Climate change and weather events are increasingly affecting the macroeconomic performance of countries and regions. However, their effects on income inequality are less understood. We estimate the dynamic impact of thunderstorms on income and wages and reveal a robust asymmetric effect. We leverage a comprehensive dataset covering more than 200,000 events affecting contiguous U.S. counties across three decades. Storms have caused the highest number of billion-dollar disaster events since the eighties, but they have the lowest average event cost. They are short-lived, locally confined, relatively frequent, difficult-to-predict, and hazardous albeit not fully destructive events. While such features are convenient for the identification of impacts, previous studies mostly focused on more extreme events. We document a robust negative association between storm activity, income and wages growth. While income tends to recover in the long run, wages exhibit a significantly more stubborn decline, suggesting persistent and adverse impacts on (functional) income inequality. A one standard deviation increase in wind exposure generates a loss of 0.15% (0.21%) in wages after three (nine) years; incomes fall by a larger extent initially (0.19% after three years) while fully recovering in the longer run. In addition to their notable asymmetry, such estimates are non-negligible—especially given the downward rigidity of U.S. wages. Our analyses also highlight a lack of effective adaptation and stronger negative impacts in economically disadvantaged regions. Finally, we find evidence for a sizable shock-absorbing role of federal assistance.

Even though data on hospital admissions are widely used in health research, hospitalization-related quantities measured using these data are not always clearly conceptualized. Consequently, estimators of these quantities can have unclear rationales and undesirable properties. We evaluate three rate estimators for measuring hospitalization-related quantities that are of interest in health economics and clinical medicine subspecialities. Using the Grossman human capital model, we motivate the importance of measuring healthy time. We show that an upper bound on healthy time can be calculated using lengths of hospital stay without assumptions about health status outside the hospital. We find that an admission rate conventionally used in clinical research is a patient follow-up time weighted average that lacks a clear basis for the weights. We evaluate the Centers for Medicare and Medicaid Services (CMS) use of risk-standardized readmission rates to penalize hospitals under the Hospital Readmissions Reduction Program (HRRP) and find that it may inadvertently conflict with disease-specific care aimed at reducing mortality risk. We show that risk-standardized rates can be sensitive to patient case mix, potentially leading to hospital rankings that do not reflect hospital quality. We also summarize debates regarding the effectiveness of risk-standardized readmission rates in reducing readmissions.