A meta-analysis of country-level studies on environmental change and migration


The impact of climate change on migration has gained both academic and public interest in recent years. Here we employ a meta-analysis approach to synthesize the evidence from 30 country-level studies that estimate the effect of slow- and rapid-onset events on migration worldwide. Most studies find that environmental hazards affect migration, although with contextual variation. Migration is primarily internal or to low- and middle-income countries. The strongest relationship is found in studies with a large share of countries outside the Organisation for Economic Co-operation and Development, particularly from Latin America and the Caribbean and sub-Saharan Africa, and in studies of middle-income and agriculturally dependent countries. Income and conflict moderate and partly explain the relationship between environmental change and migration. Combining our estimates for differential migration responses with the observed environmental change in these countries in recent decades illustrates how the meta-analytic results can provide useful insights for the identification of potential hotspots of environmental migration.

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Fig. 1: Primary quantitative country-level studies testing for a relationship between environmental factors and international and internal migration.
Fig. 2: Distribution of the precision-weighted standardized effects by type of environmental hazard.
Fig. 3: Predicted environmental effects on migration by country sample compositions.
Fig. 4: Predicted environmental migration worldwide measured in standard deviation changes in migration.

Data availability

The meta-data and country-level data generated during and/or analysed during the current study are available in the Harvard Dataverse repository107, https://dataverse.harvard.edu/dataverse/Meta-Analysis_EnvironmentalMigration.

Code availability

The data analysis was carried out in R108. The complete codes used to generate and visualize the results reported in this study are available in the Harvard Dataverse repository107, https://dataverse.harvard.edu/dataverse/Meta-Analysis_EnvironmentalMigration. All used packages are acknowledged and cited in the source code file.


  1. 1.

    Kelley, C. P., Mohtadi, S., Cane, M. A., Seager, R. & Kushnir, Y. Climate change in the Fertile Crescent and implications of the recent Syrian drought. Proc. Natl Acad. Sci. USA 112, 3241–3246 (2015).

    CAS  Google Scholar 

  2. 2.

    Gleick, P. H. Water, drought, climate change, and conflict in Syria. Weather Clim. Soc. 6, 331–340 (2014).

    Google Scholar 

  3. 3.

    De Châtel, F. The role of drought and climate change in the Syrian uprising: untangling the triggers of the revolution. Middle East. Stud. 50, 521–535 (2014).

    Google Scholar 

  4. 4.

    Selby, J., Dahi, O. S., Fröhlich, C. & Hulme, M. Climate change and the Syrian civil war revisited. Polit. Geogr. 60, 232–244 (2017).

    Google Scholar 

  5. 5.

    Myers, N. Environmental refugees: a growing phenomenon of the 21st century. Phil. Trans. R. Soc. Lond. B 357, 609–613 (2002).

    Google Scholar 

  6. 6.

    Renaud, F., Bogardi, J. J., Dun, O. & Warner, K. Control, Adapt or Flee: How to Face Environmental Migration? InterSecTions, Publication Series of United Nations University, ENS Vol. 5 (United Nations University Institute for Environment and Human Security, 2007).

  7. 7.

    Stern, N. The Economics of Climate Change: The Stern Review (Cambridge Univ. Press, 2006).

  8. 8.

    Biermann, F. & Boas, L. Preparing for a warmer world: towards a global governance system to protect climate refugees. Glob. Environ. Polit. https://doi.org/10.1162/glep.2010.10.1.60 (2010).

  9. 9.

    Cattaneo, C. et al. Human migration in the era of climate change. Rev. Environ. Econ. Policy 13, 189–206 (2019).

    Google Scholar 

  10. 10.

    Berlemann, M. & Steinhardt, M. F. Climate change, natural disasters, and migration—a survey of the empirical evidence. CESifo Econ. Stud. 63, 353–385 (2017).

    Google Scholar 

  11. 11.

