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Random forest analysis of two household surveys can identify important predictors of migration in Bangladesh

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Abstract

The decision to migrate is complex and is often influenced by a combination of economic, social, political, and environmental pressures. Household survey instruments can capture detailed information about migration histories and their contexts, but it can be challenging to identify important predictors from large numbers of covariates with standard statistical methods, such as regression analyses. Machine learning techniques are well suited to pattern identification and can identify important covariates from large datasets. We report on the application of machine learning approaches to two large surveys collected from a total of more than 2800 households in southwestern Bangladesh. We applied random forest classification and regression models to identify significant covariates with the greatest predictive power for household migration decisions. The results show that random forest models are able to identify nuances in predictors of different types of migration and migration in different communities. Random forests also outperform logistic regression and support vector machines in predicting migration in all cases analyzed. Therefore, random forest models and other machine learning methods can be useful for improving the predictive accuracy of migration models and identifying patterns in complex social datasets. Future work should continue to explore the potential of machine learning techniques applied to questions of environmental migration.

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Data availability

More information about Survey 1 and Survey 2 is available upon request from the corresponding author. The questionnaire for Survey 2 is included in full in the Electronic Supplementary Materials (Online Resource 1).

References

  1. Intergovernmental Panel on Climate Change. (2018). Global warming of 1.5 °C. Retrieved from https://www.ipcc.ch/report/sr15/.

  2. Nicholls, R. J., Wong, P. P., Burkett, V., Codignotto, J. O., Hay, J., McLean, R. F., … Woodroffe, C. D. (2007). Coastal systems and low-lying areas (pp. 315–356). Cambridge University Press. Retrieved from https://pubs.er.usgs.gov/publication/70204340.

  3. Nicholls, R. J., Wong, P. P., Burkett, V., Woodroffe, C. D., & Hay, J. (2008). Climate change and coastal vulnerability assessment: Scenarios for integrated assessment. Sustainability Science, 3(1), 89–102. https://doi.org/10.1007/s11625-008-0050-4.

    Article  Google Scholar 

  4. Myers, N. (2002). Environmental refugees: A growing phenomenon of the 21st century. Philosophical Transactions of the Royal Society of London B: Biological Sciences, 357(1420), 609–613. https://doi.org/10.1098/rstb.2001.0953.

    Article  Google Scholar 

  5. Call, M. A., Gray, C., Yunus, M., & Emch, M. (2017). Disruption, not displacement: Environmental variability and temporary migration in Bangladesh. Global Environmental Change, 46, 157–165. https://doi.org/10.1016/j.gloenvcha.2017.08.008.

    Article  Google Scholar 

  6. Gray, C. L., & Mueller, V. (2012). Natural disasters and population mobility in Bangladesh. Proceedings of the National Academy of Sciences United States of America, 109(16), 6000–6005. https://doi.org/10.1073/pnas.1115944109.

    Article  Google Scholar 

  7. Obokata, R., Veronis, L., & McLeman, R. (2014). Empirical research on international environmental migration: A systematic review. Population and Environment, 36(1), 111–135. https://doi.org/10.1007/s11111-014-0210-7.

    Article  Google Scholar 

  8. Amrith, S. S. (2013). Crossing the Bay of Bengal: The furies of nature and the fortunes of migrants. Cambridge, MA: Harvard University Press.

    Google Scholar 

  9. Black, R., Adger, W. N., Arnell, N. W., Dercon, S., Geddes, A., & Thomas, D. (2011). Migration and global environmental change. Global Environmental Change, 21, S1–S2. https://doi.org/10.1016/j.gloenvcha.2011.10.005.

    Article  Google Scholar 

  10. Perch-Nielsen, S. L., Bättig, B. M., & Imboden, D. (2008). Exploring the link between climate change and migration. Climatic Change, 91(3–4), 375–393. https://doi.org/10.1007/s10584-008-9416-y.

