Exploration of the effectiveness of public procurement for innovation: Panel analysis of EU
countries’ data
Detelj Kristina a, Jagric Timotejb, Markovic-Hribernik Tanjab
a
Faculty of Organization and Informatics Varazdin, University of Zagreb, Pavlinska 2, 42000
Varazdin, Croatia
b
Faculty of Economics and Business, University of Maribor, Razlagova 14, 2000 Maribor,
Slovenia
1
Abstract:
This research focuses on the impact of public procurement for innovation (PPI) on a
country's level of innovativeness. The available literature primarily consists of case studies
that identify PPI’s impact on the innovativeness of particular firms. Therefore, this paper
developed an econometric model to investigate the impact of PPI on the innovativeness of
EU countries. The model tested the impact of four different innovation policy measures (PPI,
R&D subsidies, regulations and cooperation). The results showed that in different model
settings, PPI was positively and significantly related to countries’ innovativeness, whereas
the other three measures showed low significance. These research findings may be
important to policy makers when selecting appropriate measures for promoting innovation
and thereby also enhancing their country’s competitiveness.
Key words: public procurement, innovation policy instruments, panel data analysis, EU
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1. INTRODUCTION
Due to the current economic and financial crisis, public finances today face reductions in tax
revenues which, together with globalization, tax competition and new technologies, force
governments to reduce inefficient public spending. This also prompts the creators of
economic policies to make the role of the state more focused. In this context, one question
becomes increasingly important: which state policy instruments promote innovation better,
as innovativeness is generally recognized as an important factor in the competitiveness of an
economy (WEF, 2013).
In the analysis of policies which promote innovation, Edler and Georghiou (2007) find that
European policies have so far focused almost exclusively on the supply side, meaning that
European governments promote innovations by supporting their creators. The other option
would be to encourage the demand side by supporting the users of innovations, which
opens innovation opportunities in the market and engages innovators in potentially
profitable activities. These authors, together with other researchers (Edler et al., 2012,
Georghiou et al. 2014; Lember et al. 2015), continue to raise the awareness of the
importance of demand-side policies and the need for their inclusion into innovation policies.
Since in the past the orientation towards supply-side innovation policies was much stronger,
this paper also takes into account the demand-side policies in research. At the centre of our
research is public procurement, which can serve as a powerful policy measure for
governments to promote innovation. Edler and Georghiou (2007) call it public procurement
for innovation (PPI). It is based on the notion that the demand can trigger and accelerate the
development and diffusion of innovation (Edler et al., 2012).
The paper begins with a brief review of the relevant literature on the topic of PPI and an
explanation of the circumstances that caused a rising interest in PPI. Firsly, the paper
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present the results of the qualitative analysis of PPI cases described in recent research. After
that the paper presents a model that was created to test the impact of the four innovation
policy measures on countries’ innovativeness level. The analysis is based on panel data for
28 EU countries in the period from 2004 to 2011. After presenting and discussing the results
of the analysis, directions for possible future research are proposed in the conclusion.
2. LITERATURE REVIEW
The majority of reviewed empirical research on PPI consists of qualitative case studies of
individual examples in which innovations were the result of public procurement. However,
there are very few quantitative studies, which are generally used to verify theoretical
models and the response of certain variables in practice.
Unlike regular public procurement, in a more narrow sense, PPI is a process in which some
public entity awards a contract to another organization for a product or service that does
not yet exist (Edquist et al., 2000). Therefore it is necessary to engage in research and
development (R&D) and to create an innovation before delivery. This is why the contracting
authority must determine the function of a product or system, rather than the product itself
(Georghiou et al., 2003). In a broader sense, PPI can turn governments into leading users of
innovations, which diffuses existing innovations and thus improves their commercialization
(Edler and Georghiou, 2007).
In the early 1980s, many studies argued that demand was an important incentive for
innovation, based on the review of Mowery and Rosenberg (1979). At that time the United
States was considerably more technologically advanced than Europe, so Rothwell and
Zegveld (1981) recommended that Europe should establish a homogenous common market.
In that way, emerging new technologies could be promoted with the help of the public
procurement system, due to the reduced level of uncertainty for potentially innovative
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firms, since it reflects true needs and represents a stable demand. After this article,
researchers mainly lost interest in PPI until the end of the last century. Then the idea was
revived by Edquist et al. (2000), who found that, in practice, in the 1990s, most policies for
stimulating innovation were directed towards the supply side (such as subsidies for R&D),
whereas the demand side (where PPI belongs) was heavily neglected. As demanding clients
are a critical driving force in creating innovations, the complex requirements of PPI support
the propensity of companies for R&D investment and their propensity to create innovation
(Nyiri et al. 2007). After the year 2000, there are increasing examples of interest in the field
of PPI, particularly at the EU level (Edler et al., 2005; Aho et al., 2006; EU and OECD, 2011).
The EU itself started and funded most of this research at the beginning of the 21st century
(mostly reviewed in Edler and Georghiou, 2007; or Aschhoff and Sofka, 2009). There are also
some newer studies, such as DETE 2009; EC DGEI 2011; EU and OECD 2011. This encouraged
many national and international research institutions to do the same (e.g., FORA and OECD,
2009; OGC and DBIS, 2009; MEE 2010), as well as caused the number of research articles to
rise (e.g., Edler and Georghiou, 2007; Aschhoff and Sofka, 2009; Uyarra and Flanagan, 2010;
Kattel and Lember, 2010; Edquist and Zabala-Iturriagagoitia, 2012; Guerzoni and Raiteri,
2012, Zelenbabic 2015). All of these explore different aspects and examine the potential of
public procurement to influence the formation of innovations in the economy.
The analysis of the 58 cases from the above-mentioned literature (see in more detail in
Detelj, 2015) shows that one part of those PPI cases includes general policies aimed at
creating a supportive environment for PPI (20 cases) and the second part includes
innovations resulting from the participation in public procurement (38 cases). The cases
come primarily from the USA, United Kingdom, Germany, the Netherlands, and the Nordic
countries, which does not generally imply that there are no such examples in other countries
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but shows a better awareness of PPI possibilities in these countries. Research on public
procurement in Central and Eastern Europe still focuses on some other issues, such as the
problem of corruption in Croatian public procurement (Ateljević and Budak 2010) enhancing
the efficiency of public procurement by centralizing procurement activities in Serbia
(Jovanovic et al. 2013) or the emergence of innovative public social services in Slovakia,
which is induced by non-governmental actors (Merickova et al. 2015). None of this research
actually pays attention to PPI.
In the review of the available literature, the paper found only three quantitative studies that
examined PPI. The first is the research on the impact of public procurement and other
selected policy measures on innovative outputs, carried out on a sample of German
companies (Aschhoff and Sofka, 2009). It showed that PPI and cooperation with others in
developing innovations were the two statistically significant policies for generating revenue
from innovative goods. Another is the research on the impact of PPI and R&D subsidies on
innovation inputs (R&D investment) and innovation outputs (revenue from innovative
products and services), conducted on a sample of European companies (Guerzoni and
Raiteri, 2012). The impact of PPI on both innovation inputs and outputs is stronger than the
impact of R&D subsidies, but these two policies affect innovation best when used together.
The third study included public procurement in an economic growth model for the American
states and examined how the public demand for high-technology products or services
influenced corporate R&D investment, in turn leading to innovation (Slavtchev and
Wiederhold, 2011). The results show that the government demand for innovative products
and services increases private R&D investments.
The literature review shows an obvious prevalence of qualitative studies over quantitative
ones. There is also one other gap: no quantitative studies exist for European countries on
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the macro level (i.e., the country level). This was the rationale behind our research—to add
further empirical evidence about the impact of PPI on EU countries’ innovativeness.
3.
RESEARCH MODEL
Since quantitative studies on PPI are very scarce, the paper could not find an adequate
macro-level model to test the assumptions made. Based on the theoretical background of
Aschhoff and Sofka's (2009) micro-level study (i.e., the level of enterprises in Germany), the
paper built an econometric model to test how PPI, combined with the other three policy
measures, affects a country’s innovativeness at the macro level of 28 EU countries. Thus, the
paper built an econometric model that included PPI, R&D subsidies, cooperation with others
for innovation development, and regulation as inputs, and a country’s innovativeness
measure as an output. This was empirically tested in the research.
