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A top-down behaviour (TDB) microsimulation toolkit for distributive analysis

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  • Luca Tiberti
  • John Cockburn
  • Martín Cicowiez

Abstract

CGE models are often combined with microsimulation (MS) models to perform distributive impact analysis for fiscal or structural policies, or external shocks. This paper describes a user-friendly Stata-based toolkit to perform microsimulations combined with CGE models in a top down fashion. The toolkit is organized in various modules. It first estimates income generation by type of work and skill of workers. Then it estimates households’ specific price deflators based on individual utility. The changes estimated by a CGE model (or from other sources) in the employment (by skill and sector), in the wage payroll (by skill), in the revenues from self-employment activities (by skill) as well as in the commodities prices are fed into the MS model in a consistent way. Once the new vector of real consumption or revenue is estimated, it performs a series of distributive analysis, such as the computation of standard poverty and inequality indices, their decomposition by income factor, robustness analysis and growth incidence curves, and compare the baseline with the simulation results. This makes it possible to run standard poverty and distributive analyses, and to see whether a given shock or policy has had some impact on household welfare and who are the most affected households. Based on such information, social protection policies can be accurately designed in order to minimize the, e.g., negative effects of a given shock in a cost-effective manner. An illustrative analysis is run on data from Uganda.

Suggested Citation

  • Luca Tiberti & John Cockburn & Martín Cicowiez, 2017. "A top-down behaviour (TDB) microsimulation toolkit for distributive analysis," Working Papers PMMA 2017-24, PEP-PMMA.
  • Handle: RePEc:lvl:pmmacr:2017-24
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    Cited by:

    1. Camara, Alhassane & Savard, Luc, 2023. "Impact of agricultural input subsidy policy on market participation and income distribution in Africa: A bottom-up/top-down approach," Economic Modelling, Elsevier, vol. 129(C).
    2. Nziengui Mamboundou, Pierre, 2019. "Agricultural economic reforms, gender inequalities and poverty in Senegal," Conference papers 333063, Purdue University, Center for Global Trade Analysis, Global Trade Analysis Project.
    3. Diallo, Souleymane Sadio & Mevel-Bidau, Simon, 2021. "Poverty Impact Assessment of the AfCFTA’s Implementation: A Macro-Micro Simulation Approach," Conference papers 333275, Purdue University, Center for Global Trade Analysis, Global Trade Analysis Project.
    4. Luca Tiberti & Martin Cicowiez & John Cockburn, 2018. "A Top-Down with Behaviour (TDB) Microsimulation Toolkit for Distributive Analysis," International Journal of Microsimulation, International Microsimulation Association, vol. 11(2), pages 191-213.
    5. Emini, Christian Arnault, 2020. "Impacts of the 2014-16 drop in oil prices on child poverty in Chad and options for a policy response: Analysis using a recursive dynamic CGE model with fully integrated microsimulations," Conference papers 333220, Purdue University, Center for Global Trade Analysis, Global Trade Analysis Project.

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    Keywords

    CGE-microsimulation model; poverty and distributive analysis; Uganda;
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