The COVID-19 state sales tax windfall.

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.

The COVID-19 state sales tax windfall

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.

Incomplete program take-up during a crisis: evidence from the COVID-19 shock in one U.S. state.

In the U.S., means-tested cash, in-kind assistance, and social insurance are part of a patchwork safety net, often run with substantial involvement of state and local governments. Take-up-participation among eligible persons in this system is incomplete. A large literature points to both neo-classical and behavioral science explanations for low take-up. In this paper, we explore the response of the safety net to COVID-19 using newly-collected survey data from one U.S. state-Utah. The rich Utah data ask about income and demographics as well as use of three social safety net programs which collectively provided a large share of relief spending: the Unemployment Insurance program, a social insurance program providing workers who lose their jobs with payments; the Supplemental Nutrition Assistance Program, which provides benefit cards for purchasing unprepared food at retailers; and Economic Impact Payments, which provided relatively universal relief payments to individuals. The data do not suffice to determine eligibility for all of the programs, so we focus on participation per capita. These data also collect information on several measures of hardship and why individuals did not receive any of the 3 programs. We test for explanations that differentiate need, lack of information, transaction costs/administrative burden, stigma, and lack of eligibility. We use measures of hardship to assess targeting. We find that lack of knowledge as well as difficulty applying, and stigma in the UI program each play a role as reasons for not participating in the programs.

High variability in transmission of SARS-CoV-2 within households and implications for control.

BACKGROUND: Severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2) poses a high risk of transmission in close-contact indoor settings, which may include households. Prior studies have found a wide range of household secondary attack rates and may contain biases due to simplifying assumptions about transmission variability and test accuracy. METHODS: We compiled serological SARS-CoV-2 antibody test data and prior SARS-CoV-2 test reporting from members of 9,224 Utah households. We paired these data with a probabilistic model of household importation and transmission. We calculated a maximum likelihood estimate of the importation probability, mean and variability of household transmission probability, and sensitivity and specificity of test data. Given our household transmission estimates, we estimated the threshold of non-household transmission required for epidemic growth in the population. RESULTS: We estimated that individuals in our study households had a 0.41% (95% CI 0.32%- 0.51%) chance of acquiring SARS-CoV-2 infection outside their household. Our household secondary attack rate estimate was 36% (27%- 48%), substantially higher than the crude estimate of 16% unadjusted for imperfect serological test specificity and other factors. We found evidence for high variability in individual transmissibility, with higher probability of no transmissions or many transmissions compared to standard models. With household transmission at our estimates, the average number of non-household transmissions per case must be kept below 0.41 (0.33-0.52) to avoid continued growth of the pandemic in Utah. CONCLUSIONS: Our findings suggest that crude estimates of household secondary attack rate based on serology data without accounting for false positive tests may underestimate the true average transmissibility, even when test specificity is high. Our finding of potential high variability (overdispersion) in transmissibility of infected individuals is consistent with characterizing SARS-CoV-2 transmission being largely driven by superspreading from a minority of infected individuals. Mitigation efforts targeting large households and other locations where many people congregate indoors might curb continued spread of the virus.

Probability-Based Estimates of Severe Acute Respiratory Syndrome Coronavirus 2 Seroprevalence and Detection Fraction, Utah, USA.

We aimed to generate an unbiased estimate of the incidence of severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2) infection in 4 urban counties in Utah, USA. We used a multistage sampling design to randomly select community-representative participants >12 years of age. During May 4-June 30, 2020, we collected serum samples and survey responses from 8,108 persons belonging to 5,125 households. We used a qualitative chemiluminescent microparticle immunoassay to detect SARS-CoV-2 IgG in serum samples. We estimated the overall seroprevalence to be 0.8%. The estimated seroprevalence-to-case count ratio was 2.5, corresponding to a detection fraction of 40%. Only 0.2% of participants from whom we collected nasopharyngeal swab samples had SARS-CoV-2-positive reverse transcription PCR results. SARS-CoV-2 antibody prevalence during the study was low, and prevalence of PCR-positive cases was even lower. The comparatively high SARS-CoV-2 detection rate (40%) demonstrates the effectiveness of Utah's testing strategy and public health response.