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Tracking the COVID-19 crisis with high-resolution transaction data

Payments systems generate vast amounts of naturally occurring transaction data rarely used for constructing official statistics. We consider billions of transactions from card data from a large bank, Banco Bilbao Vizcaya Argentaria, as an alternative source of information for measuring consumption....

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Detalles Bibliográficos
Autores principales: Carvalho, Vasco M., Garcia, Juan R., Hansen, Stephen, Ortiz, Álvaro, Rodrigo, Tomasa, Rodríguez Mora, José V., Ruiz, Pep
Formato: Online Artículo Texto
Lenguaje:English
Publicado: The Royal Society 2021
Materias:
Acceso en línea:https://www.ncbi.nlm.nih.gov/pmc/articles/PMC8355671/
https://www.ncbi.nlm.nih.gov/pubmed/34401194
http://dx.doi.org/10.1098/rsos.210218
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author Carvalho, Vasco M.
Garcia, Juan R.
Hansen, Stephen
Ortiz, Álvaro
Rodrigo, Tomasa
Rodríguez Mora, José V.
Ruiz, Pep
author_facet Carvalho, Vasco M.
Garcia, Juan R.
Hansen, Stephen
Ortiz, Álvaro
Rodrigo, Tomasa
Rodríguez Mora, José V.
Ruiz, Pep
author_sort Carvalho, Vasco M.
collection PubMed
description Payments systems generate vast amounts of naturally occurring transaction data rarely used for constructing official statistics. We consider billions of transactions from card data from a large bank, Banco Bilbao Vizcaya Argentaria, as an alternative source of information for measuring consumption. We show, via validation against official consumption measures, that transaction data complements national accounts and consumption surveys. We then analyse the impact of COVID-19 in Spain, and document: (i) strong consumption responses to business closures, but smaller effects for capacity restrictions; (ii) a steeper decline in spending in rich neighbourhoods; (iii) higher mobility for residents of lower-income neighbourhoods, correlating with increased disease incidence.
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spelling pubmed-83556712021-08-15 Tracking the COVID-19 crisis with high-resolution transaction data Carvalho, Vasco M. Garcia, Juan R. Hansen, Stephen Ortiz, Álvaro Rodrigo, Tomasa Rodríguez Mora, José V. Ruiz, Pep R Soc Open Sci Computer Science and Artificial Intelligence Payments systems generate vast amounts of naturally occurring transaction data rarely used for constructing official statistics. We consider billions of transactions from card data from a large bank, Banco Bilbao Vizcaya Argentaria, as an alternative source of information for measuring consumption. We show, via validation against official consumption measures, that transaction data complements national accounts and consumption surveys. We then analyse the impact of COVID-19 in Spain, and document: (i) strong consumption responses to business closures, but smaller effects for capacity restrictions; (ii) a steeper decline in spending in rich neighbourhoods; (iii) higher mobility for residents of lower-income neighbourhoods, correlating with increased disease incidence. The Royal Society 2021-08-11 /pmc/articles/PMC8355671/ /pubmed/34401194 http://dx.doi.org/10.1098/rsos.210218 Text en © 2021 The Authors. https://creativecommons.org/licenses/by/4.0/Published by the Royal Society under the terms of the Creative Commons Attribution License http://creativecommons.org/licenses/by/4.0/ (https://creativecommons.org/licenses/by/4.0/) , which permits unrestricted use, provided the original author and source are credited.
spellingShingle Computer Science and Artificial Intelligence
Carvalho, Vasco M.
Garcia, Juan R.
Hansen, Stephen
Ortiz, Álvaro
Rodrigo, Tomasa
Rodríguez Mora, José V.
Ruiz, Pep
Tracking the COVID-19 crisis with high-resolution transaction data
title Tracking the COVID-19 crisis with high-resolution transaction data
title_full Tracking the COVID-19 crisis with high-resolution transaction data
title_fullStr Tracking the COVID-19 crisis with high-resolution transaction data
title_full_unstemmed Tracking the COVID-19 crisis with high-resolution transaction data
title_short Tracking the COVID-19 crisis with high-resolution transaction data
title_sort tracking the covid-19 crisis with high-resolution transaction data
topic Computer Science and Artificial Intelligence
url https://www.ncbi.nlm.nih.gov/pmc/articles/PMC8355671/
https://www.ncbi.nlm.nih.gov/pubmed/34401194
http://dx.doi.org/10.1098/rsos.210218
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