Asymmetric information between employers.

Autor/in	Kahn, Lisa B.
Institution	Forschungsinstitut zur Zukunft der Arbeit
Titel	Asymmetric information between employers.
Quelle	Bonn (2013), 58 S. PDF als Volltext kostenfreie Datei Link als defekt meldenVerfügbarkeit
Reihe	IZA discussion paper. 7147
Sprache	englisch
Dokumenttyp	online; Monographie
Schlagwörter	Kompetenz; Lernen; Informelle Kommunikation; Personalauswahl; Personalbeschaffung; Personalbeurteilung; Zwischenbetriebliche Mobilität; Arbeitsleistung; Arbeitsplatzwechsel; Arbeitsproduktivität; Berufliche Qualifikation; Erwerbstätiger; Lohnfindung; Bewertung; Arbeitgeber; Arbeitnehmer; Informationsfluss; USA
Abstract	Employer learning about workers' abilities plays a key role in determining how workers sort into jobs and are compensated. This study explores whether learning is symmetric or asymmetric, i.e., whether potential employers have the same information about worker ability as the incumbent firm. I develop a model of asymmetric learning that nests the symmetric learning case and allows the degree of asymmetry to vary, yielding testable implications for the prevalence of asymmetric learning. I then show how predictions in the model can be tested using compensation data. Using the NLSY, I test the model and find strong support for asymmetric information. I first exploit the fact that groups of workers differ in their variances in ability - based on economic conditions at time of entry into a firm - to show that incumbent wages track differences in ability distributions more closely than do outside firm wages. Second, I show that learning about ability is more symmetric for occupations that require more communication outside the firm. Finally, I show how to uncover the key parameter of interest in my model representing the degree to which information is asymmetric. My estimates imply that in one period, outside firms reduce the average expectation error over worker ability by roughly a third of the reduction made by incumbent firms. Thus outside firms retain sizeable expectation errors due to asymmetric information. (Author's abstract, IAB-Doku).
Erfasst von	Institut für Arbeitsmarkt- und Berufsforschung, Nürnberg
Update	2013/3