    Hunter, L. M., Luna, J. K. & Norton, R. M. Environmental dimensions of migration. Annu. Rev. Sociol. 41, 377–397 (2015).

    Google Scholar 

  12. 12.

    Piguet, E. Linking climate change, environmental degradation, and migration: a methodological overview. Wiley Interdiscip. Rev. Clim. Change 1, 517–524 (2010).

    Google Scholar 

  13. 13.

    Borderon, M. et al. Migration influenced by environmental change in Africa: a systematic review of empirical evidence. Demogr. Res. 41, 491–544 (2019).

    Google Scholar 

  14. 14.

    Black, R., Stephen, R., Bennett, G., Thomas, S. M. & Beddington, J. R. Migration as adaptation. Nature 478, 447–449 (2011).

    CAS  Google Scholar 

  15. 15.

    Barrios, S., Bertinelli, L. & Strobl, E. Climatic change and rural-urban migration: the case of sub-Saharan Africa. J. Urban Econ. 60, 357–371 (2006).

    Google Scholar 

  16. 16.

    Naudé, W. Natural disasters and international migration from sub-Saharan Africa. Migrat. Lett. 6, 165–176 (2009).

    Google Scholar 

  17. 17.

    Ruyssen, I. & Rayp, G. Determinants of intraregional migration in sub-Saharan Africa 1980-2000. J. Dev. Stud. 50, 426–443 (2014).

    Google Scholar 

  18. 18.

    Backhaus, A., Martinez-Zarzoso, I. & Muris, C. Do climate variations explain bilateral migration? A gravity model analysis. IZA J. Migrat. 4, 3 (2015).

    Google Scholar 

  19. 19.

    Beine, M. & Parsons, C. Climatic factors as determinants of international migration. Scand. J. Econ. 117, 723–767 (2015).

    Google Scholar 

  20. 20.

    Coniglio, N. D. & Pesce, G. Climate variability and international migration: an empirical analysis. Environ. Dev. Econ. 20, 434–468 (2015).

    Google Scholar 

  21. 21.

    Ghimire, R., Ferreira, S. & Dorfman, J. H. Flood-induced displacement and civil conflict. World Dev. 66, 614–628 (2015).

    Google Scholar 

  22. 22.

    Cai, R., Feng, S., Oppenheimer, M. & Pytlikova, M. Climate variability and international migration: the importance of the agricultural linkage. J. Environ. Econ. Manage. 79, 135–151 (2016).

    Google Scholar 

  23. 23.

    Cattaneo, C. & Peri, G. The migration response to increasing temperatures. J. Dev. Econ. 122, 127–146 (2016).

    Google Scholar 

  24. 24.

    Maurel, M. & Tuccio, M. Climate instability, urbanisation and international migration. J. Dev. Stud. 52, 735–752 (2016).

    Google Scholar 

  25. 25.

    Beine, M. & Parsons, C. R. Climatic factors as determinants of international migration: Redux. CESifo Econ. Stud. 63, 386–402 (2017).

    Google Scholar 

  26. 26.

    Cattaneo, C. & Bosetti, V. Climate-induced international migration and conflicts. CESifo Econ. Stud. 63, 500–528 (2017).

    Google Scholar 

  27. 27.

    Reuveny, R. & Moore, W. H. Does environmental degradation influence migration? Emigration to developed countries in the late 1980s and 1990s. Soc. Sci. Q. 90, 461–479 (2009).

    Google Scholar 

  28. 28.

    Damette, O. & Gittard, M. Changement climatique et migrations: les transferts de fonds des migrants comme amortisseurs? Mondes Dev. 179, 85 (2017).

    Google Scholar 

  29. 29.

    Gröschl, J. & Steinwachs, T. Do natural hazards cause international migration? CESifo Econ. Stud. 63, 445–480 (2017).

    Google Scholar 

  30. 30.

    Henderson, J. V., Storeygard, A. & Deichmann, U. Has climate change driven urbanization in Africa? J. Dev. Econ. 124, 60–82 (2017).

