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Clearing the Fog: The Predictive Power of Weather for Employment Reports and Their Asset Price Responses

Author

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  • Daniel J. Wilson

Abstract

This paper exploits vast granular data—with over one million county-month observations—to estimate a dynamic panel data model of weather's local employment effects. The fitted county model is then aggregated and used to generate in-sample and rolling out-of-sample (nowcast) estimates of the weather effect on national monthly employment. These nowcasts, which use only employment and weather data available prior to a given employment report, are significantly predictive not only of the surprise component of employment reports but also of stock and bond market returns on the days of employment reports.

Suggested Citation

  • Daniel J. Wilson, 2019. "Clearing the Fog: The Predictive Power of Weather for Employment Reports and Their Asset Price Responses," American Economic Review: Insights, American Economic Association, vol. 1(3), pages 373-388, December.
    • Handle: RePEc:aea:aerins:v:1:y:2019:i:3:p:373-88
    Note: DOI: 10.1257/aeri.20180432
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    Cited by:

    1. Daniel J. Wilson, 2021. "Where Is the U.S. COVID-19 Pandemic Headed?," FRBSF Economic Letter, Federal Reserve Bank of San Francisco, vol. 2021(11), pages 01-06, April.
    2. Thibaut Duprey & Soojin Jo & Geneviève Vallée, 2024. "Let’s Get Physical: Impacts of Climate Change Physical Risks on Provincial Employment," Staff Working Papers 24-32, Bank of Canada.
    3. Mary C. Daly, 2021. "Climate Risk and the Fed: Preparing for an Uncertain Certainty," FRBSF Economic Letter, Federal Reserve Bank of San Francisco, vol. 2021(17), pages 1-08, June.
    4. Bo Liu & Barry T. Hirsch, 2021. "Winter weather and work hours: Heterogeneous effects and regional adaptation," Contemporary Economic Policy, Western Economic Association International, vol. 39(4), pages 867-881, October.
    5. Ziheng Liu, 2025. "The behavioral dimension of CO2 fertilization effect: Evidence from US harvesting records," Agricultural Economics, International Association of Agricultural Economists, vol. 56(1), pages 5-26, January.
    6. Shihan Xie & Victoria Wenxin Xie & Xu Zhang, 2024. "Extreme Weather and Low-Income Household Finance: Evidence from Payday Loans," Staff Working Papers 24-1, Bank of Canada.
    7. Daniel J. Wilson, 2025. "Estimating National Weather Effects from the Ground Up," Working Paper Series 2025-18, Federal Reserve Bank of San Francisco.
    8. Makridis, Christos A. & Schloetzer, Jason D., 2023. "Extreme local temperatures lower expressed sentiment about U.S. economic conditions with implications for the stock returns of local firms," Journal of Behavioral and Experimental Finance, Elsevier, vol. 37(C).
    9. Xie, Victoria Wenxin, 2024. "Labor market adjustment to extreme heat shocks: Evidence from Brazil," Journal of Economic Behavior & Organization, Elsevier, vol. 222(C), pages 266-283.

    More about this item

    JEL classification:

    • C53 - Mathematical and Quantitative Methods - - Econometric Modeling - - - Forecasting and Prediction Models; Simulation Methods
    • G12 - Financial Economics - - General Financial Markets - - - Asset Pricing; Trading Volume; Bond Interest Rates
    • G17 - Financial Economics - - General Financial Markets - - - Financial Forecasting and Simulation
    • H63 - Public Economics - - National Budget, Deficit, and Debt - - - Debt; Debt Management; Sovereign Debt
    • Q54 - Agricultural and Natural Resource Economics; Environmental and Ecological Economics - - Environmental Economics - - - Climate; Natural Disasters and their Management; Global Warming
    • R23 - Urban, Rural, Regional, Real Estate, and Transportation Economics - - Household Analysis - - - Regional Migration; Regional Labor Markets; Population

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