Book of Abstracts

The farming sector is one of the most carbon intensive economic sectors and is a household group that is hard to reach for household decarbonisation. The use of anaerobic digesters represents a novel technology that can convert biomass such as slurry and grass silage into biogas and ultimately into heat or electricity. It can as such provide a within farm-based source of renewable energy for often low-income farms, improving both household fuel poverty and relative income poverty through the reduction in an important cost source. In this paper we develop a bio-economic model of this process that is applied using a farm level microsimulation model to evaluate the at farm level potential feedstocks for renewable energy generation. We compare the potential impact at farm level of either using the feedstock to reduce the use of fossil fuel based energy or electricity with supplying the biomass as feedstock to commercial biomethane plants. Using a distributional analysis we consider the optimal solution across different farms by farm size, system and income level. We make policy recommendations in how to incentivise the uptake of this potentially useful source of renewable energy.

Dynamic microsimulation models represent individual life-course trajectories through stochastic transitions across multiple domains. Their credibility depends on the accuracy and robustness of the behavioral mechanisms that govern persistence and lag dependence. While the published SimPaths model for the United Kingdom demonstrates strong external validity at the aggregate level, reproducing observed trends between 2011 and 2019, further diagnostic validation is required to assess whether its behavioral structure reproduces the conditional dynamics observed in reality. This study introduces a framework for conditional and structural validation within SimPaths, focusing on employment and health transitions. Using data from Understanding Society (UKHLS), we derive empirical transition probabilities and persistence measures conditional on key socio-demographic factors. The analysis proceeds in three stages. First, matched observed–simulated datasets are compiled for key life-course variables describing employment, self-reported health, and household composition. Second, model outputs are compared with empirical transition patterns and persistence measures across age, sex, and region, quantifying the extent to which simulated dynamics replicate observed life-course behaviour. Third, controlled sensitivity experiments vary the parameters governing behavioral persistence and lag dependence within plausible ranges, tracing how these changes propagate through employment, health, and household outcomes, including inequality indicators such as the Gini coefficient and the 80/20 income ratio. The analysis is guided by two assumptions. First, employment transitions are expected to be more sensitive to persistence and lag-structure specifications than health transitions, reflecting stronger state dependence in labour-market attachment. Second, misspecification of employment persistence is expected to systematically bias projected income distributions, inflating measures of inequality and in-work poverty over the life-course. Together, these steps test both the empirical validity and structural stability of SimPaths. Substantively, the analysis highlights which life-course domains are most sensitive to behavioral specifications, helping to prioritize future calibration and validation efforts. Methodologically, it offers a reproducible procedure for module-level testing that can be replicated across countries or policy domains, promoting transparent and cumulative model development. The broader aim is to integrate systematic behavioral validation into the standard development cycle of dynamic microsimulation models, ensuring that projected outcomes reflect both credible behavioral dynamics and empirical regularities observed in longitudinal data.

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.

Microsimulation models are flexible and powerful tools with the capacity to provide important evidence to policymakers and improve the lives of families. In this presentation we will discuss the application of microsimulation to genomic medicine and related policies. In particular, we will address how we lay the groundwork for a microsimulation model of genomic medicine when there is almost no data, but the conditions have such significant impacts on quality of life and mortality that it is critical that evidence is robust. Until recently, patients with genetic disorders rarely received a definitive ‘molecular’ diagnosis. Further, what we consider to be a single condition such as genetic blindness, may be caused by many different genetic variants (each with very different symptoms, age of onset and disease trajectory), and for some conditions such as intellectual disability, the condition may be caused by thousands of different genetic variants. As a result of their unique genetic causal mechanisms, these genetic conditions each require a ‘targeted therapy’, such as corrective diets, enzyme replacement, and emerging new gene therapies, meaning that there may be no two patients in a study with the same condition or treatment. Such extreme heterogeneity makes the field of genetics and genomic medicine an ideal application for microsimulation modelling because simulations are run at the individual level. However, the rarity of many genetic conditions, combined with the low likelihood of receiving a molecular diagnosis, has meant that nationally representative survey data generally has not identified these conditions despite their high health and cost impacts for patients, carers and government, leaving a significant data gap for model development. In this presentation, we will describe how we filled substantial data gaps related to genomic medicine by designing new and comprehensive primary data collections to use as microsimulation model base populations, how we worked with our clinical colleagues to gain ethical approval to obtain primary patient data, how we collected the data to ensure a high response rate, and how the data was managed to ensure clean and robust information. We describe the development and structure of our static microsimulation models applied to genomic medicine and present some examples of analysis of model outputs. Finally, we will provide examples of how our models have influenced policy and some current policy applications in development as well as describing the impacts such policy changes have on families.

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.

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.