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Abstract Social scientists have increasingly used asset‐based wealth scores, like the Demographic and Health Survey (DHS) wealth index, to assess economic disparities. However, current indices primarily capture wealth in globalized market economies, thus ignoring other forms of prosperity, such as success in agricultural activities. Using a simple extension to the standard estimation of the DHS wealth index, we describe procedures for estimating an agricultural wealth index (AWI) that complements market‐based wealth indices by capturing household success in agricultural activities. We apply this procedure to household data from 129 DHS surveys from over 40 countries with sufficient land and livestock data to estimate a reliable and consistent AWI. We assess the construct validity of the AWI using benchmarks of growth in both adults and children. This alternative measure of wealth provides new opportunities for understanding the causes and consequences of wealth inequality, and how success along different dimensions of wealth creates different social opportunities and constraints for health and well‐being.
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DeepWealth: A generalizable open-source deep learning framework using satellite images for well-being estimation
Measuring socioeconomic indices at the scale of regions or countries is required in various contexts, in particular to inform public policies. The use of Deep Learning (DL) and Earth Observation (EO) data is becoming increasingly common to estimate specific variables like societal wealth. This paper presents an end- to-end framework ‘DeepWealth’ that calculates such a wealth index using open-source EO data and DL. We use a multidisciplinary approach incorporating satellite imagery, socio-economic data, and DL models. We demonstrate the effectiveness and generalizability of DeepWealth by training it on 24 African countries and deploying it in Madagascar, Brazil and Japan. Our results show that DeepWealth provides accurate and stable wealth index estimates with an 𝑅2 of 0.69. It empowers computer-literate users skilled in Python and R to estimate and visualize well-being-related data. This open-source framework follows FAIR (Findable, Accessible, Interoperable, Reusable) principles, providing data, source code, metadata, and training checkpoints with its source code made available on Zenodo and GitHub. In this manner, we provide a DL framework that is reproducible and replicable.
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- Award ID(s):
- 1929464
- PAR ID:
- 10525550
- Publisher / Repository:
- Elsevier
- Date Published:
- Journal Name:
- SoftwareX
- Volume:
- 27
- Issue:
- C
- ISSN:
- 2352-7110
- Page Range / eLocation ID:
- 101785
- Subject(s) / Keyword(s):
- Deep learning Poverty SDG1 Earth observation Socioeconomic indices
- Format(s):
- Medium: X
- Sponsoring Org:
- National Science Foundation
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Social scientists have increasingly used asset-based wealth scores, like the DHS wealth index, to assess economic disparities. However, current indices primarily capture wealth in globalized market economies, thus ignoring other forms of prosperity, such as success in agricultural activities. Using a simple extension to the standard estimation of the DHS Wealth Index, we describe procedures for estimating an Agricultural Wealth Index (AWI) that complements market-based wealth indices by capturing household success in agricultural activities. We apply this procedure to household data from 129 DHS surveys from over 40 countries with sufficient land and livestock data to estimate a reliable and consistent AWI. We assess the construct validity of the AWI using benchmarks of growth in both adults and children. This alternative measure of wealth provides new opportunities for understanding the causes and consequences of wealth inequality, and how success along different dimensions of wealth creates different social opportunities and constraints for health and well-being.more » « less
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- Free Publicly Accessible Full Text
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Accepted Manuscript1.0
- Journal Article:
- https://doi.org/10.1016/j.softx.2024.101785
