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Heat and drought reduce subnational population growth in the global tropics

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Abstract

In recent decades, the possibility that climate change will lead to depopulation of vulnerable areas in the global tropics via migration, mortality, or collapsing fertility has generated significant concern. We address this issue by using data on subnational population growth from 1809 subnational units across the global tropics and linked data on climate exposures to examine how decadal temperature and precipitation anomalies influence population-weighted intercensal growth rates. Our fixed-effects regression analysis reveals that the lowest predicted population growth rates occur under hot and dry conditions. The effects of heat and drought are strongest in districts that, at baseline, have high population densities, high precipitation rates, or high educational attainment. These patterns are contrary to common assumptions about these processes, and even the rare combination of hot and dry conditions, occurring in less than 7% of our sample, does not lead to local depopulation. Taken together with previous findings, this suggests that depopulation narratives do not have a strong evidentiary basis.

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Notes

  1. Censuses included: Bangladesh (1991, 2001, 2011); Benin (1979, 1992, 2002, 2013); Bolivia (1976, 1992, 2001); Botswana (1981, 1991, 2001, 2011); Brazil (1960, 1970, 1980, 1991, 2000, 2010); Cameroon (1976, 1987, 2005); Costa Rica (1963, 1973, 1984, 2000, 2011); Dominican Republic (1960, 1970, 1981, 2010); Ecuador (1962, 1974, 1982, 1990, 2001, 2010); Ghana (1984, 2000, 2010); Haiti (1971, 1982, 2003); Honduras (1961, 1974, 1988, 2001); India (employment surveys) (1983, 1987, 1993, 1999, 2004, 2009); Indonesia (1971, 1980, 1990, 2000, 2010); Jamaica (1982, 1991, 2001); Kenya (1969, 1979, 1989, 1999, 2009); Malawi (1987, 1998, 2008); Malaysia (1970, 1980, 1991, 2000); Mali (1987, 1998, 2009); Mexico (1960, 1970, 1990, 1995, 2000, 2005); Nicaragua (1971, 1995, 2005); Panama (1960, 1970, 1980, 1990, 2000, 2010); Papua New Guinea (1980, 1990, 2000); Paraguay (1962, 1972, 1982, 1992, 2002); Tanzania (1988, 2002, 2012); Thailand (1970, 1980, 1990, 2000); Venezuela (1971, 1981, 1990, 2001); Vietnam (1989, 1999, 2009); Zambia (1990, 2000, 2010).

References

  • Alam, S. A., & Pörtner, C. C. (2018). Income shocks, contraceptive use, and timing of fertility. Journal of Development Economics, 131, 96–103. https://doi.org/10.1016/j.jdeveco.2017.10.007

    Article  Google Scholar 

  • Azongo, D. K., Awine, T., Wak, G., Binka, F. N., & Rexford Oduro, A. (2012). A time series analysis of weather variables and all-cause mortality in the Kasena-Nankana Districts of Northern Ghana, 1995–2010. Global Health Action, 5(1), 19073.

    Article  Google Scholar 

  • Berlemann, M., & Steinhardt, M. F. (2017). Climate change, natural disasters, and migration—a survey of the empirical evidence. Cesifo Economic Studies, 63(4), 353–385.

    Article  Google Scholar 

  • Bohra-Mishra, P., Oppenheimer, M., Cai, R., Feng, S., & Licker, R. (2017). Climate variability and migration in the Philippines. Population and Environment, 38(3), 286–308.

    Article  Google Scholar 

  • Boyle, E. H., King, M., Sobek, M. (2019). IPUMS-Demographic and Health Surveys: version 7. Minnesota Population Center and ICF Internationalhttps://doi.org/10.18128/D080.V7

  • Carleton, T. A., Jina, A., Delgado, M. T., Greenstone, M., Houser, T., Hsiang, S. M., ... & Zhang, A. T. (2020). Valuing the global mortality consequences of climate change accounting for adaptation costs and benefits (No. w27599). National Bureau of Economic Research.

