Download - European Economic Review - UNU-MERIT
European Economic Review 121 (2020) 103330
Contents lists available at ScienceDirect
European Economic Review
journal homepage: www.elsevier.com/locate/euroecorev
Innovation procurement as capability-building: Evaluating
innovation policies in eight Central and Eastern European
countries
Nebojša Stoj ̌ci ́c
a , Stjepan Srhoj a , Alex Coad
b , c , ∗
a Department of Economics and Business Economics, University of Dubrovnik, Croatia b CENTRUM Católica Graduate Business School (CCGBS), Jr Daniel Alomia Robles 125, Santiago de Surco, 15023 Lima, Perúc Pontificia Universidad Católica del Perú (PUCP), Lima, Perú
a r t i c l e i n f o
Article history:
Received 16 June 2019
Accepted 28 October 2019
Available online 4 November 2019
JEL CODES:
O38
Keywords:
Public funding for innovation
Public procurement for innovation
Additionality
Evaluation, Central and Eastern European
countries
a b s t r a c t
After decades of impressive growth, the new member states of the European Union are
once again in transition, but this time from imitation to innovation-driven competitiveness.
This paper evaluates the relationship between both public funding and public procurement
for innovation (PPI) and firm-level innovation output and outcome additionality, in eight
Central and Eastern European countries. Matching estimates on a sample of 41,623 firms
suggest that PPI has a large effect on innovation and output, and the highest additionality
is sometimes achieved when firms receive both financial support and innovation-oriented
public procurement. We argue that policy-makers aiming to strengthen indigenous inno-
vation capabilities should place stronger emphasis on PPI.
© 2019 Elsevier B.V. All rights reserved.
1. Introduction
Increasing awareness of the role of innovation for productivity growth and economic wellbeing has led to an expanding
role of public support for innovation, highlighting how government officials may take an ‘entrepreneurial’ role in enhancing
the innovation performance of industry ( Link and Scott, 2010 ; Mazzucato, 2013 ; Hayter et al., 2018 ). The key role of the state
in the industrial development of advanced nations ( Mazzoleni and Nelson, 2007 ) has challenged the traditional view on
the crowding-out nature of government support to innovation. The rationale for public investment in innovation is further
strengthened by arguments that the social returns to innovation exceed the private ones, through horizontal and vertical
spillovers to other firms and increases in consumer welfare. The remedying effect of the state on innovation-related market
failures such as information asymmetries, barriers for access to finance, and obstacles to collaboration between business
entities also seems non-negligible. For these reasons, state support for innovation has received increasing momentum in
recent decades.
At a theoretical level, various innovation policy instruments have been championed by innovation policies in the USA,
Europe, and other parts of the world. Early attempts at innovation policy emerged from the effort s of post-World War II USA
to stimulate economic growth in times of peace, and took the form of transfer of publicly-funded technologies from federal
∗ Corresponding author.
E-mail addresses: [email protected] (N. Stoj ̌ci ́c), [email protected] (S. Srhoj), [email protected] (A. Coad).
https://doi.org/10.1016/j.euroecorev.2019.103330
0014-2921/© 2019 Elsevier B.V. All rights reserved.
2 N. Stoj ̌ci ́c, S. Srhoj and A. Coad / European Economic Review 121 (2020) 103330
laboratories to private sector firms ( Link and Scott, 2019 ). Subsequent innovation policy initiatives in the 1980s include
the stimulation of technology transfer from university laboratories and public research institutes, as well as more direct
interventions to provide financial incentives (such as grants, subsidies or tax incentives) for R&D (research and development)
activities undertaken in private sector firms ( Leyden and Link, 2015 ). For example, President Reagan introduced the first
Research and Experimentation Tax Credit in the USA in 1981 ( Bloom et al., 2019 ). More recently, emphasis has been placed
on public procurement for innovation as an innovation policy tool designed to develop innovation capabilities within firms
( Edquist and Zabala-Iturriagagoitia, 2012 ; Guerzoni and Raiteri, 2015 ). However, empirical research is still unclear regarding
which of these push and pull mechanisms are more appropriate for specific innovation contexts, how they influence firm
performance, and their combined effectiveness when implemented together.
The relevance of public support for innovation is particularly interesting in the context of catching-up countries in tran-
sition from middle to high-income levels, such as the Central and Eastern European countries (CEECs) that are new member
states of the European Union (EU). For much of the past two and a half decades, the growth of these economies has been
driven by improvements in efficiency that had little to do with their own innovation activities ( Dobrinsky et al., 2006; Alam
et al., 2008 ). The post-crisis growth rates of these economies, together with growing pressures for wage increases and in-
tensifying competition from standardized producers from other parts of the world, call for an analysis of factors that can
lead to new growth models. This coincides with the EU’s emphasis (e.g. via the Europe 2020 strategy) on providing policy
support for industrial upgrading and innovation. Whether and through which channels the state can aid this transition from
middle to high-income status has not been the subject of much research to this day and, to the best of our knowledge,
there is a gap in the literature when it comes to evaluating public support for innovation in CEECs, a fortiori regarding the
effectiveness of push and pull channels of public innovation policy.
Conceptually, our paper is close to the recently emerging literature on the link between public innovation instruments
and economic catching-up ( Mazzoleni and Nelson, 2007 ; Fernández-Sastre and Martín-Mayoral, 2017 ). The general mes-
sage coming from this literature is that public support to innovation cannot be implemented without understanding the
specificities of the national innovation system. The distance of laggard countries from the technological frontier requires
building technological and managerial capabilities to absorb existing technological knowledge before the development of
R&D capabilities and engagement in radical innovation, which requires specific design of supply-side financial incentives.
Thus, demand-side incentives must take into account the constraints of indigenous innovators and be tailored in a way that
facilitates learning and interaction. Failure of policy-makers to understand the specificities of national innovation systems in
such countries, and the application of policy prescriptions taken from different contexts, is likely to reduce the effectiveness
of designated support measures.
The objective of this paper is to explore how public support influences innovation outcomes in eight CEECs (Bulgaria,
the Czech Republic, Estonia, Croatia, Latvia, Hungary, Romania and the Slovak Republic) during 2012–2014. There are two
key reasons why the existing literature falls short on this issue. First, research papers evaluating push or pull channels
are mostly conducted in high-income countries (e.g. Aschhoff and Sofka, 2009 ; Czarnitzki et al., 2018 ). Their findings are,
therefore, of limited practical use to policymakers in catching-up countries, which may have different drivers of innovation
( Radosevic and Yoruk, 2018 ). Policy-makers need to better understand the specific nature of national innovation systems
in catching-up economies because the application of policy prescriptions from different contexts will not result in effective
policy support. Second, empirical studies are mostly concerned with the impact of R&D subsidies. But impulses to innova-
tion can come also from the demand-side, through public procurement contracts ( Edquist and Zabala-Iturriagagoitia, 2012 ).
Existing literature provides limited evidence on this channel so far ( Guerzoni and Raiteri, 2015 ; Czarnitzki et al., 2018 ), al-
though the multi-channel approach to evaluating push and pull aspects of innovation policy in middle-income countries has
not been investigated yet ( Cunningham et al., 2016; Petrin, 2018 ), to the best of our knowledge.
These latter two points place our research in a unique position to provide novel findings relevant for those countries
in transition from middle to high-income levels. We build on existing literature by providing evidence on individual and
synergetic effects of public financial support to innovation, on the one hand, and innovation-oriented public procurement
contracts – a relatively novel and unexplored policy instrument – on the other. Our findings suggest that there are strong
positive effects of both public financial support and also PPI on the introduction of innovations and the commercialization
of both radical and incremental innovations. We also observe complementarity when public financial incentives are bundled
into a policy mix with innovation-oriented public procurement. Our take-home message for policymakers is that both push
and pull channels of support for innovation yield benefits in catching-up countries, and that their combination sometimes
yields greater effects than each support channel on its own.
2. Background
2.1. Theoretical framework
2.1.1. Innovation in a catching-up country context
A common transition path from middle to high-income levels involves building production capabilities. Over the past
half a century, many economies have succeeded to reach high-income levels by following such a route. However, to
approach the world frontier, and to remain there, require different capabilities. Competition among advanced countries
takes place mostly through innovation, and to sustain high-income levels catching-up countries must develop innovation
N. Stoj ̌ci ́c, S. Srhoj and A. Coad / European Economic Review 121 (2020) 103330 3
rather than production capabilities. While advanced economies seek to stimulate the exploitation of innovation capabilities
in the direction of radical technological breakthroughs, catching-up economies require a more basic approach to boost
the absorptive capacity of the private sector, to develop basic innovation capabilities and management capabilities, and to
invest in the required skills and innovation infrastructure throughout the innovation system ( Goñi and Maloney, 2017 ). Far
from the technological frontier, firms generally produce fewer radical innovations, and benefit more from imitation and the
application of existing best practice. Their innovation process involves developing the complex managerial and technological
capabilities required for interactive learning and innovation ( Fernández-Sastre and Martín-Mayoral, 2017 ).
All of the above presents particular challenges for the formulation of innovation policies in a catching-up context. Pol-
icymakers in the innovation systems of catching-up countries may lack experience in the challenges of administering in-
novation policy, for example, if they lack the technical and legal capabilities to successfully manage PPI contracts. Most
importantly, they often face the challenge of developing a new growth model while struggling to retain existing capabili-
ties. Innovation policies in such a setting are often subordinated to policies aimed at building (non-innovative) production
capabilities, leaving indigenous innovators to struggle alone with the challenges of development and commercialization of
innovations. These features should be kept in mind as we develop our hypotheses.
2.1.2. Public financial support for innovation
The theoretical rationale for public intervention in the innovation process is built around arguments of market failures.
