COMMISSION STAFF WORKING DOCUMENT Developing an indicator of innovation output Accompanying the document COMMUNICATION FROM THE COMMISSION TO THE EUROPEAN PARLIAMENT, THE COUNCIL, THE EUROPEAN ECONOMIC AND SOCIAL COMMITTEE AND THE COMMITTEE OF THE REGIONS Measuring innovation output in Europe: towards a new indicator
Date: 2013-09-13
Institutions: European Commission
Subjects: EU statistics, competitiveness, innovation, research and development, statistical method













EUR-Lex - 52013SC0325 - EN











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52013SC0325

COMMISSION STAFF WORKING DOCUMENT Developing an indicator of innovation output Accompanying the document COMMUNICATION FROM THE COMMISSION TO THE EUROPEAN PARLIAMENT, THE COUNCIL, THE EUROPEAN ECONOMIC AND SOCIAL COMMITTEE AND THE COMMITTEE OF THE REGIONS Measuring innovation output in Europe: towards a new indicator /* SWD/2013/0325 final */









Table of Contents
Executive summary.. 3
1. Introduction.. 7
1.1. Measuring innovation output 8
1.2. The selection criteria. 10
2. The simple composite indicator.. 12
3. data used.. 14
3.1. Ability of the economy to transform knowledge into
marketable innovations. 14
3.2. How the supply of skills feeds into the economic
structure. 15
3.3. Competitiveness of the knowledge-intensive sectors. 16
3.4. Employment dynamism of fast-growing firms in
innovative sectors. 18
3.4.1. Usage of sector-level data. 20
3.4.2. Imputation technique for missing values and wider
international comparability. 21
3.5. Overview of data used and reference periods. 24
3.5.1. The data used. 24
3.5.2. The reference periods. 25
4. Measuring country performance with the indicator.. 26
4.1. Score produced by the chosen indicator 26
4.2. Country-by-country analysis of performance. 29
5. Robustness analysis. 37
5.1. Conceptual and statistical coherence in the
framework. 37
5.2. Impact of modelling assumptions on the indicator
results. 40
5.3. Distance to the efficient frontier by Data
Envelopment Analysis (DEA) 45
6. Conclusion.. 48
Annex 1. Calculation of sectoral innovation coefficients. 49
Annex 2. Data collection for fast-growing firms. 56
Annex 3. Main options examined for the composite
indicator.. 57

Executive
summary 

This Commission Staff Working Document accompanies the Commission
Communication "Measuring innovation output in Europe: towards a new
indicator ", which presents an indicator to measure performance in
innovation output.  
The rigorous measurement of innovation is critical to underpin evidence-based
policy-making and for assessing the impact of policies and reforms. The European Council has given the European Commission
the mandate to develop a single innovation indicator in the context of the
Europe 2020 strategy, taking into account the commitment in the Innovation
Union flagship initiative to "launch the necessary work for the
development of a new indicator measuring the share of fast-growing innovative
companies in the economy" (commitment 34.b). 
There is widespread agreement among experts, Member States and Commission services that such an innovation indicator should be output-oriented, measure the innovation performance of a country and its capacity to
derive economic benefits from innovation, capture the dynamism
of innovative entrepreneurial activities, and be useful for policy-makers at EU and national
level. 
The proposed indicator will support policy-makers in establishing new or
reinforced actions to remove bottlenecks that prevent innovators from translating ideas into products and
services that can be successful on the market. Improved performance will contribute to
smart growth, in line with Europe 2020 and its Innovation Union flagship
initiative.[1] 
The proposed
indicator complements the Innovation Union Scoreboard (IUS),[2] and its Summary Innovation Index (SII), which assess how the various
strengths and weaknesses of Member States and the EU determine their overall
performance, against a broad set of 24 innovation indicators, including inputs,
throughputs and outputs. In addition, the Innovation Union Competitiveness
Report, also analyses innovation performance every two years. 
The indicator
in this Communication zooms in exclusively on innovation output and monitors a
reduced set of dimensions, including the contribution to job creation of
fast-growing firms. Given its complementarity with the IUS, it is planned that
the results of the proposed indicator are published simultaneously with those
of the IUS. 
Based on the conceptual framework defined by Eurostat for the
definition of quality indicators and state-of-the-art statistical analyses,
four principles were applied to examine feasible options. Those were: policy
relevance, data quality, international availability and cross-country
comparability, and robustness.[3] 
After a comprehensive analysis of various options, a
simple composite indicator is proposed in the Commission Communication " Measuring innovation output in Europe: towards a new indicator". It measures how Member States perform in innovation output, as shown
by four indicators from the outputs and firm activities
types in the Innovation Union Scoreboard, grouped in three components
(technological innovation, skills, and competitiveness of knowledge-intensive
goods and services), as well as a new component, capturing the employment dynamism
of fast-growing innovative firms, and proposed to fill in the placeholder (3.1.3.
"High-growth innovative firms") in the Innovation Union Scoreboard. 
Figure 0 below shows the proposed composite innovation indicator, presented
in detail in section 4 of this Commission Staff Working Document. 
Figure 0. The simple composite indicator zooming in
on innovation output
Countries’
scores for 2011 (red bars) and 2010 (crosses) with respect to the EU average (100
in 2010).
In 2011, the
components reflect the situation in 2009 (PCT), 2010 (DYN) or 2011 (KIA, COMP)
In 2010, they
are based on 2008 (PCT), 2009 (DYN) or 2010 (KIA, COMP) data
Source: Commission
calculations. 
The four components, which underpin the proposed innovation indicator,
are described in detail in sections 2 and 3 of the Commission Staff Working
Document. The Commission services examined the advantages and disadvantages of those
four components, compared to other options: 
·       
The first component, measuring the ability of the
economy to transform knowledge into marketable innovations, as shown by indicator
2.3.1 of the Innovation Union Scoreboard, counts the number of patent
applications per billion units of GDP. The most recent years available for this
indicator are 2008 and 2009. 
·       
The second component, capturing how the supply of
highly skilled people feeds into the economic structure of a country, is
defined as indicator 3.2.1 of the Scoreboard, which is measuring the number of
persons employed in knowledge-intensive activities (KIA). The most recent years
available for this indicator are 2010 and 2011. The Commission services consider
that the indicator provides a good measure of how a
highly skilled labour force feeds into the economic structure of  an innovation-driven economy. 
·       
Concerning the third component, on the
competitiveness of the knowledge-intensive sectors, the Commission services analysed in detail
various options for its sub-components (as reported in section 5.2) and
selected indicators 3.2.2 and 3.2.3 of the Scoreboard. This choice has the
drawback that both sub-components are based on different definitions, the goods
trade sub-component (indicator 3.2.2) focusing on the contribution to the trade
balance, and the services trade sub-component (indicator 3.2.3) on the export
share. Nonetheless, it presents a series of advantages such as not penalising countries with large trade deficits
and avoiding some counter-intuitive (e.g. low scores for the US and JP) as well as unstable rankings. 
A detailed analysis was
also performed relating to which weights should be best used to build a
mini-composite indicator, using both sub-components. The findings are reported
in section 3.3. Three different alternatives were tested, and a sensitivity
analysis was carried out. The choice was for integrating both sub-components
using equal weights. The reference years were aligned and set to 2010 and 2011.

·       
Finally, the fourth and last component of the
proposed indicator measures employment in fast-growing innovative enterprises,
and provides an indication of the innovation dynamism of fast-growing firms as
compared to all fast-growing business activities. The data used for the test
calculations for this component required considerable efforts from the Member
States. The component draws on Eurostat voluntary data collections for the
employment figures for 2009 and 2010 (see Annex 2), and 2006/8 and 2009/10 data
for the innovation coefficient (see Annex 1). International comparability is
more limited for this component and therefore the set of missing values was
imputed using the optimal approach, which was found to be the
Expected-Maximization (EM) algorithm described in section 3.4.2. 
The sector-specific
innovation coefficients used to compute this component reflect the level of
innovativeness of the sector and serve as a proxy for distinguishing innovative
enterprises. It should be noted that EU averages were used rather than
country-specific values and this implies that these sectoral innovation
coefficients will not reflect differences in the knowledge intensity or Community
Innovations Survey (CIS) scores across Member States. While this could be seen
as a weakness, it has also the benefit of defining a common reference of the
degree of innovation of each sector against which countries can be reliably
compared over time (see Annex 1 for more details). 
The option of computing
the innovation indicator without the fourth component was also examined in
further detail. However, a wide-ranging set of policy arguments and a
comprehensive set of technical analyses, which included inter alia a principal
component analysis and counterfactual simulations, supported its inclusion in
the proposed indicator (the results are presented in section 5.2). 
In order to refine the indicator and bring it to its full potential,
four areas were identified.
First, ensuring the improvement of data on fast-growing firms in innovative sectors,
in coverage and regular production, with a mandatory request for collection as
part of the amended Commission Regulation implementing the European Parliament
and Council Regulation on Structural Business Statistics, which will cover the
financial sector. Financial services are excluded at this stage but they are
relevant, given their pervasive function and impact on the economy. The
production of these data will also improve the alignment of the reference years
of the indicator. 
Second, analysing how
the data defining the innovation coefficients can be improved to ensure larger
sets of observations across sectors and over time, and how variations in
intensities across countries can be best captured. This includes sensitivity
analysis on the coefficients using new data from the biennial CIS and the
annual Labour Force Survey (LFS). 
Third, examining
whether and how: the data on the competitiveness of knowledge-intensive goods
and services could be improved; the skills component could be refined to
capture best the contribution of education, exploring its links with the
indicator performance; other statistics of the market success of innovations
could be considered. 
Finally, enlarging its
international dimension, through a wider collection of data on fast-growing
firms and joint work with the OECD on the international coverage of the
innovation coefficients, using comparable surveys in third countries. 
After setting the background (section 1), the Commission Staff
Working Document presents the proposed indicator (section 2) and the dataset to
build it up (section 3). It then displays the resulting ranking, alongside an
analysis of the performance of the Member States and their main international
competitors (section 4). Finally, it moves on to describe the comprehensive
robustness analysis carried out (section 5). 
 

1. Introduction

Investment in research and
innovation is a relevant determinant of the capacity of an economy to generate
smart growth, high-quality jobs and competitiveness. It must, however, be
accompanied by reforms to increase the efficiency and effectiveness of the
national innovation system to support business dynamics and the move towards a
transformation of the economy into a more innovative, knowledge-intensive and
productive one. 
The rigorous
measurement of innovation is critical to underpin evidence-based policy-making,
to evaluate investment in research and innovation (R&I), and to assess the
impact of policies and reforms. Furthermore, such measurements bolster the legitimacy
of public action and the use of public funds. However, experts agree that measuring the innovation capacity of an economy is
complex,[4] and requires choices as there is a myriad of objective difficulties
in capturing such a wide-ranging phenomenon with a single indicator. 
The proposed
indicator complements the Innovation Union Scoreboard (IUS),[5] and its Summary Innovation Index (SII), which assess how the various
strengths and weaknesses of Member States and the EU determine their overall
performance, against a broad set of 24 innovation indicators, including inputs,
throughputs and outputs. In addition, the Innovation Union Competitiveness
Report, also analyses innovation performance every two years. 
The indicator
in this Communication zooms in exclusively on innovation output and monitors a
reduced set of dimensions, including the contribution to job creation of
fast-growing firms. Given its complementarity with the IUS, it is planned that
the results of the proposed indicator are published simultaneously with those
of the IUS. 
The European Council gave the Commission the
mandate to develop an indicator in the context of Europe 2020,[6] to complement the R&D intensity target,[7] taking
into account the Innovation Union request that the Commission "launch
the necessary work for the development of a new indicator measuring the share
of fast-growing innovative companies in the economy". In March 2013, the Heads of State and Government requested a
discussion on innovation in October 2013, calling on the Commission to deliver
the indicator.[8]
To advise the
Commission on its formulation, a High-Level Panel of leading innovators and
economists was set up in 2010.[9] It prompted the Commission to engage in data collections on
fast-growing firms in innovative sectors, carried out by Eurostat. In parallel,
cooperation was undertaken with the OECD to develop sectoral innovation
coefficients. Discussions with Member States on the scope and definition of the
indicator took place in workshops, in October and December 2012, and in July
2013. 
The proposed indicator proposed in this supporting document was built
on a solid methodological basis, using state-of-the-art statistical analyses
and quality data, within the limits of current data availability.  
After setting the background (section 1), the document presents the
proposed indicator (section 2) and the dataset to build it up (section 3). It
then displays the resulting ranking, alongside an analysis of the performance
of Member States and their international competitors (section 4). It then moves
on to describe the comprehensive robustness analysis (section 5). 

1.1. Measuring innovation output 

The crisis and increasing globalisation have changed the
rules of the game. According to the literature, the economies, which have
nurtured their knowledge-base and lead in innovation, are those better placed
to wave the crisis and generate growth, jobs and competitiveness.[10] Innovation makes economies more resilient to economic downturns. 
Innovation output is wide-ranging and differs from sector to sector.
Measuring it entails quantifying the extent to which ideas for new products and
services, stemming from innovative sectors, carry an economic added value and
are capable of reaching the market. 
Therefore, it can be captured by more than one measure. After
exploring a broad set of options, the Commission opted for four IUS indicators,
from the outputs and firm activities
types in the Innovation Union Scoreboard, grouped into
three components (patents, employment in knowledge-intensive activities (KIA),
and competitiveness of knowledge-intensive goods and services), and a new
measure of employment in fast-growing firms of innovative sectors.
The patents component takes into account inventions that exploit the
knowledge generated by investing in R&D and innovation, and which can be
transformed into successful technologies. Similarly, the indicators of the
intensity of employment of skilled labour, in KIA and in fast-growing firms,
provide an indication of the orientation of the economy towards the production
of goods and services with innovation added value. Finally, the trade flows
associated with those commodities measure their capacity to reach global
markets.[11] 
The first component
of the indicator is technological innovation as measured by patents,
which account for the ability of the economy to transform knowledge into
technology. The number of patent applications per billion GDP is used as a measure
of the marketability of innovations.[12]. An
intrinsic bias in favour of countries relying more on international patents
than on national ones might occur. Alternative statistics such as triadic
patents from the OECD Patent Database were thus tested.

