The determinants of Public Grants and Venture Capital financing: Evidence from Europe

Abstract

This analysis compares the characteristics of firms supported by public and private sources in early-stage financing to investigate funding patterns for innovative companies. It examines whether the two sources of funding target similar firms in the period 2008-2017 using a portfolio approach on EU-based firms raising either Venture Capital financing, public grants under the Horizon 2020 ‘SME Instrument’ scheme, or both. The findings show that venture capitalists finance more innovative and younger firms, whereas public investors focus on smaller companies. This pattern is supported by robustness checks and expansions that address multiple dimensions of heterogeneity behaviours in the interaction of private and public funding.

Full text

The determinants of Public Grants and
Venture Capital financing: Evidence from
Europe
Bellucci, A., Gucciardi, G. and
Nepelski, D.
J R C T E C H N I C A L R E P O R T S
EUR 31398 EN
ISSN 1831-9424

This publication is a Technical report by the Joint Research Centre (JRC), the European Commission’s science and knowledge service. It
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JRC132268
EUR 31398 EN
PDF ISBN 978-92-76-62017-4 ISSN 1831-9424 doi:10.2760/920754 KJ-NA-31-398-EN-N
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How to cite this report: Bellucci, A., Gucciardi, G. and Nepelski, D., The determinants of Public Grants and Venture
Capital financing: Evidence from Europe, Publications Office of the European Union, Luxembourg, 2023,
doi:10.2760/920754, JRC132268.

i
Contents
Abstract .......................................................................................................................................................................................................................................................................... 2
Executive summary ................................................................................................................................................................................................................................................. 3
1. Introduction ......................................................................................................................................................................................................................................................... 4
2. The SME Instrument in Europe ............................................................................................................................................................................................................... 6
3. Data and empirical strategy .................................................................................................................................................................................................................... 6
3.1. Hypothesis development ............................................................................................................................................................................................................................ 6
3.2. Data and variables ......................................................................................................................................................................................................................................... 7
3.3. Model specification ........................................................................................................................................................................................................................................ 7
4. Results .................................................................................................................................................................................................................................................................... 7
4.1. Baseline results ................................................................................................................................................................................................................................................ 7
4.2. Heterogenous effects ................................................................................................................................................................................................................................ 10
4.2.1. Debt and profitability .............................................................................................................................................................................................................. 10
4.2.2. Investment round and SMEi phases .............................................................................................................................................................................. 12
5. Robustness checks ...................................................................................................................................................................................................................................... 14
5.1. Alternative definitions of the dependent variables ................................................................................................................................................................ 14
5.2. Alternative definitions of explanatory variables ...................................................................................................................................................................... 14
5.2.1. Innovation ....................................................................................................................................................................................................................................... 14
5.2.2. Size...................................................................................................................................................................................................................................................... 15
5.3. Endogeneity issues...................................................................................................................................................................................................................................... 15
6. Conclusions ...................................................................................................................................................................................................................................................... 15
Annex ............................................................................................................................................................................................................................................................................ 17
References ................................................................................................................................................................................................................................................................. 23
List of tables ............................................................................................................................................................................................................................................................ 27

1
Abstract
This analysis compares the characteristics of firms supported by public and private sources in early-stage financing to
investigate funding patterns for innovative companies. It examines whether the two sources of funding target similar
firms in the period 2008-2017 using a portfolio approach on EU-based firms raising either Venture Capital financing,
public grants under the Horizon 2020 ‘SME Instrument’ scheme, or both. The findings show that venture capitalists
finance more innovative and younger firms, whereas public investors focus on smaller companies. This pattern is
supported by robustness checks and expansions that address multiple dimensions of heterogeneity behaviours in the
interaction of private and public funding.

2
Executive summary
Depending on their stage of development, young and innovative businesses rely on private and public sources of
financing for their research and innovation activities. During the start-up phase, public funding is expected to de-risk
research and technology development by covering the expenses of necessary failures, while private investors support
mature, developed and ready-to-grow enterprises. Although it can be expected that there are relationships between
different types of funding, most of research on funding for innovative companies focuses on a single source of funding
and little is known about their complementarities and interactions. Because of the interplay between many forms of
entrepreneurial finance, there is a need to take a portfolio approach rather than treating private and public sources of
funding independently.
This report looks at the patterns of funding for innovative companies through a portfolio lens. It compares the
characteristics of innovative companies supported by the SME Instrument (SMEI) grants of the European Innovation
Council (EIC), whose objective is to foster high-risk and high-potential innovation ideas and to assists innovative firms
to shape new markets, create growth, and achieve high return on investment, with the characteristics of European
firms that received Venture Capital funding. The report examines whether these two types of funding in fact target
and select companies at various stages of development and growth. This addresses one of the most common
recommendations from program assessments, which says that public actors supporting innovative companies should
make sure that they target the right beneficiaries. This should help to avoid funding firms that might receive private
financing.
The report is based on funding patterns of public and private investors in financing small, young, and innovative EU-
based enterprises in the period 2008-2017. Information on Venture Capital investors and transactions is retrieved
from VentureSource. Information on 2020 SME Instrument public grants comes from the European Commission’s
Executive Agency for Small and Medium-sized Enterprises.
First, the report analyses the characteristics of firms that Venture Capitalists and public investors target. Second, it
investigates potential heterogeneous effects that may drive differential investment behaviours such as the level of
bank indebtedness and profitability of firms. Finally, it analyses the potential differential behaviours in the interplay
between private and public investment by comparing early and later stages of private and public funding.
The analysis reveals that, firms receiving Public Grants are on average smaller, less innovative, and older than those
raising Venture Capital funding. In addition, VC reaches more innovative businesses earlier than public grants. On the
other hand, the funding of smaller and less capitalized enterprises appears to be a prerogative of public investors.
Controlling for the level of bank debt and profitability, does not considerably change the results. Companies receiving
public subsidies are more short term indebted, whereas companies funded by VCs are on average less indebted and,
more oriented to long-term bank’s debt financing. Moreover, more profitable firms show a larger probability of
receiving a public subsidy than VC-backed firms. The results show no substantial differences in behaviours between
private and public investors based on investment stage or round and are robust to alternative econometric
specifications.
Summing up, the results show that, although sharing common ex-ante goals, the EIC SMEI targets very different types
of companies than those selected by private investors. This can indicate that public sources of funding for innovation
help companies that would not receive private investments and that it does not crowd out private money. This way the
SMEI helps to overcome a market failure related to the lack of funding for innovative companies.
As the results show that VC are more eager to provide funding to relatively younger and smaller start-ups than the
SMEI, they contradict the notion that public funding de-risks the start-up stage of development and private investors
support mature, developed and ready-to-grow enterprises.
Another question is related to the fact that companies with different profiles are very likely to have different future
growth and development trajectories. It would be thus of interest to compare the return on investment from public
funding of innovation with the one of the private one.

