Σύγκριση μεθόδων

	Social Protection Targeting	Spatial Poverty Mapping
Πεδίο	Development Studies	Development Studies
Οικογένεια	Process / pipeline	Process / pipeline
Έτος προέλευσης	2004	2007
Δημιουργός	David Coady, Margaret Grosh & John Hoddinott (World Bank)	World Bank poverty-mapping programme; Bedi, Coudouel & Simler
Τύπος	Methods for identifying eligible beneficiaries of transfers	Spatial-statistical and GIS method for analysing poverty distribution
Θεμελιώδης πηγή	Coady, D., Grosh, M., & Hoddinott, J. (2004). Targeting of Transfers in Developing Countries: Review of Lessons and Experience. Washington, DC: World Bank. ISBN: 9780821356043	Henderson, J. V., Storeygard, A., & Weil, D. N. (2012). Measuring Economic Growth from Outer Space. American Economic Review, 102(2), 994-1028.
Εναλλακτικές ονομασίες	Safety Net Targeting, Proxy Means Testing, Beneficiary Targeting, Transfer Targeting Methods	Poverty mapping, Geographic targeting, Poverty maps, Spatial poverty analysis
Συναφείς	4	4
Σύνοψη	Social Protection Targeting is the set of methods used to decide who receives a transfer or safety-net benefit when resources are too scarce to cover everyone. Synthesised in the World Bank reviews of David Coady, Margaret Grosh, and John Hoddinott (2004) and the practical handbook of Grosh and colleagues (2008), it spans means testing, proxy means testing, community-based targeting, geographic targeting, and categorical targeting. Every method trades off two errors — including the non-poor (leakage) and excluding the poor (undercoverage) — and the analyst's job is to choose, calibrate, and combine mechanisms so that, given the budget and administrative capacity, benefits reach the intended population as accurately as possible.	Spatial poverty mapping visualises and analyses the geographic distribution of poverty using geographic information systems and spatial statistics, turning poverty estimates into maps that reveal where the poor live at fine spatial scales. It combines small-area poverty estimates with spatial covariates — remote-sensing data, night-time lights, accessibility, and terrain — examines spatial patterns and autocorrelation, and supports the geographic targeting of resources. Consolidated through the World Bank programme documented by Bedi, Coudouel, and Simler and energised by data such as the satellite night-lights series analysed by Henderson, Storeygard, and Weil, it has become a standard tool for evidence-based geographic targeting.