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dc.contributor.authorLázár, AN
dc.contributor.authorAdams, H
dc.contributor.authorNeil Adger, W
dc.contributor.authorNicholls, RJ
dc.date.accessioned2020-10-08T14:20:51Z
dc.date.issued2020-09-04
dc.description.abstractResource-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.sponsorshipDepartment for International Development (DFID)en_GB
dc.description.sponsorshipEconomic and Social Research Council (ESRC)en_GB
dc.description.sponsorshipNatural Environment Research Council (NERC)en_GB
dc.identifier.citationVol. 15 (9), article e0238621en_GB
dc.identifier.doi10.1371/journal.pone.0238621
dc.identifier.grantnumberNE/J000892/1en_GB
dc.identifier.grantnumberERI091/JCPen_GB
dc.identifier.urihttp://hdl.handle.net/10871/123148
dc.language.isoenen_GB
dc.publisherPublic Library of Scienceen_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.titleModelling household well-being and poverty trajectories: An application to coastal Bangladeshen_GB
dc.typeArticleen_GB
dc.date.available2020-10-08T14:20:51Z
dc.descriptionThis is the final version. Available on open access from the Public Library of Science via the DOI in this recorden_GB
dc.descriptionData Availability: All relevant data are within the manuscript and its Supporting Information files.en_GB
dc.identifier.eissn1932-6203
dc.identifier.journalPLoS ONEen_GB
dc.rights.urihttps://creativecommons.org/licenses/by/4.0/en_GB
dcterms.dateAccepted2020-08-20
exeter.funder::Natural Environment Research Council (NERC)en_GB
exeter.funder::International Development Research Centreen_GB
exeter.funder::Natural Environment Research Council (NERC)en_GB
exeter.funder::International Development Research Centreen_GB
exeter.funder::International Development Research Centreen_GB
rioxxterms.versionVoRen_GB
rioxxterms.licenseref.startdate2020-09-04
rioxxterms.typeJournal Article/Reviewen_GB
refterms.dateFCD2020-10-08T14:13:39Z
refterms.versionFCDVoR
refterms.dateFOA2020-10-08T14:20:59Z
refterms.panelCen_GB


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© 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.
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.