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.
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.
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.
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.
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.
The main contribution of the paper is to extend the RURO labor supply model by incorporating labor demand elasticities. Standard applications of structural labor supply models implicitly assume perfectly elastic labor demand, omitting wage adjustments and firm side responses. This assumption can limit the ability of these models to capture key labor market frictions. By supplementing the RURO framework with labor demand elasticities, we allow employment and wages to adjust to policy induced changes in labor supply, providing a more realistic representation of labor market equilibrium.
This integrated approach improves the interpretation of labor supply estimates and strengthens the link between individual choice behavior and aggregate labor market outcomes. More broadly, the paper contributes to the structural labor supply literature by showing how demand side adjustments can be incorporated in a tractable way, addressing an important limitation of existing models.
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.
Due to the limitations of wealth data, the analysis concerns clients’ financial wealth in the form of investment funds, shares, and investment properties. Together, these account for slightly less than one-third of the total wealth of people aged 65 and over. The main component of wealth—owner-occupied housing—is excluded from the analysis. In the simulation, wealth was taken into account by adding 15 per cent of assets exceeding €15,000 to annual income, following the formula used in the housing allowance for pensioners.
According to the results, the total amount of client fees collected for long-term residential care would increase by approximately 12 per cent under the reform. The increase is substantial considering that the change would affect only about 14 per cent of clients and that the types of assets included in the analysis represent only a small share of older people’s total wealth. Among clients whose wealth would be considered, the fees could rise considerably (average increase €830 per month, +88 %). The largest number of affected clients would be found in the third income decile, which also has the highest prevalence of residential care users, while the probability of being affected increases with income level. Following the reform, the out-of-pocket financing of residential care would rise from about 16 per cent to 18 per cent.
The static simulation is indicative as it does not account for potential behavioural or other dynamic changes—for instance, changes in how older people might use, transfer, or convert their wealth, or shift toward private services. On the other hand, the analysis covers only a limited subset of household wealth, and future cohorts of care users are likely to be both larger and wealthier than those in 2022 data.
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.
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.
