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Using linked administrative and census data for migration research

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Ernsten_2018_PS_Migrationresearch_AAM.pdf (757.2Kb)
Date
28/08/2018
Author
Ernsten, Annemarie
McCollum, David
Feng, Zhiqiang
Everington, Dawn
Huang, Zengyi
Keywords
Administrative NHS GP health data
Data linkage
Internal migration
Scottish Longitudinal Study
G Geography (General)
ZA4050 Electronic information resources
3rd-DAS
Metadata
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Abstract
Migration is a core component of population change and is both a symptom and a cause of major economic and social phenomena. However, data limitations mean that gaps remain in our understanding of the patterns and processes of mobility. This is particularly the case for internal migration, which remains under-researched, despite being quantitatively much more significant than international migration. Using the Scottish Longitudinal Study, this paper evaluates the potential value of General Practitioner administrative health data from the National Health Service that can be linked into census-based longitudinal studies for advancing migration research. Issues relating to data quality are considered and, using the illustrative example of internal migration by country of birth, an argument is developed contending that such approaches can offer novel ways of comprehending internal migration, by shedding additional light on the nature of both movers and the moves that they make.
Citation
Ernsten , A , McCollum , D , Feng , Z , Everington , D & Huang , Z 2018 , ' Using linked administrative and census data for migration research ' , Population Studies-A Journal of Demography , vol. Latest Articles . https://doi.org/10.1080/00324728.2018.1502463
Publication
Population Studies-A Journal of Demography
Status
Peer reviewed
DOI
https://doi.org/10.1080/00324728.2018.1502463
ISSN
0032-4728
Type
Journal article
Rights
© 2018 Population Investigation Committee. This work has been made available online in accordance with the publisher’s policies. This is the author created, accepted version manuscript following peer review and may differ slightly from the final published version. The final published version of this work is available at: https://doi.org/10.1080/00324728.2018.1502463
Description
This research was funded through the ESRC Secondary Data Analysis Initiative (grant number ES/N011430/1). We acknowledge the help provided by the staff of the Longitudinal Studies Centre - Scotland (LSCS). The LSCS is supported by the ESRC/JISC, the Scottish Funding Council, the Chief Scientist's Office and the Scottish Government.
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  • University of St Andrews Research
URI
http://hdl.handle.net/10023/19550

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