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Climate Change Impacts on U.S. Migration and Household Location Choice


  • Fan, Qin
  • Klaiber, H. Allen
  • Fisher-Vanden, Karen


This paper employs a two-stage residential sorting model to examine climate change impacts on residential location choices in the US. The estimated coefficients are used to simulate population changes and US migration patterns across regions under hypothetical changes in climate. The main dataset used for estimation is the Integrated Public Use Microdata Sample (IPUMS), which provides demographic characteristics of approximately 2.4 million households located in 283 Metropolitan Statistical Areas (MSAs) of the US in the year 2000. Projected climate data (i.e. extreme temperatures) used for simulation are obtained from the North American Regional Climate Change Assessment Program (NARCCAP). In the estimation component, a two-stage random utility sorting model (RUM) is employed. The first-stage discrete choice model employs a multinomial logit specification to recover heterogeneous parameters associated with MSA specific variables, migration costs, along with the mean indirect utility of each MSA. In particular, the interaction terms of temperature extremes and individual-specific characteristics, such as one’s birth region, age and educational attainment, are used to recover valuations of temperature extremes for different classes of people with potentially different preferences. The second stage of this model decomposes the mean indirect utility obtained from the first stage into its MSA-specific attributes controlling for unobservables using region fixed effects. Migration costs are statistically significant. If migration costs are high, individuals are less likely to relocate for the sake of moderate changes in weather extremes. In the simulation component, the estimated coefficients are used to simulate population changes across regions in the US under hypothetical changes in extreme temperatures. We find that extreme temperature and extreme precipitation reduce utility, and people’s preferences for temperature extremes are heterogeneous. The climate of one’s place of birth and demographic characteristics such as age and educational attainment, are significant factors that lead to preference heterogeneity. In addition, we find that population share in the Southern region and California drop, while population share in Northeastern region increases under hypothetical changes in climate.

Suggested Citation

  • Fan, Qin & Klaiber, H. Allen & Fisher-Vanden, Karen, 2012. "Climate Change Impacts on U.S. Migration and Household Location Choice," 2012 Annual Meeting, August 12-14, 2012, Seattle, Washington 124588, Agricultural and Applied Economics Association.
  • Handle: RePEc:ags:aaea12:124588

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    References listed on IDEAS

    1. Bayer, Patrick & Keohane, Nathaniel & Timmins, Christopher, 2009. "Migration and hedonic valuation: The case of air quality," Journal of Environmental Economics and Management, Elsevier, vol. 58(1), pages 1-14, July.
    2. Kuminoff, Nicolai V., 2009. "Decomposing the structural identification of non-market values," Journal of Environmental Economics and Management, Elsevier, vol. 57(2), pages 123-139, March.
    3. Mansur, Erin T. & Mendelsohn, Robert & Morrison, Wendy, 2008. "Climate change adaptation: A study of fuel choice and consumption in the US energy sector," Journal of Environmental Economics and Management, Elsevier, vol. 55(2), pages 175-193, March.
    4. 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.
    5. Rosen, Sherwin, 1974. "Hedonic Prices and Implicit Markets: Product Differentiation in Pure Competition," Journal of Political Economy, University of Chicago Press, vol. 82(1), pages 34-55, Jan.-Feb..
    6. Dennis Epple & Thomas Romer & Holger Sieg, 2001. "Interjurisdictional Sorting and Majority Rule: An Empirical Analysis," Econometrica, Econometric Society, vol. 69(6), pages 1437-1465, November.
    7. Allen Klaiber, H. & Phaneuf, Daniel J., 2010. "Valuing open space in a residential sorting model of the Twin Cities," Journal of Environmental Economics and Management, Elsevier, vol. 60(2), pages 57-77, September.
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    Cited by:

    1. Feng, Bo & Partridge, Mark & Rembert, Mark, 2016. "The Perils of Modelling How Migration Responds to Climate Change," MPRA Paper 77059, University Library of Munich, Germany.

    More about this item


    Climate Change; Extremes; Tiebout Sorting; Locational Equilibrium; Migration; Environmental Economics and Policy; Q51 and Q54;

    JEL classification:

    • Q51 - Agricultural and Natural Resource Economics; Environmental and Ecological Economics - - Environmental Economics - - - Valuation of Environmental Effects
    • Q54 - Agricultural and Natural Resource Economics; Environmental and Ecological Economics - - Environmental Economics - - - Climate; Natural Disasters and their Management; Global Warming

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