3
1. Introduction
Young and innovative businesses use various sources of financing to fund their research and innovation (R&I) activities,
depending on their stage of development. In the initial phases, research activity is mainly financed through internal
and public sources and, when a venture is sufficiently mature and established, private investors enter (Auerswald and
Branscomb, 2003). During the start-up phase, companies usually try to raise funds through private means including
Venture Capital (VC) funds (Gompers and Lerner, 2001). In this context, public funding is expected to de-risk research
and technology development by covering the expenses of necessary failures, while private investors support mature,
developed and ready-to-grow enterprises.
Since most of research on funding for innovative companies focuses on a single source of funding, little is known
about their complementarities and interactions. Because externalities exist across many forms of entrepreneurial
finance, there is a need to take a portfolio approach towards entrepreneurial finance rather than treating private and
public sources of funding independently (Cumming et al., 2018). To close this gap, the report looks at the patterns of
funding for innovative companies through a portfolio lens and compares the characteristics of innovative companies
supported by the SME Instrument (SMEI) grants, i.e. one of the most bold and innovative policy instrument to support
break-through innovations in Europe, with the characteristics of European firms that received Venture Capital funding.
The objective is to examine whether these two types of funding in fact target and select companies at various stages
of development and growth. The findings should contribute to the debate concerning the rationale and design of public
sector mechanisms that are expected to de-risk research and technology development while still bearing the
consequences of failures.
The encouragement of entrepreneurship development is prominent on the agenda of policymakers around the world
and supporting new businesses typically entails providing them with external finance (Lerner and Nanda, 2020). This
is especially relevant in countries that do not have established VC markets despite having strong economies such as
the EU (Gucciardi, 2022). Access to finance is still considered as one of the major bottlenecks to innovation
commercialization and exploitation in Europe. To overcome this issue, policymakers create new funding instruments
and allocate larger amounts of money in order to close the ‘Valley of Death’ and to secure the necessary financial
resources for commercializing new technologies and products. Public source of funding and support for companies can
take many forms. For example, the European Union annually supports and finances over 200,000 companies, including
sole proprietorships, micro-enterprises, start-ups, and small and medium-sized enterprises, operating across all
manufacturing and product sectors (Gampfert et al., 2016). Some of them are increasingly emulating the private VC
industry by selecting companies with high growth potential and providing them with direct financial grants. Despite
the recent pandemic shock, this occurs in parallel with the development of private financing instruments and the
constant inflow of angel and venture capital funding (Bellucci et al., 2021; Bellucci et al., 2022)., This could potentially
lead to either complementarity or substitution effects between these two categories of instruments. Indeed, while
early resource allocations may increase the probability that start-ups secure VC funding (Shane and Stuart, 2002),
public grants raised by young firms could also share comparable ambitions and information to VC investments
together with the possibility of contributing as a firm’s first capital investment (Berger and Hottenrott, 2021). Focusing
on the first aspects, several recent studies have documented that public grants are interpreted as ‘signals’ (Bianchi et
al., 2019) by Venture Capitalists who are more prone to invest towards such grant-backed start-ups (Lerner, 2000);
Cumming, 2007); Söderblom et al., 2015; Howell, 2017; Giraudo et al., 2019, among the others).
On the other hand, if getting one source of funding reduces the need to raise another, there may be a risk of crowding
out of investments between public subsidies and venture capital financing. Because both instruments target start-ups
at the seed stage, young companies may consider public grants as an alternative to Venture Capital, and vice versa
(Bertoni et al., 2015). Furthermore, Venture Capitalists may lose their interest in firms that have already received public
grants, possibly because they might have already reached a certain level of development that no longer meets their
investment criteria (Alperovych et al., 2020).
As a result, one of the most common recommendations from program assessments is to target the right beneficiaries.
The European Court of Auditors, for example, specifically states that, while the SME Instrument promotes enterprises
that meet the academic model of high-growth potential firms, it nonetheless invests certain SMEs that might have
been funded by the market (ECoA, 2020). However, the research on R&I subsidies focuses mostly on evaluating public
support to innovative companies (Lerner, 1999; Bronzini and Piselli, 2016; Howell, 2017) and does not examine whether
the choice of beneficiaries of public support for R&I is optimal. Most empirical papers on financing innovative
companies are based on data from single funding source (Cumming and Vismara, 2017). Only few papers use data
from a variety of financial sources in the same analysis; exceptions include Cosh et al. (2009) for the UK and Robb
and Robinson (2014) for the US. This way there is not enough evidence on what are the main criteria of selecting firms
for public support for R&I and how private and public sources of R&I funding interact. As a result, it is not unexpected
that the findings of studies examining public support for R&I remain unclear (Dimos and Pugh, 2016). The potential
explanation of these inconclusive results might be related to the issue of selection of beneficiaries of public support

4
for R&I (Mina et al., 2021). In order to address this gap, we empirically analyse the characteristics of firms that are
selected for funding by the SME Instrument and private Venture Capitalists. The objective is to use the portfolio
approach to R&I funding and to jointly analyze the two sources of funding to investigate whether the SME Instrument
targets firms similar to those backed by private investors. Our study resembles the analysis of the characteristics of
firms financed by corporate and individual Venture Capitalists (Chemmanur et al., 2014) and a study looking at the
effects of public and private funding on firms’ innovative performance (Kou et al., 2020).
To analyze the funding patterns of public and private investors in financing small, young, and innovative EU-based
enterprises in the period 2008-2017, we use collected data from several data sources. Information on Venture Capital
investors and transactions is retrieved from VentureSource, a specialized commercial database by Dow Jones. This
dataset has been integrated with information on public grants under the Horizon 2020 SME Instrument scheme,
collected by the European Commission’s Executive Agency for Small and Medium-sized Enterprises.
First, we test whether both Venture Capitalists and public investors exhibit similar patterns in financing small, young,
and innovative enterprises. Our analysis reveals that, although sharing a common ex ante end goal, private and public
investors target quite different types of firms. In particular, firms receiving Public Grants are on average smaller, less
innovative, and more experienced than those raising Venture Capital funding. In addition, VC reaches more innovative
businesses earlier than public grants. On the other hand, the funding of smaller and less capitalized enterprises appears
to be a prerogative of public investors.
Second, we investigate potential heterogeneous effects that may drive differential investment behaviours such as the
level of bank indebtedness and profitability of firms. We recognize that firms may use bank debt becoming a third
way of funding that may potentially substitute or complement both public and private capitals. In this respect, we then
investigate whether the difference in characteristics appearing for firms supported by public subsidies vs venture
capital investments vary as a function of their bank debt level. Then, being the profitability of the financed companies
the ultimate goal of both private and public financial investors, we analyze whether the differences in characteristics
emerging for firms financed by public subsidies or VC investments change as a function of their profitability. Our
findings reveal that, also controlling for bank debt and profitability, the differences in the characteristics of enterprises
(size, innovation, and age) between those financed by public subsidies and those financed by venture capitalists do
not change considerably. Interestingly, companies receiving public subsidies are more short term indebted, whereas
companies funded by VCs are on average less indebted and, more oriented to long-term bank’s debt financing.
Moreover, more profitable firms show a larger probability of receiving a public subsidy than VC-backed firms.
Finally, we investigate the potential differential behaviours in the interplay between private and public investment by
comparing early and later stages of private and public funding (i.e., Venture Capital early -and later-stages vs SME
Instrument Phase 1 and Phase 2). Results show no substantial differences in behaviours between private and public
investors based on investment stage or round.
Our findings are robust to several tests, such as the exclusion of firms that have received both types of financing from
the estimations or the adoption of alternative definitions of innovation and size of the analysed firms.
The remainder of the paper is structured as follows. Section 2 describes the institutional setting of the EU SME
Instrument. Section 3 presents the data and the empirical strategy. Section 4 explore the main findings including
heterogeneous results, while Section 5 focuses on a battery of robustness tests. Lastly, Section 6 concludes.

