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This review article surveys work that has been done using an empirical framework for analyzing selection in insurance markets developed by Einav et al. (Einav et al., Quarterly Journal of Economics 125:877–921, 2010a). We briefly review that framework, and then describe a number of empirical applications that researchers have undertaken across an array of settings in both insurance and credit markets. We also discuss some of the useful extensions to the original framework that others have made and applied. The review is intended to be useful for scholars who may want to apply the framework in their own work on insurance, credit, or other selection markets.

I describe a Bayesian persuasion problem where Receiver has a private type representing a cutoff for choosing Sender’s preferred action, and Sender has maxmin preferences over all Receiver type distributions with known mean and bounds. This problem can be represented as a zero-sum game where Sender chooses a distribution of posterior mean beliefs that is a mean-preserving contraction of the prior over states, and an adversarial Nature chooses a Receiver type distribution with the known mean; the player with the higher realization from their chosen distribution wins. I formalize the connection between maxmin persuasion and similar games used to model political spending, all-pay auctions, and competitive persuasion. In both a standard binary-state setting and a new continuous-state setting, Sender optimally linearizes the prior distribution over states to create a distribution of posterior means that is uniform on a known interval with an atom at the lower bound of its support.

Background Because of a change of government, the Colombian Ministry of Health and Social Protection is in the process of presenting a structural reform for the General System of Social Security in Health (GSSSH), in order to implement a ‘preventive and predictive health model’. However, it will always be relevant to review and analyze the fiscal implications of any proposed public policy program, to protect financial sustainability and to promote the better functioning of the system in question. Methods To contribute to this topic, we have calculated, using a financial–actuarial approach, the loss ratio for the years 2017 to 2021 for the Capitation Payment Unit (CPU) for all the Health-Promoting Entities (HPE) for both contributory and subsidized schemes. This information, derived from public reports available on the official website of the National Health Superintendency, allows us to estimate the financial burden of the institutions that guarantee access to and provision of health services and technologies in Colombia. Results The study shows that close to half of the HPEs in Colombia (which represent 11.6 million affiliates) have CPU loss ratios of more than 100% for the year 2021, evidencing insufficient resources for the operation of health insurance. Conclusions Finally, we propose some policy recommendations regarding the strengthening of informed decision-making to allow the healthy financial sustainability of the Colombian GSSSH.

We study a strict version of the notion of equilibrium robustness by Kajii and Morris (Econometrica 65:1283–1309, 1997) that allows for a larger class of incomplete information perturbations of a given complete information game, where with high probability, players believe that their payoffs are close to (but may be different from) those of the complete information game. We show that a strict monotone potential maximizer of a complete information game is strictly robust if either the game or the associated strict monotone potential is supermodular, and that the converse also holds in all binary-action supermodular games.

1. Background: Some reports have suggested that as many as one-half of all hospital inpatients identified as COVID-19-positive during the Omicron BA.1 variant-driven wave were incidental cases admitted primarily for reasons other than their viral infections. To date, however, there are no prospective longitudinal studies of a representative panel of hospitals based on pre-established criteria for determining whether a patient was, in fact, admitted as a result of the disease. 2. Materials and Methods: To fill this gap, we developed a formula to estimate the fraction of incidental COVID-19 hospitalizations that relies on measurable, population-based parameters. We applied our approach to a longitudinal panel of 164 counties throughout the United States, covering a 4-week interval ending in the first week of January 2022. 3. Results: Within this panel, we estimated that COVID-19 incidence was rising exponentially at a rate of 9.34% per day (95% CI, 8.93–9.87). Assuming that only one-quarter of all Omicron BA.1 infections had been reported by public authorities, we further estimated the aggregate prevalence of active SARS-CoV-2 infection during the first week of January to be 3.45%. During the same week, among 250 high-COVID-volume hospitals within our 164-county panel, an estimated one in four inpatients was COVID-positive. Based upon these estimates, we computed that 10.6% of such COVID-19-positive hospitalized patients were incidental infections. Across individual counties, the median fraction of incidental COVID-19 hospitalizations was 9.5%, with an interquartile range of 6.7 to 12.7%. 4. Conclusion: Incidental COVID-19 infections appear to have been a nontrivial fraction of all COVID-19-positive hospitalized patients during the Omicron BA.1 wave. In the aggregate, however, the burden of patients admitted for complications of their viral infections was far greater.

