More Like this

1. Bangladesh Environment and Migration Survey (BEMS), 2019 :Version 1

   https://doi.org/10.3886/ICPSR38846.v1  

   Carrico, Amanda; Donato, Katharine M. (January 2023, ICPSR - Interuniversity Consortium for Political and Social Research)

   The Bangladesh Environment and Migration Survey (BEMS) collects detailed retrospective information about migration trips in southwest Bangladesh, including the first, last, and second-to-last to internal destinations, India, and other international destinations. BEMS collects information about the year, origin, destination, and duration of all trips. Furthermore, BEMS includes information on migration and livelihood histories, socioeconomic conditions, agricultural resources and practices, disasters and perceptions about environment, and self-reported health.</p> Dataset 1 is a household-level file with information about household composition, economic and migratory activity of household members, land ownership/usage, business ownership, household environmental perceptions, environmental conditions, agricultural activities, and physical and psychological health/well-being of household members. Dataset 2 is an individual-level file containing details of internal and international migration trips, as well as measures of economic and social activity during those trips. It also contains information provided by household heads, spouses, and other migrants in the household. Dataset 3 is an individual-level data file that provides general demographic information and brief migration history for each member of a surveyed household. It also includes health information for the head of household and spouse.</p> The purpose of the Bangladesh Environment and Migration Survey (BEMS) is to understand patterns and processes of contemporary internal and international migration in Bangladesh. The project derives from a multi-disciplinary research effort that will generate data on the characteristics and behavior of Bangladeshi migrants and non-migrants and the communities in which they live, and examine whether and how environmental stressors (e.g., salinity, riverbank erosion) affect patterns of migration in this region. The household ethnosurvey is administered to self-identified household heads and spouses in randomly selected households. After gathering social, demographic, and economic information on households and their members, interviewers will collect basic information on each person's first, 2nd to last, and last (or most recent) internal and international migration trips. From household heads and spouses, they will compile migration histories and administer a detailed series of questions about a selection of these trips, focusing on economic livelihoods, methods of moving, connections to other migrants, and use of health and school services. In addition to detailed migration histories, the BEMS will collect information about household wealth, physical conditions of households and communities, and perceptions of environmental conditions. It will also gather some self-reported health information about household members, such as recent illnesses, use of health services, height and weight, and diet. The BEMS is closely modeled on the sampling design and ethnosurvey used in the Mexican Migration Project. The BEMS data were collected in 20 research sites from a random sample of 200 households in each site in 2019. BEMS data include a total of 4,000 households in communities broadly covering the southwest region of Bangladesh. Households in southwest Bangladesh. Smallest Geographic Unit: Administrative region For more information about this study, please visit the ISEE Bangladesh project website.</p> 

   more » « less
2. Modeling Multi-Level Patterns of Environmental Migration in Bangladesh: An Agent-Based Approach

   https://doi.org/10.1109/WSC52266.2021.9715380  

   Best, Kelsea; Gilligan, Jonathan; Qu, Ao (January 2021, Proceedings of the 2021 Winter Simulation Conference)

   S. Kim; B. Feng; K. Smith; S. Masoud; Z. Zheng; C. Szabo; M. Loper (Ed.)

   Environmental change interacts with population migration in complex ways that depend on interactions between impacts on individual households and on communities. These coupled individual-collective dynamics make agent-based simulations useful for studying environmental migration. We present an original agent-based model that simulates environment-migration dynamics in terms of the impacts of natural hazards on labor markets in rural communities, with households deciding whether to migrate based on maximizing their expected income. We use a pattern-oriented approach that seeks to reproduce observed patterns of environmentally-driven migration in Bangladesh. The model is parameterized with empirical data and unknown parameters are calibrated to reproduce the observed patterns. This model can reproduce these patterns, but only for a narrow range of parameters. Future work will compare income-maximizing decisions to psychologically complex decision heuristics that include non-economic considerations. 