5
2. The SME Instrument in Europe
Introduced in the Horizon 2020, the SMEI managed by the European Innovation Council (EIC) is aimed at highly
innovative SMEs wishing to develop their growth potential (EC, 2015). The SMEI addresses the financing needs of
internationally oriented SMEs, in implementing high-risk and high-potential innovation ideas. It aims at supporting
projects that lead to major changes in how business (product, processes, services, marketing etc.) is done. It assists
innovative SMEs to shape new markets, create growth, and achieve high return on investment. Since its inception, the
Horizon 2020 SME Instrument has become an important source of public funding for European SMEs, contributing
50% of the total amount of public grants in 2017 (Bellucci et al., 2021b).
The SMEI resembles the Small Business Innovation Research (SBIR) programme, which operates in the United States
and disburses around $2.2 billion each year (Howell, 2017). It was introduced in 1982 to strengthen the US high
technology sector and support small firms. The SBIR program is representative of many targeted subsidy programs
for high-tech new ventures at the state level and around the world.
Like the SBIR programme, the SMEI consists of three separate phases and a coaching and mentoring service for
beneficiaries (EC, 2015). Participants can apply to Phase 1 with a view to applying to Phase 2 later or directly to Phase
2. In Phase 1, a feasibility study shall be developed verifying the technological as well as economic viability of an
innovation. A successful proposal receives a lump sum of EUR 50,000. In Phase 2, innovation projects that demonstrate
high potential in terms of company competitiveness and growth underpinned by a strategic business plan are
supported. Proposals receive a contribution from the EU of between EUR 0.5 and 2.5 million. In addition, in Phase 3,
SMEs can benefit from indirect support measures and services as well as access to the financial facilities support.
During the first two years of operation, the SMEI received 31,377 applications (Phase 1 and 2) in total and it funded
2,457 individual SMEs participating in 2,344 projects (EC, 2016). The overall success rate was 8.4% for Phase 1 and
5.5% for Phase 2. These rates are similar to those of private acceleration programs, which indicates that the SMEI is
highly competitive.
3. Data and empirical strategy
3.1. Hypothesis development
There are plausible reasons to believe that the determinants of Venture Capital financing and Public Grants under the
‘SME Instrument’ scheme are similar. Since its inception, the objective of the SME Instrument is to address the financing
needs of internationally oriented SMEs, in implementing high-risk and high-potential innovation ideas. It aims at
supporting projects that lead to radical and disruptive changes in how business is done, and it supports a company’s
expansion into new markets, promote growth, and create high return on investment. Companies applying for the SME
Instrument are assessed on their business and innovation merit (EC, 2016). The award criteria focus on the
commercialization perspective, excellence in innovation and the capacity of the implementing team. Companies have
to demonstrate that there is a market for their innovation and potential customers willing to pay for it. They are
thoroughly tested against their knowledge of the market conditions, including the total potential market size and
growth-rate, their understanding of competitors and their sales projections. The innovation they are presenting needs
to have the potential to scale-up the company, which must be proved by a clear commercialization plan and a
knowledge protection strategy, including an analysis of ‘freedom to operate’. The applicant should show that its idea
is a high-risk and high-potential innovation that stands out from competition and outperforms existing solutions.
Finally, the capacity of the company's team to effectively commercialize and scale up the business is assessed. SME
Instrument Phase 2 aims at supporting close-to-market activities, focusing on breakthrough innovations with market-
creating potential and not research and innovation activities (A4SMES, 2018). Phase 2 beneficiaries are expected to
know their market and have clearly identified relevant market opportunities, have sound and scalable business models
and feasible implementation plans.
Venture Capital investors are very selective in their decisions with only 1/6th of 1% of new businesses manage to
obtain VC funding (Kaplan and Lerner, 2010). Empirical evidence shows that they select companies based on revenue
growth, expected returns, trends, sector and performance and that innovation is an important factor during the VC
selection phase (Caselli et al. 2009; Chemmanur et al. 2011; Block et al. 2019). In Venture Capitalists’ proposal
screening, key criteria include the long-term growth and profitability of the industry in which the proposed business
will operate (Hall and Hofer, 1993). In other words, relatively young firms with high growth potential and innovative
performance supported with intellectual capital assets and high-quality human capital obtain significantly more VC
financing (Baum and Silverman, 2004; Mueller et al., 2009; Behrens et al., 2012; Zhou et al., 2016; Kim and Lee, 2022).
The above comparison of the selection criteria of the SME Instrument and private Venture Capital investors show that
they are very similar, which leads us to formulate the following hypothesis:

6
Hypothesis: Both Venture Capital investors and Public Granters aim at financing (i) small, (ii) young, and (iii) innovative
enterprises.
3.2. Data and variables
For the purposes of this study, we collected data on EU-based firms raising either Venture Capital financing, public
grants under the Horizon 2020 ‘SME Instrument’ scheme, or both in the period 2008-2017. Data on Venture Capital
financing is retrieved from VentureSource, a specialized commercial database by Dow Jones, which includes
information on VC investment transactions, as well as on VC investors and VC-backed companies. This dataset was
then integrated with information on public grants. Specifically, we make use of the SME Instrument related data that
is collected by the EC’s Executive Agency for Small and Medium-sized Enterprises (EASME). The EASME plans,
administers, and monitors the execution of the SME Instrument calls. The information about awarded grants and their
beneficiaries is public and can be accessed via the CORDA database, which is the primary source of results from EU-
funded R&I projects. While VentureSource provides financial data related to the VC-backed firms for the year of the
VC transaction or of the public grant, it does not include the same information for the years before which, however,
should be investigated as potential candidates for the determinants of the VC investment or public grant. Hence, we
matched the dataset with Orbis, a commercial database by Bureau van Dijk, which provides financial and industrial
data for each accounting year retrieved from the balance sheets of firms based on information available from several
official sources as business registers, firms’ annual report, and credit bureau. Given that VentureSource and Orbis do
not share a unique reciprocal identifier for an immediate link, the merger was conducted by matching common
variables available in both databases, such as the company name, the web and e-mail addresses, and the telephone
and fax numbers. The final matched database contains 8,057 observations, with the identifier being the single
transaction (either the Venture Capital financing or the Public Grant). For each observation, the dataset includes
information both on the characteristics of the deal and of target company. First, we know when the VC or SMEI
transaction was completed, allowing us to chronologically rank transactions for the same company. This data also
allows us to implicitly determine the age of the company at the date of the transaction, as the difference of its
incorporation date and the transaction date. Second, our dataset includes both qualitative and quantitative information
on the characteristics of the target company, such as a proxy for its size (expressed in terms of total assets) and for
its ability to innovate (in terms of number of registered patents).
3.3. Model specification
To investigate the relation between public grants and Venture Capital, we adopt the following probit model
specification:
󰇛󰇜          (1)
where our dependent variable, Pr(PG), is a dummy indicator that takes the value of one if the firm receives a Public
Grant under the Horizon 2020 SME Instrument scheme, and zero if it receives Venture Capital financing in the analysed
year. Assets t-1 is the natural logarithm of the total assets reported in the balance sheet by the firm for the year before
raising a Venture Capital investment or a Public Grant. Patents t-1 is an indicator that takes the value of 1 for firms
having applied for the filing of patent in the year before raising a Venture Capital investment or a Public Grant, and 0
otherwise. Age t-1 is the natural logarithm of the age of the firm the year before raising a Venture Capital investment
or a Public Grant. Our model also includes yearly fixed effects, φt, to capture common shocks related to every
transaction in each year. We also incorporate sector, φs, and country, φc, fixed effects to control for systematic
differences in the characteristics of financed firms across sectors and countries. Lastly, εi is the error term, clustered
at the firm level.
4. Results
4.1. Baseline results
Table 1 reports the coefficient estimates of Equation (1). Column (1) reports the benchmark specification that includes
only the three variables accounting for the size, age, and innovation ability of VC/PG-backed firms, while in the
specifications in columns (2)-(4) we progressively add different sets of fixed effects. Specifically, year fixed effects
control for common time-varying shocks that might affect the probability of raising Public Grants with respect to VC
investments, sector fixed effects allow us to consider time-invariant unobservables correlated with financing that are