    Google Scholar 

  31. 31.

    Mahajan, P. & Yang, D. Taken by Storm: Hurricanes, Migrant Networks, and U.S. Immigration Working Paper 23756 (NBER, 2017); https://doi.org/10.3386/w23756

  32. 32.

    Marchiori, L., Maystadt, J.-F. & Schumacher, I. Is environmentally induced income variability a driver of human migration? Migr. Dev. 6, 33–59 (2017).

    Google Scholar 

  33. 33.

    Missirian, A. & Schlenker, W. Asylum applications respond to temperature fluctuations. Science 358, 1610–1614 (2017).

    CAS  Google Scholar 

  34. 34.

    Spencer, N. & Urquhart, M.-A. Hurricane strikes and migration: evidence from storms in central America and the caribbean. Weather Clim. Soc. 10, 569–577 (2018).

    Google Scholar 

  35. 35.

    Peri, G. & Sasahara, A. The Impact of Global Warming in Rural-Urban Migrations: Evidence from Global Big Data Working Paper 25728 (NBER, 2019).

  36. 36.

    Wesselbaum, D. & Aburn, A. Gone with the wind: international migration. Glob. Planet. Change 178, 96–109 (2019).

    Google Scholar 

  37. 37.

    Naudé, W. The determinants of migration from sub-Saharan African countries. J. Afr. Econ. 19, 330–356 (2010).

    Google Scholar 

  38. 38.

    Alexeev, A., Good, D. H. & Reuveny, R. Weather-Related Disasters and International Migration (Indiana Univ., 2011); http://www.umdcipe.org/conferences/Maastricht/conf_papers/Papers/Effects_of_Natural_Disasters.pdf

  39. 39.

    Bettin, G. & Nicolli, F. Does Climate Change Foster Emigration from Less Developed Countries? Evidence from Bilateral Data Working Paper 10 (Univ. degli Stud. di Ferrara, 2012).

  40. 40.

    Brückner, M. Economic growth, size of the agricultural sector, and urbanization in Africa. J. Urban Econ. 71, 26–36 (2012).

    Google Scholar 

  41. 41.

    Gröschl, J. Climate change and the relocation of population. In Beiträge zur Jahrestagung des Vereins für Soc. 2012 Neue Wege und Herausforderungen für den Arbeitsmarkt des 21. Jahrhunderts - Sess. Migr. II, No. D03-V1, ZBW (Verein für Socialpolitik (German Economic Association), 2012).

  42. 42.

    Hanson, G. H. & McIntosh, C. Birth rates and border crossings: Latin American migration to the US, Canada, Spain and the UK. Econ. J. 122, 707–726 (2012).

    Google Scholar 

  43. 43.

    Marchiori, L., Maystadt, J. F. & Schumacher, I. The impact of weather anomalies on migration in sub-Saharan Africa. J. Environ. Econ. Manage. 63, 355–374 (2012).

    Google Scholar 

  44. 44.

    Drabo, A. & Mbaye, L. M. Natural disasters, migration and education: an empirical analysis in developing countries. Environ. Dev. Econ. 20, 767–796 (2015).

    Google Scholar 

  45. 45.

    Black, R. et al. The effect of environmental change on human migration. Glob. Environ. Change 21, 3–11 (2011).

    Google Scholar 

  46. 46.

    Boas, I. et al. Climate migration myths. Nat. Clim. Change 9, 901–903 (2019).

    Google Scholar 

  47. 47.

    Black, R. et al. Foresight: Migration and Global Environmental Change. Future Challenges and Opportunities (The Government Office for Science, 2011).

  48. 48.

    Veroniki, A. A. et al. Methods to estimate the between-study variance and its uncertainty in meta-analysis. Res. Synth. Methods 7, 55–79 (2016).

    Google Scholar 

  49. 49.

