Research

This is the fifth 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, and the use of “equitable interoperability” as a “super tool” to restore and encourage competition in online markets.

As large amounts of data become available and can be communicated more eas­ily and processed more effectively, information has come to play a central role for economic activity and welfare in our age. This essay overviews contributions to the industrial organization of information markets and nonmarkets, while attempting to maintain a balance between foundational frameworks and more recent developments. We start by reviewing mechanism-design approaches to modeling the trade of infor­mation. We then cover ratings, predictions, and recommender systems. We turn to forecasting contests, prediction markets, and other institutions designed for collect­ing and aggregating information from decentralized participants. Finally, we discuss science as a prototypical information nonmarket with participants who interact in a non-anonymous way to produce and disseminate information. We aim to familiarize the reader with the central notions and insights in this burgeoning literature and also point to some critical open questions that future research will have to address. 

Consider a market with identical firms offering a homogeneous good. For any given ex ante distribution of the price count (the number of firms from which a consumer obtains a quote), we derive a tight upper bound on the equilibrium distribution of sales prices. The bound holds across all models of firms’ common-prior higher-order beliefs about the price count, including the extreme cases of full information and no information. One implication of our results is that a small ex ante probability that the price count is equal to one can lead to a large increase in the expected price. The bound also applies in a large class of models where the price count distribution is endogenously determined.

This cross-sectional study uses regression discontinuity to compare racial and ethnic disparities before and after age 65 years, the age at which US adults are eligible for Medicare. There are a total of 2 434 320 respondents in the Behavioral Risk Factor Surveillance System and 44 587 state-age-year observations in the US Centers for Disease Control and Prevention Wide-Ranging Online Data for Epidemiologic Research Data (eg, the mortality rate for individuals age 63 years in New York in 2017) from January 2008 to
December 2018. The data were analyzed between February and May 2021. Proportions of respondents with health insurance, as well as self-reported health and mortality. To examine access, whether respondents had a usual source of care, encountered cost-related barriers to care, or received influenza vaccines was assessed.

Immediately after age 65 years, insurance coverage increased more for Black respondents (from 86.3% to 95.8% or 9.5 percentage points; 95% CI, 7.6-11.4) and Hispanic respondents (from 77.4% to 91.3% or 13.9 percentage points; 95% CI, 12.0-15.8) than White respondents (from 92.0% to 98.5% or 6.5 percentage points; 95% CI, 6.1-7.0). This was associated with a 53% reduction compared with the size of the disparity between White and Black individuals before age 65 years (5.7% to 2.7% or 3.0 percentage points; 95% CI, 0.9-5.1; P = .003) and a 51% reduction compared with the size of the disparity between White and Hispanic individuals before age 65 years (14.6% to 7.2% or 7.4 percentage points; 95% CI, 5.3-9.5; P < .001). Medicare eligibility was associated with narrowed disparities between White and Hispanic individuals in access to care, lowering disparities in access to a usual source of care from 10.5% to 7.5% (P = .05), cost-related barriers to care from 11.4% to 6.9% (P < .001), and influenza vaccination rates from 8.1% to 3.3% (P = .01). For disparities between White and Black individuals, access to a usual source of care before and after age 65 years was not significantly different: 1.2% to 0.0% (P = .24), cost-related barriers to care from 5.8% to 4.3% (P = .22), and influenza vaccinations from 11.0% to 10.3% (P = .60). The share of people in poor self-reported health decreased by 3.8 percentage points for Hispanic respondents, 2.6 percentage points for Black respondents, and 0.2 percentage points for White respondents. Mortality-related disparities at age 65 years were unchanged. Medicare’s association with reduced disparities largely persisted after the US Affordable Care Act took effect in 2014.

This paper is concerned with competition in digital platform markets where network effects are strong. As is widely acknowledged, these markets have an inherent tendency towards concentration, leaving consumers with little competition in the market. We explain how interoperability regulation can help stimulate competition in the market in a way that benefits consumers.

We analyze the use of the concepts of fairness and contestability in the Digital Markets Act (DMA) and propose formal definitions rooted in the economic analysis of digital markets as well as the goals of the proposed law. We discuss the implication of these concepts for innovation in digital markets.

We collect a time-series database of business and related restrictions for every county in the United States from March through December 2020. We find strong evidence consistent with the idea that employee mask policies, mask mandates for the general population, restaurant and bar closures, gym closures, and high-risk business closures reduce future fatality growth. Other business restrictions, such as second-round closures of low- to medium-risk businesses and personal care/spa services, did not generate consistent evidence of lowered fatality growth and may have been counterproductive.

Even as vaccination for severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2) expands in the United States, cases will linger among unvaccinated individuals for at least the next year, allowing the spread of the coronavirus to continue in communities across the country. Detecting these infections, particularly asymptomatic ones, is critical to stemming further transmission of the virus in the months ahead. This will require active surveillance efforts in which these undetected cases are proactively sought out rather than waiting for individuals to present to testing sites for diagnosis. However, finding these pockets of asymptomatic cases (i.e., hotspots) is akin to searching for needles in a haystack as choosing where and when to test within communities is hampered by a lack of epidemiological information to guide decision makers’ allocation of these resources. Making sequential decisions with partial information is a classic problem in decision science, the explore v. exploit dilemma. Using methods—bandit algorithms—similar to those used to search for other kinds of lost or hidden objects, from downed aircraft or underground oil deposits, we can address the explore v. exploit tradeoff facing active surveillance efforts and optimize the deployment of mobile testing resources to maximize the yield of new SARS-CoV-2 diagnoses. These bandit algorithms can be implemented easily as a guide to active case finding for SARS-CoV-2. A simple Thompson sampling algorithm and an extension of it to integrate spatial correlation in the data are now embedded in a fully functional prototype of a web app to allow policymakers to use either of these algorithms to target SARS-CoV-2 testing. In this instance, potential testing locations were identified by using mobility data from UberMedia to target high-frequency venues in Columbus, Ohio, as part of a planned feasibility study of the algorithms in the field. However, it is easily adaptable to other jurisdictions, requiring only a set of candidate test locations with point-to-point distances between all locations, whether or not mobility data are integrated into decision making in choosing places to test.