   more » « less
3. Applying machine learning to social datasets: a study of migration in southwestern Bangladesh using random forests

   https://doi.org/10.1007/s10113-022-01915-1  

   Best, Kelsea; Gilligan, Jonathan; Baroud, Hiba; Carrico, Amanda; Donato, Katharine; Mallick, Bishawjit (March 2022, Regional Environmental Change)

   Abstract As researchers collect large amounts of data in the social sciences through household surveys, challenges may arise in how best to analyze such datasets, especially where motivating theories are unclear or conflicting. New analytical methods may be necessary to extract information from these datasets. Machine learning techniques are promising methods for identifying patterns in large datasets, but have not yet been widely used to identify important variables in social surveys with many questions. To demonstrate the potential of machine learning to analyze large social datasets, we apply machine learning techniques to the study of migration in Bangladesh. The complexity of migration decisions makes them suitable for analysis with machine learning techniques, which enable pattern identification in large datasets with many covariates. In this paper, we apply random forest methods to analyzing a large survey which captures approximately 2000 variables from approximately 1700 households in southwestern Bangladesh. Our analysis ranked the covariates in the dataset in terms of their predictive power for migration decisions. The results identified the most important covariates, but there exists a tradeoff between predictive ability and interpretability. To address this tradeoff, random forests and other machine learning algorithms may be especially useful in combination with more traditional regression methods. To develop insights into how the important variables identified by the random forest algorithm impact migration, we performed a survival analysis of household time to first migration. With this combined analysis, we found that variables related to wealth and household composition are important predictors of migration. Such multi-methods approaches may help to shed light on factors contributing to migration and non-migration. 

   more » « less
4. Migration and erosion in tidal and river channels in Bangladesh

   https://doi.org/10.1553/p-d9pb-h22j  

   Donato, Katharine M; Valentine, Leslie; Carrico, Amanda; Wilson, Carol A; Rogers, Kimberly G; Tonassi, Timo (August 2024, Vienna Yearbook of Population Research)

   Environmental stressors and natural disasters are changing the physical landscapes in many countries worldwide. In this paper, we ask whether and how erosion affects internal and international migration in Bangladesh. Building on prior studies, we use data from 3,600 households in 18 research sites to investigate how erosion in tidal and river channels is related to the risk of making a first internal or international trip, net of extreme weather conditions and other relevant attributes. Findings reveal that the relationship between erosion and the likelihood of making a first domestic or international trip is moderated by livelihood type and landownership. As erosion worsens, the odds ofmaking a first domestic trip rise for non-agricultural non-landowning household heads and decline for landowners working in agriculture. Estimated lifetime probabilities of making a first domestic trip are higher than those of making a first international trip, with non-agricultural non-landowners having the highest probabilities and agricultural landowners having the lowest. Together, the evidence suggests that shifts in physical landscapes, especially erosion, are tightly linked to out-migration through ties to land. 

   more » « less
5. Coastal futures: New framings, many questions, some ways forward

   https://doi.org/10.1017/cft.2023.22  

   Spencer, Tom; Adams, Janine; Le Tissier, Martin; Murray, A. Brad; Splinter, Kristen (January 2023, Cambridge Prisms: Coastal Futures)

   Abstract Although coasts are frequently seen as at the frontline of near-future environmental risk, there is more to the understanding of the future of coastal environments than a simple interaction between increasing hazards (particularly related to global sea level rise) and increasing exposure and vulnerability of coastal populations. The environment is both multi-hazard and regionally differentiated, and coastal populations, in what should be seen as a coupled social–ecological–physical system, are both affected by, and themselves modify, the impact of coastal dynamics. As the coupled dance between human decisions and coastal environmental change unfolds over the coming decades, transdisciplinary approaches will be required to come to better decisions on identifying and following sustainable coastal management pathways, including the promotion of innovative restoration activities. Inputs from indigenous knowledge systems and local communities will be particularly important as these stakeholders are crucial actors in the implementation of ecosystem-based mitigation and adaptation strategies. 

   more » « less