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Back to basics: The Comanor–Wilson MES index revisited

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Abstract

The present article attempts to investigate the validity of the Comanor–Wilson Minimum Efficient Size (MES) measure. The basic assumption is that firms that have exhausted scale economies are in non-increasing returns to scale. The same firms are also assumed to have a size greater than MES estimated on sales (total turnover), employment or fixed assets. Data Envelopment Analysis (DEA) is used, on a sample of firms in three Greek manufacturing industries, to classify firms in operation according to increasing or non-increasing returns to scale. On the basis of the results of the DEA input oriented model, the MES measure correctly predicts over 85% of the cases. A probit model is applied to those cases that are not identically predicted by MES concerning returns to scale. Results indicate that technical efficiency, size and age are the factors that compel MES to yield the same prediction as the DEA approach.

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Notes

  1. DEA development is own to Banker et al. (1984) and Färe et al. (1985). For a detailed presentation of the DEA approach see Seiford (1996), Seiford and Zhu (1999) and Cooper et al. (2006)

References

  • Acs, Z. J., & Audretsch, D. (1988). Innovation in large and small firms: An empirical analysis. American Economic Review, 78, 678–690.

    Google Scholar 

  • Acs, Z. J., & Audretsch, D. B. (1989). Births and firm size. Southern Economic Journal, 56, 467–475.

    Article  Google Scholar 

  • Acs, Z. J., & Audretsch, D. (1990). Innovation and small firms. Cambridge, MA: MIT Press.

    Google Scholar 

  • Agarwal, R., & Audretsch, D. (2001). Does entry size matter? The impact of the life cycle and technology on firm survival. The Journal of Industrial Economic, XLIX, 21–43.

    Article  Google Scholar 

  • Arauzo-Carod, J. M., & Segarra-Blasco, A. (2005). The determinants of entry are not independent of start-up size: Some evidence from Spanish manufacturing. Review of Industrial Organization, 27, 147–165.

    Article  Google Scholar 

  • Audretsch, D. (1995). Innovation and industry evolution. Cambridge, Massachusetts: MIT Press.

    Google Scholar 

  • Audretsch, D. B., Santarelli, E., & Vivarelli, M. (1999). Start up size and industrial dynamics: some evidence from Italian manufacturing. International Journal of Industrial Organization, 17, 965–983.

    Article  Google Scholar 

  • Audretsch, D., Van Leeuwen G., Menkveld, B., & Thurik, R. (2001). Market dynamics in the Netherlands: Competition policy and the role of small firms. International Journal of Industrial Organization, 19, 795–821.

    Article  Google Scholar 

  • Banker, R. D., Charnes, A., & Cooper, W. W. (1984). Some models for estimating technical and scale inefficiencies in Data Envelopment Analysis. Management Science, 30, 1078–1092.

    Article  Google Scholar 

  • Cabral, L. (1995), Sunk costs, firm size and firm growth. Journal of Industrial Economics, 43, 161–172.

    Article  Google Scholar 

  • Charnes A, Cooper, W. W., & Rhodes, E. (1978). Measuring the efficiency of decision-making units. European Journal of Operational Research, 34, 429–444.

    Article  Google Scholar 

  • Calvo, J. L. (2006). Testing Gibrat’s law for small, young and innovating firms. Small Business Economics, 26, 117–123.

    Article  Google Scholar 

  • Cooper, W. W., Seiford, L. M., & Tone, K. (2006). Introduction to data envelopment analysis and its uses. Berlin, Springer.

    Google Scholar 

  • Comanor, W. S., & Wilson, T. A. (1967). Advertising, market structure and performance. Review of Economics and Statistics, 49, 423–440.

    Article  Google Scholar 

  • Comanor, W. S., & Wilson, T. A. (1969). Advertising and the advantage of size. The American Economic Review, 59, 87–98.

    Google Scholar 

  • Cressy, R. (2006). Why do most firms die young?. Small Business Economics, 26, 103–116.

    Article  Google Scholar 

  • Damianos, D., Dimara E., Hassapoyannes, K., & Skuras, D. (1998). Greek Agriculture in a changing international environment. Ashgate: Aldershot, Hants.

