Book of Abstracts

The 2022 Council Recommendation on adequate minimum income and the EU Minimum Wage Directive form two core elements of the current EU social policy framework. Both instruments aim to improve the situation of low-income households, but they operate through different mechanisms. Minimum income schemes raise the income of people outside employment, while statutory minimum wages affect earnings at the bottom of the wage distribution. Increasing income support for those out of work, however, reduces the financial gain from taking up employment. This creates the social trilemma faced by policymakers in modern welfare states: to simultaneously provide adequate minimum income support, maintain sufficient financial incentives for people to find a job, and keep the government budget in check (Cantillon et al., 2019). This paper quantifies the fiscal cost of eliminating poverty while preserving work incentives across EU-27, and assesses whether this can be achieved within existing EU policy frameworks. We frame the problem as a constrained policy question: what level of public spending is required to achieve zero poverty while keeping participation tax rates (PTRs) not higher than their baseline level? We first establish baseline poverty rates, poverty gaps, and PTRs for transitions from inactivity into full-time minimum wage employment across member states. We then simulate raising minimum income support to poverty thresholds. These reforms eliminate measured poverty by construction but lead to substantial declines in work incentives. In many countries, PTRs for social assistance recipients increase substantially: when moving into work, the rise in disposable income is much smaller than the gross earning because benefits are withdrawn and taxes and/or contribution apply. Next, we test whether implementing the Minimum Wage Directive can compensate for this loss in work incentives. We operationalise the Directive benchmark using the reference value of 60% of the national gross median wage. We find that Directive-level minimum wages are not enough to restore baseline PTRs in all of the member states. Directive-consistent wage increases prove insufficient, and labour subsidies with additional fiscal costs – or gross minimum wage increases with additional costs for employers – would be required to bring PTRs back to their initial levels. For each reform scenario, we report the associated fiscal impact and additional labour cost for employer. We employ the EUROMOD microsimulation model with EU-SILC data covering all 27 EU member states for policy year 2023, complemented by hypothetical household data based on the Minimum Income Protection Indicators for transparent illustration of mechanisms. We examine multiple poverty thresholds including relative measures at 40%, 50%, and 60% of median income, as well as absolute budget-based thresholds from ABSPO (Menyhert et al., 2025). The results show that poverty eradication with preserved work incentives is not feasible within the existing social policy framework. The paper provides quantitative benchmarks for the coordinated implementation of EU minimum income and minimum wage policies, and highlights the structural constraints faced by different welfare systems. References: Cantillon, B., Goedemé, T. and Hills, J. (eds) (2019) Decent incomes for all: Improving policies in Europe, Oxford: Oxford University Press. Menyhert, B., Cseres-Gergely, Zs., Kvedaras, V., Mina, B., Pericoli, F. and Zec, S. 2025. Measuring and Monitoring Absolute Poverty in the European Union. Palgrave Macmillan.

