Cross-Cutting Analysis
of Scientific Publications
versus
Other Science, Technology
and Innovation Indicators
Research and
Innovation
EUR 25968 EN
EUROPEAN COMMISSION
Directorate-General for Research and Innovation
Directorate C — Research and Innovation
Unit C.6 — Economic analysis and indicators
E-mail: [email protected]
[email protected]
Contact: Carmen Marcus, Matthieu Delescluse and Pierre Vigier (Head of Unit)
European Commission
B-1049 Brussels
EUROPEAN COMMISSION
Cross-Cutting Analysis
of Scientific Publications
versus
Other Science, Technology
and Innovation Indicators
Authors of the study
David Campbell, Julie Caruso, Éric Archambault
Science Metrix, Canada
2013
Directorate-General for Research and Innovation
EUR 25968 EN
This report is part of the study Analysis and Regular Update of Bibliometric Indicators
carried out by Science Metrix-Canada under the coordination and guidance of the European Commission, Directorate-General for Research
and Innovation, Directorate Research and Innovation, Economic analysis and indicators Unit.
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Luxembourg: Publications Office of the European Union, 2013
ISSN 1831-9424
ISBN 978-92-79-29836-3
doi:10.2777/12700
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Analytical Report 2.3.2
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Table of Contents
Executive Summary ............................................................................................................................... ii
Tables..................................................................................................................................................... v
Figures ................................................................................................................................................... v
Acronyms .............................................................................................................................................. vi
1
Introduction ................................................................................................................................. 1
2
Drivers of research output and inventory of key STI indicators for the cross-cutting
analysis with scientific output ..................................................................................................... 3
2.1
2.2
3
Methods & Results .....................................................................................................................18
3.1
3.2
4
Publication output and productivity of countries and NUTS2 regions .................................................... 19
3.1.1
Factor analysis for identifying the main dimensions (i.e., factors) among selected
STI indicators and the publication output of countries ........................................................... 20
3.1.2
Regression analysis for investigating the productivity of countries in terms of
publication output per unit of the most relevant R&D input indicators .............................. 24
3.1.3
Regression analysis for investigating the productivity of NUTS2 regions in terms of
publication output per unit of the most relevant R&D input indicators .............................. 34
Publication patterns of countries across scientific fields ............................................................................. 35
Key findings of the cross-cutting analysis of scientific output vs. other STI indicators ........... 40
4.1
4.2
5
Identifying the key drivers of research output ................................................................................................. 3
Understanding patterns of scientific output and scientific productivity: key STI indicators of
research inputs ....................................................................................................................................................... 5
2.2.1
R&D investment and expenditure indicators ............................................................................... 5
2.2.2
Human resource indicators.............................................................................................................. 7
2.2.3
Innovation indicators ........................................................................................................................ 8
2.2.4
Knowledge flow indicators ............................................................................................................ 10
2.2.5
Research infrastructure indicators ................................................................................................ 12
2.2.6
Industrial specialisation .................................................................................................................. 13
2.2.7
Selection of key STI indicators for the cross-cutting analysis with scientific output .......... 13
Publication output and productivity of countries and NUTS2 regions .................................................... 40
4.1.1
Factor analysis for identifying the main dimensions (i.e., factors) among selected
STI indicators and the publication output of countries ........................................................... 40
4.1.2
Regression analysis for investigating the productivity of countries in terms of
publication output per unit of the most relevant R&D input indicators .............................. 41
4.1.3
Regression analysis for investigating the productivity of NUTS2 regions in terms of
publication output per unit of the most relevant R&D input indicators .............................. 43
Publication patterns of countries across scientific fields ............................................................................. 44
Discussion ................................................................................................................................. 46
5.1
5.2
Publication output and productivity of countries and NUTS2 regions .................................................... 46
5.1.1
Factor analysis for identifying the main dimensions (i.e., factors) among selected
STI indicators and the publication output of countries ........................................................... 46
5.1.2
Regression analysis for investigating the productivity of countries and NUTS2
regions in terms of publication output per unit of the most relevant R&D input
indicators ........................................................................................................................................... 48
Publication patterns of countries across scientific fields ............................................................................. 51
Acknowledgments................................................................................................................................ 53
References ............................................................................................................................................ 54
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Analytical Report 2.3.2
Executive Summary
Background
Science-Metrix has been selected as the provider of bibliometric indicators for the European
Commission’s Directorate-General for Research & Innovation (DG Research), beginning September
2010 and extending through September 2013. This work involves the collection, analysis and
updating
of
bibliometric
data
that
will
be
integrated
into
the
European
Commission’s
evidence-based monitoring of progress towards the objectives set forth in the Lisbon framework
and the post Lisbon Strategy for the European Research Area (ERA). The bibliometric component
of this monitoring system is part of a package of six complementary studies reporting on the
dynamics of research activities along the continuum of knowledge, from R&D investments to
publications, patents and licensing.
The analyses provided by Science-Metrix to the European Commission focus on the scientific
performance—including impact and collaboration patterns—of countries, regions and research
performers (such as universities, public research institutes and companies). The statistics
produced by Science-Metrix are based on a series of indicators designed to take into account
national and sector specificities, as well as allow for a comprehensive analysis of the evolution,
interconnectivity, performance and impact of national research and innovation systems in Europe.
They also provide an overall view on Europe’s strengths and weaknesses in knowledge production
across fields and subfields of science. In measuring progress towards past and current objectives,
this information aims to support the coherent development of research policies for the ERA.
The present report
Investigations of the existing relationships between research and development (R&D) inputs and
outputs such as publications and patents from an econometric perspective have increased in the
past decades in response to the challenges faced by Governments. In particular, as they are
operating on increasingly tight budgets, Governments are looking to maximise returns on
investments; furthermore, accountability for public spending has become a primary issue for
residents who expect to get the most value for their tax dollars. Most studies of economies and
diseconomies of scale in scientific production have been performed with a view to providing
evidence-based policy advice that will improve the allocation and management of resources in the
research sector and, ultimately, enhance efficiency (i.e., productivity).
This study adds to the growing knowledge base on the factors driving the scientific productivity
(i.e., the efficiency with which entities are converting research inputs into research outputs) at the
national and regional levels by reporting on the results of an analysis performed using the most
comprehensive dataset on science, technology and innovation (STI) indicators that is currently
available for ERA countries and Nomenclature of Territorial Units for Statistics Level 2 (NUTS2)
regions. This study’s main objectives were to investigate:
1.
2.
the factors behind the publication outputs and productivity of countries/regions, as revealed
through an analysis of scientific production; and
the factors behind the production patterns of countries, as revealed through an analysis of
scientific concentration (by research area), across fields of science.
In total, 17 R&D input indicators distributed across four categories (i.e., R&D Investment and
Expenditure, Human Resources, Innovation and Research Infrastructures) were considered. The
bibliometric indicator that was used to improve the understanding of differences between
countries’ and NUTS2 regions’ scientific output, productivity and concentration was the total
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number of publications, as measured using Scopus. The dataset included 42 countries (i.e., ERA
countries plus a few comparables) and 291 NUTS2 regions for which data were available. The
period covered by the dataset extended from 2000 to 2009. For a summary of key findings, please
refer to Section 4. For a comprehensive discussion of findings, please refer to Section 5. A brief
overview of the main findings is presented below.
Report highlights: Scientific production and productivity of countries and NUTS2
regions
Factor analysis was used to identify the main dimensions explaining patterns of variation among
selected STI indicators and the publication output of countries, whereas regression analysis was
used for investigating the productivity of countries and NUTS2 regions in terms of outputs (i.e.,
publications) per unit of the most relevant STI indicators (i.e., R&D input indicators). Regression
analysis was also used to investigate whether the innovation capability of countries (i.e., the
capacity to produce inventions from a given amount of research) changes as the size of their
scientific production increases.

Based on Exploratory Factor Analysis (EFA), the most relevant STI indicators as well as the
selected R&D output indicator (i.e., the number of publications) could be adequately
summarised using a single factor; these indicators are highly collinear.

Although there is redundant information within the relevant set of indicators (i.e., strong
multicollinearity in the dataset), slight differences exist in the way countries allocate R&D
spending across sectors (e.g., higher education, government, private) and resources (e.g.,
human resources, infrastructure). It therefore remains pertinent to investigate how the
publication output of countries scale relative to individual R&D input indicator.

Economies of scale were observed with the following R&D input indicator: employment in
technology and knowledge-intensive services, which includes the education sector and all
occupations (at the country level) and the number of researchers in the higher education
sector (likely at the country level and confirmed at the NUTS2 level).

Potential mechanisms for explaining the increased productivity of human capital (i.e.,
employment in technology and knowledge-intensive services and researchers in the higher
education sector) as a country’s or NUTS2 region’s pool of human resources increases include
the diversification and sharing of complementary expertise and competencies, as well as an
increase in specialisation and division of labour. Other studies have shown similar reuslts at
various aggregation levels.

Diminishing returns were observed at the country and NUTS2 levels with the following R&D
input indicators: Business Enterprise Expenditure on R&D (BERD), Government Intramural
Expenditure on R&D (GOVERD) and Higher Education Expenditure on R&D (HERD) (likely at
the country level and confirmed at the NUTS2 level).

A potential mechanism for explaining the observed reduction in the productivity of countries
and NUTS2 regions in terms of publications produced per euro investment in R&D is that the
number of researchers of a given entity (i.e., its units of production) does not increase as
rapidly as its financial resources. Interestingly, it was shown that the population of researchers
in the higher education sector scales less rapidly than the Gross Expenditures on R&D (GERD)
and HERD. A rationale for awarding smaller grants to a larger population of researchers
logically follows from this explanation in order to increase the productivity of a given entity as
the size of its financial resources increases. However, as explained in the discussion, low
productivity (in terms of publications) that is concurrent to increasing R&D expenditures might
be offset by an increase in the number of citations per euro investment in R&D. The policy
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Analytical Report 2.3.2
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implications of findings on economies and diseconomies of scale are examined in the
discussion.

Luxembourg is one of the least productive countries when all sources of R&D expenditure are
considered (i.e., GERD, which covers HERD, BERD and GOVERD).

On the other hand, Luxembourg is among the countries that showed the strongest
performance in terms of productivity when HERD alone was considered. Thus, its lower
productivity in terms of its number of publications produced per currency unit (i.e., euro) of
GERD is not attributable to a higher education sector that is a less efficient at converting R&D
inputs into R&D outputs.

The weaker productivity of Luxembourg is most likely due to the stronger than usual
contribution of the business sector to GERD, as the sector is less oriented towards publishing
the results of scientific research.

Recent actions taken by the Luxembourg government appear to have been effective in
increasing its population of researchers, its HERD and its scientific production relative to its
GERD. Luxembourg has begun to close the gap with other ERA countries in terms of
publication output.

The innovation capability (i.e., the capacity to produce inventions from a given amount of
research) of countries appears to remains stable as the size of their science base increases.
Report highlights: Publication patterns of countries across scientific fields
To investigate the variations in the publication patterns of countries across scientific fields, the
relationship between scientific concentration by research area (i.e., percentage of output by field)
and the concentration of the relevant R&D input indicators by research area (e.g., percentage of
HERD by field) were determined using regression analysis. This analysis could only be performed
for HERD, the number of researchers in the higher education sector and GOVERD.

The statistics indicated that the concentration patterns of the selected R&D input variables by
field of science did not adequately explain the observed patterns of concentration in output in
most fields (i.e., in the number of scientific publications).

These results are astonishing, as the number of researchers in the higher education sector and
HERD (in their raw form, not expressed as percentages) explained much of the variation seen
in the number of peer-reviewed publications of countries (in its raw form), both when all fields
were combined as well as within each of the fields.

Factors that could contribute to explaining the patterns of variation in the concentration of R&D
outputs by scientific field include differences in the publication habits of researchers across
fields and/or countries, as well as noise in the data on R&D inputs and outputs at this
aggregation level.
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Analytical Report 2.3.2
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Tables
Table I
STI Indicators—Inventory of Available Data ............................................................................................... 14
Table II
Factor loadings of selected STI indicators on the 1st factor of the exploratory factor analysis
using PCA factoring ........................................................................................................................................... 23
Table III
Factor loadings of selected STI indicators on the 1st factor of the exploratory factor analysis,
based on PCA and IPA factoring .................................................................................................................... 24
Table IV
Scale-adjusted performance score of countries in terms of productivity (i.e., published output
per unit of an R&D input indicator) for three R&D input indicators, 2000−2009 ................................ 30
Table V
Robust group mean regressions between the concentration in the number of publications
(FRAC) of countries and the corresponding concentration in their number of researchers in the
higher education sector by field of science, 2000−2009 .............................................................................. 37
Table VI
Robust group mean regressions between the concentration in the number of publications
(FRAC) of countries and the corresponding concentration in their HERD by field of science,
2000−2009 ............................................................................................................................................................ 38
Table VII
Robust group mean regressions between the concentration in the number of publications
(FRAC) of countries and the corresponding concentration in their GOVERD by field of
science, 2000−2009 ............................................................................................................................................. 39
Figures
Figure 1
Frequency distribution of selected STI indicators and matrix of the relationships between all
pairs of indicators, 2000−2009.......................................................................................................................... 21
Figure 2
Scree plot of the exploratory factor analysis of selected STI indicators using PCA factoring .............. 22
Figure 3
Robust group mean regressions between the scientific output (number of publications [FRAC])
of countries and selected R&D input indicators, 2000−2009 ..................................................................... 27
Figure 4
Robust regression between the scientific output (number of publications [FRAC]) and GERD
of countries (A) and trend in the publication output of Luxembourg (B), 2000−2009 ......................... 31
Figure 5
Robust regression between the HERD and GERD of countries (A) and trend in the HERD of
Luxembourg (B), 2000−2009 ............................................................................................................................ 32
Figure 6
Robust regression between the number of researchers in the higher education sector and the
GERD of countries (A) and trend in the number of researchers of Luxembourg (B), 2000−2009 .... 32
Figure 7
Robust group mean regressions between the technological output (number of high-tech patent
applications to the EPO) and the scientific output (number of publications [FRAC]) of
countries, 2000−2009 ......................................................................................................................................... 34
Figure 8
Robust group mean regressions between the scientific output (number of publications [FRAC])
of NUTS2 regions and selected R&D input indicators, 2000−2009 ......................................................... 35
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Acronyms
ARC
Average of Relative Citations
ARIF
Average of Relative Impact Factors
BERD
Business Enterprise Expenditure on R&D
CI
Collaboration Index
DG Research
Research Directorate-General
EFTA
European Free Trade Association
ERA
European Research Area
EU
European Union
EU-27
The 27 member countries of the European Union
FP7
Seventh Framework Programme of the European Community for Research, Technological
Development (2007 to 2013)
FRC
Fractional Counting
FUC
Full Counting
GERD
Gross Expenditures on R&D
GI
Growth Index
GIS
Geographic Information System
GOVERD
Government Intramural Expenditure on R&D
HERD
Higher Education Expenditure on R&D
IF
Impact Factor
NACE
Nomenclature generale des activites economiques dans les communautes europeennes (Industrial
Sector Classification)
vi
NSE
Natural Sciences and Engineering
NSF
United States National Science Foundation
NUTS2
Eurostat Nomenclature of Territorial Units for Statistics (Level 2)
PAI
Probabilistic Affinity Index
R&D
Research and Development
RC
Relative Citations
RIF
Relative Impact Factor
RFP
Request for Proposal
RPO
Non-university Research Performing Organisation
RTD
Research and Technological Development
S&T
Science and Technology
SI
Specialisation Index
SME
Small and Medium Enterprise
SSH
Social Sciences and Humanities
STC
Science, Technology and Competitiveness
STI
Science, Technology and Innovation
Analytical Report 2.3.2
Final Report
1 INTRODUCTION
The last two decades have seen a steady rise in the development of STI indicators. Their use is
intended not only to better manage and govern the complex European system but to measure
progress towards the achievement of an increasingly wide variety of social and economic
objectives. A greater number and variety of actors are now involved in indicator development,
contributing new guidelines, new data sources and new areas of inquiry. A major focus of these
efforts has been to find appropriate, quantitative statistical tools that are comparable across
systems (i.e., countries, regions, sectors, organisations and industries) and that can strike the
best balance between internationally comparable and nationally relevant indicators (Edler &
Flanagan, 2011; Lugones & Suarez, 2010). In order to create robust and meaningful measures,
‘positioning’ indicators must also account for the distinct contextual factors that underlie each
system, which may be at least as important as formal inputs and outputs to their ultimate
performance (Edler & Flanagan, 2011; Lepori, Barré, & Filliatreau, 2008). In the face of often
considerable underlying conceptual and methodological difficulties, newer indicators must both
consider and attempt to confirm the specific drivers of research output and performance of
countries and regions.
This report contributes to this growing body of literature by performing a cross-cutting
assessment of performance centering on the European Research Area (ERA) in addition to a
number of selected countries. The study uses bibliometric statistics computed by Science-Metrix
as part of a project conducted for the European Commission (EC). These data on scientific output
are used to examine performance in light of input indicators such as R&D investments.
Specifically, this report investigates:
1.
2.
the factors behind publication outputs and productivity (i.e., the efficiency with which entities
are converting research inputs into research outputs) of countries/regions, as revealed
through an analysis of scientific production; and
the factors behind the production patterns of countries, as revealed through an analysis of
scientific concentration (by research area), across thematic domains (e.g., FP7 thematic
priority areas; this analysis could not be performed at the regional level due to the
unavailability of data by research area at the NUTS2 regional level, see Section 2.2.7).
To perform the cross-cutting analysis of scientific output versus other STI indicators,
Science-Metrix analysts identified the types and quantity of data that could potentially be
analysed in relation to publication output, overall and by FP7 thematic priority, and then gathered
these data for 42 countries and 291 NUTS2 regions. Section 2 of this report identifies the drivers
of scientific production in the existing literature. In addition, it provides a selection of available
STI indicators that were incorporated, subject to data availability, in the cross-cutting analysis to
facilitate the understanding of differences between countries/regions’ publication patterns and
scientific productivity (Section 2.2.7).
The bibliometric indicators that were used to improve the understanding of differences between
countries’ scientific output, productivity and production patterns are based on the data produced
in WP1 as part of datasets (1)-(8) and include the number of publications and the proportion of
publications by scientific field. These indicators were cross-linked with a selected set of other STI
indicators falling under six broad indicator categories:




R&D investment and expenditure (e.g., GERD);
human resources (e.g., number of researchers);
innovation (e.g., patenting activity);
knowledge flows (e.g., cross-sectorial and/or regional partnerships);
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
Final Report
research infrastructures (e.g., number of research infrastructures by domain); and
industrial specialisation.
Factor analysis was used to identify the main dimensions explaining patterns of variation among
selected STI indicators and the publication output (i.e., production or productivity) of countries
and/or NUTS2 regions, whereas regression analysis was used for investigating the potential
relationship of the most relevant STI indicators with the scientific output, productivity and
concentration (by research area) of countries and/or NUTS2 regions. Regression analysis was also
used to investigate whether the innovation capability of countries (i.e., the capacity to produce
inventions from a given amount of research) varies as the size of their science base increases.
Section 3 provides a detailed description of the methods and results, whereas Section 4 presents
an overview of the key findings of these analyses. Section 5 provides a discussion of the results in
light of other studies’ findings and in connection with qualitative knowledge of the science
system.
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2 DRIVERS OF RESEARCH OUTPUT AND INVENTORY OF KEY STI
INDICATORS FOR THE CROSS-CUTTING ANALYSIS WITH
SCIENTIFIC OUTPUT
The review presented here pursues two mains goals: 1) to report on drivers of scientific
production of countries and regions (Section 2.2.1 to 2.2.6) and 2) to produce an inventory of
available data and propose a selection of key STI indicators for the cross-cutting analysis to
facilitate the understanding of differences between countries’ and regions’ scientific production,
scientific productivity and publication patterns (Section 2.2.7).
2.1
IDENTIFYING THE KEY DRIVERS OF RESEARCH OUTPUT
Europe’s increasingly complex S&T system, characterised by its “multiplicity of interconnected
spatial levels of organisation and governance arrangements” (Lepori, Barré, & Filliatreau, 2008),
has led to significant levels of fragmentation and duplication in European research efforts and
activities. This fragmentation—a larger problem for public than for private research—limits
resource flows across borders and hinders the formation of competitive, world-class centres of
knowledge (Foray, 2009; European Commission, 2008). This problem was central to the
development of the Lisbon Strategy, originally laid out in March 2000, as well as the vision to
achieve a common European Research Area (ERA), as discussed in the 2007 ERA Green Paper
(European
Commission,
2007).
The
“fragmented
European
market
for
innovation”
and
“fragmentation of European research” were again identified as challenges to be addressed in the
creation of more favourable Framework conditions as part of the Innovation Union Flagship
Initiative (European Commission, 2011).
The research literature has focused on the issue of thematic and/or regional directionality—often
referred to as ‘specialisation’—as an operative mechanism and key driver of productivity and one
that some believe holds multiple benefits for Europe’s many ‘unexploited scale economies’ (e.g.,
Hallet, 2000; Klitkou & Kaloudis, 2007; Laurens & Asikainen, 2010; Laursen & Salter, 2005; Peter
& Bruno, 2010; Soete, 2006; Wong & Singh, 2004). Within the European Union (EU),
specialisation is part of an important and ongoing debate on objectives relating to knowledgedriven
growth,
cohesion
policy,
competitiveness
and
sustainable
development
(Europe
INNOVA/PRO INNO Europe, 2008; Grupp et al., 2010). A key goal of the 2020 Vision for the ERA
is to facilitate ERA-wide open competition that will gradually promote “the necessary
specialisation and concentration of resources into units of excellence” (European Commission
Expert Group, 2009). The Innovation Union Competitiveness Report 2011 (European Commission,
2011) discussed the concept of ‘smart specialisation’, defining it as a “dynamic process of finding
the right areas to focus on,” a process that is “based on evidence and strategic intelligence about
a region's assets.”
This somewhat rosy view of specialisation as a solution to fragmentation and duplication has been
challenged, with some proposing that it is likely to increase systemic disparities and calling for
R&D policies that maximise diversity and dispersion—rather than concentration—of resources
(Jacob, 1969; Kyriakou, 2009; Pontikakis, Chorafakis, & Kyriako, 2011). To get a better handle
on the question, investigators (e.g., Cooke, 2009; Smith, 2009) are attempting to identify the
implications of specialisation for different research systems at various levels of aggregation or to
determine whether differences in R&D strategies, organisations and outcomes reflect the
presence of redundancy or ‘healthy diversity’ within the system. Results of other studies (e.g.,
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Analytical Report 2.3.2
Varga, Pontikakis, & Chorafakis, 2010), suggest that both may be equally necessary, as they
operate at distinct parts of the knowledge production process and are important determinants of
productivity for different types of R&D.
Nevertheless, policy makers are more keen than ever to analyse scientific (based on publications)
and technological (based on patents) specialisation profiles, as well as the more novel measure of
R&D specialisation, defined as the “relative concentration of activity in a specific thematic area,
be it scientific, technological or even industrial, within a given ‘division of labour’ in knowledge
production” (Pontikakis, Chorafakis and Kyriakou, 2009). Using this information, policymakers
can better analyse the concentration of outputs by sector and assess the relationship between
private and public inputs to policy goals (Laurens & Asikainen, 2010). Cross-specialisation
analyses, such as those by Laursen and Salter (2005), the ERAWATCH Network (2006), Klitkou
and Kaloudis (2007) and Peter and Bruno (2010) have explored the relationship between the
different types of specialisation, combining indices based on publication and patent output
indicators with those based on national, industrial input data that are drawn from numerous
databases. These studies are contributing to a better understanding of the relationship between
science and economic spheres of production and how scientific and technological specialisation
patterns tend to co-evolve with broader R&D structures, such as investment patterns.
Studies of regional or other sub-national agglomeration efforts are also being performed, such as
those by the European Cluster Observatory and the European Cluster Alliance (2009). Clusters—
defined as a “group of firms, related economic actors and institutions that are located near each
other and have reached a sufficient scale to develop specialised expertise, services, resources,
suppliers and skills” (Europe INNOVA/PRO INNO Europe, 2008)—exist within regions and depend
upon specialisation and cooperation between actors. These studies have generally found a
positive
relationship
between
cluster
strength
and
regional
innovation
strength
and
competitiveness, and have also revealed that regions that perform better tend to be more
specialised and have more fertile business environments (Europe INNOVA/PRO INNO Europe,
2008). Similar studies at the regional and cluster level have been scarce because of the lack of
available statistical data at these levels of aggregation (European Cluster Alliance, 2009; Hallet,
2000; Lugones & Suarez, 2010).
However, specialisation is only one of a great number of areas of interest in the debate on drivers
of research productivity. As a whole, at both the national and regional levels, investment in R&D
and factors related to human capital (such as workforce quality and education and training) have
been considered elemental to the overall S&T performance of countries and regions. These two
classical STI input indicators—R&D expenditures and the S&T labour force—continue to have
obvious importance in how successfully regions and countries ultimately perform and compete.
Studies continue to propose countless additional facilitators of research capacity development,
sustainable growth and competitiveness in R&D—from broad ‘social needs’ and related public
policy to specific configurations of inputs—and may suggest related indicators and interventions
(e.g., Cooke, Booth, Nancarrow, & Wilkinson, 2006). As different investigations use different,
often highly heterogeneous sources, they obtain different results. Some (e.g., Benavente, Crespi,
& Maffioli, 2007; Soete, 2006; etc.) call into question even the capacity of the two presumed
engines of growth—R&D expenditures and investments in human capital—to deliver the promised
results.
The majority of these studies also engage in ex-post evaluation methods that focus on the ratios
of outputs to inputs, results achieved and impacts and can therefore only make tenuous
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assertions about the drivers of efficiency within research units (Jiménez-Sáez, Zabala, & Zofío,
2010). This is due in no small part to the immense complexity of making direct correlations and
the frequently noted methodological difficulties involved in determining causality, such as time
lags, static versus dynamic phenomena, the lack of data comparability and availability and the
potential for diminishing returns to funding inputs (Crespi & Guena, 2004; Freeman & Soete,
2007; Ho, 2004; Statistics Canada, 2006; Laurens & Asikainen, 2010). A coherent account has
yet to be made of the exact mechanisms and structural conditions that lead to regional and
national strengths and increasing levels of scientific output in particular domains of knowledge.
Researchers have called for novel indicators based on disaggregated data and “different,
imaginative, classifications of R&D data” (Kyriakou, 2009) to answer these and other fundamental
questions. In recent years, indicator development has increasingly focused on categories of
indicators that stress areas such as innovation and value added, and new indicators are emerging
that intend to capture processes of knowledge creation and diffusion, as well as the so-called
‘intangible capital’ that examine factors such as organisational innovation and technical progress
(Eurostat, 2009; Organisation for Economic Co-operation and Development (OECD), 2007; JonaLasinio, Iommi, & Manzocchi, 2011). The following section examines the selection of indicator
categories—both classical STI indicators and newer ‘positioning’ indicators—that may be most
likely to contribute to a greater understanding of determinants and patterns of national and
regional specialisation.
2.2
UNDERSTANDING PATTERNS OF SCIENTIFIC OUTPUT AND
PRODUCTIVITY: KEY STI INDICATORS OF RESEARCH INPUTS
SCIENTIFIC
Although traditional indicators of research activity (e.g., those based on publications and patents)
have many advantages and are “currently the most established proxies for measuring scientific
and technological outputs” (European Commission, 2008), they do not reveal what policy makers
are most interested in knowing—how inputs and throughputs lead to a more effective output and,
more specifically, whether there is any correlation between investment and output in a given
scientific field or technology (Peter & Bruno, 2010). Their joint use with input indicators in the
following categories could better capture the agents and determinants of scientific specialisation
and competitive advantage.
2.2.1 R&D investment and expenditure indicators
As noted, expenditure data on basic, applied or experimental research comprise a fundamental
input indicator used to characterise the S&T system in Europe. Research investment levels and
activity vary considerably between nations, with some of the most striking differences visible
among the G7 economies (Royal Society, 2011). Within the EU, increasing R&D investment was a
crucial part of the achievement of an ERA. In 2002, the goal was established to spend 3% of GDP
on R&D by 2010, but spending has since remained stable at around 1.85% (Eurostat ERA News,
2009). In 2010, the EU decided to maintain the 3% objective for 2020 (European Commission,
2011).
The four broad sources of R&D funding (i.e., the business sector, government, private non-profit,
and overseas funding) support activity that is carried out across four sectors of performance (i.e.,
the business sector, government, the higher education sector, and private non-profit foundations)
(Cooke, 2009). The standard indicator of R&D intensity and a basic structural indicator for the
ERA is Gross Domestic Expenditure in R&D (GERD), expressed as a percentage of GDP, which
covers all R&D by all four sources and in all four sectors. Government, business enterprise and
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foreign funding sources account for over 95% of expenditure in most Member States (European
Commission, 2008; OECD, 2010).
Public expenditures—Government intramural Expenditure on R&D (GOVERD) and Higher
Education Expenditure on R&D (HERD) as a share of GDP—can be divided by socioeconomic
objective, field of science and type of receiving institution. Data on budget provisions may be
used to measure planned government investment in R&D, but not actual spending (as in
expenditure data). The Government Budget Appropriations or Outlays on R&D (GBAORD)
indicator, expressed as a percentage of GDP, covers R&D in all sectors of performance carried out
either domestically or abroad. Appropriations are first distinguished between defence and civil
programmes and then between the main objectives of civil R&D (called NABS categories), broken
down by EU-27 socio-economic objectives (Eurostat, 2009; OECD, 2010). Although different,
GBAORD and GERD are often used in a complementary manner (OECD, 2007). GBAORD data are
taken from documents on initial budget provisions, forecasts, proposals and appropriations and
are timelier than GERD, but sources of data are less harmonised (Eurostat, 2009).
Around the world, the business sector is the primary R&D performer, and in research-intensive
countries, over two-thirds of total R&D investment comes from the private sector (European
Commission, 2008; Peter & Bruno, 2010). An indicator of private involvement in R&D is the
percentage of Business Expenditure (or Business Enterprise Expenditure) in R&D (BERD) as a
share of GERD. BERD can be broken down by sector of activity based on Nomenclature of
Economic Activities (NACE) categories, the European statistical classification of economic sectors.
The EU Industrial R&D Investment Scoreboard provides information on the top 1,000 EU and
1,000 non-EU companies in terms of investment in R&D, classifying companies’ economic
activities according to the ICB (Industrial Classification Benchmark) classification. 1
Since 2000, an increasing share of domestic R&D in EU Member States has been funded from
foreign sources, which include private business, public institutions and international institutions.
Data on venture capital investment (or the private equity raised for investment in companies,
particularly early stage) or foreign ownership are also used. Companies often make the decision
to extend their research capacities and invest in R&D activities in particular geographical areas
that offer attractive framework conditions for private R&D (including a transparent business
environment, sound and enforceable rules for competition and the availability of a large pool of
skilled human resources). Foreign direct investment data can point to these ‘hot spots’ of
knowledge accumulation. However, it is not possible to break down sources within the ‘abroad’
category into public and private, nor is it possible to separate intra-EU cross-border flows from
funds from sources outside of the EU (European Commission, 2008).
How R&D expenditure and investment indicators can contribute to an understanding of
specialisation: Indicators that determine R&D intensities and their growth rates provide one of
the best indications of how the various players in countries and regions are targeting their
investments towards specific scientific areas. This understanding is possible because R&D
expenditure data for all performance sectors can be disaggregated into sufficiently fine levels of
detail, such as socio-economic objective, field of research and type of research (i.e., pure basic
research, strategic basic research, applied research or experimental development) (Cooke, 2009).
1 http://iri.jrc.ec.europa.eu/research/scoreboard_2010.htm
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For example, a study by Laurens and Asikainen (2010) used national priorities and GERD by
domain/socio-economic objective as an indicator of specialisation. Studies by Peter and Bruno
(2010) and Klitkou and Kaloudis (2007) aimed to determine countries’ relative specialisations of
GBAORD (which indicates the thematic domains and horizontal activities that are being prioritised
by public authorities) and BERD (which shows patterns of R&D investments by the private sector)
by comparing national allocations to world shares and country totals to world totals. However, as
there are only 13 broad socio-economic objectives, direct links to scientific fields, technologies or
industries cannot be made. Few studies have explored the disaggregated data across Member
States to determine whether there is any “real specialisation (or conversely duplication) of R&D
efforts across the EU” (Cooke, 2009).
2.2.2 Human resource indicators
Human resources (HR) are considered another key element of knowledge creation and
dissemination and comprise a basic area of STI indicator development. According to Eurostat
(2009), the different levels of innovation performance among countries can be chiefly explained
by factors related to knowledge workers. The OECD (2007) considers highly qualified people to be
“stores of knowledge” and “vectors of knowledge flow.” More specifically, skilled labourers
interact with relevant actors; create knowledge, inventions or patents; and shape the
innovativeness and technological capability of regions and nations (Ho, 2004). This category of
indicator measures, within a given region or country, the presence of HR directly involved in R&D
activities (employed in R&D or providing services). It can be used to determine whether the pool
of HR is growing and to identify the sectors in which changes are occurring. Employment rates
comprise the most common indicator used in this category, but indicators go beyond employment
to labour force education, training and mobility.
Data on employment are readily available—in fact, Europe INNOVA/PRO INNO Europe (2008)
noted that employment is “the only indicator that is available in Europe across all regions and
industries.” According to definitions in the Canberra and Frascati Manuals, the three broad
statistical HR categories are:



