Modelling Distributional Impacts of Carbon Taxation: a Systematic Review and Meta-Analysis

Carbon taxes are increasingly popular among policymakers but remain politically contentious. A key challenge relates to their distributional impacts; the extent to which tax burdens differ across population groups. As a response, a growing number of studies analyse their distributional impact ex-ante, commonly relying on microsimulation models. However, distributional impact estimates differ across models due to differences in simulated tax designs, assumptions, modelled components, data sources, and outcome metrics. This study comprehensively reviews methodological choices made in constructing microsimulation models designed to simulate the impacts of carbon taxation and discusses how these choices affect the interpretation of results. It conducts a meta-analysis to assess the influence of modelling choices on distributional impact estimates by estimating a probit model on a sample of 217 estimates across 71 countries. The literature review highlights substantial diversity in modelling choices, with no standard practice emerging. The meta-analysis shows that studies modelling carbon taxes on imported emissions are significantly less likely to find regressive results, while indirect emission coverage has ambiguous effects on regressivity, suggesting that a carbon border adjustment mechanism may reduce carbon tax regressivity. Further, we find that estimates using older datasets, using explicit tax progressivity or income inequality measures, and accounting for household behaviour are associated with a lower likelihood of finding regressive estimates, while the inclusion of general equilibrium effects increases this likelihood.