Abstract
We analyse the financing of R&D activity in Italy, using data at firm level that cover a wide range of sources of financing, such as internal funds, bank loans and access to financial markets. Our analysis shows the importance of relationship lending in fostering innovative activities. The relation between innovative firms and their main bank tends to be relatively long lasting, permitting the bank to reduce information asymmetry, while low credit concentration is a common feature among these firms, presumably allowing them to attenuate hold-up problems. Nonetheless, firms that rely on bonds and outside equity financing tend to have a higher propensity to invest more in R&D, suggesting that relationship lending is only a partial substitute for access to stock and bond markets.
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Notes
As underlined by Audretsch et al. (2016), in the past, entrepreneurship and finance were usually considered as separate fields: on the one hand, entrepreneurial finance was primarily referred to early stage financing mechanisms, often supplied by the entrepreneur’s personal wealth or network; on the other hand, corporate finance literature often focused on publicly traded firms. However, the evolution of both the real economy and academic research has put in contact the two fields, given that agency problems and information asymmetries were recognized crucial for both of them. The difference mainly rests on the contractual solutions adopted to tackle these issues (the role of venture capital being more relevant among entrepreneurial firms than in large, established corporations). Therefore evidence gathered analysing the financing of innovation in established firms may inform the field of entrepreneurial finance: as highlighted by the above-mentioned authors, “new ways to finance entrepreneurial ventures may emerge at the crossroads between private and public equity”. See also Cumming and Vismara (2016).
In their review, Kerr and Nanda (2014) summarize the reasons why financing R&D projects should be distinct from financing other types of projects: (i) the innovation process is inherently uncertain; (ii) the fact that the return from the innovation process is extremely skewed and standard ways of evaluating projects are therefore very difficult; (iii) the innovator knows more about the project than the financier; (iv) firms engaged in innovation have a high percentage of intangible assets, and innovation is therefore an activity that cannot be easily collateralized.
Even if the relevance of the internal sources is consistent with the predictions of the Pecking Order Theory (POT), the POT is not fully suited to describe the financing process of innovations. In fact, while the POT may be thus synthetized: (i) internal sources, (ii) debt, (iii) new equity, in order to finance innovations, the recourse to stock markets should be preferable to debt finance and, in particular, to bank finance.
Gorodnichenko and Schnitzer (2010), using a broad sample of firms located in Eastern Europe and Commonwealth of Independent States, find that financial constraints restrain the ability of domestically owned firms to innovate and export and hence to catch up to the technological frontiers. For Europe, other analyses (Mohnen and Roller 2005; Savignac 2006; Mohnen et al. 2008) confirm that insufficient finance inhibits firm innovativeness. Magri (2007) emphasizes the difficulties encountered by small innovative firms. For a general review, see also Hall and Lerner (2009).
See also the discussion in Lerner et al. (2011).
However, the role of business angels, venture capital and crowdfunding is scarce in Italy, when compared to other industrialized Countries.
Based on the formulation of the question in the survey, R&D expenditures include services, both internally made and bought from external suppliers (make and buy); expenditures for vocational training are not included.
With discrete dependent variables, the standard linear probability model (LPM) estimated by OLS is inefficient: heteroscedasticity determines biased standard errors and erroneous hypothesis testing; furthermore, LPM can bring to predicted probabilities outside the 0–1 range.
Since the dependent variable is in logs, the coefficients represent semi-elasticities: a unity increase in the cash flow variable raises the dependent variable by 0.45 (45 %). We consider a standard deviation in the regressor, which is the classical variation used in the literature to perform sensibility tests. The result is equal to one standard deviation times the coefficient and percent, which corresponds to: 0.12 × 0.46 × 100 = 5.52.
In this case, we consider a discrete change of the dummy variable from 0 to 1, and the impact corresponds to the marginal effect reported in the table.
While our analysis focuses on the role of banks and relationship lending, it is more demanding to discuss the equilibrium amount of financing from inside and outside sources; in fact, these quantities are determined by both the supply and the demand of different types of sources (Cosh et al. 2009). It is hard to believe that firms can freely choose between alternative capital providers: in the presence of obstacles due to information asymmetries, entrepreneurs may in fact simply seek capital where it is most plentiful. Cosh et al. (2009) find that rejection rates are lower in credit markets than from other sources of capital, thus suggesting that financing choices owe to supply considerations as well as demand considerations. As acknowledged by Robb and Robinson (2014), it is challenging to separate supply and demand in the absence of some quasi-experiment.
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The authors wish to thank Antonio Accetturo, Raffaello Bronzini, Paola Brighi, Andrea Caggese, Luigi Cannari, Francesca Lotti, Massimo Omiccioli, Silvia Magri, Marcello Pagnini, Alessandro Sembenelli, Giuseppe Torluccio, Salvatore Torrisi, Alberto Zazzaro, the participants at seminars held at the Bank of Italy and at the University of Bologna, and two anonymous referees for helpful comments. The authors alone are responsible for any errors. The views expressed in this paper do not necessarily reflect those of the Bank of Italy.
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Micucci, G., Rossi, P. Financing R&D investments: an analysis on Italian manufacturing firms and their lending banks. Econ Polit Ind 44, 23–49 (2017). https://doi.org/10.1007/s40812-016-0056-3
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DOI: https://doi.org/10.1007/s40812-016-0056-3