980–98 1.5

Difference in MFP (hrs adj, only) growth rate between 1980-90 abd 1990-98 Australia

1.0 Denmark United States New Zeland

0.5

Sweden

Portugal

Ireland

0.0 Germany

−0.5

Finland

Canada

Austria Greece Belgium Netherlands

Japan

France

−1.0

Spain

−1.5

−2.0 −0.4

−0.2

0.0

Correlation coefficient 0.43 t-statistic 1.83

0.2

0.4

0.6

0.8

Difference in BERD intensity between 1980-90 and 1990-98

Source: R&D data are from the OECD MSTI database.

product R&D. There is also some evidence to suggest that the role of R&D differs according to the size of the economy. In large countries, R&D operates mainly by increasing the rate of innovation while, in smaller countries, it serves primarily to facilitate technology transfer from abroad. (ii) ICT spend geared to improving firm dynamics. The importance of ICT as a driver of business performance has been recognised for some years now, but the processes involved are complex and do not yield readily to analytical methods. In recent years an alternative view of ICT has emerged,9 which suggests caution in inferring superior performance based on any single measure. Failures of economic and business researchers to demonstrate a statistically significant, direct relationship between ICT expenditure and company performance is almost certainly because the route from input to output – from ICT to innovation and growth – is contingent on more complex factors that act as enablers. According to this view, the returns to ICT investment are geared more to the receptiveness of the entrepreneurial culture than to the technology itself. If it is to provide not only a competitive impetus, but the flexibility to exploit new as yet unforeseen business opportunities, a collateral spend of several times the technology cost itself is required to change mindsets, organisational flows and work practices sufficiently to bring about real improvements in performance thresholds.

9 See

Brynjolfsson and Hitt’s MIT Sloan papers (1992–2000).

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Figure 2. Liberalisation of product markets 1.5

Difference in MFP (hrs adj.) growth rates between 1980-90 and 1990-98 Australia Denmark

1.0 0.5

Finland Sweden Canada New Zeland Portugal Greece Austria Germany Italy Japan Belgium Netherlands

United States Ireland

0.0 −0.5

France

−1.0

Spain

−1.5 0.0

0.5

Correlation coefficient −0.47 t-statistic −2.13

1.0

1.5

2.0

2.5

3.0

3.5

4.0

1

Indicator of product market regulation

Source: OECD 2000(a)

(iii) Liberalisation of product markets. Recent research has shown that public policy related to the legal institutional framework for business influences innovation, turnover growth and the diffusion of innovation. In particular, making markets more “contestable” and increasing competition can be expected to accelerate GDP and productivity growth.10 This aspect is explored in some detail in the McKinsey country reports for Sweden and the UK, while OECD research11 shows the existence of a statistically significant link between overall product market regulation and multi-factor productivity (MFP) growth during the 1990s, as illustrated by the indicators in Figure 2. More recent figures (2003) for the indicator of product market regulation show that many OECD countries have been working on the removal of obviously unnecessary regulations in this area. This shows that the significance of this indicator is widely recognised. (iv) Relaxing administrative legislation. Administrative burdens are another aspect of product market regulation that inhibits technology adoption and constrains technology diffusion. Figure 3 shows the correlation between a leading indicator of administrative legislation and productivity growth during the 1990s. This reinforces the view that countries with strict regulations and slow-moving, bureaucratic public institutions were generally associated with low productivity growth in the 1990s.12

10 This

is analysed in some detail in the McKinsey country reports. See, for example, “Sweden’s Economic Performance” (1995) and “Driving Productivity and Growth in the UK Economy” (1998). 11 OECD (2000c). Conference paper presented at the 150th Anniversary Conference of the National Bank of Belgium “How to Promote Economic Growth in the EuroArea”, Brussels, 11–12 May 2000. 12 See McKinsey reports: France & Germany (1997); Sweden (1995).

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Figure 3. Relaxing administrative regulations Difference in MFP (hrs adj.) growth rates between 1980-90 and 1990-98

1.5 Australia

1.0

Denmark

Finland

Canada

0.5

Sweden

New Zeland

Portugal Unied States Ireland Greece Austria

0.0

Germany Japan

Netherlands

−0.5

Italy Belgium France

−1.0

Spain

−1.5 0.0

0.5

1.0

Correlation coefficient −0.60 t-statistic −2.99

1.5

2.0

2.5

3.0

3.5

4.0

Indicator1 of administrative regulation

Source: OECD 2000(a)

Also in the area of reducing the administrative burden of companies due to all kinds of regulations and obligations, many OECD countries have made notable progress, considering the more recent figures in this area. (v) Raising workforce educational levels. Another important insight into the drivers of growth and productivity can be obtained by comparing the educational attainment of workers in active employment with that of the working-age population overall (Figure 4). The analysis covers nineteen 19 OECD countries for the period 1989–96, and shows a clear trend towards skill-based employment growth. Not surprisingly, it also indicates that employment prospects for workers with uppersecondary education compare favourably with the working age population at large. According to McKinsey, over the next ten years 50% of all jobs will become redundant or change beyond recognition, which means that, from a policy perspective, labour flexibility and lifelong learning must continue to be top priorities. The pace of change will be greatest in the service industries. (vi) Relaxing employment protection legislation. Labour market regulation also plays a role in influencing GDP and productivity growth. The indicators in Figure 5 show a significant negative correlation between employment protection legislation and MFP growth for selected OECD countries during the 1990s. In countries with strict employment protection regulations, firms adopt more cautious recruitment policies and this, ultimately, has an adverse affect on their propensity to invest, diversify and take risks. Mergers and take-overs are also influenced by employment legislation, for example in the freedom to divest activities that are no longer a strategic fit with the core business. Intangible capital formation Business intangibles enable knowledge-intensive economies to maintain their competitive position and out-perform resource or labour intensive economies.

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Figure 4. Rising educational levels. Share of individuals with higher educational levels in total, percentage point change Employment

12

Esp

10

8

Gbr Prt

Fra

Can

Swe

6

Ire

Bel

Fin USA

Aus

4

Dnk

Ita Nor Nld

Che

2

Deu2

Aut Nzl

0 0

1

2

4

3

5

6

7

8

9

10

11

12

Working-age population 1. High education levels refer to ISCED codes 5, 6 and 7 2. 1991-96 Source: Calculations based on data from OECD, Education at a Glance, various issues Source: OECD 2000(a)

Figure 5. Relaxing employment protection legislation Difference in MFP (hrs adj.) growth rates between 1980-90 and 1990-98 1.5 Australia Denmark

1.0

Finland Canada

0.5

Ireland

United States

0.0

Sweden

New Zeland Austria

Germany

Portugal Greece

Japan Belgium

−0.5

Italy

Netherlands France

−1.0

Spain

−1.5 0.0

0.5

1.0

Correlation coefficient −0.52 t-statistic −2.30 Source: OECD 2000(a)

1.5

2.0

2.5

3.0

3.5

4.0

Indicator1 or employment protection legislation

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Drawing on the OECD “Investment in Knowledge” paper (OECD, 2001), Hill and Youngman13 examined the scale of knowledge investment for 11 leading economies over a seven-year period from 1991 to 1998.14 The knowledge investment is expressed as a ratio to Gross Fixed Capital Formation (GFCF), which is largely, but not wholly, an aggregate of investments in physical assets. GFCF is one of the four components of GDP and will typically constitute 15–20% of GDP. As can be seen from the histogram reproduced here, the researchers were able to conclude that … “intangible investment numbers are statistically significant and deficiencies in their collation will lead to a statistical bias and materially misrepresent trends within economies. Moreover, it is apparent there are significant differences between EU countries, which might suggest structural differences in the way the economies are evolving”. They went on to recommend that … “given that intangible’s relative importance appears to be growing, this underlines the need for a new taxonomy to be developed if national accounts and macroeconomic statistics Ratio of Investment in Knowledge ("broad") to Adjusted GFCF (Residential housing and Software taken out), 1991-1998 1.2 Source: OECD/BEA 1.0 0.8 0.6 0.4 0.2

1991

1992

1993

1994

1995

1996

1997

US A

UK

Sw ed en

Ne th er la nd s

Ko re a

Ja pa n

ly Ita

G er m an y

Fr an ce

Fi nl an d

De nm ar k

0.0

1998

Source: Hill and Youngman (2003)

13 Hill

and Youngman (2003): Final Report of PRISM WP5 (available at www.euintangibles.net).

14 Their investment figure was calculated as the sum of expenditure on R&D, on education (from both public

and private sources), and software. Adjustments were made to recognise there would be overlaps between these three categories, in particular between R&D and software expenditures. Training and Development was noted as a conspicuous absentee. It would have been included if such expenditure data was readily available. Where possible, we have also made adjustments to the GFCF, taking out software (so that it is not included in both) and residential housing (as it is not considered an equivalent expenditure to the investment made in knowledge).

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are going to provide the best estimates they can of the realities of the 21st century economy”. Micro intangible’s (i) Intangible goods and other intangible assets. For over two hundred years, economists and statisticians have been constrained by an economic model based on the myth of a strict dichotomy between goods and services, a view expressed in the work of the pioneering economists Adam Smith, John Stuart Mill and Jean-Baptiste Say. In a 1997 paper to the Canadian Statistical Society, Hill proposes a third class of economic activity (in addition to goods and services), based on entities he terms “immaterial goods”. He defines these as non-physical entities that can be separated from a firm’s organisational fabric – generally in the form of intellectual property (patents, licenses, trademarks, etc.) as distinct from those which are interwoven, often in complex and subtle ways, with the enterprise’s physical and financial asset base. Such goods can be bought, sold, stocked, licensed and otherwise traded in the same manner as physical goods. In professional and academic quarters there is a growing recognition that a strict goods and services classification is too simplistic. Intangible goods are vital corporate assets in many enterprises, but until very recently were not disclosed in company statements, while the European system of national accounts (ESA) misclassifies them by scattering them all over services. As a result, there is a paucity of data for analysis at both levels. What has been clear to many of us for some time, is that there is an urgent need to flush them out and recognise them for what they are – key wealth components of a 21st century economy. In corporate reporting, a landmark event in this connection took place in July 2001, when two new US accounting standards (Statements of Financial Accounting Standards Nos. 141 and 142) proposed a different treatment of goodwill and intangibles with an indefinite life arising from business combinations. Statement of Accounting Standards No. 142 (SFAS 142) “Goodwill and other Intangible Assets” represented an historic pronouncement by the US Financial Accounting Standards Board (FASB). For the first time, business valuation (as opposed to cost accounting) was incorporated into the financial reporting framework. In addition to being a landmark accounting pronouncement, SFAS 142 is, in many respects, a valuation standard. SFAS 142 changed the game. It is now necessary for finance and accounting professionals in publicly-listed companies, and non-listed companies that adopt the standard, to develop an understanding of many of the principles, methods, and techniques of business valuation. This new accounting treatment will be extended to European listed companies from 2005 via a EU regulation that will require listed European companies to comply with International Financial Reporting Standards (IFRS) for their group financial statements, including IFRS 3 which deals with the treatment of intangible assets in business combinations along similar lines to the US pilot regime. This is the biggest change in corporate financial reporting in 25 years and makes the

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transition to IFRS an urgent issue for the 7,000 companies in the EU that currently report under national rules. It is expected that an increase in the breakout and disclosure of goodwill into its component intangible assets will stimulate a wider interest in the management of intangibles and their representation in company reports. It will also stimulate further experimentation with management tools, and valuation and audit standards for the new asset classes will be necessary. A further stimulus to both the management and disclosure protocols will come from the new search and consolidation facilities offered by eXtensible Business Reporting Language (XBRL). Figure 6 presents a taxonomy for intangible assets as developed through IFRS 3. The result is five categories of intangible assets, essentially the intangible goods segment under the 4-way PRISM asset schema (PRISM report, 2003). The IFRS 3 classification of intangible assets comprises five mutually exclusive asset classes (mirroring the FASB classification): – Marketing-related intangible assets: trademarks, trade names, service marks, collective marks, internet domain names, trade dress (unique colour, shape or package design), newspapers mastheads and non-competition agreements. – Customer-related intangible assets: customer-lists, order or production backlogs, customer contracts and their related relationships, non-contractual customer relationships. – Artistic-related intangible assets: plays, operas, ballets, books, magazines, newspapers, musical works such as compositions, song lyrics and advertising jingles, picture and photographs, video and audiovisual material, films, music videos and television programmes. Figure 6. IFRS taxonomy for intangible assets Generation Process for Intangible assets

• Separate purchase • Part of business combination • Government Grants • Exchange of assets • Self-Creation (internal Generation)

Technology-based Intangibles

Contract-Based Intangibles

Customer-related intangibles

Marketing related intangibles Artistic-related intangibles Source: Mantos Associates

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– Contract-based intangible assets: Licensing, royalty and standstill agreements, advertising, construction, management, service or supply contracts, lease agreements, construction permits, franchise agreements, operating and broadcasting rights, use rights such as drilling, water, air, and finally, servicing contracts such as mortgage servicing contracts. – Technology-based intangible assets: patented technology, computer software and mask works, unpatented technology, databases, trade secrets, such as secret formulas, processes or recipes. In Europe, this information will be available through official company filings from 2005 and will provide a rich source of data for the statistical community to tap into. In this connection early attention should be given to building a picture of the royalty flows. (ii) Intangible investment products. There is a growing awareness in OECD member countries that an increasing part of the total investment in the business enterprise sector is directed towards intangible “investment products” such as R&D, marketing, training, and software. Nevertheless, OECD data on intangible investment is still relatively scarce (PRISM, 2003), as it is in the national statistics. Given the huge gap created by our inability to measure returns to intangible investment, it might be useful – and achievable – to collate data on the input expenditures (costs) made by enterprises on the following. While these may seem like very basic data points, it should be noted that such data does not exist today. Its existence would allow an array of research and analysis that today is constrained by data limitations: Research and development – ideally split between the two. Marketing and organisational design. Software development. Training and development – ideally split between operational and vocational.

5.4.3. Innovation and the supply chain In classical business theory, major shifts are driven by discontinuities or rapid changes in market expectations. In this context, the primary drivers of economic change today are seen as: – Rapid improvements in computer power and connectivity. – Global contestability15 of business intangibles (the rapid speed with which leading-edge practices migrate around the world).

15 Global

contestability is based on the notion that best practice migrates rapidly to multinational companies everywhere. The prime agents are leading edge companies establishing operations abroad and M&A, but there is also a “halo” effect on local companies as suppliers link in via electronically-enabled supply chains. Global contestability also drives equality of productivity in locally produced, locally consumed services. In this respect, ownership changes through cross-border mergers and acquisitions are important – the pace

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The net effect of these and other firm-level changes over the past 25 years has served to create a wholesale disaggregation of the corporate value delivery system. Adding to this, the growth of outsourcing and relocation as value management tools are having a profound effect on the managerial mindset and the norms of entrepreneurial behaviour and competitiveness. At the firm level, changes in the architecture, pace and connectivity of the value chain have redefined the core business activities of innovation, operations and management of the customer interface. These aspects of the changing corporate perspective require new indicators and indicator clusters. As a starting point, the following headline groupings are proposed:16 – – – – – – – – –

Product “R” and “D” to sales (goods & services, organic & outsourced) Time to market, new product ratio Process R&D Manufacturing capacity & utilisation Services capacity & utilisation Order/production backlog Online orders/fulfilment Average time to service order Outsourced/offshore operations

Finance cluster From humble origins in the 1970s, when corporate finance operated within fixed boundaries with textbook equity and debt “products”, today’s corporate treasury function is a sophisticated and important part of the corporate financial system. Firms, especially multinationals, have highly complex financial-engineering structures and increasingly by-pass the banks for medium and long-term debt and deal directly with the capital markets. As a result, the traditional City banking practices are now being challenged by the emergence of ever-more innovative lending structures, many of which owe a significant debt of origin to Wall Street and its financial institutions. Very little of this, however, is captured in our regular statistics. In addition to headline rates & numbers – capitalisation, traded volumes, spreads, etc. – key information gaps to be addressed include, for each major capital market centre: – Cost of capital trend statistics – Corporate credit ratings (trend line) – Moodies, S&P, etc. – Structure of corporate debt – equity/debt gearing ratios

of adoption in the local economy increases dramatically in the presence of a global industry leader for a fuller description of global contestability, see Bryan, et al. (1999). 16 This list is by no means exhaustive. For a more comprehensive treatment of business performance indicators see Kaplan and Norton (1996) and Lev (2001).

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– Corporate bond issuance – statistics to include derivatives such as convertibles, contingent convertibles, etc. – Securitisation – corporate, bank, mortgage, credit card, etc. – Structure of firm-ownership – growth of equity partners – implications for corporate governance – Directors share dealings – Tracking of enterprise and household credit While on the subject of financial ratios, the market-to-book (M/B) ratio for publicly-listed firms warrants special mention. Some academic theorists and analysts believe that high M/B values are attributable mainly to the substantial build-up of hidden intangible capital that is not reported in company filings, or elsewhere in government statistics. However, the contention that the gap is attributable to unbooked “intellectual capital” is now largely discredited as too simplistic and other factors, such as rising returns to book equity and a fall in the cost of equity, must be taken into account. An interesting recent development is the industry diagnostic developed by McKinsey, which throws a different light on what the M/B relationship tells us about a firm’s competitive positioning relative to others in its industry sector, and how this moves over time. The diagnostic technique goes under the name “strategic control mapping”, and provides a useful picture of the relationship between the size and performance perspectives of market capitalisation. Sectoral studies have shown that as a rule an inverse relationship exists between the performance and size metrics for most industries. Figure 7 shows a map of the global financial services industry. It plots book value as a proxy for size and M/B ratio as a proxy for prospective performance. Mapping all the players in a given industry on the strategic control map gives a snapshot of their competitive positioning. A further dimension is obtained by looking at the trajectories of the market and book figures over time (Figure 8). This plots the course of a sample of financial services firms between 1987 and 1997.

Corporate taxation The rapid growth and expansion of the shareholder value movement in the 1990s has raised the profile of corporate taxation as a manageable cost of doing business. Little information is available and more work is needed to establish appropriate model parameters. However, tracking of tax yield and cash tax rates, for cross-sectional comparison by country, industry sector etc. would seem a sensible starting point.

Simulation modelling (of intangibles) As stated earlier, there are few models that make any serious attempt to probe and explore the causal inter-relationships in the so-called new economy – the first

Figure 7. Strategic map of the global financial services industry (simplified to show a sample of the major global players) Market capitalization isoquants ($billions) $10 $25 $50 $75 $100 $150

12

10 MBNA

8

Performance: Market-tobook ratio*

Schwab

Lloyds TSB

6 U.S. BanCrop

4

Santander

BankBoston Mellon

2

American Express

First Union BancOne/First Chicago

AXA-MIDI

AIG

Morgan Stanley Merrill Lynch JP Morgan

UBS/SBC

Deutschebank

CitiGroup HSBC

Chase

BankAmerica NationsBank

ING

Geographic incumbents

0 0

5

10

15

20

25

30

35

40

45

50

Size: Book equity ($billions)**

Source: McKinsey Global Institute

Figure 8. Strategic trajectory of key industry players (Global Financial Services Industry, 1987–97) Market capitalization isoquants ($billions) $10 $30 $50 $75 $100 $150 8

MBNA '97

7 6 5 Performance: Market-to4 book ratio*

AIG '97

'92 MBNA

3

AIG '87

2

AIG

'97 BankAmerica

'92

NationsBank

'92 NationsBank

1 '87 NationsBank

0 0

5

10

15

20

25

30

35

40

45

Size: Book equity ($billions)**

Source: McKinsey Global Institute Data sources (Figures 7–8): Compustat; Global Vantage; Bloomberg *Market Value calculated by reference to price of common stock on 31 March 1998 **Shareholders’ equity as of 31 March 1998, or most recent previous reporting date

50

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Clark Eustace

generation models were mainly heuristic rather than causal. There is much scope for probabilistic maths and risk/default models should also be more fully exploited. As a first priority, more research and experimentation effort is needed in the area of macro and micro-simulation models for intangibles. Econometric modelling of the outcome (returns) to sustained intangible investments would seem to be a sensible starting point. This book testifies of some interesting milestones reached in this work.

5.5. Conclusion This chapter pinpoints some of the key measurement gaps that have been thrown into relief by changes in the business economy over the last quarter of the 20th century, and goes on to propose appropriate indicators. The initial ideas on what aspects of intangible assets and other non-material factors of superior economic performance might usefully be captured will provide the basis for the way forward.

References Bryan, L.B.; Fraser, J.; Oppenheim, J. and Rall, W. (1999), Race for the World. Strategies to Build a Great Global Firm. Boston, HBS Press. Hill, T.P. and Youngman, R.D. (2003), Final Report of PRISM WP5 (available at www.euintangibles.net). Kaplan, R.S. and Norton, D.P. (1996), The Balanced Scorecard, Harvard Business School Press, Boston. Lev, B.L. (2001), “Intangibles – Measurement, Management and Reporting”, The Brookings Institution, Washington, DC (manuscript available at www.baruchlev.com). McKinsey (1995), “Sweden’s Economic Performance”, McKinsey Global Institute, Washington, DC Country Reports. McKinsey (1997), “Removing Barriers to Growth and Employment in France & Germany”, McKinsey Global Institute, Washington, DC Country Reports. McKinsey (1998), “Driving Productivity and Growth in the UK Economy”, McKinsey Global Institute, Washington, DC Country Reports. McKinsey (1999), “Unlocking Economic Growth in Russia”, McKinsey Global Institute, Washington, DC Country Reports. OECD (2000a), “Knowledge, Technology and Economic Growth: Recent Evidence from OECD Countries”, by Andrea Bassanini, Stefano Scarpetta and Ignazio Visco. Paper given at the 150th Anniversary Conference at the National Bank of Belgium, Brussels, 11–12 May 2000. OECD (2000b), “Is There a New Economy? First Report on the OECD Growth Project”, 14 June 2000.

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OECD (2000c), “New Sources of Economic Growth in Europe?” by Jørgen Elmeskov, and Stefano Scarpetta. Paper given at the 28th Economics Conference, Österreichische Nationalbank, Vienna, 15–16 June 2000. OECD (2000d), “A New Economy? The Changing Role of Innovation and Information Technology in Growth”, OECD Growth Project Series, July 2000. OECD (2001), “Investment in Knowledge”, Paris. PRISM Report (2003), “Research Findings and Policy Recommendations”, EC IST Programme (manuscript available at www.euintangibles.net).

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Measuring the New Economy Edited by Teun Wolters © 2007 Elsevier B.V. All rights reserved.

