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Financial constraints and the growth and survival of innovative start‐ups: An analysis of Italian firms

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Abstract

We study the impact of measures devoted to relieving financial constraints for the growth and survival of Italian innovative start‐ups. Using balance sheet data on innovative start‐ups and information on the use of the Italian Central Guarantee Fund for small and medium‐sized enterprises, we evaluate whether access to the fund, relieving financial constraints, helps innovative start‐ups survive and grow. We find innovative start‐ups benefit significantly more than similar control firms. We shed light on the relevance of policies aimed at reducing financial constraints for the growth and survival of innovative start‐ups, an issue receiving increasing attention at the European level. This article is protected by copyright. All rights reserved.
Eur Financ Manag. 2020;123. wileyonlinelibrary.com/journal/eufm
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1
DOI: 10.1111/eufm.12277
ORIGINAL ARTICLE
Financial constraints and the growth and
survival of innovative startups: An analysis of
Italian firms
Edoardo Ferrucci
1
|Roberto Guida
2
|Valentina Meliciani
1
1
Department of Business and Management,
University Luiss Guido Carli, Rome, Italy
2
Department of Economics, University of
International Studies, Rome, Italy
Correspondence
Roberto Guida, University of International
Studies, via G. Baracconi 5A, 00161 Rome,
Italy.
Email: r.guida@unint.eu
Abstract
We study the impact of measures devoted to relieving
financial constraints for the growth and survival of
Italian innovative startups. Using balance sheet data
on innovative startups and information on the use of
the Italian Central Guarantee Fund for small and
mediumsized enterprises, we evaluate whether ac-
cess to the fund, relieving financial constraints, helps
innovative startups survive and grow. We find in-
novative startups benefit significantly more than si-
milar control firms. We shed light on the relevance of
policies aimed at reducing financial constraints for
the growth and survival of innovative startups, an
issue receiving increasing attention at the European
level.
KEYWORDS
financial constraints, growth, innovative startups, survival
JEL CLASSIFICATION
D45; G14; G21; G32
EUROPEAN
FINANCIAL MANAGEMENT
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This is an open access article under the terms of the Creative Commons Attribution License, which permits use, distribution and
reproduction in any medium, provided the original work is properly cited.
© 2020 The Authors. European Financial Management published by John Wiley & Sons Ltd.
We would like to thank an anonymous referee and the Editor (John Doukas) for the very helpful comments and
suggestions on previous versions of the paper.
1|INTRODUCTION
Innovative entrepreneurship is essential for sustained economic growth (Audretsch, Aldridge,
& Sanders, 2011; Audretsch & Keilbach, 2004). From the viewpoint of evolutionary economics,
entrepreneurs serve as agents of change, bring new ideas to markets, and stimulate growth
through competitive firm selection (Audretsch, 1995; Jovanovic, 1982). However, these theories
dismiss the importance of financial conditions in allowing entrepreneurship to act as a stimulus
to economic growth, a point that Schumpeter (1912) made very clear in arguing that bank credit
acts as money capital and, therefore, constitutes the necessary premise for the realization of the
innovative processes planned by entrepreneurs.
While the financing of innovation plays a critical role in promoting economic growth, in-
novative firms often turn out to be financially constrained, because external finance can be very
difficult or costly for them to obtain due to information asymmetry problems.
1
The argument goes
as follows: innovative firms have better information about the probability of success and the nature
of their innovative project than potential investors, so that external finance can be more expensive
because it is harder for investors to distinguish good from bad projects (i.e., the lemon premium for
innovation will be higher). Moreover, the revelation of information can represent a relevant cost to
innovative firms because of the ease of imitation, which reduces the quality of their signal to the
market (Bhattacharya & Ritter, 1983). In addition, innovative firms are characterized by a high
share of intangible assets that cannot be pledged as collateral, and the investments in physical
capital are often firm specific and have little collateral value (Hall, 2010). The information asym-
metry problem can result in financial constraints, especially for innovative firms whose internal
financial resources are limited, that is, small and young firms.
Based on the recognition of the importance of socalled young innovative companies (YICs)
and their financial constraints, in 2016 the European Commission adopted an initiative to
improve the economic and regulatory situation for young enterprises that have recently started
or are in their early years of existence.
2
With the same purpose, the Italian government, already in 2012, adopted focused measures
favoring the birth and growth of YICs. In particular, Law No. 221/2012, the Italian Startup Act,
established a special section of the National Business Register to indicate the possibility for
young innovative firms to be qualified as YICs (i.e., innovative startups). Among the various
support measures that YIC registration provides, in addition to those related to tax incentives
and exemptions, the 2012 law gives these firms priority and simplified access to a government
guaranteed bank loan fund the Central Guarantee Fund (CGF) for small and mediumsized
enterprises (SMEs) to overcome market imperfections in the provision of bank loans to YICs.
Such a specific guarantee facility allows 80% of the bank credit exposure of YICs to be covered,
up to 2.5 million, and makes them fundable, independent of typical credit risk assessments,
thanks to an overriding process.
But has this law actually helped YICs to overcome financial constraints? And has it in-
creased firm life expectancy and growth rate? Despite the relevance of the policy for innovation
and growth in Italy (one of the causes of Italy's productivity slowdown is its low rate of
innovation), to our knowledge, the only studies evaluating the impact of the 2012 law policy
1
For a review of the theoretical and empirical literature on the financing of innovation, see Hall (2010).
2
See European Commission (2016), Communication from the Commission to the European Parliament, the Council,
the European Economic and Social Committee and the Committee of the Regions Europe's next leaders: the Startup
and Scaleup Initiative, Strasbourg, 22 November.
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measure are those of Finaldi Russo, Magri, and Rampazzi (2016), Giraudo, Giudici, and Grilli
(2019), and Biancalani, Czarnitzki, and Riccaboni (2020).
Based on propensity score matching, Finaldi Russo et al. (2016) find that external funding
(either debt or equity) has increased more for YICs than for other similar firms since the 2012
law was enacted. Focusing on the effectiveness and potential interrelations of different policy
instruments offered to YICs at the same time and in the same institutional context, Giraudo
et al. (2019) highlight a sort of institutional division of laborbetween the two main 2012 law
facilities: firm access to a governmentguaranteed bank loan and fiscal incentives for venture
capital (VC) equity investments. Finally, Biancalani et al. (2020) find that YICs show a greater
capacity to collect equity and debt than comparable untreated firms. However, none of these
studies investigates the specific role of the CGF, and they neglect whether changed financial
conditions affect firm growth and survival.
