Does market structure trigger efficiency? Evidence for the USA before and after the financial crisis

Halkos, George and Polemis, Michael (2018): Does market structure trigger efficiency? Evidence for the USA before and after the financial crisis.

Abstract

This paper investigates the relationship between efficiency and market structure for a sample of industrial facilities dispersed among the USA states. In order to measure the relevant efficiency scores, we use a Data Development Analysis (DEA) allowing for the inclusion of desirable and undesirable (toxic chemical releases) outputs in the production function. In the next stage, we utilise the bootstrapped quantile regression methodology to uncover possible non-linear relationships between efficiency and competition at the mean and at various quantiles before and after the global financial crisis (2002 and 2012). In this way, we impose no functional form constraints on parameter values over the conditional distribution of the dependent variable (efficiency). At the same time, we estimate at which part of its conditional distribution function, the efficiency is located and draw substantial conclusions about the range of policy measures obtained. The empirical findings, indicate that the relationship between efficiency and market concentration did not remain unchanged in the aftermath of the economic crisis. The empirical results survived robustness checks under the inclusion of an alternative market concentration indicator (CR8).

• Item Type: MPRA Paper
• Language: English
• Keywords: Market concentration; Industrial Toxic Releases; Efficiency, Financial crisis; Bootstrapped quantile regression.
• Subjects: C - Mathematical and Quantitative Methods > C1 - Econometric and Statistical Methods and Methodology: General > C14 - Semiparametric and Nonparametric Methods: General; L - Industrial Organization > L1 - Market Structure, Firm Strategy, and Market Performance; Q - Agricultural and Natural Resource Economics ; Environmental and Ecological Economics > Q5 - Environmental Economics > Q52 - Pollution Control Adoption and Costs ; Distributional Effects ; Employment Effects

References

Apergis, N., and Christou, C, (2015). The behaviour of the bank lending channel when interest rates approach the zero lower bound: Evidence from quantile regressions, Economic Modelling, 49(C): 296-307.

Apergis N. and Polemis M. (2016). Competition and efficiency in the MENA banking region: a non-structural DEA approach. Applied Economics, 48(54): 5276-5291.

Branco F. and Villas-Boas J. (2015). Competitive Vices. Journal of Marketing Research, 52(6): 801-816.

Bi X., and Khanna M. (2012) Reassessment of the Impact of the EPA’s Voluntary 33/50 Program on Toxic Releases. Land Economics, 88: 341-361.

Buchinsky M. (1995). Estimating the asymptotic covariance matrix for quantile regression models: A Monte Carlo study. Journal of Econometrics, 68: 303–338.

Buchinsky M. (1998). Recent advances in quantile regression models. Journal of Human Resources, 33(1), 88–126.

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

Cooper W.W., Seiford L.M., Tone K. (2007). Data Envelopment Analysis: A Comprehensive Text with Models, Applications, References and DEA-solver Software. 2nd Ed. Springer Science and Business Media, New York, NY.

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

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

Färe R., Grosskopf S., Lovel C.A.K. and Pasurka S. (1989). Multilateral productivity comparisons when some outputs are undesirable: a non parametric approach. Review of Economics and Statistics, 71(1): 90–98.

Färe R. and Primont D. (1995). Multi-Output Production and Duality: Theory and Applications. Kluwer Academic Publishers, Boston.

Halkos, G. (2003). Environmental Kuznets Curve for sulfur: evidence using GMM estimation and random coefficient panel data models, Environment and Development Economics, 8(04): 581-601.

Halkos G.E. (2011). Nonparametric modelling of biodiversity: Determinants of threatened species, Journal of Policy Modeling, 33(4): 618-635.

Halkos, G. E., and Polemis, M. L. (2017). Does Financial Development Affect Environmental Degradation? Evidence from the OECD Countries. Business Strategy and the Environment, 26: 1162–1180.

Halkos G. and Polemis M. (2018). The impact of economic growth on environmental efficiency of the electricity sector: A hybrid window DEA methodology for the USA. Journal of Environmental Management, 211: 334-346.

Halkos G. and Tzeremes N.G. (2009). Exploring the existence of Kuznets curve in countries’ environmental efficiency using DEA window analysis. Ecological Economics, 68: 2168–2176.

Harrington D.R., Deltas, G., and Khanna M. (2014). Does Pollution Prevention Reduce Toxic Releases? A Dynamic Panel Data Model. Land Economics, 90 (2): 199-221.

Hsueh L. (2015). Regulatory effectiveness and the long-run policy horizon: The case of U.S. toxic chemical use. Environmental Science and Policy, 52: 6 – 22.

Jones M.C. (1992). Estimating densities, quantiles, quantiles densities and density quantiles. Annals of the Institute of Statistical Mathematics, 44(4), 721-727.

Koenker R. (2005). Quantile regression. New York: Cambridge University Press.

Machado, J.A.F., Mata, J., (2000). Box–Cox quantile regression and the distribution of firm sizes. Journal of Applied Econometrics, 15 (3): 253–274.

Mansur, E (2007) Do oligopolists pollute less? Evidence from a restructured electricity market. Journal of Industrial Economics, 55 (4): 661–689.

Millimet D., List J. and Stengos T., (2003). The Environmental Kuznets Curve: Real Progress or Misspecified Models?, The Review of Economics and Statistics, 85(4), 1038-1047.

Polemis., (2017) Revisiting the Environmental Kuznets Curve: A semi-parametric analysis on the role of market structure on environmental pollution, Michael Polemis, Letters in Spatial and Resource Sciences https://doi.org/10.1007/s12076-017-0195-9.

Newson R. (2012). Sensible parameters for univariate and multivariate splines. Stata Journal, 12-3: 479-504.

Picazo-Tadeo A., Beltrá-Esteve M. And Gómez-Limón J.A. (2012). Assessing eco-efficiency with directional distance functions. European Journal of Operational Research, 220: 798-809.

Polemis M. and Stengos T. (2017). Does Competition Prevent Industrial Pollution? Evidence from a Panel Threshold Model, Working Paper Series 17-07, The Rimini Centre for Economic Analysis.

Powell J. (1986). Censored regression quantiles. Journal of Econometrics, 32: 143–155.

Scheel H. (2001). Undesirable outputs in efficiency valuations. European Journal of Operational Research, 132: 400–410.

Seiford LM, Zhu J (2002) Modeling undesirable factors in efficiency evaluation. European Journal of Operational Research, 142: 16–20.

Shephard RW (1970). Theory of cost and production function. Princeton University Press, Princeton.

Shleifer A. (2004). Does Competition Destroy Ethical Behavior? American Economic Review, 94: 414-418.

Simon, DH., and Prince J.T. (2016). The Effect of Competition on Toxic Pollution Releases, Journal of Environmental Economics and Management, 79: 40-54.

Zarzoso, I.M and Morancho. A.B., (2004). Pooled mean group estimation of an environmental Kuznets curve for CO2. Economics Letters, 82, 121–126.