Knowledge that Transforms

We find that mutual funds whose managers are socially connected with firm auditors hold more shares of these firms and generate superior portfolio returns. Cross‐sectional results reveal that the relation between social connections and mutual fund stockholdings is more pronounced: when the social connections are stronger, when the auditor is in a better position or has stronger incentives to acquire private information, when the fund manager exercises more power, for small audit firms, for auditors in areas with poor investor protection, and for public firms with greater business opacity or private information. Other results are consistent with fund managers electing to schedule their corporate site visits to coincide with the fieldwork of their connected auditors, as would be expected if fund managers time their visits to meet with these auditors to facilitate information transfer. Additionally, we observe associations between fund trading prior to earnings surprises and audit opinions, and the presence of social connections between fund managers and firm auditors. Finally, we show that mutual funds and firms in which they invest tend to appoint connected auditors and pay them higher fees. Collectively, we document empirical patterns that would arise if socially connected auditors and mutual fund managers share information.

Audit firms are investing billions of dollars to develop artificial intelligence (AI) systems that will help auditors execute challenging tasks (e.g., evaluating complex estimates). Although firms assume AI will enhance audit quality, a growing body of research documents that individuals often exhibit “algorithm aversion”—the tendency to discount computer‐based advice more heavily than human advice, although the advice is identical otherwise. Therefore, we conduct an experiment to examine how algorithm aversion manifests in auditor judgments. Consistent with theory, we find that auditors receiving contradictory evidence from their firm's AI system (instead of a human specialist) propose smaller adjustments to management's complex estimates, particularly when management develops their estimates using relatively objective (vs. subjective) inputs. Our findings suggest auditor susceptibility to algorithm aversion could prove costly for the profession and financial statements users.

Using a novel empirical approach and newly available administrative data on U.S. tax filings, we estimate the corporate elasticity of taxable income, decompose the elasticity into economic responses versus other tax‐motivated “accounting” transactions, and determine how responsiveness varies depending on accounting method, firm size, and interest rate. In response to a 10% increase in the expected marginal tax rate, private U.S. firms decrease taxable income by 9.1%, which indicates a discernibly more elastic response than prevailing estimates. This response reflects a decrease in taxable income of 3.0% arising from real economic responses to a firm's scale of operations and 6.1% arising from accounting transactions via (for example) revenue and expense timing. Responsiveness to the corporate tax rate is more elastic if a firm uses cash (9.9%) rather than accrual accounting (7.4%), if the firm is small (9.9%) rather than large (8.6%), and if the firm discounts future cash flows at a lower rate.

I study whether banks’ loan loss provisioning contributes to economic downturns, by examining the U.S. housing market. Specifically, I examine the aggregate effects of banks’ delayed loan loss recognition (DLR) on house prices during the Great Recession and the channels through which these potential effects arose. I construct ZIP‐code‐level exposure to banks’ DLR before the crisis and compare high‐ and low‐exposure ZIP codes during the crisis to examine the aggregate effects of banks’ DLR on the housing market. I find that high‐exposure ZIP codes experienced larger decreases in mortgage supply, larger increases in distressed sales, and larger decreases in house prices during the crisis. In addition, I conduct individual bank‐level analyses and find that high‐DLR banks reduced their mortgage supply more than low‐DLR banks, and mortgages issued by high‐DLR banks were more likely to become distressed during the crisis. Taken together, these findings suggest that banks’ DLR was associated with nontrivial effects on the housing market during the Great Recession, and the effects of DLR on house prices were likely driven by both the credit‐crunch and distressed‐sales channels.

We examine whether voluntary disclosure is associated with incentives for firms to collude. Public disclosure can facilitate collusion by aiding with coordination and monitoring for defections. Using common ownership (investors holding stock in competing firms) to identify reduced incentives to compete, we find a positive association between public disclosure and these incentives. We also find that common ownership is positively associated with measures of disclosure that are likely to facilitate tacit collusion and that this association is stronger in industries where collusion is easier. Our study expands the literature on disclosure and competition among firms by showing that public disclosure is positively associated with incentives for tacit collusion. This finding is consistent with managers facilitating anticompetitive outcomes using voluntary disclosure.

This paper shows that robust inference under weak identification is important to the evaluation of many influential macro asset pricing models, including (time‐varying) rare‐disaster risk models and long‐run risk models. Building on recent developments in the conditional inference literature, we provide a novel conditional specification test by simulating the critical value conditional on a sufficient statistic. This sufficient statistic can be intuitively interpreted as a measure capturing the macroeconomic information decoupled from the underlying content of asset pricing theories. Macro‐finance decoupling is an effective way to improve the power of the specification test when asset pricing theories are difficult to refute because of a severe imbalance in the information content about the key model parameters between macroeconomic moment restrictions and asset pricing cross‐equation restrictions. We apply the proposed conditional specification test to the evaluation of a time‐varying rare‐disaster risk model and the construction of robust model uncertainty sets.

We theoretically analyze the problem of testing for p ‐hacking based on distributions of p ‐values across multiple studies. We provide general results for when such distributions have testable restrictions (are non‐increasing) under the null of no p ‐hacking. We find novel additional testable restrictions for p ‐values based on t ‐tests. Specifically, the shape of the power functions results in both complete monotonicity as well as bounds on the distribution of p ‐values. These testable restrictions result in more powerful tests for the null hypothesis of no p ‐hacking. When there is also publication bias, our tests are joint tests for p ‐hacking and publication bias. A reanalysis of two prominent data sets shows the usefulness of our new tests.

We develop a unique survey that focuses on the job search behavior of individuals regardless of their labor force status and field it annually starting in 2013. We use our survey to study the relationship between search effort and outcomes for the employed and non‐employed. Three important facts stand out: (1) on‐the‐job search is pervasive, and is more intense at the lower rungs of the job ladder; (2) the employed are at least three times more effective than the unemployed in job search; and (3) the employed receive better job offers than the unemployed. We set up a general equilibrium model of on‐the‐job search with endogenous search effort, calibrate it to fit our new facts, and find that the search effort of the employed is highly elastic. We show that search effort substantially amplifies labor market responses to productivity shocks over the business cycle.

This paper investigates whether the range of an attribute's outcomes in the choice set alters its relative importance. I derive distinguishing predictions of two prominent theories of range‐dependent attribute weighting: the focusing model of Kőszegi and Szeidl (2013) and the relative thinking model of Bushong, Rabin, and Schwartzstein (2021). I test these predictions in a laboratory experiment in which I vary the prices of high‐ and low‐quality variants of multiple products. The data provide clear evidence of choice‐set dependence consistent with relative thinking: price increases that expand the range of prices in the choice set lead to more purchases. Structural estimates imply economically meaningful effect sizes: the average participant was willing to pay around 17% more when a seemingly irrelevant option is added to their choice set.

The benefits of new technologies accrue not only to high‐skilled labor but also to owners of capital in the form of higher capital incomes. This increases inequality. To make this argument, we develop a tractable theory that links technology to the distribution of income and wealth—and not just that of wages—and use it to study the distributional effects of automation. We isolate a new theoretical mechanism: automation increases inequality by raising returns to wealth. The flip side of such return movements is that automation can lead to stagnant wages and, therefore, stagnant incomes at the bottom of the distribution. We use a multiasset model extension to confront differing empirical trends in returns to productive and safe assets and show that the relevant return measures have increased over time. Automation can account for part of the observed trends in income and wealth inequality.