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.
Interstate conflict networks are frequently represented as mappings of dyadic feature combinations, which can be represented as graph-based networks. Such graphs encode structured macro-patterns, including but not limited to positions, clusters, and polarization, that require mechanistic interpretations and explanation. The research underlying this abstract develops an agent-based model (ABM) to reconstruct annual interstate conflict networks derived from the Conflict Barometer of the Heidelberg Institute for International Conflict Research (HIIK) (HIIK, various years). Rather than treating these networks as graphs and illustrations, we utilize them as empirical macro-targets for simulation-based explanation in the tradition of generative social science (Epstein, 2006).
Between 2014 and 2024, and on a yearly basis, the HIIK Conflict Barometer systematically coded political conflicts worldwide using a qualitative, measure-based methodology and an ordinal intensity scale (1-5), distinguishing disputes, crises, and wars. The resulting annual interstate networks can be used to represent states as nodes and conflict-intensity as weighted, undirected edges. These graphs, in turn, reveal recurring structural features, such as high-betweenness states, state clustering, and varying intensity distributions. However, these graphs cannot provide insights into the micro-processes that generate them. Research in international conflict networks emphasizes that dyadic conflict behavior is embedded in broader relational structures characterized by diffusion and bloc formation (Maoz, 2010), reinforcing the need for dynamic modeling.
To address this gap and support the future prediction of dyadic state combinations with the potential to escalate, we construct an ABM at monthly granularity in which state agents are endowed with heterogeneous capabilities, institutional characteristics, and endogenous stress variables. In our model, dyadic conflict intensity evolves through probabilistic escalation and de-escalation processes driven by baseline tension and retaliation, cost sensitivity and fatigue, network spillover or conflict transmissibility, as well as alignment pressure via triadic closure and alliances. Third-party mediation mechanisms further enable mediation effects to emerge endogenously. Annual networks are obtained by aggregating simulated monthly intensities. They are then evaluated and calibrated through comparison with HIIK data, including the empirical distribution of dyadic intensity levels, rank stability of high-betweenness states, network density and component structure, as well as modular clustering patterns. Our validation relies on out-of-sample yearly comparisons and tests.
Our preliminary results indicate that a parsimonious combination of dyadic retaliation and network-mediated spillovers can already be used to reproduce mediation and polarization patterns observed in HIIK networks, while alliance-related parameters affect cluster rigidity rather than aggregate conflict prevalence. Our findings thus suggest that interstate conflict networks are best understood as macro-representations of micro-interactions rather than static representations of antagonism. Our work further demonstrates how conflict data can anchor ABM calibration. Substantively, it clarifies structural drivers of mediation and alliance formation in interstate conflicts and provides a framework for counterfactual and experimental analyses.
References
Epstein, J. M. (2006). Generative Social Science. Princeton University Press. Heidelberg Institute for International Conflict Research (HIIK). (Various years). Conflict Barometer. Heidelberg. Maoz, Z. (2010). Networks of Nations. Cambridge University Press.
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.
As populations age, the sustainability of long-term care systems increasingly depends on the availability of informal care, particularly from partners. This paper addresses the question of how much care we may expect partners to provide in the future by projecting demand for long-term care (LTC), the care supply mix based on current patterns, and the resulting care gaps up to 2070. Using a comparative dynamic microsimulation model, we contrast the results for Austria and Italy, two countries at very different stages in the ageing process and with pronounced institutional differences. Our results suggest that delayed widowhood due to improvements in mortality is a mitigating factor for the increased need for formal care in ageing societies, although it can only offset this increase to a limited extent. Even under optimistic assumptions, potential care gaps substantially increase in both countries, primarily due to demographic change. The size of these gaps is influenced by institutional settings, partnership patterns and gains in longevity, but no scenario reverses the overall upward trend. These findings emphasize the need for comprehensive LTC reforms that extend beyond merely promoting informal care and highlight the necessity for substantial investment in formal care infrastructure.
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.
Because causal estimands are unobservable in reality, benchmarking causal effect estimators is inherently challenging. To overcome this, simulation studies are increasingly used to evaluate causal inference methods under realistic conditions such as small sample sizes, limited overlap, and complex confounding. Yet it remains unclear to what extent conclusions drawn from simulated settings extrapolate to real-world performance. Existing benchmarks rely on a wide range of outcome-generation strategies, from parametric structural models to flexible machine learning, without clear guidance on their reliability. This paper formalizes simulation-based benchmarking and introduces a framework to characterize the types of bias that may arise from different generative strategies, focusing particularly on Average Treatment Effect (ATE). We classify and compare several approaches proposed in the causal inference literature, including parametric structural models[1], machine-learning conditional mean–variance models[2], and adversarial generative models such as Wasserstein GANs[3]. We apply these modeling strategies to commonly used benchmark datasets in which we synthetically define an outcome-generating function so that the true ATE is known and systematic evaluation is possible. Across Monte Carlo experiments, we compare estimator performance in terms of bias, variance, and mean squared error. We examine how closely these performance metrics align across different simulation strategies relative to the structural data-generating process, and whether commonly used generator diagnostics are predictive of estimator behavior. Our preliminary results indicate that the choice of simulation strategy can substantially alter conclusions about estimator reliability relative to the underlying data distribution. These findings underscore the importance of principled design and validation of simulation frameworks when benchmarking causal methods.
