The ABCDE of Big Data : Assessing Biases in Call-Detail Records for Development Estimates

This article contributes to improving our understanding of biases in estimates of demographic indicators, in the developing world, based on Call Detail Records (CDRs). CDRs represent an important and largely untapped source of data for the developing world. However, they are not representative of th...

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Main Authors:	Pestre, Gabriel, Letouzé, Emmanuel, Zagheni, Emilio
Format:	Journal Article
Published:	Published by Oxford University Press on behalf of the World Bank 2021
Subjects:	CENSUS NATIONAL STATISTICS DEMOGRAPHICS BIG DATA DIGITAL BREADCRUMB SAMPLE BIAS CORRECTION
Online Access:	http://hdl.handle.net/10986/36153

id	okr-10986-36153
recordtype	oai_dc
spelling	okr-10986-361532021-08-18T05:10:39Z The ABCDE of Big Data : Assessing Biases in Call-Detail Records for Development Estimates Pestre, Gabriel Letouzé, Emmanuel Zagheni, Emilio CENSUS NATIONAL STATISTICS DEMOGRAPHICS BIG DATA DIGITAL BREADCRUMB SAMPLE BIAS CORRECTION This article contributes to improving our understanding of biases in estimates of demographic indicators, in the developing world, based on Call Detail Records (CDRs). CDRs represent an important and largely untapped source of data for the developing world. However, they are not representative of the underlying population. We combine CDRs and census data for Senegal in 2013 to evaluate biases related to estimates of population density. We show that: (i) there are systematic relationships between cell-phone use and socio-economic and geographic characteristics that can be leveraged to improve estimates of population density; (ii) when no ‘ground truth’ data is available, a difference-in-difference approach can be used to reduce bias and infer relative changes over time in population size at the subnational level; (iii) indicators of development, including urbanization and internal, circular, and temporary migration, can be monitored by integrating census data and CDRs. The paper is intended to offer a methodological contribution and examples of applications related to combining new and traditional data sources to improve our ability to monitor development indicators over time and space. 2021-08-17T18:57:40Z 2021-08-17T18:57:40Z 2020-02 Journal Article World Bank Economic Review 1564-698X http://hdl.handle.net/10986/36153 CC BY-NC-ND 3.0 IGO http://creativecommons.org/licenses/by-nc-nd/3.0/igo World Bank Published by Oxford University Press on behalf of the World Bank Publications & Research Publications & Research :: Journal Article Africa Africa Western and Central (AFW) Senegal
repository_type	Digital Repository
institution_category	Foreign Institution
institution	Digital Repositories
building	World Bank Open Knowledge Repository
collection	World Bank
topic	CENSUS NATIONAL STATISTICS DEMOGRAPHICS BIG DATA DIGITAL BREADCRUMB SAMPLE BIAS CORRECTION
spellingShingle	CENSUS NATIONAL STATISTICS DEMOGRAPHICS BIG DATA DIGITAL BREADCRUMB SAMPLE BIAS CORRECTION Pestre, Gabriel Letouzé, Emmanuel Zagheni, Emilio The ABCDE of Big Data : Assessing Biases in Call-Detail Records for Development Estimates
geographic_facet	Africa Africa Western and Central (AFW) Senegal
description	This article contributes to improving our understanding of biases in estimates of demographic indicators, in the developing world, based on Call Detail Records (CDRs). CDRs represent an important and largely untapped source of data for the developing world. However, they are not representative of the underlying population. We combine CDRs and census data for Senegal in 2013 to evaluate biases related to estimates of population density. We show that: (i) there are systematic relationships between cell-phone use and socio-economic and geographic characteristics that can be leveraged to improve estimates of population density; (ii) when no ‘ground truth’ data is available, a difference-in-difference approach can be used to reduce bias and infer relative changes over time in population size at the subnational level; (iii) indicators of development, including urbanization and internal, circular, and temporary migration, can be monitored by integrating census data and CDRs. The paper is intended to offer a methodological contribution and examples of applications related to combining new and traditional data sources to improve our ability to monitor development indicators over time and space.
format	Journal Article
author	Pestre, Gabriel Letouzé, Emmanuel Zagheni, Emilio
author_facet	Pestre, Gabriel Letouzé, Emmanuel Zagheni, Emilio
author_sort	Pestre, Gabriel
title	The ABCDE of Big Data : Assessing Biases in Call-Detail Records for Development Estimates
title_short	The ABCDE of Big Data : Assessing Biases in Call-Detail Records for Development Estimates
title_full	The ABCDE of Big Data : Assessing Biases in Call-Detail Records for Development Estimates
title_fullStr	The ABCDE of Big Data : Assessing Biases in Call-Detail Records for Development Estimates
title_full_unstemmed	The ABCDE of Big Data : Assessing Biases in Call-Detail Records for Development Estimates
title_sort	abcde of big data : assessing biases in call-detail records for development estimates
publisher	Published by Oxford University Press on behalf of the World Bank
publishDate	2021
url	http://hdl.handle.net/10986/36153
_version_	1764484615295205376

The ABCDE of Big Data : Assessing Biases in Call-Detail Records for Development Estimates

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