Skip to main content


Log in

A note on the estimation of competition-productivity nexus: a panel quantile approach

  • Published:
Journal of Industrial and Business Economics Aims and scope Submit manuscript


We study the impact of product market competition on total factor productivity in 462 US manufacturing sectors for the period 1958–2009 through the lens of a panel quantile regression analysis. We confirm that there is a nonmonotonic inverse-U relationship between competition and productivity. We argue that the turning point increases substantially as we move to the higher quantiles of the productivity distribution function. Our findings survive robustness checks under alternative competition measure and panel quantile estimator.

This is a preview of subscription content, log in via an institution to check access.

Access this article

Price excludes VAT (USA)
Tax calculation will be finalised during checkout.

Instant access to the full article PDF.

Fig. 1

Similar content being viewed by others


  1. As we show that the residuals in none of the estimated models follow the normal distribution, the choice of MM-QR is supported to discover the relationship at the extreme quantiles of TFP growth rate. The estimated results based on Alejo et al. (2013) panel normality test are available upon request.

  2. The relevant indicator is calculated as: \(HHI50 = \sum\nolimits_{i = 1}^{50} {s_{it}^{2} \times 10,000}\) where s denotes the market share of each firm in industry i at time t.

  3. According to the EU horizontal merger guidelines, the European Commission is likely to identify horizontal competition concerns in a merger with a post-merger HHI above 2000 and a delta above 150. The Federal Trade Commission (FTC) also uses the HHI in merger analysis and classifies markets into three types: (a) unconcentrated markets: HHI below 1500, (b)mModerately concentrated markets: HHI between 1500 and 2500 and (c) highly concentrated markets: HHI above 2500.

  4. The results are available upon request.

  5. Specifically the ratio n/T is quite large (approximately 9 in our case).

  6. Specifically, we have used the lagged value of the CR4 (HHI50) and the squared CR4 (HHI50) as reported in the manuscript. The number of lags (L = 1) was chosen by minimizing the Akaike Information Criterion (AIC).

  7. To preserve space we do not present the estimated results of the rest covariates.


  • Aghion, P., Bloom, N., Blundell, R., Griffith, R., & Howitt, P. (2005). Competition and innovation: An inverted-U relationship. Quarterly Journal of Economics, 120, 701–728.

    Google Scholar 

  • Aghion, P., & Howitt, P. (1992). A model of growth through creative destruction. Econometrica, 60, 323–351.

    Article  Google Scholar 

  • Alejo, J., Galvao, A., Montes-Rojas, G., & Sosa-Escudero, W. (2013). Tests for normality in linear panel-data models. The Stata Journal, 15(3), 822–832.

    Article  Google Scholar 

  • Altunbaş, Y., & Thornton, J. (2019). The impact of financial development on income inequality: A quantile regression approach. Economics Letters, 175, 51–56.

    Article  Google Scholar 

  • Amable, B., Ledezma, I., & Robin, S. (2016). Product market regulation, innovation, and productivity. Research Policy, 45(10), 2087–2104.

    Article  Google Scholar 

  • Arrow, K. J. (1962). Economic welfare and the allocation of resources for invention. In R. R. Nelson (Ed.), The rate and direction of inventive activity (pp. 609–625). Princeton: Princeton University Press.

    Chapter  Google Scholar 

  • Bain, J. S. (1956). Barriers to new competition: Their character and consequences in manufacturing industries. Cambridge: Harvard University Press.

    Book  Google Scholar 

  • Bartelsman, E. J., & Gray, W. (1996). The NBER manufacturing productivity database. NBER Technical Working Paper No. 205. National Bureau of Economic Research.

  • Baryshnikova, N. V., & Pham, N. (2019). Natural disasters and mental health: A quantile approach. Economics Letters, 180, 62–66.

    Article  Google Scholar 

  • Bloom, N., Draca, M., & Reenen, J. V. (2011). Trade induced technical change? The impact of Chinese imports on innovation, IT and productivity, NBER working paper 16717.

  • Blundell, R., Griffith, R., & Reenen, J. V. (1999). Market share, market value and innovation in a panel of British manufacturing firms. Review of Economic Studies, 66, 529–554.

    Article  Google Scholar 

  • Cabral, L. (2017). Introduction to industrial organization (2nd ed.). London: The MIT Press.

    Google Scholar 

  • Canay, I. A. (2011). A simple approach to quantile regression for panel data. Econometrics Journal, 14, 368–386.

    Article  Google Scholar 

  • Carlin, W., Schaffer, M., & Seabright, P. (2004). A minimum of rivalry: Evidence from transition economies on the importance of competition for innovation and growth. Journal of Economic Analysis and Policy: Contributions, 3, 1–43.

    Google Scholar 

  • Chen, C., Polemis, M. L., & Stengos, T. (2018). On the examination of non-linear relationship between market structure and performance in the US manufacturing industry. Economics Letters, 164(C), 1–4.

    Article  Google Scholar 

  • Chernozhukov, V., & Hansen, C. (2008). Instrumental variable quantile regression: A robust inference approach. Journal of Econometrics, 142, 379–398.

    Article  Google Scholar 

  • Correa, J. A. (2012). Competition and innovation: An unstable relationship. Journal of Applied Econometrics, 27(1), 160–166.