We consider a multi-state, general-equilibrium model with collateralized financial promises to study how allowing an asset to back multiple financial contracts (i.e., tranching) affects price bases. A basis emerges when one asset can be tranched to issue more derivative securities than can be backed by another asset. Variations in the ability to tranche an asset or to pyramid derivative debt lead to variations in price bases. Tranching a CDS, as occurs with the CDX index, increases the basis on the underlying asset. Our theory correctly predicts that inclusion in the CDX index increases the underlying CDS basis.

In many cities, incentives and regulations lead developers to integrate low-income housing into market-rate buildings. How cost-effective are these policies? I study take-up of a tax incentive in New York City using a model in which developers trade off between tax savings and pre-tax income. Estimating the model using policy variation and microdata on development from 2003 to 2015, I find a citywide marginal fiscal cost of $1.6 million per low-income unit. Differences in neighborhoods, not developer incidence, explain the cost premium over other housing programs. Weighing costs against estimates of neighborhood effects, I conclude middle-class neighborhoods offer “opportunity bargains.”, National Science Foundation (NSF)

Background More than two years into the coronavirus disease (COVID-19) pandemic, it remains unclear whether financial incentives can reduce vaccine hesitancy and improve uptake among key unvaccinated populations. This study estimated the willingness of racial/ethnic minority adults in the United States to accept financial incentives for COVID-19 vaccination and the minimum amount needed to vaccinate a sufficiently high percentage of this population. Methods From August through September 2021, we conducted an online survey of 367 Black/African American and Hispanic patients, age ≥ 18 years, from 8 community health centers in Rhode Island. Contingent valuation questions assessed respondents’ willingness-to-accept (WTA) incentives for COVID-19 vaccination using random-starting-points and iterative incentive offers of $5 to $50 per dose. Ordered logistic regression models examined associations between respondent characteristics and WTA. Predictive probabilities were modeled using both within-survey range and out-of-survey range incentive offer amounts and compared against vaccination thresholds needed to reach herd immunity. Results Less than 30% of unvaccinated survey respondents were WTA an incentive of $50/dose for vaccination. Models using out-of-survey incentive offer amounts greater than $50 suggested that 85% of respondents would agree $140/dose (95% CI: $43-$236) could convince other people to accept vaccination, while $209/dose (95% CI: -$91-$509) would be needed for 85% of respondents to accept vaccination themselves. Conclusions Findings from this analysis may inform the design of incentive schemes aiming to reduce racial/ethnic disparities in vaccine and booster uptake, which will continue to be important as new variants of SARS-CoV-2 emerge.

This paper develops a model of Bayesian learning from online reviews and investigates the conditions for learning the quality of a product and the speed of learning under different rating systems. A rating system provides information about reviews left by previous customers. observe the ratings of a product and decide whether to purchase and review it. We study learning dynamics under two classes of rating systems: full history, where customers see the full history of reviews, and summary statistics, where the platform reports some summary statistics of past reviews. In both cases, learning dynamics are complicated by a selection effect—the types of users who purchase the good, and thus their overall satisfaction and reviews depend on the information available at the time of purchase. We provide conditions for complete learning and characterize and compare its speed under full history and summary statistics. We also show that providing more information does not always lead to faster learning, but strictly finer rating systems do.

MethodsWe relied on reports of confirmed case incidence and test positivity, along with data on the movements of devices with location-tracking software, to evaluate a novel scheme of three concentric regulatory zones introduced by then New York Governor Cuomo to address an outbreak of COVID-19 in South Brooklyn in the fall of 2020. The regulatory scheme imposed differential controls on access to eating places, schools, houses of worship, large gatherings and other businesses within the three zones, but without restrictions on mobility.ResultsWithin the central red zone, COVID-19 incidence temporarily declined from 131.2 per 100,000 population during the week ending October 3 to 62.5 per 100,000 by the week ending October 31, but then rebounded to 153.6 per 100,000 by the week ending November 28. Within the intermediate orange and peripheral yellow zones combined, incidence steadily rose from 28.8 per 100,000 during the week ending October 3 to 109.9 per 100,000 by the week ending November 28. Data on device visits to pairs of eating establishments straddling the red-orange boundary confirmed compliance with access controls. More general analysis of device movements showed stable patterns of movement between and beyond zones unaffected by the Governor's orders. A geospatial regression model of COVID-19 incidence in relation to device movements across zip code tabulation areas identified a cluster of five high-movement ZCTAs with estimated reproduction number 1.91 (95% confidence interval, 1.27–2.55).DiscussionIn the highly populous area of South Brooklyn, controls on access alone, without restrictions on movement, were inadequate to halt an advancing COVID-19 outbreak.

