The use of survey data to study migration-environment relationships in developing countries: alternative approaches to data collection
Open accessAbstract
Growing interest in the environmental aspects of migration is not matched by research on their interrelationships, due partly to the lack of adequate data sets on the two together. Focusing on the microlevel, we describe the data required to effectively investigate these interrelationships. Data sources are discussed, be collected, focusing on household surveys and remote sensing. The main section of the paper describes three alternative approaches to data collection: (a) using existing population and environmental data from different sources, illustrated by Burkina Faso; (b) adding questions to a survey developed for another purpose, illustrated for Guatemala using a DHS survey; and (c) designing a new survey specifically to collect both migration and environmental data to investigate interrelationships, illustrated by Ecuador. Methods used and summary findings are described, followed by a discussion of their advantages and limitations. We conclude with recommendations as to effective use of each approach as research on migration-environment linkages moves forward.
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Both retrospective questions and indirect information about absent household members can be prone to biases.
“Both forms of data collection can be prone to certain biases (Bilsborrow et al., 2012).”
From Improving the evidence base: A methodological review of the quantitative climate migration literature (2021) · cites this paper as Bilsborrow et al., 2012
In the Burkina Faso case-study section (page unavailable), the paper says a single-round retrospective survey risks memory lapses, supporting the concern about retrospective questions. The Ecuador case-study section (page unavailable) says household members provided information about absent migrants, but does not say that this proxy information was biased. (LLM, conf=0.82)
Experimental — accuracy not yet measured
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- Authors
- Bilsborrow, R. E., & Henry, S. J. F.
- Affiliations
- University of North Carolina at Chapel Hill (US)
- University of Namur (BE)
- Corresponding author
- Bilsborrow, Richard E.
- Henry, Sabine J. F.
- Venue
- Population and Environment
- Published
- 2012-08-10
- Volume
- 34
- Issue
- 1
- Pages
- 113-141
- ISSN
- 0199-0039 (ISSN-L)
- 1573-7810
- PMC
- PMC3972128
- OpenAlex
- W1989608291
- Cited by
- 50
- References
- 96
- Citations by year
- 2025: 2 · 2024: 4 · 2023: 4 · 2022: 6 · 2021: 4 · 2020: 8 · 2019: 4 · 2018: 3 · 2017: 3 · 2016: 6 · 2015: 4 · 2014: 1 · 2013: 1
- FWCI
- 6.33
- Type
- journal article
- Language
- en
- Open access
- green
- europepmc.org
- Licence
- www.springer.com/tdm (tdm)
- Locations
- Population and Environment (publishedVersion)
- PubMed (publishedVersion)
- Europe PMC (PubMed Central) (acceptedVersion)
- PubMed Central (acceptedVersion)
- Carolina Digital Repository (University of North Carolina at Chapel Hill) (submittedVersion)
- Repository of the University of Namur (submittedVersion)
- UNC Libraries
- Funders
- Université de Montréal
- National Institutes of Health
- Eunice Kennedy Shriver National Institute of Child Health and Human Development (R21HD052092)
- Carolina Population Center, University of North Carolina at Chapel Hill
- Topics
- Social Sciences › Sociology and Political Science › Climate Change, Adaptation, Migration
- Social Sciences › Sociology and Political Science › Hydropower, Displacement, Environmental Impact
- Social Sciences › Sociology and Political Science › Migration and Labor Dynamics
- Keywords
- environmental migration, household surveys, remote sensing, data collection methods, data integration, developing countries, Burkina Faso, Guatemala, Ecuador
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Registry, retraction, Cites and statistics results are computed, not judged, with one exception: a printed reference that no index lists is read once by a model before it counts as a citation error. Claims are extracted by a model and kept only when their quote appears verbatim in the text; each cited source is then checked, with a model’s help, for whether it says what the claim relies on. Those verdicts can be wrong — read the claim’s quote and the cited source before relying on one.
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