Book of Abstracts

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. Indeed, in the BEAMM context, we use a large number of variables available in different databases. We thus need to analyze data from different sources; the observations, which only share a subset of the variables, cannot always be paired to detect common individuals. This is the case, for example, when the information required to study a certain phenomenon comes from different sample surveys. Statistical matching is a common practice to combine these data sets. In this talk, we investigate and extend to statistical matching three methods based on Kernel Canonical Correlation Analysis (KCCA; [6]), Super-Organizing Map (Super-OM; [1]) and Autoencoders-Canonical Correlation Analysis (ACCA; [7]). These methods are designed to deal with various variable types, sampling weights and incompatibilities among categorical variables ([2, 3, 5]). We additionally implement methods for recalculating the sampling weights. In our context, data privacy and anonymization are important. Under these circumstances, the need for synthetic databases that replicate the characteristics of the population while preserving privacy is arising. In this presentation, we also investigate how we can employ a range of data generation approaches utilizing various advancements in the Wasserstein Generative Adversarial Network (WGAN) literature to create survey databases. WGANs were introduced by Arjovsky 2017 ([8]) in the context of image synthesis. Our algorithms have been adjusted to account for sampling weights ([4, 5]). Moreover, survey and adminstrative data have the specificity of mixing continuous and categorical data, which should be taken into account in the architecture of the WGANs. References [1] Kohonen, T. (1982), Self-organized formation of topologically correct feature map. Biological Cybernetics, 43 (1), 59–69. [2] Annoye, H., Beretta, A. and Heuchenne, C. (2024). Statistical matching using kernel canonical correlation analysis and super-organizing map. Expert Systems with Applications, 246, 123–134. [3] Annoye, H., Beretta, A. and Heuchenne, C. (2025). Statistical Matching using Autoencoders- Canonical Correlation Analysis, Kernel Canonical Correlation Analysis and Multi-output Multilayer Perceptron, Knowledge-Based Systems, 330,114626. [4] Annoye, H. and Heuchenne, C. (2025) Generating survey databases with Wasserstein Generative Adversarial Networks, Applied Intelligence, 55 (17), 1-17. [5] Annoye, H. (2024), Thesis: Statistical matching and data generation Prom. : Heuchenne, C.. [6] Lai, P. L. and Fyfe, C. (2000), Kernel and nonlinear canonical correlation analysis. International Journal of Neural Systems, 10 (05), 365–377. [7] Rumelhart, D. E., Hinton, G. E. and Williams, R. J. (1986), Learning Internal Representations by Error Propagation in Parallel Distributed Processing: Explorations in the Microstructure of Cognition. Cambridge: MIT Press, 318–362. [8] Arjovsky, M., Chintala, S., and Bottou, L. (2017, July). Wasserstein generative

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. This work is part of MMUST+, an Interreg project developing a multimodal mobility model for the Luxembourg cross-border area using the synthetic population as input. We propose a multi-stage framework that combines iterative proportional fitting (IPF) (Deming and Stephan 1940) and stochastic synthetic reconstruction (Lenormand and Deffuant 2013) to generate synthetic populations that are statistically and structurally realistic. The first stage consists of generating several entities: individuals, family nuclei, households, and dwellings. For each entity, we generate attributes covering socio-demographics, employment, education, household structure, dwelling information and spatial location. To generate these entities, we chose the IPF method for its efficiency and low algorithmic complexity. We ran a separate IPF instance for each entity using as many Eurostat marginals as possible. Since no survey includes all variables, we used a uniform seed with some structural zeros, but the multi-variable marginals preserve most dependency information. In the future, if survey data or microdata from statistical institutes become available, these could be used as seed for IPF to improve accuracy. Finally, we applied the truncate-replicate-sample (TRS) (Lovelace and Ballas 2013) integerisation to obtain integer counts. In the second stage, dwelling attributes are assigned to households by probabilistically drawing dwellings for each household, with probabilities derived from IPF weights. If a dwelling and household are incompatible (different locations or mismatched number of occupants), the probability is set to zero. The third stage addresses the assignment of individuals to family nuclei and the grouping of isolated individuals and family nuclei into households. We implemented the stochastic sample-free synthetic reconstruction algorithm described in (Lenormand and Deffuant 2013). The probabilities required by this method were computed using age-gap distributions derived from data available from Eurostat and the Human Fertility Database, in order to guide realistic relationships between partners and between parents and children. Hard constraints (e.g. maximum two parents per nucleus) were imposed by setting the corresponding probabilities to zero. Preliminary validation demonstrates that the population reproduces key aggregate statistics, household structures, and family compositions across the cross-border region. While the current approach relies on aggregated data, future integration of survey microdata from national institutes could further improve accuracy. Overall, this method offers a practical and flexible approach for generating synthetic populations. By combining IPF-based synthesis, TRS integerisation and stochastic synthetic reconstruction, it produces populations that are consistent with aggregate statistics, household- and individual-level structures.

