Book of Abstracts

The current German system of means-tested social benefits, which include citizen’s benefit, housing benefit, and supplementary child benefit, is characterized by high effective marginal tax rates, which are often higher than 90 percent across wide income brackets. As a result, even substantial increases in working hours typically yield small gains in disposable household income. These high effective marginal tax rates apply not only to citizen’s benefit, but also to the housing benefit and supplementary child benefit. Additionally, the coexistence of competing benefits – citizen’s benefit on the one hand, housing benefit and supplementary child benefit on the other – makes the social benefits system complex to navigate for those affected. For these reasons, and against the backdrop of considerable fiscal pressures, there is currently intense political and academic debate about reforming the system. This study evaluates reform proposals currently under political consideration, categorized into two distinct approaches. The first approach comprises variants of a far-reaching reform in which housing benefits and child benefits are combined with citizen’s benefit and basic income support for the elderly to form a single means-tested benefit that is conceptually modeled on the existing citizen’s benefit framework. Variants within this group differ primarily in earned income exemption design. The second approach represents a more moderate intervention in the existing system: housing and supplementary child benefit are not abolished, but are modified and merged, while adjustments to citizen’s benefit exemptions are introduced. Both reform approaches have in common that they aim to reduce the high effective marginal tax rates in the existing system while simplifying the system for recipients and reducing administrative costs. We analyze the reform proposals with the behavioral tax and transfer microsimulation model of the Institute for Employment Research (IAB-MSM), which uses household microdata from the German Socio-Economic Panel (GSOEP). The IAB-MSM consists of two components: First, a static tax-and-transfer module that simulates the effects of a tax-benefit reform on the disposable income of individual households, including taxes on income, social security contributions, and public transfers. Second, a discrete choice labor supply model that also endogenously models benefit take-up decisions. We use the IAB-MSM to simulate labor supply effects, fiscal effects, the change in the number of claiming households, and distributional impacts of the reform proposals. We highlight conflicting goals that must be weighed against each other in political decisions. In particular, “generous” reform scenarios, which attempt to reduce marginal tax rates while ensuring that households are not worse off financially than under the current framework, are generally associated with relatively high fiscal costs. “Restrictive” scenarios, on the other hand, which reduce incentives to work in marginal employment or part-time and strengthen incentives to work full-time, sometimes involve significant budget savings, but are accompanied by income losses for households with low earned income, at least in the short term.

Population ageing in the United Kingdom is shaped by the interaction of multiple demographic processes, including longevity improvements, fertility dynamics, and migration flows. This paper presents ongoing work using SimPaths to study plausible population ageing paths in the UK from a life-course perspective. The analysis models individual-level demographic transitions, including age- and sex-specific mortality, fertility behaviour, and migration. Particular attention is given to immigration, distinguishing age at arrival, cohort replacement effects, and heterogeneity in demographic characteristics between migrant and native-born populations. The framework allows population ageing to emerge endogenously from cohort size, survival, and population composition. We outline a set of counterfactual scenarios that vary assumptions on longevity improvements, fertility patterns, and migration regimes. Planned simulations will compare resulting population age structures, cohort distributions, and old-age dependency measures across alternative demographic trajectories. By using microsimulation to decompose population ageing into its underlying demographic mechanisms, the analysis provides a structured basis for refining longer-term research on demographic change in the UK and for identifying which population dynamics are most influential for future ageing trajectories.

Extreme heat events cause substantial excess mortality, yet their long-term demographic consequences extend far beyond immediate death counts. Each heatwave creates demographic memory—the cascading effects of lost individuals who would have reproduced, aged, and shaped future population structures. In this work, I will develop a microsimulation framework to quantify how a single extreme heat event reshapes population trajectories over subsequent decades, comparing outcomes with and without the heatwave to isolate its lasting demographic imprint. I will implement microsimulations using the SOCSIM package in R, which simulates individual life events—births, deaths, migrations—through probabilistic rules applied at monthly time steps. This granular approach captures how mortality shocks propagate through populations in ways that aggregate models cannot. I will construct synthetic populations for Western European countries affected by the July 2022 heatwave, initializing from the Human Mortality Database and Human Fertility Database. Two parallel projections will be analyzed: one incorporating observed mortality from July 2022 (including heat-attributable excess deaths), and a counterfactual where mortality follows forecasted baseline rates without the heatwave. I will use the Lee-Carter method to project baseline mortality, allowing the observed July 2022 mortality spike to capture the heat events demographic shock. By the conference, I expect to present proof-of-concept results showing how the two scenarios diverge. Core outputs will include: excess mortality attributable to the July 2022 heatwave, measures of bereavement quantifying how many individuals lost family members due to heat-attributable mortality, and projections comparing population structures with and without this event over subsequent decades. Focusing on a single extreme heat event establishes methodological foundations while demonstrating that even one event matters demographically. While climate change will bring repeated heat extremes that compound these effects, understanding one events impact provides the baseline for assessing cumulative effects. The difference between the two projected populations quantifies the full demographic burden: not only immediate deaths, but also potential altered age structures, depleted cohorts, and expanded bereavement.

