Climate-induced cross-border migration and change in demographic structure

Abstract

As climate change threatens livelihoods in Bangladesh, migration to neighboring countries in South Asia may accelerate. We use multiple types of data to predict how changes in the environment affect cross-border migration. Nationally representative migration data are combined with remote-sensing measures of flooding and rainfall and in situ measures of monsoon onset, temperature, radiation, and soil salinity to characterize environmental migration patterns. We further evaluate which groups are more susceptible to cross-border migration to examine how environmental factors shape the demographic composition of the country. We find migration to neighboring countries declines with short-term, adverse weather but increases with soil salinity. The soil salinity effect remains particularly persistent among poorer households. Investments targeting risks faced by the poor and non-poor remain crucial, as retention of the earnings skills, and experience of the latter enhances national resilience.

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Fig. 1
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Notes

  1. 1.

    While these adaptive measures are critical coping strategies against crop losses from flooding, the creation of fish ponds can lead to deterioration of river embankments, exacerbating future flood risk. In coastal areas, households often engage in shrimp cultivation and voluntarily introduce saline water to create brackish ponds. This endangers future crop production through the enhancement of salinity in the water and soil, often leading to discord and conflict within communities (Sovacool, 2018).

  2. 2.

    We are broadly generalizing the migrant’s destination decision, where the density of the social network at a particular destination proxies reductions in moving costs and risks of unemployment through job contacts (Carrington et al., 1996; Munshi, 2003). However, Nawrotzki et al. (2015) interestingly find that access to social networks suppresses the probability of environmental migration as they offer would-be-migrants and their corresponding households other forms of adaptation to climate change at the origin.

  3. 3.

    A few macroeconomic studies have examined the effects of climate on bilateral migration flows (Beine and Parsons, 2015; Cattaneo and Peri, 2016). A novel global study calculates the economic effect of sea level rise accounting for changes in a variety of factors, including migration using population data (Desmet et al., 2018).

  4. 4.

    Emerging evidence from Chinese manufacturing firms suggests there may be long-term risks from gradual increases in temperature through its effect on both labor and capital (Zhang et al., 2017). Previous macroeconomic studies link changes in economic growth with changes in climate (Hsiang, 2010; Dell et al., 2012; Burke et al., 2015).

  5. 5.

    Numerous assessments have deemed the prolonged submergence of land attributable to the 1998 “flood of the century” particularly damaging to markets and welfare in Bangladesh (del Ninno et al., 2003; del Ninno and Lundberg, 2005; Mueller and Quisumbing, 2011). Recent work has directed attention to the deleterious impacts of soil salinity, given changes in the amplitude and frequency of sea- level extremes from storm events and tides, on rice productivity (Alauddin et al., 2013, 2014).

  6. 6.

    There are numerous additional barriers to migration that we cannot explicitly address in this paper. These barriers include having insecure property rights (de Brauw and Mueller, 2012; de Janvry et al., 2015), having a strong attachment to place (Bell et al., 2018), or even the psychic costs associated with moving (Sjaastad, 1962; Chen et al., 2019).

  7. 7.

    The selection of household characteristics is limited to what was available from the survey data used in the paper.

  8. 8.

    With a dataset of this size, it is not unusual to have outliers particularly with respect to the age and household size variables. The outliers can arise at various stages of the survey process, from the miscalculation of the respondent at the interview stage, from the documentation of the interview at the transcription stage, and from the transfer of the information from paper to electronic form at the data entry stage. Fortunately, these occurrences of high values for the head of household’s age and household size are rather sparse. Only 0.03% of the households in our sample report having more than 20 members and 0.2% of the households report having a head older than 90 years old.

  9. 9.

    Migration to these countries accounts for 38% of international moves, where the remaining share of migrants arrives in the Middle East.

  10. 10.

    A value of 1 is assigned to the following districts: Satkhira, Jessore, Narail, Gopalganj, Khulna, Bagerhat, Pirojpur, Barguna, Jhalokati, Patuakhali, Barisal, Bhola, Shariatpur, Chandpur, Noakhalim, Feni, Lakshmipur, Chittagong, Cox’s Bazar, and Madaripur. Note that, because salinity data is available only for these districts, the coastal indicator accounts for both regional differences in migration and the mass point at zero in our salinity measure.

