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Productivity growth persistence: firm strategies, size and system properties

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Abstract

This paper investigates the dynamics of productivity in a large sample of Italian manufacturing firms, focusing on the determinants of firm-level persistence in time of high total factor productivity (TFP) growth rates relative to the corresponding sectoral distributions. In particular, we assess the impact of both the internal characteristics of companies, including size and management strategies, and external systemic conditions, including business cycles and regional innovation performance. In order to disentangle the effects of the mix of internal and systemic factors in shaping firm-level persistence, we implement both transition probability matrices and dynamic probit models. Results reveal the presence of significant persistence in TFP growth rates. Such persistence turns out to be path-dependent since it is shaped by a number of complementary and contingent factors that locally affect the dynamics of the process.

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Notes

  1. Our previous analyses studied persistence in innovation (Antonelli Crespi Scellato 2012), whereas this paper focuses specifically on the determinants of persistence in productivity growth taking into account in the analysis the role of firm characteristics such as size, their evolving internal capabilities and management strategies, on the one hand, and systems properties such as the macroeconomic, sectoral and regional contexts into which persistence displays its effects, on the other.

  2. The level of yearly depreciation of physical capital was chosen following the approach applied in previous studies that applied perpetual inventory techniques to estimate yearly fixed capital levels, adopting depreciation parameters in the 5–10 % range for physical capital. Since the adopted depreciation parameter is constant across industries, changes should not be expected in the significance of estimate coefficients for slight changes in δ.

  3. This measure of persistence is substantially different from the one adopted in Antonelli et al. (2013). As in the previous study, the state variable simply reflected the existence of positive changes in TFP over time.

  4. Let P ij and \(\hat{P}_{ij}\) denote the population and sample probabilities of a transition of a company from status i to status j. This transition process can also be seen as the outcome of a binomial distribution. Hence, standard errors of the estimated transition probabilities can be calculated as a binomial standard deviation: \(\sqrt {P_{ij} \times (1 - P_{ij} )/N}\) where N equals the number of companies in status i. As N increases, \(\hat{P}_{ij}\) tends to P ij.

  5. Sectors were divided into High-Tech and Low-Tech according to the Italian 3 digit ATECO industry classification.

  6. In order to simplify the description of results, we report only the probability associated with persistent top performers. Moreover, to be consistent with subsequent econometric models, controlling for the same-system level factors in productivity persistence, we consider the same selection criteria to identify top performers, i.e., the first 15 % in the sectoral distribution of TFP growth rates. Persistency patterns do not significantly change with respect to different thresholds. All results are available upon request from the authors.

  7. We carried out a sensitivity analysis to investigate whether, and to what extent, the results are related to the selected threshold. Results are largely confirmed for different thresholds and are available upon request from the authors.

  8. This intuition is particularly relevant given that the sample is composed of manufacturing companies that operate to a large extent in traditional sectors and that during the observed years have carried out significant restructuring outsourcing of the production activities.

  9. The empirical evidence on the relationship between profitability measures and productivity is mixed, even when taking into account operative profitability (ROI or ROA). In general, the identification of linear effects appears to be difficult (See Antonelli and Scellato (2011) for a discussion).

  10. We also tested different specifications for the growth rate of TFP, using both a two-year and a four-year interval. Results were not affected.

  11. Given that the effects of relevant control variables do not largely differ across estimates produced on different samples, we do not report results for all estimated coefficient in order to save space. All results are available upon request from authors.

  12. See Antonelli et al. (2014) and Antonelli and Scellato (2015) for complementary evidence. This interpretation for which we acknowledge the suggestion of one of the anonymous referees might be the object of further empirical investigations on a comparative basis.

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Authors and Affiliations

  1. Dipartimento di Economia, Università di Torino, Turin, Italy

    Cristiano Antonelli

  2. BRICK (Bureau of Research in Complexity, Knowledge, Innovation), Collegio Carlo Alberto, Moncalieri, Italy

    Cristiano Antonelli, Francesco Crespi & Giuseppe Scellato

  3. Dipartimento di Economia, Università Roma Tre, Rome, Italy

    Francesco Crespi

  4. Dipartimento di Ingegneria Gestionale e della Produzione, Politecnico di Torino, Turin, Italy

    Giuseppe Scellato

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  1. Cristiano Antonelli
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  2. Francesco Crespi
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  3. Giuseppe Scellato
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Correspondence to Cristiano Antonelli.

Appendix: Robustness checks

Appendix: Robustness checks

See Table 8.

Table 8 Transition probability matrix on quartiles of the sectoral distribution TFP growth rates
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Antonelli, C., Crespi, F. & Scellato, G. Productivity growth persistence: firm strategies, size and system properties. Small Bus Econ 45, 129–147 (2015). https://doi.org/10.1007/s11187-015-9644-2

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