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Financial constraints to investing in intangibles: Do innovative and non-innovative firms differ?

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Abstract

This paper investigates the extent to which financial constraints on investments in intangible activities differ with respect to the kind of intangible and to the firms’ innovative status. Through an original pseudo-panel extension of a recent European Innobarometer survey, we are capable to address these research questions by attenuating the risks of reverse causality and simultaneity bias and to obtain interesting new results. Financial barriers significantly hamper the firms’ investments in intangibles with respect to R&D, design, software, and organisation or business process improvements. With respect to branding and reputation, and training, instead, financial constraints do not emerge to hinder the relative investments. Furthermore, while innovative firms tend to invest more in intangibles, the hampering role of financial barriers does not seem to differ between innovative and non-innovative firms. Financial barriers reduce firms’ investments in intangibles selectively, but the strength of this effect is the same in deterring and in restraining their possible innovative use by non-innovative and innovative firms, respectively.

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Notes

  1. Innovation barriers can also be non-financial, and rather refer to market conditions or institutional factors. For a recent review of the literature on the barriers to radical innovations, see Sandberg and Aarikka-Stenroos (2014). For the methodological problems in “selecting” rather than “deducing” different barriers and in confusing their “underlying reasons”, see Mirow et al. (2008).

  2. Just to make an example, the obstacles that market competition can pose to a successful innovation comprehend problems of appropriability, diffusion, and standardization (to mention a few), which do not reduce to the obstacles posed by market competition/concentration to the firm’s capacity of investing in intangibles per se.

  3. The majority of these studies make use of the Community Innovation Survey (in particular, from the CIS-2010 onwards) and refer to financial barriers either in a narrow meaning, in terms of available (internal/external) resources, or in a broad one, as part of cost barriers (including costs of finance, other innovation costs and risks) (on the correlation among the two, see Mohnen and Rosa 2001). Using the same source, the firms’ engagement in innovation is captured by referring to a basket of “innovation activities”, which encompasses R&D, software, training, design, and marketing. With respect to the same activities, a risk of selection-bias (Savignac 2008is avoided by referring to “potential innovators” and ruling out those firms that have a nil engagement in them and thus implicitly signal no interest in innovating.

  4. The two waves of the survey present very close sample characteristics (available at: https://data.europa.eu/euodp/it/data/dataset/S2054_415_ENG).

  5. The turnover shares of each type of intangible investments are collected in the following four categories: equal to 0%, below 1%, in-between 1 and 5%, and above 5%.

  6. The computer-assisted telephone interviewing (CAT I) is one of the most popular survey techniques, carried out through a telephone call with the interviewer generally following a script provided by a software program.

  7. See https://www.ecb.europa.eu/stats/ecb_surveys/safe/html/index.en.html.

  8. Although the adjective “innovative” has been dropped from the survey question posed to non-innovators, descriptive statistics (available from the authors upon request) reveal that innovators and non-innovators did not statistically differ in reporting to the question, suggesting that the two have meant to be asked about the same kind of financial shortage.

  9. The macro-sectors are defined according to the NACE nomenclature: manufacturing = category C, retail = category G, services = categories H/I/J/K/L/M/N, industry = categories D/E/F. As for the employees, these are available in the Innobarometer in the following four classes: ' < 10′, '10–49′, '50–249′, and '250 + '.

  10. In deciding the number of subgroups, a crucial trade-off emerges. A higher number increases the between group heterogeneity, but also decreases the average number of observations per group, thus leading to less precise estimates of the group statistics. As recommended by the pseudo-panel literature, groups should have at least 30 observations each. Consistently with this criterion, our smaller group contains 36 firms and only 4 groups have less than 100 observations (see Table 1 in Sect. 4 for further info).

  11. In brief, the higher the share of firms in a certain group at t-1 that appears to be financially supported in their innovation or winner of a public procurement, the higher the probability that a focal firm i of the correspondent group at t will also be financially supported or winning a public procurement.

  12. A t-test suggests that there is no significant difference between the means of the two groups (p-val 0.212).

  13. Similar results about the tests, available from the authors upon request, are obtained for the other intangibles, with respect to which the role of financial barriers appears consistent with that we will comment in the next Sect. 4.

  14. We have also estimated our focal relationship with a multivariate probit model, which accounts for the possible correlation between investments in different intangibles, but do not provide a suitable framework for an instrumental variable approach and also makes the construction and interpretation of the marginal effects particularly cumbersome. Results, available from the authors upon request, are generally consistent with the ones reported in the next Section.

  15. The hypothesis of exogeneity of financial barriers is supported by the data only for reputation and branding (and for training in the full specification reported in Tables 4 and A5). Therefore, we have also tried to estimate the same equations for these intangibles without instrumenting the financial barriers. The coefficients attached to financial barriers, innovation and their interaction are not different from those reported in the tables. Results are available upon request.

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Authors and Affiliations

  1. Social Sciences, Gran Sasso Science Institute (GSSI), Viale Francesco Crispi, 7, 67100, L’Aquila, Italy

    Sandro Montresor

  2. Department of Economics, Roma Tre University, Via Silvio D’Amico, 77, 00145, Rome, Italy

    Antonio Vezzani

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Appendix

Appendix

See Tables 6, 7, 8, 9, 10, and 11.

Table 6 Description of variables and of the relative survey questions
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Table 7 Descriptive statistics of the sample firms and by their innovative status
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Table 8 Correlations across variables
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Table 9 Investing in R&D and financial barriers: IV reg linear probability model
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Table 10 Investing in intangibles and (instrumented) financial barriers: second-step probit estimates with the innovation dummy (Eq. 1)
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Table 11 Investing in intangibles and (instrumented) financial barriers: second-step probit estimates with innovation dummy and innovation interaction (Eq. 1)
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Montresor, S., Vezzani, A. Financial constraints to investing in intangibles: Do innovative and non-innovative firms differ?. J Technol Transf 47, 1–32 (2022). https://doi.org/10.1007/s10961-020-09842-1

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