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.
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.
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.
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.
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
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.
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 tutorial introduces the microWELT model and modular modelling platform for comparative dynamic microsimulation. MicroWELT is a portable, continuous time interacting population model built to work with readily available data for many countries, and it supports optional alignment to aggregate targets. It is “X-compatible”: the same model code can be compiled using Modgen or the open-source openM++ environment. As documented on the project website www.microWELT.eu, the model is also extendable to refined national applications such as the microDEMS model, which applies the same platform to an Austrian setting using detailed longitudinal administrative records, illustrating how the shared core can be refined when richer data are available.
Participants will learn (i) the conceptual architecture of microWELT as an interacting population model (entities, states, events, and exposures in continuous time); (ii) the platform’s modular structure and (iii) how to use the documentation and web resources to adapt the platform to new research questions. We will show examples of comparative and national applications developed in different research contexts. The emphasis is on “how to get started”: using microWELT as a reference implementation, a starting template for new applications, and a training resource for dynamic microsimulation workflows. We will also point participants to the platform’s step-by-step implementation material, organised to introduce core concepts first and usable as a textbook-style reference when extending models.
MicroWELT lowers the barrier to comparative analysis by providing a shared, well-documented model core that can be reused across countries and projects. Its continuous-time framework is well suited to life-course processes and interacting individuals (e.g., partnership formation and dissolution), while its modularity allows users to extend the core demography to topics such as education, labour force projections, health, long-term care, pensions and other policy-relevant outcomes. Because aggregate outcomes can be aligned to official projections, users can combine micro-level heterogeneity with macro-level consistency - useful when communicating scenarios to policy audiences and when comparing results across countries. Cross-compatibility with Modgen/openM++ also supports both “production-style” workflows and reproducible open-source deployment.
Our team brings 25 years of experience developing dynamic microsimulation models with the Modgen/openM++ programming technology and pioneering comparative, cross-national models. MicroWELT is implemented at the Austrian Institute of Economic Research (WIFO), and our work spans model architecture, parameterization, implementation, documentation, and applied comparative studies.
The tutorial is aimed at applied researchers, graduate students, and policy analysts who want a concrete, working example of a comparative continuous-time dynamic microsimulation platform and guidance on how to build their own applications. For participants not familiar with Modgen/openM++, we recommend also attending the companion tutorial led by Doug Manuel introducing openM++ and the repository stcOpenMpp.
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.
Mediterranean agricultural systems face increasing pressure from water scarcity, climate variability, and environmental degradation, calling for transition pathways that reconcile environmental sustainability with economic viability. Agroecological practices are increasingly proposed as systemic alternatives, yet their impacts remain insufficiently quantified at the micro level. This paper develops a microsimulation framework to assess the environmental and economic performance of agroecological transitions in water-scarce Mediterranean contexts, using a case study from Tunisia.
The model relies on micro-level farm data to simulate alternative production systems, comparing a conventional irrigated orange-tree monoculture with a diversified agroecological intercropping system combining olive trees and aloe vera. It integrates biophysical indicators—carbon balance and water use—with economic variables including production costs, yields, and farm income, enabling scenario-based analysis under different adoption and policy assumptions.
Results show that microsimulation captures heterogeneity, trade-offs, and non-linear effects that are overlooked by aggregate approaches. The findings identify conditions under which olive–aloe vera intercropping systems can improve water efficiency and carbon performance relative to orange monoculture, while maintaining or enhancing economic resilience, as well as constraints related to transition costs and scale effects. Methodologically, the paper extends the application of microsimulation to agroecological and climate-related questions. Substantively, it provides quantitative evidence to support climate-smart agricultural strategies and investment decisions in Mediterranean regions facing increasing water scarcity.
