Effectiveness of R&D Expenditures Supporting Innovation: A Case

Study of OECD Countries

Viktor Prokop, Jan Stejskal, and Petr Hajek

University of Pardubice, Faculty of Economics and Administration, Studentska 05, Pardubice, Czech Republic

Keywords: Effectiveness; Public policy; Innovation; R&D support; OECD countries

Abstract: R&D expenditures have been proven to be a key determinant of innovation activities in all developed

countries. These are primarily private sources of in-house research, intramural government expenditure on

R&D, research and development spending by universities and the public sector (e.g., public laboratories and

research institutes). These expenditures are often part of public policies whose proper targeting and

management should lead to allocation efficiency and optimal use of available production resources and

funding. This paper aims to analyze the effectiveness of these resources used to support R&D activities in

36 OECD countries. We have used the available data from the OECD databases for 2014 and employed the

DEA (VRS model) method. The output variable was % share of innovative firms and the GDP of the

economy. The results confirm that the efficiency of allocated resources is considerably variable. Only a

handful of countries have achieved maximum efficiency in the analyzed period (Estonia, Belgium, Ireland,

Chile, South Korea, Mexico, and New Zealand). At the end of the paper, the results were discussed and

practical recommendations defined.

1 INTRODUCTION

There are thousands of research studies dealing with

innovation, their importance for growth and

development, both by individual economic entities

and by regions, states, or supranational communities

(Autio et al., 2014). At this point, it is an undeniable

fact that innovation is a key factor in success

(whatever the economic entity thinks of success).

Great attention is also paid to the innovation

environment and to individual factors, which is

perceived as one of the important new factors of

production (apart from standard production factors,

for example, the quality of the innovation

environment is included (Lundvall, 2016; Mairesse

and Mohnen, 2004). His work is devoted to all

developed countries. Conversely, the backwardness

or lack of innovation environment is the reason for

the low maturity of some countries. It is, therefore,

one of the goals of governments to create an

innovation environment in their states (regions) that

foster the emergence of innovations and enable all

the benefits to be realized (Prajogo, 2016).

There are several scholars who are discussing the

above approach to innovation. E.g. Osborne and

Brown (2011) are discussing three flaws that can be

noticed in the application of public policies to

promote innovation. The first problem is the wrong

choice of the innovative model. They described the

perception of innovation in New Public

Management and its interest that innovation is at the

center of events and a competitive advantage (De

Vries et al., 2016). This was followed by

developments inspired by Porter's work, where the

engine of development was competition that led to

increased efficiency in service delivery (Furman,

Porter and Stern, 2002). Further development within

the innovation model was directed towards the

development of the innovation environment and its

components (Carlin et al., 2004).

The second flaw is perceived as the result of a

constant effort to improve, which ultimately leads to

a high inefficiency in any attempts to create public

policies. It turns out that it is necessary to perceive

public policy as a complex of processes and too

much emphasis on some of the details can

overshadow the resulting effect and cause

inefficiency. It is therefore, essential for the public

sector to understand very well what the innovation

process is, what its purpose is, how it is to achieve it.

From the observation of practice, it is necessary to

point out that innovation processes differ, whether

Prokop, V., Stejskal, J. and Hajek, P.

Effectiveness of RD Expenditures Supporting Innovation: A Case Study of OECD Countries.

DOI: 10.5220/0008928500050013

In Proceedings of the 1st International Conference on IT, Communication and Technology for Better Life (ICT4BL 2019), pages 5-13

ISBN: 978-989-758-429-9

they are products or services, as well as differing in

individual countries. This greatly hinders the

application of unified policies (such as EU territory),

leading to an ineffective approach “one-size-fits-all”

(Veugelers and Schweiger, 2016).

Osborne and Brown (2011) also put forward the

arguments of several scholars who also point to the

normative dimension of the word "innovation,"

which in many cases is perceived as synonymous

with the word "good." Even within this perception, it

should be remembered that if some of the support

processes are financed by public finances, it is

possible to perceive them to a certain extent as a

public good (Mazzucato and Semieniuk, 2017).