7
sector-specific, while country fixed effects account for time-invariant unobservables correlated with financing that are
specific to the country, respectively.
We find that the coefficient for the Assets indicator is negative and highly statistically significant across the
specifications of the model, with the coefficients ranging between -19 and -22%. Moreover, when we look at Patents
we get that, again, the coefficients are negative and statistically significant, with the magnitude of the coefficients
materially varying across the specifications. Lastly, we find the Age of target companies being positively correlated –
and with a high level of statistical significance – with the probability of raising a Public Grant (vs a VC). In this case,
the coefficients range between 0.65 and 0.8.
Overall, these findings seem to suggest that - despite being moved by a similar ex ante ultimate goal - private and
public investors target quite different types of firms. In particular, firms receiving Public Grants are on average smaller,
less innovative and more experienced than those raising a Venture Capital investment. This is an interesting result
especially if compared with the spirit of the SMEI, whose aim is to finance high-potential (and innovative) young firms.
Indeed, it seems that private financing manages to reach more innovative firms at an earlier stage than public
investors. On the other hand, it seems that the other differential outcome of public with respect to private investors is
the financing of smaller and less capitalized firms, which is probably linked to the fact that such companies are less
marketable.
Overall, these results corroborate the view that public and private investors, despite being guided by a similar spirit,
show very different outcomes. In particular, Venture Capitalists manage to finance more innovative and younger firms,
while public investors focus on smaller companies. This result is consistent with previous findings suggesting that there
are qualitative differences in start-ups raising public vs private funds (Bellucci et al., 2021b). These findings recall the
results of a study analysing the characteristics of firms financed by corporate and independent Venture Capitalists
(Chemmanur et al., 2014). In a direct comparison, corporate VC tend to fund more innovative, younger and riskier,
although less profitable firms than independent VC. In the context of this study, SMEI beneficiaries resemble the profile
of firms backed by independent rather than corporate VC.

8
Table 1: Baseline Results
Panel A – Probit
Public Grant
Dependent Variable
(1)
(2)
(3)
(4)
Assets t-1
-0.197***
-0.205***
-0.223***
-0.196***
(0.014)
(0.016)
(0.018)
(0.019)
D_Patents t-1
-0.811***
-0.384***
-0.625***
-0.458***
(0.079)
(0.094)
(0.104)
(0.108)
Age t-1
0.794***
0.799***
0.646***
0.668***
(0.032)
(0.036)
(0.041)
(0.045)
Observations
4,742
4,742
4,742
4,742
Year Fixed Effects
No
Yes
Yes
Yes
Sector Fixed Effects
No
No
Yes
Yes
Country Fixed Effects
No
No
No
Yes
Panel B – Marginal Effects
Public Grant
Dependent Variable
(1)
(2)
(3)
(4)
Assets t-1
-0.060***
-0.049***
-0.046***
-0.037***
(0.004)
(0.003)
(0.003)
(0.003)
D_Patents t-1
-0.245***
-0.093***
-0.129***
-0.086***
(0.023)
(0.022)
(0.021)
(0.019)
Age t-1
0.240***
0.193***
0.133***
0.126***
(0.007)
(0.007)
(0.008)
(0.008)
Observations
4,742
4,742
4,742
4,742
Year Fixed Effects
No
Yes
Yes
Yes
Sector Fixed Effects
No
No
Yes
Yes
Country Fixed Effects
No
No
No
Yes
Note: The analysis covers Venture Capital and Public Grants raised in the period between 2008 to 2017 by firms operating in the European
Union. The table reports regression results of the Probit estimation of equation (1) on the full sample (Panel A) and its marginal effects
(Panel B). Public Grants is a categorical variable which takes the value of 1 if the firm raises a Public Grant and 0 if the firm raises a Venture
Capital investment in the analysed year. Assets t-1 is the natural logarithm of the total assets reported in the balance sheet by the firm for the
year before raising a Venture Capital investment or a Public Grant. Patents t-1 is an indicator that takes the value of 1 for firms having applied
for the filing of patent in the year before raising a Venture Capital investment or a Public Grant, and 0 otherwise. Age t-1 is the natural
logarithm of the age of the firm the year before raising a Venture Capital investment or a Public Grant. The table reports coefficients of a
Probit estimation followed by standard errors, clustered at the firm level, in parentheses. ***, **, and * indicate statistical significance at the
1%, 5%, and 10% level, respectively.

9
4.2. Heterogeneous effects
Our baseline estimations consider three among the most relevant aspects that both private and public investors look
at when financing start-ups. In this section, we investigate potential heterogeneous effects due to two relevant aspects
which can also drive differential investment behaviours such as the level of bank indebtedness and profitability of
firms. In a second analysis, we also investigate the potential differential behaviours in the interplay between private
and public investment by comparing early and later Venture Capital stages Phase 1 and 2 SME Instrument, respectively.
4.2.1. Debt and profitability
Start-ups face greater challenges in obtaining bank funding (Colombo and Grilli, 2017), owing to the inherent riskiness
of the concept and the lack of or limited availability of information - particularly formal (e.g. financial statements) -
that banks must analyze in order to offer loans. At the same time, we cannot rule out a priori the possibility that these
companies may be able to obtain bank financing, allowing them to use financial leverage for both long-term debt and
current operations in the early months and years of their existence. From this point of view, bank debt is therefore a
third way to access the funding of these companies, potentially substituting for or complementing both public and
private capital in the form of equity.
In this spirit, we then investigate whether the difference in characteristics appearing for firms supported by public
subsidies vs venture capital investments vary as a function of their bank debt level. If the results are in line with those
of the baseline, we could conclude that bank debt per se does not constitute a distinguishing factor in Venture
Capitalists’ and public investors’ investing strategies.
To test this effect, we estimate an augmented version of Eq. (1) now including three different variables in two distinct
estimates. First, we introduce two indicators for short-term and long-term bank debt. This estimation allows us to
control for the level of bank debt, as well as distinguishing between its use for current activities or for investments. In
the second estimation, we introduce the leverage ratio indicator, which is computed as the debt-to-total-assets ratio.
This estimation allows us to test the relevance of bank debt while parametrizing it to the equity size of the company.
Table 2 (Columns 1 and 2) shows the estimation results. First, findings reveal that when we control for bank debt, the
differences in the characteristics of enterprises (size, innovation, and age) between those financed by public subsidies
and those financed by venture capitalists do not change considerably. Indeed, these results confirm those of the
baseline, i.e. a larger probability that companies financed by public grants are on average smaller (probability between
2 and 4%), less innovative (10-12%) and older (13%). Interestingly, we can also note that companies receiving public
subsidies are on average more indebted - even compared to their capital size (Leverage) - but that this debt is mainly
driven by short term or current activities. On the other hand, companies raising VCs are on average less indebted and,
where they get finance by a bank, are on average more oriented to finance long-term investments. This result seems
also consistent with their greater ability to generate innovation through patents.
The profitability of the financed companies is the ultimate goal of the financial players, be they public (through
subsidies) or private (via VC investments). The former category because policymakers often have the strengthening of
the financed companies among their policy objectives; the latter because Venture Capitalists look for an increase in
the value of the acquired shares as well as a profitable exit option when investing. In this perspective, the level of
profitability of companies could influence the investor’s behavior by modifying the strategy of public and private
interventions. As a result, we look into whether the differences in characteristics emerging for firms financed by public
subsidies or VC investments change as a function of their profitability.
Hence, we augment Eq. (1) with three indicators in three distinct estimations, by proxying profitability with EBIT, ROE,
and Profit Margin indicators, respectively. Again, should we find no changes in the signs of the coefficients related to
the size, innovation, and age, then we can conclude that current profitability does not act as a distinctive factor in the
investment strategies of public and VC investors.
Table 2, Columns 3 through 5, shows the estimation results. Results confirm what already emerged from the baseline
estimations, thus controlling for past profitability of VC/PG-backed firms does not affect the differential probability of
raising one or the other based on size, level of innovation, and age. At the same time, we find that firms that are more
profitable have a larger probability of receiving a public subsidy than VC-backed firms. One probable explanation is
that, in comparison to start-ups, older firms are more likely to generate profits.

