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Post-flood selective migration interacts with media sentiment and income effects

Nature Climate Change volume 15, pages 619–626 (2025)Cite this article

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Abstract

Escalating flood risks from climate change cause economic losses and alter migration patterns, although their impacts across socioeconomic groups remain underexplored. Here we investigate flood-induced inter-county migration in the United States between 2006 and 2019, and find that floods increase outflow and inflow migration by 2.7% and 1.9%, respectively. Younger, better-educated and employed residents leave, while older, less-educated and unemployed individuals move into affected counties. Such patterns can be amplified by media sentiment on flood risks. Selective migration lowers housing prices but raises rent, suggesting structural changes in flood-prone housing markets. Flood-induced selective migration also has salient impacts on the local labour markets, with net annual income losses estimated to be US$9.3 million and $1.98 million, conditional on education and age profiles, respectively. Our results shed light on how natural disasters influence selective migration conditional on socioeconomic profiles and how information provision interacts with migration incentives.

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Fig. 1: Impact of floods on selective migration: event-study results.
Fig. 2: Selective migration patterns after floods.

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Data availability

The migration data were obtained from the Integrated Public Use Microdata Series of the Annual American Community Survey (https://usa.ipums.org/usa/acs.shtml). The flood data are available from the public website of the National Center for Environmental Information (https://www.ncei.noaa.gov/). The housing market data are publicly available from the Zillow website (https://www.zillow.com/research/data/). The data on GDP, population size, unemployment rate and personal income data are publicly available from the US Bureau of Economic Analysis (https://www.bea.gov/data). The public firm entry and exit data were obtained from the Augmented 10-X Header Database (https://sraf.nd.edu/sec-edgar-data/10-x-header-data/), which is publicly available. The information on government subsidies provided for post-flood economic recoveries is publicly available from the OpenFEMA Dataset (https://www.fema.gov/about/openfema/data-sets). The data on flood-related news articles were downloaded from the Dow Jones Factiva database (https://www.dowjones.com/professional/factiva/), which requires private subscription for access. Source data are provided with this paper.

Code availability

The code used for this analysis is available on Zenodo at https://doi.org/10.5281/zenodo.15254509 (ref. 53).

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Acknowledgements

We thank seminar participants at the National University of Singapore, Monash University, Deakin University, Queensland University of Technology, Asian Real Estate Society (AsRES) Annual Meeting 2023, Asian Bureau of Finance and Economics Research (ABFER) Annual Meeting 2024, SMU-Jinan Conference on Urban and Regional Economics 2024, and other conferences where authors presented this paper. This paper was previously circulated with the title ‘Gone with the Flood: Natural Disasters, Selective Migration, and Media Sentiment’.

Author information

Authors and Affiliations

  1. Department of Real Estate, NUS Business School, National University of Singapore, Singapore, Singapore

    Yi Fan & Qiuxia Gao

  2. Synapxe, Singapore, Singapore

    Yinghao Elliot Sitoh

  3. Department of Banking and Finance, Monash University, Clayton, Victoria, Australia

    Wayne Xinwei Wan

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Contributions

Y.F., Q.G. and W.X.W. conceived the research question and developed the methodology. Q.G. and Y.E.S. contributed to the data curation. Q.G., Y.E.S. and W.X.W. performed the data analysis under the supervision of Y.F. and W.X.W. Y.E.S. wrote the first draft. Y.F. and W.X.W. led the revisions of the paper. Y.F., Q.G. and W.X.W. rewrote the paper. Y.F., Q.G., Y.E.S. and W.X.W. contributed to the refinement, interpretation and discussion of the analysis. All authors contributed equally to this article.

Corresponding authors

Correspondence to Yi Fan or Wayne Xinwei Wan.

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The authors declare no competing interests.

Peer review

Peer review information

Nature Climate Change thanks Linguere Mbaye, Jianghao Wang and the other, anonymous, reviewer(s) for their contribution to the peer review of this work.

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Extended data

Extended Data Fig. 1 Correlation between Net Migration and Flood Risk in the US.

