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Population and income sensitivity of private and public weather forecasting


  • Anbarci, Nejat
  • Boyd III, John
  • Floehr, Eric
  • Lee, Jungmin
  • Song, Joon Jin


Accurate weather forecasts have substantial economic value. We examine the provision of accurate forecasts both theoretically and empirically. Theoretically, we use a simple Neo-Hotelling model. In that model, the public forecaster, the National Weather Service (NWS), tries to achieve socially-efficient forecast accuracy operating under a per capita tax constraint; on the other hand, the private providers compete against each other for profits by choosing their optimal level of forecast accuracy in a monopolistically competitive market in which each private provider caters to a market niche while co-existing with the NWS. Empirically, we use a unique data set on daily maximum temperature forecasts for 704 U.S. cities and estimate the nearest neighbor matching and the state fixed effect (FE) models. Our empirical findings are consistent with the predictions of our simple public good model: we find that forecast accuracy is sensitive to economic variables such as population and average household income in that the accuracy increases in these economic variables. Our most surprising theoretical and empirical finding is that population and income sensitivity is found not only for private forecasters but also for the public forecaster, the NWS.

Suggested Citation

  • Anbarci, Nejat & Boyd III, John & Floehr, Eric & Lee, Jungmin & Song, Joon Jin, 2011. "Population and income sensitivity of private and public weather forecasting," Regional Science and Urban Economics, Elsevier, vol. 41(2), pages 124-133, March.
  • Handle: RePEc:eee:regeco:v:41:y:2011:i:2:p:124-133

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    References listed on IDEAS

    1. Kelvin Lancaster, 1990. "The Economics of Product Variety: A Survey," Marketing Science, INFORMS, vol. 9(3), pages 189-206.
    2. Bruce A. Babcock, 1990. "The Value of Weather Information in Market Equilibrium," American Journal of Agricultural Economics, Agricultural and Applied Economics Association, vol. 72(1), pages 63-72.
    3. James H. Stock & Mark W. Watson, 2008. "Heteroskedasticity-Robust Standard Errors for Fixed Effects Panel Data Regression," Econometrica, Econometric Society, vol. 76(1), pages 155-174, January.
    4. Sean D. Campbell & Francis X. Diebold, 2005. "Weather Forecasting for Weather Derivatives," Journal of the American Statistical Association, American Statistical Association, vol. 100, pages 6-16, March.
    5. Roll, Richard, 1984. "Orange Juice and Weather," American Economic Review, American Economic Association, vol. 74(5), pages 861-880, December.
    6. Babcock, Bruce A., 1990. "Value of Weather Information in Market Equilibrium (The)," Staff General Research Papers Archive 10592, Iowa State University, Department of Economics.
    7. Mueser Peter R. & Graves Philip E., 1995. "Examining the Role of Economic Opportunity and Amenities in Explaining Population Redistribution," Journal of Urban Economics, Elsevier, vol. 37(2), pages 176-200, March.
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    Cited by:

    1. L. Zirulia, 2015. "“Should I stay or should I go?”: Weather forecasts and the economics of “short breaks”," Working Papers wp1034, Dipartimento Scienze Economiche, Universita' di Bologna.


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