HR in S&T (HRST), or individuals who either have higher education or persons who are
employed in positions that normally require such education;
R&D personnel, or all persons employed directly in R&D, as well as those providing direct
services such as R&D managers, administrators, and clerical staff; and
Researchers, who are professionals engaged in the conception or creation of new knowledge,
products, processes, methods and systems and also in the management of the projects
concerned.
Both HRST and R&D personnel indicators focus on the stock of qualified personnel; however, the
population of R&D personnel is much smaller than that of HRST and excludes everyone not
currently employed in R&D activities. The category of researchers is the narrowest of the three
and, in general, it is the population of greatest interest, particularly in terms of their stocks, their
mobility and their career trajectories (OECD, 2007). Eurostat (2009) examines researchers by
institutional sector, by economic activity and by field of science. Employed personnel may be
further broken down by Full Time Equivalent (FTE) or Head Count; countries and institutions may
use either method, and the OECD uses both.
Data on education inflows can also be used. The development of human capital in the form of
better education and skills is a determinant of economic growth in a knowledge-based economy
and is a major concern for the EU. In particular, graduates from tertiary education and doctoral
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graduates are commonly used as a measure of the current and future supply of HRST (European
Commission, 2008; Eurostat, 2009). HRST data are often used as a proxy for the current HRST
pool and graduate data as a proxy for the prospective pool. Graduates are generally defined by
the levels of education classified in UNESCO’s International Standard Classification of Education
(ISCED).
Researcher mobility, a newer indicator, is based on the assumption that because qualified
labour is generally marked by a higher mobility, they will move faster to exploit countries’ and
regions’ incentives and higher wages (Stirböck, 2002). In 2000, the European Commission
established the central ERA objective of increasing the number of mobile researchers in Europe,
and this goal was reconfirmed in the 2007 Green Paper (European Commission, 2007) and again
in the Innovation Union Competitiveness Report (European Commission, 2011). However, intercountry and even intra-country mobility within the EU remain low (European Union, 2011).
Current statistics on the mobility of HRST, available through Eurostat, provide information by
country on non-national researchers and on the balance of outgoing and incoming researchers,
but precise data are lacking at the geographical and sectoral levels (European Commission,
2008). Doctorate holders are a particular concern in mobility, according to the OECD (2007).
While data are partially available for doctoral candidates, doctorate holders and mobility funded
by select European instruments (European Commission, 2008), there is as yet no coherent
framework for collecting data on doctorate holders (Gault, 2011).
How HR indicators can contribute to an understanding of specialisation: Specialisation in
S&T is completely dependent on the researchers and engineers that are available to undertake
specialised research activities (Peter & Bruno, 2009). In its Science, Technology and
Competitiveness Key Figures report, the European Commission (2008) considers information on
researcher mobility to be a very rough proxy for the level of openness and attractiveness of
national research institutions. HR trends are an important dimension of structural change, and
these indicators can be used to determine changing patterns of specialisation across Member
States (European Commission, 2009). Few studies have empirically investigated the relationship
between mobility and specialisation; however, it has been determined that the countries with the
highest research capacities encourage both inward and outward mobility, while those with weaker
capacities—often those that exhibit specialisation on specific thematic areas—have the most
severe mobility problems (namely low mobility and high net outward flows) (Fernández-Zubieta &
Guy, 2010).
2.2.3 Innovation indicators
Innovation is a large and complex subject, but given its close ties to productivity, growth,
competitiveness, even “social well-being,” broaching the topic is crucial in any discussion of S&T
performance. Although innovation and R&D are often considered separately, both in concept and
policy approach, the two are considered “intricately and systemically linked processes in the
framework of a larger, knowledge-driven socioeconomic system” (Eurostat, 2009). The concept of
innovation has become perhaps even more popular than that of R&D (see, for example, the “blue
sky of innovation” 2) (Freeman & Soete, 2007). In response to the ‘innovation gap’ and
‘competitiveness challenge’ in Europe, a host of European instruments supporting innovation were
2 http://www.oecd.org/document/29/0,3746,fr_2649_34451_37075032_1_1_1_1,00.html
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introduced under the Competitiveness and Innovation Framework Programme, the Seventh
Research Framework Programme and related Structural Funds and the Innovation Union Flagship
Initiative (Reid, Denekamp, & Galvao, 2008). Innovation will play a large role in the upcoming
Eighth Framework Programme (‘Horizon 2020’).
Measuring the activity of innovation presents challenges. Most essentially, not all R&D leads to
innovation and not all innovation stems from R&D (Gault, 2011; Laursen & Salter, 2005).
Furthermore, the locus of innovation could take place anywhere throughout the economy—well
upstream or downstream from the firm or sector that carried out the research—and the
generation of innovation relies on a variety of inputs beyond technological activity (Freeman &
Soete, 2007; OECD, 2007). Nevertheless, the measurement of innovative activity rests heavily on
traditional technology-based output indicators, such as R&D and patent activity. As a result,
indicators of the activity of innovation are not as well developed as those for R&D (Gault, 2011).
According to Eurostat (2009), innovation generally belongs to two input indicator categories—
innovation drivers and knowledge creation—as well as three output indicators—innovation &
entrepreneurship, applications and intellectual property. The OECD’s Oslo Manual covers
indicators of innovation and provides guidelines on the measurement of innovative processes,
particularly in the private sector. A number of innovation surveys—including the Community
Innovation Survey—focus on small and medium enterprises (SMEs) as the main innovative agent
and provide innovation counts and innovation input indicators. Innovations within enterprises are
generally broken down by their NACE class. The recently introduced Innovation Union Scoreboard
will provide comparative benchmarking of EU and Member State performance against 25 core
research and innovation indicators on an annual basis, benchmarking some against major
international partners.
In terms of indicators, a focus for innovation and technological progress has been high-tech and
knowledge service activities and industries. High-tech industries are defined by their R&Dintensity (or the average shares of their expenses dedicated to R&D), and high-tech products
result from significant R&D investment (European Commission, 2008; Peter & Bruno, 2010).
Indicators focus on shares of product or process innovators, sales of new-to-market or new-tofirm products, and shares and types of employment in high- or medium-tech industries or
knowledge-intensive services (Grupp et al., 2010). International high-tech trade data, or data on
the exports and imports of products manufactured using a high intensity of R&D, are also used.
The European Union (2011) noted that “fast-growing enterprises in the most innovative sectors of
the economy are key actors for the development of emerging industries and for the acceleration
of the structural changes that Europe requires.” This is why the European Commission proposed
that a new single innovation headline indicator be the share of fast-growing enterprises in the
most innovative sectors; the definition of innovative sectors is being elaborated in collaboration
with the OECD and covers non-technology (non-manufacturing) sectors. 3
Value added is an additional indicator of knowledge intensity. The European Commission (2008)
defined value added as “current gross value added measured at producer prices or at basic
prices, depending on the valuation used in the national accounts.” According to the European
Union (2011), competitive advantage relies on the ability to compete on high value-added
3 http://epp.eurostat.ec.europa.eu/portal/page/portal/statistics_policymaking_europe_2020/documents/
Presentation_Delescluse.pdf
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products, and value is added to products through intensified labour or capital. Technology- and
knowledge-intensive SMEs are believed to be a source of high value added, as they generate new
products and services, create high-paying jobs, use resources more efficiently and conduct
research that has spill-over benefits (Eurostat, 2009). The indicator provides an indication of how
industry sectors within a country contribute to its GDP. Both the Value Added Scoreboard 4, which
is published by the Department for Innovation, Universities & Skills and the Department for
Business, Enterprise & Regulatory Reform, and the EU KLEMS database 5 (available up to 2007)
provide data and rankings on value added.
How innovation data can contribute to an understanding of specialisation: Innovation is a
‘distributed activity’—processes of innovation typically have a spatial element, as they involve
several contributing and coordinated firms or organisations (Coombs, Harvey, & Tether, 2001).
Innovation also takes place within sectoral systems—indicators are meant to capture high-growth
sectors and areas of leading-edge research activity. For instance, Peter and Bruno (2010)
calculated countries’ relative value added specialisation as an indicator of their economic
specialisation and their relative high-tech trade specialisation as an indicator of their product
specialisation by country. Knowledge-intensive or high-tech services, in particular, are an
important indicator of the overall knowledge intensity of an economy, one that is closely linked to
the “growing specialisation of industries and the need for more specialisation in other services
and in manufacturing sectors” (European Commission, 2008). Very few empirical assessments of
innovation and specialisation, however, have been done in service areas beyond the application of
information and communication technologies (ICT) (Tether, Hipp, & Miles, 1999). Because they
deal primarily with technology development and activity, non-technological innovation is largely
overlooked, and innovation indicators are most often used to gain an understanding of strictly
technological, rather than scientific, specialisation.
2.2.4 Knowledge flow indicators
The 2000 ERA Communication, the 2007 Green Paper and the 2011 Innovation Union
Competitiveness Report stressed the need for partnerships between existing centres of excellence
across European countries, better coordination between national and European research activities
and the generation of knowledge spillovers (European Commission, 2007). This goal has largely
involved making use of “spatial and cultural proximity between firms and supporting institutions”
within the EU context, particularly at the regional level (European Commission, 2009). Studies
continue to demonstrate that a robust STI system is built on networks of relationships between
universities, governments and firms; that emerging and high-growth scientific fields are
characterised by high degrees of diversity and complementarities requiring active cooperation;
and that linkages between various actors in various sectors have therefore become crucial to the
production of S&T knowledge and innovation production (Bonaccorsi, 2005; Lepori, Barré &
Filliatreau, 2008; Mota, 2001, as cited in Sartori & dos Santos Pacheco, 2006).
Knowledge flow indicators are relative newcomers to the assemblage of established STI
indicators. The European Commission presented a chapter on knowledge flows and new indicators
to measure transnational knowledge flows and integration of research in its various dimensions
4 http://www.research-interfaces.org/resources/article/default.aspx?objid=2764
5 http://www.euklems.net
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for the first time in its 2008 Science, Technology and Competitiveness Key Figures Report. The
report noted that these indicators are currently experimental due to the lack of coverage that
these dimensions have in European and international statistical systems, particularly with respect
to the public sector; linkages at the firm level, however, are more easily determined because of
the prevalence of innovation surveys. A report on scientific collaboration by the Royal Society
(2011) confirmed a specific lack of data on the flow and migration of talented scientists and their
diaspora networks, asserting that better indicators are required from organisations like UNESCO
and the OECD in order to properly evaluate global science.
The analysis of S&T flows enables an understanding of the transfer of both embodied and
disembodied knowledge and the dissemination and exploitation of S&T advances by examining
the dynamics of research-driven innovation through activities and network of actors (JiménezSáez, Zabala, & Zofío, 2010). Codified knowledge flows are registered in scientific and technical
literature and patents, and output indicators are generally based on data on co-publication and
co-patenting cooperation, including patent-to-patent and patent-to-non-patent citations and
references. The Technological Balance of Payments (TBP) indicator may be considered a
‘commercial’ flow indicator, as it measures the current exchange of technological know-how and
services into and out of a country by recording the flow of funds for transactions concerning
industrial property rights (OECD, 2010). Exchanges of the tacit knowledge embodied in individual
workers can be gauged by HR mobility indicators. Meanwhile, less ‘visible’ flows come in a variety
of forms and may involve public domain sources, co-operative knowledge exchanges, university
spin-offs, trade literature and electronic academic links (e.g., data on Open Access to scientific
publications and journals and webometrics) (European Commission, 2008; European Union,
2011; OECD, 2007; Pontikakis, Chorafakis, & Kyriakou, 2009). In aggregate, this information
leads to an idea of the competitiveness of countries or regions based on their “potential as
creators and disseminators of new knowledge” (Lugones & Suarez, 2010).
How knowledge flow data can contribute to an understanding of specialisation: Although
this is likely changing in the encroaching age of ‘virtual organisations’ and ‘virtual critical mass’,
knowledge flow has been largely geographically dependent. Based on network analyses, the
European Union (2011) found that there is a strong concentration of knowledge flows amongst a
few Western European countries, with only marginal involvement of other EU-12 (new) Member
States and most southern European countries. Additionally, while science is happening in more
places, with greater numbers of widely dispersed major hubs of scientific production, scientific
activity is actually becoming more concentrated—and hubs are growing more interconnected.
Regions and cities, rather than countries, are frequently perceived as the more relevant loci for
corporate R&D investment, scientific facilities or global talent because they are better able to
facilitate knowledge exchange between clustered institutions and organisations (Royal Society,
2011). Measures on the interconnectedness of agents in the STI system show the degree of
‘clustering’ within a network, the linkage between the specific clusters and the common
innovation infrastructure and the centrality of nations or regions within larger networks of
collaborations (OECD, 2007).
Pontikakis, Chorafakis and Kyriakou (2009) argued that many questions in the debate on
specialisation versus diversity could be answered through a better understanding of the
characteristics and consequences of these so-called ‘untraded flows of knowledge’. Knowledge
flow indicators could potentially measure not only variations in the specialisation/diversity axis,
but the degree of structural change over time. Combined with other significant variables in an
appropriate modelling framework, the authors noted that these measures could help “gauge the
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effects of different specialisation patterns on R&D productivity, EU cohesion and the flexibility of
research systems” and ultimately identify determinants of variation in specialisation. Knowledge
flow indicators could also help researchers to determine whether increased networking and
collaboration is more achievable and more efficient than geographic agglomeration for reaching
critical mass. Similarly, the European Commission (2009) suggested that maps of network links
and specialisation patterns, based on readily available summary statistics, would enable a direct
picture of the linkages (and their intensity) between institutions and how they evolve over time
“without imposing some ad hoc geographical partition.” Using such maps, these patterns and
structures could be compared to those observed in other regions of the world.
2.2.5 Research infrastructure indicators
Another stated objective for the ERA was to develop strategic and large-scale research
infrastructures (RI) in Europe. This was followed by the establishment in 2002 of the European
Strategic Forum on Research Infrastructures (ESFRI) 6. This goal, and a common method for
financing large RI in Europe, was a major part of FP6 and FP7 and related Structural Funds. The
Innovation
Union
Competitiveness
Report
(European
Commission,
2011)
discussed
the
importance of building a framework for pan-European RI. There are currently seven major
intergovernmental European research organisations operating new large-scale infrastructures.
Resources under the Structural Funds cover physical capital for research activities, including land,
buildings, instruments and equipment in laboratories. Infrastructure in higher education and in
government laboratories has been a major focus of these efforts, with Member States introducing
numerous reforms aimed at improving the functioning of the public research base (European
Union, 2011). Despite considerable funding for their design, preparation and construction, severe
imbalances persist, however, in the distribution of RI in Europe.
The term RI refers to “facilities, resources and related services used by the scientific community
to conduct top-level research in their respective fields, ranging from social sciences to astronomy,
genomics to nanotechnologies.” 7 The European Commission (2008) Key Figures report used data
on Structural Funds and expenditures on RI to determine the creation of new large-scale RI at the
European level, particularly those that have national or regional dimensions (especially in the new
Member States). RI-related indicators also included the most active research universities, funding
models for universities (types of funding) and additional economic indicators such as the share of
GOVERD in total public sector expenditure on R&D (GOVERD + HERD). European RI-related
instruments include the Survey of European Research Infrastructures 8 conducted in 2006−2007
(first trial conducted in 2004−2005) by the European Commission, European Science Foundation
and European Heads of Research Councils and the resulting RI Database Portal 9 and impact
studies, as well as the 2010 Roadmap of the European Strategy Forum on Research
Infrastructures (ESFRI).
How RI data can contribute to an understanding of specialisation: While very few
investigations have empirically examined the relationship between RI and specialisation, it is clear
6 http://ec.europa.eu/research/infrastructures/index_en.cfm?pg=esfri
7 http://ec.europa.eu/research/infrastructures/index_en.cfm?pg=what
8 http://cordis.europa.eu/infrastructures/survey.htm
9 http://ec.europa.eu/research/infrastructures/index_en.cfm?pg=landscape
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that the development of national and regional research and technical infrastructure is crucial to
enabling STI systems to realise their full potential as creators and disseminators of R&D. In
particular, high-quality RI better enable public research institutions and organisations to build
critical mass in specialised domains of knowledge by establishing networks and partnerships,
creating private and cooperative research organisations and supporting technology transfer
agencies. Through sharing specialised RI and testing facilities, countries or regions may seek to
build strong clusters or cluster cooperation and facilitate knowledge transfer for cross-border
cooperation (Europe INNOVA/PRO INNO Europe, 2008). At the Week of Innovative Regions in
Europe 2011 conference in Debrecen, Hungary, members consulted and agreed on the “Debrecen
Declaration”. The resulting document 10 stresses the important synergies between clusters,
regional specialisation and RI. In it, the EU is encouraged to develop a holistic approach to the
design of “smart specialization strategies through roadmaps where clusters and RI play a crucial
role” in contributing to European competitiveness and facilitating the emergence of strong
innovative European regions.
2.2.6 Industrial specialisation
Although this review focuses primarily on R&D specialisation, the concept of industrial
specialisation is no doubt an issue of crucial importance to overall country specialisation. The
relationship between science, technology and innovation is believed to be linear, with R&D and
technological specialisation driving industrial specialisation, which in turn drives competitiveness,
leadership, growth, incomes and standards of living (Bonaccorsi, 2009; Giannitsis, 2009).
Industrial specialisation—often called ‘industrial concentration’—is looked to as an effective
approach to regional growth and is commonly analysed in relation to regional specialisation
(Goschin, Constantin, Roman, & Ileanu, 2009).
Employment indicators are some of the most frequently used measures to determine the
concentration of industries and specialisation of regions. These include regional concentrations of
employment in high-tech and medium-high-tech industries or employment in knowledgeintensive services (e.g., employment in knowledge intensive economic activities as a percentage
of total employment). Relative wage rate dynamics, or impacts on regional income caused by
exogenous increases in demand in particular industries, may also be used (as in Stanton &
Mason, 2007), measured as the total increase in the value of wages and salaries paid by each
industry in the region to its employees.
Aside from employment and income indicators, value added (GVA, as described in the section on
innovation indicators) is often used as a relative measure of industrial specialisation/
concentration. GVA is an indicator of the contribution of each industry to GDP, enabling
comparisons of one region with the overall economy.
2.2.7 Selection of key STI indicators for the cross-cutting analysis with scientific
output
Table I presents an inventory of STI indicators for which data are available at the level of
countries (always available for EU-27) and regions (NUTS2; when specified). Data for these
indicators were not found by FP7 thematic priorities. As such, indicators for which data are
10 http://www.wire2011.eu/upload/document/34/Debrecen%20Declaration.pdf
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available by field of science (i.e., Natural Sciences, Engineering & Technology, Medical & Health
Sciences, Agricultural Sciences; data were not available at a lower aggregation levels) were used
to investigate the factors behind the scientific specialisation and the scientific productivity of
countries (such data do not seem to be available at the NUTS2 regional level). Selected indicators
are illustrated by the following symbol, ‡, placed after their name in Table I.
Table I
STI Indicators—Inventory of Available Data
Category of Indicator
R&D Investment and
Expenditure
Indicator
Gross Domestic Expenditure in
R&D (GERD) ‡
Aggregation Level
GERD can be broken down by
four sectors of performance:
•
•
•
•
BERD
GOVERD
HERD
PNPRD
Source(s)
Eurostat rd_e database
OECD Main Science and
Technology Indicators
GERD can also be broken down
by four sources of funding:
•
•
•
•
R&D Investment and
Expenditure
R&D Intensity (GERD as % of
GDP)
R&D Investment and
Expenditure
Government intramural
Expenditure on R&D
(GOVERD) ‡
Business enterprise
Government
Other national sources
Abroad
GERD can also be broken down
by field of science (Country level)
R&D Intensity can be broken
down at the level of:
Eurostat rd_e database
GOVERD can be expressed as:
OECD Main Science and
Technology Indicators
Eurostat rd_e database
•
OECD Main Science and
Technology Indicators
•
•
•
•
Sector of performance
NUTS 2
GOVERD as % of GDP
(GOVERD intensity)
GOVERD: Compound annual
growth rate (constant prices)
% of GOVERD financed by
industry
GOVERD can also be broken
down by field of science (Country
R&D Investment and
Expenditure
Higher Education Expenditure
on R&D (HERD) ‡
level)
HERD can be expressed as:
Eurostat rd_e database
•
OECD Main Science and
Technology Indicators
•
•
R&D Investment and
Expenditure
Government Budget
Appropriations or Outlays on
R&D (GBAORD)
HERD as % of GDP (HERD
intensity)
HERD: Compound annual
growth rate (constant prices)
% of HERD financed by
industry
HERD can also be broken down
by field of science (Country level)
GBAORD data can be broken
down by:
•
NABS categories
(socioeconomic objectives)
Central government
statistics
GBAORD can be expressed as:
•
R&D Investment and
Expenditure
Business Expenditure in R&D
(BERD) ‡
% of general government
expenditure
BERD can be broken down by:
•
•
NACE categories
Field of science (Country
level)
BERD can be broken down by
four sources of funding:
•
•
14
Business enterprise
Government
Eurostat rd_e database
OECD Main Science and
Technology Indicators
Final Report
Analytical Report 2.3.2
Category of Indicator
Indicator
Aggregation Level
•
•
Other national sources
Abroad
Source(s)
BERD can be expressed as:
•
•
•
R&D
Investment
Expenditure
and
Private Non-Profit Expenditure
on R&D (PNPRD)
BERD as % of GDP (BERD
intensity)
BERD: Compound annual
growth rate (constant prices)
BERD as % of value added in
industry
PNPRD can be broken down
by:
•
•
NACE categories
Field of science (Country
level)
Eurostat rd_e database
OECD Main Science and
Technology Indicators
PNPRD can be expressed as:
•
PNPRD as % of GDP (BERD
Human Resources (HR)
HR in S&T (HRST) ‡ (precise
selection of indicators in this
category remains to be
established)
Human Resources (HR)
R&D Personnel
intensity)
HRST can be broken down by:
•
•
•
•
•
•
•
Core (HRSTC)
Education (HRSTE)
Occupation (HTSTO)
Scientists & Engineers (S&E)
Gender
Nationality based on
citizenship: Nationals or nonnationals
NUTS2
Total R&D Personnel can be
broken down by:
•
•
Eurostat hrst database
Eurostat rd_p database
Field of science (Country
level)
NUTS2
R&D Personnel can be
expressed in:
•
•
Human Resources (HR)
Researchers ‡
Full-Time Equivalent (FTE)
Personnel in Head Count
(HC)
R&D Personnel can be broken
down by:
•
•
Eurostat rd_p database
Field of Science (Country
level)
NUTS2
R&D Personnel can be
expressed in:
•
•
Human Resources (HR)
Education Inflows ‡
HRST education inflows can be
broken down by:
Eurostat hrst Database
Doctoral graduates can be
broken down by:
Eurostat hrst Database
•
•
Human Resources (HR)
Doctoral Graduates ‡
Full-Time Equivalent (FTE)
Personnel in Head Count
(HC)
•
•
•
Levels of tertiary education
Field of study: Total (all fields)
vs. Science & Engineering
Flows: incoming plus
outgoing, as % of total
PhD/doctoral graduates
Gender
Country of origin
R&D Personnel can be
expressed in:
•
•
Human Resources (HR)
Researcher Mobility ‡ (subject
to data quality)
Number of degrees awarded,
per thousand population
Average Annual Growth Rate
(AAGR)
Mobility patterns of individual
researchers over time are
Eurostat hrst Database
15
Final Report
Analytical Report 2.3.2
Category of Indicator
Indicator
Aggregation Level
based on:
•
•
Source(s)
Nationality
Place of birth
Similarly, patterns of student or
doctorate holder mobility over
time are based on:
•
•
Human Resources (HR)
Number of graduates ‡
Innovation
Patents as Inventive R&D
Output ‡ (Can the Commission
grant Science-Metrix access to
data collected in the
‘Measurement and analysis of
knowledge and R&D
exploitation flows, assessed by
patent and licensing data’
study)
Value Added
Innovation
Number of graduates by field of
study
Patents can be broken down by,
for example:
•
•
•
•
US Patent and Trademark
Office (USPTO)
Eurostat
•
OECD
•
Manufacturing value added:
% distribution by type of
industry
High-tech value added as %
of total national
manufacturing value added
Value added of knowledge
intensive high-tech services
as % of total national services
value added
NUTS 2
•
•
NACE categories
Size (e.g., SMEs)
•
Enterprises in KnowledgeIntensive Services (KIS)
Patent applications per million
population
Patents granted
High-tech patents per million
population (NACE)
Patent applications filed
under PCT
OECD Online Education
Database
European Patent Office
(EPO)
Value added can be broken
down by:
•
Innovation
Country of permanent
residence
Country of prior education
Enterprises in KIS can be
broken down by:
Eurostat Structural
Business Statistics (SBS)
Eurostat htec database
They can also be broken down
into:
•
•
Innovation
Innovation
Employment in KnowledgeIntensive Services (KIS) ‡
High-Tech KIS (HTKIS)
Less Knowledge-intensive
Services (LKIS)
Employment in KIS are
aggregated at the level of:
•
Venture Capital Investment
(VCI) ‡
NUTS 2
Employment in KIS can be
broken down by:
•
Employment in HTKIS
VCI is expressed as:
•
% of GDP
VCI can be broken down by:
•
•
Innovation
Trade in High-Tech Products
Early stage (seed + start-up)
capital
Expansion and replacement
capital
Trade in High-Tech Products is
expressed as:
•
Exports/imports of high-tech
products as % of total
High-tech products are
determined based on:
•
Standard International Trade
Knowledge Flow
16
Technology
Balance
of
N/A
Eurostat htec database
Classification (SITC)
European Private Equity
and Venture Capital
Association (EVCA)
Eurostat htec database
Eurostat COMEXT
Database
United Nations
COMTRADE Database
Central government
Final Report
Analytical Report 2.3.2
Category of Indicator
Knowledge Flow
Knowledge Flow
Knowledge Flow
Indicator
Payments (TBP)
Scientific Co-Publications
Scientific Co-Patenting (Can
the Commission grant ScienceMetrix access to data collected
in the ‘Measurement and
analysis of knowledge and
R&D exploitation flows,
assessed by patent and
licensing data’ study)
Scientific Publications
Cited in Patents
Knowledge Flow
Open Access (OA)
Repositories
Research Infrastructures (RI)
RI Projects Funded
(Construction or Upgrade) ‡
(Subject to the feasibility of
downloading information in
bulk from the data source)
Research Infrastructures (RI)
Expenditures on RI ‡
(Subject to the feasibility of
downloading information in
bulk from the data source)
Aggregation Level
Scientific Co-Publications can
be broken down into:
•
Any required unit
•
•
•
Single-country co-patents
Transnational co-patents
AAG
Scientific Co-Patenting can be
broken down into:
Source(s)
statistics
Science-Metrix
European Patent Office
(EPO)
OECD
US Patent and Trademark
Office (USPTO)
Scientific Publications
Cited in Patents can be broken
down into:
European Patent Office
(EPO)
Establishment of OA
repositories can be broken
down by:
•
Countries
RI Projects Funded can be
broken down by:
•
Countries
Field of science
•
Directory of Open Access
Journals (DOAJ)
•
•
•
All cited publications
Highly-cited publications
Science-intensive fields
Expenditures on RI can be
broken down by:
•
Countries
•
Field of Science
US Patent and Trademark
Office (USPTO)
European Strategic Forum
on Research Infrastructures
(ESFRI) Roadmap 2010
European Portal on
Research Infrastructures’
services
European Strategic Forum
on Research Infrastructures
(ESFRI) Roadmap 2010
European Portal on
Research Infrastructures’
services
17
Analytical Report 2.3.2
Final Report
3 METHODS & RESULTS
This report adds a highly meaningful level of analysis to the bibliometric data collected so far in
this study by performing a cross-cutting analysis of scientific output versus other STI indicators.
Specifically, this report investigates:
1.
2.
the factors behind the publication outputs and productivity (i.e., the efficiency with which
entities are converting research inputs into research outputs) of countries and NUTS2
regions, as revealed through an analysis of scientific production (Section 3.1); and
the factors behind the production patterns of countries (data were not available at the
NUTS2 level), as revealed through an analysis of scientific concentration (by research area),
across scientific fields (data were not available to perform the analysis by thematic domain
[i.e., FP7 thematic priority areas]) (Section 3.2).
For this report, a total of 17 STI indicators distributed across four categories (i.e., R&D
Investment and Expenditure, Human Resources, Innovation and Research Infrastructures) were
considered, although some were not available for analysing NUTS2 regions and the production
patterns of countries by scientific field (see Section 2.2.7 and description of the indicators below).
Data were downloaded in bulk from Eurostat 11 for the 42 countries and 291 NUTS2 regions for
which bibliometric data were available. The downloaded data covered the years 2000 to 2009,
where available. The European Portal on Research Infrastructures’ Services 12 was also used to
download data for some of the selected STI indicators. These data were then uploaded on
Science-Metrix’ SQL server and structured into a relational database that could be linked with
Science-Metrix’ relational database of bibliometric indicators produced for DG Research as part of
the same study (i.e., Analysis and Regular Update of Bibliometric Indicators; Science-Metrix,
2011). These indicators are as follows: 13
R&D Investment and Expenditure




