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: The impact of in-work conditionality of Universal Credit on benefit take-up and employment

Universal Credit (UC) is the main means-tested benefit in the UK welfare system, supporting low-income individuals and families. UC replaced multiple benefits with a single payment, while introducing strict job search requirements and in-work conditionality. Individuals who are not working and are deemed capable of work are usually required, among other things, to actively look for a job, while claimants who are working but earning below a threshold are required to take steps to increase their earnings, including looking for alternative jobs and increasing work hours. Failure to comply can result in benefit sanctions. Research shows that UC conditionality can have detrimental effects on individual well-being and mental health, while evidence of its employment effects is mixed. In this study, we jointly model the take-up behaviour and labour supply decisions through the lens of a structural random utility model. Individuals anticipate that receiving UC negatively affects their well-being, and job search requirements may reduce the utility they derive from income and leisure. As a result, they might choose not to take up UC even if they are eligible and modify their labour supply accordingly. In this paper, we compare baseline simulations with estimated parameters with counterfactual simulations where the effects of conditionality are muted/removed. This allows us to quantify the impact of conditionality on a number of outcomes of interest, including benefit take-up and employment.

A Novel Weighting-Based Approach to Cohort Replenishment in Dynamic Microsimulations

We propose a new method for generating replenishment cohorts in dynamic microsimulation models. Standard dynamic microsimulations project the future states of an initial population through the recursive application of one-step-ahead predictions. Over time, sample size declines due to attrition (e.g., mortality), and without the integration of new individuals, the projected population progressively departs from the target population structure. To preserve representativeness, replenishment cohorts must therefore be introduced at each simulation step.

A venue-based population-wide individual-based microsimulation model for COVID-19 transmission

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.

Alternatives for financing social security in Luxembourg by resident and cross-border households

Given the rapidly evolving structural socio-demographic determinants in Luxembourg (ageing, migrations, cross-border households) and social needs induced, the concern about the funding of the Luxembourg social security system is high on the agenda. This concern could become even more pressing over time, given Luxembourgs specificity in several respects. According to the 2024 Ageing Report of the EPC’s Ageing Working Group, Luxembourg might face an increase of age-related expenditure from 17.2% of GDP in 2022 to 27.9% in 2070, mostly due to pensions (+ 8.3% of GDP over the period). This is the biggest increase expected in EU-countries, a first particularity for Luxembourg. A second specificity is the importance of cross-border commuters in the Luxembourg economy. These represent 43% of total employment in 2022. Given such a context, the countrys social players are looking for avenues of reflection for future concrete proposals. This paper precisely aims to contribute to such a debate. It examines day-after impact of hypothetical parametric changes in social contributions and personal income taxes (the “alternatives”) on the distribution of household disposable income and total public financial receipts from these sources for Luxembourg. Moreover and given the importance of cross-border commuters for the country, we need a microsimulation modelling EUROMOD-based covering both residents and cross-border commuters’ households, the latter population involving an essential innovative extension to previous assessments. We emphasize the structural discrepancies between residents and cross-border households in terms of socio-economic status as well as regarding gross labor and taxable income. Consequently, we show that total receipts from residents are greater than those from cross-border households, even when controlling for population size. Next, we examine 42 alternatives based on the concerns of a key Luxembourg social partner in the context of an ongoing public debate. This examination takes into account the values achieved for a triplet of standard indicators chosen for their simplicity and acceptability to a broad public, and this within the framework of an independent external expertise : total revenues (cross-border households included), the inequality Gini coefficient and the poverty rate (the latter two for the resident population only). We then complete our detailed overview with an evaluation of each alternative according to the selected dimensions altogether. Finally, we evoke a basic “Global Performance Index” which may enlighten conclusions derived from a one or two-dimensional analysis. Although the analysis is specific to Luxembourg, the policy alternatives and methodology considered here may also be relevant for other countries with comparable socio-economic fundamentals, particularly in the context of the EU-wide concern regarding the fiscal implications of population ageing.

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.

Bimic+: microsimulation with labor supply

This paper introduces BIMic+, the labor supply extension of the tax and benefit microsimulation model of the Bank of Italy, BIMic (Curci, Savegnago and Cioffi, 2017). The model follows the Random Utility approach (McFadden, 1974; Aaberge, Dagsvik, and StrØm,1995; Van Soest, 1995). The model focuses on the labor supply behavior of wage earners and imputes wages for workers who are not employed through a two-step Heckman estimation procedure. The utility function departs from the quadratic functional form, which is common in this literature, to avoid decreasing utility in disposable income, a violation of a critical assumption in consumer theory and that underlies all redistributive analyses and is crucial for computing equivalent variations. The main arguments of the utility function are hours and disposable income. The latter is calculated through the static module, BIMic, for each counterfactual hours option. With respect to the literature, we innovate by: (i) matching the observed distribution of hours as a constraint into the optimization problem to avoid overfitting issues (as opposed to the usual approach of drawing taste shocks until the estimated hours match the observed ones). We do so in a way that also matches the distribution of labor income from aggregated tax returns. (ii) organizing the output of the model according to a strand of the public finance literature theoretically connected to optimal taxation. For each policy, we want to characterize the willingness to pay of beneficiaries and the net government cost, taking into account behavioral responses to the policy. We also propose to use these quantities to compute the marginal value of spending public funds in such a policy (Hendren and Sprung-Keyser, 2020; Bourguignon and Landais, 2022). In the last section of our paper, we simulate the labor supply effects of a policy reform as an illustration of how to use our model and its output; specifically, we focus on a cut in social security contribution for mothers with at least two children introduced in Italy in 2024.

Building 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.

Carbon Pricing and Redistribution: A Microsimulation Analysis for Belgium

We simulate the distributional effects of a €45/tCO2 carbon price on Belgian households’ heating and transport fuels using microdata from the 2016 Household Budget Survey. Without compensation, the policy is regressive and increases energy poverty, with especially large burdens for singles, seniors, and households heating with oil. We compare three revenue-recycling designs: equal transfers per household, equal transfers per capita, and a fuel-type-differentiated scheme that provides larger supplements to fossil-heated households. Per-household recycling protects vulnerable households better than per-capita recycling, which tends to undercompensate small households. Differentiating transfers by heating fuel further reduces large losses and within-income-group dispersion, and it prevents an increase in energy poverty while preserving overall progressivity of the reform.

Challenges in measuring subjective poverty: a policy application to Ecuador

Traditional monetary poverty metrics used in policy analysis have well-known limitations: small changes in thresholds or methodology can markedly alter who is counted as poor. Subjective poverty indicators—based on individuals’ own assessments—offer a complementary lens by capturing perceived deprivation.

Comparative Pension Microsimulation using microWELT: Lessons from Benchmarking to a Detailed National Model

Comparative pension microsimulation supports coherent cross-country policy analysis by us-ing harmonised inputs such as EU-SILC. Yet institutional heterogeneity and limited life-course information in standardised surveys constrain how credibly national rules, accrual mecha-nisms, and retirement pathways can be represented within a portable framework. Portability is feasible, but for most countries the feasibility frontier is set by what can be inferred from harmonised data and how much institutional detail can be included without sacrificing com-parability. We explore the validity domain of comparative modelling by benchmarking a comparative model against a detailed national model in a controlled “sister-model” setting. Our compar-ative model is microWELT, designed for multi-country applications and therefore built around portable representations of labour-market and retirement transitions and simplified pension benefit calculations. Retirement timing follows parsimonious rules centred on statutory ages, and benefits are approximated via mappings from pre-retirement earnings. microWELT is pa-rameterised for eight European countries and serves as a uniform base for refining national applications. We compare microWELT to microDEMS, a closely related detailed Austrian model built on longitudinal administrative data. microDEMS reconstructs employment and insurance careers and implements Austrian pension law at a granular level, including path-way-specific eligibility and benefit calculations that depend on accumulated insurance peri-ods and full contribution histories. This pairing allows us to attribute projection differences directly to data richness and institutional detail - rather than to unrelated modelling choices. Empirically, we use Austria’s reform harmonising women’s statutory retirement age with men’s as a demanding test case. Although the reform is simple to describe, it is difficult to capture with stylised comparative rules because it is phased in over time and interacts with multiple exit routes from the labour market whose availability depends on career histories. We run matched baseline and reform scenarios focussing on retirement transitions over the next decade under harmonised demographic and macro assumptions and compare age- and cohort-specific retirement and employment profiles as well as the timing of pension claiming and aggregate expenditure trajectories.

Conditional diffusion for uncertainty-aware dynamic microsimulation: multivariate trajectory inpainting, forecasting, and scenario generation

Dynamic microsimulation requires generating coherent multivariate micro-trajectories over time across multiple outcomes (e.g., employment, income, hours), while handling panel gaps and propagating uncertainty into downstream indicators. Common approaches—transition models, chained regressions, and hot-deck imputation—often yield a single deterministic completion and can struggle to preserve high-dimensional joint structure, especially under block nonresponse.

Constructing a historic microsimulation model to measure tax-benefit policy intentions in the Netherlands: 1950-1975

Microsimulation techniques are widely used to study the impact of (reforms in) fiscal and social policies in mature welfare states, providing interesting insights into how today policies reduce poverty, redistribute resources among population groups and shape incentives for instance to work or save. While a lot has been written on the historical emergence and expansion of welfare states on an institutional level, hardly anything is known about the impact early-stage welfare states had on the lives of ordinary people. Most European countries saw the emergence and rapid expansion of their welfare states in the three decades after the Second World War, usually referred to as the Golden Age. It is often inferred that this must have been a period in which everyone was better off, in large part attributed to strong social protection. Yet, the empirical evidence is lacking as the literature typically focuses on the top 10% and the role of top marginal tax rates.