© 2019 by the Association for Public Policy Analysis and Management As we approach the tenth anniversary of the passage of the Affordable Care Act, it is important to reflect on what has been learned about the impacts of this major reform. In this paper, we review the literature on the impacts of the ACA on patients, providers, and the economy. We find strong evidence that the ACA's provisions have increased insurance coverage. There is also a clearly positive effect on access to and consumption of health care, with suggestive but more limited evidence on improved health outcomes. There is no evidence of significant reductions in provider access, changes in labor supply, or increased budgetary pressures on state governments, and the law's total federal cost through 2018 has been less than predicted. We conclude by describing key policy implications and future areas for research.

The 2020 John Bates Clark Medal of the American Economic Association was awarded to Melissa Dell, Professor of Economics at Harvard University, for her path-breaking contributions in political economy, economic history, and economic development. This article summarizes Melissa Dell's work, places it in the context of the broader literature, and emphasizes how, with its data collection, careful empirical implementation, and audacious ambition, it has revolutionized work in political economy and economic history.

I develop a simple model of social learning in which players observe others’ outcomes but not their actions. A continuum of players arrives continuously over time, and each player chooses once-and-for-all between a safe action (which succeeds with known probability) and a risky action (which succeeds with fixed but unknown probability, depending on the state of the world). The actions also differ in their costs. Before choosing, a player observes the outcomes of K earlier players. There is always an equilibrium in which success is more likely in the good state, and this alignment property holds whenever the initial generation of players is not well informed about the state. In the case of an outcome-improving innovation (where the risky action may yield a higher probability of success), players take the correct action as K → ∞. In the case of a cost-saving innovation (where the risky action involves saving a cost but accepting a lower probability of success), inefficiency persists as K → ∞ in any aligned equilibrium. Whether inefficiency takes the form of under-adoption or over-adoption also depends on the nature of the innovation. Convergence of the population to equilibrium may be nonmonotone. (JEL D81, D83, O32, Q12, Q16)

This paper examines the link between electoral incentives and environmental degradation by exploiting a satellite dataset on 107,000 forest fires and 879 asynchronous district elections in Indonesia. Fires represent a cheap but illegal means of converting forested land to other uses, but they risk burning out of control and creating substantial negative environmental externalities. We find a significant electoral cycle in forest fires. Ignitions and area burned decline during election years but steeply increase in the year after. The results suggest that politicians may suppress this activity at times when it might particularly dent their electoral chances.

According to the previous literature, only a few papers found better accuracy than a chance to detect dishonesty, even when more information and verbal cues (VCs) improve precision in detecting dishonesty. A new classification of dishonesty profiles has recently been published, allowing us to study if this low success rate happens for all people or if some people have higher predictive ability. This paper aims to examine if (dis)honest people can detect better/worse (un)ethical behavior of others. With this in mind, we designed one experiment using videos from one of the most popular TV shows in the UK where contestants make a (dis)honesty decision upon gaining or sharing a certain amount of money. Our participants from an online MTurk sample (N = 1,582) had to determine under different conditions whether the contestants would act in an (dis)honest way. Three significant results emerged from these two experiments. First, accuracy in detecting (dis)honesty is not different than chance, but submaximizers (compared to maximizers) and radical dishonest people (compare to non-radicals) are better at detecting honesty, while there is no difference in detecting dishonesty. Second, more information and VCs improve precision in detecting dishonesty, but honesty is better detected using only non-verbal cues (NVCs). Finally, a preconceived honesty bias improves specificity (honesty detection accuracy) and worsens sensitivity (dishonesty detection accuracy).

Abstract We argue theoretically and document empirically that aging leads to greater (industrial) automation, because it creates a shortage of middle-aged workers specializing in manual production tasks. We show that demographic change is associated with greater adoption of robots and other automation technologies across countries and with more robotics-related activities across U.S. commuting zones. We also document more automation innovation in countries undergoing faster aging. Our directed technological change model predicts that the response of automation technologies to aging should be more pronounced in industries that rely more on middle-aged workers and those that present greater opportunities for automation and that productivity should improve and the labor share should decline relatively in industries that are more amenable to automation. The evidence supports all four of these predictions.

