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.
Carbon taxation is widely seen by economists as one of the top instruments for reducing greenhouse gas emissions and achieving ambitious decarbonization targets. Yet, despite its efficiency, it often faces public opposition, mainly driven by distributional concerns. A broad consensus in the literature holds that, in the absence of revenue recycling, carbon taxes tend to be regressive, disproportionately burdening low-income households. Consequently, a substantial body of research has focused on designing compensatory mechanisms,such as lump-sum rebates, tax shifts or targeted transfers, to restore progressivity and improve public acceptability.
While these approaches have provided important insights, they often conflate two distinct objectives: making carbon taxation more progressive on average, and reducing the heterogeneity and intensity of its adverse impacts across households. In practice, large disparities in energy consumption and bills persist even within standard socioeconomic groups (e.g. income deciles or geographic areas), meaning that the households most negatively affected by carbon pricing are not always those conventionally identified as vulnerable. In this paper, we therefore focus on the latter objective, namely reducing the “painfulness” of carbon taxation rather than progressivity per se. We argue that an effective compensation scheme should primarily protect households facing large increases in energy costs, smooth the distribution of impacts, avoid overcompensation of lightly affected households and preserve incentives to reduce carbon-intensive energy use. In this perspective, the change in the Energy Effort Rate (EER), that is the variation in the share of household income devoted to energy expenditures following a carbon price increase, emerges as a relevant indicator of household vulnerability.
We use the Prometheus microsimulation model, developed at the French General Commission for Sustainable Development, to analyze the distributional effects of alternative revenue-recycling schemes in France. Prometheus provides a fine-grained simulation of households’ energy bills for housing and transport, accounting for own- and cross-price elasticities, and is specifically designed to assess the impact of energy transition policies and compensatory measures. Using 2019 as the baseline year, we simulate a €10/tCO₂ increase in the carbon tax and compare three polar, budget-neutral redistribution scenarios, each leading to identical aggregate CO₂ emissions from household energy consumption: (i) a reduction in electricity taxation, in the context of France’s low-carbon electricity mix (ii) a uniform lump-sum rebate, and (iii) a progressive lump-sum rebate targeted at the bottom 50% of the income distribution. The microsimulation framework allows us to characterize the full distribution of changes in households’ EER under each scenario.
Our results highlight important trade-offs between progressivity and the smoothing of impacts from carbon taxation. While the targeted lump-sum rebate is the most progressive scheme by construction, it performs poorly in reducing the dispersion of changes in EER, leaving a non-negligible fraction of households exposed to substantial increases. The uniform rebate and the electricity tax shift exhibit comparable average redistributive profiles, but differ in their ability to smooth the impact of carbon taxation. Although the uniform rebate is slightly more progressive, the electricity tax shift consistently minimizes extreme variations in the EER and achieves the lowest overall dispersion according to several quantitative indicators.
Overall, our findings show that some revenue-recycling instruments, when carefully designed to account for context-specific energy and institutional conditions, can effectively smooth the burden of carbon taxation across households and thereby help improve public support for carbon pricing without compromising its environmental effectiveness.
This paper introduces BIMic+, the labor supply extension of the tax and benefit microsimulation model of the Bank of Italy, BIMic (Curci, Savegnago and Cioffi, 2017). The model follows the Random Utility approach (McFadden, 1974; Aaberge, Dagsvik, and StrØm,1995; Van Soest, 1995). The model focuses on the labor supply behavior of wage earners and imputes wages for workers who are not employed through a two-step Heckman estimation procedure. The utility function departs from the quadratic functional form, which is common in this literature, to avoid decreasing utility in disposable income, a violation of a critical assumption in consumer theory and that underlies all redistributive analyses and is crucial for computing equivalent variations. The main arguments of the utility function are hours and disposable income. The latter is calculated through the static module, BIMic, for each counterfactual hours option. With respect to the literature, we innovate by: (i) matching the observed distribution of hours as a constraint into the optimization problem to avoid overfitting issues (as opposed to the usual approach of drawing taste shocks until the estimated hours match the observed ones). We do so in a way that also matches the distribution of labor income from aggregated tax returns. (ii) organizing the output of the model according to a strand of the public finance literature theoretically connected to optimal taxation. For each policy, we want to characterize the willingness to pay of beneficiaries and the net government cost, taking into account behavioral responses to the policy. We also propose to use these quantities to compute the marginal value of spending public funds in such a policy (Hendren and Sprung-Keyser, 2020; Bourguignon and Landais, 2022). In the last section of our paper, we simulate the labor supply effects of a policy reform as an illustration of how to use our model and its output; specifically, we focus on a cut in social security contribution for mothers with at least two children introduced in Italy in 2024.
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.
With the B2 Fiscal Policy Analysis Unit in Sevilla, we developed a User Interface to our labour supply model that is integrated with EUROMOD. The model is going to be publicly available in end of Q1 2026. I could give a tutorial on:
the underlying model when to apply this model how the UI works how to make use of this resource
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.
