Hysteresis in an Evolutionary Labor Market with Adaptive Search

dc.contributor.author Tesfatsion, Leigh
dc.contributor.department Department of Economics (LAS)
dc.date 2018-02-16T05:04:24.000
dc.date.accessioned 2020-06-30T02:04:30Z
dc.date.available 2020-06-30T02:04:30Z
dc.date.embargo 2015-04-16
dc.date.issued 1999-10-01
dc.description.abstract <p>This study undertakes a systematic experimental investigation of hysteresis (path dependency) in an agent-based computational labor market framework. It is shown that capacity asymmetries between work suppliers and employers can result in two distinct hysteresis effects, network and-behavioral, when work suppliers and employers interact strategically and evolve their work site behaviors over time. These hysteresis effects result in persistent heterogeneity in earnings and employment histories across agents who have no observable structural differences. At a more global level, these hysteresis effects are shown to result in a one-to-many mapping between treatment factors and experimental outcomes. These hysteresis effects may help to explain why excess earnings heterogeneity is commonly observed in real-world labor markets.</p>
dc.identifier archive/lib.dr.iastate.edu/econ_las_economicreports/51/
dc.identifier.articleid 1048
dc.identifier.contextkey 6994143
dc.identifier.s3bucket isulib-bepress-aws-west
dc.identifier.submissionpath econ_las_economicreports/51
dc.identifier.uri https://dr.lib.iastate.edu/handle/20.500.12876/21269
dc.source.bitstream archive/lib.dr.iastate.edu/econ_las_economicreports/51/EconReportSeries_50.pdf|||Sat Jan 15 00:43:05 UTC 2022
dc.subject.disciplines Behavioral Economics
dc.subject.disciplines Business Intelligence
dc.subject.disciplines Economic Policy
dc.subject.disciplines Numerical Analysis and Scientific Computing
dc.title Hysteresis in an Evolutionary Labor Market with Adaptive Search
dc.type article
dc.type.genre report
dspace.entity.type Publication
relation.isAuthorOfPublication 1b45fd18-54a5-46e7-a853-62262cd64b39
relation.isOrgUnitOfPublication 4c5aa914-a84a-4951-ab5f-3f60f4b65b3d
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