We develop a model where risk-averse workers can costly invest in their skills before matching with heterogenous firms. At the investment stage, workers face multiple sources of risk. They are uncertain about how skilled they will turn out and also about their income shock realizations at the time of employment. We analyse the equilibria of two versions of the model that depend on when uncertainty resolves, which determines the available risk-sharing possibilities between workers and firms. We provide a thorough analysis of equilibrium comparative statics regarding changes in risk, worker and firm heterogeneity, and technology. We derive conditions on the match output function and risk attitudes under which these shifts lead to more investment and show how this affects matching and wages. To illustrate the applied relevance of our theory, we provide a stylized quantitative assessment of the model and analyse the sources (risk, heterogeneity, or technology) of rising U.S. wage inequality. We find that changes in risk were the most important driver behind the surge in inequality, followed by technological change. We show that these conclusions are significantly altered if one neglects the key feature of our model, which is that educational investment is endogenous.

Consumer protection law is vital for ensuring that market-based economies work in the economic interest of consumers as well as businesses, and thus to the benefit of civil society. This is the case for online markets just as it is for offline markets. However, despite broad consensus on these points, too little has been done to ensure that the various standards applicable in offline markets are sufficient or adequate to guarantee efficiency and fairness in online markets. This paper outlines eleven key features of online markets that might necessitate standards additional to or different from those that are applicable offline, and provides a menu of possible policies in relation to each. Many of these are general to all online markets, but some are specific to the largest digital platfoms. Many if not most of our policy proposals could be enacted through minor changes to existing law or regulation or through decisional law interpreting existing legislation. Some have already been implemented in some jurisdictions. What is needed in all jurisdictions, however, is a regulator or regulators with sufficient expertise around technical issues such as A/B testing and algorithmic decision-making to understand, anticipate, and remedy the myriad ways that online firms can disadvantage consumers.

This paper identifies a set of possible regulations that could be used both to make the search market more competitive and simultaneously ameliorate the harms flowing from Google’s current monopoly position. The purpose of this paper is to identify conceptual problems and solutions based on sound economic principles and to begin a discussion from which robust and specific policy recommendations can be drafted.

We conduct the first survey experiment to understand public attitudes about the realization rule for capital gains. This rule requires that assets usually must be sold before gains on them are taxed and thus makes taxing capital income much harder. We have three main findings. First, respondents strongly prefer to wait to tax gains on stocks until sale: 75% to 25%. But the flip side is that there is surprisingly strong support for taxing gains on assets at sale or transfer, including at death, in areas where current law never taxes those gains. Second, these stated views change only modestly when randomized participants observe a policy debate composed of videos explaining both the pros and cons of taxing before sale, though the pro and con treatments have large effects individually. And, third, among many possible explanations of these attitudes, we find particular evidence for three: mental accounting; status quo effects; and a desire to tax consumption, not income.

 

Many centralized school admissions systems use lotteries to ration limited seats at oversubscribed schools. The resulting random assignment is used by empirical researchers to identify the effects of schools on outcomes like test scores. I first find that the two most popular empirical research designs may not successfully extract a random assignment of applicants to schools. When are the research designs able to overcome this problem? I show the following main results for a class of data-generating mechanisms containing those used in practice: The first-choice research design extracts a random assignment under a mechanism if the mechanism is strategy-proof for schools. In contrast, the other qualification instrument research design does not necessarily extract a random assignment under any mechanism. The former research design is therefore more compelling than the latter. Many applications of the two research designs need some implicit assumption, such as large-sample approximately random assignment, to justify their empirical strategy.

Competition in health insurance markets may fail to improve health outcomes if consumers are not able to identify high-quality plans. We develop and apply a novel instrumental variables framework to quantify the variation in causal mortality effects across plans and measure how much consumers attend to this variation. We first document large differences in the observed mortality rates of Medicare Advantage plans in local markets. We then show that when plans with high mortality rates exit these markets, enrollees tend to switch to more typical plans and subsequently experience lower mortality. We derive and validate a novel “fallback condition” governing the subsequent choices of those affected by plan exits. When the fallback condition is satisfied, plan terminations can be used to estimate the relationship between observed plan mortality rates and causal mortality effects. Applying the framework, we find that mortality rates unbiasedly predict causal mortality effects. We then extend our framework to study other predictors of plan mortality effects and estimate consumer willingness to pay. Higher-spending plans tend to reduce enrollee mortality, but existing quality ratings are uncorrelated with plan mortality effects. Consumers place little weight on mortality effects when choosing plans. Good insurance plans dramatically reduce mortality, and redirecting consumers to such plans could improve beneficiary health.

We estimate the returns to a broad set of graduate degrees. To control for heterogeneity in preferences and ability, we use fixed effects for combinations of field-specific undergraduate and graduate degrees obtained by the last time we observe an individual. Basically, we compare earnings before the graduate degree to earnings after it. Using National Science Foundation data, we find large differences across graduate fields in earnings effects. The returns often depend on the undergraduate major. The contribution of occupational upgrading to the earnings gain varies across degrees. Finally, simple regression-based estimates of returns to graduate fields are often highly misleading.