[1] T. Wendling, K. Jung, A. Callahan, A. Schuler, N. H. Shah, and B. Gallego, “Comparing methods for estimation of heterogeneous treatment effects using observational data from health care databases,” Stat. Med., vol. 37, no. 23, pp. 3309–3324, Oct. 2018, doi: 10.1002/sim.7820. [2] A. Schuler, K. Jung, R. Tibshirani, T. Hastie, and N. Shah, “Synth-Validation: Selecting the Best Causal Inference Method for a Given Dataset,” Oct. 31, 2017, arXiv: arXiv:1711.00083. doi: 10.48550/arXiv.1711.00083. [3] S. Athey, G. W. Imbens, J. Metzger, and E. Munro, “Using Wasserstein Generative Adversarial Networks for the design of Monte Carlo simulations,” J. Econom., vol. 240, no. 2, p. 105076, Mar. 2024, doi: 10.1016/j.jeconom.2020.09.013.
This research explores how microsimulation modelling can be used to develop an integrated framework that evaluates economic efficiency, social justice, and ecological effectiveness simultaneously.
When social policies are designed and implemented, analysis often focusses on one or maximum two main objectives: economic efficiency and/or social justice. Policymakers and researchers frequently examine how policy reforms can reduce poverty and inequality, or how they can strengthen work incentives and increase employment levels. Microsimulation models, such as the EU tax-benefit microsimulation model EUROMOD, are useful to analyse these distributive and labour market effects of policies. However, policy designs in the context of the just transition towards climate neutrality increasingly require a multi‑dimensional perspective that goes beyond the traditional efficiency-equity trade-off. Current research shows that a third factor affects the existing trade-off: next to economic efficiency and social justice, policies must also consider ecological effectiveness.
A policy measure that illustrates this challenge, is the implementation of a carbon tax. Research shows that it is an economically and ecologically efficient tool to reduce GHG emissions (Baranzini et al., 2017; Timilsina, 2022). On the one hand, it encourages households, businesses, and governments to reduce their emissions, and it provides incentives for innovation. On the other hand, the cost to the government is relatively low, particularly compared to other policies aimed at reducing GHG emissions. However, empirical findings also show that it is very often regressive: households in lower income groups face a higher tax burden than those in higher income groups (Boyce, 2018). The combination of ecological effectiveness, economic efficiency, and potential social inequity highlights the need for an integrated framework that addresses these trade-offs.
Although existing microsimulation models can analyse each dimension separately, we argue that an integrated framework that considers efficiency, justice, and effectiveness simultaneously is needed. Our research tries to contribute to this by exploring how microsimulations models can be used to examine a framework that integrates various indicators (economic, social, and environmental outcomes) together. As an example, we will apply our framework to carbon taxation in Belgium, using Green EUROMOD (the recently developed environmental extension of EUROMOD), building further on Bursens et al. (2026).
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.