The second component of the indicator focuses
on how a highly skilled labour force feeds into the economic structure of
a country. Investing in people is one of the main challenges for Europe in the years ahead, as education and training provide workers with the skills for generating
innovations. This component captures the structural orientation of the economy
towards knowledge-intensive activities, as measured by the number of persons
employed in those activities in business industries over total employment. 
The third
component of the proposed indicator is the competitiveness of
knowledge-intensive goods and services. This is a fundamental dimension of
a well-functioning economy, given the close link between growth, innovation and
internationalisation. Competitiveness-enhancing measures and innovation strategies
can be mutually reinforcing for the growth of employment, export shares and
turnover at the firm level. This component is built integrating in equal
weights the contribution of the trade balance of high-tech and medium-tech
products to the total trade balance, and knowledge-intensive services as a
share of the total services exports of a country. It reflects the ability of an
economy, notably resulting from innovation, to export products with high levels
of value added, and successfully take part in knowledge-intensive global value
chains. 
Finally, the last component measures the employment in
fast-growing firms in innovative sectors. Sector-specific innovation
coefficients, reflecting the level of innovativeness of each sector, serve here
as a proxy for distinguishing innovative enterprises. The component reflects
the degree of innovativeness of successful entrepreneurial activities. The
specific target of fostering the development of fast-growing firms in
innovative sectors is an integral part of modern R&D and innovation policy.
Studies show that while there are fewer fast-growing innovative firms in the EU
than in the US, overall employment growth depends critically upon them given
that they generate directly or indirectly a disproportionally large share of
jobs, and can contribute to increased innovation investments during economic
downturns.[13] Moreover, it has been estimated that variations in firm growth
dynamics between the US and the EU may account for more than two thirds of the
EU's underperformance vis-à-vis the US in productivity growth in the recent
decades.[14]
The Scoreboard data were used for the proposed indicator
in two different ways: 
1.     
First, three indicators of the
"outputs" type (employment in knowledge-intensive activities,
contribution of medium- and high-tech products to the trade balance and
knowledge-intensive services exports as percentage of total service exports)
defined the skills and competitiveness dimensions of the proposed indicator. 
2.     
Second, one indicator from the "firm
activities" type of the Scoreboard, measuring PCT (Patent Cooperation
Treaty) patent applications, was used as a proxy of the ability of transforming
knowledge into marketable technology. 
Finally, the Commission services computed a new measure
intended to fill in the placeholder in the Scoreboard under the
"outputs" type (number 3.1.3. High-growth innovative firms),[15] reserved for an indicator reflecting the contribution of fast-growing
firms in innovative sectors to market dynamics, as foreseen by the Innovation
Union flagship initiative. It could thus be the 25th
Scoreboard indicator. 
The underlying data for all components is available in Table
1 and at the European Commission’s Innovation Union website.[16] 

1.2. The selection criteria 

There is widespread
agreement among experts, Member States' representatives and Commission services
on the need to develop an indicator which is output-oriented, measures the innovation performance of a country and its capacity to
derive economic benefits from it, captures the dynamism
of innovative entrepreneurial activities, and is useful for policy-makers at EU and national
level. 
The indicator should also be easy to understand, built on solid foundations, and cover different types
of innovation.[17] It should draw on representative,
comparable and validated data and rely on a robust methodology for its
construction.
Throughout this work, the international
standards for quality indicators put forward by Eurostat, OECD and IMF,[18] which are widely accepted in the economic literature, were taken as reference. 
A large set of options were comprehensively tested in the process of
defining this indicator.[19] The necessary data were put together and comprehensive calculations
were run to identify the option that best complied with the set of criteria
defined in this section. 
Based on the
conceptual framework defined by Eurostat,[20] the
following four principles were applied by the Commission services in its
analysis of the set of options for the innovation indicator.  
1.      Policy relevance. Focus was set on a
simple and intuitive interpretation, with sizeable and direct links to measured
facts. The indicator permits monitoring dimensions such
as IPR conditions, the upgrading of the skills demanded by the market in
knowledge-intensive and innovative sectors, the creation of a breeding ground
for trade in knowledge-intensive commodities, and framework conditions for
fast-growing firms. 
2.      Data quality. The availability of
timely, representative and validated time series, and the exploitation of all
available sources, was deemed essential. 
3.      International availability and cross-country comparability. The aim was to set the basis for an indicator suitable for
meaningful cross-country comparisons and benchmarking. 
4.      Robustness. Composite
indicators are used worldwide by a large number of actors, including
international organisations. Their construction requires such state-of-the-art validation and robustness analyses[21] that the picture produced enables
benchmarking and meets policy needs. 
A detailed analysis was performed on the various options for the innovation
indicator. A set of more stringent robustness tests and associated analyses has
been carried out for the selected option, namely the simple composite indicator.
Because of data limitations, criteria 2 and 3 could
only be met partially at this stage, and remain areas for future analysis. The
indicator relies on imputations for missing values and international
comparability, carried out in the fourth indicator component for four Member
States and international partners. Those imputations were tested for robustness
(section 3.4.2).

 
2. The simple composite indicator 

Work to develop the indicator departed from
the premise that the selected indicator of innovation output shall reflect the
objectives assigned to innovation policy in the context of the Europe 2020
strategy for smart, sustainable and inclusive growth. 
After a comprehensive analysis of all options, the Commission services
(Secretariat-General, and Directorates-General ECFIN, ENTR, Eurostat,[22] JRC and RTD) reached consensus
on a simple composite indicator zooming in on four components measuring innovation
output. Eurostat's role was
limited to providing advice on the statistics it collects. Three of those components
are from the "outputs" and "firm activities" types in the
Innovation Union Scoreboard (technological innovation, skills , and
competitiveness of knowledge-intensive goods and services) and there is a new
component, which captures the employment dynamism of fast-growing firms in
innovative sectors. This latter is proposed to fill in the existing placeholder
(number 3.1.3.) in the Innovation Union Scoreboard. 
The equation representing this indicator
is: 
 Box 1: Equation for the simple composite indicator zooming in on innovation output.[23] where: PCT = number of patent applications filed under the Patent Cooperation Treaty per billion GDP. KIA = employment in knowledge-intensive activities in business industries (including financial services) as % of total employment. , where: [24]   GOOD = contribution of medium and high-tech products exports to the trade balance; SERV = knowledge-intensive services exports as % of total service exports; DYN = employment in fast-growing firms of innovative business industries, excluding financial services: , where is the innovation coefficient of sector s, resulting from the product of Community Innovation Survey and Labour Force Survey scores for each sector at EU level;[25] and is the employment in fast-growing firms in sector s and country c.  are the weights of the component indicators (23, 18, 43, 15), fixed over time.[26] These are statistically computed in such a way that the component indicators are equally balanced.   
It is to be noted that the weights for the
indicator components are used as ‘scaling coefficients’ and not as
‘importance coefficients’, with the aim of arriving at composite
scores that are balanced in their underlying components. This implies taking a
first decision on the relative importance of the variables, e.g. two given
variables should be equally important. The corresponding nominal weights are
subsequently assigned to these two variables in such a way that they are of
truly equal statistical importance. The nominal weights might thus diverge from
50%-50%. This procedure aims to avoid that the two variables are equally
important in nominal terms but that statistically the index depends more on one
variable than on the other.[27] 

3. data
used  

The simple composite indicator proposed in
this supporting document is based to the maximum possible extent on existing
and internationally used definitions and variables and on the best data sources
available for its underlying components,[28]
be it national accounts, national business registers, European Union Labour
Force Survey (LFS), Community Innovation Survey, European Patent Office
Database, Commodity trade statistics, Balance of Payments. 
Composite indicators are used across the
board by a large number of public and private actors, including international
organisations.[29]
They have the advantage of permitting intuitive and straightforward comparisons
of countries in issues which would otherwise prove of a wide-ranging and
multifaceted nature, and avoiding the disadvantage of possibly offering a simplistic
picture of what is being measured. According to the authors of the Stiglitz
report, composite indicators may also hide non transparent normative stances
behind their weighting process[30].
Thus, their construction requires the
application of advanced validation and robustness analysis so that the picture
produced supports the derivation of sound analytical and policy conclusions, while
allowing to solidly benchmark relative performances. The battery of tests
carried out for the selected composite indicator is presented in section 5
below. 
Below, the supporting
document presents in detail the data used to construct the four components of
the proposed composite indicator, with particular attention to the dynamism
component, given the fact that this latter variable is a new construct, which fully
exploits the results from the ad hoc data collections on fast-growing
firms undertaken by Eurostat and from the workshops conducted with the Member
States. This component is intended to fill in the placeholder reserved in the Innovation
Union Scoreboard for an indicator on fast-growing firms. 

3.1. Ability of the economy to transform knowledge
into marketable innovations

The first
component, labelled as PCT, is indicator 2.3.1. of the Innovation Union
Scoreboard and counts the number of patent applications per billion GDP. The
numerator is defined as the number of patent applications filed, in
international phase, which name the European Patent Office (EPO) as designated
office under the Patent Cooperation Treaty (PCT).[31] Patent counts are based on the
priority date, the inventor's country of residence and fractional counts to
account for patents with multiple attributions. The denominator is the GDP in Euro-based
purchasing power parities. 
The most
recent years available for this indicator are 2008 and 2009, which are
considered in the calculation of the composite indicator for respectively 2010 and
2011. PCT data are also available for US, JP and the BRIC countries. However,
the composite indicator has not been computed for BRIC countries as it would introduce
too many missing data points (the same applies to the data for the dynamism
component, DYN, below). 
An intrinsic
bias in favour of countries relying more on international patents than on
national ones might occur. The work undertaken examined the possibility of
using triadic patents from the OECD Patent Database, instead of PCT patents.[32] Among the benefits of such
approach was the avoidance of the implicit "home bias" for the US in
the PCT data. The analysis carried out showed a very high correlation between
both indicators and stability in the final ranking, therefore that option was finally
dropped.[33]
The Commission will examine whether and how other statistics of the market
success of innovations could be considered in future analyses related to the
innovation indicator. 
 
Figure 1. Number of PCT patent applications
per billion GDP, PPP
Source:
Innovation Union Scoreboard 2013, indicator 2.3.1. (original source: EPO)

3.2. How the supply of skills feeds into the
economic structure 

The second component,
KIA, is indicator 3.2.1. of the Innovation Union Scoreboard and measures the
number of employed persons in knowledge-intensive activities (KIA) in business
industries as a percentage of total employment. The KIA component is calculated
from EU Labour Force Survey data, as all NACE Rev.2 industries at 2-digit level,[34] where at least 33% of
employment has a tertiary degree (ISCED5 or ISCED6). The most recent years
available for this indicator are 2010 and 2011, which are considered in the
calculation of the composite indicator for respectively 2010 and 2011. KIA data
are also available for the US and JP.
Figure 2. Employment in knowledge-intensive
activities in business industries as % of total employment
Source:
Innovation Union Scoreboard 2013, indicator 3.2.1. (original source: Eurostat)

3.3. Competitiveness of the knowledge-intensive sectors 

The third
component, named COMP, is made of indicators 3.2.2. and 3.2.3. of the
Innovation Union Scoreboard, and integrates in equal weights the contribution
of the trade balance of high-tech and medium-tech products to the total trade
balance and of knowledge-intensive services exports as a share of the total
services exports of a country. 
The first
part of the component, GOOD, measures the contribution of the trade balance of high-tech
and medium-tech products to the total trade balance. The contribution to the
trade balance is calculated as follows: (XMHT-MMHT)-(X-M)*[(XMHT+MMHT)/(X+M)], where
(XMHT-MMHT) is the observed trade balance for medium and high-tech products and
(X-M)*[(XMHT +MMHT)/(X+M)] is the theoretical trade balance (where X denotes
exports and M denotes imports of respectively MHT products and all products).
MHT exports include exports of the following Standard International Trade
Classification (STIC) Rev.3 products: 266, 267, 512, 513, 525, 533, 54, 553,
554, 562, 57, 58, 591, 593, 597, 598, 629, 653, 671, 672, 679, 71, 72, 731,
733, 737, 74, 751, 752, 759, 76, 77, 78, 79, 812, 87, 88 and 891. The
denominator is the value of the total trade balance.  
The most
recent years available for this indicator are 2010 and 2011, which are
considered in the calculation of the composite indicator for the years 2010 and
2011. GOOD data are also available for US, JP and the BRIC countries. 
Figure 3a. Contribution of medium and
high-tech products to trade balance
Source:
Innovation Union Scoreboard 2013, indicator 3.2.2. (original source: UN)
The second part
of the component, SERV, measures exports of knowledge-intensive services as captured
by the sum of credits in EBOPS (Extended Balance of Payments Services
Classification) 207, 208, 211, 212, 218, 228, 229, 245, 253, 260, 263, 272,
274, 278, 279, 280 and 284.  The denominator is the total services exports as
measured by credits in EBOPS 200.  
The most
recent years available for this indicator are 2010 and 2011, which are
considered in the calculation of the composite indicator for the years 2010 and
2011. The SERV data for 2011 come directly from Eurostat, as the Scoreboard
indicator only covers figures up to 2010. SERV data are not available for Norway
in 2010, so the 2009 value is considered. SERV data are also available for US,
JP and the BRIC countries. For CH, SERV data is not available for 2011, so the
2010 value has been imputed as the best proxy. Data from the Scoreboard for
Greece showed a large discrepancy in value between year 2010 (5.4%) and 2011
(54.2%). This was due to the fact that the value for sector 208 (freight
transport by sea) is not available for 2011, due to confidentiality
constraints. In order to calculate the SERV indicator, the 2011 value for EL was
thus imputed using the 2010 value for that Member State. 
Figure 3b. Knowledge-intensive services
exports as % of total service exports
Source:
Innovation Union Scoreboard 2013, indicator 3.2.3. for 2010, and Eurostat for
2011 
(original
sources for both years: UN/Eurostat)
Three different alternatives
were duly tested to weight the two components of COMP:   
1.     
First,
to use country-specific weights. Despite the fact that this would have been a
most valuable solution, in that case the weights for GOOD and SERV would need
balancing,[35]
else their relevant ratio of importance would not be obtained. Regrettably
country-specific weights cannot be balanced per se, as balancing
requires examining the variances across countries, which by definition cannot
be done with a single observation. This option was thus abandoned. 
2.     
Second,
to compute country-independent though well-balanced weights, so that the GOOD
and SERV variables would be in a ratio of about 1:4 in importance. This implied
aggregating both components linearly, using as weights the share of products
and services in the economy calculated at EU level and to normalise them to sum
up to one (i.e. 17% for GOOD and 83% for SERV), using gross value added at
basic prices from National Accounts data (10-branch breakdown according to NACE
Rev.2) to compute the share of manufacturing (sector C) and services (sectors G
to U) for each country in 2010. This option was finally discarded because,
although technically feasible, it risked attaching larger weight to the
services sector in countries in which knowledge-intensive services actually
represent a much smaller share of the economy than the production of
medium-tech and high-tech goods does.

