Book of Abstracts

The persistent erosion of corporate tax bases through profit shifting by MNEs has long represented a key challenge in international tax policy. Evidence of profit shifting by large companies operating in international markets has been well documented in the economic literature, but tackling the problem at the international level requires strong coordination between countries, which has always seemed utopian. The 2021 OECD/G20 agreement on a Global Minimum Tax (GMT) under the BEPS Pillar Two framework has marked an unprecedented step toward coordinated international taxation. At the same time, the design of the OECD BEPS Plan may not have provided the necessary incentives to eliminate profit shifting. Yet the effectiveness of this reform in curbing profit shifting and harmful tax competition remains uncertain. Moreover, the evolution of the original OECD agreement has been marked by significant fragmentation. A large number of signatory countries have yet to legislate the GloBE rules. Above all, together with the position of China, the recent G7 agreement and the OECD implementation of a ‘side-by-side’ approach and the exclusion of US MNE from the application of the minimum taxation cast serious doubt on the GMT’s global reach and effectiveness. This paper makes two main contributions. First, it provides a critical assessment of the GMT framework under Pillar Two, examining key structural limitations — including the role of the Substance-Based Income Exclusion (SBIE), the treatment of tax incentives, and the fragmented pattern of international adoption — that may substantially constrain its capacity to reduce profit shifting and tax avoidance by MNE. Particular attention is devoted to the positions of China and the United States, whose non-compliance significantly undermines the scope of the agreement. Second, drawing on the MEDITA microsimulation model developed at the Italian Parliamentary Budget Office, the paper provides the first empirical assessment of the GMT’s impact on MNE groups located in Italy. Using firm-level data matched with tax return information, the analysis documents systematic differences in effective tax rates (ETRs) between single domestic firms, domestic groups, and MNE groups — consistent with widespread profit shifting behavior. Results show that, in 2022, 25.49% of MNE groups located in Italy have an ETR below the 15% GloBE threshold and would thus be liable for the Qualified Domestic Minimum Top-up Tax (QDMTT). However, after applying for the SBIE, only 24.14% of potentially liable groups face a positive GMT payment, indicating that substance carve-out provisions reduce GMT liability to zero in approximately 70% of eligible cases. These findings suggest that, together with the recent side-by-side approach for US MNE, the design of the GMT leaves substantial room for large MNE groups to continue reducing their effective tax burden, and that the additional taxation generated is insufficient to equalize the tax load between multinational and purely domestic firms.

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.

Understanding spatial disparities in income distribution is crucial for designing effective and targeted public policies. However, small-area analysis is constrained by the limited representativeness of household surveys at fine geographical levels and by the lack of comprehensive income information in administrative and census data. This paper develops a spatial microsimulation framework to map and decompose income inequality across municipalities in Luxembourg by combining EU-SILC survey data, census information, and administrative statistics to simulate complete disposable income distributions at the municipal level for 2012 and 2022. The model integrates labour market behaviour, multiple income sources—including capital income and private transfers—and the tax-benefit system using EUROMOD. Spatial heterogeneity is captured through a two-step procedure that combines census-based reweighting with regression-based alignment to local demographic and labour market control totals. We further decompose overall inequality into between- and within-municipality components, observing a stronger within-municipality component. This suggests that factors operating at the local level—such as demographic composition, labour market participation, and access to specific income sources—play a central role in shaping income disparities. Our results reveal the dominant role of demographic and local structural factors in driving these disparities. By producing timely and spatially detailed estimates of disposable income and inequality, this paper demonstrates how combining spatial microsimulation with dynamic income generation can overcome data limitations and provide a powerful tool for analysing the drivers of spatial inequality and supporting evidence-based local policy design.

Dynamic microsimulation models such as SimPaths are increasingly used to evaluate long-term policy impacts by generating synthetic trajectories for individuals and households. They are often estimated on longitudinal household surveys and then validated by comparing simulated trajectories with the same survey over an overlapping period. This practice is appealing but can become misleading when the survey panel is selectively attrited. A dynamic simulation preserves its initial synthetic cohort except through explicitly modelled exits, whereas a survey panel increasingly represents respondents who remain observable. The resulting comparison may therefore conflate model error with a changing validation target. This paper asks how dynamic microsimulation models should be validated when the longitudinal survey used for estimation and validation is subject to selective attrition. It presents ongoing work on strengthening validation frameworks in SimPaths, with a focus on developing a target-aware framework. The approach combines insights from a Monte Carlo validation exercise and empirical application with SimPaths. Monte Carlo evidence identifies how alternative correction strategies map onto distinct validation targets. The empirical application uses education processes to show how apparent validation gaps can be decomposed into equation fit, response selection, simulated risk-set composition, and realised model dynamics. The paper therefore argues that validation should proceed by making the target explicit before judging model performance. For internal debugging, the relevant question is whether the implemented model reproduces the empirical processes it was designed to simulate under comparable sample conditions. For policy projection, the relevant question is whether the simulated population remains credible as a representation of the population of interest. Selective attrition makes these questions diverge. A defensible validation protocol must therefore separate implementation checks, equation-level fit, dynamic propagation, and external population realism, rather than treating a single simulated-versus-observed comparison as decisive evidence of model validity.

