BEAMM project : How do we deal with data ? Statistical matching and WGAN generation.
In the framework of the BEAMM project (BElgian Arithmetic Micro-simulation Model), we propose several methods to address data issues. The core of this project is to develop a tax-benefit microsimulation model for Belgium accessible online, requiring intensive data handling. Our challenges consist in creating a unified data set containing variables from different surveys and developing a completely synthetic database for the online development of the BEAMM platform.
Read MoreBuilding a cross-border synthetic population for Luxembourg and neighbouring regions
Synthetic populations are essential for modeling complex systems that require individual-level data. However, they are typically limited to a single country. Creating a synthetic population across multiple countries is challenging because the data available from national statistical institutes are inconsistent: the variables available differ, and for shared variables, the categories may not align. Fortunately, Eurostat provides access to a large amount of aggregated socio-economic data that is consistent across EU countries. Although these data are less detailed than what can be obtained from national statistical institutes, they provide a solid basis for generating synthetic populations.
Read MoreImputing lifetime incomes: Baseline projections for the UK
Most studies that report distributional comparisons of income focus on income evaluated over periods that vary between one week and one year. Distributional studies of weekly income recognise the importance of short-term constraints, particularly in relation to material deprivation and poverty. Distributional studies of annual income recognise the capacity of many people to save the proceeds of temporary income peaks to carry them through temporary income troughs. Income measured over longer periods is rarely analysed due to the relative (in)availability of survey data, rather than any more fundamental motivation. Unfortunately, analysis of lifetime incomes for contemporary population cross-sections is complicated in part by the limited historical context captured by existing panel studies, and in part because future incomes are unobservable. Microsimulation is one method to fill gaps in the available statistical record. This study describes how microsimulation methods were used to project lifetime incomes for a contemporary population cross-section of the UK.
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