3
By investigating the impact of the Italian Startup Act (through access to the government
guaranteed bank loan fund) on firm growth and survival, this paper integrates the literature on
financial constraints for innovative and risky firms (Hall, 2010;Hall&Lerner,2010;Huyghebaert&
Van de Gucht, 2011; Kerr, Lerner, & Schoar, 2011) with studies on the impact of financial
constraints on growth and survival (e.g., Bottazzi, Secchi, & Tamagni, 2014; Cefis, Bartoloni, &
Bonati, 2020; Liu & Li, 2017; Musso & Schiavo, 2008).Inparticular,itshedslightonwhether
relieving financial constraints is especially beneficial for the growth rate and survival of innovative
startups with respect to noninnovative ones. The investigation of this issue, which has been
neglected by the literature, has important policy implications, since it can inform policymakers on
the opportunity to adopt special policies for YICs, a topic that has been receiving increasing
attention at the European level (European Commission, 2016).
The empirical analysis is based on balance sheet data for innovative startups and for a
control sample from the Bureau van Dijk's Aida database merged with information on the use
of the CGF for Italian SMEs.
The remainder of the paper is organized as follows. Section 2reviews the literature on
financial constraints for young innovative firms and relates it to the literature on the growth
and survival of small firms. Section 3describes the dataset and provides descriptive statistics
comparing the sample of innovative startups with the control group. Section 4describes the
methodology. Section 5reports and comments on the results. Section 6concludes the paper.
2|LITERATURE REVIEW
Many strands of literature, both theoretical contributions and empirical evidence, are sig-
nificant for the topic developed in this paper. The scientific debate is based on the presence of
relevant capital market imperfections that particularly impact young and innovative firms
(Hall, 2002; Revest & Sapio, 2012), lessening their capacity to access external resources and
increasing their likelihood of ending up financially constrained. The literature investigates a
causal link between financial constraints, investment in research and development (R&D), and
innovation as well. The following reviews the most relevant works in these fields of research
and highlights the main novelties of our contribution.
3
A few studies in other countries have directly focused on the impact of support measures for startups, generally
finding that these programs foster performance (Bertoni et al., 2019; Cantner & Kosters, 2015; Hottenrott &
Richstein, 2020; Howell, 2017; Soderblom, Samuelsson, Wiklund, & Sandberg, 2015).
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The literature suggests various reasons for a mismatch between the demand and supply of
finance capital for a highly risky (i.e., young and innovative) firm that ends up financially
constrained (Hall & Lerner, 2010). Among the determinants of the mismatch, studies highlight
several factors, such as adverse selection and moral hazard problems, due to the presence of
information asymmetries between the firm and potential investors (Brown, Fazzari, &
Petersen, 2009; Carpenter & Petersen, 2002; Kerr et al., 2011; Leland & Pyle, 1977; Peneder,
2008), which can be caused by unstable cash flow or the lack of a solid track record or tangible
assets (Hall, 2002; O'Sullivan, 2005). As found by Hall (2010), knowledge assets created by
innovation investments are hard to use as collateral, since they are intangible, firm specific, and
mainly embedded in human capital. Moreover, investors find it difficult to evaluate innovative
projectsquality,
4
because banks have limited capacity to assess technologies in the early stages
of adoption (Atanassov, Nanda, & Seru, 2007; Rajan & Zingales, 2003; Ueda, 2004).
Based on these theoretical considerations, most of the empirical literature uses investment cash
flow sensitivity to test financial constraints on innovative firms (for a review, see Carreira & Sil-
va, 2010). A number of studies find a significant positive cash flow effect on R&D investments,
supporting the hypothesis that innovative firms are credit constrained. This is the case for US
manufacturing firms (Hall, 1992)andsmallUSfirmsinhightech industries (Himmelberg & Pe-
tersen, 1994). Moreover, this result is not found for large firms (Hao & Jaffe, 1993). Brown et al.
(2009) estimate dynamic R&D models for hightech firms and find significant effects of cash flow
and external equity for young, but not mature, firms. A positive cash flow effect on R&D investments
is also found for French firms (Mulkay, Hall, & Mairesse, 2000) and Irish firms (Bougheas, Görg, &
Strobl, 2003). Hillier, Pindado, de Queiroz, and de la Torre (2011) show that R&D at the firm level is
less sensitive to internal cash flow in countries with effective investor protection, developed financial
systems, and strong corporate control mechanisms. In the case of Italy, Scellato (2007) finds that only
firms showing lower financial constraints, approximated by cash flowinvestment sensitivity, are
able to maintain a sustained patenting profile. In a longitudinal sample of 379 Italian unlisted new
technologybased firms (NTBFs), Bertoni, Colombo, and Croce (2010) observe that their investment
rate is strongly positively correlated with their current cash flow and that, after receiving VC
financing, they increase their investment rate. Colombo, Croce, and Guerini (2013) also find that the
investment of small NTBFs (unlike that of large NTBFs) is sensitive to internal cash flows.
Financial constraints on innovation are also noted from survey data. The Community In-
novation Survey carried out by European national statistical offices shows that the main obstacles to
innovation in the majority of European firms are financial. In particular, the lack of appropriate
sources of finance is one of the main obstacles to increasing both the probability and intensity of
innovation throughout Europe (Canepa & Stoneman, 2004; D'Este, Iammarino, Savona, & von
Tunzelmann, 2012; Mohnen, Palm, Loeff, & Tiwari, 2008; Mohnen & Roller, 2005;Savignac,2009).
Given the importance, in the current paper, of the role of external funding sources for YICs, our
work is also related to the literature on the role of the banking channel and other sources of financing
for small firms (Berger & Udell, 1995; Petersen & Rajan, 1994,1997). Along this line, Cosh, Cumming,
and Hughes (2009) find that banks provide less capital to smaller risky ventures, while Freel (2007)
finds that innovative firms are less successful in loan markets than their less innovative peers,
confirming that they end up being financially constrained. Huyghebaert and Van de Gucht (2011)
show that, in the context of business startups, banks finance a smaller fraction of debt when adverse
4
The existence of market imperfections due to information problems implies that firms could be rationed by their
lenders (Stiglitz & Weiss, 1981), resulting in a hierarchy (pecking order) of financial sources for firms (Myers &
Majluf, 1984).
