Small Area Estimation-Based Prediction Methods to Track Poverty: Validation and Applications
46 Pages Posted: 20 Apr 2016
Date Written: June 1, 2011
Abstract
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 -- an assumption that cannot usually be tested directly. This study analyzes the performance of poverty prediction models based on small area estimation 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.
Keywords: Rural Poverty Reduction, Regional Economic Development, Debt Markets, Achieving Shared Growth
Suggested Citation: Suggested Citation
Do you have negative results from your research you’d like to share?
Recommended Papers
-
Micro-Level Estimation of Welfare
By Peter F. Lanjouw, Jean O. Lanjouw, ...
-
Combining Census and Survey Data to Study Spatial Dimensions of Poverty a Case Study of Ecuador
By Peter F. Lanjouw and Jesko Hentschel
-
Crime and Local Inequality in South Africa
By Gabriel Demombynes and Berk Ozler
-
Picking the Poor: Indicators for Geographic Targeting in Peru
-
Poverty Comparisons with Noncompatible Data: Theory and Illustrations
By Jean O. Lanjouw and Peter F. Lanjouw
-
On the Unequal Inequality of Poor Communities
By Chris Elbers, Peter F. Lanjouw, ...
-
Poverty Alleviation Through Geographic Targeting: How Much Does Disaggregation Help?
By Chris Elbers, Tomoki Fujii, ...
-
Do School Facilities Matter? The Case of the Peruvian Social Fund (Foncodes)