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.
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.
This study challenges the perception that universalism is fiscally unattainable by examining an alternative, progressive source of financing: the recovery of Illicit Financial Flows (IFFs). IFFs, linked to practices such as tax evasion, trade mis-invoicing, and money laundering, undermine fiscal capacity and reduce resources available for social investment. Despite their scale, they remain largely absent from debates on financing universal social protection.
The analysis focuses on the feasibility and impacts of a Universal Child Benefit (UCB), a policy choice that is both strategic and urgent given the high incidence of child poverty and the long-term developmental consequences of deprivation. The empirical application centers on Ghana, a country characterized by a large child population, limited child-focused social protection, persistent rural poverty, and substantial revenue losses associated with trade mis-invoicing. These features make Ghana an informative case for assessing whether IFF recovery could meaningfully expand fiscal space for universal policies.
The study simulates two budget-neutral UCB schemes financed through the hypothetical recovery of revenues lost to trade mis-invoicing. The first scheme provides a flat transfer to all households with at least one child, while the second offers a higher benefit to households with four or more children. The analysis combines a tax-benefit microsimulation model with a Social Accounting Matrix to estimate revenue losses and redistribution effects under the existing tax structure. To complement household-level impacts on poverty and inequality, a macro-development framework is used to project potential effects on broader development outcomes if recovered revenues were allocated following historical public spending patterns.
Results indicate that financing a UCB through IFF recovery can generate meaningful reductions in poverty and inequality, particularly among rural households, larger families, and children. Both schemes achieve substantially higher coverage and greater equity than existing targeted programmes, despite relying on a relatively limited revenue base. Beyond income effects, projections suggest positive spillovers for child-related development outcomes, including health, education, and access to essential services.
The study makes three main contributions. First, it demonstrates that universal social protection can be fiscally plausible in LMICs when financed through progressive and underutilized revenue sources. Second, it provides empirical evidence on the distributional and developmental impacts of a UCB in a sub-Saharan African context marked by high child poverty and targeting challenges. Third, it bridges social protection and tax policy, highlighting how revenue composition critically shapes equity, effectiveness, and political feasibility.
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.
In this session, I’m going to introduce a firm microsimulation methodology for policy analysis. This framework generates synthetic firm populations calibrated to official statistics through multi-objective optimisation, matching distributional targets across turnover, sector, and employment from multiple administrative data. We then layer behavioural responses onto this synthetic population using bunching estimation methods from Saez (2010), Kleven and Waseem (2013), and Liu and Lockwood (2015). We demonstrate the methodology through UK VAT threshold analysis: firms near the registration threshold face incentives to stay just below it, creating bunching in the turnover distribution; we estimate how strongly firms respond by comparing the observed distribution to a no-bunching counterfactual, then simulate how firms would redistribute under alternative policy designs. The session covers synthetic data generation, PyTorch-based calibration, behavioural response estimation, and policy simulation using PolicyEngine’s open-source Python-based platform.
While household microsimulation is widely used, the absence of firm microdata results in firm-level microsimulation being less common, despite businesses being central to tax policy. Developing firm-level microsimulation opens new research directions, particularly when linked to household models, enabling analysis of how business taxes pass through to consumer prices, how firm-level policy changes affect employment and wages, and how supply-side interventions are distributed across income groups.
One direct application is VAT threshold analysis: the UK requires businesses to register for VAT and charge 20 percent on sales once annual turnover exceeds £90,000, and there is active policy debate about raising this threshold. Firm microsimulation enables analysis of different registration thresholds and smoothed phase-ins on revenues and firm counts, questions relevant to the economic growth literature. We benchmark static results against HMRCs published projections for raising the threshold from £85,000 to £90,000, achieving approximately 90 percent alignment with official costings.
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.
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.
The purpose of the change has been to improve the simulation of social assistance for households that only require support during part of the year. Relying on annual data results in an underestimation of households that receive social assistance only intermittently throughout the year. A transition to monthly simulation has been made possible by the availability of monthly information on wages and other taxable incomes.
Social assistance is a means-tested benefit provided by municipalities to households that cannot support themselves. Households must actively apply for the benefit. FASIT includes different models for this (take-up), which will also be reviewed in the presentation.
In the simulation of social assistance, both the total amount of assistance paid out and the number of households receiving assistance are important to consider. Depending on how underlying incomes vary for each household throughout the year, and the likelihood of applying for assistance during short periods of reduced income, the monthly model may risk overestimation of the total number of households receiving assistance over the year.
We will describe how the simulation was performed before the transition, the challenges encountered during the shift, how these challenges were addressed, and the differences in outcomes between the two simulation approaches.
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
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.
A defining feature of a digital twin is the continuous feedback loop with the real world. New empirical data are regularly used to update and recalibrate the model, while simulated policy scenarios generate testable expectations that can be compared to observed outcomes. Deviations between simulation and reality are used to iteratively refine behavioural rules and parameters. In this way, the model becomes an adaptive, data-driven representation that evolves in parallel with the society it describes.
Another focus of the contribution is on data-related challenges. Classical surveys and commuting studies are subject to systematic sampling biases that distort or underrepresent specific population groups. We show how synthetic populations can be used to correct such distortions and to generate coherent, robust microdata. On this basis, we argue that for certain research questions synthetic samples may yield more reliable inference than direct subsamples, without questioning the indispensable role of empirical surveys as training and calibration data for simulation models.
Using current applications on childcare expansion, labour markets, pensions, and long-term care, the presentation illustrates how a digital twin can serve as an experimental environment for analysing long-term policy effects. The contribution thus provides a methodological clarification of the distinction between microsimulation and a digital twin of society, and advances the role of synthetic data in evidence-based policy analysis.
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.
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.
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.