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selection and riskshifting incentives are potentially large, although these problems do not affect the
maturity structure of bank loans. Ferrando, Marchica, and Mura (2017) show that financial flexibility
attained through a conservative leverage policy is more important for investment for those companies
that face higher external financing costs (small and young companies), while Bigelli, MartínUgedo,
and SánchezVidal (2014) find that financially conservative firms are smaller and have more in-
tangible and fewer tangible assets. As for young innovative Italian firms, Magri (2009) suggests that
supply factors can play a role in explaining the lower usage of bank loans, while Herrera and Minetti
(2007) and Cosci, Meliciani, and Sabato (2016) find that strong relationships with banks promote
innovation. On the importance of credit markets for the formation and success of young firms,
Lemmon, Roberts, and Zender (2008) suggest that such firms are especially sensitive to changes in
bank lending conditions, finding evidence of reliance on formal bank capital and its importance to
firm growth. In the same strand of studies, Hirsch and Walz (2011)andRobbandRobinson(2012)
show the importance of startup external debt financing. Overall, although VC can be viewed as the
most suitable form of external finance for YICs, bank loans remain the most important source of
external finance for innovative firms in Europe (Colombo & Grilli, 2007;Giudici&Paleari,2000).
Directly related to our paper are the studies by Finaldi Russo et al. (2016), Giraudo et al. (2019),
and Biancalani et al. (2020) investigating the effects of the 2012 Italian law on YICsfinancial
structures. Using propensity score matching, Finaldi Russo et al. (2016) find that external funding
(either debt or equity) has increased more for YICs than for other similar firms since the 2012 law.
Focusing on the effectiveness and potential interrelations of different policy instruments offered to
YICs at the same time and in the same institutional context, Giraudo et al. (2019)highlightasortof
institutional division of labor between the two main 2012 law facilities, namely, firm access to
governmentguaranteed bank loans and fiscal incentives for VC equity investments. Finally,
Biancalani et al. (2020) find that YICs show greater capacity to collect equity and debt than
comparable untreated firms. All these studies, however, neglect to investigate whether changed
financial conditions affect overall firm performance or survival time.
Our paper asks whether relieving financial constraints truly helps innovative firms to grow and
survive longer. Our contribution is therefore also related to studies investigating the problems of the
growth and survival of YICs. The literature on firm growth finds financial constraints to have a
negative impact on firm growth (Aghion, Fally, & Scarpetta, 2007; Bottazzi et al., 2014; Musso &
Schiavo, 2008). Several studies also show that SMEs suffer more than large firms from financial
constraints, which limits their growth rate (for a review, see Beck & DemirgüçKunt, 2006). But do
innovative startups face more problems than other SMEs? While hightech firms are more likely to
grow (Morone & Testa, 2008; Westhead & Cowling, 1995), innovative activity is highly uncertain
and, although it can increase the probability of superior performance, it cannot guarantee it (Coad
&Rao,2008). The risk associated with innovation activity can therefore limit firm growth, since
innovative firms can be denied the financial resources necessary for their growth. Westhead and
Storey (1997) find that, irrespective of the measure of technological sophistication chosen for a
sample of independent hightech firms, the most technologically sophisticated firms seemed much
more likely to report that continual financial constraint impeded firm growth, compared with less
technologically sophisticated firms. Hyytinena and Toivanen (2005) show that government funding
in Finland is particularly beneficial for firmsR&D and growth in industries that depend more on
external financing. The evidence is consistent with the view that financial constraint holds back
innovation and growth.
Financial constraint can be detrimental not only for firmsgrowth, but also for their sur-
vival. The few studies including financial variables in equations modelling survival probabilities
(Bridges & Guariglia, 2008; Bunn & Redwood, 2003; Cefis et al., 2020; Fotopoulos & Louri, 2000;
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Liu & Lee, 2017; Musso & Schiavo, 2007; Vartia, 2004; Zingales, 1998) find a significant asso-
ciation between financial variables and survival in different countries. On the other hand, while
the literature has found a robust positive relation between firmssurvival and firmssize and
age, the results on the role of innovation on survival are mixed. While most studies find a
positive impact of innovation on firm survival (Cefis & Marsili, 2005,2006; EstevePerez et al.,
2007; Hall, 1987; Rosenbusch, Brinckmann, & Bausch, 2011), others find that the survival times
of (especially small) innovative enterprises can be significantly shorter than those of
noninnovative ones (Boyer & Blazy, 2014; Buddelmeyer, Jensen, & Webster, 2010; Cader &
Leatherman, 2011; Hyytinena, Pajarinenb, & Rouvinen, 2015).
Linking the literature on the financial constraints of innovative firms with studies on the negative
impact of financial constraint on firm growth and survival, we test whether relieving financial
constraints is especially beneficial to the growth rate and survival probability of innovative startups.
3|DATA AND DESCRIPTIVE STATISTICS
The empirical analysis is based on the following sources:
1. Comprehensive balance sheet data from Aida (the Italian version of the Bureau van Dijk's
Amadeus database) and
2. A database on SMEsaccess to the governmentguaranteed bank loan facility, provided by
the Italian Ministry of Economic Development.
5
In 2012, the Italian Parliament approved a law aimed at stimulating the creation and devel-
opment of innovative startups.
6
The law gives these firms priority and simplified access to a
governmentguaranteed bank loan fund (i.e., the CGF for SMEs) to overcome market imperfec-
tions in the provision of bank loans to YICs. Such a specific guarantee facility allows 80% of YIC's
bank credit exposure to be covered, up to a limit of 2.5 million, and makes them fundable,
independent of typical credit risk assessments, thanks to an overriding process. To gain access to
this and other benefits, firms must meet eligibility criteria to register as innovative startups in a
special section of the Business Register of the Chamber of Commerce. These criteria include being
a limited liability company (either an Italian company or a branch of a European Union company
registered in Italy), less than 5 years old, and operating in a technologyrelated business. In
addition, YICs have to meet at least one of the following conditions: (a) the firm is a holder,
depositary, or licensee of a patent or the owner and author of registered software; (b) at least one
third of the firm's employees are highly educated (with a PhD or a research tenure, or twothirds
have an MSc degree); and (c) the firm's annual R&D investments comprise at least 15% of its
revenues (or operating costs, if higher).
7
Note that these firms can be easily identified, since they
are registered in a special section of the Business Register of the Chamber of Commerce. Starting
with an initial sample of 2,898 YICs, we downloaded their balance sheet data from Bureau van
Dijk for the period from the year of their establishment to 2017.
5
The ministerial decree for fast track access to the Central Guarantee Fund for SMEs was adopted in May 2013.
6
See Law No. 221/2012.
7
To be recognized as a YIC and maintain the right to belong to the Register, firms are prohibited from paying dividends
or listing on a stock exchange. In addition, they cannot have revenues of over 5 million a year and must not have been
created as a spinoff or a merger.