Alignment is a critical calibration technique in microsimulation, ensuring individual-level transitions aggregate to known macro-targets. While indispensable for updating populations to match demographic projections or macroeconomic forecasts, the statistical properties of various alignment algorithms remain under-researched. This paper provides a systematic evaluation of alignment methods for discrete-time models to guide researchers in method selection. We categorize existing methods based on a robust taxonomy: variable type (continuous vs. discrete), the nature of the outcome (exact match vs. stochastic), and time-step logic (continuous vs discrete). Building on the work of Stephenson (2018), we demonstrate that most contemporary alignment methods can be unified under a single constrained optimization framework, differing only in their choice of objective functions. This unification allows us to establish previously unrecognized relationships between seemingly disparate techniques. Furthermore, we derive closed-form expressions and Taylor series approximations for computationally intensive iterative methods. These mathematical shortcuts allow modellers to reduce simulation run-times significantly while converting exact alignment processes into stochastic ones without losing desirable statistical properties. We then study the impact of these methods on the predicted distributions and covariance of outcomes between simulation runs. Our analysis reveals that common techniques, such as Sorting by Predicted Probability (SBP) and Sorted By Difference between predicted Probability and a random number (SBD), are undesirable for microsimulation modelling as they can introduce biases and path dependence into the simulation outcomes. In contrast, parameter variation methods (i.e. recalibration of model coefficients such as intercept shifting) offer modellers control over the impact of alignment and better interpretability. Furthermore, methods like logit scaling, variance-weighted additive scaling for probabilities, and additive scaling for continuous variables offer greater robustness due to their foundations in probability theory. We conclude by providing a decision matrix for modellers, weighing the trade-offs between computational efficiency, distribution preservation, and variance reduction.

SimPaths is an open-source framework for modelling individual and household life course events, jointly developed at the Centre for Microsimulation and Policy Analysis and the University of Glasgow (Bronka et al., 2025). 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 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. SimPaths builds upon standardised assumptions and data sources, which facilitates adaptation to alternative countries. The presentation will focus on recent developments of the SimPaths framework, centred around: • Introduction of new health and well-being variables (MCS, PCS, life satisfaction) for the UK model • Development of a new wealth module for the UK model • Updated release for the model for Italy • First release for the models for Poland, Spain, Germany, Spain • Development of macro modules (all countries) • Development of migration modules (all countries) • Development of inter-household transfer modules (all countries) • Improved model documentation Strategies for internal validation will also be discussed.

This paper analyses the distributional effects of a fiscally neutral tax reform in Croatia that shifts the tax burden from consumption to labour income, capital income, and property. Such a reform can be considered justified given the imbalances of the Croatian tax system, which is characterised by an exceptionally high share of indirect taxes and relatively low taxation of labour, capital, and property income compared to the EU average, contributing to regressivity and greater income inequality. The proposed reform consists of increasing the progressivity of the personal income tax (through the introduction of higher tax rates), raising taxation of capital income and property, while simultaneously reducing the standard VAT rate and expanding the reduced VAT rate. The analysis is based on the application of microsimulation models of direct and indirect taxes, using EU-SILC and Household Budget Survey (HBS) data, with corrections to the income distribution using administrative data to ensure a credible simulation of direct and indirect taxes. In addition to distributional effects, the paper also assesses the impact of the reform on efficiency using a behavioural labour supply model. The results show that the reform does not have a significant negative effect on labour supply, increases the fairness of the tax system by reducing inequality, and delivers the largest gains to lower-income households and more vulnerable social groups, while the richest households incur losses due to increased direct tax burdens. Overall, the reform increases progressivity and the redistributive effect of the system without compromising economic efficiency.

Dynamic microsimulation models such as SimPaths are increasingly used to evaluate long-term policy impacts by generating synthetic trajectories for individuals and households. Their credibility, however, depends on rigorous validation: demonstrating that simulated outcomes can reliably reproduce observed data. Despite their growing role in policy analysis, validation practices remain fragmented and only partially automated (e.g., O’Donoghue et al., 2015; Gosseries & Van der Heyden, 2018). This paper presents ongoing work on strengthening validation frameworks in SimPaths, with a focus on discriminator-based methods and econometric consistency checks. First, we apply classifiers (e.g., Gradient Boosted Machines) to distinguish between simulated and survey data. Discriminator accuracy provides an interpretable quantitative score of similarity: the closer performance is to random guessing, the more realistic the simulated data. Second, we explore the re-estimation of key behavioural regressions using simulated data and assess parameter recovery. This helps identify whether discrepancies arise from implementation issues, estimation limitations, or structural differences between datasets. By combining machine-learning discriminators with regression-based diagnostics, the paper contributes to more automated, transparent, and reproducible validation practices for complex microsimulation models.