    Google Scholar 

  • Davies, S., Lyons B., Dixon H., & Geroski, P. (1988). Economics of industrial organisation. London: Longmans.

    Google Scholar 

  • Dimara, D., Pantzios, C., Skuras, D., & Tsekouras, K. (2005). The impacts of regulated notions of quality on farms’ productive efficiency: A DEA application. European Journal of Operational Research, 161, 416–431.

    Article  Google Scholar 

  • Färe, R., Grosskopf S., & Lovell, C. A. K. (1985). The measurement of efficiency of production. Kluwer Academic Publishers, Boston.

    Google Scholar 

  • Farrell M. J. (1957). The measurement of productive efficiency. Journal of the Royal Statistical Society, 120(Part III), 253–278.

    Google Scholar 

  • Geroski, P. A. (1995). What do we know about entry?. International Journal of Industrial Organization, 13, 421–440.

    Article  Google Scholar 

  • Greene W. (1997). Econometric analysis. Prentice Hall International, London.

    Google Scholar 

  • Hall, G. J. (2000). Non-convex costs and capital utilization: A study of production scheduling at automobile assembly plants. Journal of Monetary Economics, 45, 681–716.

    Article  Google Scholar 

  • Huang Yasheng (2002). Between two coordination failures: Automotive industrial policy in China with a comparison to Korea. Review of International Political Economy, 9, 538–573.

    Article  Google Scholar 

  • Lotti, F., Santarelli, E., & Vivarelli, M. (2001). The relationship between size and growth: the case of Italian newborn firms. Applied Economics Letters, 8, 451–454.

    Article  Google Scholar 

  • Maddala, G. (1983). Limited dependent and qualitative variables in econometrics. MA: Cambridge University Press.

    Google Scholar 

  • Manjon-Antolin, M. C. (2004). Firm size and short-term dynamics in aggregate entry and exit. CentER Discussion Paper, 2, Center for Economic Research, Tilburg University.

  • Rotemberg, J. J., & Saloner, G. (2000). Competition and human capital accumulation: A theory of interregional specialization and trade. Regional Science and Urban Economics, 30, 373–404.

    Article  Google Scholar 

  • Sakakibara, M. (2001). Cooperative research and development: Who participates and in which industries do projects take place? Research Policy, 30, 993–1018.

    Article  Google Scholar 

  • Scott, F., & Anstine, J. (2002). Critical mass in the production of Ph.Ds: A multidisciplinary study. Economics of Education Review, 21, 29–42.

    Article  Google Scholar 

  • Seiford, L. M. (1996). Data envelopment analysis: The evolution of the state of the art (1978–1995). Journal of Productivity Analysis, 7, 99–137.

    Article  Google Scholar 

  • Seiford, L. M., & Zhu, J. (1999). Sensitivity and stability of the classifications of returns to scale in data envelopment analysis. Journal of Productivity Analysis, 12, 55–75.

    Article  Google Scholar 

  • Yatchew, A., & Griliches, Z. (1984). Specification error in probit models. Review of Economics and Statistics, 66, 134–139.

    Google Scholar 

  • Weiss, C. R. (1998). Size, growth, and survival in the upper Austrian Farm sector. Small Business Economics, 10, 305–312.

    Article  Google Scholar 

  • Varian, H. R. (1999). Intermediate microeconomics: A modern approach, (5th ed.). Norton, W. W. & Co, New York.

    Google Scholar 

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Acknowledgements

The authors would like to thank Professor David Audretsch and participants of the 2nd Hellenic Workshop on Productivity and Efficiency Measurement (HEWPEM, (http://hewpem.econ.upatras.gr/) for useful comments on an earlier draft of this work. We are also grateful to two anonymous referees for useful comments and suggestions. All errors and omissions remain our responsibility. This publication arises out of the ‘KARATHEODORIS’ research program No. 1946, financed and administered by the University of Patras’ Research Committee.

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Correspondence to Kostas Tsekouras.

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Tsekouras, K., Dimara, E., Skuras, D. et al. Back to basics: The Comanor–Wilson MES index revisited. Small Bus Econ 32, 111–120 (2009). https://doi.org/10.1007/s11187-007-9081-y

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