The usual measure of innovative activity has always been patent application, but that is not
a very good measure for many industries (Arundel and Kabla, 1998). For this reason,
Fagerberg and Srholec (2008) argue that the measurement of innovation based on patents is
often misleading because it measures only the global inventions, while the "small" and
incremental innovations, representing the majority of modern innovation activity, are not
covered by such measurements. Similarly, Rodriguez and Montalvo (2007) emphasize that
the focus should also be extended from research, which is seen as a primary source of
innovation, to the development and diffusion of innovation. Due to the interconnectedness
of modern technologies with all the spheres of modern life, innovation is now usually
incremental and not radical (Markard et al., 2012). This is why it was decided that the
dependent variable in the model would be an indicator of business sophistication and
innovation (BS&I) from competitiveness research by the World Economic Forum (WEF,
2013), representing the outputs in the presented econometric model. The BS&I index
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includes a business sophistication pillar, which covers the knowledge, skills and working
conditions embedded in the organizations; and the technological innovation pillar, attached
to the traditional view of innovation as new products, services and processes (Sala-i-Martin
et al., 2011). The index ranges from 1 to 7, with 7 representing the best result.
The basic regression model is as follows:
𝐵𝑆&𝐼𝑖𝑡 = 𝛽1 + 𝛽2 𝐶𝑂𝑂𝑃𝑖𝑡 + 𝛽3 𝑅𝑄𝑖𝑡 + 𝛽4 𝐺𝐵𝐸𝑅𝐷𝑖𝑡 + 𝛽5 𝑃𝑃𝐼𝑖𝑡 + 𝑢𝑖𝑡 (1)
The explanatory variables in our focus are the four policy measures through which the state
can encourage the formation of innovation. These are:

COOP - the proportion of firms that cooperate with others in the process of creating
innovation

RQ - regulatory quality

GBERD - share of Gross Business Expenditures for R&D financed by the public sector
(subsidies)

PPI - a measure of public procurement for innovation
3.1. Choice of indicators for explanatory variables
Cooperation with others in the creation of innovation (COOP) was represented by Eurostat
data (n.d.), based on the Community Innovation Survey (CIS). COOP represents the share of
enterprises in a country that create their innovations in cooperation with others.
Participants in this cooperation may be manifold (customers, suppliers, competitors,
research institutes, universities, etc.). The impact of regulatory conditions on businesses is
measured by an indicator from Worldwide Governance Indicators (WGI), examining the
quality of governance. The Regulatory Quality (RQ) "captures the perception of the ability of
the government to formulate and implement sound policies and regulations that permit and
promote private sector development" (WGI project, 2012). It is a proxy measure for the
8
characteristics of the regulatory environment in EU countries. This indicator reflects changes
in business conditions affected by changes in the regulatory framework. Public subsidies in
R&D are calculated based on Eurostat’s science and technology indicators. The first
measures the total Business Enterprise R&B Expenditure (BERD) and the second shows the
amount of euros spent on BERD coming from public funds. Their quotient is our indicator,
which shows the share of Gross Business R&D Expenditures financed by the public sector
(GBERD). For most of the countries the data was available for the years 2004-2011.
Determining the most appropriate indicator for monitoring PPI proved to be the biggest
problem. The EU prescribes regulatory thresholds above which the public entities are
obliged to implement a mandatory procurement process (EC, 2013). But, there is no
coordination in collecting the data about performed procurement above the thresholds, let
alone below them (Kapff, 2013). In the published literature to date, there are no relevant
indicators, except for the estimate of the total amount of public procurement in the EU and
the share of public procurement in GDP, published by the European Commission (EC, 2009,
2012). The data for the years 2004-2011 is available for the 25 EU Member States, but has
only been available for Bulgaria and Romania since the year 2007, while for Croatia these
reports contain no data. The data for Croatia was thus calculated based on the figures listed
in public procurement annual reports of the Croatian Ministry of Economy (MINGORP, 2013)
divided by GDP (Worldbank n.d.). Croatian data is also only available from the year 2007
onwards.
However, the data on total public procurement (PP) does not distinguish between regular
and innovative procurement, which would be more appropriate. Therefore, the paper
creates a combined variable which consists of the PP total multiplied by a dummy indicator
reflecting a government’s propensity to use PP for acquiring innovative goods. The dummy
9
was derived from the results of the Executive Opinion Survey (EOS), where the respondents
had to answer whether their governments' decisions in public procurement promoted
innovation (WEF 2013). Responses ranged from 1 (no, not at all) to 7 (yes, extremely
efficiently). Since this indicator is also a component of the BS&I index, which is our
dependent variable, authors could not include it directly into our model as a numeric
variable. Therefore, authors used it to create the dummy variable (DPPI) based on the
average value of the indicator for the country in a specific year. The countries with results
below a certain threshold were given the value of the dummy variable DPPI=0 and the ones
with results above the threshold value were given a DPPI=1. This way, the authors created
two variables to distinguish between more and less innovative public procurement: PPI_D1
where the dummy variable has taken a value of 1 where the results from EOS were above
4.0, and PPI_D2 has taken a value of 1 where the results from EOS were above 4.5.
The econometric model therefore includes two variables instead of one for monitoring the
PPI:
𝑃𝑃𝐼𝑖𝑡 = 𝛽5 𝑃𝑃𝑖𝑡 + 𝛽6 (𝑃𝑃𝑖𝑡 ∗ 𝐷𝑃𝑃𝐼𝑖𝑡 )
(2)
By including (2) in our basic model (1), it becomes the following:
𝐵𝑆&𝐼𝑖𝑡 = 𝛽1 + 𝛽2 𝐶𝑂𝑂𝑃𝑖𝑡 + 𝛽3 𝑅𝑄𝑖𝑡 + 𝛽4 𝐺𝐵𝐸𝑅𝐷𝑖𝑡 + 𝛽5 𝑃𝑃𝑖𝑡 + 𝛽6 (𝑃𝑃𝑖𝑡 ∗ 𝐷𝑃𝑃𝐼𝑖𝑡 ) + 𝑢𝑖𝑡
(3)
The (un)availability of data for Bulgaria, Romania and Croatia for the whole period (20052011) resulted in an unbalanced panel.
In addition to these variables the outputs can also be affected by other factors, which the
paper discuss in the extended model as control variables. The authors examine how the
BS&I index reflects the impact of the following: GDP per capita (GDP_pc), GDP growth rate
(GDP_gr), gross expenditures on R&D as a percentage of GDP (GERD), government financing
10
share in GERD (G_GERD), share of employment in high-technology and knowledge-intensive
activities in the economy (TRD_emp and RES_emp), export of high-technology products
(HTP_xp) and the export of knowledge-intensive services (KIS_xp ). The goal was to find out
which among them significantly affected the dependent variable. Moreover, the literature
review suggests that the effects of certain innovation policy measures (e.g. public
procurement or changes in legislation) could only be seen after a longer period of time
(medium to long term) and not immediately after their use (Edler et al. 2012). Considering
this, our econometric model also tested the effects of the explanatory variables with a oneyear time lag. Unfortunately, short time-series data availability does not allow us to test our
models for longer time lags.
3.2. Methodology and data
In order to study data at a national level, which is available for more years, the paper opted
for the method of panel data analysis. Panel analysis takes into account the diversity of
individual research units while the combination of time and cross-sectional dimensions
allows for more informative, more various and less collinear variables, providing more
degrees of freedom and better efficiency and reliability of the estimators (Baltagi, 2005). The
characteristics of the dependent variable determine which is more appropriate to use: the
static or the dynamic panel model. The dynamic models result in unbiased and consistent
estimators only with a larger number of cross-section units (N) and time periods (T) (Škrabić,
2009). Since this study included only 28 EU countries and the longest time series for some
variables extends up to 8 periods, this dataset is not appropriate to apply the dynamic panel
analysis. Therefore, the paper used only static models.
