Care&WorkMOD is a static microsimulation model developed to estimate the long-term economic costs of lost labour force participation of informal carers due to their caring responsibilities. The model estimates economic impacts such as lost Productivity Life Years (PLYs) due to informal caring, lost personal incomes, lost taxation revenue, increase in welfare payments, lost gross domestic product (GDP) and economic hardship. It uses static ageing methods to project socio-demographic and economic profiles of Australian informal carers aged 15-64 years every five years from 2015 to 2030.

The CPITMS model established by Zhan et al. (2019). To distinguish the impact of PIT from that of other social security payments, the model defines and classifies income in the following categories: (1) Market income: the total amount of income received by individuals from the market; (2) Pretax income: market income minus social security contributions; (3) Post-tax income: pretax income minus income tax expenditure; (4) Disposable income: post-tax income plus the net income of various transfers.

This model is the result of a project funded by the Chambre des Salariés du Luxembourg/CSL, the aim of which is to analyse the impact of various changes in social contributions and direct taxes on the distribution of net income (welfare) and total revenue for Luxembourg. The analysis aims to focus on the resident population as far as distributional and total effects are concerned, with an innovative extension to "cross-border commuters/cross-borders" (these play an important role in Luxembourg). In order to make the two sides of the coin, residents and cross-borders, comparable, we have built two EUROMOD-type models on HFCS data, the latter being also available for cross-borders (active households), which SILC data do not offer. The EUROMOD/HFCS-R model deals with the resident side and has been updated from a version developed by third parties as part of previous projects.

This model is the result of a project funded by the Chambre des Salariés du Luxembourg/CSL, the aim of which is to analyse the impact of various changes in social contributions and direct taxes on the distribution of net income (welfare) and total revenue for Luxembourg. The analysis aims to focus on the resident population as far as distributional and total effects are concerned, with an innovative extension to "cross-border commuters/cross-borders" (these play an important role in Luxembourg). In order to make the two sides of the coin, residents and cross-borders, comparable, we have built two EUROMOD-type models on HFCS data, the latter being also available for cross-borders (active households), which SILC data do not offer. The EUROMOD/HFCS-XB model deals with the cross-borders.

MIDAS_CH is a discrete-time DMSM using dynamic cross-sectional ageing. It is developed to simulate the distribution of pension income, and its underlying processes, including labour supply, migration, family formation and dissolution, and many others. As such, the model creates a synthetic panel dataset out of a cross-sectional observed starting dataset, by simulating the life spans of the existing individuals and their households while adding new-borns and immigrants, and removing emigrants and deceased individuals. MIDAS_CH runs on LIAM2, just as other models including MIDAS_BE in Belgium, MIDAS_LU in Luxembourg, MOSART in Norway, SESIM in Sweden, DESTINIE in France, T-DYMM in Italy and CBOLT in the US. This contribution is based on the project MIGAPE (see WEBSITE), supported by the European Commission DG Just programme (Grant Agreement no. 820798). We thanks to the support of other teams in MIGAPE and particularly Karel Van den Bosch and Philippe Liégeois as to Nicolas Bauman for excellent research assistance. We are very thankful for the support by the InGRID2 programme, which initiated the ‘Special Interest Group on Dynamic Microsimulation’ (SIG), provided possibilities for training in dynamic microsimulation models as well as access to research infrastructure and fostered international collaboration.

MiDAS_LU is designed to simulate for Luxembourg the long-term prospective impact (up to 2070) of demographic ageing and social policies on income inequality and poverty measures. For its Demographic and Labour modules, it is inspired from MIDAS_BE, a model developed since years by the Federal Planning Bureau (FPB) in Belgium. “MiDAS_LU 2020” is an update of a former one set up in 2015 and part of the MIGAPE project (see WEBSITE) was devoted to the upgrade of this old version, thanks to the support of other teams in MIGAPE and particularly FPB (Gijs Dekkers, Karel Van den Bosch and Raphaël Desmet).

SimPaths is a family of models for individual and household life course events, all sharing common components. The framework is designed to project life histories through time, building up a detailed picture of career paths, family (inter)relations, health, and financial circumstances. The framework builds upon standardised assumptions and data sources, which facilitates adaptation to alternative countries – versions currently exist for the UK and Italy. Careful attention is paid to model validation, and sensitivity of projections to key assumptions. The modular nature of the SimPaths framework is designed to facilitate analysis of alternative assumptions concerning the tax and benefit system, sensitivity to parameter estimates and alternative approaches for projecting labour/leisure and consumption/savings decisions. Projections for a workhorse model parameterised to the UK context are reported in Bronka et al. (2023)., which closely reflect observed data throughout a 10-year validation window.

STINMOD+ is a tax microsimulation model designed to evaluate the distributional and fiscal impacts of tax and transfer policies in Australia. Covering all major taxes and welfare schemes, the model has utilized a consistent policy database since 2001. This includes comprehensive income and asset testing. STINMOD+ is frequently employed to produce detailed policy impact analyses for different household and family types.