Learning from potentially biased statistics

Autores
Cavallo, Alberto; Cruces, Guillermo Antonio; Perez-Truglia, Ricardo
Año de publicación
2016
Idioma
inglés
Tipo de recurso
artículo
Estado
versión publicada
Descripción
When forming expectations, households may be influenced by perceived bias in the information they receive. In this paper, we study how individuals learn from potentially biased statistics using data from both a natural experiment and a survey experiment during a period (2007-15) when the government of Argentina was manipulating official inflation statistics. This period is interesting because attention was being given to inflation information and both official and unofficial statistics were available. Our evidence suggests that, rather than ignoring biased statistics or naively accepting them, households react in a sophisticated way, as predicted by a Bayesian learning model. We also find evidence of an asymmetric reaction to inflation signals, with expectations changing more when the inflation rate rises than when it falls. These results could also be useful for understanding the formation of inflation expectations in less extreme contexts than Argentina, such as the United States and Europe, where experts may agree that statistics are unbiased but households are not.
Fil: Cavallo, Alberto. Massachusetts Institute of Technology; Estados Unidos
Fil: Cruces, Guillermo Antonio. Consejo Nacional de Investigaciones Científicas y Técnicas. Centro Científico Tecnológico Conicet - La Plata; Argentina. Universidad Nacional de La Plata. Facultad de Ciencias Económicas. Departamento de Ciencias Económicas. Centro de Estudios Distributivos Laborales y Sociales; Argentina
Fil: Perez-Truglia, Ricardo. Microsoft Research; Estados Unidos
Materia
Expectations
Households
Biased statistics
Experiment
Nivel de accesibilidad
acceso abierto
Condiciones de uso
https://creativecommons.org/licenses/by-nc-sa/2.5/ar/
Repositorio
CONICET Digital (CONICET)
Institución
Consejo Nacional de Investigaciones Científicas y Técnicas
OAI Identificador
oai:ri.conicet.gov.ar:11336/85462

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spelling Learning from potentially biased statisticsCavallo, AlbertoCruces, Guillermo AntonioPerez-Truglia, RicardoExpectationsHouseholdsBiased statisticsExperimenthttps://purl.org/becyt/ford/5.2https://purl.org/becyt/ford/5When forming expectations, households may be influenced by perceived bias in the information they receive. In this paper, we study how individuals learn from potentially biased statistics using data from both a natural experiment and a survey experiment during a period (2007-15) when the government of Argentina was manipulating official inflation statistics. This period is interesting because attention was being given to inflation information and both official and unofficial statistics were available. Our evidence suggests that, rather than ignoring biased statistics or naively accepting them, households react in a sophisticated way, as predicted by a Bayesian learning model. We also find evidence of an asymmetric reaction to inflation signals, with expectations changing more when the inflation rate rises than when it falls. These results could also be useful for understanding the formation of inflation expectations in less extreme contexts than Argentina, such as the United States and Europe, where experts may agree that statistics are unbiased but households are not.Fil: Cavallo, Alberto. Massachusetts Institute of Technology; Estados UnidosFil: Cruces, Guillermo Antonio. Consejo Nacional de Investigaciones Científicas y Técnicas. Centro Científico Tecnológico Conicet - La Plata; Argentina. Universidad Nacional de La Plata. Facultad de Ciencias Económicas. Departamento de Ciencias Económicas. Centro de Estudios Distributivos Laborales y Sociales; ArgentinaFil: Perez-Truglia, Ricardo. Microsoft Research; Estados UnidosBrookings Institution Press2016-03info:eu-repo/semantics/articleinfo:eu-repo/semantics/publishedVersionhttp://purl.org/coar/resource_type/c_6501info:ar-repo/semantics/articuloapplication/pdfapplication/pdfhttp://hdl.handle.net/11336/85462Cavallo, Alberto; Cruces, Guillermo Antonio; Perez-Truglia, Ricardo; Learning from potentially biased statistics; Brookings Institution Press; Brookings Papers on Economic Activity; 2016; SPRING; 3-2016; 59-1080007-23031533-4465CONICET DigitalCONICETenginfo:eu-repo/semantics/altIdentifier/url/https://muse.jhu.edu/article/629296/summaryinfo:eu-repo/semantics/altIdentifier/doi/10.1353/eca.2016.0013info:eu-repo/semantics/openAccesshttps://creativecommons.org/licenses/by-nc-sa/2.5/ar/reponame:CONICET Digital (CONICET)instname:Consejo Nacional de Investigaciones Científicas y Técnicas2025-09-10T13:20:07Zoai:ri.conicet.gov.ar:11336/85462instacron:CONICETInstitucionalhttp://ri.conicet.gov.ar/Organismo científico-tecnológicoNo correspondehttp://ri.conicet.gov.ar/oai/requestdasensio@conicet.gov.ar; lcarlino@conicet.gov.arArgentinaNo correspondeNo correspondeNo correspondeopendoar:34982025-09-10 13:20:07.576CONICET Digital (CONICET) - Consejo Nacional de Investigaciones Científicas y Técnicasfalse
dc.title.none.fl_str_mv Learning from potentially biased statistics
title Learning from potentially biased statistics
spellingShingle Learning from potentially biased statistics
Cavallo, Alberto
Expectations
Households
Biased statistics
Experiment
title_short Learning from potentially biased statistics