  11. 11.

    A value of 1 is assigned to the following districts: Bogra, Joypurhat, Naogaon, Pabna, Rajshahi, Dinajpur, Rangpur, Nilphamari, Sirajgong, Kurigram, Lalmonirhat, Nawabgong, and Natore.

  12. 12.

    Specifically, the variables used to build the index are: whether the primary water source comes from a tap, whether the primary water source comes from a well, whether the secondary water source comes from a tap, whether the secondary water source comes from a well, whether the household has its own water source, and whether the household has a modern or sanitary latrine.

  13. 13.

    An incidence rate ratio less than one indicates a lower risk of the incident occurring, while a ratio greater than one indicates a higher risk.

  14. 14.

    Due to lack of data, we are unable to trace out a similar effect for salinity.

  15. 15.

    The figure is calculated by multiplying the incidence rate ratio (1.039) by the standard deviation of salinity reported in Table 1 (17.255).

  16. 16.

    Additionally, we find a significant negative effect of minimum temperature, suggesting that colder weather may “tip” households into international migration, particularly for males.

  17. 17.

    All districts outside the coastal zone are assigned a value of zero for salinity, based on the design of the soil sampling survey. Therefore, the effect of salinity can be estimated only for the coastal region.

  18. 18.

    Recall, however, that our data on soil salinity cover only the coastal zone; therefore, our findings cannot be extrapolated to increased salinity outside these districts.

  19. 19.

    We also compared households above and below the median for the asset index (results available upon request). Estimates were similar in sign but less precise, suggesting a steeper wealth gradient.

References

  1. Ahmed, R., & Karmakar, S. (1993). Arrival and withdrawal dates of the summer monsoon in Bangladesh. International Journal of Climatology., 13, 727–740.

    Google Scholar 

  2. Alam, S. (2003). Environmentally induced migration from Bangladesh to India. Strategic Analysis, 27(3), 422–438.

    Google Scholar 

  3. Alauddin, M., Amarasinghe, U., & Sharma, B. (2014). Four decades of rice water productivity in Bangladesh: a spatio-temporal analysis of district level panel data. Economic Analysis and Policy, 44, 51–64.

    Google Scholar 

  4. Alauddin, M., & Sharma, B. (2013). Inter-district rice water productivity differences in Bangladesh: an empirical exploration and implications. Ecological Economics, 93, 210–281.

    Google Scholar 

  5. Alpuerto, V., Norton, G., Alwang, J., & Ismail, A. (2009). Economic impact analysis of market-assisted breeding for tolerance to salinity and phosphorous deficiency in rice. Review of Agricultural Economics, 31(4), 779–792.

    Google Scholar 

  6. Angelucci, M. (2015). Migration and financial constraints: evidence from Mexico. Review of Economics and Statistics, 97(1), 224–228.

    Google Scholar 

  7. Auffhammer, M., Ramanathan, V., & Vincent, J. R. (2012). Climate change, the monsoon, and rice yield in India. Climatic Change., 111(2), 411–424.

    Google Scholar 

  8. Banerjee, L. (2010). Effects of flood on agricultural productivity in Bangladesh. Oxford Development Studies, 38(3), 339–356.

    Google Scholar 

  9. Bazzi, S. (2017). Wealth heterogeneity and the income elasticity of migration. American Economic Journal: Applied Economics, 92(2), 219–255.

    Google Scholar 

  10. Beine, M., & Parsons, C. (2015). Climatic factors as determinants of international migration. The Scandinavian Journal of Economics, 117(2), 723–767.

    Google Scholar 

  11. Bell, A., C. Hernandez, and M. Oppenheimer (2018). “Migration, intensification, and diversification as adaptive strategies (MIDAS)”. Socio-Environmental Systems Modelling.

  12. Bertrand, M., Duflo, E., & Mullainathan, S. (2004). How much should we trust differences-in-differences estimates? Quarterly Journal of Economics, 119(1), 249–275.