GERD: Gross Domestic Expenditure in R&D (GERD) expressed in millions of PPS at 2000
prices (Source: Eurostat rd_e_gerdsc table [country & field levels] and rd_e_gerdreg [NUTS2
level])
HERD: Higher Education Expenditure on R&D (HERD) expressed in millions of PPS at 2000
prices (Source: Eurostat rd_e_gerdsc table [country & field levels] and rd_e_gerdreg [NUTS2
level])
GOVERD: Government intramural Expenditure on R&D (GOVERD) expressed in millions of
PPS at 2000 prices (Source: Eurostat rd_e_gerdsc table [country & field levels] and
rd_e_gerdreg [NUTS2 level])
BERD: Business Expenditure in R&D (BERD) expressed in millions of PPS at 2000 prices
(Source: Eurostat rd_e_gerdsc table [country level; too many missing entries at the field
level] and rd_e_gerdreg [NUTS2 level])
Human Resources

Researchers in the Higher Education Sector: Number of researchers (both genders in all
fields) in the higher education sector expressed in head count (Source: Eurostat rd_p_perssci
table [country & field levels] and rd_p_persreg [NUTS2 level])
11 http://epp.eurostat.ec.europa.eu/portal/page/portal/statistics/bulk_download
12 http://www.riportal.eu/public/ index.cfm?fuseaction=ri.search
13 For more details on these indicators, see Eurostat’s metadata at:
http://epp.eurostat.ec.europa.eu/portal/page/portal/statistics/metadata
18
Final Report
Analytical Report 2.3.2





HRST with Tertiary Education: Number of human resources (both genders in all fields; 15
to 74 years) in science and technology (HRST) with tertiary education (employed) expressed
in thousands (Source: Eurostat hrst_st_nfiesex table)
PhD Students: Number of PhD students (both genders in all fields) participating in tertiary
education (ISCED 97: Level 6) expressed in thousands (Source: Eurostat hrst_fl_tepart
table)
PhD Graduates: Number of PhD graduates (both genders in all fields) from tertiary
education (ISCED 97: Level 6) expressed in thousands (Source: Eurostat hrst_fl_tegrad
table)
Foreign Students in Tertiary Education: Number of foreign students (both genders in all
fields) participating in tertiary education (ISCED 97: Levels 5 and 6) expressed in thousands
(Source: Eurostat hrst_fl_tefor table)
Job-to-Job Mobility of HRST: Job-to-Job Mobility of HRST (25-64 years; Employed) in all
knowledge-intensive services expressed in thousands (Source: Eurostat hrst_fl_mobsect
table)
Innovation





Employment in Technology and Knowledge-Intensive Sectors: Employment in
technology and knowledge-intensive sectors (all NACE activities; all occupations) expressed
in thousands (Source: Eurostat htec_emp_nisco table)
High-Tech Patent Applications to the EPO: Number of high-tech (total) patent
applications to the EPO (Unit = All (no breakdown); Source: Eurostat pat_ep_ntec table)
VCI (Expansion & Replacement): Venture Capital Investments (VCI) for expansion &
replacement expressed in millions of euro (Source: Eurostat htec_VCI_exre table)
VCI (Buyout): VCI for buyout expressed in millions of euro (Source: Eurostat
htec_VCI_buyout table)
VCI (Early Stage): VCI for early stage research expressed in millions of euro (Source:
Eurostat htec_VCI_earl table)
Research Infrastructure 14


