Data Gaps, Data Incomparability, and Data Imputation: A Review of Poverty Measurement Methods for Data-Scarce Environments

65 Pages Posted: 20 Dec 2017 Last revised: 21 May 2020

See all articles by Hai-Anh Dang

Hai-Anh Dang

World Bank - Development Data Group (DECDG); IZA Institute of Labor Economics; Indiana University Bloomington - School of Public & Environmental Affairs (SPEA); Global Labor Organization (GLO); Vietnam National University Ha Noi; Vietnam Academy of Social Sciences (VASS) - Centre for Analysis and Forecasting

Dean Jolliffe

World Bank, DECDG; IZA Institute of Labor Economics; Global Labor Organization (GLO); Johns Hopkins University, Paul H. Nitze School of Advanced International Studies (SAIS), Students

Calogero Carletto

World Bank; World Bank - Development Research Group (DECRG)

Calogero Carletto

affiliation not provided to SSRN

Multiple version iconThere are 3 versions of this paper

Date Written: December 19, 2017

Abstract

This paper reviews methods that have been employed to estimate poverty in contexts where household consumption data are unavailable or missing. These contexts range from completely missing and partially missing consumption data in cross-sectional household surveys, to missing panel household data. The paper focuses on methods that aim to compare trends and dynamic patterns of poverty outcomes over time. It presents the various methods under a common framework, with pedagogical discussion on the intuition. Empirical illustrations are provided using several rounds of household survey data from Vietnam. Furthermore, the paper provides a practical guide with detailed instructions on computer programs that can be used to implement the reviewed techniques.

Keywords: Inequality, Poverty Diagnostics, Poverty Monitoring & Analysis, Poverty Lines, Poverty Impact Evaluation, Small Area Estimation Poverty Mapping, Poverty Assessment, Educational Sciences, Labor & Employment Law, Demographics

Suggested Citation

Dang, Hai-Anh H. and Jolliffe, Dean and Carletto, Calogero and Carletto, Calogero, Data Gaps, Data Incomparability, and Data Imputation: A Review of Poverty Measurement Methods for Data-Scarce Environments (December 19, 2017). World Bank Policy Research Working Paper No. 8282, Available at SSRN: https://ssrn.com/abstract=3090768

Hai-Anh H. Dang (Contact Author)

World Bank - Development Data Group (DECDG) ( email )

1818 H. Street, N.W.
MC2-846
Washington, DC 20433
United States

HOME PAGE: http://sites.google.com/site/haianhhdang/

IZA Institute of Labor Economics ( email )

P.O. Box 7240
Bonn, D-53072
Germany

Indiana University Bloomington - School of Public & Environmental Affairs (SPEA) ( email )

1315 East Tenth Street
Bloomington, IN 47405
United States

Global Labor Organization (GLO) ( email )

Collogne
Germany

Vietnam National University Ha Noi ( email )

Vietnam Academy of Social Sciences (VASS) - Centre for Analysis and Forecasting ( email )

1 Lieu Giai Street
Hanoi
Vietnam

Dean Jolliffe

World Bank, DECDG ( email )

1818 H Street NW
Washington, DC 20433
United States

HOME PAGE: http://www.deanjolliffe.net

IZA Institute of Labor Economics ( email )

P.O. Box 7240
Bonn, D-53072
Germany

HOME PAGE: http://www.iza.org/en/webcontent/index_html

Global Labor Organization (GLO) ( email )

Collogne
Germany

HOME PAGE: http://https://glabor.org/

Johns Hopkins University, Paul H. Nitze School of Advanced International Studies (SAIS), Students ( email )

1740 Massachusetts Avenue, NW
Washington, DC 20036-1984
United States

Calogero Carletto

World Bank ( email )

1818 H Street, NW
Washington, DC 20433
United States

World Bank - Development Research Group (DECRG)

1818 H. Street, N.W.
MSN3-311
Washington, DC 20433
United States

Calogero Carletto

affiliation not provided to SSRN

No Address Available

Do you have a job opening that you would like to promote on SSRN?

Paper statistics

Downloads
78
Abstract Views
482
rank
314,462
PlumX Metrics