10
Table 2: Heterogeneous Effects – Debt and Profitability (Panel A – Probit)
Public Grant
Dependent Variable
(1)
(2)
(3)
(4)
(5)
Assets t-1
-0.248***
-0.160***
-0.308***
-0.297***
-0.301***
(0.027)
(0.031)
(0.047)
(0.034)
(0.047)
D_Patents t-1
-0.699***
-0.616***
-0.460*
-0.546***
-0.453*
(0.150)
(0.155)
(0.273)
(0.180)
(0.271)
Age t-1
0.773***
0.812***
0.983***
0.905***
0.976***
(0.059)
(0.068)
(0.097)
(0.070)
(0.097)
ST debt t-1
0.259***
0.186**
(0.083)
(0.091)
LT debt t-1
-2.821***
-3.114***
(0.368)
(0.334)
Leverage (ln) t-1
0.381***
(0.133)
EBIT t-1
0.011***
(0.002)
ROE t-1
0.002***
(0.000)
Profit Margin t-1
0.011***
(0.002)
Observations
2,666
2,340
1,165
1,808
1,139
Year Fixed Effects
Yes
Yes
Yes
Yes
Yes
Sector Fixed Effects
Yes
Yes
Yes
Yes
Yes
Country Fixed Effects
Yes
Yes
Yes
Yes
Yes

11
Table 2 (cntd): Heterogeneous Effects – Debt and Profitability (Panel B – Marginal Effects)
Public Grant
Dependent Variable
(1)
(2)
(3)
(4)
(5)
Assets t-1
-0.043***
-0.026***
-0.049***
-0.058***
-0.048***
(0.004)
(0.005)
(0.007)
(0.006)
(0.008)
D_Patents t-1
-0.121***
-0.100***
-0.073*
-0.106***
-0.072*
(0.025)
(0.024)
(0.043)
(0.034)
(0.042)
Age t-1
0.133***
0.131***
0.156***
0.175***
0.155***
(0.009)
(0.010)
(0.014)
(0.012)
(0.014)
Observations
2,666
2,340
1,165
1,808
1,139
Year Fixed Effects
Yes
Yes
Yes
Yes
Yes
Sector Fixed Effects
Yes
Yes
Yes
Yes
Yes
Country Fixed Effects
Yes
Yes
Yes
Yes
Yes
Note: The analysis covers Venture Capital investments and Public Grants raised in the period between 2008 to 2017 by firms operating in
the European Union. The table reports regression results of the Probit estimation of equation (1) on the full sample (Panel A) and its
marginal effects (Panel B). Public Grants is a categorical variable which takes the value of 1 if the firm raises a Public Grant and 0 if the
firm raises a Venture Capital investment in the analysed year. The table reports coefficients of a Probi t estimation followed by standard
errors, clustered at the firm level, in parentheses. ***, **, and * indicate statistical significance at the 1%, 5%, and 10% level, respectively.
4.2.2. Investment round and SMEI phases
As already anticipated, companies seeking for a SME Instrument can either apply to a Phase 1 instrument, directly to
a Phase 2 one, or to both. The Phase 1 instrument is a lump sum of 50,000 euros that companies utilize to establish
their initial stages of operations (e.g., feasibility studies, business ideas). In other terms, this type of instrument seems
to mimic early stages of VC funding which have similar objectives. On the other hand, Phase 2 contribution is based
on a proposal, which might provide resources in a range between EUR 500,000 and EUR 2.5 million, with applying
firms that need to demonstrate high potential in terms of corporate competitiveness and growth supported by a
strategic business strategy. Given the amounts and the underlying objectives, Phase 2 instrument seems to be more
comparable to later stage VC investments. Different objectives may lead to different investment behaviours, which
may affect the features of firms that are more likely to receive private or the public investment.
So far, our results have not taken into account this difference. We now aim at controlling for such a potential
heterogeneous behavior by estimating Eq. (1) on two subsets of firms, i.e. the first limited to those start-ups that have
raised either a SMEI Phase 1 or an Early Stage VC investments (or both); the second limited to those that have received
either a SMEI Phase 2 or a Later Stage VC investments (or both).
Table 3 shows the results, with Col. 1 and 2 focusing on the first and second subsets of firms, respectively. Interestingly,
we find no substantial differences in behaviours between private and public investors based on investment stage or
round. Indeed, we get negative signs for Assets and Patents, as well as a positive one for Age, thus corroborating our
baseline results.

12
Table 3: Heterogeneous effects – Early Stages vs SMEI Phase 1, Later Stages vs SMEI Phase 2
Panel A – Probit
SMEI Ph.1
SMEI Ph.2
Dependent Variable
(1)
(2)
Assets t-1
-0.392***
-0.249***
(0.079)
(0.030)
Patents t-1
-1.622***
-1.289***
(0.395)
(0.199)
Age t-1
0.588***
0.947***
(0.158)
(0.070)
Observations
376
1,854
Year Fixed Effects
Yes
Yes
Sector Fixed Effects
Yes
Yes
Country Fixed Effects
Yes
Yes
Panel B – Marginal Effects
SMEI Ph.1
SMEI Ph.2
Dependent Variable
(1)
(2)
Assets t-1
-0.083***
-0.052***
(0.015)
(0.006)
Patents t-1
-0.345***
-0.270***
(0.075)
(0.040)
Age t-1
0.125***
0.198***
(0.031)
(0.013)
Observations
376
1,854
Year Fixed Effects
Yes
Yes
Sector Fixed Effects
Yes
Yes
Country Fixed Effects
Yes
Yes
Note: The analysis covers Venture Capital investments and Public Grants raised in the period between 2008 to 2017 by firms operating in
the European Union. The table reports regression results of the Probit estimation of equation (1) on the full sample (Panel A) and its
marginal effects (Panel B). SMEI Phase 1 is a categorical variable which takes the value of 1 if the firm raises a SMEI Phase 1 and 0 if the
firm raises an Early stage of Venture Capital investments in the analysed year. SMEI Phase 2 is a categorical variable which takes the value
of 1 if the firm raises a SMEI Phase 2 and 0 if the firm raises a Later stage of Venture Capital investments in the analysed year. Assets t-1 is
the natural logarithm of the total assets reported in the balance sheet by the firm for the year before raising a Venture Capital investment or
a Public Grant. Patents t-1 is an indicator that takes the value of 1 for firms having applied for the filing of patent in the year before raising a
Venture Capital investment or a Public Grant, and 0 otherwise. Age t-1 is the natural logarithm of the age of the firm the year before raising
a Venture Capital investment or a Public Grant. The table reports coefficients of a Probit estimation followed by standard errors, clustered
at the firm level, in parentheses. ***, **, and * indicate statistical significance at the 1%, 5%, and 10% level, respectively.