CHAPTER 6

IT Investment, ICT Use and UK Firm Productivity Tony Clayton1 (ONS) Raffaella Sadun (Centre for Economic Performance, LSE) Shikeb Farooqui (ONS and University of Pompeu Fabre)

6.1. Introduction During the period 2004–2005 the UK Office for National Statistics and London School of Economics were sponsored by the Department of Trade and Industry to research the impact of IT investment and Information and Communication Technology (ICT) use on firm level productivity across the UK economy. As well as providing an evidence base for policy, this study set out to identify some of the measurement issues which need resolution to improve the support to policy makers. The first major step in this research was constructing firm level IT investment data from a range of UK sources, and analysing the effects of the resulting IT capital stock on multifactor productivity. Particularly interesting results were found for the differential productivity effects of IT investment in multinational firms compared to “UK only” operations, and in firms with US ownership. Subsequent research looked at the productivity effects of ICT use – by employees, through electronic transactions and through use of communications services – in addition to the effects associated directly with IT investment. Analysis was also undertaken on different industry sectors to test whether differences in productivity effects associated with IT investment and ICT use are substantial. The UK data indicates that they are. Taken together, the results of this work help to explain differences in productivity seen in macro-economic comparisons between the UK and US. They are also consistent with industry comparisons undertaken at the University of Groningen which show the performance of ICT enabled service industries to be an important factor in the productivity performance of the US compared to EU countries.2

1 His

e-mail address: [email protected] and Goodridge (2004), Bloom et al. (2005), Sadun (2005), Farooqui (2005).

2 Clayton

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Tony Clayton et al.

This chapter summarises the various results of the research done within the indicated framework.

6.2. Research background Up to 2002, most work on the effects of ICT on economic performance focused on macro-economic measures, looking at whole economy or industry growth, and the role of ICT as one of the inputs. Many of these analyses, especially those which made North American-European comparisons, concluded that evidence for IT investment as a positive driver of productivity growth was emerging in the US (after a ten-year wait), but was difficult to find in Europe. More recent work using growth accounting (van Ark, 2003) shows productivity growth in ICT using sectors in Europe lagging well behind similar industries in the USA. This conclusion has led to extensive work at firm level, to identify how ICT affects behaviour and performance of individual firms and to see whether the European failure to capitalise ICT runs across the board, or whether there are successful examples. Researchers have also been concerned to understand whether the differences are explicable in terms of market failure which can be tackled by government policy, or whether there are lasting structural differences.3 See also annex 1 (at the end of this chapter). The new UK research outlined in this chapter seeks to bring together firm-level data on hardware and software investment, ICT use by employees, use of electronic transactions and use of communication services to test their combined productivity effects on one data set. ONS research to date uses surveys collected up to 2003. Surveys for IT investment at firm level are recent, providing only short investment series to create estimates of capital stock. Annex 2 gives the data sources used for the analyses done. By linking together firm level investment data, making industry level assumptions on initial levels of IT investment, and modelling IT depreciation, we have built firmlevel estimates of hardware and software capital stock for over 50,000 firms. Linking this to other measures of firm inputs and outputs, and to estimates of non-IT capital we have been able to analyse: • • •

behaviour of firm investment in IT assets, the relative importance for productivity of different ICT indicators at firm level, types of firms with productivity benefits associated with ICT investment or use.

3 International

work from the US, Japan and Europe on firm level ICT impacts which advances this research agenda is summarised in two collections of papers: Comparative Analysis of Enterprise Data proceedings (ONS London, 2003, www.statistics.gov.uk/events/ caed) and Economic Impact of ICT – Measurement, Evidence and Implications (OECD, 2004).

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6.3. Definition and measurement of IT investment Before looking at productivity effects, we tested relationships between IT investment – compiled from all sources – and firm characteristics. This helps understand effects of third factors in productivity relationships, and aspects of IT investment behaviour. Hardware data is more reliably measured at firm level than software. In discussions at the OECD with other National Statistical Institutes who measure software, reasons identified for this include:  



software is often bundled with IT equipment, and not a separate purchase by firms; purchased software may be treated as an operating cost, not identified as investment; “own account” software and databases, built by firms for own use, is captured badly in surveys.

Due to the structure of UK surveys, we have more UK observations on software investment than for hardware. Yet, as we shall see, the robustness of software influence on productivity in our analyses is almost always lower than for hardware. This supports the conclusion that it is more subject to measurement error. Measuring hardware investment does not pose these problems. However, definitional difficulties occur with “embodied” IT equipment (e.g. processors) built into other equipment and not captured by firms as an IT purchase. Retail IT investment is affected by this problem, both in measuring investment and in some ICT use surveys which may not recognise point of sale equipment as computers. While economists and statisticians refer to the technology products and services they expect to show productivity effects as “ICT”. In practice, assets identified in firm-level purchase surveys are usually confined to “IT” – information processing equipment and software. Communications technology input is, for most firms outside the telecoms sector, dominated by purchase of telecoms services. In this analysis, therefore, we have focused attention on the purchase of telecommunications services by firms, and its combined effect on productivity together with their IT assets. Differences in IT investment at firm level show systematic influences, some related to industry and region, but a range of factors can be identified: ■



Industry sectors: Services show a closer link than manufacturing between IT and total capital investment; IT is a large part of total capital formation for certain service sectors, especially financial services. Employee pay: Manufacturing shows stronger correlation than services between higher wages and more IT investment. Across manufacturing industries, IT substitution for low cost labour appears well advanced, and more of the remaining employees in high IT using manufacturing firms tend to be more skilled, active, “information users”. Service sectors include some where high IT use is associated with use of lower cost labour; examples are retailing or call centres.

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Firm size: Hardware investment is strongly correlated with firm size (measured by employment) in manufacturing and services; this is consistent with results from ICT use surveys across the EU, which show computer network use driven by firm size. Ownership: US-owned firms invest more in hardware than similar UK firms, or other foreign owned firms. However, for software, foreign and group owned units report lower investment levels. This suggests that software and business systems are shared across countries and sites, and large multinational firms gain from economies of scale. Region: Significant regional variations show higher investment levels in IT hardware in the south-east/London for both manufacturing and services, in a few other regions for services, and lower levels in Scotland. Regional variation in software investment is weaker.

In addition, we looked at the relationship between IT investment and firm level purchases of computer services. Computer services data in the Annual Business Inquiry (ABI) captures firm purchases of computer related services not capitalised (maintenance, support, outsourced operations), which might be a substitute for IT investment, where firms buy computing capability and facilities management rather than their own systems. The evidence suggests that most computer service spend by firms is in fact complementary to capital. Computer service spend is strongly correlated with hardware and software investment. It is positively correlated with firm age and IT intensity, showing that it captures support or maintenance spending for IT capital. However, it is not a significant determinant of productivity when included in regressions alongside hardware and software investment.

6.4. IT hardware and software investment, and multi-factor productivity Standard modelling of productivity effects uses a basic Cobb–Douglas production function: ln Qit = αK ln Kit + αL ln Lit + αM ln Mit + βH ln(KHARD)it + βS ln(KSOFT)it + γ ′ Xit + uit where Q is gross output of establishment i at time t, KHARD is the capital stock of computer hardware, KSOFT is the capital stock of computer software, K is noncomputer capital, L is labour, M is materials and X are other possible observable drivers of productivity. The error term is uit . Assuming perfectly competitive factor and product markets, and constant returns to scale, in the long-run the parameters on each factor input will be equal to the factor share of revenue. Materials, energy and other intermediate inputs may be hard

IT Investment, ICT Use and UK Firm Productivity

107

to measure; an alternative is to estimate a value-added (VA) version of the equation above as: ln VAit = αK ln Kit + αL ln Lit + αM ln Mit + βH ln(KHARD)it + βS ln(KSOFT )it + γ ′ Xit + uit In this research both approaches have been used. Among the “other possible observable drivers of productivity” the analysis has included sector dummies, firm size, age, regional effects and foreign ownership for many of the relationships identified and, firm level fixed effects. 6.4.1. Results Results based on the specifications above are summarised in Tables 1 and 2 (taken from Sadun, 2005). These use measures of hardware and software capital alongside “non-computer” capital. They show significant, positive, returns to both hardware and software across both manufacturing and services, broadly consistent with growth accounting results. Across the period, and across sectors, returns range from: – for hardware, 4.4% (OLS) to 2.0% (Olley–Pakes method) – for software, 4.9% (OLS) to 1.9% (Olley–Pakes method) Positive returns for IT hardware and software are robust for both value added and gross output specifications, after taking account of all other characteristics modelled, including firm fixed effects, and other tests for links through other factors. This represents very strong evidence for positive productivity impact of IT investment across the economy over the period 1999–2003. Similar results are obtained by substituting IT investment (a flow) into the model outlined above for IT capital. This is done as a check on methods used to calculate IT capital which, in a data set with short investment series, is sensitive to assumptions on starting capital, and on depreciation rates. The results suggest that these assumptions do not affect the model. 6.4.2. Effects per firm type To analyse the effects by firm type, to test how far the productivity gains are sector or size specific, the models have been applied to subsets of the linked data set, with the following conclusions: High/low IT use The data has been split into two parts, based on a categorisation of “high IT use” and “low IT use”, taking a sector classification scheme developed by Bart van Ark.

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Table 1. Hardware capital and firm productivity Estimation Method

(1) OLS, No FE

(2) OLS, FE

(3) OLS, FE

(4) GMM, Static

(5) (6) GMM, GMM, COMFAC Dynamic Unrestricted (Restricted)

(7) Olley– Pakes

Dependent variable: ln(GO) = ln(Gross Output) Ln(Ht ) Hardware capital Ln(Ht−1 ) Hardware capital, lagged Ln(Me ) Materials Ln(Me−1 ) Materials, lagged Ln(Kt ) Non-IT Capital Ln(Kt−1 ) Non-IT Capital, lagged Ln(Lt ) Labour

0.0440∗∗∗ 0.0299∗∗∗ 0.0265∗∗∗ 0.0391∗∗∗ (0.0023) (0.0040) (0.0063) (0.0171) -

-

-

0.0656∗ (0.0373)

0.0430∗∗∗ (0.0211)

0.0204∗∗∗ (0.0030)

−0.0343 (0.0242)

-

-

0.3293∗∗∗ (0.0750)

0.3595∗∗∗ (0.0494)

0.5562∗∗∗ (0.0102)

−0.0715 (0.0534)

-

-

0.3618∗∗∗ (0.0869)

0.2937∗∗∗ (0.0526)

0.1511∗∗∗ (0.0115)

0.1815∗∗∗ (0.0592)

-

0.2981∗∗∗ (0.0829)

0.3524∗∗∗ (0.0560)

-

0.0091 (0.0624)

-

-

0.5384∗∗∗ 0.4665∗∗∗ 0.4702∗∗∗ 0.3998∗∗∗ (0.0080) (0.0193) (0.0283) (0.0402) -

-

-

-

0.1193∗∗∗ 0.1650∗∗∗ 0.1953∗∗∗ 0.1584∗∗∗ (0.0063) (0.0153) (0.0234) (0.0410) -

-

-

0.2868∗∗∗ 0.3177∗∗∗ 0.2979∗∗∗ 0.4158∗∗∗ (0.0062) (0.0198) (0.0209) (0.0479)

0.2611∗∗∗ (0.0080)

Ln(Lt−1 ) Labour, lagged

-

-

Ln(Ye−1 ) Gross Output, lagged

-

-

-

-

0.2330∗∗∗ (0.0581)

-

-

Rho, ρ

-

-

-

-

-

0.3488∗∗∗ (0.0291)

-

Observations

22,736

22,736

6,763

6,763

6,763

6,763

12,069

Fixed effects

NO

YES

YES

YES

YES

YES

YES

1st order serial correlation test (p value)

-

-

-

−3.634 (0.000)

−5.223 (0.000)

-

-

2nd order serial correlation test (p value)

-

-

-

−0.239 (0.811)

0.953 (0.341)

-

-

Sargan-Hansen test (p value)

-

-

-

34.38 (0.354)

24.65 (0.852)

-

-

COMFAC (p value)

-

-

-

-

-

6.7474 (0.1500)

-

109

IT Investment, ICT Use and UK Firm Productivity

Table 2. Software capital and firm productivity Estimation Method

(1) OLS, No FE

(2) OLS, FE

(3) OLS, FE

(4) GMM, Static

(5) GMM, Dynamic Unrestricted

(6) GMM, COMFAC (Restricted)

(7) Olley– Pakes

0.0235 (0.0151)

0.0232∗∗∗ (0.0118)

0.0192∗∗∗ (0.0017)

0.4533∗∗∗ (0.0464)

0.529∗∗∗ (0.0074)

0.1733∗∗∗ (0.0411)

0.1534∗∗∗ (0.0003)

0.3347∗∗∗ (0.0479)

0.2945∗∗∗ (0.0057)

Dependent variable: ln(GO) = ln(Gross Output) Ln(St ) Software capital

0.0491∗∗∗ 0.0222∗∗∗ 0.0163∗∗∗ 0.0231∗∗∗ (0.0013) (0.0025) (0.0033) (0.0081)

Ln(St−1 ) Software capital, lagged Ln(Me ) Materials

−0.0053 (0.0080) 0.5145∗∗∗ 0.4061∗∗∗ 0.4299∗∗∗ 0.4457∗∗∗ (0.0046) (0.0137) (0.0191) (0.0343)

−0.0972∗∗∗ (0.0262)

Ln(Me−1 ) Materials, lagged Ln(Kt ) Non-IT Capital

0.1007∗∗∗ 0.2103∗∗∗ 0.1899∗∗∗ 0.1504∗∗∗ (0.0040) (0.0130) (0.0189) (0.0303)

Ln(Kt−1 ) Non-IT Capital, lagged Ln(Lt ) Labour

0.4244∗∗∗ (0.0531)

0.2536∗∗∗ (0.0637) −0.1465 (0.0436)∗∗∗

0.3227∗∗∗ 0.3511∗∗∗ 0.3589∗∗∗ 0.3857∗∗∗ (0.0035) (0.0127) (0.0182) (0.0387)

0.2554 (0.0738)∗∗∗

Ln(Lt−1 ) Labour, lagged

0.0148 (0.0519)

Ln(Ye−1 ) Gross Output, lagged

0.2766∗∗∗ (0.0370) 0.3405∗∗∗ (0.0312)

Rho, ρ

Observations

58283

58283

13072

13072

13072

13072

26463

Fixed effects

NO

YES

YES

YES

YES

YES

YES

1st order serial correlation test (p value)

−8.624 (0.000)

−9.517 (0.000)

2nd order serial correlation test (p value)

−0.172 (0.863)

−0.704 (0.481)

Sargan-Hansen test (p value)

44.87 (0.065)

39.62 (0.198)

COMFAC (p value)

14.11 (0.007)

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Modelling each group separately show that overall returns to hardware and software capital are similar for both low IT using and high IT using sectors. There is no indication from this analysis that higher returns are earned in higher using sectors (although there are differences related to ownership – see section on multinationals).

Manufacturing vs. services Splitting the data set by a different criterion, into manufacturing and service firms, shows productivity results for both hardware and software capital around 40% greater in services than in manufacturing firms. The difference narrows after taking account of firm fixed effects. This result reflects the fact that services firms are, in many sectors, more dependent on IT capital as a proportion of total capital than manufacturing. Within manufacturing, productivity effects of IT hardware and software capital are around 50% bigger in newer firms (i.e. younger than the average for their sector); in services productivity effects of IT hardware capital are typically 20–30% bigger in longer established enterprises. These age effects related to IT may reflect different competitive dynamics in manufacturing and services: 



manufacturing firms are more likely to use IT to optimise value chains in operations, which can change quickly; service firms often use IT in managing customer relationships, which takes time to establish.

Other “non IT” capital The research has looked at the relationship between IT capital and other forms of assets, and their combined effect in productivity models. The results show IT investment has a complementary “compounding” effect, raising the productivity of other forms of fixed capital (plant and equipment, vehicles land and buildings) in capital intensive firms.

Employee skills Two approaches have been used to look at the effects of employee skills on the relationship between IT investment and productivity, one an endogenous measure based on wage levels in the firm (wage costs divided by number of employees) and the other an exogenous measure based on Labour Force Survey measures of the proportion of employees in a NUTS region/2 digit industry sector with higher education attainment. This second measure represents the quality of labour input available to firms in the sector/region, and is a potential policy lever. Not surprisingly, both skill measures show significant productivity effects on their own. In addition to this, the IT investment effect on productivity is reinforced by

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the education attainment measure. Firms investing in IT in areas with better qualified labour force show better returns to IT, although the effect loses significance when firm fixed effects are added to the analysis. Analysis with better definition of labour quality in local areas will follow.

US multinationals The comparison between US firm performance and firms owned by enterprise groups based in other countries has become important in understanding international productivity differences. Detailed analysis has been undertaken on this question. The results show that US owned firms in the UK have large productivity advantages attributable to investment in hardware, compared to UK-owned multinationals or to those with other national ownership. These other multinationals, in turn, are more productive than purely domestic firms. The difference in IT effects could be due to complementary assets (skills or organisation), or to US-owned firms’ scale effect in global software and business systems (likely to reflect both the scale of the US market, and their greater degree of multinational scope). However, there is no similar effect for software or other forms of capital. Over 80% of the advantage in productivity for US owned subsidiaries is explained by level of hardware investment. Much of the US owned productivity differential is concentrated in IT intensive sectors. In these sectors, doubling of IT hardware capital is associated with 2% higher productivity for a domestic UK firm, 2.5% for non-US multinationals, and 5% for US multinationals. All the statistically significant advantage for US-owned firms is accounted for by IT investment. LSE economists attribute US advantages to organisation and management approaches. Additional tests on the data show that the US-IT advantage is not explained by US firms investing in higher skills in their UK operations, or by US firms operating in specific industries where the returns to IT are particularly high. However, there is a limited amount of evidence to show that US firms invest more in IT after making a takeover of a UK firm, while other acquirers do not. In low IT using sectors, IT productivity effects are similar across all ownership types.

Computer services The analysis has been extended to include “computer services expenditure” as an input to productivity modelling. These expenditures (measured by the ABI), are strongly correlated with IT capital expenditure. They are not significantly related to productivity once hardware and software capital stock are taken into account. However, as we see later, telecoms services purchases have a significant, separate, impact.

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6.5. Employee use of ICT and multi-factor productivity Two questions in the Eurostat sponsored e-Commerce survey ask about ■ ■

the use of computers by firms’ employees, and the proportion of employees who use/access a computer; the use of computers with internet access by employees, and the proportion with access.

These questions capture the degree of employee involvement with ICT, and may also capture skills. In Finnish studies, employee use has also been used as a proxy for firm-level IT investment data. In practice, these metrics are also likely to measure the electronic exchange of information between employees – and with outside sources – so may give an approximate measure of knowledge management currently available at firm level. The econometric strategy to tackle employee use of computers or internet is set out in Farooqui (2005), building on Sadun’s model above. It allows workers to have different marginal productivities according to whether they use ICT or not. The analysis is made separately for employees using computers and employees using the internet. The data on employees using computers and the internet is, not surprisingly, correlated with IT investment at firm level. It is also strongly dependent on industry sector. However, after taking account of IT investment, the identifiable effects of employee computer/internet use on firm level productivity are large and significant.

6.5.1. Results in manufacturing On their own, these measures show large, positive, and statistically highly significant links to multi-factor productivity. After taking IT hardware and software investment into account, employee use in manufacturing is associated with higher productivity to the tune of: ■ ■

2.2% for every additional 10% of employees IT-enabled. 2.9% for every additional 10% of employees internet-enabled.

What is particularly interesting is that these impacts can be seen (Table 3) together with the effects of IT hardware and software capital, and do not appear to diminish the impact of either by much. This result suggests that employee use of ICT is – as an indicator – far more than a proxy for IT investment. In manufacturing firms which are below the average age for their 2-digit sector, the positive effects of computer enablement of employees on productivity are much stronger: ■ ■

4.4% for every additional 10% of employees IT-enabled. 3.4% for every additional 10% of employees internet-enabled.

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Table 3. Employee use of ICT (Internet-equipped labour share) in Manufacturing, and firm productivity Dependent Variable Sector

(1) (2) (3) (4) (5) ln (VA/EMP) ln (VA/EMP) ln (VA/EMP) ln (VA/EMP) ln (VA/EMP) Manufacturing Manufacturing Manufacturing Manufacturing Manufacturing

Log of non-IT capital per employee

0.245*** (0.028)

0.231*** (0.028)

0.256*** (0.029)

Log of employee

0.053** (0.023)

0.055** (0.023)

0.043* (0.023)

0.056** (0.022)

0.043* (0.023)

Log of hardware per employee

0.051*** (0.015)

0.042*** (0.015)

0.038** (0.015)

0.042*** (0.015)

0.037** (0.015)

Log of software per employee

0.040*** (0.013)

0.038*** (0.013)

0.032** (0.013)

0.037*** (0.013)

0.031** (0.013)

0.217*** (0.063)

0.215*** (0.064)

Computer Equipped Labour Share Skills Proportion of people with a college degree in industry-region cell

0.233*** (0.028)

0.257*** (0.029)

0.235

0.300

(0.262)

(0.261)

Internet Equipped Labour Share

0.294*** (0.072)

0.295*** (0.073)

Observations

1394

1394

1317

1394

1317

R-squared

0.62

0.63

0.64

0.63

0.65

Robust standard errors in parentheses. *significant at 10%; **significant at 5%; ***significant at 1%. The dependent variable in all columns is the log of value added per employee. The time period is 2000–2003. Standard errors in brackets under coefficients are clustered by establishment and robust to heteroskedasticity and serial autocorrelation. All variables are expressed in deviations from the 4dig industry mean in the same year. All regressions include age, region, ownership and group dummies.

In these young firms the productivity effects of equipping employees with computers are more statistically significant (and bigger in terms of impact) than productivity effects associated with the level of IT investment. This suggests that employee use of ICT is a particularly valuable metric for younger firms entering manufacturing sectors. 6.5.2. Results in services Across service business as a whole (Table 4), employee use of computers is not significantly related to productivity, perhaps due to measurement issues in areas like retail.

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Table 4. Employee use of ICT (Internet-equipped labour share) in services, and firm productivity Dependent Variable Sector

(1) ln (VA/EMP) Services

(2) ln (VA/EMP) Services

(3) ln (VA/EMP) Services

(4) ln (VA/EMP) Services

(5) ln (VA/EMP) Services

0.275*** (0.024)

0.277*** (0.024)

0.274*** (0.025)

0.267*** (0.025)

0.264*** (0.025)

−0.055*** (0.015)

−0.056*** (0.015)

−0.060*** (0.015)

−0.051*** (0.015)

−0.055*** (0.015)

Log of hardware per employee

0.100*** (0.014)

0.100*** (0.014)

0.097*** (0.014)

0.097*** (0.014)

0.095*** (0.014)

Log of software per employee

0.041*** (0.012)

0.041*** (0.012)

0.046*** (0.012)

0.039*** (0.012)

0.044*** (0.012)

0.154*** (0.046)

0.022*** (0.053)

Log of non-IT capital per employee Log of employee

Computer Equipped Labour Share Skills Proportion of people with a college degree in industry-region cell

0.206

0.158

(0.420)

(0.419)

Internet Equipped Labour Share

0.145*** (0.056)

0.154*** (0.056)

Observations

2272

2272

2231

2272

2231

R-squared

0.70

0.70

0.70

0.70

0.71

Robust standard errors in parentheses. *significant at 10%; **significant at 5%; ***significant at 1%. The dependent variable in all columns is the log of value added per employee. The time period is 2000–2003. Standard errors in brackets under coefficients are clustered by establishment and robust to heteroskedasticity and serial autocorrelation. All variables are expressed in deviations from the 4dig industry mean in the same year. All regressions include age, region, ownership and group dummies.

