Small area estimation-based prediction methods to track poverty: Validation and applications

Tracking poverty is predicated on the availability of comparable consumption data and reliable price deflators. However, regular series of strictly comparable data are only rarely available. Price deflators are also often missing or disputed. In response, poverty prediction methods that track consumption correlates as opposed to consumption itself have been developed. These methods typically assume that the estimated relation between consumption and its predictors is stable over time-assumptions that cannot usually be tested directly. This study analyzes the performance of poverty prediction models based on small area estimation (SAE) techniques. Predicted poverty estimates are compared with directly observed levels in two country settings where data comparability over time is not a problem. Prediction models that employ either non-staple food or non-food expenditures or a full set of assets as predictors are found to yield poverty estimates that match observed poverty well. This offers some support to the use of such methods to approximate the evolution of poverty. Two further country examples illustrate how an application of the method employing models based on household assets can help to adjudicate between alternative price deflators.

Title	Small area estimation-based prediction methods to track poverty: Validation and applications
Creator	Christiaensen, L.
	Luoto, J.
	Stifel, David C.
	Lanjouw, P.
Publisher	Journal of Economic Inequality
Academic Department	Economics
Division	Social Sciences
Organization	Lafayette College
Date Issued	June 2012
Date Available	2015-08-10T19:22:22Z
Type	Article
Language	English
Keyword	Vietnam
	Russia
	poverty dynamics
	China
	small area estimation
	Kenya
	price deflator
	consumption prediction
Bibliographic Citation	Christiaensen, L., et al. (2012 June) "Small area estimation-based prediction methods to track poverty: Validation and applications." Journal of Economic Inequality 10 (2): 267-97.
Standard Identifier	DOI 10.1007/s10888-011-9209-9
Standard Identifier	Handle 10385/2017
Permalink	http://hdl.handle.net/10385/2017
Rights Statement	In Copyright
Rights Holders	Springer Science+Business Media LLC