Tax aggressiveness presents nontax risks to firms’ cash flow. Evaluating these risks requires information beyond the accounting function’s expertise, resulting in high processing costs to acquire and integrate risk information relevant to tax strategies. Managers can rationally adapt by making assumptions about risk information, potentially resulting in decision biases when evaluating the risk-reward tradeoff of tax aggressiveness. Using a novel regulatory setting in the U.S. insurance industry, I examine whether the adoption of mandated enterprise risk assessments updates managers’ prior beliefs about the nontax risks of tax aggressiveness. I find that as regulation requires managers to accept processing costs to acquire and integrate risk information, managers learn about previously underestimated nontax risks and significantly reduce tax aggressiveness. Results suggest that absent firm-wide internal risk information, managers can use aggressive tax positions without fully considering nontax risks. Data Availability: Data used in this study are available from public sources identified in the paper.

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.

We examine the role of private equity in alleviating earnings myopia induced by public markets. We first construct a measure of earnings myopia and show that this measure varies as predicted with determinants and effects of myopia. Then we show that public firms exhibiting earnings myopia realize an increased likelihood of takeover by private equity buyers. Cross-sectional analyses indicate that this relation is strongest when costs of earnings myopia are likely higher. Following private equity takeovers, firms exhibiting greater measures of earnings myopia realize improvements to R&D investment and productivity. The results add to the understanding of the role of private equity in identifying and alleviating earnings myopia within U.S. capital markets. This is important given the increasing size of private equity assets under management. Takeover premiums paid for myopic firms suggest a cost of earnings myopia at approximately 6.9% of firm value.

We examine how progressive individual tax rates affect risk‐taking by pass‐through businesses (PTBs). PTBs generate over 60% of US business income and make up roughly 95% of business tax returns, yet there is limited research on how progressive tax rates affect project selection. We study PTBs using the setting of thoroughbred racing and examine how progressive tax rates affect the decision to enter a risky stakes race or a less risky allowance race. This setting provides a unique opportunity to observe the choice between two mutually exclusive projects that differ only in expected payoffs and risk. Using a difference‐in‐differences design surrounding the reduction in progressivity under the Tax Cuts and Jobs Act, we find that investment in stakes races increases in the United States relative to Canada. We find further evidence of a negative relation between progressive tax rates and risk‐taking using a plausibly exogenous shock in progressivity in California and exploiting cross‐sectional variation in the progressivity of state tax rates. Overall, our findings should be of interest to policy‐makers considering changes to progressive rates. Results indicate that increases to progressive tax rates may discourage risk‐taking by the small businesses that drive economic growth.