The effects of IT investment are hardly changed in terms of either impact or of significance by adding computer equipped labour share to a regression. This is true for both young and old firms. Employee use of the internet, however, is significantly related to productivity, but only in younger firms. For these firms internet equipped labour share raises productivity by 1.7% for each additional 10% of employees enabled and it does so without affecting the relationship between IT investment and productivity, suggesting that the skills, communication links and organisation measured by internet-equipped employees have a specific role in new service firms.

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Differences between service industries help explain the relatively weak overall relationship: – Wholesale firms show a large productivity effect associated with computer or internet use by employees; the “computer” effects are as large as for “internet”. These impacts are additional to those due to IT hardware and software capital. – In retailing the “employee use” effect is absent, but this may be due to the definition of “computer” in OECD and Eurostat surveys, which exclude electronic point of sale equipment – the technology most retail employees use. In fact EPOS units include processing power similar to simple PCs. – The absence of productivity impact from IT enabled employees in other business activities (business services) is puzzling. Plenty of case studies show performance gains associated with knowledge management, tele-working and other forms of ICT use requiring employee involvement. The small number of observations with data on output, IT investment and employee use of IT in this sector may explain the lack of significance.

6.6. Telecommunications spending, IT investment, and ICT use In an early review of this work, experts at London Business School suggested that the focus on IT investment in the research did not take full account of the role of communications technology as a complement to information processing. In part this is met by our analyses which cover e-commerce and internet use. Data is available to go one step further and make specific allowance in the productivity equations shown above for firm level purchase of communication services. Because most firms use external telecommunications infrastructure – purchased from outside service suppliers – this cannot be done in the same way as we have for IT. Information technology assets (to store or process information) can be captured by firm level IT investment, but the links to gather or transfer information cannot, except for firms which have their own dedicated communications infrastructure. National Accounts input/output data shows that most telecommunications investment is made in the telecoms sector, which sells its services to other sectors. Less than 15% of investment in telecommunications products is made by non-telecoms firms for own use. The best measure of telecommunications use by firms we have is their external spend on purchased services, which is identified at firm level in the ABI. Firms are asked to identify purchases of telecoms services as part of their overall purchases of good materials and services in the “long form” version of ABI. We add telecoms spend per employee, as a measure of communications intensity for a firm, to the analyses outlined earlier to test – whether communications has a significant link with productivity – how this measure interacts with others

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6.6.1. Telecommunications use Adapting the productivity model outlined earlier to include telecommunications service purchases as another input, alongside IT capital use, Table 5 shows that: •

Telecoms use has a consistent, positive and significant link with productivity across manufacturing and services, without reducing the effects of IT hardware and software investment. There is a specific “communications effect” which associates an extra £7 in net output for each extra £100 spent on communications.

Table 5. Communications use (expenditure) in manufacturing and services, and firm productivity Dependent Variable Sector

(1) (2) (3) (4) (5) (6) In (VA/EMP) ln (VA/EMP) ln (VA/EMP) In (VA/EMP) ln (VA/EMP) ln (VA/EMP) Manufacturing Manufacturing Manufacturing Services Services Services

Log of non-IT capital per employee

0.206*** (0.014)

0.207*** (0.014)

0.221*** (0.028)

0.244*** (0.012)

0.245*** (0.012)

0.249*** (0.025)

Log of employee

0.037*** (0.010)

0.044*** (0.010)

0.068*** (0.023)

−0.019** (0.007)

−0.011 (0.008)

−0.047*** (0.016)

Log of hardware per employee

0.051*** (0.007)

0.012 (0.020)

−0.019** (0.043)

0.075*** (0.007)

0.032** (0.016)

0.094** (0.039)

Log of software per employee

0.038*** (0.006)

0.038*** (0.006)

0.034*** (0.013)

0.053*** (0.006)

0.053*** (0.006)

0.037*** (0.012)

Log of telecom spend per employee

0.066*** (0.012)

0.075*** (0.013)

0.093*** (0.026)

0.066*** (0.012)

0.074*** (0.013)

0.064*** (0.022)

0.009** (0.005)

0.011 (0.009)

0.010*** (0.003)

−0.001 (0.007)

Hardware* telecom spend

0.239*** (0.073)

Internet Equipped Labour Share

0.128** (0.055)

Observations

5397

5397

1394

8255

8255

2272

R-squared

0.47

0.47

0.64

0.60

0.60

0.70

Robust standard errors in parentheses. *significant at 10%; **significant at 5%; ***significant at 1%. The dependent variable in all columns is the log of value added per employee. The time period is 2000–2003. Standard errors in brackets under coefficients are clustered by establishment and robust to heteroskedasticity and serial autocorrelation. All variables are expressed in deviations from the 4dig industry mean in the same year. All regressions include age, region, ownership and group dummies.

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In manufacturing, we see strong interactions between communications spending and IT investment. •



If we interact communications spend with hardware investment, the productivity effects associated with hardware alone are much weaker; this suggests that hardware intensive firms which transfer large quantities of information between sites or with suppliers or customers are most productive, and the joint use of IT and CT provides the biggest productivity gains If we add Internet-equipped labour share, the effects of hardware disappear altogether, but effects of software and telecoms use remain strong and significant, suggesting that systems, communication, and the effective use of both IT and CT by employees matter most

Across all services, the interactions are significant, but do not weaken the effects of hardware investment: • •

If we interact communications spend with hardware investment, the interaction is significant, but so is hardware on its own If we add Internet-equipped labour share to the model, the CT*IT hardware interaction drops out, leaving all the other terms significant; this also suggests interdependence between systems, communication and use of IT and CT by employees, but influenced by the higher proportion of services investment accounted for by hardware and software.

Links between telecoms spend and productivity, are particularly strong for wholesale distribution (elasticity 8%) and retail distribution (elasticity 13%) – both in addition to the effects of hardware and software investment. In both sectors, the CT*IT hardware interaction is both significant and strong; it effectively replaces hardware investment as a productivity influence. This suggests that IT as an internal driver of efficiency in distribution is much less critical than its role in managing complex coordination in supply chains, and is consistent with the results for e-commerce in distribution in Section 5. It is clear from these results that telecommunications services available to firms are an important enabler for productivity. Further work should develop this conclusion to include quality measures of communications infrastructure available and used (e.g. broadband).

6.7. e-Commerce and multi-factor productivity Criscuolo and Waldren (Economic Trends, 2003) demonstrated that UK manufacturing firms using e-commerce experience: • •

value-added productivity gains associated with electronic buying smaller productivity losses associated with electronic selling.

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As there is also evidence showing a tendency for prices to decline among firms selling electronically compared to those which do not, this was interpreted to show: • •

an overall efficiency gain associated with electronic process use market price effects favouring e-buyers, through stronger price competition.

This research has repeated Criscuolo and Waldron’s analysis over a longer period (including 2003), and extended it to service sector firms. The modelling approach uses a Cobb–Douglas production function as the basis for all regression specifications of the form Q = AK α Lβ M γ where K, L and M are capital, labour and material inputs (labour and materials are available from the ARD and capital is calculated from it using perpetual inventory methods). A is the disembodied technology parameter which we assume is a function of the use of computer/electronic networks for buying or selling, taking the form: A = exp (δ0 + δ1 eActivity) eActivity is a binary indicator with value 1 if a reporting unit uses an electronic network for buying or selling and 0 if not. Taking logs and expressing all variables per employee gives:       Qit Kit Mit ln = δ0 + δ1 eActivityit + α ln + γ ln Lit Lit Lit + (α + β + γ − 1) ln Lit + uit

(1)

Differences between gross output and value added regressions highlight the price effects of e-commerce. The results reported in this study are based on value added. Gross output regression is the basis of robustness checks. Unobserved firm characteristics are represented by a full set of four digit industry dummies and time dummies. Also included are dummies controlling for ownership effects, multi-plant effects, regional differences and the age of a plant. In addition, research includes an interaction between the e-commerce variables and year. This is to capture the time trend in eActivity impact that is apparent from descriptive statistics suggesting that the “price effect” of e-commerce found by Criscuolo and Waldron is diminishing over time. 6.7.1. Results in manufacturing Manufacturing firms identified as using e-commerce (buying/selling) continue to show (Table 6) overall positive productivity effects identified by Criscuolo & Waldron. These are a negative 3% “price effect” for firms engaged in e-selling, against a positive 4% associated with e-buying. These effects are strongest in newer firms.

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Table 6. IT investment and e-commerce use in manufacturing, and firm level productivity Dependent Variable Sector

(1) ln (VA/EMP) Manufacturing Pooled

(2) ln (VA/EMP) Manufacturing Pooled

(3) ln (VA/EMP) Manufacturing Young Firms

(4) ln (VA/EMP) Manufacturing Old Firms

Log of non-IT capital per employee

0.206*** (0.014)

0.205*** (0.014)

0.169*** (0.021)

0.241*** (0.018)

Log of employee

0.036*** (0.010)

0.037*** (0.010)

0.010*** (0.016)

0.054*** (0.014)

Log of hardware per employee

0.051*** (0.007)

0.050*** (0.007)

0.058** (0.012)

0.041*** (0.008)

Log of software per employee

0.038*** (0.006)

0.038*** (0.006)

0.051*** (0.011)

0.031*** (0.007)

Log of telecom spend per employee

0.066*** (0.012)

0.065*** (0.012)

0.064*** (0.020)

0.075*** (0.017)

esell

−0.037** (0.017)

−0.082*** (0.030)

−0.011 (0.022)

ebuy

0.041*** (0.016)

0.084*** (0.031)

0.024 (0.020)

Observations

5397

5397

2104

3293

R-squared

0.47

0.47

0.53

0.54

Robust standard errors in parentheses. *significant at 10%; **significant at 5%; ***significant at 1%. The dependent variable in all columns is the log of value added per employee. The time period is 2000–2003. Standard errors in brackets under coefficients are clustered by establishment and robust to heteroskedasticity and serial autocorrelation. The age of a firm is determined by median age in its four digit sector. All variables are expressed in deviations from the 4dig industry mean in the same year. All regressions include age, region, ownership and group dummies.

Our analysis which shows large, widespread, benefits associated with e-procurement but not with electronic selling is supported by trends in the use of e-commerce, measured by the ONS e-commerce survey. This shows that while the value of e-commerce transactions in the UK continues to rise rapidly over 2000–2003, and the number of firms purchasing electronically increased significantly, the number selling electronically is now roughly stable. Changes in e-commerce effects over time Although identifying negative value added effects associated with e-selling, the new research on manufacturing firms shows that the differential effect of e-buying and e-selling on productivity has diminished over time. Compared with the base

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year (2000), e-sellers perform 4% better by 2002, and e-buyers perform 5% worse, confirming the trend in descriptive data. By 2003 the average negative “price effect” of e-selling had dropped to around 1%. This suggests that firms undertaking e-selling are overcoming initial set up costs, gaining scale and learning how to operate the process more effectively. It is also possible that differential e-price effects are transitional; the initial effect of electronic transactions being to commoditise markets, driving down prices for firms that use them, but price effects later spread more widely to affect all firms (e-sellers or not) and the difference disappears.

Age of firms in manufacturing The analysis shows (Table 7) that there are differences in the impact of e-commerce use, depending on firm age. Firms which are below median age for their 2 digit sector show larger and more significant gains (8%) associated with e-buying compared to longer established firms. This may be due to the fact that younger firms have more flexibility to procure efficiently and effectively through electronic buying. Related results covering firms which are below average size for their sector suggest that smaller firms gain more from the access to wider supply sources and reduced search costs which electronic procurement brings.

6.7.2. Results in service firms Descriptive data on labour productivity shows a more complex pattern of productivity influences of e-commerce for services. There is little evidence of negative e-selling effects. Regression analysis using the ABI indicator of e-commerce use is possible for ten broadly defined sectors within services, some of which are summarised in Table 7, and shows that effects are highly sector dependent, reflecting different business dynamics, and perhaps different roles of internet compared with older electronic systems. Significant industry effects are as follows: –





wholesaling: electronic procurement shows a significant, positive, effect on value added productivity, but appears to be diminishing slightly over time, perhaps as price effects of electronic markets become more widely diffused. retail sector: e-selling here is usually internet based B2C business. There are significantly positive productivity effects for established firms (large and small), which are larger (6%) than those found anywhere else for e-selling; this may show that gains depend on established brands and customer relationships. E-procurement shows a beneficial effect on value added productivity for smaller enterprises, suggesting that e-buying benefits firms which have not previously had access to wide sources of supply. hotels/catering: e-selling shows large, significant, productivity effects.

Table 7. IT investment and e-Commerce use by industry, and firm level productivity Dependent Variable Sector Sample

(1) (2) (3) (4) ln (VA/EMP) ln (VA/EMP) ln (VA/EMP) ln (VA/EMP) Manufacturing Manufacturing Manufacturing Wholesale Pooled Young Firms Old Firms Pooled

(5) ln (VA/EMP) Wholesale Young Firms

(6) ln (VA/EMP) Wholesale Old Firms

(7) ln (VA/EMP) Retail Pooled

(8) ln (VA/EMP) Retail Young Firms

(9) ln (VA/EMP) Retail Old Firms

Log of capital per employee

0.229*** (0.008)

0.211*** (0.010)

0.263*** (0.012)

0.293*** (0.008)

0.334*** (0.013)

0.266*** (0.010)

0.257*** (0.010)

0.293*** (0.018)

0.237*** (0.012)

Log of employee

0.064*** (0.005)

0.068*** (0.007)

0.062*** (0.007)

0.025*** (0.006)

0.022*** (0.009)

0.027*** (0.008)

−0.000 (0.005)

0.006 (0.008)

−0.007 (0.006)

esell

−0.024** (0.010)

−0.028* (0.017)

−0.019 (0.012)

0.025 (0.017)

−0.003 (0.030)

0.041** (0.020)

0.054** (0.023)

0.024 (0.038)

0.063** (0.028)

ebuy

0.051*** (0.010)

0.057*** (0.016)

0.051*** (0.012)

0.042** (0.017)

0.052* (0.029)

0.037* (0.021)

−0.017 (0.020)

−0.008 (0.034)

−0.013 (0.024)

Observations R-squared

21277

9507

11770

13659

5837

7822

11905

4861

7044

0.30

0.32

0.37

0.53

0.52

0.55

0.19

0.18

0.21

Robust standard errors in parentheses. *significant at 10%; **significant at 5%; ***significant at 1%. The dependent variable in all columns is the log of value added per employee. The time period is 2000–2003. Standard errors in brackets under coefficients are clustered by establishment and robust to heteroskedasticity and serial autocorrelation. The age of a firm is determined by median age in its four digit sector. All variables are expressed in deviations from the 4dig industry mean in the same year. All regressions include age, region, ownership and group dummies.

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transport services: e-commerce effects are negative and large (but diminishing) for selling, positive for buying; they are concentrated in large firms, and appear to reflect the types of price effects seen in manufacturing. other business activities (which include a wide range of business services): e-selling is associated with lower productivity; there are strongly positive effects for e-procurement especially for small enterprises.

The overall pattern suggested by these industry results is that: •





where firms use internet e-commerce to reduce costs, and simplify the complexity associated with large numbers of end users, (retail, wholesale, hotels, restaurants and catering) there are positive productivity gains from e-selling. service sectors where electronic networks are used to sell commodity services (e.g. transport) behave like manufacturing, with negative effects on prices and value added. in most service sectors there are positive productivity effects associated with e-procurement, but the impacts vary across large and small firms.

6.8. Implications for indicators and measurement In this section we draw together the results of available research and consider their implications for the choice of “best” indicators based on surveys and measures already available, possible alternative or new indicators to consider and the implications for measurement of ICT and its use. The work summarised in this report is consistent with US studies by Atrostic and Nguyen showing productivity impacts from IT investment and from use of networks as well as with Scandinavian analysis by Maliranta on employee use of ICT and productivity, research in Japan by Motohashi on networks and transactions, and work in the Netherlands by van Leeuwen and van der Wiel (2003) on productivity effects of IT investment (see for references, Annex 1 at the end of chapter). In addition, it has shown that: • • • • •



many of the IT investment effects apply across manufacturing and services; employee involvement with ICT has a significant, additional, effect on productivity; communications inputs have significant, and separate, productivity impacts; for IT investment, and communications, productivity impacts are larger in services than in manufacturing; productivity effects of e-commerce use are sector specific, with differences between manufacturing and services, and between young and established firms; in addition to productivity effects within firms, electronic transactions improve market efficiency and price competitiveness.

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Indicators development should focus on measures which are associated with better productivity performance, with productivity growth, or market efficiency. This suggests the priority should be given to the following indicators, which we and others have shown to be related to productivity through firm level econometric analysis.

Metrics

Source

Relevant Research

ICT capital or investment levels, usually SBS and Investment measured as computer hardware and software surveys (Europe) per employee ICT survey (US)

ONS/LSE (UK)4 van Leeuwen & van der Wiel5 Atrostic & Nguyen (US)6

Employees using computers, or using computers connected to the internet

ICT use/e-commerce surveys

Maliranta (Finland)7 ONS/LSE (UK)

Use of electronic networks to link business processes, multiple e-business process use

ICT use/e-commerce surveys (EU), CNUS (US)

ONS (UK)8 Atrostic & Nguyen (US)

e-Commerce use; significant positive effects are application specific: - e-procurement over all networks - e-selling in most service sectors

ICT use/e-commerce surveys (Europe/Japan)

ONS/LSE (UK)9 Motohashi (Japan)10 ONS/LSE (UK) and also NIESR work

Availability of skills in local labour market, associated with IT investment

Labour Force Survey (EU)

ONS/LSE (UK)

Firm innovation capacity, associated with firm investment in ICT

IT use/e-commerce, and innovation surveys (EU)

van Leeuwen & van der Wiel (Netherlands) supported by Maliranta (Finland)

UK evidence in this research indicates that most of these metrics are as strongly linked to superior performance in smaller enterprises as they are in large firms and multinationals. Since much of the remaining work to be done on e-adoption (outside the public sector) is in small to medium sized enterprises, the applicability of these metrics beyond medium to larger firms is important. If we are looking for measures that underpin productivity improvement across the economy, over the next few years

4 (Bloom

et al., 2005) and (Farooqui, 2005) Leuween, 2003) 6 (Atrostic and Sang Nguyen, 2004) 7 (Maliranta and Rouvinen, 2003) 8 (Clayton and Goodridge, 2004) 9 (Clayton et al., 2003) 10 (Motohashi, 2003) 5 (Van

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tracking the intensity of IT investment, ICT use and other measures of capability outlined above, for SMEs, would be a good start. There are significant sector differences in the impact of these measures, but these are not yet well enough understood to form the basis of differentiated sector metrics. It appears from UK and US work that IT investment, and e-business use, show strong impacts which differ systematically across sectors. It may make sense for national metrics on IT use to be normalised for differences in sector make-up of national economies. For example, services sectors where IT use is high are proportionately greater in the UK, so an un-normalised “national indicator” should show the UK as significantly ahead in ICT use. 6.8.1. Possible new indicators, and implications for measurement A contribution of the research outlined in this report has been better understanding of IT investment data from purchase and investment surveys, and to consolidate the various sources. It has already led to a clearer demonstration of productivity effects associated with IT assets. It should lead to a better approach to estimating hardware and software across the economy. A similar exercise is under way in a number of countries led by Eurostat. This report reinforces the value of better quality ICT investment and expenditure data across Europe for understanding economic impacts. If achieved, ICT investment would be a more significant measure (and should replace IT expenditure in the Lisbon “structural indicators”). The main area where the current UK/Eurostat survey approach misses out significant applications of ICT is in the use of electronic business processes not associated with e-commerce. For example, the US Bureau of Census’ computer network use survey covers all links within businesses, and with customers and suppliers, which provides a more complete measure of e-activity. However, the US survey focuses on manufacturing, and may miss a major application of e-business which case evidence shows in the service sector – knowledge management. EU surveys miss this area too, but largely because it is not connected with e-commerce, and because it is difficult to define. If these two gaps: – lack of information on e-business applications not linked to e-commerce – lack of data on use of ICT based knowledge management can be filled by extending existing surveys, our picture of electronic process use across the economy would be more complete. Finally, there is continuing debate on the definition of ICT. We have seen that the definition of a “computer” can affect the way in which behaviour of employees is surveyed, possibly understating employee involvement in e-business in retailing. In addition to tackling this measurement issue on a consistent international basis, we need to consider how to measure “processor content” in products which are definitely not computers, but where ability to process information adds to functionality, and therefore to productivity.

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References Atrostic, B.K. and Sang Nguyen, C.E.S. (2004), “How Businesses use Information Technology, Insights for measuring Capital and Productivity”, May 2004, www.ipeer.ca/papers/AtrosticNguyenMay282002ConferenceFinal.pdf Bloom, N.; Sadun, R. and Van Reenen, J. (2005), “It ain’t what you do, it’s the way that you do IT”, available on the ONS website at http://www.statistics.gov.uk/ cci/nugget.asp?ID=1240 Clayton, T.; Criscuolo, C. and Goodridge C. (2003), “e-Commerce and Firm Performance”, Eurostat 2003, http://epp.eurostat.cec.eu.int/portal/page?_pageid=1073, 1135281,1073_1135295&_dad=portal&_schema=PORTAL&p_product_code= KS-04-001 Clayton, T. and Goodridge, C. (2004), “e-Business and labour productivity in manufacturing and services”, Economic Trends 2004 services’, http://www.statistics.gov. uk/articles/economic_trends/ET609Good.pdf Farooqui, R. (2005), “IT use by firms and employees; productivity evidence across industries”, Economic Trends, 2005, also available on the ONS website at http:// www.statistics.gov.uk/cci/nugget.asp?ID=1240 Maliranta, M. and Rouvinen P. (2003), “Productivity Effects of ICT in Finnish Business”; www.statistics.gov.uk/events/caed Marcel Timmer; Gerard Ypma and Bart van Ark (2003), “IT in the European Union: Driving Productivity Divergence?”, Research Memorandum GD-67, Groningen Growth and Development Centre. Motohashi, K. (2003), “IT Revolution’s Implications for the Japanese Economy”; www.nesb.go.th/data%20index/Japanese/5%Motohashi%2005-WBI-Bankok.ppt Sadun, R. (2005), “The role of IT in firm productivity; evidence from UK microdata”, Economic Trends 2005, also. available on the ONS website at http://www.statistics.gov.uk/cci/nugget.asp?ID=1240 van Leeuwen, G. and van der Wiel, H. (2003), “ICT, Innovation and Productivity”; www.statistics.gov.uk/events/caed

Annex 1 The main strands of analysis in this literature include: •

• •

Atrostic and Nguyen (2002), using US Bureau of Census manufacturing data, show that computer network use is associated with higher productivity, and follow up work (2004) identifies additional effects associated with IT investment. Motohashi (2003) uses Japanese data on e-commerce and network use to show productivity gains associated with e-commerce. Maliranta and Rouvinen (2003) use Finnish survey data on employee use of computers and the internet to show productivity gains in newer enterprises.

