The empirical methodology used by Persson and Rossin-Slater (2018) to estimate the causal effect of in utero exposure to stress contains a potentially significant flaw. They define the control group in a way that may bias their causal estimates and can lead to the finding of a significant relationship when there is none. In this note, I describe the source of the bias and suggest an alternative specification of the control group. (JEL I12, J12, J13)
Fang and Gong (2017) develop a procedure to detect potential over-billing of Medicare by physicians. In their empirical analysis, they use aggregated claims data that can overstate the number of services performed due to features of Medicare billing. In this comment, I show how auditors can use detailed claims-level data to better target improper overbilling. (JEL H51, I13, I18, J22, J44)
Average prices Consumer price indexes Consumer prices
How the data are obtainedSurvey of businesses, Survey of households
Classification system Commodity
Periodicity of data availability Bimonthly, Monthly
Geographic detailCensus region, Metro area, U.S. City average Scope Urban consumers
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