3.      Finally,
to integrate with equal weights the contribution of exports of high-tech and
medium-tech products to the trade balance and of knowledge-intensive services
exports as a share of the total services exports of a country. This was chosen
as the final option as it reduces biases in favour of the competitiveness in
knowledge-intensive services. 
The
sensitivity analysis confirmed that alternatives 2 and 3 are largely equivalent
in terms of country rankings (see section 5). The result of combining the two
sub-components is shown in Figure 4 below.
Figure
4.
Combination of indicators GOOD and SERV into component COMP, using equal
weights
Source:
Commission calculations, based on Innovation Union Scoreboard 2013 
The most recent years available for COMP, as for
its sub-components, are 2010 and 2011. Because of its composition, COMP reflects the strengths of
countries with high scores in GOOD, such as Japan or Germany, and with a very good
performance in SERV, such as Luxembourg and Ireland. Countries with good
performance in both sub-components, rank naturally also well in COMP. This is
the case of the UK, France or the United States. For a sub-set of countries,
relative strengths and weaknesses in both sub-components lead to a number of countries
being located around the EU average. Beyond the four top performers, the differences
in score for the majority of the remaining countries are not particularly large.


3.4. Employment
dynamism of fast-growing firms in innovative sectors

The fourth
and last component of the composite indicator, DYN, is a measure of innovation
dynamism newly developed by the work of the Commission services. Commitment
34.b of the Innovation Union flagship initiative requested to measure "the
share of fast-growing innovative companies in the economy" and a
placeholder was reserved for such measure in the Innovation Union Scoreboard. 
This new
measure focuses on employment in fast-growing enterprises in innovative sectors.
Sector-specific innovation coefficients, reflecting the level of innovativeness
of each sector, serve here as a proxy for distinguishing innovative
enterprises. These coefficients are weighted with sectoral shares of employment
in fast-growing enterprises, providing an indication of the dynamism of fast-growing
firms in innovative sectors. 
The employment
data used for the calculation of this component comes from the ad hoc
data collections undertaken in 2011 and 2012.[36]
In statistical terms, it is calculated on the basis of a ‘basket’
of all business economic sectors, with the exception of financial economic
activities, characterised by their innovativeness and knowledge intensity,
weighted with the sectoral shares of employment in fast-growing enterprises.[37] 
The formula
representing this fourth component is: 
Equation 2. Component DYN (dynamism) of the composite
indicator 
where  is the innovation coefficient and  is the
number of employees in fast-growing enterprises in sector s and country c,
being . Note that in this formula the term  plays the role of a weight as.

This
indicator has been shown to be resistant to crisis-induced fluctuations in employment
growth. 
Based on the above definitions, the dynamism component is calculated
as follows: 
-        
Fast-growing enterprises are defined as
enterprises with average annualised growth in number of employees of more than
10 % a year, over a three-year period, and with 10 or more employees at
the beginning of the observation period (period of growth).[38] 
-        
The economic sectors included are the three-digit
NACE business economy sectors, excluding the financial sector (i.e. NACE Rev. 2
sections B-N & S95, excluding section K), as identified by the national statistical
office based on national business register data and based on the number of
employees in these enterprises.[39]

-        
Sectoral innovation coefficients are computed in
line with the methodology outlined in Annex 1 and weighted according to the
importance of the sectors in the economy in terms of high growth, measured as
the sector’s share of total employment in fast-growing enterprises. 
-        
The expected maximisation technique is used to
impute the data for four Member States EL, HR, LU, MT (no data available), as
well as for TR, IS, CH, US and JP. BRIC countries,[40] are not included because of missing
data for the KIA component. 

3.4.1. Usage of sector-level data 

In building the dynamism component for the indicator,
data at sectoral level was used, in order to take into account the different economic
structures of the Member States, and then those data were aggregated. In
particular, although the value of the component is communicated at the level of
the whole non-financial business sector,[41]
innovation coefficients and employment data are compiled at a fine-grained
sectoral level (NACE Rev.2 three-digit level) and then aggregated. 
This is linked to the fact that innovation does not develop at a
uniform speed within a given sector. Usually, it starts in one part of this
sector, and then diffuses to the whole sector and even to the rest of the
economy. This phenomenon is rarely measured, as it requires a degree of
granularity in statistics (NACE three-digit level) which is seldom used, not
least for confidentiality reasons. 
In this supporting document, the dynamism
component has been calculated using data at NACE three-digit level, instead of
the two-digit level commonly in use. Nonetheless, the decision
on the precise level of disaggregation of the sectoral data used for this
component is still to be decided with Eurostat, taking into consideration that
the quality and the amount of non-confidential data for
the purposes of dissemination is considered higher for the 2-digit level data.  
As presented in Annex 1, the innovation performance of the economic sectors is in fact measured by a set of sectoral innovation
coefficients reflecting each sector’s innovation intensity according to a
taxonomy which involves using Community Innovation Survey (CIS) and knowledge intensity (KIA)
data, the latter on the basis of the EU Labour Force Survey (LFS). The OECD elaborated the CIS and EU-LFS based scores used, in the framework of a contract financed by
the Commission. The list of coefficients was updated following the meetings with
Member States experts on 23 October and 13 December 2012, where agreement was
reached on this principle. 
The use of EU averages rather than country-specific values implies
that these sectoral innovation coefficients will not reflect differences in the
knowledge intensity or CIS score across Member States. While this could be seen
as a weakness, it has also the benefit of defining a common reference of the
degree of innovation of each sector against which countries can be reliably
compared over time (see Annex 1). 
In statistical terms, the dynamism component is calculated on the
basis of a ‘basket’ of all business economy sectors, with the
exception of financial economic activities, characterised by their innovativeness
and knowledge intensity, weighted according to the sectoral shares of employment in fast-growing enterprises, providing an indication of
the dynamism of innovative fast-growing firms. The
changes in the index over time will show the trend. 
All economic sectors of the non-financial business economy are
included in the component. However, the contribution of each sector depends on its
innovation intensity as defined by multiplying its degree of innovativeness by
its knowledge intensity.[42]

The top innovative sectors include among others R&D in natural sciences and engineering, software publishing, satellite
telecommunications activities, manufacture of pharmaceutical preparations, computer
programming, consultancy and related activities, wireless telecommunications
activities, manufacture of basic pharmaceutical products, architectural and
engineering activities and related technical consultancy.
Well-functioning and performing financial services are crucial to
the innovative capacity of an economy. Financial services have been excluded from
the indicator at this stage but they are considered relevant for the
measurement of innovation given their pervasive function and impact in the
non-financial economy. The contribution of the financial sector is furthermore
included in the other three components of the composite, i.e. in the
technological innovation (to a lesser extent), skills component and
competitiveness components.
The Commission services therefore underline the need to ensure the improvement of data on fast-growing
firms in innovative sectors, in coverage and regular production, with a
mandatory request for collection as part of the amended Commission Regulation
implementing the European Parliament and Council Regulation on Structural
Business Statistics, which will cover the financial sector. 

3.4.2. Imputation technique for
missing values and wider international comparability

For the purpose of this supporting document, the dynamism component is
based on two voluntary test data collections relating to four years (2008,
2009, 2010 and 2011), of which one (2010) is almost complete (with the
exclusion of EL, HR, LU and MT). The other reference years are covered for a
wide range of Member States.[43]

Unlike the Innovation Union Scoreboard based components, for which a
good level of international comparability beyond the EU is ensured, as
described above, for the dynamism component international comparability is more
limited. The actual capacity to provide data to
calculate the indicator on an international basis is constrained by two main factors.
First of all, while statistical business registers are available in
countries such as Brazil, Canada, New Zealand or the United States, a proper
register does not exist in other major global economies such as China, India or
Japan. In order to calculate the dynamism component for these countries, all
that can be used are firm-level data from different types of data sources, with
several representativeness and quality problems. For instance, the available
Chinese data only cover manufacturing enterprises while India provides
information at plant (not company) level, again mostly in manufacturing
industry.
Second, estimates of employment in fast-growing innovative
enterprises are based on European sector-specific innovation coefficients. 
Countries outside the EU use the International System of Industrial
Classifications (ISIC) or national classifications convertible to ISIC, in
order to organise economic data. Innovation coefficients thus need to be
reported in both NACE and ISIC for the calculation of the indicator to be
accurate outside the EU. The base classifications, i.e. ISIC4 and NACE Rev.2,
are identical (NACE Rev.2 added a set of sub-aggregates at the three-digit
level, which can be aggregated to ISIC4 three-digit level codes), but the data
currently produced by non-EU countries are not necessarily at ISIC4 three-digit
level and hence a methodology for converting such data to NACE Rev.2 still
needs to be developed.
However, it is standard practice to calculate composite indicators
by the statistical imputation of missing values. DG JRC
has applied a set of ten different imputation techniques from which the optimal
one is chosen based on a cross-validation test. For the
data considered here, the optimal imputation approach was found to be the Expectation-Maximization (EM) algorithm technique.[44]

This is an iterative procedure to find the maximum likelihood
estimates of the parameter vector by repeating the following steps. 
1.      Expectation "E-step": given a set of parameter estimates,
such as a mean vector and covariance matrix for a multivariate normal
distribution, the E-step calculates the conditional expectation of the
complete-data log likelihood given the observed data and the parameter
estimates. 
2.      Maximization "M-step": given a complete-data log
likelihood, the M-step finds the parameter estimates which maximize the
complete-data log likelihood from the E-step. These two steps are iterated
until the iterations converge. 
The imputation is thus carried out for component DYN for four EU Member
States, EL, HR, LU, MT, , as well as for TR, IS, CH, US and JP. BRIC countries
have not been included in the composite indicator because they also display
missing data for the KIA component. The implications of the imputation
procedure on countries ranking are tested in the sensitivity auditing of the
innovation composite (see section 5). 
 Figure 5. Employment in fast-growing firms in innovative sectors
as a % of total employment in fast-growing firms 
Source: Commission calculations
Figure 6. Number of employees in fast-growing firms 
as a share of the total number of employees, 2010
Source: Commission
calculations, using Eurostat data.

3.5. Overview of data used and
reference periods 
3.5.1. The data used 

Table 1 below presents the data used for
all the components of the selected indicator.
   ||   || PCT || KIA || GOOD || SERV || DYN 
 Country || Code || 2008 || 2009 || 2010 || 2011 || 2010 || 2011 || 2010 || 2011 || 2009 || 2010 
 EU || EU || 3.8 || 3.9 || 13.5 || 13.6 || 1.0 || 1.3 || 45.1 || 54.8 || 16.2 || 16.2 
 Belgium || BE || 3.5 || 3.7 || 14.6 || 14.8 || 1.5 || 2.4 || 41.3 || 42.9 || 18.3 || 16.8 
 Bulgaria || BG || 0.4 || 0.3 || 8.6 || 8.4 || -4.8 || -4.8 || 26.8 || 27.6 || 12.7 || 11.8 
 Czech Republic || CZ || 1.0 || 0.9 || 11.8 || 12.3 || 3.4 || 3.8 || 27.3 || 33.0 || 15.2 || 15.6 
 Denmark || DK || 7.6 || 7.0 || 15.8 || 15.6 || -3.8 || -2.8 || 63.3 || 65.8 || 22.0 || 19.2 
 Germany || DE || 7.2 || 7.4 || 15.3 || 15.1 || 7.8 || 8.5 || 56.7 || 57.2 || 18.8 || 18.3 
 Estonia || EE || 2.0 || 2.4 || 9.8 || 10.7 || -3.0 || -2.7 || 37.4 || 41.8 || 14.2 || 14.1 
 Ireland || IE || 2.9 || 2.8 || 19.5 || 19.8 || 2.4 || 2.6 || 73.1 || 75.7 || 16.7 || 19.2 
 Greece || EL || 0.4 || 0.4 || 10.9 || 11.3 || -4.2 || -5.7 || 54.2 || 54.2 || 14.7 || 14.8 
 Spain || ES || 1.4 || 1.4 || 11.5 || 11.8 || 2.6 || 3.1 || 21.6 || 29.9 || 15.2 || 15.5 
 France || FR || 4.0 || 4.2 || 13.8 || 14.4 || 4.8 || 4.7 || 32.6 || 37.8 || 19.2 || 18.2 
 Croatia || HR || 0.7 || 0.6 || 9.9 || 10.3 || 2.1 || 3.0 || 15.0 || 17.6 || 14.4 || 14.3 
 Italy || IT || 2.1 || 2.1 || 13.7 || 13.4 || 4.0 || 5.0 || 27.2 || 34.0 || 14.3 || 14.4 
 Cyprus || CY || 0.5 || 0.6 || 14.4 || 15.0 || 0.7 || 1.7 || 48.5 || 48.5 || 13.7 || 12.8 
 Latvia || LV || 0.8 || 1.2 || 9.6 || 9.1 || -5.0 || -5.4 || 35.3 || 36.5 || 12.7 || 12.6 
 Lithuania || LT || 0.5 || 0.3 || 8.7 || 9.0 || -1.1 || -1.3 || 13.7 || 14.7 || 11.7 || 12.7 
 Luxembourg || LU || 1.6 || 1.6 || 25.7 || 24.8 || -4.4 || -3.3 || 78.3 || 76.2 || 18.1 || 18.1 
 Hungary || HU || 1.4 || 1.5 || 12.8 || 13.1 || 5.9 || 5.8 || 26.5 || 28.9 || 15.9 || 17.8 
 Malta || MT || 1.1 || 0.3 || 15.8 || 16.4 || 3.2 || 0.9 || 13.6 || 21.4 || 14.9 || 14.5 
 Netherlands || NL || 6.5 || 6.2 || 15.2 || 14.9 || 0.5 || 1.7 || 26.3 || 31.0 || 17.2 || 16.4 
 Austria || AT || 4.6 || 5.1 || 14.4 || 14.0 || 2.6 || 3.2 || 22.2 || 25.3 || 17.4 || 15.3 
 Poland || PL || 0.4 || 0.5 || 9.1 || 9.3 || 0.4 || 0.9 || 26.1 || 32.5 || 12.9 || 13.7 
 Portugal || PT || 0.6 || 0.6 || 8.6 || 9.1 || -3.5 || -1.2 || 29.0 || 31.2 || 12.3 || 13.3 
 Romania || RO || 0.2 || 0.2 || 6.0 || 6.5 || 0.3 || 0.4 || 43.0 || 47.5 || 14.0 || 15.2 
 Slovenia || SI || 3.0 || 3.0 || 13.4 || 13.7 || 6.1 || 6.1 || 20.9 || 26.6 || 13.9 || 14.3 
 Slovakia || SK || 0.3 || 0.4 || 10.1 || 10.5 || 4.0 || 4.4 || 19.6 || 24.5 || 16.6 || 14.6 
 Finland || FI || 9.6 || 10.5 || 15.2 || 15.3 || 2.0 || 1.7 || 35.9 || 36.8 || 18.8 || 17.9 
 Sweden || SE || 10.6 || 10.5 || 17.1 || 17.4 || 1.8 || 2.0 || 38.7 || 41.6 || 20.6 || 20.4 
 United Kingdom || UK || 3.4 || 3.2 || 17.0 || 17.6 || 3.0 || 3.1 || 57.6 || 64.8 || 16.4 || 15.8 
 Turkey || TR || 0.8 || 0.9 || 4.8 || 4.7 || -2.8 || -2.2 || 21.3 || 22.0 || 13.5 || 13.3 
 Iceland || IS || 2.7 || 3.9 || 18.1 || 18.5 || -12.8 || -13.6 || 50.3 || 51.4 || 16.6 || 16.6 
 Norway || NO || 2.9 || 3.6 || 14.2 || 15.1 || -16.5 || -17.4 || 49.4 || 54.0 || 15.1 || 16.7 
 Switzerland || CH || 8.3 || 8.1 || 19.8 || 20.0 || 8.0 || 8.4 || 26.5 || 26.5 || 18.7 || 18.0 
 United States || US || 3.7 || 3.4 || 16.6 || 16.8 || 2.4 || 1.9 || 45.3 || 44.8 || 16.7 || 16.4 
 Japan || JP || 8.1 || 8.8 || 17.5 || 17.5 || 20.4 || 21.4 || 33.9 || 31.6 || 18.5 || 17.8 
Table 1. Country
performance by indicator
Note:
imputed values displayed in yellow background
Note: For DYN, the figures presented here include imputations,
on the basis of the expectation maximization method, for BG, CZ, DE, EL,
ES, HR, CY, LU, MT, PT, UK, TR, IS, CH, US and JP for 2010 and EL, HR, LU, MT,
TR, IS, CH, US and JP for 2011. For CH, SERV for 2011 was n.a., therefore the
2010 value was imputed. For EL, SERV for 2011 was imputed using the 2010 value.[45] 
Source: For DYN, Commission calculations, using Eurostat data.
For PCT, KIA, GOOD, SERV, Innovation Union Scoreboard (indicators 2.3.1, 3.2.1,
3.2.2., and 3.2.3., respectively). For SERV 2011, the source is Eurostat. 

