Modeling the effect of competition using robust conditional nonparametric frontiers: Evidence from U.S. manufacturing sector

Polemis, Michael and Stengos, Thanasis and Tzeremes, Nickolaos (2018): Modeling the effect of competition using robust conditional nonparametric frontiers: Evidence from U.S. manufacturing sector.

Abstract

The study applies the probabilistic framework of nonparametric frontier estimation in order to model the effect of competitive conditions on sectors’ production efficiency levels. We utilize conditional Order-m robust frontiers modeling the dynamic effects of competitive conditions on a sample of 462 U.S. 6-digit manufacturing sectors over the period 1958-2009. The results derived from the time-dependent robust conditional estimators unveil a non-linear relationship between market competition and productive efficiency. Our findings suggest that for higher competitive conditions the effect is positive up to a certain threshold point after which the effect becomes negative.

• Item Type: MPRA Paper
• Language: English
• Keywords: Probabilistic frontier analysis; Conditional efficiency; Order-m estimators; U.S. manufacturing; Competition.
• Subjects: C - Mathematical and Quantitative Methods > C1 - Econometric and Statistical Methods and Methodology: General > C14 - Semiparametric and Nonparametric Methods: General; L - Industrial Organization > L6 - Industry Studies: Manufacturing > L60 - General; O - Economic Development, Innovation, Technological Change, and Growth > O1 - Economic Development > O14 - Industrialization ; Manufacturing and Service Industries ; Choice of Technology

References

Aghion, P, Bloom, N, Blundell, R, Griffith, R. & Howitt, P. (2005). Competition and Innovation: An Inverted-U Relationship. Quarterly Journal of Economics, 120(2): 701–728.

Bădin, L., Daraio, C., & Simar, L. (2010). Optimal bandwidth selection for conditional efficiency measures: a data-driven approach. European Journal of Operational Research, 201(2), 633-640.

Bădin, L., Daraio, C., & Simar, L. (2012). How to measure the impact of environmental factors in a nonparametric production model. European Journal of Operational Research, 223(3), 818-833.

Cazals, C., Florens, J. P., & Simar, L. (2002). Nonparametric frontier estimation: a robust approach. Journal of Econometrics, 106(1), 1-25.

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

Daraio, C., & Simar, L. (2005). Introducing environmental variables in nonparametric frontier models: a probabilistic approach. Journal of Productivity Analysis, 24(1), 93-121.

Daraio, C., & Simar, L. (2014). Directional distances and their robust versions: computational and testing issues. European Journal of Operational Research, 237(1), 358-369.

Daraio, C., Simar, L., & Wilson, P. W. (2018). Central limit theorems for conditional efficiency measures and tests of the ‘separability’ condition in non‐parametric, two‐stage models of production. Econometrics Journal, 21(2), 170-191.

DOJ (2010). Horizontal Merger Guidelines, U.S. Department of Justice and the Federal Trade Commission, Can be downloaded from: https://www.justice.gov/sites/default/files/atr/legacy/2010/08/19/hmg-2010.pdf

Hashmi A.R., & Biesebroeck, J.V. (2016). The Relationship between Market Structure and Innovation in Industry Equilibrium: A Case Study of the Global Automobile Industry. Review of Economics and Statistics 98 (1): 192-208.

Hashmi, A. R., (2013). Competition and Innovation: The Inverted-U Relationship Revisited. Review of Economics and Statistics, 95(5): 1653–1668.

Hayfield, T., & Racine, J. (2008). Nonparametric Econometrics: The np Package. Journal of Statistical Software, 27(5), 1 - 32. doi:http://dx.doi.org/10.18637/jss.v027.i05

Huang, CW., Foo Nin Ho, Yung-ho Chiu (2014). Measurement of tourist hotels׳ productive efficiency, occupancy, and catering service effectiveness using a modified two-stage DEA model in Taiwan, Omega, 48: 49-59,

Jeong, S. O., Park, B. U., & Simar, L. (2010). Nonparametric conditional efficiency measures: asymptotic properties. Annals of Operations Research, 173(1), 105-122.

Li, Q., and Racine, J.S. (2007). Nonparametric Econometrics: Theory and Practice. Princeton University Press.

López, G.P., Diego Prior, José L. Zafra-Gómez (2018). Temporal scale efficiency in DEA panel data estimations. An application to the solid waste disposal service in Spain, Omega, 76: 18-27.

Mastromarco, C., & Simar, L. (2015). Effect of FDI and time on catching up: New insights from a conditional nonparametric frontier analysis. Journal of Applied Econometrics, 30(5), 826-847.

Mukoyama, T, (2003). Innovation, Imitation and Growth with Cumulative Technology. Journal of Monetary Economics 50: 361–380

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

Simar, L., & Wilson, P. W. (2011). Two-stage DEA: caveat emptor. Journal of Productivity Analysis, 36(2), 205.

Tran, T.H., Mao, Y., Nathanail, P., Siebers, P.O., and Darren R.(2018). Integrating slacks-based measure of efficiency and super-efficiency in data envelopment analysis, Omega, https://doi.org/10.1016/j.omega.2018.06.008.