What do you want to teach?
This hands-on tutorial introduces participants to PolicyEngine, a free, open-source microsimulation platform for analysing tax and benefit policy reforms in the US and UK. Participants will learn to use PolicyEngines web interface (policyengine.org) to: (1) model a tax or benefit reform by adjusting policy parameters, (2) compute hypothetical household impacts showing how the reform affects a hypothetical households taxes, benefits, and net income, (3) run population-level microsimulation analysis to estimate budgetary cost or revenue, distributional effects across income deciles, poverty impacts, and winner/loser breakdowns, and (4) use PolicyEngines AI assistant (a Claude Code plugin) to conduct policy analysis from natural language prompts, including generating charts, policy briefs, and congressional district or constituency-level breakdowns. The session will use live examples relevant to current policy debates in both the US and UK.
Why is it useful?
PolicyEngine is used by governments (including No. 10 Downing Street), think tanks (Brookings Institution, CRFB, Niskanen Center), and researchers for rapid policy analysis. Unlike proprietary microsimulation models, PolicyEngine is fully open-source and requires no software installation—all analysis runs in the browser or through a Python package. This makes it accessible to researchers, students, and policy practitioners who need to evaluate reform proposals but lack access to established microsimulation infrastructure. The AI-assisted workflow further lowers the barrier, enabling users to conduct distributional analysis and generate publication-quality outputs from plain-language policy descriptions.
What is your expertise?
Max Ghenis is co-founder and CEO of PolicyEngine. He previously founded the UBI Center, a think tank researching universal basic income policies, and worked as a data scientist at Google. He holds a masters degree in Data, Economics, and Development Policy from MIT and a bachelors degree in operations research from UC Berkeley.
Spatial dynamic microsimulations probabilistically project geographically referenced units with individual characteristics over time. Like any stochastic projection method, their outcomes are inherently uncertain and sensitive to multiple factors. In discrete time dynamic microsimulations, for each simulated time step (often years), each unit passes through different modules addressing different life events (births, deaths, ageing, partnership, employment, …), evaluating if a status transition occurs for them via a Monte Carlo experiment. This inherently introduces uncertainty due to the methods stochastic nature. However, simulations may also be sensitive to other factors, such as the choice of model types and complexity, as well as the parameter estimations, among others.
Few articles detail the uncertainty in dynamic microsimulations, and the importance of its components is often overlooked. This is due to the high computational effort required for testing numerous simulation configurations and individual runs are necessary for the analysis. A complete sensitivity analysis testing the sensitivity to each single parameter in every module of the simulation would be unfeasible due to the complex structure of these microsimulations and the resulting computational power required to run them. Moreover, since dynamic microsimulations are typically developed to address specific problems and vary significantly in design and complexity, one-size-fits-all solutions are unattainable. Lastly, there is no commonly agreed-upon standard for reporting uncertainty in dynamic microsimulations.
Applying variance-based sensitivity analyses to both direct and indirect effects within the employment module of the MikroSim model for Germany, we show that commonly considered sources of uncertainty, namely coefficient and parameter uncertainty, are less influential than qualitative modelling choices. Dynamic microsimulations being inherently complex and computationally intensive, it is crucial to consider potential factors of uncertainty and their influence on simulation outputs in order to more carefully design simulation setups and better communicate results. We find that simple summary measures do not adequately capture overall model uncertainty and therefore urge modellers to account for these broader sources when designing microsimulations and interpreting their results.
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.
In the arid and semi-arid lands of northwest Kenya, climate change threatens to compound existing disparities in agricultural activities and health infrastructure and exacerbate food insecurity and malnutrition. To estimate the distribution of nutrition-associated health outcomes and assess projected disease burdens under climate change scenarios, a microsimulation model was combined with health impact assessment methods. A synthetic population covering Turkana, Samburu, and Laikipia counties was generated from census data and a survey conducted in the study region in 2025 provided socioeconomic attributes and baseline dietary intake data. Population growth estimates were obtained from the national statistics authority and forecasts of future edible food availability were estimated up to 2050 from a validated open-source agricultural land-use model. Relative risks of nutrition-associated health outcomes were simulated at the individual-level using standard exposure-response functions at baseline and then at five-year intervals under four representative concentration pathways. The attributable burden in disability-adjusted life years was then estimated to generate the projected morbidity burden in demographic groups. This work expands on an earlier microsimulation model framework developed to assess policies to promote the use of clean cooking fuels on individual exposure to ambient and household air pollution emissions and associated gender-specific mortality in three densely populated Kenyan municipalities. With this additional iteration, we demonstrate how geographic- and hazard-specific assessments of disease burdens in rural and urban populations may contribute toward an improved understanding of differential climate vulnerability in Kenya.
This paper examines the gendered distributional effects of carbon pricing across six European Union countries, focusing on differences in household carbon footprints and exposure to carbon-induced price increases. While prior research has primarily emphasized the income-based regressivity of carbon taxes (Maier et al., 2025), this study addresses the relative neglect of gender disparities in emissions patterns and welfare impacts.
The study employs a harmonised microsimulation framework combining two components. First, the Green-EUROMOD model integrates disposable income simulated through EUROMOD using EU-SILC microdata with detailed consumption profiles from the Household Budget Survey (Dreoni et al., 2025). Household-level greenhouse gas emissions are calculated by applying environmentally extended input-output CO2-per-euro emission intensities from EXIOBASE to harmonised consumption categories (EXIOBASE Consortium, 2015). This enables a distinction between direct emissions from home energy and transport fuels and indirect emissions embedded in goods and services, allowing a decomposition of gender differentials by emission source and supporting distributional analysis. Second, a demand system supports the welfare analysis of carbon pricing by linking consumption, emissions, price changes, and behavioural responses, following Creedy and Van De Ven (1997), Sologon et al. (2025) and Sologon et al. (2024). To assess distributional heterogeneity, we apply quantile functions and quantile regression techniques, examining how gender differences in carbon footprints and exposure to carbon-related price increases vary across income and emissions distributions and interact with household composition.
Preliminary results reveal systematic gender differences in emissions by source. Across countries, women-led households tend to exhibit higher emissions from heating and electricity. However, this gender gap declines with income: disparities are largest among lower-income households and narrow progressively at higher income levels. At the same time, home-energy emissions increase with income for all households, indicating that higher-income groups generate larger energy-related footprints even after controlling for observable characteristics. Evidence on transport-related emissions is more heterogeneous. In some countries (e.g. Germany, Finland, and Ireland), men-led households display higher motor fuel emissions, while in others (e.g. Portugal, Hungary, and Poland), women-led households emit more. Nevertheless, gender gaps in transport emissions generally decline along the income distribution, while overall emissions rise with income.
This study highlights that carbon pricing may interact with gendered consumption and energy-use patterns in complex ways. By identifying which groups are most exposed to price-based climate instruments, the paper aims to inform the design of compensatory measures that enhance equity without undermining environmental effectiveness.