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Since the analysis aims to study the effectiveness of the CGF in relieving financial con-
straints on YICs in comparison to other noninnovative startups (NISs), we also identify this
second group of firms. To do so, we select a group of Italian limited liability companies less than
5 years old, with a production value of no more than 5 million (startups) but not registered as
YICs in the special section of the Business Register of the Chamber of Commerce because they
do not meet the criteria to be defined as an innovative startup (i.e., operating in a technology
related business and meeting conditions (a), (b), or (c) above). We refer to this group of firms as
NISs, and it consists of 15,789 companies. Since some firms might not strictly meet the criteria
to be classified as YICs (or have not applied to be registered as such) but are nevertheless
innovative, we perform robustness checks in the empirical analysis by deleting from the group
of NISs all firms with positive R&D investments.
Table 1breaks down the sample based on economic activity.
8
As expected, YICs are highly concentrated in hightech sectors: 36% operate in software
development and information technology (IT) consultancy, while almost 20% are in R&D. The
number of NISs that belong to these sectors is rather low (about 2% combined). On the other
hand, the top sectors for NISs are trade, retail, and construction, where YICs are rather scarce
(less than 5%).
In Table 2, we report the definitions of all the variables used in the empirical analysis and
the set of financial and performance indicators used in the matching procedure.
In Table 3, we compare YICs and NISs based on all the variables used in the empirical
analysis and the matching procedure, and in Table 4we perform the same comparison for failed
and nonfailed firms.
We can see that YICs are younger and smaller and have a higher ratio of loans covered by
the CGF and total assets (CG Fund), a higher ratio of intangible assets to total assets, and a
higher growth rate with respect to NISs. Additionally, the profitability and financial structures
of the two groups are extremely different: YICs are less profitable and more financially in-
dependent. These results hold across the samples of failed and nonfailed firms. The main
difference between the two samples is that the CG Fund ratio and the growth rate of YICs are
larger than for NISs, but only among nonfailed firms, whereas the opposite holds for failed
firms. The high level of heterogeneity between the two samples is a concern for the empirical
analysis and will be tackled through a matching procedure.
4|ESTIMATED EQUATIONS AND METHODOLOGY
The empirical analysis is devoted to studying the impact of innovative startupseasy access to the
CGF on their growth and survival rate. We therefore estimate two equations, one where the de-
pendent variable is firm growth (Equation (1)) and the other where it is firm survival time
(Equation (3)). In both equations, we estimate the impact of the ratio of loans covered by the CGF and
total assets (CG Fund) to growth and survival and test whether it differs between YICs and NISs.
Several possible variables can be used to measure firm growth, but the most widely used in
empirical studies are sales and employment (Delmar, 1997). While the consensus seems to be
that, if only one indicator is chosen, the preferred measure of firm growth should be sales, sales
does not always lead growth among hightech startups, since the number of employees is more
8
Firms are classified according to the 2007 ATECO version. For a complete description, see https://www.istat.it/it/
archivio/17888
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TABLE 1 Distribution of firms across sectors
This table reports the initial sample of firms by ATECO sector. The ATECO classification of sectors of economic
activity was adopted by the Italian National Institute of Statistics in 2008, and it corresponds to NACE Rev. 2.
Activity description
(ATECO, 2007)
Non
innovative
Non
innovative (%) YIC YIC (%) All All (%)
Wholesale trade 1,929 14.48% 31 1.24% 1,960 12.39%
Retail 1,651 12.39% 61 2.45% 1,712 10.82%
Buildings construction 1,363 10.23% 11 0.44% 1,374 8.69%
Software production and IT
consultancy
250 1.88% 900 36.13% 1,150 7.27%
Specialized constructions 1,009 7.57% 31 1.24% 1,040 6.58%
Catering activities 960 7.20% 5 0.20% 965 6.10%
Land transport 655 4.92% 0 0.00% 655 4.14%
Research and development 36 0.27% 490 19.67% 526 3.33%
Repair of motor vehicles 523 3.92% 1 0.04% 524 3.31%
Manufacturing of metal products 504 3.78% 14 0.56% 518 3.27%
Real estate 505 3.79% 3 0.12% 508 3.21%
Information services 181 1.36% 241 9.67% 422 2.67%
Business management and
consulting
337 2.53% 84 3.37% 421 2.66%
Other professional, scientific, and
technical activities
262 1.97% 105 4.22% 367 2.32%
Support activities for office
functions
297 2.23% 52 2.09% 349 2.21%
Manufacture of machinery and
equipment
224 1.68% 101 4.05% 325 2.05%
Tests and technical analysis 207 1.55% 113 4.54% 320 2.02%
Storage and transport support
activities
314 2.36% 4 0.16% 318 2.01%
Service activities for buildings
and landscape
266 2.00% 3 0.12% 269 1.70%
Food industries 256 1.92% 13 0.52% 269 1.70%
Accommodation 249 1.87% 0 0.00% 249 1.57%
Supply of electricity, gas, steam 229 1.72% 20 0.80% 249 1.57%
Sports and entertainment
activities
212 1.59% 3 0.12% 215 1.36%
Repair, maintenance, and
installation of machinery
195 1.46% 14 0.56% 209 1.32%
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likely to grow first in such firms (Delmar, Davidsson, & Gartner, 2003). Given the nature of the
firms in our sample (i.e., YICs operating mainly in hightech businesses, versus NISs operating
in more traditional sectors), the empirical analysis on firm growth is repeated with different
growth measures (sales growth and growth in the number of employees). We estimate the
following equation:
Size Size ββSize βYIC βCG Fund
βCG Fund YIC βAge βAge
βCash flow βIntangibles e
ln( ) ln( ) = + ln( ) + + ln( )
+ ln( ) * + ln( ) + ln( )
+ln( ) +ln( ) +
it it it i it
it i it it
it it it
,,101 ,123 ,1
4,15,162,1
7,18,1,1
(1)
TABLE 1 (Continued)
Activity description
(ATECO, 2007)
Non
innovative
Non
innovative (%) YIC YIC (%) All All (%)
Health care 192 1.44% 5 0.20% 197 1.25%
Advertising and market research 153 1.15% 36 1.45% 189 1.19%
Manufacture of computers and
electronic products
52 0.39% 127 5.10% 179 1.13%
Packaging of clothing articles 172 1.29% 3 0.12% 175 1.11%
Education 143 1.07% 20 0.80% 163 1.03%
Total 13,326 2,491 15,817
TABLE 2 Variable definitions
Name Definition
YIC Dichotomous variable that takes the value of one for innovative startup firms, and
zero otherwise
Age Number of years since the firm's establishment year
Employees Firm's total number of employees
Sales Firm's value of revenues
CG Fund Firm's ratio of loans covered by the CGF to total assets
Total assets Firm's value of total assets
Earnings Firm's value of total profits (or losses)
Intangibles Firm's ratio of intangible assets to total assets
Cash flow Firm's aftertax operating profits plus depreciation and amortization
Growth of sales Annual growth rate of a firm's sales
ROA Return on assets
Equitytoasset ratio Firm's ratio of equity to total assets
Debt ratio Firm's ratio of total debt to equity
Operating income Difference between a firm's gross income and operating expenses
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TABLE 3 Descriptive statistics
This table reports descriptive statistics for all the variables used in the regression analysis and in the matching procedure. In the table, we perform a series of ttests on the
means between innovative startups (YIC) and noninnovative ones (nonYIC) and a series of nonparametric Ksample tests on the equality of the median values.
