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
- Institución
- Consejo Nacional de Investigaciones Científicas y Técnicas
- OAI Identificador
- oai:ri.conicet.gov.ar:11336/85462
Ver los metadatos del registro completo
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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 |
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info:eu-repo/semantics/openAccess https://creativecommons.org/licenses/by-nc-sa/2.5/ar/ |
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openAccess |
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https://creativecommons.org/licenses/by-nc-sa/2.5/ar/ |
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application/pdf application/pdf |
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Brookings Institution Press |
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Brookings Institution Press |
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