The data for the analysis was collected from secondary sources. They refer to the 28
members of the European Union. The dependent variable is the level of innovativeness
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measured by the business sophistication and innovation index (BS&I). The current way of
measuring BS&I is available only for the last 8 consecutive periods through the data platform
(WEF, 2013). Most of the explanatory variables data was extracted from web-based data
platforms (Eurostat, n.d.; Worldbank, n.d.). In addition, data on PP and PPI was collected
from the reports of the European Commission on public procurement indicators (EC, 2009,
2012) and from the global competitiveness report database platform (WEF, 2013). European
Commission reports do not include data for Croatia (which joined the EU in 2013) and
therefore our estimate for Croatia used the annual reports of the Croatian Ministry of
Economy (MINGORP, 2013) and data on the Croatian GDP (Worldbank, n.d.). The advantage
of secondary sources is the fact that their data collection methodology is known and relies
upon a theoretical basis, thus producing comparable data for different countries. The
disadvantage of secondary sources is practical field collection of the data by many national
organizations, where there may be different interpretations of the otherwise same
guidelines. Also, sometimes there are countries that lack data for certain time periods, which
creates an unbalanced panel. Even though our data is unbalanced, the Stata software used
in the analysis has procedures that correct the estimates for unbalanced data.
Although our analysis focuses on the impact of PPI on a country's innovativeness, the
emergence of innovation can also be affected by some other conditions, so econometric
analysis also has to consider other variables. Therefore, the authors checked additional
variables in the extended model: GDP per capita (GDP_pc), GDP growth rate (GDP_gr), gross
expenditures on R&D as a percentage of GDP (GERD), government financing share in GERD
(G_GERD), share of employees in high-technology and knowledge-intensive activities in the
economy (TRD_emp - total R&D personnel and RES_emp - number of researchers), the
export of high-technology products (HTP_xp) and the export of knowledge-intensive services
12
(KIS_xp ). All the data was extracted from secondary sources with some missing data, thus
resulting in an unbalanced panel.
3.3. Descriptive statistics
The data includes 28 countries of the European Union. Together with the usual statistics
such as mean and standard deviation (SD) the authors additionally calculated the variation
among different countries ("between" SD) and the variation of a particular indicator through
time for the same country ("within" SD). Some variables show greater variability due to
changes in certain phenomena over time, while others change due to the differences
between the countries. It is important to emphasize that we have an unbalanced panel
because of data unavailability; therefore we had less observations for some variables than
we would have in a balanced panel. The results are given in Table 1.
Table 1 here
Descriptive analysis shows that most variables show greater variability based on differences
between countries compared to the within variability for the same country over time. This
feature suggests that it will probably be more appropriate to use fixed effects models (FEM)
for panel analysis. This kind of analysis takes into account certain differences in the
behaviour of different units of analysis, based on their individual characteristics. However,
the authors tested all the possibilities and the results of formal tests determined the
adequacy of the models.
4.
RESULTS OF THE ECONOMETRIC ANALYSIS
In the panel data analysis we first need to check if there is multicollinearity in the model (a
correlation among some or all of the explanatory variables), because in its presence
regression coefficients would be indeterminate and standard errors would become infinite
(Gujarati 2004). Therefore, the authors first analysed variables by using Pearson and
13
Spearman correlation coefficients. A strong correlation (usually above 0.6) indicates which
variables should not be put into the same econometric model in order to avoid
multicollinearity. Both correlation coefficients show a strong correlation among some
variables. In the basic model, the biggest problems are caused by the variable regulatory
quality (RQ), which is strongly correlated with PPI_D1, GDP per capita (GDP_pc), Gross R&D
Expenditures (GERD), government financing of total R&D (G_GERD) and indicators of
employment in R&D activities (TRD_emp and RES_emp). Except for the variable PPI_D1, RQ
is not strongly correlated with the other variables in the basic model. Therefore, in the basic
model equation, the authors focus on the model with PPI_D2 for measuring the propensity
of government to innovative public procurement instead of PPI_D1. In the later extension of
the model, the authors added other variables that revealed low correlation with our four
innovation policy measures. When the regression coefficients show similar properties and
significance in both the basic and extended models, this suggests their robustness.
Other relatively stronger correlations were observed among PPI_D2 and indicators of
employment in the R&D sector (TRD_emp and RES_emp). Therefore, if the authors include
them in the model (after removing RQ) the authors shall focus on observing the estimates
for PPI_D1 instead of PPI_D2. Among other variables, several higher correlations are found
for the indicator GDP_pc (with G_GERD, KIS_xp and R&D employment), as well as between
the indicators GERD and G_GERD, and particularly strong correlations are present among
expenditures for R&D and employment in these activities. Correlation between indicators of
employment in R&D (TRD_emp and RES_emp) is not important, because they will not be
included in the same model.
The Ramsey Regression Specification Error Test cannot be carried out after the panel
regression. Škrabić (2009) explains this through the fact that the panel analysis usually does
14
not use the R-squared coefficient as an indicator of the adequacy of the model chosen and in
the panel analysis it is not justified to interpret this indicator. Indeed, the purpose of the
panel data analysis is to determine which explanatory variables significantly affect the
dependent variable in different settings, by using several different models and the purpose
is not to choose the best model.
For all the models, our results suggested that the fixed effects model (FEM) or the random
effects model (REM) were more appropriate than the pooled OLS model—therefore we had
to choose between the two by applying the Hausman test. The test confirmed that it was
more appropriate to use FEM estimates in all our models. Moreover, we had already
expected this based on the nature of the data.
In the next step we had to check for heteroskedasticity and/or autocorrelation in the
residuals. Heteroskedasticity does not result in unbiased regression coefficients, but
standard errors become invalid (Wooldridge 2002). We used a modified Wald statistic for
heteroskedasticity in the panel data, as suggested by Greene, but with Baum's modification
which allows for the testing in unbalanced panel data (Baum 2013). The next step was to
test if there was autocorrelation (serial correlation) in the model because this would lead to
biased standard errors and the inefficient estimates of regression coefficients (Wooldridge
2002). The Wooldridge test checks if there is a correlation between the residuals in
successive periods (Drukker 2003). In all the estimated models both heteroskedasticity and
autocorrelation were found to be present. Then the authors used the procedure
recommended by Wooldridge (2011) to estimate robust regression coefficients, which
account for heteroskedasticity and autocorrelation in the data. These robust coefficients
allow the authors to make valid conclusions because they have already taken into
consideration the effects that heteroskedasticity and autocorrelation have on the data.
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4.1.
Basic model analysis
The basic model estimates the equation with the mentioned four innovation policy
measures and their relation to the BS&I index. Each combination of variables was tested four
times. In the first version (the model label is an odd number with no letters) the authors
used all the selected variables in the model and the PPI_D2 variable for PPI (the dummy
variable had a value of 1 for the assessment of innovative public procurement above 4.5). In
the second version (odd number with the letter a) everything is the same, except for the PPI,
where the authors used PPI_D1 (assessment of PPI above 4.0). The third version (labelled by
an even number) used the same variables as the first model, but now with a time lag of one
year, and the fourth version (an even number with the letter a) is the same as the second,
but with time lag in the explanatory variables. A list of equations for all the tested models is
displayed in Appendix A.
Table 2 here
In the basic models without the time lag (Models 1 and 1a in Table 2) the only statistically
significant variables are the ones connected with public procurement. The share of PP has a
negative impact on innovativeness level, i.e. the growth in the share of public procurement
in GDP by one percentage point is associated with the average reduction of the BS&I index
by 0.02. On the other hand, PPI has a positive impact, i.e. growth of PPI by one percentage
point is associated with an increase in the BS&I index by 0.007. This implies that larger public
procurement generally deteriorates the level of innovativeness, but if it is innovationoriented, this mitigates the negative impact. As in Model 2a, the authors used PPI_D1 (which
is as a slightly less innovation-oriented PP calculated with dummy variable 1 for values above
4.0 vs. 4.5 in PPI_2), PPI is no longer significant for the BS&I index. However, in this case,
regulatory quality becomes significant at a significance level of 5%. In the literature review,
16
the authors found contradicting empirical evidence which could explain this. Some research
claimed that better regulations could improve innovativeness, and another claimed that
worse regulations could improve innovativeness. Our results here are in line with evidence
from the research of Montalvo and Koops (2011) or Munos (2009). This means that stricter
regulations (lower graded in our data) have a positive impact on the emergence of
innovations. An intuitive explanation for this would be that companies in countries with
weaker regulations are forced to become more innovative and thereby overcome the
obstacle of poor regulatory framework.