Joint taxation of married couples remains a central feature of many income tax systems, with significant implications for labor supply and household welfare. By pooling partners’ incomes into a single tax base, joint filing can create disincentives for secondary earners and generate marriage-related penalties, raising concerns about efficiency and equity. This paper studies the impact of joint taxation on the labor supply of couples in Belgium, where the personal income tax is formally individual but substantially adjusted at the household level. We estimate a Random Utility Random Opportunity (RURO) model of labor supply using rich administrative data linking tax records and demographic information. Using the FANTASI microsimulation model of the Belgian personal income tax, we perform a counterfactual analysis of a shift from joint to individual taxation.

This research explores how microsimulation modelling can be used to develop an integrated framework that evaluates economic efficiency, social justice, and ecological effectiveness simultaneously. When social policies are designed and implemented, analysis often focusses on one or maximum two main objectives: economic efficiency and/or social justice. Policymakers and researchers frequently examine how policy reforms can reduce poverty and inequality, or how they can strengthen work incentives and increase employment levels. Microsimulation models, such as the EU tax-benefit microsimulation model EUROMOD, are useful to analyse these distributive and labour market effects of policies. However, policy designs in the context of the just transition towards climate neutrality increasingly require a multi‑dimensional perspective that goes beyond the traditional efficiency-equity trade-off. Current research shows that a third factor affects the existing trade-off: next to economic efficiency and social justice, policies must also consider ecological effectiveness. A policy measure that illustrates this challenge, is the implementation of a carbon tax. Research shows that it is an economically and ecologically efficient tool to reduce GHG emissions (Baranzini et al., 2017; Timilsina, 2022). On the one hand, it encourages households, businesses, and governments to reduce their emissions, and it provides incentives for innovation. On the other hand, the cost to the government is relatively low, particularly compared to other policies aimed at reducing GHG emissions. However, empirical findings also show that it is very often regressive: households in lower income groups face a higher tax burden than those in higher income groups (Boyce, 2018). The combination of ecological effectiveness, economic efficiency, and potential social inequity highlights the need for an integrated framework that addresses these trade-offs. Although existing microsimulation models can analyse each dimension separately, we argue that an integrated framework that considers efficiency, justice, and effectiveness simultaneously is needed. Our research tries to contribute to this by exploring how microsimulations models can be used to examine a framework that integrates various indicators (economic, social, and environmental outcomes) together. As an example, we will apply our framework to carbon taxation in Belgium, using Green EUROMOD (the recently developed environmental extension of EUROMOD), building further on Bursens et al. (2026).

Carbon pricing combined with revenue recycling through lower labor income taxation achieves carbon mitigation and a decrease in distortionary labor income tax. However, due to distributional concern, there is large societal opposition towards such reforms. The burden of the carbon price is higher for low-income households due to their higher relative expenditures on carbon-intensive goods, such as heating and transport. Moreover, also indirect effects of the carbon price, e.g. job loss in the economy, are feared to additionally fall on the shoulders of those same households. In this paper we combine a micro- and macroeconomic approach to gauge the distributional direct and indirect impacts of green tax reform. A computable general equilibrium (CGE) model is used to simulate impacts on commodity prices and real wage rates for different types of labor. These impacts are fed to a microsimulation model (MSM) of incomes and expenditures, so that we can gauge the distributional impact of several scenarios in green tax reform. We build on the existing top-down literature, discuss consistency between the two models, the choice of the numéraire and the (implicit) assumption on the uprating of the tax schedule and benefit amounts. Moreover, we show the importance of allowing automatic stabilizers to play out in the computable general equilibrium model, i.e. the role of progressive income taxation and benefits. In a traditional CGE, income taxation is modelled as a (macroeconomically calibrated) proportional tax rate. Change in market incomes would not change the tax burden in such model. However, since taxation is progressive, the tax burden responds to (real) changes in market income. The MSM, with the detailed modelling of the non-linear tax-and-benefit system captures this. We propose in this paper a simple bottom-up feedback, in which we update the proportional tax rates in the CGE with the results of a first run of the MSM, as an alternative to the estimation of a parametric (macroeconomic) progressive tax-and-benefit function to be included in the CGE. Not accounting for automatic stabilizer, overestimates the revenue recycling budget available by one half. This is also relevant for fully integrated CGE-MSM models. We find that medium-skilled employees are on average net losers of the impacts on prices and labor demand. Traditional revenue recycling schemes, such as lumpsum transfers or linear labor income tax cuts cannot overturn this welfare loss for medium-skilled, while still guaranteeing progressivity of the net impacts of the reform. However, more targeted revenue recycling schemes, inspired by the existing low wage subsidies in Belgium (the work boni) are equipped to target revenue recycling towards those most hit by the impacts on the labor market. However, robustness checks show that the adequacy of such revenue recycling design depends on the labor market assumptions in the model, specifically whether the decreased demand for medium-skilled can be translated in higher involuntary unemployment in equilibrium.