However, this aspect is very difficult to apply in

practice and encounters many obstacles (Smith et al.,

2019).

It is undeniable that innovation is and will be of

interest to policy makers in different countries and

that the level of effectiveness of these public

interventions is very low (Seaden and Manseau,

2001). That is why we need to continuously analyze

the effectiveness of individual intervention measures

in different countries and define appropriate policy

implications for improving public policies.

The paper is structured as follows. The following

part presents the theoretical framework in relation to

the public policies supporting innovation. The next

part describes the data, variables, and methodology.

In the third part, the obtained results are displayed

and discussed. Finally, the fourth part summarizes

the main conclusions.

2 LITERATURE REVIEW

In the beginning, it should be noted that public

intervention must be linked to impending market

failures or a clearly documented reduction in

community welfare. This is the conclusion resulting

from a neoclassical economic theory that points to

the causes of market failure due to, for example, the

existence of externalities or imperfect competition

with which clean market mechanisms cannot

effectively solve. Sometimes, failure occurs only in

the second phase, that is, at the actual

implementation of government intervention.

Lundvall (2016) describes this as a failure of a

system in which there is not enough linkages

between the academic sector and industry (despite

all government efforts, the application of public

policy and the allocation of public funds). Here we

can call it as a government failure.

There are several studies that address the

effectiveness of state interference, optimization,

setting goals and resources, assessing the conditions

for achieving Pareto efficiency or optimality (Potts,

2009). All this is happening in dynamic conditions,

which are represented by normal economic growth

and changes in the structure of the economy (change

of economic subjects, their goals, changing customer

preferences, etc.). It is mainly about the internal

transformation of processes in which determinants

(drivers) come in the form of new technologies, new

knowledge, revised business strategies, new

globalization tendencies, etc. These are the

manifestations of the so-called economic evolution

described in the early 1980s (Nelson and Winter,

1982). Part of this evolution is the creation of

innovation, which since then has been one of the

most important drivers of economic transformation

everywhere in the world.

In classical, but above all in neoclassical,

economic doctrines, scholars have stated that the

equilibrium market situation represents a Pareto

optimum and does not require any external

interference (government intervention in the form of

public policy). However, economic development

violates this equilibrium and worsens allocation

efficiency, which is the reason for public

intervention. This requires new types, new goals,

and new public policy tools to achieve higher

allocation efficiency. However, the new element is

the so-called dynamic efficiency (Abel et al., 1989).

The general objective remains to achieve maximum

social well-being and to maximize the efficiency of

the use of production resources.

An example of public policy that is applied in

developed countries is a policy of support for small

and medium-sized enterprises, innovation, R&D, or

industrial policy. The common denominator of these

types of public policies is expenditures on research

and development activities. Stimulation of each type

of expenditure (private, public, university,

government, etc.) is the subject of all. Many scholars

also draw attention to the crowding-out effect

(public subsidies are crowding the private

investment out), and that is precisely in support of

science and research activities.

Several studies examine the effectiveness of

R&D policies in different countries. González et al.

(2005) examined the effects of public subsidies on

stimulating scientific and research activities in

Spanish enterprises. They found subsidies are

stimulating the in-house research, but some

enterprises did not develop these activities without

subsidies at all. These scholars did not find the

ICT4BL 2019 - International Conference on IT, Communication and Technology for Better Life

crowding-out effect of Spanish enterprises in their

target group. Czarnitzki et al. (2007) analyzed the

effectiveness of the innovation policy supporting

cooperation on R&D activities in Germany and

Finland. For German companies, no significant

impact on R&D activities was found; innovation

performance could be improved by public incentives

financed from public budgets directed to R&D

cooperation. Conversely, the results from Finland

confirm that public spending on R&D funding for

these companies is crucial. Without subsidies, R&D

activities are almost unrealized.