    Article  Google Scholar 

  11. Cai, R., Feng, S., Oppenheimer, M., & Pytlikova, M. (2016). Climate variability and international migration: The importance of the agricultural linkage. Journal of Environmental Economics and Management, 79, 135–151. https://doi.org/10.1016/j.jeem.2016.06.005.

    Article  Google Scholar 

  12. Black, R., Arnell, N. W., Adger, W. N., Thomas, D., & Geddes, A. (2013). Migration, immobility and displacement outcomes following extreme events. Environmental Science & Policy, 27, S32–S43. https://doi.org/10.1016/j.envsci.2012.09.001.

    Article  Google Scholar 

  13. Cattaneo, C., & Peri, G. (2016). The migration response to increasing temperatures. Journal of Development Economics, 122, 127–146. https://doi.org/10.1016/j.jdeveco.2016.05.004.

    Article  Google Scholar 

  14. Joarder, M. A. M., & Miller, P. W. (2013). Factors affecting whether environmental migration is temporary or permanent: Evidence from Bangladesh. Global Environmental Change, 23(6), 1511–1524. https://doi.org/10.1016/j.gloenvcha.2013.07.026.

    Article  Google Scholar 

  15. Bohra-Mishra, P., Oppenheimer, M., & Hsiang, S. M. (2014). Nonlinear permanent migration response to climatic variations but minimal response to disasters. Proceedings of the National Academy of Sciences of the United States of America, 111(27), 9780–9785. https://doi.org/10.1073/pnas.1317166111.

    Article  Google Scholar 

  16. Gray, C., & Wise, E. (2016). Country-specific effects of climate variability on human migration. Climatic Change, 135(3–4), 555–568. https://doi.org/10.1007/s10584-015-1592-y.

    Article  Google Scholar 

  17. Thiede, B., Gray, C., & Mueller, V. (2016). Climate variability and inter-provincial migration in South America, 1970–2011. Global Environmental Change, 41, 228–240. https://doi.org/10.1016/j.gloenvcha.2016.10.005.

    Article  Google Scholar 

  18. Gray, C., & Mueller, V. (2012). Drought and population mobility in rural Ethiopia. World Development, 40(1), 134–145. https://doi.org/10.1016/j.worlddev.2011.05.023.

    Article  Google Scholar 

  19. Gray, C. L., & Bilsborrow, R. E. (2014). Consequences of out-migration for land use in rural Ecuador. Land Use Policy, 36, 182–191. https://doi.org/10.1016/j.landusepol.2013.07.006.

    Article  Google Scholar 

  20. Mueller, V., Gray, C., & Kosec, K. (2014). Heat stress increases long-term human migration in rural Pakistan. Nature Climate Change, 4(3), 182–185. https://doi.org/10.1038/nclimate2103.

    Article  Google Scholar 

  21. Gray, C. L. (2011). Soil quality and human migration in Kenya and Uganda. Global Environmental Change, 21(2), 421–430. https://doi.org/10.1016/j.gloenvcha.2011.02.004.

    Article  Google Scholar 

  22. Gray, C., Frankenberg, E., Gillespie, T., Sumantri, C., & Thomas, D. (2014). Studying displacement after a disaster using large scale survey methods: Sumatra after the 2004 tsunami. Annals of the Association of American Geographers. Association of American Geographers, 104(3), 594–612. https://doi.org/10.1080/00045608.2014.892351.

    Article  Google Scholar 

  23. Marchiori, L., Maystadt, J.-F., & Schumacher, I. (2012). The impact of weather anomalies on migration in sub-Saharan Africa. Journal of Environmental Economics and Management, 63(3), 355–374. https://doi.org/10.1016/j.jeem.2012.02.001.

    Article  Google Scholar 

  24. Alam, G. M. M., Alam, K., & Mushtaq, S. (2017). Climate change perceptions and local adaptation strategies of hazard-prone rural households in Bangladesh. Climate Risk Management, 17, 52–63. https://doi.org/10.1016/j.crm.2017.06.006.