4.2.
Extended models analysis
The dependent variable is the same (BS&I index) and control variables are added to the four
focus variables in various combinations depending on their mutual correlations. Strongly
correlated variables are not included in the same models in order to eliminate
multicollinearity. Therefore, the models labelled 7-14a (equations can be checked in
Appendix A) exclude the variable RQ as it was correlated with several other control
variables. The results of the models with odd labels (3-13a) are presented in summary form
in Table 3.
Table 3 here
The table consists of the tested models without a time lag of explanatory variables with
respect to the dependent variable. In all of the models, the variable PPI was found to be
significant (which is similar to the basic model), with a slightly stronger impact of PPI_D2
compared to PPI_D1. Based on the calculations in all of the models the authors assume that
the increase of PPI_D2 by 1 percentage point is connected with the increase in the BS&I
index of about 0.008 (approximate value of the estimator in all the tested models). In
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contrast to the basic model, the share of public procurement in the GDP (PP_GDP) is no
longer significant for the BS&I index.
The other variables from the basic model (COOP, RQ and GBERD) are not statistically
significant. For the regulatory quality (RQ) and the government subsidies for R&D (GBERD), it
is similar to the conclusion of the study by Aschhoff and Sofka (2009). But this study, as well
as some others (Lichtenthaler, 2009; Zeng et al., 2010; Schroll and Mild, 2011), showed that
cooperation between firms in the innovation process at the company level leads to an
improvement in their innovativeness. Therefore, the authors expected that greater
cooperation with others when creating innovation (COOP) would also be positively
associated with the BS&I index of a country. However, this was not confirmed by our
analysis through a variety of paper panel models. A possible explanation lies in the fact that
we do not know if the cooperation was only with domestic or with foreign partners, where
in case of foreign partners, the positive effect of innovations could have spilled over to the
foreign partner, therefore improving the other country's innovativeness instead of domestic
innovativeness.
In Table 3, it can also be seen that among other variables the GDP growth rate has significant
regression coefficients in all the models at a 1% level of significance. The authors take this
indicator as a sign of the environment in which businesses operate. The authors expected
that the crisis would have an impact on the BS&I index, but we were not sure of the
direction of the impact. Innovations are usually responsive to deteriorating business
conditions, which results in shrinking budgets for the activities that are not necessary (R&D
is sometimes considered to be as such, especially by finance executives). Examples from
Madrid-Guijarro et al. (2013) showed that SMEs in times of crisis severely abandoned
innovation activities, particularly in the field of process innovation. The same is seen in the
18
analysis of data on innovation management, which demonstrates the decreasing propensity
of SMEs to innovate during the last economic crisis (Diedrichs, 2013). On the other hand,
many managers witness that innovation emerging from the crisis can help enterprises to
outperform their rivals and maintain their market position (Collins, 2010). Similarly, the
results of a survey on the innovation climate on a sample of managers from developed
countries showed that a larger proportion of firms (47%) improved the climate for
innovation, while for 26% it was worse than before the crisis (Frey, 2009). In the developed
models, the regression estimator for GDP growth is not high, but shows that in the observed
period in EU countries higher GDP growth was associated with a higher BS&I index.
The coefficients of most of the other variables are not statistically significant, except for the
Gross Expenditures for R&D (GERD) and the share of researchers (RES_emp), which are
significant only at a level of 5% in two models with PPI_D1. But in this case the regression
estimator has the opposite direction (sign) than expected. It turns out to be negative, when
the direct correlations of these indicators and BS&I index indicate positive association.
Obviously, there is a possibility that the growth of R&D expenditures and a larger number of
researchers may lead to reduced efficiency in their usage.
The models where authors included explanatory variables with a time lag (Models labelled
with even numbers) are not presented here because the results of these models show that
in nearly half of the models (5/12) none of the variables are statistically significant (not even
at the level of 5%). In other models there is only one variable (PPI_D2) that is statistically
significant, but at a lower level of significance than in the same models without a time lag.
4.3. Additional analyses
The low coefficients of determination (R-squared) indicate that multicollinearity still exists in
our models. To eliminate it, the authors first did not include highly correlated variables in
19
the same model. Next, the authors attempted to transform all of the variables into their
logarithmic form and repeated the analysis with double-logarithmic (log-log) models. An
analysis showed that log-log models have very similar regression coefficients and R-squared
values, and in most cases R-squared was also lower compared to the previously described
models without transformation.
After that it was also attempted to transform some variables based on the visual analysis of
scatter plot diagrams, which suggested that it may be appropriate to apply a logtransformation for variables like public funding of enterprises' R&D (GBERD), export of hightechnology products (HTP_xp), and government financing share in GERD (GGERD). The
dependent variable and other explanatory variables were left in their original form, whereas
those three were included in all the models in logarithmic form. The obtained results show
that R-squared coefficients were higher in the models with the original data than in partially
linear-logarithmic models. In only two models was the R-squared higher than in the models
with original data, and that by merely 0.003. Thus it can be concluded that such a
transformation does not remove the problem of multicollinearity. Tables with comparisons
are presented in Appendix B.
5. CONCLUSIONS AND PROPOSAL FOR FOLLOW-UP WORK
Today, public procurement constitutes a large part of GDP and government expenditures,
which stresses the importance of using public funds more effectively, particularly in times of
crisis. Therefore, the authors presented a concept that developed countries have been using
in practice for a long time—public procurement for innovation (PPI).
On the basis of selected statistical data for EU countries, the authors ran a panel data
analysis to examine the impact of the four policy measures through which the state can
encourage the emergence of innovation. The basic model has shown that statistically
20
significant variables are the share of public procurement in the GDP and PPI. The share of
public procurement has a negative impact, but PPI influences innovativeness positively. Time
lag only weakens the impact of significant variables and other variables mainly do not
become statistically significant by introducing the time lag. Even though authors found no
evidence that a one-year time lag is important for this matter, the availability of data
covering longer timespans leaves much room for testing the models further. The authors
expected that PPI would have a positive impact on innovation based on the literature
review, but the authors also expected that the cooperation of enterprises in innovative
processes would significantly and positively affect the level of innovativeness of a country as
well, which was not backed up by our results. In the literature, no evidence was found that,
at the macro level, cooperation between participants in the economic system led to a
general increase of innovativeness at the country level, which authors confirmed through
the insignificance of regression estimators for the variable COOP through a variety of panel
models. One possible explanation is that the cooperation included not only the domestic but
also the foreign partners, which would reduce the impact on innovation in each country due
to spill-over effects.
The extended models show that two variables are perseveringly statistically significant
through all the tested models: PPI and GDP growth rate. The other variables are not
statistically significant, except for the Gross expenditures for R&D and the number of
researchers that are significant only at a level of 5% in two models, when the authors used a
less innovation-oriented variable for PPI (PPI_D1). In almost half of the models with time lag,
no variable is statistically significant (not even at the level of 5%) and in the majority of other
models only PPI is a statistically significant variable.
21
Hence, our results imply that PPI significantly contributes to the higher level of
innovativeness measured by the country's BS&I index, thus confirming our assumption that
public procurement can stimulate the emergence of innovations in a national economy.
Due to the restrictions of the described analysis (unavailability of appropriate indicators and
short timespans) there is a need to improve the monitoring of PPI data. Moreover, the
qualitative study of PPI cases shows a considerably more frequent occurrence of PPI in the
old EU member countries, which opens another possible research venue—exploring if there
are significant differences between new and old EU members. In addition, many correlated
variables in the model indicate that the use of methods such as the integration of certain
variables based on principal component analysis could reduce multicollinearity and improve
estimates and the significance of regression coefficients.
Our results are in line with the conclusions of many previous studies based on qualitative
case analyses, which confirms the existing literature rather than challenging it. But they also
represent an empirical confirmation of the claims that PPI has a positive impact on
innovativeness, which is a significant contribution to PPI research. The authors hope that this
article represents a pathway for continuing research on public procurement for innovation.