Context specificity of childcare out-of-pocket costs and child-contingent benefits

This paper examines the interplay between child contingent income support and out of pocket (OOP) childcare costs in four European countries—Belgium, Poland, Spain, and Sweden. While existing research has extensively analysed cash benefits and early childhood education and care (ECEC) services separately, considerably less is known about how these policies jointly shape families’ income adequacy and labour market participation. Using EUROMOD, enriched with detailed information with regard to legislation on childcare fees, we introduce a novel indicator—the compensation ratio—which captures the degree to which child contingent benefits offset OOP childcare expenses.

Continuous-Time Labour Activity Transitions in Comparative Microsimulation: Alignment, Validation, and Benchmarking

We present a continuous-time longitudinal microsimulation module for labour activity careers in microWELT, a comparative microsimulation model for socio-demographic projections and policy scenario analysis. Individual careers evolve through transitions among employment, unemployment, non-participation, retirement, and family-related leave using an event-driven waiting-time approach. Transition processes are modelled with piecewise-constant hazard regressions featuring competing risks and explicit duration dependence, enabling realistic spell dynamics - especially for unemployment - that are often difficult to reproduce in discrete-time transition models. A central design feature is alignment to scenario targets for aggregate unemployment and labour force participation. Because microWELT, like most policy microsimulations, do not model market mechanisms, these targets provide macro-level closure for projections and a transparent instrument for counterfactual scenario design. Estimating transition hazards in microWELT is challenging because the underlying comparative survey data typically have limited longitudinal depth and small effective sample sizes, implying higher parameter uncertainty than administrative-history models such as Austria’s microDEMS. To address this, we adapt the microDEMS hazard-based framework and implement two alignment mechanisms. First, simulated aggregates are calibrated to scenario targets for overall unemployment and participation. Second, an optional but pivotal mechanism for microWELT constructs group-specific targets using cross-sectional imputation models defined by categorical characteristics (age group, gender, education, health, and family status). These group targets are reconciled with aggregate constraints via monthly calibration of the cross-sectional model during the simulation. Operationally, alignment exploits simulated waiting times for selected transitions - entries into unemployment and exits from the labour force - to rank individuals by implied event timing and then induces the required number of events by selecting those with the shortest waiting times to meet target margins. All other transitions remain fully continuous-time; only alignment-induced events are implemented on monthly steps to enforce cross-sectional constraints. We evaluate the framework through benchmarking and retrospective validation, comparing careers generated from hazards estimated on administrative versus survey data and assessing fit to recent observed outcomes. Validation focuses on the distribution of unemployment spell durations and heterogeneity in unemployment risk across population groups, quantifying how alignment delivers target consistency while preserving plausible continuous-time dynamics and empirically observed group differences.

Decomposing Child Poverty Drivers using UKMOD and EUROMOD: A Comparative Analysis of the UK and selected European countries

This study investigates the drivers behind the divergent trends in child poverty between the UK and five European counterparts—Finland, Hungary, Ireland, Poland, and Spain—from 2011 to 2024. These countries were selected to represent a diverse spectrum of trajectories: Finland and Spain serve as references for persistently low and high rates, respectively, while Poland reports a significant reduction over the analysed period of time. The analysis further incorporates Hungary, which follows the UK’s experience of rising child poverty, and Ireland, which offers a divergent path despite sharing initial similarities with the UK. The analysis decomposes these trajectories into three core determinants: demographic differences, labour market dynamics, and tax-benefit systems. To capture differences due to demographic characteristics, we employ a coarsened exact matching and reweighting technique to generate synthetic populations. Differences in labour market dynamics are captured using four-stage statistical models (estimating the probability of employment, the probability of unemployment, hourly wages, and hours worked) based on counterfactual projections for employment and earnings in the UK that reflect labour market conditions in the comparator countries. Finally, the contribution of the tax-benefit system is isolated through a difference-in-differences (DiD) approach, comparing child poverty rates computed on original versus disposable incomes. This is facilitated by utilizing the static microsimulation models UKMOD and EUROMOD that allows to compute disposable incomes. By quantifying these components, we determine the extent to which demographic characteristics, labour market dynamics, or tax-benefit systems have driven the UK’s relative child poverty trends.

Demand and Supply of Care Over the Life Course

We project the effects of changes in fertility and mortality rates on both the receipt and provision of care in the UK. We investigate the impact on the level and cost of care, as well as its share of total GDP, through the life course and across income and wealth distributions. SimPaths, an open-source dynamic microsimulation model, is employed to design different scenarios over a half-century period. This framework projects life histories over time, developing detailed representations of career paths, family and intergenerational relationships, health, and financial circumstances. Our estimates show that the value of care, as a share of GDP, almost doubles over the five decades of our analysis, with informal care accounting for most of the projected rise.

Developing Reporting Standards for Population Health Microsimulation: A Scoping Review of Current Practices

Background Population health simulation models—including microsimulation, agent-based, system dynamics, and Markov models—are essential tools for understanding noncommunicable disease (NCD) burden and evaluating policy interventions. However, inconsistent reporting practices limit the transparency, reproducibility, and credibility of this work. Unlike clinical trials and observational studies, which benefit from established reporting guidelines (CONSORT, STROBE), no comprehensive standards exist for population health simulation models.

Distributional Effects of Distance-Based Road Pricing: A Behavioral Microsimulation Study for the Brussels-Capital Region

At the intersection of transport economics and public finance, this research contributes to the empirical literature on transport pricing as a policy tool for addressing the externalities of vehicle use. In line with recent technological developments and ongoing policy debates, it provides an ex-ante evaluation of a distance-based road pricing scheme that varies by time (peak and off-peak), location (congested and non-congested zones), and vehicle characteristics. While such systems are widely recognized as efficient, as they better align driving costs with externalities, their implementation in passenger transport remains limited. This absence is largely driven by concerns about distributional effects, highlighting the need for robust empirical evidence on equity implications as a key input for policy feasibility.

Drivers of Income Inequality in Ireland and Northern Ireland

The distribution of income differs in Ireland and Northern Ireland. Differences in demographics, working patterns, wage levels and the tax-benefit system all contribute to these differences and could prove a barrier to increased co-operation on the Shared Island of Ireland. Using harmonised microsimulation models for Ireland (SWITCH) and Northern Ireland (UKMOD), we identify the drivers of the differences in income distribution between Ireland and Northern Ireland. Using a decomposition technique, we isolate the relative contributions of market income differences - attributable to demographics, labour market participation and wage levels - and the tax-benefit system to differences in income distribution in the two jurisdictions. In a final step, we simulate the implementation of the Irish tax-benefit system in Northern Ireland and vice versa. This exercise indicates the potential costs and distributional effect of harmonising the direct tax and welfare system across the two jurisdictions.

Effectiveness of Minimum Income Support in Bulgaria: A Microsimulation Analysis of 2023 Reform of the Monthly Social Assistance Benefit

Over 15 years since the EU integration of Bulgaria an ambitious reform has been introduced in 2023 regarding the minimum income protection scheme operated within the social policy mix, namely the Monthly Social Assistance (MSA) benefit. The reform addressed European Council’s country specific recommendations (CSRs) to Bulgaria within the framework of the European Semester and had several ambitious goals, among which to improve benefit coverage, adequacy, and targeting. The long years maintained MSA scheme had limited reach due to many strict eligibility criteria so the reform aimed in expanding the inclusion of more vulnerable individuals and families. Besides, the monetary values of the benefit were previously not indexed to inflation as well as outdated means-test thresholds were implemented. From this point of view, the 2023 reform sought to tie the benefit amounts to the national poverty line in order to make them more responsive to the rapidly shifting economic circumstances since the 2022 energy crisis and related inflationary pressures. Moreover, the introduction of annual adjustment aimed in improved reflection of MSA regarding the needs of different groups (e.g., elderly, disabled, single parents). The microsimulation analysis of such effects expected to be achieved by the 2023 MSA reform is performed for the period 2023-2025 using the Bulgarian component of EUROMOD: the tax-benefit microsimulation model of the EU. Most up-to-date dataset is utilized, derived from SILC 2023 survey measuring the incomes for year 2022. Particular indications for the shifts in selected poverty and inequality indicators are evaluated, having in mind that the reform was intended to narrow the poverty gap and improve social inclusion.

Environmental Engel Curves over four decades – The case of the Republic of Ireland

The relationship between income and carbon dioxide (CO₂) emissions has been debated in the environmental economics literature, primarily through two complementary but conceptually distinct frameworks. At the macro level, the Environmental Kuznets Curve (EKC) hypothesis posits a non-linear relationship between economic development and emissions, whereby emissions initially increase with income growth before stabilising or declining at higher income levels. At the micro level, Environmental Engel Curves (EECs) describe how household-level emissions vary with income within a given country, reflecting differences in consumption patterns, energy use, and access to carbon-intensive goods and services. These two literatures typically developed separately, leaving a gap: we have limited evidence on how the within-country income-emissions relationship evolves as an economy moves along its development path. To our knowledge, only one study examine temporal changes in the distribution of household CO₂ emissions within a single country, notably Sager (2019) for the United States. This paper studies the evolution of Environmental Engel Curves over time using six waves of the Irish Household Budget Survey (HBS) from the early 1980s to 2015, a period during which Ireland experienced exceptionally rapid income growth and major changes in consumption opportunities and energy use. We estimate income-emissions relationship for multiple points along Ireland’s development trajectory and ask: How does the shape of the EEC change as a country transitions from lower- to higher-income status? In doing so, we explicitly separate two mechanisms that are easily confounded in cross-sectional work: (i) changes in the carbon content of given consumption categories (i.e. carbon intensity of heating) and (ii) changes in the prevalence of carbon-intensive durables (i.e. personal vehicles) across the income distribution (extensive margin). We focus, in particular, on direct household energy use, focusing on residential heating fuels and private transport (vehicle ownership), which are plausible channels through which development alters EEC shape. These categories are where diffusion, infrastructure constraints, and policy-induced technology change are most likely to generate non-linearities. By documenting how EECs shift and re-shape across three decades of development within a single country, the paper complements cross-country evidence on heterogeneous distributional incidence (Dorband et al., 2019) and time-series evidence for the United States (Sager, 2019). We show that the EKC can be understood as the outcome of changing Environmental Engel Curves over the development process, driven by the diffusion and saturation of carbon-intensive household technologies. References Dorband, I. I., Jakob, M., Kalkuhl, M., & Steckel, J. C. (2019). Poverty and distributional effects of carbon pricing in low- and middle-income countries – A global comparative analysis. World Development, 115, 246–257. https://doi.org/10.1016/j.worlddev.2018.11.015 Sager, L. (2019). Income inequality and carbon consumption: Evidence from Environmental Engel curves. Energy Economics, 84, 104507. https://doi.org/10.1016/j.eneco.2019.104507