Hall et al. (2000) conducted a broad study on

public tax incentives for the development of R&D

activities. The individual research studies from many

OECD countries were their target group. Results

confirmed the trend that there is a shift away from

direct subsidies to the R&D area in favor of tax

incentives, which, according to the authors of the

study, increases the efficiency of the whole

innovation system. Guellec et al. (2004) conducted

longitudinal research of 16 countries around the

world and examined the factors that affect their

productivity growth. They found that there are three

main factors affecting the performance growth: a

source of the funds, the socio-economic objectives

of state aid, and the type of public institution

conducting research.

It has been demonstrated that different types of

R&D funding contribute to increasing innovation

potential and consequently increase the performance

of the economy. All developed economies apply

some form of public policy aimed at supporting

R&D activities. It is different how these policies

finance and differ significantly in achieving dynamic

efficiency. Therefore, the aim of this paper is to

provide an overview of the effectiveness of pro-

innovation policies implemented in different OECD

countries by analyzing different types of R&D

funding.

3 DATA AND METHODS

One of the significant approaches to evaluate the

efficiency, performance, and productivity of

production units (decision-making units - DMUs,

e.g., OECD countries) based on the size of inputs

and outputs is known as Data Envelopment Analysis

(DEA). DEA is encompassing the linear

programming technique to depict the efficiency

frontier (Hudec and Prochádzková, 2013) while

DMUs should be comparable or homogenous. These

units convert multiple inputs into outputs, meaning a

set of units that produce the same or equivalent

effects that are referred to as the outputs of these

units (Prokop, Stejskal and Hajek, 2018).

The mathematical formulation of DEA models

considers the existence of a set of homogeneous

production units U1, U2, …, Un, wherein each unit

produces r outputs and subsequently using m inputs.

Then according to Prajogo (2006), we can write:

X = {xij, i = 1, 2, …, m, j = 1, 2, ..., n} (1)

is considered as input matrix and

Y = {yij, i = 1, 2, …, r, j = 1, 2, ..., n} (2)

is considered as output matrix.

The efficiency rate of Uq unit is generally

expressed as the weighted sum of inputs/weighted

sum of outputs. The principle of DEA models is that

when evaluating the efficiency of a production unit

Uq it maximizes its efficiency level, if the efficiency

rate of all other DMUs cannot be higher than 1 (100

%). The weights of all inputs and outputs must be

greater than zero so that all the considered

characteristics in the model are included (to see

more, e.g. (Halaskova, Halaskova, and Prokop,

2018; Prokop and Stejskal, 2007).

In this study, we measure the efficiency of

different R&D expenditures´ sources (inputs) within

36 OECD countries in 2014 by using input-oriented

VRS model operating with variable returns to scale

and data from OECD database (available at

http://stats.oecd.org). The assumption of variable

returns to scale (VRS) considering all types of

returns: increasing, constant, or decreasing. As

output variables, we are using innovation creation

and growth of GDP (all input and output variables

are described below in Table 1). According to

Griliches (1998) who empirically proved that there

is no time delay with a significant impact on the

results of analyses, we do not consider time delay

between input and output variables.

Table 1. Description of input and output variables.

Input variables

R&D exp. Variables Description

BERD Business enterprise

expenditure on R&D

BERD is seen as an essential factor affecting firms' performance

and innovation capacity (Siedschlag et al., 2005; Wang et al.,

Effectiveness of RD Expenditures Supporting Innovation: A Case Study of OECD Countries

2013). Karahan (2015), e.g., showed that BERD is one of the main

determinants of high-tech sectors.

GOVERD Gov. intramural

expenditure on R&D

Finance, human resources, and risks in innovation always limit

companies. Therefore, GOVERD represents one of the strategic

resources that could support firms´ R&D (Jin et al., 2016).

Government support also has a positive relationship with firms´

(industrial) innovation (Doh and Kim, 2014).

