Please use this identifier to cite or link to this item: https://hdl.handle.net/10419/246706 
Year of Publication: 
2020
Series/Report no.: 
ADB Economics Working Paper Series No. 629
Publisher: 
Asian Development Bank (ADB), Manila
Abstract: 
The spatial granularity of poverty statistics can have a significant impact on the efficiency of targeting resources meant to improve the living conditions of the poor. However, achieving granularity typically requires increasing the sample sizes of surveys on household income and expenditure or living standards, an option that is not always practical for government agencies that conduct these surveys. Previous studies that examined the use of innovative (geospatial) data sources such as those from highresolution satellite imagery suggest that such method may be an alternative approach of producing granular poverty maps. This study outlines a computational framework to enhance the spatial granularity of government-published poverty estimates using a deep layer computer vision technique applied on publicly available medium-resolution satellite imagery, household surveys, and census data from the Philippines and Thailand. By doing so, the study explores a potentially more cost-effective alternative method for poverty estimation method. The results suggest that even using publicly accessible satellite imagery, in which the resolutions are not as fine as those in commercially sourced images, predictions generally aligned with the distributional structure of government-published poverty estimates, after calibration. The study further contributes to the existing literature by examining robustness of the resulting estimates to user-specified algorithmic parameters and model specifications.
Subjects: 
big data
computer vision
data for development
machine learning algorithm
official statistics
poverty
SDG
JEL: 
C19
D31
I32
O15
Persistent Identifier of the first edition: 
Creative Commons License: 
cc-by Logo
Document Type: 
Working Paper

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