    Google Scholar 

  13. Bohra-Mishra, P., Oppenheimer, M., & Hsiang, S. (2014). Nonlinear permanent migration response to climatic variations but minimal response to disasters. Proceedings of the National Academy of Sciences, 111(27), 9780–9785.

    Google Scholar 

  14. Bryan, G., Chowdhury, S., & Mobarak, A. (2014). Under-investment in a profitable technology: the case of seasonal migration in Bangladesh. Econometrica, 82(5), 1671–1748.

    Google Scholar 

  15. Burke, M., Hsiang, S., & Miguel, E. (2015). Global non-linear effect of temperature on economic production. Nature, 527, 235–239.

    Google Scholar 

  16. 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.

    Google Scholar 

  17. Call, M., Gray, C., Yunus, M., & Emch, M. (2017). Disruption, not displacement: environmental variability and temporary Mmigration in Bangladesh. Global Environmental Change, 46, 157–165.

    Google Scholar 

  18. Carrico, A. and K. Donato (2019). “Extreme weather and migration: evidence from Bangladesh.” Population and Environment.

  19. Carrington, W., Detragiache, E., & Vishwanath, T. (1996). Migration with endogenous moving costs. American Economic Review, 86(4), 909–930.

    Google Scholar 

  20. Cattaneo, C., & Peri, G. (2016). The migration response to increasing temperatures. Journal of Development Economics, 122, 127–146.

    Google Scholar 

  21. Chen, J., Mueller, V., Jia, Y., & Tseng, S. K.-H. (2017). Validating migration responses to flooding using satellite and vital registration data. American Economic Review, 107, 446–450.

    Google Scholar 

  22. Chen, J., Kosec, K., & Mueller, V. (2019). Moving to despair? Migration and well-being in Pakistan. World Development, 113, 186–203.

    Google Scholar 

  23. Chen, J., & Mueller, V. (2018). Coastal climate change, soil salinity and human migration in Bangladesh. Nature Climate Change, 8, 981–985.

    Google Scholar 

  24. Clark, D., Williams, S., Jahiruddin, M., Parks, K., & Salehin, M. (2015). Projections of on-farm salinity in coastal Bangladesh. Environmental Science: Processes & Impacts, 17, 1127–1136.

    Google Scholar 

  25. Colmer, J. (2018). “Weather, labor reallocation and industrial production: evidence from India,” Center for Economic Performance Discussion Paper 1544.

  26. Cox, Z., Eser, E., & Jimenez, E. (1998). Motives for private transfers over the life cycle: an analytical framework and evidence from Peru. Journal of Development Economics, 55(1), 57–81.

    Google Scholar 

  27. Dasgupta, S., Hossain, M., Huq, M., & Wheeler, D. (2016). Facing the hungry tide: climate change, livelihood threats, and household responses in coastal Bangladesh. Climate Change Economics, 7, 1–25.

    Google Scholar 

  28. Dasgupta, S., F. Kamal, Z. Khan, S. Choudhury, and A. Nishat (2014). “River salinity and climate change: evidence from coastal Bangladesh.” World Bank Policy Research Working Paper 6817.

  29. Davis, K., Bhattachan, A., D’Odorico, P., & Suweis, S. (2018). A universal model for predicting human migration under climate change: examining future sea level rise in Bangladesh. Environmental Research Letters, 13, 064030.

    Google Scholar 

  30. De Brauw, A., & Mueller, V. (2012). Do limitations in land rights transferability influence mobility rates in Ethiopia? Journal of African Economies, 21(4), 548–579.

    Google Scholar 

  31. De Brauw, A., Mueller, V., & Woldehanna, T. (2013). Motives to remit: evidence from tracked internal migrants in Ethiopia. World Development, 50, 13–23.

    Google Scholar 

  32. De Janvry, A., Emerick, K., Gonzalez-Navarro, M., & Sadoulet, E. (2015). Delinking land rights from land use: certification and migration in Mexico. American Economic Review, 105(10), 3125–3149.

    Google Scholar 

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

    Google Scholar 

  34. Del Ninno, C., Dorosh, P., & Smith, L. (2003). Public policy, markets and housing coping strategies in Bangladesh: avoiding a food security crisis following the 1998 floods. World Development, 31(7), 1221–1238.