Housing markets are very important in modern societies because of their effect on households’ ability to find suitable accommodation at an affordable price and because of they lock in huge amounts of wealth, often in a way that is highly unequal. As a result, in many countries, and specifically in England, housing policy is a highly contentious and difficult issue. In this presentation, I will consider how one might model the English Housing market, from simple statistical approaches, through microsimulation and agent-based modelling, and illustrate the latter with a description of an agent-based model that has been developed over the last two decades and now incorporates owner-occupation, the rental sector, social housing and buy-to-lets. The model allows the testing of the implications on market prices and rents of a range of actual and proposed policies, such as changing the basis of property ‘council’ taxes, a ‘mansion’ tax on expensive properties, and transaction taxes, such as the English stamp duty land tax. I will comment on the advantages of using an agent-based modelling approach, but also on the problems and difficulties we had to overcome to obtain a working and validated model and suggest avenues for future development.

Background Population health simulation models—including microsimulation, agent-based, system dynamics, and Markov models—are essential tools for understanding noncommunicable disease (NCD) burden and evaluating policy interventions. However, inconsistent reporting practices limit the transparency, reproducibility, and credibility of this work. Unlike clinical trials and observational studies, which benefit from established reporting guidelines (CONSORT, STROBE), no comprehensive standards exist for population health simulation models. The POPCORN initiative (Population Health Modelling Consensus Reporting Network) aims to address this gap by developing the first EQUATOR Network reporting guideline specifically for population health NCD models. Following EQUATOR methodology, guideline development requires three phases: (1) a scoping review to identify current reporting practices and gaps, (2) international Delphi consensus to prioritise reporting items, and (3) pilot testing with modellers and journal editors. This abstract presents findings from the first phase. Methods We conducted a scoping review following the Arksey and OMalley framework with Joanna Briggs Institute guidance. Scoping reviews systematically map the evidence base to identify key concepts, gaps, and research needs—making them ideal for informing guideline development. We searched MEDLINE, Embase, Scopus, and Global Health for studies published July–December 2025. Eligible studies were computational simulation models examining population-level outcomes for eight NCD groups (cardiovascular, cancer, diabetes, respiratory, mental health, neurological, musculoskeletal, injury) or six risk factors (tobacco, diet, physical activity, alcohol, obesity, hypertension). We applied PICOSIM criteria adapted for simulation studies, covering population, intervention/comparator, outcome, study approach, integration of data sources, and model adaptability. Given the high volume of literature, we implemented AI-assisted screening using large language models with retrieval-augmented generation to support title/abstract review alongside five human reviewers. Data extraction captured reporting elements across model structure, inputs, validation, and outputs. Results From 8,474 records, deduplication yielded 6,427 unique citations. Our database search identified 90% of studies from a reference set of 65 known eligible studies. AI-assisted screening achieved 90% sensitivity and 95% specificity compared to human consensus (κ=0.69), missing only 2 of 20 eligible studies in the validation set. Identifying simulation models proved challenging: key discriminating terms were absent from nearly 10% of titles and abstracts. Preliminary extraction revealed systematic reporting gaps in model specification, parameter uncertainty quantification, and validation procedures—precisely the areas where standardised guidance could improve practice. Conclusions This scoping review establishes the evidence base for POPCORN reporting guideline development. Findings confirm substantial variation in how population health models are reported, supporting the need for consensus-based standards. The microsimulation community will play a central role in the upcoming Delphi process, and we welcome collaborators interested in shaping guidelines that serve both modellers and the policy audiences who rely on their work.

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.

Ensuring accessible and adequate outpatient healthcare close to patients homes is a central concern in Germanys health policy and societal debates. Against the backdrop of demographic change, an ageing medical profession, and changing professional priorities, the shortages in care are intensifying, particularly in rural regions. Concurrently, rising life expectancy is leading to an increase in age-associated, multimorbid conditions that require complex management. These developments affect both the supply of physicians and patient demand. This work presents a district-level model of future healthcare utilisation and relates it to projected physician supply trends. These demand-side projections build directly upon a previous supply-side microsimulation model, which projected the future number of physicians, their specialities, and working patterns. Methodologically, the study employs a dynamic microsimulation of the entire German population within the MikroSim framework. The model is developed using data from national health surveys, from which sociodemographic and morbidity-related determinants of utilisation behaviour are identified. Considering the systemic differences in access, distinct models are developed for individuals covered by statutory (SHI) and private (PHI) health insurance, with the primary focus on simulating utilisation for the SHI-insured population. The primary outcome measures are the projected annual frequency of physician contacts, differentiated between general practitioner and specialist consultations on a regional district level. The results, contingent upon improved future data availability and ongoing model refinement, can provide an evidence-based contribution to enhancing regional and sectoral needs-based planning in outpatient medical care.