We develop an equivalence between the equilibrium effects of incomplete information and those of two behavioral distortions: myopia, or extra discounting of the future; and anchoring of current behavior to past behavior, as in models with habit persistence or adjustment costs. We show how these distortions depend on higher-order beliefs and GE mechanisms, and how they can be disciplined by evidence on expectations. We finally illustrate the use of our toolbox with a quantitative application in the context of inflation, a bridge to the HANK literature, and an extension to networks. (JEL C53, D83, D85, E12, E31, E37)

© 2018 by The University of Chicago. All rights reserved. We thank Kamal Chavda, Carol Johnson, Carleton Jones, Tim Nicollette, and Jack Yessayan for their expertise and for granting permission to undertake this study. Edward L. Glaeser, Arda Gitmez, Fuhito Kojima, Yusuke Narita, Peng Shi, and several seminar audiences provided helpful comments. Kominers is grateful for the support of National Science Foundation grants CCF-1216095 and SES-1459912, the Harvard Milton Fund, an American Mathematical Society–Simons Foundation Travel Grant, the Ng Fund and the Mathematics in Economics Research Fund of the Harvard Center of Mathematical Sciences and Applications, and the Human Capital and Economic Opportunity Working Group sponsored by the Institute for New Economic Thinking. Much of this work was conducted while Kominers was a research scholar at the Becker Friedman Institute at the University of Chicago. Pathak and Sönmez’s work was supported by NSF grant SES-1426566. Sönmez also acknowledges the research support of Goldman Sachs Gives via Dalinc Ariburnu—Goldman Sachs Faculty Research Fund. ering the precedence of reserve seats at a school or increasing the school’s reserve size weakly increases reserve-group assignment at that school. Using data from Boston Public Schools, we show that reserve and precedence adjustments have similar quantitative effects. Transparency about these issues—in particular, how precedence unintentionally undermined intended policy—led to the elimination of walk zone reserves in Boston’s public school match.

© 2020 American Economic Association. All rights reserved. This paper studies the problem of experiment design by an ambiguity-averse decision-maker who trades off subjective expected performance against robust performance guarantees. This framework accounts for real-world experimenters’ preference for randomization. It also clarifies the circumstances in which randomization is optimal: when the available sample size is large and robustness is an important concern. We apply our model to shed light on the practice of rerandomization, used to improve balance across treatment and control groups. We show that rerandomization creates a trade-off between subjective performance and robust performance guarantees. However, robust performance guarantees diminish very slowly with the number of rerandomizations. This suggests that moderate levels of rerandomization usefully expand the set of acceptable compromises between subjective performance and robustness. Targeting a fixed quantile of balance is safer than targeting an absolute balance objective.

We study the firm-level implications of robot adoption in France. Of 55,390 firms in our sample, 598 adopted robots between 2010 and 2015, but these firms accounted for 20 percent of manufacturing employment. Adopters experienced significant declines in labor shares, the share of production workers in employment, and increases in value added and productivity. They expand their overall employment as well. However, this expansion comes at the expense of competitors, leading to an overall negative association between adoption and employment. Robot adoption has a large impact on the labor share because adopters are larger and grow faster than their competitors.

Abstract There are two broad views as to why people stay poor. One emphasizes differences in fundamentals, such as ability, talent, or motivation. The poverty traps view emphasizes differences in opportunities that stem from access to wealth. To test these views, we exploit a large-scale, randomized asset transfer and an 11-year panel of 6,000 households who begin in extreme poverty. The setting is rural Bangladesh, and the assets are cows. The data support the poverty traps view—we identify a threshold level of initial assets above which households accumulate assets, take on better occupations (from casual labor in agriculture or domestic services to running small livestock businesses), and grow out of poverty. The reverse happens for those below the threshold. Structural estimation of an occupational choice model reveals that almost all beneficiaries are misallocated in the work they do at baseline and that the gains arising from eliminating misallocation would far exceed the program costs. Our findings imply that large transfers, which create better jobs for the poor, are an effective means of getting people out of poverty traps and reducing global poverty.