    Google Scholar 

  35. Del Ninno, C., & Lundberg, M. (2005). Treading water: the long-term impact of the 1998 flood on nutrition in Bangladesh. Economics & Human Biology, 3(1), 67–96.

    Google Scholar 

  36. Desmet, K., R. Kopp, S. Kulp, D. Nagy, M. Oppenheimer, E. Rossi-Hansberg, and B. Strauss (2018). “Evaluating the economic cost of coastal flooding.” NBER Working Paper No. 24918.

  37. Dillon, A., Mueller, V., & Salau, S. (2011). Migratory responses to agricultural risk in Northern Nigeria. American Journal of Agricultural Economics, 93(4), 1048–1061.

    Google Scholar 

  38. Drabo, A., & Mbaye, L. (2015). Natural disasters, migration and education: an empirical analysis in developing countries. Environment and Development Economics, 20(6), 767–796.

    Google Scholar 

  39. Dutta, A. (2018). Political destiny of immigrants in Assam: national register of citizens. Economic and Political Weekly, 53(8), 18–21.

    Google Scholar 

  40. Feng, S., Krueger, A., & Oppenheimer, M. (2010). Linkages among climate change, crop yields, and Mexico-US Cross-border migration. Global Environmental Change, 28, 182–191.

    Google Scholar 

  41. Filmer, D., & Pritchett, L. (2001). Estimating wealth effects without expenditure data—or tears: an application to educational enrollments in States of India. Demography, 38, 115–132.

    Google Scholar 

  42. Fussell, E., Hunter, L., & Gray, C. (2014). Measuring the environmental dimensions of human migration: the demographer’s toolkit. Global Environmental Change, 28, 182–191.

    Google Scholar 

  43. Gray, C., & Mueller, V. (2012). Natural disasters and population mobility in Bangladesh. Proceedings of the National Academy of Sciences, 109(16), 6000–6005.

    Google Scholar 

  44. Guiteras, R., Jina, A., & Mobarak, A. (2015). Satellites, self-reports, and submersion: exposure to floods in Bangladesh. American Economic Review, 105(5), 232–236.

    Google Scholar 

  45. Halliday, T. (2006). Migration, risky, and liquidity constraints in El Salvador. Economic Development and Cultural Change, 54(4), 893–925.

    Google Scholar 

  46. Hill, R. V., Kumar, N., Magnan, N., Makhija, S., de Nicola, F., Spielman, D., & Ward, P. (2019). Ex ante and ex post effects of hybrid index insurance in Bangladesh. Journal of Development Economics, 136, 1–17.

    Google Scholar 

  47. Hirvonen, K. (2016). Temperature changes, household consumption, and internal migration: evidence from Tanzania. American Journal of Agricultural Economics, 98(4), 1240–1249.

    Google Scholar 

  48. Hoddinott, J. (1992). Rotten kids or manipulative parents: are children old age security in Western Kenya? Economic Development and Cultural Change, 40(3), 545–566.

    Google Scholar 

  49. Hoddinott, J. (1994). A model of migration and remittances applied to Western Kenya. Oxford Economic Papers, 46, 459–476.

    Google Scholar 

  50. Hsiang, S. (2010). Temperature and cyclones strongly associated with economic production in the Caribbean and Central America. Proceedings of the National Academy of Sciences, 107(35), 15367–15372.

    Google Scholar 

  51. Hussain, M., Ahmand, S., Hussain, S., Lal, R., Ul-Allah, S., & Nawaz, A. (2018). Chapter six—rice in saline soils: physiology, biochemistry, genetics, and management. Advances in Agronomy., 148, 231–287.

    Google Scholar 

  52. Islam, N., & Uyeda, H. (2007). Use of TRMM in determining the climatic characteristics of rainfall over Bangladesh. Remote Sensing of Environment, 108(3), 264–276.

    Google Scholar 

  53. Islam, A. S., Bala, S. K., & Haque, M. A. (2010). Flood inundation map of Bangladesh using MODIS time-series images. Journal of Flood Risk Management., 3, 210–222.

    Google Scholar 

  54. Jayachandran, S. (2006). Selling labor low: wage responses to productivity shocks in developing countries. Journal of Political Economy, 114(3), 538–575.

