Innovation procurement as capability-building: Evaluating innovation policies in eight Central and Eastern European countries

Innovation procurement as capability-building: Evaluating innovation policies in eight Central and Eastern European countries

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Innovation procurement as capability-building: Evaluating innovation policies in eight Central & Eastern European Countries Nebojˇsa Stojˇci´c PhD , Stjepan Srhoj PhD , Alex Coad PhD PII: DOI: Reference:

S0014-2921(19)30190-4 EER 103330

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European Economic Review

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16 June 2019 28 October 2019

Please cite this article as: Nebojˇsa Stojˇci´c PhD , Stjepan Srhoj PhD , Alex Coad PhD , Innovation procurement as capability-building: Evaluating innovation policies in eight Central & Eastern European Countries, European Economic Review (2019), doi:

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Innovation procurement as capability-building: Evaluating innovation policies in eight Central & Eastern European Countries Nebojša Stojčić, PhD Associate professor University of Dubrovnik Department of Economics and Business Economics [email protected] Stjepan Srhoj, PhD Postdoctoral research fellow University of Dubrovnik Department of Economics and Business Economics [email protected] Alex Coad, PhD Professor CENTRUM Católica Graduate Business School (CCGBS), Lima, Perú Pontificia Universidad Católica del Perú (PUCP), Lima, Perú [email protected]

ABSTRACT: 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 & 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 innovationoriented public procurement. We argue that policy-makers aiming to strengthen indigenous innovation capabilities should place stronger emphasis on PPI. KEYWORDS: public funding for innovation, public procurement for innovation, additionality, evaluation, Central and Eastern European countries JEL CODES: O38 ACKNOWLEDGEMENTS: 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 National Research Foundation of Korea funded by the Korean Government (Grant NRF-2018S1A3A2075175).


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 & 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 nonnegligible. 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 efforts 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 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 transition 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 intensifying competition from standardised 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 & Nelson, 2007; FernándezSastre & Martin-Mayoral, 2017). The general message 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 & Sofka, 2009; Czarnitzki, Hünermund & Moshgbar, 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 innovation 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), although the multichannel approach to evaluating push and pull aspects of innovation policy in middle-income countries has not been investigated yet (Cunningham et al., 2013; 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 4

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 highincome 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 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 & Martin-Mayoral, 2017).

All of the above presents particular challenges for the formulation of innovation policies in a catching-up context. Policymakers in the innovation systems of catching-up countries may lack experience in the challenges of administering innovation 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 capabilities. 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 & Howitt, 1992) that the non-rival nature of knowledge creates economy-wide spillovers. Recent literature suggests that these 5

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 (1936) 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 & 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 & Mohnen, 2012).

In addition to these traditional market failure arguments, a third theory was recently put forward in favour of public support to innovation (Cunningham, Gök & Larédo, 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 6

policies include financial and non-financial measures to instigate additionality effects in the level of investment in innovation, and to influence the behaviour 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 guarantees in the arrangements of private business entities with financial institutions (Bloom et al., 2019). Nonfinancial measures 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 Investment (FDI) in the hope that indigenous firms could benefit from learning opportunities and technology transfer from multinationals (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 7

effects, knowledge spillovers through labour flows, and also upstream-downstream supply chain linkages between multinationals and indigenous firms (Javorcik, 2004; Stojčić 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 multinationals (Stojčić and Orlic, 2019). Furthermore, multinational firms seem to operate in segregated labour 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 collaborative and developmental relationship. This is succinctly stated in a recent cross-country study into PPI practices by the OECD (2017, page 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 & Georghiou, 2007; Aschhoff & Sofka, 2009; Czarnitzki, Hünermund & Moshgbar, 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 favour of demand-side instruments. The first is centred 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 commercializing 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 & Sofka, 2009). A second argument for demand-side 8

instruments is related to addressing societal needs and grand challenges.

PPI can help

governments obtain innovative solutions 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 innovation that emerge from R&D grants. Different policies may reach different firms, or address different needs within firms. Edler (2009, p3) explicitly states that the underlying assumption of his research into demandbased 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 & 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


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, p269).


Mixing together (supply-side and demand-side) innovation policies also runs the risk that firms may benefit simultaneously from several policy instruments (e.g. R&D subsidies as well as PPI contracts), which could create a culture of dependence 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; Zuñiga-Vicente et al., 2014; Guerzoni and Raiteri, 2015; Howell, 2017). Existing findings are mostly concerned with three types of additionalities generated through state intervention in the innovation process: input additionality or the supplementing role to private innovation investment; behavioural additionality or the shift in organizational attitude and behaviour 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, Guo & Jiang, 2016; Zúñiga-Vicente 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, Hünermund & Moshgbar, 2018). The general message is that, while most studies find that public support enhances private 11

innovation efforts, 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 subsidised 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 & Fier, 2002; Czarnitzki, & Licht 2006; Hud & Hussinger, 2015; Radicic & 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 undertaken by Cunningham et al. (2013) and Petrin (2018) suggest that additionality effects are more common among smaller firms, those operating in standardised sectors and in economically challenging regions. Findings from some individual studies 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 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 support 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 12