This project evaluates the labor supply and distributional effects of the 2018 reform of the Luxembourg minimum income scheme, which replaced the Guaranteed Minimum Income (RMG) with the Social Inclusion Income (REVIS). The reform was motivated by growing concerns about inactivity traps, weak coherence between income support and activation policies, rising poverty risks among children and single-parent families, and excessive administrative complexity. REVIS aims to promote social inclusion, strengthen work incentives, improve child and single-parent poverty outcomes, and simplify administration. A key innovation of the reform is the introduction of a direct income immunization mechanism, whereby 25 per cent of household income is disregarded when calculating benefit entitlements. This represents a paradigm shift away from the pre-reform system, which effectively imposed very high implicit marginal tax rates on low earnings. To assess the effects of this reform, the project combines detailed tax–benefit microsimulation with a microeconometric Random Utility Random Opportunity (RURO) labor supply model, using Luxembourg Income Study (LIS) data. Standard “arithmetical” microsimulation models are well suited to analyzing first-round, non-behavioral effects of tax–benefit reforms, such as changes in disposable income, poverty, inequality, and public budgets. However, they may be misleading when reforms are non-marginal or when medium- to long-run impacts are considered, as individuals may adjust their labor supply by changing employment status, hours of work, or job characteristics. Incorporating behavioral responses is therefore essential for evaluating reforms like REVIS, which explicitly aim to alter w The RURO approach departs from traditional labor supply models in which individuals choose hours of work from a continuous set assumed to be equally available. Instead, individuals are modeled as choosing among discrete job opportunities characterized by different packages of hours, wages, and so on Empirically, the project proceeds in several steps. First, various versions of the RURO model are estimated and validated using LIS data for Luxembourg, with particular attention to labor market exclusion and heterogeneity in opportunities between “insiders” and “outsiders.” Single mothers are a focal group, given the reforms explicit concern with child and single-parent poverty and their sensitivity to work incentives. Second, the RURO model is integrated into the LuxTaxBen microsimulation framework, a policy tool designed for ex ante analysis of Luxembourgs tax–benefit system. LuxTaxBen provides detailed simulations of disposable income and benefit eligibility under alternative policy scenarios and is adapted here by replacing its standard labor supply module with the RURO model. Finally, the integrated model is used to evaluate the effects of REVIS relative to RMG. The reform replaces the near-100 per cent implicit marginal tax rates of the pre-reform scheme with a structure closer to a negative income tax, allowing guaranteed income to increase with earnings and thereby strengthening incentives to work. The analysis compares simulated and observed labor supply outcomes under pre- and post-reform regimes, using both forward and inverse simulation strategies. This dual approach allows not only an assessment of the reforms impacts on labor supply, poverty, and public finances, but also a validation of the modeling framework and deeper insight into the mechanisms.

Spain has the second-highest child poverty rate in the European Union (EU). Moreover, child poverty has been increasing since 2018, despite a context of economic growth and a decreasing overall poverty rate. The At-risk-of-poverty-or-social-exclusion (AROPE) rate for individuals under 18 was 34.6% according to the latest official statistics, substantially higher than the (also high) overall population rate of 25.8%. From both an economic and ethical standpoint, reducing child poverty yields high returns. Drawing on Heckmans work on early childhood investment, resources directed at children during developmental periods generate the highest long-term returns by preventing future skill gaps and reducing social costs. From a social investment perspective, child benefits represent strategic investments in future workforce productivity rather than mere expenditure. Furthermore, Roemers theory of equality of opportunity underscores that children bear no responsibility for their households economic circumstances, and therefore child poverty would always violate fundamental fairness. This research addresses a key policy question: What fiscal policies could most efficiently reduce child poverty in Spain, and what are the trade-offs between targeting and universalism? We employ EUROMOD, the EU tax-benefit microsimulation model, which simulates the effects of policy changes on household incomes using representative microdata from the EU Statistics on Income and Living Conditions (EU-SILC). We compare current policy rules against ambitious reforms targeted at reducing (child) poverty, including a universal child benefit and improvements to Spains national minimum income. Our methodological contribution lies in systematically exploring the policy space through thousands of counterfactual scenarios, varying benefit levels and take-up ratios across different policy instruments. This comprehensive approach, enabled by running EUROMOD via an Application Programming Interface (API), allows us to map the efficiency frontier of poverty reduction, therefore identifying optimal policy combinations. Unlike conventional studies that compare a handful of discrete policy options, our method estimates the functional relationship between spending and poverty outcomes, providing policymakers with a complete menu of alternatives. Results suggest that the current policy rules are markedly inefficient, as even a universal child benefit (a relatively expensive tool) shows a 33% higher cost-effectiveness. We discuss the causes for this, and suggest specific policy reforms capable of improving the tax-benefit system both in terms of budgetary cost and societal outcomes.

