An international cohort comparison of size effects on job growth

For a, now slightly dated, summary of different cross-country data sets, see Vale (2006).

Two other OECD studies make cross-country comparisons of (amongst other things) job creation and destruction: the first uses the Amadeus and Orbis databases and excludes firms with less than 20 employees, see (Bassanini and Marianna (2009), pp. 33–35); the second, Schreyer (2000), was organised as a cross-country project involving researchers from six participating countries, the data were compiled from a range of administrative, public and private surveys, in most cases it excluded firms with less than 20 employees and considered only firms which survived the study period (between 3 and 9 years, depending on the country). Moreover, as its ”Methodological Annex” recorded: ”\(\ldots\) major methodological differences remain and the present analysis is faced with the problem of harmonisation and consistency. The results obtained in each country are strongly marked by these differences” (Schreyer (2000), p. 40)

See, for example: Kirchhoff (1994), Phillips and Kirchhoff (1989), and most recently Headd and Kirchhoff (2009).

Cabral and Mata (2003) compared a cohort of Portuguese manufacturing firms at birth and age 7 to provide the empirical foundation for the suggestion that “financial constraints” play a key role in the early growth performance of firms. However, of the many papers which cite Cabral and Mata (2003) and claim to be following their approach, relatively few have analysed cohort data.

They do, however, offer some somewhat speculative remarks about the contrast between US and European growth performance and its connection with size at birth ((Bartelsman et al. (2009), pp. 53–57).

For example Haltiwanger at al draw the following methodological conclusion given the character of business dynamics, “Lumping together all firms of the same age is clearly misleading \(\ldots\)”(Haltiwanger et al. (2009), p.2)

Indeed, the subtitle of his 1987 book was:“how our smallest companies put the most people to work”.

This may also affect Sweden’s firm count: firms in which every employee’s main job is elsewhere would not be included.

For a discussion of the implications of measurement issues in harmonised cross-country data sets see (Bartelsman et al. (2009), pp. 27–32).

We will return to this issue later and look at the size distribution in a little more detail.

It might be conjectured that Sweden’s relatively slow growth might be connected to the different measure of employees. Of course, it is not possibly to know, however, to make such a difference to the growth calculation would require not just multiple job holding but increased multiple job holding in cohort98 over the decade.

Moreover, in smaller countries, with relatively few firms born very large, the statistical authorities do not permit publication of data which might allow individual firms to be identified.

Precise definitions and a derivation are provided in the Appendix Sect. 9.1

Austria was chosen after some experimentation with alternative approaches to constructing a cross-country “average”.

A more detailed treatment of the Austrian decomposition is laid out in the Appendix 9.2 and its accompanying table. It displays the size-band detail which evidences some of the comments in the text about the relative importance of different effects.

Remember growth is being measured here as the ratio between average jobs per firm in survivors at birth and in the terminal period, 10 years later.

With the partial exception of Finland where the job numbers are full-time equivalents and so some firms in the 1–4 size-band have, in practice, less than one job.

In Germany, as we saw from Fig. 2, 20+ firms contracted and this produces a negative contribution of equal absolute value to size-band 1–4 growth.

Our data do not allow us to infer whether these firms remained in the same size-band throughout the decade: they may have moved out and moved back, though a priori this does not seem very likely to be a widespread phenomenon.

In Norway, for example, with the greatest mobility, much of the movement out of the birth size-band, much more than in other countries is into the 5–9 and 10–19 size-bands, see Appendix for details.

The influence of HJM is very noticeable in three out of the four 2014 papers we cited earlier, that is Ayyagari et al. (2014), Criscuolo et al. (2014), Lawless (2014) discuss HJM; oddly de Wit and de Kok (2014) does not, perhaps because it ignores the significance of age altogether.

They also add a generalisation which must be regarded as a conjecture since they provide no specific evidence: “Importantly, because new firms tend to be small, the finding of a systematic inverse relationship between firm size and net growth rates in prior analyses is entirely attributable to most new firms being classified in small size classes” (Haltiwanger et al. (2013), p. 348)

The approach adopted by HJM had previously been applied to similar problems: see Evans (1987) and Dunne et al. (1989).

Although the “slope” of the relationship is pretty flat between size 5–9 and size 10–19.

HJM actually use “average” size rather than terminal size, but this is not crucial here.

This figure can be calculated from Appendix Table 9 as the product of \(firmshb\) (column (2) and \(rsrb\) (column(3)).

Whilst Decker et al. (2014) use the term “high-growth” this is not the conventional (OECD) usage (see Anyadike-Danes et al. (2012) for discussion of the high-growth firm definition and its application); indeed, in this context, young and very fast growing, they could be referring to “gazelles”, though not as conventionally defined, see Henrekson and Johansson (2010).

This is a big subject and outside the scope of this paper, however, it is explored in a deliberately provocative way in Shane (2009) and more recently Coad and Nightingale (2014).

There is \(\Delta \hbox {firmsh}^{bs}\) term because, by definition, \(\hbox {firmsh}^{t}\) is equal to \(\hbox {firmsh}^{bs}\).