* denotes significance at the 0.01 level.
Test on means Test on medians
Variables Obs. Min. Max. Mean
Mean
nonYIC Mean YIC Diff.
Median
nonYIC
Median
YIC Chisquared
Age 140,729 0.000 10.000 4.488 4.500 4.386 0.114* 5.000 4.000 53.069*
Employees 112,801 0.000 1,518.000 5.998 6.421 2.265 4.155* 3.000 1.000 3,433.940*
Sales (thousands of euros) 115,215 0.000 166,151.000 673.771 728.574 191.973 536.601* 408.000 36.000 7,944.213*
CG Fund 117,109 0.000 13.342 0.203 0.175 0.442 0.266* 0.000 0.000 280.5724*
Total assets (thousands of
euros)
83,512 0.000 832,485.000 985.262 1,077.296 412.591 664.706* 427.000 108.000 4,296.617*
Earnings (thousands of euros) 115,211 14,148.000 112,394.000 9.971 15.008 34.304 49.311* 5.000 1.000 3,066.023*
Intangibles 83,412 0.024 1.000 0.086 0.057 0.269 0.211* 0.007 0.178 5,722.334*
Cash flow (thousands of euros) 83,466 1.000 18,480.008 30.761 31.125 28.499 2.626 10.000 5.000 859.842*
Growth of sales 115,182 6.672 8.392 0.325 0.321 0.374 0.053* 0.121 0.288 258.034*
ROA 113,510 956.010 603.560 2.867 4.777 13.949 18.725* 4.140 0.580 1,801.688*
Equitytoasset ratio 115,074 0.499 1.000 0.242 0.226 0.384 0.157* 0.146 0.335 2,183.070*
Debt ratio 140,729 1,988.660 9,790.000 14.704 15.491 7.790 7.701* 5.580 2.315 3,005.672*
Operating income (thousands
of euros)
115,224 7,063.000 88,836.000 27.590 34.140 29.932 64.073* 15.000 0.000 4,767.977*
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where ln indicates the natural logarithm of the variable; Size
i,t
is the value of sales (or the number of
employees, for robustness checks) for firm iat time t;YIC is a dummy variable for innovative start
ups; CG Fund is the ratio of loans covered by the CGF to total assets; Age is the age of the firm at time
t1; Cash flow denotes funds collected within an enterprise from its operations, calculated as after
tax operating profits plus depreciation and amortization; Intangibles is the ratio of intangible assets to
total assets; and εis the error term. Since the dependent variable is the growth rate of the firm
between times t1andt, all the explanatory variables are measured at time t1. All specifications
include sectoral, regional, juridical form, and calendar year dummies. Equation (1) is estimated with
generalized least squares, with standard errors clustered at the firm level.
The coefficients of interest in this paper are the dummy for YICs (β
2
), the coefficient on the
logarithm of the CG Fund (β
3
), and its interaction with the dummy for YICs (β
4
). In particular, we
have no strong a priori expectations for the coefficient β
2
of the YIC variable, since, on the one hand,
innovative startups, when successful, can have good market opportunities and grow more than the
control group, but, on the other hand, are potentially more credit constrained due to their risky
activity, which can be detrimental for their growth rate. However, we expect that the greater intensity
of CGF use is more beneficial for the growth of YICs than for NISs, since it helps relieve financial
constraints that are expected to be more severe for the former. Thus, we expect a positive sign for the
coefficient β
3
.
As for the control variables, according to Gibrat's law, the firm's proportional rate of growth
is independent of its initial size. If the law holds, the distribution of firm size is lognormal
TABLE 4 tTests for failed versus nonfailed firms
This table reports two sets of ttests, depending on whether the firms belong to the group of failed firms or not.
The table compares the means of the variables used in the regression analysis and in the matching procedure
between innovative startups (YIC) and noninnovative ones (nonYIC). * denotes significance at the 0.01 level.
Failed firms Nonfailed firms
NonYIC YIC Diff. NonYIC YIC Diff.
Age 4.500 4.348 0.152* 4.500 4.388 0.112*
Employees 7.905 0.721 7.184* 5.984 2.152 3.832*
Sales 867.318 54.877 812.442* 682.168 177.808 504.359*
CG Fund 0.408 0.289 0.118* 0.142 0.468 0.326*
Total assets 1,168.950 141.809 1,027.142* 1,022.323 387.230 635.093*
Earnings 0.760 31.378 32.138 17.095 32.687 49.781*
Intangibles 0.047 0.217 0.170* 0.057 0.269 0.211*
Cash flow 35.556 12.383 23.174* 29.159 26.725 2.434
Growth of sales 0.373 0.139 0.235* 0.330 0.385 0.055*
ROA 0.839 45.570 46.409* 5.441 15.282 20.722*
Equitytoasset ratio 0.182 0.448 0.266* 0.232 0.379 0.147*
Debt ratio 80.525 28.663 51.862 55.934 27.391 28.543*
Operating income 43.818 84.596 128.415* 33.990 16.666 50.656*
Percentage 15.370 11.890 84.630 88.110
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(with a large number of small firms and very few large firms). In that case, we should expect β
1
to equal zero. However, many empirical studies find that small firms tend to grow more than
large ones (e.g., Santarelli, Klomp, & Thurik, 2006). The age of the firm is expected to negatively
affect firmsgrowth (old firms are expected to grow less than young ones), although we do not
rule out the possibility of nonlinear effects. The availability of internal funds (Cash flow)is
expected to positively affect firm growth by providing means to expand sales (Bottazzi
et al., 2014). A greater proportion of intangible assets can hamper growth when firms need to
provide collateral to obtain credit (Bottazzi et al., 2014), but it can also have a positive effect,
since intangibles are a major driver of firm competitiveness and are expected to sustain firms
longrun performance (Denicolai, Cotta Ramusino, & Sotti, 2015).
