Even though economists prefer the instrument of carbon pricing to reduce greenhouse gas emissions over regulatory policies, fears of regressive effects that disproportionately burden low-income households lead to low public support, hindering the political feasibility of carbon pricing. In light of an ongoing policy debate and lacking evidence for the German context, this paper investigates the distributional effects of carbon pricing in Germany, as well as possible redistributive polices. I analyze three channels through which carbon pricing affects the distributional outcome: the direct price effect, the indirect price effect and the behavioral response. I then compare a lump-sum transfer, a targeted transfer per income deciles and reductions in labor and income taxes as redistributive policies, providing evidence on their effect on counteracting adverse distributional consequences from carbon pricing.
Methodologically, this thesis extends the ifo Tax and Transfer Microsimulation Model, which is based on the GSOEP, with a carbon pricing module. This extension models the direct and indirect price effects by using household consumption data from the EVS combined with data on emission intensities and estimates the behavioral responses with Engel curves. Using the model, I simulate the introduction of a 200€ carbon price per ton of CO2-equivalent on all direct and embedded emissions in household expenditures for the year 2018.
The analysis of the simulation results yields three main findings. First, the carbon price achieves substantial emission reductions of 221 Mt CO2eq and generates 97.09 bn. € in revenue but also decreases household consumption expenditures by an average of 5,171€ per year, corresponding to an average price burden of 2,090€ per household. Second, the policy exhibits regressive distributional effects, as the price burden amounts to 8.1% of disposable income for households in the lowest income decile and 4.4% in the highest decile. Decomposing the channels shows that regressivity is mainly driven by the disproportionately high energy burden of lower-income households and their lower ability to respond to price increases. Third, the evaluation of revenue rebating policies reveals that lump-sum transfers and transfers differentiated by income deciles significantly reduce regressivity, whereas reductions in labor or income taxes have limited equality-enhancing effects. These results enable a detailed ex-ante distributional analysis by embedding carbon pricing into a microsimulation model of the German tax-benefit system. Further, the findings provide empirical evidence to a scarce study base on the distributional effects of carbon pricing and different redistributive policies for the German context. The analysis also highlights the persistence of horizontal inequalities within income groups, suggesting the need for more targeted and differentiated redistribution mechanisms. Beyond contributions to literature, this thesis provides insights for policymaking. Introducing a high carbon price requires the design of effective and equi-table rebating, considering heterogeneous effects over and within deciles.
Nearly half of the unemployed persons are excluded from the unemployment benefit system by the re-form which has been gradually implemented since 1 January 2026 in Belgium, particularly those with a long unemployment history. The main feature of the reform is to limit the duration of unemployment benefit payments to a maximum of two years, depending on work experience, instead of unlimited payments in time as in the old system. Further, the progressivity of the benefit scheme has been strengthened by an increase of 10% of the ceiling amount during the six first months of unemployment, and a re-duction to a lump sum for unemployed persons who are entitled to payments after one year of unemployment.
This paper gives the ex-ante evaluation results on income and labour supply of this reform. The simulation exercise concludes that approximately one third of the excluded persons are expected to return to work, approximately 40% are predicted to receive the social minimum income. The remaining fourth withdraw from the labour market without replacement income, resulting in a significant loss of disposable revenue. Nonetheless the impact on the poverty rate is moderate. Older unemployed, aged 55 years and over, are particularly affected by the reform as more than three fourth of them lose their benefit, representing nearly 22% of the excluded sample. Regional differences are substantial, with Flanders being the least impacted and Brussels-Capital region the most.
To fulfil the evaluation, we use:
The data from the Crossroad Bank which is a representative sample of the Belgian population with more than 300,000 individuals. It includes a wealth of economic, social and demographic information at both individual and household level, The microsimulation model EXPEDITION which is a highly detailed transcription of the Belgian fiscal and social laws into a microsimulation program, which enables to compute a.o. the dis-posable income and its components for each household based on gross salary, labour supply and all the individual and household characteristics. For this exercise, we are particularly interested in the net income from work, replacement income from unemployment or social minimum allowance, depending on eligibility, and additional benefits (children benefit e.g.). EXPEDITION offers also summary statistics and distribution analysis, such as inequality measures depending on income decile, family type, age group, etc. The labour supply model LASER which is a Van Soest-style microeconomic model, enhanced by non-parametric random coefficients on the household preference parameters. Furthermore, we introduce unemployment as an additional labour supply ‘’choice’’. Nonetheless, unlike most similar models, we explicitly define equations capturing the unemployment probability, and the potential unemployment duration. In addition, the replacement income from unemployment is de-fined as the average benefit over the expected unemployment period to account for the degressive scheme of the Belgian unemployment benefit (and the limitation in time after reform), when calculating the income associated with the unemployment ‘’choice’’ alternative. Subsequently, each individual (not only those directly affected by the reform) is impacted when choosing the best employment alternative.
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.