HERD Higher education

expenditure on R&D

HERD expenditure could support, e.g., university research, which

is the catalyst of new knowledge and driving force of advanced

(knowledge) economies (Sharif and Tang, 2014).

PSERD Public sector expenditure

on R&D

Voutsinas et al. (2008) proved that the public R&D expenditure has

a positive influence on business and total innovation, which

indicates the existence of significant externalities of public sector

research.

Output variables

INNOV Innovations (Product

and/or Process)

Product and/or process innovations are two distinct mechanisms

through which countries (firms) can improve their performance and

support their competitive advantage in the current global economy

(Najafi-Tavani et al., 2018). Therefore, innovations could be

underlying drivers of a firm's innovative performance, which can,

besides, contribute to general economic development (Prokop,

Odei and Stejskal, 2018).

GDP Gross domestic product Gross domestic product (GDP) and its growth represent one of the

most frequently used indicators of economic growth.

Source: own processing

4 RESEARCH RESULTS

In this part, the results of DEA are showed. We

distinguish all 36 OECD countries into two groups –

EU countries (23 countries) and the rest of the world

(13 countries). Countries that efficiently used

selected R&D expenditures´ sources (inputs) in the

processes of innovation creation and reaching GDP

growth (output variables) reached the rate of

effectiveness 1.000. Countries that did not reach the

rate of effectiveness 1.000 were not considered

effective – less rate of effectiveness means less

efficiency of the country.

Surprisingly, only 3 out of 23 (13 %) EU

countries were considered as efficient. These

countries are Belgium, Estonia and Ireland. Belgium

and Estonia are small open economies, characterized

by a relatively high dependence on foreign

subsidiaries of multinational firms, both in terms of

employment and output generation and innovation.

In Belgium, agglomeration can be an important

catalyst in the innovation process of firms which

enjoy a significantly positive impact from for

example increased sectoral concentration,

controlling for research and development intensity,

export intensity, foreign ownership, funding, and

own sector employment concentrations (De Beule et

al., 2012; Hansen et al. 2011). In Estonia, foreign

ownership or participation in larger corporate

groups, international markets and cooperation seems

to be main determinants of firms´ innovation

activities. Moreover, the positive impact of public

funding shows that public support has not crowded

out private expenditure on innovation in Estonia

(Masso and Vahter, 2008). In Ireland, innovation in

combination with increased export activities are

proved as the main drivers of productivity gains and

innovations (Love, 2010). On the other hand, less

efficient countries in this group were Czech

Republic (0.430), France (0.456), Greece (0.458)

and Lithuania (0.458).

In the group, Rest of the World, 4 out of 13 (31

%) countries were considered as efficient. These

countries are Chile, Korea, Mexico and New

Zealand.

Chile is one of the Latin America countries

where business, economic, and policy environments

differ between countries and generally diverge from

OECD countries and where innovation policy work

has made greater strides in the last decade (Crespi

and Zuniga, 2012). As in Estonia, foreign ownership

or participation in larger corporate groups seems to

be an important factor influencing R&D investment

and innovations in Chile because the economic

superiority of multinational firms can be associated

with more sophisticated knowledge assets and easier

access to finance and human capital (Girma and

Görg, 2007). In Korea and Mexico, increasing ICT

penetration is found to be strong, positive, and

statistically significant innovation determinant

ICT4BL 2019 - International Conference on IT, Communication and Technology for Better Life

(Lechman and Marszk, 2015). Moreover, R&D

activities and government innovation support

systems are considered essential factors for service

and technological innovation performance in Korea.

Korean Innovation Support System showed that

innovation support programs could be classified as

supports for tax incentives, finance, technology

development, human resources, purchasing, law, and

institutional infrastructure, or other indirect supports

based on the expenditure approach (Kim et al.,

2016). In New Zealand, firms and their performance

differ according to the extent to which they have

adopted knowledge-management practices (Darroch

and McNaughton, 2003) while public research

institutes play an essential role in the creation of new

knowledge (Lee et al., 2012). On the other hand, less

efficient countries in this group were Israel (0.246 –

the less efficient country within OECD countries),

USA (0.308) and Japan (0.476).