    Google Scholar 

  55. Ji, L., Zhang, L., & Wylie, B. (2009). Analysis of dynamic thresholds for the normalized difference water index. Photogrammetric Engineering and Remote Sensing, 75(11), 1307–1317.

    Google Scholar 

  56. Khanom, T. (2016). Effect of salinity on food security in the context of interior coast of Bangladesh. Ocean and Coastal Management, 130, 205–212.

    Google Scholar 

  57. Kleemans, N. (2015). “Migration choice under risk and liquidity constraints.” Unpublished. Retrieved from https://sites.google.com/site/mariekekleemans/research. Accessed 27 November 2018.

  58. Lu, X., Wrathall, D., Sundsoy, P., Nadiruzzaman, M., Wetter, E., Iqbal, A., Qureshi, T., Tatem, A., Canright, G., Engo-Monsen, K., & Bengtsson, L. (2016). Unveiling hidden migration and mobility patterns in climate stress regions: a longitudinal study of six million anonymous mobile phone users in Bangladesh. Global Environmental Change, 38, 1–7.

    Google Scholar 

  59. Melkonyan, T., & Grigorian, D. (2012). Microeconomic implications of remittances in an overlapping generations model with altruism and a motive to receive inheritance. Journal of Development Studies, 48(8), 1026–1044.

    Google Scholar 

  60. Mueller, V., & Quisumbing, A. (2011). How resilient are labor markets to natural disasters? The case of the 1998 Bangladesh flood. Journal of Development Studies, 47(12), 1954–1971.

    Google Scholar 

  61. Mueller, V., Doss, C., & Quisumbing, A. (2018). Youth migration and labor constraints in African agrarian households. Journal of Development Studies, 54(5), 875–894.

    Google Scholar 

  62. Munshi, K. (2003). Networks in the modern economy: Mexican migrants in the U.S. labor market. The Quarterly Journal of Economics, 118(2), 549–599.

    Google Scholar 

  63. Nawrotzki, R., Riosmena, F., Hunter, L., & Runfola, D. (2015). Amplification or suppression: social networks and the climate change-migration association in Rural Mexico. Global Environmental Change, 35, 463–474.

    Google Scholar 

  64. Neumann, B., Vafeidis, A., Zimmermann, J., & Nicholls, R. (2015). Future coastal population growth and exposure to sea-level rise and coastal flooding—a global assessment. PLOS One, 10(6), e0131375.

    Google Scholar 

  65. Ogilvie, A., Belaud, G., Delenne, C., Bailly, J. S., Bader, J. C., Oleksiak, A., Ferry, L., & Martin, D. (2015). Decadal monitoring of the Niger inner delta flood dynamics using MODIS optical data. Journal of Hydrology, 523, 368–383.

    Google Scholar 

  66. Payo, A., Lazar, A., Clarke, D., Nicholls, R., Bricheno, L., Mashfiqus, S., & Haque, A. (2017). Modeling daily soil salinity dynamics in response to agricultural and environmental Cchanges in coastal Bangladesh. Earth’s Future, 5, 495–514.

    Google Scholar 

  67. Penning-Rowsell, E., Sultana, P., & Thompson, P. (2013). The ‘last resort’? Population movement in response to climate-related hazards in Bangladesh. Environmental Science & Policy, 27(S1), S44–S59.

    Google Scholar 

  68. Quiñones, E. (2018). Anticipatory Migration and Local Labor Responses to Rural Climate Shocks. Unpublished. Accessed online on November 27, 2018 at: https://sites.google.com/view/ejquinones/.

  69. Redfern, S.K., Azzu, N., Binamira, J.S., Meybeck, A., Lankoski, J., Redfern, S., and Gitz, V. (2012). “Rice in Southeast Asia: facing risks and vulnerabilities to respond to climate change. In: Building resilience for adaptation to climate change in the agriculture sector.” Proceedings of a Joint FAO/OECD Workshop, Rome, Italy, 23–24 April 2012 (Food and Agriculture Organization of the United Nations (FAO)), pp. 295–314. Quiñones, E. (2018). “Anticipatory migration and local labor responses to rural climate shocks.” Unpublished. Retrieved from https://sites.google.com/view/ejquinones/. Accessed 27 November 2018

  70. Rosenzweig, M. (1993). Women, insurance capital, and economic development in rural India. Journal of Human Resources, 28(4), 735–758.

