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LINDA: A dynamic microsimulation model for analysing policy effects on the evolving population cross-section

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  • Justin van de Ven

Abstract

This paper describes a structural dynamic microsimulation model that generates individual-specific data over a range of demographic and economic characteristics at discrete intervals through-out a simulated time horizon. The model is designed to analyse the distributional implications of policy alternatives over appreciable periods of time. This focus motivates endogenous simulation of savings and labour supply decisions, taking explicit account of uncertainty regarding the evolving decision environment. In contrast to the existing literature of savings in context of uncertainty, the model described here takes an overlapping generations form which is adapted to the needs of policy makers, and which has distinct advantages for empirical investigations.

Suggested Citation

  • Justin van de Ven, 2016. "LINDA: A dynamic microsimulation model for analysing policy effects on the evolving population cross-section," National Institute of Economic and Social Research (NIESR) Discussion Papers 459, National Institute of Economic and Social Research.
    • Handle: RePEc:nsr:niesrd:459
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    References listed on IDEAS

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    16. Orazio Attanasio & Renata Bottazzi & Hamish Low & Lars Nesheim & Matthew Wakefield, 2012. "Modelling the Demand for Housing over the Lifecycle," Review of Economic Dynamics, Elsevier for the Society for Economic Dynamics, vol. 15(1), pages 1-18, January.
    17. James Sefton & Justin vandeVen & Martin Weale, 2008. "Means Testing Retirement Benefits: fostering equity or discouraging savings?," Economic Journal, Royal Economic Society, vol. 118(528), pages 556-590, April.
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    22. Dr Justin van de Ven, 2009. "Optimal Design of Means Tested Retirement Benefits," National Institute of Economic and Social Research (NIESR) Discussion Papers 332, National Institute of Economic and Social Research.
    23. Gustman, Alan L. & Steinmeier, Thomas L., 2005. "The social security early entitlement age in a structural model of retirement and wealth," Journal of Public Economics, Elsevier, vol. 89(2-3), pages 441-463, February.
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    27. repec:sae:niesru:v:188:y::i:1:p:56-72 is not listed on IDEAS
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    29. Yingxu Kuang & Ted Englebrecht & Otis W. Gilley, 2011. "A Distributional Analysis of the FairTax Plan: Annual and Lifetime Income Considerations," Southern Economic Journal, John Wiley & Sons, vol. 78(2), pages 358-381, October.
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    Citations

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    Cited by:

    1. Cathal O'Donoghue & Gijs Dekkers, 2018. "Increasing the Impact of Dynamic Microsimulation Modelling," International Journal of Microsimulation, International Microsimulation Association, vol. 11(1), pages 61-96.
    2. repec:ijm:journl:v109:y:2017:i:1:p:135-166 is not listed on IDEAS
    3. Justin W. van de Ven, 2017. "Parameterising a detailed dynamic programming model of savings and labour supply using cross-sectional data," International Journal of Microsimulation, International Microsimulation Association, vol. 10(1), pages 135-166.
    4. Justin Van de Ven, 2016. "Parameterising the LINDA microsimulation model of benefit unit savings and labour supply," National Institute of Economic and Social Research (NIESR) Discussion Papers 464, National Institute of Economic and Social Research.
    5. Angus Armstrong & Justin Van de Ven, 2016. "The Impact of Possible Migration Scenarios after ‘Brexit’ on the State Pension System," Economies, MDPI, vol. 4(4), pages 1-13, October.
    6. van de Ven, Justin, 2017. "SIDD: An adaptable framework for analysing the distributional implications of policy alternatives where savings and employment decisions matter," Economic Modelling, Elsevier, vol. 63(C), pages 161-174.

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    More about this item

    Keywords

    dynamic programming; savings; labour supply;
    All these keywords.

    JEL classification:

    • C51 - Mathematical and Quantitative Methods - - Econometric Modeling - - - Model Construction and Estimation
    • C61 - Mathematical and Quantitative Methods - - Mathematical Methods; Programming Models; Mathematical and Simulation Modeling - - - Optimization Techniques; Programming Models; Dynamic Analysis
    • C63 - Mathematical and Quantitative Methods - - Mathematical Methods; Programming Models; Mathematical and Simulation Modeling - - - Computational Techniques
    • H31 - Public Economics - - Fiscal Policies and Behavior of Economic Agents - - - Household

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