    Article  Google Scholar 

  • Correa, J. A., & Ornaghi, C. (2014). Competition and innovation: Evidence from US patent and productivity data. The Journal of Industrial Economics, 62(2), 258–285.

    Article  Google Scholar 

  • Dai, M., Liu, Q., & Serfes, K. (2014). Is the effect of competition on price dispersion nonmonotonic? Evidence from the US airline industry. Review of Economics and Statistics, 96(1), 161–170.

    Article  Google Scholar 

  • Das, D., & Dutta, A. (2020). Bitcoin’s energy consumption: Is it the achilles heel to miner’s revenue? Economics Letters, 186, 108530.

    Article  Google Scholar 

  • Dhaene, G., & Jochmans, K. (2015). Split-panel jackknife estimation of fixed-effect models. Review of Economic Studies., 82, 991–1030.

    Article  Google Scholar 

  • Distante, R., Petrella, I., & Santoro, E. (2018). Gibrat’s law and quantile regressions: An application to firm growth. Economics Letters, 164, 5–9.

    Article  Google Scholar 

  • Gorodnichenko, Y., Svejnar, J., & Terrell, K. (2010). Globalization and innovation in emerging markets. American Economic Journal: Macroeconomics, 2, 194–226.

    Google Scholar 

  • Hall, P., & Sheather, S. (1988). On the distribution of a studentized quantile. Journal of the Royal Statistical Society Series B, 50, 381–391.

    Google Scholar 

  • Hashmi, R. A. (2013). Competition and innovation: The inverted-U relationship revisited. The Review of Economics and Statistics, 95(5), 1653–1668.

    Article  Google Scholar 

  • Hicks, J. R. (1935). Annual survey of economic theory: The theory of monopoly. Econometrica, 3, 1–20.

    Article  Google Scholar 

  • Li, Q., & Racine, J. (2006). Nonparametric econometrics: Theory and practice. Princeton: Princeton University Press.

    Google Scholar 

  • Machado, J. A. F., & Silva, S. (2019). Quantiles via moments. Journal of Econometrics, 213(1), 145–173.

    Article  Google Scholar 

  • Marshall, G., & Parra, A. (2019). Innovation and competition: The role of the product market. International Journal of Industrial Organization, 65, 221–247.

    Article  Google Scholar 

  • Nickell, S. J. (1996). Competition and corporate performance. Journal of Political Economy, 104(4), 724–746.

    Article  Google Scholar 

  • Polemis, M. L., & Stengos, T. (2015). Does market structure affect labour productivity and wages? Evidence from a smooth coefficient semiparametric panel model. Economics Letters, 137(1), 182–186.

    Article  Google Scholar 

  • Polemis, M., & Stengos, T. (2019). Does competition prevent industrial pollution? Evidence from a panel threshold model. Business Strategy and the Environment, 28(1), 98–110.

    Article  Google Scholar 

  • Polemis, M. L., & Tzeremes, N. (2019). Competitive conditions and sectors’ productive efficiency: A conditional non-parametric frontier analysis. European Journal of Operational Research, 276(3), 1104–1118.

    Article  Google Scholar 

  • Schmitz, J. (1980s). What determines productivity? Lessons from the dramatic recovery of the US and Canadian iron ore industries following their early 1980s crisis. Journal of Political Economy, 113, 582–625.

    Article  Google Scholar 

  • Schumpeter, J. (1943). Capitalism, socialism and democracy. London: Allen Urwin.

    Google Scholar 

  • Symeonidis, G. (2008). The effect of competition on wages and productivity: Evidence from the UK. The Review of Economics and Statistics., 901, 134–146.

    Article  Google Scholar 

  • Tsionas, M., & Polemis, M. (2019). On the estimation of total factor productivity: A novel Bayesian non-parametric approach. European Journal of Operational Research, 277(3), 886–902.

    Article  Google Scholar 

  • Van Reenen, J. (2011). Does competition raise productivity through improving management quality? International Journal of Industrial Organization, 29(3), 306–316.

    Article  Google Scholar 

  • Zhao, Q. (2000). Restricted regression quantiles. Journal of Multivariate Analysis, 72, 78–99.

    Article  Google Scholar 

Download references


I would like to expresses my gratitude to the Editor Professor Carlo Cambini for giving me the opportunity to revise and thus improve the paper. Special thanks also go to the two anonymous reviewers of the journal for their constructive comments and suggestions that enhanced the quality of this work. I would also like to thank Professor Santos Silva for his constructive comments along with the simulations STATA code for the Machado and Silva (2019) paper. Lastly, I am indebted to Professor Thanasis Stengos for enriching the paper with fruitful comments. All errors belong to the author. Usual disclaimer applies.

Author information

Authors and Affiliations


Corresponding author

Correspondence to Michael L. Polemis.

Additional information

Publisher's Note

Springer Nature remains neutral with regard to jurisdictional claims in published maps and institutional affiliations.



See Table 4.

Table 4 Summary statistics per quantile

Rights and permissions

Reprints and permissions

About this article

Check for updates. Verify currency and authenticity via CrossMark

Cite this article

Polemis, M.L. A note on the estimation of competition-productivity nexus: a panel quantile approach. J. Ind. Bus. Econ. 47, 663–676 (2020).

Download citation

  • Received:

  • Revised:

  • Accepted:

  • Published:

  • Issue Date:

  • DOI:


JEL Classification