    Article  Google Scholar 

  25. Koubi, V., Spilker, G., Schaffer, L., & Bernauer, T. (2016). Environmental stressors and migration: Evidence from Vietnam. World Development, 79, 197–210. https://doi.org/10.1016/j.worlddev.2015.11.016.

    Article  Google Scholar 

  26. Svart, L. M. (1976). Environmental preference migration: A review. Geographical Review, 66(3), 314–330. https://doi.org/10.2307/213888.

    Article  Google Scholar 

  27. Farris, S. R. (2010). Interregional migration: The challenge for gender and development. Development, 53(1), 98–104. https://doi.org/10.1057/dev.2009.89.

    Article  Google Scholar 

  28. Gioli, G., Khan, T., Bisht, S., & Scheffran, J. (2014). Migration as an adaptation strategy and its gendered implications: a case study from the Upper Indus Basin. Mountain Research and Development, 34(3), 255–265. https://doi.org/10.1659/MRD-JOURNAL-D-13-00089.1.

    Article  Google Scholar 

  29. Donato, K. M., Massey, D. S., Donato, K. M., Carrico, A. R., Sisk, B., & Piya, B. (2016). Different but the same: How legal status affects international migration from Bangladesh. The Annals of the American Academy of Political and Social Science, 666(1), 203–218. https://doi.org/10.1177/0002716216650843.

    Article  Google Scholar 

  30. Dustmann, C., & Okatenko, A. (2014). Out-migration, wealth constraints, and the quality of local amenities. Journal of Development Economics, 110, 52–63. https://doi.org/10.1016/j.jdeveco.2014.05.008.

    Article  Google Scholar 

  31. McLeman, R. (2013). Developments in modelling of climate change-related migration. Climatic Change, 117(3), 599–611. https://doi.org/10.1007/s10584-012-0578-2.

    Article  Google Scholar 

  32. Neumann, K., & Hilderink, H. (2015). Opportunities and challenges for investigating the environment-migration nexus. Human Ecology, 43(2), 309–322. https://doi.org/10.1007/s10745-015-9733-5.

    Article  Google Scholar 

  33. Renaud, F. G., Dun, O., Warner, K., & Bogardi, J. (2011). A decision framework for environmentally induced migration: Framework for environmentally induced migration. International Migration, 49, e5–e29. https://doi.org/10.1111/j.1468-2435.2010.00678.x.

    Article  Google Scholar 

  34. Hino, M., Field, C. B., & Mach, K. J. (2017). Managed retreat as a response to natural hazard risk. Nature Climate Change, 7(5), 364–370. https://doi.org/10.1038/nclimate3252.

    Article  Google Scholar 

  35. Cai, R., & Oppenheimer, M. (2013). An Agent-based model of climate-induced agricultural labor migration. In 2013 Annual meeting, August (pp. 4–6).

  36. Hassani-Mahmooei, B., & Parris, B. W. (2012). Climate change and internal migration patterns in Bangladesh: An agent-based model. Environment and Development Economics, 17(06), 763–780. https://doi.org/10.1017/S1355770X12000290.

    Article  Google Scholar 

  37. Kniveton, D., Smith, C., & Wood, S. (2011). Agent-based model simulations of future changes in migration flows for Burkina Faso. Global Environmental Change, 21, S34–S40. https://doi.org/10.1016/j.gloenvcha.2011.09.006.

    Article  Google Scholar 

  38. Silveira, J. J., Espindola, A. L., & Penna, T. J. P. (2006). An agent-based model to rural-urban migration analysis. Physica A: Statistical Mechanics and its Applications, 364, 445–456. https://doi.org/10.1016/j.physa.2005.08.055.

    Article  Google Scholar 

  39. Smith, C. D. (2014). Modelling migration futures: Development and testing of the Rainfalls Agent-Based Migration Model—Tanzania. Climate and Development, 6(1), 77–91. https://doi.org/10.1080/17565529.2013.872593.

    Article  Google Scholar 

  40. Fussell, E., Hunter, L. M., & Gray, C. L. (2014). Measuring the environmental dimensions of human migration: The demographer’s toolkit. Global Environmental Change, 28, 182–191. https://doi.org/10.1016/j.gloenvcha.2014.07.001.

