Advertisement

Journal of Industry, Competition and Trade

, Volume 19, Issue 4, pp 661–678 | Cite as

Efficiency, Productivity, and Congestion Performance: Analysis of the Automotive Cluster in Mexico

  • Alfonso Mendoza-Velázquez
  • Francisco BenitaEmail author
Article
  • 92 Downloads

Abstract

By using data envelopment analysis and productivity measures obtained via Malmquist index, this paper investigates the patterns and dynamics of efficiency, productivity, and technological change of the automotive sector in Mexico. Particularly, we examine five subclusters (automotive parts, gasoline engines and engine parts, motor vehicles, small vehicles, and metal mills and foundries) and four regions (border, center, Bajio, and others) over the period 2003–2013. It is also studied the impact of the Great Crisis on the automotive cluster and subclusters efficiency and productivity performance before and after 2009. We also identify input congestion, and distinguish its source. Among other results, we find congestion in the center and border regions and various degrees of resilience to the Great Crisis.

Keywords

Automotive industry Efficiency Congestion Mexico 

JEL Classification

E24 D24 L62 J21 

Notes

Acknowledgments

The authors are indebted to the reviewer for the constructive comments. The second author would like to acknowledge CONACYT CVU 369933 (Mexico).

References

  1. Benita F (2019) On the performance of creative industries: Evidence from Mexican metropolitan areas. Pap Reg Sci 98(2):825–842CrossRefGoogle Scholar
  2. Benita F, Urzúa CM (2018) Efficient creativity in Mexican metropolitan areas. Ecol Model 71:25–33CrossRefGoogle Scholar
  3. Brockett PL, Cooper WW, Wang Y, Shin HC (1998) Inefficiency and congestion in Chinese production before and after the 1978 economic reforms. Socioecon Plann Sci 32(1):1–20CrossRefGoogle Scholar
  4. Carbajal-Suárez Y (2015) Evolución, condiciones actuales y retos del sector automotriz en México y en el Estado de México. Red Innovación y Trabajo en la Industria Automotríz Mexicana (ITIAM)-UAEMexGoogle Scholar
  5. Cedillo-Campos MG, Sánchez-Garza J, Ramirez CS (2006) The new relational schemas of inter-firms cooperation: the case of the Coahuila automobile cluster in Mexico. Int J Automot Technol Manag 6(4):406–418CrossRefGoogle Scholar
  6. Charnes A, Cooper WW, Rhodes E (1978) Measuring the efficiency of decision making units. Eur J Oper Res 2(6):429–444CrossRefGoogle Scholar
  7. Contreras OF, Carrillo J, Alonso J (2012) Local entrepreneurship within global value chains: a case study in the Mexican automotive industry. World Dev 40 (5):1013–1023CrossRefGoogle Scholar
  8. Cook WD, Tone K, Zhu J (2014) Data envelopment analysis: prior to choosing a model. Omega 44:1–4CrossRefGoogle Scholar
  9. Cooper WW, Thompson RG, Thrall RM (1996) Introduction: extensions and new developments in DEA. Ann Oper Res 66(1):1–45CrossRefGoogle Scholar
  10. Cooper WW, Deng H, Gu B, Li S, Thrall R (2001) Using DEA to improve the management of congestion in Chinese industries (1981–1997). Socioecon Plann Sci 35(4):227–242CrossRefGoogle Scholar
  11. Delgado M, Porter ME, Stern S (2016) Defining clusters of related industries. J Econ Geogr 16(1):1–38CrossRefGoogle Scholar
  12. Färe R, Grosskopf S (1983) Measuring congestion in production. J Econ 43 (3):257–271Google Scholar
  13. Färe R, Primont D (2012) Multi-output production and duality: theory and applications. Springer Science & Business MediaGoogle Scholar
  14. Färe R, Svenson L (1980) Congestion of production factors. Econometrica 48(7):1745–1753CrossRefGoogle Scholar
  15. Färe R., Grosskopf S, Lindgren B, Roos P (1992) Productivity changes in Swedish pharmacies 1980-1989: a non-parametric Malmquist approach. J Prod Anal 3(1):85–101CrossRefGoogle Scholar
  16. Färe R, Grosskopf S, Lindgren B, Roos P (1994) Productivity developments in Swedish hospitals: a Malmquist output index approach. In: Charnes A, Cooper WW, Lewin AY, Seiford LM (eds) Data envelopment analysis: theory, methodology, and applications. Kluwer Academic Publishers, Boston, pp 253–272Google Scholar
  17. Färe R, Grosskopf S, Lovell CK (2013) The measurement of efficiency of production, vol 6. Springer Science & Business MediaGoogle Scholar