Our results indicate that most OECD countries

(29 out of 36; 81 %) have been inefficient in using

expenditures on R&D. Therefore, in the next part,

we propose some practical implications for these

countries (based on the practices that influence

innovation and performance in efficient countries,

see above). Moreover, DEA models also provide

practical implications for each inefficient country.

Therefore, we show (in Table 2 in appendix) both

original values (that each country reached) and

adjusted values (provided by DEA) that show how

the input (output) variables should be changed. Note

that input-oriented models propose changes focusing

primarily on input variables (or even minor changes

on the output side). These results show that there is a

need to focus on each financial source to avoid

increasing inefficiency and to reduce the number of

countries that are inefficient because (with the

current R&D expenditures) the necessary outputs are

not achieved. Therefore, DEA proposes to increase

outputs at given inputs or to reduce current inputs.

Moreover, DEA also provides information about

countries that could be benchmarked for other

inefficient countries. Chile and Belgium were

proposed as benchmarks for other countries in most

cases.

5 CONCLUSIONS

Like the studies above, our study also shows that

most of the countries analyzed do not achieve

effectiveness in implementing their R&D policy.

Only a few countries from the selected file have

achieved the highest possible efficiency. For non-

European countries, it is mainly Chile, South Korea,

Mexico, and New Zealand. These are countries

where the financial distribution of R&D has been

optimally distributed over the past period. The

results do not show that these countries have the

greatest innovation performance or the highest GDP

growth. The method shows the highest efficiency,

i.e., the individual inputs correspond to the

maximum achievable outputs. That is why Chile is

the most common benchmark for others.

European countries have the highest efficiency in

Belgium, Estonia, and Ireland. These are countries

that have been continually profiling for many years

as a knowledge-based economy, a high degree of

openness, digitization, and high education. These are

the so-called economic tigers of the European

Union. It turns out that setting their public policies is

optimal and allows for maximum efficiency.

Our results are confirmed by the findings of

Thomas et al. (2009) who result in a growing trend

in R&D efficiency in Asia, especially in South

Korea. Similarly, we confirm the results of Wang et

al. (2007) who came with the conclusion that less

than half of the 23 OECD countries (analyzed in

their study) are fully competent in their R&D policy.

We also confirm their conclusions that the country's

English proficiency indicator is a crucial driver of

success in science and research.

It can be said that our research is also evidenced

by the lower effectiveness of R&D policies and the

investment of financial resources in the new

Member States. Apart from Estonia, none of these

new states have achieved a high level of R&D

efficiency. This is confirmed by the findings of the

Conte study (Conte et al., 2009) which revealed by

their study that there are significant differences in

the effectiveness of R&D spending between old and

new member countries.

The results of our study show that, despite

significant efforts to implement relevant public

policies and massive financial support, there is not a

significant shift in output indicators in most of

surveyed countries. There are crowding out effects,

mainly by public funds. It is possible to imply and

recommend certain proposals for improvements:

countries should be more involved in supporting

high technologies, investing in education, supporting

specific science and research projects with clearly

specified and measurable outputs, applying tax

savings or incentive tools that affect the willingness

of firms to implement in-house research and invest

in it continuously. It is necessary to reduce the

dependence of companies and universities on

European fund funding, to better define the

Effectiveness of RD Expenditures Supporting Innovation: A Case Study of OECD Countries

objectives of the policies in place and to focus more

precisely on investment.

This research has also some limitations. One is

the quality of data that is input to our analysis.

Therefore, the results can only be related to the

countries included in the target group.

Generalization to other countries or groups of

countries can be realized only approximate and often

illustrative. The second limitation is the choice of

both input and output indicators. It is not possible to

avoid any random combination that will not be

realistic.