Estimation and Simulation of RURO Labor Supply Models with Administrative Data: Re-assessing the Evidence from Belgium

This paper estimates a Random Utility Random Opportunity model of labor supply using linked Belgian administrative data . The framework allows individuals to choose among stochastic wage and hours offers, capturing both participation decisions and hours adjustments within a unified structure. By combining tax records, social security data, and demographic registers, we construct precise measures of earnings, hours, and household characteristics.

Evaluating Labour Supply Responses to an In-Work Benefit for Spain

Spain records one of the highest rates of in-work poverty in the European Union (Eurostat, 2024). Despite this, the development of policies specifically targeted at supporting low-income workers has been limited, especially when compared to other European countries (Laun, 2019). This policy gap, combined with the expansion of minimum income guarantees schemes, may weaken work incentives by narrowing the income gap between employment and nonemployment, thereby increasing the risk of poverty traps among low-income households (Domínguez-Olabide & Zalakain, 2023).

Evaluating the results of a social benefit simulation using individual administrative data on benefit receipt

The simulation of social benefits is an important application of tax-benefit microsimulation models in social policy research. Simulation outcomes inform about the (potential) effects of social policies and policy reforms. Furthermore, tax-benefit simulations allow for an evaluation of the interactions of different benefits in complex benefit systems. However, the quality of the simulation outcomes has consequences for the assessment of the effectiveness of the policy. An increasing number of recent studies on benefit non-take-up as one measure for the ineffectiveness of social policies explicitly address the difficulties in determining benefit entitlements using tax-benefit microsimulation models (Tasseva 2016, Bruckmeier et al. 2021, Doorley and Kakoulidou 2024, Bargain et al. 2012, Harnisch 2019). Consequently, the validation of the simulation outcomes is an important step in the application of tax-benefit simulations. Our study contributes to the literature on validating the results of tax-benefit simulation models. We examine how well the results of an open-source tax-benefit microsimulation model for Germany (GETTSIM) on means-tested minimum income (UBII) entitlements match the benefits contained in administrative data on UBII. Our analysis has two objectives: First, the results should provide an assessment of the validity of the UBII simulation results using GETTSIM. Second, generalized conclusions for policy and non-take-up analyses based on tax-benefit microsimulation models will be drawn. The results show that UBII entitlements are in most cases correctly simulated. In an adapted version of GETTSIM we have used, only for 3 to 4 percent of all observations no UBII entitlement was simulated (beta error). The simulation also allowed a precise distinction between UBII and existing similar benefits (housing and means-tested child benefit) for most observations. A closer look at individual deviations between recorded and simulated entitlements reveals significant deviations for migrants, especially from crisis countries, which was particularly relevant in 2017 and 2018. Furthermore, for households with many family members, with children or employed persons, the simulation at the individual becomes less precise. The results also provide some insights for the analysis of eligibility based on tax-benefit simulations in general. In social policy systems with overlapping benefits, even with very good data quality, misspecification of benefit entitlements cannot be avoided to a relevant extent, especially when the benefits pursue similar objectives and discretionary decisions occur at the administrative level. Since the mean values of various large sociodemographic groups are relatively accurately determined in the simulation, calibrating the simulated recipient numbers can compensate for these inaccuracies. The analysis at the individual level has shown that simulation quality decreases particularly for subgroups with more complex life circumstances, such as households with children. This applies in particular to comprehensive last-resort minimum income systems that provide benefits in the household context and take all types of household income into account. Temporary special circumstances, like a national or global crisis, can also lead to simulation results that do not reflect actual payments. In crisis years, consideration should be given to excluding certain particularly affected subgroups from the analysis or to choose other simulation years, if possible.

Financing a Universal Child Benefit by taxing Illicit Financial Flows in Ghana

Universal social protection is increasingly recognized as a central instrument for achieving the United Nations 2030 Agenda commitment to “leave no one behind.” By guaranteeing income security for all, universal policies enhance equity, reduce stigma, and limit exclusion errors that commonly affect means-tested programmes, particularly in low-capacity settings. However, their adoption in low- and middle-income countries (LMICs) is often constrained by concerns over fiscal affordability, driven by cost estimates that assume financing through broad-based and potentially regressive taxation.

Firm microsimulation and VAT policy analysis

I am a Research Associate at PolicyEngine, a nonprofit that provides free, open-source software to compute the impact of public policy in the US and UK. Previously, I served as a researcher at the London School of Economics. My work focuses on microsimulation, economic modelling, and public policy analysis, particularly the UK tax and benefit system.

Forecasting ADRD in European Elderly Population Using Dynamic Microsimulation

Population ageing is rapidly increasing the prevalence of Alzheimer’s disease and related dementias (ADRD) across Europe, creating major challenges for health and long-term care systems. Existing European projections typically rely on static prevalence assumptions or self-reported diagnoses and rarely model individual cognitive trajectories within a unified, forward-looking framework. This paper presents a major extension of the European Future Elderly Model (EU-FEM), introducing a dedicated microsimulation module for dementia and cognitive decline applicable across multiple European countries. Using harmonized longitudinal data from SHARE Waves 1–9 (2004–2022), we develop and integrate a dynamic ADRD module that simulates transitions in cognitive status for individuals aged 50 and over. Cognitive decline status is defined using the Langa–Weir (LW) classification, adapted to the European context by combining episodic memory tests with functional limitations in instrumental activities of daily living (IADLs). Country-specific cut-offs are calibrated against OECD dementia prevalence benchmarks and tested for robustness across alternative SHARE waves. The classification algorithm incorporates deterministic and stochastic imputation procedures and enforces the absorptive nature of dementia over time. The ADRD module is embedded within EU-FEM’s first-order Markov Monte Carlo framework, allowing cognitive decline to evolve jointly with chronic conditions, demographic characteristics, and socioeconomic factors. Transition equations condition on prior Socio Economic Status (education, income, wealth, labour market status), health and cognition, enabling heterogeneous life-course trajectories. The enhanced model expands EU-FEM coverage to twelve European countries, including new Central and Eastern European populations, and produces internally consistent projections of dementia prevalence and cognitive trajectories. Validation exercises show close alignment with external epidemiological benchmarks and substantial improvements over self-reported dementia measures. This work demonstrates how dynamic microsimulation can be used to model cognitive decline in a harmonized, multi-country setting, providing a flexible platform for forecasting dementia prevalence and evaluating counterfactual scenarios involving risk-factor modification, prevention policies, and future care needs. The extended EU-FEM establishes a foundation for integrating cost-of-illness and long-term care modules, supporting policy analysis in ageing European societies.

From Annual to Monthly Simulation of Social Assistance in Sweden

FASIT is a microsimulation model primarily used by the Swedish Parliament and Government to calculate the effects of various regulatory changes. Statistics Sweden (SCB) is responsible for the maintenance and development of the model. This presentation explores experiences moving from annual to monthly data when simulating social assistance.

From Marital Entitlements to Individual Risks: Vertical Solidarity and the Future of Survivors’ Pensions in Beveridgean Systems

Survivors’ pensions have long served as a central pillar of social protection in Beveridgean pension systems, offering intrafamilial insurance against the economic consequences of widowhood. Yet demographic ageing, evolving family structures, and gendered labour‑market patterns increasingly call into question the appropriateness and sustainability of marital-status‑based entitlements. Drawing on recent European jurisprudence and ongoing reform debates in Switzerland, Finland, Germany, and Japan, the project examines how the abolition or redesign of survivors’ pensions would affect vertical solidarity in a Beveridgean first‑pillar pension system. Using the Swiss Old‑Age and Survivors’ Insurance (OASI) as an illustrative case, the analysis explores how interpersonal redistribution, intrafamilial solidarity, and spousal equalisation mechanisms jointly shape pension outcomes across marital statuses and along the pension‑income distribution. The study employs MIDAS‑CH, a dynamic microsimulation model calibrated to Swiss SILC data, to generate long‑term counterfactual life‑course trajectories under shifting gender labour‑market behaviours. This enables an assessment of how redistribution embedded in the Swiss first pillar—minimum and maximum pensions, care credits, splitting rules, and survivor benefits—operates in a context where men’s and women’s participation, hours, and wages converge. As family structures diversify and dual-earner households become the norm, traditional justifications for marital entitlements weaken, suggesting that solidarity mechanisms may need to shift from status-based to individualised forms. To identify the redistribution channels at work, the project applies a Recentered Influence Function (RIF) decomposition, which separates differences in characteristics (earnings histories, contribution years, care credits) from differences in coefficients, interpreted as the redistributive valuation embedded in the benefit formula. This distinction allows a distribution‑sensitive quantification of vertical solidarity and an assessment of how strongly various institutional components compensate gendered labour‑market inequalities. The RIF approach provides insight into nonlinear redistribution at the bottom, middle, and top of the pension distribution—precisely where floors, ceilings, credit valuation, and splitting rules bite. Simulation results show that survivors’ benefits play a meaningful but declining role in equalising outcomes, particularly as women accumulate stronger contributory records. Removing survivor pensions lowers intrafamilial solidarity but increases the relative importance of interpersonal redistribution, especially for low‑income individuals. When labour‑market participation, work intensity, and wages between men and women are equalised, women’s pensions rise markedly, while men’s decline modestly. Consequently, the gender pension gap narrows substantially through improved earnings capacity rather than through marital entitlements. In scenarios of full labour‑market convergence, marital-status‑based mechanisms—splitting, capping, and survivors’ benefits—become less central, while individualised solidarity instruments such as minimum pensions and care credits remain decisive. These findings highlight a fundamental shift in the organisation of solidarity within Beveridgean pension systems. As gender labour‑market trajectories converge and family forms diversify, the rationale for marital entitlements weakens, and the effectiveness of solidarity increasingly depends on individualised, needs‑based instruments rather than derived rights. Vertical solidarity remains essential for cushioning fragmented careers and low earnings, but its incidence evolves as women’s labour‑market attachment strengthens. In this environment, survivor protection can be better targeted through time‑limited or earnings‑tested supplements, while care credits and progressive benefit formulas remain central to mitigating gendere

