IDEAS home Printed from https://ideas.repec.org/p/ags/pugtwp/332827.html
   My bibliography  Save this paper

How Many Households Does a CGE Model Need and How Should They Be Disaggregated?

Author

Listed:
  • Cicowiez, Martin
  • Lofgren, Hans
  • Escobar, Pamela

Abstract

In this paper we analyze how the impact of shocks (in terms of changes in aggregate welfare, poverty, size distribution on income, and functional distribution of income) are influenced by the number of representative households (RHs) that are included and the criteria according to which they are disaggregated (“strategically” on the basis of sources of income or, alternatively, on the basis of levels of per-capita income or consumption). By varying the number of production factors, it also tests the sensitivity of the results to the functional disaggregation. The hypotheses are that (a) starting from a single RH, initial increases in the number of RHs has a strong impact on the results when the disaggregation is strategic but that the impact quite soon becomes miniscule; (b) the larger the number of income sources, the larger the payoffs from household disaggregation; and (c) there is a sharp contrast between the results from disaggregation by quantile and strategic disaggregation, reflecting more limited sensitivity to changes in the functional distribution when households are disaggregated on the basis of per-capita incomes. In short, it is hypothesized that there is a strong case for strategic disaggregation of households and that the payoffs from fine household disaggregation are limited. To study these issues, we built a simple static CGE model that works with alternative disaggregations of households and income sources. Specifically, our CGE model is applied to several variants – in terms of factor and/or household disaggregation – of a 2011 dataset for Guatemala. In its most disaggregated form, the dataset has 8 factors (unskilled salaried labor, skilled salaried labor, unskilled non-salaried labor, skilled non-salaried labor, capital, land, and two other natural resources), 24 sectors, and 13,100 households. In addition, households receive transfer incomes from the government and abroad

Suggested Citation

  • Cicowiez, Martin & Lofgren, Hans & Escobar, Pamela, 2017. "How Many Households Does a CGE Model Need and How Should They Be Disaggregated?," Conference papers 332827, Purdue University, Center for Global Trade Analysis, Global Trade Analysis Project.
  • Handle: RePEc:ags:pugtwp:332827
    as

    Download full text from publisher

    File URL: https://ageconsearch.umn.edu/record/332827/files/8539.pdf
    Download Restriction: no
    ---><---

    References listed on IDEAS

    as
    1. Dorothée Boccanfuso & Luc Savard & Antonio Estache, 2013. "The Distributional Impact of Developed Countries’ Climate Change Policies on Senegal: A Macro-Micro CGE Application," Sustainability, MDPI, vol. 5(6), pages 1-24, June.
    2. Dorothée Boccanfuso & Luc Savard, 2007. "Poverty and Inequality Impact Analysis Regarding Cotton Subsidies: A Mali-based CGE Micro-accounting Approach," Journal of African Economies, Centre for the Study of African Economies, vol. 16(4), pages 629-659, August.
    3. John Cockburn & Erwin L. Corong & Caesar B. Cororaton, 2008. "Poverty Effects of the Philippines’ Tariff Reduction Program: Insights from a Computable General Equilibrium Analysis," Asian Economic Journal, East Asian Economic Association, vol. 22(3), pages 289-319, September.
    Full references (including those not matched with items on IDEAS)