ACKNOWLEDGEMENTS

This work was supported by a grant provided by the

scientific research project of the Czech Sciences

Foundation Grant No. 17-11795S.

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Effectiveness of RD Expenditures Supporting Innovation: A Case Study of OECD Countries

APPENDIX

Table 1 Results of input-oriented VRS DEA model

Source: own



 Inputs



Country

Efficien

cy

Bench‐

marks

BERD

(%ofGDP)

GOVERD

(%ofGDP)

HERD

(%ofGDP)

PSERD

(%offirms)

Orig

.

Adjust. Orig

.

Adjust. Orig. Adjust

.

Orig. Adjust.

EU28 Austria

0.533 Chile 1.95 1.04 0.15 0.08 0.73 0.37 0.87 0.45

Countries Belgium 1.000‐1.52 1.52 0.18 0.18 0.52 0.52 0.7 0.70

 CzechRep.

0.430 Chile 1.01 0.43 0.35 0.12 0.52 0.22 0.86 0.35

 Denmark

0.852 Chile 1.96 0.88 0.07 0.06 0.95 0.30 1.01 0.37

 Estonia

1.000‐1.26 1.26 0.2 0.20 0.7 0.70 0.91 0.91

 Finland

0.568 Belgium 2.44 1.39

 0.32 0.18 0.77 0.44 1.09 0.62

 France 0.456 Chile 1.48 0.67 0.31 0.13 0.47 0.21 0.78 0.35

 Germany

0.814 Belgium 2.02 1.64 0.43 0.35 0.53 0.43 0.96 0.78

 Greece

0.458 Chile 0.24 0.11 0.17 0.01 0.28 0.12 0.45 0.14

 Hungary

0.662 Chile 0.85 0.55 0.19 0.07 0.24 0.16

0.43 0.24

 Ireland

1.000‐1.2 1.20 0.08 0.08 0.38 0.38 0.46 0.46

 Italy 0.968 Belgium 0.69 0.67 0.17 0.16 0.36 0.35 0.54 0.52

 Latvia

0.733 Chile 0.15 0.11 0.18 0.01 0.33 0.12 0.51 0.14

 Lithuania

0.458 Chile 0.24 0.11 0.18 0.01 0.48 0.12 0.66 0.14

 Luxembourg

0.941 Chile

1 0.67 0.28 0.09 0.18 0.17 0.46 0.27

 Netherlands 0.902 Chile 1.22 1.10 0.23 0.21 0.7 0.63 0.94 0.84

 Poland

0.540 Chile 0.33 0.18 0.25 0.05 0.31 0.16 0.56 0.22

 Portugal

0.982 Chile 0.7 0.69 0.1 0.10 0.58 0.39 0.68 0.50

 SlovakRep.