Türkiye experienced the highest inflation experience in the OECD during the cost of living crisis during the cost of living crisis in the early mid-2020s. While the European Union inflation rate was 9.2% in 2022, declining to 6.4% in 2023 and 2.6% for 2025 - Eurostat, year on year inflation peaked at 85% in Türkiye in October 2022 and with annual inflation remaining above 65% at the end of 2023 before dipping to about 45% at the end of 2024 - Turkstat. Such large price changes impact the income distribution in many ways. In this presentation, we describe a portfolio of research that has employed microsimulation based decomposition methods to disentangle the impact of large macro-economic changes on inequality. The research begins by describing the historical macro-economic volatility that Türkiye. Using the new ARIA microsimulation model we undertake a variety of different analyses focusing on different dimensions. We begin by examining the distribution of price changes before the crisis and after the peak crisis in 2022. We then explore the policy response in terms of the poverty effectiveness efficiency and the poverty gap efficiency social transfers, which as an archetypal Southern European Welfare state mainly focuses on pension age work replacement benefits. With a progressive income tax system, we explore the nature of the fiscal drag within the system during this period. We contrast it with impact of price change on the regressive indirect tax system. With data from before the crisis and peak-crisis, we are employ a unique decomposition of the consumption and savings response during the crisis, emphasising in particular the differential savings response and the importance of durables as a source of hedging inflation for high income households on the one hand and the prioritisation of necessities by low income households. Furthermore, we explore the inequality increasing nature of the labour market, where some sectors have been resilient to price inflation in terms of wage growth, combined with other sectors that have not. A key conclusion is the distributional impact of price change has a greater impact when behavioural responses are considered than the literature that focuses on pre-behavioural response. As a result the consumption patterns have a greater impact than income changes.

While prediction markets in decentralized finance aggregate dispersed information into probabilistic prices, their accuracy strongly dependents on design, incentives, and environment. Under suitable incentives, markets can theoretically approximate the likelihood of events (Wolfers & Zitzewitz, 2004). However, recent agent-based research suggests that in prediction markets with order-based matching, biased traders with large capital endowments (so-called “whales”) can move prices away from fundamentals. Such distortions may persist when updating beliefs is slow and herding reinforces such deviations (Smart et al., 2026). Such distortion dynamics thus depend not only on trader heterogeneity but also on the mechanisms translating demand into prices and spreads. We analyze this challenge at the micro-level by simulating an automated market maker building on the principles of logarithmic market scoring rule (LMSR). LMSR derives prices from a convex cost function over outstanding shares and secures continuous liquidity with bounded worst-case loss (Hanson, 2003; Chen & Pennock, 2007). Unlike price-impact systems in which demand moves prices without relevant lag (Smart et al., 2026), LMSR embeds liquidity in the curvature of the cost function. This structural difference alters how capital accumulation and biased trading affect price formation. Our contribution lies not just in the replication of an order-book market but rather in an analysis of distortion mechanisms under scoring-rule-based liquidity provision. Our model approximates a binary event market with heterogeneous traders, including informed traders with high-precision signals, noise traders generating random order flows, arbitrage-oriented traders reacting to publicly available information, strategic traders with biased valuations and larger budgets, and conviction-driven traders who update beliefs slowly. A latent “true” probability evolves stochastically with occasional shocks. Agents update beliefs in log-odds space using heterogeneous learning weights and submit trades proportional to the gap between belief and LMSR price, scaled by both risk aversion and trading intensity. Performance is evaluated dynamically and at settlement. During trading we trace mispricing (price - true probability), volatility, volume, and wealth distribution across traders. At settlement, forecasting accuracy is measured using both Brier score and log loss. The experimental design focuses on three questions: (i) how the LMSR liquidity parameter affects the trade-off between responsiveness and stability; (ii) how large and persistent distortions become under high-budget biased trading; and (iii) how liquidity design interacts with stubborn belief updating. In our preliminary analyses, we test whether distortion mechanisms identified in price-impact order-matching environments (Smart et al., 2026) also emerge under scoring-rule-based automated market makers and to which extent liquidity curvature mitigates persistence. This work’s limitations emerge primarily from generalized trading strategies and abstracted order-book dynamics. It is further limited by the exogeneity of true probabilities. References Chen, Y., & Pennock, D.M. (2007). A Utility Framework for Bounded-Loss Market Makers. Conference on Uncertainty in Artificial Intelligence. 49-56. Hanson, R. (2003). Combinatorial Information Market Design. Information Systems Frontiers, 5(1), 107-119. Smart, J., et al. (2026). Manipulation in Prediction Markets: An Agent-Based Modeling Experiment. ArXiv. Wolfers, J., & Zitzewitz, E. (2004). Prediction Markets. Journal of Economic Perspectives, 18(2), 107–126.