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van Leeuwen and van der Wiel (2003) use Netherlands data on IT investment and innovation to show strong links between the two in producing productivity gains for firms. Milana (2003) uses Italian firm level data to suggest that ICT produces productivity gains by sector, which are offset by adjustment costs and structural effects. Clayton, Criscuolo, Goodridge and Waldron (2003) use UK data to identify productivity and pricing effects related to electronic buying/selling.

Annex 2 Firm level data sources used for the analyses include: •





• •



The Annual Business Inquiry (ABI), used in longitudinally linked form as the Annual Respondents Database (ARD) in the ONS Business Data Laboratory, to provide data on sales, value added measures of output, employment, software capital and maintenance spend on IT, spend on telecommunications services, and simple e-commerce use indicators, for around 70,000 firms a year. The annual Business Spending on Capital Inquiry (BSCI – from 1998), which provides very detailed data on capital expenditure for around 2,500 firms, including measures of hardware and software. The Quarterly Inquiry on Capital Expenditure (Quarterly Capex – from 2000) which provides summary data (including hardware and software alongside plant and machinery, vehicles, land and buildings) for around 30,000 firms, each quarter in a rotating panel. A specific Fixed Asset Register (FAR) survey for a limited number of firms, carried out for 1995–2000. The e-Commerce Survey from 2000 onwards which provides data on forms of electronic commerce (internet and closed e-commerce systems), on ICT systems in place in firms, on electronic business processes and on employees using computers and the internet, for up to 12,000 firms per year. The Labour Force Survey (LFS), which has been used to develop indicators of skills available to firms by region and industry sector.

These surveys (except for the LFS) have been linked at reporting unit level, the survey unit covering similar activities within each firm. Also used are: 



Inter-Departmental Business Register data on industrial sector for reporting units, and on nationality of ownership for UK subsidiaries of foreign multinationals. Data from the Foreign Direct Investment survey, to identify UK based firms with substantial overseas investments, a definition for UK multinationals.

Measuring the New Economy Edited by Teun Wolters © 2007 Elsevier B.V. All rights reserved.

CHAPTER 7

Travel Agents and ICT Technologies Cees van Beers1 and Harry Bouwman (Delft University of Technology)

7.1. Introduction2 This chapter examines the relationship between emerging Internet(-based) technologies and the performance of the travel agents in the Netherlands. Internet technologies affect business performance through internal and external organisational change. The present study focuses on Information and Communication Technology (ICT)-related organisational changes and the performance of travel agents. Internal organisational changes inter alia involve internal information exchange, monitoring and management information systems, shared services and enterprise integration solutions. External organisational changes comprise different modes of co-operation with other firms (such as tour operators) facilitated by web-based electronic data interchange or total business integration. The performance indicators used and tested in this study consist of share of turnover achieved through the Internet, competitiveness and opportunities to adapt products to customer preferences. The next section discusses the background of the research. The structure of the Dutch travel sector in relation to ICT/internet technologies is presented in Section 7.3. Section 7.4 discusses recent empirical data on the use of Internet and Internet technology by the Dutch travel agents. Section 7.5 presents an overview of major technology trends that affect the travel agencies. Section 7.6 introduces a number of relevant concepts and a simple model of the relationship between internet technology investments/use by travel agents and their performance. Section 7.7 deals with the data and the operationalisation of the model. The empirical results are reported in Section 7.8. The final section draws some conclusions.

1 E-mail:

[email protected] chapter results from a research project initiated by Statistics Netherlands to define indicators that help to understand the impact of ICT and Internet-related technology on the performance of intermediaries in the services sector. In this chapter the terms ICT and IT are used interchangeably. 2 This

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7.2. Background of the research: Productivity paradox The present research is motivated by the productivity paradox. Effects of ICT investments in the United States could not be found in productivity numbers. The paradox was first noted in the 1970s: although the capacity of and investment in computers showed a remarkable increase, the average productivity fell during this period – and most importantly in the ICT-intensive service sector. Baily and Chakrabarti (1988) report that in some sectors, investments in ICT went together with lower productivity numbers. Morrison and Berndt (1990) show that every dollar spent on ICT yields 80 cents in return. Roach (1991) argues how, especially in the services sector, investments in ICT were followed by a notably lower productivity figures. Brynjolfsson (1993) argues that these negative evaluations are based on incorrect assumptions. He suggests four explanations for this paradox: 







Measurement errors: there are various indicators to measure output, but these are unreliable, especially in the services sector (where the paradox is predominant). Delayed impact: it takes some time (several years) before the benefits of ICT are translated into solid statistics of productivity measurements. Redistribution: this explanation assumes a different distribution of the benefits. ICT divides the pie differently, without making it bigger. Mismanagement: the most pessimistic explanation argues that managers take the wrong decisions when it comes to the use of ICT and deploy the wrong systems, as a result of which they will never reach any efficiency gains.

Brynjolfsson considers the first explanation to be the most important one. Other authors (for instance: Yorukoglu, 1998; Anderson et al., 2003) argue that positive relations between ICT investments and productivity do exist. Crafts (2001) even argues that the contribution of ICT to productivity has exceeded that of steam technology and at least has matched that of electricity – while, on the other hand, Gordon (2000) contends that ICT’s contribution is not comparable to “the great inventions of the past”, such as electricity and the internal combustion engine. At a microeconomic level, ICT’s contribution to efficiency and effectiveness of individual employees is sometimes found to be negative, which is explained by factors such as miscommunication, information overload and technical failures (McAfee, 2005a, b). Altogether, studies on the effects of ICT on productivity and efficiency do not present a coherent picture. A similar lack of coherence is apparent in discussions on the issue of dis- and re-intermediation.

7.3. Internet(-based) technologies and the travel industry Traditionally, the travel agents represent the point in the value chain where tour operators’ confronts the wishes of their potential clients. Travel agents give information

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on the available travel products, considering prices and service levels. They produce value added as advisers and mediating agents with the ability to realise economies of scale and match (bundled) services (Spulber, 1999). Due to Internet and Internet technology, the position of the traditional travel agents is subject to great pressure. In many studies, the travel industry illustrates newly emerging technologies leading to structural change (Barry, 2003). Airlines allow customers to check the status of their flight, update frequent flyer miles and book flights via wireless services. Online booking and electronic tickets are changing the primary processes in this industry, omitting traditional agents and desk functions in airports. Hotels and car rentals can be booked online, or even via mobile devices (i.e. Dollar Rent a Car developed a Palm-based mobile application based on XML Web services).3 Virtual travel agencies offer almost all the services that the conventional agencies can provide, while offering auxiliary services like travel hints, electronic travel magazines, price comparisons, or even online travel auctions (e.g. cathaypacific.com auction of tickets on competitive routes, and aerlingus.ie auctions tickets that are set to expire; Turban et al., 2002). For customers, this is beneficial as it leads to lower travel costs, customised services, better insight into what is offered thanks to multimedia presentations, paperless processes, self-management, etc., while on the supply side it means product innovation, new competitive vendors like online travel portals (Travelocity, Expedia, and so on), direct sales by airlines, fierce price competition, but also new value-added configurations created by travel agencies e.g. by providing total solutions, dynamic packaging (bundling of services based on wishes of individual customers), certifications and trusted third party control, as well as developing niche strategies. It is widely believed that Internet and Internet technology most strongly affects the intermediaries in the travel industry, i.e. the travel agents. Although Internet technology tends to promote disintermediation (Benjamin and Wigand, 1995), there is also reintermediation taking place (Sarkar et al., 1998). Travel agents as intermediaries are likely to continue to play a role of some significance, but not as outlets at the corner of the street. Traditional travel agents not only look into the opportunities Internet is offering, but also develop new strategies, for instance, by focusing on niche markets, i.e. specific target groups such as the elderly or the adventurous or by focusing on a specific type of holidays: e.g. concert travels or treks. Therefore, to understand the performance of travel agents, it is important to take into account their strategic objectives, their product-market orientation as well as their orientation towards IT and IT investment.

3 http://www.microsoft.com/resources/casestudies/CaseStudy.asp?CaseStudyID=11626, retrieved 3 August, 2005.

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Table 1. Collecting travel information: 2005 General travel organizations Web site

Tour operators Number of visitors

Web site

Number of visitors

658,000 617,000 571,000 406,000 244,000

D-reizen Cook/Neckermann

287,000 247,000

ANWB (Dutch AAA) Locatienet (Route information) NS.NL (Dutch Rail) 9292ov.nl (Dutch Public Transport) Map24 (Route information) Source: http://www.etcnewmedia.com/review

7.4. Empirical data on the Dutch travel sector Information on the consumers’ use of Internet in the Dutch Travel Industry is scarce, and if available, fragmented and mainly based on informal sources. We first present some highlights with regard to the demand side and then report some data on the supply side. In 2005, 60% of the Dutch population used internet to collect travel information, while only 14% preferred a travel agent. The most popular travel web site – in terms of unique audience – is that of the Dutch Automobile Association (ANWB), with more than half a million visitors per year (Table 1). Although the ANWB also involves travel agencies, their business is far more diverse. Tour operators and travel agents attract fewer visitors. In 2003, 35% of the Dutch consumers booked their travels on line, while 40% still used a traditional travel agent.4 Other channels are direct bookings, for instance via contact centres. Other sources estimate that 30% of the Dutch consumer book their holidays online, and 20% via a travel agent (www.anvr.nl/, retrieved September 15, 2005). CBS (2005) reports the percentage of online buyers that booked a holiday, hotel or flight, increased from 30 in 2002 to 37 in 2004. Findings released in August 2002 by Jupiter Research show that online travel sales in the Netherlands are expected to reach E 1.0 million by 2007, up from E 0.3 million in 2002. (eMarketer, June 2003, retrieved 3 August, 2005). In 2004, the travel industry generated E 335 million from online sales, while the total of online transaction amounted to E 775 million (http://www.sales-online.nl/nieuws/index2004-38.html, retrieved 3 August, 2005). Other sources indicate that travel combined with insurance and ticket sales are responsible for 53.4% of the total e-commerce turnover (http://www.adformatie.nl/adfodirect/nieuws2005-23.html, retrieved 3 August, 2005). These trends suggest that investments in and use of IT in the travel sector cannot but have an effect on the performance of the travel agents. So far, clear insights on 4 http://www.deloitte.com/dtt/press_release.

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the impact of Internet and the shift from traditional channels towards the Internet are missing.

7.5. Technology trends In order to gain insight into how Internet and Internet-related technology affects the travel industry, it is important to understand technological trends. These trends indicate what computer manufacturers, network providers, software developers and application designers can offer. They have an impact on numerous areas such as energy production and mobility, network-increased interoperability, and on the intelligence of applications (for an extended overview of technology trends, see Bouwman et al., 2005). Especially middleware applications are relevant to understanding the impact of new technologies on organisational and performance indicators Middleware makes it possible for services to be delivered through the Internet. These so-called Web services are based on standards such as WSDL (Web Service Definition Language) and UDDI (Universal Description, Discovery and Integration) from Microsoft, JINI (a Java based technology), and JXTA (JAVA P2P protocol) from SUN and open standards as SOAP (Simple Object Access Protocol) and XML (Extensible Mark-up Language). These new standards and protocols enable web services such as single sign-on, authentication, notification and messaging, personalisation, integrated search, management and data exchange. Web services can be integrated with legacy systems; in that case we speak of Enterprise Applications Integration. Web services help to separate business logic from implementation by making use of standardised service interfaces. Web services in fact perform encapsulated business functions. These technologies ensure a “loose coupling” for instance in cases of Enterprise Application Integration. Web services also help to couple IT-systems across the value chain. Adoption, implementation and use of web-services are indicative for IT maturity and an orientation towards external partners: web services enable flexible connections with other parties in the value chain. It is assumed that web services will lead to horizontal disintegration of value chains within and between organisations, and increased flexibility in offering (new) end-user services. This implies that in the long run, the value chain will transform into a networked environment. In this networked environment traditional nodal actors, like tour operators, will be less dominant and be frequently passed by. Although this is what we expect for the future, at present IT use by intermediaries is rather traditional. IT use by intermediaries is characterised by the existence of a web site, containing information on different topics, and the use of communication facilities such as e-mail in the front office and by administrative information systems (ERP) in the back office. Back office applications might be integrated with the systems of dominant nodal actors like tour operators in cases of value chain integration. Then there is almost no direct connection between the information systems of travel agents with systems used by other organisations in the value chain, like e.g. financial services, airlines or rental companies. If interoperability exists, it is most likely achieved with the help of web services.

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7.6. Concepts and model The main aim of this chapter is to investigate the impact of ICT and Internet technologies on the performance of the travel sector. The performance determinants are presented in the following model; they are based on Figure 1: Performancet = f (IT − uset , IT − investmentst , maturityt , firmsizet−1 )

(1)

An important indicator of firm performance is internet sales. The other indicator is market performance as measured by improved competitiveness. IT use is an important predictor of performance. If a web site is used to perform more transaction enabling functions it is expected to affect Internet sales positively. As regards the other two performance indicators, the web site information index is assumed to reflect how well advanced the firm is in terms of ICT use. Therefore we also expect a positive effect here. A positive effect on changes in internal organisation and internet sales can be expected for travel agencies that make extensive use of web functionality, while we expect a negative effect for travel agents that are more connected to back-office processes of tour operators. Back-office integration is expected to affect Internet sales negatively. Firms that are (strongly) connected to the tour operators in their back office

Figure 1. Conceptual model IT use Website information

Use of online databases Strategic objectives

Turnover attributed to Internet sales

(Internal and external) Organizational effects of IT IT investment

Maturity ICT

Level of investment in software Dependencies in investment decisions Investment in type software

Market performance of IT

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are more locked in and therefore have fewer opportunities to sell through the Internet. Back-office systems in the travel industry tend to be rather user unfriendly and mainly used by employees of travel agencies. Interfaces to the Internet are seldom available. We expect that investments in specific web site software influence the Internet sales positively. But investments in software like middleware and web services are not expected to make a difference here. However, investments in middleware and web services software are expected to influence the other two performance indicators positively. Web services and middleware enable organisations to transact more flexible with other parties in the value chain and to form (temporary) networks on a more flexible basis. Therefore, we also expect this software to affect the external data exchange with other firms positively. Dependency on prescriptions of software suppliers, age of IT systems, and predetermined standards for investment decisions, as well as dependency of ICT expenditures on wishes or requirements of tour operators are expected to work out negatively on the three performance indicators. According to business-IT alignment concepts (Henderson and Venkatraman, 1993), a firm has to align the development of information systems with its business strategies. Firms that systematically invest and use information systems as part of a plan are assumed to be well informed about the business opportunities these IT systems can produce. Therefore, we expect that implementation of IT systems which is duly planned along these lines (internal maturity), will have a positive effect on the three performance indicators. External maturity can have two appearances. On the one hand, we see electronic hierarchies in which dominant partners define how other players have to use information systems and applications within a traditional value chain, for instance based on EDI-based systems. On the other hand, we see examples – based on for instance web services – of loose connections between parties in a value web, enabling flexible forms of collaboration. In cases where information systems and applications are directly connected with information systems of other firms, internet sales are expected to be negatively affected by such connections. Firm size is a variable reported in regular production statistics and has been incorporated with a lag of one year (year is 2002). A larger firm is expected to invest more in IT capital but also to know better how to use IT as it has sufficient means to hire IT expertise. Therefore, we expect a priori a positive effect of firm size on the relevant performance indicators.

7.7. Data and model operationalization 7.7.1. Survey To test the model (see also Figure 1), we used a survey approach. The survey was sent out by Statistics Netherlands to 205 travel agents and tour operators having their own outlets. Response was received from 127 firms. The data were merged with

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Table 2. Size distribution of Dutch travel firm’s sample: 2003 Size Distribution (number of employees)

1 1–3 4–9 10–19 20–49 50–99 100–199 200–499 >499 Total

Sent Out

Number 47 24 22 29 44 16 9 10 4

cumulative% 22,9 34,6 45,3 59,5 80,9 88,7 93,1 98,0 100

205

Response

Number 33 13 13 22 28 8 2 5 3

cumulative% 26,0 36,2 46,5 63,8 85,8 92,1 93,7 97,6 100

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available data on productivity from 110 respondents derived from annual business surveys of Statistics Netherlands. In Table 2 (see above), the size distribution of the survey sample is shown. The sample consists of many small firms, which is typical for the travel sector. Forty six per cent of the responding firms have less than 10 employees. 7.7.2. Measurement scales and indexes Strategic objectives are measured by a number of items with respect to competitive advantage, new business, cost reduction and interorganisational collaboration. Factor analysis (principal components) results in two relevant factors (explained variance 72%). The first three items (competitive advantage, new business, cost reduction) have a factor loading of 0.84, 0.80 and 0.65 on the first factor, while interorganisational collaboration is the only item that loads on the second factor: 0.95. Interorganisational collaboration appears to be a strategy that clearly differs from a strategy based on competitive advantage, new business, and cost reduction. We only use the first scale for further analysis. Reliability of the scale is indicated by Cronbach’s α of 0.66. For an exploratory study like this one, this is considered to be sufficient. Strategy variables – although relevant in the conceptual model of Figure 1 – have not been included as discriminatory factor analysis shows that these variables and maturity-items are strongly correlated with each other. IT maturity was measured by a number of items. These items were concerned with the degree in which the development of information systems were aligned with the business strategy, based on (internal) process analysis, the degree into which internal systems were integrated, the degree to which internal systems were linked to systems of value chain partners, and the degree to which one uses (open) standards

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for integration such as for example XML, Soap, or UDDI. Factor analysis resulted in two factors (explained variance 71%). The first factor is composed of two items on the development of IT systems based on a pre-defined process analysis (0.84) and on alignment of information and communication technology with a firm’s strategy (0.85). Its values range between 2 (both process and strategy very weakly present) and 8 (both process and strategy very strongly present). We will label this factor as Internal Maturity. Cronbach’s α for this two items scale is 0.74. The other two items load on the first factor and have factor loadings of 0.88 (external integration), and 0.87 for the use of standards for integration. Cronbach’s α for this three items scale is also 0.74. We will label this factor as External Maturity. The variable’s values also range between 2 (both connections and use of open standards are weakly present) and 8 (both connections and use of open standards are very strongly present). IT use is measured by indices based on two variables, related to back and frontoffice IT use. For front-office IT use, survey questions were formulated about different types of information supplied by the travel agents web site and the provided possibilities for on-line transactions. Based on these questions, a web site information index was constructed. This index is an ordered numbered variable ranging from 1 to 9. Value 1 indicates that the reporting firm has no web site. Values ranging from 2 to 9 indicate that the web site is used for more functions ranging from simple information provision (2) to payments or continuous information on travel schedules and times (9). For back-office integration, three questions relating to access to (online) databases and applications are important, (1) the ability to access systems and applications of tour operators, (2) the interoperability of the systems and applications of the tour operator and the travel agents and (3) internal systems, for instance financial or ERP systems. The back-office integration index is also an ordered numbered variable with a value 1 if the travel agent does not make use of databases that are provided by tour operators, value 2 if the travel agent does use on-line databases without access to ICT systems of the tour operators, value 3 if the firm has access to databases and applications of the tour operators and uses them only for making reservations and receiving orders, and a value 4 if the firm has access to databases and applications of the tour operators and uses them for reservations and receiving orders but also for payments and issuing of tickets. This index indicates the degree to which the travel agent is “locked-in” to the databases, applications and systems of the tour operator. IT investments consist of two variables. The first one describes whether or not the firm performed IT-investments in 2003, with 1 if the answer is yes and 0 if the answer is no. The question addresses seven kinds of software, ranging from simple desktop software to software for total business integration like middleware and web services. We use two of these specified software investments, i.e. (1) investment in software for content management and web editing in case internet sales share is the dependent performance indicator, and (2) a composite index with investments in software customer relations management, middleware, and integrated web services. The second variable – technological dependencies – measures whether or not investment

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decisions are dependent on the prescriptions of software suppliers, age of IT-systems, and predetermined standards. This variable is 3 if none of these limitations exist and 9 if they are strongly present. Performance consist of (1) turnover share achieved with internet sales, (2) market performance, i.e. improved competitiveness at input and output markets.

7.8. Empirical results The average part of turnover achieved with Internet sales in the sample is 30%. A positive number on this performance indicator is achieved by 54% of all firms in the sample. This is quite high as compared with the average of all Dutch firms in 2003, which is 22% (CBS, 2005, p. 66). The distribution of the sample over the range of Internet sales as share of total turnover is shown in Figure 2. Figure 2 shows the firms reporting percentages larger than zero. The number of observations in each stack is shown on top of it. For example, 24 firms revealed percentage greater than zero and lesser than or equal to 10. It is clear that the distribution is skewed to lower values. The median is 20%.

Figure 2. Sample distribution of Internet sales as share of turnover: Dutch travel sector, 2003

25

Internet sales: Dutch travel sector 2003

15 10

8

8 6

5

4

5

5 2

3

3

0

Number of firms

20

24

0

20

40

60

Internet sales as % of turnover

80

100

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model.5

Table 3 reports the estimates of the Some 26 firms reported 0 Internet sales. This means that the distribution is truncated at the left side.6 A Tobit estimator has been used to take this characteristic into account. With regard to IT use, two counteracting factors can be distinguished. The web site information index works out positively on the share of turnover achieved through internet sales. The existence of a web site and its use to perform more transactionenabling functions encourage the share of internet sales. This suggests the existence of an electronic market. The back-office connection to the tour operators on-line databases and applications by the travel agents shows a negative coefficient, which is in line with expectations. The use of on-line databases and applications from the tour operators might have a positive effect on back-office process and supply chain integration but have a negative effect on the use of the Internet as a sales channel. Back-office integration indicates the existence of a lock-in relation between tour operators and travel agents, which means that travel agents have limited possibilities to exploit the Internet channel for transaction purposes. The negative effect of the backoffice connection is stronger than the positive effect of the web site functionality. This indicates that electronic hierarchy forces are stronger than the forces that drive electronic markets in the Dutch travel sector. The variable web software affects the dependent variable negatively but not significantly. Investments in (certain types of) software are not important. This is an unlikely conclusion but can be explained of the fact that only investments in one year, i.e. 2003 are reported. The best way to model software effects would be to take into account the investments in e.g. the last five years. The questionnaire is not able to reveal that information. Technological dependency, i.e. limitations on ICT expenditures due to prescriptions of software suppliers, age of IT-systems and predetermined standards work out negatively on the dependent variable as expected. Internal maturity’s effect on the internet sales’ share of turnover is significantly positive, as expected. External maturity shows a positive effect as well but is not significant. Both internal and external organisation variables affect turnover share achieved with internet sales negatively. This implies that firms in which the effects of ICT in the period 2001–2003 have affected the internal or external organisation strongly report a lower internet sales share. As most firms in the sample are small, it might be that organisational matters guided companies away from the sale process. Another explanation is that strong effects of ICT on data exchange with other firms implied substantial efforts to sell products through other channels than internet. Firm size affects internet sales negatively but not significant. In the travel sector smaller firms sell a larger part of their turnover through internet than large firms. 5As

the dependent and independent variables are measured at the same moment in time endogenous problems exist. Therefore, although our specification can be justified theoretically, our estimates are correlates and do not necessarily mean the existence of causality. 6 The distribution is also truncated at the right side as the upper limit is 100%. However, not imposing the upper limit does not affect the estimation results while exclusion of the lower limit would affect the estimations.

