Skip to main content

Advertisement

SpringerLink
Log in

Are small firms less efficient?

  • Published:
Small Business Economics Aims and scope Submit manuscript

Abstract

This paper compares the technical efficiency of small and medium-sized enterprises (SMEs) with that of large firms and studies the factors influencing technical efficiency for Taiwan’s electronics industry. Unlike conventional studies, we use two alternative approaches to control for the influence of size effect. One is the two-stage switching regression to correct for endogenous size effect on technical efficiency and, the other is, a metafrontier production function for firms in different groups. The main results are as follows. First, the average technical efficiency for large firms is higher than that of SMEs, without considering the size effect, and lower when considering the endogenous choice on firm size. This study cannot, therefore, conclude that there is a negative size–technical efficiency relationship. It however, sheds light on the importance of size effect on the size–technical efficiency nexus. Second, the estimates on the determinants of technical efficiency show that being a subcontractor has a statistically significant positive influence on SMEs’ technical efficiency, but the effect decreases with firm size.

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

Access this article

Log in via an institution

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

Instant access to the full article PDF.

Institutional subscriptions

Similar content being viewed by others

Notes

  1. For a study on the role of SMEs in economic development and growth, please refer to Acs (1992). On the basis of both economics and welfare, You (1995) argued that an expansion of the small-firm segment leads to more efficient resource allocation, less unequal income distribution, and less underemployment, because small firms tend to use more labor-intensive technologies.

  2. The concepts of efficiency, allocative efficiency, and technical efficiency, please refer to Kumbhakar and Lovell (2000).

  3. In fact, this is just why the statement that SMEs tend to employ a more labor-intensive technology is usually stressed in literature (e.g., You 1995; Berry and Mazumdar 1991; Kitching 1982).

  4. X-efficiency is the effectiveness with which a given set of inputs are used to produce outputs. If a firm is producing the maximum output it can, given the resources it employs and the best technology available, it is said to be x-efficient. Therefore, X-inefficiency occurs when X-efficiency is not achieved. The concept of X-efficiency was introduced by Leibenstein (1966).

  5. For the linkage between factor demand and a firm’s technology and factor price, see Little et al. (1987) for an extensive discussion.

  6. As for the pre-1990 studies, please refer to Lundvall and Battese (2000) for a comprehensive review.

  7. A large body of related literature defines this behavior as “outsourcing.” We prefer to adopt the term “subcontracting” in this article, because the term is traditionally used in many of the Asian countries.

  8. For the calculation of technical efficiency in the stochastic frontier production function, please refer to Jondrow et al. (1982).

  9. There is also a two-stage approach using the technique of the Tobit model as the second-step to investigate the determinants of technical efficiency. However, recent research has shown how inappropriate the two-step framework is and suggests that the single-stage estimation procedure is more appropriate (Wang and Schmidt 2002; Greene 2005; Simar and Wilson 2007).

  10. Due to the null values of inputs or output, 246 observations are dropped from our empirical work.

  11. The value of the generalized likelihood-ratio (LR) statistic not displayed in this article shows that the Cobb–Douglas technology is rejected, meaning that input and substitution elasticities are not constant among firms.

  12. There is also literature developing a single-stage approach combining the latent class structure and the stochastic frontier approach, without the need for the a priori sample separation information (see Kumbhakar and Tsionas 2006; Orea and Kumbhakar 2003; Greene 2002).

  13. Refer to Battese et al. (2004) for more details.

  14. The LR statistic is defined by \(\lambda=-2\ \{\ln[\hbox{H}_{0}/\hbox{H}_{1}]\}= -2\ \{\ln[(\hbox{H}_{0})]-\ln[(\hbox{H}_{1})]\},\) where \(\ln[(\hbox{H}_{0})]\) is the value of the log likelihood function for the stochastic frontier estimated by pooling the data for LEs and SMEs and \(\ln[(\hbox{H}_{1})]\) is the sum of the values of the likelihood functions for production frontiers of LEs and SMEs.

References

  • Abraham, K. G., & Taylor, S. K. (1996). Firms’ use of outside contractors: Theory and evidence. Journal of Labor Economics, 14, 394–424.

    Article  Google Scholar 

  • Acs, Z. J. (1992). Small business economics: A global perspective. Challenge, 35(6), 38–44.

    Google Scholar 

  • Aigner, D. J., Lovell, C. A. K., & Schmidt, P. (1977). Formulation and estimation of stochastic frontier function models. Journal of Econometrics, 6, 21–37.

    Article  Google Scholar 

  • Alvarez, R., & Crespi, G. (2003). Determinants of technical efficiency in small firms. Small Business Economics, 20, 233–244.

    Article  Google Scholar 

  • Aw, B. Y., & Batra, G. (1998). Technological capability and firm efficiency in Taiwan. World Bank Economic Review, 12(1), 59–79.

