Skip to main content

Product market regulation and innovation efficiency

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.

This is a preview of subscription content, access via your institution.

Fig. 1
Fig. 2

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).

References

  • Abiad A, Detragiache E, Tressel T (2008) A new database of financial reform. MF Working Paper WP/08/266

  • Acharya RC, Keller W (2009) Technology transfer through imports. Can J Econ 42:1411–1448

    Article  Google Scholar 

  • Aghion P, Harris C, Vickers J (1997) Competition and growth with step-by-step innovation; an example. Eur Econ Rev 41:771–782

    Article  Google Scholar 

  • Aghion P, Bloom N, Blundell R, Griffith R, Howitt P (2005) Competition and innovation: an inverted-U relationship. Q J Econ 120:701–728

    Google Scholar 

  • Aghion P, Blundell R, Griffith R, Howitt P, Prantl S (2009) The effects of entry on incumbent innovation and productivity. Rev Econ Stat 91:20–32

    Article  Google Scholar 

  • Aghion P, Akcigit U, Howitt P, (2013a). What do we learn from Schumpeterian growth theory? NBER working paper No. 18824

  • Aghion P, Howitt P, Prantl S (2013b) Patent rights, product market reforms, and innovation. NBER working paper No. 18854

  • Aigner D, Lovell K, Schmidt P (1977) Formulation and estimation of stochastic frontier production function models. J Econom 6:21–37

    Article  Google Scholar 

  • Alesina A, Ardagna S, Nicoletti G, Schiantarelli F (2005) Regulation and investment. J Eur Econ Assoc 4:791–825

    Article  Google Scholar 

  • Amable B, Demmou L, Ledezma I (2009) Product market regulation, innovation and distance to frontier. Ind Corp Change 19:117–159

    Article  Google Scholar 

  • Ang JB (2011) Financial development, liberalization and technological deepening? Eur Econ Rev 55:688–701

    Article  Google Scholar 

  • Arnold J, Nicoletti G, Scarpetta S (2011) Does anti-competitive regulation matter for productivity? Evidence from European Firms. IZA Discussion Papers No. 5511

  • Barbosa N, Faria AP (2011) Innovation across Europe: How important are institutional differences? Res Policy 40:1157–1169

    Article  Google Scholar 

  • Barone G, Cingano F (2011) Service regulation and growth: evidence from OECD countries. Econ J 121:931–957

    Article  Google Scholar 

  • Bassanini A, Brunello G (2011) Barriers to entry, deregulation and workplace training: a theoretical model with evidence from Europe. Eur Econ Rev 55:1152–1176

    Article  Google Scholar 

  • Bassanini A, Nunziata L, Venn S (2009) Job protection legislation and productivity growth in OECD countries. Econ Policy 24:349–402

    Article  Google Scholar 

  • Beck T, Demirgü-Kunt A (2009) Financial institutions and markets across countries and over time: data and analysis. World Bank Policy Research Working Paper No. 4943

  • Blind K (2012) The influence of regulations on innovation: a quantitative assessment for OECD countries. Res Policy 41:391–400

    Article  Google Scholar 

  • Boldrin M, Levine DK (2013) The case against patents. J Econ Perspect 27:3–22

    Article  Google Scholar 

  • Bottazzi L, Peri G (2007) The international dynamics of R&D and innovation in the short and long run. Econ J 117:486–511

    Article  Google Scholar 

  • Bourlès R, Cette G, Lopez J, Mairesse J, Nicoletti G (2013) Do product market regulations in upstream sectors curb productivity growth? Panel data evidence for OECD countries. Rev Econ Stat 95:1750–1768

    Article  Google Scholar 

  • Buccirossi P, Ciari L, Duso T, Spagnolo G, Vitale C (2013) Competition policy and productivity growth: an empirical assessment. Rev Econ Stat 95:1324–1336

    Article  Google Scholar 

  • Caudill S, Ford J (1993) Biases in frontier estimation due to heteroskedasticity. Econ Lett 41:17–20

