We examine whether information processing constraints limit managers’ ability to effectively integrate tax planning and core business strategies (i.e., effective tax planning). We propose that artificial intelligence (AI) tools, such as machine learning, can mitigate these constraints by providing enhanced predictive information for key business decisions (e.g., customer demand, supply chain), thereby reducing processing costs. Using a recently developed firm-year measure of investment in AI-related human capital for a broad sample of U.S. nontechnology firms between 2010 and 2018, we find that AI investment is positively associated with tax effectiveness. This effect is concentrated among more complex firms and those where the tax function holds a higher status. Consistent with AI reducing information processing costs, we find that it improves tax effectiveness by enhancing internal information quality and internal capital management. We provide novel evidence that processing constraints hinder effective tax planning and show that AI can mitigate these constraints.

Using IRS tax return data for private C corporations, we examine the relation between GAAP audits and IRS enforcement outcomes. Among IRS-audited tax returns, we find that those with audited GAAP financial statements have fewer and smaller proposed deficiencies. These effects persist after controlling for tax preparers. Furthermore, we estimate that the explanatory power of audited GAAP financial statements is comparable to that of external debtholders. Audited GAAP financial statements are also associated with a higher likelihood of unagreed proposed deficiencies, more detailed tax return disclosures, and fewer IRS audit hours, all of which are consistent with more defensible tax positions. Tax returns with audited GAAP financial statements exhibit greater initial voluntary compliance and generate smaller proposed deficiencies per IRS audit hour. These results inform private firms as they weigh the costs and benefits of financial statement audits and the IRS as it allocates enforcement resources. Data Availability: The IRS provided confidential tax information to Michele S. Mullaney pursuant to an agreement under the Intergovernmental Personnel Act of 1970 through the Statistics of Income Division’s Joint Statistical Research Program.

Drawing on confidential Internal Revenue Service (IRS) data, we examine whether privately held corporations are more aggressive tax planners than their publicly held peers. Contrary to conventional wisdom, we find no consistent evidence that private firms are more aggressive tax planners. We then examine whether private firms’ tax planning differs from that of public firms more generally. We find that private firms engage in more conforming tax planning (planning that also reduces pretax accounting income). However, tests of nonconforming tax planning reveal that private firms generally engage in the same or less planning relative to their public peers. Overall, our findings cast doubt on the belief that private firms are generally more aggressive tax planners than are public firms, but confirm that they engage in more of some forms of general (i.e., conforming) planning. Data Availability: The IRS provided confidential tax information to Michele S. Mullaney pursuant to an Intragovernmental Personnel Act of 1970 (IPA) agreement through the Statistics of Income (SOI) Joint Statistical Research Program (JSRP).