    Google Scholar 

  • Aw, B. Y., & Huang, A. R. (1995). Productivity and the export market: A firm-level analysis. Journal of Development Economics, 47(2), 313–332.

    Article  Google Scholar 

  • Aw, B. Y., Chen, X., & Roberts, M. J. (2001). Firm-level evidence on productivity differentials and turnover in Taiwanese manufacturing. Journal of Development Economics, 66, 51–86.

    Article  Google Scholar 

  • Battese, G. E., & Coelli, T. J. (1995). A model for technical inefficiency effects in a stochastic frontier production function for panel data. Empirical Economics, 20, 325–332.

    Article  Google Scholar 

  • Battese, G. E., & Rao, D. S. P. (2002). Technology gap, efficiency and a stochastic metafrontier function. International Journal of Business and Economics, 1(2), 1–7.

    Google Scholar 

  • Battese, G. E., Rao, D. S. P., & O’Donnell, C. J. (2004). A metafrontier production function for estimation of technical efficiencies and technology gaps for firms operation under different technologies. Journal of Productivity Analysis, 21, 91–103.

    Article  Google Scholar 

  • Berry, A., & Mazumdar, D. (1991). Small-scale industry in the Asian-Pacific region. Asian-Pacific Economic Literature, 5, 35–67.

    Article  Google Scholar 

  • Biggs, T., Shah, M., & Srivastava, P. (1996). Technological capabilities and learning in African enterprises. Case studies series. Washington: Regional Program of Enterprise Development (RPEP), World Bank.

    Google Scholar 

  • Brada J. C., King, A. E., & Ma, C. Y. (1997). Industrial economics of transition: Determinants of enterprise efficiency in Czechoslovakia and Hungary. Oxford Economic Paper, 49, 104–127.

    Google Scholar 

  • Bunch, D., & Smiley, R. (1992). Who deters entry? Evidence on the use of strategic entry deterrents. Review of Economics and Statistics, 79, 509–521.

    Article  Google Scholar 

  • Cabral, L. M. B., & Mata, J. (2003). On the evolution of the firm size distribution: Facts and theory. American Economic Review, 93, 1075–1090.

    Article  Google Scholar 

  • Coelli, T. J. (1996). A Guide to FRONTIER Version 4.1: A computer program for frontier production function estimation. CEPA Working Paper 97/07, Department of Econometrics, University of New England, Australia.

  • Fixler, D., & Siegel, D. (1999). Outsourcing and productivity growth in services. Structural Change and Economic Dynamic, 10, 177–194.

    Article  Google Scholar 

  • Garen, J. E. (1984). The returns to schooling: A selectivity bias approach with a continuous choice variable. Econometrica, 25, 1199–1218.

    Article  Google Scholar 

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

    Article  Google Scholar 

  • Glass, A. J., & Saggi, K. (2001). Innovation and wage effects of international outsourcing. European Economic Review, 45(1), 67–86.

    Article  Google Scholar 

  • Greene, W. (2002). Alternative panel data estimators for stochastic frontier models. Working Paper, Department of Economics, Stern School of Business, NYU.

  • Greene, W. (2005). Reconsidering heterogeneity in panel data estimators of the stochastic frontier model. Journal of Econometrics, 126(2), 269–303.

    Article  Google Scholar 

  • Haddad, M., & Harrison, A. (1993). Are there positive spillovers from foreign direct investment? Evidence from panel data for Morocco. Journal of Development Economics, 42, 51–74.

    Article  Google Scholar 

  • Hayami, Y. (1969). Sources of agricultural productivity gap among selected countries. American Journal of Agricultural Economics, 51, 564–575.

    Article  Google Scholar 

  • Heckman, J. J. (1979). Sample selection bias as a specification error. Econometrica, 47, 153–161.

    Article  Google Scholar 

  • Heshmati, A. (2003). Productivity growth, efficiency and outsourcing in manufacturing and service industries. Journal of Economic Surveys, 17, 79–112.

    Article  Google Scholar 

  • Hill, H., & Kalirajan, K. P. (1993). Small enterprise and firm-level technical efficiency in the Indonesian garment industry. Applied Economics, 25(9), 1137–1144.

    Article  Google Scholar 

  • Hu, M. W., & Schive, C. (1997). A study on the productivity and efficiency of SMEs in Taiwan manufacturers. Taiwan Economic Review, 25(1), 1–26.

    Google Scholar 

  • Jondrow, J., Lovell, C. A. K., Materov, I. S., & Schmidt, P. (1982). On the estimation of technical inefficiency in the stochastic frontier production function model. Journal of Econometrics, 19, 233–238.

    Article  Google Scholar 

  • Jovanovic, B. (1982). Selection and the evolution of industries. Econometrica, 50(3), 649–670.

    Article  Google Scholar 

  • Kimura, F. (2002). Subcontracting and the performance of small and medium firms in Japan. Small Business Economics, 18, 163–175.