  • Carletto, C., & Gourlay, S. (2019). A thing of the past? Household surveys in a rapidly evolving (agricultural) data landscape: Insights from the LSMS-ISA. Agricultural Economics, 50, 51–62.

    Article  Google Scholar 

  • De Sherbinin, A., Levy, M., Adamo, S., MacManus, K., Yetman, G., Mara, V., ... & Pistolesi, L. (2015). Global estimated net migration grids by decade: 1970–2000. Palisades, NY: NASA Socioeconomic Data and Applications Center (SEDAC). https://doi.org/10.7927/H4319SVC

  • De Sherbinin, A., Levy, M., Adamo, S., MacManus, K., Yetman, G., Mara, V., ... & Pistolesi, L. (2012). Migration and risk: net migration in marginal ecosystems and hazardous areas. Environmental Research Letters, 7(4), 045602.

  • Dell, M., Jones, B. F., & Olken, B. A. (2012). Temperature shocks and economic growth: Evidence from the last half century. American Economic Journal: Macroeconomics, 4(3), 66–95.

    Google Scholar 

  • Deville, P., Linard, C., Martin, S., Gilbert, M., Stevens, F. R., Gaughan, A. E., ... & Tatem, A. J. (2014). Dynamic population mapping using mobile phone data. Proceedings of the National Academy of Sciences, 111(45), 15888–15893.

  • Diboulo, E., Sie, A., Rocklöv, J., Niamba, L., Ye, M., Bagagnan, C., & Sauerborn, R. (2012). Weather and mortality: A 10 year retrospective analysis of the Nouna Health and Demographic Surveillance System. Burkina Faso. Global Health Action, 5(1), 19078.

    Article  Google Scholar 

  • Egondi, T., Kyobutungi, C., Kovats, S., Muindi, K., Ettarh, R., & Rocklöv, J. (2012). Time-series analysis of weather and mortality patterns in Nairobi’s informal settlements. Global Health Action, 5(1), 19065.

    Article  Google Scholar 

  • Eissler, S., Thiede, B. C., & Strube, J. (2019). Climatic variability and changing reproductive goals in Sub-Saharan Africa. Global Environmental Change, 57, 101912.

    Article  Google Scholar 

  • Geruso, M., & Spears, D. (2018). Heat, humidity, and infant mortality in the developing world (No. w24870). National Bureau of Economic Research.

  • Gray, C., & Wise, E. (2016). Country-specific effects of climate variability on human migration. Climatic Change, 135(3–4), 555–568.

    Article  Google Scholar 

  • Gutmann, M. P., Deane, G. D., Lauster, N., & Peri, A. (2005). Two population-environment regimes in the Great Plains of the United States, 1930–1990. Population and Environment, 27(2), 191–225.

    Article  Google Scholar 

  • Harris, I. P. D. J., Jones, P. D., Osborn, T. J., & Lister, D. H. (2014). Updated high‐resolution grids of monthly climatic observations–the CRU TS3. 10 Dataset. International Journal of Climatology, 34(3), 623–642.

  • Hasegawa, T., Fujimori, S., Havlík, P., Valin, H., Bodirsky, B. L., Doelman, J. C., Fellmann, T., Kyle, P., Koopman, J. F., Lotze-Campen, H., & Mason-D’Croz, D. (2018). Risk of increased food insecurity under stringent global climate change mitigation policy. Nature Climate Change, 8(8), 699–703.

    Article  Google Scholar 

  • Hawelka, B., Sitko, I., Beinat, E., Sobolevsky, S., Kazakopoulos, P., & Ratti, C. (2014). Geo-located Twitter as proxy for global mobility patterns. Cartography and Geographic Information Science, 41(3), 260–271.

    Article  Google Scholar 

  • Heris, M. P., Foks, N. L., Bagstad, K. J., Troy, A., & Ancona, Z. H. (2020). A rasterized building footprint dataset for the United States. Scientific Data, 7(1), 1–10.

    Google Scholar 

  • Hoffmann, R., Dimitrova, A., Muttarak, R., Cuaresma, J. C., & Peisker, J. (2020). A meta-analysis of country-level studies on environmental change and migration. Nature Climate Change, 10(10), 904–912.