Food pricing policies are increasingly discussed as instruments to internalise environmental externalities and improve diets. The motivation is clear: animal-based foods account for a disproportionate share of food-system greenhouse gas (GHG) emissions (Clark et al., 2019; Poore & Nemecek, 2018), while dietary patterns high in red and processed meat are strongly associated with cardiovascular disease (CVD) risks (Afshin et al., 2019; Willett et al., 2019). Yet the case for food carbon taxes cannot be made on emissions alone. In low- and middle-income countries, food expenditure shares are high and nutritional adequacy is often fragile, so price shocks can have first-order welfare and nutrition consequences (FAO, 2022). Whether carbon pricing in food delivers health co-benefits, or instead worsens diet quality for vulnerable households, is fundamentally an empirical question about substitution behaviour. This paper develops a behavioural microsimulation framework that links food carbon taxation → prices → consumption substitution → protein intake → predicted CVD risk. The core behavioural engine is a Quadratic Almost Ideal Demand System (QUAIDS), estimated on household microdata from Pakistan. QUAIDS is well-suited here because it accommodates flexible Engel-curve shapes and non-linear expenditure effects (Banks et al., 1997), which matter in settings with substantial heterogeneity in food budgets and diet composition. The demand system follows the standard AIDS/QUAIDS tradition (Deaton & Muellbauer, 1980) and is estimated with demographic controls and survey weights. Food is grouped into nine aggregates that reflect both emissions and nutrition channels: cereals; red meat; poultry; fish; dairy; fruit and vegetables; beverages; processed foods; and plant-based protein (legumes/peas/beans). Prices are constructed from unit values and total expenditure is equivalised using a square-root scale. From the estimated parameters, we recover own- and cross-price elasticities and propagate policy-induced price changes through predicted budget shares and implied quantities. Nutritional outcomes focus on protein (given its policy relevance for affordability and adequacy), derived from group-specific nutrient coefficients. Health outcomes are evaluated using comparative risk assessment logic by mapping dietary changes into shifts in cardiovascular disease (CVD) risk factors, drawing on established evidence on diet–disease relationships from large-scale comparative risk assessments (Lim et al., 2012; Micha et al., 2017) and widely used dietary benchmarks for healthy and sustainable diets (Springmann et al., 2016; Tilman & Clark, 2014). We simulate three carbon-tax scenarios applied to all food: no revenue recycling, full revenue recycling via an equal per-capita lump-sum transfer, and targeted partial recycling, where 20% of revenues subsidise plant-based protein (legumes/peas/beans). The tax is calibrated at €5 per tCO₂e, consistent with low-end carbon prices observed across many existing instruments, particularly in low- and middle-income settings (World Bank, 2023). This design captures a key tension: while carbon pricing can reduce diet-related emissions, substitution toward cheaper calories or processed foods may dilute nutritional and health co-benefits (Smed et al., 2016). The partial-recycling scenario tests whether modest earmarking can redirect substitution toward lower-emission, nutritionally favourable foods relative to the pure-tax and lump-sum benchmarks. Methodologically, the paper embeds a demand system within a fiscal microsimulation framework to jointly evaluate climate, nutrition, and health outcomes, and shows how recycling design governs whether behavioural responses translate into co-benefits.
The simulation of social benefits is an important application of tax-benefit microsimulation models in social policy research. Simulation outcomes inform about the (potential) effects of social policies and policy reforms. Furthermore, tax-benefit simulations allow for an evaluation of the interactions of different benefits in complex benefit systems. However, the quality of the simulation outcomes has consequences for the assessment of the effectiveness of the policy. An increasing number of recent studies on benefit non-take-up as one measure for the ineffectiveness of social policies explicitly address the difficulties in determining benefit entitlements using tax-benefit microsimulation models (Tasseva 2016, Bruckmeier et al. 2021, Doorley and Kakoulidou 2024, Bargain et al. 2012, Harnisch 2019). Consequently, the validation of the simulation outcomes is an important step in the application of tax-benefit simulations. Our study contributes to the literature on validating the results of tax-benefit simulation models. We examine how well the results of an open-source tax-benefit microsimulation model for Germany (GETTSIM) on means-tested minimum income (UBII) entitlements match the benefits contained in administrative data on UBII. Our analysis has two objectives: First, the results should provide an assessment of the validity of the UBII simulation results using GETTSIM. Second, generalized conclusions for policy and non-take-up analyses based on tax-benefit microsimulation models will be drawn. The results show that UBII entitlements are in most cases correctly simulated. In an adapted version of GETTSIM we have used, only for 3 to 4 percent of all observations no UBII entitlement was simulated (beta error). The simulation also allowed a precise distinction between UBII and existing similar benefits (housing and means-tested child benefit) for most observations. A closer look at individual deviations between recorded and simulated entitlements reveals significant deviations for migrants, especially from crisis countries, which was particularly relevant in 2017 and 2018. Furthermore, for households with many family members, with children or employed persons, the simulation at the individual becomes less precise. The results also provide some insights for the analysis of eligibility based on tax-benefit simulations in general. In social policy systems with overlapping benefits, even with very good data quality, misspecification of benefit entitlements cannot be avoided to a relevant extent, especially when the benefits pursue similar objectives and discretionary decisions occur at the administrative level. Since the mean values of various large sociodemographic groups are relatively accurately determined in the simulation, calibrating the simulated recipient numbers can compensate for these inaccuracies. The analysis at the individual level has shown that simulation quality decreases particularly for subgroups with more complex life circumstances, such as households with children. This applies in particular to comprehensive last-resort minimum income systems that provide benefits in the household context and take all types of household income into account. Temporary special circumstances, like a national or global crisis, can also lead to simulation results that do not reflect actual payments. In crisis years, consideration should be given to excluding certain particularly affected subgroups from the analysis or to choose other simulation years, if possible.