    Article  Google Scholar 

  • Ciccone A, Papaioannou E (2010) Estimating cross-industry cross-country models using benchmark industry characteristics. Working Papers 504, Barcelona Graduate School of Economics

  • Conway P, Nicoletti G (2006) Product market regulation in the non-manufacturing sectors of OECD countries: measurement and highlights. OECD Economics Department Working Papers 530

  • Conway P, De Rosa D, Nicoletti G, Steiner F (2006) Regulation, competition and productivity convergence. OECD Econ Stud 2:9

    Google Scholar 

  • Correa JA, Ornaghi C (2014) Competition and innovation: evidence from U.S. patent and productivity data. J Ind Econ 62:258–285

    Article  Google Scholar 

  • Cullmann A, Schmidt-Ehmcke J, Zloczysti P (2012) R&D efficiency and barriers to entry: a two stage semi-parametric DEA approach. Oxf Econ Pap 64:176–196

    Article  Google Scholar 

  • Fiori G, Nicoletti G, Scarpetta S, Schiantarelli F (2012) Employment effects of product and labour market reforms: Are there synergies? Econ J 122:F79–F104

    Article  Google Scholar 

  • Franco C, Montresor S, Vittucci Marzetti G (2011) On indirect trade-related R&D spillovers: the ‘Average Propagation Length’ of foreign R&D. Struct Change Econ Dyn 22:227–237

    Article  Google Scholar 

  • Fu X, Yang QG (2009) Exploring the cross-country gap in patenting: a stochastic frontier approach. Res Policy 38:1203–1213

    Article  Google Scholar 

  • Ginarte JC, Park WG (1997) Determinants of patents rights: a cross-national study. Res Policy 26:283–301

    Article  Google Scholar 

  • Greene WH (2005) Reconsidering heterogeneity in panel data estimators of the stochastic frontier model. J Econom 126:269–303

    Article  Google Scholar 

  • Greene WH (2008) The econometric approach to efficiency analysis. In: Fried HO, Lovell CAK, Schmidt SS (eds) The measurement of productive efficiency and productivity change. Oxford University Press, Oxford, pp 92–250 Chap. 2

    Chapter  Google Scholar 

  • Griffith R, Macartney G (2014) Employment protection legislation, multinational firms and innovation. Rev Econ Stat 96:135–150

    Article  Google Scholar 

  • Griffith R, Harrison R, Macartney G (2007) Product market reforms, labour market institutions and unemployment. Econ J 117:C142–C166

    Article  Google Scholar 

  • Griffith R, Harrison R, Simpson H (2010) Product market reform and innovation in the EU. Scand J Econ 112:389–415

    Article  Google Scholar 

  • Hall BH, Jaffe AB, Trajtenberg M (2001) The NBER citations data file: lessons, insights and methodological tools. NBER Working papers 8498

  • Henry M, Kneller R, Milner C (2009) Trade, technology transfer and national efficiency in developing countries. Eur Econ Rev 53:237–254

    Article  Google Scholar 

  • Inklaar R, Timmer MP, van Ark B (2008) Market services productivity across Europe and the US. Econ Policy 23:139–194

    Article  Google Scholar 

  • Jondrow J, Lovell CAK, Materov S, Schmidt P (1982) On the estimation of technical efficiency in the stochastic frontier production function model. J Econom 19:233–238

    Article  Google Scholar 

  • Kneller R, Stevens P (2006) Frontier technology and absorptive capacity: evidence from OECD manufacturing industries. Oxf Bull Econ Stat 68:1–21

    Article  Google Scholar 

  • Kodde D, Palm F (1986) Wald criteria for jointly testing equality and inequality restrictions. Econometrica 54:1243–1248

    Article  Google Scholar 

  • Kumbhakar S, Lovell C (2000) Stochastic frontier analysis. Cambridge University Press, Cambridge

    Book  Google Scholar 

  • Kumbhakar S, Lien G, Hardaker BJ (2014) Technical efficiency in competing panel data models: a study of Norwegian grain farming. J Product Anal 41:321–337

    Article  Google Scholar 

  • Liu Y, Myers R (2009) Model selection in stochastic frontier analysis with an application to maize production in Kenya. J Product Anal 31:33–46