It was noted already by evolutionary economists ( Nelson, 1959 ) and later in the endogenous growth literature ( Aghion and
Howitt, 1992 ) that the non-rival nature of knowledge creates economy-wide spillovers. Recent literature suggests that these
social returns to innovation exceed private ones by two to three times ( Frontier Economics, 2014 ; BEIS, 2017 ) thus making
innovation desirable from a social standpoint. Yet, if the cost of innovation falls entirely on private investors, their inability to
fully appropriate the returns to innovation leads to underinvestment in such activities. One way to remedy this suboptimal
allocation of resources is through intellectual property rights. However, this still leads to a suboptimal level of innovation
from a social standpoint ( Arrow, 1962 ). The barriers to knowledge flows create information asymmetries and reduce the
stock of knowledge available to potential innovators, thus reducing the emergence of new ideas.
A second type of market failure that calls for public support for innovation are barriers regarding access to finance
and innovation infrastructure. The large scale of innovation investments is one reason why, for instance, it was noted by
early Schumpeter (1934) that large firms are the bearers of innovation. The inability of small and medium-sized (SME)
firms to attain the required amount of financial resources is likely to result in a socially suboptimal level of innovation.
Similarly, innovation requires general infrastructure for the production of basic research and collaborative platforms, all of
which produce beneficial effects for innovators, but their development costs may exceed innovators’ available resources
( Aschhoff and Sofka, 2009 ). Hence, public support is required to increase the level of overall innovation output, reduce
information asymmetries and provide the required innovation infrastructure ( Falk, 2007 ; Lokshin and Mohnen, 2012 ).
In addition to these traditional market failure arguments, a third theory was recently put forward in favor of public
support to innovation ( Cunningham et al., 2016 ). According to this alternative view, public support to private innovation
is required to promote the international competitiveness of domestic firms, ensure catching-up with the advanced world
and the protection of infant industries. The foundations of such reasoning have been laid forward centuries ago ( List, 1841 ).
However, in the light of current globalization of economic activity and increasing debates about the need for protectionism,
it is regaining popularity. Public support to innovation, therefore, has three important missions: market failure correction,
establishment of cooperation with other entities in the innovation process, and fulfilling the mission to meet public demand.
The existing literature has identified that intervention in the innovation process can take place through either supply-
side (push) or demand-side (pull) channels ( Petrin, 2018 ). Supply-side policies include financial and non-financial measures
to instigate additionality effects in the level of investment in innovation, and to influence the behavior of innovating firms
and their success in the production of innovation outputs. Financial incentives to private innovation activities are perhaps
the most known instruments of public support to private innovation. The existing literature has identified several of these
channels such as direct grants and subsidies, cost-sharing arrangements, tax exemptions, or the provision of financial guar-
antees in the arrangements of private business entities with financial institutions ( Bloom et al., 2019 ). Non-financial mea-
sures include technology transfer from government labs or universities. Regardless of the form, financial incentives to private
innovations are at the core of concerns about the crowding out of public support to innovation, as their direct effect is to
reduce the R&D costs of beneficiaries.
Overall, therefore, we hypothesize that:
Hypothesis 1. public financial support has positive effects on firm innovation and performance outcomes, in the catching-up
economy context
2.1.3. PPI and the development of innovation capabilities
We suggest a fourth theory why public support for innovation – and in particular public procurement for innovation (PPI)
– is needed in transition economies. One of the main barriers to innovation for firms in these countries is that they have not
yet developed the innovation capabilities to be able to convert opportunities into success stories. Indeed, if the innovation
capabilities are not already established, giving grants and tax breaks to firms will not result in successful innovation, and the
effects of public funding for innovation given to ill-prepared firms may even be negative ( Goñi and Maloney, 2017 ). Indeed,
4 N. Stoj ̌ci ́c, S. Srhoj and A. Coad / European Economic Review 121 (2020) 103330
it is not clear in the literature how new firms in transition economies can be exposed to learning opportunities to develop
the advanced capabilities that are required for innovation.
Previously, governments seeking to develop the capabilities of indigenous firms sought to attract Foreign Direct Invest-
ment (FDI) in the hope that indigenous firms could benefit from learning opportunities and technology transfer from multi-
nationals ( Javorcik, 2004 ). The spillover channels though which these learning opportunities operate include demonstration
effects (as indigenous firms imitate and learn about markets and technologies), competition effects, knowledge spillovers
through labor flows, and also upstream-downstream supply chain linkages between multinationals and indigenous firms
( Javorcik, 2004 ; Stoj ̌ci ́c and Orlic, 2019 ). Nevertheless, the effectiveness of FDI policy for stimulating learning and technology
transfer from multinationals ended up disappointing, because multinationals may shroud their technologies and processes
in secrecy, and there are limited opportunities for knowledge transfer and interaction between local firms and multination-
als ( Stoj ̌ci ́c and Orlic, 2019 ). Furthermore, multinational firms seem to operate in segregated labor market pools, such that
employees rarely leave their jobs at multinationals, and when they do, they tend to move to a different multinational rather
than to a local firm ( Holm et al., 2019 ). In addition, the potential for knowledge transfer via supply chain linkages is reduced
by the fact that multinationals often source their inputs from overseas instead of interacting with local firms ( Barrios et al.,
2011 ).
Against this backdrop, PPI offers indigenous firms a valuable opportunity to develop new routines and capabilities, to
take risks with new products, and to engage in close learning with stakeholders (such as partnering ministries, government
entities, municipalities, state-owned enterprises, partnering academic researchers, etc.) in the context of a long-term collab-
orative and developmental relationship. This is succinctly stated in a recent cross-country study into PPI practices by the
OECD (2017 , p. 42): "Innovation often originates from fruitful collaboration rather than from isolation. In most countries,
innovative ideas emerged from a dialogue between government entities and business, as well as end-users/beneficiaries of
the service."
Demand-side policies have received increasing attention in recent years as an instrument of innovation policy ( Edler and
Georghiou, 2007 ; Aschhoff and Sofka, 2009 ; Czarnitzki et al., 2018 ; Uyarra et al., 2020 ). Demand-side policies are more
concerned with creating lead-user or lead-market effects and addressing information asymmetries. Two arguments are
commonly put forward in favor of demand-side instruments. The first is centered around von Hippel’s (1986) concept of
the lead user or lead market premium. Public procurement of innovative solutions can reduce the costs of learning and
product-refining while offering scale economies to business entities, hence reducing their costs of developing and commer-
cializing innovations. This may be particularly beneficial for small and medium-sized companies struggling to develop their
innovative capabilities in the face of market uncertainties ( Aschhoff and Sofka, 2009 ). A second argument for demand-side
instruments is related to addressing societal needs and grand challenges. PPI can help governments obtain innovative solu-
tions to meet certain policy goals such as providing healthcare for an ageing population ( Uyarra et al., 2020 ), environmental
protection, and energy efficiency and sustainability ( De Marchi, 2012 ; Costantini et al., 2015 ).
PPI may also be especially valuable in emerging countries, where governments seek to temporarily protect and nurture
their infant industries during a vulnerable early developmental stage. Simultaneously, public procurement may be used by
policymakers to signal to private agents the forthcoming market trends, and thus help to boost preparedness. The purchase
of products by the government serves another purpose as a signal of product quality, and thus enhances chances of adoption
and commercialization in later stages of product development. PPI may, therefore, act as a form of infant industry protection
policy and have an advantage over financial push incentives, in that it may help develop product innovation capabilities as
well as technological capabilities ( Geroski, 1990 ).
While in the context of advanced innovation-driven economies such opportunities are provided by the market, this is
not the case in a catching-up context. Learning opportunities are simply not available in the latter contexts, customers and
investors are risk-averse, and firms don’t risk producing new products and developing new routines if this can be avoided.
We therefore suggest that PPI has a role in supporting firms to develop new routines and capabilities, and develop dynamic
capabilities for innovation and exploration, in ways that are simply not possible via other public innovation schemes such
as R&D tax credits (which usually go to large mature firms with established innovation capabilities, Brown et al., 2017 ).
Hypothesis 2. Public procurement for innovation (PPI) has positive effects on firm innovation and performance outcomes,
in the catching-up economy context
2.1.4. Complementarity of push and pull channels
Innovation policy is a multifaceted phenomenon, and several innovation policy instruments may be operating at the
same time, in the context of a policy mix ( Flanagan et al., 2011 ). The policy mix may well include both push and pull
instruments, and the effectiveness of one may depend on the existence of the other ( Mohnen and Roller, 2005 ; Guerzoni and
Raiteri, 2015 ).
On the one hand, supply-side and demand-side policies may complement each other. For example, while incumbents
may benefit from R&D tax credits, entrants may benefit more from R&D grants or PPI. Czarnitzki et al. (2018) note that
PPI may result in innovations which are incremental (e.g. technology diffusion or upgrading of existing product portfolios)
rather than radical, because of the technical and legal challenges of allowing for radical innovations in the context of PPI
contracts. The incremental nature of innovation from PPI may, therefore, complement the more radical types of innova-
tion that emerge from R&D grants. Different policies may reach different firms, or address different needs within firms.
N. Stoj ̌ci ́c, S. Srhoj and A. Coad / European Economic Review 121 (2020) 103330 5
V
Edler (2009 , p. 3) explicitly states that the underlying assumption of his research into demand-based innovation policies in
CEECs is that demand-side policies complement (rather than substitute for) supply-side measures.
On the other hand, it cannot be taken for granted that push and pull instruments will complement each other. Much
of the controversy about public support for innovation arises from its potential negative effects. For example, demand-side
public incentives may be directed to satisfying particular user needs, and thus limit the lead-user or lead-market effects
( Edler and Georghiou, 2007 ). The provision of financial incentives bears a potential risk of crowding out of private innovation
expenditure, as firms may be keen to reallocate their own innovation resources to other uses, and substitute them with
public support to innovation ( Bloom et al., 2019 ).
