Job market signaling and employer learning

Alós-Ferrer, Carlos; Prat, Julien

Job market signaling and employer learning

Files

There are no files associated with this item.

Date

2012

Authors

Alós-Ferrer, Carlos

Prat, Julien

DOI (citable link)

10.1016/j.jet.2012.01.018

Collections

Wirtschaftswissenschaften

Publication type

Journal article

Publication status

Published

Published in

Journal of Economic Theory ; 147 (2012), 5. - pp. 1787-1817. - Elsevier. - ISSN 0022-0531. - eISSN 1095-7235

Abstract

We consider a signaling model where the senderʼs continuation value after signaling depends on his type, for instance because the receiver is able to update his posterior belief. As a leading example, we introduce Bayesian learning in a variety of environments ranging from simple two-period to continuous-time models with stochastic production. Signaling equilibria present two major departures from those obtained in models without learning. First, new mixed-strategy equilibria involving multiple pooling are possible. Second, pooling equilibria can survive the Intuitive Criterion when learning is efficient enough.

Subject (DDC)

330 Economics

Keywords

Employer learning; Signaling games; Intuitive Criterion; Multiple pooling

Cite This

ISO 690

ALÓS-FERRER, Carlos, Julien PRAT, 2012. Job market signaling and employer learning. In: Journal of Economic Theory. Elsevier. 147(5), pp. 1787-1817. ISSN 0022-0531. eISSN 1095-7235. Available under: doi: 10.1016/j.jet.2012.01.018

BibTex

@article{AlosFerrer2012-09marke-49256,
  year={2012},
  doi={10.1016/j.jet.2012.01.018},
  title={Job market signaling and employer learning},
  number={5},
  volume={147},
  issn={0022-0531},
  journal={Journal of Economic Theory},
  pages={1787--1817},
  author={Alós-Ferrer, Carlos and Prat, Julien}
}

RDF

<rdf:RDF
    xmlns:dcterms="http://purl.org/dc/terms/"
    xmlns:dc="http://purl.org/dc/elements/1.1/"
    xmlns:rdf="http://www.w3.org/1999/02/22-rdf-syntax-ns#"
    xmlns:bibo="http://purl.org/ontology/bibo/"
    xmlns:dspace="http://digital-repositories.org/ontologies/dspace/0.1.0#"
    xmlns:foaf="http://xmlns.com/foaf/0.1/"
    xmlns:void="http://rdfs.org/ns/void#"
    xmlns:xsd="http://www.w3.org/2001/XMLSchema#" > 
  <rdf:Description rdf:about="https://kops.uni-konstanz.de/server/rdf/resource/123456789/49256">
    <foaf:homepage rdf:resource="http://localhost:8080/"/>
    <dc:creator>Prat, Julien</dc:creator>
    <dcterms:available rdf:datatype="http://www.w3.org/2001/XMLSchema#dateTime">2020-04-22T12:32:42Z</dcterms:available>
    <dcterms:title>Job market signaling and employer learning</dcterms:title>
    <void:sparqlEndpoint rdf:resource="http://localhost/fuseki/dspace/sparql"/>
    <dspace:isPartOfCollection rdf:resource="https://kops.uni-konstanz.de/server/rdf/resource/123456789/46"/>
    <bibo:uri rdf:resource="https://kops.uni-konstanz.de/handle/123456789/49256"/>
    <dc:language>eng</dc:language>
    <dcterms:abstract xml:lang="eng">We consider a signaling model where the senderʼs continuation value after signaling depends on his type, for instance because the receiver is able to update his posterior belief. As a leading example, we introduce Bayesian learning in a variety of environments ranging from simple two-period to continuous-time models with stochastic production. Signaling equilibria present two major departures from those obtained in models without learning. First, new mixed-strategy equilibria involving multiple pooling are possible. Second, pooling equilibria can survive the Intuitive Criterion when learning is efficient enough.</dcterms:abstract>
    <dc:contributor>Prat, Julien</dc:contributor>
    <dc:creator>Alós-Ferrer, Carlos</dc:creator>
    <dc:date rdf:datatype="http://www.w3.org/2001/XMLSchema#dateTime">2020-04-22T12:32:42Z</dc:date>
    <dcterms:isPartOf rdf:resource="https://kops.uni-konstanz.de/server/rdf/resource/123456789/46"/>
    <dcterms:issued>2012-09</dcterms:issued>
    <dc:contributor>Alós-Ferrer, Carlos</dc:contributor>
  </rdf:Description>
</rdf:RDF>

Bibliography of Konstanz

Yes

Refereed

Yes