Returns-to-Scale in Software Production: A Comparison of Approaches

  • Patricia E. Byrnes
  • Thomas P. Frazier
  • Thomas R. Gulledge


The literature on the software development industry (summarized in [2]) contains references to returns-to-scale as a factor in software development productivity. As noted in [2], most studies have typically related project size to labor productivity. A general finding is that software development tools and more specialized labor are usually employed on larger, in terms of project size, software projects. However, this observation does not necessarily imply increasing returns to a particular input, such as software development tools or more specialized labor. The confusion in the literature stems from the use of the term scale and the more general observation that large projects rarely have the same capital-labor mix as their smaller counterparts. In this paper returns-to-scale estimates are allowed to vary with both project capital-labor mix and project size.


Production Function Software Development Software Project Project Size Software Development Project 
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  1. [1]
    Banker, R.D., Estimating Most Productive Scale Size Using Data Envelopment Analysis, European Journal of Operational Research, Vol. 17 (1984), 35–44.CrossRefGoogle Scholar
  2. [2]
    Banker, R.D. and C.F. Kemerer, Scale Economies in New Software Development, IEEE Transactions on Software Engineering, Vol. 15, 1199–1205.Google Scholar
  3. [3]
    Boehm, Barry W. Software Engineering Economics. Englewood Cliffs: Prentice-Hall, 1981.Google Scholar
  4. [4]
    Boyd, G., Factor Intensity and Site Geology as Determinants of Returns-to-Scale in Coal Mining, Review of Economics and Statistics, Vol. 69 (1987), 18–23.CrossRefGoogle Scholar
  5. [5]
    Defense Logistics Agency. DoD Automated Resources Management System (ARMS), Users Guide, April, 1985.Google Scholar
  6. [6]
    Byrnes, P., R. Färe, and S. Grosskopf, Measuring Productive Efficiency: An Application to Illinois Strip Mines, Management Science, Vol. 30 (1984), 671–681.CrossRefGoogle Scholar
  7. [7]
    Färe, R., Fundamentals of Production Theory. Berlin: Springer-Verlag, 1988.Google Scholar
  8. [8]
    Färe, R., On Scaling Laws for Production Functions, Zeitschrift für Operations Research, Vol. 17 (1973), 195–205.CrossRefGoogle Scholar
  9. [9]
    Ferguson, C.E., The Neoclassical Theory of Production and Distribution. Cambridge: Cambridge University Press, 1969.CrossRefGoogle Scholar
  10. [10]
    Levitan, K.B., J. Salasin, T.P. Frazier, and B.N. Angier, Final Report on the Status of Software obsolescence in the DoD, P-2126, Institute for Defense Analyses, 1988.Google Scholar
  11. [11]
    Lovell, C.A.K. and P. Schmidt, A Comparison of Alternative Approaches to the Measurement of Productive Efficiency, In A. Dogramaci and R. Färe, Editors, Applications of Modern Production Theory: Efficiency and Productivity. Boston: Kluwer Academic Publishers, 1988.Google Scholar
  12. [12]
    Shephard, R.W., Theory of Cost and Production Functions. Princeton: Princeton University Press, 1970.Google Scholar
  13. [13]
    Zellner, A., J. Kmenta, and J. Dreze, Specification and Estimation of Cobb-Douglas Production Function Models, Econometrica, Vol. 34 (1966), 784–795.CrossRefGoogle Scholar

Copyright information

© Springer-Verlag Berlin · Heidelberg 1993

Authors and Affiliations

  • Patricia E. Byrnes
    • 1
  • Thomas P. Frazier
    • 2
  • Thomas R. Gulledge
    • 3
  1. 1.School of Public Policy and ManagementOhio State UniversityColumbusUSA
  2. 2.Institute for Defense AnalysesAlexandriaUSA
  3. 3.The Institute of Public PolicyGeorge Mason UniversityFairfaxUSA

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