License: Creative Commons Attribution 4.0 International license (CC BY 4.0)
When quoting this document, please refer to the following
DOI: 10.4230/LIPIcs.GIScience.2023.25
URN: urn:nbn:de:0030-drops-189204
URL: http://dagstuhl.sunsite.rwth-aachen.de/volltexte/2023/18920/
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Cunningham, Angela R. ; Tuccillo, Joseph V. ; Frazier, Tyler J.

Building Alternative Indices of Socioeconomic Status for Population Modeling in Data-Sparse Contexts (Short Paper)

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LIPIcs-GIScience-2023-25.pdf (8 MB)

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.

BibTeX - Entry

@InProceedings{cunningham_et_al:LIPIcs.GIScience.2023.25,
  author =	{Cunningham, Angela R. and Tuccillo, Joseph V. and Frazier, Tyler J.},
  title =	{{Building Alternative Indices of Socioeconomic Status for Population Modeling in Data-Sparse Contexts}},
  booktitle =	{12th International Conference on Geographic Information Science (GIScience 2023)},
  pages =	{25:1--25:7},
  series =	{Leibniz International Proceedings in Informatics (LIPIcs)},
  ISBN =	{978-3-95977-288-4},
  ISSN =	{1868-8969},
  year =	{2023},
  volume =	{277},
  editor =	{Beecham, Roger and Long, Jed A. and Smith, Dianna and Zhao, Qunshan and Wise, Sarah},
  publisher =	{Schloss Dagstuhl -- Leibniz-Zentrum f{\"u}r Informatik},
  address =	{Dagstuhl, Germany},
  URL =		{https://drops.dagstuhl.de/opus/volltexte/2023/18920},
  URN =		{urn:nbn:de:0030-drops-189204},
  doi =		{10.4230/LIPIcs.GIScience.2023.25},
  annote =	{Keywords: Demographic and Health Survey, multiple correspondence analysis, population modeling, socioeconomic status, spatial statistics}
}

Keywords: Demographic and Health Survey, multiple correspondence analysis, population modeling, socioeconomic status, spatial statistics
Collection: 12th International Conference on Geographic Information Science (GIScience 2023)
Issue Date: 2023
Date of publication: 07.09.2023

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