We build a model in which the Fed and the market disagree about future aggregate demand. The market anticipates monetary policy “mistakes,” which affect current demand and induce the Fed to partially accommodate the market’s view. The Fed expects to implement its view gradually. Announcements that reveal an unexpected change in the Fed’s belief provide a microfoundation for monetary policy shocks. Tantrum shocks arise when the market misinterprets the Fed’s belief and overreacts to its announcement. Uncertainty about tantrums motivates further gradualism and communication. Finally, disagreements affect the market’s expected inflation and induce a policy trade-off similar to “ cost-push” shocks. (JEL D83, E12, E31, E43, E44, E52, E58)

Abstract This article explores the consequences of extremely low real interest rates in a world with integrated but heterogeneous capital markets, nominal rigidities, and an effective lower bound [a zero lower bound (ZLB) for simplicity]. We establish four main results: (1) At the ZLB, creditor countries export their recession abroad, which we illustrate with a new Metzler diagram in quantities; (2) Beggar-thy-neighbour currency and trade wars provide stimulus to the undertaking country at the expense of other countries; (3) (Safe) public debt issuances and increases in government spending anywhere are expansionary everywhere; and (4) When there is a scarcity of safe assets, net issuers of these assets import the recession from abroad.

Abstract We study the sources of high end-of-life spending for cancer patients. Even among patients with similar initial prognoses, spending in the year post diagnosis is over twice as high for those who die within the year than those who survive. Elevated spending on decedents is predominantly driven by higher inpatient spending, particularly low-intensity admissions. However, most such admissions do not result in death, making it difficult to target spending reductions. Furthermore, end-of-life spending is substantially more elevated for younger patients, compared to older patients with similar prognosis. Results highlight sources of high end-of-life spending without revealing any natural “remedies.”

Significance Prediction problems are important in many contexts. Examples include cross-sectional prediction, time series forecasting, counterfactual prediction and synthetic controls, and individual treatment effect prediction. We develop a prediction method that works in conjunction with many powerful classical methods (e.g., conventional quantile regression) as well as modern high-dimensional methods for estimating conditional distributions (e.g., quantile neural networks). Unlike many existing prediction approaches, our method is valid conditional on the observed predictors and efficient under some conditions. Importantly, our method is also robust; it exhibits unconditional coverage guarantees under model misspecification, under overfitting, and with time series data.

© 2020 American Economic Association. All rights reserved. Around mid-March 2020, as the United States and much of the rest of the world was facing an unprecedented health threat in the form of COVID-19, an abrupt shift in the tone and policies of the United States and United Kingdom occurred. In early March, Prime Minister Boris Johnson said that "we should all basically just go about our normal daily lives." Likewise, on March 11, President Donald Trump reassured the American people that for "[t]he vast majority of Americans, the risk is very, very low." Just five days later, the Trump administration recommended that "all Americans, including the young and healthy, work to engage in schooling from home when possible. Avoid gathering in groups of more than 10 people. Avoid discretionary travel. And avoid eating and drinking at bars, restaurants, and public food courts" (as reported by Keith 2020). The British government likewise markedly changed course, with a series of partial measures preceding a March 23 lockdown order. Although Trump and Johnson had been receiving briefings about COVID-19 for several weeks, the proximate cause of the shift in both countries appears to have been the March 16 release of a headlinegrabbing epidemiological model produced by London's Imperial College, which predicted that there could be as many as 2,200,000 deaths in the United States and 510,000 in the United Kingdom" (as reported by Landler and Castle 2000).

Background: Cost-effective demand-side interventions are needed to increase childhood immunization. Multiple studies find tying income support programs (≥USD 50 per year) to immunization raises coverage. Research on maximizing impact from small mobile-based conditional cash transfers (mCCTs) (≤USD 15 per fully immunized child) delivered in lower-income settings remains sparse. Methods: Participants in Karachi, Pakistan, were individually randomized into a seven arm, factorial open label study with five mCCT arms, one reminder (SMS) only arm, and one control arm. The mCCT arms varied by amount (high ∼USD 15 per fully immunized child versus low ∼USD 5 per fully immunized child), schedule (flat versus rising payments over the schedule), design (certain versus lottery payments), and payment method (airtime or mobile money). Children were enrolled at BCG, pentavalent-1 (penta-1) or pentavalent-2 (penta-2) vaccination and followed until at least 18 months of age. A serosurvey in 15% sub-sample validated reported study coverage. The full immunization coverage (FIC) at 12 months (primary outcome) was analyzed using logit regression. ClinicalTrials.gov (NCT03355989), 3ie registry (58f6ee7725fc1), and AEA RCT Registry (AEARCTR-0001953). Findings: Between November 6, 2017, and October 10, 2018, a total of 11,197 caregiver-child pairs were enrolled, with 1598-1600 caregiver-child pairs per arm. FIC at 12 months was statistically significantly higher for any mCCT versus SMS (OR:1.18, 95% CI: 1.05-1.33; p = 0.005). Within the mCCT arms, FIC was statistically significantly higher for high versus low amount (OR: 1.16, 95% CI: 1.04-1.29; p = 0.007), certain versus lottery payment (OR: 1.30, 95% CI: 1.17-1.45; p < 0.001) and airtime versus mobile money (OR: 1.17, 95% CI:1.01-1.36; p = 0.043). There was no statistically significant difference between a flat and increasing schedule (OR: 1.03, 95% CI: 0.93-1.15; p = 0.550). SMS had a marginally statistically significant impact on FIC ve