  18. Flegg AT, Allen DO (2009) Congestion in the Chinese automobile and textile industries revisited. Socioecon Plann Sci 43(3):177–191CrossRefGoogle Scholar
  19. Ito K (2004) Foreign ownership and plant productivity in the Thai automobile industry in 1996 and 1998: a conditional quantile analysis. J Asian Econ 15(2):321–353CrossRefGoogle Scholar
  20. Ji Yb, Lee C, et al. (2010) Data envelopment analysis. Stata J 10(2):267–280CrossRefGoogle Scholar
  21. Khalifah NA (2013) Ownership and technical efficiency in Malaysia’s automotive industry: a stochastic frontier production function analysis. J Int Trade Econ Develop 22(4):509–535CrossRefGoogle Scholar
  22. Lieberman MB, Dhawan R (2005) Assessing the resource base of Japanese and US auto producers: a stochastic frontier production function approach. Manag Sci 51 (7):1060–1075CrossRefGoogle Scholar
  23. Lovold K (2013) A note on input congestion. Econ Lett 120(3):599–502CrossRefGoogle Scholar
  24. Medina-Alvarez MdL (2015) The demand for automobiles in Mexico before and after the 2008 economic crisis. In: Jetin B (ed) Global automobile demand: major trends in emerging economies, vol 2. Palgrave Macmillan, UK, pp 90–112Google Scholar
  25. Mendoza-Velazquez A, Santillana JA, Zárate-Mirón VE, Cabanas M (2018) Labor congestion in the automotive cluster: the role of wages. Competitiveness Review: Int Business J 28(4):386–407CrossRefGoogle Scholar
  26. Odeck J (2006) Congestion, ownership, region of operation, and scale: their impact on bus operator performance in Norway. Socioecon Plann Sci 40(1):52–69CrossRefGoogle Scholar
  27. Porter ME (2008) On competition. Harvard Business PressGoogle Scholar
  28. Porter ME (2014) Microeconomics of competitiveness, core concepts and course structure. Microeconomics of Competitiveness Faculty WorkshopGoogle Scholar
  29. Praet NV (2008) Caw girds for war. Financial Post, Wednesday, June 04Google Scholar
  30. Sáenz-Royo C, Salas-Fumás V (2013) Learning to learn and productivity growth: evidence from a new car-assembly plant. Omega 41(2):336–344CrossRefGoogle Scholar
  31. Sáenz-Royo C, Salas-Fumás V (2014) Long-and short-term efficiency in an automobile factory: an econometric case study. Int J Prod Econ 156:98–107CrossRefGoogle Scholar
  32. Sanchez-Ramirez C, Gastón Cedillo-Campos M, Perez-Villanueva P, Martinez-Flores JL (2011) Global economic crisis and Mexican automotive suppliers: impacts on the labor capital. Simulation 87(8):711–725CrossRefGoogle Scholar
  33. SE (2016) La industria automotriz Mexicana: situación actual, retos y oportunidades. Secretaría de Economía, Mexico CityGoogle Scholar
  34. SE (2017) Capacidades de los servicios de I+D+i en la industria automotriz mexicana. Secretaría de Economía, Mexico CityGoogle Scholar
  35. Sturgeon T, Van Biesebroeck J, Gereffi G (2008) Value chains, networks and clusters: reframing the global automotive industry. J Econ Geogr 8(3):297–321CrossRefGoogle Scholar
  36. Sueyoshi T, Sekitani K (2009) DEA congestion and returns to scale under an occurrence of multiple optimal projections. Eur J Oper Res 194(2):592–607CrossRefGoogle Scholar
  37. Tone K, Sahoo B (2004) Degree of scale economies and congestion: a unified DEA approach. Eur J Oper Res 158(3):755–772CrossRefGoogle Scholar
  38. Wei Q, Yan H (2004) Congestion and returns to scale in data envelopment analysis. Eur J Oper Res 153(3):641–660CrossRefGoogle Scholar
  39. Wójtowicz M (2019) The relocation of the automotive industry in Brazil and Mexico: between corporate strategies and industrial policies. In: Capik P, Dej M (eds) Relocation of economic activity: contemporary theory and practice in local, regional and global perspectives. Springer International Publishing, Cham, pp 33–51Google Scholar
  40. Yang Z, Shi Y, Yan H (2017) Analysis on pure e-commerce congestion effect, productivity effect and profitability in China. Socioecon Plann Sci 57:35–49CrossRefGoogle Scholar

Copyright information

© Springer Science+Business Media, LLC, part of Springer Nature 2019

Authors and Affiliations

  1. 1.IESDE School of ManagementPueblaMexico
  2. 2.Engineering Systems and DesignSingapore University of Technology and DesignSingaporeSingapore

Personalised recommendations