From Microsimulation to a Digital Twin of Society: Methodological and Data Foundations of project InnoTwin

Digital twins are increasingly regarded as a key technology for analysing complex systems. While the concept is well established in engineering and industry, its transfer to societal systems remains methodologically underdeveloped. This presentation discusses, using our BMFTR-founded project InnoTwin (www.innotwin.de) as a case study, what it scientifically means to construct a digital twin of society, and how this approach differs from classical microsimulation. InnoTwin is based on an agent-based model that explicitly represents individual life courses, behavioural responses, and social interactions, thereby moving beyond the analysis of average effects.

Household Demand Responses to Carbon Pricing by Energy Poverty Status: Evidence from Belgium

This paper develops a behavioral microsimulation framework to analyse how carbon pricing affects energy vulnerable households in Belgium. We focus on two groups: energy poor (EP) households, who devote a large share of their income to energy, and hidden energy poor (hEP) households, who spend little on energy as they severely restrict their consumption. Using eleven cross-sections of the Belgian Household Budget Survey (2003–2016), we first document structural differences between EP and hEP households with logistic regressions. We then estimate a demographically specified Quadratic Almost Ideal Demand System (Banks et al., 1997) and develop a two-stage residual inclusion procedure to address the endogeneity of energy poverty statuses. The resulting price and income elasticities by vulnerability profiles are used to simulate responses and welfare impacts of a carbon price on heating and transport fuels, consistent with the forthcoming EU ETS 2. Behavioral adjustments are highly heterogeneous: lower-income and energy vulnerable households show higher fuel price elasticities than wealthier groups, with hEP households responding most strongly to price increases. Consequently, EP households face substantial carbon costs but comparatively smaller welfare losses, whereas hEP households suffer disproportionately large welfare losses despite their low expenditure exposure. These results reveal horizontal equity concerns that are invisible in income-based metrics alone and highlight the need to integrate detailed energy vulnerability profiles into carbon pricing design.

Imputing 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.

Incorporating the Prebound Effect in Retrofit Policy Analysis: Distributional Results for Belgium

This paper compares the distributional incidence of three decarbonization instruments in the Belgian residential sector: EPC‑based minimum standards, carbon pricing with an equal per‑household dividend, and renovation subsidies financed by a uniform lump‑sum tax. Using Woonsurvey 2018 and a dwelling‑level microsimulation model that evaluates renovation profitability on observed energy use, we quantify household monetary impacts, renovation take‑up, and equity (across and within income groups) for budget neutral policies calibrated to common C02 targets. Three results stand out. First, EPC standards concentrate burdens on low‑income and low‑use households and generate high dispersion because they compel renovations where realized savings are small. Second, universal subsidies are costly on average and distribute benefits unevenly, with sizable transfers to infra‑marginal projects. Third, carbon pricing with revenue recycling yields the lowest and most evenly distributed household burdens, largely because it triggers heat‑pump adoption in dwellings with the highest energy consumption. We further show that combining a modest carbon price with targeted heat‑pump support can meet the same emissions target at lower cost and with a smaller variance of household impacts than under the carbon dividend. Results are robust to rebound, landlord–tenant limits, and reasonable variations in discounting, horizons, and costs.

Increasing the minimum wage to decrease labor cost ? An analysis by microsimulation for the case of France

This paper analyzes the reduction in total labor costs induced by an increase in the minimum wage in France. Using the Ines microsimulation model developed by the French National Statistical Institute (Insee), I simulate a 2% increase in wages for all workers paid at the minimum wage. Due to the complex of exemptions of the French socio-fiscal system, an increase in the minimum wage leads to a reduction in employers’ social security contributions (SSCs) for workers earning between 1.01 and 3.5 times the minimum wage. I confirm existing results from L’horty (2000): overall, a 1% increase in the minimum wage reduces employers’ SSCs by approximately €1.67 billion.

INFORM2, DWP’s main forecasting model for Universal Credit

INFORM2 is a dynamic microsimulation model developed within the Department for Work and Pensions (DWP) for forecasting claim volumes that underpin the benefit expenditure forecast for Universal Credit. Development began in 2018 and builds on earlier iterations of the INFORM framework. The model simulates independent benefit units and individuals on a monthly time step, using Universal Credit (UC) administrative data as its core input.

Integrating Labor Demand Frictions in a Random Utility Random Opportunity Labor Supply Model

This paper studies labor market responses to tax policy using a structural labor supply model estimated within a Random Utility - Random Opportunity framework. The RURO model represents labor supply as a choice among a finite set of work options, where individuals compare the utility of different employment and hours combinations given the opportunities available to them. Preferences are modeled in a random utility framework, while heterogeneity in job availability and constraints is captured through the opportunity structure. This allows the model to account for both choice behavior and limitations in feasible options. We combine this structural labor supply framework with a detailed microsimulation model based on Belgian administrative data, allowing for an accurate mapping from labor supply choices to disposable income and fiscal outcomes.

Introducing an asset test into client fees for long-term social care: a simulation study using Finnish administrative data

As the population ages and the sustainability gap in public finances in Finland widens, new solutions are needed to ensure sufficient funding for public services. One potential solution is to place greater emphasis on private wealth in the financing of care services. At present, client fees for long-term social and health care services in Finland are determined based on clients’ income. This study examines the potential effects of also taking clients’ assets into account. We focus on the fiscal and distributional implications of such a reform. The analysis is based on the SOTE-SISU static microsimulation model and unique administrative register including wealth.

Introducing Retirees into Discrete Labor Choice Models - the Case of Germany

Demographic change poses profound challenges to labor markets across advanced economies. Population ageing is increasing pressure on public social security systems in many countries. These developments have intensified the policy debate on how to extend working lives and increase labor force participation at older ages. Against this background, promoting labor force participation among individuals close to or beyond statutory retirement age has gained increasing importance. In the German policy debate, one prominent example of such an approach is the “active pension” scheme (Aktivrente) recently introduced in 2026. This policy allows employed pensioners to earn additional income up to a specified monthly threshold without being subject to income taxation. While the intended goal of these measures is to encourage voluntary labor supply at older ages, their actual quantitative effects on employment and public finances remain uncertain. Traditional models in pension economics typically conceptualize retirement as a discrete and absorbing state, in which labor force participation ends entirely upon retirement. In light of changing employment biographies and policy initiatives aimed at extending working lives, this limitation has become increasingly problematic. A methodological extension of existing microsimulation models that explicitly accounts for labor supply decisions at older ages, through differentiated transition scenarios, earnings rules, or tax allowances, is therefore required to reliably assess the effects of such reforms. The paper at hand addresses this methodological challenge using Germany as a case study. It examines to what extent microsimulation approaches behavioral adjustments can be further developed to analyze policy measures that create labor supply incentives for pensioners. The paper identifies and discusses both the limits and the potential of extending existing modeling frameworks. We were able to transfer the methodology of microsimulation to the group of retirees, using a microsimulation model based on the German Socio-Economic Panel. Simulations of hypothetical reform scenarios, as well as estimated labor supply elasticities, yield plausible results that are consistent with findings on labor supply responses among the younger working-age population. We plan to advance this model such that labor supply effects of realistic reform scenarios, like the active pension, can be estimated, as well as distributional effects of such reforms. We also plan to study and estimate in further detail the labor supply elasticities of pensioners with the model.