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Table 3. Tobit-estimates of ICT determinants on Dutch travel firms’ performance: 2003 Regression

1

2

3

4

Dependent variable: internet sales as % turnover IT-uset

ITinvestmentst

Maturityt

Web site information index

0.245∗∗ (2.087)

0.289∗∗ (2.434)

0.255∗∗ (2.142)

0.273∗∗ (2.187)

Back-office integration index

−0.326∗∗∗ (−2.910)

−0.321∗∗∗ (−2.923)

−0.389∗∗∗ (−3.321)

−0.398∗∗∗ (−3.373)

Web site software

−0.025 (−0.219)

−0.019 (−0.168)

−0.058 (−0.510)

−0.064 (−0.546)

Technological dependencies

−0.125 (−1.088)

−0.093 (−0.820)

−0.090 (−0.810)

−0.083 (−0.735)

Internal maturity

0.291∗∗∗ (2.593)

0.298∗∗∗ (2.694)

0.278∗∗∗ (2.540)

0.313∗∗∗ (2.482)

0.185 (1.552)

0.191 (1.495)

External maturity Organisationt

Sizet−1

Internal

−0.073 (−0.552)

External

−0.003 (−0.026)

Number of employees

−0.206 (−1.560)

−0.213 (−1.638)

−0.207 (−1.586)

−274.862

−273.508

−272.311

−272.147

0.0008

0.0006

0.0005

0.0016

74

74

74

74

Constant Log likelihood Chi2 Observations

Remarks: ∗ = significant at 10%; ∗∗ = significant at 5%; ∗∗∗ = significant at 1%; t-values in parentheses. Reported coefficients are fully standardized.

For all regressions in Table 3, variance inflation factors have been calculated in order to test for multicollinearity. Although some multicollinearity exists it cannot be considered of great weight (average vif = 1.44 with minimum = 1.15 and maximum = 1.65). The zero hypothesis of no heteroskedasticity (Breusch–Pagan test) cannot be rejected. The total number of respondents consists of 127 firms of which 34 reported that they were part of a larger firm(s) or a franchising organisation. When the regressions

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139

in Table 3 include multiplication dummies with value 1 for dependent firms and 0 otherwise, we find that the significant estimates for web site information index, back-office integration and internal maturity can be attributed to the independent firms.7 The negative effect of technological dependencies on internet sales is entirely due to the dependent firms. In other words, independent firms do not experience technological limitations on their ICT expenditures. In Table 4 the estimates on the other performance indicator – improved competitiveness – are shown. Firms that use web sites for more (advanced) functions report that their competitiveness has been improved due to ICT use. Back-office integration with tour operators is reported by firms that did not observe improved competitiveness because of ICT. The negative effect is not so strong as in case of internet sales as a performance indicator. This is caused by the dependent firms. Independent firms would experience a negative effect at 10% significance (coefficient would become −0.274 in regression 7). Just like in Table 3 and for the same reasons, we also observe here the irrelevance of specific software investments. Technological dependencies affect the reporting of improved competitiveness positively but not significantly. This is valid for both dependent and independent firms. The internal maturity (the alignment of information systems to the business strategy) clearly goes together with improved competitiveness, especially in the case of firms that are independent. Stronger connections with other firms and the use of open standards also go together with improved competitiveness. This effect is stronger for independent firms than for dependent firms. It is likely that the latter’s external links are dealt with at the level of the mother company. Firms that report strong effects of ICT on changes in the internal and external organisation also experience strong improved competitiveness effects due to ICT. There is a very small positive effect of firm size but insignificant. It suggests that the size of the firm as measured by the number of employers is not important for the competitiveness effects of ICT.

7.9. Conclusions The results show that Internet sales strongly correlate with the degree to which the web site of tour operators offers advanced functionality. More functionality and higher share of sales achieved through internet go together. This indicates the existence of an electronic market. At the same time also an electronic hierarchy seems to exist as on-line databases affect the internet sales negatively. The force of electronic hierarchy seems even to be stronger than the tendency towards electronic markets. Consumer and travel sales through internet have increased substantially in the last five years and are expected to rise further in the near future. It seems that travel agents

7 These

estimates are not reported due to lack of space.

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Table 4. Ordered Logit-estimates of ICT determinants on Dutch travel firms’ performance: 2003 Regression

5

6

7

8

Dependent variable: improved competitiveness IT-uset

ITinvestmentst

Maturityt

Web site information index

0.379∗∗∗ (3.630)

0.366∗∗∗ (3.503)

0.331∗∗∗ (3.118)

0.207∗∗ (2.221)

Back-office integration index

−0.122 (−1.287)

−0.119 (−1.253)

−0.170∗ (−1.658)

−0.137 (−1.516)

CRM, middle-ware and webservices software

0.063 (0.682)

0.052 (0.565)

0.018 (0.191)

0.007 (0.078)

Technological dependencies

0.120 (1.300)

0.109 (1.181)

0.104 (1.134)

0.023 (0.276)

Internal maturity

0.347∗∗∗ (3.791)

0.350∗∗∗ (3.829)

0.344∗∗∗ (3.781)

0.114 (1.309)

0.143 (1.303)

0.050 (0.469)

External maturity Organisationt Internal

0.322∗∗∗ (2.801)

External

0.288∗∗∗ (2.714)

Sizet−1

Number of employees

0.085 (0.872)

0.079 (0.821)

0.052 (0.635)

−137.839

−137.443

−136.581

−124.419

0.0000

0.0000

0.0000

0.0000

96

96

96

96

Constant Log likelihood Chi2 Observations

Remarks: ∗ = significant at 10%; ∗∗ = significant at 5%; ∗∗∗ = significant at 1%; t-values in parentheses. Reported coefficients are fully standardised.

are well prepared to take advantage of these opportunities, as long as they succeed in making optimal use of web site functionality. An important factor appears to be a clear understanding of the relationship between business strategy and IT opportunities as illustrated by the importance of the internal alignment of both. Although backward compatibility might be in the interest of the tour operator, it doesn’t help the travel agent to get more Internet-based sales. Dependence

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on legacy systems, software providers and existing proprietary standards impede the exploitation of Internet transactions but not very strongly. After correcting for whether or not the firms are dependent, i.e. part of a larger firm or a franchising organisation, we find that this impediment is only valid for dependent firms, which constitute 27% of the sample. In contrast to use of ICT, the availability of and investments in software is not considered important for the internet sales indicator. This is quite likely caused by flaws in the questionnaire with regard to investment questions. Smaller firms sell a larger part of their turnover through internet than large firms. The explanation of the other dependent variable, improved competitiveness due to ICT, again illustrates the importance of a clearly aligned strategy and use of ICT, as well as dependence on legacy systems and standards that limit the opportunities to strive for a well-organised firm. External organisational changes – electronic data exchange on purchase and sales activities – lead to improved competitiveness but are not translated in higher internet sales as a share of total turnover. This brings us to a major point for the discussion. We developed indicators like front-office IT-use and a back-office integration index. We found that both electronic markets and electronic hierarchies exist in the Dutch travel sector. We did not succeed in developing indicators that can help to understand the difference between hierarchical organisational value chains, in which EDI-based standards play an important role, and more loosely coupled value webs, based on web services and related standards. These issues are dealt with in another survey whose results were not yet available at the time of writing this chapter. Our results are limited by the explorative nature of the research and the limited number of observations. The population of travel agents is not very large. Our response rate is at an acceptable level (62%). Due to requirements of analysis, the number of observations was reduced. Furthermore, the research is focused on one specific industry sector, albeit a sector in which the effects of Internet and Internet-based technologies appear to be very explicit. Further research in other (services) sectors is needed and will take place by us – particularly technical wholesale sector – as well as more extended investigations on the concepts introduced such as web site information index, back-office integration index, dependency of IT investment on legacy systems, standards and providers, as well as on business and IT alignment as used in the IT maturity concept.

References Anderson, M.C.; Banker, R.D. and Ravindran, S. (2003), “The New Productivity Paradox”, Communications of the ACM, 46(3), pp. 91–94. Baily, M.N. and Chakrabarti, A. (1988), “Innovation and the Productivity Crisis”, Brookings Institution, Washington DC.

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Barry, D.K. (2003), “Web services and Service-Oriented Architectures. Your Roadmap to Emerging IT”: Imprint of Elsevier Science. Morgan Kaufman Publishers, Amsterdam. Benjamin, R. and Wigand, R. (1995), “Electronic Markets and Virtual Value Chains”, Sloan Management Review Winter, 1995, pp. 62–72. Bouwman, H.; Van den Hooff, B.; Van de Wijngaert, L. and Van Dijk, J. (2005), “Information and Communication Technology in Organizations”, London: Sage. ISBN 1-4129-0089-1, p. 223. Brynjolfsson, E. (1993), “The Productivity Paradox of Information Technology: Review and Assessment”, Communications of the ACM, 26(12), pp. 67–77. CBS (2005), “De Digitale Economie, 2004”, Centraal Bureau voor de Statistiek, Voorburg/Heerlen (English title: The Digital Economy 2004). Crafts, N. (2001), “Historical Perspectives on Development”, in G. Meier and J. Stiglitz (eds.), Frontiers of Development Economics, Oxford University Press, Oxford, pp. 301–334. Gordon, R.J. (2000), “Does the ‘New Economy’ Measure up to the Great Inventions of the Past?”, Journal of Economic Perspectives, 4(14), pp. 49–74. Henderson, J.C. and Venkatraman, N. (1993), “Strategic Alignment: Leveraging Information Technology for Transforming Organizations”, IBM Systems Journal 32(1), pp. 4–16. McAfee, A. (2005a), “Will Web Services Really Transform Collaboration?”, Sloan Management Review, Winter, pp. 78–84. McAfee, A. (2005b), “Two Electronic Hierarchies Hypotheses”, Harvard Business School Working Paper 50, Harvard University. Morrison, C.J. and Berndt, E.R. (1990), “Assessing the Productivity of Information Technology Equipment in the U.S. Manufacturing Industries”. National Bureau of Economic Research Working Paper #3582, (January, 1990). Roach, S.S. (1991), “Services Under Siege – The Restructuring Imperative”, Harvard Business Review, 69(5), pp. 82–92. Sarkar, M.B.; Butler, B. and Steinfield, C. (1998), “Cybermediaries in the Electronic Marketspace: Towards Theory Building”, Journal of Business Research, 41(3), pp. 215–221. Spulber, D. (1999), Market Microstructure: Intermediaries and the Theory of the Firm, Cambridge University Press, Cambridge. Turban, E.; King, D.; Lee, J.; Warketin, M. and Chung, H.M. (2002), Electronic Commerce, A Managerial Perspective, Prentice Hall, Upper Saddle River, NJ. Yorukoglu, M. (1998), “The Information Technology Productivity Paradox”, Review of Economic Dynamics, 1(2), pp. 551–592.

Measuring the New Economy Edited by Teun Wolters © 2007 Published by Elsevier B.V.

CHAPTER 8

ICT Maturity and Firm Productivity Adding Organisational Aspects to the Equation Xander de Graaf†1 (Vrije Universiteit, Amsterdam)

8.1. Introduction 8.1.1. Statistics Netherlands and ICT statistics Statistics Netherlands operates various projects to keep abreast of ICT-related developments.. This chapter goes into one of these projects, called “E-Business, ICT and Statistics” – a collaboration between Statistics Netherlands and the Vrije Universiteit Amsterdam. The way firms use ICT is rapidly changing. Politicians and economists are concerned with these developments because they feel they have an impact on the nation’s economic health and dynamics. ICT is an important technology that becomes more and more visible in economic life. Being a General Purpose Technology (Bresnahan and Trajtenberg, 1995), ICT can be used in many ways. For instance, it allows (knowledge) workers to be increasingly mobile. Moreover, an increasing part of turnover is realised through electronic commerce. In 1999, Dutch firms with 10 or more employees realised 2% of their turnover through electronic selling. In 2003, this percentage increased to 7% (CBS, 2005). From a company perspective and focusing on ICT investments, Brynjolfsson was, in the mid-1990s, one of the first researchers who showed that ICT investments raise firm level productivity (Brynjolfsson and Hitt, 1996). Corporate managers view productivity as an indicator of the total overall efficiency of their firm (Craig and Harris, 1973). Productivity is the amount of output produced per unit of input (Brynjolfsson and Hitt, 1998). Statistically, productivity can be seen as an

1 We deeply regret to announce that Xander died on 1 August 2006. For questions about the chapter, please contact Professor Wouter Weller ([email protected]).

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efficiency ratio i.e. total Euro-value of inputs divided by total Euro-value of output (for more details, see Box 1).

BOX 1: ICT’s influence on productivity The concept of productivity as an efficiency ratio is adequate but requires further explanation since (at firm level) it actually has three dimensions, input euro-value, output euro-value and the efficiency of converting inputs to output. van Ark and de Jong distinguish three sources of value creation for firms, namely productivity (through increased operational efficiency and technological change), prices (lower input and/or higher output prices) and activities (improved input mix, improved output mix and economies of scale) (van Ark and de Jong, 2004). All these sources of value creation are expressed in the productivity statistics. Besides improving the input–output ratio, ICT investments may also allow firms to successfully introduce new products and services, to raise output prices and quantities, and to lower input prices and quantities (see Section 8.2 for examples of ICT Impacts).

The measurement of ICT developments calls for new ICT indicators. The research reported here involve the development and testing of theoretical models that can be used to derive new ICT impact indicators. These indicators measure the impact of ICT on firms in terms of (ICT related) changes that go beyond the basic technical (readiness) level of having computers, networks and Internet access.2 To develop ICT impact indicators, the project has reviewed several academic research fields as well as recent ICT developments. The field of Productivity & ICT provided a suitable background for testing new indicators. This field uses production functions and other techniques to relate various firm inputs such as ICT to outputs such as total sales. Based on a review of ICT developments, a layered ICT productivity model had been constructed to visualise the relationship between ICT readiness and productivity (Section 8.2). This model made it possible to create an instrument to measure the maturity of a firm’s ICT (Architecture Matrix, see Section 8.3). Section 8.4 translates the concept of ICT maturity into a questionnaire. ICT maturity describes the context and basic features of a firm’s ICT within a development perspective. Quantitative results from the ICT maturity questionnaire have been linked with existing official productivity statistics (Section 8.5). This chapter builds on the work of inter alia Brynjolfsson (1996), Bresnahan (2002) and various statistical agencies. An important scientific goal is to further explain the relationship between ICT investments and productivity. Successive ICT investments lead to the build-up of an ICT stock. The research involved looks

2 Sambamurthy

and Zmud have defined ICT Impacts in 1994 using statements (Sambamurthy & Zmud, 1994). These statements are summarised in Table 1 (Section 8.3).

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into the mechanisms behind the phenomenon that ICT investments tend to increase productivity (called ICT productivity). Other researchers have investigated ICT productivity from different angles. For instance, Clayton, Criscuolo and Goodridge have looked at the role of electronic linkages between business processes (Goodridge et al., 2004) and van der Wiel and van Leeuwen have focused on the interplay between firm innovation and ICT stock (van Leeuwen and van der Wiel, 2003). Brynjolfsson has used a scatter plot to visualise the positive association between ICT stock and productivity. Figure 1 is a replication of such a scatter plot using Statistics Netherlands’ data. The vertical axis represents value added per employee divided by the SBI 2-digit industry median of this number. The horizontal axis represents the amount of ICT hardware stock per employee, again relative to the industry median. Besides the positive association, Figure 1 also shows a big variation in ICT productivity between seemingly similar firms. When measured directly, the relationship between ICT investments and productivity is positive but very weak. This weak association seems logical since ICT investments affect productivity via several intermediate steps. Details on the data sets used for this research are given in Appendix A. After Brynjolfsson, many researchers have demonstrated that there is a positive relationship between ICT investments and productivity. However, according to what at present can be measured, ICT productivity strongly differs between seemingly similar firms. (Section 8.1.2). For a great part, this enormous spread seems to be due to a lack of theoretical understanding and relevant data. This chapter aims to further clarify the mechanisms between ICT investments and productivity so as to decrease the variation in observed ICT productivity figures.

Figure 1. Relative value added per employee versus relative HW-stock per employee Value Added (2002) and HW-Stock (2002), whole sample (N=826)

Value Added / Labor (relative per sector)

3.0 2.5 2.0 1.5 1.0 0.5

y = 0.1375x + 0.9019 R2 = 0.098

0.0 0

1

2

3

4

5

HW Stock / Labor (relative per sector)

6

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8.1.2. ICT productivity research The use of econometric techniques that relate firm output to a set of inputs including labour, non-ICT capital stock and ICT capital stock3 is a common research method. The current view is that ICT investments contribute to firm productivity,4 and show higher gross marginal returns than non-ICT investments (Dedrick et al., 2002). In 1996 Brynjolfsson was one of the first researchers who showed that the rate of return on ICT investments is positive, i.e. the contribution of ICT to productivity is positive and greater than its cost. Although the overall contribution of ICT to firm productivity is now widely believed to be positive, negative relationships have also been reported5 In the course of time it has been shown that many reported negative or zero ICT payoffs can be attributed to insufficient theory or data problems, such as incorrect deflators (Barua and Lee, 1997) or output measures (Strassman, 1990). Concerning these problems Appendix B discusses nonlinear relationships, time lags, firm output, ICT inputs and capital stocks. In spite of the possible pitfalls mentioned in Appendix B, the body of knowledge on ICT productivity is growing fast. The current view that the rate of return on ICT investments is positive seems to be very consistent. Furthermore, a lot of knowledge on the nature of the indirect relationship between ICT investments and productivity exists. Numerous case studies and quantitative work suggest that organisational variables to a large extent determine the returns on ICT investments. Organisational variables are for instance characteristics of business processes, organisational skills and managerial practices (Box 2) (Brynjolfsson and Hitt, 1995; Brynjolfsson et al., 2000). ICT systems do not provide business benefits as such, however they may prove beneficial by first improving for instance business operations (e.g. better availability of information) and thereafter firm level aggregates such as profit or productivity. Such quality increases may very well not be picked up by (official) statistics. If so, ICT is associated with unmeasured value, in nominal terms. Brynjolfsson refers to unmeasured variables as “intangible organizational capital” (Brynjolfsson et al., 2000)

3 Most productivity calculations use ICT (capital) stocks as inputs instead of ICT investment flows.

For each type of asset a firm extracts a flow of productive services from the cumulative stock of past investments (OECD, 2001). Investments are a poor proxy of capital stocks because levels of investment flows are not constant. Computer investments are a typical example of non-constant investments (Broersma et al., 2003). The usage of capital stocks, however, tend to reduce the reliability of production function estimates, as they require several “heroic” assumptions, for instance on depreciation rates. See (Martin, 2005) for a review of the measurement problems associated with constructing ICT stocks. 4 Studies that report a positive relationship between ICT investments and productivity are e.g. (Lehr and Lichtenberg, 1999) and (Brynjolfsson et al., 1996). The empirical evidence is summarized in e.g. (Brynjolfsson and Hitt, 2000; Dedrick et al., 2002; United Nations – UNCTAD, 2003; OECD, 2004). 5 Robert Solow noted in 1987: “Computers are found everywhere but in the productivity data” (Solow, 1987). This statement became known as the Solow (productivity) paradox. Strassman finds no relationship between ICT spending and profit (Strassman, 1990). Loveman reports no relationship between ICT spending and productivity (Loveman, 1994).

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BOX 2: ICT investment → usage (e-buying and e-selling) → productivity Work of the Office of National Statistics (UK) contains a clear example of measurable business process characteristics that affect productivity. First it is shown that electronic buying and electronic selling have a different effect on productivity (Eurostat, 2004), (Criscuolo and Waldron, 2003). Productivity gains associated with e-procurement are larger than those associated with e-selling. Eurostat suggests that these gains to buyers and losses to sellers are due to pricing effects, internal efficiency accounting for the difference. This reasoning is in accordance with theories on consumer surplus (Hitt and Brynjolfsson, 1996) which predict that in competitive markets ICT-benefits eventually will be passed on to end-consumers. Focusing on business processes, a more recent UK study suggests that sellside linkages (customers–firm) are more beneficial in the service sector, whereas buy-side linkages (suppliers–firm) are more beneficial for manufacturing firms. These industry differences can be explained by differences in value creation mechanisms. In manufacturing, value is created at various points through the value chain. However, in services value is often added at the point where customer needs and firm capabilities meet (Goodridge and Clayton, 2004).

and “intangible outputs” such as product quality or variety (Brynjolfsson et al., 1996). The argument can be summarised as follows: “higher ICT returns are due, in part, to complementary investments that firms make in organisational assets which are not properly accounted for as investments in a firm’s financial statements” (Dedrick et al., 2002).

8.1.3. Research approach and focus ICT investments thrive if they go together with certain organisational characteristics. As long as they are not (fully) identified, these characteristics are intangible assets, which are absent from the productivity equations. When measured, these characteristics may turn out to be complementary to ICT investments (Bresnahan et al., 2002). In productivity equations, such additional variables are expected to decrease the error term and ceteris paribus reduce the productivity variations between firms. This research combines theories on intangibles and complementarities by distinguishing an organisational variable called ICT maturity. ICT maturity describes the design and the surroundings of a firm’s ICT within a perspective of moving to higher stages of achievement. As a central hypothesis, ICT maturity when growing, is seen to induce increases in ICT productivity (Section 8.4). ICT maturity is modeled as interacting with ICT investments, because ICT productivity effects have been shown to exist as such, but now are are seen to be reinforced by ICT maturity (Figure 2).