3.1
Research Infrastructures (RI): Number of new research infrastructures (Unit = All (no
breakdown); Source: http://www.riportal.eu/public/ index.cfm?fuseaction=ri.search);
Average Lower Bound of RI investment: Average lower bound of research infrastructure
investment (i.e., for initial construction/setting up) expressed in millions of euro (Source:
http://www.riportal.eu/public/ index.cfm?fuseaction=ri.search).
PUBLICATION OUTPUT
NUTS2 REGIONS
AND PRODUCTIVITY OF COUNTRIES AND
The bibliometric indicator that was used to improve the understanding of differences between
countries’ and NUTS2 regions’ scientific output and productivity is the total number of
publications indexed in Scopus (the data cover the 2000−2009 period). The data were produced
by Science-Metrix (2011) for DG research. The indicator is defined as follows:
Number of peer-reviewed scientific publications written by authors located in a
given geographical or organisational entity (e.g., the world, a country, a NUTS2
region, a university, an RPO or a company). Fractional counting (FRAC) was
used. The fractioning was done at the level of author addresses.
14 For more details on these indicators, see: European Commission, European Science Foundation. (2007).
Trends in European Research Infrastructures: Analysis of data from the 2006/07 survey. 96 pages,
http://ec.europa.eu/research/infrastructures/pdf/survey-report-july-2007_en.pdf#view=fit&pagemode=none.
19
Analytical Report 2.3.2
Final Report
Factor analysis was used to identify the main dimensions (i.e., factors) explaining patterns of
variation among selected STI indicators and the publication output of countries (Section 3.1.1),
whereas regression analysis was used for investigating the productivity of countries and NUTS2
regions in terms of outputs (i.e., publications) per unit of the most relevant STI indicators (i.e.,
R&D input indicators) (Section 3.1.2 and 3.1.3). Section 3.1.2 also presents the results of a
regression analysis aimed at investigating whether the innovation capability of countries (i.e., the
capacity to produce inventions from a given amount of research) varies as the size of their
science base increases.
3.1.1 Factor analysis for identifying the main dimensions (i.e., factors) among
selected STI indicators and the publication output of countries
Exploratory Factor Analysis (EFA) was used for identifying the main dimensions (i.e., factors)
explaining patterns of variation among selected STI indicators and the publication output (i.e.,
production) of countries. Prior to performing EFA, the frequency distributions of all indicators
were examined to see whether or not the indicators needed to be transformed prior to
undertaking the analyses. Indeed, the normality of individual variables is an assumption
underlying many factoring methods used in performing EFA. As expected, all indicators had a
high positive skew, with most countries having low scores and a few having high scores (e.g.,
similarly to heavy tails). As such, they were log transformed, and although the resulting
distributions did not always satisfy the condition of normality (based on a Kolmogorov-Smirnov
Test of normality, data not shown), neither did they appear to present a strong departure from
normality (based on a visual inspection of the distributions, see Figure 1). Additionally, this
transformation made (if it was not already the case) the relationships between all observed
variables linear, which is another assumption often underlying EFA using different factoring
methods (based on a visual inspection of the relationships, Figure 1).
The two indicators on research infrastructure (Q and R in Figure 1) were left aside, as the volume
of data available for them was small (N = 31; many missing values). Additionally, the GERD was
also removed from the analysis because it is redundant with HERD, GOVERD and BERD.
Therefore, the remaining indicators included R&D input indicators (13), as explanatory variables
for the number of publications (FRAC) produced by various entities, and the number of high-tech
patent applications to the EPO, as another R&D output variable to be cross-linked with scientific
output (i.e., the number of publications).
The dataset used in this report consisted of a panel data structure made up of cross-sections
(i.e., countries) and time-series (i.e., years = 10), the latter being nested within the former.
Thus, the input and output variables to be analysed have two dimensions. Each observation has a
cross-sectional unit (i.e., country i) and a temporal reference (i.e., year t). The result is that the
input and output variables, which consist of time-series, do not fully satisfy the assumption of
random and independent sampling of observations. However, since the goal of the current
analysis is to describe the underlying structure of the dataset rather than to perform inferential
statistics, this violation has a limited impact. Because the data were not always available for all
countries and years among the set of retained variables, the panel data was unbalanced.
Consequently, missing data were dealt with using pairwise deletion. The sample size for each of
the variables submitted to the EFA are indicated in the note for Figure 1.
20
Analytical Report 2.3.2
Final Report
Figure 1
Frequency distribution of selected STI indicators and matrix of the relationships
between all pairs of indicators, 2000−2009
Note:
A = Publications (FRAC; N = 405), B = GERD (N = 339), C = HERD (N = 339), D = BERD (N = 336),
E = GOVERD (N = 340), F = HRST with Tertiary Education (N = 218), G = Researchers in the Higher
Education Sector (N = 263), H = Foreign Students in Tertiary Education (N = 237), I = PhD Graduates (N
= 288), J = PhD Students (N = 262), K = Job-to-Job Mobility of HRST (N = 186), L = High-Tech Patent
Applications to the EPO (N = 355), M = Employment in Technology and Knowledge-Intensive Sectors (N
= 269), N = VCI (Buyout) (N = 183), O = VCI (Early Stage) (N = 201), P = VCI (Expansion &
Replacement) (N = 210), Q = Research Infrastructure (N = 31) and R = Average Lower Bound of RI
investment (N = 31)
Computed by Science-Metrix using Scopus (Elsevier) and Eurostat data
Source:
Because the variables did not fully satisfy the condition of normality, an attempt was first made
to perform EFA using Iterated Principal Axis (IPA) factoring, which does not rely on any
distributional assumption (Fabrigar et al., 1999). Unfortunately, the IPA procedure failed, as the
correlation matrix consisted of a singular matrix that could not be inverted. This is due to the
very small determinant (0) of the correlation matrix, which provided evidence of high
multicollinearity in the dataset (Field, 2000).
Consequently, a second attempt was made using PCA factoring, which relies on the assumption of
normality of the variables. Using the Kaiser criterion (i.e., eigenvalue > 1; a factor should be
dropped when it carries less information than the average single input variable) for retaining the
meaningful factors resulted, in this case, in an overextraction of factors, as is often the case with
this approach (Costello & Osborne, 2005).
In identifying the meaningful factors, the scree plot approach was used in conjunction with the
Kaiser criterion (Figure 2). Based on this approach, it was found that the 15 selected variables
21
Final Report
Analytical Report 2.3.2
could be adequately summarised using a single factor. Indeed, although the two main factors
have an associated eigenvalue greater than 1, there is a sharp break in the distribution of
eigenvalues between these two factors, and the first factor alone explains 83% of the variance in
the dataset (Table II). The presence of negative eigenvalues for the two last factors provided
further evidence of high multicollinearity in the dataset. In fact, all variables had at least 50% of
their variance explained by the first factor, and the output variable (i.e., number of publications)
was almost perfectly correlated (R2 = 0.98) with the first factor (Table II).
14
12
Eigenvalue
10
8
6
4
2
0
0
2
4
6
8
10
12
14
16
Number of factors
Figure 2
Scree plot of the exploratory factor analysis of selected STI indicators using PCA
factoring
Source:
Computed by Science-Metrix using Scopus (Elsevier) and Eurostat data
To assess whether the departure from normality in the selected variables was sufficiently
pronounced to create distortion in the results obtained using PCA factoring, an attempt was made
to reduce the multicollinearity in the dataset to allow an EFA to be performed using IPA factoring,
again using pairwise deletion. This was achieved by removing variables (i.e., job-to-job mobility
of HRST, VCI Buyout, VCI Early Stage, VCI Expansion & Replacement, and high-tech patent
applications to the EPO), which appeared to cause problems. Among them, the job-to-job
mobility of HRST was highly correlated with both the publication output and HERD of countries
(data not shown) and was, with these two variables, almost perfectly correlated with the first
factor based on PCA factoring (Table II).
22
Final Report
Analytical Report 2.3.2
Table II
Factor loadings of selected STI indicators on the 1st factor of the exploratory
factor analysis using PCA factoring
Indicator
Publications (FRAC)
Job-to-Job Mobility of HRST
HERD
HRST with Tertiary Education
BERD
GOVERD
PhD Graduates
Researchers in the Higher Education Sector
Employment in Technology and Knowledge-Intensive Sectors
Foreign Students in Tertiary Education
PhD Students
High-tech patent applications to the EPO
VCI (Expansion & Replacement)
VCI (Buyout)
VCI (Early Stage)
% of Total Variance Explained by the 1st Factor
Source:
R2
0.98
0.95
0.95
0.89
0.91
0.89
0.88
0.84
0.82
0.84
0.79
0.77
0.72
0.62
0.54
R
0.99
0.98
0.97
0.95
0.95
0.94
0.94
0.92
0.91
0.92
0.89
0.88
0.85
0.79
0.74
83%
Computed by Science-Metrix using Scopus (Elsevier) and Eurostat data
The reasons behind the impact of the three indicators on venture capital investments (VCI) and
the impact of the number of high-tech patent applications to the EPO on the high multicollinearity
of the dataset is more difficult to grasp. However, their removal was not a major concern, as
they are the least correlated with the publication output of countries, each explaining 70% or less
of the variance in this variable. In addition, they are not intrinsically linked with this variable, at
least not to the extent that R&D expenditures and HRST indicators are linked with it. Indeed,
although early- and expansion- stage venture capital supports a significant level of R&D, which is
captured in BERD, this R&D is mostly oriented towards development rather than research (OECD,
2002). This redundancy with BERD—as revealed by the high correlation coefficients of early- and
expansion- stage VCI with this indicator, as well as between them—might very well be the cause
for their impact on the high multicollinearity in the dataset. The number of high-tech patent
applications to the EPO was also highly correlated with BERD. Finally, the job-to-job mobility of
the HRST variable and the three indicators on VCI had the highest number of missing values in
the dataset.
This time, the IPA factoring did work, although the multicollinearity in the dataset was still high
(determinant of the correlation matrix < 0.00001; Field, 2002), and it provided results that were
highly comparable to those obtained using PCA factoring (Table III). All variables were again
adequately summarised using a single dimension (only one meaningful factor with an eigenvalue
above 1) and the output variable (i.e., the number of publications [FRAC]) was again almost
perfectly correlated with the first factor, meaning that it is almost equivalent to it (i.e., the first
factor is a good approximation of the scientific production of countries). It should be noted that
the dataset used to perform the EFA included repeated measures over time (from 2000 to 2009)
for each country. Thus, the assumption of independence in the observations was violated.
However, since the goal was to explore the data rather than to perform confirmatory factor
analysis (CFA), this violation does not offset the main conclusion drawn from this analysis—that
the selected indicators are highly collinear.
23
Final Report
Analytical Report 2.3.2
As all R&D input indicators are highly correlated with the first factor (R ranging from 0.88 to
0.97), it is likely that they are strongly correlated with the scientific production of countries.
Indeed, all remaining R&D input indicators had a correlation coefficient greater than 0.89 with the
publication output of countries (data not shown). This does not come as a surprise, as all of these
indicators are to some extent dependant upon the GERD of countries: the more a country invests
in R&D, the more resources (e.g., human resources, infrastructure) it is likely to have in S&T.
Table III
Factor loadings of selected STI indicators on the 1st factor of the exploratory
factor analysis, based on PCA and IPA factoring
PCA Factoring
Indicator
Publications (FRAC)
HRST with Tertiary Education
PhD Graduates
Employment in Technology and Knowledge-Intensive Sectors
Researchers in the Higher Education Sector
HERD
PhD Students
GOVERD
BERD
Foreign Students in Tertiary Education
% of Total Variance Explained by the 1st Factor
Source:
2
R
0.99
0.97
0.97
0.96
0.95
0.95
0.95
0.94
0.92
0.90
R
0.98
0.94
0.93
0.92
0.91
0.90
0.89
0.89
0.84
0.81
90%
IPA Factoring
R2
0.99
0.94
0.93
0.91
0.90
0.89
0.88
0.88
0.82
0.78
R
1.00
0.97
0.96
0.95
0.95
0.94
0.94
0.94
0.91
0.88
89%
Computed by Science-Metrix using Scopus (Elsevier) and Eurostat data
However, given that there are differences in the way countries allocate R&D spending across
sectors (e.g., higher education, government, private) and resources (e.g., human resources,
infrastructure), it is of interest to investigate how the publication output of countries scale relative
to individual R&D input indicators (12 indicators, including those on VCI but excluding the job-tojob mobility of HRST because of its very high correlation with HERD and the response variable).
This was achieved through regression analysis. Regression analysis was also used to investigate
whether countries with a larger publication output also have a stronger innovation capabilities
(measured in terms of high-tech patent applications to the EPO).
3.1.2 Regression analysis for investigating the productivity of countries in terms
of publication output per unit of the most relevant R&D input indicators
This section presents the results of a regression analysis aimed at investigating the productivity
of countries in terms of outputs (i.e., publications) per unit of the most relevant STI indicators
(i.e., R&D input indicators) (Section 3.1.2.1). Based on this analysis, it subsequently ranks
countries based on their scientific productivity (Section 3.1.2.2). Finally, the section concludes
with the results of a regression analysis aimed at investigating whether the innovation capability
of countries (i.e., the capacity to produce inventions from a given amount of research) varies as
the size of their science base increases (Section 3.1.2.3).
3.1.2.1
Economies and diseconomies of scale in scientific production
Investigating the impact of individual R&D input indicators on the production of countries (i.e.,
the output variable) involved determining whether the two variables were scaling linearly (i.e., no
change in the ratio as one of the variable increases; isometric pattern) or whether one variable
was scaling exponentially relative to the other (i.e., change in ratio as one of the variable
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Analytical Report 2.3.2
increases; allometric pattern). In the latter case, the relationship between the two measured
quantities is expressed as a power law:
y = kx a ;
or, equivalently, as a linear relationship using the logarithm of the variables:
log(y) = a log(x) + log(k),
where y = output variable and x = explanatory variable.
When attempting to interpret the pattern of change in the ratio between two variables as one
increases, estimating the regression coefficient using the logarithm of the two variables can yield
the answer (Smith, 2009). In the present case, when the slope of the regression line using the
logarithmic form equals 1, there is isometric scaling between the two variables, meaning that the
relationship between the two variables is linear (e.g., if y is equal to twice the value of x, it will
remain so for any value of x). When the slope of the regression line using the logarithmic form is
smaller than 1, there is negative allometric scaling between the two variables, meaning that y
increases less rapidly than x (e.g., the ratio of y to x decreases as x increases). In this report,
negative allometric scaling will be referred to as “diminishing returns”, whereby an increase in a
factor of production (i.e., an R&D input indicator) while holding all others constant will yield lower
per-unit returns (i.e., peer-reviewed publications per unit of the R&D input indicator).
Alternatively, when the slope of the regression line using the logarithmic form is greater than 1,
there is positive allometric scaling between the two variables, meaning that y increases more
rapidly than x (e.g., the ratio of y to x increases as x increases). In this report, positive allometric
scaling will be refered to as “economies of scale”, whereby an increase in a factor of production
(i.e., an R&D input indicator) while holding all others constant will yield higher per-unit returns
(i.e., peer-reviewed publications per unit of the R&D input indicator). When the 95% confidence
interval of the slope does not overlap with the value of 1, which is indicative of an isometric
scaling between the variables, it is concluded that there is a significant allometric scaling,
indicating either “diminishing returns” (i.e., slope smaller than 1) or “economies of scale” (i.e.,
slope greater than 1).
Multiple regression could not be used to investigate the productivity of countries in terms of
outputs (i.e., publications) per unit of the most relevant R&D input indicators due to the high
multicollinearity in the dataset. This could have resulted in spurious conclusions regarding the
significance of the regression coefficients and led to coefficients of unexpected sign (Zar, 1999).
Additionally, the dataset used in this report consisted of a panel data structure made up of crosssections (i.e., countries) and time-series (i.e., years = 10), the latter being nested within the
former. Thus, the input and output variables to be regressed have two dimensions. Each
observation has a cross-sectional unit (i.e., country i) and a temporal reference (i.e., year t). The
result is that the input and output variables, which consist of time-series, are likely to be
autocorrelated as a result of the non-independence in the observations. For instance, the HERD of
a country at time t+1 is likely dependent upon its HERD at time t such that it is correlated with
itself; in other words, a lagged variable of HERD could likely be regressed with itself (i.e.,
autoregression). Due to autocorrelations in the data, estimating the regression coefficients on the
pooled dataset (i.e., all coutries and years) would likely compress the confidence intervals of the
slopes, increasing the likelihood of falsely concluding that there are either “diminishing returns” or
“economies of scale”.
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Analytical Report 2.3.2
Final Report
Several regression models have been developed to deal with the peculiarities of panel datasets, in
particular with the autocorrelation that often occurs in time-series as well as with unbalanced
panels as in this study (i.e., data were not always available for all countries and years among the
set of retained variables). Common models in panel data analysis include the fixed-effects model,
the between-effects model, the random-effects model and the dynamic panel data model. When
the goal of the analysis is the population response means, as in this study, the need to account
for the within cross-section (i.e., country) variation and autocorrelation does not matter as much
as when the analysis aims to investigate subject-specific (i.e., country-specific) effects of
explanatory variables. In such cases, one can go for robust inference (Gardiner et al., 2009)
using, for example, a between-effects model, which fits a group-mean regression.
In this study, group-mean regressions were fitted by means of S-estimators (robust regression)
(Rousseeuw & Yohai, 1984). This method is adequate for fitting a regression line when outliers
might be present in both the response and explanatory variables, which is highly likely with the
data used in this report. Furthermore, this regression technique is also robust to violations of the
assumptions of normality and homoscedasticity of the residuals, which was the case in several of
the fitted regressions. The regressions were performed using a c-value of 2.937, which provided a
good compromise between the breakdown point (i.e., the percentage of outliers above which the
estimator is likely to be biased; 25%) and efficiency (i.e., 75%) of the estimator (Rousseeuw &
Yohai, 1984).
Figure shows the regression coefficients obtained by fitting a group-mean regression between the
scientific production (i.e., number of publications [FRAC]) of countries and each of the R&D input
indicators retained in the factor analysis (12 indicators in total, see Section 3.1.1) in decreasing
order of the strength of the correlation between the scientific output of countries and each of
these variables (correlation coefficients based on group means ranged from 0.72 to 0.96). In
total, twelve group-mean regressions were fitted using S-estimators, one for each of the relevant
R&D input indicators. In nearly half of the regressions, when the 95% confidence intervals of the
regression coefficients presented in Figure 3 did not overlap with the value of 1, which is
indicative of an isometric scaling between the variables, it was concluded that there was a
significant allometric scaling indicative of either “diminishing returns” (i.e., slope smaller than 1)
or “economies of scale” (i.e., slope greater than 1).
Significant “diminishing returns” in terms of publication output are observed for five out of six
R&D input indicators related to expenditures (i.e., GOVERD, BERD and all three VCI indicators).
“Diminishing returns” appear to be stronger with the VCI indicators followed by BERD and
GOVERD, whereas there appears to be isometric scaling with HERD.
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Robust R2 = 0.94
Slope = 0.93
95% CI = [0.84 - 1.02]
Log(Publications)
Log(Publications)
Analytical Report 2.3.2
Robust R2 = 0.93
Slope = 1.00
95% CI = [0.93 - 1.06]
Robust R2 = 0.92
Slope = 0.98
95% CI = [0.85 - 1.10]
Log(PhD Graduates)
Log(Publications)
Log(Publications)
Log(HERD)
Robust R2 = 0.79
Slope = 0.87
95% CI = [0.74 - 1.00]
Log(HRST with Tertiary Education)
Log(Publications)
Log(Publications)
Log(Researchers in the HES)
Robust R2 = 0.90
Slope = 1.12
95% CI = [0.96 - 1.29]
Robust R2 = 0.93
Slope = 0.86
95% CI = [0.77 - 0.95]
Robust R2 = 0.85
Slope = 1.2
95% CI = ]1.00 - 1.39]
Log(GOVERD)
Log(Publications)
Log(Publications)
Log(PhD Students)
Robust R2 = 0.93
Slope = 0.71
95% CI = [0.63 - 0.80]
Log(BERD)
Robust R2 = 0.84
Slope = 0.89
95% CI = [0.72 - 1.06]
Log(Foreign Students in TE)
Log(Publications)
Log(Publications)
Log(Employment in Tech & KIS)
Robust R2 = 0.80
Slope = 0.57
95% CI = [0.44 - 0.71]
Log(VCI [Expansion & Replacement])
Figure 3
Robust group mean regressions between the scientific output (number of
publications [FRAC]) of countries and selected R&D input indicators, 2000−2009
Source:
Computed by Science-Metrix using Scopus (Elsevier) and Eurostat data
27
Final Report
Robust R2 = 0.85
Slope = 0.43
95% CI = [0.33 0.53]
Log(VCI [Buyout])
Log(Publications)
Log(Publications)
Analytical Report 2.3.2
Robust R2 = 0.62
Slope = 0.48
95% CI = [0.29 - 0.67]
Log(VCI [Early Stage])
Figure 3 (Cont’d)
Robust group mean regressions between the scientific output (number
of publications [FRAC]) of countries and selected R&D input indicators,
2000−2009
Source:
Computed by Science-Metrix using Scopus (Elsevier) and Eurostat data
However, there might also be slight “diminishing returns” with respect to HERD. Indeed, the slope
for HERD at the country level is below 1 (i.e., 0.93) and the 95% confidence intervals slightly
overlap with the value of 1, indicative of isometric scaling. This overlap might have been
nonexistent with a larger system size. As will be shown later with NUTS2 regions, moderate
decreasing returns with respect to HERD are confirmed.
Moderate “diminishing returns” in terms of publication output with respect to the number of
students participating in a doctoral program are also very likely. Indeed, the regression
coefficient is equal to 0.87, and although the 95% confidence interval for the slope of the
regression includes the value of 1, which is indicative of isometric scaling, it does not overlap with
it (i.e., it is its upper boundary).
In contrast, significant “economies of scale” in terms of publication output are observed with
employment in technology and knowledge-intensive services, which includes the education sector
and all occupations (i.e., professionals, technicians and other occupations). The scaling coefficient
is high (1.20), and its confidence interval does not overlap the value of 1. Regarding the number
of researchers in the higher education sector, the result at the country level suggests isometric
scaling. However, as will be seen later with NUTS2 regions, there appears to be moderate
“economies of scale” in terms of publication output as the number of researchers increases. Since
this result is based on a much larger system (i.e., there are more NUTS2 regions than countries
within the ERA), it is considered more reliable.
Regarding the number of PhD graduates, the number of employed HRST with tertiary education,
and the number of foreign students participating in tertiary education, it was not possible to
reject the hypothesis of isometric scaling, as the 95% confidence interval overlaps with the value
of 1.
The results for the three expenditure indicators as well as for researchers in the higher education
sector can be considered more reliable, as they could also be examined at the NUTS2 level with
better sample sizes. This is due to the fact that the NUTS2 system is larger than the country
system at the European level (i.e., there are more NUTS2 regions than countries within the ERA)
and there are more datapoints at the NUTS2 level to study the European system. Given the small
system sizes used for the remaining indicators, the findings should be considered preliminary and
used with care.
3.1.2.2
Comparative analysis of the scientific productivity of countries
Some countries undoubtedly deviate, positively or negatively, from the general tendency of the
systems (i.e., from the regression line) described above. In cases where a power law relationship
28
Analytical Report 2.3.2
Final Report
exists between two variables (i.e., when “diminishing returns” or “economies of scale” are
confirmed), it is better to use scale-adjusted indicators instead of ratios to appropriately take
account of the relative size of entities when comparing their performance; ratios, like the number
of publications produced per euro investment in R&D in the higher education sector, assume a
linear relationship (Katz, 2000). To take account of the non-linear relationship between the two
variables, the performance score (in terms of productivity) is obtained by dividing the score for
the output variable (not log transformed) by its expected score, as determined from the general
tendency of the system (i.e., from the projection of the datapoint on the regression line; not log
transformed). A score above one therefore indicates a stronger performance than expected for
the size of the input (e.g., for HERD), whereas a score below one indicates the opposite. When
the relationship between the two variables is linear (isometric scaling), this approach is still valid
and provides rankings that are equivalent to those obtained using simple ratios.
Table V presents the scale-adjusted performance score of countries in terms of productivity (i.e.,
published output per unit of an R&D input indicator) for the three R&D input indicators that
correlate the most with the number of publications of countries (i.e., HERD, the number of PhD
graduates and the number of researchers in the higher education sector). The country that
showed the strongest performance in terms of productivity when considering all three
scale-adjusted indicators is Luxembourg. Indeed, the country ranks 2nd in terms of scientific
output given the size of its population of PhD graduates and researchers in the higher education
sector. It also has a good productivity performance given the size of its HERD. In other words,
given the amount of financial and human resources (PhD graduates and researchers) it devoted
to the higher education sector, Luxembourg managed to produce more scientific publications than
would be expected, to a greater extent than most of the selected countries. Other countries that
fared well include Russia, Slovenia, the Netherlands and Belgium. Among the countries that
showed the weakest performance in terms of productivity are Latvia, Lithuania, Portugal, Estonia
and Austria.
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Analytical Report 2.3.2
Table IV
Scale-adjusted performance score of countries in terms of productivity (i.e.,
published output per unit of an R&D input indicator) for three R&D input
indicators, 2000−2009
Publications/HERD
Country
Austria
Belgium
Bulgaria
China
Croatia
Cyprus
Czech Republic
Denmark
Estonia
Finland
France
Germany
Hungary
Iceland
Ireland
Italy
Japan
Latvia
Liechtenstein
Lithuania
Luxembourg
Macedonia
Malta
Netherlands
Norway
Poland
Portugal
Rep. of Korea
Romania
Russia
Slovakia
Slovenia
Spain
Sweden
Switzerland
Turkey
United Kingdom
United States
Note:
Source:
30
Publications/PhD Graduates
Publications/Researchers
Score
Rank
Score
Rank
Score
Rank
0.60
0.99
4.65
3.04
1.38
1.05
1.76
0.83
0.81
0.89
0.96
0.99
1.38
0.47
0.91
0.93
0.81
0.51
33
17
1
3
10
16
8
25
28
23
20
18
11
36
22
21
27
35
0.77
1.56
0.75
26
7
28
0.83
1.03
1.06
19
10
9
18
17
12
6
31
11
7
13
26
24
15
4
22
34
32
9
0.24
1.99
33
2
0.53
0.82
0.76
1.83
0.85
1.13
2.42
2.85
3.07
2.11
1.19
0.77
0.96
0.73
1.30
1.20
34
26
30
7
24
15
5
4
2
6
14
29
19
31
12
13
9
3
24
5
25
23
14
30
19
1
18
12
11
27
15
31
2
33
4
6
8
29
34
0.84
0.86
0.99
1.20
0.39
1.00
1.15
0.94
0.54
0.58
0.92
1.45
0.72
0.14
0.65
1.64
1.41
6.38
0.86
1.77
0.83
0.89
1.13
0.63
0.98
7.91
1.05
1.30
1.34
0.76
1.12
0.59
6.63
0.42
2.03
1.60
1.47
0.63
0.26
0.21
35
0.47
1.20
0.93
0.91
0.95
1.08
1.09
1.38
32
13
21
22
20
17
16
10
0.28
2.62
0.76
0.49
0.48
0.91
0.57
1.89
0.41
1.44
0.65
0.93
1.07
0.47
0.72
32
1
20
27
28
16
25
3
30
5
23
14
8
29
21
Only countries for which data were available are included. The rankings based on the scale-adjusted incidator
were almost the same as those based on simple ratios for the number of PhD graduates and the number of
researchers in the higher education sector. A score above one indicates a stronger performance than expected
for the size of the input (e.g., for HERD), whereas a score below one indicates the opposite.
Computed by Science-Metrix using Scopus (Elsevier) and Eurostat data
Final Report
Analytical Report 2.3.2
The case of Luxembourg
Luxembourg was the country most often identified, by the robust regression technique, as an outlier with regards
to R&D expenditures. To investigate this case in more detail, a robust regression analysis was performed between
the number of publications of countries and their GERD, which combines three of the previous indicators on
R&D expenditures (i.e., HERD, BERD and GOVERD). The regression was performed on the pooled dataset, as
the estimation of the confidence interval on the slope is not important in this case.
Figure 4A shows the regression line between the log of the number of publications and the log of the GERD of
countries. The robust regression technique identified 10 outliers in the dataset (highlighted in orange in Figure 4A),
all of which belong to Luxembourg (i.e., one datapoint for each year in the 2000−2009 period). According to an
ERAWATCH report on Luxembourg’s research system and policies, the country’s GERD is low relative to the
EU27 (Alexander, 2008); it actually ranks 19th among EU27 member states). The current results indicate that its
R&D output in terms of peer-reviewed publications, considering its GERD, is lower than would be expected based
A
B
Robust R2 = 0.95
Slope = 0.82
Publications
Log(Publications)
on the general pattern observed for the 42 selected countries.
y = 9E-141e 0.16x
R2 = 0.93
Log(GERD)
Year
Figure 4
Robust regression between the scientific output (number of publications [FRAC])
and GERD of countries (A) and trend in the publication output of Luxembourg
(B), 2000−2009
Source:
Computed by Science-Metrix using Scopus (Elsevier) and Eurostat data
When the analysis is performed independently on HERD, BERD and GOVERD, similar findings are observed for
BERD and, to some extent, for GOVERD, but not for HERD (data not shown). In fact, with HERD, none of
Luxembourg’s datapoints is an outlier. Thus, the lower productivity of Luxembourg in terms of its number of
publications produced per currency unit (i.e., Euro) investment in R&D (based on the GERD, including all three
sources), is not attributable to a higher education sector that is less efficient at converting R&D inputs into R&D
outputs. In fact, Luxembourg ranks within the top 10 among selected countries for the size of its scientific output
relative to HERD (see Table IV).
Since the business sector is less oriented than the higher education sector towards producing scientific publications,
this observation is likely due to the stronger than usual contribution of the business sector—or, conversely, to the
smaller than usual contribution of the higher education sector—to R&D expenditures in Luxembourg (see Figure
5A; Luxembourg’s datapoints are highlighted in orange).
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Analytical Report 2.3.2
The case of Luxembourg (Continued)
For instance, Luxembourg has the largest average ratio of BERD to GERD (85%, as opposed to an average of
about 54% for EU27 countries) and the lowest average ratio of HERD to GERD (3%, as opposed to an average
of about 26% for EU27 countries) among the 42 selected countries for the 2000−2009 period.
A
HERD
Log(HERD)
B
Threefold increase in HERD between
2008 (13.2 millions of PPS at 2000
prices) and 2009 (40.2 millions)
Robust R2 = 0.96
Slope = 0.89
Log(GERD)
Year
Figure 5
Robust regression between the HERD and GERD of countries (A) and trend in
the HERD of Luxembourg (B), 2000−2009
Source:
Computed by Science-Metrix using Scopus (Elsevier) and Eurostat data
An investigation of the relationships between the R&D input indicators selected in this study and the GERD of
countries also revealed that the size of Luxembourg’s population of researchers in the higher education sector,
which is linked with HERD, contributes to its lower productivity in terms of its number of publications produced
per currency unit (i.e., Euro) of overall investment in R&D. Indeed, Luxembourg systematically has fewer
researchers in the higher education sector than would be expected given its overall expenditures in R&D (i.e., its
GERD); all datapoints available for Luxembourg were identified as outliers, and they all fall below the regression
A
Robust R2 = 0.84
Slope = 0.67
Researchers in the HES
Log(Res. in the HES)
lines between the number of researchers in the higher education sector and the GERD of countries (Figure 6A).
B
Log(GERD)
y = 1E-280e 0.32x
R2 = 0.92
Year
Figure 6
Robust regression between the number of researchers in the higher education
sector and the GERD of countries (A) and trend in the number of researchers of
Luxembourg (B), 2000−2009
Source:
Computed by Science-Metrix using Scopus (Elsevier) and Eurostat data
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Analytical Report 2.3.2
Final Report
The case of Luxembourg (Continued)
In fact, Luxembourg’s lag appears to be stronger with respect to researchers in the higher education sector than
with respect to HERD. Indeed, the most recent datapoint for the year 2009 is no longer an outlier for the latter
indicator (compared to the former, for which all datapoints are outliers). Interestingly, the limited access to
qualified researchers had previously been identified has a weakness of Luxembourg’s research system (Alexander,
2008).
This led, in the years 2000, to several policy actions aimed at resource mobilisation by the Luxembourg
government. Among these was the creation of a national university in 2003, the opening of visas to researchers
from new member states and the easing of requirements for issuing visas to other researchers (Alexander, 2008).
These policies appear to have been effective, as Luxembourg’s population of researchers and its scientific
production grew exponentially in 2000−2009 (Figure 4B and Figure 6B), in conjunction with a rapid growth in
HERD (with a threefold increase seen between 2008 and 2009; Figure 5B). In fact, all three indicators experienced
a stronger growth rate than Luxembourg’s GERD, which has been increasing linearly (data not shown). Although
Luxembourg was the lowest ranked country in the EU27 in 2009 for the share of GERD allocated to the higher
education sector (i.e., HERD), it came closer to the EU27 level of about 28% in 2009, having increased it from
0.2% in 2000 to 9% in 2009 (data not shown). Similarly, while it was the highest ranked country in the EU27 in
2009 for the share of GERD allocated to the business sector, its share declined significantly from 93% in 2000 to
74% in 2009, coming closer to the EU27 level of about 53%. In addition, among the datapoints for Luxembourg
in Figure 4A, Figure 6A and Figure 6A, the most recent are also the closest to the regression line. Thus, it can
safely be concluded that the research system in Luxembourg has begun to close the gap with the other countries of
the ERA in terms of publication output.
3.1.2.3
Regression analysis for investigating the innovation capability of countries in relation
to the size of their science base
The number of high-tech patent applications to the EPO is one of the four indicators (the others
are the three VCI indicators) that are the least correlated with the number of publications. This is
not surprising, as all four indicators are conceptually more related to the business sector, and
thus to innovation, than to the higher education sector, which is clearly more oriented towards
publishing research results. Nevertheless, the correlation between the number of publications and
the number of high-tech patent applications to the EPO is still important (R = 0.84 based on
group means to ensure the independence of observations) and statistically significant (p <
0.001).
Assuming a linear model of innovation whereby research occurs upstream of invention, a
regression analysis was used to investigate whether the innovation capability of countries (i.e.,
the capacity to produce inventions from a given amount of research) increases (analogously to
“economies of scale”), decreases (analogously to “diminishing returns”) or remains stable (i.e.,
isometric scaling) as the size of their science base increases. In this case, the number of
pubications is the explanatory variable and the number of high-tech patent applications to the
EPO is the response variable (Figure 7). Although the slope of the regression line is above 1, it
cannot be concluded that the innovation capability of countries increases with the size of their
science base, as the 95% confidence interval of the slope overlaps with the value of one, which is
indicative of isometric scaling. Thus, based on the current results, it is not possible to reject the
hypothesis that the innovation capability of countries increases linearly with the size of their
science base. Given the small system size used for this analysis (N = 41 countries; data was
missing for one of the selected countries), this finding should be considered preliminary and used
with care.
33
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Analytical Report 2.3.2
Log(Publications)
Robust R2 = 0.69
Slope = 1.08
95% CI = [0.82 - 1.35]
Log(High-tech patent applications to the EPO)
Figure 7
Robust group mean regressions between the technological output (number of
high-tech patent applications to the EPO) and the scientific output (number of
publications [FRAC]) of countries, 2000−2009
Source:
Computed by Science-Metrix using Scopus (Elsevier) and Eurostat data
Of course, both small and large countries can deviate from the general tendency of the system
(i.e., from the regression line) as a result of various factors that may affect their research and
innovation systems (e.g., resources, policies, culture). For example, Germany, which has one of
the largest levels of scientific production (ranked 5th out of the 41 countries shown in Figure 7),
ranks 7th for its ratio of patent applications to scientific publications, whereas Luxembourg, which
has one of the smallest levels of scientific production (ranked 38th), ranks 4th for its ratio of
patent applications to scientific publications. In the case of Luxembourg, divergence from the
general tendency of the system is easily explained by the fact that it has, as shown above, a
higher-than-usual share of its GERD allocated to the business sector. Thus, assuming a linear
model of innovation, Luxembourg’s innovation may have relied heavily on the knowledge bases of
other countries in the past. However, it was demonstrated above that the research system in
Luxembourg has begun to close the gap with the other countries in the ERA in terms of
publication output.
3.1.3 Regression analysis for investigating the productivity of NUTS2 regions in
terms of publication output per unit of the most relevant R&D input
indicators
Since data were only available for four R&D input indicators at the NUTS2 level (i.e., HERD,
BERD, GOVERD, and the number of researchers in the higher education sector), the exploratory
factor analysis was not performed again, and it was assumed that the four indicators were again
highly collinear. Thus, to investigate the productivity of NUTS2 regions in terms of outputs (i.e.,
publications) per unit of these four R&D input indicators, the same approach was applied to the
regions as that applied at the country level (see Section 3.1.2).
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Analytical Report 2.3.2
Again, there appear to be significant “diminishing returns” in terms of publication output with
increases in BERD and GOVERD. “Diminishing returns” are also significant, although more
moderate, in HERD at the NUTS2 level. Similarly to observations at the country level,
“diminishing returns” are strongest with respect to BERD, followed by GOVERD and HERD (Figure
8). Finally, there are significant but moderate “economies of scale” in terms of publication output
with respect to the number of researchers in the higher education sector (Figure 8). It should be
noted that the R&D input indicators that have the strongest allometric relationship with the
number of peer-reviewed publications are also those which explain the least variation in the
scientific production of NUTS2 regions, namely BERD (robust R2 = 0.54) and GOVERD (robust R2
= 0.67). These variables are therefore less relevant to the analysis of the scientific output of
NUTS2 regions; this is expected, given that they are less tightly linked, conceptually, with this
Robust R2 = 0.86
Slope = 0.84
95% CI = [0.81 - 0.88]
Log(Publications)
Log(Publications)
type of output than HERD and the population of researchers in the higher education sector.
Robust R2 = 0.67
Slope = 0.69
95% CI = [0.63 - 0.74]
Log(Researchers in the HES)
Log(Publications)
Log(Publications)
Log(HERD)
Robust R2 = 0.82
Slope = 1.08
95% CI = [1.03 - 1.13]
Robust R2 = 0.54
Slope = 0.57
95% CI = [0.51 - 0.64]
Log(GOVERD)
Log(BERD)
Figure 8
Robust group mean regressions between the scientific output (number of
publications [FRAC]) of NUTS2 regions and selected R&D input indicators,
2000−2009
Source:
Computed by Science-Metrix using Scopus (Elsevier) and Eurostat data
3.2
PUBLICATION PATTERNS OF COUNTRIES ACROSS SCIENTIFIC FIELDS
To investigate the factors behind the publication patterns of countries across scientific fields, the
relationship between scientific concentration by research area (i.e., percentage of output by field)
and concentration of the relevant R&D input indicators by research area (e.g., percentage of
HERD by field) was investigated using regression analysis. The rationale behind this analysis is
that if a country allocates 50% of its HERD to a given field, its should publish roughly 50% of its
scientific output in the area. It should be noted that, as was observed above when all scientific
fields were combined, the scientific output of countries (in its raw form; i.e., not expressed as a
percentage) still correlates strongly with each of the R&D input indicators (in their raw form) for
which data were available in each of the fields analysed.
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Data on R&D input indicators were only available for the following fields of S&T (FOS; see OECD,
2002B):




