Is Predicted Data a Viable Alternative to Real Data?

It is costly to collect the household- and individual-level data that underlies official estimates of poverty and health. For this reason, developing countries often do not have the budget to update their estimates of poverty and health regularly,...

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Main Authors:	Fujii, Tomoki, van der Weide, Roy
Format:	Working Paper
Language:	English en_US
Published:	World Bank, Washington, DC 2016
Subjects:	double sampling survey costs poverty prediction model
Online Access:	http://documents.worldbank.org/curated/en/2016/09/26822026/predicted-data-viable-alternative-real-data http://hdl.handle.net/10986/25156

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recordtype	oai_dc
spelling	okr-10986-251562021-12-10T18:04:12Z Is Predicted Data a Viable Alternative to Real Data? Fujii, Tomoki van der Weide, Roy double sampling survey costs poverty prediction model It is costly to collect the household- and individual-level data that underlies official estimates of poverty and health. For this reason, developing countries often do not have the budget to update their estimates of poverty and health regularly, even though these estimates are most needed there. One way to reduce the financial burden is to substitute some of the real data with predicted data. An approach referred to as double sampling collects the expensive outcome variable for a sub-sample only while collecting the covariates used for prediction for the full sample. The objective of this study is to determine if this would indeed allow for realizing meaningful reductions in financial costs while preserving statistical precision. The study does this using analytical calculations that allow for considering a wide range of parameter values that are plausible to real applications. The benefits of using double sampling are found to be modest. There are circumstances for which the gains can be more substantial, but the study conjectures that these denote the exceptions rather than the rule. The recommendation is to rely on real data whenever there is a need for new data, and use the prediction estimator to leverage existing data. 2016-10-13T20:46:52Z 2016-10-13T20:46:52Z 2016-09 Working Paper http://documents.worldbank.org/curated/en/2016/09/26822026/predicted-data-viable-alternative-real-data http://hdl.handle.net/10986/25156 English en_US Policy Research Working Paper;No. 7841 CC BY 3.0 IGO http://creativecommons.org/licenses/by/3.0/igo/ World Bank World Bank, Washington, DC Publications & Research Publications & Research :: Policy Research Working Paper
repository_type	Digital Repository
institution_category	Foreign Institution
institution	Digital Repositories
building	World Bank Open Knowledge Repository
collection	World Bank
language	English en_US
topic	double sampling survey costs poverty prediction model
spellingShingle	double sampling survey costs poverty prediction model Fujii, Tomoki van der Weide, Roy Is Predicted Data a Viable Alternative to Real Data?
relation	Policy Research Working Paper;No. 7841
description	It is costly to collect the household- and individual-level data that underlies official estimates of poverty and health. For this reason, developing countries often do not have the budget to update their estimates of poverty and health regularly, even though these estimates are most needed there. One way to reduce the financial burden is to substitute some of the real data with predicted data. An approach referred to as double sampling collects the expensive outcome variable for a sub-sample only while collecting the covariates used for prediction for the full sample. The objective of this study is to determine if this would indeed allow for realizing meaningful reductions in financial costs while preserving statistical precision. The study does this using analytical calculations that allow for considering a wide range of parameter values that are plausible to real applications. The benefits of using double sampling are found to be modest. There are circumstances for which the gains can be more substantial, but the study conjectures that these denote the exceptions rather than the rule. The recommendation is to rely on real data whenever there is a need for new data, and use the prediction estimator to leverage existing data.
format	Working Paper
author	Fujii, Tomoki van der Weide, Roy
author_facet	Fujii, Tomoki van der Weide, Roy
author_sort	Fujii, Tomoki
title	Is Predicted Data a Viable Alternative to Real Data?
title_short	Is Predicted Data a Viable Alternative to Real Data?
title_full	Is Predicted Data a Viable Alternative to Real Data?
title_fullStr	Is Predicted Data a Viable Alternative to Real Data?
title_full_unstemmed	Is Predicted Data a Viable Alternative to Real Data?
title_sort	is predicted data a viable alternative to real data?
publisher	World Bank, Washington, DC
publishDate	2016
url	http://documents.worldbank.org/curated/en/2016/09/26822026/predicted-data-viable-alternative-real-data http://hdl.handle.net/10986/25156
_version_	1764458693305303040

Is Predicted Data a Viable Alternative to Real Data?

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