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Small Business Economics

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01.10.2015

Productivity, market selection, and corporate growth: comparative evidence across US and Europe

verfasst von: Giovanni Dosi, Daniele Moschella, Emanuele Pugliese, Federico Tamagni

Erschienen in: Small Business Economics | Ausgabe 3/2015

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Abstract

This paper analyses the patterns of market selection in manufacturing industries of France, Germany, UK, and USA. We first disentangle the contribution to industry-level productivity growth of within-firm productivity changes and between-firm reallocation of shares. The evidence corroborates the notion that within-firm learning prevails over market selection forces, with larger firms driving such innovation and learning processes. Second, we address the “strength” of selection by exploring to what extent firm growth rates are shaped by relative productivity levels as compared to variation thereof. Our key finding is that, although changes in relative efficiency have a greater impact on growth than relative efficiency levels, there is an overall weak relationship between productivity and growth and, therefore, a weak power of selection forces in all countries. The results hold across firms of different size, but we also find that selection bites more on SMEs.

Vorheriger Artikel Do labor tax rebates facilitate firm growth? An empirical study on French establishments in the manufacturing industry, 2004–2011

Nächster Artikel Performance of small- and medium-sized enterprises in services trade: evidence from French firms

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See Bartelsman and Doms (2000), Dosi (2007), and Syverson (2011) for surveys and discussions.

See Foster et al. (2001) for a discussion of sensitivity of decomposition results to different methodologies and Petrin and Levinsohn (2012) for specific treatment of decompositions based on plant-level data.

See also Bottazzi et al. (2008) for a more descriptive analysis of Italian manufacturing based on rank correlation, also suggesting weak competitive selection.

Plehn-Dujowich (2009) extends the standard framework to also incorporate reallocation across industries, that is looking at existing firms exiting from one industry and reallocating their assets via entry into a different industry or opening a new product line.

See also Dosi et al. (1995), Silverberg and Verspagen (1995), Metcalfe (1998), among others, for models sharing the same structure. Models in the Nelson and Winter (1982) formalism yield the same qualitative prediction in that more efficient (productive) firms operating in a competitive, price-taking market would get higher profits and (under some reasonable assumption of imperfect capital markets) would invest and produce more relative to the universe of competitors (see also Bottazzi et al. 2001).

Indeed, as one argues at greater length in Dosi and Grazzi (2006), total factor productivity (TFP) measures of productivity might be biased and misleading in the presence of technologically heterogeneous firms and complementarity among inputs. As a robustness check, however, we also repeat our main regression analysis with a TFP index, yielding qualitatively similar conclusions (see Appendix 2).

Constant price sales and value added are obtained by deflating all nominal variables with appropriate sectoral price indexes, from EUROSTAT and from the BLS (base year 2005).

Aggregate country-level data are from OECD STAN database. Coverage is similar in other years.

Since application of the formula requires information on two consecutive years, incumbent firms need here to be intended as firms for which data for at least two consecutive years are available. Also recall that we cannot properly distinguish entry and exit from simple missing values in one of the variables, so we cannot meaningfully compute the contribution from entry and exit. Further notice that our decomposition, as in Griliches and Regev (1995), does not separate out the covariance effect. It is easy to show that formula (3) splits the covariance term in equal parts between the within and the between components.

Notice that the percentage contribution of each component obtained with our formula is equivalent to the weighted sum of the yearly contributions. Take for example the within component. Its total contribution is equal to \(\left.{\left(\sum _{t}\sum _{i\in j}\bar{s}_{i}\Delta \pi _{i,t}\right)}\right/{\left(\sum _{t}\Delta {\tilde{\Pi }}_{j,t}\right)}=\sum _{t}\left[\left(\frac{\sum _{i \in j}\bar{s}_{i}\Delta \pi _{i,t}}{\Delta {\tilde{\Pi }}_{j,t}}\right)\left(\frac{\Delta {\tilde{\Pi }}_{j,t}}{\sum _{t}\Delta {\tilde{\Pi }}_{j,t}}\right)\right]\).

In preliminary analysis, we have also explored the validity of the linear specification. Kernel regressions show that the linear fit is in the 95 % confidence interval across sectors and countries, and the lack of nonlinearities is confirmed by a parametric binned regression with three bins for low-, medium-, and high-productivity firms.

Lagged values also help in alleviating violations of strict exogeneity of the error term. Indeed, the presence of significant lags helps in ensuring that there are no shocks to the dependent variable that are correlated with past values of the independent variable. More formally, strict exogeneity \(\left(E\left(\epsilon _{i,t}| \pi _{i,s},u_{i}\right)=0,\,\forall t,s\right)\) also requires that future values of the dependent variable are uncorrelated with present shocks. We tested this hypothesis by including \(\pi _{t+1}\) in our regressions. The coefficients of this variable were not statistically significant in the large majority of the cases.

More precisely, \(R^{2}\) also considers the contribution of time dummies, whereas \(S^{2}\) also considers the covariances between time dummies and productivity variables. Year dummies are however found to explain a negligible part of total variation in our case, so that in practice the \(c_i\) terms explain the entire difference \(R^{2}-S^{2}\).

Also notice that in Sect. 6 below we explore robustness of our main conclusions across firms of different size and that (see Appendix 2) we are also able to confirm our main results when using TFP in place of labour productivity, implicitly controlling for capital intensity. Other potentially interesting controls, such as age and R&D in particular, are unfortunately not available or rather full of missing values in the data, especially in AMADEUS.

In Appendix 2, we show that the overall picture does not change if we use a TFP proxy for productivity.

We have verified that the same result is confirmed also if we estimate two separate specifications with \(\pi _{i,t}\) and \(\pi _{i,t-1}\) alternatively entering as the only regressor, so that the finding does not simply originates from some “perverse” collinearity between current and lagged productivity.

For completeness, Table 12 in Appendix 1 shows corresponding coefficient estimates. Notice that \(\beta _{\Delta }\) and \(\beta _{m}\) are related to the coefficients of Eqs. (6) and (7) through \(\beta _{0}=\frac{\beta _{m}}{2}+ \beta _{\Delta }\) and \(\beta _{1}=\frac{\beta _{m}}{2} - \beta _{\Delta }\).

Also in this case the main conclusions remain valid under alternative regressions using TFP in place of labour productivity (see Appendix 2).

Corresponding coefficient estimates are reported, for completeness, in Table 13 in Appendix 1.

Young age, together with small size, is a complementary characteristic of these firms, which we cannot unfortunately measure in our data.

We do not report results for sectors wherein the number of observations was too small to obtain reliable estimates. This applies in particular when we consider the group of SMEs in the US-COMPUSTAT database.

However, Van Beveren (2012) shows that the “simple” TFP measure is highly correlated with the TFP derived from more sophisticated estimators: in his data, the TFP obtained through the Levinsohn–Petrin estimation algorithm has a 0.9262 correlation with the OLS measure.

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Titel: Productivity, market selection, and corporate growth: comparative evidence across US and Europe
verfasst von: Giovanni Dosi
Daniele Moschella
Emanuele Pugliese
Federico Tamagni
Publikationsdatum: 01.10.2015
Verlag: Springer US
Erschienen in: Small Business Economics / Ausgabe 3/2015
Print ISSN: 0921-898X
Elektronische ISSN: 1573-0913
DOI: https://doi.org/10.1007/s11187-015-9655-z

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