0.540 Chile 0.34 0.18 0.2 0.04

 0.28 0.15 0.48 0.20

 Slovenia

0.719 Chile 1.99 1.12 0.34 0.16 0.29 0.21 0.64 0.38

 Spain 0.916 Belgium 0.69 0.63 0.25 0.23 0.36 0.33 0.61 0.56

 Sweden

0.861 Chile 2.31 1.22 0.16 0.14 0.92 0.53 1.09 0.68

 UK

0.485 Belgium 1.1 0.53 0.14 0.06 0.46 0.22 0.6 0.29

Restof

the

Australia

0.654 Belgium 1.23 0.80 0.24 0.16 0.58 0.38 0.86 0.54

World Canada

0.810 Chile 0.88 0.71 0.15 0.12 0.65 0.43 0.8 0.56

 Chile

1.000‐0.11 0.11 0.01 0.01 0.12 0.12 0.14 0.14

 Iceland 0.693 Estonia 1.38 0.96 0.46 0.28 0.69 0.48 1.15 0.76

 Israel 0.246 Chile 3.32 0.11 0.07 0.01 0.49 0.12 0.57 0.14

 Japan

0.476 Chile 2.57 0.99

 0.29 0.13 0.45 0.21 0.74 0.35

 Korea

1.000‐3.4 3.40 0.49 0.49 0.41 0.41 0.91 0.91

 Mexico

1.000‐0.17 0.17 0.13 0.13 0.12 0.12 0.25 0.25

 New

Zealand

1.000‐0.57 0.57 0.29 0.29 0.4 0.40 0.69 0.69

 Norway

0.751 Belgium 0.86 0.65 0.27 0.20 0.52 0.39 0.79 0.59

 Switzerland

0.500 Chile 2.17 0.11 0.02 0.01 0.88 0.12 0.9 0.14

 Turkey 0.805 Chile 0.42 0.34 0.1 0.08 0.4 0.24 0.51 0.33

 USA

0.308 Chile 1.95 0.16 0.34 0.10 0.39 0.12 0.73 0.22

ICT4BL 2019 - International Conference on IT, Communication and Technology for Better Life

Table 3 Results of output-oriented VRS DEA model



Outputs



Country Efficiency Bench‐marks

INNOV

(%ofallfirms)

GDP

(%change)

Orig. Adjust. Orig. Adjust.

EU28 Austria

0.533 Chile 12.61 12.61 2.22 2.74

Countries Belgium

1.000‐17.88 17.88 1.53 1.53

 CzechRep.

0.430 Chile 9.26 9.26 2.27 4.22

 Denmark

0.852 Chile 10.92 10.92 1.9 2.96

 Estonia

1.000‐20.07 20.07 3.95 3.95

 Finland

0.568 Belgium 15.86 15.86 1.94 2.36

 France

0.456 Chile 9.47 9.47 1.57 4.40

 Germany

0.814 Belgium 17.66 17.66 1.96

 3.24

 Greece

0.458 Chile‐5.38 1.79 4.92

 Hungary

0.662 Chile 7.5 7.50 1.65 4.80

 Ireland

1.000‐13.19 13.19 2.16 2.16

 Italy

0.968 Belgium 12.66 12.66 1.42 3.31

 Latvia

0.733 Chile‐5.38‐4.92

 Lithuania

0.458 Chile‐5.38‐4.92

 Luxembourg

0.941 Chile 8.08 8.08 2.33 4.76

 Netherlands

0.902

Chile 18.66 18.66 0.87 3.81

 Poland

0.540 Chile 6.81 6.81 3.33 4.61

 Portugal

0.982 Chile 12.36 12.36 1.11 4.31

 SlovakRep.

0.540 Chile 6.5 6.50 2.92 4.70

 Slovenia

0.719 Chile 10.22 10.22 0.63 4.64

 Spain

0.916 Belgium 12.91 12.91 0.98 3.43

 Sweden

0.861 Chile 16.48 16.48 3.04 3.04



 UK

0.485 Belgium 8.75 8.75 2.49 4.06

Restof

the

Australia

0.654 Belgium 13.4 13.40 3.05 3.16

World Canada

0.810 Chile 13.3 13.30 2.6 4.31

 Chile

1.000‐5.38 5.38 4.92 4.92

 Iceland  0.693 Estonia 16.77 16.77 2.77 3.22

 Israel

0.246 Chile 5.1 5.38 3.45 4.92

 Japan

0.476 Chile 9.87 9.87 0.96 4.42

 Korea

1.000‐21.22 21.22 3.99 3.99

 Mexico

1.000 ‐ ‐ ‐ 4.17 4.17

 NewZealand

1.000‐15.01 15.01 2.85 2.85

 Norway

0.751 Belgium 13.73 13.73 3.14 3.35

 Switzerland

0.500 Chile‐5.38 2.7 4.92

 Turkey 0.805 Chile 8.9 8.90 4.14 4.46

 USA

0.308 Chile‐1.24 3.38 4.34

Source: own

Effectiveness of RD Expenditures Supporting Innovation: A Case Study of OECD Countries