This figure shows (a) the net migration in the U.S. at the state level between 2006 and 2019 and (b) the average flood risk in the same period at the county level. Data on state-to-state migration flows is obtained from the United States Census Bureau; the National Flood Risk Index is obtained from the Federal Emergency Management Agency. Both figures are generated using QGIS, keeping the same coordinate projection, and the base map is obtained from ESRI (https://www.arcgis.com/home/item.html?id=f16090f6d3da48ec8f144a0771c8fec4).

Source data

Extended Data Fig. 2 Conceptual Framework: Relationships among Flood Disasters, Media Sentiment, and Selective Migration.

This figure presents the conceptual framework in our study, illustrating the relationships of flood disasters, media sentiment, and selective migration.

Extended Data Fig. 3 Impact of Floods on Migration: Placebo Test Results.

This figure shows the distribution of the placebo-test estimates for the impacts of floods on (a) outflow migration and (b) inflow migration, respectively. For each flood event in a county, we randomly assign an event year as a placebo treatment, and we repeat this process 1,000 times to get the distribution. The y-axis in the figures represents the p-values of the estimates, and the horizontal dashed line refers to the 5% significance level. Statistical significance is determined using two-sided tests without adjustment for multiple comparisons.

Source data

Extended Data Fig. 4 Event Study Results: Robustness Checks Using the Synthetic Control Approach.

This figure plots the estimated coefficients of the impact of flood events on outflow and inflow migration from the event study using Equation (1), with the synthetic control approach. Data are presented as mean values ± 95% confidence intervals. For both regressions, the total number of observations is 12824. Statistical significance is determined using two-sided tests without adjustment for multiple comparisons.

Source data

Extended Data Fig. 5 Selective Migration Patterns after Floods: Robustness Checks Using the Synthetic Control Approach.

This figure plots the estimated coefficients of the impact of flood events on (a) outflow migration and (b) inflow migration of different sociodemographic subgroups, using the synthetic control approach. Each estimate indicates the average change in outflow (or inflow) migration in the unit of log (number of migrants per year) in three years after the flood event, relative to the 3-year period before the flood event. High (low) education refers to migrants with degrees at or above (below) the college level. Employed (unemployed) individuals are classified by their employment status in 1 year before the flood. Young (old) individuals are those under (in or above) the age of 40. Data are presented as mean values ± 95% confidence intervals. For all regressions, the total number of observations is 12824. Statistical significance is determined using two-sided tests without adjustment for multiple comparisons.

Source data

Extended Data Fig. 6 Correlation between Google Search Index of Flood and Number of News Articles.

This figure shows the correlation between the weekly Google search index (GSI) of the keyword ‘flood’ in the U.S. and the number of flood-related news articles in the Factiva database between 2005 and 2019. The GSI is obtained from https://trends.google.com/trends/.

Source data

Supplementary information

Supplementary Information

Conceptual framework and hypothesis, robustness checks and Supplementary Tables 1–15.

Reporting Summary

Source data

Source Data Fig. 1

Statistical source data for Fig. 1a (Fig1_A_Pretrend_twfe) and 1b (Fig1_B_Pretrend_csdid).

Source Data Fig. 2

Statistical source data for Fig. 2a (Fig2_A_Selective_outmigration) and 2b (Fig2_B_Selective_inmigration).

Source Data Extended Data Fig. 1

Statistical source data for Extended Data Fig. 1a (ExtendedData_Fig1_A_State_to_state_migration) and 1b (ExtendedData_Fig1_B_Flood_risk).

Source Data Extended Data Fig. 3

Statistical source data for Extended Data Fig. 3a (ExtendedData_Fig3_A_PlaceboTest_out) and 3b (ExtendedData_Fig3_B_PlaceboTest_in).

Source Data Extended Data Fig. 4

Statistical source data.

Source Data Extended Data Fig. 5

Statistical source data for Extended Data Fig. 5a (ExtendedData_Fig5_A_SCM_outflow) and 5b (ExtendedData_Fig5_B_SCM_inflow).

Source Data Extended Data Fig. 6

Statistical source data.

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Fan, Y., Gao, Q., Sitoh, Y.E. et al. Post-flood selective migration interacts with media sentiment and income effects. Nat. Clim. Chang. 15, 619–626 (2025). https://doi.org/10.1038/s41558-025-02345-7

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