Constructing Social Vulnerability Indexes with Increased Data and Machine Learning Highlight the Importance of Wealth Across Global Contexts

Yuan Zhao (), Ronak Paul (), Sean Reid (), Carolina Coimbra Vieira (), Chris Wolfe (), Yan Zhang () and Rumi Chunara ()
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Yuan Zhao: New York University
Ronak Paul: International Institute for Population Sciences
Sean Reid: University of California
Carolina Coimbra Vieira: Max Planck Institute for Demographic Research
Chris Wolfe: East Carolina University
Yan Zhang: University of Oxford
Rumi Chunara: New York University

Social Indicators Research: An International and Interdisciplinary Journal for Quality-of-Life Measurement, 2024, vol. 175, issue 2, No 12, 639-657

Abstract: Abstract We consider the availability of new harmonized data sources and novel machine learning methodologies in the construction of a social vulnerability index (SoVI), a multidimensional measure that defines how individuals’ and communities may respond to hazards including natural disasters, economic changes, and global health crises. The factors underpinning social vulnerability—namely, economic status, age, disability, language, ethnicity, and location—are well understood from a theoretical perspective, and existing indices are generally constructed based on specific data chosen to represent these factors. Further, the indices’ construction methods generally assume structured, linear relationships among input variables and may not capture subtle nonlinear patterns more reflective of the multidimensionality of social vulnerability. We compare a procedure which considers an increased number of variables to describe the SoVI factors with existing approaches that choose specific variables based on consensus within the social science community. Reproducing the analysis across eight countries, as well as leveraging deep learning methods which in recent years have been found to be powerful for finding structure in data, demonstrate that wealth-related factors consistently explain the largest variance and are the most common element in social vulnerability.

Keywords: Social vulnerability; Principal component analysis; Autoencoder (search for similar items in EconPapers)
Date: 2024
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DOI: 10.1007/s11205-024-03386-9

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