    Most related items

    These are the items that most often cite the same works as this one and are cited by the same works as this one.
    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. Saeed Solaymani, 2016. "Impacts of energy subsidy reform on poverty and income inequality in Malaysia," Quality & Quantity: International Journal of Methodology, Springer, vol. 50(6), pages 2707-2723, November.
    3. George Verikios & Xiao-guang Zhang, 2016. "Structural change and income distribution: the case of Australian telecommunications," Journal of the Asia Pacific Economy, Taylor & Francis Journals, vol. 21(4), pages 549-570, October.
    4. Balié, Jean & Minot, Nicholas & Valera, Harold Glenn A., 2021. "Distributional impacts of the rice tariffication policy in the Philippines," Economic Analysis and Policy, Elsevier, vol. 69(C), pages 289-306.
    5. Verikios, George & Zhang, Xiao-guang, 2013. "Structural change in the Australian electricity industry during the 1990s and the effect on household income distribution: A macro–micro approach," Economic Modelling, Elsevier, vol. 32(C), pages 564-575.
    6. Zhang, Xiao-Guang, 2015. "Incorporating household survey data into a CGE model," Conference papers 332628, Purdue University, Center for Global Trade Analysis, Global Trade Analysis Project.
    7. Bourguignon, François & Bussolo, Maurizio, 2013. "Income Distribution in Computable General Equilibrium Modeling," Handbook of Computable General Equilibrium Modeling, in: Peter B. Dixon & Dale Jorgenson (ed.), Handbook of Computable General Equilibrium Modeling, edition 1, volume 1, chapter 0, pages 1383-1437, Elsevier.
    8. Bouët, Antoine & Estrades, Carmen & Laborde, David, 2012. "Cooperation vs. non cooperation in the multilateral trading system: the impact on poverty and inequality in developing countries," Conference papers 332287, Purdue University, Center for Global Trade Analysis, Global Trade Analysis Project.
    9. Umed Temursho & Matthias Weitzel & Toon Vandyck, 2020. "Distributional impacts of reaching ambitious near-term climate targets across households with heterogeneous consumption patterns: A quantitative macro-micro assessment for the 2030 Climate Target Plan," JRC Research Reports JRC121765, Joint Research Centre.
    10. Yuanying Chi & Zhengquan Guo & Yuhua Zheng & Xingping Zhang, 2014. "Scenarios Analysis of the Energies’ Consumption and Carbon Emissions in China Based on a Dynamic CGE Model," Sustainability, MDPI, vol. 6(2), pages 1-26, January.
    11. Verikios, George & Zhang, Xiao-guang, 2015. "Reform of Australian urban transport: A CGE-microsimulation analysis of the effects on income distribution," Economic Modelling, Elsevier, vol. 44(C), pages 7-17.
    12. Dorothée Boccanfuso & G. Rodolphe A. Missinhoun & Luc Savard, 2010. "Réformes economiques et croissance pro-pauvre : une application macro-micro aux Philippines," Recherches économiques de Louvain, De Boeck Université, vol. 76(3), pages 257-288.
    13. García Merchán, Gabriela, 2023. "Agricultural Subsidies in the Economy of Ecuador – An Assessment of Impact Through CGE Modelling," Papers 1413, World Trade Institute.
    14. Essama-Nssah, B., 2008. "Assessing the redistributive effect of fiscal policy," Policy Research Working Paper Series 4592, The World Bank.
    15. Dorothée Boccanfuso & Véronique Gosselin & Jonathan Goyette & Luc Savard & Clovis Tanekou Mangoua, 2014. "An impact analysis of climate change and adaptation policies on the forestry sector in Quebec. A dynamic macro-micro framework," Cahiers de recherche 14-04, Departement d'économique de l'École de gestion à l'Université de Sherbrooke.
    16. Xinyao Wang & Dan Li & Yue Yu, 2022. "Current Situation and Optimization Countermeasures of Cotton Subsidy in China Based on WTO Rules," Agriculture, MDPI, vol. 12(8), pages 1-16, August.
    17. 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.
    18. Fousseini Traoré, 2012. "Do global cotton subsidies affect the Malian economy? New evidence from a multimarket-general equilibrium model," Economics Bulletin, AccessEcon, vol. 32(2), pages 1640-1652.
    19. Hans Lofgren & Martin Cicowiez, 2017. "A Proximity-Based Approach to Labor Mobility in CGE Models with an Application to Sub-Saharan Africa," Journal of Global Economic Analysis, Center for Global Trade Analysis, Department of Agricultural Economics, Purdue University, vol. 2(1), pages 120-165, June.
    20. Luc Savard & Dorothee Boccanfuso & Jonathan Goyette & Véronique Gosselin & Clovis Tanekou Mangoua, 2014. "An impact analysis of the impact of climate change and adaptation policies on the forestry sector in Quebec. A dyanamic macro-micro framework," EcoMod2014 6787, EcoMod.

    Corrections

    All material on this site has been provided by the respective publishers and authors. You can help correct errors and omissions. When requesting a correction, please mention this item's handle: RePEc:ags:pugtwp:332827. See general information about how to correct material in RePEc.

    If you have authored this item and are not yet registered with RePEc, we encourage you to do it here. This allows to link your profile to this item. It also allows you to accept potential citations to this item that we are uncertain about.

    If CitEc recognized a bibliographic reference but did not link an item in RePEc to it, you can help with this form .

    If you know of missing items citing this one, you can help us creating those links by adding the relevant references in the same way as above, for each refering item. If you are a registered author of this item, you may also want to check the "citations" tab in your RePEc Author Service profile, as there may be some citations waiting for confirmation.

    For technical questions regarding this item, or to correct its authors, title, abstract, bibliographic or download information, contact: AgEcon Search (email available below). General contact details of provider: https://edirc.repec.org/data/gtpurus.html .

    Please note that corrections may take a couple of weeks to filter through the various RePEc services.

    IDEAS is a RePEc service. RePEc uses bibliographic data supplied by the respective publishers.