Compensation Design for Carbon Pricing with Horizontal Heterogeneity: Evidence from 88 Countries

We analyze the horizontal and vertical distributional impacts of climate policy by examining heterogeneity in households’ carbon intensity of consumption. We construct a novel dataset that includes information on the carbon intensity of 1.5 million individual households from 88 countries. First, we show that horizontal differences are generally larger than vertical differences. Then, we use supervised machine learning to analyze the non-linear contribution of household characteristics to the prediction of carbon intensity of consumption. Household income, proxied by total household expenditures, is usually an insufficient predictor for the additional costs of carbon pricing. Including household-level information beyond household income increases the accuracy of prediction. We identify six clusters of countries that differ in the distribution of climate policy costs and their determinants. Our results highlight that, depending on the context, some compensation policies may be more effective in reducing horizontal heterogeneity than others.