    Article  Google Scholar 

  41. Cutler, D. R., Edwards, T. C., Beard, K. H., Cutler, A., Hess, K. T., Gibson, J., et al. (2007). Random forests for classification in ecology. Ecology, 88(11), 2783–2792. https://doi.org/10.1890/07-0539.1.

    Article  Google Scholar 

  42. Prasad, A. M., Iverson, L. R., & Liaw, A. (2006). Newer classification and regression tree techniques: Bagging and random forests for ecological prediction. Ecosystems, 9(2), 181–199. https://doi.org/10.1007/s10021-005-0054-1.

    Article  Google Scholar 

  43. Passalacqua, P., Lanzoni, S., Paola, C., & Rinaldo, A. (2013). Geomorphic signatures of deltaic processes and vegetation: The Ganges-Brahmaputra-Jamuna case study. Journal of Geophysical Research: Earth Surface, 118(3), 1838–1849. https://doi.org/10.1002/jgrf.20128.

    Article  Google Scholar 

  44. Walsham, M. (2010). Assessing the Evidence: Environment. International Organization for Migration: Climate Change and Migration in Bangladesh.

    Google Scholar 

  45. Black, R., Kniveton, D., Skeldon, R., Coppard, D., Murata, A., & Schmidt-Verkerk, K. (2008). Demographics and climate change: Future trends and their policy implications for migration. Development Research Centre on Migration, Globalisation and Poverty. Brighton: University of Sussex.

    Google Scholar 

  46. Nicholls, R. J., Wong, P. P., Burkett, V., Codignotto, J., Hay, J., McLean, R., … Arblaster, J. (2007). Coastal systems and low-lying areas. Cambridge : Cambridge University Press.

  47. Ackerly, B. A., Anam, M. M., & Gilligan, J. (2015). Environment, political economies and livelihood change. In B. Mallick & B. Etzold (Eds.), Environment, migration and adaptation: Evidence and politics of climate change in Bangladesh. Dhaka, Bangladesh: AH Development Publishing House (AHDPH). Retrieved from https://eprints.qut.edu.au/84192/.

  48. Auerbach, L. W., Goodbred Jr, S. L., Mondal, D. R., Wilson, C. A., Ahmed, K. R., Roy, K., … Ackerly, B. A. (2015). Flood risk of natural and embanked landscapes on the Ganges–Brahmaputra tidal delta plain. Nature Climate Change, 5(2), 153–157. https://doi.org/10.1038/nclimate2472.

  49. Benneyworth, L., Gilligan, J., Ayers, J. C., Goodbred, S., George, G., Carrico, A., … Piya, B. (2016). Drinking water insecurity: water quality and access in coastal south-western Bangladesh. International Journal of Environmental Health Research, 26(5–6), 508–524. https://doi.org/10.1080/09603123.2016.1194383.

  50. Brammer, H. (2014). Bangladesh’s dynamic coastal regions and sea-level rise. Climate Risk Management, 1, 51–62. https://doi.org/10.1016/j.crm.2013.10.001.

    Article  Google Scholar 

  51. Tessler, Z. D., Vorosmarty, C. J., Grossberg, M., Gladkova, I., Aizenman, H., Syvitski, J. P. M., et al. (2015). Profiling risk and sustainability in coastal deltas of the world. Science, 349(6248), 638–643. https://doi.org/10.1126/science.aab3574.

    Article  Google Scholar 

  52. Xu, J., Grumbine, R. E., Shrestha, A., Eriksson, M., Yang, X., Wang, Y., et al. (2009). The melting himalayas: Cascading effects of climate change on water, biodiversity, and livelihoods. Conservation Biology, 23(3), 520–530. https://doi.org/10.1111/j.1523-1739.2009.01237.x.