3.5.2. The reference periods 

In defining the
reference years for the data underpinning the various components of the simple
composite indicator, two main aspects have been considered. First, to use the
most recent data available. Second, to have the longest possible time series in
order to compute relevant growth rates for the different components. A series
of different options were thus examined for the four components involved (it is
recalled that component COMP is made of two sub-components: GOOD and SERV).
Table 2 below summarises the final choice of years, determined by the
availability of the data for PCT in the Innovation Union Scoreboard,[46] and by the methodology used to
compute the innovation coefficient, which is described in Annex  below. 
 Composite indicator || PCT || KIA || GOOD || SERV || DYN 
 CIS score || KIA score || Fast-growing enterprises[47] 
 2010 || 2008 || 2010 || 2010 || 2010 || 2006/8 || 2009/10 || 2009 with imputations 
 2011 || 2009 || 2011 || 2011 || 2011 || 2006/8 || 2009/10 || 2010 with imputations 
Table 2. Reference years used
for each component of the simple composite indicator  
With the regular production of the data on DYN, the reference years behind
the indicator will become further aligned .

4. Measuring country performance with
the indicator      

The indicator provides an outcome-oriented
measure of innovation in a country, balanced between technological and
non-technological innovation, manufacturing and services, as captured by its four
components: patents, skills, competitiveness and the employment dynamism of fast-growing
enterprises in innovative sectors.

4.1. Score produced by the chosen
indicator

Figure 7 and Table 3 show
the scores of the innovation indicator for each EU Member State, Switzerland,
Norway, Turkey, United States and Japan in comparison with the EU average. Countries’ scores for year 2011 (red bars) and 2010 (crosses) are
displayed in Figure 7 with respect to the EU average, set at 100 in 2010. 
Figure 7. The simple composite indicator measuring
innovation output
Countries’
scores for 2011 (red bars) and 2010 (crosses) with respect to the EU average (100
in 2010).
In 2011, the
components reflect the situation in 2009 (PCT), 2010 (DYN) or 2011 (KIA, COMP)
In 2010, they
are based on 2008 (PCT), 2009 (DYN) or 2010 (KIA, COMP) data
Source: Commission
calculations. 
Improved time series, based on longer observation periods and
further aligned reference years, are essential and will become available in the
medium term. This will enhance the possibilities for analysing performance in
relation to progress and will offer new possibilities to derive policy
recommendations.[48]
 Country ||   || 2010 || 2011 
   ||   ||   ||   
 Japan || JP || 133.9 || 134.2 
 Sweden || SE || 126.4 || 127.5 
 Germany || DE || 125.9 || 126.1 
 Ireland || IE || 118.7 || 124.8 
 Switzerland || CH || 122.6 || 121.5 
 Luxembourg || LU || 121.6 || 120.7 
 Denmark || DK || 124.7 || 119.7 
 Finland || FI || 117.5 || 117.9 
 United Kingdom || UK || 110.8 || 112.8 
 France || FR || 105.5 || 106.7 
 United States || US || 106.0 || 104.4 
 European Union || EU || 100.0 || 104.4 
 Belgium || BE || 103.8 || 103.1 
 Netherlands || NL || 102.4 || 102.8 
 Austria || AT || 98.0 || 96.4 
 Hungary || HU || 90.9 || 96.0 
 Iceland || IS || 92.6 || 95.2 
 Slovenia || SI || 89.5 || 92.8 
 Italy || IT || 89.0 || 92.3 
 Cyprus || CY || 90.1 || 90.3 
 Czech Republic || CZ || 85.2 || 89.0 
 Norway || NO || 81.1 || 87.7 
 Spain || ES || 82.8 || 87.4 
 Estonia || EE || 80.5 || 84.3 
 Greece || EL || 84.7 || 83.9 
 Malta || MT || 84.5 || 83.5 
 Romania || RO || 76.8 || 81.5 
 Slovakia || SK || 81.9 || 81.0 
 Poland || PL || 72.7 || 77.6 
 Croatia || HR || 74.7 || 76.6 
 Portugal || PT || 68.6 || 74.3 
 Latvia || LV || 72.0 || 72.1 
 Lithuania || LT || 63.9 || 65.9 
 Turkey || TR || 64.2 || 64.9 
 Bulgaria || BG || 66.7 || 64.9 
   ||   ||   
Table 3. Countries’ scores for the years 2010
and 2011,
reported with
respect to the EU average, which is set at 100 in 2010
Source: Commission
calculations. 
Overall, in 2011 six categories of performers are identified
according to the country scores.[49]
Sweden, Germany, Ireland and Luxembourg are “top performers”, with
scores of over 120 and high values in all four components. These are followed
by Denmark, Finland, and the UK, which appear as “very good
performers”, with scores of between 110 and 120. France, Belgium and the
Netherlands are “good performers” with indicator values of between
100 and 110, followed closely by a group of “medium-level
performers”, including Austria, Hungary, Slovenia, Italy, and Cyprus, in
the score range of 90 to 100. “Medium-low performers”, with values
of between 80 and 90, include the Czech Republic, Spain, Estonia, Greece,
Malta, Romania, and Slovakia. Finally, the countries with scores of less than
80 are considered “low performers”. These include Poland, Croatia,
Portugal, Latvia, as well as Lithuania and Bulgaria, the latter two with particularly
low scores close to 65, around half of the top score.[50] 
Box 2.
Performance of four selected Member States on the indicator 
Sweden,
the top EU performer, has a knowledge-intensive economy with one of the world's
highest R&D intensities, increased high-tech and medium-high-tech
specialisation, and framework conditions prone to innovation and the creation
of fast-growing firms. Therefore, it has a strong performance in three of the
indicator components: patents, and employment in knowledge-intensive activities
and in fast-growing firms of innovative sectors. Sweden’s success in deriving
economic benefits from a well-performing research and innovation system is an
example for other Nordic countries.
France
is a good performer in the indicator. With its
large and competitive science base, it has particular strengths in the
contribution of medium- and high-tech products to the trade balance and in
employment in fast-growing firms of innovative sectors. In contrast, its share
of knowledge-intensive exports is much lower than the EU value. Although this
can partially reflect the weight of tourism in France's economy, policies such
as those aimed at linking up internationalisation and innovation strategies at
the firm level and at valorising research results, will contribute to a higher
performance on the indicator.  
Italy,
as a medium-level performer, is strong on the contribution of its medium- and
high-tech goods to the trade balance, in relation to its lower performance on
the other components. Improving the national framework conditions for
innovation, such as further pursuing the on-going simplification of the IPR
system, and policies aimed at fostering an increased correspondence between education
curricula and labour market needs, as well as the reduction of administrative
burdens for SMEs, all contribute to a better overall performance. 
Bulgaria
ranks as a low performer in the indicator, with small levels of
knowledge-intensive economic activity. Improving this position requires
fostering adequate framework conditions for an upgrade of the innovation
capacity of its economy, including a more stable regulatory environment for
companies, better access to financing and a reduction of existing
administrative burdens for creating new enterprises. The failure to channel
skilled people into domestic employment, linked to the relevance of making
working conditions more attractive for highly productive researchers, is also a
relevant bottleneck for increased performance. Policies to favour the
development of knowledge, technology-intensive clusters and the upgrading of
its manufacturing sector through R&D contribute to higher patenting and
harness innovation to create new high value-added exports. 
Given the data
constraints for component DYN, the indicator is calculated for years 2009 and
2010. The policy relevance of the indicator will gradually grow as time series based on longer observation periods and further aligned reference
years, become available in the medium term and it will
make it possible to assess the progress achieved over time by individual
countries and by the EU as a whole. 

4.2. Country-by-country analysis
of performance 

Most of the countries show modest changes in the composite indicator
from year 2010 to year 2011. However, for a few countries this variation is
more significant. This can be explained by the dynamics of components DYN and
SERV over time whereas KIA, PCT and GOOD display much more moderate changes.
Country size does not have a particular effect neither on the composite nor on
the underlying indicators except for GOOD for which most of the larger
countries score better. The top performing countries are on top at least on two
of the component indicators. The same occurs for those countries which are at
the bottom. 
 
The ranking shows, for the year 2011, Japan (134) and Sweden (128) on
top, thanks to the excellent score in GOOD for Japan, in DYN for Sweden, and
the remarkable performance in PCT for both countries. Germany (126), strong in
PCT, GOOD, SERV as well as DYN, follows in the ranking. Ireland (125) comes
fourth, also strong in both KIA and SERV as well as in DYN. Switzerland (122),
strong in KIA, GOOD and PCT, occupies the fifth position. Luxembourg (121), is leading
in components KIA and SERV, with a strong financial
sector, follows thereafter. The score of Finland (118), with a number
one performance in PCT and a strong position in DYN, is close to that of
Denmark (120), strong also on those two components, as well as in SERV.
After a gap in score of 5 points ranks the United
Kingdom (113), which shows an average performance in PCT, GOOD and DYN and a
strong position in KIA and SERV explained, to a large extent, by the
country’s international competitiveness in the financial sectors. The
United States and France share a similar score (104), 7 points below that of
the UK. The United States has nearly the same score as the EU in 2011 (104),[51] maintaining its relative
stronger KIA and somewhat weaker SERV scores, while its performance in the PCT,
GOOD (somewhat above EU) and DYN components is very similar to that of the EU.
France (107) has higher scores than Belgium in DYN and GOOD but
weaker in SERV and similar in PCT and KIA, with Belgium (103) and the Netherlands
(103) scoring just below EU average (one point difference) in 2011. Austria (96)
and Hungary (96) follow in the ranking with a 7 points difference with respect
to the Netherlands.  
Iceland (95) is particularly strong in KIA. Slovenia (93), strong in
GOOD but poor in SERV, and Italy (92), with a solid score in GOOD and SERV and
an average performance in KIA, follow. Cyprus (90), appears solid in KIA and
SERV, and the Czech Republic (89), with average scores in all components, has relatively
better ranking in GOOD and DYN. Norway (88) performs just below EU average in SERV,
although it has the lowest score in GOOD as a result of oil and natural gas
exports. 
Spain (87) has a low score in SERV and PCT but higher
scores in GOOD, KIA and DYN. Estonia (84) follows with score below the EU
average in all components, although closer to it in PCT. Greece (84) appears
particularly strong on SERV,[52] Malta (84) is below average in
PCT and SERV but strong in KIA, and Romania (82) has
low performance in both PCT and KIA but performs better in SERV and somewhat in
DYN, together with Slovakia (81), which has strong performance in GOOD. 
Poland (78) and Croatia (77), follow in the ranking, the first one
scoring better in GOOD and SERV while the latter with an average score for GOOD
but weak in SERV, slightly ahead of Portugal (74), which scores low in all
components but with higher performance in GOOD. Latvia (72), with performance under
that of Portugal although with an average position in SERV and scoring better
in PCT, is ahead of Lithuania (66) and Turkey (65), both having stronger scores
in GOOD, and of Bulgaria, weak in all components except SERV, which shows the
lowest score. 
The radar charts shown below for all
countries can assist in the interpretation of the results of the composite
indicator. We refer to Table 1 for the exact country’s scores. 
The figures below display the reference
years for the composite indicator (2010 and 2011), using normalised unweighted
scores. Table 2 above shows the reference years selected for each of the
components, which have been reflected into the 2010 and 2011 values of the
proposed composite indicator. 
 Belgium || Bulgaria 
 || 
 Czech Republic || Denmark 
 || 
 Germany || Estonia 
 || 
 Ireland || Greece 
 || 
 Spain || France 
 || 
 Croatia || Italy 
 || 
 Cyprus || Latvia 
 || 
 Lithuania || Luxembourg 
 || 
 Hungary || Malta 
 || 
 The Netherlands || Austria 
 || 
 Poland || Portugal 
 || 
 Romania ||     Slovenia 
 || 
 Slovakia || Finland 
 || 
 Sweden || United Kingdom 
 || 
     Turkey ||     Iceland 
 || 
 Norway || Switzerland 
 || 
 United States || Japan 
 || 
Figure 9. Country results.  Note: The graphs
include imputations for missing data, as shown in Table 1.

