Estimating National Weather Effects from the Ground Up

Understanding the effects of weather on macroeconomic data is critically important, but it is hampered by limited time series observations. Utilizing geographically granular panel data leverages greater observations but introduces a “missing intercept” problem: “global” (e.g., nationwide spillovers and GE) effects are absorbed by time fixed effects. Standard solutions are infeasible when the number of global regressors is large. To overcome these problems and estimate granular, global, and total weather effects, we implement a two-step approach utilizing machine learning techniques. We apply this approach to estimate weather effects on U.S. monthly employment growth, obtaining several novel findings: (1) weather, and especially its lags, has substantial explanatory power for local employment growth, (2) shocks to both granular and global weather have significant immediate impacts on a broad set of macroeconomic outcomes, (3) responses to granular shocks are short-lived while those to global shocks are more persistent, (4) favorable weather shocks are often more impactful than unfavorable shocks, and (5) responses of most macroeconomic outcomes to weather shocks have been stable over time but the consumption response has fallen.