    Article  Google Scholar 

  • Hornbeck, R. (2012). The enduring impact of the American Dust Bowl: Short- and long-run adjustments to environmental catastrophe. American Economic Review, 102(4), 1477–1507.

    Article  Google Scholar 

  • Horton, R. M., de Sherbinin, A., Wrathall, D., & Oppenheimer, M. (2021). Assessing human habitability and migration. Science, 372(6548), 1279–1283.

    Article  Google Scholar 

  • Hsiang, S. M., & Sobel, A. H. (2016). Potentially extreme population displacement and concentration in the tropics under non-extreme warming. Scientific Reports, 6(1), 1–7.

    Article  Google Scholar 

  • Jin, Z., Azzari, G., You, C., Di Tommaso, S., Aston, S., Burke, M., & Lobell, D. B. (2019). Smallholder maize area and yield mapping at national scales with Google Earth Engine. Remote Sensing of Environment, 228, 115–128.

    Article  Google Scholar 

  • Kaczan, D. J., & Orgill-Meyer, J. (2020). The impact of climate change on migration: A synthesis of recent empirical insights. Climatic Change, 158(3), 281–300.

    Article  Google Scholar 

  • Lowder, S. K., Sánchez, M. V., & Bertini, R. (2021). Which farms feed the world and has farmland become more concentrated? World Development, 142, 105455.

    Article  Google Scholar 

  • Mastrorillo, M., Licker, R., Bohra-Mishra, P., Fagiolo, G., Estes, L. D., & Oppenheimer, M. (2016). The influence of climate variability on internal migration flows in South Africa. Global Environmental Change, 39, 155–169.

    Article  Google Scholar 

  • McLeman, R. A. (2011). Settlement abandonment in the context of global environmental change. Global Environmental Change, 21, S108–S120.

    Article  Google Scholar 

  • McLeman, R., Fontanella, F., Greig, C., Heath, G., & Robertson, C. (2021). Population responses to the 1976 South Dakota drought: insights for wider drought migration research. Population, Space and Place, e2465.

  • McLeman, R., Herold, S., Reljic, Z., Sawada, M., & McKenney, D. (2010). GIS-based modeling of drought and historical population change on the Canadian Prairies. Journal of Historical Geography, 36(1), 43–56.

    Article  Google Scholar 

  • Minnesota Population Center. (2018). Integrated Public Use Microdata Series, International: version 7.1. https://doi.org/10.18128/D020.V7.1

  • Mora, C., Dousset, B., Caldwell, I. R., Powell, F. E., Geronimo, R. C., Bielecki, C. R., Counsell, C. W., Dietrich, B. S., Johnston, E. T., Louis, L. V., & Lucas, M. P. (2017). Global risk of deadly heat. Nature Climate Change, 7, 501–506.

    Article  Google Scholar 

  • Mueller, V., Gray, C., & Hopping, D. (2020a). Climate-induced migration and unemployment in middle-income Africa. Global Environmental Change, 65, 102183.

    Article  Google Scholar 

  • Mueller, V., Sheriff, G., Dou, X., & Gray, C. (2020b). Temporary migration and climate variation in eastern Africa. World Development, 126, 104704.

    Article  Google Scholar 

  • Nawrotzki, R. J., & DeWaard, J. (2018). Putting trapped populations into place: Climate change and inter-district migration flows in Zambia. Regional Environmental Change, 18(2), 533–546.

    Article  Google Scholar 

  • Niva, V., Kallio, M., Muttarak, R., Taka, M., Varis, O., & Kummu, M. (2021). Global migration is driven by the complex interplay between environmental and social factors. Environmental Research Letters, 16(11), 114019.

    Article  Google Scholar 

  • Nordkvelle, J., Rustad, S. A., & Salmivalli, M. (2017). Identifying the effect of climate variability on communal conflict through randomization. Climatic Change, 141(4), 627–639.

    Article  Google Scholar 

  • Ortiz-Bobea, A., Ault, T. R., Carrillo, C. M., Chambers, R. G., & Lobell, D. B. (2021). Anthropogenic climate change has slowed global agricultural productivity growth. Nature Climate Change, 11(4), 306–312.

