Global tax enforcement policies have received increased attention since the financial crisis, with much stated focus on curbing perceived harmful tax practices of multinational corporations. Yet there is a dearth of evidence on possible differential effects of home‐country tax enforcement on multinationals. We take a step toward filling this void in the tax policy discussion by examining whether there is a differential relation between changes in home‐country enforcement and the tax avoidance of domestic versus multinational corporations. Using OECD data on 50 countries from 2005 to 2019, we find increases in home‐country enforcement are associated with lower levels of tax avoidance for domestic firms than for multinational corporations. Using a subset of firms from the Bureau van Dijk database, we find that multinationals avoid more tax in foreign countries when home‐country enforcement increases. Results are stronger for multinationals with a higher proportion of subsidiaries in low‐tax countries and when enforcement spending is low. These findings have implications for policy‐makers and highlight the importance of coordinated enforcement efforts across jurisdictions—such as the recently proposed global minimum tax—to successfully curb multinationals' worldwide tax avoidance.

We investigate (1) how well a machine learning algorithm can predict one-year ahead effective tax rates (ETRs) and (2) which items in the financial statements and notes are most useful for these predictions. We compare our machine-generated ETR predictions with those from ETRs implied by analysts’ earnings forecasts and find the algorithm’s predictions are less biased, more precise, and explain more of the variance in future ETRs. We then use Explainable AI (based on Shapley values) to measure the usefulness of each disclosure item in the algorithm’s predictions. We find that while some tax-related items are useful, others offer minimal value. Using the machine learning algorithm’s use of information as a benchmark, we then further use Shapley values to examine which information is underweighted or overweighted by analysts. Overall, our results help inform standard setters on the relevance of certain tax disclosures in achieving the objective of predicting future ETRs.