Machine Learning Approaches to Predicting Consumption Expenditure: A Comparative Analysis for SILC–HBS Statistical Matching

This study examines whether supervised machine learning can improve the prediction of household expenditure shares within the standard statistical matching pipeline that fuses EU SILC–type microdata with Household Budget Survey (HBS) expenditures. The conventional approach uses a transparent two part econometric design: a probit model for participation (extensive margin) and an OLS regression for conditional spending (intensive margin). While robust, this framework is known to struggle in categories with pronounced zero inflation, nonlinear participation boundaries, heterogeneous spending patterns, or timing noise. We assess whether replacing the parametric steps with Gradient Boosted Trees (GBT) for participation and Gradient Boosted Regression (GBR) for conditional expenditure yields systematically better predictions without altering the downstream imputation workflow. We combine Swiss SILC 2020 as the recipient dataset and Swiss HBS 2015–2017 as the donor survey. Because these samples have no shared identifiers, we harmonize variables following established Eurostat/JRC practices. Seventeen covariates present in both sources are aligned through recoding and aggregation, and we uprate nominal incomes and expenditures using the harmonized index of consumer prices (HICP) to ensure comparability with the SILC reference year. We apply EUROMOD style categorical aggregation to mitigate incidental zeros, remove extreme expenditure to income ratios, and enforce a common structure for the predictors used in both stages of the model. This creates a coherent evaluation environment in which alternative prediction models can be compared fairly. The imputation pipeline remains unchanged to ensure comparability with policy applications. First, we estimate participation for each aggregated COICOP category using the selected model (probit baseline or GBT alternative). Second, we model conditional expenditure given participation using OLS (baseline) or GBR (alternative). Third, we compute fitted shares and apply a pseudo R² screen to restrict attention to categories where covariates meaningfully explain variation. All diagnostics and matching steps are identical across methods so that any downstream differences are attributable solely to the prediction component. The design yields (i) cross validated probability and error metrics for extensive and intensive margins; (ii) threshold sweep summaries to document operating point sensitivity under imbalance; and (iii) downstream compatibility with the standard donor selection step used in EUROMOD/SWISSMOD type applications. Because the imputation workflow and diagnostics are held constant, the study isolates the contribution of flexible predictors relative to the classical probit–OLS baseline in a way that is transparent for policy use.

Mapping the drivers of spatial inequality in Luxembourg.

Understanding spatial disparities in income distribution is crucial for designing effective and targeted public policies. However, small-area analysis is constrained by the limited representativeness of household surveys at fine geographical levels and by the lack of comprehensive income information in administrative and census data. This paper develops a spatial microsimulation framework to map and decompose income inequality across municipalities in Luxembourg by combining EU-SILC survey data, census information, and administrative statistics to simulate complete disposable income distributions at the municipal level for 2012 and 2022. The model integrates labour market behaviour, multiple income sources—including capital income and private transfers—and the tax-benefit system using EUROMOD. Spatial heterogeneity is captured through a two-step procedure that combines census-based reweighting with regression-based alignment to local demographic and labour market control totals. We further decompose overall inequality into between- and within-municipality components, observing a stronger within-municipality component. This suggests that factors operating at the local level—such as demographic composition, labour market participation, and access to specific income sources—play a central role in shaping income disparities. Our results reveal the dominant role of demographic and local structural factors in driving these disparities. By producing timely and spatially detailed estimates of disposable income and inequality, this paper demonstrates how combining spatial microsimulation with dynamic income generation can overcome data limitations and provide a powerful tool for analysing the drivers of spatial inequality and supporting evidence-based local policy design.

Measuring and Modelling Migrant Fertility: Using Hazard Models and Dynamic Microsimulation to Simultaneously Account for Multiple Clocks

The share of individuals with a migration background in European societies is increasing, both directly because of migration and indirectly because migrants’ descendants give rise to an increasing second and third generation, raising questions on the potential impact of unfolding diversity by migration background on fertility trends in Europe. Life course research has identified a large number of mechanisms and clocks that shape patterns of family formation in migrant populations, but the translation of such micro-level (inter)actions into macro-level population outcomes remains a key challenge. Using population-wide longitudinal microdata from Belgian registers, we use a multistate discrete-time hazard model of entry into parenthood and parity progression that simultaneously considers conventional determinants of family formation (e.g. age, education, parity, time since index birth), migration-specific factors (origin group, migrant generation, age and parity at migration, duration of residence), while additionally incorporating unobserved heterogeneity that shapes transitions over the life course. We subsequently feed parameter estimates and variance estimates into a dynamic microsimulation model that allows to quantify the sensitivity of macro-level demographic trends in timing and quantum of order-specific fertility to unfolding diversity by migration background and contrasting migration scenarios.

Microsimulation at Scale for Chronic Disease Modelling: Executing 100 Million Individual Life-Course Simulations in 100 Seconds

Microsimulation is a uniquely powerful technique for chronic disease modelling because it simulates outcomes at the level of the individual over time, capturing heterogeneity, history-dependent progression, multimorbidity, and complex clinical pathways that cohort averages cannot. In an era when chronic diseases account for the majority of global mortality and impose escalating pressure on health systems, decisions about their prevention, treatment, pricing, and resource allocation carry profound long-term clinical and financial consequences. Consequently, accurate long-horizon modelling of these diseases has become central to policy, reimbursement, and investment decisions. Historically, however, microsimulation has been constrained by computational performance. Statistical precision requires large, simulated populations to reduce Monte Carlo error, and probabilistic sensitivity analysis multiplies this burden through repeated parameter sampling. Many models built in spreadsheets or high-level languages require hours or days to run, limiting scenario exploration, delaying iteration, and reducing their practical utility in time-sensitive decision environments. To address these limitations, a legacy microsimulation stack was rebuilt into a high-performance platform capable of executing 100 million life-course simulations in approximately 100 seconds. Performance gains were achieved through several core engineering innovations. The microsimulation core was implemented in modern C++, enabling direct control over memory allocation, cache locality, and execution flow. Compared with interpreted (e.g. Python, R) or spreadsheet-based environments, compiled C++ dramatically reduces runtime overhead and enables predictable, deterministic execution, strengthening validation processes and supporting regulatory-grade transparency and auditability. Memory architecture was optimised to maximise Central Processing Unit (CPU) cache efficiency and minimise allocation costs. Modelled individuals’ attributes, state transitions, and event processes were encoded in compact, structured formats, allowing large virtual populations to be simulated without performance degradation. The engine exploited modern multi-core CPU architectures through multi-threading, allowing independent patient simulations to run concurrently. Because individual life trajectories are largely independent within Monte Carlo microsimulation, the model parallelises naturally, enabling near-linear scaling with available cores. Beyond single-machine performance, the system supports horizontal scaling via containerised simulation instances, allowing elastic expansion across the infrastructure based on workload demand, without reliance on specialised high-performance computing clusters. The platform includes integrated pipelines for data ingestion, preprocessing, simulation execution, and post-processing. Outputs are automatically aggregated into epidemiological, and economic metrics, including incidence, prevalence, costs, and healthcare resource use outcomes, ready for decision analysis. A user-facing interface abstracts technical complexity, allowing domain experts to configure scenarios and execute simulations without interacting directly with the code or infrastructure. The entire platform is securely hosted in the cloud, allowing for easy set up and access anywhere in the world. The system is comprised of cross-cloud components that allow it to be hosted in any of the major cloud providers. These advances represent a fundamental shift in capability: complex simulations once requiring hours or days may now be completed in seconds, enabling real-time exploration of uncertainty, and rapid scenario iteration to expedite decision-making. Microsimulation can therefore operate at the scale and speed demanded by modern policy, reimbursement, and investment strategies, amid growing chronic disease complexity and multimorbidity.

MIDAS DE – A LIAM2 based dynamic microsimulation of German pension incomes using linked RV–SOEP data

We introduce MIDAS DE, a LIAM2 based microsimulation model for analysing German pension incomes under current law and counterfactual policy scenarios. The model reproduces the statutory formula for earnings point accrual, access and type factors, and the current pension value, and is designed to evaluate distributional, gender, and adequacy effects of reforms such as pension splitting and survivor benefit adjustments within a unified framework. MIDAS DE is implemented in LIAM2 using discrete time processes over entities (individuals, households), typed fields (e.g., insured status, pension points), and explicit links (spouse/partner, parent–child) necessary for survivor pensions and splitting eligibility. The model combines SOEP RV administrative insurance records with SOEP survey microdata. Linkage relies on rv_id (SOEP RV↔SOEP) and pid to reconstruct households and partnerships from ppath/ppathl, household matrices from pbrutto/pl, and family histories from biofam/biomars. This enables (i) identification of spouses; (ii) retrieval of pension relevant histories for groups under represented in DRV (e.g., civil servants, self employed) via biowork/biojob; and (iii) construction of household attributes needed for survivor benefit means tests. To harmonise labour income for accrual, we estimate gender and occupation specific Heckman selection models for three groups—salaried employees, self employed, and civil servants—ensuring segment specific participation mechanisms and wage processes. Predictions are selection corrected (inverse Mills ratio) and back transformed with lognormal adjustments; observed wages replace predictions when available. This captures institutional heterogeneity (e.g., civil service pay scales, self employment volatility) and mitigates bias from missing or misreported earnings, feeding consistent contributory bases into earnings point calculations. Robustness checks consider exclusion restrictions (household composition and partner status), outlier trimming, and alternative retransformation (smearing). Technically, MIDAS DE shows how LIAM2 can host a law consistent German pension engine calibrated on linked RV–SOEP microdata with explicit household links, enabling faithful simulation of survivor pensions, pension splitting, and income offsets. Substantively, the model structures policy scenarios along current law splitting, VersAusglG style variants (with and without 25 year conditions and cross pillar coverage), and a universal splitting regime, providing outcomes on the gender pension gap, poverty at retirement, and fiscal effects.