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Figure 2. Research approach: Investigating ICT maturity Cluster of complements

ICT investment

Productivity

ICT maturity

This research focuses on ICT and its complements as they transpire within a firm. The role of ICT in value chains and networks is excluded here.6 Indicators developed describe ICT in the firm and the firm’s “culture” towards ICT. This does not mean that for instance external linkages between firms using ICT are not considered. The capability to interconnect is a capability of the internal systems. Consequently, “the extent of external linkages” and “interoperability” are concepts measured by this research project (Section 8.4). The existence of such linkages has often been measured and correlated with ICT productivity. Using firm level data for Japanese manufacturers and distributors from 1991, Motohashi reports positive productivity effects of ICT networks in production, sales and inventory, control systems and logistics management (Motohashi, 2001). Using UK firm level data from 2001, Goodridge inter alia shows that the presence of any of the electronic links surveyed is associated with higher average labour productivity (Goodridge et al., 2004). Such effects are also present in descriptive statistics of Dutch data for 2002 (Box 3).

8.2. Modeling ICT maturity and productivity The research approach involves investigating the joint effect of ICT investments and ICT maturity on productivity. The Information Systems (IS) literature gives conceptual models about the relationships between ICT investments (ICT stock, ICT spending) and productivity. A model of Soh and Markus describes how ICT investments may be converted into business performance via intermediate steps (Figure 4). Their model follows Lucas (Lucas, 1993) according to whom welldesigned ICT and appropriate ICT use are necessary conditions for improving organisational performance. Soh’s model first links ICT investments with ICT assets.

6 See

Chapter 7 for a focus on the external aspects.

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BOX 3: Existence of external linkages Statistics Netherlands’ questionnaire on ICT usage asks inter alia whether the firm has electronic ordering, sales or purchasing systems. If a firm appears to have such e-selling and/or e-buying systems, it is asked to specify with which (select all that apply) other ICT applications these systems are linked: (a) internal systems for inventory management, (b) systems for invoicing and payments, (c) systems for production planning, (d) logistic systems, (e) marketing systems, (f) customers’ ICT systems, and (g) suppliers’ ICT systems. In 2002 more than 60% of the firms had in place ICT systems for e-selling and/or e-buying. Of this group 92% had at least one electronic linkage with other systems and 80% had at least the linkage with systems for invoicing and payments (CBS, 2005). Considering productivity, larger firms realised a significantly higher labour productivity when they had more external linkages. Figure 3 shows that, for firms with more than 150 employees and value added labour productivity measures, the best performing firms are those which have a number of above average electronic linkages (N = 1031).

Figure 3. Value added per employee versus number of electronic linkages in 2002 Number of electronic linkages and productivity 2002 110 Value Added per Employee

100 90 80 Firms >= 150 employees

70 60 50 40 30 0

1

2

3

4

5

6

7

Number of Electronic Linkages

Figure 4. How ICT creates business value (Soh et al., 1995) ICT

ICT assets

ICT impacts

Business performance

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ICT assets comprise the application portfolio, ICT infrastructure and user skill (Markus and Soh, 1993).7 ICT assets are linked with ICT impacts. Finally, ICT impacts are related to business performance. Various authors provide examples for the different model components (Sambamurthy et al., 1994; Soh et al., 1995; Barua et al., 1997). A flexible ICT infrastructure and high levels of user ICT knowledge and skills are mentioned as ICT Assets. Examples of ICT impacts are new or improved products and services, changed business processes, better decision-making, dynamic organisational structures and improved coordination flexibility. Many indicators of business performance besides productivity exist. Empirically testing Soh’s model is difficult because it is a process theory (as opposed to a variance theory (Mohr, 1982)). In order to develop a variance theory, the concepts of intangibles and complements are used as starting points for developing a theory which does allow empirical validation. Measuring ICT impacts in order to gain insight into ICT productivity – a seemingly simple approach – would allow empirical validation but has two disadvantages. First, showing relationships between ICT impacts and productivity does not tell us how ICT investments have caused these impacts. Second, ICT impacts differ per industry, which would make meeting the requirement (Section 8.4.4) of one questionnaire for all industry sectors highly complex. These considerations have led to the ICT productivity model given in Figure 5. First, ICT investments alone or in combination with ICT maturity lead to ICT impacts. ICT maturity is an extension of the ICT assets concept. As discussed before, ICT maturity describes the design and the surroundings of a firm’s ICT and is operationalised in the next sections. ICT impacts are partial performance indicators that precede ultimate business outcomes. In order to meet the requirements for the data-gathering instrument, ICT impacts are not directly modeled or measured.

8.3. Concepts of ICT maturity In order to be able to measure ICT maturity a measurement framework has been developed. This framework – the Architecture Matrix – measures ICT maturity and describes the assumed factors that affect ICT productivity. The Architecture Matrix describes the ICT scope level of the different ICT architecture layers at a functional level. ICT scope is the organisational extent to which ICT related measures are implemented. By looking at larger firms, this research focuses on the standardisation of business functions. The Architecture Matrix measures the level 7 The

applications portfolio consists of all uses of ICT in the organisation, measurable in terms of the applications software it has deployed. ICT infrastructure is comprised of the basic building blocks of hardware and operating systems, shared services such as networking services, and expertise of ICT personnel (Weill, 1992). User skill – what users actually know how to do with their applications and infrastructure – is also a critical ICT asset, since without user skill, the potential of the portfolio and the infrastructure can never be realised (Soh and Markus, 1995).

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Figure 5. ICT productivity model Cluster of complements ICT investment - Hardware, software - Employees

ICT impacts - Flexibility - Customer service

Productivity

ICT maturity ICT architecture - Infrastructure - Applications - Information - Processes Business architecture - Business-ICT alignment - Attitudes towards ICT of various groups - Customer focus - Integration of business functions - Standardisation of ICT Management - Centralization of execution

of standardisation per Architecture Layer. The general hypothesis is that firms with higher ICT scope levels per architecture layer and per business function will show higher ICT productivity. Because the matrix is geared to the situation in larger firms, much attention is paid to the concepts ICT architecture, standardisation and ICT scope. The resulting Architecture Matrix is given in Table 1. Architecture Matrix concepts are discussed below and operationalised in Section 8.4. ICT architecture, standardisation, and ICT scope are particularly pronounced in large firms as these have a stratified organisational structure (departments, business units, and divisions) and many external networks, requiring unifying Table 1. Architecture matrix Layer (Hardly) none

◮ ICT culture ICT architecture



−·−·−·−·−·−·−·−·

ICT scope: standardisation per …

Business Process Information Application Infrastructure

Department

Business unit

Company

Chain

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corporate strategies. Such strategies involve increasing system requirements of information systems and may also increase the complexity of ICT systems (e.g. decentralised ICT management in the wake of a merger may cause problems).

8.3.1. ICT architecture ICT architecture measures the particular design that ICT takes in an organisation to achieve specific goals or functionalities. Most architecture models contain separate layers for at least technology, applications, information and business. In short, applications need hardware and networks (technology), and contain information. The information demands are defined by the information layer. At the business layer the focus is on business processes and business outcomes. Architecture Models serve to consider the coherence between the various layers, which together determine the quality of a firm’s ICT.

BOX 4: US ICT architecture layers In 1996, the US implemented the Clinger–Cohen Act “to reform acquisition laws and information technology management of the Federal Government” (Federal CIO Council, 2005). This act (US Senate, 1996) was issued “to ensure that agencies improve the initial capital planning process for large acquisitions to develop realistic cost, schedule, and performance goals that are tied directly to agency strategic mission goals within available budget resources”. The act prescribed that agencies should develop, maintain, facilitate and implement a sound and integrated information technology architecture, but it did not define what an architecture was. To remedy this omission (Bloem and van Doorn, 2004), the Enterprise Architecture Extension (Federal CIO Council, 2003) of the Clinger–Cohen Act was issued in the autumn of 2003. Based upon the Federal Enterprise Architecture Framework (FEAF) (CIO Council, 2001), this extension defined the four ICT related architectures given below. Clinger–Cohen Act ICT architecture layer Business

Information Application Technology

Definition Addresses the business mission, strategy, line of businesses, organisation structure, business process models, business functions, etc. Defines what information needs to be made available to accomplish the mission, to whom, and how. Focuses on the application portfolio required to support the business mission and information needs of the organisation. Defines the technology services needed to support the application portfolio of the business. It also documents the software, hardware and network product standards.

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The layers of the Architecture Matrix are mainly based on work in the Netherlands (van den Dool et al., 2002). In the US the usage of four of these five layers is mandatory for many governmental organisations (Box 4). By combining the Dutch and the US model, five Architecture layers have been defined: infrastructure, applications, information and process. The fifth architecture layer – business – represents the outcomes of the other four layers. It is called ICT culture, representing the business setup which surrounds a firm’s ICT. Most architecture models, including the previously mentioned two models, apply a broad definition of the business layer. They describe for instance what business is being done, with whom etc. This research uses a fairly narrow definition of the business layer so as to exactly coincide with the construct of ICT culture. As a second adjustment, business applications (Table 2) are a more important software category than for instance operating systems when considering ICT productivity. Therefore, the application layer of Table 1 explicitly addresses business applications, whereas other software, such as (network) operating systems, is part of the infrastructure. Davenport describes the importance of Architecture. ICT applications can be fragmented involving different departments. When applications are incompatible, fragmentation may be the result, leading to repetitious storing of the same data Davenport describes how information can be spread across dozens or even hundreds of separate computer systems, each housed in an individual function, business unit, region, factory, or office (legacy applications). Davenport states that in combination, these separate applications represent one of the greatest impediments to higher business productivity and better performance now in existence (Davenport, 1998). They increase for instance the complexity of linking applications and processes. Considering processes, many ICT projects have failed due to interrupted business processes and/or disagreement on business rules and terminology (Dyché, 2002). Table 2. Architecture layers of this research Architecture layer

Definition

Business

Defines the particular business setup that surrounds a firm’s ICT

Process

Describes the (primary and secondary) business processes that are used to produce goods and services (van den Dool et al., 2002). “Business processes are defined as the unique ways in which organisations coordinate and organise work activities, information, and knowledge to produce a product or service (Laudon et al., 2004).”

Information

Defines what information needs to be made available to accomplish the mission, for whom, and how (Federal CIO Council, 2003).

Application

Describes the design of the set of business applications that support the business mission. A business application is an application that supports one or more business functions.

Infrastructure

Defines the technology services needed to support the application portfolio of the organisation (Federal CIO Council, 2003).

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An example of a fragmented business process is the sales department which does not check stocks when accepting an order. Furthermore, stocks can be checked efficiently if for instance the sales department and the warehouse both use the same product definitions. A general point is that ICT makes it possible to avoid the nuisence of having to provide the same information over and over again. Most larger companies nowadays use ICT for their customer service.

8.3.2. Standardisation Standardisation means having common criteria. This reduces complexity. Concerning ICT systems, it is estimated that solving complexity related problems may account for up to 80% of total ICT budget (Gianotten, 2003a). Non-standardisation may lead to for instance island-automation, redundancy, program data dependence, inflexibility, poor data security, and inability to share data among applications (Laudon et al., 2004). Thus a lack of standardisation may easily make for greater complexity. Complexity (possibly due to an inadequate architecture) may cause a firm to fail to quickly respond to changing market conditions or demands. Secondly, simplicity (less complexity) in design reduces development and maintenance costs. Of course, standardisation cannot increase business performance at all times. It may for instance frustrate change or provoke devious practices. However, it is assumed that in general, standardisation is better than no standardisation. The factors that inhibit better business performance come down to a lack of standardisation and integration when the overall firm ICT is considered. Integration8 can be defined as: “combining two or more things in order to become more effective” (Cambridge University Press, 2003). Integration of firm ICT systems is conducive to both effectiveness (e.g. more sales) and efficiency (e.g. lower costs). When looking at business functions such as finance, manufacturing, human resources, and sales, standardisation is good for business performance. In most larger organisations, ICT systems tended to develop ad hoc and not according to an overall strategy. Each functional area tended to develop systems in isolation from other areas. All developed their own systems and data files. As a result, the organisation is saddled with hundreds of programs and applications which are not or are poorly documented. With a change in personnel, no one knows how they work, what data they use, and who is using the data. Again, this leads to unwanted complexities which are costly and reduce overall business performance. Box 5 elaborates on the interplay between the different architectural and functional parts of an information system.

8 Integration

is related to interoperability. A definition of interoperability is “the condition achieved among electronic communication systems when information or services can be exchanged directly and satisfactorily between them and/or their users” (Wikipedia, 2005).

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BOX 5: Dependence between applications, information and processes Applications exchange information, either with other applications, with people, or both. Information is exchanged both within and between firms and within and between business functions. Improved application-to-application exchange may be beneficial to business performance even stronger than improved application-to-person exchange, as the former bypasses traditional work such as retyping, and speeds up processes and make them less prone to errors, cheaper etc. Nonetheless, as various parts in a firm use different systems for the same tasks, even routine activities may become unnecessarily complex. As Davenport notes: “if a company’s systems are fragmented, its business is fragmented” (Davenport, 1998). The same holds for information. “Program-data-dependence is the close relationship between databases the specific programs required to update and maintain them. In a traditional file environment, every computer program manages the location and nature of the data with which it works. In many situations, any change in data requires a change in all programs that access the data. This results for instance in lack of flexibility, reduced data-sharing and availability (Laudon and Laudon, 2004).” “Database technology is an example of a technology that can cut through many of the problems traditional file organisation creates. A database management system permits a firm to centralise its data, manage them effectively, and provide access to the stored data by application programs. A database management system specifies the content and structure of the database. For instance, a data dictionary can be used. This is an automated or manual file that stores definitions of data elements and data characteristics such as usage, physical representation, ownership, authorisation and security (Laudon et al., 2004).” With the applications in place, some analysts argue that only companies seeking to streamline business processes, to standardise data, or to standardise processes can achieve a positive return on their enterprise system investment (Connolly, 1999). The overall problem is illustrated well by Zachman: “In the industrial age, the value proposition was cost justification. Start manufacturing before you do any engineering. This however will lead to cost increases in the long term. Long-term thinking involves doing the engineering before you start the manufacturing. A current observation is: we are just not getting the value of IT. This is caused by short term thinking (Zachman, June 17, 2004).”

8.3.3. ICT scope ICT scope is the organisational extent to which ICT architecture and ICT culture related measures are implemented. To describe organisational diversity and complexity of the larger organisations, we use the organisational excellence model of the European Foundation for Quality Management (EFQM, 2003) and their Dutch association INK (Instituut Nederlandse Kwaliteit). This leads to five ICT scope levels.

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Organisational excellence models describe a generalised development from internal task orientation towards streamlining processes within and across company borders. The basic idea is that the products and services which a company delivers result from a combination of different tasks. Failing to coordinate these mutually supplementary tasks causes unnecessary duplication (Hammer, 2001b). A (serial) coordination of tasks into processes likely cuts out waste and will increase customer satisfaction, because customers do not have to contact multiple departments for the same question. In a task-oriented firm, isolated islands of business processes, information and/or technology may all create organisational inefficiencies (Laudon et al., 2004). In general, reaching a higher stage of development needs to be done from experience gained at lower stages (Instituut Nederlandse Kwaliteit, 2001). Companies will gradually learn to execute more complex business processes. For instance, information sharing between firms will be run more smoothly when information sharing within the firm is executed (almost) error-free. The idea to move from a task orientation to a process orientation (and eventually reaching organisational excellence) is applied to the extent of standardisation of Architecture Matrix’ measures. ICT productivity will be larger if measures are implemented on a larger scale. Following Maes, we argue that scope can be best seen as a boundary condition for an information system (Maes and Dedene, 2001). Thus, ICT characteristics can for instance be implemented per department or per business unit. Using the concepts of the INK model (Instituut Nederlandse Kwaliteit, 2001), we developed (and fine-tuned by means of case studies) a five-stage scope model (Table 3). 8.3.4. ICT culture ICT culture measures the particular surroundings of the firm’s ICT. ICT culture is related to the Architectural Business layer (Section 8.3.1). The relevance of ICT culture is illustrated well by research on success factors of ICT projects. Factors such as executive support, user involvement, experienced project manager, clear Table 3. ICT scope INK stage

ICT scope (level of implementation of ICT measures, operationalisation results from case studies, see Section 8.5 for details)



None (hardly)

Activity oriented

Per smallest organisational unit, e.g. department

Process oriented

Per highest organisational unit, e.g. business unit

System (firm) oriented

Per company

Chain oriented

Also with (some) other companies

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business objective and minimised scope, are more decisive when it comes to the success of an ICT project than a technical factor such as standard software architecture (The Standish Group, 2001). High failure rates of business process reengineering projects are due to an overemphasis on engineering and a lack of attention paid to the human dimension (Foster, 2004). This, however, should not play down the significance of ICT architecture. A recent Atos Consulting study finds that three quarters of Dutch firms who are process-oriented from a business point-of-view are faced with major application-integration related problems (spaghetti-integration) and are therefore not process-oriented from an application point-of-view (Automatiseringsgids, 2005).

8.3.5. Business functions To be able to operationalise the Architecture Matrix, six very broadly defined business functions were developed that together cover most company activities.9 ‘Business functions are specialised tasks performed in a business organisation, including manufacturing and production, sales and marketing, finance, accounting, and human resources (Laudon et al., 2004)’. They are defined broadly. For instance, marketing and after sales services both are seen as parts of the sales function. The business functions also represent distinct ICT areas that are found in all industries. Cells from the Architecture Matrix are informed by each of these functions (for a sample of survey questions, see Section 8.4). The following business functions are distinguished (translated from Dutch): •

• •





Sales: “Broad area of tasks belonging to your sales function, including marketing, submitting quotations, processing orders, sales support, (possibly ongoing) customer contacts and after sales service”. Procurement: “Including tasks for procurement, sourcing and ordering as well as support activities such as order monitoring and contract management”. Production planning and logistics: “All activities for planning and logistics aimed at producing goods and services. Include accompanying information streams. Exclude ICT embedded production automation”. Financial management: “The whole area of financial administrations, safeguards and reports of your company, both at the operational (e.g. invoicing and payments) and a higher (e.g. consolidation, annual returns) level”. Human resource management: “Broad spectrum of activities associated with managing employees, such as recruitment, development, training, payroll and retirement, including associated administrations”.

9 The business functions have been based on various ICT usage related questionnaires of Statistics Netherlands, the OECD and Eurostat, and Porter’s Internet Value Chain (Porter, 2001) and have been fine-tuned by mena of case studies.

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Knowledge management: “Encompassing all activities specifically aimed at storing employee knowledge in documents, web pages etc. and the exchange of this (semi-structured) information with other persons both inside and outside (your) firm”.

Embedded production automation is excluded, because it is also excluded in the financial ICT questions from our data panel. Our definition of knowledge management (KM) focuses on the management of unstructured and semi-structured information. This is a topic not covered by other business functions that focus mainly on documents instead of records. We see firms as information processing entities (Galbraith, 1983). We use the term knowledge management instead of information management because this term is better recognised by respondents. Because of our focus on information management, our definition of KM is quite narrow when compared to some KM literature. 8.3.6. Architecture matrix Using the concepts defined above, the general research hypothesis is: General hypothesis

Firms with higher ICT scope levels per architecture layer and per business function will show higher ICT productivity.

Note that pursuing higher ICT scope levels for ICT architecture is a trade-off between costs and benefits, while pursuing higher ICT scope levels for ICT culture is a matter of “organising things differently”. The general hypothesis is visualised in the so-called architecture matrix (Figure 6). This matrix combines ICT architecture, ICT culture, ICT scope and standardisation. In general, ICT productivity increases when the ICT scope levels of Architecture layers increase. All cells of the Architecture Matrix are surveyed for each business function.

8.4. Measuring ICT maturity This section presents the empirical variables that are used to measure ICT maturity. These variables have been derived from theory and were fine-tuned by means of case studies. The questionnaire uses propositions, asks for exact numbers and uses a new kind of question design especially developed for inquiring the Architecture Matrix, namely Augmented Design. An Augmented Design question goes together with ample information needed to provide a complete description of what is being asked. However, for most respondents the questions have a natural appeal so that there is no need for them to read all the information to give adequate answers. Substantial case study effort went into ensuring construct validity of the augmented design. All details on the questionnaire design are given in appendix C. For reasons of clarity, an example of the Augmented Design is provided in Table 4.

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Table 4. Content survey questions ICT architecture Topic

Design*

Question Content (text between quotation marks is translated from Dutch)

Processes

A

“What is the level of standardisation of activities within each of the following business functions”? “Think of work practices, fixed task sequences, responsibilities and “process steps”. The higher the organisational level on which such matters are agreed upon, the higher the level of standardisation of the activities in question”.

Information

A

“To what extent has your company standardised their definitions of each of the following groups of data”? “Here standardisation refers to the extent to which rules are applied to the dataformat and the meaning of your company data. The higher the organisational level on which such rules exists, the stronger your data definitions are standardised”. Distinguishing seven data categories: customer (e.g. information on customers such as name, address, order history), supplier, own products and services, internal logistics, finance, employees, and knowledge.

Applications

A

“What is the level of standardisation of activities within each of the following business functions”? Here standardisation refers “to work routines, fixed task sequences, responsibilities and “process steps”. The higher the organisational level on which such matters are agreed upon, the higher the level of standardisation of the activities in question”.

Infrastructure



Item dropped after case studies.

∗: A: augmented design.