13
5. Robustness checks
5.1. Alternative definitions of the dependent variables
In our baseline model, we have investigated how differently the size, innovation ability, and age of firms are associated
to their probability to obtain a Public Grant with respect to a Venture Capital investment. At the same time, some firms
can be recipient of both instruments – even though in different times – thus potentially leading to a misinterpretation
of our findings. In this case, it could be possible that the results obtained on the entire sample of companies are (at
least partially) influenced by the companies that received both instruments.
To exclude this possibility and confirm our previous findings, we then replicate the model presented in Eq. (1) limiting
the sample to companies that exclusively received Venture Capital or Public Grants in the analyzed period. The results,
shown in the Table 4, reassure about the robustness of our main results. Indeed, excluding enterprises that have
received both types of financing from the estimations has no effect on the signs and significance of the estimated
coefficients, which are consistent with the baseline model. These findings are robust to each specification of the model,
regardless of whether the different sets of fixed effects are incorporated (see Col. (1) to (4) of Table 4).
At the same time, this category of firms could behave similarly to the recipient of Public Grants or VC only. If this were
not the case, we would have a hint of the fact that this category has independent and distinct determinants from the
other two. We then replicate the model of Eq. (1) limiting the analysis to companies that received both instruments in
the sample period. The result of the estimate shows coefficients in line with the expected signs, but statistically not
significant. This suggests that such firms are not immediately comparable to those that receive only one type of
funding and suggest that they should be considered as a distinct category of investigation.
Hence, we consider firms being recipient of both Venture Capital and Public Grants in the sample period within a
separate group of firms. In order to test whether the behaviour of this specific category of firms is statistically different
from the others, we replicate again the model of Eq. (1) modifying the dependent variable to allow three different
categories: (i) the group of firms raising VC investments only (“VC”); (ii) the group of firms receiving both VC and PG
(“VC + PG”); (iii) the group of firms recipient of Public Grants only (“PG”). We then estimate an ordered probit model
with a three-category dependent variable. This estimation allows us to determine whether the characteristics of firms
ultimately receiving both types of financing are different from the ones of the VC-backed or the public granted only.
Panel A of Table 5 shows that our three investigated determinants (i.e., size, innovation, and age) are relevant in
determining the probability of getting a VC, a Public Grant, or both. We then look at the related marginal effects. First,
the results presented in Col. 1 and 3 of Panel B confirm the differences in the characteristics of firms that receive a
VC and those that receive a PG. Indeed, VC-backed firms are typically significantly larger in terms of total assets
(+0.074) than public granted firms (-0.066). Similarly, the probability of raising a VC is significantly larger for more
innovative firms (+0.226), while the probability of receiving a Public Grant is higher in case of older firms (+0.265).
When we look at the specific characteristics of firms who received both instruments, we find that the estimated
probabilities lay in the range of coefficients estimated for the VC and PG categories only. At the same time,
interestingly, the marginal effects have the same signs of public granted firms, despite the estimated coefficients are
smaller in magnitude for assets (-0.008 vs -0.066), patents (-0.038 vs -0.188), and age (0.033 vs 0.265). These
findings seem to suggest that the characteristics of firms associated with a higher probability of raising both
instruments are similar to those receiving a Public Grant only.
5.2. Alternative definitions of explanatory variables
In this section, we test the robustness of our results adopting alternative definitions of the innovation and size of the
analysed firms.
5.2.1. Innovation
In our baseline model, we have adopted patents as the indicator signalling the presence of technological innovations.
This approach is consistent with the developed literature on innovation (e.g., Soete and Wyatt, 1983; Griliches, 2007;
Trajtenberg, 1990; Eaton and Kortum, 1996; Eaton and Kortum, 1999; Kortum, 1997; Kanwar and Evenson, 2003;
Furman et al., 2002; Hagedoorn and Cloodt, 2003). In particular, we have taken as regressor a dummy variable
indicating the presence of new patents the year before the firm receives either the VC financing or the Public Grant. In
order to assess the robustness of these findings, we replicate the baseline model of Eq. (1) replacing this indicator
with two other alternative proxies. On one side, we use a dummy variable, D_Patent evert-1, indicating whether the firm
has ever filed a patent prior to receiving the private or public financing. In this way, we control for the fact that the

14
beneficial effects of innovation – proxied by the presence of patents – can materialize after more than one year from
the filing. On the other side, we adopt a continuous variable, Patent countt-1, which provides quantitative information
on the number of registered patents. This approach allows us to test whether the intensity of innovation – rather than
only its presence –differently determines how private and public financing are raised. The results of these analyses
are presented in Table 6, Col. (1) and (2), and show that the probability of raising a VC vs a Public Grant is significantly
higher also in the presence of patents registered some years before the financing (-0.746) and when the intensity of
innovation is higher (-0.143).
As a further test, we also estimate a new version of the baseline model using a different proxy for innovation which
is often adopted to analyse the macro-economic determinants of equity financing, i.e. the expenditure in Research and
Development (e.g., Gompers and Lerner, 1999; Cherif and Gazdar, 2011; Pradhan et al., 2017). In particular, we use a
dummy indicator, R&Dt-1, that is equal to 1 if the firm has a positive R&D expenditure the year before the financing,
and zero otherwise. The result of this estimation is shown in Table 6, Col. (3), and confirms our previous findings.
Specifically, firms that have spent financial resources on research and development in the past year have a higher
probability of raising a VC instead of a public grant.
5.2.2. Size
In the economic literature, the size of a firm is usually proxied by three main indicators (Dogan, 2013): total assets
(e.g., Deesomsak, 2004; Isik et al., 2017; Nanda and Panda, 2018; Khatap et al., 2011; Saliha and Abdessatar, 2011),
total sales (e.g., Rajan and Zingales, 1995; Wiwattanakantang, 1999; Huang, 2006; Isik et al., 2017; Serrasqueiro and
Nunes, 2008; Shubita and Alsawalhah, 2012), and the number of employees (e.g., Holzmuller and Kasper, 1991; Isik
et al., 2017; Bonaccorsi, 1992; Archarungroj and Hoshino, 1998; Isik et al., 2017; Serrasqueiro and Nunes, 2008). While
in most cases the choice of which indicator should be used is not discussed (Dang et al., 2018) or is mainly motivated
by constraints on data availability (Hart and Oulton, 1996), a few more recent studies suggest that the choice of the
size indicator could in principle affect the results of estimated models (Vijh and Yang, 2013). Hence, similarly to Didier
et al. (2015), we test the robustness of our baseline specification of the model by alternatively substituting total assets
– our main indicator – with total sales and the number of employees as proxies for the firms’ size. If the results were
consistent with those obtained using total assets, we may confirm that the company size is one of the predictors of
VC financing regardless of how it is defined.
Table 6, Columns (4) and (5), show the results of the robustness test replicating the estimation of the model in Eq. (1)
but substitutes total assets with (the natural log of lagged) total sales and number of employees, respectively. We
find that in both cases the probability of raising a Public Grant – with respect to VC-backed firms – is higher for smaller
firms, as evidenced by the negative and statistically significant coefficient associated to total sales ( -0.259) and to
the number of the employees (-0.042), with the related estimated marginal effects equal to -5.3% and -0.8%,
respectively. Hence, our baseline results are reassuringly robust to alternative definitions for the firms’ size.
5.3. Endogeneity issues
In the previous sections we investigated which characteristics of the companies could be considered as potential
determinants of public and private investors. To do so, we examined the size, innovation, and age indicators in the year
prior to the VC investment or the Public Grant. This choice allows us to reduce the risk of possible endogeneity issues
of the estimated models, given that the characteristics of the firms may not be influenced by the subsequent private
or public financing under investigation.
At the same time, obtaining a VC investment or Public Grant frequently necessitates a lengthy period of negotiation
and evaluation by the investors. In certain circumstances, this period might also be longer than one year, thus
potentially invalidating our results due to endogeneity concerns.
To overcome this potential issue and further test the robustness of our results, we replicate the estimations of the Eq.
(1), anticipating the lag of the regressors from one to two years ahead of year of the VC investment or of the Public
Grant. Reassuringly, the results shown in Table 7 are in line with what we obtained in the baseline estimations,
independently of the inclusion of the different sets of fixed effects.
6. Conclusions
Motivated by previous research that examined the characteristics of firms financed by corporate and individual Venture
Capitalists, as well as the effect of public and private funding on firms’ innovative performance (Chemmanur et al.,

