Abstract
Population modeling requires clear definitions of socioeconomic status (SES) to ensure overall estimate accuracy and locate potentially underserved subpopulations. This presents a challenge as SES can be measured in myriad ways and for divergent purposes, and the data required to calculate these metrics may be lacking, particularly in low and middle income countries (LMICs). To support more refined SES measurement, we explore improvements upon the Demographic and Health Survey’s (DHS) Wealth Index (DHS-WI) using alternative characterizations of SES based on multiple correspondence analysis (MCA) and hierarchical clustering. We produce the MCA-derived metrics first on a full suite of household economic, demographic, and dwelling variables, then on a reduced set of occupant-only SES characteristics. We explore the utility of these metrics relative to DHS-WI based on their ability to 1) differentiate DHS household types and 2) identify mixtures of SES levels within DHS samples and mapped at high spatial resolution. We find that our full suite MCA yields more clearly defined SES segments and that our reduced MCA delineates occupant SES most clearly, suggesting potential pathways to improve upon the DHS-WI in future population modeling efforts for LMICs.
Original language | English |
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Title of host publication | 12th International Conference on Geographic Information Science, GIScience 2023 |
Editors | Roger Beecham, Jed A. Long, Dianna Smith, Qunshan Zhao, Sarah Wise |
Publisher | Schloss Dagstuhl- Leibniz-Zentrum fur Informatik GmbH, Dagstuhl Publishing |
ISBN (Electronic) | 9783959772884 |
DOIs | |
State | Published - Sep 2023 |
Event | 12th International Conference on Geographic Information Science, GIScience 2023 - Leeds, United Kingdom Duration: Sep 12 2023 → Sep 15 2023 |
Publication series
Name | Leibniz International Proceedings in Informatics, LIPIcs |
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Volume | 277 |
ISSN (Print) | 1868-8969 |
Conference
Conference | 12th International Conference on Geographic Information Science, GIScience 2023 |
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Country/Territory | United Kingdom |
City | Leeds |
Period | 09/12/23 → 09/15/23 |
Funding
Funding This material is based upon the work supported by the U.S. Department of Energy under contract no. DE-AC05-00OR22725.
Keywords
- Demographic and Health Survey
- multiple correspondence analysis
- population modeling
- socioeconomic status
- spatial statistics