Policymakers were surprised to find increases in sales tax revenues in 2020 due to expectations that they would drop 8–20%. We investigate this puzzle and provide novel insights into consumption taxes based on this experience. Using a case study from the State of Utah, we document that shifts in the structure of consumption played a significant role in the robustness of sales tax revenue. Two factors stand out in our results. The first factor is the structure of the tax base for sales taxes in the USA. This tax base covers only a subset of personal consumption, excluding, for example, many services. During the pandemic, when services were restricted or shut down, this caused a shift in spending toward goods that are more likely to be in the sales tax base. The second factor is the boom in e-commerce during the pandemic, which boosted sales tax collections. This was catalyzed by recent legal changes that made the collection of sales taxes in e-commerce easier. Interestingly, this e-commerce boost also shifted the point of sale and related sales tax revenues away from urban areas toward suburban areas. Our case study of the pandemic's effect on sales taxes in the USA generally, and Utah's experience specifically, provides lessons for consumption taxes, such as the VAT more broadly, and lessons on the role of consumption taxes for tax revenue volatility.

We provide a method to track the active prevalence of COVID-19 in real time, correcting for time-varying sample selection in symptom-based testing data and incomplete tracking of recovered cases and fatalities. Our method only requires publicly available data on positive testing rates in combination with one parameter, which we estimate based on a representative randomized sample of nearly 10,000 individuals tested in Utah in May and June 2020. We validate our method using external studies in Indiana in April 2020 and two counties in Utah in March 2021. In all three locations and times, our estimates of latent prevalence are within the 95 percent confidence intervals of prevalence estimates from randomized testing. Applying our method to all 50 states, we show that true prevalence is 2--3 times higher than publicly reported.

The Accounting literature has recently grown its adoption of textual data as a source of accounting information. Recent studies have employed various methods to do large scale natural language processing to compare textual documents across firms and across time. This paper studies the extant technical methods adopted in the literature, and compares them to new advances in computational linguistics to demonstrate the strengths and weaknesses of the various employed techniques. We demonstrate that the commonly used approaches limit disclosure comparison to literal matches, effectively requiring exact word usage similarity when comparing documents. Our approach allows us to capture the context, or or underlying meaning, of disclosures while being agnostic about which specific words are chosen. We demonstrate the flexibility of our method in identifying ex-ante predicted network effects in disclosures, such as industry clustering and compensation consultant similarity.

This project's aim was to generate an unbiased estimate of the incidence of SARS-CoV-2 infection in four urban counties in Utah. A multi-stage sampling design was employed to randomly select communityrepresentative participants 12 years and over. Between May 4 and June 30, 2020, surveys were completed and sera drawn from 8,108 individuals belonging to 5,125 households. A qualitative chemiluminescent microparticle immunoassay was used to detect the presence of IgG antibody to SARS-CoV-2. The overall prevalence of IgG antibody to SARS-CoV-2 was estimated at 0.8%. The estimated seroprevalence-to-case count ratio was 2.4, corresponding to a detection fraction of 42%. Only 0.2% of individuals who had a nasopharyngeal swab collected were reverse transcription polymerase chain reaction (RTPCR) positive. The prevalence of antibodies to SARS-CoV-2 in Utah urban areas between May and June was low and the prevalence of positive RTPCR even lower. The detection fraction for COVID-19 in Utah was comparatively high.

Personal data markets have become ubiquitous. At the same time, the non-rivalry of data suggests that the social returns to personal data sharing will often exceed its private returns. Using a unique sequence of RCTs for randomized COVID-19 testing among tens of thousands of households in Utah, we analyze different tools to stimulate personal data sharing. We contrast the effectiveness of incentives for data sharing with mechanisms suggested by behavioral economics, including moral engagement, image motivation, and identity. Our results suggest that incentives by themselves can easily backfire and are highly complementary with framing effects. Furthermore, image motivation and identity are an order of magnitude more effective in influencing data sharing than monetary incentives.