8.4.1. ICT architecture Infrastructure Infrastructure (e.g. hardware, operating systems, network connections) supports all other ICT architecture layers. All the pieces of infrastructure must be properly interlaced to ensure e-readiness (Economist Intelligence Unit, 2005). Infrastructure is associated with the statistical concept of readiness. From the readiness, intensity, impact model the concept of readiness is by far the most well documented. Currently most readiness indicators approach their saturation point of for instance 90% internet penetration (CBS, 2005). While conducting case studies, we tested several prototypes of a question on the degree of complexity of firm ICT infrastructure. Each prototype produced a low interfirm variance. Furthermore, most firms told us that their infrastructure did not inhibit business performance. With the restriction of posing only one question on infrastructure, we were unable to clearly earmark those firms where infrastructure does inhibit performance. Another argument not to include infrastructure in a questionnaire

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on ICT maturity is that ICT infrastructure parameters contain no information on actual usage. Considering these reasons, infrastructure was dropped from the questionnaire. Applications The survey is focused at business applications that support (parts of) business functions. Our main hypothesis on applications is that having an IS-design with a low number of different applications per business function increases performance. In case studies we tested several prototypes that inquired this concept. The question that differentiated best between complex and non-complex application portfolios is given in Figure 3. Information Just because all users use the same hardware and software does not guarantee common, integrated systems. Some central authority in the firm must establish data standards with which sites are to comply. For instance, technical accounting terms such as the beginning and end of the fiscal year must be standardised (Laudon et al., 2004). The questionnaire proxies standardisation of information by surveying the extent to which data format and data meaning of different categories of data are standardised. This is inquired for seven data groups which are derived from the six business functions (Section 8.3). Processes For the process layer the questionnaire inquires the degree to which activities are standardised. Strictly speaking, the properties of ICT support of activities should be inquired. However, case study respondents found such a concept too difficult to distinguish from application standardisation. Therefore, we inquire the standardisation of activities in general (without an ICT context) and use this as a proxy for the standardisation of the ICT support of activities (Figure 3). 8.4.2. ICT culture Information Systems literature proposes a large amount of business characteristics that might affect ICT productivity. This research measures ICT culture using the following concepts: – – – –

level and change options of business-ICT alignment; attitudes towards ICT of various employee categories; level of customer focus; integration of business functions (as an end-result of the combined ICT architecture levels); – standardisation of ICT management;

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– centralisation of execution of business functions; – presence of ICT skills. Business-ICT alignment Business-ICT alignment measures the extent to which a company’s ICT is able to fulfill business needs. Business-ICT alignment comes in as important especially when there is a tendency to one-sidedly focus on technology and underrate the key issues of business, management, and organisational strength. A firm’s organisational structure and business processes should reflect the interdependence of enterprise strategy and information technology capabilities (Luftman et al., 1993). The literature describes many types of business-IT alignment, such as Henderson and Venkatraman (1993): – strategic integration: the link between business strategy and IT strategy; – operational integration: the link between organisational infrastructure and processes and IT infrastructure and processes. Business-ICT alignment is positioned between the operational and the tactical level using four items. The full questionnaire content is given in Table 5. – In order to obtain a base level of Business-ICT alignment, the survey measures the level of agreement between business goals and the existing ICT systems. – We inquire the percentage of companies’ total ICT budget that is available to change its ICT applications and functionalities. Maintaining business-IT alignment requires constantly adjusting ICT capabilities to changing business needs. However, due to for example legacy costs, license fees and safeguarding operational continuity, companies cannot freely allocate their total IT budget to new systems that align better with new business needs. Complexity related costs may consume up to 80% of ICT budgets (Section 8.3.2). – Information system–business integration can be measured in terms of (Premkumar and King, 1992) (a) senior management’s understanding of the potential of information systems in their business operations and (b) information planners’ understanding of the business plans of the firm. We measure the knowledge that business and ICT employees have of each other’s work.

Attitudes towards ICT of various employee categories Commitment of various employee categories has been proven to determine the success of ICT projects. Concerning individual ICT projects, all factors – executive support, user involvement, experienced project manager, clear business objectives and minimised scope – are more important for the success of an ICT project than technical factors (The Standish Group, 2001). In general, high failure rates of business process reengineering projects have been attributed to an overemphasis on engineering and a neglect of the human

Table 5. Content survey questions ICT culture Topic

Business-ICT alignment

Design

N

P P P Commitment of various user groups

Customer centricity

P P P N

P

P

Question Content (text between quotation marks is translated from Dutch) “What percentage of its total ICT budget1 has your company spent on the procurement, development and/or implementation of new or highly ameliorated ICT applications and/or functionalities, average over the last three years?” 1 : definition total ICT budget. “Currently, our ICT capabilities do not match our company goals.” “Business employees (excluding ICT employees) have a good knowledge of ICT possibilities.” “ICT employees understand our business needs.” “Employees are willing to change their work practices to new processes made possible by ICT.” “New business projects lack the support of ICT management.” “New ICT projects lack the support of business management.” “What percentage of your total number of employees annually receives at least one day per year of training in the usage of ICT applications, averaged over the last three years?” “Our company coordinates all communication with its customers using an integrated Front Office1 . 1 Here the group of employees that themselves have customer contacts is the Front Office. Integrated means that the Front Office coordinates the customer contacts as regards to content between multiple communication channels (e.g. telephone and Internet) and organisational parts (e.g. departments) of our company.” “The Back Offices cannot process the amount of information the Front Office delivers.”

Process integration

A

“To what extent is each of the business functions below electronically linked with other business functions within our organisation?” “To what extent is each of the business functions below electronically linked with business functions of third parties outside your organisation?” “These [linkages] exist when data is exchangeable in digital form without human processing between different business functions using (an) ICT application(s). The more tasks (parts) of a business function that can be processed this way, the stronger the linkage.”

ICT management

A

“To what extent has your company standardised its Application Management for each of the following business functions?” “Application Management involves tuning decisions concerning application development and maintenance, like analyzing user needs and application parameterisation and configuration. The higher the organisational level on which your company conducts application management, the more standardised your application management is.”

Centralization of execution of business functions (except KM)

A

“To what extent is the execution of the following business functions centralized in your company?” “Very decentralized means that a business function is executed by almost all organisational parts. Fully centralized means that execution is conducted by a single organisational unit.”

Presence of ICT skills

P

“Business employees (excluding ICT employees) have a good knowledge of ICT possibilities.” “Our employees (including ICT employees) lack adequate ICT skills.”

P

∗: A, Augmented Design; P, proposition, scale from “highly disagree” (1) to “highly agree” (5); N: exact number (Section 8.4).

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dimension (Foster, 2004). As the introduction of new ICT often goes together with business process change (Teng et al., 1994; Grover and Kettinger, 2000), it is important to take these failure rates and the reason why into account. Business applications may both constrain and enable human action (Soh et al., 2003). Research of Bresnahan strongly suggests that ICT investments, human capital and productivity are complements, with the most important human capital variable being “the fraction of workers receiving training” (Bresnahan et al., 2002). Many single and multiple item scales for commitment, involvement and support of for instance top management and users exist (Grover, 1993; Premkumar et al., 1992). The attitudes of the three user groups top management, business management and employees are measured using the items given in Table 5. Level of customer focus Organisational excellence models stress the importance of customer centric processes. We measure the level of customer centricity using the concepts of front office and back office. A front office is a department which operates as a window to the outside world for other departments and/or business units. Front-offices coordinate the external communication of back offices with suppliers (buy-side), customers (sell-side) and other third parties. In the extreme case, back offices only keep external contacts via one or more front offices (van den Dool et al., 2002). To limit the response burden, this survey only inquires aspects of the sell-side front office. The level of customer centricity is proxied by the degree to which customer contacts take the route via a front office. Without a sell-side front office, there will be fragmented customer contacts with different departments and/or business units. This will be to the detriment of customer service, whilst a good front office increases customer satisfaction. Ultimately, “loyalty economics” means greater revenue and profit (Thompson, 2003). With business customers (B2B) similar effects are expected. The survey measures customer centricity using a two-item construct (Table 5). Integration of business functions From a supply chain perspective, electronic linkages between business functions are said to lower costs, increase sales and thus to increase productivity (Clark and Lee, 2000; Simchi-Levi, 2000; Hammer, 2001a; Goodridge et al., 2004). In her research on EDI Usage, Massetti has measured the existence and the intensity of linkages between different functions (Massetti, 1991; Massetti and Zmud, 1996). She has not measured the mere existence of linkages, but instead the number of associated types of document a linkage is capable of processing electronically. This is a relatively rich intensity measure, because when for instance orders can be placed electronically but cannot be changed or cancelled electronically, the linkage is of moderate value. Concerning our questionnaire requirements, we cannot measure an exact number. Instead, the questionnaire measures the extent to which tasks between functions are electronically linked (Table 5).

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Standardisation of ICT management As an overarching item, we include a question on ICT management. ICT management keeps the ICT infrastructure in such a condition that the information function will continue to comply with the requirements and needs of those who pay for this (Thiadens, 2005). ICT management is a very broad item including e.g. managing user needs and day-to-day server maintenance. Considering the questionnaire requirements, we do not inquire after technical characteristics as these are expected to show high collinearity with industry sector. Instead, we focus on the management of the applications for six business functions (Box 2). Application management includes two tasks: a functional, more user-oriented task and an application management or more informatics-oriented task (Looijen, 1998). Application management is part of the ITIL series from the Office of Government Commerce. It covers the software development life cycle and provides details on business change with an emphasis on requirement definitions and implementation to meet business users’ needs (Office of Government Commerce, 2005). We ask for the extent to which a firm analyses user needs and coordinates application configuration (Table 5). Thereby we measure how well organisations coordinate decisions that affect their applications, information, as well as their processes. Centralisation of execution of business functions A complex organisational structure may require a complex ICT architecture. Therefore, we hypothesise that a centralised execution of business functions increases ICT productivity. For the business function of knowledge management the surveying is focused on the level of its management rather than the level of its execution (as the latter by definition is executed in a decentralised manner (Table 5). Presence of ICT skills A necessary condition for developing and maintaining a good ICT architecture and ICT culture is the presence of adequate ICT skills. In this context, ICT skills do not refer to technical (e.g. programming) knowledge. After all, technical ICT-related tasks can be outsourced. Instead, we refer to knowledge of the possibilities and pitfalls that come with up-to-date ICT (Table 5). 8.4.3. Case studies In order to pre-test the measurement instrument for ICT maturity, we conducted 22 case studies. The case study sample consisted of firms from the following industries: – retail, 5 firms; – finance, 5 firms;

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– manufacturing, 7 firms; – publishing, 5 firms. Nineteen of these 2 firms had more than 250 employees, 3 firms had more than 100 employees. Interviews were conducted with either Chief Information Officers, Information Managers or ICT (department) managers. The primary goal was to improve the questionnaire itself, not the theory behind the questionnaire. The small sample size does not allow drawing general conclusions. Instead, the case studies served to gain a deeper understanding of how respondents interpreted the survey questions. This makes it possible to improve the questionnaire. Appendix C extends on the case study methodology and outcomes. 8.4.4. Questionnaire Requirements for the questionnaire are: • • •

developing one questionnaire applicable in all industry sectors; keeping the administrative burden on responding firms as low as possible; revealing enough variation between firms and industry sectors to further explain ICT productivity.

As a general rule, survey questions have to be as objective, simple, and factual as possible. Subjective questions such as “are your applications easily accessible” generally lead to biased responses because most respondents wish to make a good impression. This brings in the survey design. Simply asking for exact numbers or percentages – very objective – often was deemed infeasible. The interpretation of numbers highly differs between industry sectors. Case studies showed that the usage of percentages made respondents spend too much attention to each individual business function or architecture level. A higher level of generalisation was needed. Therefore, an augmented question design for surveying the Architecture Matrix was developed (Table 4). The full questionnaire is available upon request. Appendix C elaborates on augmented design and propositions.

8.5. First results First analyses of questionnaire responses show that ICT scope levels per Architecture layer and per business function of firms roughly affirm the research hypotheses. As expected, for all business functions except knowledge management, applications are standardised at higher ICT scope levels than processes (Figure 6). Note that considering the differences in ICT scope between functions, the largest difference is observed between financial management and knowledge management. Financial management is standardised at higher ICT scope levels. This seems logical since financial management is more heavily bounded by for instance legal requirements.

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Figure 6. ICT scope versus business functions per architecture layer ICT scope and business functions 4,5

ICT scope level

4 3,5

Processes

3

Application management Applications

2,5 2 1,5 Sales

Procurement Prod.plan. & Financial logistics management

HRM

KM

Business function

By way of initial analysis, the differences between financial and knowledge management have been analysed in more detail. Figure 7 shows that most firms reported to have standardised their financial processes at ICT scope level 4 (within the whole firm, not with third parties), while knowledge management is often standardised at ICT scope level 2 (per smallest organisational unit, e.g. department) or level 3

Figure 7. Number of responses versus ICT scope levels per business function Distribution of ICT scope levels for process standardisation 140

Number of firms

120 100 80

Financial management Knowledge management

60 40 20 0 1

2

3

ICT scope level 2005

4

5

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Figure 8. Productivity versus ICT scope levels for process layer per business function Labor productivity and ICT scope levels for process standardisation

Value added per employee 2002 +/- SE

90

80

70 Financial management

60

Knowledge management

50

40

30 1

2

3

4

5

ICT scope level for process standardisation 2005

(per highest organisational unit, e.g. business unit). Figure 8 shows that financial management standardised at higher ICT scope levels correlates with a higher labour productivity. For knowledge management, a similar but less clear pattern has been observed. Appendix A presents details on the data used to construct these first results.

8.6. Conclusion Measuring ICT readiness is followed by measuring how ICT is actually used. Airaksinen points to a lack of intensity information between ICT readiness and ICT impact indicators. “On e-commerce there is the intensity aspect involved in the ICT surveys in the form of value of e-commerce. There also exist questions on the use of Internet and homepages in the form of usage of them for different purposes. But the richness to do e-commerce and wider e-business is not really dealt with very deeply (Airaksinen, 2004).” Furthermore, Airaksinen notes that ICT-related systems and processes inside the enterprise need more attention as compared to systems and

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processes between enterprises. Eurostat notes that when it comes to impacts of ICT investments on business processes and the role of intangible capital, there is a severe lack of indicators (Eurostat, 2005). The ICT productivity model conceptually links ICT investments and ICT maturity with firm productivity. Using the Architecture Matrix, this research specifically addresses: (1) ICT and business architecture. The questionnaire addresses all conceptual layers between business applications and the business effects. Many other studies do share our notions of intangibles and complementarities, but only gather data on a limited number of architecture layers. (2) ICT scope (Organisational complexity). The questionnaire specifically gathers information on the extent to which ICT and Business Architecture are implemented. Furthermore, it gathers data on the number of organisational parts and the degree of homogeneity of organisational parts, in order to be able to control for organisational complexity in productivity equations. (3) Business functions. ICT scope levels per Architecture layer are investigated for six broadly defined business functions that together cover most company activities. The level of abstractness and the relative straightforwardness of the concepts above and the questionnaire design keep the response burden within reasonable bounds. The questionnaire usually takes 15–20 minutes to complete. First analyses show that the level of ICT maturity (or ICT scope level per Architecture layer) correlates with productivity. Forthcoming research publications will analyse such relationships in more detail. One of the research outcomes will be a set of ICT indicators primarily based on ICT usage within business functions.

References Airaksinen, A. (2004), “Impacts of ICT usage on Business Organization and Business Processes – Final Report”. NESIS Deliverable D5.4.2. Alderighi, M. (2003), “Investigating the IT productivity paradox”, Paper presented at the Productivity, Competitiveness and the New Information Economy – Business, Systemic and Measurement Issues, Rome. Automatiseringsgids. (2005), IT wordt hinderpaal voor innovatie, Nr. 20. Barua, A. and Lee, B. (1997), “The IT productivity paradox revisited: A theoretical and empirical investigation in the manufacturing sector”. The International Journal of Flexible Manufacturing Systems, 7, pp. 145–166. Berry, W.D. (1993), “Understanding Regression Assumptions”, Sage University Paper 07-092: Sage Publications. Bloem, J. and Van Doorn, M. (2004), “Realisten aan het roer – Naar een prestatiegerichte governance van IT”, Verkenningsinstituut Nieuwe Technologie (ViNT).

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1977–1993, The Canadian Journal of Economics, 32, pp. 335–362 (Special Issue on Service Sector Productivity and the Productivity Paradox). Looijen, M. (1998), Information Systems Management, Control and Maintenance, Kluwer Bedrijfsinformatie, Deventer. Loveman, G.W. (1994), “An assessment of the Productivity Impact of the Information Technologies”, in T. J. Allen and M. S. S. Morton (eds.), Information technology and the corporation of the 1990s: research studies. Oxford University Press, New York. Lucas, H.C. (1993), “The Business Value of Information Technology: A Historic Perspective and Thoughts for Future Research”. in R.D. Banker, R.J. Kauffman and M.A. Mahmood (eds.), Strategic Information Technology Management: Perspectives on Organizational Growth and Competitive Advantage. (pp. 359–374). Idea Group Publishing, Harrisburg, Pennsylvania. Luftman, J.N.; Lewis, P.R., & Oldach, S.H. (1993). “Transforming the enterprise: The alignment of business and information technology strategies”, IBM Systems Journal, 32, p. 198. Maes, R. and Dedene, G. (2001), “Towards an integrative framework for software architecture”, Working Paper, Universiteit van Amsterdam, Primavera, 2001–07. Markus, M.L. and Soh, C. (1993), Banking on Information Technology: Converting IT Spending into Firm Performance. Idea Group Publishing, Harrisburg. Martin, R. (2005), “Providing evidence base from Musiness Micro Data – Methods and Results”, University of London, London. Massetti, B. (1991), “The effects of Electronic Data Interchange on Corporate Organizations”, Doctoral Dissertation. Massetti, B. and Zmud, R.W. (1996), “Measuring the extent of EDI usage in complex organizations: Strategies and illustrative examples”, MIS Quarterly: Vol. 20, Issue 3, pp. 331–345. Mohr, L.B. (1982), Explaining organizational behavior. Jossey-Bass, San Francisco, London. Motohashi, K. (Januari 2001), “Economic Analysis of Information Network Use: Organizational and Productivity Impacts on Japanese Firms”. Not published. OECD (2001), “Measuring Productivity”, OECD Manual, measuring of aggregate and industry level productivity growth. OECD (2004), “The Economic Impact of ICT – Measurement, Evidence and Implications”, Paris. Office of Government Commerce (2005, 041005), “ITIL (IT Infrastructure Library)”. Available at http://www.get-best-practice.co.uk/itilProducts.aspx Oz, E. (2005), “Information technology productivity: in search of a definite observation”, Information & Management, 42, pp. 789–798. Porter, M.E. (2001), “Strategy and the Internet”, Harvard Business Review, Reprint R0103D. Premkumar, G. and King, W.R. (1992), “An empirical assessment of information systems planning and the role of information systems in organizations”, Journal of Management Information Systems, 9, p. 99 (27 pp.).

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Sambamurthy, V. and Zmud, R.W. (1994), “IT Management Competency Assesment: A Tool for Creating Business Value Through IT”. Working Paper, “Financial Executives Research Foundation”: Morristown, N.J. Schreyer, P. (2002), “Computer Price Indices and International Growth and Productivity Comparisons”, OECD, Review of Income and Wealth: Vol. 48, pp. 15–31. Simchi-Levi, D. (2000), Designing and managing the supply chain, McGraw-Hill Higher Education. Snijkers, G. (2002), “Cognitive Laboratory Experiences – on pre-testing computerised questionnaires and data quality”, Universiteit Utrecht, Utrecht. Soh, C. and Markus, M.L. (1995), “How IT Creates Business Value: A Process Theory Synthesis”, paper presented at the International Conference on Information Systems. Soh, C.; Sia, S.K.; Boh, W.F. and Tang, M. (2003). “Misalignments in ERP Implementation: A Dialectic Perspective”. International Journal of Human-Computer Interaction, 16, pp. 81–100. Solow, R. (1987), “We’d better watch out”, New York Times Book Review, July 12. Strassman, P.A. (1990), “The Business Value of Computers: An Executive’s Guide”, Information Economics Press, New Canaan, CT. Taris, T.W. (2000), A Primer in Longitudinal Data Analysis. Sage, London. Teng, J.T.C.; Grover, V. and Fiedler, K.D. (1994), “Re-designing business processes using information technology”, Long Range Planning, 27, pp. 95–106. The Standish Group, J.H.J. (2001), “Micro Projects Cause Constant Change”, Paper presented at the Extreme Programming 2001, Cagliari, Italy. Thiadens, T. (2005), “IT Service Management”, available at http://home.aim.avans.nl/ thth/ Thompson, B. (2003), “Multi-Channel Service - Boosting Customer Value and Loyalty”, available at www.crmguru.com United Nations – UNCTAD (2003), “E-Commerce and Development Report 2003”. New York and Geneva. US Senate (1996), Clinger-Cohen Act of 1996. van Ark, B. and de Jong, G. (2004), Productiviteit in Dienstverlening – deel 1: Wat het is en waarom het moet. Koninklijke Van Gorcum BV, Assen. Van den Dool, F.; Keller, W.J.; Wagenaar, R. and Hinfelaar, J.A.F. (2002), “Architectuur elektronische overheid – Samenhang en Samenwerking”, Ministerie van Binnenlandse Zaken. Van Leeuwen, G. and Van der Wiel, H. (2003), “ICT, innovaties en productiviteit – Een analyse met Nederlandse bedrijfsgegevens”, CPB Memorandum 61. Weill, P. (1992). “The Role and Value of Information Technology Infrastructure: Some Empirical Observations”, CISR Sloan Working Paper No. 240. Wikipedia. (2005, 110905), “Interoperability”, Available at http://en.wikipedia.org/ wiki/Interoperability Yin, R.K. (1994), Case Study Research – design and methods – second edition. Sage Publications Inc., Thousand Oaks. Zachman, J.A. (June 17, 2004), Lecture at Conference “IT and Business”.

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Appendix A: Data sets In this chapter two data sets are used: (1) Responses to the Questionnaire on ICT maturity. (2) Statistics Netherlands’ data on external linkages. Questionnaire on ICT maturity Firms that have been surveyed on ICT maturity had to match several criteria. Figures from the same firms over a number of consecutive years were needed, e.g. to construct capital stocks (Section 8.1.2). To restrict the survey to larger firms, the ICT maturity data panel included firms with 85 or more employees in the 1995–2001 period (85 employees is the minimum that should have been achieved at least in one year during this period). The resulting data panel includes 1139 firms that had participated in the annual business inquiries. Furthermore, it contains figures on hardware and software investments and spending from the 1995–2002 period. From the annual ICT usage survey hardware and software investments and spending are known for at least three years. From the annual investment survey, additional data on hardware and total investments are available for approximately 890 of the 1139 firms for the 1995–2002 period. Figure 1 is derived from the data panel of 1139 firms. The research data panel consists of firms from various industries namely manufacturing, construction, retail and wholesale trade, hotels and restaurants, transport, storage and communication, business activities and other personal service activities. A total of 974 copies of the questionnaire on ICT maturity were sent out. The response was approximately 60%. To be able to test our ICT maturity model (Architecture Matrix, see Section 8.4.3) we have put aside a set of 265 firms. These 265 firms have not been included in the analyses presented in Section 8.5. Statistics Netherlands’ data on external linkages The analysis presented in Box 3 had made use of all firms which had at least 150 employees in 2002 and whose 2002 productivity and external linkages data were available. This set included a total of 1031 firms.