    Article  Google Scholar 

  53. World Bank. (2016). Bangladesh: Growing the economy through advances in agriculture. World Bank. Text/HTML. Retrieved from https://projects-beta.worldbank.org/en/results/2016/10/07/bangladesh-growing-economy-through-advances-in-agriculture

  54. Black, R., Natali, C., & Skinner, J. (2005). Migration and inequality. Washington, DC: World Bank.

    Google Scholar 

  55. Martin, M., Billah, M., Siddiqui, T., Abrar, C., Black, R., & Kniveton, D. (2014). Climate-related migration in rural Bangladesh: A behavioural model. Population and Environment, 36(1), 85–110. https://doi.org/10.1007/s11111-014-0207-2.

    Article  Google Scholar 

  56. Afsar, R. (2003). Internal migration and the development nexus: The case of Bangladesh. In Regional Conference on Migration, Development and Pro-Poor Policy Choices in Asia (pp. 22–24).

  57. Khandker, S. R. (2012). Seasonality of income and poverty in Bangladesh. Journal of Development Economics, 97(2), 244–256. https://doi.org/10.1016/j.jdeveco.2011.05.001.

    Article  Google Scholar 

  58. Ahsan, R., Karuppannan, S., & Kellett, J. (2011). Climate migration and urban planning system: A study of Bangladesh. Environmental Justice, 4(3), 163–170. https://doi.org/10.1089/env.2011.0005.

    Article  Google Scholar 

  59. Chen, J., & Mueller, V. (2018). Coastal climate change, soil salinity and human migration in Bangladesh. Nature Climate Change. https://doi.org/10.1038/s41558-018-0313-8.

    Article  Google Scholar 

  60. Carrico, A. R., & Donato, K. (2019). Extreme weather and migration: Evidence from Bangladesh. Population and Environment. https://doi.org/10.1007/s11111-019-00322-9.

    Article  Google Scholar 

  61. Bernzen, A., Jenkins, J. C., & Braun, B. (2019). Climate change-induced migration in coastal Bangladesh? A critical assessment of migration drivers in rural households under economic and environmental stress. Geosciences, 9(1), 51. https://doi.org/10.3390/geosciences9010051.

    Article  Google Scholar 

  62. Mallick, B., & Vogt, J. (2014). Population displacement after cyclone and its consequences: Empirical evidence from coastal Bangladesh. Natural Hazards, 73(2), 191–212. https://doi.org/10.1007/s11069-013-0803-y.

    Article  Google Scholar 

  63. Rakib, M. A., Sasaki, J., Matsuda, H., & Fukunaga, M. (2019). Severe salinity contamination in drinking water and associated human health hazards increase migration risk in the southwestern coastal part of Bangladesh. Journal of Environmental Management, 240, 238–248. https://doi.org/10.1016/j.jenvman.2019.03.101.

    Article  Google Scholar 

  64. Hosmer, D. W., Jr., Lemeshow, S., & Sturdivant, R. X. (2013). Applied Logistic Regression. New York: Wiley.

    Book  Google Scholar 

  65. Suykens, J. A. K., & Vandewalle, J. (1999). Least squares support vector machine classifiers. Neural Processing Letters, 9(3), 293–300. https://doi.org/10.1023/A:1018628609742.

    Article  Google Scholar 

  66. Liu, Y., & De, A. (2015). Multiple imputation by fully conditional specification for dealing with missing data in a large epidemiologic study. International journal of statistics in medical research, 4(3), 287–295. https://doi.org/10.6000/1929-6029.2015.04.03.7.

    Article  Google Scholar 

  67. van Buuren, S., & Groothuis-Oudshoorn, K. (2011). mice: Multivariate imputation by chained equations in R. Journal of Statistical Software, 45(1), 1–67. https://doi.org/10.18637/jss.v045.i03.

    Article  Google Scholar 

  68. Cutler, F. original by L. B. and A., & Wiener, R. port by A. L. and M. (2018). randomForest: Breiman and Cutler’s random forests for classification and regression. Retrieved from https://CRAN.R-project.org/package=randomForest.

  69. James, G., Witten, D., Hastie, T., & Tibshirani, R. (Eds.). (2013). An introduction to statistical learning: with applications in R. New York: Springer.