    Crespo Cuaresma, J., Fidrmuc, J. & Hake, M. Demand and supply drivers of foreign currency loans in CEECs: a meta-analysis. Econ. Syst. 38, 26–42 (2014).

    Google Scholar 

  50. 50.

    Hsiang, S. M., Burke, M. & Miguel, E. Quantifying the influence of climate on human conflict. Science 341, 1235367 (2013).

    Google Scholar 

  51. 51.

    Nawrotzki, R. J. & Bakhtsiyarava, M. International climate migration: evidence for the climate inhibitor mechanism and the agricultural pathway. Popul. Space Place 23, 1–16 (2016).

    Google Scholar 

  52. 52.

    Beine, M. & Jeusette, L. A Meta-Analysis of the Literature on Climate Change and Migration CREA Discussion Paper Series (Center for Research in Economic Analysis, Univ. Luxembourg, 2018).

  53. 53.

    Auffhammer, M., Hsiangy, S. M., Schlenker, W. & Sobelz, A. Using weather data and climate model output in economic analyses of climate change. Rev. Environ. Econ. Policy 7, 181–198 (2013).

    Google Scholar 

  54. 54.

    Hsiang, S. Climate econometrics. Annu. Rev. Resour. Econ. 8, 43–75 (2016).

    Google Scholar 

  55. 55.

    Hugo, G. Future demographic change and its interactions with migration and climate change. Glob. Environ. Change 21(Suppl.), S21–S33 (2011).

  56. 56.

    Martin, P. L. & Taylor, J. E. in Development Strategy, Employment and Migration: Insights from Models 43–62 (OECD, 1996).

  57. 57.

    Feng, S., Krueger, A. B. & Oppenheimer, M. Linkages among climate change, crop yields and Mexico–US cross-border migration. Proc. Natl Acad. Sci. USA 107, 14257–14262 (2010).

    CAS  Google Scholar 

  58. 58.

    Mendelsohn, R. & Dinar, A. Climate change, agriculture, and developing countries: does adaptation matter? World Bank Res. Obs. 14, 277–293 (1999).

    Google Scholar 

  59. 59.

    Schlenker, W. & Roberts, M. J. Nonlinear temperature effects indicate severe damages to U.S. crop yields under climate change. Proc. Natl Acad. Sci. USA 106, 15594–15598 (2009).

    CAS  Google Scholar 

  60. 60.

    Schlenker, W. & Lobell, D. B. Robust negative impacts of climate change on African agriculture. Environ. Res. Lett. 5, 014010 (2010).

    Google Scholar 

  61. 61.

    Abel, G. J., Brottrager, M., Crespo Cuaresma, J. & Muttarak, R. Climate, conflict and forced migration. Glob. Environ. Change 52, 239–249 (2019).

    Google Scholar 

  62. 62.

    Burke, M., Hsiang, S. M. & Miguel, E. Climate and conflict. Annu. Rev. Econ. 7, 577–617 (2015).

    Google Scholar 

  63. 63.

    Barnett, J. & Adger, W. N. Climate change, human security and violent conflict. Polit. Geogr. 26, 639–655 (2007).

    Google Scholar 

  64. 64.

    Schlenker, W., Hanemann, W. M. & Fisher, A. C. The impact of global warming on U.S. agriculture: An econometric analysis of optimal growing conditions. Rev. Econ. Stat. https://doi.org/10.1162/003465306775565684 (2006).

  65. 65.

    Dimitrova, A. & Bora, J. K. Monsoon weather and early childhood health in India. PLoS ONE 15, e0231479 (2020).

    CAS  Google Scholar 

  66. 66.

    Muttarak, R. & Dimitrova, A. Climate change and seasonal floods: potential long-term nutritional consequences for children in Kerala, India. BMJ Glob. Health 4, e001215 (2019).

    Google Scholar 

  67. 67.

    Deschênes, O. & Greenstone, M. Climate change, mortality, and adaptation: evidence from annual fluctuations in weather in the US. Am. Econ. J. Appl. Econ. 3, 152–185 (2011).