The second hypothesis we aim to test is whether the CGF helps innovative startups survive
longer. In Aida, the survival status of firms is traceable until the end of 2017. For each firm, we
construct a dichotomous variable Fail
i,t
that equals one if firm iis recorded as either dissolved,
liquidated, or bankrupted in year t, and zero otherwise.
The standard estimation strategy to analyze firm survivability is the Cox proportional hazard
model. This model relies on the definition of the survival function S
i
(t), which describes the
probability of firm isurviving beyond a generic time period t*, specifically S
i
(t)=Pr(Tt),
where Tis a continuous random variable that indicates the survival time and that assumes one
of the values is in the range [t
0
,t*]. The hazard rate H
i
(t) describes the rate at which a firm fails
in year t, given it was active in year t1, and it is defined as f
i
(t)/S
i
(t), where f
i
(t) is the
probability density function of T. The hazard function in the Cox model has the form
Ht H t BX()= ()exp(
)
iit0(2)
where Ht()
0is the baseline hazard and Xis a list of covariates. While the model offers a high
degree of flexibility, given that the parameters can be estimated without a specific functional
form for the baseline hazard, H
0
(t), it also requires regressors in Equation (2) to be multi-
plicatively associated with hazard (i.e., proportional hazards assumption).
The multiplicity assumption is very stringent: given two random units from the sample, the
ratio of hazards must be constant over time. We run two instances of Schoenfeld's test and in
both cases we reject the null hypothesis of hazard rate proportionality.
9
We therefore opt for a more conservative strategy through estimation of an accelerated
failure time (AFT) model. Unlike proportional hazards models, the parameter estimates in AFT
models are robust to omitted variable bias and, perhaps more importantly, are less affected by
the choice of probability distribution (Keiding, Andersen, & Klein, 1997). The approach is
frequently used in the literature on firm survival (e.g., Cader et al., 2011; Falck, 2007; Saridakis,
Mole, & Storey, 2008; Strotmann, 2007).
In its loglinear form, our AFT model is given by
TγYIC γCG Fund γCG Fund YIC γSize
γAge γAge γGrowth γROA
γEquity to asset ratio γDebt ratio αε
log( ) = + ln ( ) + ln ( ) * + ln ( )
+ ln( ) + ln( ) + ln( ) + ln( )
+ln( ‐‐ )+ln( )+
i i it it i it
it it it it
it it i
0,1,12,13,1
4,152,16,17,1
7,18,1
(3)
9
The results are available on request.
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where log(T
i
)isthelogtransformed survival time Twhile ε
i
and αrepresent an error term and a
scale parameter, respectively. To test whether greater intensity of use of the CGF affects the survival
times of YICs and NISs differently, we include among the regressors a dummy for YICs, the ratio of
loans covered by the CGF to total assets, and an interaction term between the two. Following the
literature (e.g., Cefis & Marsili, 2005;Iwasaki&Kočenda, 2020), the richest specification includes as
control variables firm size (measured by sales in the basic specification and by employees in
robustness checks) and age (also allowing for nonlinear effects), firm growth (given the firm's size,
the likelihood of survival could differ between firms growing and declining in size), and firm
performance variables, such as the return on assets (ROA), the equitytoasset ratio, and the debt
ratio (ratio of total debt to assets).
10
All the variables are expressed in natural logarithms. In all
estimations we also control for sectoral, regional, juridical form, and calendar year dummies.
5|RESULTS
Table 5reports the results for different specifications of Equation (1). Column (1) reports the
results for a basic specification in which firm growth depends on firm size and age; the ratio of
loans covered by the CGF and total assets (CG Fund); a dummy for YICs; the interaction term
between the YIC dummy and CG Fund; and regional, juridical status, and sectoral dummies.
Column (2) allows for the nonlinear effect of age on growth, and in column (3) we include
controls for the intensity of intangible assets (Intangibles) and for a proxy of firmsability to self
finance their growth (Cash flow).
Table 5shows that small firms tend to grow more than large ones (size, measured by sales,
always has a negative impact on growth, violating Gibrat's law). We also find that innovative start
ups grow, on average, less than similar startups (4.25.3 percentage points less), supporting the
hypothesis that the most technologically sophisticated firms suffer more from financial constraints
that limit their growth than less technologically sophisticated ones (Westhead & Storey, 1997). We
find that growth decreases with age (in a specification, age has a nonmonotonic effect on growth,
with relatively younger and older firms growing more). Finally, we observe that the proportion of
intangible assets and cash flow have a positive impact (Hall, 2002).
The results of all the specifications show that obtaining more funds from banks helps firms
grow. The use of these funds helps innovative startups significantly more than noninnovative
young firms, with an additional effect of around 0.025 (a 1% increase in the ratio of loans
covered by the CGF and total assets increases firm growth by about 0.01% for NISs and by about
0.035% for YICs). This evidence provides indirect support for the hypothesis that, while SMEs
suffer from financial constraints, which limit their growth rate (Beck & DemirgüçKunt, 2006),
financial constraints are more severe for innovative firms (Hall, 2002; Revest & Sapio, 2012).
Overall, access to a guarantee fund appears to be an effective way of overcoming information
asymmetries favoring the growth of YICs.
However, does the CGF also help innovative startups live longer? Table 6shows the results
of the survival analysis (Equation (3)), where we report time ratios.
11
We can use time ratios to
compare the rates at which different firms approach failure. Specifically, a time ratio greater
than one for a predictor indicates that an increase in this predictor delays the firm's time to
10
To apply a logarithmic transformation, we rescaled variables with negative values so that the minimum equals one.
11
In all the specifications, we model survival times using a lognormal distribution. This is a standard choice in this
literature (Che, Lu, & Tao, 2017). The choice of a loglogistic distribution provides very similar estimates.
FERRUCCI ET AL.EUROPEAN
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failure (while the opposite applies for a time ratio lower than one). Table 6reports different
specifications: column (1) reports the results of the basic specification, in which the survival
time depends on size (sales); age; sectoral, regional, time, and juridical form dummies; the ratio
of loans covered by the CGF and total assets; a dummy for innovative startups; and its in-
teraction with CG Fund (to investigate whether the impact of access to bank resources varies
between innovative startups and similar young firms). In column (2), we allow for nonlinear
TABLE 5 Regression results for the growth equation
This table reports the results of panel regressions of the growth equation specified in Equation (1). Explanatory
variables are expressed in logarithms and are lagged by 1 year, and the coefficients are estimated with
generalized least squares. All specifications include year, sectoral, regional, and firm juridical form dummies.
