European Economic Review - UNU-MERIT

European Economic Review 121 (2020) 103330

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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.


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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,


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.


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).


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 ).


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.

https://ec.europa.eu/digital-single-market/en/news/benchmarking-national-innovation-procurement-policy-frameworks-across-europe


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


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.


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


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)


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.



(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)


(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)


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)


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)



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 treated firms 2.790 688 849 132 156 581 131 156 97



Table 4

Treatment effects for public procurement for innovation and financial support.



(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)


(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)


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)


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)



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 treated firms 342 55 75 29 38 41 44 41 19




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.


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.


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


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.


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 .

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