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

Jules Linden, Cathal O'Donoghue, Denisa Sologon

TL;DR

This paper tackles how ex-ante microsimulation studies estimate the distributional impacts of carbon taxation, noting substantial cross-study variation arising from modelling choices. It combines a systematic literature review with a meta-analysis of 217 estimates across 71 countries, using a probit model to assess how conceptual and implementation choices influence the likelihood of finding regressive outcomes, with the key relationship: Pr(y_i=1|X_i) = Φ(X_iβ). The analysis finds that incorporating household behavioural responses reduces estimated regressivity, while coverage of imported indirect emissions (e.g., via a carbon border adjustment) can further lessen regressivity; older data and explicit inequality measures are associated with progressive results, whereas general-equilibrium effects tend to increase regressivity. The study highlights the lack of standard practices in microsimulation modelling for carbon taxes and argues for open data, standardised benchmarks, and collaborative platforms to improve comparability and policy relevance across contexts.

Abstract

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.

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

TL;DR

This paper tackles how ex-ante microsimulation studies estimate the distributional impacts of carbon taxation, noting substantial cross-study variation arising from modelling choices. It combines a systematic literature review with a meta-analysis of 217 estimates across 71 countries, using a probit model to assess how conceptual and implementation choices influence the likelihood of finding regressive outcomes, with the key relationship: Pr(y_i=1|X_i) = Φ(X_iβ). The analysis finds that incorporating household behavioural responses reduces estimated regressivity, while coverage of imported indirect emissions (e.g., via a carbon border adjustment) can further lessen regressivity; older data and explicit inequality measures are associated with progressive results, whereas general-equilibrium effects tend to increase regressivity. The study highlights the lack of standard practices in microsimulation modelling for carbon taxes and argues for open data, standardised benchmarks, and collaborative platforms to improve comparability and policy relevance across contexts.

Abstract

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.
Paper Structure (24 sections, 3 equations, 13 figures, 1 table)

This paper contains 24 sections, 3 equations, 13 figures, 1 table.

Table of Contents

  1. Introduction
  2. Methodology
  3. Microsimulation modelling of carbon taxation
  4. Theoretical overview and study focus
  5. Systematic Review
  6. Literature review - Conceptual modelling choices and their implementation
  7. Country coverage and the unit of analysis
  8. Implementation: Data Quality, Timeliness, and study scope
  9. Tax coverage and design
  10. Implementation: Energy Data Granularity and Input–Output Choices
  11. Behaviour
  12. Implementation: Price elasticity estimation and Micro–Macro Linkages
  13. Revenue recycling
  14. Implementation: Data Linkages for Revenue Recycling
  15. Welfare
  16. ...and 9 more sections

Figures (13)

  • Figure 1: Modelling steps and choices.
  • Figure 2: Comparison of carbon tax burdens by welfare concept and ranking variable for Hungary.
  • Figure 3: Modelling choices as a percentage of total studies.
  • Figure 4: Regressive and progressive impacts by developed and developing countries.
  • Figure 5: Percent of Studies Finding Regressive Effects, by Modelling choice.
  • ...and 8 more figures