Small Area Estimation of Monetary Poverty in Mexico Using Satellite Imagery and Machine Learning

72 Pages Posted: 3 Oct 2022

See all articles by David Locke Newhouse

David Locke Newhouse

World Bank

Joshua D. Merfeld

KDI School

Anusha Ramakrishnan

World Bank Group

Tom Swartz

affiliation not provided to SSRN

Partha Lahiri

University of Maryland

Abstract

Estimates of poverty are an important input into policy formulation in developing countries. The accurate measurement of poverty rates is therefore a first-order problem for development policy. This paper shows that combining satellite imagery with household surveys can improve the precision and accuracy of estimated poverty rates in Mexican municipalities, a level at which the survey is not considered representative. It also shows that a household-level model outperforms other common small area estimation methods. However, poverty estimates in 2015 derived from geospatial data remain less accurate than 2010 estimates derived from household census data. These results indicate that the incorporation of household survey data and widely available satellite imagery can improve on existing poverty estimates in developing countries when census data are old or when patterns of poverty are changing rapidly, even for small subgroups.

Keywords: poverty, small area estimation, poverty mapping, satellite data, machine learning

Suggested Citation

Newhouse, David Locke and Merfeld, Joshua D. and Ramakrishnan, Anusha and Swartz, Tom and Lahiri, Partha, Small Area Estimation of Monetary Poverty in Mexico Using Satellite Imagery and Machine Learning. Available at SSRN: https://ssrn.com/abstract=4235976 or http://dx.doi.org/10.2139/ssrn.4235976

David Locke Newhouse (Contact Author)

World Bank ( email )

1818 H Street, NW
Washington, DC 20433
United States

Joshua D. Merfeld

KDI School ( email )

P.O. Box 184
Seoul, 130-868
Korea, Republic of (South Korea)

Anusha Ramakrishnan

World Bank Group ( email )

10 Marina Boulevard
Marina Bay Financial Center, Tower 2, #34-02
Singapore, DC 018983
Singapore

Tom Swartz

affiliation not provided to SSRN ( email )

No Address Available

Partha Lahiri

University of Maryland ( email )

College Park
College Park, MD 20742
United States

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