Policymakers have bounded rationality, and have a limited ability to absorb, process and transform all the available
information about market failures into knowledge required for their solution ( Edler and Fagerberg, 2017 ). Policymakers may
thus lack the legal and technical capabilities required for certain innovation policy instruments such as PPI ( Uyarra et al.,
2020 ), especially in catching-up economies. Ineffective policies may also arise if policymakers inappropriately introduce
policies that were successful in dissimilar countries and contexts. Furthermore, policymakers may have a self-serving bias
towards their own projects ( Hayter et al., 2018 ). For their part, firms may use public instruments in inefficient ways, if
for example government funds crowd out firm’s investments in innovative activities, or if firms with low capabilities are
somehow able to receive recurring rounds of funding. 1
Mixing together (supply-side and demand-side) innovation policies also runs the risk that firms may benefit simultane-
ously from several policy instruments (e.g. R&D subsidies as well as PPI contracts), which could create a culture of depen-
dence and decrease the effectiveness of innovation policy expenditures. Hence, the sum of innovation policies put forward
by different government departments may not be well coordinated, featuring overlaps and lacunae. ‘Government failure’
may therefore occur, if the government’s interventions in market activities result in an inefficient use of resources ( Link and
Link, 2009 ). To the extent that existing policies are difficult to remove once they are established ( Flanagan et al., 2011 ),
government failure may persist for years.
As a consequence, we investigate whether financial support for innovation (supply-side) and PPI (demand-side) policies
enhance each other’s effectiveness:
Hypothesis 3. Public financial support and public procurement for innovation complement each other in their effects on
firm innovation and performance, in the catching-up country context.
2.2. Empirical literature
The role of public support to innovation received considerable attention in the empirical literature ( Bozeman and
Link, 1984 , 2014; Zúñiga-Vicente et al., 2014 ; Guerzoni and Raiteri, 2015 ; Howell, 2017 ). Existing findings are mostly con-
cerned with three types of additionalities generated through state intervention in the innovation process: input additionality
or the supplementing role to private innovation investment; behavioral additionality or the shift in organizational attitude
and behavior towards innovation; and output additionality referring to the increased innovation output or greater success
in commercialization of innovation activities, job creation, export competitiveness and growth. Most existing studies are
concerned with developed countries and supply-side mechanisms such as innovation subsidies ( Guo et al., 2016 ; Zúñiga-
icente et al., 2014 ). To a lesser extent, recent research on demand-side instruments has focused on the role of public
procurement in stimulating innovation ( Czarnitzki et al., 2018 ). The general message is that, while most studies find that
public support enhances private innovation effort s, there is nevertheless considerable heterogeneity in the results, and the
relevance of individual instruments depends on contextual factors such as economic development, industry characteristics
or firm features.
The public support instrument of primary interest for many researchers is the provision of R&D subsidies. This is because,
for many policy makers, financial barriers remain the largest obstacle to the innovation activities of private business entities.
While at a theoretical level there is much debate about the crowding-out effect of public R&D subsidies, empirical evidence
has generally pointed to the positive effects of these instruments on R&D investment. Almus and Czarnitzki (2003) find the
R&D intensity of subsidized firms to be about 4 percentage points higher than that of their non-recipient rivals in Germany,
and similar findings are reported by several authors for different countries ( Czarnitzki and Fier, 2002 ; Czarnitzki, and Licht
2006 ; Hud and Hussinger, 2015 ; Radicic and Pugh, 2017 ). Falk (2007) finds that the probability of an innovation project
taking place increases by more than 70% in the presence of public support.
The above results, however, are not uniform and depend on the innovation system context. Literature reviews under-
taken by Cunningham et al. (2016) and Petrin (2018) suggest that additionality effects are more common among smaller
firms, those operating in standardized sectors and in economically challenging regions. Findings from some individual stud-
ies concur. Cano-Kollmann et al. (2017) suggest that the crowding-out effect is moderated by the level of own innovation
intensity. Firms of high innovation intensity who possess sufficient capacity to carry out their innovation activities alone are
more likely to substitute private resources with public ones, while the opposite holds for those firms with scarce financial
resources to undertake innovation activities. Relatedly, Guellec and Van Pottelsberghe de la Potterie (2003) show that the
1 This is the case of ’SBIR mills’ – firms with low innovation and commercial capabilities that nevertheless can successfully navigate the SBIR application
process to obtain multiple rounds of innovation funding (see Link and Scott, 2009 , p. 269).
6 N. Stoj ̌ci ́c, S. Srhoj and A. Coad / European Economic Review 121 (2020) 103330
complementarity of subsidies exists at lower levels of financial support, while above the threshold of 20% the crowding-out
effect kicks in.
Besides the interest in input additionality, the work of recent years was also concerned with the impact of public sup-
port for innovation on output additionality. Here too the existing literature emphasizes the role of public financial support
through R&D subsidies and other forms of state grants, although several studies also examined the role of university-industry
links and demand-side incentives such as public procurement or regulation. Findings from developed countries, both EU
and OECD countries, suggest that greater R&D public support increases the propensity of firms to innovate, and also their
involvement in radical innovations ( Hewitt Dundas and Roper, 2010 ; De Marchi, 2012 ; Lucena and Afcha, 2014 ). Romero-
Martinez et al. (2010) find that these effects are warranted for product and process innovations as well as organizational,
institutional or managerial innovations, among Spanish SMEs. Among the few studies to consider sectoral differences, they
find stronger effects among services than manufacturing firms.
Direct innovation outputs (such as those mentioned above) are only an intermediate stage towards the ultimate objective
of better firm performance. For this reason, several authors addressed the relationship between public financial incentives
and performance dimensions such as job creation, turnover, productivity or survival. BEIS (2017) suggests a positive effect
of R&D subsidies on firm survival and employment in the short-run, as well as turnover effects in the period up to 5
years since receipt of support. Link and Scott (2012) investigate the relationship between public support to innovation and
employment growth in the US, and their findings suggest an absence of any effects on employment in recipient firms,
but they also mention the indirect effects on job creation in firms adopting innovations developed by award beneficiaries.
Hashi and Stoj ̌ci ́c (2013) find that the provision of subsidies creates input additionality across EU firms, but has an adverse
effect on innovation outputs.
The effects of other public support instruments on innovation output are rather ambiguous. Positive effects of public pro-
curement (i.e. PP not PPI) are found on the proportion of sales coming from the new products ( Aschhoff and Sofka, 2009 ),
while Czarnitzki et al. (2018) find PPI to yield limited positive effects only on products and services new to the firm. Sim-
ilar findings seem to hold at the meso level with respect to productivity growth ( Haskel and Wallis, 2013 ). Lucena and
Afcha (2014) report a positive effect of R&D subsidies on patent counts and the introduction of new products, but suggest
that these effects are mediated through openness of innovation and the extent of investment in intramural R&D. There is
also evidence of a positive effect on exports ( Guo et al., 2016 ). However, it appears that output additionality is higher when
public subsidies are complemented with additional measures.
Un and Montoro-Sanchez (2010) show that the propensity of firms to innovate increases when public funds are com-
plemented with own resources. Czarnitzki and Licht (2006) find similar evidence on complementarity between public and
private innovation investment. Bozeman and Link (2015) show how private-sector R&D investment benefited from a com-
bination of policies, including those aimed to encourage technology transfer from universities, collaboration, and R&D tax
credits for the development and commercialization of innovations. Finally, Bérubé and Mohnen (2009) suggest that the
addition of grants to tax credits increases the propensity of firms to innovate, their success in the commercialization of
innovations, and their involvement in radical innovations.
The provision of public support does not only affect the innovation input and output of firms. The link between the
two goes through the innovation throughput stage. Several studies suggest that access to public sources of innovation
also changes the behavior of recipient firms ( Clarysse et al., 2009 ; Gök and Edler, 2012 ). Empirical evidence suggests a
non-negligible effect on the behavior of beneficiary firms. Falk (2007) notes that the provision of subsidies increases the
speed of launching, the duration and the publication of results for publicly-funded research projects. Hewitt-Dundas and
Roper (2010) found evidence of extensive and improved product additionality (the probability of undertaking innovation
and doing incremental innovation). Finally, Albors-Garrigos and Barrera (2011) suggest that the effect of received subsidies
is higher if recipient firms have high innovation capabilities and the potential to develop cooperation linkages in the devel-
opment of innovations.
Most of the above studies are undertaken on firms in developed economies. However, there are also studies evaluating
the effectiveness of public sources of innovation in catching-up economies. For Colombia, Crespi et al. (2011) suggest a pos-
itive effect of R&D subsidies on productivity, employment and sales of new products. The evidence for new EU member
states is scarce. Radosevic (2007) discusses the limited role of domestic demand (including public sector demand) for the
development of innovations. Rather, it appears that firms in these countries follow doing-using-interacting modes of inno-
vation based on non-scientific drivers such as learning-by-doing, learning-by-using, and learning-by-interacting. Results of
Zemplinerová and Hromádková (2012) for the Czech Republic are in line with Hashi and Stoj ̌ci ́c (2013) , who suggest that
access to subsidies has a negative effect on innovation output as it leads to a quiet-life behavior. Similar results are reported
by Szczygielski et al. (2017) , who report a positive effect of government support to R&D activities on the innovation perfor-
mance of Polish firms, but a negative effect of grants provided for the upgrading of physical and human capital capabilities.
Our brief literature review broadly suggests a positive effect of public support to private innovation activities with spo-
radic evidence of crowding out. Variation in results is no doubt affected by the choice of methodological approach, for ex-
ample regarding selection bias and endogeneity ( Radicic and Pugh, 2017 ). Petrin (2018) recommends that most older studies
should be approached with caution, as the above-mentioned concerns were often neglected. More recent studies, how-
ever, have adopted econometric strategies nested in Rubin’s causal framework, to address selection bias and endogeneity
( Radicic and Pugh, 2017 ).
N. Stoj ̌ci ́c, S. Srhoj and A. Coad / European Economic Review 121 (2020) 103330 7
Fig. 1. Gross and government expenditure on R&D in new EU member states.
Source: Eurostat. Key to country codes: BG: Bulgaria; CZ: Czech Republic; EE: Estonia; HR: Croatia; LV: Latvia; LT: Lithuania; HU: Hungary; PL: Poland;
RO: Romania; SI: Slovenia; SK: Slovak Republic.