5. Robustness
analysis 

Monitoring innovation at the national scale
across the European Union Member States and with respect to benchmark countries
raises practical challenges related to the quality of data and the combination
of these into a single number. This section discusses the assessment of the
indicator along two main axes: the conceptual and statistical coherence of the
structure, and the impact of key modelling assumptions on the country ranks.[53] 
These are necessary steps to ensure the
transparency and reliability of the indicator, to enable policymakers to derive
informed and meaningful conclusions, and to potentially guide choices on
priority setting and policy formulation.
The conceptual and statistical coherence is
carried out for two statistical approaches, one based on global sensitivity
analysis and using the Pearson correlation ratio (the non-linear equivalent of
the Pearson correlation coefficient), and another based on multivariate
analysis and using principal component analysis.[54]  
The key modelling assumptions tested include
imputation (estimation of missing data), alternative aggregation formulas (arithmetic,
geometric), alternative indicators for KIA, SERV and DYN and random weights for
the indicators GOOD and SERV underlying the component COMP.
The analysis complements the country
rankings with confidence intervals, in order to better appreciate the
robustness of these ranks to the index computation methodology. In addition,
the analysis includes a measure of distance to the efficient frontier of
innovation by using data envelopment analysis. 

5.1.
Conceptual and statistical coherence in the framework

The options for the innovation indicator were
assessed in an iterative process that aimed at setting the foundation for a
balanced index. The process followed four steps (see Figure 10).
Figure 10. Conceptual and statistical
coherence in the indicator framework
Step 1: Conceptual Consistency
Candidate indicators were selected for
their relevance to innovation, on the basis of literature review, expert
opinion, country coverage, and timeliness. To represent a fair picture of
country differences, they were scaled (e.g. dividing by GDP) when appropriate
and needed. 
Step 2: Data Checks
The most recently released data (see Table 1)
were used for each country. The data availability in the two years across the
components is 93% (9 countries have not reported data on DYN in 2010, 15
countries in 2009). There were no potentially problematic components, which could
bias the overall results, as skewness and kurtosis for all components were
within acceptable limits (skewness less than 2 and kurtosis less than 3.5[55]).
Step 3: Statistical Coherence
Weights as ‘scaling
coefficients’
The nominal weights for the components were
chosen as ‘scaling coefficients’ and not as ‘importance
coefficients’, with the aim to arrive at an index that is balanced in the
underlying components. Similar choices were made by INSEAD in the development
of the Global Innovation Index, and Yale and Columbia University in the
development of the Environmental Performance Index. 
More specifically, the requirements for the
innovation indicator were: a) equal importance to SERV and GOOD, and b) equal
importance to PCT, KIA, COMP and DYN. Herein, ‘importance’
was quantified via the Pearson correlation ratio (which is the non-linear
equivalent of the Pearson correlation coefficient). This importance measure describes
‘the expected reduction in the variance of the index scores that would be
obtained if a given indicator could be fixed’. For a discussion of why
the Pearson correlation ratio is a suitable measure of importance in the
context of aggregate measures, see Paruolo et al. (2013)[56].
Table 4 shows the
nominal weights that were assigned to the indicators in order to achieve the
required importance when building the innovation indicator.
   || Nominal Weight || Importance measure || Objective 
 Balancing the two indicators within COMP 
 GOOD || 50 % || 0.39 || Equal importance to SERV and GOOD 
 SERV || 50 % || 0.39 
 Balancing the four components of the indicator 
 PCT || 23  % || 0.70 || Equal importance to all four components 
 KIA || 18 % || 0.71 
 COMP || 43 % || 0.70 
 DYN || 15  % || 0.79 
Note: Importance measures were calculated using kernel estimates
of the Pearson correlation ratio (),
as in Paruolo et al., 2013.[57]
Table 4. Nominal weights and importance in the indicator
framework
Source: Commission calculations
Principal components analysis
Principal
component analysis confirms the presence of a single latent dimension that
captures 70% of the total variance in the four components. This result suggests
that the arithmetic average is a suitable aggregation formula to build the
indicator. 
Yet, as this latent dimension is more
influenced by the DYN, the Commission services opted instead to build the
innovation indicator using a balanced structure, whereby all four components
have the same importance (see corresponding nominal weights in Table 4. The
indicator captures 69.5% of the total variance in the four components. 
A comparison was made in this respect when
excluding DYN from the framework. Again, a single latent dimension is
identified in the three remaining components of the innovation indicator
(namely PCT, KIA, COMP), yet, in this case, the first principal component
captures only 67.5% of the variance in the three components. A further
justification for including DYN in the innovation indicator framework is
offered by reliability item analysis using the coefficient Cronbach alpha
(c-alpha)[58]. A high c-alpha, or equivalently a high
“reliability”, indicates that the individual indicators measure the
latent phenomenon well [59]. The c-alpha value is 0.85 for the innovation indicator, which
confirms the high reliability of the indicator. When either PCT or KIA is
excluded from the framework, the reliability drops slightly at 0.81. When the
COMP is eliminated, the reliability remains unaffected. Instead, when DYN is
eliminated from the framework, the reliability drops notably at 0.74. This
result gives a further justification for including DYN in the framework, as it
increases the reliability (measured here by c-alpha) of the proposed innovation
indicator.
These results reveal that the choices made in
building the indicator have assured the statistical coherence of the index. 
Step 4: Qualitative Review 

Finally, the country
scores and ranks for the innovation indicator were evaluated to verify that the
overall results were, to a great extent, consistent with current evidence,
existing research or prevailing theory. 

Notwithstanding these statistical tests and
the positive outcomes on the statistical coherence of the proposed indicator, it
is important to mention that it should remain open for future improvements as
new relevant research studies become available. A potential revision of the
framework in five to ten years can thus be envisaged.

5.2.
Impact of modelling assumptions on the indicator results

Every country score depends on modelling
choices:  components’ selection, imputation or not of missing data,
normalization, weights, aggregation method, among other elements. These choices
are based on expert opinion (e.g., selection of components), or common practice
(e.g., standardisation), driven by statistical analysis (e.g., weights assigned
to the components). The robustness analysis is aimed at assessing the simultaneous
and joint impact of these modelling choices on the rankings. The data are
error-free since eventual errors and typos were corrected during the
computation phase. 
The robustness assessment of the innovation
indicator was based on a combination of a Monte Carlo experiment and a
multi-modelling approach that dealt with seven issues: (1) missing data, (2) aggregation
formula, (3) weights for GOOD and SERV, (4) alternative indicator for KIA, (5)
alternative indicator for SERV, (6) alternative indicator for DYN, and (7)
exclusion of SERV. This type of assessment aims to anticipate eventual
criticism that the indicator scores were calculated under conditions of
certainty (Saisana et al., 2005;[60]
Saisana et al., 2011). 
The Monte Carlo simulation was played on
the weights for the two components underlying COMP, namely GOOD and SERV, and
comprised 1,000 runs, each corresponding to a different set of weights,
randomly sampled from uniform continuous distributions that were determined as
follows. The ratio of the share of services to products in the economies
analysed ranges between 2.1 in Romania in 2010 to 16.26 in Luxembourg in 2009.
These ratios are considered as notions of importance and subsequently lead to
nominal weights for GOOD and SERV in the following range: 28-43% for GOOD and
57-72% for SERV. This choice of the range for the weights’ variation
ensures a wide enough interval to have meaningful robustness checks.
When building aggregate measures and for
reasons of transparency and replicability, international organisations often
prefer not to estimate missing data. Yet, this “no imputation”
choice is practically equivalent to replacing missing values with the weighted
average of the available (normalized) data. Furthermore, the ‘no
imputation’ choice might encourage countries not to report low data
values. To overcome these limitations, the Commission services opted to estimate
missing data using the Expectation Maximization (EM) algorithm.[61] 
The next type of uncertainty considered
relates to the use of the arithmetic average in the calculation of the
indicator, a formula that received statistical support from principal component
analysis. Yet, decision-theory practitioners have challenged the use of simple
arithmetic averages because of their fully compensatory nature, in which a
comparative high advantage on a few indicators can compensate a comparative
disadvantage on many indicators (Munda, 2008).[62] In
order to account for this criticism, the geometric average was considered as an
alternative. The geometric average[63] is a partially compensatory approach that rewards countries with similar
performance on the underlying indicators and motivates them to improve in the indicators
in which they perform poorly, and not just in any indicator. 
Finally, although the Commission services made
a clear choice on the use of indicators KIA, SERV, and DYN, there have been
discussions as to whether KIA2, or SERV2 or DYN2 could have been used instead.[64] These alternatives were hence included in the uncertainty analysis,
together with a consideration as to whether SERV should be excluded.  
Fourty-eight models were tested based on
the combination of EM imputation versus no imputation, arithmetic versus
geometric average, KIA versus KIA2, SERV versus SERV2, DYN versus DYN2, and
inclusion or not of SERV. Combined with 1,000 simulations per model for the random
weights assigned to GOOD and SERV, a total of 48,000 simulations for the indicator
were carried out (see Table 5 for a summary of the uncertainties considered in
the indicator).  
 1. Uncertainty in the treatment of missing values 
 Reference || Alternative 
 Expectation Maximization (EM) || no estimation of missing data 
 2. Uncertainty in the aggregation formula at pillar level 
 Reference || Alternative  
 arithmetic average || geometric average  
 3. Uncertainty in the weights 
 COMP component || Reference value || Distribution for robustness 
 GOOD || 50% || Uniform between 28-43%   
 SERV || 50% || Uniform between 57-72% 
 4-7. Uncertainty in the indicators  
 Reference || Alternative  
 KIA || KIA2 
 SERV || SERV2 
 DYN || DYN2   
 SERV || Excluding SERV 
Table 5. Uncertainty analysis for the innovation
indicator 
Sensitivity analysis results
Sensitivity analysis has been used to
identify which of the modelling assumptions have the highest impact on country
ranks, and thereafter to help focus the discussion of on those uncertainties. Figure
11 presents the box plots of ranking shifts for the seven assumptions tested.
The median shift in rank across all simulations is the red segment. The
vertical boxes show the 75% of the distributions (percentiles P25 and P75 are
the horizontal edges of the boxes) and vertical lines extend from minimum to
maximum.
Three assumptions are highly influential: the choice of
SERV versus SERV2, DYN versus DYN2, and the inclusion or not of the SERV
indicator within the COMP. If SERV2 were used instead of SERV, four countries
would move more than 3 positions (up to 6) in 2010 and only one country would
move 4 positions in 2011. All other countries would shift less than 3 positions
in either year. If DYN2 were used instead of DYN, seven countries would move
more than 3 positions (up to 6). If SERV were excluded from the framework, two
countries would lose over 12 positions in 2011 and 2010, whilst three countries
would gain over 6 positions in 2011. Ten more countries would also move over 4
positions in the classification either in 2011 and/or in 2010. Of all the choices
considered, this is also the only choice that has a notable impact to EU
(decline of almost 5 positions if SERV were excluded). 
 
Figure 11. Sensitivity analysis: Impact of
assumptions on the innovation indicator ranks
Source: Commission calculations
The Commission services engaged as well in
detailed discussions as to whether KIA, SERV and DYN were the most suitable indicators,
or whether their alternatives should have been used instead. Those discussions
also related to the inclusion of SERV or not within the COMP component. The
results of sensitivity analysis confirm how important it is to have focused the
discussions about the development of the indicator around these three issues. 
In the following, we take for granted the
structure of the indicator composed of PCT, KIA, COMP (GOOD plus SERV) and DYN.

Uncertainty analysis results
The main results of the robustness analysis,
accounting for the three remaining issues on imputation, weights for GOOD and
SERV, and the aggregation formula are summarised in Table 6, which reports the
country ranks in 2011 and 2010 and the respective 90% confidence intervals. It
can be verified that all country ranks lay within the simulated intervals, and
that these are narrow enough for all countries (3 or less positions) to allow
for meaningful inferences to be drawn. Given the uncertainties for example, Japan outperforms all countries in the dataset in 2011, yet it is on equal footing with Sweden in 2010. On the other hand, Bulgaria, Turkey and Lithuania have similar performance
in 2011 and in 2010, hence their rank should not be taken at face value.  
 
