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Product market regulation and innovation efficiency

Journal of Productivity Analysis volume 45, pages 299–315 (2016)Cite this article

Abstract

We study the role of upstream product market regulation (PMR) in innovation efficiency. By estimating a knowledge production function on OECD industries through a stochastic frontier analysis, we find that service regulation reduces R&D efficiency in the manufacturing sector. These results are robust to controlling for the institutional setting of the technology, the labour and the financial market, and to various forms of heterogeneity. The marginal impact of PMR is higher in less regulated economies indicating that large improvements in R&D efficiency cannot be obtained at the earlier stages of deregulation. Potential efficiency gains for late reformers are however sizeable.

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Notes

  1. Bourlès et al. (2013, working paper version) builds upon the model originally developed by Lopez (2010) which consists in an extension of Aghion et al. (1997).

  2. Aghion et al. (2013b) report that the EU SMP reforms raised innovation in countries characterized by strong patent rights, but not elsewhere; this effect was more relevant in industries relying on patenting as a main tool of innovation.

  3. Unreported sensitivity results indicate that (in)efficiency estimates are robust to alternative distributions (i.e., the exponential function). The choice of the distribution is dictated only by computational tractability (Greene 2008, p. 180).

  4. This procedure outperforms the two-step methodology mostly used in earlier works. The latter consists of first estimating inefficiency scores from a baseline function (such as for instance our KPF), and then regressing these values on a set of additional explanatory variables (product market regulation). The two-step procedure has been shown to yield biased estimates of the (in)efficiency parameters in presence of omitted variables in the first-step estimation.

  5. See Kumbhakar and Lovell (2000, pp. 272–273) and Caudill and Ford (1993).

  6. Our work extends previous research in the field. For instance, Fu and Yang (2009) disentangle patenting at the economy-wide level into the effects of innovation capacity and efficiency, allowing for country fixed-effects within the (in)efficiency equation but no deterministic element within the frontier. Comparable works, using a similar specification without fixed effects but focused on output production efficiency, are Kneller and Stevens (2006) and Henry et al. (2009).

  7. Details are provided in the Web Appendix. Industry list: Food, beverage and tobacco; Chemicals; Pharmaceuticals; Rubber and plastics; Other non-metallic minerals; Basic metals; Fabricated metal products; Machinery; Office machinery; Electrical eq. and apparatus; Communication eq.; Medical and scientific instruments; Motor vehicles; Other transport eq.; Other manufacturing. Country list: Australia, Canada, Germany, France, Great Britain, Italy, Japan, The Netherlands, Sweden, US.

  8. Although patent data mainly account for the output of formal innovation, they present some important advantages. Firstly, using applications at the USPTO, we work with a standardized measure of innovation output that reduces measurement errors, as all innovators are subjected to the same IPR law. Secondly, we cover the portion of innovations with higher quality as firms demand patent protection in the US –the world-wide leading technology market– only for their most valuable ideas. As patent application data move closer to the last date in the data set (2006), our series increasingly suffer from missing observations consisting of patents filed in recent years that have not yet been granted (Hall et al. 2001). To circumvent such truncation problem, we are forced to work with data until 2002. However, we use information on patent applications at the European Patent Office as robustness checks.

  9. Notice that parameters of the inefficiency equation cannot be interpreted as marginal effects (Liu and Myers 2009). This issue will be addressed in Sect. 5.

  10. The relationship between IPR stringency and innovation remains a debated issue (Boldrin and Levine 2013). Furthermore, the strength of patent protection may interplay with the policies pursued in other markets, for instance enhancing the positive impact of product market liberalization on R&D and patenting (Aghion et al. 2013b).

  11. We thank an anonymous referee for emphasizing this point.

  12. Unreported LR tests indicate that industry- and country-specific time trends included into the inefficiency term are always different from zero. It confirms that, to consistently estimate the effect of upstream regulation, it is crucial to control for the deterministic evolution of this variable over time. In accordance with Fiori et al. (2012), when time trends are omitted PMR turns out to be insignificant. Our model is sufficiently articulated and introducing country-year fixed effects, to control for country-specific economic shocks that may induce product market reforms, does not improve estimates due to convergence problems.

  13. Asset tangibility is given by the average share of structure, transport and non-ICT equipment on total capital expenditure, observed on the US industries in the 1980s. Notice that the high correlation between the measures of financial input based on external finance dependence and asset tangibility inhibits the inclusion of both variables within the same specification.

  14. For the US, we consider the cross-country average value of import shares.

  15. We also inspect whether there is heterogeneity in the impact of PMR according to the technological base of production. There is evidence that high-tech sectors are less influenced by PMR; however, this finding is quite sensitive to the grouping criterion.

  16. Union density is defined as the percentage ratio between trade union members and the total number of wage and salary earners.

  17. This is also witnessed by running regressions (3a)–(3b) separately for the EU and non-EU countries; for the latter, as our modelling of R&D efficiency shows there is a very low explanatory power.