    Google Scholar 

  68. 68.

    Deschenes, O. Temperature, human health, and adaptation: a review of the empirical literature. Energ. Econ. https://doi.org/10.1016/j.eneco.2013.10.013 (2014).

  69. 69.

    Burgess, R., Deschênes, O., Donaldson, D. & Greenstone, M. Weather, Climate Change and Death in India Working Paper (LSE, 2017).

  70. 70.

    Zivin, J. G. & Neidell, M. Environment, health, and human capital. J. Econ. Lit. 51, 689–730 (2013).

    Google Scholar 

  71. 71.

    Gemenne, F. Why the numbers don’t add up: a review of estimates and predictions of people displaced by environmental changes. Glob. Environ. Change 21, 41–49 (2011).

    Google Scholar 

  72. 72.

    Findley, S. E. Does drought increase migration? A study of migration from rural Mali during the 1983–1985 drought. Int. Migr. Rev. 28, 539 (1994).

    CAS  Google Scholar 

  73. 73.

    Black, R., Arnell, N. W., Adger, W. N., Thomas, D. & Geddes, A. Migration, immobility and displacement outcomes following extreme events. Environ. Sci. Policy 27, S32–S43 (2013).

    Google Scholar 

  74. 74.

    Bohra-Mishra, P., Oppenheimer, M., Cai, R., Feng, S. & Licker, R. Climate variability and migration in the Philippines. Popul. Environ. 38, 286–308 (2017).

    Google Scholar 

  75. 75.

    Bardsley, D. K. & Hugo, G. J. Migration and climate change: examining thresholds of change to guide effective adaptation decision-making. Popul. Environ. 32, 238–262 (2010).

    Google Scholar 

  76. 76.

    Mertz, O., Mbow, C., Reenberg, A. & Diouf, A. Farmers’ perceptions of climate change and agricultural adaptation strategies in rural Sahel. Environ. Manage. 43, 804–816 (2009).

    Google Scholar 

  77. 77.

    Garcia, A. J., Pindolia, D. K., Lopiano, K. K. & Tatem, A. J. Modeling internal migration flows in sub-Saharan Africa using census microdata. Migr. Stud. 3, 89–110 (2015).

    Google Scholar 

  78. 78.

    Naudé, W. Conflict, Disasters and No Jobs: Reasons for International Migration from sub-Saharan Africa. WIDER Research Paper 85 (United Nations Univ., 2008).

  79. 79.

    Dell, M., Jones, B. F. & Olken, B. A. What do we learn from the weather? The new climate-economy literature. J. Econ. Lit. 52, 740–798 (2014).

    Google Scholar 

  80. 80.

    Gemenne, F. & Blocher, J. How can migration serve adaptation to climate change? Challenges to fleshing out a policy ideal. Geogr. J. 183, 336–347 (2017).

    Google Scholar 

  81. 81.

    Zickgraf, C. Keeping people in place: political factors of (im)mobility and climate change. Soc. Sci. 8, 1–17 (2019).

    Google Scholar 

  82. 82.

    Ayeb-Karlsson, S. et al. I will not go, I cannot go: cultural and social limitations of disaster preparedness in Asia, Africa, and Oceania. Disasters 43, 752–770 (2019).

    Google Scholar 

  83. 83.

    Oakes, R. Culture, climate change and mobility decisions in pacific small island developing states. Popul. Environ. 40, 480–503 (2019).

    Google Scholar 

  84. 84.

    Gharad, B., Chowdhury, S. & Mobarak, A. M. Underinvestment in a profitable technology: the case of seasonal migration in Bangladesh. Econometrica 82, 1671–1748 (2014).

    Google Scholar 

  85. 85.

    Kniveton, D., Black, R. & Schmidt-Verkerk, K. Migration and climate change: towards an integrated assessment of sensitivity. Environ. Plan. A 43, 431–450 (2011).