title_full Learning from potentially biased statistics
title_fullStr Learning from potentially biased statistics
title_full_unstemmed Learning from potentially biased statistics
title_sort Learning from potentially biased statistics
dc.creator.none.fl_str_mv Cavallo, Alberto
Cruces, Guillermo Antonio
Perez-Truglia, Ricardo
author Cavallo, Alberto
author_facet Cavallo, Alberto
Cruces, Guillermo Antonio
Perez-Truglia, Ricardo
author_role author
author2 Cruces, Guillermo Antonio
Perez-Truglia, Ricardo
author2_role author
author
dc.subject.none.fl_str_mv Expectations
Households
Biased statistics
Experiment
topic Expectations
Households
Biased statistics
Experiment
purl_subject.fl_str_mv https://purl.org/becyt/ford/5.2
https://purl.org/becyt/ford/5
dc.description.none.fl_txt_mv When forming expectations, households may be influenced by perceived bias in the information they receive. In this paper, we study how individuals learn from potentially biased statistics using data from both a natural experiment and a survey experiment during a period (2007-15) when the government of Argentina was manipulating official inflation statistics. This period is interesting because attention was being given to inflation information and both official and unofficial statistics were available. Our evidence suggests that, rather than ignoring biased statistics or naively accepting them, households react in a sophisticated way, as predicted by a Bayesian learning model. We also find evidence of an asymmetric reaction to inflation signals, with expectations changing more when the inflation rate rises than when it falls. These results could also be useful for understanding the formation of inflation expectations in less extreme contexts than Argentina, such as the United States and Europe, where experts may agree that statistics are unbiased but households are not.
Fil: Cavallo, Alberto. Massachusetts Institute of Technology; Estados Unidos
Fil: Cruces, Guillermo Antonio. Consejo Nacional de Investigaciones Científicas y Técnicas. Centro Científico Tecnológico Conicet - La Plata; Argentina. Universidad Nacional de La Plata. Facultad de Ciencias Económicas. Departamento de Ciencias Económicas. Centro de Estudios Distributivos Laborales y Sociales; Argentina
Fil: Perez-Truglia, Ricardo. Microsoft Research; Estados Unidos
description When forming expectations, households may be influenced by perceived bias in the information they receive. In this paper, we study how individuals learn from potentially biased statistics using data from both a natural experiment and a survey experiment during a period (2007-15) when the government of Argentina was manipulating official inflation statistics. This period is interesting because attention was being given to inflation information and both official and unofficial statistics were available. Our evidence suggests that, rather than ignoring biased statistics or naively accepting them, households react in a sophisticated way, as predicted by a Bayesian learning model. We also find evidence of an asymmetric reaction to inflation signals, with expectations changing more when the inflation rate rises than when it falls. These results could also be useful for understanding the formation of inflation expectations in less extreme contexts than Argentina, such as the United States and Europe, where experts may agree that statistics are unbiased but households are not.
publishDate 2016
dc.date.none.fl_str_mv 2016-03
dc.type.none.fl_str_mv info:eu-repo/semantics/article
info:eu-repo/semantics/publishedVersion
http://purl.org/coar/resource_type/c_6501
info:ar-repo/semantics/articulo
format article
status_str publishedVersion
dc.identifier.none.fl_str_mv http://hdl.handle.net/11336/85462
Cavallo, Alberto; Cruces, Guillermo Antonio; Perez-Truglia, Ricardo; Learning from potentially biased statistics; Brookings Institution Press; Brookings Papers on Economic Activity; 2016; SPRING; 3-2016; 59-108
0007-2303
1533-4465
CONICET Digital
CONICET
url http://hdl.handle.net/11336/85462
identifier_str_mv Cavallo, Alberto; Cruces, Guillermo Antonio; Perez-Truglia, Ricardo; Learning from potentially biased statistics; Brookings Institution Press; Brookings Papers on Economic Activity; 2016; SPRING; 3-2016; 59-108
0007-2303
1533-4465
CONICET Digital
CONICET
dc.language.none.fl_str_mv eng
language eng
dc.relation.none.fl_str_mv info:eu-repo/semantics/altIdentifier/url/https://muse.jhu.edu/article/629296/summary
info:eu-repo/semantics/altIdentifier/doi/10.1353/eca.2016.0013
dc.rights.none.fl_str_mv info:eu-repo/semantics/openAccess
https://creativecommons.org/licenses/by-nc-sa/2.5/ar/
eu_rights_str_mv openAccess
rights_invalid_str_mv https://creativecommons.org/licenses/by-nc-sa/2.5/ar/
dc.format.none.fl_str_mv application/pdf
application/pdf
dc.publisher.none.fl_str_mv Brookings Institution Press
publisher.none.fl_str_mv Brookings Institution Press
dc.source.none.fl_str_mv reponame:CONICET Digital (CONICET)
instname:Consejo Nacional de Investigaciones Científicas y Técnicas
reponame_str CONICET Digital (CONICET)
collection CONICET Digital (CONICET)
instname_str Consejo Nacional de Investigaciones Científicas y Técnicas
repository.name.fl_str_mv CONICET Digital (CONICET) - Consejo Nacional de Investigaciones Científicas y Técnicas
repository.mail.fl_str_mv dasensio@conicet.gov.ar; lcarlino@conicet.gov.ar
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