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The Role of Survey Data in Microsimulation Models for Social Policy Analysis

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  • Alberto Martini
  • Ugo Trivellato

Abstract

Questions about the impact of social policies cannot be answered solely on the basis of official statistics made available in tabular format. The access to microdata allows policy analysts to answer these questions with a larger set of analytical tools, in particular microsimulation models. This paper examines the interface between survey data and microsimulation models. We review different types of microsimulation models (static and dynamic) and their data requirements. We then restrict the attention to survey‐based microdata, and examine issues in survey design, questionnaire content, data quality, and dissemination policy that are important from the perspective of microsimulation.

Suggested Citation

  • Alberto Martini & Ugo Trivellato, 1997. "The Role of Survey Data in Microsimulation Models for Social Policy Analysis," LABOUR, CEIS, vol. 11(1), pages 83-112, April.
    • Handle: RePEc:bla:labour:v:11:y:1997:i:1:p:83-112
    DOI: 10.1111/1467-9914.00030
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    Cited by:

    1. M. Spielauer, 2000. "Microsimulation of Life Course Interactions between Education, Work, Partnership Forms and Children in Five European Countries," Working Papers ir00032, International Institute for Applied Systems Analysis.
    2. Marike Knoef & Rob Alessie & Adriaan Kalwij, 2013. "Changes in the Income Distribution of the Dutch Elderly between 1989 and 2020: a Dynamic Microsimulation," Review of Income and Wealth, International Association for Research in Income and Wealth, vol. 59(3), pages 460-485, September.
    3. Aurélien Goutsmedt & Cléo Chassonnery-Zaïgouche, 2023. "Modeling intervention: The Political element in Barbara Bergmann's micro-to-macro simulation projects," Working Papers hal-04208686, HAL.
    4. Eugenio Zucchelli & Andrew M Jones & Nigel Rice, 2012. "The evaluation of health policies through dynamic microsimulation methods," International Journal of Microsimulation, International Microsimulation Association, vol. 5(1), pages 2-20.
    5. Ana Kreter & Gianni Betti & Renata Del-Vecchio & Jefferson Staduto, 2015. "The Siena Micro-Simulation Model (SM2): a contribution for informality studies in Brazil," Quality & Quantity: International Journal of Methodology, Springer, vol. 49(6), pages 2251-2268, November.
    6. Rembert De Blander & Ingrid Schockaert & André Decoster & Patrick Deboosere, 2017. "Projected Population, Inequality and Social Expenditures: The Case of Flanders," International Journal of Microsimulation, International Microsimulation Association, vol. 10(3), pages 92-133.
    7. Delli Gatti,Domenico & Fagiolo,Giorgio & Gallegati,Mauro & Richiardi,Matteo & Russo,Alberto (ed.), 2018. "Agent-Based Models in Economics," Cambridge Books, Cambridge University Press, number 9781108400046, October.
    8. Zucchelli, E & Jones, A.M & Rice, N, 2010. "The evaluation of health policies through microsimulation methods," Health, Econometrics and Data Group (HEDG) Working Papers 10/03, HEDG, c/o Department of Economics, University of York.
    9. Elisa Baroni & Matteo Richiardi, 2007. "Orcutt’s Vision, 50 years on," LABORatorio R. Revelli Working Papers Series 65, LABORatorio R. Revelli, Centre for Employment Studies.

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