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.

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. This study uses Ecuador’s ENEMDU household survey (2009–2022), combining repeated cross-sections with a two-period panel spanning a major reform of the Bono de Desarrollo Humano (BDH) cash transfer program. In 2013–2014, a sharp tightening of the welfare index cutoff increased benefits for households below the threshold while making those just above it ineligible, generating an abrupt loss of transfers for some near-cutoff households. The panel allows us to track poverty dynamics around this shock. We compare two objective poverty measures (income-based, using official poverty lines) with two subjective measures (self-reported poverty status and a minimum-income-based “subjective poverty line”). First, we document trends over time and test basic coherence, including whether higher income is associated with lower subjective poverty and how the subjective poverty line evolves. Second, exploiting the BDH reform as a quasi-experiment, we compare households just below and just above the eligibility cutoff to estimate—via a regression discontinuity design—how losing the transfer affects each poverty metric. We expect objective and subjective measures to diverge in informative ways: some households above the monetary poverty line may still feel poor, while some income-poor households may not self-identify as such, reflecting adaptation and social comparison. We also hypothesize that subjective poverty is more responsive to the transfer loss than income poverty status. The results will clarify what each metric captures, whether different subjective measures behave similarly, and inform poverty targeting and social policy design by combining “poverty on paper” with perceived economic vulnerability.

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. In this paper we present for the first time preliminary results from a project that develops a historical microsimulation model for the Netherlands, a particularly interesting case as it went from being one of the lowest to one of the highest spending welfare states. In practice, we simulate cash social transfers, social insurance contributions and the personal income tax for several years between 1950 and 1975 based on official legislative documents. For the present paper the model will be combined with hypothetical household data in order to analyse key policy indicators for household types differing along socio-demographic characteristics and income levels. Such an analysis provides valuable insights into the intentions of historical policymakers. In a later stage the aim is to combine the microsimulation model with representative microdata of the Dutch population in order to study policy outcomes such as redistribution and poverty reduction and to contrast outcomes with intentions.

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.

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. In this context, this research assesses the distributional impacts of a potential distance-based tax in the Brussels-Capital Region, one of the most congested urban areas in Europe. The analysis adopts a behavioral microsimulation approach to examine longer-term effects. By explicitly incorporating behavioral responses, the study addresses an important gap in the literature, which has largely relied on static and aggregate analyses that overlook how individuals adjust their travel behavior in response to pricing policies. The central research question is: what are the distributional impacts of a distance-based road pricing scheme, differentiated by time, location, and vehicle characteristics, considering both static (immediate) and dynamic (post-adaptation) effects? Methodologically, the approach is structured in two complementary layers. The first layer estimates individual behavioral responses using a stated preference survey based on a discrete choice experiment administered to a representative sample of car users. Respondents are asked to evaluate a recent trip and choose between maintaining it or selecting alternatives that vary in cost, travel time, mode, and timing. These choices are used to estimate individual-level price elasticities through a Mixed Multinomial Logit model. The resulting behavioral parameters are then incorporated into the second layer. The second layer consists of a behavioral microsimulation model built on a synthetic microdataset. This dataset is built by enriching the Brussels Travel Behavior Survey (OVG, 2024) with administrative fiscal data and the estimated behavioral responses. Individuals in the OVG (receiver dataset) are matched with similar profiles from the two complementary sources (donor datasets) using machine learning techniques, including kernel canonical correlation analysis, which captures nonlinear relationships and projects observations into a common latent space. Variables of interested are then imputed using similarity-based weighting. The resulting dataset is aligned with aggregate statistics to ensure consistency with real-world distributions. This integrated framework enables a detailed assessment of policy impacts across the income distribution and supports the simulation of compensation mechanisms. Preliminary results suggest that, in static terms, a distance-based tax is not more regressive on average than the current vehicle ownership tax, largely due to lower car ownership among low-income households. However, conditional on being a driver, some regressive effects emerge, driven not only by uniform tariffs but also by the higher prevalence of less efficient vehicles among lower-income groups, which are expected to pay higher per-kilometer charges. Dynamic results indicate stronger behavioral responses among lower-income individuals, raising important policy considerations regarding accessibility and fairness. Overall, the findings aim to provide new evidence on the extent to which distance-based road pricing can better reconcile efficiency and equity objectives.

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.

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. A detailed micro simulator tailored to the Belgian tax system maps gross income into disposable income, ensuring an exact representation of institutional rules and nonlinear budget constraints. Compared to survey based data, administrative records substantially reduce measurement error and improve the credibility of simulated behavioral responses. The model closely replicates observed distributions of hours, wages, and income across gender and household types. We then revisit an in-work benefit reform previously analyzed using survey data, highlighting how data precision and institutional detail affect predicted labor supply responses and budgetary implications.

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.