Environmental tax reforms are often criticized for their regressive effects. However, distributional impacts depend crucially on the design of revenue recycling mechanisms. This paper evaluates a comprehensive green fiscal reform package in Spain inspired by the recommendations of the Spanish White Paper on Tax Reform (2022) combining (i) the equalization of diesel and gasoline taxation, (ii) a general increase in fuel excise duties, and (iii) the elimination of electricity-related distortive taxes to promote electrification. Using microdata from the Spanish Household Budget Survey (EPF 2024), we microsimulate the direct incidence of price changes across income deciles, household types and urban-rural location. We show that vulnerability and fiscal loss do not perfectly overlap: while lower-income households face higher budget shares in energy consumption, significant losses are also observed among middle-income and rural households. We then simulate alternative revenue recycling schemes using the fiscal surplus generated by the reform: (1) a universal green transfer, (2) a targeted ecological supplement to the Minimum Income Scheme, and (3) a mixed design combining universal and targeted elements. The full reform packages are evaluated in terms of coverage, inequality (Gini, Reynolds-Smolensky, Kakwani), poverty (FGT indices), and fiscal neutrality. Our results highlight the importance of moving beyond vulnerability-based compensation towards a broader design of revenue recycling that balances environmental incentives, equity and political feasibility. The findings contribute to the literature on green tax reform and just transition by showing how distributional outcomes critically depend on the architecture of the policy mix rather than on the tax instrument alone.
With the ongoing diffusion of information and communication technologies (ICT) across large parts of Europe, security awareness is becoming increasingly important for mitigating societal security risks and long-term costs of cybercrime. At the macro-structural level, the diffusion of ICT is to a large extent shaped by institutional factors, such as legislative decisions at the national level, technological innovations, or considerations within larger organizational units. By contrast, macro-structural changes in security awareness cannot be governed in a comparable top-down manner. Instead, micro-founded models at the individual level are required, whose contextual conditions systematically change as a result of social-structural and demographic transformation processes.
In this context, security awareness is fundamentally linked to the use of ICT. At the macro-structural level, ICT use constitutes a diffusion process that shapes the socio-demographic contexts of exposure in which security awareness becomes relevant in the first place. At the individual level, ICT use acts as a predictor of security awareness, as only sustained engagement with ICT over time makes individual experiences with cybersecurity issues and cybercrime more likely and thus gives rise to security awareness as a dynamic, process-based phenomenon.
This leads to a macro-level research question that has so far remained unresolved: Is the expansion of ICT use associated with an increase, stagnation, or decline in security awareness within European populations, and to what extent can these changes be attributed to (a) shifts in the social and demographic composition of the user population and (b) differences in group-specific developmental trajectories? To date, population-level dynamics of security awareness have predominantly been examined from a univariate and descriptive perspective. An explanatory approach to the macro-structural dynamics of security awareness in country-specific populations that systematically links ICT diffusion with social structure and demography remains largely absent from the existing literature.
To address this gap, the presentation is structured around three components. First, it discusses the extent to which ICT, cybersecurity in general, and security awareness in particular are incorporated into existing microsimulation models. This review demonstrates that dynamic microsimulations constitute a well-suited yet largely underutilized instrument for analyzing population-level change in these phenomena. Second, a theoretical model is presented to understand macro-structural changes in security awareness, placing particular emphasis on the micro-structural relevance of exposure dynamics related to ICT use and composition dynamics related to demographic and social-structural factors. Third, the mechanisms derived from this model are empirically examined using Eurobarometer data for Germany as an illustration. They are then implemented in a dynamic microsimulation with modules on demography, social structure, ICT use and cybersecurity to simulate scenario-based developments in security awareness.
Overall, the analysis shows that dynamics in security awareness can only be fully understood through the simulation of temporal trajectories in ICT use, which are in an interdependent relationship with security awareness and follow socially unequal patterns of development. In this sense, the results provide an empirically grounded basis for discussing potential interventions aimed at improving security awareness within populations.
Through their ability to fill important data gaps, synthetic populations have become well-established resources in research spanning a wide range of population geography aligned disciplines. By providing readily available data on key life domains and for entire populations, the role of synthetic populations is ever growing – for example within the context of modelling policy questions around public budgets, urban planning, climate mitigation, or health inequalities. Nevertheless, many approaches to creating synthetic populations present important limitations, impacting their robustness and utility for policy and research. In this talk I will outline the creation and utility of synthetic population data, covering important innovations such as the nesting of household and individual level structures, validation, approaches to sharing datasets, and undertaking applied research based on these datasets.
The ability to access and use high-quality data is becoming a key enabler, and bottleneck, for innovation across AI and digital systems. Yet privacy constraints, regulation, and data scarcity continue to limit what organizations and researchers can do. Synthetic data generation is increasingly emerging as a powerful ingredient for enabling responsible, inclusive, and scalable data-driven innovation.
In this talk, I’ll introduce a broader vision for data democratization, with synthetic data playing a central role. I’ll walk through how generative AI models can be used to synthesize rich, realistic tabular datasets, and how these can be safely shared and applied across a wide range of use cases, from AI model development and testing to fairness research, simulation, and beyond.
The session will include a live walkthrough of open-source tools, showcasing how accessible and practical synthetic data generation can be today.