Missing Out on Social Assistance: The Consequences of Benefit Non Take-Up in the UK

Benefit non take-up refers to situations in which individuals or households do not claim social benefits to which they are legally entitled, due to low expected financial gains or costs associated with claiming, including administrative complexity, time and effort, stigma (Moffitt, 1983). While public debate has often focused on benefit fraud, non take-up is considerably more widespread (Ko and Moffitt, 2022). Low take-up undermines the redistributive capacity of welfare states and limits their effectiveness in protecting households against poverty and economic insecurity (Van Oorschot, 1991; Matsaganis et al., 2008). An alternative interpretation, however, views non take-up as a screening mechanism that contains public expenditure by discouraging claims from less needy households (Nichols and Zeckhauser, 1982). In the UK, existing micro-level studies of benefit take-up are relatively dated and rely largely on cross-sectional data (Blundell et al., 1987; Pudney et al., 2006; Zantomio et al., 2010). As a result, there is little evidence on the longer-term consequences of non take-up, partly due to the lack of longitudinal data linking benefit eligibility to observed outcomes. This study addresses this gap by combining longitudinal survey data with tax-benefit microsimulation, complementing recent work on the determinants of non take-up in the UK (Vella and Richiardi, 2024) by focusing instead on its consequences. The main objective of the study is to examine the consequences of non take-up of means-tested benefits for eligible individuals over time, with a focus on labour market outcomes, poverty, physical and mental health, and subjective wellbeing. The analysis combines the UK Household Longitudinal Study (UKHLS) with the UK tax-benefit microsimulation model, UKMOD. UKHLS provides annual panel data on income, employment, household composition, health, and wellbeing. Embedding UKMOD within a longitudinal survey represents a key methodological innovation, allowing benefit eligibility to be reconstructed consistently over time and take-up to be modelled dynamically. Non take-up is identified by comparing observed benefit receipt, based on self-reported information in UKHLS, with simulated eligibility derived from UKMOD. The analysis covers the main social assistance programmes in the UK, namely Universal Credit and its legacy benefits, Pension Credit, and Child Benefit. Prior research shows that eligible individuals who claim benefits differ systematically from those who do not, with non-claimants typically having higher incomes, higher levels of education, and lower dependency loads. In addition, take-up is subject to state dependency, in the sense that individuals who claim benefits once are likely to continue claiming in subsequent periods, and vice versa. To address this selection, propensity score matching is used to construct comparable groups of eligible claimants and eligible non-claimants based on observed characteristics measured prior to take-up. Outcomes are then analysed using a difference-in-differences design, comparing changes over time between the two groups. In addition, individual fixed effects regressions are estimated, exploiting within-person variation in take-up status across waves.

Modelling cancer incidences and mortality in the Austrian population using dynamic microsimulation

Population projections indicate that by 2045, the Austrian population aged 65 and older will increase by approximately 47% compared to 2023. Since the likelihood of a cancer diagnosis increases with age, a corresponding rise in cancer cases is expected. To address this and support evidence-based decision-making, a model has been developed on behalf of the Ministry of Health to project cancer incidence, prevalence, and mortality within the population up to the year 2045. Our cancer projection model builds on the microsimulation model used by Statistics Austria for official population projections (Pohl et al, 2025). It introduces a new module for calculating cancer diagnoses and refines existing ones, such as the module for calculating mortality. A key advantage of microsimulation is its ability to account for individual characteristics, allowing factors such as existing diagnoses to influence future disease states and determine cause specific mortality outcomes. In addition, microsimulation offers the possibility of further developing the model in the future, e.g. through extensions such as the consideration of risk factors, as is already done in well-known microsimulation models such as OncoSim. (Ruan et al., 2023). The model parameters are calculated using administrative register data, including the Central Population Register and Cause of Death Statistics, linked with the National Cancer Register – enabling detailed tracking of individual life histories.

Modelling France’s Agirc-Arrco supplementary pension scheme

Modelling France’s Agirc-Arrco supplementary pension scheme. The Agirc-Arrco federation runs a pension scheme complementary to France’s first pillar CNAV Old-Age pension scheme. This compulsory pay-as-you-go point system is second only to the CNAV by its size. It covers most private sector wage-earners, with 27 million contributors over a given year, and represents on average a third of pension income paid to 14 million pensioners. It is run together by trade unions and employer organisations. They determine most regulatory aspects of the scheme, such as contribution rates or the point’s buying price. Most notably, they set the annual revision of the point value to guarantee Agirc-Arrco’s reserve funds are at least equivalent to 6 months of benefits for the next 15 years. To cover this and other projection needs, the Agirc-Arrco technical department developed a microsimulation model in native Python to project the system’s future income and expenditure at an individual level until 2070 through a range of economic, demographic and regulatory hypotheses. Using a random sample of more than 2 million contributors, retirees and survivor pensioners, it models affiliation to the Agirc-Arrco scheme, labour-market transitions, wages, mortality and retirement decisions. It computes individual contributions, points bought during individual careers, and retirement and survivor pensions consequently paid by Agirc-Arrcos pension system. It is thus a key input for Agirc-Arrco’s decision makers, to ensure the scheme’s sustainability goals are met. It also provides periodic insights regarding the system’s joint management by social partners and is used for aggregate projections of the French pension system coordinated by the Retirement Orientation Council (COR). This presentation provides an overview of Agirc-Arrcos microsimulation model main features and graphical results, shedding light on France’s second most important pension scheme and sharing methodological choices of interest to the microsimulation community,

Modelling future ambulatory care utilisation in Germany: A Microsimulation of patient demand and physician supply

Ensuring accessible and adequate outpatient healthcare close to patients homes is a central concern in Germanys health policy and societal debates. Against the backdrop of demographic change, an ageing medical profession, and changing professional priorities, the shortages in care are intensifying, particularly in rural regions. Concurrently, rising life expectancy is leading to an increase in age-associated, multimorbid conditions that require complex management. These developments affect both the supply of physicians and patient demand.

Modelling Social Assistance Take-Up with Machine Learning in Slovakia

The Material Need Benefit (MNB) constitutes the core component of the Slovak social assistance system. It is a means-tested transfer whose eligibility depends on household composition, income thresholds, and asset tests. In the TATRASK model, which is a static microsimulation model of the Slovak tax and transfer system based on linked administrative data collected by government authorities, the MNB is simulated through the application of legislative rules subject to data availability constraints. As is common in microsimulation models of this type, both the number of simulated beneficiaries and the aggregate fiscal cost are substantially overestimated. This limitation stems primarily from the inability to model benefit take-up accurately due to missing or unobserved information in the data. To address this issue, first an expert-based adjustment approach is implemented, where the number of potential beneficiaries is reduced through a set of rules reflecting observed behavioural patterns in the data. While this method improves model performance and reduces overestimation, it remains limited in its ability to fully capture take-up behaviour. As an alternative, this contribution applies machine learning techniques to model MNB take-up. Specifically, XGBoost models are trained to predict the probability of benefit receipt and to identify likely beneficiaries within the underlying dataset. The results demonstrate that the machine learning approach clearly and substantially outperforms both the baseline TATRASK simulation and the expert rule-based adjustment in terms of accuracy. Moreover, cross-temporal validation confirms the robustness and stability of the ML model, even in the presence of policy changes affecting MNB eligibility.

Navigating Trade-offs in German Social Benefit Reform

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.

Nowcasting the BELMOD input dataset: Comparing techniques for an administrative dataset

The BELMOD microsimulation model of the Belgian Federal Public Service Social Security is based on an administrative input dataset. While the model’s policy simulations are updated twice a year, the most recent input dataset currently available refers to 2019. As a result, the discrepancy between the input data and the present situation has grown to several years. This gap can be reduced through nowcasting methods, by updating the outdated input data with more recent information to bring it more in line with the present situation, thereby making the data more suitable for simulations relating to the most recent years. We applied nowcasting to the BELMOD input dataset by incorporating both demographic changes and changes in individuals’ labour market status. To assess which nowcasting approach is most suitable for BELMOD, we developed and compared three different methods. In the first two methods, labour market status transitions are modelled using, respectively, a parametric and a non-parametric approach. For individuals experiencing a labour market status transition, the relevant variables in the dataset are subsequently adjusted. In addition, demographic changes are incorporated in these two methods through reweighting. In the third method, both labour market status and demographic changes are implemented through reweighting. We validated these three methods using external statistics on the number of income and benefit recipients, as well as aggregate income and benefit amounts.

Population Ageing Trajectories in the United Kingdom: A Microsimulation Approach

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.

Populations remember: projecting the intergenerational consequences of heat extremes

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.

Prediction Markets in Decentralized Finance: An Agent-Based Model with Heterogeneous Traders

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.

Projecting Demand for Senior Day-Care Facilities in Slovakia

Population ageing represents one of the most significant long-term challenges for social and health care systems in Europe, with powerful implications for long-term care infrastructure. In Slovakia, demographic ageing is expected to intensify markedly over the coming decades, generating substantial pressure on the capacity and spatial distribution of residential care facilities for older persons. This paper aims to quantify the future demand for residential senior day-care facilities using the Slovak Labour Microsimulation Model (SLAMM). The analysis employs a dynamic microsimulation approach that models individual life-course transitions related to ageing, household structure, health status, and care dependency. Based on demographic projections of the Slovak population, the model simulates the evolution of the senior population and estimates the number of individuals requiring institutional or semi-institutional care services. The analysis is conducted at the district (LAU 1) level, allowing for the identification of significant regional disparities in both demographic ageing and projected care needs. The primary output of the model is a district-level forecast of demand for residential day-care facilities for seniors over the medium- and long-term horizon. Building on these demand estimates, the paper further quantifies the investment costs associated with expanding care infrastructure. Using unit cost estimates for the construction and capacity expansion of senior care facilities, the study derives projected capital expenditure requirements necessary to meet future demand under different demographic scenarios. The results provide a comprehensive picture of how population ageing will translate into spatially differentiated demand for senior care services in Slovakia. By linking microsimulation-based demand projections with infrastructure cost estimates, the paper offers an integrated analytical framework that supports evidence-based planning of long-term care capacity. The findings are particularly relevant for policymakers and regional authorities responsible for social service provision, as they highlight districts facing the most significant future capacity gaps and investment needs. Overall, the study demonstrates the usefulness of microsimulation modelling as a tool for strategic planning in the area of ageing and long-term care, enabling policymakers to anticipate demographic pressures and to design more efficient and equitable infrastructure investment strategies.

Properties of alignment methods in discrete time dynamic microsimulation models

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.