    Article  Google Scholar 

  • Lopez J (2010) Essais sur la Reglementation du March des Biens, les Technologies de l’Information et de la Communication, la Recherche et Developpement et la Productivité PhD Dissertation, Ecoles des Hautes Etudes en Sciences Sociales, GREQAM D’Aix-Marseille

  • Martin J, Scarpetta S (2012) Setting it right: employment protection, labour reallocation and productivity. De Economist 160:89–116

    Article  Google Scholar 

  • Maskus KE, Neumann R, Seidel T (2012) How national and international financial development affect industrial R&D. Eur Econ Rev 56:72–83

    Article  Google Scholar 

  • Mason G, O’Leary B, Vecchi M (2012) Certified and uncertified skills and productivity growth performance: cross-country evidence at industry level. Labour Econ 19:351–360

    Article  Google Scholar 

  • Meeusen W, van den Broeck J (1977) Efficiency estimation for Cobb–Douglas production functions with composed error. Int Econ Rev 18:435–444

    Article  Google Scholar 

  • Menezes-Filho N, Van Reenen J (2003) Unions and innovation: a survey of the theory and empirical evidence. International handbook of trade unions. Edward Elgar Publishing Ltd, pp 293–334

  • Nelson RR (2008) What enables rapid economic progress: what are the needed institutions. Res Policy 37:1–11

    Article  Google Scholar 

  • Nicoletti G, Scarpetta S (2003) Unions and innovation: a survey of the theory and empirical evidence. Regulation, productivity and growth: OECD evidence. Econ Policy 18:9–72

    Article  Google Scholar 

  • Pieri F, Zaninotto E (2013) Vertical integration and efficiency: an application to the Italian machine tool industry. Small Bus Econ 40:397–416

    Article  Google Scholar 

  • Rajan RG, Zingales L (1998) Financial dependence and growth. Am Econ Rev 88:559–586

    Google Scholar 

  • Samaniego RM (2013) Knowledge spillovers and intellectual property rights. Int J Ind Organ 31:50–63

    Article  Google Scholar 

  • Sanyal P, Ghosh S (2013) Product market competition and upstream innovation: theory and evidence from the US electricity market restructuring. Rev Econ Stat 95:237–254

    Article  Google Scholar 

  • Schmidt P (2011) One-step and two-step estimation in SFA models. J Product Anal 36:201–203

    Article  Google Scholar 

  • Venn D (2009) Legislation, collective bargaining and enforcement: Updating the OECD employment protection indicators. Social, Employment and Migration Working Paper no. 89, OECD, Paris

  • Venturini F (2012) Looking into the black-box of Schumpeterian theory: an assessment of R&D races. Eur Econ Rev 56:1530–1545

    Article  Google Scholar 

  • Venturini F (2014) The modern drivers of productivity. Res Policy (forthcoming). doi:10.1016/j.respol.2014.10.011

  • Von Furstenberg GM, Von Kalckreut U (2006) Dependence on external finance: An inherent industry characteristic? Open Econ Rev 17:541–559

    Article  Google Scholar 

  • Wang EC (2007) R&D efficiency and economic performance: a cross-country analysis using the stochastic frontier approach. J Policy Model 39:345–360

    Article  Google Scholar 

  • Wang H-J, Schmidt P (2002) One-Step and two-step estimation of the effects of exogenous variables on technical efficiency levels. J Product Anal 18:129–144

    Article  Google Scholar 

Download references

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).

Author information

Authors and Affiliations

Authors

Corresponding author

Correspondence to Fabio Pieri.

Electronic supplementary material

Below is the link to the electronic supplementary material.

Supplementary material 1 (pdf 118 KB)

Rights and permissions

Reprints and Permissions

About this article

Verify currency and authenticity via CrossMark

Cite this article

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

Download citation

  • Published:

  • Issue Date:

  • DOI: https://doi.org/10.1007/s11123-015-0441-3

Keywords

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

JEL Classification

  • L5
  • L6
  • O3
  • O5