22
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28
Table 1: Descriptive statistics (. xtsum)
Variable
id
yr
si
coop
rq
g_berd
pp_gdp
ppi_d1
ppi_d2
gdp_pc
gdp_gr
gerd
g_gerd
trd_emp
res_emp
htp_xp
kis_xp
Mean
overall
between
within
overall
between
within
overall
between
within
overall
between
within
overall
between
within
overall
between
within
overall
between
within
overall
between
within
overall
between
within
overall
between
within
overall
between
within
overall
between
within
overall
between
within
overall
between
within
overall
between
within
overall
between
within
overall
between
within
Std. Dev.
14,5
2008
4,334509
33,69906
1,224554
8,18
17,64546
7,70377
2,182199
26426,18
1,90996
1,488318
40,54783
1,041982
0,6623963
14,49117
34,02487
8,093822
8,225975
0
2,587127
0
2,587127
0,7412991
0,7447426
0,1108748
12,11054
10,91735
5,371546
0,4042529
0,3994099
0,0943386
7,418624
6,422634
3,626905
4,186964
4,029725
1,541734
9,411754
7,856675
5,357294
5,674416
4,526577
3,455295
11517,09
11636,01
1249,85
4,148132
1,301501
3,945514
0,9140558
0,9120507
0,1723202
12,74025
12,03575
4,323725
0,527925
0,5193126
0,1071975
0,3299183
0,3223463
0,0795088
10,51057
10,17798
3,08905
17,52221
16,45369
6,153653
Min
1
1
14,5
2004
2008
2004
3,2
3,26125
4,030759
12,11
13,78143
11,82478
0,16
0,49625
0,8720536
0
0,5675
-6,7025
7,65
10,61625
12,49796
0
0
-8,333373
0
0
-12,96494
8965
10539,56
22691,73
-17,95
-0,6744445
-19,38004
0,37
0,44375
0,9983182
13,9
22,55
22,84782
0,28
0,32375
0,6544816
0,17
0,21125
0,3873963
3,04
4,61125
-2,322579
3,8
13,51429
7,55344
Max
28
28
14,5
2012
2008
2012
5,79
5,575
4,619509
68,79
57,42714
47,07763
1,93
1,85125
1,482054
47
32,6425
24,44375
30,3
27,24
21,22171
30,3
27,75429
23,4752
20,3
15,14714
14,84363
74021
69180,89
31266,29
12,23
4,528889
10,79996
3,92
3,64375
2,530818
70,1
66,22857
52,87282
2,46
2,31125
1,524482
1,73
1,64875
0,9673963
58,13
51,65125
25,99742
81,8
78,71429
61,77487
Observations
N
n
T
N
n
T
N
n
T
N
n
T
N
n
T
N
n
T
N
n
T
N
n
T
N
n
T
N
n
T
N
n
T
N
n
T
N
n
T
N
n
TN
bar
n
TN
bar
n
T
N
n
T
=
=
=
=
=
=
=
=
=
=
=
=
=
=
=
=
=
=
=
=
=
=
=
=
=
=
=
=
=
=
=
=
=
=
=
=
=
=
=
=
=
=
=
=
=
=
=
=
=
=
=
252
28
9
252
28
9
224
28
8
192
28
6,85714
224
28
8
209
28
7,46429
216
28
7,71429
191
28
6,82143
191
28
6,82143
252
28
9
252
28
9
220
28
7,85714
207
28
7,39286
217
28
7,75
217
28
7,75
222
28
7,92857
189
28
6,75
Source: authors' display from Stata software
29
Table 2: Variants of the basic model (Models 1, 1a, 2 and 2a)
Model 1
Model 1a
b/(se)
-0.0012
(0.002)
b/(se)
-0.0016
(0.002)
rq
0.0270
(0.141)
-0.0292
(0.128)
gberd
0.0038
(0.005)
pp_gdp
-0.0198**
(0.007)
ppi_d2
0.0079***
(0.002)
bs&i
coop
ppi_d1
Model 2
Model 2a
b/(se)
-0.0025
(0.002)
b/(se)
-0.0030
(0.002)
L.rq
-0.2480
(0.133)