The lower number of observations in column (3) is due to the missing values for the variables Cash flow and
Intangibles. Robust standard errors are clustered at the firm level and are in parentheses. *p< 0.1,
**p< 0.05, ***p< 0.01.
(1) (2) (3)
Growth (sales) Growth (sales) Growth (sales)
Ln(Sales)0.334*** 0.333*** 0.268***
(0.0037) (0.0037) (0.0051)
YIC 0.535*** 0.519*** 0.421***
(0.0231) (0.0230) (0.0211)
Ln(CG Fund) 0.012*** 0.011*** 0.007***
(0.0008) (0.0008) (0.0007)
Ln(CG Fund)*YIC 0.023*** 0.026*** 0.0260***
(0.0032) (0.0032) (0.0030)
Ln(Age)0.208*** 0.751*** 0.911***
(0.0332) (0.0655) (0.0666)
Ln(Age
2
) 0.286*** 0.300***
(0.0313) (0.0306)
Ln(Cash flow) 0.044***
(0.0026)
Ln(Intangibles) 0.162***
(0.0255)
Intercept 2.700*** 2.695*** 2.233***
(0.0287) (0.0287) (0.1080)
Year fixed effects (FE) Yes Yes Yes
ATECO sector FE Yes Yes Yes
Regional FE Yes Yes Yes
Legal form FE Yes Yes Yes
Observations 86,204 86,204 60,444
R
2
0.391 0.392 0.301
Loglikelihood 87,285 87,185 54,640
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effects of age and size and include a control for firm performance (i.e., firm growth). In
column (3), we also control for a set of financial indicators (i.e., ROA, equitytoasset ratio, and
debt ratio).
A robust finding is that innovative startups have longer survival times with respect to non
innovative ones. The interpretation of the estimated time ratios is straightforward: innovative
startups survive longer than noninnovative ones; specifically, those for which the dummy YIC
equals one fail at a rate that is 5070% slower. Again, the result is consistent with studies that
identify innovation as one of the main drivers of firm survival (Cefis & Marsili, 2005,2006;
EstevePerez et al., 2007; Hall, 1987; Rosenbusch et al., 2011). As expected, survival increases
with age, size, and firm growth. The ROA and the equitytoasset ratio both have a small and
positive effect on firm survival.
Focusing on the interaction term between CG Fund and the YIC dummy variable, we
observe that CG Fund contributes to increasing firm survival only for YICs (for a unitary
increase in the ratio of loans covered by CGF to total assets, the YIC failure rate decreases by
about 35%). These results are partially in line with studies showing that financial variables
matter for survival probabilities (Bridges & Guariglia, 2008; Bunn & Redwood, 2003; Fotopoulos
& Louri, 2000; Musso & Schiavo, 2008; Vartia, 2004; Zingales, 1998). Overall, the result that the
CGF contributes to increasing firm survival only for YICs (robust in all specifications) suggests
an opportunity to establish specific policies directed at innovative startups to help them
overcome market imperfections in the provision of bank loans and allow them not only to grow
more, but also to survive longer.
The results reported in Tables 5and 6are based on a comparison between YICs and a group
of noninnovative firms that are also startups (limited liability companies less than 5 years old,
with a production value of no more than 5 million). However, as shown in section 3, YICs and
NISs present serious heterogeneity, especially in terms of some financial indicators. To more
accurately evaluate the impact of the CGF, we build a control sample of NISs that closely
resembles the set of YICs.
We adopt the coarsened exact matching (CEM) algorithm described by Iacus, King, and Porro
(2012). We select three financial indicators, namely,Operatingincome,Assets,andEarnings,plusa
variable indicating the ATECO sector in which the firms operate. On the basis of these variables, we
trim observations to reduce the imbalance between the YIC group and the group of NISs. We run
the algorithm following the FreedmanDiaconis rule for distribution coarsening (Freedman &
Diaconis, 1981), substantially reducing imbalance: the imbalance statistic described by Iacus et al.
(2012) drops from an original value of 0.87 to 0.13. This strategy, although resulting in the exclusion
of a portion of the data, given that the new matched sample consists of only 3,753 firms (1,917 NISs
and 1,836 YICs), produces excellent results in terms of the overall balance of both financial in-
dicators and the distribution of firms across ATECO sectors.
12
In Tables 7and 8, we replicate the econometric exercises described in Equations (1) and (3),
respectively, over the matched samples.
The main results are confirmed: in the growth equation, innovative startups grow, on average,
less than the control group; growth depends positively on the ratio of loans covered by the CGF and
total assets for both YICs and the control group, but the advantage is more marked for YICs.
Looking at the estimates of the survival equation, we find that most of the results are
confirmed. In agreement with previous estimations (see Table 6), borrowing increases the
12
The results are available on request.
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probability of survival only for YICs. We emphasize once more the conclusion that favoring
firmsaccess to bank loans through the CGF is particularly important for innovative startups
suffering from financial constraint due to asymmetric information. At the same time, the results
raise doubts about the advantages of the policy measure for the survival of SMEs in general.
TABLE 6 Regression results for the survival equation
This table reports the results of panel regressions of the survival equation specified in Equation (3). Explanatory
variables are expressed in logarithms and are lagged by 1 year, and we report time ratios estimated with an AFT
model. All specifications include year, sectoral, regional, and firm juridical form dummies. Robust standard
errors are clustered at the firm level, in parentheses. *p< 0.1, **p< 0.05, ***p< 0.01.
(1) (2) (3)
Survival Survival Survival
YIC 1.589*** 1.720*** 1.535***
(0.0227) (0.0128) (0.0470)
Ln(CG Fund) 0.992 0.987 0.965
(0.0030) (0.0123) (0.0222)
Ln(CG Fund)*YIC 1.194*** 1.172*** 1.215**
(0.0711) (0.0702) (0.1020)
Ln(Sales) 1.052*** 1.028*** 1.038***
(0.0055) (0.0042) (0.0046)
Ln(Age) 1.071*** 1.069*** 1.038**
(0.0150) (0.0162) (0.0152)
Ln(Age
2
) 1.000*** 1.000***
(0.0000) (0.0000)
Growth of sales 1.347*** 1.365***
(0.0409) (0.0441)
Ln(ROA) 1.006***
(0.0008)
Ln(Equitytoasset ratio) 1.007***
(0.0011)
Ln(Debt ratio) 1.092
(0.0626)
Year fixed effects (FE) Yes Yes Yes
ATECO sector FE Yes Yes Yes
Regional FE Yes Yes Yes
Legal form FE Yes Yes Yes
Observations 98,532 98,460 84,932
Firms 18,627 17,761 17,422
Failed 2,053 1,938 1,811
Chisquared 40,124 37,494 36,426
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We have also performed robustness checks for the growth and survival equations, respec-
tively.