Table 6. Country ranks and 90% intervals for the innovation
indicator
Source: Commission calculations

5.3.
Distance to the efficient frontier by Data Envelopment Analysis (DEA)

Several
innovation-related policy issues at the national level entail an intricate
balance between global priorities and country-specific strategies. Comparing the
performance of countries on innovation by subjecting them to a fixed and common
set of weights may prevent acceptance of the indicator on grounds that a given
weighting scheme might not be fair to a particular country. An appealing
feature of the more recent Data Envelopment Analysis (DEA) literature applied
in real decision-making settings is to determine endogenous weights that
maximize the overall score of each decision-making unit given a set of other
observations (see Box 1 for a brief mathematical formulation of DEA).
In this
section, the assumption of fixed component weights common to all countries is
relaxed once more; this time country-specific weights
that maximize a country’s score are determined endogenously by DEA. In theory, each country is free to decide on the relative
contribution of each component to its score, so as to achieve the highest possible
score in a computation that reflects its innovation strategy. In practice, the
DEA method assigns a higher (or lower) contribution to those components in
which a country is relatively strong (or weak). 
Reasonable
constraints on the weights are assumed to preclude the possibility of a country
achieving a perfect score by assigning a zero weight to weak components: for
each country, the share of each component score (i.e. the component score
multiplied by the DEA weight over the total score) has upper and lower bounds
of 10% and 30% respectively. The DEA score is then measured as the weighted
average of all four component scores, where the weights are the
country-specific DEA weights, compared to the best performance among all other
countries with those same weights. The DEA score can be interpreted as a
measure of ‘the ‘distance to the efficient frontier’. 
Table 7 presents
the pie shares and DEA scores for all countries in 2011. All pie shares are in
accordance with the starting point of granting leeway to each country when
assigning shares, while not violating the (relative) upper and lower bounds.
The pie shares are quite diverse, reflecting the different national innovation
strategies. For example Austria, Bulgaria, Cyprus assign 30% of their DEA score
to PCT, whilst this component accounts for no more than 10% of
Luxembourg’s DEA score. The EU assigns 30% of its score to KIA and GOOD,
and so does the USA. Two countries– Sweden, and Japan – reach a perfect DEA score of 1. Figure 12 shows how close the DEA scores and
the innovation indicator scores are for all 34 countries plus the EU in 2011 (correlation
of 0.987).  
 
 
Table 7. DEA results (2011): pie shares and
Efficiency scores
Source: Commission calculations
Notes: Values in bold indicate that this
value equals the lower 10% (or upper 30%) bound of the pie share constraint
associated with this component.
Figure 12.
Innovation
indicator scores and Data Envelopment Analysis ‘distance to the efficient
frontier’ scores 
Source: Commission calculations
Box 3. Data Envelopment Analysis
The original question in
the DEA-literature was how to measure each unit’s relative efficiency in
production compared to a sample of peers, given observations on input and
output quantities and, often, no reliable information on prices (Charnes and
Cooper, 1985).[65] A notable difference between the original DEA
question and the one applied here is that no differentiation between inputs and
outputs is made (Melyn and
Moesen, 1991; Cherchye et al., 2008).[66] To
estimate DEA-based distance to the efficient frontier scores, we consider the m
= 4 components in the innovation indicator for n = 35 countries,
with yij the value of component j in country i.
The objective is to combine the component scores per country into a single
number, calculated as the weighted average of the m components, where wi
represents the weight of the i-th component. In absence of reliable
information about the true weights, the weights that maximize the DEA-based
scores are endogenously determined. This gives the following linear programming
problem for each country j:
                               (bounding constraint)
Subject
to  
, where,
               (non-negativity
constraint)
In this basic programming problem, the
weights are non-negative and a country’s score is between 0 (worst) and 1
(best). 

6. Conclusion 

In response to the European Council, this Communication presents an
indicator of innovation output, building on the Commission’s efforts to improve the quality of
its evidence in support of policy-making and to assess the impact of
innovation. 
By zooming in
on innovation output, the indicator complements the Innovation Union Scoreboard
and its Summary Innovation Index. 
In line with Europe 2020 and its Innovation Union flagship
initiative, the indicator will support policy-makers in creating an
innovation-friendly environment. 
It was developed using international quality standards and state-of-the-art statistical analyses. Nonetheless, the Commission identified
four areas to bring it to its full potential, including widening its international comparability, improving
its data on fast-growing firms, and analysing how the
innovation coefficient datasets could be improved. 
The indicator is a composite index,
quantifying four dimensions of innovation output:
patents, skills, trade in knowledge-intensive goods and services, and
employment in fast-growing firms. 

Annex 1.
Calculation of sectoral innovation coefficients  

The component DYN makes use of a set of innovation
coefficients which characterise the degree of innovation of each sector of the
business economy. This annex presents the method used to compute these
coefficients, highlighting in particular the advantages and disadvantages of
using EU averages for each sector rather than country-specific values. It also
proposes a way forward for further improving the quality and relevance of these
coefficients. 
For each
sector, the innovation coefficient is calculated at EU level as the product of
two elements: a CIS-based innovation intensity score (),[67] and a Labour Force Survey (LFS)-based knowledge intensity score ().[68]

It is
represented as.
Advantages and
disadvantages of sectoral EU-level coefficients 
Using EU averages rather
than country-specific values implies that these sectoral innovation
coefficients will not reflect differences in the knowledge intensity or CIS
score across Member States. While this could be seen as a weakness in the
approach, it has the main benefit of defining a common reference of the degree
of innovation of each sector against which countries can be reliably compared
over time. 
Advantages of using
a uniform coefficient across countries for each sector
Using a uniform
coefficient at EU level is also justified given the fact that the cross-country
comparability of detailed CIS survey results can be limited, partly as a result
of differences between enterprises in the perception of what constitutes an
innovation. These differences may become noticeable in the responses to the CIS
at country level. This problem becomes even more acute at the sectoral-level, where
limitations of the sample size and the small number of observations per sector
and country affect negatively the statistical reliability of results.
Country-specific sectoral CIS-scores in their current form are hence considered
not reliable enough for inter-country comparisons.  
Moreover, the source
micro-data for both the CIS and KIA scores are not available for each of the
Member States, making it impossible to produce country-specific coefficients
for all of them. Since CIS is only carried out in EU Member States, non-EU EEA countries
and some candidate countries, country-specific CIS scores cannot be produced
for non-European countries. Despite the fact that CIS is the best available
source for innovation statistics in European countries, calculations of
sectoral country-specific CIS scores could furthermore be based only on those
sectors defined as 'core', according to the CIS methodology. Participating
countries self-select the sectors beyond 'core' to be covered by their national
CIS. For sectors included in the CIS on a voluntary basis, different countries
took different priorities.

In response to the
limitations outlined above, pooling together all available CIS micro-data for
each sector increases noticeably the statistical reliability of CIS scores. Similarly,
the sample sizes of the Labour Force Survey (LFS) are not sufficient for
producing reliable results for most economic activities, unless data is pooled
at the sectoral level. Moreover, LFS data is not available at 3-digit level
from all countries (voluntary provision only). 
Disadvantages of
using a uniform coefficient across countries for each sector
There might indeed be
differences between countries in their sectoral CIS and KIA scores, reflecting
the fact that the same sector can be more innovative in one country than in
another. Sector differences between countries will not be addressed by a
uniform coefficient which will apply the same value to both modest innovators
and innovation leaders. In other words, pooling all available data to calculate
the uniform coefficient leads to an EU average which risks overestimating the
innovation coefficient for modest innovators and underestimating it for
innovation leaders.  
Another option would be
to calculate the uniform coefficient based on the data of the countries close
to the innovation frontier. The results would be statistically less robust,
because based on fewer data, and less representative for the EU as a whole. 
Despite these
shortcomings, there is regrettably currently no alternative to using uniform
coefficients, since data gaps, sample size limitations and potential
comparability issue at detail level do not allow to produce meaningful country
specific coefficients for inter-country comparison. 
Computation of the coefficients 
In order to compute the dynamism component, all sectors of the non-financial
business economy are considered. Innovation coefficients are calculated for all
economic sectors of the non-financial business economy, i.e. NACE Rev.2
sections B to N, plus S95, excluding K. Therefore, no economic sector covered
by CIS, except the financial sector, is excluded from the list of innovative
sectors when calculating the indicator. 
Each sector (NACE Rev.2 three-digit level),[69] is given a sector-specific
coefficient reflecting its degree of innovativeness. This sector-specific
coefficient is calculated on the basis of the sector’s scores on a set of
Community Innovation Survey (CIS) variables in all countries providing CIS
micro-data (21 countries), and also on the share of tertiary-educated persons
employed in this sector (data source: Labour Force Survey, 19 countries), which
is used as a measure of the knowledge intensity of the sector (designated as
KIA score). CIS variables quantify the level of innovation in a sector.
Knowledge intensity provides insight into the innovation potential of the
sector, as innovation is in essence based on knowledge and requires highly
qualified human resources.
In the case of a few
three-digit sectors, the number of CIS observations was judged too small to
allow for statistics to be displayed, even with pooled data. In those cases,
the CIS-score was imputed from the two-digit level. In the case of the Labour Force
Survey, KIA scores of a few three-digit sectors (representing 1.5% of total
employment) were also judged statistically unreliable, due to the small size of
their populations. In other words, the KIA scores related to 98.5% of total
employment are statistically reliable, a very good performance from a
statistical point of view. 
The innovation
coefficient of a sector is defined as the product of this sector’s normalised
CIS-based score and normalised LFS-based score. The KIA score is the normalised
share of tertiary attainment (ISCED 5 and 6) in NACE sections based on the
Labour Force Survey results 2009 and 2010 (average for the two years). The normalisation
is performed by dividing the share of a section by the highest share and giving
the highest share a value of 1.

The coefficient of each
sector in the indicator is larger or smaller, depending on the innovativeness
of this sector. Firms can innovate in any business
sector. Including in the indicator only a subset of business sectors would have
led to the innovation carried out in other sectors to be ignored. The option of
assigning innovation coefficients to all sectors avoids this exclusion and
allows innovation in all business sectors to be taken into account. 
By construction, the
innovation coefficient is sector-specific although uniform for all countries
and constant over time, in order to provide an
indication of the structural features of economic sectors. This is in line with the long-established
OECD taxonomy of high-tech, medium-high-tech, medium-low-tech and low-tech
sectors, which is also based on the R&D intensity of economic activities in
a defined pool of countries and kept unchanged for years in order to build
meaningful time series. 
Calculation
of CIS scores 
CIS aims at capturing
a broad range of innovation activities such as product and process development and
marketing and organisational changes. A total of 33 CIS 2008 variables
distributed among four groups (see Table 12) and reflecting the different
aspects of innovation were used to rank economic sectors according to their
innovation intensity. These variables were assigned equal weights so that, for
instance, R&D performers and marketing innovators are given the same weight
when constructing sectoral innovation intensities. This addresses the concern
that the innovation performance of some sectors, especially services, is not linked
to R&D performance only, as has traditionally been the case in
manufacturing. The CIS-based sector-specific scores,[70] are
based on a methodology developed by OECD which can be summarised as: [71]
1. Weighted country data were pooled together:
firms’ responses from all available countries were weighted by their
statistical representativeness (provided by each national statistical office)
before being pooled together. This approach has the main advantage of being
able to account for the relative importance of the countries included in the
analysis. This on the other hand leads to statistics that are more heavily
influenced by the behaviour of respondents located in the biggest countries
surveyed; it also assumes that non-surveyed firms behave in exactly the same
way as their surveyed counterparts do.
2. For each CIS 2008 dichotomous variable
considered, sectors’ ‘performance’ was obtained as the ratio
of the number of firms answering ‘yes’ to the total number of firms
answering the same question. Conversely, for those variables asking respondents
to quantify investment or amounts (e.g. innovation-related expenses), sectors were
ranked on the basis of average expenditure per respondent firm.
3. For each of the 33 CIS 2008 variables
considered and included in the four different groups created (see Table 11), business
sectors were ranked according to their relative ‘performance’ and
given a score proportional to their position in the ranking: the first sector
in the ranking is attributed the highest score, the second one is attributed
the highest score minus one, and so on until the last sector in the ranking,
which only receives 1 point.
4. Variable-specific scores were then normalised
so that all variable-specific rankings are defined between ]0, 1]. To this end,
the normalised score of sector x (called xnorm) was calculated
for each variable i as:
xnorm i 
= xi  / ximax
where ximax
is the maximum score by any of the sectors included in the ranking of variable i.[72] 
5. For each group j of
variables, with j = [product and process innovators; innovation-related expenditures;
organisation and market innovations; environmental innovations], group-specific
sectoral scores were calculated as the average of the scores obtained from each
of the variables included in group j.
6. Overall CIS-based sectors scores were finally calculated as the
average of the sector-specific scores obtained from each of the four groups of
variables considered. Overall CIS-based scores thus range between ]0, 1].[73]
Calculation
of KIA scores 
Innovation is always the tangible or intangible translation of new
ideas and knowledge. Knowledge-intensive economic activities are more likely to
be subject to innovations and to offer innovations with a high potential for
economic and societal transformations. In order to account for the role of
knowledge in the innovation potential of an economic sector, the CIS-based
score of each sector was multiplied by a second component,[74] the knowledge intensity score
of this sector, defined as the share of tertiary-educated persons employed
in that sector, based on Labour Force Survey 2009-2010 data and normalised by the highest share among
all sectors.[75]
The sector-specific knowledge intensity coefficient thus ranges between [0, 1].
Reference years and
disaggregation level 
The sectoral innovation
coefficients were calculated using the latest years available at the time of
the calculation (CIS 2008, covering the years 2006-2008, and LFS 2009-2010).[76]  
To provide an illustration of these coefficients, Table 11 and
Figure 15 below summarise their value if they are computed
as averages at the 1-digit level NACE classification. Note that the
computations for the indicator have been carried out at the 3-digit level
following the method outlined above. The data below just provide a rough
indication. 
 NACE || Sector || CIS score || KIA score || 
 B || Mining and quarrying || 0.42 || 0.17 || 0.07 
 C || Manufacturing || 0.64 || 0.30 || 0.21 
 D || Electricity, gas, steam and air conditioning supply || 0.57 || 0.40 || 0.23 
 E || Water supply; sewerage, waste management and remediation activities || 0.52 || 0.25 || 0.13 
 F || Construction || 0.28 || 0.26 || 0.08 
 G || Wholesale and retail trade; repair of motor vehicles and motorcycles || 0.33 || 0.28 || 0.10 
 H || Transportation and storage || 0.48 || 0.31 || 0.14 
 I || Accommodation and food service activities || 0.33 || 0.22 || 0.08 
 J || Information and communication || 0.63 || 0.71 || 0.45 
 L || Real estate activities || 0.37 || 0.47 || 0.17 
 M || Professional, scientific and technical activities || 0.52 || 0.77 || 0.40 
 N || Administrative and support service activities || 0.45 || 0.33 || 0.15 
 S || Other service activities || 0.39 || 0.24 || 0.09 
   || Total || 0.53 || 0.35 || 0.20 
Table 11. CIS and KIA scores by NACE letters
(arithmetic average over all sections)[77]
Source: Commission calculations
Figure 15. CIS and KIA scores by NACE letters
(arithmetic average over all sections)
Source: Commission calculations
In order to analyse
innovation in greater detail, one can use the three-digit level of the economic
activities classification NACE. Two-digit NACE economic sectors can prove heterogeneous
in the innovation intensity of their sub-sectors and such differences can lead
to three-digit CIS and KIA scores differing from the two-digit ones.[78] These differences can be particularly
noticeable in the services sectors. 
For the computation of the innovation coefficients, each NACE Rev.2 three-digit
sector was therefore assigned an innovation coefficient between ]0, 1]
obtained by multiplying its normalised CIS-based score by its normalised knowledge
intensity score. The overall innovation coefficient of the sector is uniform
for all countries (see the CIS and KIA scores at: http://ec.europa.eu/research/innovation-union/index_en.cfm?pg=keydocs)
and represented by the country-specific score for each sector. 
 CIS wave || Group name || Variable name || Content || Type of variable 
 CIS 2008 || Product and innovators process innovators || INPDGD || New or significantly improved goods || Dichotomous, 0/1 
     || process innovators   || INPDSV || New or significantly improved services || Dichotomous, 0/1 
     ||     || NEWMKT || New to the market || Dichotomous, 0/1 
     ||     || NEWFRM || New to the firm || Dichotomous, 0/1 
     ||     || INPSPD || New or significantly improved methods of manufacturing or producing goods or services || Dichotomous, 0/1 
     ||     || INPSLG || New or significantly improved logistics, delivery or distribution methods || Dichotomous, 0/1 
     ||     || INPSSU || New or significantly improved supporting activities for your processes || Dichotomous, 0/1 
     ||   || INPSNM || New or significantly improved processes new to the market || Dichotomous, 0/1 
     || Innovation-   Expenditures || RRDINX || Intramural (in-house) R&D || Discrete, го 
     || related expenditures   || RRDEXX || Acquisition of R&D (extramural R&D) || Discrete, го 
     ||     || RMACX || Acquisition of machinery, equipment and software || Discrete, го 
     ||     || ROEKX || Acquisition of other external knowledge || Discrete, го 
     ||     || RTOT || Total of innovation expenditures || Discrete, го 
     || Organisation and innovations || RTR || Internal or external training for your personnel || Dichotomous, 0/1 
     || market innovations   || RMAR || Activities for the market introduction of goods and services, including market research and launch advertising || Dichotomous, 0/1 
     ||     || RPRE || Procedures and technical preparations, not covered elsewhere || Dichotomous, 0/1 
     ||     || ORGPUB || New business practices for organising procedures || Dichotomous, 0/1 
     ||     || ORGWKP || New methods of organising work responsibilities and decision making  || Dichotomous, 0/1 
     ||     || ORGEXR || New methods of organising external relations with other firms or public institutions || Dichotomous, 0/1 
     ||     || MKTDGP || Significant changes to the aesthetic design or packaging of a good or service || Dichotomous, 0/1 
   ||   || MKTPDP || New media or techniques for product promotion          || Dichotomous, 0/1   
   ||   || MKTPDL   || New methods for product placement or sales channels   || Dichotomous, 0/1 
     ||     || MKTPRI || New methods of pricing goods or services   || Dichotomous, 0/1 
 CIS 2008 || Environmental || ECOMAT || Reduced material use || Dichotomous, 0/1 
     || innovations   || ECOEN || Reduced energy use || Dichotomous, 0/1 
     ||     || ECOCO || Reduced CO2 footprint || Dichotomous, 0/1 
     ||     || ECOSUB || Replaced materials with less polluting or hazardous substitutes || Dichotomous, 0/1 
     ||     || ECOPOL || Reduced soil, water, noise, or air pollution || Dichotomous, 0/1 
     ||     || ECOREC || Reduced waste, water or materials || Dichotomous, 0/1 
     ||     || ECOENU || Reduced energy use (by end user) || Dichotomous, 0/1 
     ||     || ECOPOS || Reduced air, water, soil or noise pollution (by end user) || Dichotomous, 0/1 
     ||     || ECOREA || Improved recycling of product after use (by end user) || Dichotomous, 0/1 
     ||     || ENVI D || Procedure to improve environmental impact || Discrete, os.xs.1 
Table 12. Ordering criteria
groups and CIS 2008 variables considered

