scispace - formally typeset
M

Manolis Galenianos

Researcher at Royal Holloway, University of London

Publications -  40
Citations -  858

Manolis Galenianos is an academic researcher from Royal Holloway, University of London. The author has contributed to research in topics: Productivity & Wage. The author has an hindex of 14, co-authored 40 publications receiving 783 citations. Previous affiliations of Manolis Galenianos include Pennsylvania State University.

Papers
More filters
Journal ArticleDOI

Directed Search with Multiple Job Applications

TL;DR: In this paper, the authors developed an equilibrium directed search model of the labor market where workers can simultaneously apply for multiple jobs and the main theoretical contribution is to integrate the portfolio choice problem faced by workers into an equilibrium framework.
Journal ArticleDOI

Hiring through referrals

TL;DR: In this article, an equilibrium search model of the labor market is combined with a social network and the key features are that the workers' network transmits information about jobs and that wages and firm entry are determined endogenously.
Posted Content

A Search-Theoretic Model of the Retail Market for Illegal Drugs

TL;DR: In this paper, a search-theoretic model of the retail market for illegal drugs is developed, which produces testable implications regarding the distribution of purity offered in equilibrium, and the duration of the relationships between buyers and sellers.
Journal ArticleDOI

Learning About Match Quality and the Use of Referrals

TL;DR: In this paper, a theoretical model of the labor market is developed to study the firm's decision to use referrals as a hiring method, which is characterized by search frictions and uncertain quality of the match between a worker and a job.
Journal ArticleDOI

Learning about match quality and the use of referrals

TL;DR: In this paper, the authors studied the decision to use referrals as a hiring method in a theoretical model of the labor market and found that using referrals increases the arrival rate of applicants and provides more accurate signals regarding a worker's suitability for the job.