Recent developments of the SimPaths dynamic microsimulation framework

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.

Reconstructing Interstate Conflict Networks - An Agent-Based Model Anchored in HIIK Data

Interstate conflict networks are frequently represented as mappings of dyadic feature combinations, which can be represented as graph-based networks. Such graphs encode structured macro-patterns, including but not limited to positions, clusters, and polarization, that require mechanistic interpretations and explanation. The research underlying this abstract develops an agent-based model (ABM) to reconstruct annual interstate conflict networks derived from the Conflict Barometer of the Heidelberg Institute for International Conflict Research (HIIK) (HIIK, various years). Rather than treating these networks as graphs and illustrations, we utilize them as empirical macro-targets for simulation-based explanation in the tradition of generative social science (Epstein, 2006).

Self-interest, stated preferences and the taxation of couples

A recent and growing literature studies policy preferences empirically using survey experiments. A frequent approach in welfare economics, cost-benefit-analysis and political economy is to aggregate policy preferences assuming rationality and self-interest. In this paper we compare the two approaches in a specific policy domain, the tax treatment of married couples. We explore whether political economy arguments can explain the persistence of joint taxation in Germany. First, we use a sufficient statistics approach rooted in utility-maximizing behavior. With this approach we estimate the population shares of winners and losers from a revenue-neutral reform towards individual taxation, according to material self-interest. Second, we report on a large scale survey experiment to elicit stated preferences among a representative sample of the German population. Both methods show that the tax treatment of couples in Germany is highly controversial. Based on material self-interest, the support for a reform towards individual taxation is slightly below the majority threshold. Since the race is so close, views on just taxation or on the status of marriage can actually make a difference. We find that, according to the preferences stated in the survey experiment, support for a reduction of marriage bonuses is lower than suggested by the analysis based on self-interest.

Shifting the Tax Burden from Consumption to Income in Croatia: Preserving Efficiency while Reducing Inequality

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.

Signals on Budget Day: Media Attention and Public Beliefs in Ireland

Ireland is an outlier among European countries in that it has no automatic indexation of tax and welfare parameters. As a result, the annual Budget announcement plays a significant role, both in shaping the government’s discretionary policy priorities and in influencing people’s expectations about the future. In this project, we study Budget Day as a signalling device along two margins. First, we ask how discretionary policy choices across spending areas respond to political economy forces: media attention, organised interest-group pressure, and electoral cycles. Second, we examine whether Budget announcements shift individuals beliefs about redistribution and uncertainty about the future, and whether these responses are heterogeneous by predicted exposure to the announced measures. In terms of conceptual framework and methods, we build on the literature on central bank communication, which uses language analysis and treats policy announcements as signals that shape expectations (Hansen et al., 2018; Ehrmann and Talmi, 2020). A related strand of work studies the political economy of budget trade-offs (Adolph et al., 2020) and the role of information flows in distinguishing anticipated from unanticipated fiscal policy in budget announcements (Leeper et al., 2013). Balladares and Garcia-Miralles (2025) trace fiscal drag in Spain over time, while in the Irish context (Boyd and McIndoe-Calder, 2025) the focus is on personal income tax over a shorter horizon (2019-2023), using microsimulation modelling with EUROMOD. However, there is little long-run evidence on Irish budget cycles or their political determinants beyond the aggregate analysis in Cousins (2007). In the first part of this paper, we build a new historical dataset of Irish budgets from the mid-1990s onward, coding discretionary changes by broad policy area. We benchmark each years policy package against a counterfactual that mechanically indexes key tax and welfare parameters to price and wage inflation. Deviations from this benchmark provide a transparent measure of discretionary policy priorities. We then relate these deviations to (i) topic-level media salience indices constructed using text analysis of newspaper coverage, (ii) measures of lobbying intensity proxied by pre-budget submissions and public representations by sector, and (iii) electoral timing and macroeconomic conditions. In the second part, we analyse individual-level survey data to examine perceptions of redistribution, fairness, and uncertainty about the future in relation to budget announcements. Microsimulation using EUROMOD allows us to account for heterogeneity in responses, in particular whether individuals or households that are likely to gain or lose from policy changes react differently. We exploit variation in interview timing relative to Budget Day using the Irish sample of the European Social Survey, complemented by Eurobarometer data. Our work provides a systematic assessment of Irish budget priorities and contributes to the debate on whether indexation would reduce fiscal drag and depoliticise distributional choices, or whether discretionary budgets are instead responsive to public preferences.

Simulating the Simulation: Evaluating Simulation Strategies in Causal Inference

Because causal estimands are unobservable in reality, benchmarking causal effect estimators is inherently challenging. To overcome this, simulation studies are increasingly used to evaluate causal inference methods under realistic conditions such as small sample sizes, limited overlap, and complex confounding. Yet it remains unclear to what extent conclusions drawn from simulated settings extrapolate to real-world performance. Existing benchmarks rely on a wide range of outcome-generation strategies, from parametric structural models to flexible machine learning, without clear guidance on their reliability. This paper formalizes simulation-based benchmarking and introduces a framework to characterize the types of bias that may arise from different generative strategies, focusing particularly on Average Treatment Effect (ATE). We classify and compare several approaches proposed in the causal inference literature, including parametric structural models[1], machine-learning conditional mean–variance models[2], and adversarial generative models such as Wasserstein GANs[3]. We apply these modeling strategies to commonly used benchmark datasets in which we synthetically define an outcome-generating function so that the true ATE is known and systematic evaluation is possible. Across Monte Carlo experiments, we compare estimator performance in terms of bias, variance, and mean squared error. We examine how closely these performance metrics align across different simulation strategies relative to the structural data-generating process, and whether commonly used generator diagnostics are predictive of estimator behavior. Our preliminary results indicate that the choice of simulation strategy can substantially alter conclusions about estimator reliability relative to the underlying data distribution. These findings underscore the importance of principled design and validation of simulation frameworks when benchmarking causal methods.

Strengthening Validation Frameworks in Dynamic Microsimulation: Evidence from SimPaths

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.

Taxing Couples as Singles? A Structural Analysis of Labor Supply for Belgium

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.

The development of an integrated framework combining economic efficiency, social justice, and ecological effectiveness using microsimulation modelling

This research explores how microsimulation modelling can be used to develop an integrated framework that evaluates economic efficiency, social justice, and ecological effectiveness simultaneously.

The direct and indirect effects of green tax reform in Belgium. A micro-macro approach.

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.

The Distributional Effects of Carbon Pricing in Germany

Even though economists prefer the instrument of carbon pricing to reduce greenhouse gas emissions over regulatory policies, fears of regressive effects that disproportionately burden low-income households lead to low public support, hindering the political feasibility of carbon pricing. In light of an ongoing policy debate and lacking evidence for the German context, this paper investigates the distributional effects of carbon pricing in Germany, as well as possible redistributive polices. I analyze three channels through which carbon pricing affects the distributional outcome: the direct price effect, the indirect price effect and the behavioral response. I then compare a lump-sum transfer, a targeted transfer per income deciles and reductions in labor and income taxes as redistributive policies, providing evidence on their effect on counteracting adverse distributional consequences from carbon pricing.

The ex-ante distributional impact of the Italian reform of the participation exemptions regime

Corporate tax microsimulation models are essential tools to estimate tax revenue, assess the impact of tax reforms, and carry out distributional analyses at the firm level. The microsimulation model for firms currently being developed at the Bank of Italy aims at accurately simulating the effective tax burden on corporations to allow for the routine evaluation of fiscal policies geared towards the corporate sector. The model is the first to exploit information on tax liabilities contained in detailed financial statements to reconstruct the corporate income tax base at the firm level, which is next used for model validation. This represents an innovative approach to carry out micro-based model validation despite the lack of access to tax return microdata. The model can quantify the gap between financial accounting and tax accounting results, thanks to the modelling of major fiscal adjustments, such as participation exemptions, limits on interest deductibility, and tax depreciation, and replicate a few key stylized facts, such as: i) the widespread divergence between accounting and tax profits, with one fifth (one fourth) of firms with negative (positive) accounting profits showing positive (negative) tax profits; ii) the inverse U-shape of effective tax rates in firm size; iii) the greater impact of participation exemptions and interest payment deductions on large firms’ effective tax rates; iv) the countercyclical usage of loss carryforwards; v) the weaker impact on effective tax rates of accelerated depreciation schemes for intangible vs. tangible assets The work proposes an ex-ante evaluation of the most sizeable fiscal policy geared towards firms enacted with the Italian budget law in 2026, namely the introduction of a 5% participation threshold for the eligibility to the participation exemption (PEX) regime. The PEX regime seeks to eschew double taxation of corporate profits by making participation income (in the form of either dividends or capital gains) exempt. The EU “Parent-Subsidiary” Directive establishes that double taxation arises when the parent company holds, directly or indirectly, a participation stake worth at least 10%. Prior to the budget law 2026, the Italian PEX regime exempted all participation income. The newly introduced policy therefore partially aligns the Italian regime with the EU benchmark. The ex-ante distributional impact of the reform is hard to gauge. On the one hand, firms receiving participation income are less than 10% of total firms. On the other, there is a large empirical literature documenting that shareholding portfolio diversification increases with firm size. By exploiting Orbis information on the size of direct and indirect shareholdings, this work calculates the impact of the reform on firm effective tax rates, and tries to understand whether it was overall progressive, namely it redistributed the fiscal burden towards the largest firms. Finally, it discusses how potential endogenous responses in terms of portfolio reallocation might influence the results.

