Research

We obtain a necessary and sufficient condition under which random-coefficient discrete choice models such as the mixed logit models are rich enough to approximate any nonparametric random utility models across choice sets. The condition turns out to be very simple and tractable. When the condition is not satisfied and, hence, there exists a random utility model that cannot be approximated by any random-coefficient discrete choice model, we provide algorithms to measure the approximation errors. After applying our theoretical results and the algorithms to real data, we find that the approximation errors can be large in practice.

This paper studies optimal second-best corrective regulation, when some agents/activities cannot be perfectly regulated. We show that policy elasticities and Pigouvian wedges are sufficient statistics to characterize the marginal welfare impact of regulatory policies in a large class of environments. We show that a subset of policy elasticities, leakage elasticities, determine optimal second-best policy, and characterize the marginal value of relaxing regulatory constraints. We apply our results to scenarios with unregulated agents/activities, uniform regulation across agents/activities, and costly regulation. We illustrate our results in applications to financial regulation with environmental externalities, shadow banking, behavioral distortions, asset substitution, and fire sales.

Incomplete vaccine uptake and limited vaccine availability for some segments of the population could lead policymakers to consider re-imposing restrictions to help reduce fatalities. Early in the pandemic, full business shutdowns were commonplace. Given this response, much of the literature on policy effectiveness has focused on full closures and their impact. But were complete closures necessary? Using a hand-collected database of partial business closures for all U.S. counties from March through December 2020, we examine the impact of capacity restrictions on COVID-19 fatality growth. For the restaurant and bar sector, we find that several combinations of partial capacity restrictions are as effective as full shutdowns. For example, point estimates indicate that, for the average county, limiting restaurants and bars to 25% of capacity reduces the fatality growth rate six weeks ahead by approximately 43%, while completely closing them reduces fatality growth by about 16%. The evidence is more mixed for the other sectors that we study. We find that full gym closures reduce the COVID-19 fatality growth rate, while partial closures may be counterproductive relative to leaving capacity unrestricted. Retail closures are ineffective, but 50% capacity limits reduce fatality growth. We find that restricting salons, other personal services and movie theaters is either ineffective or counterproductive.

We analyze whether receiving care from higher-priced hospitals leads to lower mortality. We overcome selection issues by using an instrumental variable approach which exploits that ambulance companies are quasi-randomly assigned to transport patients and have strong preferences for certain hospitals. Being admitted to a hospital with two standard deviations higher prices raises spending by 52% and lowers mortality by 1 percentage point (35%). However, the relationship between higher prices and lower mortality is only present at hospitals in less concentrated markets. Receiving care from an expensive hospital in a concentrated market increases spending but has no detectable effect on mortality.

What are the effects of school closures during the Covid-19 pandemic on children’s education? Online education is an imperfect substitute for in-person learning, particularly for children from low-income families. Peer effects also change: schools allow children from different socio-economic backgrounds to mix together, and this effect is lost when schools are closed. Another factor is the response of parents, some of whom compensate for the changed environment through their own efforts, while others are unable to do so. We examine the interaction of these factors with the aid of a structural model of skill formation. We find that school closures have a large, persistent, and unequal effect on human capital accumulation. High school students from low-income neighborhoods suffer a learning loss of 0.4 standard deviations after a one-year school closure, whereas children from high-income neighborhoods initially remain unscathed. The channels operating through schools, peers, and parents all contribute to growing educational inequality during the pandemic.

The COVID-19 pandemic led to a worldwide lockdown and school closures, which have placed a substantial mental health burden on children and college students. Through a systematic search of the literature on PubMed and Collabovid of studies published January 2020–July 2021, our findings of five studies on children and 16 studies on college students found that both groups reported feeling more anxious, depressed, fatigued, and distressed than prior to the pandemic. Several risk factors such as living in rural areas, low family socioeconomic status, and being a family member or friend to a healthcare worker were strongly associated with worse mental health outcomes. As schools and researchers discuss future strategies on how to combine on-site teaching with online courses, our results indicate the importance of considering social contacts in students’ mental health to support students at higher risk of social isolation during the COVID-19 pandemic.

We characterize the revenue-maximizing information structure in the second price auction. The seller faces a classic economic trade-o§: providing more information improves the e¢ - ciency of the allocation but also creates higher information rents for bidders. The information disclosure policy that maximizes the revenue of the seller is to fully reveal low values (where competition will be high) but to pool high values (where competition will be low). The size of the pool is determined by a critical quantile that is independent of the distribution of values and only dependent on the number of bidders. We discuss how this policy provides a rationale for conáation in digital advertising.

The relationship between the use of nonpharmaceutical interventions and COVID-19 vaccination among U.S. child care providers remains unknown. If unvaccinated child care providers are also less likely to employ nonpharmaceutical interventions, then a vaccine mandate across child care programs may have larger health and safety benefits. To assess and quantify the relationship between the use of nonpharmaceutical interventions and COVID-19 vaccination among U.S. child care providers, we conducted a prospective cohort study of child care providers (N = 20,013) from all 50 states, the District of Columbia, and Puerto Rico. Child care providers were asked to complete a self-administered email survey in May-June 2020 assessing the use of nonpharmaceutical interventions (predictors) and a follow-up survey in May-June 2021 assessing COVID-19 vaccination (outcome). Nonpharmaceutical interventions were dichotomized as personal mitigation measures (e.g., masking, social distancing, handwashing) and classroom mitigation measures (e.g., temperature checks of staff/children, symptom screening for staff/children, cohorting).

For each unendorsed personal mitigation measure during 2020, the likelihood of vaccination in 2021 decreased by 7% (Risk Ratio = 0.93 [95% 0.93 – 0.95]). No significant association was found between classroom mitigation measures and child care provider vaccination (Risk Ratio = 1.01 [95% CI 1.00-1.01]).

Child care providers who used less personal mitigation measures were also less likely to get vaccinated for COVID-19 as an alternative form of protection. The combined nonadherence to multiple types of preventative health behaviors, that is, both nonpharmaceutical interventions and vaccination, among some child care providers may support a role for mandatory vaccination to achieve pandemic control.