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.

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.

Understanding infectious disease transmission requires insight into who interacts with whom, where and how these interactions take place, and under which conditions. While individual-based models (IBMs) allow interactions to be represented at the level of individuals, most models aggregate information on interaction partners (e.g. by age), without specifying where contacts occur, how they take place, or which individuals are co-present in the same setting. As a result, interactions outside households, schools or workplaces are commonly represented using aggregated community structures, and detailed mobility or venue-level contact data are rarely available. This poses a key challenge for microsimulation-based transmission modelling. We present a methodological extension of the STRIDE individual-based model (Willem et al., 2021) that introduces an explicit, population-wide representation of community venues. Starting from aggregated community interaction pools, individuals are assigned to specific venue types (i.a. such as shops, restaurants, and other social locations) using empirical time-use data. This venue-based decomposition makes it possible to explicitly represent where interactions occur at the population scale, without relying on detailed mobility trajectories. Such fine-grained representations are straightforward when modelling a single setting, but become substantially more challenging when extending to all venues across an entire population. The venue-based structure allows heterogeneous environmental characteristics to be incorporated at the setting level, including ventilation, occupancy, and exposure duration. Within this framework, we explicitly integrate multiple transmission pathways (i.a. close-range droplet transmission and airborne transmission) within STRIDE. The contribution of each pathway depends on individual behaviour and venue-specific conditions. Most epidemiological models, including IBMs, focus on a single dominant transmission route. By contrast, jointly modelling multiple pathways across all venues makes it possible to examine how these routes combine and interact to shape transmission at the population scale. A key challenge is the limited availability of venue-level data. To this end, we developed an algorithm to redistribute aggregated community contacts across venues based on occupancy sizes and the time individuals spend in each setting. Where time-use data were unavailable, venue attendance patterns were imputed using age-stratified contact information. Many additional venue-specific characteristics required for transmission modelling, (i.a., contact duration, proximity, and environmental parameters) were not directly observed. In such cases, we introduced assumptions, guided as much as possible by existing literature. By representing individuals and venues explicitly, the model can study superspreading caused by differences in contacts, infectiousness, and venue conditions. Because empirical data on the drivers of superspreading are limited, variability in key characteristics was introduced using mathematically defined distributions, allowing heterogeneity to be explored systematically. Our new model was applied to a computer-generated population of 600,000 virtual individuals designed to statistically mirror the Belgian population, enabling the simulation of intervention scenarios corresponding to Belgium’s first COVID-19 lockdown. Moreover, it allowed us to investigate a variety of what if scenarios, including ventilation interventions. Overall, this work demonstrates how venue-based microsimulation with heterogeneous transmission and individual-level variability can enhance the realism and policy relevance of population-scale infectious disease models.

