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Linking CGE and microsimulation models: a comparison of different approaches

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  • Giulia Colombo

    (Department of Economic and Social Science - Catholic University of Milan and SiNSYS NV/SA)

Abstract

In the literature that studies income inequality and poverty, a recent development has been the development of models that link together a macroeconomic model (usually a Computable General Equilibrium (CGE) model) and a microsimulation model. Linking the two types of model allows the modeller to take into account full agents? heterogeneity, whilst at the same time considering the general equilibrium effects of a proposed policy reform. In this paper, I first review in detail three approaches to building linked CGE-microsimulation models: one in accordance with the fully integrated approach, and two following the layered approach (the so-called Top-Down and Top-Down/Bottom-Up approaches). The principal goal of the paper is to present a considered evaluation of the merits and demerits of these alternative methods currently used to link CGE and microsimulation models. To do so I use all three approaches to model the macro- and micro-economic impacts of a policy shock to an archetypical economy (constructed using fictitious data), and compare the results. This analysis highlights the importance of (i) consistency between the underlying macro- and micro-data; and (ii) the precise mechanisms by which feedback effects are passed between the macro and micro models. I develop this latter point further by detailed analysis of the TD/BU approach outlined by Savard (2003), leading to a proposed refinement in the way that feedback effects from the micro level of analysis are incorporated back into the CGE model.

Suggested Citation

  • Giulia Colombo, 2010. "Linking CGE and microsimulation models: a comparison of different approaches," International Journal of Microsimulation, International Microsimulation Association, vol. 3(1), pages 72-91.
  • Handle: RePEc:ijm:journl:v:3:y:2010:i:1:p:72-91
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    6. Li, Jinjing & O'Donoghue, Cathal, 2012. "A methodological survey of dynamic microsimulation models," MERIT Working Papers 2012-002, United Nations University - Maastricht Economic and Social Research Institute on Innovation and Technology (MERIT).
    7. Jann Lay, 2010. "Sequential macro-micro modelling with behavioural microsimulations," International Journal of Microsimulation, International Microsimulation Association, vol. 3(1), pages 24-34.
    8. Debowicz, Dario & Golan, Jennifer, 2012. "The impact of Oportunidades on human capital and income distribution: a top-down/bottom-up approach," Conference papers 330252, Purdue University, Center for Global Trade Analysis, Global Trade Analysis Project.
    9. Yogi Vidyattama & Maheshwar Rao & Itismita Mohanty & Robert Tanton, 2014. "Modelling the impact of declining Australian terms of trade on the spatial distribution of income," International Journal of Microsimulation, International Microsimulation Association, vol. 7(1), pages 100-126.
    10. Liyanaarachchi, Tilak S. & Naranpanawa, Athula & Bandara, Jayatilleke S., 2016. "Impact of trade liberalisation on labour market and poverty in Sri Lanka. An integrated macro-micro modelling approach," Economic Modelling, Elsevier, vol. 59(C), pages 102-115.
    11. Gerald Nelson & Jessica Bogard & Keith Lividini & Joanne Arsenault & Malcolm Riley & Timothy B. Sulser & Daniel Mason-D’Croz & Brendan Power & David Gustafson & Mario Herrero & Keith Wiebe & Karen Coo, 2018. "Income growth and climate change effects on global nutrition security to mid-century," Nature Sustainability, Nature, vol. 1(12), pages 773-781, December.
    12. Matteo Richiardi & John Cockburn & Helene Maisonnave & Luca Tiberti, 2016. "Editorial," Post-Print hal-02314206, HAL.
    13. Andrew Feltenstein & Luciana Lopes & Janet Porras-Mendoza & Sally Wallace, 2014. "Modeling tax reform in developing countries," Chapters, in: Richard M. Bird & Jorge Martinez-Vazquez (ed.), Taxation and Development: The Weakest Link?, chapter 3, pages 69-102, Edward Elgar Publishing.
    14. Norbert Švarda & Jana Valachyová & Matúš Senaj & Michal Horváth & Zuzana Siebertová, 2018. "The end of the flat tax experiment in Slovakia: An evaluation using behavioural microsimulation linked with a dynamic macroeconomic framework," Discussion Papers 50, Central European Labour Studies Institute (CELSI).
    15. van Ruijven, Bas J. & O’Neill, Brian C. & Chateau, Jean, 2015. "Methods for including income distribution in global CGE models for long-term climate change research," Energy Economics, Elsevier, vol. 51(C), pages 530-543.
    16. Cecilia Llambi & Silvia Laens & Marcelo Perera, 2016. "Assessing the Impacts of a Major Tax Reform: a CGE-microsimulation analysis for Uruguay," International Journal of Microsimulation, International Microsimulation Association, vol. 9(1), pages 134-166.
    17. Matteo Richiardi & John Cockburn & Hélène Maisonnave & Luca Tiberti, 2016. "Editorial," International Journal of Microsimulation, International Microsimulation Association, vol. 9(1), pages 1-4.
    18. Melnikov, Nikolai B. & O’Neill, Brian C. & Dalton, Michael G. & van Ruijven, Bas J., 2017. "Downscaling heterogeneous household outcomes in dynamic CGE models for energy-economic analysis," Energy Economics, Elsevier, vol. 65(C), pages 87-97.
    19. Zhang, Xiao-Guang, 2016. "Solving a partial equilibrium model in a CGE framework: the case of a BMS model," Conference papers 332742, Purdue University, Center for Global Trade Analysis, Global Trade Analysis Project.
    20. Miriam Frey, 2016. "Income Inequality Effects of Ukraine’s Trade Liberalization with the EU. Are there 'two Ukraines'?," EcoMod2016 9664, EcoMod.
    21. Steven Gronau & Etti Winter & Ulrike Grote, 2020. "Aquaculture, fish resources and rural livelihoods: a village CGE analysis from Namibia’s Zambezi Region," Environment, Development and Sustainability: A Multidisciplinary Approach to the Theory and Practice of Sustainable Development, Springer, vol. 22(2), pages 615-642, February.

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