Using Satellite Imagery and Deep Learning to Evaluate the Impact of Anti-Poverty Programs / Luna Yue Huang, Solomon M. Hsiang, Marco Gonzalez-Navarro.

By:

Huang, Luna Yue

Contributor(s):

Material type: Text

TextSeries: Working Paper Series (National Bureau of Economic Research) ; no. w29105.Publication details: Cambridge, Mass. National Bureau of Economic Research 2021.Description: 1 online resource: illustrations (black and white)Subject(s):

Online resources:

Available additional physical forms:

Hardcopy version available to institutional subscribers

Abstract: The rigorous evaluation of anti-poverty programs is key to the fight against global poverty. Traditional approaches rely heavily on repeated in-person field surveys to measure program effects. However, this is costly, time-consuming, and often logistically challenging. Here we provide the first evidence that we can conduct such program evaluations based solely on high-resolution satellite imagery and deep learning methods. Our application estimates changes in household welfare in a recent anti-poverty program in rural Kenya. Leveraging a large literature documenting a reliable relationship between housing quality and household wealth, we infer changes in household wealth based on satellite-derived changes in housing quality and obtain consistent results with the traditional field-survey based approach. Our approach generates inexpensive and timely insights on program effectiveness in international development programs.

Tags from this library: No tags from this library for this title. Log in to add tags.

Average rating: 0.0 (0 votes)

Holdings
Item type	Home library	Collection	Call number	Status	Date due	Barcode	Item holds
Working Paper	Biblioteca Digital	Colección NBER	nber w29105 (Browse shelf(Opens below))	Not For Loan

Total holds: 0

Collection: Colección NBER Close shelf browser (Hides shelf browser)

Previous	No cover image available	No cover image available	No cover image available	No cover image available	No cover image available	No cover image available	No cover image available	Next
Previous	nber w29102 Internet Access and its Implications for Productivity, Inequality, and Resilience /	nber w29103 Concentration and Resilience in the U.S. Meat Supply Chains /	nber w29104 The Heterogeneous Value of a Statistical Life: Evidence from U.S. Army Reenlistment Decisions /	nber w29105 Using Satellite Imagery and Deep Learning to Evaluate the Impact of Anti-Poverty Programs /	nber w29106 Why do Bank Boards have Risk Committees? /	nber w29107 Intangible Assets, Corporate Taxes and the Relocation of Pharmaceutical Establishments: The case of Puerto Rico /	nber w29108 Racial Disparities in the Health Effects from Air Pollution: Evidence from Ports /	Next

July 2021.

The rigorous evaluation of anti-poverty programs is key to the fight against global poverty. Traditional approaches rely heavily on repeated in-person field surveys to measure program effects. However, this is costly, time-consuming, and often logistically challenging. Here we provide the first evidence that we can conduct such program evaluations based solely on high-resolution satellite imagery and deep learning methods. Our application estimates changes in household welfare in a recent anti-poverty program in rural Kenya. Leveraging a large literature documenting a reliable relationship between housing quality and household wealth, we infer changes in household wealth based on satellite-derived changes in housing quality and obtain consistent results with the traditional field-survey based approach. Our approach generates inexpensive and timely insights on program effectiveness in international development programs.