Less to Assistance, More to Work: Territorial and Distributional Effects of Italian fiscal Policies in 2022–2025
In recent years, Italy has experienced a phase of significant discontinuity in the design of its redistributive policies. The national government has introduced a broad set of measures that, taken together, have reshaped the relationship between the instruments to tackle poverty, the structure of personal income taxation and the measures affecting labour costs. On the one hand, the abolition of the guaranteed minimum income, introduced by the previous government, the so-called Reddito di cittadinanza, and its replacement with two new categorical measures, Assegno di inclusione and Supporto per la formazione e il lavoro, have led to a reduction in resources allocated to households facing economic disadvantage. These reforms have profoundly restructured the target population of recipients, introducing new eligibility criteria and a stronger link with activation and labour-market participation pathways. On the other hand, the reduction of the tax and social security wedge and the revision of personal income tax (IRPEF)—with the broader legislative process aimed at moving towards a flat-tax model—have increased the resources available to employees and taxpayers with low- and middle-income levels, producing differentiated effects along the income distribution and across occupational groups. This paper aims to provide an integrated analysis of the costs and benefits of this “redistributive trade-off” between reduced social assistance spending and lower tax pressure on labour, at both the individual and territorial levels. Using the IRPET static microsimulation model, MicroReg*, the study quantifies the direct and indirect effects of the reforms implemented between 2022 and 2025 on Italian households. Particular attention is devoted to: (i) vertical and horizontal redistribution across income groups and household types (e.g. households with children, single-parent households, households’ employment status of their members); (ii) potential effects on absolute and relative poverty; and (iii) territorial impacts, measured at the regional level, in relation to differences in socio-demographic, labour-market, and income structures across the country. Territorial comparisons are carried out by constructing a net redistributive balance for each region. The objective is to systematically assess which groups are overall beneficiaries and which are disadvantaged by the new equilibrium between reduced cash assistance and increased tax relief, highlighting potential concerns in terms of equity, territorialisation of social risk, and the coherence of the redistributive system with ongoing demographic and economic dynamics. *M. Luisa Maitino, L. Ravagli, N. Sciclone; 2017; Microreg: A traditional tax-benefit microsimulation model extended to indirect taxes and in-kind transfers; International Journal of Microsimulation; 10(1); 5-38. DOI: 10.34196/IJM.00148.
Read MoreThe Horizontal Distribution of Carbon Emissions in Türkiye
This paper extends the literature on the distributional impacts of carbon pricing in Türkiye by examining both vertical (income-based) and horizontal (non-income-based) inequalities in household carbon emissions. Using the ARIA (Analytical Routine for Inequality Assessment) microsimulation framework with data from the 2019 Turkish Household Budget Survey, we simulate direct and indirect carbon emissions linked to household expenditures. Consistent with prior findings, emissions rise with income but decline as a share of income, indicating regressivity. However, horizontal inequality in emissions exceeds vertical inequality. By analyzing emissions relative to personal characteristics, we find that older individuals and car owners exhibit higher carbon intensity, while larger households and those with children show lower intensity. These insights underscore the importance of incorporating diverse personal traits into climate policy design.
Read MoreProjecting Demand for Senior Day-Care Facilities in Slovakia
Population ageing represents one of the most significant long-term challenges for social and health care systems in Europe, with powerful implications for long-term care infrastructure. In Slovakia, demographic ageing is expected to intensify markedly over the coming decades, generating substantial pressure on the capacity and spatial distribution of residential care facilities for older persons. This paper aims to quantify the future demand for residential senior day-care facilities using the Slovak Labour Microsimulation Model (SLAMM). The analysis employs a dynamic microsimulation approach that models individual life-course transitions related to ageing, household structure, health status, and care dependency. Based on demographic projections of the Slovak population, the model simulates the evolution of the senior population and estimates the number of individuals requiring institutional or semi-institutional care services. The analysis is conducted at the district (LAU 1) level, allowing for the identification of significant regional disparities in both demographic ageing and projected care needs. The primary output of the model is a district-level forecast of demand for residential day-care facilities for seniors over the medium- and long-term horizon. Building on these demand estimates, the paper further quantifies the investment costs associated with expanding care infrastructure. Using unit cost estimates for the construction and capacity expansion of senior care facilities, the study derives projected capital expenditure requirements necessary to meet future demand under different demographic scenarios. The results provide a comprehensive picture of how population ageing will translate into spatially differentiated demand for senior care services in Slovakia. By linking microsimulation-based demand projections with infrastructure cost estimates, the paper offers an integrated analytical framework that supports evidence-based planning of long-term care capacity. The findings are particularly relevant for policymakers and regional authorities responsible for social service provision, as they highlight districts facing the most significant future capacity gaps and investment needs. Overall, the study demonstrates the usefulness of microsimulation modelling as a tool for strategic planning in the area of ageing and long-term care, enabling policymakers to anticipate demographic pressures and to design more efficient and equitable infrastructure investment strategies.
