Research

Staying home and avoiding unnecessary contact is an important part of the effort to contain COVID-19 and limit deaths. Every state in the United States enacted policies to encourage distancing and some mandated staying home. Understanding how these policies interact with individuals' voluntary responses to the COVID-19 epidemic is a critical initial step in understanding the role of these nonpharmaceutical interventions in transmission dynamics and assessing policy impacts. We use variation in policy responses along with smart device data that measures the amount of time Americans stayed home to disentangle the extent that observed shifts in staying home behavior are induced by policy. We find evidence that stay-at-home orders and voluntary response to locally reported COVID-19 cases and deaths led to behavioral change. For the median county, which implemented a stay-at-home order with about two cases, we find that the response to stay-at-home orders increased time at home as if the county had experienced 29 additional local cases. However, the relative effect of stay-at-home orders was much greater in select counties. On the one hand, the mandate can be viewed as displacing a voluntary response to this rise in cases. On the other hand, policy accelerated the response, which likely helped reduce spread in the early phase of the pandemic. It is important to be able to attribute the relative role of self-interested behavior or policy mandates to understand the limits and opportunities for relying on voluntary behavior as opposed to imposing stay-at-home orders.

This is the sixth in a series of papers prepared by a collection of economists and policy experts in the United States, the UK, and the European Union who have studied, and are committed to the improvement of, competition in digital markets. Previous papers addressed consumer protection in online markets, regulating the market for general search services, the concepts of “fairness” and “contestability” as used in the Digital Markets Act, the use of “equitable interoperability” as a “super tool” to restore and encourage competition in online markets, and coherence between US and European approaches to digital regulation.

Democracy is widely believed to contribute to economic growth and public health in the 20th and earlier centuries. We find that this conventional wisdom is reversed in this century, i.e., democracy has persistent negative impacts on GDP growth during 2001-2020. This finding emerges from five different instrumental variable strategies. Our analysis suggests that democracies cause slower growth through less investment and trade. For 2020, democracy is also found to cause more deaths from Covid-19.

We use geospatial data to examine the unprecedented national program currently underway in the United States to distribute and administer vaccines against COVID-19. We quantify the impact of the proposed federal partnership with the company Dollar General to serve as vaccination sites and compare vaccine access with Dollar General to the current Federal Retail Pharmacy Partnership Program. Although dollar stores have been viewed with skepticism and controversy in the policy sector, we show that, relative to the locations of the current federal program, Dollar General stores are disproportionately likely to be located in Census tracts with high social vulnerability; using these stores as vaccination sites would greatly decrease the distance to vaccines for both low-income and minority households. We consider a hypothetical alternative partnership with Dollar Tree and show that adding these stores to the vaccination program would be similarly valuable, but impact different geographic areas than the Dollar General partnership. Adding Dollar General to the current pharmacy partners greatly surpasses the goal set by the Biden administration of having 90% of the population within 5 miles of a vaccine site. We discuss the potential benefits of leveraging these partnerships for other vaccinations, including against influenza.

This paper documents, using a newly-constructed data set, the evolution of the characteristics of employer-sponsored DC schemes. The features we focus on are their match schedules, vesting schedules, and the extent of ‘auto-features’ (i.e. presence of auto-enrollment, the level of any default contribution, and presence and details of auto-escalation). The data we construct is formed by hand-coding the details in narrative plan descriptions attached to plan fillings. Our data covers approximately 5,000 plans, covering up to 37 million participants annually, for the period 2003-2017. We document that matching schedules, when they are offered, have become more generous over time. However, the proportion of firms offering a match fell sharply during the Great Recession and the proportion offering one did not recover to its pre-financial crisis level for almost a decade. Vesting schedules for DC plans have remained essentially unchanged since 2003, while the proportion of plans with auto-enrollment has increased dramatically over the same period. We find that the vast majority of plans that offer auto-enrollment have a default rate that is substantially lower than the level that would fully exploit the match offered by the employers.

The advent of mobile devices and digital media platforms in the past decade represents the biggest shock to cognition in human history. Robust medical evidence is emerging that digital media platforms are addictive and, when used in excess, harmful to users’ mental health. Other types of addictive products, like tobacco and prescription drugs, are heavily regulated to protect consumers. Currently, there is no regulatory structure protecting digital media users from these harms. Antitrust enforcement and regulation that lowers entry barriers could help consumers of social media by increasing competition. Economic theory tells us that more choice in digital media will increase the likelihood that some firms will vie to offer higher-quality and safer platforms. For this reason, evaluating harm to innovation (especially safety innovation) and product variety may be particularly important in social media merger and conduct cases. Another critical element to antitrust enforcement in this space is a correct accounting of social media’s addictive qualities. Standard antitrust analysis seeks to prohibit conduct that harms consumer welfare. Economists have taught the antitrust bar that the output of a product or service is a reliable proxy for consumer welfare. However, output and welfare do not have this relationship when a product is addictive. Indeed, in social media markets, increased output is often harmful. We argue that antitrust analysis must reject the output proxy and return to a focus on consumer welfare itself in cases involving addictive social media platforms. In particular, courts should reject defenses that rely only on gross output measures without evidence that any alleged increases in output actually benefit consumers.

We study data linkages among heterogeneous firms and examine how they shape the outcome of privacy regulation. A single consumer interacts sequentially with two firms: one firm collects data on consumer behavior; the other firm leverages the data to set a quality level and a price. A data linkage benefits the consumer in equilibrium when the recipient firm is sufficiently similar to the collecting firm. We then endogenize linkage formation under various forms of privacy regulation. We show that voluntary consent requirements are beneficial to consumers in equilibrium but that bans on discriminatory price and quality offers are harmful.

With dispersed information, how much can agents learn from past endogenous aggregates such as prices or output? In a rational-expectations equilibrium, if general equilibrium effects are strong enough, aggregates no longer perfectly reveal underlying fundamentals. In this con- founding regime, the effects of informational frictions are persistent over time, and the aggregate outcome displays an initial underreaction followed by a delayed overreaction relative to its perfect- information counterpart. In a standard New Keynesian model, we show that the confounding regime is more likely to arise under a dovish monetary policy rule.

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.