This paper analyses the distributional effects of a fiscally neutral tax reform in Croatia that shifts the tax burden from consumption to labour income, capital income, and property. Such a reform can be considered justified given the imbalances of the Croatian tax system, which is characterised by an exceptionally high share of indirect taxes and relatively low taxation of labour, capital, and property income compared to the EU average, contributing to regressivity and greater income inequality. The proposed reform consists of increasing the progressivity of the personal income tax (through the introduction of higher tax rates), raising taxation of capital income and property, while simultaneously reducing the standard VAT rate and expanding the reduced VAT rate. The analysis is based on the application of microsimulation models of direct and indirect taxes, using EU-SILC and Household Budget Survey (HBS) data, with corrections to the income distribution using administrative data to ensure a credible simulation of direct and indirect taxes. In addition to distributional effects, the paper also assesses the impact of the reform on efficiency using a behavioural labour supply model. The results show that the reform does not have a significant negative effect on labour supply, increases the fairness of the tax system by reducing inequality, and delivers the largest gains to lower-income households and more vulnerable social groups, while the richest households incur losses due to increased direct tax burdens. Overall, the reform increases progressivity and the redistributive effect of the system without compromising economic efficiency.

All microsimulation models require the same core functionalities such as event scheduling, output logging, model calibration, and common random number techniques. Few frameworks provide a comprehensive package of ready-to-use core functionalities, and modelers often need to implement these functionalities themselves, resulting in duplicate work and non-standardized and unvalidated code. We present the MISCore (MISCAN Core) Python package with ready-to-use core functionalities for health-economic modelling. Besides the aforementioned core functionalities, it provides built-in tools for cost-effectiveness analyses, probabilistic sensitivity analyses, output stratification, and more. MISCore was developed as the simulation framework for the MISCAN (Microsimulation Screening Analysis) family of disease models. These models have a 40‑year history of informing screening policy through microsimulation modelling; they have provided key evidence for USPSTF guidelines on colorectal, cervical, and lung cancer screening as well as various guidelines throughout Europe. Although MISCore was originally developed to support the MISCAN models, it can be used for models for a wide range of applications, and offers an accessible way to begin microsimulation modelling in Python using an established framework. We are currently in the process of making MISCore publicly available as a Python package for non-commercial purposes. During this course, participants will be introduced to the MISCore Python package to get a head start with microsimulation modelling in Python. The course will follow the three phases of simulation modeling: model development, model application, and model analysis. Each phase will start with a short presentation, followed by hands-on exercises in Python. Starting with model application, participants will learn the general structure of MISCore models, perform simulations with the MISCAN model for endometrial cancer (MISCAN-Endometrium), and evaluate endometrial cancer screening strategies. In the model analysis phase, participants will analyze model outputs of MISCAN-Endometrium and do a cost-effectiveness analysis using MISCore functionalities. Third, we cover the model development phase where participants will adjust the underlying code of a simple disease model and learn the underlying code structure of MISCore models, enabling them to build their own models in the future. Finally, we will shortly discuss other features available in MISCore, and its license which prescribes the permitted uses of MISCore and any MISCAN model made publicly available in the future. This schedule anticipates 3.5-hour tutorial, but we are flexible to shorten if less time is available. At the end of this tutorial, participants will be familiar with discrete-event microsimulation modelling in Python using the MISCore package. They can continue expanding their skills through the elaborate tutorials provided on our online documentation webpage. Pre-Tutorial Preparation Participants should be familiar with a scientific programming language such as Python, R, or a comparable programming language. We will share instructions for installing Python, PyCharm and the MISCore package before the course. Expertise The Department of Public Health at Erasmus University Medical Center has a long track record of informing screening policy with the MISCAN microsimulation models. The faculty of this tutorial are researchers that used or developed MISCAN models for gastric, colorectal, lung, cervical and prostate cancer screening, as well as dementia. They were all involved in the development of MISCore over the past 5 years. Moreover, they organize a course on health-economic modelling using MISCore for BSc students in econometrics, as well as an EU-funded course on decision modelling in Slovenia which aims to build capacity for screening evaluations in Slovenia. Our tutorial will be based on these courses.