Research

Face masks are an important component in controlling COVID-19, and policy orders to wear masks are common. However, behavioral responses are seldom additive, and exchanging one protective behavior for another could undermine the COVID-19 policy response. We use SafeGraph smart device location data and variation in the date that US states and counties issued face mask mandates as a set of natural experiments to investigate risk compensation behavior. We compare time at home and the number of visits to public locations before and after face mask orders conditional on multiple statistical controls. We find that face mask orders lead to risk compensation behavior. Americans subject to the mask orders spend 11–24 fewer minutes at home on average and increase visits to some commercial locations—most notably restaurants, which are a high-risk location. It is unclear if this would lead to a net increase or decrease in transmission. However, it is clear that mask orders would be an important part of an economic recovery if people otherwise overestimate the risk of visiting public places.
 

The COVID-19 pandemic and resulting childcare closures have left many parents and guardians struggling to find care for their children while continuing to work, leading to adverse mental health and financial outcomes for families. Thus, keeping childcare programs open safely is of paramount importance. Although exposure to childcare early in the pandemic demonstrated no increased risk of contracting COVID-19, the highly contagious B.1.617.2 (Delta) variant has increased community prevalence, and COVID-19 outbreaks in childcare and among younger children are now well described. Furthermore, the attack rate for the B.1.1.7 (Alpha) variant, another highly contagious strain, is similar for both children and adults during childcare outbreaks.

Face masks reduce SARS-CoV-2 respiratory droplet transmission in the community and high-risk environments. In kindergarten through 12th grade schools, masks are part of successful risk mitigation bundles that facilitate a safe return to in-person education. Studies suggest that with strict masking policies social distancing can be safely reduced from 6 to 3 feet. However, child masking has not been studied in childcare, where children are typically younger than 5 years, social distancing is challenging, and adherence to masking is less than in older children. This gap in science is particularly problematic given current public debate regarding the benefits and risks of masking younger children not yet eligible for vaccination. We hypothesized that child masking, regardless of social distancing practices, is associated with reduced risk of a childcare program closing because of COVID-19 cases in either staff or children.

We study where privately insured individuals receive planned MRI scans. Despite significant out-of-pocket costs for this undifferentiated service, privately insured patients often  receive care in high-priced locations when lower priced options were available. The median patient in our data has 16 MRI providers within a 30-minute drive of her home. On average, patients bypass 6 lower-priced providers between their homes and their actual treatment locations. Referring physicians heavily influence where patients receive care. The share of the variance in the prices of patients’ MRI scans that referrer fixed effects (52 percent) explain is dramatically greater than the share explained by patient cost-sharing (< 1 percent), patient characteristics (< 1 percent), or patients’ home HRR fixed effects (2 percent). In order to access lower cost providers, patients must generally diverge from physicians’ established referral patterns.

Tracking human activity in real time and at fine spatial scale is particularly valuable during episodes such as the COVID-19 pandemic. In this paper, we discuss the suitability of smartphone data for quantifying movement and social contact. These data cover broad sections of the US population and exhibit pre-pandemic patterns similar to conventional survey data. We develop and make publicly available a location exposure index that summarizes county-to- county movements and a device exposure index that quantifies social contact within venues. We also investigate the reliability of smartphone movement data during the pandemic.

Data were obtained from US child care providers (N = 57 335) reporting whether they had ever tested positive or been hospitalized for COVID-19 (n = 427 cases) along with their degree of exposure to child care. Background transmission rates were controlled statistically, and other demographic, programmatic, and community variables were explored as potential confounders. Logistic regression analysis was used in both unmatched and propensity score–matched case-control analyses. No association was found between exposure to child care and COVID-19 in both unmatched (odds ratio [OR], 1.06; 95% confidence interval [CI], 0.82–1.38) and matched (OR, 0.94; 95% CI, 0.73–1.21) analyses. In matched analysis, being a home-based provider (as opposed to a center-based provider) was associated with COVID-19 (OR, 1.59; 95% CI, 1.14–2.23) but revealed no interaction with exposure.

Randomized controlled trials (RCTs) enroll hundreds of millions of subjects and involve many human lives. To improve subjects’ welfare, I propose a design of RCTs that I call Experiment-as-Market (EXAM). EXAM produces a welfare-maximizing allocation of treatment-assignment probabilities, is almost incentive-compatible for preference elicitation, and unbiasedly estimates any causal effect estimable with standard RCTs. I quantify these properties by applying EXAM to a water-cleaning experiment in Kenya. In this empirical setting, compared to standard RCTs, EXAM improves subjects’ predicted well-being while reaching similar treatment-effect estimates with similar precision.

Manufacturers perform the majority of US patenting and R&D. The decades-long decline of US manufacturing employment raises concerns that US innovation will falter. We investigate the relationship between between physical production and innovation by constructing a new dataset linking all US firms and their establishments to location geocodes and innovative activities. Pre- liminary results indicate that while firms with both manufacturing and innovation establishments exhibit higher patenting when these facilities are more spatially proximate, manufacturing firms’ overall contribution to US innovation declines steadily and substantially over time. Moreover, cohorts of firms permanently exiting manufacturing in the 1990s and 2000s continue to patent at their prior pace.

We evaluate the quantitative effects of trade policy on the location of firms across space and over time. We develop a multi-country, multi-sector dynamic general-equilibrium trade and spatial model with forward-looking decisions of workers on where to supply labor, forward-looking decisions of firms on where to locate produc-tion, endogenous capital structure accumulation, and trade in intermediate goods with sectoral linkages. We bring the model to data using trade, production, and data on firm demographics across sector and locations. We use the model to study if trade protectionism can revert the declining trend in the U.S. manufacturing em-ployment and firms; and its impact on the location of production across space and over time. We feed into the model the raise in import tariffs between the U.S. and its major trade partners in the year 2018. We find that these changes in trade policy can result in a persistent increase on manufacturing employment and firms. However, these effects do not revert the long run decline in manufacturing employment and firms. Importantly, the relocation of production comes at the cost of higher prices, lower welfare for households, and heterogeneous effects on firm entry across space.

Economists often point to the superiority of cash transfers over in-kind assistance as a means of redistribution because recipients can choose how to use these resources. However, among the trillions of dollars of annual U.S. transfers, redistribution is mostly in-kind. We conducted a survey experiment—using a choice between a cash transfer and a transfer that could be spent only on a bundle of “necessities”—to help explain why. We show that the general population overwhelmingly prefers in-kind redistribution, largely for paternalistic reasons. This preference was common to a majority of virtually all segments of the general population, though not to a sample of educational elites. A persuasion treatment on the value of choice, while impactful, did not change this overall preference for in-kind. For an equal-sized program, below-poverty respondents preferred receiving cash. But they appeared to prefer the larger in-kind transfer to the smaller cash transfer that the general population was willing to support. This suggests that an in-kind transfer may be preferable to both recipients and the general population.