3. Data
3.1. EU context
As one of its Europe 2020 objectives, the EU set forth a target of meeting the threshold of investment in R&D of 3% of
GDP. This goal is ambitious for the EU, and even more so for new EU member states from Central and Eastern Europe ( Fig. 1 ).
In all these countries, the gross amount of R&D investment is below the EU28 average ( Fig. 1 , left). Boosting innovative
activity in our CEECs is, therefore, a priority for policy. However, several countries have above-average performance in terms
of government expenditure on R&D, namely the Czech Republic and Slovenia ( Fig. 1 , right).
The information on innovation behavior and accompanying public support to innovation across these countries is rather
scarce, and few data sources exist for several of these countries. The most prominent such source is the Community Innova-
tion Survey (CIS) database, compiled from surveys undertaken biannually by Eurostat in cooperation with national statistical
offices of EU member states and candidate countries. Since its introduction, CIS received lots of attention from the academic
community (e.g. Mohnen and Roller, 2005 ; Raymond et al., 2015 ) which enabled continuous improvements of its survey
methodology.
CIS data is anonymized, which precludes follow up surveys or qualitative analyses. However, it is a reliable source for
quantitative analyses of firm innovation behavior ( Mairesse and Mohnen, 2010 ). Moreover, it contains information on differ-
ent types of public support including public financial incentives from local, national and EU authorities, and demand-side
interventions such as purchasing agreements between government and private business entities that involve innovation.
Another feature of the CIS dataset is the inclusion of information on firm performance and various characteristics, which
enables the evaluation of input, output, and behavioral additionality. The dataset is not without caveats. The biannual na-
ture of the survey, and the anonymization of data, mean that it is possible to trace only the short-run innovation behavior
of firms and potential additionality effects of various public support instruments. Moreover, survey results are released with
a 2–3 year lag. Nevertheless, it is the most comprehensive cross-country dataset on the innovation behavior of European
firms.
CIS data yield insights in innovation activities of firms in new EU member states. The collection of data is undertaken
with the consent of each participating country, which all have the freedom to decide whether to make the results available
for wider use. For the purpose of this research, the data from the most recent CIS round, covering the 2012–2014 period,
have been provided on only eight new member states, namely Bulgaria, the Czech Republic, Estonia, Croatia, Latvia, Hungary,
Romania, and the Slovak Republic. Fig. 2 shows that our database covers 41,623 firms in eight countries, of which about
8135 have engaged in either product or process innovation during the survey period. The proportion of innovators within
surveyed firms seems to follow our findings on the amount of expenditure on R&D in general and government expenditure
in particular. The greatest proportion of innovators can be found in the Czech Republic and Croatia, while at the opposite
end are Romania and Bulgaria.
Fig. 3 shows that public financial support to innovation seems to be the dominant support channel across all analyzed
CEECs. In all countries, the share of firms receiving either PPI alone, or in combination with public financial support for
innovation, is below 2%. This clearly shows that CEECs still rely on conventional “push” channels of public support, while
the use of novel demand-side support instruments is still in its infancy. To some extent, this finding is understandable given
the state of development of the framework for public procurement for innovation in these countries during the analyzed
period. Legal reforms for the facilitation of the procurement of innovative products were introduced at the EU-level only in
2014. A recent European Commission study 2 shows that these directives were incorporated into the national legislation of
2 I.e. SMART 2016/0040, see https://ec.europa.eu/digital- single- market/en/news/benchmarking- national- innovation- procurement- policy- frameworks-
across-europe , last accessed 26th Sept, 2019.
8 N. Stoj ̌ci ́c, S. Srhoj and A. Coad / European Economic Review 121 (2020) 103330
Fig. 2. Number of firms in the sample.
Fig. 3. Access to public support for innovation.
the analyzed countries in the period 2015–2017. However, even in 2018, the majority of these countries (with the exception
of Estonia) were ranked as least progressive in the implementation of the formal framework for PPI. This is not to say that
PPI did not take place, but that the lack of a formal PPI framework may have hindered its development. It can therefore
be concluded that part of the explanation for weak reliance of firms on PPI in our sample lies in the fact that, during the
analyzed period, PPI was not a highly-developed innovation policy instrument, but rather occurred as a response to the
specific needs of the public sector in standard procurement contracts.
3.2. Treatment variables
Our analysis investigates the effect of the introduction of a particular policy measure or event on a specific outcome. To
this end, we assess the effect of two types of innovation policies, defined as demand (or pull) mechanisms and supply (or
push) innovation instruments. The former correspond to PPI. Conventional public procurement (PP) is a policy tool that acts
through the purchase of various products and services by the state. As such it can act as a powerful generator of demand for
innovations. A fortiori, PPI can be expected to strongly encourage firm-level innovation. The CIS asks respondents whether
their public procurement contracts required the development of innovations. This enables us to examine whether such con-
tracts induce differences in innovation outcomes. Regarding push instruments, we analyze financial support for innovation
from local, national and EU bodies. To this end, we introduce three types of treatment in our baseline specification defined
as: (i) receipt of PPI only, (ii) receipt of financial support for innovation only, and (iii) receipt of both PPI and financial
support for innovation.
3.3. Outcome variables
The effectiveness of public innovation policies is assessed for several firm output measures. Firstly, we use conventional
indicators of product and process innovation, categorical variables taking the value of one if the firm introduced product or
N. Stoj ̌ci ́c, S. Srhoj and A. Coad / European Economic Review 121 (2020) 103330 9
process innovation during the 2012–2014 period. We are also interested in the success of firms in the commercialization of
innovations. It is often stated that the true test of an innovation is its adoption by consumers. For this reason, three outcome
variables are introduced which are defined as: (i) share of sales coming from products new to the market, (ii) share of sales
coming from products new to the firm, and (iii) share of sales coming from products that are either new to the firm or to the
market. In this way, we distinguish between firms which introduce genuine, or radical, innovation and those which introduce
imitations of products already available on the market. Finally, we introduce a measure of firm performance defined as the
growth of turnover over 2012–2014. In innovation-driven economies, one would expect that firms which are innovators and
which rely on innovation-oriented instruments achieve stronger performance results. However, the opposite may hold in
settings dominated by the production of standardized activities.
3.4. Control variables
We control for firm characteristics as well as sectoral and country effects (Appendix Table A1 defines the variables). Firm
size is captured with three dummy variables for small, medium-sized and large firms based on their number of employees.
Ideally, one would use a continuous variable as a measure of firm size, but confidentiality of our dataset is partly ensured
through anonymization of the employment variable. Controlling for firm size is relevant from the perspective of the ability
to meet requirements of public procurement (and especially PPI), but more importantly it can be considered as a proxy
for the possession of intellectual, technological and infrastructural resources for innovation. Besides firm size, three dummy
variables are included reflecting the innovation experience of firms, namely whether the firm introduced a patent, or an
organizational or marketing innovation.
The ability of firms to benefit from policy measures or from interactions within the innovation system in general depends
on their absorptive capacity ( Cohen and Levinthal, 1990 ). To this end, our model includes a dummy variable for firms in
which more than 25% of personnel possess a tertiary degree of education. Absorptive capacity may also be strengthened
through knowledge flows from related firms. For this reason, we include a dummy variable that equals 1 if the firm is part
of an enterprise group. Similarly, firms may have access to new knowledge, skills and technology but also have greater need
to innovate if they participate in international markets. For this reason, we introduce two categorical variables for firms
that sell their products on the EU market and firms that sell their products on other international markets. The model also
includes categorical variables for firms that had a positive export intensity in 2012, in order to reflect potential learning-by-
exporting. Finally, the model includes sectoral and country dummy variables to control for universal cross-sectional shocks
affecting all firms. Descriptive statistics are in Appendix Table A2.
4. Methodology
Program evaluations often apply matching techniques to compare a treatment group to a control group, where the two
groups are as similar as possible in terms of observable characteristics ( Imbens and Wooldridge, 2009 ; Guerzoni and Rai-
teri, 2015 ). In this spirit, the conditional independence assumption (CIA) states that given a set of observable covariates,
the selection into treatment is assumed to be as good as random. Finding exact matches on all the relevant covariates can
lead to the so-called ’curse of dimensionality ’, which is why a univariate propensity score is used to decrease the dimen-
sionality by using a probit or logit model ( Imbens and Wooldridge, 2009 ). After matching there should be no statistically
significant differences in the means of all relevant covariates between the treated and control groups, while the distri-
bution of propensity scores for treated and control groups should have a good overlap. In this regard, the most intuitive
matching estimator is one-to-one nearest neighbor matching ( nnm ). In nnm , firstly, a propensity score of the probability of
receiving a treatment is estimated using a probit or logit model, secondly, one control firm is selected for each treated
firm by minimizing the distance of the propensity score between treated and control firms, and thirdly the difference
in potential outcome means of the two samples is calculated. The nnm
3 average treatment effect on the treated (ATT) is
given by:
AT T nnm
=
1
N
N ∑
i =1
( Y i ( 1 ) − Y i ( 0 ) )
Our main estimator is nnm . However, in order to assess the sensitivity of our findings, we also perform sensitivity analy-
sis with propensity score matching and with a regression-based technique called the inverse probability weighted regression
adjustment (ipwra) estimator. We also applied different propensity matching algorithms (including kernel and radius). Esti-
mations obtained with these techniques confirm the robustness of our findings. Details about these alternative techniques
and results of the sensitivity analysis can be found in an Online Appendix to the paper (Section A2.1).
Another issue that we must take into account is potential hidden bias. For example, recipients of public support may pos-
sess superior characteristics that affect both their receipt of support (treatment) and the analyzed outcome. In the presence
3 Nearest neighbour matching is conducted with replacement, implying one control firm can be used as a control firm for several treated firms as
suggested by Lechner (2002) . We conduct the procedure also without replacement. The results remain similar and are available upon request.
10 N. Stoj ̌ci ́c, S. Srhoj and A. Coad / European Economic Review 121 (2020) 103330
Table 1
Probit regression models: determinants of receipt of public procurement for innovation, public financial support and both.