To investigate barriers to universal health insurance in developing countries, we designed a randomized experiment involving about 6,000 households in Indonesia who are subject to a government health insurance program with a weakly enforced mandate. Time-limited subsidies increased enrollment and attracted lower-cost enrollees, in part by reducing the strategic timing of enrollment to correspond with health needs. Registration assistance also increased enrollment, but increased attempted enrollment much more, as over one-half of households who attempted to enroll did not successfully do so. These findings underscore how weak administrative capacity can create important challenges in developing countries for achieving widespread coverage. (JEL D82, G22, H51, I13, I18, O15)

We present a speculative application of model estimates from Fudenberg and Puri (2021) to prize-linked savings in South Africa. The models used include one combining simplicity theory (Puri 2018, 2022), a preference for lotteries with fewer possible outcomes, with cumulative prospect theory. The results and those of prior literature indicate that both simplicity and probability weighting have a role to play in understanding behavior in choice under risk. We discuss the properties of these models and their implications for behavior.

Abstract We introduce two empirical strategies harnessing the randomness in school assignment mechanisms to measure school value-added. The first estimator controls for the probability of school assignment, treating take-up as ignorable. We test this assumption using randomness in assignments. The second approach uses assignments as instrumental variables (IVs) for low-dimensional models of value-added and forms empirical Bayes posteriors from these IV estimates. Both strategies solve the underidentification challenge arising from school undersubscription. Models controlling for assignment risk and lagged achievement in Denver and New York City yield reliable value-added estimates. Estimates from models with lower-quality achievement controls are improved by IV.

We characterize revenue maximizing mechanisms in a common value environment where the value of the object is equal to the highest of the bidders' independent signals. If the revenue maximizing solution is to sell the object with probability 1, then an optimal mechanism is simply a posted price, namely, the highest price such that every type of every bidder is willing to buy the object. If the object is optimally sold with probability less than 1, then optimal mechanisms skew the allocation toward bidders with lower signals. The resulting allocation induces a “winner's blessing,” whereby the expected value conditional on winning is higher than the unconditional expectation. By contrast, standard auctions that allocate to the bidder with the highest signal (e.g., the first-price, second-price, or English auctions) deliver lower revenue because of the winner's curse generated by the allocation. Our qualitative results extend to more general common value environments with a strong winner's curse.

© 2017 Project HOPE- The People-to-People Health Foundation, Inc. Oregon's 2008 Medicaid expansion significantly increased the use of prescription medications in 2009-10., National Institute on Aging (Grants P30AG012810, RC2AGO36631, and R01AG0345151)

We studied the possible role of the subways in the spread of SARS-CoV-2 in New York City during late February and March 2020. Data on cases and hospitalizations, along with phylogenetic analyses of viral isolates, demonstrate rapid community transmission throughout all five boroughs within days. The near collapse of subway ridership during the second week of March was followed within 1–2 weeks by the flattening of COVID-19 incidence curve. We observed persistently high entry into stations located along the subway line serving a principal hotspot of infection in Queens. We used smartphone tracking data to estimate the volume of subway visits originating from each zip code tabulation area (ZCTA). Across ZCTAs, the estimated volume of subway visits on March 16 was strongly predictive of subsequent COVID-19 incidence during April 1–8. In a spatial analysis, we distinguished between the conventional notion of geographic contiguity and a novel notion of contiguity along subway lines. We found that the March 16 subway-visit volume in subway-contiguous ZCTAs had an increasing effect on COVID-19 incidence during April 1–8 as we enlarged the radius of influence up to 5 connected subway stops. By contrast, the March 31 cumulative incidence of COVID-19 in geographically-contiguous ZCTAs had an increasing effect on subsequent COVID-19 incidence as we expanded the radius up to three connected ZCTAs. The combined evidence points to the initial citywide dissemination of SARS-CoV-2 via a subway-based network, followed by percolation of new infections within local hotspots.