Agricultural sciences
Engineering and technology
Medical and health sciences
Natural sciences
These data were only available at the country level, and the amount of data was sufficient for
analysis of only two R&D expenditure indicators, namely HERD and GOVERD, as well as the
number of researchers in the higher education sector (the data covers the 2000−2009 period).
HERD and the number of researchers in the higher education sector were among the most highly
correlated with the scientific production of countries (see Section 3.1.1).
Data on the number of publications of countries for the above areas were available from the
bibliometric data produced by Science-Metrix (2011) for DG Research (the data covers the
2000−2009 period). They were obtained by matching the four above areas, as defined in the
revised FOS classification in the Frascati Manual (OCDE, 2007B), to the fields and subfields of
science found in Science-Metrix’s ontology (http://www.science-metrix.com/OntologyExplorer).
The match is as follows:




Agricultural sciences = Agriculture, Fisheries & Forestry (field level);
Engineering and technology = Engineering (field level), plus the following subfields:
Computer Hardware & Architecture, Networking & Telecommunications, Energy, Materials,
Nanoscience & Nanotechnology, Optoelectronics & Photonics, Architecture, Building &
Construction;
Medical and health sciences = Biomedical Research, Clinical Medicine, and Public Health &
Health Services (field level);
Natural sciences = Biology, Chemistry, Earth & Environmental Sciences, Mathematics &
Statistics and Physics & Astronomy; plus the following subfields: Bioinformatics, Medical
Informatics, Artificial Intelligence & Image Processing, Computation Theory & Mathematics,
Distributed Computing, Information Systems, Software Engineering (field level).
For each area, the relationship between the concentration of output (% of a country’s publication)
in the corresponding area and the concentration of each R&D input indicator (e.g., % of a
country’s HERD) in the same area was investigated using regression analysis. Given that the
structure of the dataset was the same as that used in Section 3.1.2, the regressions were fitted
using a between-effects model, and the robust regression coefficients were estimated by means
of S-estimators. It should be noted that the variables (which are proportions) are not
log-transformed, as it was not required. Thus, regression coefficients in this section should not be
interpreted in terms of isometric/allometric scaling. Additionally, a Pearson correlation coefficient
was computed for each pair, and the significance of the correlation was assessed using a Bartlett
test. Since the variables were not perfectly normal, a Spearman correlation coefficient was also
computed.
Prior to performing the regression analysis, an EFA was performed using IPA factoring to study
the correlation structure among the selected variables (i.e., proportion of papers, HERD, GOVERD
and researchers in the higher educaton sector by field), combining all four areas in the analysis
(i.e., each country has up to four datapoints per indicator and year; data not shown). This
analysis indicated that a single factor was significant and explained 52% of the variance in the
dataset. The concentration of HERD and the number of researchers in the higher education sector
had the strongest factor loading with the first factor (correlation coefficient of, respectively, 0.93
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and 0.95) and were followed by the concentration in the publication output (R = 0.55) and in the
GOVERD (R = 0.22). This indicates collinearity between the concentrations of HERD and the
number of researchers in the higher education sector. It also shows that these two variables are
only moderately correlated with the concentration in the peer-reviewed scientific publications of
countries, but to a greater extent than GOVERD. This is not surprising, as the former variables
are more directly linked with this type of output than the latter variable.
Table V shows the result of the analysis of the relationship between the percentage of
publications of countries in a given field and the percentage of their population of researchers in
the higher education sector in the corresponding field for each of the four areas considered. The
results indicate that a concentration of this type of human resources contributes, to a large
extent, to explaining the pattern of publication of countries in the medical and health sciences,
but not in the other areas. Indeed, more than 50% of variation in the level of concentration in
number of publications across countries in the medical and health sciences can be accounted for
by concentration in the number of researchers in the same area (R2 = 0.58). In this area, the
concentration of output increases by one percentage point, with each additional percentage point
in the concentration of input (regression coefficient = 0.91; 95% confidence interval = [0.55 –
1.27]).
Table V
Robust group mean regressions between the concentration in the number of
publications (FRAC) of countries and the corresponding concentration in their
number of researchers in the higher education sector by field of science,
2000−2009
Field of Science & Technology
Agricultural sciences
Engineering and technology
Medical and health sciences
Natural sciences
Source:
N
31
31
31
31
Pearson
Spearman
Coef. p -value
0.35
0.06
0.45
0.01
0.71
0.00
0.31
0.09
Coef.
0.39
0.24
0.77
0.21
Robust regression
Coef. 95% Conf. Interval
0.35
[0.07 - 0.64]
0.18
[-0.01 - 0.38]
0.91
[0.55 - 1.27]
0.27
[-0.14 - 0.67]
R2
0.47
0.09
0.58
0.23
Computed by Science-Metrix using Scopus (Elsevier) and Eurostat data
In the other areas, the statistics that were produced indicate that the relationship between these
variables are subtle and do not adequately explain the observed patterns of variation. The
absence of clear relationships between these variables is exemplified by looking at extreme cases
of a high concentration of output combined with a low concentration of researchers, and viceversa. For instance, Ireland and Norway each have a concentration of output that is above the
average for the countries considered (i.e., 6.7% and 6.1%, respectively, compared to an average
of 3.5%) but a concentration of researchers well below the average (i.e., 1.8% and 2.2%,
respectively, compared to an average of 4.6%) in the agricultural sciences. On the other hand,
Romania and Latvia each have a concentration of output well below the average for the countries
considered (i.e., 0.3% and 1.6%, respectively, compared to an average of 3.5%) but a
concentration of researchers near or above the average (i.e., 4.4% and 6.3%, respectively,
compared to an average of 4.6%) in the area. In engineering and technology, Latvia and Cyprus
have a concentration of output above the average (i.e., 35% and 32%, respectively, compared to
an average of 24%), whereas their concentration of researchers is below the average (i.e., 16%
and 14%, respectively, compared to an average of 22%). At the other end of the spectrum,
Croatia and the Czech Republic have a concentration of output below the average (i.e., 16% and
17%, respectively, compared to an average of 24%), whereas their concentration of researchers
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is above the average (i.e., 29% and 32%, respectively, compared to an average of 22%). Finally,
in the natural sciences, Romania and Bulgaria have a concentration of output above the average
(i.e., 53% and 40%, respectively, compared to an average of 31%), whereas their concentration
of researchers is below the average (i.e., 8% and 10%, respectively, compared to an average of
20%). On the contrary, Luxembourg and Ireland have a concentration of output below the
average (i.e., 20% and 21%, respectively, compared to an average of 24%), whereas their
concentration of researchers is above the average (i.e., 26% and 28%, respectively, compared to
an average of 22%). As explained in the discussion (Section 5.2), these numbers should not be
used to compare the performance of countries by field.
Table VI shows the result of the analysis of the relationship between the percentage of
publications of countries in a given field and the percentage of their HERD in the corresponding
field for each of the four areas considered. The results for the concentration of HERD are highly
similar to those observed for the concentration of researchers in the higher education sector. This
is not surprising, as both indicators are highly correlated. For instance, the analysis indicates that
the concentration of R&D expenditures in the higher education sector accounts for 58% of the
variation in the concentration in the number of publications across countries in the medical and
health sciences, which is exactly the same as was observed with the concentration of researchers
in the higher education sector. Also, as was the case with the concentration of researchers in the
higher education sector, the concentration of output increases by about one percentage point
with each additional percentage point in the concentration of HERD (regression coefficient =
0.86; 95% confidence interval = [0.55 – 1.18]).
Table VI
Robust group mean regressions between the concentration in the number of
publications (FRAC) of countries and the corresponding concentration in their
HERD by field of science, 2000−2009
Field of Science & Technology
Agricultural sciences
Engineering and technology
Medical and health sciences
Natural sciences
Source:
N
34
34
34
34
Pearson
Coef. p -value
0.28
0.11
0.57
0.00
0.56
0.00
0.22
0.22
Spearman
Coef.
0.35
0.38
0.62
0.20
Robust regression
Coef. 95% Conf. Interval
0.20
[-0.03 - 0.43]
0.15
[0.01 - 0.30]
0.86
[0.55 - 1.18]
0.27
[-0.06 - 0.61]
R2
0.35
0.14
0.58
0.20
Computed by Science-Metrix using Scopus (Elsevier) and Eurostat data
In the other areas, the statistics that were produced again indicate that the relationship between
these variables are subtle and that they do not adequately explain the observed patterns of
variation. For example, Ireland and Norway each have a concentration of output above the
average for the countries considered (i.e., 6.9% and 6.4%, respectively, compared to an average
of 3.6%) but a concentration of HERD well below the average (i.e., 2.6% and 4.6%, respectively,
compared to an average of 5.8%) in the agricultural sciences. On the other hand, Romania and
Latvia each have a concentration of output well below the average for the countries considered
(i.e., 0.4% and 1.3%, respectively, compared to an average of 3.6%) but a concentration of
HERD above the average (i.e., 7.4% and 7.7%, respectively, compared to an average of 5.8%) in
the area. In engineering and technology, Latvia and Cyprus have a concentration of output above
the average (i.e., 33% and 32%, respectively, compared to an average of 23%), whereas their
concentration of HERD is below the average (i.e., 20% and 11%, respectively, compared to an
average of 26%). At the other end of the spectrum, Iceland and the Czech Republic have a
concentration of output below the average (i.e., 13% and 17%, respectively, compared to an
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average of 23%), whereas their concentration of HERD is above the average (i.e., 40% and 38%,
respectively, compared to an average of 26%). Finally, in the natural sciences, Romania and
Bulgaria have a concentration of output above the average (i.e., 54% and 40%, respectively,
compared to an average of 31%), whereas their concentration of HERD is below the average (i.e.,
15% and 17% compared to an average of 25%). On the contrary, Luxembourg and Ireland have
a concentration of output below the average (i.e., 19% and 21%, respectively, compared to an
average of 31%), whereas their concentration of HERD is near or above the average (i.e., 26%
and 34%, respectively, compared to an average of 25%). Again, these numbers should not be
used to compare the performance of countries by field (see Discussion, Section 5.2).
Table VII shows the result of the analysis of the relationship between the percentage of
publications of countries in a given field and the percentage of their GOVERD in the corresponding
field for each of the four areas considered. The results for the concentration of GOVERD indicates
that it does not adequately explain the observed patterns of variation in the publication output of
countries in any of the fields considered. Although the strength of the relationships between the
scientific output of countries and their GOVERD were much larger when expressed in absolute
terms (both overall and by field, data not shown), this finding is not as surprising as it was for
HERD and the number of researchers in the higher education sector, as R&D output in the form of
peer-reviewed publications is not as important in the government sector as it is in the education
sector.
Table VII
Robust group mean regressions between the concentration in the number of
publications (FRAC) of countries and the corresponding concentration in their
GOVERD by field of science, 2000−2009
Field of Science & Technology
Agricultural sciences
Engineering and technology
Medical and health sciences
Natural sciences
Source:
N
31
31
31
32
Pearson
Coef. p -value
0.25
0.17
0.38
0.03
0.33
0.07
0.26
0.16
Spearman
Coef.
0.36
0.10
0.27
0.36
Robust regression
Coef. 95% Conf. Interval
0.12
[0.06 - 0.17]
-0.02
[-0.13 - 0.10]
0.45
[-0.17 - 1.07]
0.24
[0.05 - 0.43]
R2
0.25
0.04
0.16
0.02
Computed by Science-Metrix using Scopus (Elsevier) and Eurostat data
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4 KEY FINDINGS OF THE CROSS-CUTTING ANALYSIS OF
SCIENTIFIC OUTPUT VS. OTHER STI INDICATORS
This section provides an overview of the key findings of the Methods & Results section (Section
3). These findings relate to the two following aims of the study:
1. Examining the factors behind the publication outputs and productivity (i.e., the efficiency
2.
with which entities are converting research inputs into research outputs) of countries and
NUTS2 regions, as revealed through an analysis of scientific production (Section 4.1); and
Examining the factors behind the production patterns of countries, as revealed through an
analysis of scientific concentration (by research area), across scientific fields (Section 4.2).
In total, 17 R&D input indicators distributed across four categories (i.e., R&D Investment and
Expenditure, Human Resources, Innovation and Research Infrastructures) were considered,
although some were not available for analysing NUTS2 regions and the production pattern of
countries by scienfic field. The bibliometric indicator that was used to improve the understanding
of differences between countries’ and NUTS2 regions’ scientific output, productivity and
concentration was the total number of publications as measured using Scopus. The dataset
included 42 countries and 291 NUTS2 regions for which data were available, and the period
covered by the dataset extended from 2000 to 2009.
4.1
PUBLICATION OUTPUT
NUTS2 REGIONS
AND PRODUCTIVITY OF COUNTRIES AND
Factor analysis was used to identify the main dimensions explaining the patterns of variation
among selected STI indicators and the publication output of countries (Section 4.1.1), whereas
regression analysis was used for investigating the productivity of countries and NUTS2 regions in
terms of outputs (i.e., publications) per unit of the most relevant STI indicators (i.e., R&D input
indicators) (Section 4.1.2 and 4.1.3). Section 4.1.2 also presents the results of a regression
analysis aimed at investigating whether the innovation capability of countries (i.e., the capacity to
produce inventions from a given amount of research) changes as the size of their science
production increases.
4.1.1 Factor analysis for identifying the main dimensions (i.e., factors) among
selected STI indicators and the publication output of countries
Exploratory Factor Analysis (EFA) was used to identify the most relevant STI indicators to study
the patterns of variation in the publication output of countries.

