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Efforts to raise the productivity of the U.S. health care system have proceeded slowly. One potential explanation is the fragmentation of payment across insurers. Each insurer's efforts to improve care could influence how doctors practice medicine for other insurers, leading to unvalued externalities. We study these externalities by examining the unintended private insurance spillovers of a public insurer's intervention. In 2015, Medicare randomized warning letters to doctors to curtail overuse of antipsychotics. Even though the letters did not mention private insurance, they reduced prescribing to privately insured patients by 12%. The reduction to Medicare patients was 17%, and we cannot reject one-for-one spillovers. The results imply th.
The past few decades have ushered in an experimental revolution in economics whereby scholars are now much more likely to generate their own data. While there are virtues associated with this movement, there are concomitant difficulties. Several scientific disciplines, including economics, have launched research registries in an effort to attenuate key inferential issues. This study assesses registries both empirically and theoretically, with a special focus on the AEA registry. We find that over 90% of randomized control trials (RCTs) in economics do not register, only 50% of the RCTs that register do so before the intervention begins, and the majority of these preregistrations are not detailed enough to significantly aid inference. Our em.
The COVID-19 pandemic has already led to dramatic policy responses in most advanced economies, and in particular sustained lockdowns matched with sizable transfers to much of the workforce. This paper provides a preliminary quantitative analysis of how aggregate policy responses should differ in developing countries. To do so we build an incomplete-markets macroeconomic model with epidemiological dynamics that features several of the main economic and demographic distinctions between advanced and developing economies relevant for the pandemic. We focus in particular on differences in population structure, fiscal capacity, healthcare capacity, the prevalence of ``hand-to-mouth'' households, and the size of the informal sector. The model pred.
We revisit several leading puzzles about the aggregate stock market by incorporating into a standard dividend discount model survey expectations of earnings of S&P 500 firms. Using survey expectations, while keeping discount rates constant, explains a significant part of “excess” stock price volatility, price-earnings ratio variation, and return predictability. The evidence is consistent with a mechanism in which good news about fundamentals leads to excessively optimistic forecasts of earnings, especially at long horizons, which inflate stock prices and lead to subsequent low returns. Relaxing rational expectations of fundamentals in a standard asset pricing model accounts for stock market anomalies in a parsimonious way.
The constant-maturity zero-coupon Treasury yield curve is one of the most studied datasets. We construct a new dataset using a non-parametric kernel-smoothing method with a novel adaptive bandwidth specifically designed to fit the Treasury yield curve. Our curve is globally smooth while still capturing important local variation. Economically, we show that applying our data leads to different conclusions from using the leading alternative data of Gurkaynak et al. (2007) (GSW) when we repeat two popular studies of Cochrane and Piazzesi (2005) and Giglio and Kelly (2018). Statistically, we show our dataset preserves information in the raw data and has much smaller pricing errors than GSW. Our new yield curve is maintained and updated online, c.
Consumers often face an overwhelming amount of information when deciding between products, and one of the primary policymaking tools available to improve their informativeness is the framing of this information. We introduce a general theoretical approach that characterizes when one frame is revealed to provide robustly higher welfare than another. Because it is testable, adaptable, and both necessary and sufficient, our condition determines both whether frames are robustly welfare ranked in a particular data set and the overall proportion of data sets in which frames can be so ranked.
Governments around the world use grant and loan programs to ease the financial constraints that contribute to socioeconomic gaps in college completion. A growing body of research assesses the impact of grants; less is known about how loan programs affect persistence and degree completion. We use detailed administrative data from Chile to provide rigorous regression-discontinuity-based evidence on the impacts of loan eligibility for university students who retake the national admission test after their first year of studies. Those who score above a certain threshold become eligible for loans covering around 85% of tuition costs for the duration of their program. We find that access to loans increases the fraction who return to university for.
The COVID-19 epidemic in emerging markets risks a combined health, economic, and debt crisis. We integrate a standard epidemiology model into a sovereign default model and study how default risk impacts the ability of these countries to respond to the epidemic. Lockdown policies are useful for alleviating the health crisis but they carry large economic costs and can generate costly and prolonged debt crises. The possibility of lockdown induced debt crises in turn results in less aggressive lockdowns and a more severe health crisis. We find that the social value of debt relief can be substantial because it can prevent the debt crisis and can save lives.
Unlike most countries, Korea did not implement a lockdown in its battle against COVID-19, instead successfully relying on testing and contact tracing. Only one region, Daegu-Gyeongbuk (DG), had a significant number of infections, traced to a religious sect. This allows us to estimate the causal effect of the outbreak on the labor market using difference-in-differences. We find that a one per thousand increase in infections causes a 2 to 3 percent drop in local employment. Non-causal estimates of this coefficient from the US and UK, which implemented large-scale lockdowns, range from 5 to 6 percent, suggesting that at most half of the job losses in the US and UK can be attributed to lockdowns. We also find that employment losses caused by lo.