Read MoreBeyond IPF: Generative Modeling of Synthetic Populations with Variational Autoencoders
Synthetic populations are a key component of many transportation and urban analysis frameworks, as they provide disaggregated representations of individuals or households used to feed traffic simulators and exposure models. Beyond mobility studies, they are increasingly mobilized to assess territorial sensitivity to external factors such as environmental nuisances or construction noise. Traditionally, synthetic populations are generated using calibration-based methods such as Iterative Proportional Fitting (IPF), which adjust a micro-sample to match aggregated census constraints. While robust and interpretable, these approaches are limited in high-dimensional settings and can only reproduce individuals that are already present in the initial sample.
Read MoreEvaluating Synthetic Data Quality for Regional Microsimulation: Comparing Model-Generated and Commercial Data Sources for Population Modelling
Background and Motivation Regional microsimulation models face critical data challenges: survey data lack local representativeness, administrative data access is often restricted, and commercial datasets are costly. Increasingly, researchers turn to synthetic data generated through statistical models, but questions remain about their validity for policy analysis compared to established commercial alternatives like Experian.
Read MoreUncertainty assessment in dynamic microsimulation: the case of MikroSim (Germany)
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.
Read MorePopulation Ageing Trajectories in the United Kingdom: A Microsimulation Approach
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.
Read MoreFrom Microsimulation to a Digital Twin of Society: Methodological and Data Foundations of project InnoTwin
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.
Read MoreAgroecological Transitions under Mediterranean Water Scarcity: A Microsimulation of Carbon, Water, and Economic Performance
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.
Read MoreModelling the Distributional Impact of Farm Level Renewable Energy
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.
Read MoreMapping the drivers of spatial inequality in Luxembourg.
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.
Read MoreDistributional Effects of Distance-Based Road Pricing: A Behavioral Microsimulation Study for the Brussels-Capital Region
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.
Read MoreModelling future ambulatory care utilisation in Germany: A Microsimulation of patient demand and physician supply
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.
Read MoreGeo-referencing buildings to census grids: an optimization-based approach
The geo-referencing of buildings and their inhabitants at detailed geographical levels can be required for multiple applications, for example, transportation planning, evaluation of housing policies, provision of health services, or flooding information. Indeed, the precise location of residence as well as place of employment of the individuals and households is crucial information in these fields. Spatial dynamic microsimulations especially project these individual units, over time and spatial dimensions, instead of aggregates to better understand behaviour at the individual level, when histories and heterogeneity are crucial. The German model MikroSim, in particular, represents the over 80 million German inhabitants and their households as a fully synthetic statistical twin and directly benefits from a detailed representation of their location. Individuals and household data often comes from census datasets, which depict aggregate population information in a grid cell representation of various resolutions. On the other hand, micro-level buildings datasets, such as the Official Real Estate Cadastral Information System (ALKIS), represent individual buildings, their type, associated geospatial attributes and locations. The merging of these two sources of geographical information consists in a new task, the assignment of building objects to grid cells. The allocation of buildings to the census grid cells is neither fully transparent, nor conducted purely on geographic localization alone, meaning that buildings can in principle be attributed to a neighbouring cell rather than the one they are geographically located in. It is suggested, that assignment of buildings to grid-cells occurs by address, rather than building location. However, the exact location of an address is also ambiguous. Indeed, this information is currently not published by the German National Statistical Institute (DESTATIS). Therefore, this allocation of buildings has to be conducted otherwise. This problem can be represented as a bipartite graph, where each building must then be assigned to close grid cells, ensuring that the total estimated population of assigned buildings does not exceed the grid cells capacity. This problem is similar to the class of generalized assignment problems (GAP). In this study, we present different mathematical formulations of this problem that aim at minimising differences in estimated building inhabitants and grid cells population level; and minimising distances between buildings and assigned grid cells. This problem presents some challenges, such as allocation of building complexes, heterogeneous building types or uncertainty in buildings capacity estimates as well as potential register errors.
Read MoreL0 regularisation for subnational microsimulation calibration
Tax-benefit microsimulation models typically operate at the national level, using household survey weights calibrated to aggregate population targets. Subnational analysis—at the level of states, congressional districts, or local authorities—requires datasets that simultaneously satisfy geographic distributional constraints while preserving household-level detail. We present a method based on L0 regularisation that jointly optimises survey weight magnitudes and sparsity to produce calibrated subnational microsimulation datasets.
Read MoreBuilding a cross-border synthetic population for Luxembourg and neighbouring regions
Synthetic populations are essential for modeling complex systems that require individual-level data. However, they are typically limited to a single country. Creating a synthetic population across multiple countries is challenging because the data available from national statistical institutes are inconsistent: the variables available differ, and for shared variables, the categories may not align. Fortunately, Eurostat provides access to a large amount of aggregated socio-economic data that is consistent across EU countries. Although these data are less detailed than what can be obtained from national statistical institutes, they provide a solid basis for generating synthetic populations.
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