increases the propensity of firms to innovate, and also their involvement in radical innovations (Hewitt Dundas & Roper, 2010; De Marchi, 2012; Lucena & 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 Stojčić (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 procurement (i.e. PP not PPI) are found on the proportion of sales coming from the new products (Aschhoff & Sofka, 2009), while Czarnitzki, Hünermund and Moshgbar (2018) find PPI to yield limited positive effects only on products and services new to the firm. Similar findings seem to hold at the meso level with respect to productivity growth (Haskel & Wallis, 2013). Lucena & 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 complemented with own resources. Czarnitzki and Licht (2006) find similar evidence on complementarity between public and private innovation investment. 13

Bozeman and Link (2014) show how private-sector R&D investment benefited from a combination 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 behaviour of recipient firms (Clarysse et al., 2009; Gök & Edler, 2012). Empirical evidence suggests a nonnegligible effect on the behaviour 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 development 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 positive 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 innovation based on non-scientific drivers such as learning-by-doing, learning-by-using, and learning-by-interacting. Results of Zemplinerova and Hromadkova (2012) for the Czech Republic are in line with Hashi and Stojčić (2013), who suggest that access to subsidies has a negative effect on innovation output as it leads to a quiet-life behaviour. Similar results are reported by Szczygielski et al. (2017), who report a positive effect of government support to R&D activities on the innovation performance 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 sporadic evidence of crowding out. Variation in results is no doubt affected by the choice of methodological approach, for example 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, however, have adopted econometric strategies nested in Rubin‘s causal framework, to address selection bias and endogeneity (Radicic & Pugh, 2017).

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 (Figure 1). In all these countries, the gross amount of R&D investment is below the EU28 average (Figure 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 (Figure 1, right).

Figure 1: Gross and government expenditure on R&D in new EU member states GERD as % of GDP 2016 2.50 2.00 1.50 1.00 0.50 0.00

GOV R&D as % of GDP 2016 0.40 0.30 0.20 0.10 0.00

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.

The information on innovation behaviour 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 Innovation Survey (CIS) 15

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 behaviour (Mairesse and Mohnen, 2010). Moreover, it contains information on different 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 behavioural additionality. The dataset is not without caveats. The biannual nature of the survey, and the anonymization of data, mean that it is possible to trace only the short-run innovation behaviour 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 behaviour 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. Figure 2 shows that our database covers 41‘623 firms in eight countries, of which about 8‘135 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.


Figure 2: Number of firms in the sample

Figure 3 shows that public financial support to innovation seems to be the dominant support channel across all analysed 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 analysed 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 study2 shows that these directives were incorporated into the national legislation of the analysed 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 analysed period, PPI was


I.e. SMART 2016/0040, see , last accessed 26th Sept 2019.


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.

Figure 3: Access to public support for innovation

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 contracts induce differences in innovation outcomes. Regarding push instruments, we analyse 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 18

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 standardised 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, mediumsized 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 anonymisation 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 & 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 19

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 & Wooldridge, 2009; Guerzoni & Raiteri, 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 dimensionality by using a probit or logit model (Imbens & 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 distribution 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 neighbour 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 nnm3 average treatment effect on the treated (ATT) is given by:



Our main estimator is nnm. However, in order to assess the sensitivity of our findings, we also perform sensitivity analysis with propensity score matching and with a regression-based technique called the inverse probability weighted regression adjustment (ipwra) estimator. We 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.


also applied different propensity matching algorithms (including kernel and radius). Estimations 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 possess superior characteristics that affect both their receipt of support (treatment) and the analysed outcome. In the presence of such self-selection, the outcomes can no longer be considered independent of treatment status, and conventional estimation methods may produce biased results. One way around this problem is randomization of the sample through modelling 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 matching procedure should remove any statistically significant differences between treated and control firms, and standardised differences between two groups of firms should converge to 0 while the variance ratio should near 1 (Busso et al., 2014). Appendix Figure 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% (Table 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 unobserved 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 21

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. Table 1. Probit regression models: determinants of receipt of public procurement for innovation, public financial support and both Treatment/variables Medium firm Large firm Patent application Organisational innovation Marketing innovation Enterprise group EU market Other markets Human capital Constant Country fixed effects Sector fixed effects Number of obs.

PPI -0.050 (0.062) 0.072 (0.074) 0.587*** (0.098) 0.506*** (0.051) 0.658*** (0.050) 0.212*** (0.055) -0.030 (0.051) -0.021 (0.053) 0.337*** (0.049) -3.02*** (0.066) Yes Yes 38.730

PS 0.035 (0.031) 0.028 (0.042) 0.941*** (0.051) 0.500*** (0.028) 0.392*** (0.028) 0.222*** (0.030) 0.272 *** (0.027) 0.234*** (0.025) 0.256 *** (0.023) -2.45*** (0.031) Yes Yes 40.993

Both -0.048 (0.063) -0.079 (0.085) 1.042*** (0.083) 0.499*** (0.057) 0.467*** (0.056) 0.345*** (0.058) 0.067 (0.058) 0.179*** (0.055) 0.090* (0.052) -3.16*** (0.073) Yes Yes 38.572 22

Pseudo R2




Notes: ***, ** and * denote significance at 1%, 5% and 10% level of significance respectively. Country and sector dummy variables included. Standard errors in parentheses.