We highlight the importance of signaling effects in determining whether public policy should be implemented at a decentralized or centralized level. For example, although a public policy may have the same direct effect if enacted at a state or county level, people may perceive these policies differently, leading to different indirect effects. We explore this mechanism using the patchwork of mask mandate orders in the U.S. from April to September 2020. State-wide mask mandates stimulate economic activity while also reducing COVID-19 case growth. Surprisingly, county-level mask mandates generally have the opposite effect, depressing economic activity. We argue that different unintended signaling effects can explain these differences in policy effects: households infer from county mask mandates that infection risks have increased in their local area and, therefore, socially distance more and spend less. In contrast, state mask mandates do not lead to similar local inferences, and thus overall, they stimulate the economy.

We combine structural estimation with ideas from Machine Learning to estimate a model with information-based voluntary social distancing and state lockdowns to analyze the factors driving the effect of social distancing in mitigating COVID-19. The model allows us to estimate how contagious social interactions are by state and enables us to control for several unobservable, time-varying confounders such as asymptomatic transmission, sample selection in testing and quarantining, and time-varying fatality rates. We find that information-based voluntary social distancing has saved three times as many lives as lockdowns. Second, information policy effects are asymmetric: 'least informed' responses would have implied 240,000 more fatalities by June 2020 while 'most informed' responses would have saved 25,000 more lives. Third, our estimates suggest that contagion externalities from social interactions are large enough that a lockdown response could have been 25% less costly for the median state and still saved an equivalent number of lives.

We provide the first examination of the determinants of firms' decision to use a list of keywords in SEC filings to identify forward-looking statements and obtain 'safe harbor' protection under the Private Securities Litigation Reform Act. We show that proxies for ex ante litigation risk, disclosure supply, economic uncertainty, and disclosure herding are strongly associated with the decision to include the keyword list. In addition, when we examine the determinants of the number of keywords, we find that both structural variables and proxies for transient forces are statistically significant, with the latter being consistent with lower disclosure costs. Finally, using exploratory factor analysis, we identify five disclosure attributes that capture the most frequent keywords that managers choose. We find that managers use specific keywords that evolve over time, potentially to tailor the language of their forward-looking statements to reflect the economic circumstances they face. Together this evidence provides an important first look at the determinants of firms' decisions regarding a central feature of 'safe harbor' protection.

This paper shows that firms adjust CEO compensation policies when creditors' interests are more salient. This effect helps explain controversial compensation practices such as weak performance incentives and short pay duration. Our findings also show that to mitigate the agency cost of debt, compensation contracts can reflect not only the firm's capital structure but the debt contract itself. For example, firms tend to contract on accounting-based goals when creditors do as well. Our analysis relies on a regression discontinuity design around loan covenant violations. We also confirm our conclusions studying a broad sample of financially constrained firms seeking debt financing.

I find that managers time their identification of new risk factors and removal of previously identified ones to align with the expected occurrence of future adverse outcomes. By using individual risk factors as the unit of disclosure, I am able to provide novel evidence that managers remove stale disclosures on a timely basis. My results are inconsistent with concerns of uninformative boilerplate or purely 'copy and paste' disclosure. To shed light on what shapes the disclosure equilibrium, I study the managerial response to demand 'shocks' from public and private enforcement actions. The results show that firms respond to investor demand in a manner consistent with the litigation shield hypothesis, and that this effect persists for multiple years. Consistent with the regulatory cost-benefit function, public enforcement does not result in a net increase in disclosed risk factors, but does evoke more definitive disclosures through more specific language and an increased use of numbers.

Change of management restrictions (CMRs) in loan contracts give lenders explicit ex-ante control rights over managerial retention and selection. This paper shows that lenders use CMRs to mitigate risks arising from CEO turnover, especially those related to the loss of human capital and replacement uncertainty, thereby providing evidence that human capital risk affects debt contracting. With a CMR in place, the likelihood of CEO turnover decreases by more than half, and future firm performance improves when retention frictions are important, suggesting that lenders can influence managerial turnover, even outside of default states, and help the borrower to retain talent.