    Google Scholar 

  71. Rosenzweig, M., & Binswanger, H. (1993). Wealth, weather risk and the composition and profitability of agricultural investments. Economic Journal, 103, 56–78.

    Google Scholar 

  72. Rosenzweig, M., & Stark, O. (1989). Consumption smoothing, migration, and marriage: evidence from rural India. Journal of Political Economy, 97(4), 905–926.

    Google Scholar 

  73. Schultz, K. (2018). “As India clamps down on migration, millions may lose citizenship.” The New York Times. Retrieved Online from https://nyti.ms/2K4ZdJ3. Accessed 5 Dec 2018

  74. Sjaastad, L. (1962). The costs and returns of human migration. Journal of Political Economy, 70(5), 80–93.

    Google Scholar 

  75. Soil Resource Development Institute. (2012). Saline soils of Bangladesh. Bangladesh: Dhaka.

    Google Scholar 

  76. Sovacool, B. (2018). Bamboo beating bandits: conflict, inequality, and vulnerability in the political ecology of climate change adaptation in Bangladesh. World Development, 102, 183–194.

    Google Scholar 

  77. Stark, O., & Lucas, R. E. B. (1988). Migration, remittances, and the family. Economic Development and Cultural Change, 36(3), 465–481.

    Google Scholar 

  78. Stecklov, G., Winters, P., Stampini, M., & Davis, B. (2005). Do conditional cash transfers influence migration? A study using experimental data from the Mexican PROGRESA Program. Demography, 42(4), 769–790.

    Google Scholar 

  79. Tarek, M. H., Hassan, A., Bhattacharjee, J., Choudhury, S. H., & Badruzzaman, A. B. (2017). Assessment of TRMM data for precipitation measurement in Bangladesh. Meteorological Applications, 24, 349–359.

    Google Scholar 

  80. Tripathi, S. (2016). “Illegal immigration from Bangladesh to India: toward a comprehensive solution.” Carnegie India. Available Online from https://carnegieindia.org/2016/06/29/illegal-immigration-from-bangladesh-to-india-toward-comprehensive-solution-pub-63931. Accessed 26 Nov 2018

  81. Welch, J., Vincent, J., Auffhammer, M., Moya, P., Dobermann, A., & Dawe, D. (2010). Rice yields in tropical/subtropical Asia exhibit large but opposing sensitivities to minimum and maximum temperatures. Proceedings of the National Academy of Sciences, 107(33), 14562–14567.

    Google Scholar 

  82. Xu, H. (2006). Modification of normalised difference water index (NDWI) to enhance open water features in remotely sensed imagery. International Journal of Remote Sensing, 27(14), 3025–3033.

    Google Scholar 

  83. Zhang, P., O. Deschenes, K. Meng, and J. Zhang (2017). “Temperature effects on productivity and factor reallocation: evidence from a Half Million Chinese manufacturing plants.” NBER Working Paper. No. 23991.

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Acknowledgments

We thank Kathryn Dotzel, Yuanyuan Jia, and Varuni Sureddy for excellent research assistance and Steven Kuo-Hsin Tseng for graciously sharing his code.

Funding

Support from the National Science Foundation via the Belmont Forum/IGFA Program (ICER-1342644) and the Mershon Center for International Security Studies at The Ohio State University is gratefully acknowledged.

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Appendix 1

Appendix 1

Fig. 3
figure3

Bangladesh Meteorological Department Weather Stations. Source: Bangladesh Meteorological Department, http://www.bmd.gov.bd

Table 5 Environmental migration patterns, separate time lags
Table 6 Environmental migration patterns, alternate measures
Table 7 Environmental migration patterns, multinomial logit

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Chen, J., Mueller, V. Climate-induced cross-border migration and change in demographic structure. Popul Environ 41, 98–125 (2019). https://doi.org/10.1007/s11111-019-00328-3

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Keywords

  • Cross-border migration
  • Climate change
  • Bangladesh