Treatment/variables PPI PS Both
Medium firm −0.050 (0.062) 0.035 (0.031) −0.048 (0.063)
Large firm 0.072 (0.074) 0.028 (0.042) −0.079 (0.085)
Patent application 0.587 ∗∗∗ (0.098) 0.941 ∗∗∗ (0.051) 1.042 ∗∗∗ (0.083)
Organizational innovation 0.506 ∗∗∗ (0.051) 0.500 ∗∗∗ (0.028) 0.499 ∗∗∗ (0.057)
Marketing innovation 0.658 ∗∗∗ (0.050) 0.392 ∗∗∗ (0.028) 0.467 ∗∗∗ (0.056)
Enterprise group 0.212 ∗∗∗ (0.055) 0.222 ∗∗∗ (0.030) 0.345 ∗∗∗ (0.058)
EU market −0.030 (0.051) 0.272 ∗∗∗ (0.027) 0.067 (0.058)
Other markets −0.021 (0.053) 0.234 ∗∗∗ (0.025) 0.179 ∗∗∗ (0.055)
Human capital 0.337 ∗∗∗ (0.049) 0.256 ∗∗∗ (0.023) 0.090 ∗ (0.052)
Constant −3.02 ∗∗∗ (0.066) −2.45 ∗∗∗ (0.031) −3.16 ∗∗∗ (0.073)
Country fixed effects Yes Yes Yes
Sector fixed effects Yes Yes Yes
Number of obs. 38.730 40.993 38.572
Pseudo R 2 0.200 0.204 0.214
Notes: ∗∗∗ , ∗∗ and ∗ denote significance at 1%, 5% and 10% level of significance, respectively.
Country and sector dummy variables included. Standard errors in parentheses.
of such self-selection, the outcomes can no longer be considered independent of treatment status, and conventional estima-
tion methods may produce biased results. One way around this problem is randomization of the sample through modeling
of the treatment assignment process as a function of all factors that could drive the assignment of firms into groups of
e.g. recipients or non-recipients of public support. Well-designed models, including all relevant determinants, can make the
treatment assignment process as good as random, conditional on the included variables ( Cattaneo, 2010 ).
Tests were undertaken to investigate the presence of hidden bias that might affect our results. A well-specified match-
ing procedure should remove any statistically significant differences between treated and control firms, and standardized
differences between two groups of firms should converge to 0 while the variance ratio should near 1 ( Busso et al., 2014 ).
Appendix Fig. A1 verifies the covariate balance for all three treatments. We further examined the sensitivity of the model
to hidden bias with the Rosenbaum (2002) bounds approach after matching estimation, which revealed the robustness of
our model to hidden bias of over 100% (Tables A5a–A5c). A placebo test was undertaken after our matching estimator. The
treated firms were excluded and their control firms from the original matching were assigned as the placebo-treated group.
New control firms were subsequently allocated with the matching procedure to estimate the effect of the placebo treatment
(Table A6). Results from all placebo estimations were insignificant, further confirming the robustness of our model to un-
observed selection bias. Finally, Section A2.3 compares our findings with those from other studies. Results and explanations
for all these tests are contained in our Online Appendix.
Our starting point is the analysis of the effect of our three treatments on firms from eight countries. To estimate each
of the desired treatments, we exclude those firms which have received any of the other two treatments ( Guerzoni and
Raiteri, 2015 ). However, apart from the analysis of an entire sample, we also undertake analysis on the subsample of small
and medium-sized firms and on the subsample of large firms. In each case, we assess the effect of public push and pull
programmes, as well as their combined effects.
5. Results
Three types of policy ‘treatment’ are considered: (i) award of a PPI contract, (ii) receipt of financial support for innovation
from the local, national or EU level (including Framework and Horizon 2020 programmes) and (iii) synergy effects of receipt
of both PPI and financial support. Probit models are estimated to investigate the determinants of the probability of receiving
either a PPI contract ( Table 1 ), public financial support for innovation ( Table 2 ), or both together ( Table 3 ).
5.1. Selection equation
Several interesting findings emerge from Table 1 . Engagement in innovation activities seems relevant for the probability
of receiving push and pull incentives. Having applied for a patent, or having introduced an organizational or marketing
innovation, are all positively associated with the probability of receiving a public procurement contract or financial support
for innovation (or both). It is thus likely that experience of innovative activity, efficiency improvements and experimentations
with marketing issues all matter when it comes to gains from push and pull public incentives. A similar finding is obtained
with respect to knowledge flows within groups of firms. Those firms that are part of a group have a higher probability of
receiving either type of public support, which can be associated with superior knowledge, better management routines and
innovation capabilities, higher skills and better use of technology – all of which are usually characteristics of foreign-owned
firms.
It is often held that firms participating in international markets have superior capabilities and technologies and thus
outperform their indigenous rivals in a number of ways ( Barrios et al., 2005 ). Table 1 indicates that participation in the
N. Stoj ̌ci ́c, S. Srhoj and A. Coad / European Economic Review 121 (2020) 103330 11
Table 2
Treatment effects of public procurement for innovation.
Outcome All BG CZ EE HR HU LV RO SK
Product innovation 1/0 0.363 ∗∗∗
(0.027)
0.423 ∗∗∗
(0.059)
0.353 ∗∗∗
(0.060)
0.192
(0.124)
0.337 ∗∗∗
(0.067)
0.303 ∗∗∗
(0.068)
0.253 ∗∗
(0.124)
0.290 ∗∗∗
(0.102)
0.550 ∗∗∗
(0.058)
Process innovation 1/0 0.231 ∗∗∗
(0.027)
0.216 ∗∗∗
(0.063)
0.243 ∗∗∗
(0.065)
0.244 ∗
(0.130)
0.225 ∗∗∗
(0.061)
0.215 ∗∗∗
(0.063)
0.244 ∗∗∗
(0.089)
0.335 ∗∗∗
(0.085)
0.175 ∗∗
(0.084)
Turnover from products new to
the market (in %)
6.935 ∗∗∗
(1.202)
12.038 ∗∗∗
(3.067)
5.854 ∗∗∗
(1.913)
−4.051
(3.644)
7.350 ∗∗∗
(2.555)
5.752 ∗
(3.172)
3.999
(5.615)
5.615
(4.521)
7.882 ∗∗
(4.003)
Turnover from products new to
the firm (in %)
6.038 ∗∗∗
(1.116)
7.382 ∗∗∗
(2.473)
10.970 ∗∗∗
(3.355)
2.748
(3.757)
2.503
(2.323)
4.583
(2.799)
8.443 ∗
(4.515)
10.819 ∗∗
(4.852)
1.536
(2.397)
Turnover from innovative
products (new to firm or
market) (in %)
12.905 ∗∗∗
(1.657)
19.420 ∗∗∗
(4.111)
16.824 ∗∗∗
(3.844)
0.138
(5.748)
9.853 ∗∗∗
(3.450)
10.335 ∗∗
(4.105)
10.376
(7.216)
15.766 ∗∗∗
(6.082)
9.417 ∗
(5.028)
Growth in turnover (in %) −0.575
(1.592)
−0.365 ∗∗
(0.163)
−0.480 ∗
(0.285)
17.188
(16.706
−5.633
(5.685
−3.152
(3.533)
−0.125
(0.144)
−0.769
(0.558
0.035
(0.267)
Number of observations 38.730 13.283 4.050 1.568 2.480 5.976 1.336 7.738 2.299
Number of treated firms 411 88 63 26 72 66 22 30 44
Number of control firms 38.319 13.195 3.987 1.542 2.408 5.910 1.314 7.708 2.255
∗∗∗ , ∗∗ and ∗ denote significance at the 1%, 5% and 10% level, respectively. Standard errors in parentheses.
Table 3
Treatment effects for public financial support.
Outcome All BG CZ EE HR HU LV RO SK
Product innovation 1/0 0.371 ∗∗∗
(0.012)
0.363 ∗∗∗
(0.021)
0.314 ∗∗∗
(0.024)
0.451 ∗∗∗
(0.050)
0.414 ∗∗∗
(0.042)
0.407 ∗∗∗
(0.023)
0.361 ∗∗∗
(0.056)
0.450 ∗∗∗
(0.046)
0.444 ∗∗∗
(0.056)
Process innovation 1/0 0.391 ∗∗∗
(0.011)
0.428 ∗∗∗
(0.020)
0.362 ∗∗∗
(0.023)
0.391 ∗∗∗
(0.050)
0.456 ∗∗∗
(0.039)
0.392 ∗∗∗
(0.024)
0.412 ∗∗∗
(0.051)
0.411 ∗∗∗
(0.044)
0.250 ∗∗∗
(0.059)
Turnover from products new to
the market (in %)
3.175 ∗∗∗
(0.462)
4.462 ∗∗∗
(0.824)
2.104 ∗∗
(0.878)
4.499 ∗∗
(2.171)
1.601
(1.206)
2.662 ∗∗∗
(1.026)
4.563 ∗∗
(2.106)
4.728 ∗∗
(2.373)
2.597
(2.280)
Turnover from products new to
the firm (in %)
4.516 ∗∗∗
(0.452)
5.531 ∗∗∗
(1.023)
3.165 ∗∗∗
(0.734)
3.919 ∗∗
(1.742)
3.561 ∗∗
(1.499)
3.727 ∗∗∗
(0.888)
5.222 ∗∗
(2.333)
9.894 ∗∗∗
(2.376)
6.661 ∗∗
(3.055)
Turnover from innovative
products (new to firm or
market) (in %)
9.975 ∗∗∗
(0.635)
12.764 ∗∗∗
(1.373)
9.257 ∗∗∗
(1.167)
7.868 ∗∗∗
(2.504)
11.026 ∗∗∗
(2.236)
9.269 ∗∗∗
(1.395)
9.275 ∗∗∗
(2.335)
5.980 ∗∗∗
(1.976)
11.553 ∗∗∗
(3.589)
Growth in turnover (in %) −2.505 ∗∗∗
(0.913)
−4.492
(2.829)
−0.748
(0.479)
0.011
(0.083)
−9.780
(7.557)
−0.340
(0.041)
−2.332 ∗∗
(1.222)
−5.790
(5.126)
−2.917
(2.746)
Number of observations 40.993 13.883 4.836 1.643 2.564 6.491 1.428 7.826 2.322
Number of treated firms 2.790 688 849 132 156 581 131 156 97
Number of control firms 38.203 13.195 3.987 1.511 2.408 5.910 1.297 7.670 2.225
∗∗∗ , ∗∗ and ∗ denote significance at the 1%, 5% and 10% level, respectively. Standard errors in parentheses.
