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.
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.
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.
References Pohl, P., Slepecki, P., & Spielauer, M. (2025). STATSIM: Statistics Austrias dynamic microsimulation model for official population projections. Statistical Journal of the IAOS, 18747655241308058. Ruan, Y., Poirier, A., Yong, J., Garner, R., Sun, Z., Than, J., Brenner, D. R. (2023) “Long-term projections of cancer incidence and mortality in Canada: The OncoSim All Cancers Model”, Preventive Medicine, Vol. 168.
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,
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.
This work presents a district-level model of future healthcare utilisation and relates it to projected physician supply trends. These demand-side projections build directly upon a previous supply-side microsimulation model, which projected the future number of physicians, their specialities, and working patterns. Methodologically, the study employs a dynamic microsimulation of the entire German population within the MikroSim framework. The model is developed using data from national health surveys, from which sociodemographic and morbidity-related determinants of utilisation behaviour are identified. Considering the systemic differences in access, distinct models are developed for individuals covered by statutory (SHI) and private (PHI) health insurance, with the primary focus on simulating utilisation for the SHI-insured population. The primary outcome measures are the projected annual frequency of physician contacts, differentiated between general practitioner and specialist consultations on a regional district level.
The results, contingent upon improved future data availability and ongoing model refinement, can provide an evidence-based contribution to enhancing regional and sectoral needs-based planning in outpatient medical care.
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.
The current German system of means-tested social benefits, which include citizen’s benefit, housing benefit, and supplementary child benefit, is characterized by high effective marginal tax rates, which are often higher than 90 percent across wide income brackets. As a result, even substantial increases in working hours typically yield small gains in disposable household income. These high effective marginal tax rates apply not only to citizen’s benefit, but also to the housing benefit and supplementary child benefit. Additionally, the coexistence of competing benefits – citizen’s benefit on the one hand, housing benefit and supplementary child benefit on the other – makes the social benefits system complex to navigate for those affected. For these reasons, and against the backdrop of considerable fiscal pressures, there is currently intense political and academic debate about reforming the system.
This study evaluates reform proposals currently under political consideration, categorized into two distinct approaches. The first approach comprises variants of a far-reaching reform in which housing benefits and child benefits are combined with citizen’s benefit and basic income support for the elderly to form a single means-tested benefit that is conceptually modeled on the existing citizen’s benefit framework. Variants within this group differ primarily in earned income exemption design. The second approach represents a more moderate intervention in the existing system: housing and supplementary child benefit are not abolished, but are modified and merged, while adjustments to citizen’s benefit exemptions are introduced. Both reform approaches have in common that they aim to reduce the high effective marginal tax rates in the existing system while simplifying the system for recipients and reducing administrative costs.
We analyze the reform proposals with the behavioral tax and transfer microsimulation model of the Institute for Employment Research (IAB-MSM), which uses household microdata from the German Socio-Economic Panel (GSOEP). The IAB-MSM consists of two components: First, a static tax-and-transfer module that simulates the effects of a tax-benefit reform on the disposable income of individual households, including taxes on income, social security contributions, and public transfers. Second, a discrete choice labor supply model that also endogenously models benefit take-up decisions. We use the IAB-MSM to simulate labor supply effects, fiscal effects, the change in the number of claiming households, and distributional impacts of the reform proposals.
We highlight conflicting goals that must be weighed against each other in political decisions. In particular, “generous” reform scenarios, which attempt to reduce marginal tax rates while ensuring that households are not worse off financially than under the current framework, are generally associated with relatively high fiscal costs. “Restrictive” scenarios, on the other hand, which reduce incentives to work in marginal employment or part-time and strengthen incentives to work full-time, sometimes involve significant budget savings, but are accompanied by income losses for households with low earned income, at least in the short term.
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 in the United Kingdom is shaped by the interaction of multiple demographic processes, including longevity improvements, fertility dynamics, and migration flows. This paper presents ongoing work using SimPaths to study plausible population ageing paths in the UK from a life-course perspective. The analysis models individual-level demographic transitions, including age- and sex-specific mortality, fertility behaviour, and migration. Particular attention is given to immigration, distinguishing age at arrival, cohort replacement effects, and heterogeneity in demographic characteristics between migrant and native-born populations. The framework allows population ageing to emerge endogenously from cohort size, survival, and population composition. We outline a set of counterfactual scenarios that vary assumptions on longevity improvements, fertility patterns, and migration regimes. Planned simulations will compare resulting population age structures, cohort distributions, and old-age dependency measures across alternative demographic trajectories. By using microsimulation to decompose population ageing into its underlying demographic mechanisms, the analysis provides a structured basis for refining longer-term research on demographic change in the UK and for identifying which population dynamics are most influential for future ageing trajectories.
Extreme heat events cause substantial excess mortality, yet their long-term demographic consequences extend far beyond immediate death counts. Each heatwave creates demographic memory—the cascading effects of lost individuals who would have reproduced, aged, and shaped future population structures. In this work, I will develop a microsimulation framework to quantify how a single extreme heat event reshapes population trajectories over subsequent decades, comparing outcomes with and without the heatwave to isolate its lasting demographic imprint.
I will implement microsimulations using the SOCSIM package in R, which simulates individual life events—births, deaths, migrations—through probabilistic rules applied at monthly time steps. This granular approach captures how mortality shocks propagate through populations in ways that aggregate models cannot.
I will construct synthetic populations for Western European countries affected by the July 2022 heatwave, initializing from the Human Mortality Database and Human Fertility Database. Two parallel projections will be analyzed: one incorporating observed mortality from July 2022 (including heat-attributable excess deaths), and a counterfactual where mortality follows forecasted baseline rates without the heatwave. I will use the Lee-Carter method to project baseline mortality, allowing the observed July 2022 mortality spike to capture the heat events demographic shock.
By the conference, I expect to present proof-of-concept results showing how the two scenarios diverge. Core outputs will include: excess mortality attributable to the July 2022 heatwave, measures of bereavement quantifying how many individuals lost family members due to heat-attributable mortality, and projections comparing population structures with and without this event over subsequent decades.
Focusing on a single extreme heat event establishes methodological foundations while demonstrating that even one event matters demographically. While climate change will bring repeated heat extremes that compound these effects, understanding one events impact provides the baseline for assessing cumulative effects. The difference between the two projected populations quantifies the full demographic burden: not only immediate deaths, but also potential altered age structures, depleted cohorts, and expanded bereavement.