-0.3129*
(0.136)
0.0039
(0.005)
L.gberd
0.0071
(0.006)
0.0059
(0.006)
-0.0192*
(0.007)
L.pp_gdp
-0.0094
(0.006)
-0.0073
(0.007)
L.ppi_d2
0.0030***
(0.001)
bs&i
L.coop
0.0050**
(0.002)
L.ppi_d1
0.0010
(0.002)
_cons
4.6726***
(0.201)
4.7247***
(0.205)
_cons
4.8693***
(0.208)
4.9199***
(0.263)
R-sq
N
0.1536
155
0.1474
155
R-sq
N
0.1096
155
0.1118
155
*
p < 0.05, ** p < 0.01, *** p < 0.001
Source: authors' display from Stata software
30
Table 3: Variants of the extended models
Model 3
β/(se)
-0.0009
(0.002)
Model 3a
β/(se)
-0.0014
(0.002)
Model 5
β/(se)
-0.0007
(0.002)
Model 5a
β/(se)
-0.0013
(0.002)
rq
0.1046
(0.151)
0.0443
(0.139)
0.1191
(0.159)
0.0559
(0.146)
gberd
0.0019
(0.005)
0.0019
(0.005)
0.0023
(0.005)
pp_gdp
-0.0069
(0.009)
-0.0059
(0.009)
-0.0057
(0.009)
ppi_d2
0.0084***
(0.002)
bs&i
coop
Model 9
β/(se)
-0.0012
(0.002)
Model 9a
β/(se)
-0.0015
(0.002)
Model 11
β/(se)
-0.0014
(0.002)
Model 11a
β/(se)
-0.0022
(0.002)
Model 13
β/(se)
-0.0008
(0.002)
Model 13a
β/(se)
-0.0011
(0.002)
0.0023
(0.005)
0.0008
(0.005)
0.0010
(0.005)
0.0027
(0.005)
0.0028
(0.004)
0.0013
(0.004)
0.0006
(0.004)
0.0030
(0.004)
0.0033
(0.004)
-0.0049
(0.009)
-0.0040
(0.010)
-0.0033
(0.010)
0.0004
(0.011)
0.0020
(0.010)
-0.0075
(0.008)
-0.0059
(0.008)
-0.0018
(0.009)
0.0013
(0.009)
0.0093**
(0.003)
0.0078***
(0.002)
0.0052**
(0.002)
0.0075**
(0.003)
0.0052*
(0.002)
0.0080***
(0.001)
0.0054*
(0.002)
0.0092**
(0.003)
0.0093**
(0.003)
0.0093**
(0.003)
0.0095**
(0.003)
0.0089**
(0.003)
0.0092**
(0.003)
htp_xp
0.0019
(0.004)
0.0012
(0.003)
0.0022
(0.004)
0.0016
(0.003)
0.0008
(0.004)
0.0003
(0.003)
kis_xp
0.0022
(0.002)
0.0010
(0.002)
0.0025
(0.002)
0.0014
(0.002)
0.0024
(0.003)
0.0015
(0.002)
0.0043
(0.005)
0.0033
(0.005)
gdp_gr
0.0091**
(0.003)
Model 7a
β/(se)
-0.0010
(0.002)
0.0079***
(0.002)
0.0056**
(0.002)
ppi_d1
Model 7
β/(se)
-0.0005
(0.002)
g_gerd
gdp_pc
0.0075***
(0.002)
0.0062**
(0.002)
0.0092**
(0.003)
0.0103**
(0.003)
-0.0000
(0.000)
-0.0000
(0.000)
gerd
res_emp
-0.2923
(0.181)
-0.3444*
(0.143)
0.0057**
(0.002)
0.0081**
(0.002)
0.0084**
(0.002)
-0.1362
(0.068)
-0.1746*
(0.064)
_cons
4.3357***
(0.231)
4.3833***
(0.242)
4.1809***
(0.252)
4.2911***
(0.268)
4.1175***
(0.354)
4.1840***
(0.368)
4.4305***
(0.234)
4.4606***
(0.216)
4.6875***
(0.219)
4.8958***
(0.281)
4.5720***
(0.197)
4.5537***
(0.181)
R-sqr
N
0.279
155
0.279
155
0.274
153
0.276
153
0.282
152
0.284
152
0.294
151
0.306
151
0.278
155
0.299
155
0.295
155
0.314
155
Notes: * p < 0.05, ** p < 0.01, *** p < 0.001
Source: authors' display from Stata software
31
Appendix A: List of the tested models
1
𝑆&𝐼𝑖𝑡 = 𝛽1 + 𝛽2 𝐶𝑂𝑂𝑃𝑖𝑡 + 𝛽3 𝑅𝑄𝑖𝑡 + 𝛽4 𝐺𝐵𝐸𝑅𝐷𝑖𝑡 + 𝛽5 𝑃𝑃_𝐺𝐷𝑃𝑖𝑡 + 𝛽6 𝑃𝑃𝐼_𝐷2𝑖𝑡 + 𝑢𝑖𝑡
1a
𝑆&𝐼𝑖𝑡 = 𝛽1 + 𝛽2 𝐶𝑂𝑂𝑃𝑖𝑡 + 𝛽3 𝑅𝑄𝑖𝑡 + 𝛽4 𝐺𝐵𝐸𝑅𝐷𝑖𝑡 + 𝛽5 𝑃𝑃_𝐺𝐷𝑃𝑖𝑡 + 𝛽6 𝑃𝑃𝐼_𝐷1𝑖𝑡 + 𝑢𝑖𝑡