 Annex 2. Data collection for fast-growing firms 

Fast-growing or high-growth enterprises have been
defined statistically as part of the joint OECD/Eurostat Entrepreneurship
Indicators Programme (EIP).[79]
The definition used for the dynamism component is the
employment-based definition established by the EIP, with the exception of the
minimum growth rate, which is set to 10 % for DYN instead of 20 % for
the EIP. While still being very selective, this lower threshold allows for a
more significant coverage of fast-growing firms.[80] 
Member States
have been involved in the development of the component through their national
statistical offices and through providing regular information to the European
Research Area Committee (ERAC). 
This
internationally agreed definition is now in use in the production of business
demography statistics.[81]
National statistics on fast-growing enterprises are based on national business
registers, leaving therefore enterprises' response burden unchanged. To define
the dynamism component, fast-growing enterprises are those with average
annualised growth in number of employees of more than 10 % a year, over a
three-year period, and with ten or more employees at the beginning of the
observation period (period of growth).
The relevant data
for fast-growing enterprises was gathered by Eurostat on the basis of two
voluntary test data collections on sectoral employment and fast-growing
enterprises’ employment in Member States, which took place in 2011 and
2012 from April to September.[82]
The
data collected have been extensively used for calculations of the DYN component.
The voluntary
test data collections relate to four years (2008, 2009, 2010 and 2011), of
which one (2010) is almost complete in country coverage, the other reference
years being covered by 
15 Member
States for 2008, 19 for 2009, and 17 for 2011. In fact, 25 countries
participated in the 2011 and 2012 collection exercise (24 EU Member States plus
Norway).  Data for the 4 reference
years are available for 12 countries, for 3 reference years for 6 countries,
for 2 reference years for 6 countries, and for 1 reference year for 1 country.
For the reference year 2010, data are available for 25 countries. 
Those data should
be regularly produced.          

Annex 3. Main options examined for the composite
indicator 
 
   || Variables || Formula || Pros || Cons 
   ||   ||   ||   ||   
 1. || PCT, KIA, COMP || || - Timely data currently available. - All indicators in Scoreboard. - Correlation structure fit for aggregation. || - Does not include aspect of fast-growth. 
 2. || PCT, TER, COMP || || - Timely data currently available. - Correlation structure fit for aggregation. - All indicators but TER in Scoreboard.   || - Does not include aspect of fast-growth. - TER not about skills employed in economy.   
 3. || PCT, KIA2, COMP || || - Timely data currently available. - Correlation structure fit for aggregation. - All indicators but KIA2 in Scoreboard. || - Does not include aspect of fast-growth.   
 4. || PCT, KIA, (GOOD, SERV2) || || - Timely data currently available. - Correlation structure fit for aggregation. - All indicators but SERV2 in Scoreboard. || - Does not include aspect of fast-growth.   
 5. || PCT, KIA2, (GOOD, SERV2) || || - Timely data currently available. - Correlation structure fit for aggregation. || - Does not include aspect of fast-growth. - KIA2 and SERV2 not in the Scoreboard. 
 6. || PCT, TER, COMP, SALE || || - Fair coverage of output-oriented innovation. - All indicators but TER in Scoreboard.   || - Does not include aspect of  fast-growth. - TER not about skills employed in economy. - Correlation not permitting fine aggregation. 
 7. || PCT, KIA, COMP, SALE || || - Fair coverage of output-oriented innovation. - All indicators in Scoreboard. || - Does not include aspect of fast-growth - Correlation not permitting fine aggregation. 
 8. || PCT, KIA, COMP, DYN || || - Direct inclusion of dynamism component. - Fair coverage of output-oriented innovation. - Correlation structure fit for aggregation. - DYN robust to economic downturn. || - DYN to be improved: coverage & production.   
 9. || PCT, KIA, COMP, DYN2 || || - Direct inclusion of dynamism component. - Fair coverage of output-oriented innovation.   || - DYN2 to be improved: coverage & production. - Correlation not permitting fine aggregation. - DYN2 more sensitive to economic downturns. 
 10 || PCT, KIA, COMP, DYN3 || || - Direct inclusion of dynamism component. - Fair coverage of output-oriented innovation.   || - DYN3 to be improved: coverage & production. - Correlation not permitting fine aggregation.  - DYN3 more sensitive to economic downturns. 
 11. || PCT, KIA, (GOOD, SERV2), DYN || || - Direct inclusion of dynamism component. - Fair coverage of output-oriented innovation. - Correlation structure fit for aggregation. - DYN robust to economic downturn. || - DYN to be improved: coverage & production. - SERV2 not in the Scoreboard. 
 11b. || PCT, KIA, (GOOD2, SERV), DYN || || - Direct inclusion of dynamism component. - Fair coverage of output-oriented innovation. - Correlation structure fit for aggregation. - DYN robust to economic downturn. || - DYN to be improved: coverage & production. - GOOD2 not any longer in the Scoreboard. 
 11c. || PCT, KIA, (GOOD2, SERV2), DYN || || - Direct inclusion of dynamism component. - Fair coverage of output-oriented innovation. - Correlation structure fit for aggregation. - DYN robust to economic downturn. || - DYN to be improved: coverage & production. - GOOD2 not any longer in the Scoreboard. - SERV2 not in the Scoreboard. 
 12. || PCT, KIA, (GOOD, SERV2), DYN2 || || - Direct inclusion of dynamism component. - Fair coverage of output-oriented innovation.   || - DYN2 to be improved: coverage & production. - SERV2 not in the Scoreboard. - Correlation not permitting fine aggregation.. - DYN2 more sensitive to economic downturns. 
 13. || PCT, KIA2, COMP, DYN2 || || - Direct inclusion of dynamism component. - Fair coverage of output-oriented innovation.   || - DYN2 to be improved: coverage & production. - KIA2 not in the Scoreboard. - Correlation not permitting fine aggregation. - DYN2 more sensitive to economic downturn. 
 14. || PCT, KIA2, COMP, DYN || || - Direct inclusion of dynamism component. - Fair coverage of output-oriented innovation. - Correlation structure fit for aggregation. - DYN robust to economic downturn. || - DYN to be improved: coverage & production. 
 15. || PCT, KIA2, (GOOD, SERV2), DYN2 || || - Direct inclusion of dynamism component. - Fair coverage of output-oriented innovation.   || - DYN2 to be improved: coverage & production. - KIA2 and SERV2 not in the Scoreboard. - Correlation not permitting fine aggregation. - DYN2 more sensitive to economic downturn. 
 16. || PCT, KIA2, (GOOD, SERV2), DYN || || - Direct inclusion of dynamism component. - Fair coverage of output-oriented innovation.   || - DYN2 to be improved: coverage & production. - KIA2 and SERV2 not in the Scoreboard. - Correlation not permitting fine aggregation.. - DYN2 more sensitive to economic downturn. 
 17. || PCT, KIA, COMP, SALE, DYN2 || || - Direct inclusion of dynamism component. - Fair coverage of output-oriented innovation.   || - DYN2 to be improved: coverage & production. - Correlation not permitting fine aggregation.. - DYN2 sensitive to economic downturns.   
Legend
PCT:                                       Number
of patent applications filed under the PCT per billion GDP. 
KIA:                                       Employment
in knowledge-intensive activities in business industries as a % of total
employment. 
KIA2:                                     Same
as KIA but expressed as % of the total employment in business industries. 
TER:                                       Share
of tertiary educated persons as a % of total employment. 
COMP:                                   Aggregated
measure of competitiveness drawing on the indicators below: 
GOOD:                                   contribution of the trade
balance of medium-tech and high-tech products to the total trade balance. 
SERV:                                     knowledge-intensive services
as % of total services exports. 
SERV2:                                   contribution of
knowledge-intensive services exports to the trade balance.  
SALE:                                     Sales
of new-to-market and new-to-firm innovations. 
DYN:                                       
DYN2:                                     
DYN3:                                    
CISscore:                                   Normalised
innovativeness score based on the Community Innovation Survey. 
KIAscore:                                 Normalised measure
of knowledge-intensity in a given sector using Labour Force Survey data. 
Esc:                                          Employment in
sector s of country c. 
HG:                                         Fast-growing or high-growth
(enterprises). 
[1]               "Europe 2020 Flagship
Initiative Innovation Union", COM(2010) 546 final, of 6 October 2010. 
[2]               http://ec.europa.eu/enterprise/policies/innovation/files/ius-2013_en-pdf.

[3]               A set of robustness tests and associated analyses is presented
in this Commission Staff Working Document and its annexes.
[4]               OECD(2010), "Measuring innovation: a new
perspective".  
[5]               http://ec.europa.eu/enterprise/policies/innovation/files/ius-2013_en-pdf.

[6]               http://ec.europa.eu/europe2020/index_en.htm.

[7]               Conclusions of 4/2/2011 (Council doc. EUCO 2/1/11
REV1) and 1-2/3/2012 (EUCO 4/2/12 REV2). 
[8]               The European Council noted "a debate next year
on the Europe 2020 Strategy" and called for "preparatory work to be
conducted giving priority to: (…) (b) innovation (October 2013)",
looking forward to "the presentation by the Commission of (…) its
communication on the 'State of the Innovation Union 2012', including the single
innovation indicator, in time for its discussions.", Council doc. EUCO 23/13.

[9]               Report of the High Level Panel on the Measurement of
Innovation, A. Mas-Colell (Chair), September 2010. 
[10]             This relation has been tested inter alia by Mairesse
and Mohnen "Using Innovation Surveys for Econometric Analysis", in
Hall and Rosenberg (2010) Handbook of the Economics of Innovation. 
[11]             In selecting the final
components for the proposed indicator, the possibility
of using any of the four additional indicators of the output type in the IUS
(SMEs with products or process innovations, SMEs with marketing or organisation
innovations, sales of new to market and new to firms innovations and license
and patent revenues from abroad) was examined. A set of
considerations were taken into account in this respect, among which the
recommendations by the High-Level Panel of leading economists and innovators
set up in 2010 to advise the Commission on the development of the indicator,
the relevance of exploiting the ad hoc data collection on fast-growing
firms by Eurostat, the fact that the first three indicators above draw on
reported CIS data –also used to build the innovation coefficients of the
component on employment in fast-growing firms of innovative sectors-, and that
PCT patenting covers for the technological innovation dimension. 
[12]             Despite the fact that these data might fail to capture
innovation which occurs in industries where investors rely on alternative
mechanisms to protect intellectual property such as secrecy or lead-time. Moser
(2013) Journal of Economic Perspectives—Volume
27, Number 1—Winter 2013—Pages 23–44. 
[13]             OECD (2010), "High-growth Enterprises: What
Governments Can Do to Make a Difference". Archibugi, D et al.
(2013) "Economic crisis and innovation: is destruction prevailing over
accumulation?" Research Policy 42, 2. 
[14]             See the Report on the State of the Innovation Union
2011. 
[15]             The development of the DYN component of the indicator
is the fruit of joint work by Directorates-General RTD and JRC with Eurostat. 
[16]             See http://ec.europa.eu/research/innovation-union/index_en.cfm. 