  18. Efficiency scores are calculated for each country \(j\) on an annual base as: \(\sum _{i}TE_{ij,t}/I\), where \(TE_{ij,t}=exp(-\widehat{u}_{ij,t})\). Inefficiency scores are obtained through the conditional (to the overall residual) mean estimator, corrected for heteroskedasticity, developed by Jondrow et al. (1982):

    $$\begin{aligned} \widehat{u}_{ij,t}=E\left( u_{ij,t}|\epsilon _{ij,t} \right) =\frac{\sigma _{v}\sigma _{u_{ij,t}}}{\sigma _{ij,t}} \left[ \frac{\phi \left( \frac{\epsilon _{ij,t}\lambda _{ij,t}}{\sigma } \right) }{1-\Phi \left( \frac{\epsilon _{ij,t} \lambda _{ij,t}}{\sigma _{ij,t}}\right) }- \left( \frac{\epsilon _{ij,t}\lambda _{ij,t}}{\sigma _{ij,t}} \right) \right] , \end{aligned}$$

    where \(\sigma _{ij,t}=\sqrt{\sigma ^{2}_{v}+\sigma ^{2}_{u_{ij,t}}}\), \(\lambda _{ij,t}=\sigma _{u_{ij,t}}/\sigma _{v}\), and \(\phi (\cdot )\) and \(\Phi (\cdot )\) denote, respectively, the density function and the cumulative function of the standard normal distribution. Estimates of \(\epsilon _{ij,t}\) are directly recoverable from Eq. (3): \(\widehat{\epsilon }_{ij,t}=I_{ij,t}-\widehat{\alpha }- \widehat{\theta } \ln Z_{ij,t} - \widehat{\alpha }_{ij} - \widehat{\tau }_{t}.\) The employed empirical specification (Eq. 3) of the KPF is well-suited to separate out the time-invariant unobserved heterogeneity component, \(\widehat{\alpha }_{ij}\), from time-variant inefficiency at the industry/country level, \(\widehat{u}_{ij,t}\). Nonetheless, along with ’pure’ heterogeneity, \(\widehat{\alpha }_{ij}\) may also capture the time-invariant part of efficiency (as underlined by Pieri and Zaninotto 2013, pp. 411–412 among others), and it could be consequently used in the calculation of the efficiency scores. However, as Kumbhakar et al. (2014, pp. 325–326) point out, the method used to compute the efficiency scores is mainly a matter of parameters’ interpretation decided by the researcher. Furthermore, rather encouragingly, the Pearson and Spearman rank correlation coefficients between \(TE_{ij,t}=exp(\widehat{u}_{ij,t})\) and \(\widehat{\alpha }_{ij}\)- \(\widehat{u}_{ij,t}\) (0.57 and 0.65) indicate a wide similarity between these two ’ways’ of computing the efficiency scores.

  19. Following Liu and Myers (2009), marginal effects are defined as:

    $$\begin{aligned} \frac{\partial \left[ E\left( u_{ij,t}|\ln Z_{ij,t},PMR_{ij,t},{\mathbf {W}}\right) \right] }{\partial PMR_{ij,t}}. \end{aligned}$$

  20. See respectively Fiori et al. (2012), Alesina et al. (2005), Bourlès et al. (2013).

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Acknowledgments

The authors wish to thank two anonymous referees for their useful remarks on a previous version of the paper. Comments by Jimmy Lopez, Xose-Luis Varela-Irimia, Michela Vecchi, Andrea Lasagni and seminar participants at 17th Spring Meeting of Young Economists (Mannheim), SAEe 2012 (Vigo), SIE 2012 (Matera), NIESR (London), Middlesex Business School, Universitat Rovira i Virgili, SIEPI 2014 (Naples), IO Workshop (Alberobello) and University of Trento are also greatly acknowledged. The usual disclaimer applies. Fabio Pieri acknowledges the financial support by the Spanish Ministry of Science and Innovation (Project MINECO ECO2011-27619 co-financed with FEDER). Chiara Franco acknowledges the financial support of the National Research Project PRIN-MIUR 2010-11 “Climate changes in the Mediterranean area: scenarios, mitigation policies and technological innovation” (2010S2LHSE).

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Authors and Affiliations

  1. Department of International Economics, Institutions and Development, Catholic University of the Sacred Heart of Milan, Via Necchi 5, 20123, Milan, Italy

    Chiara Franco

  2. Departamento de Economia Aplicada, Universitat de Valencia, Avinguda dels Tarongers, s/n, 46022, Valencia, Spain

    Fabio Pieri

  3. Department of Economics, University of Perugia, Via Pascoli 20, 06123, Perugia, Italy

    Francesco Venturini

  4. NIESR, London, UK

    Francesco Venturini

Authors
  1. Chiara Franco
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  2. Fabio Pieri
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  3. Francesco Venturini
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Correspondence to Fabio Pieri.

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Franco, C., Pieri, F. & Venturini, F. Product market regulation and innovation efficiency. J Prod Anal 45, 299–315 (2016). https://doi.org/10.1007/s11123-015-0441-3

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Keywords

  • R&D
  • Knowledge production
  • Efficiency
  • Product market regulation

JEL Classification

  • L5
  • L6
  • O3
  • O5