Estimating Poverty Rates in Target Populations: An Assessment of the Simple Poverty Scorecard and Alternative Approaches

56 Pages Posted: 9 Sep 2016

See all articles by Alexis Diamond

Alexis Diamond

Harvard University

Michael Gill

Harvard University

Miguel Angel Rebolledo Dellepiane

World Bank - International Finance Corporation (IFC)

Emmanuel Skoufias

World Bank

Katja Vinha

World Bank

Yiqing Xu

University of California, San Diego (UCSD) - Department of Political Science

Date Written: August 15, 2016

Abstract

The performance of the Simple Poverty Scorecard is compared against the performance of established regression-based estimators. All estimates are benchmarked against observed poverty status based on household expenditure (or income) data from household socioeconomic surveys that span nearly a decade and are representative of subnational populations. When the models all adopt the same "one-size-fits-all" training approach, there is no meaningful difference in performance and the Simple Poverty Scorecard is as good as any of the regression-based estimators. The findings change, however, when the regression-based estimators are "trained" on "training sets" that more closely resemble potential subpopulation test sets. In this case, regression-based models outperform the nationally calculated Simple Poverty Scorecard in terms of bias and variance. These findings highlight the fundamental trade-off between simplicity of use and accuracy.

Keywords: Inequality

Suggested Citation

Diamond, Alexis J. and Gill, Michael and Rebolledo Dellepiane, Miguel Angel and Skoufias, Emmanuel and Vinha, Katja and Xu, Yiqing, Estimating Poverty Rates in Target Populations: An Assessment of the Simple Poverty Scorecard and Alternative Approaches (August 15, 2016). World Bank Policy Research Working Paper No. 7793. Available at SSRN: https://ssrn.com/abstract=2836540

Alexis J. Diamond (Contact Author)

Harvard University ( email )

Michael Gill

Harvard University ( email )

1875 Cambridge Street
Cambridge, MA 02138
United States

Miguel Angel Rebolledo Dellepiane

World Bank - International Finance Corporation (IFC)

2121 Pennsylvania Avenue, NW
Washington, DC 20433
United States

Emmanuel Skoufias

World Bank ( email )

1818 H Street, NW
Washington, DC 20433
United States

Katja Vinha

World Bank

1818 H Street, N.W.
Washington, DC 20433
United States

Yiqing Xu

University of California, San Diego (UCSD) - Department of Political Science ( email )

9500 Gilman Drive
Code 0521
La Jolla, CA 92093-0521
United States

HOME PAGE: http://yiqingxu.org

Register to save articles to
your library

Register

Paper statistics

Downloads
44
Abstract Views
343
PlumX Metrics