ABSTRACT We investigate the relation between corporate tax payments and corporate social responsibility. Because existing theory and empirical studies find inconsistent evidence on the relation between these constructs, we investigate whether the two activities act as complements or substitutes. We estimate the relation between measures of corporate social responsibility and (1) the amount of corporate taxes paid, and (2) the amount invested in tax lobbying activities using both ordinary least squares and a system of simultaneous equations. We find consistent evidence that corporate social responsibility is negatively related to five-year cash effective tax rates and positively related to tax lobbying expenditures. Our evidence suggests that, on average, corporate social responsibility and tax payments act as substitutes. Data Availability: Data are available from sources identified in the paper.

ABSTRACT 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. JEL Classifications: G17.