2*
𝑆&𝐼𝑖𝑡 = 𝛽1 + 𝛽2 𝑙. 𝐶𝑂𝑂𝑃𝑖𝑡 + 𝛽3 𝑙. 𝑅𝑄𝑖𝑡 + 𝛽4 𝑙. 𝐺𝐵𝐸𝑅𝐷𝑖𝑡 + 𝛽5 𝑙. 𝑃𝑃_𝐺𝐷𝑃𝑖𝑡 + 𝛽6 𝑙. 𝑃𝑃𝐼_𝐷2𝑖𝑡 + 𝑢𝑖𝑡
2a*
𝑆&𝐼𝑖𝑡 = 𝛽1 + 𝛽2 𝑙. 𝐶𝑂𝑂𝑃𝑖𝑡 + 𝛽3 𝑙. 𝑅𝑄𝑖𝑡 + 𝛽4 𝑙. 𝐺𝐵𝐸𝑅𝐷𝑖𝑡 + 𝛽5 𝑙. 𝑃𝑃_𝐺𝐷𝑃𝑖𝑡 + 𝛽6 𝑙. 𝑃𝑃𝐼_𝐷1𝑖𝑡 + 𝑢𝑖𝑡
3
𝑆&𝐼𝑖𝑡 = 𝛽1 + 𝛽2 𝐶𝑂𝑂𝑃𝑖𝑡 + 𝛽3 𝑅𝑄𝑖𝑡 + 𝛽4 𝐺𝐵𝐸𝑅𝐷𝑖𝑡 + 𝛽5 𝑃𝑃_𝐺𝐷𝑃𝑖𝑡 + 𝛽6 𝑃𝑃𝐼_𝐷2𝑖𝑡 + 𝛽7 𝐺𝐷𝑃_𝑔𝑟𝑖𝑡 + 𝑢𝑖𝑡
3a
𝑆&𝐼𝑖𝑡 = 𝛽1 + 𝛽2 𝐶𝑂𝑂𝑃𝑖𝑡 + 𝛽3 𝑅𝑄𝑖𝑡 + 𝛽4 𝐺𝐵𝐸𝑅𝐷𝑖𝑡 + 𝛽5 𝑃𝑃_𝐺𝐷𝑃𝑖𝑡 + 𝛽6 𝑃𝑃𝐼_𝐷1𝑖𝑡 + 𝛽7 𝐺𝐷𝑃_𝑔𝑟𝑖𝑡 + 𝑢𝑖𝑡
4*
𝑆&𝐼𝑖𝑡 = 𝛽1 + 𝛽2 𝑙. 𝐶𝑂𝑂𝑃𝑖𝑡 + 𝛽3 𝑙. 𝑅𝑄𝑖𝑡 + 𝛽4 𝑙. 𝐺𝐵𝐸𝑅𝐷𝑖𝑡 + 𝛽5 𝑙. 𝑃𝑃_𝐺𝐷𝑃𝑖𝑡 + 𝛽6 𝑙. 𝑃𝑃𝐼_𝐷2𝑖𝑡 + 𝛽7 𝑙. 𝐺𝐷𝑃_𝑔𝑟𝑖𝑡 + 𝑢𝑖𝑡
4a*
𝑆&𝐼𝑖𝑡 = 𝛽1 + 𝛽2 𝑙. 𝐶𝑂𝑂𝑃𝑖𝑡 + 𝛽3 𝑙. 𝑅𝑄𝑖𝑡 + 𝛽4 𝑙. 𝐺𝐵𝐸𝑅𝐷𝑖𝑡 + 𝛽5 𝑙. 𝑃𝑃_𝐺𝐷𝑃𝑖𝑡 + 𝛽6 𝑙. 𝑃𝑃𝐼_𝐷1𝑖𝑡 + 𝛽7 𝑙. 𝐺𝐷𝑃_𝑔𝑟𝑖𝑡 + 𝑢𝑖𝑡
5
𝑆&𝐼𝑖𝑡 = 𝛽1 + 𝛽2 𝐶𝑂𝑂𝑃𝑖𝑡 + 𝛽3 𝑅𝑄𝑖𝑡 + 𝛽4 𝐺𝐵𝐸𝑅𝐷𝑖𝑡 + 𝛽5 𝑃𝑃_𝐺𝐷𝑃𝑖𝑡 + 𝛽6 𝑃𝑃𝐼_𝐷2𝑖𝑡 + 𝛽7 𝐺𝐷𝑃_𝑔𝑟𝑖𝑡 + 𝛽8 𝐻𝑇𝑃_𝑥𝑝𝑖𝑡 + 𝛽9 𝐾𝐼𝑆_𝑥𝑝𝑖𝑡 + 𝑢𝑖𝑡
5a
𝑆&𝐼𝑖𝑡 = 𝛽1 + 𝛽2 𝐶𝑂𝑂𝑃𝑖𝑡 + 𝛽3 𝑅𝑄𝑖𝑡 + 𝛽4 𝐺𝐵𝐸𝑅𝐷𝑖𝑡 + 𝛽5 𝑃𝑃_𝐺𝐷𝑃𝑖𝑡 + 𝛽6 𝑃𝑃𝐼_𝐷1𝑖𝑡 + 𝛽7 𝐺𝐷𝑃_𝑔𝑟𝑖𝑡 + 𝛽8 𝐻𝑇𝑃_𝑥𝑝𝑖𝑡 + 𝛽9 𝐾𝐼𝑆_𝑥𝑝𝑖𝑡 + 𝑢𝑖𝑡
6*
6a*
𝑆&𝐼𝑖𝑡 = 𝛽1 + 𝛽2 𝑙. 𝐶𝑂𝑂𝑃𝑖𝑡 + 𝛽3 𝑙. 𝑅𝑄𝑖𝑡 + 𝛽4 𝑙. 𝐺𝐵𝐸𝑅𝐷𝑖𝑡 + 𝛽5 𝑙. 𝑃𝑃_𝐺𝐷𝑃𝑖𝑡 + 𝛽6 𝑙. 𝑃𝑃𝐼_𝐷2𝑖𝑡 + 𝛽7 𝑙. 𝐺𝐷𝑃_𝑔𝑟𝑖𝑡 + 𝛽8 𝑙. 𝐻𝑇𝑃_𝑥𝑝𝑖𝑡 + 𝛽9 𝑙. 𝐾𝐼𝑆_𝑥𝑝𝑖𝑡
+ 𝑢𝑖𝑡
𝑆&𝐼𝑖𝑡 = 𝛽1 + 𝛽2 𝑙. 𝐶𝑂𝑂𝑃𝑖𝑡 + 𝛽3 𝑙. 𝑅𝑄𝑖𝑡 + 𝛽4 𝑙. 𝐺𝐵𝐸𝑅𝐷𝑖𝑡 + 𝛽5 𝑙. 𝑃𝑃_𝐺𝐷𝑃𝑖𝑡 + 𝛽6 𝑙. 𝑃𝑃𝐼_𝐷1𝑖𝑡 + 𝛽7 𝑙. 𝐺𝐷𝑃_𝑔𝑟𝑖𝑡 + 𝛽8 𝑙. 𝐻𝑇𝑃_𝑥𝑝𝑖𝑡 + 𝛽9 𝑙. 𝐾𝐼𝑆_𝑥𝑝𝑖𝑡
+ 𝑢𝑖𝑡
7
𝑆&𝐼𝑖𝑡 = 𝛽1 + 𝛽2 𝐶𝑂𝑂𝑃𝑖𝑡 + 𝛽4 𝐺𝐵𝐸𝑅𝐷𝑖𝑡 + 𝛽5 𝑃𝑃_𝐺𝐷𝑃𝑖𝑡 + 𝛽6 𝑃𝑃𝐼_𝐷2𝑖𝑡 + 𝛽7 𝐺𝐷𝑃_𝑔𝑟𝑖𝑡 + 𝛽8 𝐻𝑇𝑃_𝑥𝑝𝑖𝑡 + 𝛽9 𝐾𝐼𝑆_𝑥𝑝𝑖𝑡 + 𝛽10 𝐺𝐺𝐸𝑅𝐷𝑖𝑡 + 𝑢𝑖𝑡
7a
𝑆&𝐼𝑖𝑡 = 𝛽1 + 𝛽2 𝐶𝑂𝑂𝑃𝑖𝑡 + 𝛽4 𝐺𝐵𝐸𝑅𝐷𝑖𝑡 + 𝛽5 𝑃𝑃_𝐺𝐷𝑃𝑖𝑡 + 𝛽6 𝑃𝑃𝐼_𝐷1𝑖𝑡 + 𝛽7 𝐺𝐷𝑃_𝑔𝑟𝑖𝑡 + 𝛽8 𝐻𝑇𝑃_𝑥𝑝𝑖𝑡 + 𝛽9 𝐾𝐼𝑆_𝑥𝑝𝑖𝑡 + 𝛽10 𝐺𝐺𝐸𝑅𝐷𝑖𝑡 + 𝑢𝑖𝑡
8*
8a*
𝑆&𝐼𝑖𝑡 = 𝛽1 + 𝛽2 𝑙. 𝐶𝑂𝑂𝑃𝑖𝑡 + 𝛽4 𝑙. 𝐺𝐵𝐸𝑅𝐷𝑖𝑡 + 𝛽5 𝑙. 𝑃𝑃_𝐺𝐷𝑃𝑖𝑡 + 𝛽6 𝑙. 𝑃𝑃𝐼_𝐷2𝑖𝑡 + 𝛽7 𝑙. 𝐺𝐷𝑃_𝑔𝑟𝑖𝑡 + 𝛽8 𝑙. 𝐻𝑇𝑃_𝑥𝑝𝑖𝑡 + 𝛽9 𝑙. 𝐾𝐼𝑆_𝑥𝑝𝑖𝑡
+ 𝛽10 𝑙. 𝐺𝐺𝐸𝑅𝐷𝑖𝑡 + 𝑢𝑖𝑡
𝑆&𝐼𝑖𝑡 = 𝛽1 + 𝛽2 𝑙. 𝐶𝑂𝑂𝑃𝑖𝑡 + 𝛽4 𝑙. 𝐺𝐵𝐸𝑅𝐷𝑖𝑡 + 𝛽5 𝑙. 𝑃𝑃_𝐺𝐷𝑃𝑖𝑡 + 𝛽6 𝑙. 𝑃𝑃𝐼_𝐷1𝑖𝑡 + 𝛽7 𝑙. 𝐺𝐷𝑃_𝑔𝑟𝑖𝑡 + 𝛽8 𝑙. 𝐻𝑇𝑃_𝑥𝑝𝑖𝑡 + 𝛽9 𝑙. 𝐾𝐼𝑆_𝑥𝑝𝑖𝑡
+ 𝛽10 𝑙. 𝐺𝐺𝐸𝑅𝐷𝑖𝑡 + 𝑢𝑖𝑡
9
𝑆&𝐼𝑖𝑡 = 𝛽1 + 𝛽2 𝐶𝑂𝑂𝑃𝑖𝑡 + 𝛽4 𝐺𝐵𝐸𝑅𝐷𝑖𝑡 + 𝛽5 𝑃𝑃_𝐺𝐷𝑃𝑖𝑡 + 𝛽6 𝑃𝑃𝐼_𝐷2𝑖𝑡 + 𝛽7 𝐺𝐷𝑃_𝑔𝑟𝑖𝑡 + 𝛽8 𝐻𝑇𝑃_𝑥𝑝𝑖𝑡 + 𝛽9 𝐾𝐼𝑆_𝑥𝑝𝑖𝑡 + 𝛽13 𝑅𝐸𝑆_𝑒𝑚𝑝𝑖𝑡 + 𝑢𝑖𝑡
9a
𝑆&𝐼𝑖𝑡 = 𝛽1 + 𝛽2 𝐶𝑂𝑂𝑃𝑖𝑡 + 𝛽4 𝐺𝐵𝐸𝑅𝐷𝑖𝑡 + 𝛽5 𝑃𝑃_𝐺𝐷𝑃𝑖𝑡 + 𝛽6 𝑃𝑃𝐼_𝐷1𝑖𝑡 + 𝛽7 𝐺𝐷𝑃_𝑔𝑟𝑖𝑡 + 𝛽8 𝐻𝑇𝑃_𝑥𝑝𝑖𝑡 + 𝛽9 𝐾𝐼𝑆_𝑥𝑝𝑖𝑡 + 𝛽13 𝑅𝐸𝑆_𝑒𝑚𝑝𝑖𝑡 + 𝑢𝑖𝑡
10*
10a*
𝑆&𝐼𝑖𝑡 = 𝛽1 + 𝛽2 𝑙. 𝐶𝑂𝑂𝑃𝑖𝑡 + 𝛽4 𝑙. 𝐺𝐵𝐸𝑅𝐷𝑖𝑡 + 𝛽5 𝑙. 𝑃𝑃_𝐺𝐷𝑃𝑖𝑡 + 𝛽6 𝑙. 𝑃𝑃𝐼_𝐷2𝑖𝑡 + 𝛽7 𝑙. 𝐺𝐷𝑃_𝑔𝑟𝑖𝑡 + 𝛽8 𝑙. 𝐻𝑇𝑃_𝑥𝑝𝑖𝑡 + 𝛽9 𝑙. 𝐾𝐼𝑆_𝑥𝑝𝑖𝑡
+ 𝛽13 𝑙. 𝑅𝐸𝑆_𝑒𝑚𝑝𝑖𝑡 + 𝑢𝑖𝑡
𝑆&𝐼𝑖𝑡 = 𝛽1 + 𝛽2 𝑙. 𝐶𝑂𝑂𝑃𝑖𝑡 + 𝛽4 𝑙. 𝐺𝐵𝐸𝑅𝐷𝑖𝑡 + 𝛽5 𝑙. 𝑃𝑃_𝐺𝐷𝑃𝑖𝑡 + 𝛽6 𝑙. 𝑃𝑃𝐼_𝐷1𝑖𝑡 + 𝛽7 𝑙. 𝐺𝐷𝑃_𝑔𝑟𝑖𝑡 + 𝛽8 𝑙. 𝐻𝑇𝑃_𝑥𝑝𝑖𝑡 + 𝛽9 𝑙. 𝐾𝐼𝑆_𝑥𝑝𝑖𝑡
+ 𝛽13 𝑙. 𝑅𝐸𝑆_𝑒𝑚𝑝𝑖𝑡 + 𝑢𝑖𝑡
11
𝑆&𝐼𝑖𝑡 = 𝛽1 + 𝛽2 𝐶𝑂𝑂𝑃𝑖𝑡 + 𝛽4 𝐺𝐵𝐸𝑅𝐷𝑖𝑡 + 𝛽5 𝑃𝑃_𝐺𝐷𝑃𝑖𝑡 + 𝛽6 𝑃𝑃𝐼_𝐷2𝑖𝑡 + 𝛽7 𝐺𝐷𝑃_𝑔𝑟𝑖𝑡 + 𝛽11 𝐺𝐷𝑃_𝑝𝑐𝑖𝑡 + 𝑢𝑖𝑡
11a
𝑆&𝐼𝑖𝑡 = 𝛽1 + 𝛽2 𝐶𝑂𝑂𝑃𝑖𝑡 + 𝛽4 𝐺𝐵𝐸𝑅𝐷𝑖𝑡 + 𝛽5 𝑃𝑃_𝐺𝐷𝑃𝑖𝑡 + 𝛽6 𝑃𝑃𝐼_𝐷1𝑖𝑡 + 𝛽7 𝐺𝐷𝑃_𝑔𝑟𝑖𝑡 + 𝛽11 𝐺𝐷𝑃_𝑝𝑐𝑖𝑡 + 𝑢𝑖𝑡
12*
𝑆&𝐼𝑖𝑡 = 𝛽1 + 𝛽2 𝑙. 𝐶𝑂𝑂𝑃𝑖𝑡 + 𝛽4 𝑙. 𝐺𝐵𝐸𝑅𝐷𝑖𝑡 + 𝛽5 𝑙. 𝑃𝑃_𝐺𝐷𝑃𝑖𝑡 + 𝛽6 𝑙. 𝑃𝑃𝐼_𝐷2𝑖𝑡 + 𝛽7 𝑙. 𝐺𝐷𝑃_𝑔𝑟𝑖𝑡 + 𝛽11 𝑙. 𝐺𝐷𝑃_𝑝𝑐𝑖𝑡 + 𝑢𝑖𝑡
12a*
𝑆&𝐼𝑖𝑡 = 𝛽1 + 𝛽1 + 𝛽2 𝑙. 𝐶𝑂𝑂𝑃𝑖𝑡 + 𝛽4 𝑙. 𝐺𝐵𝐸𝑅𝐷𝑖𝑡 + 𝛽5 𝑙. 𝑃𝑃_𝐺𝐷𝑃𝑖𝑡 + 𝛽6 𝑙. 𝑃𝑃𝐼_𝐷2𝑖𝑡 + 𝛽7 𝑙. 𝐺𝐷𝑃_𝑔𝑟𝑖𝑡 + 𝛽11 𝑙. 𝐺𝐷𝑃_𝑝𝑐𝑖𝑡 + 𝑢𝑖𝑡
