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SME access to market-based finance across Eurozone countries

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Abstract

This paper provides an in-depth analysis of small and medium enterprise (SME) access to capital markets across Eurozone countries. First, we determine which factors—at firm and country level—influence the likelihood of SME access to market-based finance. Second, we construct an index of market suitability to indicate the percentage of firms potentially fit for market-based finance. Our results suggest that while several Eurozone countries have realised SMEs’ ‘potential’ for capital market financing, a large number of firms fit for market-based finance remain unexploited. We also find that overall business conditions—measured by GDP growth, the development degree of domestic financial markets and the quality of the legal and judicial enforcement system—considerably influence a firm’s market suitability. In the studied period (2009–2014), macro-economic and institutional factors tended to reduce the likelihood of SMEs accessing market-based finance in most countries in our sample.

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Notes

  1. For example, Invest Europe (2018) shows that, in the last four years, European private-equity funds have raised over EUR 240 billion to invest in companies in Europe, representing more than double the amount raised in the four years following the financial crisis. Meanwhile, the private placement channel of the bond market has grown rapidly in Europe, from EUR 4.7 billion in 2014 to EUR 8.4 billion in 2015 (EC 2017b, p. 33).

  2. Using tax identification codes, each survey reply is matched with the Bureau van Dijk Amadeus (hereafter simply ‘Amadeus’) dataset, which includes information on firms’ balance sheet and profit and loss accounts.

  3. Firms that operate in countries with weak creditor protection and low-quality judicial enforcement systems may even face a competitive disadvantage relative to firms located in countries with strong protection and high-quality enforcement (Moro et al. 2016).

  4. SAFE firms are exclusively non-financial corporations. Size is based on number of employees: micro firms are defined as those with less than 10 employees; small firms as those with 10–49 employees; medium-sized firms as those with 50–249 employees; and large firms as those with 250 or more employees.

  5. More detailed information about SAFE is available at: https://www.ecb.europa.eu/stats/ecb_surveys/safe/html/index.en.html. Detailed information on the SAFE weighting methodology is available at: https://www.ecb.europa.eu/stats/pdf/surveys/sme/methodological_information_survey_and_user_guide.pdf?3193098a993584e5bd27d9c68d13bd36, p.11.

  6. Amadeus-sourced financial statement data include annual financial reports from 2009 until 2014.

  7. Some changes in the questionnaire may have caused a break in the series for German firms, as the questions related to equity changed from ‘Kapitalbeteiligung in Ihrer Firma’ (wave 2) to ‘Eigenkapital’ (wave 3) and then to ‘Anteilskapital’ (wave 4). For this reason, percentages in wave 3 are calculated as averages of those in waves 2 and 4.

  8. Our Amadeus data was collected following the approach of Kalemli-Ozcan et al. (2015). It covers 75–80% of economic activity reported in Eurostat.

  9. This methodology has often been employed in the literature, starting from Pagano et al. (1998).

  10. Since some firm-specific variables could be persistent, we check the presence of endogeneity by running the same analyses considering the following variables at time t−2 instead of t−1: leverage, turnover growth, current ratio, fixed asset growth and size. The results are similar; the only difference concerns the current ratio which becomes not significant.

  11. The variable is based on question Q4 of the SAFE questionnaire (ECB 2016).

  12. Average turnover growth is calculated as the difference between the turnover value in period t and the average value of variable turnover in periods t−1 and t−2, scaled by the average value in periods t−1 and t−2. Fixed asset growth is calculated as the difference between the value of fixed assets in period t and the average value of variable fixed assets in periods t−1 and t−2, scaled by the average value in periods t−1 and t−2.

  13. The Rule of Law Index is sourced from the World Bank’s World Governance Indicators and captures perceptions of the extent to which agents have confidence in and abide by the rules of society, particularly the quality of contract enforcement, property rights and the courts, as well as the likelihood of crime and violence.

  14. The Property Rights Index is sourced from the Heritage Foundation’s Index of Economic Freedom. This variable measures individuals’ ability to accumulate private property, secured by clear laws that are fully enforced by the state. Hence, it measures the degree to which a country’s laws protect private property rights and the degree to which its government enforces those laws. It also assesses the likelihood that private property will be expropriated. The more certain the legal protection of property, the higher a country’s score (between 0 and 100).

  15. We source data from the World Bank’s Doing Business dataset. A procedural step is defined as any interaction, required by law or commonly used in practice, between the parties or between them and a judge or court officer. Other procedural steps, internal to the court or between the parties and their counsel, are also counted. Procedural steps include those to file and serve a case, to assign a case to a judge, for trial and judgement, and as necessary to enforce a judgement.

