Melitz Equals Armington Plus Endogenous Productivity and Preferences

  • Peter B. Dixon
  • Michael Jerie
  • Maureen T. Rimmer
Chapter
Part of the Advances in Applied General Equilibrium Modeling book series (AAGEM)

Abstract

This chapter continues the exploration from Chap.  2 of the relationship between the Melitz and Armington models. We find that the principal results from a Melitz model can be obtained from an Amington model with additional equations that endogenize factor productivity for industries and preferences by households between goods obtained from different supplying regions. In short, Melitz equals Armington plus endogenous productivity and preferences (M = A+) This idea comes out of the algorithm devised by Balistreri and Rutherford (BR 2013) for solving general equilibrium models that contain Melitz sectors. As we describe in this chapter, the BR algorithm involves: solving Melitz sectors one at a time with guessed values of economy-wide variables; passing productivity and preference results from the Melitz sectoral computations to an Armington general equilibrium model; solving the Armington model and passing results for economy-wide variables back to the Melitz sectoral computations. We don’t think an algorithmic approach such as this is necessary for solving Melitz general equilibrium models. Nevertheless the basic insight encapsulated in the (M = A+) equation is of considerable interest. It means that the results from a Melitz general equilibrium model for the effects of a trade reform can be interpreted as the sum of the effects in an Armington model of the reform and particular productivity and preference changes. With this interpretation, CGE modelers can draw on 40 years’ experience with Armington models to help them understand results from Melitz models. We illustrate this in Chap.  6.

Keywords

Decomposition algorithm Balistreri Rutherford Melitz Armington Result interpretation 

References

  1. Balistreri, E., & Rutherford, T. (2013). Computing general equilibrium theories of monopolistic competition and heterogeneous firms (Chapter 23). In P. B. Dixon & D. W. Jorgenson (Eds.), Handbook of computable general equilibrium modeling (pp. 1513–1570). Amsterdam: Elsevier.Google Scholar
  2. Bisschop, J., & Meeraus, A. (1982). On the development of a general algebraic modeling system in a strategic planning environment. Mathematical Programming Study, 20, 1–19.CrossRefGoogle Scholar
  3. Brooke, A., Meeraus, A., & Kendrick, D. (1992). Release 2.25: GAMS a user’s guide. San Francisco: The Scientific Press.Google Scholar
  4. Harrison, W. J., Horridge, J. M., Jerie, M., & Pearson, K. R. (2014). GEMPACK manual, GEMPACK Software, ISBN 978-1-921654-34-3. Available at http://www.copsmodels.com/gpmanual.htm.
  5. Horridge, J. M., Meeraus, A., Pearson, K., & Rutherford, T. (2013). Software platforms: GAMS and GEMPACK (Chap. 20). In P. B. Dixon & D. W. Jorgenson (Eds.), Handbook of computable general equilibrium modeling (pp. 1331–1382). Amsterdam: Elsevier.CrossRefGoogle Scholar
  6. Pearson, K. R. (1988). Automating the computation of solutions of large economic models. Economic Modelling, 5(4), 385–395.CrossRefGoogle Scholar

Copyright information

© Springer Nature Singapore Pte Ltd. 2018

Authors and Affiliations

  • Peter B. Dixon
    • 1
  • Michael Jerie
    • 1
  • Maureen T. Rimmer
    • 2
  1. 1.Centre of Policy StudiesVictoria UniversityMelbourneAustralia
  2. 2.Centre of Policy StudiesVictoria UniversityMelbourneAustralia

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