Table 4
Treatment effects for public procurement for innovation and financial support.
Outcome All BG CZ EE HR HU LV RO SK
Product innovation 1/0 0.401 ∗∗∗
(0.026)
0.621 ∗∗∗
(0.050)
0.421 ∗∗∗
(0.055)
0.213 ∗∗
(0.093)
0.366 ∗∗∗
(0.070)
0.543 ∗∗∗
(0.069)
0.077
(0.075)
0.399 ∗∗∗
(0.083)
0.603 ∗∗∗
(0.104)
Process innovation 1/0 0.301 ∗∗∗
(0.028)
0.384 ∗∗∗
(0.065)
0.319 ∗∗∗
(0.063)
0.079
(0.099)
0.378 ∗∗∗
(0.069)
0.407 ∗∗∗
(0.105)
0.155 ∗∗
(0.066)
0.346 ∗∗∗
(0.076)
0.471 ∗∗∗
(0.130)
Turnover from products new to
the market (in %)
−4.157 ∗∗∗
(0.702)
−7.169 ∗∗∗
(2.131)
−3.845 ∗∗∗
(0.656)
−1.186
(0.940)
−6.795 ∗∗∗
(2.062)
−6.700 ∗∗
(3.103)
−1.959
(1.704)
−2.067
(1.943)
−0.397
(3.789)
Turnover from products new to
the firm (in %)
−0.885
(1.066)
−0.740
(2.453)
−6.770 ∗∗∗
(0.846)
−0.569
(1.620)
−7.784 ∗∗∗
(1.501)
−3.534 ∗
(1.944)
−0.912
(1.701)
15.821 ∗∗∗
(5.412)
12.635 ∗
(6.508)
Turnover from innovative
products (new to firm or
market) (in %)
16.542 ∗∗∗
(1.693)
29.099 ∗∗∗
(4.849)
27.995 ∗∗∗
(3.413)
4.056
(4.134)
12.438 ∗∗∗
(4.860)
14.371 ∗∗∗
(4.891)
6.260 ∗
(3.345)
2.163
(2.848)
30.058 ∗∗∗
(8.730)
Growth in turnover (in %) −2.900 ∗∗
(1.354)
−1.673 ∗
(0.863)
−0.169
(0.134)
−0.364 ∗∗
(0.164)
−13.639
(10.240)
−5.878
(5.644)
−3.124 ∗∗
(1.454)
−1.064 ∗
(0.605)
−0.324
(0.199)
Number of observations 38.572 13.241 4.062 1.550 2.446 5.951 1.328 7.725 2.269
Number of treated firms 342 55 75 29 38 41 44 41 19
Number of control firms 38.230 13.186 3.987 1.521 2.408 5.910 1.284 7.684 2.250
∗∗∗ , ∗∗ and ∗ denote significance at the 1%, 5% and 10% level, respectively. Standard errors in parentheses.
12 N. Stoj ̌ci ́c, S. Srhoj and A. Coad / European Economic Review 121 (2020) 103330
EU market increases the probability of receiving financial incentives for innovation, although this is not observed for PPI. A
similar finding holds for firms serving other markets. Appendix Tables A8–A10 disaggregate the results in Table 1 for each
of our 8 CEECs.
Finally, as expected, higher levels of human capital in firms increase the probability of receiving any type of public
support. Propensity scores obtained from probit models are used to obtain nearest neighbors with exact matching at the
country level.
Fig. 4. Plots of ATTs and their 95% confidence intervals, based on Tables 2 , 3 and 4 . See text for details on interpretation.
N. Stoj ̌ci ́c, S. Srhoj and A. Coad / European Economic Review 121 (2020) 103330 13
Fig. 4. Continued
5.2. Treatment effects
If the matching assumptions are verified, including if there is no difference between treatment and control groups in
terms of unobserved variables (i.e. no ’hidden bias’), then our results can be interpreted as causal effects. However, by
definition, we cannot rule out hidden bias (because we have no information on unobserved variables). Therefore, while our
results may be suggestive of, or consistent with, a causal interpretation, nevertheless the cautious reader should interpret
our results as associations rather than definite causal effects.
14 N. Stoj ̌ci ́c, S. Srhoj and A. Coad / European Economic Review 121 (2020) 103330
Fig. 4. Continued
5.2.1. Treatment effects on all firms
The ATTs are calculated for the three treatments in Tables 2 –4 . These shed some light on whether push and pull in-
centives affect the innovation behavior of firms. Strong positive effects of PPI ( Table 2 ) and also public financial support
( Table 3 ) are found for the innovation outcomes, thus supporting Hypotheses 1 and 2. The interpretation of effect sizes is
straightforward: e.g. receiving PPI increases the probability of product innovation by 36.3 percentage points ( Table 2 ), while
receiving public financial support increases the probability of product innovation by 37.1 percentage points ( Table 3 ). These
positive estimates suggest that both push (public financial support) and pull (PPI) incentives can stimulate the successful de-
velopment and application of innovation capabilities of firms in transition economies. These push and pull policies therefore
N. Stoj ̌ci ́c, S. Srhoj and A. Coad / European Economic Review 121 (2020) 103330 15
seem appropriate for the context of CEE countries, whose firms face challenges of moving from the standardized production
of components for global value chains, to the production of innovative products and services.
Negative results are found for the effects of innovation policies on growth of turnover, in a minority of countries, and in
particular for public financial support ( Table 3 ’s estimates for the full sample, and also for Latvia and Slovakia), but also vis-
ible for PPI in the cases of Bulgaria and the Czech Republic ( Table 2 ). While in most countries the effect is not significantly
different from zero, these few cases of negative effects are puzzling. At face value, they suggest that innovation support
has a negative effect on turnover growth. One speculative interpretation could be that recipients shift their priorities to-
wards having higher margins from lower sales. Another speculative interpretation could be that our matching estimates do
not represent causal effects (i.e. if unobserved variables differ between treatment and control groups), for example, if re-
cipients tend to operate in low-growth submarkets or have different strategies (e.g. cost-reduction in the context of global
value chains). This negative effect of innovation support on turnover growth, found for a few countries, would merit fur-
ther investigation in future work. In most cases, however, there is no statistically significant effect of financial support for
innovation on a firm’s turnover growth. This result is in line with the impact evaluations for separate grant schemes in the
Czech Republic ( Dvouletý et al., 2019 ), Croatia ( Srhoj et al., 2019 ) and Slovenia ( Burger and Rojec, 2018 ).
Particularly interesting findings emerge for the effects on the commercialization of innovative products. As noted repeat-
edly in the innovation literature, the true test of innovation success is the acceptance of products by the market. In our
analysis, we distinguished between the sales of innovative products that are new to the market and those that are new to
the firm. While the former can be regarded as ‘genuine’ innovations, the latter are sometimes referred to as imitation. We
also introduce the combined share of turnover coming from products that are either new to the firm or market. Our findings
suggest that PPI matters more than public financial support for innovative sales (compare for example the ATT of 6.935 in
Table 2 with the ATT of 3.175 in Table 3 , for new-to-market sales).
Table 4 contains the ATTs when firms receive both push and pull support. In the case of product innovation, the ATT
of receiving both is slightly larger (but not significantly so) than the ATT of receiving just one. In other cases, however,
the ATT of receiving both push and pull is lower than the ATT of one policy instrument individually. This hints to the
potential mismatch between different types of innovation instruments. Such a mismatch may appear when two instruments
are applied jointly and firms struggle to meet the requirements imposed on them from either type of support. We therefore
obtain mixed evidence for Hypothesis 3.
Fig. 4 below plots the ATTs for the full sample (top row in each case), as well as for individual countries, for the 6 perfor-
mance outcomes (product and process innovation, percentage of sales from new/new-to-market/new-to-firm products, and
turnover growth). Dark blue dots refer to point estimates from Tables 2 , 3 and 4 , horizontal lines represent 95% confidence
intervals, and the vertical reference line at 0 is shown to help assess the statistical significance of individual ATT estimates.
For example, graph (a) in Fig. 4 (i) shows that public procurement for innovation has a statistically significant positive effect
on the probability of product innovation, both for the full sample (top row: “All”) and for individual countries (with the
exception of Estonia, where the ATT is not significantly different from zero).
Appendix Tables A3 and A4 also report the results for subsamples of manufacturing vs services sectors, and for subsam-
ples of SMEs vs large firms. In some cases, such as the introduction of product innovations, there are no differences between
SMEs and large firms. One interesting finding is that large firms are less likely to convert innovation support into process
innovations. SMEs might thus disproportionately benefit from innovation support in terms of process innovations, if this
enables them to cover the fixed costs of introducing improved business processes. Another interesting finding is that large
firms more likely to have new-to-firm innovations (while there is no difference between SMEs and large firms in terms of
new-to-market innovations).
6. Conclusions
It is widely accepted that innovation is the driving force for long-term productivity growth and economic development.
