Predicting Poverty Using Geospatial Data in Thailand

Nattapong Puttanapong, Arturo Martinez, Jr. (), Mildred Addawe (), Joseph Bulan (), Ron Lester Durante () and Marymell Martillan ()
Additional contact information
Arturo Martinez, Jr.: Asian Development Bank
Mildred Addawe: Asian Development Bank
Joseph Bulan: Asian Development Bank
Ron Lester Durante: Asian Development Bank
Marymell Martillan: Asian Development Bank

No 630, ADB Economics Working Paper Series from Asian Development Bank

Abstract: Poverty statistics are conventionally compiled using data from household income and expenditure survey or living standards survey. This study examines an alternative approach in estimating poverty by investigating whether readily available geospatial data can accurately predict the spatial distribution of poverty in Thailand. In particular, geospatial data examined in this study include night light intensity, land cover, vegetation index, land surface temperature, built-up areas, and points of interest. The study also compares the predictive performance of various econometric and machine learning methods such as generalized least squares, neural network, random forest, and support vector regression. Results suggest that intensity of night lights and other variables that approximate population density are highly associated with the proportion of an area’s population who are living in poverty. The random forest technique yielded the highest level of prediction accuracy among the methods considered in this study, perhaps due to its capability to fit complex association structures even with small and medium-sized datasets. Moving forward, additional studies are needed to investigate whether the relationships observed here remain stable over time, and therefore, may be used to approximate the prevalence of poverty for years when household surveys on income and expenditures are not conducted, but data on geospatial correlates of poverty are available.

Keywords: big data; computer vision; data for development; machine learning algorithm; multidimensional poverty; official statistics; poverty; SDG; Thailand (search for similar items in EconPapers)
JEL-codes: C19 D31 I32 O15 (search for similar items in EconPapers)
Pages: 38 pages
Date: 2020-12-29
New Economics Papers: this item is included in nep-big, nep-cmp, nep-dev and nep-sea
References: View complete reference list from CitEc
Citations: View citations in EconPapers (1)

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