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Microsimulation as an Instrument to Evaluate Economic and Social Programmes

Author

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  • Joachim Merz

    (LEUPHANA University Lüneburg,Department of Economic, Behaviour and Law Sciences, Research Institute on Professions (Forschungsinstitut Freie Berufe (FFB)))

Abstract

In recent years microsimulation models (MSMs) have been increasingly applied in quantitative analyses of the individual impacts of economic and social programme policies. The suitability of using microsimulation as an instrument to analyze main and side policy impacts at the individual level will be discussed in this paper by characterizing: the general approach and principles of the two general microsimulation approaches: static and dynamic (cross-section and lifecycle) microsimulation, the structure of MSMs with institutional regulations and behavioural response, panel data and behavioural change, deterministic and stochastic microsimulation, the 4M-strategy to combine microtheory, microdata, microestimation and microsimulation, and pinpointing applications and recent developments. To demonstrate the evaluation of economic and social programmes by microsimulation, two examples concerning a dynamic (cross-section and life-cycle) microsimulation of the German retirement pension reform and a combined static/dynamic microsimulation of the recent German tax reform with its behavioural impacts on formal and informal economic activities of private households are briefly described. Evaluating the evaluation of economic and social programmes with microsimulation models finally is followed by concluding remarks about some future developments.

Suggested Citation

  • Joachim Merz, 1993. "Microsimulation as an Instrument to Evaluate Economic and Social Programmes," FFB-Discussionpaper 05, Research Institute on Professions (Forschungsinstitut Freie Berufe (FFB)), LEUPHANA University Lüneburg.
    • Handle: RePEc:leu:wpaper:05
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    File URL: http://www.leuphana.de/fileadmin/user_upload/Forschungseinrichtungen/ffb/files/publikationen/diskussion/DP_05_microsimulation_instrument.pdf
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    Cited by:

    1. Joachim Merz & Lars Rusch, 2015. "MICSIM-4j - A General Microsimulation Model User Guide (Version 1.1)," FFB-Discussionpaper 100, Research Institute on Professions (Forschungsinstitut Freie Berufe (FFB)), LEUPHANA University Lüneburg.
    2. Chyzheuskaya, Aksana & O'Donoghue, Cathal & O'Neill, Stephen, 2014. "Using a farm micro-simulation model to evaluate the impact of the nitrogen reduct," International Journal of Agricultural Management, Institute of Agricultural Management, vol. 3(4), pages 1-11.
    3. Merz, Joachim, 1995. "MICSIM : Concept, Developments and Applications of a PC-Microsimulation Model for Research and Teaching," MPRA Paper 16029, University Library of Munich, Germany.
    4. Chyzheuskaya, Aksana & O’Donoghue, Cathal & O’Neill, Stephen & Hynes, Stephen, 2015. "Using A Spatialmicrosimulationmodel To Estimate The Potential Economic Impact On Agriculture Of Possible Freshwater Pearl Mussel Protection Strategies," 150th Seminar, October 22-23, 2015, Edinburgh, Scotland 212669, European Association of Agricultural Economists.
    5. Joachim Merz, 1994. "Microdata Adjustment by the Minimum Information Loss Principle," FFB-Discussionpaper 10, Research Institute on Professions (Forschungsinstitut Freie Berufe (FFB)), LEUPHANA University Lüneburg.
    6. Franziska Gassmann & Cecile Cherrier & Andrés Mideros Mora & Pierre Mohnen, 2013. "Making the Investment Case for Social Protection: Methodological challenges with lessons learnt from a recent study in Cambodia," Papers inwopa694, Innocenti Working Papers.
    7. Tony Lawson, 2016. "How the Ageing Population Contributes to UK Economic Activity: A Microsimulation Analysis," Scottish Journal of Political Economy, Scottish Economic Society, vol. 63(5), pages 497-518, November.
    8. Widmaier, Ulrich & Niggemann, Hiltrud & Merz, Joachim, 1994. "What makes the Difference between Unsuccessful and Successful Firms in the German Mechanical Engineering Industry?," MPRA Paper 7230, University Library of Munich, Germany.
    9. Aksana Chyzheuskaya & Cathal O'Donoghue & Cathal Buckley & Mary Ryan & Stuart Green, 2012. "Modelling the Marginal Abatement Cost of Mitigating Nitrogen Loss from Agricultural Land," Working Papers 1205, Rural Economy and Development Programme,Teagasc.

    More about this item

    Keywords

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    JEL classification:

    • C80 - Mathematical and Quantitative Methods - - Data Collection and Data Estimation Methodology; Computer Programs - - - General
    • C81 - Mathematical and Quantitative Methods - - Data Collection and Data Estimation Methodology; Computer Programs - - - Methodology for Collecting, Estimating, and Organizing Microeconomic Data; Data Access
    • H20 - Public Economics - - Taxation, Subsidies, and Revenue - - - General
    • H22 - Public Economics - - Taxation, Subsidies, and Revenue - - - Incidence

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