After extensive analyses, of the 17 STI indicators, 13 were deemed relevant to the analysis.
Twelve indicators are R&D input indicators useful for studying the productivity of countries
and NUTS2 regions in terms of outputs (i.e., publications) per unit of an explanatory variable.
They are distributed across three categories, as follows.
R&D Investment and Expenditure
−
−
−
40
HERD: Higher Education Expenditure on R&D;
GOVERD: Government intramural Expenditure on R&D;
BERD: Business Expenditure in R&D;
Analytical Report 2.3.2
Final Report
Human Resources
−
−
−
−
−
Researchers in the Higher Education Sector: Number of researchers (both
genders in all fields) in the higher education sector;
HRST with Tertiary Education: Number of human resources (both genders in all
fields; 15 to 74 years) in science and technology (HRST) with tertiary education
(employed);
PhD Students: Number of PhD students (both genders in all fields) participating in
tertiary education (ISCED 97: Level 6);
PhD Graduates: Number of PhD graduates (both genders in all fields) from
tertiary education (ISCED 97: Level 6);
Foreign Students in Tertiary Education: Number of foreign students (both
genders in all fields) participating in tertiary education (ISCED 97: Levels 5 and 6);
Innovation
−
−
−
−

Employment in Technology and Knowledge-Intensive Sectors: Employment
in technology and knowledge-intensive sectors (all NACE activities; all occupations).
VCI (Expansion & Replacement): Venture Capital Investments (VCI) for
expansion & replacement stage;
VCI (Buyout): VCI for buyout; and
VCI (Early Stage): VCI for early stage.
The last indicator is another measure of R&D outputs and is useful for studying the innovation
capability of countries as the size of their scientific output increases. It falls in the innovation
category of indicators and is defined as follows:
−

High-Tech Patent Applications to the EPO: Number of high-tech (total) patent
applications to the EPO.
Based on EFA, the most relevant STI indicators as well as the selected R&D output indicator
(i.e., the number of publications) could be adequately summarised using a single factor; a
single variable (the primary factor) explained 83% of the variance in the dataset.

In fact, the 13 STI indicators presented above were highly correlated, although to a lesser
extent in the case of VCI indicators and the number of high-tech patent applications to the
EPO, with the number of peer-reviewed publications of countries (all correlation coefficients
greater than 0.72, or greater than 0.89 if VCI indicators and that on patents are excluded);
the 12 most relevant R&D input indicators are highly collinear.
4.1.2 Regression analysis for investigating the productivity of countries in terms
of publication output per unit of the most relevant R&D input indicators
The results of a regression analysis on the productivity of countries in terms of outputs (i.e.,
publications) per unit of the most relevant STI indicators (i.e., R&D input indicators) are
presented in Section 4.1.2.1. Based on this analysis, countries are ranked based on their
scientific productivity (Section 4.1.2.2). Finally, the results of a regression analysis aimed at
investigating whether the innovation capability of countries (i.e., the capacity to produce
inventions from a given amount of research) varies as the size of their science base increases is
presented in Section 4.1.2.3.
4.1.2.1
Economies and diseconomies of scale in scientific production
Although there is strong multicollinearity in the dataset (i.e., redundant information among the
selected R&D input variables), there can exist slight differences in the way countries allocate R&D
spending across sectors (e.g., higher education, government, private) and resources (e.g.,
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human resources, infrastructure). It is therefore of interest to investigate how the publication
output of countries scale relative to individual R&D input indicators. This was achieved through
regression analysis.

“Diminishing returns”, whereby an increase in a production factor (i.e., an R&D input
indicator), while holding all others constant, yields lower per-unit returns (i.e., peer reviewed
publications per unit of the R&D input indicator), were observed with the following R&D input
indicators: BERD, GOVERD and the number of students participating in a PhD program. It was
also observed for VCI indicators, which is not surprising as two of these are captured in
BERD.

“Economies of scale”, whereby an increase in a production factor (i.e., an R&D input
indicator), while holding all others constant, yields higher per-unit returns (i.e., peer reviewed
publications per unit of R&D input indicator), were observed with the following R&D input
indicator: employment in technology and knowledge-intensive services, which includes the
education sector and all occupations (i.e., professionals, technicians and other occupations).
4.1.2.2
Comparative analysis of the scientific productivity of countries
The performance of countries in terms of productivity (i.e., published output per unit of an R&D
input indicator) was measured for the three R&D input indicators that correlate the most with the
number of publications of countries (i.e., HERD, the number of PhD graduates and the number of
researchers in the higher education sector).

The country that showed the strongest performance in terms of productivity when considering
all three dimensions was Luxembourg.

Other countries that fared well included Russia, Slovenia, the Netherlands and Belgium.

Countries that showed the weakest performance in terms of productivity were Latvia,
Lithuania, Portugal, Estonia, and Austria.
A case study on Luxembourg was performed, as it is the country most often identified as an
outlier with regards to R&D expenditures. To study this case in more detail, an analysis was
performed between the number of publications of countries and their GERD, which combines the
three main categories of R&D expenditures (i.e., HERD, BERD and GOVERD).

Luxembourg is one of the least productive countries when taking into account all three
sources of R&D expenditure (i.e., HERD, BERD and GOVERD).

The lower productivity of Luxembourg in terms of its number of publications produced per
currency unit (i.e., euro) of GERD is not attributable to a higher education sector that is a less
efficient at converting R&D inputs into R&D outputs.

In fact, Luxembourg ranks within the top 10 among selected countries for the size of its
scientific output relative to HERD.

The weaker productivity of Luxembourg is most likely due to the stronger than usual
contribution of the business sector (85% of GERD, compared to an average of 54% for EU27
countries) or, conversely, the smaller than usual contribution of the higher education sector
(3% of GERD, compared to an average of 26% for EU27 countries) to GERD, as the former
sector is less oriented towards publishing the results of scientific research.

Also, Luxembourg systematically has fewer researchers in the higher education sector than
would be expected, given its GERD.
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Final Report
Recent actions taken by the Luxembourg government appear to have been effective in
increasing its population of researchers, its HERD and its scientific production relative to its
GERD.

Luxembourg has begun to close the gap with the other ERA countries in terms of publication
output.
4.1.2.3
Regression analysis for investigating the innovation capability of countries in relation
to the size of their science base
Assuming a linear model of innovation whereby research occurs upstream of invention, a
regression analysis was used to investigate whether the innovation capability of countries (i.e.,
the capacity to produce inventions from a given amount of research) increases (analogously to
“economies of scale”), decreases (analogously to “diminishing returns”) or remains stable (i.e.,
isometric scaling) as the size of their science base increases.

Based on this analysis, it is not possible to reject the hypothesis that the innovation capability
of countries remains stable as the size of their science base increases.

Countries with both small or large production performed well in terms of their innovation
capability.

Here, Luxembourg performs strongly, ranking 4th for its ratio of patent applications to
scientific publications despite its very small scientific output (it is ranked 38th for its number
of publications).

The strong performance of Luxembourg in this respect is easily explained by the fact that it
has, as shown above, a higher than usual share of its GERD allocated to the business sector.

Thus, Luxembourg’s innovation may have relied more heavily on the knowledge bases of
other countries in the past.
4.1.3 Regression analysis for investigating the productivity of NUTS2 regions in
terms of publication output per unit of the most relevant R&D input
indicators
Since data were only available for four R&D input indicators at the NUTS2 level (i.e., HERD,
BERD, GOVERD and the number of researchers in the higher education sector), the EFA was not
performed again, and it was assumed that the four indicators were again highly collinear. Thus,
to investigate the productivity of NUTS2 regions in terms of outputs (i.e., publications) per unit of
these four R&D input indicators, the same approach was applied as that used at the country level.

“Diminishing returns” in terms of publication output per unit of a given R&D input indicator
are confirmed for BERD and GOVERD.

“Diminishing returns” appear to be stronger for BERD than GOVERD.

Whereas the scientific output appeared to scale linearly with HERD at the country level,
moderate “diminishing returns” are confirmed at the NUTS2 level. Because the regression
coefficient for HERD at the country level was below 1 (0.93) and its 95% confidence interval
only slightly overlapped with the value of 1, indicative of isometric scaling, diminishing
returns might have been confirmed at the country level given a larger system size.

“Diminishing returns” are strongest with respect to BERD, followed by GOVERD and HERD.
The observed order in the intensity of “diminishing returns” in terms of publication output for
these three R&D expenditure indicators does not come as a surprise, as the tradition to
publish scientific results in peer-reviewed journals is strongest in the academic sector,
followed by the government and private sectors. In fact, the private sector is oriented
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Analytical Report 2.3.2
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towards development rather than research and secrecy as opposed to making results publicly
available.

Regarding the number of researchers in the higher education sector, although the result at
the country level did not indicate either “diminishing returns” or “economies of scale”,
significant and moderate “economies of scale” are confirmed at the NUTS2 level. Because the
latter result is based on a much larger sample size, it is considered more reliable.

From these findings, it seems that R&D expenditures indicators are associated with
“diminishing returns” in terms of publication output, whereas some human resource indicators
appear to be associated with “economies of scale”.

The results for the three expenditure indicators as well as for researchers in the higher
education sector can be considered more reliable, as they could also be examined at the
NUTS2 level with better sample sizes. This is due to the fact that the NUTS2 system is larger
than the country system at the European level (i.e., there are more NUTS2 regions than
countries within the ERA), such that there are more datapoints at the NUTS2 level to study
the European system. Given the small system sizes used for the remaining indicators, the
findings should be considered preliminary and used with care.
4.2
PUBLICATION PATTERNS OF COUNTRIES ACROSS SCIENTIFIC FIELDS
To investigate the variations in publication patterns of countries across scientific fields, the
relationship between scientific concentration by research area (i.e., percentage of output by field)
and the concentration of the relevant R&D input indicators, again by research area (e.g.,
percentage of HERD by field), a regression analysis was performed.
Data on R&D input indicators were only available for the following fields of science and
technology:




Agricultural sciences
Engineering and technology
Medical and health sciences
Natural sciences
These data were only available at the country level, and a sufficient amount of data for analysis
was only available for two R&D expenditure indicators, namely HERD and GOVERD, as well as the
number of researchers in the higher education sector.
For each area, the relationship between the concentration of output (% of a country’s publication)
in the corresponding area and the concentration of each R&D input indicator (e.g., % of a
country’s HERD) in the same area was investigated using regression analysis. The findings are as
follows:

The concentration of researchers and R&D expenditures in the higher education sector
accounted for 58% of the variance in the concentration of publications across countries in the
medical and health sciences.

In the medical and health sciences, where the relationship is the strongest, the concentration
of output increases by one percentage point with each additional percentage point in the
concentration of input for both R&D input indicators (i.e., the number of researchers in the
higher education sector and HERD).

In the other areas, the statistics that were produced indicate that the relationship between
these variables (i.e., the number of researchers in the higher education sector and HERD) are
subtle and that they do not adequately explain the observed patterns of variation.
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These results are astonishing, as the number of researchers in the higher education sector
and HERD (in their raw form, not expressed as percentages) explained much of the variation
in the number of peer-reviewed publications of countries (in its raw form) when all fields were
combined, as well as within each of the fields. Refer to Section 5.2 for a discussion of
hypotheses that could explain these findings.