We study the use of excuses to justify socially stigmatized actions, such as opposing minority groups. Rationales to oppose minorities change some people’s private opinions, leading them to take anti-minority actions even if they are not prejudiced against minorities. When these rationales become common knowledge, prejudiced people who are not persuaded by the rationale can pool with unprejudiced people who are persuaded. This decreases the stigma associated with anti-minority expression, increasing public opposition to minority groups. We examine this mechanism through several large-scale experiments in the context of anti-immigrant behavior in the United States. In the first main experiment, participants learn about a study claiming that
We introduce a general-equilibrium model of a “granular” spatial economy populated by a finite number of people. Our quantitative model is designed for application to the growing body of fine spatial data used to study economic outcomes for regions, cities, and neighborhoods. Conventional approaches invoking the law of large numbers are ill-suited for such empirical settings. We evaluate quantitative spatial models’ out-of-sample predictions using event studies of large office openings in New York City. Our granular framework improves upon the conventional continuum-of-individuals model, which perfectly fits the pre-event data but produces predictions uncorrelated with the observed changes in commuting flows.
We study the effects of negative supply shocks and shocks to the composition of final demand on aggregate output in a disaggregated neoclassical model with multiple sectors, factors, and input-output linkages. We show how nonlinearities associated with complementarities in consumption and production amplify the effect of negative supply shocks by creating supply bottlenecks and disrupting supply chain networks. These nonlinearities are particularly potent when the shocks are more heterogeneous as the worst-affected sectors drag down the other sectors. Nonlinearities are strengthened when changes in preferences lead households to tilt the composition of their demand towards the crippled sectors directly and indirectly through their supply ch.
Nearly one-quarter of married, fertile-age women in Sub-Saharan Africa say that they want to avoid pregnancy but are not using contraceptives. To the best of our knowledge, this paper is the first to study this puzzle in a developing country using detailed data on women’s subjective probabilistic beliefs about contraception and contraceptive attributes. Policy counterfactuals based on a structural model suggest that costly interventions such as eliminating supply constraints would only have modest effects on contraceptive use. Alternatively, increasing partners’ approval of methods, aligning partners’ fertility preferences with women’s, and correcting women’s expectations about pregnancy risk absent contraception have the potential to incre.
We examine how policymakers should react to a pandemic when there is significant uncertainty regarding key parameters relating to the disease. In particular, this paper explores how optimal mitigation policies change when incorporating uncertainty regarding the Case Fatality Rate (CFR) and the Basic Reproduction Rate (R0) into a macroeconomic SIR model in a robust control framework. This paper finds that optimal policy under parameter uncertainty generates an asymmetric optimal mitigation response across different scenarios: when the disease’s severity is initially underestimated the planner increases mitigation to nearly approximate the optimal response based on the true model, and when the disease’s severity is initially overestimated the.
This paper uses a dataset from Tanzania that contains information on consumption, income and income shocks within and across family networks. A unique feature of this data is that it contains data on the degree of information existing between each pair of households within family networks. We use these data to construct a novel measure of the quality of information both at the level of household pairs and at the level of the network. We study risk sharing within these networks and explore whether the rejection of perfect risk sharing that we observe can be related to imperfections in the information network members have about each. We show that households within family networks with better information are less vulnerable to idiosyncratic sh.
We analyze how investor expectations about economic growth and stock returns changed during the February-March 2020 stock market crash induced by the COVID-19 pandemic, as well as during the subsequent partial stock market recovery. We surveyed retail investors who are clients of Vanguard at three points in time: (i) on February 11-12, around the all-time stock market high, (ii) on March 11-12, after the stock market had collapsed by over 20%, and (iii) on April 16-17, after the market had rallied 25% from its lowest point. Following the crash, the average investor turned more pessimistic about the short-run performance of both the stock market and the real economy. Investors also perceived higher probabilities of both further extreme stock.
What is the pathway to development in a world with less international integration? We answer this question within a model that emphasizes the role of demand-side constraints on national development, which we identify with sustained poverty reduction. In this framework, development is linked to the adoption of an increasing returns to scale technology by imperfectly competitive firms, who need to pay the fixed setup cost of switching to that technology. Sustained poverty reduction is measured as a continuous decline in the share of the population living below $1.90/day PPP in 2011 US dollars over a five year period. This outcome is affected in a statistically significant and economically meaningful way by both domestic market size, which is
As part of the public health response to the COVID-19 epidemic, states enacted a set of social distancing policies between March and April of 2020. These actions together with voluntary social distancing have reduced the rate of new COVID-19 cases and deaths. But there are growing concerns that the social distancing that occurred during March and April also imposed large costs on workers and businesses who were mandated or encouraged to cease operating and stay at home. In this paper, we examine the impact of social distancing policies on work related mobility, unemployment internet search, initial unemployment claims, and individual measures of employment, hours worked, and earnings. Our main analysis is based on monthly CPS data, and leve.
Several high-profile news stories have linked post-September 11 (9/11) combat service to violent crime among veterans. Nevertheless, there is scant causal evidence for this claim. We exploit the administrative procedures by which U.S. Armed Forces senior commanders conditionally randomly assign active duty servicemen to overseas deployments to estimate the causal impact of modern warfare on crime. Using data from two national surveys and a unified framework, we find consistent evidence that post-9/11 combat service substantially increased the probability of crime commission among veterans. Combat increases the likelihood of property and violent crime, arrest, gang membership, trouble with police, and punishment under the Uniform Code of Mil.

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