Impacts of refugee influx on the local economy and environmental degradation in Bangladesh: A spatial multilevel autoregressive analysis

Maiko Sakamoto, S.M. Asik Ullah and Masakazu Tani

World Development, 2024, vol. 183, issue C

Abstract: The number of people who have been forcibly displaced has increased steadily over the past decade. The recent Rohingya refugee exodus of an estimated 700,000 individuals surging into Bangladesh from Myanmar is just one example of this growing issue. Refugees generally affect the economic and social conditions and the local environment where they resettle. This study aims to examine the impacts of the Rohingya refugee influx on livelihood choice and income status in the host community as well as local environmental degradation. We conducted a questionnaire survey in all 147 villages of the Teknaf Upazila before and after the Rohingya refugee influx that included 5,769 and 6,825 households respectively. We used land cover maps created from remote sensing images to assess the region’s environmental degradation in a holistic fashion. Inherent regional characteristics may affect livelihood choice and income; therefore, we applied two statistical modeling approaches to mitigate such inherent regional biases—Multilevel Modeling and Multilevel Intrinsic Conditional Autoregressive Modeling. The statistical analyses used a combination of the household survey results and the land cover maps. Our study found significant income decreases between the two study periods, specifically among those engaged in farming and miscellaneous labor work. Furthermore, farmers with small agricultural land were crowded out of farming as a livelihood. The results also revealed the natural resource dependency of the host community and its association with ongoing environmental degradation. We located those who were left behind and did not benefit from relief interventions in the middle-south area and the middle of the west coast—this was likely due to geographical and topographical disadvantages. Our results illuminated the limitations of the current humanitarian system and emphasized the need for a sustainable perspective to be more strongly incorporated into future humanitarian efforts.

Keywords: Displacement; Humanitarian aid; Deforestation; Spatial statistical modeling; Bayesian inference; Remote sensing (search for similar items in EconPapers)
Date: 2024
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Persistent link: https://EconPapers.repec.org/RePEc:eee:wdevel:v:183:y:2024:i:c:s0305750x24001992

DOI: 10.1016/j.worlddev.2024.106729

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