Modelling household well-being and poverty trajectories: An application to coastal Bangladesh
dc.contributor.author | Lázár, AN | |
dc.contributor.author | Adams, H | |
dc.contributor.author | Neil Adger, W | |
dc.contributor.author | Nicholls, RJ | |
dc.date.accessioned | 2020-10-08T14:20:51Z | |
dc.date.issued | 2020-09-04 | |
dc.description.abstract | Resource-based livelihoods are uncertain and potentially unstable due to variability over time, including seasonal variation: this instability threatens marginalised populations who may fall into poverty. However, empirical understanding of trajectories of household wellbeing and poverty is limited. Here, we present a new household-level model of poverty dynamics based on agents and coping strategies–the Household Economy And Poverty trajectory (HEAP) model. HEAP is based on established economic and social insights into poverty dynamics, with a demonstration of the model calibrated with a qualitative and quantitative household survey in coastal Bangladesh. Economic activity in Bangladesh is highly dependent on natural resources; poverty is widespread; and there is high variability in ecosystem services at multiple temporal scales. The results show that long-term decreases in poverty are predicated more on the stability of, and returns from, livelihoods rather than their diversification. Access to natural resources and ecosystem service benefits are positively correlated with stable income and multidimensional well-being. Households that remain in poverty are those who experience high seasonality of income and are involved in small scale enterprises. Hence, seasonal variability in income places significant limits on natural resources providing routes out of poverty. Further, projected economic trends to 2030 lead to an increase in well-being and a reduction in poverty for most simulated household types. | en_GB |
dc.description.sponsorship | Department for International Development (DFID) | en_GB |
dc.description.sponsorship | Economic and Social Research Council (ESRC) | en_GB |
dc.description.sponsorship | Natural Environment Research Council (NERC) | en_GB |
dc.identifier.citation | Vol. 15 (9), article e0238621 | en_GB |
dc.identifier.doi | 10.1371/journal.pone.0238621 | |
dc.identifier.grantnumber | NE/J000892/1 | en_GB |
dc.identifier.grantnumber | ERI091/JCP | en_GB |
dc.identifier.uri | http://hdl.handle.net/10871/123148 | |
dc.language.iso | en | en_GB |
dc.publisher | Public Library of Science | en_GB |
dc.rights | © 2020 Lázár et al. This is an open access article distributed under the terms of the Creative Commons Attribution License, which permits unrestricted use, distribution, and reproduction in any medium, provided the original author and source are credited. | en_GB |
dc.title | Modelling household well-being and poverty trajectories: An application to coastal Bangladesh | en_GB |
dc.type | Article | en_GB |
dc.date.available | 2020-10-08T14:20:51Z | |
dc.description | This is the final version. Available on open access from the Public Library of Science via the DOI in this record | en_GB |
dc.description | Data Availability: All relevant data are within the manuscript and its Supporting Information files. | en_GB |
dc.identifier.eissn | 1932-6203 | |
dc.identifier.journal | PLoS ONE | en_GB |
dc.rights.uri | https://creativecommons.org/licenses/by/4.0/ | en_GB |
dcterms.dateAccepted | 2020-08-20 | |
exeter.funder | ::Natural Environment Research Council (NERC) | en_GB |
exeter.funder | ::International Development Research Centre | en_GB |
exeter.funder | ::Natural Environment Research Council (NERC) | en_GB |
exeter.funder | ::International Development Research Centre | en_GB |
exeter.funder | ::International Development Research Centre | en_GB |
rioxxterms.version | VoR | en_GB |
rioxxterms.licenseref.startdate | 2020-09-04 | |
rioxxterms.type | Journal Article/Review | en_GB |
refterms.dateFCD | 2020-10-08T14:13:39Z | |
refterms.versionFCD | VoR | |
refterms.dateFOA | 2020-10-08T14:20:59Z | |
refterms.panel | C | en_GB |
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Except where otherwise noted, this item's licence is described as © 2020 Lázár et al. This is an open access article distributed under the terms of the Creative Commons Attribution License, which permits unrestricted use, distribution, and reproduction in any medium, provided the original author and source are credited.