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 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 neighbours with exact matching at the country level.


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.


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 incentives affect the innovation behaviour 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 development 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 visible 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 towards 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 recipients 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 further 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, Čadil & Mirošník, 2019), Croatia (Srhoj, Škrinjarić & Radas, 2019) and Slovenia (Burger & Rojec, 2018).

Table 2: Treatment effects of public procurement for innovation Outcome Product




















innovation 1/0 Process innovation 1/0 Turnover from products new to the market (in %) Turnover from products new to the firm (in %) Turnover from innovative products (new to firm or market) (in %) Growth in turnover (in %) Number of observations Number of treated firms Number of control firms










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)

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)

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)

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)

-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)




























Source: Authors. ***, ** and * denote significance at the 1%, 5% and 10% level respectively. Standard errors in parentheses.

Table 3: Treatment effects for public financial support Outcome










Product innovation 1/0 Process innovation 1/0 Turnover from products new to the market (in %) Turnover from products new to the firm (in %) Turnover from innovative products (new to firm or market) (in %) Growth in turnover (in %) Number of observations Number of treated firms Number of control firms

0.371*** (0.012) 0.391*** (0.011) 3.175*** (0.462)

0.363*** (0.021) 0.428*** (0.020) 4.462*** (0.824)

0.314*** (0.024) 0.362*** (0.023) 2.104** (0.878)

0.451*** (0.050) 0.391*** (0.050) 4.499** (2.171)

0.414*** (0.042) 0.456*** (0.039) 1.601 (1.206)

0.407*** (0.023) 0.392*** (0.024) 2.662*** (1.026)

0.361*** (0.056) 0.412*** (0.051) 4.563** (2.106)

0.450*** (0.046) 0.411*** (0.044) 4.728** (2.373)

0.444*** (0.056) 0.250*** (0.059) 2.597 (2.280)

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)

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)

-2.505*** (0.913) 40.993

-4.492 (2.829) 13.883

-0.748 (0.479) 4.836

0.011 (0.083) 1.643

-9.780 (7.557) 2.564

-0.340 (0.041) 6.491

-2.332** (1.222) 1.428

-5.790 (5.126) 7.826

-2.917 (2.746) 2.322



















Source: Authors. ***, ** 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










Product innovation 1/0 Process innovation 1/0 Turnover from products new to

0.401*** (0.026) 0.301*** (0.028) -4.157*** (0.702)

0.621*** (0.050) 0.384*** (0.065) -7.169*** (2.131)

0.421*** (0.055) 0.319*** (0.063) -3.845*** (0.656)

0.213** (0.093) 0.079 (0.099) -1.186 (0.940)

0.366*** (0.070) 0.378*** (0.069) -6.795*** (2.062)

0.543*** (0.069) 0.407*** (0.105) -6.700** (3.103)

0.077 (0.075) 0.155** (0.066) -1.959 (1.704)

0.399*** (0.083) 0.346*** (0.076) -2.067 (1.943)

0.603*** (0.104) 0.471*** (0.130) -0.397 (3.789)


the market (in %) Turnover from products new to the firm (in %) Turnover from innovative products (new to firm or market) (in %) Growth in turnover (in %) Number of observations Number of treated firms Number of control firms

-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)

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)

-2.900** (1.354)

-1.673* (0.863)

-0.169 (0.134)

-13.639 (10.240)

-5.878 (5.644)

-0.324 (0.199)





3.124** (1.454) 1.328

-1.064* (0.605)


0.364** (0.164) 1.550





















Source: Authors. ***, ** and * denote significance at the 1%, 5% and 10% level respectively. Standard errors in parentheses.

Particularly interesting findings emerge for the effects on the commercialization of innovative products. As noted repeatedly 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.

Figure 4 below plots the ATTs for the full sample (top row in each case), as well as for individual countries, for the 6 performance 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 4, 5 and 6, horizontal lines represent 95% confidence intervals, and the vertical reference line at 0 is shown to help assess the statistical 26

significance of individual ATT estimates. For example, graph a) in Figure 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).


Figure 4: Plots of ATTs and their 95% confidence intervals, based on Tables 4, 5 and 6. See text for details on interpretation. i)








Appendix Tables A3 and A4 also report the results for subsamples of manufacturing vs services sectors, and for subsamples 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 30

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 innovation 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 situations 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 31

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 innovation – 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 labour flows and upstream/downstream supplier relations. Nevertheless, collaborative and developmental relationships 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 analyse 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 anonymised 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 together. 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.

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