Governments have long sought to stimulate innovation, putting forward an impressive range of innovation policies. At the
end of World War II, the USA sought to transfer publicly developed technology from the public to the private sectors of
the economy, so that technologies developed for military applications might lead to economic growth during times of peace
( Link and Scott, 2019 ). More recently, innovation policy has sought to facilitate the transfer of publicly-funded technology
from universities and national laboratories to private sector firms via the Bayh–Dole Act of 1980 and the Stevenson-Wydler
Act of 1980, respectively ( Bozeman and Link, 2015 ), leading to the reconfiguration of national innovation systems to provide
an expanding role for technology transfer offices at universities ( Link and van Hasselt, 2019 ). Shortly afterwards, the R&D
Tax Credit Act of 1981 was introduced to offer financial incentives to stimulate R&D investments undertaken within firms’
R&D laboratories ( Leyden and Link, 2015 ). R&D tax credits have since become a central innovation policy instrument in the
USA, Europe, and elsewhere. Since then, governments have expanded upon the innovation policy tools set up to encourage
firms to invest their funds in internal R&D activities, including public procurement for innovative solutions as a demand-side
policy to encourage firms to develop innovation capabilities to meet specific user needs. Public procurement for innovation
remains a little-known channel for innovation policy, however, especially regarding its role alongside other elements of the
innovation policy mix. In this paper, we evaluated the effectiveness of a mix of innovation policies (both financial incentives
for R&D and public procurement for innovation) in eight Central and East European Countries.
16 N. Stoj ̌ci ́c, S. Srhoj and A. Coad / European Economic Review 121 (2020) 103330
Our results reveal the beneficial effects of both types of policy instruments. Firms receiving public procurement for inno-
vation contracts or financial support for innovation have a higher probability to innovate and achieve higher sales from new
products. However, the push channel seems to be the dominant mechanism of innovation. This is particularly true in situa-
tions when public procurement is not tailored in a way that requires firms to come up with novel products and processes.
In such circumstances, two policy channels are likely to produce weaker effects than those achieved through push policies
alone. The opposite finding, however, holds when public procurement is structured in a way that specifically stimulates
innovation. Our findings show that such measures alone – and particularly in combination with financial support to inno-
vation – provide the largest positive results, and what is more important they generate the strongest effects on innovations
which are new to the market and not only to firms.
Firms in emerging countries must explore and learn in order to develop their innovation capabilities. These kinds of
valuable learning opportunities are rare in advancing markets – for example, spillovers from multinationals are often weak
in terms of labor flows and upstream/downstream supplier relations. Nevertheless, collaborative and developmental rela-
tionships with state-owned innovation procurement offices and other PPI stakeholders may be a valuable opportunity for
firms to make the first faltering steps towards improving innovation capabilities, in a nurturing and relatively forgiving
environment.
Our research is not without limitations. Chiefly, this refers to our cross-sectional survey data. The availability of longer
time series would enable discerning some of the longer-term effects which are hard to find in the short run. Primarily
this refers to the effects on output such as turnover or exports, where it takes time for innovations to materialize. Future
research could also analyze heterogeneous treatment effects of push and pull factors stemming from local/regional, national
and EU levels. Future research might also apply dose-response models to better understand the optimal doses of innovation
policy interventions. The anonymized nature of our dataset prevented the introduction of additional variables from other
datasets that could help to decrease the potential role of unobserved confounders. Given that treatment and control groups
may differ in terms of unobserved variables, we cannot completely rule out that our results may be affected by selection
bias, which would hinder a causal interpretation of our results. Finally, future studies should investigate complementarities
between technology transfer activities, and push and pull channels of public support to innovation in advancing economies,
something that with current datasets is not possible.
Overall, our results signal that both push and pull mechanisms are relevant public mechanisms to stimulate innovation
for catching-up countries. Furthermore, these push and pull mechanisms are sometimes more effective when applied to-
gether. Innovation policy, in future, faces the challenge of boosting its overall effectiveness by aligning innovation support
schemes in the context of a multipronged innovation policy mix.
Acknowledgments
We are grateful to the editor and two anonymous reviewers for many helpful comments and suggestions. Any remaining
errors are ours alone.
Funding
This work was supported by the Croatian Science Foundation under the project IP-2016-06-3764 , as well as by the Na-
tional Research Foundation of Korea funded by the Korean Government (Grant NRF-2018S1A3A2075175 ).
Supplementary materials
Supplementary material associated with this article can be found, in the online version, at doi: 10.1016/j.euroecorev.2019.
103330 .
References
Aghion, P. , Howitt, P. , 1992. A model of growth through creative destruction. Econometrica 60 (2), 323–351 .
Alam, A. , Casero, P.A. , Khan, F. , Udomsaph, C. , 2008. What do we learn from Schumpeterian Growth Theory? NBER working paper series no. 18824. February .Albors-Garrigos, J. , Barrera, R.R. , 2011. Impact of public funding on a firm’s innovation performance: analysis of internal and external moderating factors.
Int. J. Innov. Manag. 15 (06), 1297–1322 .
Almus, M. , Czarnitzki, D. , 2003. The effects of public R&D subsidies on firms’ innovation activities: the case of Eastern Germany. J. Bus. Econ. Stat. 21 (2),226–236 .
Arrow, K. , 1962. Economic welfare and the allocation of resources for invention. In: Nelson, R. (Ed.), The Rate and Direction of Inventive Activity. PrincetonUniversity Press, Princeton, NJ .
Aschhoff, B. , Sofka, W. , 2009. Innovation on demand—Can public procurement drive market success of innovations? Res. Policy 38 (8), 1235–1247 . Barrios, S. , Görg, H. , Strobl, E. , 2005. Foreign direct investment, competition and industrial development in the host country. Eur. Econ. Rev. 49 (7),
1761–1784 .
Barrios, S. , Görg, H. , Strobl, E. , 2011. Spillovers through backward linkages from multinationals: measurement matters!. Eur. Econ. Rev. 55 (6), 862–875 . BEIS (2017). The impact of public support for innovation on firm outcomes. BEIS research paper 3. Accessed June 13 2019 : https://www.gov.uk/government/
uploads/system/uploads/attachment _ data/file/604841/innovation- public- support- impact- report- 2017.pdf . Bérubé, C. , Mohnen, P. , 2009. Are firms that receive R&D subsidies more innovative? Can. J. Econ./Revue Canadienne D’économique 42 (1), 206–225 .
Bloom, N. , Van Reenen, J. , Williams, H. , 2019. A toolkit of policies to promote innovation. J. Econ. Perspect. 33 (3), 163–184 . Bozeman, B. , Link, A.N. , 1984. Tax incentives for R&D: a critical evaluation. Res. Policy 13 (1), 21–31 .
N. Stoj ̌ci ́c, S. Srhoj and A. Coad / European Economic Review 121 (2020) 103330 17
Bozeman, B., Link, A.N., 2015. Toward an assessment of impacts from US technology and innovation policies. Sci. Publ. Policy 42 (3), 369–376. doi: 10.1093/scipol/scu058 .
Brown, J.R. , Martinsson, G. , Petersen, B.C. , 2017. What promotes R&D? Comparative evidence from around the world. Res. Policy 46 (2), 447–462 . Burger, A. , Rojec, M. , 2018. Impotence of crisis-motivated subsidization of firms: the case of Slovenia. East Eur. Econ. 56 (2), 122–148 .
Busso, M., DiNardo, J., McCrary, J., 2014. New evidence on the finite sample properties of propensity score reweighting and matching estimators. Rev. Econ.Stat. 96 (5), 885–897. doi: 10.1162/REST _ a _ 00431 .
Cano-Kollmann, M. , Hamilton III, R.D , Mudambi, R. , 2017. Public support for innovation and the openness of firms’ innovation activities. Ind. Corp. Change
26 (3), 421–442 . Cattaneo, M.D., 2010. Efficient semiparametric estimation of multi-valued treatment effects under ignorability. J. Econometrics 155 (2), 138–154. doi: 10.
1016/j.jeconom.2009.09.023 . Clarysse, B. , Wright, M. , Mustar, P. , 2009. Behavioural additionality of R&D subsidies: a learning perspective. Res. Policy 38 (10), 1517–1533 .
Cohen, W.M. , Levinthal, D.A. , 1990. Absorptive Capacity: A New Perspective on Learning and Innovation. Admin. Sci. Quart. 35, 128–152 . Costantini, V. , Crespi, F. , Martini, C. , Pennacchio, L. , 2015. Demand-pull and technology-push public support for eco-innovation: the case of the biofuels
sector. Res. Policy 44 (3), 577–595 . Cunningham, P. , Gök, A. , Larédo, P. , 2016. The impact of direct support to R&D and innovation in firms. In: Edler, J., Cunningham, P., Gök, A., Shapira, P.
(Eds.), Handbook of Innovation Policy Impact. Eu-SPRI Forum on Science, Technology and Innovation Policy series, Edward Elgar Publishing, Cheltenham .
Czarnitzki, D. , Fier, A. , 2002. Do innovation subsidies crowd out private investment? Evidence from the German service sector (No. 02-04) ZEW DiscussionPapers: Mannheim, Germany .
Czarnitzki, D. , Licht, G. , 2006. Additionality of public R&D grants in a transition economy: the case of Eastern Germany. Econ. Transit. 14 (1), 101–131 . Czarnitzki, D. , Hünermund, P. , Moshgbar, N. , 2018. Public Procurement as Policy Instrument For Innovation. ZEW – Centre for European Economic Research
Discussion Paper No. 18-001 . Crespi, G. , Maffioli, A. , Arjona, Meléndez , M , 2011. Public Support to Innovation: The Colombian COLCIENCIAS’ Experience. Inter-American Development
Bank .
De Marchi, V. , 2012. Environmental innovation and R&D cooperation: empirical evidence from Spanish manufacturing firms. Res. Policy 41 (3), 614–623 . Dobrinsky, R. , Hesse, D. , Traeger, R. , 2006. Understanding The Long-Term Growth Performance Of The East European And CIS Economies. UNECE Discussion
Paper 2006-1. United Nations . Dvouletý, O., Cadil, J., Mirošník, K., 2019. Do firms supported by credit guarantee schemes report better financial results 2 years after the end of interven-
tion? BE J. Econ. Anal. Policy 19 (1). doi: 10.1515/bejeap- 2018- 0057 . Edler, J. (2009). Demand Policies for Innovation in EU CEE Countries. Manchester Business School Working Paper No. 579: Manchester, UK.
Edler, J. , Georghiou, L. , 2007. Public procurement and innovation—resurrecting the demand side. Res. Policy 36 (7), 949–963 .