    Article  Google Scholar 

  • Peri, G., & Sasahara, A. (2019). The impact of global warming on rural-urban migrations: evidence from global big data (No. w25728). National Bureau of Economic Research. http://www.nber.org/papers/w25728

  • Randell, H., & Gray, C. (2019). Climate change and educational attainment in the global tropics. Proceedings of the National Academy of Sciences, 116(18), 8840–8845.

    Article  Google Scholar 

  • Rigaud, K. K., de Sherbinin, A., Jones, B., Bergmann, J., Clement, V., Ober, K., ... & Midgley, A. (2018). Groundswell: preparing for internal climate migration. Washington, DC: The World Bank.

  • Ruggles, S., Manson, S., Kugler, T., Haynes, D., II., Van Riper, D., & Bakhtsiyarava, M. (2018). IPUMS Terra: Integrated Data on Population and Environment: version 2. https://doi.org/10.18128/D090.V2

  • Šedová, B., Čizmaziová, L., & Cook, A. (2021). A meta-analysis of climate migration literature (No. 29). Center for Economic Policy Analysis. Retrieved March 24, 2021, from, https://publishup.uni-potsdam.de/opus4-ubp/frontdoor/deliver/index/docId/49982/file/cepa29.pdf

  • Sellers, S., & Gray, C. (2019). Climate shocks constrain human fertility in Indonesia. World Development, 117, 357–369.

    Article  Google Scholar 

  • Springmann, M., Mason-D'Croz, D., Robinson, S., Garnett, T., Godfray, H. C. J., Gollin, D., ... & Scarborough, P. (2016). Global and regional health effects of future food production under climate change: a modelling study. The Lancet, 387(10031), 1937–1946.

  • Thiede, B., Gray, C., & Mueller, V. (2016). Climate variability and inter-provincial migration in South America, 1970–2011. Global Environmental Change, 41, 228–240.

    Article  Google Scholar 

  • Verdin, A., Funk, C., Peterson, P., Landsfeld, M., Tuholske, C., & Grace, K. (2020). Development and validation of the CHIRTS-daily quasi-global high-resolution daily temperature data set. Scientific Data, 7(1), 1–14.

    Article  Google Scholar 

  • Vicedo-Cabrera, A. M., Scovronick, N., Sera, F., Royé, D., Schneider, R., Tobias, A., ... & Gasparrini, A. (2021). The burden of heat-related mortality attributable to recent human-induced climate change. Nature Climate Change, 11(6), 492–500.

  • Zhang, D. D., Brecke, P., Lee, H. F., He, Y. Q., & Zhang, J. (2007). Global climate change, war, and population decline in recent human history. Proceedings of the National Academy of Sciences, 104(49), 19214–19219.

    Article  Google Scholar 

  • Zhang, D. D., Lee, H. F., Wang, C., Li, B., Pei, Q., Zhang, J., & An, Y. (2011). The causality analysis of climate change and large-scale human crisis. Proceedings of the National Academy of Sciences, 108(42), 17296–17301.

    Article  Google Scholar 

  • Zhang, Q., Pandey, B., & Seto, K. C. (2016). A robust method to generate a consistent time series from DMSP/OLS nighttime light data. IEEE Transactions on Geoscience and Remote Sensing, 54(10), 5821–5831.

    Article  Google Scholar 

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Funding

This research was supported by the National Institute of Child Health and Human Development via grant R03HD098357 to CG and via infrastructure support from grant P2CHD050924 to the Carolina Population Center. This work was also supported by the National Socio-Environmental Synthesis Center (SESYNC) under funding received from the National Science Foundation (DBI-1639145).

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Conceptualization: CG, MC. Methodology: CG, MC. Formal analysis and investigation: CG, MC. Writing—original draft preparation: CG, MC.

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Correspondence to Clark Gray.

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Gray, C., Call, M. Heat and drought reduce subnational population growth in the global tropics. Popul Environ 45, 6 (2023). https://doi.org/10.1007/s11111-023-00420-9

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