15
2014; Kou et al., 2020), we compare the characteristics of European firms supported by public and private sources in
early-stage financing from 2008 to 2017. Using a portfolio approach based on firms raising either Venture Capital
financing, public grants under the Horizon 2020 SME Instrument scheme, or both, we empirically test whether: (i) both
Venture Capitalists and public investors exhibit a similar pattern in financing small, young, and innovative enterprises;
(ii) potential heterogeneous effects that may drive differential investment behaviours in terms of level of bank
indebtedness and profitability of firms; (iii) the potential differential behaviours in the interplay between private and
public investment by comparing early and later stages of private and public funding.
Our analysis shows that, despite having the same ex-ante goals, private and public investors target very different
types of firms. Firms that receive Public Grants are generally smaller, less innovative, and more experienced than those
receiving Venture Capital funding. Furthermore, VC reaches more innovative enterprises earlier than public grants. On
the other hand, public investors seem to be more prone to fund smaller and less capitalized firms. When bank debt
and profitability are accounted, the differences in the characteristics of enterprises (size, innovation, and age) between
those financed by public subsidies and those financed by venture capitalists are not significant. Companies receiving
public subsidies are more indebted in the short term, whereas companies funded by VCs are less indebted and, more
oriented to long-term bank’s debt financing. Moreover, profitable firms are more likely to receive public subsidies than
VC-backed firms. In terms of financing stage, there are no significant differences in behaviours between private and
public investors based on investment stage or round. These findings are robust to several tests like the adoption of
alternative definitions of innovation and size of the analyzed firm or the exclusion of firms that have received both
types of financing from the analysis.

16
Annex
Table 4: Robustness test – Alternative definitions of the dependent variable: Exclusion of the mixed category
Panel A – Probit
Public Grant
Mixed excluded
Mixed only
Dependent Variable
(1)
(2)
(3)
(4)
(5)
Assets t-1
-0.222***
-0.251***
-0.268***
-0.237***
0.015
(0.016)
(0.019)
(0.024)
(0.026)
(0.087)
D_Patents t-1
-1.443***
-1.026***
-1.371***
-1.133***
0.265
(0.127)
(0.164)
(0.190)
(0.196)
(0.273)
Age t-1
0.867***
0.940***
0.801***
0.828***
0.078
(0.036)
(0.043)
(0.052)
(0.057)
(0.217)
Observations
4,282
4,282
4,282
4,282
119
Year Fixed Effects
No
Yes
Yes
Yes
No
Sector Fixed Effects
No
No
Yes
Yes
No
Country Fixed Effects
No
No
No
Yes
No
Panel B – Marginal Effects
Public Grant
Mixed excluded
Mixed only
Dependent Variable
(1)
(2)
(3)
(4)
(5)
Assets t-1
-0.062***
-0.054***
-0.046***
-0.036***
0.005
(0.004)
(0.004)
(0.004)
(0.004)
(0.028)
D_Patents t-1
-0.405***
-0.222***
-0.237***
-0.172***
0.085
(0.034)
(0.033)
(0.029)
(0.027)
(0.087)
Age t-1
0.243***
0.203***
0.138***
0.126***
0.024
(0.007)
(0.007)
(0.008)
(0.008)
(0.070)
Observations
4,282
4,282
4,282
4,282
119
Year Fixed Effects
No
Yes
Yes
Yes
No
Sector Fixed Effects
No
No
Yes
Yes
No
Country Fixed Effects
No
No
No
Yes
No
Note: The analysis covers Venture Capital investments and Public Grants raised in the period between 2008 to 2017 by firms operating in
the European Union. The table reports regression results of the Probit estimation of equation (1) on the full sample (Panel A) and its
marginal effects (Panel B). The dependent variable, Public Grant – Mixed excluded (Columns (1) to (4)), is a dummy variable which takes the
value of 1 if the firm raises a Public Grant only, and 0 if the firm raises a Venture Capital investment only in the analysed period (firms
raising both Public Grants and Venture Capital investments are excluded from the sample). The dependent variable, Public Grant – Mixed
only (Column (5)), is a dummy variable which takes the value of 1 if the firm raises a Public Grant, and 0 if the firm raises a Venture Capital
investment, limitedly to firms raising both Public Grants and Venture Capital investments in the analysed period. Assets t-1 is the natural
logarithm of the total assets reported in the balance sheet by the firm for the year before raising a Venture Capital investm ent or a Public
Grant. Patents t-1 is an indicator that takes the value of 1 for firms having applied for the filing of patent in the year before raising a Venture
Capital investment or a Public Grant, and 0 otherwise. Age t-1 is the natural logarithm of the age of the firm the year before raising a Venture
Capital investment or a Public Grant. This table reports coefficients of a Probit estimation followed by standard errors, clustered at the
firm level, in parentheses. ***, **, and * indicate statistical significance at the 1%, 5%, and 10% level, respectively.

17
Table 5: Robustness test – Alternative definitions of the dependent variable
Panel A – Ordered Probit
Categorical variable (0 = VC; 1 = VC+PG; 2 = PG)
Dependent Variable
(1)
(2)
(3)
(4)
Assets t-1
-0.190***
-0.189***
-0.211***
-0.185***
(0.014)
(0.015)
(0.017)
(0.019)
D_Patents t-1
-0.632***
-0.261***
-0.479***
-0.334***
(0.074)
(0.084)
(0.096)
(0.097)
Age t-1
0.766***
0.734***
0.582***
0.606***
(0.032)
(0.035)
(0.038)
(0.043)
Observations
4,742
4,742
4,742
4,742
Year Fixed Effects
No
Yes
Yes
Yes
Sector Fixed Effects
No
No
Yes
Yes
Country Fixed Effects
No
No
No
Yes
Panel B – Marginal Effects
VC
VC+PG
PG
Dependent Variable
(1)
(2)
(3)
Assets t-1
0.074***
-0.008***
-0.066***
(0.006)
(0.001)
(0.005)
D_Patents t-1
0.226***
-0.038***
-0.188***
(0.023)
(0.005)
(0.018)
Age t-1
-0.299***
0.033***
0.265***
(0.012)
(0.003)
(0.011)
Observations
4,742
4,742
4,742
Year Fixed Effects
No
No
No
Sector Fixed Effects
No
No
No
Country Fixed Effects
No
No
No
Note: The analysis covers Venture Capital investments and Public Grants raised in the period between 2008 to 2017 by firms
operating in the European Union. The dependent is a Categorical Variable which takes the value of 2 if the firm raises a Publ ic
Grant only, 1 if it raises both a Venture Capital investment and a Public Grant, 0 if the firm raises a Venture Capital investment
only in the analysed period. Assets t-1 is the natural logarithm of the total assets reported in the balance sheet by the firm for the year
before raising a Venture Capital investment or a Public Grant. Patents t-1 is an indicator that takes the value of 1 for firms having
applied for the filing of patent in the year before raising a Venture Capital investment or a Public Grant, and 0 otherwise. Age t-1 is
the natural logarithm of the age of the firm the year before raising a Venture Capital investment or a Public Grant. Panel A reports
coefficients of an Ordered Probit estimation followed by standard errors, clustered at the firm level, in parentheses. ***, **, and *
indicate statistical significance at the 1%, 5%, and 10% level, respectively. Panel B reports the marginal effects of the Ordered Probit
estimation (model in Panel (A) col (1)) followed by standard errors, clustered at the firm level, in parentheses. ***, **, and * indicate
statistical significance at the 1%, 5%, and 10% level, respectively.

