It is well established that a small number of firms, known as fast-growth firms or Gazelles, create most new jobs. Despite the importance of this topic from a policy point of view, most studies are descriptive and explore a limited number of characteristics of fast-growth firms. The existence of some correlation between two or more of the determinants of fast growth could yield, however, spurious results. To avoid that problem, this paper performs a multivariate analysis of the determinants of fast growth using a panel of Spanish firms. The variables explored include sector of activity, region and newness of the firm as well as access to external finance and firms’ human resource practices. We control for the presence of unobserved time-invariant, firm-specific heterogeneity as well as for the possible existence of state dependence. We find that past extreme growth episodes increase the probability of current fast growth, which is in contrast to previous findings on the topic. We also find that human resource practices, such as employing qualified personnel or the mix of contracts offered, are important determinants of fast growth. Lastly, newness and access to credit are found to be important to explain firm growth, but they are not significant determinants of fast or extreme employment growth, thereby reflecting the existence of non-linearities in the growth process of firms.
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See Sutton (1997) for a survey on Gibrat’s Law.
For a recent survey, see Coad (2007).
However, Haltiwanger et al. (2010) in a very thorough and recent analysis using U.S. plant and firm data conclude that what matters most for growth is age. This conclusion is based on the disappearance of the negative relation between firm size and growth once firm age is properly controlled for.
Acs and Mueller (2008) study the net employment effect of different types of start-ups in narrowly defined U.S. regions. They find that the largest and more long-lasting impact is that of “Gazelles” defined as new firms that grow quickly and set up a second establishment once they reach the 100 employee threshold.
Size, as it will become clear shortly, is crucial to construct the dependent variable and, therefore, cannot be included among the explanatory variables.
They include two finance-related variables: (1) a dummy that takes the value 1 if the firm requested, but did not receive, credit, and (2) the firm’s leverage ratio. When they perform the analysis that includes both surviving and non-surviving firms, they find that whereas credit rationing has always a negative impact on growth, the leverage ratio has a negative impact on small firms’ growth. If we interpret higher leverage as better access to credit, these two results seem to be contradicting each other. On the other hand, higher leverage could be increasing a firm’s riskiness, which could restrict future access to credit.
Europe’s 500 focus on the 500 most dynamic European entrepreneurs between 1979 and 1984.
Schreyer (2000) offers further support to the inclusion of these variables among the determinants of fast growth. The authors of a number of the country-level studies that he has reviewed distinguish between independent firms and those that are partly or wholly owned by another firm. All of these authors conclude that dependent firms play a more than proportional part in the group of high-growth firms. This finding has been recently confirmed by Levratto et al. (2010) using a sample of French manufacturing SMEs. The importance of belonging to a group is attributed by Schreyer (2000) to the following factors: (1) dependent firms have easier access to finance; (2) dependent firms have better access to human capital whenever recruitment, training and mobility involve fixed costs and/or a minimum size; (3) dependent firms have easier access to information on markets, products and technologies.
One could argue, for example, that the relationship between financial resources and fast growth goes in the reverse order, that is, from fast growth in the past to better access to credit in the present.
Firms in Spain are constituted as trading companies (sociedades mercantiles) or as a physical person or self-employed (autónomos). There are important differences between both types of firms. Most importantly, trading companies are entities with a legal status that is different from that of their owners, and most of them limit the responsibility of the owners to their particular capital share. About three-quarters of all trading companies are “Sociedades de Responsabilidad Limitada”, less than 10% are corporations or “Sociedades Anónimas” and the rest are cooperatives and other associations. This later group of trading companies has a poorer quality of data than the other two and, therefore, we decided to exclude these from our dataset. All trading firms are obliged by law to disclose annually their accounts. On the other hand, the firm constituted as a physical person (self-employed) is indistinguishable from the owner who, therefore, responds with all his personal assets (unlimited liability). Self-employed are not obliged to publish their accounts, which makes it extremely difficult to obtain firm-level information about them. For that reason, they are excluded from the BSFDD. In any case, more than two-thirds of self-employed have no employees, which means that their weight in total employment is quite low, about 15%.
We have also incorporated the information of firms collaborating on voluntary grounds with the Bank of Spain (mainly large firms) to our database.
We have considered broad sectors of activity, namely, mining, manufacturing, utilities, construction, trade, hotels and restaurants, transport, post and telecommunications and other market services, as well as three employment segments, i.e., fewer than 20 employees, between 20 and 500 employees and more than 500 employees.
We checked whether firms with no data in one specific year in their history were systematically different from those providing information that year, given both groups of firms had the same age and belonged to the same sector, by comparing their average employment and productivity when the information was provided. We found no systematic differences.
DIRCE is the Central Directory of Firms and it is managed by the National Statistics Institute.
It is important to stress from the outset that DIRCE records an entry whenever a new fiscal identification number is given to a firm. An exit is recorded when a fiscal identification number disappears. This means that any restructuring of firms (M&A) resulting in a new identification number will be recorded as an entry by DIRCE. Consequently, in this study we have no way to distinguish organic from acquired growth. Using information from large firms collaborating with the Bank of Spain, we have estimated that around 5% of entries of firms with fewer than 20 employees and most of the entries recorded by DIRCE of firms with more than 100 employees could be the result of some kind of restructuring process or “false” entries. The reader should keep this caveat in mind when interpreting the results of the paper.
They require a 2-year growth of at least 60% with a minimum of 20% growth per year for a firm to be defined as a fast grower. Additionally, the firm had to have at least 15 employees at the beginning of the period.
we consider the Birch-Schreyer indicator to be the most appropriate for our Spanish analysis.
Hall (2002) reviews a number of papers testing the existence of financing restrictions to innovative firms and concludes that debt is a disfavoured source of finance for Research & Development (R&D) investment.
We have also tried the analysis with other (more aggregate) sector classifications and found similar results.
Using simple algebra, it can be shown that the Birch-Schreyer indicator can be expressed as the product of a quadratic growth term and a size term.
Although they do not interpret it as a proxy for human capital but rather as a proxy for total costs.
The nine sectors are mining, manufacturing, utilities, construction, retail, hotels and restaurants, telecommunications, transport and other services.
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Lopez-Garcia, P., Puente, S. What makes a high-growth firm? A dynamic probit analysis using Spanish firm-level data. Small Bus Econ 39, 1029–1041 (2012). https://doi.org/10.1007/s11187-011-9321-z
- High-growth firms
- Job creation
- Panel firm-level data
- Dynamic probit analysis