The future of long-term care in Europe. A microsimulation analysis of potential demand based on benefit eligibility rules

European long-term care (LTC) systems face mounting sustainability pressures as population ageing increases the number of older adults living with functional and cognitive limitations. Yet projections of future LTC needs often rely on simplified “need” definitions (e.g., at least one ADL/iADL limitation), overlooking a critical institutional determinant of public expenditure: eligibility rules. Access to publicly funded LTC is not automatic; it is regulated by national (and in some cases regional) legislation that combines multiple vulnerability dimensions—limitations in activities of daily living (ADL), instrumental ADL (iADL), cognition, behavioural issues, and medical needs—into non-linear thresholds for benefit entitlement. Because these rules differ markedly across Europe, they can generate substantially different trajectories of potential demand for publicly financed LTC even under identical demographic and epidemiological trends. This study investigates how the heterogeneity of LTC eligibility criteria shapes the evolution of potential demand for formal domiciliary LTC across European countries in the coming decades. We use longitudinal microdata from the Survey of Health, Ageing and Retirement in Europe (SHARE), Waves 1–9 to operationalize country-specific eligibility rules. Using SHARE’s information on ADL/iADL limitations, mobility constraints, depression symptoms, and cognitive impairment, we construct harmonized indicators of “objective vulnerability” consistent with the assessment-of-need frameworks embedded in legislation. These eligibility indicators are then embedded in a dynamic microsimulation model, the EU-FEM (the SHARE-based version of the Future Elderly Model), which simulates individual life courses through a first-order Markov Monte Carlo process. Transition models generate probabilities of changes in health and functional status over time, allowing heterogeneous trajectories by age, risk factors, and baseline conditions, while the simulated population evolves by ageing and cohort replacement. We produce projections of the population-level prevalence of eligibility for publicly funded domiciliary LTC—interpreted as potential demand—under a baseline “no policy change” scenario. We then conduct counterfactual exercises to disentangle the role of rules versus epidemiology: (i) applying alternative countries’ eligibility rules to the same underlying population trajectories; (ii) simulating a synthetic “single-country” setting to isolate institutional effects; and (iii) evaluating stylized healthy-ageing interventions that reduce the incidence of selected physical and mental limitations by 25% among individuals aged 65–75. The simulations highlight that eligibility-rule heterogeneity translates into markedly different projected pathways of potential LTC demand across Europe. Moreover, the same healthy-ageing intervention can yield very different reductions in projected eligibility depending on which functional domains are targeted and how each country’s rules weight physical, cognitive, and mental limitations. These findings imply that cross-country comparisons of future LTC burdens—and evaluations of prevention-oriented strategies—must explicitly account for institutional eligibility design. Ongoing work extends the framework by incorporating newer SHARE waves, improving harmonization with broader international platforms, and translating projected eligibility into cost trajectories of potential demand.

The Impact of Demographic Change on Spousal Caregiving and Future Gaps in Long-term Care: Microsimulation Projections for Austria and Italy

As populations age, the sustainability of long-term care systems increasingly depends on the availability of informal care, particularly from partners. This paper addresses the question of how much care we may expect partners to provide in the future by projecting demand for long-term care (LTC), the care supply mix based on current patterns, and the resulting care gaps up to 2070. Using a comparative dynamic microsimulation model, we contrast the results for Austria and Italy, two countries at very different stages in the ageing process and with pronounced institutional differences. Our results suggest that delayed widowhood due to improvements in mortality is a mitigating factor for the increased need for formal care in ageing societies, although it can only offset this increase to a limited extent. Even under optimistic assumptions, potential care gaps substantially increase in both countries, primarily due to demographic change. The size of these gaps is influenced by institutional settings, partnership patterns and gains in longevity, but no scenario reverses the overall upward trend. These findings emphasize the need for comprehensive LTC reforms that extend beyond merely promoting informal care and highlight the necessity for substantial investment in formal care infrastructure.

The impact of social transfers for self-employed

We examine the role of social transfers for self-employed in Belgium using the microsimulation model BELMOD. The self-employed represent a significant and increasing group in Belgium. The measurement of income from self-employment comes with particular methodological challenges and research shows that material deprivations tends to be lower for self-employed compared to employees. Despite the existing limitations we will focus on financial poverty for self-employed. The combination of microsimulation techniques and administrative data allows us to analyze which parts of the tax-benefit system contribute most to the poverty reduction for self-employed. We use administrative data and BELMOD to study the poverty reducing role of various household income sources for the self-employed in more detail. Other incomes in the household, besides the income from the individual self-employed person, reduce the poverty risk significantly. For example, the poverty risk of self-employed is reduced by more than half when taking into account labour incomes from other household members. Furthermore, it is reduced by about a third when taking into account all social benefits in the household. Our results indicate that poverty is reduced substantially by social transfers, but to a different degree for various family types. Child allowances clearly contribute to the reduction of the poverty risk for self-employed with children. For families without children, we see the largest reduction in the poverty risk for the household by pension related benefits, followed by contributory sickness/disability benefits. This work is a first step in trying to identify groups of vulnerable self-employed and assessing the role of the tax-benefit system.

The impact on income and labour supply of the limitation of the unemployment benefit in Belgium

Nearly half of the unemployed persons are excluded from the unemployment benefit system by the re-form which has been gradually implemented since 1 January 2026 in Belgium, particularly those with a long unemployment history. The main feature of the reform is to limit the duration of unemployment benefit payments to a maximum of two years, depending on work experience, instead of unlimited payments in time as in the old system. Further, the progressivity of the benefit scheme has been strengthened by an increase of 10% of the ceiling amount during the six first months of unemployment, and a re-duction to a lump sum for unemployed persons who are entitled to payments after one year of unemployment. This paper gives the ex-ante evaluation results on income and labour supply of this reform. The simulation exercise concludes that approximately one third of the excluded persons are expected to return to work, approximately 40% are predicted to receive the social minimum income. The remaining fourth withdraw from the labour market without replacement income, resulting in a significant loss of disposable revenue. Nonetheless the impact on the poverty rate is moderate. Older unemployed, aged 55 years and over, are particularly affected by the reform as more than three fourth of them lose their benefit, representing nearly 22% of the excluded sample. Regional differences are substantial, with Flanders being the least impacted and Brussels-Capital region the most. To fulfil the evaluation, we use:

Towards a distributionally painless carbon tax through revenue recycling

Carbon taxation is widely seen by economists as one of the top instruments for reducing greenhouse gas emissions and achieving ambitious decarbonization targets. Yet, despite its efficiency, it often faces public opposition, mainly driven by distributional concerns. A broad consensus in the literature holds that, in the absence of revenue recycling, carbon taxes tend to be regressive, disproportionately burdening low-income households. Consequently, a substantial body of research has focused on designing compensatory mechanisms,such as lump-sum rebates, tax shifts or targeted transfers, to restore progressivity and improve public acceptability.

Uncertainty assessment in dynamic microsimulation: the case of MikroSim (Germany)

Spatial dynamic microsimulations probabilistically project geographically referenced units with individual characteristics over time. Like any stochastic projection method, their outcomes are inherently uncertain and sensitive to multiple factors. In discrete time dynamic microsimulations, for each simulated time step (often years), each unit passes through different modules addressing different life events (births, deaths, ageing, partnership, employment, …), evaluating if a status transition occurs for them via a Monte Carlo experiment. This inherently introduces uncertainty due to the methods stochastic nature. However, simulations may also be sensitive to other factors, such as the choice of model types and complexity, as well as the parameter estimations, among others.

Understanding Dynamics in Security Awareness through ICT Diffusion using Microsimulation

With the ongoing diffusion of information and communication technologies (ICT) across large parts of Europe, security awareness is becoming increasingly important for mitigating societal security risks and long-term costs of cybercrime. At the macro-structural level, the diffusion of ICT is to a large extent shaped by institutional factors, such as legislative decisions at the national level, technological innovations, or considerations within larger organizational units. By contrast, macro-structural changes in security awareness cannot be governed in a comparable top-down manner. Instead, micro-founded models at the individual level are required, whose contextual conditions systematically change as a result of social-structural and demographic transformation processes.

Use of Microsimulation Methods to Assess Nutrition-associated Health Outcomes of Climate Change in Northwest Kenya

In the arid and semi-arid lands of northwest Kenya, climate change threatens to compound existing disparities in agricultural activities and health infrastructure and exacerbate food insecurity and malnutrition. To estimate the distribution of nutrition-associated health outcomes and assess projected disease burdens under climate change scenarios, a microsimulation model was combined with health impact assessment methods. A synthetic population covering Turkana, Samburu, and Laikipia counties was generated from census data and a survey conducted in the study region in 2025 provided socioeconomic attributes and baseline dietary intake data. Population growth estimates were obtained from the national statistics authority and forecasts of future edible food availability were estimated up to 2050 from a validated open-source agricultural land-use model. Relative risks of nutrition-associated health outcomes were simulated at the individual-level using standard exposure-response functions at baseline and then at five-year intervals under four representative concentration pathways. The attributable burden in disability-adjusted life years was then estimated to generate the projected morbidity burden in demographic groups. This work expands on an earlier microsimulation model framework developed to assess policies to promote the use of clean cooking fuels on individual exposure to ambient and household air pollution emissions and associated gender-specific mortality in three densely populated Kenyan municipalities. With this additional iteration, we demonstrate how geographic- and hazard-specific assessments of disease burdens in rural and urban populations may contribute toward an improved understanding of differential climate vulnerability in Kenya.

Who bears the cost of carbon pricing? Gender differentials in carbon footprint and distributional impact of carbon pricing across Europe

This paper examines the gendered distributional effects of carbon pricing across six European Union countries, focusing on differences in household carbon footprints and exposure to carbon-induced price increases. While prior research has primarily emphasized the income-based regressivity of carbon taxes (Maier et al., 2025), this study addresses the relative neglect of gender disparities in emissions patterns and welfare impacts.