Appendix B: Measuring ICT productivity Non-linear relationships The large ICT productivity differences between firms of similar size and sectors are caused by the fact that the relationship between firm ICT investments (ICT stock) and productivity is unlikely to be direct and linear. Studies that directly link

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investments with performance in fact are testing two hypotheses at the same time: firstly, that investments and use are correlated; secondly that use and output are correlated (Alderighi, 2003). Estimates of an independent ICT variable will be positively biased when other independent variables that correlate with both productivity and ICT (such as usage variables) are not included in the productivity equations (Berry, 1993). Time lags The time lag between investment and productivity effect is not fully understood. In general, after the ICT investments take place, a period of learning and adjustment will be necessary (Brynjolfsson et al., 1996) before benefits become fully visible. The length of this time lag cannot be stated precisely, it probably differs between industries (Oz, 2005) and firms. Gretton finds that the initial impact of computer use on labour productivity is negligible (slightly positive or negative) and becomes positive after 2–5 years (Gretton and Gali, 2003). Others find that the impact of ICT on MFP growth is maximised after a lag of four to seven years (Brynjolfsson et al., 1998). Concerning CRM investments, companies report that if implementations went back less than two years ago, it was too early to judge their success. When implementations occurred between 2 and 4 years ago, 61% of companies reported that costs and benefits broke even. When implementations occurred more than 4 years ago, 55% of companies reported a positive result of CRM investments (Gianotten, 2003b). The problem is that if the time lag chosen in a particular equation is shorter than the underlying causal process, it is likely that effects of the causal variable on the effect variable are underestimated. If the time lag is too long, it is possible that other processes have influenced the effect variable, implying that the causal effects are blurred as well (Taris, 2000). Firm output Firm output is hard to measure as well. Conventional measures of output do not account for improvements in product quality or the creation of new products (Barua et al., 1997). This is especially relevant for the service sector. Measuring manufacturing output is easier than measuring service output. For example, Brynjolfsson describes the increase in convenience ATMs have created for bank customers (Brynjolfsson and Hitt, 1993). Such ICT-related output growth is not picked up in conventional statistics. ICT inputs Considering ICT inputs, almost all data sources contain data on hardware investments (or spending) and most sources also include software. However, an ideal dataset for ICT productivity research should contain more financial variables. Oz lists the

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following: computing hardware, telecommunications hardware and software, purchased software, software development, consulting services, and personnel training (Oz, 2005). Firm spending is not evenly distributed over ICT categories. For instance, Forrester Research estimated that – for setting up an ERP system – typical software costs are 4 times larger than hardware costs. Non-ICT costs associated with the implementation were estimated to be four times larger than the combined hardware and software costs. Those non-ICT costs included hiring consultants to help change business processes and to train employees (Gormely et al., 1998). This clearly shows that data sets containing only hardware expenses actually tell a very small proportion of the entire story. However, many ICT stocks used for productivity calculations are based on hardware investments only. Capital stocks (instead of investment flows) To be able to investigate time lags and to construct Capital Stocks, time series need to be available. In order to allow calculations on such data, all financial values have to be converted from nominal values to base year values. Ideally, this conversion takes into account both inflation and quality changes. Especially for ICT hardware this can be a source of error. As Schreyer notes, price indices are constructed by comparing prices of sampled products between two periods in time. At least two conditions should be fulfilled: the products in the sample have to be representative of a whole product group and they should be comparable between the two periods. When technical change is fast, neither of these conditions holds easily (Schreyer, 2002). The approximation of quality changes over time may introduce severe measurement error.

Appendix C: Case studies and questionnaire Case study methodology The case study protocols used for this research have been based on the survey methodology and experiences of the “Questionnaire Laboratory” at Statistics Netherlands (Snijkers, 2002). The background of pre-testing a questionnaire is the notion that data quality is largely determined by questionnaire aspects such as instructions, task difficulty, question wording, and question structure (open, closed, number of response options and so on). Pre-testing is an iterative process by which several prototypes of a questionnaire are pre-tested several times. The method of in-depth interviews has been used. Interview techniques where e.g.: •



Meaning-oriented probing. The probe is used to investigate how the respondent interprets a word or a term in a question, e.g. by asking: “What do you mean by Knowledge Management?” Thinking aloud. The respondent is asked to express his thoughts while answering a survey question.

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Case study outcomes During the case studies we made two major questionnaire revisions (omitting questions, change question structure) and several minor adjustments to each question (wording, rephrasing, layout). Minor adjustments included fine-tuning the definitions of business functions, ICT culture concepts, ICT architecture layers and ICT scope levels. The result has been a questionnaire that is relatively easy to answer (this will increase the response rate) and is relatively easy to understand (this will increase validity of the response and decrease measurement errors). Furthermore, the case studies have shown that the architecture layers are recognized well by (the CIO) Dutch companies. Question design: Augmented design Many concepts of the integration matrix are relatively easy to understand but require many words to be sufficiently explained. The problem with long texts was that most respondents showed “kick-and-rush” behaviour. The augmented design is a trade-off between doing justice to the sophistication of the concepts involved and considering the impatience of most respondents. Case studies have showed that most respondents are able to provide a sufficiently informed answer to this question after only reading the text printed in bold and possibly the main question. In order to also facilitate those respondents who want more details, the design does contain further explanation. The latter can be placed after the arrow underneath the title or in footnotes. A large part of the case study work was devoted to testing the construct–validity (establishing correct operational measures for the concepts being studied (Yin, 1994)) of the scores of “kick-and-rush” respondents. After testing several prototypes, we are reasonably sure that the current questionnaire shows construct-validity. Table 2 provides an example of this augmented design. Question design: Propositions Most ICT culture concepts were expressed in propositions and there relevance measured by means of a five-point summated rating scale. To prevent automatisms, positive and negative formulations of propositions were mixed (Korzelius, 2000). In order to keep respondents focused, the question wording was varied whenever possible.

Measuring the New Economy Edited by Teun Wolters © 2007 Elsevier B.V. All rights reserved.

CHAPTER 9

Innovation in Services: A Search for New Indicators Jeroen de Jong (EIM Business and Policy Research)1 Gerhard Meinen (Statistics Netherlands) Patrick Vermeulen (Tilburg University)

9.1. Introduction The measurement of innovation has developed significantly in recent years. The introduction of the Community Innovation Survey (CIS) on a European scale is the most visible exponent of this process. The subject of measuring innovation is, however, still in an early phase of development. One of the challenges facing statisticians and researchers is to improve the measurement of innovation in services as many of these innovations tend to be intangible in character. Existing indicators of innovative output – such as the introduction of technologically upgraded products – are less suitable to record innovation in services, implying that current measures of innovation bring along serious underestimations (Archibugi and Sirilli, 2001; Tether and Miles, 2001; de Gimel, 2003). This is probably caused by the fact that most innovation studies tend to focus on manufacturing firms, but as the share of the services sector has increased significantly over the last few decades, this is actually remarkable (Drejer, 2004). Although many European studies have proposed new indicators to measure innovation in services, only a few of them have tested them empirically. We aim to contribute to the debate by exploring and testing some new indicators. An empirical project executed by the Dutch research institute EIM and commissioned by Statistics Netherlands provides the basis for this contribution (de Jong et al., 2003; de Jong et al., 2004). The project has developed and tested six new indicators for the innovative output of service firms. Four indicators relate to the dimensions of innovation in services (renewal in the service concept, the client interface, the supply system and

1 E-mail

address: [email protected]

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technological options) while the other two deal with particular sources of innovation (supplier-driven and customer-driven). Contrary to previous attempts, the project used explicit criteria for the validation of its indicators. Drawing on a survey among small- and medium-sized service firms, we investigated whether the survey questions on the new indicators were easy to be answered, whether there were notable variations in the innovative output across sectors, whether the new indicators measured an aspect of innovation truly different from traditional innovation indicators, and finally whether they mirrored innovations that had actually been implemented. In this contribution we first indicate how innovation in firms is commonly measured, and we outline some recent developments in the field (Section 9.2). Next, the focus is on the exploration and testing of new indicators. Section 9.3 provides the results of a literature survey aimed to find new indicators that might enrich the measurement of innovation in services. It is followed by a qualitative screening in four service industries to see if entrepreneurs would actually recognise the new indicators and if entrepreneurs could actually mention concrete examples related to them. Next, Section 9.4 presents the methodology and results of a validation study to assess the indicators’ added value. Subsequently, Section 9.5 reveals how EIM and Statistics Netherlands have adopted the new indicators in their own innovation surveys, and what results have been found. Finally, Section 9.6 concludes and provides suggestions for future research.

9.2. Measuring innovation at the firm level: State of the art and recent developments 9.2.1. State of the art An important distinction in the measurement of innovation is a focus on either innovative objects or innovative subjects (Archibugi and Sirilli, 2001). Object-based approaches focus on actual innovations; in the collection of data, counts and characteristics of actual innovations are central. Subject-based approaches look at the innovativeness of actors, for example an individual, team or organisation. The CIS is an example of a subject-based approach, directed towards the measurement of innovation in firms. Our study followed the subject-based approach. We searched for new indicators enabling a better measurement of innovation in service firms. In the subject-based approach, a commonly used classification of indicators is based on the distinction between input, throughput and output criteria. Output indicators show the results of an innovation process (introduction of new products or services on the market, implementation of new work processes, etc.), input indicators attempt to measure the nature and scope of investments/sacrifices in terms of capital, labour etc., and throughput indicators provide information on how input is converted into output (for example, by adapting the internal organisation, using an innovative strategy, etc.). In our search for indicators to improve the measurement of the nature of innovations in services, we aimed for new output indicators.

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The common indicators for the innovative output of firms that are frequently used in innovation surveys include the following (de Jong et al., 2003): The announcement and actual introduction of innovations. The first step taken by a firm towards achieving better results is the actual introduction of innovations, mostly related to new products (product innovation) or business processes (process innovation). In innovation surveys, such as the CIS, these are often the core questions to determine whether a firm is innovative or not (Kleinknecht, 1996; Brouwer, 1997). The effects of innovation. Along with the introduction of innovations, it is also useful to examine their effect on main business processes. The actual sales of new products/services is an interesting criterion as it enables to judge what innovation contributes to a firm’s financial position (Kleinknecht, 2000). Patents. Firms may protect their inventions by applying for a patent. The number of patent applications can serve as an indicator for innovative output (Kleinknecht et al., 1990; Kleinknecht, 2000). This indicator has the advantage of being publicly available. However, disadvantages include that many firms do not actually use their patents to secure revenues from their innovations (Brouwer, 1997; van Ark et al., 1999). Moreover, smaller firms hardly ever apply for patents, whilst firms with a limited technological intensity (such as service firms) are not allowed to use this method of appropriation (Ebling et al., 1999). Organisational renewal. In the past few years, however, researchers have recognised that innovation relates to much more than just new products or processes. Recent innovation surveys like the CIS also involve organisational changes, including changes in strategy, management, marketing and organisation. Such changes are meant to optimise existing processes (Gupta and Wilemon, 1990; Davenport, 1993; Miles, 1996), so that innovative activities can be conducted more efficiently. Traditional output indicators are difficult to apply in a services context. For instance, new services and their production processes tend to be closely intertwined, making it difficult to distinguish between product and process innovation. Producing separate sales figures for new services can be problematic. As service enterprises perform no R&D (in the traditional meaning of the word) they are unlikely to apply for patents. Patents therefore undervalue the innovation activities undertaken by service enterprises. Yet, measuring organisational and marketing renewals improves the measurement of innovation in services. One practical problem is the fact that it is not clear whether this involves an output indicator. Changes in strategy, management, marketing or a reorganisation can be viewed not only as the end result (output), but also as a determinant of product innovation. In the latter case, it would be a throughput indicator. Finding better ways of making innovation in services measurable therefore remains a challenge. 9.2.2. Recent developments Over the past ten years, the rapid growth of the services sector has led to a growing interest in service innovation. Besides the economic importance, there are at least two other reasons why innovation in the services is a hot topic: innovations in services are

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believed to extend beyond the services sector influencing the service activities in all sectors of the economy, and some services play central roles in innovation processes throughout the economy, as agents of transfer, innovation support and sources of innovations for other sectors (Miles, 2005). The growing interest in innovation in services manifests itself in the third edition of the International Handbook for Innovation Statistics. This Oslo Manual (OECD, 2005) has recently expanded the innovation measurement framework in several ways. The manual now recognises that innovation is also important in less R&D intensive industries, such as services and low-technology manufacturing. While the second edition of the Oslo Manual already covered these sectors, the focus of its proposed questions was still very much in favour of manufacturing firms. Innovation in services, however, can often be less formally organised, is more incremental in nature and less technological. In order to form a framework that better accommodates this, a number of modifications have been made to the formulation of definitions, terms and concepts used in the manual. To identify the full range of changes undertaken by firms to improve performance, and their success in improving economic outcomes, the Oslo Manual now broadens the innovation framework by adding two types of innovation: organisational and marketing innovations. Organisational innovation may emanate from technical change, but may also be a necessary condition for technical innovation (Lam, 2005). Besides facilitating product and process innovations, organisational innovations as such may have a major impact on firm performance. Organisational innovations are there to improve the quality and efficiency of work, enhance the exchange of information, and improve the ability of the firm to generate and utilise new knowledge and technologies. Marketing innovation is now a separate category rather than just a mechanism to integrate marketing methods with organisational or process innovations. This opens up new ways of analysing how market innovation relates to other types of innovation. The economic rationale of marketing innovation may strongly differ from that of process innovation. Although the Oslo Manual has expanded its scope, it considers the measurement of innovation in services to a limited extent only. In several countries, however, specific research has been done in this area. In the Netherlands, from the second Community Innovation Survey (CIS2) onwards (i.e. innovation survey 1994–1996) the survey population covered a large part of the services sector. Furthermore, Dutch innovation surveys included questions on non-technological innovations (such as marketing, organisation, strategy and management innovations) from CIS2 onwards. Results from the Dutch innovation survey covering the reference period 2000–2002 show that in the services the percentage of enterprises with technological innovations is much lower than in manufacturing (16% vs. 40%), but when non-technological innovations are taken into consideration, the difference in the percentage of enterprises is smaller (23% of the services firm and 30% of the manufacturing firms). Further insight can be gained by dividing innovating enterprises into three groups: enterprises with purely technological innovations, with purely non-technological innovations, and enterprises with both types of innovation.

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Figure 1. Percentage of firms with innovations, 2000–2002 50

%

45 40 35 30 25 20 15 10 5 0

Manufacturing Technological innovation

Both types of innovations

Services Non-technological innovation

Source: Statistics Netherlands, Innovation surveys.

Figure 1 shows that the percentage of enterprises with only non-technological innovations is higher in services (14%) than in manufacturing (9%). Over 50% of the enterprises with technological innovation activities had also introduced nontechnological innovations. This holds for both manufacturing and services, indicating that innovations in both sectors have things in common as well.

9.3. Exploring and screening of new indicators 9.3.1. Potential new indicators The project by EIM and Statistics Netherlands aimed to explore, screen and empirically validate some new output indicators for innovation in services. It started with a literature survey to find new indicators that might enrich the measurement of this phenomenon. Eventually six indicators were identified (de Jong et al., 2003). These indicators reflected: (1) Objects of innovation: renewing the service concept, client interface, supply system and technological options. (2) Sources of innovation: supplier-driven innovation and client-driven innovation. 9.3.2. Ad 1. Objects of innovation Innovation surveys usually distinguish between two types of innovation: new or significantly improved products (product innovation) and processes (process innovation).

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This distinction is rooted in Schumpeter’s (1934) classification of the objects of creative destruction, and is advocated by modern innovation textbooks (e.g. Tidd et al., 2001; Afuah, 2003). Although the new Oslo Manual includes questions on organisational and marketing changes, de Jong et al. (2003) followed a different approach by embracing a model that attempts to capture the dimensions of innovation in services. This model was developed by Den Hertog (2000) and colleagues as part of the European SIIS (“Services in Innovation, Innovation in Services”) program (also see Bilderbeek et al., 1998). The model proposes four dimensions to characterise a service innovation: • • • •

Innovation in Innovation in Innovation in Innovation in

the service concept, the client interface, the delivery system and the application of technology.

Innovation in the service concept relates to the changes in the content and characteristics of a service (cf. Lancaster, 1966). Manufactured products (and processes) are typically highly tangible and visible, but services usually involve more intangible features. A new service concept relatively often includes new combinations of existing activities. Take, for instance, the service concept offered by Center Parcs. This formula combines various catering and recreation services and has now been copied by many competitors. Another example of innovation in the service concept includes call-centre services. Nowadays there are agencies that specialise in staffing call centres. Innovation in the client interface relates to new types of distribution or other ways of meeting client specifications when services are sold and distributed. Service offerings are increasingly marketed and produced in a client-specific way, even with client-specific pricing. It is often that characteristics and desires of existing and potential clients challenge a service firm to make adjustments in the client interface. The Wehkamp catalogue, for example, is no longer distributed as hard copy only. Wehkamp has invested considerably in online services to meet the requirements of clients who wish to shop and buy on the internet. A number of researchers have therefore suggested to also include distribution-oriented indicators in innovation surveys (e.g. Miles, 1996; Djellal and Gallouj, 2001; Tether and Miles, 2001). The third dimension consists of adjustments in the service delivery system. The service delivery system facilitates the adequate production and delivery of service products. It could be interpreted as the internal work processes and arrangements. Changes in the service delivery system are often interlinked with the other dimensions of service innovation. The fourth dimension (technological options) is the focus of much analysis and debate (e.g. Kandampully, 2002). Service innovation is certainly possible without technological innovation, but often service innovation are driven by technological developments. For example, IT is often perceived as the great enabler of service innovation.

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Den Hertog (2000) rightfully observed that “any service innovation involves some combination of the (four) dimensions”. In practice, innovative services embody a mixture of the four types. The four dimensions can be regarded as extremes that help to understand the complex real world. However, innovative services frequently display a dominant feature related to one of the four dimensions, making the dimensions potential output indicators in innovation surveys. 9.3.3. Ad 2. Sources of innovation Another way to characterise innovation in services is to examine the source of innovation: who takes the initiative? Pavitt (1984) was among the first to provide a typology of innovative firms, consisting of specialised suppliers, science-based firms, supplierdominated firms and scale intensive firms. He and other pioneers (e.g. Barras, 1986) tended to see service firms as being passive innovators: innovations were supposed to be initiated by suppliers only. More recently, the idea that innovation in service firms is exclusively supplier-dominated has been abandoned (e.g. Evangelista, 2000; Miozzo and Soete, 2001). Bilderbeek et al. (1998) enhanced the Pavitt taxonomy by adjusting it to the reality of innovative service firms. Drawing upon a sample of 1232 firms, de Jong and Marsili (2006) built a classification of Dutch innovative enterprises using variables on innovative outputs, inputs, sources of innovation and innovative strategies. They demonstrated that innovation processes in services in essence do not differ from those in manufacturing firms. This latter result indicates that Pavitt’s (1984) typology of innovating firms may actually be a useful source of new, general innovation indicators. Despite the typology being over twenty years old, it is remarkable that no attempts have been made to include Pavitt’s various types of innovation as indicators in innovation surveys. de Jong et al. (2003) arrived at two new indicators to be tested: supplier-driven and client-driven innovation. In the spirit of Pavitt (1984), supplier-driven innovation measures innovations which are mainly adoptions of novelties that have been developed by other enterprises, for example, producers of scanning equipment. The indicator for client-driven innovation measures innovations which are a direct response to the needs of clients. 9.3.4. Qualitative screening With respect to the objects and sources of innovation, de Jong et al. (2003) performed a qualitative screening, consisting of in-depth interviews with service entrepreneurs and sector specialists. The aim was to see if entrepreneurs would actually recognise the conceptual distinctions made, and if they were able to provide concrete examples. The interviews were applied to four pilot sectors: the engineering branch, financial services, wholesale trade in electrical consumer goods and the do-it-yourself (DIY) retail trade. Thus, the pilot sectors were very different in terms of knowledge intensity, nature of products and services, share of highly educated staff and business dynamics.

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During the interviews, respondents were asked to provide real examples of innovations in their firms over the past two years. A content analysis showed that the previously mentioned objects and sources were recognised in at least 20% of the recorded examples.2 In all pilot sectors entrepreneurs were able to give real examples of the four objects of innovation. The same applied to the supply-driven and clientdriven types of innovation. Examples of innovation categorised with the objects of innovation are listed in Table 1.

9.4. Validation study 9.4.1. Methodology The fact that entrepreneurs in the four pilot sectors were able to name the objects and sources of innovation is not enough to regard them as suitable indicators for innovation surveys. A subsequent, quantitative validation study was carried out to test the new indicators (de Jong et al., 2004). The survey again focused on smalland medium-sized enterprises (SMEs) in four sectors: engineering (NACE code 742), financial services (codes 651, 652, 660), wholesale trade in electronic consumer goods (code 5143) and DIY retail trade (code 5246). Data was collected over telephone using a questionnaire. The sampling frame consisted of Marktselect’s DMCD, a database containing information on all firms active in the Netherlands. A random sample was drawn for each sector according to the Dutch definition of SMEs as being firms with no more than 100 employees. Approximately 100 entrepreneurs per sector were interviewed. The average response rate was 60%, which is good for a telephone survey. The questionnaire first inquired for the aforementioned traditional indicators for the innovative output of firms, i.e. product and process innovation. Next, it asked for the six new indicators (Table 2). Following the established practice in innovation surveys, most questions were dichotomous to keep them as simple as possible. In the third part of the questionnaire, the respondent was asked to identify real innovations implemented by his/her firm for a pre-specified list of items. These innovations mirrored the examples of innovations presented in Table 1. They served to analyse whether the new indicators adequately correspond with innovations that have actually been implemented. This part of the questionnaire thus varied according to the sector. The questions on real-life examples were presented to the respondents in random sequence to prevent biased answers. Previous empirical attempts to develop new indicators for innovation in services usually lack formal validation criteria (e.g. de Gimel, 2003). This usually elicits the application of innovation indicators which are strongly biased by the 2 We note that de Jong et al. (2003) tested additional indicators, including so-called enterprise-driven and paradigmatic innovations. For various reasons these indicators did not qualify for empirical validation and were discarded.

Table 1. Examples of innovations in four pilot sectors Dimension/Object

Engineering

Financial Services

Wholesale Trade in Electronic Goods

DIY Retail Trade

– Application of new themes, methods or techniques – Serving new markets

– Changes in product offerings – Strategic renewal

– New product assortment – Maintenance services

– New product assortment – New shop formulas

Client interface

– Online communication and/or distribution of services – New marketing methods

– Online distribution/ selling – Online communication with customers

– Online distribution of product support information – Online ordering/sales

– Online communication/ sales – Savings and loyalty programs

Delivery system

– Quality certification – New division of tasks among employees

– Changes in the internal organisation – New request and acceptance methods

– Investment in employees’ knowledge/skills – Internal knowledge exchange systems

– Stock management – Logistic process optimisation

Technological options

– Dynamic websites – Document management systems – Management information systems – Instruments for analysis and/or measurement – Design and spreadsheet software

– E-commerce – Data Warehousing/ -Mining – CRM-software – Security systems – New ATM systems – ERP-systems

– Data Warehousing/ -Mining – Knowledge management systems – E-commerce – CRM-systems – RFID-technology

– Scanning – Web technology – Wireless security systems – RFID technology – New pay systems

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Service concept

Source: de Jong et al. (2003). 185

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Table 2. Indicators and questions in validation study Indicator

Question

Common indicators: Product innovation 1

In the past two years, did your firm introduce any products or services which are new or significantly improved? (yes-no).

Product innovation 2

If yes, could you estimate the share of your current revenues of these products or services? (