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What do we know about the capital structure of small firms?

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Abstract

Firm data from ten Western European countries is used in this paper to contrast the sources of leverage across small and large, as well as across listed and unlisted firms. Specifically, the explanatory power of firm-specific, country of incorporation institutional, and macroeconomic factors is evaluated. Using data that is more comprehensive in coverage than that used in the existing research the stylized facts of the capital structure literature for large and listed firms is confirmed, but contrasting evidence is obtained for smaller companies. First, the country of incorporation carries much more information for small firms, supporting the idea that small firms are more financially constrained and face non-firm-specific hurdles in their capital structure choice. Second, using two different leverage measures it is shown that the relationship of firm size and tangibility to leverage is robust to the measure used for listed, but not for unlisted firms.

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Notes

  1. This is largely due to the data availability. Stock market-listed firms are required to report annual financial records by law, and usually the accounting standards for those firms across countries are the same.

  2. For example, Harris and Raviv’s (1990) agency cost model shows that leverage is positively related to firm value and liquidation value, and Myers (1977) points out the importance of firm growth opportunities. In Modigliani and Miller’s (1963) trade-off theory, firms trade off the benefits from tax shields of debt with potential bankruptcy costs. Hence, the tax rate is considered an important determinant of firm leverage as is the legal and administrative costs of bankruptcy. Jensen and Meckling (1976) stress the importance of investor protection in a country. Finally, Levy and Hennessy (2007) spell out the importance of domestic macroeconomic factors.

  3. Morck et al. (2000) show that stock prices move together more in poor economies than in rich ones—country factors matter more for firm stock price in poor markets. Campbell et al. (2001) show that in the U.S., firm-specific factors gained importance over market factors during 1967–1997. Hence, it would be interesting to analyse how the importance of firm, industry and country effects has changed in terms of firm market leverage on long time-series data.

  4. See also Harris and Raviv (1991) for a detailed review of theoretical and empirical capital structure studies.

  5. Frank and Goyal (2009) use Compustat data.

  6. MacKay and Phillips (2005) show that not only industry dummies but also firm position in its industry matters (e.g. proximity to median industry capital/labor ratio). Frank and Goyal (2009) show that omitting industry from the leverage regression turns many other firm characteristics significant. Hence, apparently industry captures a number of different effects.

  7. They use three macroeconomic variables—2-year aggregate domestic non-financial corporation profit growth, 2-year equity market returns and commercial paper spreads—to describe overall tendencies in the market.

  8. Frank and Goyal (2009) use only U.S. data and therefore only observe the time variation of country variables while not observing cross-country variation.

  9. Rajan and Zingales (1995) divide the leverage measures into two groups. The first group includes measures that evaluate the ratio of debt to assets, where the definitions of debt and assets vary across different measures. The debt can be measured as broadly as total liabilities. The second group includes measures that evaluate interest coverage.

  10. For listed firms we computed both book and market leverage ratios. We estimated exactly the same leverage specification as Rajan and Zingales (1995) and received similar estimates (the results of those estimates are available upon request). Hence, we conclude that our data quality is comparable to the data used in the existing capital structure studies.

  11. The ANOVA estimation finds the total sum of squares of the dependent variable (SST), which is decomposed to the sum of squares of the model (SSR) and the sum of squares of the error term (SSE). Note that the ratio SSR/SST is the R 2 in the OLS regression. Also, ANOVA calculates for each explanatory variable the partial sum of squares.

  12. Class 1 firms have total assets smaller than $1 million. Class 2 firms have total assets between $1 and 2 million. Class 3 firms have total assets between $2 and 5 million. Class 4 firms have total assets between $5 to 50 million and finally, Class 5 firms have total assets above $50 million. The median number of employees in each size group is 10, 15, 22, 55 and 391, respectively. Given the European Commission definition of SME’s (less than 250 employees and 27 million Euro in total assets) the four smallest groups roughly correspond to SME’s in our study.

  13. Amadeus data cover firms from 42 European countries.

  14. The financial intermediation sector has a specific balance sheet structure. It is standard to disregard these firms in capital structure studies.

  15. The outliers compile less than 2 % of total sample.

  16. For more details about “Enterprises in Europe” see Kumar et al. (2002).

  17. Note that firms are divided into size classes based on the number of employees. Only two-thirds of firms in the data report employment. Therefore, the coverage figures presented should be taken as proxies for the coverage of a full sample.

  18. Small- and medium-sized German firms are not legally forced to disclose the financial data (Desai et al. 2003).

  19. The representativeness of the Amadeus data is also presented in Gomez-Salvador et al. (2004). They find that firms in the Amadeus data cover on average 25 % of the employment in National Labor Force Surveys. Different industries are well represented in Amadeus data. Gomez-Salvador et al. (2004) conclude that the industry coverage is similar across countries and stable over time.

  20. See also a discussion about French and Italian firms’ balance sheet structure in Giannetti (2003, pp. 190–191), who used the Amadeus sample as well.

  21. Except for unlisted French and Italian firms’ narrow leverage. Those unlisted firms use very little debt finance.

  22. See Strebulaev (2007) for details about dynamic capital structure.

  23. Firm and country variables are not introduced here since they do not change the pattern of results.

  24. All regression results are robust if, instead of the logarithm of total assets, the discrete size variables—the five size groups exploited in the ANOVA section—are used.

  25. The results of year-specific and country-specific regressions are available on request.

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Acknowledgments

I am indebted to Ron Anderson and Štěpán Jurajda for many valuable discussions and comments. I thank Jan Svejnar, two anonymous referees and the editor for their helpful comments. The paper was written while I was visiting the William Davidson Institute (WDI), University of Michigan Business School. I would like to thank WDI for their hospitality and data access. I gratefully acknowledge financial support from CERGE-EI and partial support from Charles University research grant GAUK 348/2005.

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  1. Management School, Queen’s University Belfast, Belfast, BT9 5EE, UK

    Karin Jõeveer

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Correspondence to Karin Jõeveer.

Appendix

Appendix

See Tables 11 and 12.

Table 11 Representativeness of data, Amadeus data versus “Enterprises in Europe”
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Table 12 Cross-country capital structure studies
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Jõeveer, K. What do we know about the capital structure of small firms?. Small Bus Econ 41, 479–501 (2013). https://doi.org/10.1007/s11187-012-9440-1

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