13
𝑆&𝐼𝑖𝑡 = 𝛽1 + 𝛽2 𝐶𝑂𝑂𝑃𝑖𝑡 + 𝛽4 𝐺𝐵𝐸𝑅𝐷𝑖𝑡 + 𝛽5 𝑃𝑃_𝐺𝐷𝑃𝑖𝑡 + 𝛽6 𝑃𝑃𝐼_𝐷2𝑖𝑡 + 𝛽7 𝐺𝐷𝑃_𝑔𝑟𝑖𝑡 + 𝛽12 𝐺𝐸𝑅𝐷𝑖𝑡 + 𝑢𝑖𝑡
13a
𝑆&𝐼𝑖𝑡 = 𝛽1 + 𝛽2 𝐶𝑂𝑂𝑃𝑖𝑡 + 𝛽4 𝐺𝐵𝐸𝑅𝐷𝑖𝑡 + 𝛽5 𝑃𝑃_𝐺𝐷𝑃𝑖𝑡 + 𝛽6 𝑃𝑃𝐼_𝐷1𝑖𝑡 + 𝛽7 𝐺𝐷𝑃_𝑔𝑟𝑖𝑡 + 𝛽12 𝐺𝐸𝑅𝐷𝑖𝑡 + 𝑢𝑖𝑡
14*
𝑆&𝐼𝑖𝑡 = 𝛽1 + 𝛽2 𝑙. 𝐶𝑂𝑂𝑃𝑖𝑡 + 𝛽4 𝑙. 𝐺𝐵𝐸𝑅𝐷𝑖𝑡 + 𝛽5 𝑙. 𝑃𝑃_𝐺𝐷𝑃𝑖𝑡 + 𝛽6 𝑙. 𝑃𝑃𝐼_𝐷2𝑖𝑡 + 𝛽7 𝑙. 𝐺𝐷𝑃_𝑔𝑟𝑖𝑡 + 𝛽12 𝑙. 𝐺𝐸𝑅𝐷𝑖𝑡 + 𝑢𝑖𝑡
14a*
𝑆&𝐼𝑖𝑡 = 𝛽1 + 𝛽2 𝑙. 𝐶𝑂𝑂𝑃𝑖𝑡 + 𝛽4 𝑙. 𝐺𝐵𝐸𝑅𝐷𝑖𝑡 + 𝛽5 𝑙. 𝑃𝑃_𝐺𝐷𝑃𝑖𝑡 + 𝛽6 𝑙. 𝑃𝑃𝐼_𝐷1𝑖𝑡 + 𝛽7 𝑙. 𝐺𝐷𝑃_𝑔𝑟𝑖𝑡 + 𝛽12 𝑙. 𝐺𝐸𝑅𝐷𝑖𝑡 + 𝑢𝑖𝑡
*In models labelled with even numbers (2, 2a, 4, 4a ...) the STATA notation for time lag is used, with an ‘l.’
before the variable name for the lagged variable, instead of the ‘t-1’ subscript behind the variable (e. g.,
l.COOPit, instead of COOPit-1)
32
Appendix B: Comparison of R-squared coefficients after log-transformation of variables
Table B.1: Double logarithmic models - Determination coefficients (R-sqr)
Basic models
M1
M1a
M2
M2a
Original data
Log-transformed
0.154
0.102
0.147
0.127
0.110
0.126
0.112
0.118
The difference
0.052
0.02
-0.016
-0.006
M3
M3a
M5
M5a
M7
M7a
M9
M9a
M11
M11a
M13
M13a
Log-transformed
0.279
0.238
0.279
0.268
0.274
0.238
0.276
0.269
0.282
0.272
0.284
0.291
0.294
0.280
0.306
0.319
0.278
0.248
0.299
0.294
0.295
0.252
0.314
0.287
The difference
0.041
0.011
0.036
0.007
0.010
-0.007
0.014
-0.013
0.030
0.005
0.043
0.027
M4
M4a
M6
M6a
M8
M8a
M10
M10a
M12
M12a
M14
M14a
Log-transformed
0.145
0.140
0.127
0.132
0.145
0.139
0.133
0.134
0.101
0.085
0.084
0.075
0.133
0.111
0.119
0.104
0.286
0.311
0.288
0.320
0.120
0.111
0.104
0.101
The difference
0.005
-0.005
0.006
-0.001
0.016
0.009
0.022
0.015
-0.025
-0.032
0.009
0.003
Extended models
Original data
Extended models with
time-lag
Original data
Source: authors' calculation based on Stata results
Table B.2: Partially linear-logarithmic models (log-transformed variables GBERD, HTP_xp and
G_GERD) - Determination coefficients (R-sqr)
Basic models
M1
M1a
M2
M2a
Original data
0.154
0.147
0.11
0.112
Partially lin-log
0.145
0.138
0.103
0.085
The difference
0.009
0.009
0.007
0.027
M3
M3a
M5
M5a
M7
M7a
M9
M9a
M11
M11a
M13
M13a
Original data
0.279
0.279
0.274
0.276
0.282
0.284
0.294
0.306
0.278
0.299
0.295
0.314
Partially lin-log
0.277
0.276
0.269
0.273
0.285
0.287
0.293
0.306
0.277
0.299
0.290
0.308
The difference
0.002
0.003
0.005
0.003
-0.003
-0.003
0.001
0.000
0.001
0.000
0.005
0.006
M4
M4a
M6
M6a
M8
M8a
M10
M10a
M12
M12a
M14
M14a
Original data
0.145
0.127
0.145
0.133
0.101
0.084
0.133
0.119
0.286
0.288
0.12
0.104
Partially lin-log
0.124
0.105
0.120
0.107
0.095
0.077
0.113
0.099
0.286
0.288
0.093
0.093
0.021 0.022 0.025 0.026 0.006
Source: authors' calculation based on Stata results
0.007
0.020
0.020
0.000
0.000
0.027
0.011
Extended models
Extended models
with time-lag
The difference
33