Edler, J. , Fagerberg, J. , 2017. Innovation policy: what, why and how. Oxf. Rev. Econ. Policy 33 (1), 2–23 . Edquist, C. , Zabala-Iturriagagoitia, J.M. , 2012. Public procurement for innovation as mission-oriented innovation policy. Res. Policy 41 (10), 1757–1769 .
Falk, R. , 2007. Measuring the effects of public support schemes on firms’ innovation activities: survey evidence from Austria. Res. Policy 36 (5), 665–679 . Fernández-Sastre, J. , Martín-Mayoral, F. , 2017. Assessing the impact of public support for innovation in an emerging innovation system. Int. J. Technol. Learn.
Innov. Dev. 9 (1), 42–64 . Flanagan, K. , Uyarra, E. , Laranja, M. , 2011. Reconceptualising the ‘policy mix’ for innovation. Res. Policy 40 (5), 702–713 .
Frontier Economics, 2014. Rates of Return to Investment in Science and Innovation. Department for Business, Energy and Industrial Strat-
egy. Available at www.gov.uk/government/uploads/system/uploads/attachment _ data/file/333006/bis- 14- 990- rates- of- return- to- investment- in-science- and- innovation- revised- final- report.pdf .
Geroski, P.A. , 1990. Procurement policy as a tool of industrial policy. Int. Rev. Appl. Econ. 4 (2), 182–198 . Gök, A. , Edler, J. , 2012. The use of behavioural additionality evaluation in innovation policy making. Res. Eval. 21 (4), 306–318 .
Goñi, E. , Maloney, W.F. , 2017. Why don’t poor countries do R&D? Varying rates of factor returns across the development process. Eur. Econ. Rev. 94, 126–147 .Guellec, D. , Van Pottelsberghe De La Potterie, B. , 2003. The impact of public R&D expenditure on business R&D. Econ. Innov. New Technol. 12 (3), 225–243 .
Guerzoni, M. , Raiteri, E. , 2015. Demand-side vs. supply-side technology policies: hidden treatment and new empirical evidence on the policy mix. Res.
Policy 44 (3), 726–747 . Guo, D. , Guo, Y. , Jiang, K. , 2016. Government-subsidized R&D and firm innovation: evidence from China. Res. Policy 45 (6), 1129–1144 .
Hashi, I. , Stoj ̌ci ́c, N. , 2013. The impact of innovation activities on firm performance using a multi-stage model: evidence from the community innovationsurvey 4. Res. Policy 42 (2), 353–366 .
Haskel, J. , Wallis, G. , 2013. Public support for innovation, intangible investment and productivity growth in the UK market sector. Econ. Lett. 119 (2),195–198 .
Hayter, C.S. , Link, A.N. , Scott, J.T. , 2018. Public sector entrepreneurship. Oxf. Rev. Econ. Policy 34 (4), 676–694 .
Hewitt-Dundas, N. , Roper, S. , 2010. Output additionality of public support for innovation: evidence for Irish manufacturing plants. Eur. Plan. Stud. 18 (1),107–122 .
Holm, J.R. , Timmermans, B. , Østergaard, C.R. , Coad, A. , Grassano, N. , Vezzani, A. , 2019. Labor mobility from R&D-intensive multinational companies: Impli-cations for knowledge and technology transfer. Paper presented at DRUID Society Conference 2019, Copenhagen, Denmark .
Howell, S.T. , 2017. Financing innovation: evidence from R&D grants. Am. Econ. Rev. 107 (4), 1136–1164 . Hud, M. , Hussinger, K. , 2015. The impact of R&D subsidies during the crisis. Res. Policy 44 (10), 1844–1855 .
Imbens, G.W. , Wooldridge, J.M. , 2009. Recent developments in the econometrics of program evaluation. J. Econ. Lit. 47 (1), 5–86 . Javorcik, B.S. , 2004. Does foreign direct investment increase the productivity of domestic firms? In search of spillovers through backward linkages. Am.
Econ. Rev. 94 (3), 605–627 .
Lechner, M., 2002. Some practical issues in the evaluation of heterogenous labour market programmes by matching methods. J. Roy. Stat. Soc. A Sta. 165,59–82. doi: 10.1111/1467-985X.0asp2 .
Leyden, D.P. , Link, A.N. , 2015. Public Sector Entrepreneurship: US Technology and Innovation Policy. Oxford University Press . Link, A.N. , Link, J.R. , 2009. Government as Entrepreneur. Oxford University Press .
Link, A.N. , Scott, J.T. , 2009. Private investor participation and commercialization rates for government-sponsored research and development: would a pre-diction market improve the performance of the SBIR programme? Economica 76 (302), 264–281 .
Link, A.N. , Scott, J.T. , 2010. Government as entrepreneur: evaluating the commercialization success of SBIR projects. Res. Policy 39 (5), 589–601 .
Link, A.N. , Scott, J.T. , 2012. Employment growth from public support of innovation in small firms. Econ. Innov. New Technol. 21 (7), 655–678 . Link, A.N., Scott, J.T., 2019. The economic benefits of technology transfer from U.S. federal laboratories. J. Technol. Transf. doi: 10.1007/s10961- 019- 09734- z ,
Online first. Link, A.N. , van Hasselt, M. , 2019. On the transfer of technology from universities: the impact of the Bayh–Dole Act of 1980 on the institutionalization of
university research. Eur. Econ. Rev. 119, 472–481 . List, F. , 1841. The National System of Political Economy, English Edition (1904) Longman, London .
Lokshin, B. , Mohnen, P. , 2012. How effective are level-based R&D tax credits? Evidence from the Netherlands. Appl. Econ. 44 (12), 1527–1538 .
Lucena, A., & Afcha, S. (2014). Public support for R&D, knowledge sourcing and firm innovation: Examining a mediated model with evidence from themanufacturing industries. Centrum Catolica Working Paper Series No. 2014-06-0 0 02 / June 2014. Lima, Peru.
Mairesse, J. , Mohnen, P. , 2010. Using innovation surveys for econometric analysis. In: Hall, B.H., Rosenberg, N. (Eds.), Chapter 26 in: Handbook of theEconomics of Innovation, Vol. 2. North Holland publishing, Amsterdam, Netherlands, pp. 1129–1155 .
Mazzoleni, R. , Nelson, R.R. , 2007. Public research institutions and economic catch-up. Res. Policy 36 (10), 1512–1528 .
18 N. Stoj ̌ci ́c, S. Srhoj and A. Coad / European Economic Review 121 (2020) 103330
Mazzucato, M. , 2013. The Entrepreneurial State. Anthem Press, London . Mohnen, P. , Röller, L.H. , 2005. Complementarities in innovation policy. Eur. Econ. Rev. 49 (6), 1431–1450 .
Nelson, R.R. , 1959. The simple economics of basic scientific research. J. Polit. Econ. 67 (3), 297–306 . OECD, 2017. Public Procurement for Innovation: Good Practices and Strategies. OECD Public Governance Reviews, OECD Publishing, Paris, doi: 10.1787/
9789264265820-en . Petrin, T. , 2018. A Literature Review On the Impact and Effectiveness of Government Support for R&D and Innovation. ISIgrowth Working Paper 5/2018 .
Radicic, D. , Pugh, G. , 2017. R&D programmes, policy mix, and the ‘European paradox’: evidence from European SMEs. Sci. Publ. Policy 44 (4), 497–512 .
Radosevic, S. , 2007. National Systems of Innovation and Entrepreneurship: In search of a Missing Link. Centre for the Study of Economic and Social Changein Europe, London Working Paper 73 .
Radosevic, S. , Yoruk, E. , 2018. Technology upgrading of middle income economies: a new approach and results. Technol. Forecast. Soc. Change 129, 56–75 . Raymond, W. , Mairesse, J. , Mohnen, P. , Palm, F. , 2015. Dynamic models of R&D, innovation and productivity: panel data evidence for Dutch and French
manufacturing. Eur. Econ. Rev. 78, 285–306 . Romero-Martínez, A.M. , Ortiz-de-Urbina-Criado, M. , Ribeiro Soriano, D , 2010. Evaluating European Union support for innovation in Spanish small and
medium enterprises. Serv. Ind. J. 30 (5), 671–683 . Rosenbaum, P.R. , 2002. Observational studies. In: Rosenbaum, P.R. (Ed.), Observational Studies. Springer, New York, pp. 1–17 .
Schumpeter, J.A. , 1934. The Theory of Economic Development. Mass: Harvard University Press, Cambridge .
Srhoj, S., Škrinjari ́c, B., Radas, S., 2019. Bidding against the odds? The impact evaluation of grants for young micro and small firms during the recession.Small Bus. Econ. doi: 10.1007/s11187- 019- 00200- 6 .
Stoj ̌ci ́c, N., Orlic, E., 2019. Spatial dependence, foreign investment and productivity spillovers in new EU member states. Reg. Stud. doi: 10.1080/00343404.2019.1653451 .
Szczygielski, K. , Grabowski, W. , Pamukcu, M.T. , Tandogan, V.S. , 2017. Does government support for private innovation matter? Firm-level evidence from twocatching-up countries. Res. Policy 46 (1), 219–237 .
Un, C.A. , Montoro-Sanchez, A. , 2010. Public funding for product, process and organisational innovation in service industries. Serv. Ind. J. 30 (1), 133–147 .
Uyarra, E. , Zabala-Iturriagagoitia, J.M. , Flanagan, K. , Magro, E. , 2020. Public procurement, innovation and industrial policy: rationales, roles, capabilities andimplementation. Res. Policy 49 (1), 103844 .
Von Hippel, E. , 1986. Lead users: a source of novel product concepts. Manag. Sci. 32 (7), 791–805 . Zemplinerová, A. , Hromádková, E. , 2012. Determinants of firm’s innovation. Prague Econ. Pap. 21 (4), 487–503 .
Zúñiga-Vicente, J.Á. , Alonso-Borrego, C. , Forcadell, F.J. , Galán, J.I. , 2014. Assessing the effect of public subsidies on firm R&D investment: a survey. J. Econ.Surv. 28 (1), 36–67 .