    Article  Google Scholar 

  • Kitching, G. (1982). Development and underdevelopment in historical perspective. London: Metheun.

    Google Scholar 

  • Kumbhakar, S. C., & Lovell, C. A. K. (2000). Stochastic frontier analysis. Cambridge: Cambridge University Press.

    Google Scholar 

  • Kumbhakar, S. C., & Tsionas, E. G. (2006). Does deregulation change economic behavior of firms? A latent class approach. Working Paper, Department of Economics, University of Crete.

  • Lau, L. J., & Yotopoulos, P. A. (1989). The meta-production function approach to technological change in world agriculture. Journal of Development Economics, 31, 241–269.

    Article  Google Scholar 

  • Leibenstein, H. (1966). Allocative efficiency versus X-efficiency. American Economic Review, 56(3), 392–415.

    Google Scholar 

  • Little, I. M. D., Mazumdar, D., & Page, J. M. Jr. (1987). Small manufacturing enterprises: A comparative analysis of Indian and other economies. Washington: Oxford University Press.

    Google Scholar 

  • Lundvall, K., & Battese, G. E. (2000). Firm size, age and efficiency: Evidence from kenyan manufacturing firms. Journal of Development Studies, 36, 146–163.

    Article  Google Scholar 

  • Marsili, O. (2002). Technological regimes and source of entrepreneurship. Small Business Economics, 19, 217–231.

    Article  Google Scholar 

  • Orea, L., & Kumbhakar, S. C. (2003). Efficiency measurement using a stochastic frontier latent class model. State University of New York at Binghamton, mimeo.

  • Pack, H. (1982). Aggregate implications of factor substitution in industrial processes. Journal of Development Economics, 11, 1–38.

    Article  Google Scholar 

  • Roberts, M. J., & Tybout, J. R. (1996). Industrial evolution in developing countries: Micro patterns of turnover, productivity and market structure. Oxford: Oxford University Press

    Google Scholar 

  • Sharma, K. R., & Leung, P. S. (2000). Technical efficiency of carp production in India: A stochastic frontier production function analysis. Aquaculture Research, 31, 937–947.

    Article  Google Scholar 

  • Simar, L., & Wilson, P. W. (2007). Estimation and inference in two-stage, semi-parametric models of production processes. Journal of Econometrics, 136(1), 31–64.

    Article  Google Scholar 

  • Soderbom, M., & Teal, F. (2004). Size and efficiency in African manufacturing firms: Evidence from firm-level panel data. Journal of Development Economics, 73, 369–394.

    Article  Google Scholar 

  • Taylor, C. R., & Wiggins, S. N. (1997). Competition or compensation: Supplier incentives under the American and Japanese subcontracting systems. American Economic Review, 87(4), 598–618.

    Google Scholar 

  • Taymaz, E. (2005). Are small firms really less productive? Small Business Economics, 25, 429–445.

    Article  Google Scholar 

  • Taymaz, E., & Saatci, G. (1997). Technical change and efficiency in Turkish manufacturing industries. Journal of Productivity Analysis, 8, 461–475.

    Article  Google Scholar 

  • Wang, H. J., & Schmidt, P. (2002). One-step and two-step estimation of the effects of exogenous variables on technical efficient levels. Journal of Productivity Analysis, 18, 129–144.

    Article  Google Scholar 

  • Yang, C. H. (2003). Exporting and productivity—a firm-level analysis of the Taiwan electronics industry. Developing Economies, 41(3), 340–361.

    Article  Google Scholar 

  • You, J. I. (1995). Small firms in economic theory. Cambridge Journal of Economics, 19(3), 441–462.

    Google Scholar 

Download references

Acknowledgments

We thank anonymous referees and the Editor for their helpful suggestions and Chia-Hui Huang for excellent research assistant.

Author information

Authors and Affiliations

  1. Department of Economics, National Central University, 300, Jhongda Road, Jhongli, 320, Taiwan

    Chih-Hai Yang

  2. Department of Industrial Economics, Tamkang University, Taipei, Taiwan

    Ku-Hsieh Chen

Authors
  1. Chih-Hai Yang
    View author publications

    You can also search for this author in PubMed Google Scholar

  2. Ku-Hsieh Chen
    View author publications

    You can also search for this author in PubMed Google Scholar

Corresponding author

Correspondence to Chih-Hai Yang.

 

 

Appendix 1 The estimations of metafrontier production function
Full size table

Rights and permissions

Reprints and permissions

About this article

Cite this article

Yang, CH., Chen, KH. Are small firms less efficient?. Small Bus Econ 32, 375–395 (2009). https://doi.org/10.1007/s11187-007-9082-x

Download citation

  • Received:

  • Accepted:

  • Published:

  • Issue Date:

  • DOI: https://doi.org/10.1007/s11187-007-9082-x

Keywords

JEL Classifications

Search

Navigation