We study the sources of high end-of-life spending for cancer patients. Even among patients with similar initial prognoses, spending in the year postdiagnosis is over twice as high for those who die within the year than those who survive. Elevated spending on decedents is predominantly driven by higher inpatient spending, particularly low-intensity admissions. However, most such admissions do not result in death, making it difficult to target spending reductions. Furthermore, end-of-life spending is substantially more elevated for younger patients, compared to older patients with similar prognoses. Results highlight sources of high end-of-life spending without revealing any natural “remedies.”

Prior research shows that individuals exhibit a propensity to surrogate performance measures for their underlying strategy, resulting in suboptimal strategic decisions. We investigate whether the incorporation of flexibility in contemporary performance measurement systems (PMSs) reduces surrogation propensity in the context of product innovation and whether this effect varies depending on environmental dynamism. We conduct a 2 × 2 experiment and find that PMS flexibility significantly lowers managers' surrogation propensity when the business environment is more dynamic and when the investment decisions have opportunity costs. Our study contributes to the literature by identifying a viable way to reduce managers' surrogation propensity.

We conduct an experiment in the securities-based crowdfunding setting to investigate whether some investors avoid accounting information for psychological reasons, even when they understand the information is useful in their decision-making. Results suggest investors who experience relatively more psychological discomfort when working with quantitative information are relatively less likely to acquire the financial statements of a potential crowdfunding investment. Importantly, this effect is incremental to any effect of investors' quantitative ability (i.e., their numeracy) and attenuates with an intervention designed to help investors overcome their psychological discomfort. Altogether, the results extend our understanding of the theory of information avoidance, provide a behavioral explanation for investors' documented underuse of accounting information, and can inform regulators as they revise crowdfunding regulations.

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.

After a natural experiment is first used, other researchers often reuse the setting, examining different outcome variables. We use simulations based on real data to illustrate the multiple hypothesis testing problem that arises when researchers reuse natural experiments. We then provide guidance for future inference based on popular empirical settings including difference‐in‐differences, instrumental variables, and regression discontinuity designs. When we apply our guidance to two extensively studied natural experiments, business combination laws and the Regulation SHO pilot, we find that many results that were statistically significant using single hypothesis testing do not survive corrections for multiple hypothesis testing.

Improving academic outcomes for economically disadvantaged students has proven challenging, particularly for children at older ages. We present two large-scale randomized controlled trials of a high-dosage tutoring program delivered to secondary school students in Chicago. One innovation is to use paraprofessional tutors to hold down cost, thereby increasing scalability. Participating in math tutoring increases math test scores by 0.18 to 0.40 standard deviations, and increases math and nonmath course grades. These effects persist into future years. The data are consistent with increased personalization of instruction as a mechanism. The benefit-cost ratio is comparable to many successful early childhood programs.