Achieving a fair distribution of resources is one of the key goals of fiscal policy. To do this, governments often transfer tax resources from rich to marginalized areas. We study whether lower transfers dampen the incentives of local authorities to curb tax evasion in the context of mining in Colombia. To overcome the challenge of measuring evasion, we use machine learning on satellite images. Using differencein- differences strategies, we find that a reduction in the share of revenue transferred back to mining municipalities led to an increase in illegal mining. This result illustrates the difficulties of redistributing tax revenues.
