State and local governments imposed social distancing measures in March and April of 2020 to contain the spread of novel coronavirus disease 2019 (COVID-19). These included large event bans, school closures, closures of entertainment venues, gyms, bars, and restaurant dining areas, and shelter-in-place orders (SIPOs). We evaluated the impact of these measures on the growth rate of confirmed COVID-19 cases across US counties between March 1, 2020 and April 27, 2020. An event-study design allowed each policy's impact on COVID-19 case growth to evolve over time. Adoption of government-imposed social distancing measures reduced the daily growth rate by 5.4 percentage points after 1-5 days, 6.8 after 6-10 days, 8.2 after 11-15 days, and 9.1 after 16-20 days. Holding the amount of voluntary social distancing constant, these results imply 10 times greater spread by April 27 without SIPOs (10 million cases) and more than 35 times greater spread without any of the four measures (35 million). Our paper illustrates the potential danger of exponential spread in the absence of interventions, providing relevant information to strategies for restarting economic activity.
This paper helps close the gap between theory and empirical evidence in the literature on asymmetric employer learning. If an employer's private learning is reflected in a worker's wage and one employer's private information is transmitted to the next when the worker makes a job-to-job transition, then asymmetric employer learning will appear in wage regressions as learning over an employment spell. Extending previous work that assumes all learning takes place publicly, this paper develops wage regressions that test for both asymmetric employer learning and public learning. The empirical results, including tests of alternative explanations, are consistent with asymmetric employer learning's having at least as much of an effect on wages during an employment spell as does public learning. The model developed in this paper illustrates how the story suggested by the empirical work might unfold. It shows that outside firms can profitably compete with a better-informed employer through bidding wars, even when the worker is equally productive in all firms. Furthermore, this competition results in different wages for workers with the same publicly observable characteristics, a result that previous models of asymmetric learning have not produced. Copyright © 2009 The Review of Economic Studies Limited.
A growing literature examines the effects of economic variables on obesity, typically focusing on only one or a few factors at a time. We build a more comprehensive economic model of body weight, combining the 1990-2010 Behavioral Risk Factor Surveillance System with 27 state-level variables related to general economic conditions, labor supply, and the monetary or time costs of calorie intake, physical activity, and cigarette smoking. Controlling for demographic characteristics and state and year fixed effects, changes in these economic variables collectively explain 37% of the rise in body mass index (BMI), 43% of the rise in obesity, and 59% of the rise in Class II/III obesity. Quantile regressions also point to large effects among the heaviest individuals, with half the rise in the 90th percentile of BMI explained by economic factors. Variables related to calorie intake-particularly restaurant and supercenter/warehouse club densities-are the primary drivers of the results. easy to avoid. Philipson and Posner (1999) formalize this notion by modeling weight as the result of eating and exercise decisions made through a utility-maximization process. 2 Individuals tradeoff the disutility from excess weight with the enjoyment of eating and having a sedentary lifestyle, subject to a budget constraint. The model predicts that lower food prices and reduced on-the-job physical activity increase weight, while the effect of additional income on weight varies across the income distribution. Cutler, Glaeser, and Shapiro (2003) point out that time costs of eating should matter in addition to monetary costs, and discuss how innovations such as vacuum packing, improved preservatives, and microwaves have reduced the time cost of food preparation. Later theoretical models (e.g., Komlos 2004;Ruhm 2012;Courtemanche Heutel, and McAlvanah 2015) add an intertemporal dimension, noting that the enjoyment from eating and sedentary activities occurs in the present but the health costs occur in the future. The prediction that the weights of at least some individuals respond to economic incentives persists in these models, regardless of whether or not preferences are time consistent.Motivated by these theoretical considerations, a large number of empirical studies investigate links between various economic factors and obesity. 3 Lakdawalla and Philipson (2002) document an inverted U-shaped association between income and BMI in individual fixed effects models. Lindahl (2005) and Cawley et al. (2010) find no evidence that income affects weight using lottery prizes and variations in Social Security payments as natural experiments, while Schmeiser (2009) finds that Earned Income Tax Credit benefits increase weight.Several articles document a connection between the costs of eating and BMI. Lakdawalla and
Standard-Nutzungsbedingungen:Die Dokumente auf EconStor dürfen zu eigenen wissenschaftlichen Zwecken und zum Privatgebrauch gespeichert und kopiert werden.Sie dürfen die Dokumente nicht für öffentliche oder kommerzielle Zwecke vervielfältigen, öffentlich ausstellen, öffentlich zugänglich machen, vertreiben oder anderweitig nutzen.Sofern die Verfasser die Dokumente unter Open-Content-Lizenzen (insbesondere CC-Lizenzen) zur Verfügung gestellt haben sollten, gelten abweichend von diesen Nutzungsbedingungen die in der dort genannten Lizenz gewährten Nutzungsrechte. www.econstor.eu We propose a new method for using validation data to correct self-reported weight and height in surveys that do not weigh and measure respondents. The standard correction from prior research regresses actual measures on reported values using an external validation dataset, and then uses the estimated coefficients to predict actual measures in the primary dataset. This approach requires the strong assumption that the expectations of actual weight and height conditional on the reported values are the same in both datasets. In contrast, we use percentile ranks rather than levels of reported weight and height. Our approach requires the much weaker assumption that the conditional expectations of actual measures are increasing in reported values in both samples, making our correction more robust to differences in measurement error across surveys. We then examine three nationally representative datasets and confirm that misreporting is sensitive to differences in survey context such as data collection mode. When we compare predicted BMI distributions using the two approaches, we find that the standard correction is biased by differences in misreporting while our correction is not. Finally, we present several examples that demonstrate the potential importance of our correction for future econometric analyses and estimates of obesity rates. Terms of use: Documents in D I S C U S S I O N P A P E R S E R I E SJEL Classification: C18, I1
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