Score contribution per author:
α: calibrated so average coauthorship-adjusted count equals average raw count
Understanding carbon tax incidence is critical. However, standard approaches for calculating the incidence of subnational policies are prone to inaccuracy due to coarse aggregation. We evaluate an alternative approach: a spatial microsimulation (SMS) method that generates granular household-level incidence estimates. SMS provides unique and more nuanced insights into the distributional consequences of carbon taxes, including across geographies. We demonstrate this method for a recent carbon tax initiative in Washington State and counterfactual variations on its revenue recycling provisions. Comparing across counterfactuals, we pinpoint how different targeting provisions in revenue recycling designs will have disparate consequences for the progressivity/regressivity of the policy package and for the geographic distribution of incidence. We furthermore analyze and discuss potential implications for political economy analysis of carbon taxes. Methodologically, we examine the reliability and robustness of SMS estimates, and we show the superiority of SMS to approaches that aggregate household characteristics over geographic areas.