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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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Notes

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

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

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

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

  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. 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. 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.

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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
figure 3

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