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

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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.

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  • 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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    References listed on IDEAS

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    1. John H. Boyd & Jian Hu & Ravi Jagannathan, 2005. "The Stock Market's Reaction to Unemployment News: Why Bad News Is Usually Good for Stocks," Journal of Finance, American Finance Association, vol. 60(2), pages 649-672, April.
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    7. Olivier Deschênes & Michael Greenstone, 2007. "The Economic Impacts of Climate Change: Evidence from Agricultural Output and Random Fluctuations in Weather," American Economic Review, American Economic Association, vol. 97(1), pages 354-385, March.
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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. 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.
    3. 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.
    4. 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.
    5. 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).

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    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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