  16. We again use the World Bank’s Doing Business dataset, using the average time needed to resolve a dispute in calendar days. The time is counted from the moment the plaintiff decides to file a lawsuit in court until payment. It includes both the days when actions occur and the waiting periods between them.

  17. The estimations reported in the paper were performed using robust standard errors. To verify the robustness of results, we also performed the same estimations clustering standard errors at country level. The results are not reported here but are available on request from the authors.

  18. We tested for the presence of country differences using a chi-square test. First, we considered a restricted model in which we only included firm-specific variables, without considering the presence of potential differences across countries. Next we analysed an unrestricted model in which country dummies were inserted to account for possible differences between countries. The chi-square test verified the null hypothesis that all countries’ coefficients were equal. This result allows us to reject the null hypothesis (chi-square (6) = 112 Prob > χ2 = 0.000). In these circumstances, we adopt the unrestricted model with country dummies, taking account of country differences. We also fit a mixed effect binomial model with a random intercept at the country level. A likelihood ratio test comparing this random intercept model with the one-level binomial regression model favours the former, indicating that there is significant variation in access to market-based financial instruments across countries.

  19. Excessive dependence on short-term bank financing by distressed-country SMEs has increased, particularly since 2011 in the aftermath of the Eurozone sovereign debt crisis.

  20. Because our analysis categorises both new equity and bond financing as market-based funding, it is not surprising that creditworthiness indicators, such as the current ratio, appear to be relevant for prospective bond investors.

  21. We use the specification with sample weights to compute our novel MSI, following the methodology discussed for the restricted model with only firm-specific variables.

  22. These tests were performed on the model specification with country-specific variables and without sample weights (column 4 of Table 6). The percentage of cases correctly classified was as high as 93.83%; the predictive power, measured by the area under the ROC curve, reached a value of 0.674, indicating that the model has good predictive power. Goodness of fit was assessed using Pearson’s chi-square and Hosmer-Lemeshow tests. Both tests confirmed that the model fits the data reasonably well, with Prob > χ2 = 0.314 and Prob > χ2 = 0.608, respectively.

  23. We ran the same analysis adding also country fixed effects to test for cultural and institutional factors that we could not consider with country-specific variables. The results for firm-specific variables are quite similar to those in the previous analysis, whereas many country/institutional variables become non-significant.

  24. To compute the predicted SAFE score, we first consider the coefficients of the estimated probit, as specified in column 2, panel A of Table 6, as this regression also considers country fixed effects.

  25. In Table 9, panel A, the MSI is computed by employing the model specification of column 1 in Table 6 (firm-specific variables only; no country fixed effects).

  26. Germany and Belgium were in the economically strongest phases of the business cycle in our sample period, with an average GDP growth of 1.6% and 1% respectively.

  27. The CMU Action Plan (European Commission 2016a, 2016b) identifies six areas of intervention, representing the objectives of the CMU project: financing for innovation, start-ups and non-listed companies; making it easier for companies to raise funds on capital markets; investing for long term, infrastructure and sustainable investment; fostering retail and institutional investment; leveraging banking capacity to support the wider economy; facilitating cross-border investing.

  28. See also CMU Action Plan objective n. 5: leveraging banking capacity to support the wider economy (European Commission 2016a, 2016b, 2017b).

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Acknowledgements

This paper was developed through CEPR’s Restarting European Long-Term Investment Finance (RELTIF) Programme, which is funded by Emittenti Titoli. We would also like to thank the participants in the RELTIF Authors’ Meetings and various seminars and conferences for their comments. In particular, we are grateful to Colin Mayer, Stefano Micossi, Marco Pagano, Vikrant Vig, Ioannis Ganoulis, Andrew Ellul, Flavio Bazzana, Christian Masiak and Frank Lang for helpful discussions and comments. All errors and omissions remain our own. Earlier versions of the paper were under the title, ‘Suitable or non-suitable? An investigation of Eurozone SME access to market-based finance’.

Funding

The authors are grateful to the CEPR RELTIF Research Programme for the financial support of the research used in this paper.

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Authors and Affiliations

  1. Department of Business and Law, University of Milano-Bicocca, Via Bicocca degli Arcimboldi 8, 20126, Milan, Italy

    Paola Bongini, Emanuele Rossi & Monica Rossolini

  2. European Central Bank, Sonnemannstraße 20, 60314, Frankfurt am Main, Germany

    Annalisa Ferrando

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  1. Paola Bongini
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  2. Annalisa Ferrando
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  4. Monica Rossolini
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Correspondence to Emanuele Rossi.

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Table 10 Description of the variables
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Bongini, P., Ferrando, A., Rossi, E. et al. SME access to market-based finance across Eurozone countries. Small Bus Econ 56, 1667–1697 (2021). https://doi.org/10.1007/s11187-019-00285-z

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