18
Table 6: Robustness test – Alternative definitions of the explanatory variables: Innovation and Size
Panel A – Probit
Public Grant
Dependent Variable
(1)
(2)
(3)
(4)
(5)
Assets t-1
-0.185***
-0.201***
-0.211***
(0.019)
(0.019)
(0.019)
Employees t-1
-0.259***
(0.037)
Sales t-1
-0.042**
(0.017)
D_Patent t-1
-0.538***
-0.562***
(0.121)
(0.134)
D_Patent ever t-1
-0.746***
(0.087)
Patent count t-1
-0.143**
(0.058)
R&D t-1
-0.436**
(0.203)
Age t-1
0.663***
0.669***
0.681***
0.663***
0.584***
(0.046)
(0.045)
(0.045)
(0.054)
(0.052)
Observations
4,742
4,742
4,742
2,723
2,528
Year Fixed Effects
Yes
Yes
Yes
Yes
Yes
Sector Fixed Effects
Yes
Yes
Yes
Yes
Yes
Country Fixed Effects
Yes
Yes
Yes
Yes
Yes

19
Table 6: continued
Panel B – Marginal Effects
Public Grant
Dependent Variable
(1)
(2)
(3)
(4)
(5)
Assets t-1
-0.034***
-0.038***
-0.040***
(0.004)
(0.003)
(0.004)
Employees t-1
-0.053***
(0.007)
Sales t-1
-0.008**
(0.003)
D_Patent t-1
-0.110***
-0.109***
(0.024)
(0.025)
D_Patent ever t-1
-0.137***
(0.015)
Patent count t-1
-0.027**
(0.011)
R&D t-1
-0.083**
(0.039)
Age t-1
0.122***
0.127***
0.129***
0.136***
0.113***
(0.008)
(0.008)
(0.008)
(0.010)
(0.009)
Observations
4,742
4,742
4,742
2,723
2,528
Year Fixed Effects
Yes
Yes
Yes
Yes
Yes
Sector Fixed Effects
Yes
Yes
Yes
Yes
Yes
Country Fixed Effects
Yes
Yes
Yes
Yes
Yes
Note: The analysis covers Venture Capital investments and Public Grants raised in the period between 2008 to 2017 by firms
operating in the European Union. The table reports regression results of the Probit estimation of equation (1) on the full sample
(Panel A) and its marginal effects (Panel B). The dependent variable, Public Grant, is a dummy variable which takes the value of 1 if
the firm raises a Public Grant, and 0 if the firm raises a Venture Capital investment in the analysed period. This table reports
coefficients of a Probit estimation followed by standard errors, clustered at the firm level, in parentheses. ***, **, and * indicate
statistical significance at the 1%, 5%, and 10% level, respectively.

20
Table 7: Robustness test – Endogeneity
Panel A – Probit
Public Grant
Dependent Variable
(1)
(2)
(3)
(4)
Assets t-2
-0.188***
-0.199***
-0.199***
-0.166***
(0.014)
(0.016)
(0.019)
(0.020)
D_Patents t-2
-0.795***
-0.432***
-0.716***
-0.563***
(0.082)
(0.100)
(0.116)
(0.120)
Age t-2
0.671***
0.744***
0.568***
0.587***
(0.031)
(0.037)
(0.041)
(0.047)
Observations
4,264
4,264
4,264
4,264
Year Fixed Effects
No
Yes
Yes
Yes
Sector Fixed Effects
No
No
Yes
Yes
Country Fixed Effects
No
No
No
Yes
Panel B – Marginal Effects
Public Grant
Dependent Variable
(1)
(2)
(3)
(4)
Assets t-2
-0.060***
-0.049***
-0.042***
-0.032***
(0.004)
(0.004)
(0.004)
(0.004)
D_Patents t-2
-0.253***
-0.107***
-0.153***
-0.109***
(0.025)
(0.024)
(0.024)
(0.022)
Age t-2
0.214***
0.185***
0.121***
0.113***
(0.008)
(0.007)
(0.008)
(0.008)
Observations
4,264
4,264
4,264
4,264
Year Fixed Effects
No
Yes
Yes
Yes
Sector Fixed Effects
No
No
Yes
Yes
Country Fixed Effects
No
No
No
Yes
Note: The analysis covers Venture Capital and Public Grants raised in the period between 2008 to 2017 by firms operating in the
European Union. The table reports regression results of the Probit estimation of equation (1) on the full sample (Panel A) and its
marginal effects (Panel B). Public Grants is a categorical variable which takes the value of 1 if the firm raises a Public Grant and 0
if the firm raises a Venture Capital investment in the analysed year. Assets t-2 is the natural logarithm of the total assets reported in
the balance sheet by the firm two years before raising a Venture Capital investment or a Public Grant. Patents t-2 is an indicator that
takes the value of 1 for firms having applied for the filing of patent two years before raising a Venture Capital investment or a Public
Grant, and 0 otherwise. Age t-2 is the natural logarithm of the age of the firm two years before raising a Venture Capital investment
or a Public Grant. The table reports coefficients of a Probit estimation followed by standard errors, clustered at the firm level, in
parentheses. ***, **, and * indicate statistical significance at the 1%, 5%, and 10% level, respectively.

21
Table 8: Summary Statistics
Variables
Obs.
Mean
Std. Dev.
Min
Max
VC
PG
VC
PG
VC
PG
VC
PG
VC
PG
Assetst-1 (ln)
3,047
1,695
6.997
6.721
1.948
2.039
0.000
0.001
13.762
12.899
D_Patentst-1
3,047
1,695
1.956
0.049
0.397
0.216
0.000
0.000
1.000
1.000
Aget-1 (ln)
3,047
1,695
1.389
2.070
0.702
1.069
0.000
0.000
4.533
4.745
ST debtt-1
1,770
1,555
0.411
0.537
0.492
0.499
0.000
0.000
1.000
1.000
LT debtt-1
1,770
1,555
0.998
0.758
0.047
0.429
0.000
0.000
1.000
1.000
Leveraget-1 (ln)
2,991
1,294
0.231
0.217
0.372
0.259
0.000
0.000
4.251
2.634
EBITt-1
700
1,000
-28.986
0.267
34.185
25.086
-99.968
-98.590
67.536
98.196
ROEt-1
1,206
1,190
-87.310
-17.289
145.195
106.616
-919.655
-963.023
206.549
593.390
Profit Margint-1
690
995
-29.783
-0.849
33.856
25.177
-99.183
-97.508
71.090
98.196
Employeest-1 (ln)
1,941
1,266
2.547
2.416
1.237
1.180
0.000
0.000
7.952
5.805
Salest-1 (ln)
1,873
1,247
5.365
5.771
2.858
2.750
0.000
0.000
12.659
11.194
D_Patents ever
3,047
1,695
0.326
0.080
0.469
0.272
0.000
0.000
1.000
1.000
Patents countt-1 (ln)
3,047
1,695
0.351
0.088
0.829
0.436
0.000
0.000
4.942
3.920
R&Dt-1
3,047
1,695
0.018
0.009
0.134
0.097
0.000
0.000
1.000
1.000
Assetst-2 (ln)
2,683
1,581
6.993
6.655
1.977
2.116
0.000
0.000
13.762
13.007
D_Patentst-2
2,683
1,581
0.196
0.048
0.397
0.214
0.000
0.000
1.000
1.000
Aget-2 (ln)
2,683
1,581
1.465
2.051
0.737
1.106
0.000
0.000
4.522
4.736

22
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27
List of tables
Table 1: Baseline Results............................................................................................... 9
Table 2: Heterogenous Effects – Debt and Profitability (Panel A – Probit) ........................................11
Table 3: Heterogeneous effects – Early Stages vs SMEI Phase 1, Later Stages vs SMEI Phase 2 ..............13
Table 4: Robustness test – Alternative definitions of the dependent variable: Exclusion of the mixed category
..........................................................................................................................17
Table 5: Robustness test – Alternative definitions of the dependent variable ....................................18
Table 6: Robustness test – Alternative definitions of the explanatory variables: Innovation and Size ..........19
Table 7: Robustness test – Endogeneity .............................................................................21
Table 8: Summary Statistics ..........................................................................................22

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