Achieving a fair distribution of resources is one of the goals of fiscal policy. To this end, governments often transfer tax resources from richer to more marginalized areas. In the context of mining in Colombia, we study whether lower transfers to the locality where the taxed economic activity takes place dampen local authorities’ incentives to curb tax evasion. Using machine learning predictions on satellite imagery to identify mines allows us to overcome the challenge of measuring evasion. Employing difference-in-differences strategies, we find that reducing the share of revenue transferred back to mining municipalities leads to an increase in illegal mining. This result highlights the difficulties inherent in adequately redistributing tax revenues.

实现资源的公平分配是财政政策的目标之一。为此，政府经常将税收资源从较富裕的地区转移到较边缘化的地区。在哥伦比亚采矿业的背景下，我们研究了向征税经济活动发生地的较低转移是否会抑制地方当局遏制逃税的动机。在卫星图像上使用机器学习预测来识别地雷使我们能够克服测量规避的挑战。采用差异中的差异策略，我们发现减少转移回采矿市政当局的收入份额会导致非法采矿的增加。这一结果凸显了充分重新分配税收收入的固有困难。