Child poverty remains a persistent challenge across the European Union: approximately 20 million children are at risk of poverty or social exclusion, and the economic costs of child poverty are estimated at an average of 3.5% of GDP annually in Europe (Clarke et al., 2024). Child-Contingent Payments (CCP), comprising cash transfers and tax reliefs linked to the presence of children in the household, constitute a primary policy instrument for addressing this issue. The period 2011-2024 encompasses significant economic and policy shifts: post-Great Recession austerity, the COVID-19 pandemic, subsequent inflation shocks, and the launch of the European Child Guarantee in 2021. Understanding how CCP have evolved and contributed to child poverty reduction during this turbulent period is crucial for evidence-based policymaking. This paper provides a comprehensive analysis of the level, composition, distribution, and poverty/inequality impacts of CCP across all 27 EU Member States from 2011 to 2024. We extend previous research (Figari et al., 2011; Ferrarini et al., 2013) by covering a longer and more recent period, offering novel decompositions by payment type and targeting mechanism, and quantifying both poverty and inequality impacts with high granularity. We use EUROMOD, the EU tax-benefit microsimulation model, combined with EU-SILC microdata. CCP are decomposed into child benefits, other benefit supplements for children, and child tax reliefs. We measure poverty using the at-risk-of-poverty (AROP) rate and gap, and inequality using the Gini coefficient. We rely on the Shapley decomposition to address sequential bias when assessing individual CCP components. CCP reduce child poverty by 10.6 percentage points on average across the EU (from 26.5% to 15.9%) and decrease the Gini coefficient by 5.7%. However, we document substantial cross-country heterogeneity. Poland’s Family 500+ program, introduced in 2016, tripled the country’s CCP effectiveness, i.e. poverty reduction impact improved from 5.4% to 16.7% between 2011 and 2024. Slovakia achieved the largest inequality reduction (15.3% Gini decrease) alongside a 106% increase in benefit levels. In contrast, Spain’s CCP achieved only a 3.9% poverty reduction, reflecting persistent coverage gaps. We identify targeting and adequacy shortfalls: only 37% of CCP expenditure reaches poor households, and only 51% of payments to poor families are adequate to lift them above the poverty line. Countries with stronger targeting (Ireland, Romania) do not necessarily achieve higher adequacy, suggesting that benefit generosity matters as much as targeting design. Policy design choices, such as universal versus targeted benefits, or cash transfers versus tax reliefs,significantly determine CCP effectiveness in reducing child poverty and inequality. Our findings provide timely evidence for the implementation of the European Child Guarantee and national reform efforts, highlighting that both adequate benefit levels and appropriate targeting mechanisms are necessary for meaningful poverty reduction.

Microsimulation models are flexible and powerful tools with the capacity to provide important evidence to policymakers and improve the lives of families. In this presentation we will discuss the application of microsimulation to genomic medicine and related policies. In particular, we will address how we lay the groundwork for a microsimulation model of genomic medicine when there is almost no data, but the conditions have such significant impacts on quality of life and mortality that it is critical that evidence is robust. Until recently, patients with genetic disorders rarely received a definitive ‘molecular’ diagnosis. Further, what we consider to be a single condition such as genetic blindness, may be caused by many different genetic variants (each with very different symptoms, age of onset and disease trajectory), and for some conditions such as intellectual disability, the condition may be caused by thousands of different genetic variants. As a result of their unique genetic causal mechanisms, these genetic conditions each require a ‘targeted therapy’, such as corrective diets, enzyme replacement, and emerging new gene therapies, meaning that there may be no two patients in a study with the same condition or treatment. Such extreme heterogeneity makes the field of genetics and genomic medicine an ideal application for microsimulation modelling because simulations are run at the individual level. However, the rarity of many genetic conditions, combined with the low likelihood of receiving a molecular diagnosis, has meant that nationally representative survey data generally has not identified these conditions despite their high health and cost impacts for patients, carers and government, leaving a significant data gap for model development. In this presentation, we will describe how we filled substantial data gaps related to genomic medicine by designing new and comprehensive primary data collections to use as microsimulation model base populations, how we worked with our clinical colleagues to gain ethical approval to obtain primary patient data, how we collected the data to ensure a high response rate, and how the data was managed to ensure clean and robust information. We describe the development and structure of our static microsimulation models applied to genomic medicine and present some examples of analysis of model outputs. Finally, we will provide examples of how our models have influenced policy and some current policy applications in development as well as describing the impacts such policy changes have on families.