    Google Scholar 

  86. 86.

    Hornbeck, R. The enduring impact of the American Dust Bowl: Short- and long-run adjustments to environmental catastrophe. Am. Econ. Rev. 102, 1477–1507 (2012).

    Google Scholar 

  87. 87.

    Libecap, G. D. & Steckel, R. H. in The Economics of Climate Change: Adaptations Past and Present (eds Libecap, G. D. & Steckel, R. H.) 1–22 (Univ. Chicago Press, 2011).

  88. 88.

    Hsiang, S. M. & Narita, D. Adaptation to cyclone risk: evidence from the global cross-section. Clim. Change Econ. 03, 1250011 (2012).

    Google Scholar 

  89. 89.

    Borenstein, M., Hedges, L. V., Higgins, J. P. T. & Rothstein, H. R. A basic introduction to fixed-effect and random-effects models for meta-analysis. Res. Synth. Methods 1, 97–111 (2010).

    Google Scholar 

  90. 90.

    Hedges, Larry V. & Olkin, I. Statistical Method for Meta-Analysis (Academic Press, 1998).

  91. 91.

    Lipsey, M. W. & Wilson, D. B. Practical meta-analysis (SAGE Publications, 2001).

  92. 92.

    Hedges, L. V. & Olkin, I. Vote-counting methods in research synthesis. Psychol. Bull. 88, 359–369 (1980).

    Google Scholar 

  93. 93.

    Combs, J. G., Ketchen, D. J., Crook, T. R. & Roth, P. L. Assessing cumulative evidence within ‘macro’ research: why meta-analysis should be preferred over vote counting. J. Manage. Stud. 48, 178–197 (2011).

    Google Scholar 

  94. 94.

    Beine, M., Bertoli, S. & Fernández-Huertas Moraga, J. A practitioners’ guide to gravity models of international migration. World Econ. 39, 496–512 (2016).

    Google Scholar 

  95. 95.

    Angrist, J. D. & Pischke, J.-S. Mostly Harmless Econometrics (Princeton Univ. Press, 2009).

  96. 96.

    Dell, M., Jones, B. F. & Olken, B. A. Temperature shocks and economic growth: evidence from the last half century. Am. Econ. J. Macroecon. https://doi.org/10.1257/mac.4.3.66 (2012).

  97. 97.

    Bohra-Mishra, P., Oppenheimer, M. & Hsiang, S. M. Nonlinear permanent migration response to climatic variations but minimal response to disasters. Proc. Natl Acad. Sci. USA 111, 9780–9785 (2014).

    CAS  Google Scholar 

  98. 98.

    World Development Indicators (The World Bank, 2019).

  99. 99.

    Marshall, M. G. Major Episodes of Political Violence (MEPV) and Conflict Regions, 1946–2018 (Center for Systemic Peace, 2019).

  100. 100.

    Sundberg, R. & Melander, E. Introducing the UCDP georeferenced event dataset. J. Peace Res. 50, 523–532 (2013).

    Google Scholar 

  101. 101.

    Högbladh, S. UCDP GED Codebook Version 19.1 (Department of Peace and Conflict Research, Uppsala Univ., 2019).

  102. 102.

    Harris, I., Jones, P. D., Osborn, T. J. & Lister, D. H. Updated high-resolution grids of monthly climatic observations—the CRU TS3.10 dataset. Int. J. Climatol. 34, 623–642 (2014).

    Google Scholar 

  103. 103.

    EM-DAT: The Emergency Events Database (Centre for Research on the Epidemiology of Disasters, 2020); www.emdat.be

  104. 104.

    Bell, M. et al. Internal migration data around the world: assessing contemporary practice. Popul. Space Place 21, 1–17 (2015).

    Google Scholar 

  105. 105.

    Bell, M. & Charles-Edwards, E. Cross-National Comparisons of Internal Migration: An Update of Global Patterns and Trends Technical Paper 2013/1 (United Nations, 2013).

