The Anatomy of Sorting—Evidence From Danish Data.
In this paper, we formulate and estimate a flexible model of job mobility and wages with two‐sided heterogeneity. The analysis extends the finite mixture approach of Bonhomme, Lamadon, and Manresa (2019) and Abowd, McKinney, and Schmutte (2019) to develop a new Classification Expectation‐Maximizatio...
| Publicado en: | Econometrica Vol. 91; no. 6; pp. 2409 - 2456 |
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| Autores principales: | , , |
| Formato: | Artículo |
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Wiley-Blackwell
Nov2023
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| Materias: | |
| Acceso en línea: | Ver este registro en EBSCOhost |
| fields | @attributes: recordID: 1 pdfLink: plink: https://search.ebscohost.com/login.aspx?direct=true&db=ssf&AN=174064836&site=ehost-live header: @attributes: shortDbName: ssf uiTerm: 174064836 longDbName: Social Sciences Full Text (H.W. Wilson) uiTag: AN controlInfo: bkinfo: jinfo: jid: 00129682 ECN jtl: Econometrica issn: 00129682 maglogo: Y pubinfo: dt: Nov2023 vid: 91 iid: 6 pid: 480 pub: Wiley-Blackwell artinfo: ui: 174064836 10.3982/ECTA16425 ppf: 2409 ppct: 47 formats: tig: atl: The Anatomy of Sorting—Evidence From Danish Data. aug: au: Lentz, Rasmus Piyapromdee, Suphanit Robin, Jean‐Marc affil: Department of Economics, University of Wisconsin–Madison Dale T. Mortensen Centre, University of Aarhus Department of Economics, University College London Department of Economics, Sciences Po, Paris su: McKinney (Tex.) Occupational mobility Life cycles (Biology) Net present value Expectation-maximization algorithms Classification algorithms sug: subj: Occupational mobility McKinney (Tex.) Life cycles (Biology) Net present value Expectation-maximization algorithms Classification algorithms keyword: classification algorithm decomposition of wage inequality EM algorithm employment and job mobility finite mixtures Heterogeneity mutual information sorting wage distributions classification algorithm decomposition of wage inequality EM algorithm employment and job mobility finite mixtures Heterogeneity mutual information sorting wage distributions ab: In this paper, we formulate and estimate a flexible model of job mobility and wages with two‐sided heterogeneity. The analysis extends the finite mixture approach of Bonhomme, Lamadon, and Manresa (2019) and Abowd, McKinney, and Schmutte (2019) to develop a new Classification Expectation‐Maximization algorithm that ensures both worker and firm latent‐type identification using wage and mobility variations in the data. Workers receive job offers in worker‐type segmented labor markets. Offers are accepted according to a logit form that compares the value of the current job with that of the new job. In combination with flexibly estimated layoff and job finding rates, the analysis quantifies the four different sources of sorting: job preferences, segmentation, layoffs, and job finding. Job preferences are identified through job‐to‐job moves in a revealed preference argument. They are in the model structurally independent of the identified job wages, possibly as a reflection of the presence of amenities. We find evidence of a strong pecuniary motive in job preferences. While the correlation between preferences and current job wages is positive, the net present value of the future earnings stream given the current job correlates much more strongly with preferences for it. This is more so for short‐ than long‐tenure workers. In the analysis, we distinguish between type sorting and wage sorting. Type sorting is quantified by means of the mutual information index. Wage sorting is captured through correlation between identified wage types. While layoffs are less important than the other channels, we find all channels to contribute substantially to sorting. As workers age, job arrival processes are the key determinant of wage sorting, whereas the role of job preferences dictate type sorting. Over the life cycle, job preferences intensify, type sorting increases, and pecuniary considerations wane. pubtype: Academic Journal doctype: Article src: R language: English refInfo: copyright: @attributes: flag: N holdings: @attributes: islocal: N |
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