Dataset Information

Microestimates of wealth for all low- and middle-income countries.

ABSTRACT: Many critical policy decisions, from strategic investments to the allocation of humanitarian aid, rely on data about the geographic distribution of wealth and poverty. Yet many poverty maps are out of date or exist only at very coarse levels of granularity. Here we develop microestimates of the relative wealth and poverty of the populated surface of all 135 low- and middle-income countries (LMICs) at 2.4 km resolution. The estimates are built by applying machine-learning algorithms to vast and heterogeneous data from satellites, mobile phone networks, and topographic maps, as well as aggregated and deidentified connectivity data from Facebook. We train and calibrate the estimates using nationally representative household survey data from 56 LMICs and then validate their accuracy using four independent sources of household survey data from 18 countries. We also provide confidence intervals for each microestimate to facilitate responsible downstream use. These estimates are provided free for public use in the hope that they enable targeted policy response to the COVID-19 pandemic, provide the foundation for insights into the causes and consequences of economic development and growth, and promote responsible policymaking in support of sustainable development.

SUBMITTER: Chi G

PROVIDER: S-EPMC8784134 | biostudies-literature | 2022 Jan

REPOSITORIES: biostudies-literature

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Microestimates of wealth for all low- and middle-income countries.

Chi Guanghua G Fang Han H Chatterjee Sourav S Blumenstock Joshua E JE

Proceedings of the National Academy of Sciences of the United States of America 20220101 3

Many critical policy decisions, from strategic investments to the allocation of humanitarian aid, rely on data about the geographic distribution of wealth and poverty. Yet many poverty maps are out of date or exist only at very coarse levels of granularity. Here we develop microestimates of the relative wealth and poverty of the populated surface of all 135 low- and middle-income countries (LMICs) at 2.4 km resolution. The estimates are built by applying machine-learning algorithms to vast and h ...[more]

PMID: 35017299

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Project description:IntroductionLow levels of household access to basic environmental health assets (EHAs)-including technologies such as clean cookstoves and bed nets or infrastructure such as piped water and electricity-in low- and middle-income countries (LMICs) are known to contribute significantly to the global burden of disease. This low access persists despite decades of promotion of many low-cost, life-saving technologies, and is particularly pronounced among poor households. This study aims to characterize variation in access to EHAs among LMIC households as a function of wealth, as defined by ownership of various assets.MethodsDemographic and Health Survey (DHS) data from 41 low- and middle-income countries were used to assess household-level access to the following EHAs: 1) improved water supply; 2) piped water supply; 3) improved sanitation; 4) modern cooking fuels; 5) electricity; and 6) bed nets. For comparison, we included access to mobile phones, which is considered a highly successful technology in terms of its penetration into poor households within LMICs. Ownership levels were compared across country-specific wealth quintiles constructed from household assets using bivariate analysis and multivariable linear regression models.ResultsAccess to EHAs was low among the households in the bottom three quintiles of wealth. Access to piped water, modern cooking fuels, electricity and improved sanitation, for example, were all below 50% for households in the bottom three wealth quintiles. Access to certain EHAs such as improved water supply and bed nets increased only slowly with concomitant increases in wealth, while gaps in access to other EHAs varied to a greater degree by wealth quintile. For example, disparities in access between the richest and poorest quintiles were greatest for electricity and improved sanitation. Rural households in all wealth quintiles had much lower levels of access to EHAs, except for bed nets, relative to urban households.ConclusionsThe findings of this study provide a basis for understanding how EHAs are distributed among poor households in LMICs, elucidate where inequalities in access are particularly pronounced, and point to a need for strategies that better reach the poor, if the global environmental burden of disease is to be reduced.

Dataset Information

Microestimates of wealth for all low- and middle-income countries.

Publications

Microestimates of wealth for all low- and middle-income countries.

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