    Google Scholar 

  70. Adger, W. N., Arnell, N. W., Black, R., Dercon, S., Geddes, A., & Thomas, D. S. G. (2015). Focus on environmental risks and migration: Causes and consequences. Environmental Research Letters, 10(6), 060201. https://doi.org/10.1088/1748-9326/10/6/060201.

    Article  Google Scholar 

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

    Article  Google Scholar 

  72. Haug, S. (2008). Migration networks and migration decision-making. Journal of Ethnic and Migration Studies, 34(4), 585–605. https://doi.org/10.1080/13691830801961605.

    Article  Google Scholar 

  73. Hunter, L. M., Murray, S., & Riosmena, F. (2013). Rainfall patterns and U.S. migration from rural Mexico. The International Migration Review, 47(4), 874–909. https://doi.org/10.1111/imre.12051.

    Article  Google Scholar 

  74. Sraboni, E., Malapit, H. J., Quisumbing, A. R., & Ahmed, A. U. (2014). Women’s empowerment in agriculture: What role for food security in Bangladesh? World Development, 61, 11–52. https://doi.org/10.1016/j.worlddev.2014.03.025.

    Article  Google Scholar 

  75. Lu, X., Wrathall, D. J., Sundsøy, P. R., Nadiruzzaman, Md., Wetter, E., Iqbal, A., … Bengtsson, L. (2016). Unveiling hidden migration and mobility patterns in climate stressed regions: A longitudinal study of six million anonymous mobile phone users in Bangladesh. Global Environmental Change, 38, 1–7. https://doi.org/10.1016/j.gloenvcha.2016.02.002.

  76. Islam, M. R., & Hasan, M. (2016). Climate-induced human displacement: a case study of Cyclone Aila in the south-west coastal region of Bangladesh. Natural Hazards, 81(2), 1051–1071. https://doi.org/10.1007/s11069-015-2119-6.

    Article  Google Scholar 

  77. Stern, N. (2006). The price of change. IAEA Bulletin, 48(2), 25.

    Google Scholar 

  78. Biermann, F., & Boas, I. (2010). Preparing for a warmer world: Towards a global governance system to protect climate refugees. Global Environmental Politics, 10, 60–88. https://doi.org/10.1162/glep.2010.10.1.60.

    Article  Google Scholar 

  79. Hugo, G. (2011). Future demographic change and its interactions with migration and climate change. Global Environmental Change, 21, S21–S33. https://doi.org/10.1016/j.gloenvcha.2011.09.008.

    Article  Google Scholar 

  80. Shayegh, S. (2017). Outward migration may alter population dynamics and income inequality. Nature Climate Change, 7(11), 828–832. https://doi.org/10.1038/nclimate3420.

    Article  Google Scholar 

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Acknowledgements

This work was supported by the National Science Foundation Coupled Human-Natural Systems Grant No. 1716909 and Office of Naval Research through Grant No. N00014-11–1-0683.

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Authors and Affiliations

  1. Department of Earth and Environmental Sciences, Vanderbilt University, Nashville, TN, USA

    Kelsea B. Best & Jonathan M. Gilligan

  2. Department of Civil and Environmental Engineering, Vanderbilt University, Nashville, TN, USA

    Hiba Baroud

  3. Environmental Studies Program, University of Colorado at Boulder, Boulder, CO, USA

    Amanda R. Carrico

  4. Institute for the Study of International Migration, Georgetown University, Washington, DC, USA

    Katharine M. Donato

  5. Department of Political Science, Vanderbilt University, Nashville, TN, USA

    Brooke A. Ackerly

  6. Environmental Sciences, Technische Universität Dresden, Dresden, Germany

    Bishawjit Mallick

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Best, K.B., Gilligan, J.M., Baroud, H. et al. Random forest analysis of two household surveys can identify important predictors of migration in Bangladesh. J Comput Soc Sc 4, 77–100 (2021). https://doi.org/10.1007/s42001-020-00066-9

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