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Gibrat’s law and the learning process

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Abstract

This paper discusses the speed of convergence of small firms in the context of Gibrat’s law for manufacturing and service industries. We analyze unbalanced panel data from 139,922 firms belonging to the Spanish manufacturing and service industries between 1994 and 2002. The results show that small firms grow faster than large firms. The evidence supports the proposition that market structure affects the capacity of firms to grow. In particular, small firms in service industries do not grow as quickly as small firms in manufacturing industries. This is mainly due to the lower medium efficient size (MES) in the service industries diminishing the incentives to grow and the positive effect of MES on the speed of convergence.

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Notes

  1. Between 1994 and 2002, the number of SMEs (firms with more than 1 employee and fewer than 20 employees) increased by 4% in the manufacturing industries and by 23% in the service industries (Source: Directorio Central de Empresas).

  2. Source: Encuesta Anual de Servicios and Encuesta Industrial (Spanish Statistics Institute).

  3. Spanish empirical evidence shows that firms in the service sector are more likely to fail: 58.86% of service firms created in 1994 had failed by the end of 2005. This may be related to the lower sunk costs and more short-term strategies for facing unemployment situations in the service sector.

  4. These authors applied a chi-square test and the equation of the persistence of firm growth. With the chi-square test, Gibrat’s law was accepted for firms operating above the MES. With the equation of persistence of firm growth, Gibrat’s law was accepted in 11 out of 15 cases. However, their results varied according to year and business group.

  5. Oliveira and Fortunato (2004a, b) used unbalanced panel data with 8,072 firms, but with only 419 firms from the service sectors.

  6. As the dependent variable they used current size and as the independent variable they used previous size. In this case, their coefficient was <1.

  7. NACE (from the French abbreviation for nomenclature statistique des activités économiques dans la Communauté Européenne) is a general industrial classification of economic activities within the European Union. This classification is officially recognized by the Accounting Economic System (National Institute of Statistics).

  8. We excluded sectors 16 (industries related to tobacco products) and 23 (industries related to coke, refined petroleum products, and nuclear fuel) because there are few firms in these traditional monopolistic industries.

  9. The original database had 162,208 firms: 80% of the firms survived until 2002. The percentages of firms surviving until 2002 were 68% of those born in 1994, 69% of those born in 1995, 70% of those born in 1996, 74% of those born in 1997, 77% of those born in 1998, 82% of those born in 1999, and 91% of those born in 2000. This evidence is in concordance with previous empirical literature (Segarra et al. 2002; Segarra and Teruel 2007).

  10. Calculations are available upon request.

  11. The income per worker includes the sales per worker plus other incomes obtained by the firm.

  12. Evans (1987a, b), Hall (1987) and Dunne et al. (1989) differentiated between a latent firm growth equation and a real firm growth equation. The former includes surviving and nonsurviving firms and would raise a sample selection bias arising from exit. The latter estimates the growth of surviving firms.

  13. The Hausman test does not accept the null hypothesis about the absence of correlation between individual effects and explanatory variables. Furthermore, the Breusch and Pagan (B and P) test does not accept the null hypothesis of homoskedasticity in the error distribution, conditional on a set of variables which are presumed to influence the error variance.

  14. Calculations are available upon request.

  15. The F and chi-squared test are the values reported by the fixed effects and random effects estimations. They test whether the model is significant or not. The tests do not accept the null hypothesis about the nonsignificance of the model.

  16. For example, in 2006 the capital per worker intensity in the manufacturing industries was equal to 15.113 euros and in the service industries was equal to 9.376 euros (Spanish Statistical Institute).

  17. The estimations of the effect of the effect of capital per worker intensity and the microfirms on individual sectors are available upon request.

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Acknowledgments

This research was partially funded by the Xarxa de Referència d’R+D+I en Economia Aplicada and CICYT (SEJ2004-07824/ECON) entitled “La dinámica empresarial: entornos urbanos, mercado de trabajo y modelización econométrica”. I also would like to acknowledge helpful and supportive comments from Agustí Segarra, seminar participants at X Encuentro de Economía Aplicada (Logroño, Spain), and two anonymous referees for useful comments and suggestions. The usual disclaimer applies.

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  1. Rovira i Virgili University, Tarragona, Spain

    Mercedes Teruel-Carrizosa

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  1. Mercedes Teruel-Carrizosa
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Correspondence to Mercedes Teruel-Carrizosa.

Appendix

Appendix

Annex 1
figure a

Kernel densities depending on the age

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Annex 2 Descriptive statistics and test of mean of the lagged firm size for manufacturing and service industries
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Teruel-Carrizosa, M. Gibrat’s law and the learning process. Small Bus Econ 34, 355–373 (2010). https://doi.org/10.1007/s11187-008-9127-9

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