13
First, we measure size by the number of employees rather than sales; second, we
exclude from the group of NISs (for both the whole sample and the matched sample) all firms
TABLE 7 Regression results for the growth equation (matched sample)
This table reports the results of panel regressions of the growth equation specified in Equation (1). Explanatory
variables are expressed in logarithms and are lagged by 1 year, and the coefficients are estimated with
generalized least squares. All specifications include year, sectoral, regional, and firm juridical form dummies.
The lower number of observations in column (3) is due to missing values for the variables Cash flow and
Intangibles. The sample is obtained via CEM, as discussed in section 5. Robust standard errors are clustered at
the firm level, in parentheses. *p< 0.1, **p< 0.05, ***p< 0.01.
(1) (2) (3)
Growth (sales) Growth (sales) Growth (sales)
Ln(Sales)0.357*** 0.356*** 0.308***
(0.0055) (0.0055) (0.0074)
YIC 0.555*** 0.544*** 0.463***
(0.0250) (0.0249) (0.0239)
Ln(CG Fund) 0.012*** 0.0123*** 0.008***
(0.0012) (0.0012) (0.0012)
Ln(CG Fund)*YIC 0.022*** 0.0251*** 0.025***
(0.0036) (0.0036) (0.0034)
Ln(Age)0.253*** 0.713*** 0.842***
(0.0353) (0.0718) (0.0724)
Ln(Age
2
) 0.239*** 0.253***
(0.0343) (0.0336)
Ln(Cash flow) 0.043***
(0.0039)
Ln(Intangibles) 0.218***
(0.0374)
Intercept 2.956*** 2.948*** 2.604***
(0.0440) (0.0441) (0.0649)
Year fixed effect (FE) Yes Yes Yes
ATECO sector FE Yes Yes Yes
Regional FE Yes Yes Yes
Legal form FE Yes Yes Yes
Observations 44,055 44,055 31,804
R
2
0.370 0.371 0.341
Loglikelihood 41,265 41,214 31,986
13
The results of the robustness checks are available on request.
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TABLE 8 Regression results for the survival equation (matched sample)
This table reports the results of panel regressions of the survival equation specified in Equation (3). Explanatory
variables are expressed in logarithms and are lagged by 1 year, and we report time ratios estimated with an AFT
model. All specifications include year, sectoral, regional, and firm juridical form dummies. The sample is
obtained via CEM, as discussed in section 5. Robust standard errors are clustered at the firm level, in
parentheses. *p< 0.1, **p< 0.05, ***p< 0.01.
(1) (2) (3)
Survival Survival Survival
YIC 1.188*** 1.241*** 1.326***
(0.0351) (0.0410) (0.0452)
Ln(CG Fund) 0.975 0.953 0.966
(0.0327) (0.0357) (0.0380)
Ln(CG Fund)*YIC 1.051*** 1.030*** 1.032***
(0.0085) (0.0085) (0.0090)
Ln(Sales) 1.042*** 1.026*** 1.025***
(0.0092) (0.0086) (0.0087)
Ln(Age) 1.100*** 1.112*** 1.124***
(0.0101) (0.0121) (0.0137)
Ln(Age
2
) 1.000*** 1.000***
(0.0000) (0.0000)
Growth of sales 1.324*** 1.321***
(0.0183) (0.0188)
Ln(ROA) 1.003***
(0.0004)
Ln(Equitytoasset ratio) 1.001**
(0.0004)
Ln(Debt ratio) 1.000
(0.0001)
Year fixed effects (FE) Yes Yes Yes
ATECO sector FE Yes Yes Yes
Regional FE Yes Yes Yes
Legal form FE Yes Yes Yes
Observations 19,838 18,982 17,344
Firms 3,741 3,560 3,370
Failed 402 366 351
Chisquared 26,514 23,661 22,240
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with positive R&D investments. The main results of the paper are confirmed: use of the CGF
increases growth and survival more for YICs than for NISs.
6|CONCLUSIONS
This paper studies the impact of measures devoted to relieving financial constraints on the
growth and survival of innovative startups in Italy. Taking advantage of the 2012 law allowing
innovative startups that meet certain eligibility criteria to have simplified and prioritized access
to the CGF for SMEs, we investigate whether use of the fund benefits these startups more than
similar firms in the control group. While several studies have shown that SMEs suffer more
than large firms from financial constraints, which limits their growth rate (for a review, see
Beck & DemirgüçKunt, 2006), we provide indirect evidence that innovative startups face more
problems than other SMEs. Although access to the CGF increases firm growth in general, its
impact is much higher in the case of innovative startups. Moreover, the use of the fund reduces
the hazard rate of innovative firms, but not that of similar SMEs.
Overall, this evidence supports the view that financial constraints are particularly severe for
innovative firms due to the uncertainty that surrounds the innovation process, the high degree
of information asymmetry, and the intangibility of the knowledge assets created by innovative
investments. All these factors make it costly to carry out such investments using external
finance. Although entrepreneurs could resort to internal sources, this can be difficult for start
ups and could limit their growth potential. Moreover, in Italy, as in many other bankbased
continental European countries, the capital market is not yet a valid alternative to bank loans,
and VC represents a small share of firmsfunds. Given the importance of innovation for long
term growth and the financial problems faced by innovative firms, this is an area where
policymakers at the national and European levels can introduce effective measures to mitigate
financial constraints and foster the survival and growth of innovative firms.
Although we have shown that the use of the CGF has helped innovative startups grow and
survive longer, further studies could evaluate the costs and benefits of different policies devoted
to alleviating financial constraints, including those favoring the financing of innovative start
ups in the financial market and the growth of VC. Moreover, given the European Commission's
interest in improving the economic and regulatory situation for young enterprises and the
initiatives adopted in many European countries, similar analyses could be performed in other
economies to determine the extent to which the results for Italy can be generalized to other
bankand marketbased systems.
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How to cite this article: Ferrucci E, Guida R, Meliciani V. Financial constraints and the
growth and survival of innovative startups: An analysis of Italian firms. Eur Financ
Manag. 2020;123. https://doi.org/10.1111/eufm.12277
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