[17]             While the first component of the proposed indicator
focuses solely on technological innovation, the other three components might
capture, to a certain extent, various dimensions of non-technological
innovations, as measured for instance by skills, marketability elements in
competitive knowledge-based goods and services, and additional aspects such as
the organisation of business processes in fast-growing firms. 
[18]             See Eurostat and European Statistical System (2011)
"European Statistics Code of Practice for the National and Community
Statistical Authorities", OECD (2011) “Quality Framework and
Guidelines for OECD Statistical Analysis”, and OECD-JRC (2008)
"Handbook on constructing composite indicators: methodology and user
guide", as well as the report of the High-Level Panel, referred to in
footnote 4.  
[19]             The options examined are not
reported in this supporting document, for the sake of conciseness. 
[20]             See
Eurostat and European Statistical System (2011) "European Statistics Code
of Practice for the National and Community Statistical Authorities". 
[21]             See: http://composite-indicators.jrc.ec.europa.eu/
[22]             Providing advice on the statistics it collects. 
[23]             For better comparability all components have been
standardized. This procedure implies subtracting from each component its mean
and then dividing the result by the component’s standard variation.
[24]             Section 3.3. explains in detail the selected weighting
and why the chosen weights are not country-specific. 
[25]             Annex 1 presents in detail the computation of the
innovation coefficient
[26]             The sum of the weights adds up to 99, as each weight
has been rounded to the closest integer. 
[27]             Paruolo P., Saisana M., Saltelli A., “Ratings and
Rankings: Voodoo or Science?”, Journal Royal Statistical Society, A176(3),
609-634, show that in weighted arithmetic averages, the ratio of two nominal
weights gives the rate of substitutability between the two indicators, and
hence can be used to reveal the relative importance of individual indicators.
Subsequently, a correction of the ‘scaling coefficients’ can be
made to achieve component indicators with the desired relative importance. 
[28]             For all components based on data from the Innovation
Union Scoreboard, the EU average refers to EU27, since the Scoreboard
indicators were computed and published prior to Croatia's accession. For the DYN
component, the EU average was computed making use of all countries with
available data (see section 3.4). 
[29]             Recent studies find almost 180 composite indicators
being used worldwide on a regular basis for policy-making purposes. Those
indicators usually assess performance of countries in multiple areas such as
competitiveness, environment, governance, and globalization, amongst others.
Some can be found in the field of research and innovation. For more details, see
Bandura (2008), UNDP/ODS Working Paper, " A Survey
of Composite Indices Measuring Country Performance: 2008 Update". 
[30]             Stiglitz, J. E., A. Sen, and J. Fitoussi (2009). Report
by the commission on the measurement of economic performance and social
progress. Technical report, www.stiglitz-sen-fitoussi.fr.

[31]             PCT is an international patent law treaty concluded in 1970, unifying
procedures for filing patent
applications. An application filed under PCT is called an "international
application". An international patent is subject to two phases. The first one
is the "international phase" (protection pends under a single
application filed with the patent office of a contracting state of the PCT).
The second one is the "national and regional phase" in which rights
are continued by filing documents with the patent offices of the various PCT states.

[32]             Patents taken in various
countries to protect inventions get linked together to build triadic patent
families. Those are a set of patents taken at the European Patent Office (EPO),
the Japanese Patent Office (JPO), and the US Patent and Trademark Office
(USPTO) sharing one or more priorities. 
[33]             The Pearson correlation coefficient obtained was of
0.92. The ranking produced was stable in relation to the baseline with marginal
switches of positions between the countries, with the exception of Switzerland
losing three positions and Iceland gaining four positions. 
[34]             NACE
(Nomenclature statistique des activités
économiques)
is the statistical classification of economic activities in the European Union and the subject of legislation
at the EU level, which guarantees the use of the classification uniformly
within all the Member States. It is a basic element of the international
integrated system of economic classifications, based on classifications of the
UN Statistical Commission, Eurostat as well as national classifications; all of
them strongly related each to the others, allowing the comparability of
economic statistics produced worldwide by different institutions.
[35]             See footnote 19. 
[36]             See details in Annex 2. 
[37]             Annex 2 provides details on the underlying dataset. 
[38]             Different thresholds were tested (such as 7% and 20%),
and 10% was judged sufficient to capture the phenomenon. 
[39]                    There is a need to collect data on the financial sector for this
component. See section 3.4.1. 
[40]                    Brazil, Russia, India and China   HYPERLINK
"http://en.wikipedia.org/wiki/South_Africa" \o "South
Africa" . 
[41]             For confidentiality
reasons on sectoral employment data.
[42]             See Annex 1 for a comprehensive
overview. 
[43]             See Annex 2 for more details.  
[44]             For further reference see: Dempster, A.P.; Laird, N.M.;
Rubin, D.B., 1977. Maximum Likelihood from Incomplete Data via the EM
Algorithm, Journal of the Royal Statistical Society. B 39 (1): 1–38, and
Little, R.J.A., Rubin, D.B., 2002. Statistical Analysis with missing data. IInd
edition; John Wiley & Sons, Inc. 
[45]             The 2011 value for sector 208 (freight transport by
sea) in EL is n.a. due to confidentiality constraints. 
[46]             See section 3.1. for more details. 
[47]             The reference year of the DYN component will be the
same as for the indicator for its regular production. 
[48]             The potential of the indicator to inform policies will
be further tested using research and econometric analyses.
[49]             For 2011 the statistics for the score distribution are:
average 96.1, standard deviation 19.4, median 92.8
[50]                    The country-level results are correlated with those of the Summary
Innovation Index of the Innovation Union Scoreboard (coefficient: 0.90) and the
R&D headline indicator on research expenditure (coefficient: 0.72).
Nonetheless, the statistical properties of the proposed composite indicator are
different from those of the SII. As an illustration of this fact, Principal
Component Analysis reveals that the SII accounts for five different latent
dimensions, capturing a wide range of innovation aspects, while the indicator
proposed in the Communication reveals a single latent dimension, capturing
innovation output. 
[51]                    The US performs slightly above the EU average in the indicator,
mostly as a result of its KIA performance, with high shares in ICT, health and
professional, scientific and technical activities. It performs however below EU
average in PCT patents and knowledge intensive services exports, the latter
notably as a result of the relevance of royalties and license fees (not
classified as KIS) for the US economy. The US also performs near the EU average
in the contribution of medium/high-tech goods to the trade balance. 
[52]             The 2010
SERV data for Greece was imputed by the 2011 values, due to the
disproportionate.
[53]                    See for example Saisana, M., D'Hombres, B., Saltelli, A., 2011. 
Rickety numbers: Volatility of university rankings and policy implications,
Research Policy 40(1), 165-177. 
[54]             Details on the applied methodologies are available at
http///composite-indicators.jrc.ec.europa.eu
[55]                    Groeneveld and Meeden (1984) set the criteria for absolute skewness
above 1 and kurtosis above 3.5. The skewness criterion was relaxed to account
for the small sample (142 countries). Groeneveld, R.A. and Meeden, G. 1984.
Measuring skewness and kurtosis. The Statistician 33, 391-399. 
[56]             The Pearson correlation
ratio or first order sensitivity measure offers a precise definition of
importance, that is ‘the expected reduction in variance of the CI that
would be obtained if a variable could be fixed’; it can be used
regardless of the degree of correlation between variables; it is model-free, in
that it can be applied also in non-linear aggregations; it is not invasive, in
that no changes are made to the index or to the correlation structure of the
indicators.
[57]             See footnote 19. 
[58]             Cronbach L. J. 1951. Coefficient alpha and the internal
structure of tests. Psychometrika 16: 297-334.
[59]             Nunnally (1978) suggests 0.7 as an acceptable
reliability threshold (yet some authors use 0.75 or 0.8, whist others are as
lenient as to go to 0.6). Nunnaly J. 1978. Psychometric theory. New York: McGraw-Hill.
[60]                    Saisana, M., Saltelli, A., Tarantola, S., 2005. Uncertainty and
sensitivity analysis techniques as tools for the analysis and validation of
composite indicators. Journal of the Royal Statistical Society A 168(2),
307-323.
[61]                    The Expectation-Maximization (EM) algorithm (Little, R.J.A., Rubin,
D.B. 2002. Statistical Analysis with missing data. 2nd edition; John Wiley
& Sons, Inc.) is an iterative procedure that finds the maximum likelihood
estimates of the parameter vector by repeating two steps: (1) The expectation
E-step: Given a set of parameter estimates, such as a mean vector and covariance
matrix for a multivariate normal distribution, the E-step calculates the
conditional expectation of the complete-data log likelihood given the observed
data and the parameter estimates. (2) The maximization M-step: Given a
complete-data log likelihood, the M-step finds the parameter estimates to
maximize the complete-data log likelihood from the E-step. The two steps are
iterated until the iterations converge.
[62]                    Munda, G. 2008. Social Multi-Criteria Evaluation for a Sustainable
Economy. Berlin Heidelberg: Springer-Verlag.
[63]             In the geometric average, indicators are multiplied as
opposed to summed in the arithmetic average. Indicator weights appear as
exponents in the multiplication. 
[64]                    KIA2 defines KIA as a percentage of total employment in business
industries. SERV2 captures the contribution of KIS exports to the trade
balance. DYN2 is a variant of DYN focussing on the top-third tier of the
innovative sectors and on employment in fast-growing firms over the total
employment in the economy. 
[65]                    Charnes, A., Cooper, W.W. 1985. Preface to Topics in Data
Envelopment Analysis, Annals of Operations Research 2, 59-94.
[66]                    Cherchye, L., Moesen, W., Rogge, N., Van Puyenbroeck, T., Saisana,
M., Saltelli, A., Liska, R., Tarantola, S. 2008. Creating Composite Indicators
with DEA and Robustness Analysis: the case of the Technology Achievement Index.
Journal of Operational Research Society 59, 239-251. Melyn, W. and Moesen, W.
1991. Towards a Synthetic Indicator of Macroeconomic Performance: Unequal
Weighting when Limited Information is Available, Public Economics Research
Paper 17, Leuven: Centre for Economic Studies.
[67]             Community Innovation Survey. 
[68]             Labour Force Survey. 
[69]             Statistical Classification of Economic Activities in
the European Community.
[70]             All sectors covered in the CIS 2008 survey were
included in order to assess innovativeness across the whole spectrum of
economic activities. This means that, as sectoral coverage varies across
countries, the statistics for some sectors may rely on a subset of countries only.
Reference years of the CIS 2008: 2006-2008.
[71]             The OECD methodology is fully described in
"Innovation Intensity in Sectors; An Experimental Taxonomy" (2011).
Calculations at 3-digit level of NACE Rev. 2 are based on CIS 2008 micro-data from
21 European countries (BG, CY, CZ, EE, IE, ES, FI, FR, HR, HU, IT, LT, LU, LV,
MT, NO, PT, RO, SE, SI, SK). 
[72]             The methodology leads to first ranked sectors that
receive normalised scores equal to one, and to last ranked sectors that receive
small (i.e. close to zero) but positive values, with stepwise distances (i.e.
between a sector n and the sectors ranked n+1 or n-1) that
depend on the variable-specific number of sectors included.
[73]             For three-digit sectors where the number of
observations was judged too small to allow for statistics to be displayed, the
CIS score was imputed from the two-digit level. This concerns 27 three-digit
sectors out of 218 included in the indicator (non-financial business economy).
[74]             The arithmetic average between CIS and KIA scores of a
sector was also envisaged. Multiplication was preferred to avoid a substitution
effect, whereby one component can compensate for the other. 
[75]                    Calculations at three-digit level of NACE Rev. 2 are based on the
LFS data available for 19 Member States (AT, CZ, DE, EE, ES, FI, FR, GR, HU,
LU, LT, MT, NL, PL, PT, RO, SE, SK, UK). Values for 25 three-digit sectors out
of 272 in total economy (i.e. including public sector), accounting for 0.3% of
total persons employed in 2010 in the same dataset, are considered unreliable
and not published, because they are below the 6500 population threshold applied
to LFS data; 28 additional three-digit sectors out of 272 in total economy,
accounting for 1.2% of total persons employed in 2010 in the same data set, are
considered unreliable but are published with a flag (between 6500 and 15000
population threshold applied to LFS data).
[76]             The list of sectors included in
the indicator and their associated innovation coefficients is available at http://ec.europa.eu/research/innovation-union/index_en.cfm?pg=keydocs.
[77]                    Calculation of CIS scores directly at the 1-digit level of NACE 2
based on pooled CIS micro-data may not result exactly comparable to the figures
in Table 11, calculated as an arithmetic average over the sectors.  
[78]                    See Mariagrazia Squicciarini and Colin Webb (2011),
“Innovation intensity in sectors. An experimental taxonomy”, Mimeo,
OECD, Paris.
[79]             The joint OECD/Eurostat EIP programme started in 2006.
See http://www.oecd.org/fr/industrie/statistiquessurlentreprenariatetlesentreprises/theentrepreneurshipindicatorsprogrammeeipbackgroundinformation.htm.

[80]             The rationale of the choice of this threshold was
agreed with Member States' experts on 23 October 2012. 
[81]             See the joint Eurostat/OECD manual on business
demography at: http://epp.eurostat.ec.europa.eu/portal/page/portal/product_details/publication?p_product_code=KS-RA-07-010.
[82]                    See DOC.8/en/Eurostat/g2/sbs/nov2012 of the Meeting of 15-16
November 2012 of the Structural Business Statistics Steering Group. In the 2011
collection in addition to data with 10+ employees, data were also collected for
the enterprise threshold 5+ employees and for 7% and 20% annualized growth in
employment.