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Exploring the contribution of micro firms to innovation: does competition matter?

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Abstract

With a special focus on firms with fewer than 10 employees, we examine how small businesses participate in innovation and how perceived competition affects their innovative behavior. Statistics from a large sample of European micro-, small-, and medium-sized enterprises document a relevant share of innovative firms, including micro ones. We empirically explore the relationship between competition and the likelihood of being innovative, the degree of complexity of the innovation strategy, and its frequency. Estimates provide evidence of an inverted-U-shaped relationship, whereby innovation initially increases with competition and then it slightly declines. The results hold for all firms, regardless of their size, but the negative effect seems to be more marked for smaller firms. Competition shows a stronger relationship with technical and external innovation. By including micro firms, this paper contributes to the understanding of innovative patterns and activities in firms of all size.

Plain English Summary

Are micro firms marginal players in innovation? It seems not. Exploring a large sample of small European businesses, we find a non-negligible share of innovative firms with fewer than 10 employees. How does competition affect their innovative behavior? We find that as competition increases, innovation also increases if the initial level of competition is low, but innovation declines if the initial level of competition is high. The results hold for all firms regardless of size. Our findings have two important implications for research and policy. First, micro firms must be considered as significant players in innovation and more comprehensive innovation data should be collected from them. Second, competition fosters small businesses’ innovation, but excessive competition can hamper it. Thus, policies aimed at promoting well-balanced competitive markets are crucial if micro firms are to exploit their full innovation potential.

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Fig. 1
Fig. 2

Notes: Authors’ calculations on SAFE data. The figure reports the weighted percentage of firms that introduced at least one type of innovation (innovative) and product, process, organization, or marketing innovation. It does so by size (a), sector (b), and both sector and size (c). The statistics refer to the five Common round waves from 2014 to 2018

Fig. 3

Notes: Authors’ calculations on SAFE data. The figure reports the weighted percentages of MSMEs that introduced at least one innovation (a) and that introduced a product (b), process (c), organization (d), and marketing (e) type in each country. The statistics refer to the five Common round waves from 2014 to 2018

Fig. 4

Notes: Authors’ calculations on SAFE data. The figure reports the weighted percentages of firms with fewer than 10 employees that introduced at least one innovation (a) and that introduced a product (b), process (c), organizational (d) and marketing (e) type in each country. The statistics refer to the five Common round waves from 2014 to 2018

Fig. 5

Notes: Authors’ calculations on SAFE data. The figure reports weighted statistics about firms’ perceived level of competition: the full sample and subsamples distributions (a–d), the weighted mean by size (e), sector (f), by sector and size (g) and by country (h and i). The statistics refer to the five Common round waves from 2014 to 2018

Fig. 6

Notes: Authors’ calculations on SAFE data. The binned scatterplot in panel (a) reports the percentage of innovative firms for different level of competition: Low (values 1, 2, 3), Medium (values 4, 5, 6, 7), and High (values 8, 9, 10). Panel (b) does the same for the separate subsamples of micro, small, and medium firms. The statistics refer to the five Common round waves from 2014 to 2018

Fig. 7

Notes: Authors’ calculations on SAFE data. The figure plots the frequency of innovation over competition. The statistics refer to the five Common round waves from 2014 to 2018

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Notes

  1. For instance, the Community Innovation Survey (CIS) excludes companies with fewer than 10 employees.

  2. For a deeper overview of the methodological information on the survey see https://www.ecb.europa.eu/stats/ecb_surveys/safe/html/index.en.html.

  3. Wave 11 (reference period April–September 2014), wave 13 (reference period April–September 2015), wave 15 (reference period April-September 2016), wave 17 (reference period April–September 2017), and wave 19 (reference period April–September 2018). We include the UK since it was still part of the EU during this period.

  4. These options correspond to the four types of innovations defined by the Oslo Manual (OECD, 2018).

  5. In CIS, an innovation-active enterprise is one that has had innovation activities during the period under review. Innovation activities are all scientific, technological, organizational, financial, and commercial steps that actually, or are intended to, lead to the implementation of innovations. An innovation is defined as a new or significantly improved product (good or service) introduced to the market, or the introduction within an enterprise of a new or significantly improved process. For more details we refer to https://ec.europa.eu/eurostat/web/products-eurostat-news/-/ddn-20210115-2.

  6. Given the reference period of CIS 2018 (2016–2018), to make things comparable we compute the SAFE country percentages of innovative firms considering the 2016–2018 period only. Moreover, since the CIS excludes enterprises with fewer than 10 employees, we compute the SAFE country averages excluding micro firms.

  7. See Table 11 in the Appendix for further details.

  8. See, for instance, Rammer and Schubert (2018), the KfW SME Innovation Report (2019), and the Germany-SBA Fact Sheet 2019 (European Commission). The OECD (2019) SME and Entrepreneurship Outlook states that German SMEs with fewer than 250 employees spend less on R&D than the OECD median.

  9. Again, the penultimate place of Germany might seem unusual. In this regard, Baumann and Kritikos (2016) shows that around 50 percent of German micro firms engaged in innovative activities between 2005 and 2012. If we combine this information with the reported decline in the innovativeness rate of German SMEs, our value (43%) looks less astonishing. See also note 8.

