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Editorial

  1. Matteo G Richiardi  Is a corresponding author
  1. Institute for Social and Economic Research, United Kingdom
Editorial
Cite this article as: M. G Richiardi; 2023; Editorial; International Journal of Microsimulation; 16(3); I-II. doi: 10.34196/ijm.00285

The winter 2023 issue of the International Journal of Microsimulation is published as the International Microsimulation Association heads to its 9th World Congress (Vienna, Austria, 8th - 10th January 2024). The issue contains five articles, covering different microsimulation techniques and applications. The first article, by Karl Widerquist and Georg Arndt, is a static tax-benefit analysis of a Universal Basic Income (UBI) scheme for the United Kingdom (UK). The analysis reflects a growing interest in UBI worldwide, and adds to an active discussion of UBI schemes for the UK in particular (see Torry , 2023). The second article, by Montserrat Gonzalez Garibay, contains a review of how retirement decisions are modelled in dynamic microsimulation models (DMSs). The paper fills a gap in the literature, as existing reviews of DMSs (e.g. O’Donoghue, 2001; Li and O’Donoghue, 2013; O’Donoghue, 2014; O’Donoghue and Dekkers, 2018; Schofield et al., 2018) generally favour breadth of coverage to depth of modelling insights. The third article, by Edlira Narazani, Ugo Colombino and Bianey Palma, pushes forward the boundaries of tax-benefit modelling by adding both behavioural labour supply effects and endogenous wage adjustments to a static tax-benefit framework. Their model is estimated on all EU member states, but is unfortunately not available outside the European Commission. The fourth article, by Rachel Bacon, George Hodulik, David Voas, Ivan Puga-Gonzalez and Wesley Wildman, confronts with the problem of replicating official population projections in discrete-time DMSs where events like births, deaths, ageing, fertility and migration are simulated in turn - implying that the order of execution matters - while in reality the related risks are competing. The solution envisaged is to “split fertility”, attributing half of the risk at the beginning of the simulation period, and the remaining half at the end of the simulation period. The last article, by Tanja Kirn and Gijs Dekkers, is ideally linked to the review by Gonzalez Garibay, illustrating a new DMS for projecting pension entitlements in Switzerland. The model builds on the popular LIAM2/MIDAS simulation frameworks (de Menten et al., 2014; Dekkers et al., 2022) to analyse the likely impact of a recent pension reform on the gender pension gap.

Suggestions for further reding

Microsimulation modelling is becoming increasingly popular in public health research. The short manifesto of Kopasker et al. (2023) brings it to the attention of a general audience, identifying “six priority areas particularly suitable for microsimulation analysis in the context of health inequalities”: regulatory policies, healthcare policies, social security policies, tax policies, crisis responses, and other policies aimed at improving life opportunities, in particular education and active labour market policies.

If microsimulations are becoming mainstream in the field, de Oliveira et al. (2023) find only few models focused on mental health, with fewer still deemed to be of “high quality”. Their critical review will surely help addressing this shortcoming.

References

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  2. 2
    LIAM2: A new open source development tool for discrete-time dynamic microsimulation models
    1. G de Menten
    2. G Dekkers
    3. G Bryon
    4. P Liégeois
    5. C O’Donoghue
    (2014)
    Journal of Artificial Societies and Social Simulation 17:9.
    https://doi.org/10.18564/jasss.2574
  3. 3
    Microsimulation Models on Mental Health: A Critical Review of the Literature
    1. C de Oliveira
    2. MA Matias
    3. R Jacobs
    (2023)
    Value in Health 8:S1098-3015(23)06162-4.
    https://doi.org/10.1016/j.jval.2023.10.015
  4. 4
  5. 5
    A survey of dynamic microsimulation models: uses, model structure and methodology
    1. J Li
    2. C O’Donoghue
    (2013)
    International Journal of Microsimulation 6:3–55.
    https://doi.org/10.34196/ijm.00082
  6. 6
    Dynamic microsimulation: A methodological survey
    1. C O’Donoghue
    (2001)
    Brazilian Electronic Journal of Economics 4:77.
  7. 7
    Handbook of microsimulation modelling
    1. C O’Donoghue
    (2014)
    Contributions to Economic Analysis 293:305–343.
    https://doi.org/10.1108/S0573-85552014293
  8. 8
    Increasing the impact of dynamic microsimulation modelling
    1. C O’Donoghue
    2. G Dekkers
    (2018)
    International Journal of Microsimulation 11:61–96.
    https://doi.org/10.34196/ijm.00174
  9. 9
  10. 10
    The Palgrave International Handbook of Basic Income
    (2023)
    XLVI–629, The Palgrave International Handbook of Basic Income, 2nd edition, Palgrave Macmillan Cham, p, 10.1007/978-3-031-41001-7.

Article and author information

Author details

  1. Matteo G Richiardi

    Institute for Social and Economic Research, Colchester, United Kingdom
    For correspondence
    matteo.richiardi@essex.ac.uk

Publication history

  1. Version of Record published: December 31, 2023 (version 1)

Copyright

© 2023, Richiardi

This article is distributed under the terms of the Creative Commons Attribution License, which permits unrestricted use and redistribution provided that the original author and source are credited.

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