The results for the concentration of GOVERD indicates that it does not adequately explain the
observed patterns of variation in the publication output of countries in any of the fields
considered. As R&D output in the form of peer-reviewed publications is not as important in
the government sector as it is in the education sector, this finding is not as surprising as it
was for HERD and the number of researchers in the higher education sector.
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5 DISCUSSION
An important aspect of the assessment of R&D performance that is often overlooked in
bibliometric studies is the link between R&D inputs and outputs, such as papers and patents. For
instance, bibliometric indicators do not inform on the driving factors that make some
countries/regions more efficient in certain scientific domains. This report adds a highly meaningful
level of analysis to the bibliometric data collected so far for the European Commission’s
Directorate-General for Research & Innovation (DG Research) by performing a cross-cutting
analysis of scientific output versus other STI indicators, such as R&D investments. This study’s
main objectives were to investigate:
3.
4.
the factors behind the publication outputs and productivity (i.e., the efficiency with which
entities are converting research inputs into research outputs) of countries/regions, as
revealed through an analysis of scientific production (Section 5.1); and
the factors behind the production patterns of countries, as revealed through an analysis of
scientific concentration (by research area), across fields of science (Section 5.2).
In total, 17 R&D input indicators distributed across four categories (i.e., R&D Investment and
Expenditure, Human Resources, Innovation and Research Infrastructures) were considered,
although some were not available for analysing NUTS2 regions and the production patterns of
countries by scientific field. The bibliometric indicator that was used to improve the understanding
of differences between countries’ and NUTS2 regions’ scientific output, productivity and
concentration was the total number of publications, as measured using Scopus. The dataset
included 42 countries (i.e., ERA countries plus a few comparables) and 291 NUTS2 regions for
which data were available, and the period covered by the dataset extended from 2000 to 2009.
5.1
PUBLICATION OUTPUT
NUTS2 REGIONS
AND PRODUCTIVITY OF COUNTRIES AND
Factor analysis was used to identify the main dimensions explaining the patterns of variation
among selected STI indicators and the publication output of countries (Section 5.1.1), whereas
regression analysis was used for investigating the productivity of countries and NUTS2 regions in
terms of outputs (i.e., publications) per unit of the most relevant STI indicators (i.e., R&D input
indicators) (Section 5.1.2). Section 5.1.2 also presents the results of a regression analysis aimed
at investigating whether the innovation capability of countries (i.e., the capacity to produce
inventions from a given amount of research) changes as the size of their science production
increases.
5.1.1 Factor analysis for identifying the main dimensions (i.e., factors) among
selected STI indicators and the publication output of countries
Exploratory Factor Analysis (EFA) was used to identify the most relevant STI indicators for
studying the patterns of variation in the publication output of countries. After extensive analyses,
it was found that 15 of the selected indicators could be adequately summarised using a single
factor; a single variable (the primary factor) explained 83% of the variance in the dataset. The
high level of multicollinearity observed in the dataset does not come as a surprise, as all of the
indicators considered are, to varying extent, intrinsically linked with the total R&D expenditures of
countries (i.e., GERD). Indeed, as a country increases its investment in R&D, it is likely to gain
more resources (e.g., human resources, infrastructure) in S&T, such that other STI indicators are
expected to correlate positively with the GERD. Of course, countries may differ in the way they
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allocate R&D spending across sectors (e.g., higher education, government, private), resources
(e.g., human resources, infrastructure) and fields (e.g., natural sciences, engineering and
technology), creating variations in the strength and slope of the relationships between the GERD
and
each of the
selected
STI indicators. Consequently, even though there
is strong
multicollinearity in the dataset (i.e., redundant information among the selected STI indicators),
an investigation of the relationship between individual STI indicators and the publication output of
countries and NUTS2 regions can shed light on the complex factors that make some countries
more efficient at converting R&D inputs into outputs.
Thirteen of the 17 selected indicators were deemed relevant to the subsequent analysis of
countries’ and NUTS2 regions’ publication output. Twelve of these are R&D input indicators that
can be used to study the productivity of countries and NUTS2 regions in terms of outputs (i.e.,
publications) per unit of an explanatory variable. They are distributed across three categories and
are as follows:
R&D Investment and Expenditure



HERD: Higher Education Expenditure on R&D;
GOVERD: Government intramural Expenditure on R&D;
BERD: Business Expenditure in R&D;
Human Resources





Researchers in the Higher Education Sector: Number of researchers (both genders in all
fields) in the higher education sector;
HRST with Tertiary Education: Number of human resources (both genders in all fields; 15
to 74 years) in science and technology (HRST) with tertiary education (employed);
PhD Students: Number of PhD students (both genders in all fields) participating in tertiary
education (ISCED 97: Level 6);
PhD Graduates: Number of PhD graduates (both genders in all fields) from tertiary
education (ISCED 97: Level 6);
Foreign Students in Tertiary Education: Number of foreign students (both genders in all
fields) participating in tertiary education (ISCED 97: Levels 5 and 6);
Innovation




Employment in Technology and Knowledge-Intensive Sectors: Employment in
technology and knowledge-intensive sectors (all NACE activities; all occupations).
VCI (Expansion & Replacement): Venture Capital Investments (VCI) for expansion &
replacement stage;
VCI (Buyout): VCI for buyout; and
VCI (Early Stage): VCI for early stage.
The last indicator is another measure of R&D outputs and is useful for studying the innovation
capability of countries as the size of their scientific output increases. It falls in the innovation
category of indicators and is defined as follows:

High-Tech Patent Applications to the EPO: Number of high-tech (total) patent
applications to the EPO.
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5.1.2 Regression analysis for investigating the productivity of countries and
NUTS2 regions in terms of publication output per unit of the most
relevant R&D input indicators
This section first discusses the results of a regression analysis aimed at investigating the
productivity of countries in terms of outputs (i.e., publications) per unit of the most relevant STI
indicators (i.e., R&D input indicators) (Section 5.1.2.1). Based on this analysis, it subsequently
ranks countries based on their scientific productivity (Section 5.1.2.2). Finally, the section ends
with a discussion of the results of a regression analysis aimed at investigating whether the
innovation capability of countries (i.e., the capacity to produce inventions from a given amount of
research) varies as the size of their science base increases (Section 5.1.2.3).
5.1.2.1
Economies and diseconomies of scale
The cross-linking of R&D inputs with outputs from an econometric perspective has increased in
the past decades, as governments operate on increasingly tight budgets and seek ways to
maximise returns on investments, particularly as accountability for public spending has become a
primary issue for residents who expect to get the most value for their tax dollars (OECD, 2008).
Most studies of economies and diseconomies of scale in scientific production have been performed
with a view to providing evidence-based policy advice that will improve the allocation and
management of resources in the research sector, with the ultimate goal of improving efficiency
(i.e., productivity) (Bonaccorsi and Daraio, 2005). These studies have used various methods to
measure research productivity in S&T systems, including regression analysis (e.g., the knowledge
production function, as in Griliches, 1979) and the production frontier approach (i.e., Stochastic
Frontier Analysis [SFA] and Data Envelopment Analysis [DEA]), at a number of units of analysis,
from the organisational level (Pandit, Wasley and Zach, 2009; Xia and Buccola, 2005) to the
country level (Meng, et al., 2006; Rousseau and Rousseau, 1997; Sharma and Thomas, 2008;
Wang and Huang, 2007).
This study adds to the growing knowledge base on the factors behind the scientific productivity
(i.e., the efficiency with which entities are converting research inputs into research outputs) of
countries and NUTS2 regions by reporting on the results of a regression analysis performed using
the most comprehensive dataset on STI indicators that is currently available at the national and
regional levels for the ERA.
In investigating the impact of individual R&D input indicators on the production of countries and
NUTS2 regions (i.e., the output variable), it was necessary to determine whether two variables
scale linearly (i.e., an isometric pattern, wherein there is no change in the ratio, productivity, as
one variable increases) or whether one variable scales exponentially relative to the other (i.e., an
allometric pattern, wherein there is a change in ratio, productivity, as one variable increases).
Allometry was investigated using log-log linear regressions. When the slope of the regression line
was significantly smaller than 1, it was concluded that there were “diminishing returns”, whereby
an increase in a factor of production (i.e., an R&D input indicator), while holding all others
constant, yielded lower per-unit returns (i.e., peer-reviewed publications per unit of the R&D
input indicator). Alternatively, when the slope of the regression line was significantly greater than
1, it was concluded that there were “economies of scale” whereby an increase in a factor of
production (i.e., an R&D input indicator), while holding all others constant, yielded higher per-unit
returns (i.e., peer-reviewed publications per unit of the R&D input indicator). When the 95%
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confidence interval of the slope overlapped with the value of 1, it was concluded that there was
no significant allometric scaling.
Economies of scale in terms of publication output at the country level were observed with
employment in technology and knowledge-intensive services, which includes the education sector
and all occupations (i.e., professionals, technicians and other occupations). Regarding the
number of researchers in the higher education sector, the result at the country level suggested
isometric scaling. However, there appears to be moderate economies of scale in terms of
publication output, as the number of researchers in the higher education sector increases at the
NUTS2 regional level. Since the latter result is based on a much larger system (i.e., there are
more NUTS2 regions than countries within the ERA), it is considered more reliable (no data were
available for employment in technology and knowledge-intensive services at the NUTS2 level).
Potential
mechanisms for
explaining the
increased
productivity of human capital
(i.e.,
employment in technology and knowledge-intensive services and researchers in the higher
education sector) as a country’s or NUTS2 region’s pool of human resources increases include, for
example, the diversification and sharing of complementary expertise and competencies, as well
as an increase in the specialisation and division of labour. Bonaccorsi and Daraio (2005) also
found preliminary evidence of economies of scale as the size of teams in laboratories increases.
In terms of policy implications, the authors asserted that specific policies regarding the growth of
laboratories within institutions would be required if economies of scale were to be realised
through the creation of mega organisations. This is because the relevant unit through which the
mechanisms behind economies of scale would operate, with respect to human resources, is the
laboratory.
Significant diminishing returns in terms of publication output were observed for five out of six
R&D input indicators related to expenditures (i.e., GOVERD, BERD and all three VCI indicators).
Diminishing returns appear to be stronger with the VCI indicators followed by BERD and GOVERD,
whereas there appears to be isometric scaling with HERD. However, diminishing returns are also
likely with respect to HERD. Indeed, the slope for HERD at the country level was below 1 (i.e.,
0.93), with the 95% confidence intervals only slightly overlapping the value of 1, indicative of
isometric scaling. In addition, diminishing returns with respect to HERD, as well as for BERD and
GOVERD, were confirmed at the level of NUTS2 regions; no data were available for VCI indicators
at this aggregation level). Again, diminishing returns appeared strongest with respect to BERD,
followed by GOVERD and HERD.
The observed order in the intensity of diminishing returns in terms of publication output for
BERD, GOVERD and HERD does not come as a surprise, as the tradition to publish scientific
results in peer-reviewed journals is strongest in the academic sector, followed by the government
and private sectors. In fact, the private sector is mostly oriented towards development rather
than research, and there are stronger incentives to keep results secret. The fact that the
regression coefficients for the VCI indicators are closer to that of BERD than to those of GOVERD
and HERD is not unexpected, as both early- and expansion- stage venture capital are captured in
BERD, making them somewhat redundant with this indicator.
A potential mechanism for explaining the observed reduction in the productivity of countries and
NUTS2 regions in terms of publications produced per euro investment in R&D would be that the
number of researchers of a given entity (i.e., its units of production) does not increase as rapidly
as its financial resources; the maximum production capacity of a an entity’s researchers would
therefore be reached in spite of increasing financial resources. Interestingly, the population of
researchers in the higher education sector was shown to scale less rapidly than GERD (see
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Luxembourg’s case study, Section 3.1.2) and HERD (data not shown) at the NUTS2 level. A
rationale for awarding smaller grants to a larger population of researchers logically follows from
this explanation in order to increase the productivity of a given entity as the size of its financial
resources increases. However, research teams operating on larger budgets are more likely to
carry out projects that could not be conducted with less financial resources (e.g., the Human
Genome Project [HGP]). Although the cost of publications produced from such projects likely
exceeds that of publications produced by less expensive projects, they likely impact a
much
larger community. In turn, these publications are likely to have a higher scientific impact (as
measured by citations), such that entities with larger R&D expenditures might generally have a
higher scientific impact per euro investment in R&D. In fact, Hung, Lee, and Tsai (2009) found
that human capital carries more weight in terms of the quantity of academic research, whereas
capital accumulation plays a more important role in the citation impact of academic research.
Future research efforts will look at how citations scale relative to HERD at the country and NUTS2
levels to test the above hypothesis.
The publication output of countries may also show very slight diminishing returns with respect to
one of the selected R&D input indicators in the human resource category, namely the number of
students participating in a doctoral program. A hypothesis that could potentially explain
diminishing returns as the size of a population of PhD students increases would be a concomitant
decrease in the amount of researchers per student if the population of PhD students scales more
rapidly than the population of researchers in the higher education sector. Indeed, if students
receive, on average, less supervision from their thesis director, it seems likely that fewer students
would successfully publish the results of their research. However, based on the data analysed in
this study, the population of PhD students appears to scale at about the same rate as the
population of researchers in the higher education sector (i.e., regression coefficient = 1.03 and
95% confidence interval = [0.87 – 1.19], graph not shown).
Within the research policy context, any attempt at increasing the productivity of a country or
region should take account of the complex interplay between the many factors that contribute to
their efficiency, such as the country’s or region’s characteristics (e.g., funding schemes and
disciplinary portfolios) and development stages (Leydesdorff and Wagner, 2009). For instance,
Archambault and Larivière (2010) showed that the average cost per publication was higher in the
basic medical sciences compared to the humanities. Thus, if a larger share of its R&D budget is
allocated to the humanities, a country might exhibit stronger productivity in terms of publications
per dollar investment in R&D but lesser productivity in terms of received citations per dollar
investment in R&D than another country.
5.1.2.2
Comparative analysis of the scientific productivity of countries
Countries undoubtedly vary in regards to the efficiency with which they transform R&D inputs into
scientific publications. Using
the
log-log
regressions used
in analysing
economies and
diseconomies of scale, scale-adjusted indicators of scientific productivity were computed for the
three R&D input indicators that showed the strongest correlation with the number of publications
of countries (i.e., HERD, number of PhD graduates and number of researchers in the higher
education sector).
When all three dimensions were considered, the country that showed the strongest performance
in terms of productivity was Luxembourg. On the other hand, Luxembourg did not perform well
at all in terms of publication ouput in relation to GERD, which covers BERD, GOVERD and HERD. A
case study on Luxembourg was performed, as it is the country most often identified as an outlier
with regards to R&D expenditures. The results indicated that the outlying behavior of Luxembourg
is attributable to a larger than usual share of BERD within its total R&D expenditures. Thus, in
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Analytical Report 2.3.2
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spite of the smaller than usual share of its total R&D expenditures that is allocated to the higher
education sector (i.e., HERD), it has a good level of productivity given the absolute size of its
HERD. In addition, the study’s findings indicate that recent actions taken by the Luxembourg
government appear to have been effective in increasing its population of researchers, its HERD
and its scientific production relative to its GERD. Thus, Luxembourg has clearly begun to close the
gap with the other countries of the ERA in terms of publication output.
Countries that fare well in terms of productivity based on the above three measures also included
Russia, Slovenia, the Netherlands and Belgium. Countries that showed the weakest performance
in terms of productivity were Latvia, Lithuania, Portugal, Estonia and Austria.
5.1.2.3
Regression analysis for investigating the innovation capability of countries in relation
to the size of their science base
Assuming a linear model of innovation whereby research occurs upstream of invention, a
regression analysis was used to investigate whether the innovation capability of countries (i.e.,
the capacity to produce inventions from a given amount of research) increases (analogously to
“economies of scale”), decreases (analogously to “diminishing returns”) or remains stable (i.e.,
isometric scaling) as the size of their science base increases. In this case, the number of
publications is the explanatory variable and the number of high-tech patent applications to the
EPO is the response variable.
Based on this analysis, it is not possible to reject the hypothesis that the innovation capability of
countries remains stable as the size of their scientific production increases. Countries with both
small or large levels of production performed well in terms of their innovation capability. Here,
Luxembourg’s performance is strong, as it ranks 4th for its ratio of patent applications to scientific
publications despite its very small scientific output (it is ranked 38th for its number of
publications). The strong performance of Luxembourg in this respect is easily explained by the
fact that it has, as shown above, a higher than usual share of GERD allocated to the business
sector. Therefore, Luxembourg’s innovation may have relied more heavily on the knowledge
bases of other countries in the past. However, the high efficiency of Luxembourg in converting
knowledge into innovation might decrease in the future, given its rising HERD and scientific
output in combination with its stable BERD.
5.2
PUBLICATION PATTERNS OF COUNTRIES ACROSS SCIENTIFIC FIELDS
To investigate the factors behind the publication patterns of countries across scientific fields, the
relationship between scientific concentration by research area (i.e., percentage of output by field)
and concentration of the relevant R&D input indicators by research area (e.g., percentage of
HERD by field) was investigated using regression analysis. The rationale behind this analysis is
that if a country allocates 50% of its HERD to a given field, its should publish roughly 50% of its
scientific output in this area.
The analyses could be performed for countries using three R&D input indicators (i.e., HERD,
GOVERD, and the number of researchers in the higher education sector) for the following fields of
science and technology (FOS; see OECD, 2002B):




Agricultural sciences
Engineering and technology
Medical and health sciences
Natural sciences
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The results show that the concentrations of researchers and R&D expenditures in the education
sector by field of science do not explain the concentration of peer-reviewed publications by field
of science in three out of the four areas considered; they explain only about 60% of the variation
in the concentration of peer-reviewed publications in the medical and health sciences. This comes
as a surprise, as these R&D input indicators (in their raw form; i.e., not expressed as
percentages) explained much of the variation in the number of peer-reviewed publications of
countries (in its raw form) when all fields were combined, as well as within each of the fields. The
absolute amount of publications produced by a country is highly dependent on the absolute
amount of money it spends on R&D, as well as on the absolute size of its population of
researchers in the higher education sector irrespective of the field. However, the current results
indicate that the concentration of a country’s scientific output in a given field and its specialisation
in the given field cannot easily be predicted based on its percentage of HERD or its population of
researchers in the higher education sector that is allocated to the given field.
It is difficult to explain such findings, as they are counterintuitive. Other factors besides the
concentration in HERD and in the population of researchers in the higher education sector can
probably explain the patterns of variation in the concentration of R&D outputs by scientific field,
such as differences in the publication habits of researchers across fields and/or countries. For
example, it is well known that conference proceedings are used proportionately more frequently
by researchers in engineering, in particular in the computer sciences, than in the natural sciences
or the medical and health sciences, which rely more heavily on journal articles to disseminate the
results of scientific research (Lisée, et al., 2008). Because the publication outputs of countries
were measured using Scopus, which has a coverage of conference proceedings that is not as
comprehensive as its coverage of journal articles, variations across countries in the use and
coverage of conference proceedings in engineering could create distortions in the relationships
between the concentration of R&D inputs and outputs. Not only would these alterations affect the
field of engineering, they would also likely impact other areas, as an underestimation of the
percentage of outputs in a given field must be balanced out by a concomitant overestimation in
other areas (i.e., the sum of percentages across fields cannot exceed 100%).
A good example of disparities that could potentially be explained by the above hypothesis is the
very low ratio of the concentration of output to that of HERD in engineering and technology in
Iceland (0.32) compared to the average for the countries considered (0.87). This is
counterbalanced by Iceland’s larger than usual ratio in the medical and health sciences (i.e., 4.8
compared to an average of 2). However, it seems unlikely that only one factor could create
disparities as strong as those observed. Since the accuracy of most indicators increases as the
size of the system being measured increases, the selected indicators may carry more noise at the
field level, especially for smaller countries. As many of the strongest departures from the average
behaviour in the ratio of the concentration of output to that of input were observed for small
countries, noise may create potential distortions. Given the likelihood that various factors created
noise in the relationships between scientific concentration by research area and concentration in
the above input indicators, the results of the current study should not be used to compare the
performance (e.g., in terms of productivity) of countries by scientific field. Clearly, more data and
research are needed to interpret this study’s observations at the field level.
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Acknowledgments
The authors are grateful to Matthieu Delescluse and Carmen Marcus of DG research as well as to
Grégoire Côté and Guillaume Roberge of Science-Metrix for their thoughtful comments and
advices.
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59
European Commission
EUR 25968 - C
ross-Cutting Analysis of Scientific Publications versus other Science,
Technology and Innovation Indicators
Luxembourg: Publications Office of the European Union
2013 — I-II, i-vi, 58 pp — 21 x 29,7 cm
ISSN1831-9424
ISBN978-92-79-29836-3
doi:10.2777/12700
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KI-NA-25-968-EN-N
Investigations of existing relationships between R&D inputs and outputs
from an econometric perspective have increased in past decades in
response to the challenges faced by governments. As they are operating
on increasingly tight budgets, governments are looking to maximise
returns on investments; furthermore, accountability for public spending
has become a primary issue for residents who expect to get the most
value for their tax dollars.
Most studies of economies and diseconomies of scale in scientific
production have been performed with a view to providing evidencebased policy advice that will improve the allocation and management of
resources in the research sector and, ultimately, enhance efficiency.
This study adds to the growing knowledge base on the factors driving
scientific productivity (i.e., the efficiency with which research inputs are
converted into research outputs) at the national and regional levels
by reporting on the results of an analysis performed using the most
comprehensive dataset on STI indicators that is currently available for
European Research Area (ERA) countries and NUTS2 regions.
Diminishing returns were observed for R&D investment and expenditure
indicators, whereas economies of scale were observed for human
resource indicators. These results are discussed in light of their
implications for research policy.
Studies and reports
doi:10.2777/12700