  10. Figures 8, 9, and 10 in the Appendix provide more details in terms of MSMEs innovative behavior according to the size and sector of activity for each of the 28 countries.

  11. See Table 12 in the Appendix for descriptive statistics.

  12. We also compared the logit and probit estimates of the full sample model with all regressors including time, country, and sector fixed effects. Since the former shows a lightly higher log-pseudolikelihood, we preferred to use logit. Results are available in Table OA1 in the Online Appendix.

  13. Including a wide set of individual controls and dummies should mitigate potential omitted variable bias. Nevertheless, they can act as bad controls if they are determined simultaneously with our measure of innovativeness (see Angrist and Pischke, 2008). Thus, we estimate regression (1) with and without those controls.

  14. A firm is classified as a panel if it participated in the survey at least twice, though not necessarily in consecutive waves. A one-period lag may not then correspond to a one year lag.

  15. See Table 14 in the Appendix for details of the distribution among countries for the observations.

  16. We run these checks considering the full sample unbalanced panel.

  17. Cyprus received financial assistance from the European Stability Mechanism (ESM) of €6.3 billion over the 2013–2015 period. Greece obtained a total of €245.7 billion over the 2010–2018 period from three different programs: €52.9 billion from bilateral EU and IMF loans (2010–2012), €130.9 billion from the European Financial Stability Facility (EFSF) (2012–2015), and €61.9 billion from the ESM (2015–2018). See https://www.esm.europa.eu/financial-assistance for further details about the ESM-EFSF financial assistance programs. Figure 12 in the Appendix shows that the introduction of the assistance programs (2013 in Cyprus and 2010 in Greece) corresponds to the beginning of an upward trend in R&D expenditures in both countries.

  18. Tang (2006) finds a similar correlation coefficient (0.40) between product and process innovation using a sample of Canadian firms.

  19. For innovation complexity, since the normalized index has values between 0 and 1, we replicate the estimation using a fractional logistic regression model. We also develop a Poisson regression with a count dependent variable indicating the number of types of innovation a firm has introduced. The inverted-U relationship is confirmed both for the full sample and for micro firms. Results are available in Table OA2 in the Online Appendix.

  20. See Table 13 in the Appendix for a more detailed definition.

  21. As for innovation complexity, we replicate the estimation using both fractional and Poisson regressions. For the latter, the count dependent variable is used. The findings reported in Table 9 are confirmed. Results are available in Table OA3 in the Online Appendix.

  22. We check this hypothesis by doing regressions considering small and medium firms only. Statistically significant quadratic estimates emerge indeed for medium firms only. Results are available in Table OA4 in the Online Appendix.

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Acknowledgements

I am particularly grateful to Marcus Dejardin, Eric Toulemonde, Vincenzo Verardi and two anonymous referees for their insightful comments. I also thank seminar participants at BURENet Workshop, Université de Namur, 7th Greater Region PhD Workshop, and 18th ISS Conference for useful suggestions.

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    Luca Farè

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Appendix. Additional figures and tables

Appendix. Additional figures and tables

Correlation between innovation indicators

In Table 11, we correlate the SAFE country percentages of innovative firms with three alternative measures derived from the Community Innovation Survey (CIS), covering all the EU27 countries (excluding the UK). From the CIS 2018, as reported in Eurostat, we select results for the country percentages during 2016 and 2018 of (i) firms with innovation activities; (ii) firms with research and development (R&D) activities; and (iii) firm turnover from new or significantly improved products. Columns (1) to (3) in panel (a) show positive and statistically significant correlations between our measure of innovation and the CIS measures. In panel (b) we do the same exercise, but excluding Romania (where the SAFE over-estimation of innovative firms is higher); we find that the correlations look stronger with higher significance levels. Overall, despite overestimating firms’ innovative activity in some countries, our measure moves in the same direction as the considered alternative indicators.

Table 10 Observations by country — Full sample
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Table 11 Innovation measures correlations
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Table 12 Descriptive statistics
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Table 13 Main variables definitions
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Fig. 8
figure 8

Notes: Authors’ calculations on SAFE data. The figure reports the weighted percentage of firms that introduced at least one types of innovation (innovative) and product, process, organization, marketing innovation in each country. The statistics refer to the five Common round waves from 2014 to 2018

Innovative firms by country.

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Fig. 9
figure 9

Notes: Authors’ calculations on SAFE data. The figure reports the weighted percentage of innovative firms by size in each country. The statistics refer to the five Common round waves from 2014 to 2018

Innovative firms by country and size.

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Fig. 10
figure 10

Notes: Authors’ calculations on SAFE data. The figure reports the weighted percentage of innovative firms by sector in each country. The statistics refer to the five Common round waves from 2014 to 2018

Innovative firms by country and sector.

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Fig. 11
figure 11

Notes: Authors’ calculations on SAFE data. The figure reports the weighted average level of innovation complexity by sector (a), size (b), sector and size, (c) and country (d). The statistics refer to the five Common round waves from 2014 to 2018

Innovation complexity by firm size, sector, and country.

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Table 14 Observations by country — Panel component
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Fig. 12
figure 12

World Bank

R&D expenditures (% of GDP).

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Farè, L. Exploring the contribution of micro firms to innovation: does competition matter?. Small Bus Econ 59, 1081–1113 (2022). https://doi.org/10.1007/s11187-021-00575-5

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