Abstract The earnings of individuals depend on the demand for the factor services they supply. International trade may therefore affect earnings inequality because either (i) foreign consumers and firms demand domestic factor services in different proportions than domestic consumers and firms do, an export channel; or (ii) domestic consumers and firms change their demand for domestic factor services in response to the availability of foreign goods, an import channel. Building on this idea, we develop new measures of export and import exposure at the individual level and provide estimates of their incidence across the earnings distribution. The key input fed into our empirical analysis is a unique administrative data set from Ecuador that merges firm-to-firm transaction data, employer-employee matched data, owner-firm matched data, and firm-level customs transaction records. We find that export exposure is pro-middle class, import exposure is pro-rich, and in terms of overall incidence, the import channel is the dominant force. As a result, earnings inequality in Ecuador is higher than it would be in the absence of trade.

Credit cards have often been blamed for consumer overspending and for the growth in household debt. Indeed, laboratory studies of purchase behavior have shown that credit cards can facilitate spending in ways that are difficult to justify on purely financial grounds. However, the psychological mechanisms behind this spending facilitation effect remain conjectural. A leading hypothesis is that credit cards reduce the pain of payment and so ‘release the brakes’ that hold expenditures in check. Alternatively, credit cards could provide a ‘step on the gas,’ increasing motivation to spend. Here we present the first evidence of differences in brain activation in the presence of real credit and cash purchase opportunities. In an fMRI shopping task, participants purchased items tailored to their interests, either by using a personal credit card or their own cash. Credit card purchases were associated with strong activation in the striatum, which coincided with onset of the credit card cue and was not related to product price. In contrast, reward network activation weakly predicted cash purchases, and only among relatively cheaper items. The presence of reward network activation differences highlights the potential neural impact of novel payment instruments in stimulating spending—these fundamental reward mechanisms could be exploited by new payment methods as we transition to a purely cashless society.

© 2020, Japanese Economic Association. This paper presents a simple framework that allows us to survey and relate some different strands of the game theory literature. We describe a “canonical” way of adding incomplete information to a complete information game. This framework allows us to give a simple “complete theory” interpretation (Kreps in Game theory and economic modelling. Clarendon Press, Oxford, 1990) of standard normal form refinements such as perfection, and to relate refinements both to the “higher-order beliefs literature” (Rubinstein in Am Econ Rev 79:385–391, 1989; Monderer and Samet in Games Econ Behav 1:170–190, 1989; Morris et al. in Econ J Econ Soc 63:145–157, 1995; Kajii and Morris in Econ J Econ Soc 65:1283–1309, 1997a) and the “payoff uncertainty approach” (Fudenberg et al. in J Econ Theory 44:354–380, 1988; Dekel and Fudenberg in J Econ Theory 52:243–267, 1990).

Effective collaboration within and between organizations requires efficient adaptation to unforeseen change. We study how parties build relational contracts that achieve this goal. We focus on the “clarity problem”—whether parties have a shared understanding of the promises they make to each other. Specifically, (a) a buyer and seller play a trading game in several periods; (b) they know their environment will change but do not know how; and (c) before any trading occurs, they can reach a nonbinding agreement about how to play the entire game. We hypothesize that pairs whose initial agreement defines a broad principle rather than a narrow rule are more successful in solving the clarity problem and in achieving efficient adaptation after unforeseen change. In our baseline condition, we indeed observe that pairs who articulate principles achieve significantly higher performance after change occurred. Underlying this correlation, we also find that pairs with principle-based agreements were more likely to both expect and take actions that were consistent with what their agreement prescribed. To investigate a causal link between principle-based agreements and performance, we implement a “nudge” intervention that induces more pairs to articulate principles. The intervention succeeds in coordinating more pairs on efficient quality immediately after the unforeseen change, but it fails to coordinate expectations on price, ultimately leading to conflicts and preventing an increase in long-run performance after the shock. Our results suggest that (1) principle-based agreements may improve organizational performance but (2) high-performing relational contracts may be difficult to build.