  106. 106.

    Chindarkar, N. Gender and climate change-induced migration: proposing a framework for analysis. Environ. Res. Lett. 7, 025601 (2012).

    Google Scholar 

  107. 107.

    Hoffmann, R., Dimitrova, A., Muttarak, R., Crespo Cuaresma, J. & Peisker, J. A meta-analysis of country level studies on environmental change and migration | replication data and code. Harvard Dataverse, V1 https://doi.org/10.7910/DVN/HYRXVV (2020).

  108. 108.

    R Core Team R: A Language and Environment for Statistical Computing (R Foundation for Statistical Computing, 2019); https://www.r-project.org/

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We thank the authors of the papers included in the meta-analysis who kindly shared their data and codes with us. This research was funded by the Austrian Science Fund, grant number Z171-G11.We also thank the IIASA for funding, as well as the National Member Organizations that support the institute. Further funding was provided by the International Climate Initiative (IKI: www.international-climate-initiative.com) and the German Federal Ministry for the Environment, Nature Conservation and Nuclear Safety (BMU). The Potsdam Institute for Climate Impact Research is a member of the Lebniz Association.

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R.H. and R.M. conceived the project and designed research; A.D., R.H. and R.M. collected and reviewed the literature; J.C.C. helped with statistical techniques and procedures; A.D., R.H. and J.P. collected, compiled and analysed data; J.C.C., A.D., R.H., R.M. and J.P. interpreted the results and wrote the manuscript.

Corresponding author

Correspondence to Roman Hoffmann.

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Peer review information Nature Climate Change thanks Cristina Cattaneo, Clark Gray, Jessica Gurevitch and Robert Oakes for their contribution to the peer review of this work.

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Extended data

Extended Data Fig. 1 Relationships between environmental hazards and migration for different parts of the world.

Blue lines show climate trends and dark gray bars show population movement variables. Lines for temperature and drought are smoothed moving averages. Temperature and rainfall anomalies are yearly deviations from the long-term average (1901–2016). The standardized precipitation and evapotranspiration index (SPEI) is measured at a 3-month scale. Rapid-onset disasters include climatological, hydrological and meteorological disasters. Sources: a) North Africa, SPEIbase (Beguería et al., Bull. Am. Meteorol. Soc. 91, 2010) and Cai et al. (J. Environ. Econ. Manage. 7922); b) Syria, SPEIbase and UNHCR Refugee Statistics; c) South Asia, CRU TS 3.25 (Harris et al., Int. J. Climatol. 34104) and Cai et al22; d) Central America and Mexico, CRU TS 3.25 and Cai et al22; e) Angola, CRU TS 3.25 and UN World Urbanization Prospects 2018; f) Philippines, EM-DAT and IDMC Global Internal Displacement Database.

Extended Data Fig. 2 Density distributions of standardized effects.

Panel a shows the distribution of (unweighted) standardized effects of all model estimates (k = 1803) across studies (displayed range −1 to +1). Positive effects are highlighted in darker grey. Panel b shows the mean effect distribution on study level (n = 30, between-study variation). Panel c shows the distribution of the deviations of the individual estimates (k = 1803) from the study mean effects (within-study variation).

Extended Data Fig. 3 Differences in sample composition by study cases.

Panel a shows boxplots of the share of countries in the model samples belonging to a specific category of countries. Panel b shows the regional focus of the study models. SSA, Sub Saharan Africa, MENA, Middle East North Africa, LAC, Latin America and the Caribbean, OECD, Organization for Economic Co-Operation and Development. Conflict countries are countries with a recurring conflict for at least five years in the period of 1960 to 2000 (Major Episodes of Political Violence database).

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Hoffmann, R., Dimitrova, A., Muttarak, R. et al. A meta-analysis of country-level studies on environmental change and migration. Nat. Clim. Chang. (2020). https://doi.org/10.1038/s41558-020-0898-6

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