Strategic Referral and On-the-Job Search Equilibrium

Jihie Moon
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引用次数: 1

Abstract

A majority of workers use referral when searching for a job, looking for a better match. To study how and to what extent referral affects match quality and aggregate labor market, I build an equilibrium on-the-job search model in which workers have two ways to search, formal application and referral, and information on information on match quality is endogenously transmitted through referral. The model characterizes the extent of information transmission in different jobs as an outcome of a strategic game, and the equilibrium of the game and workers' optimal search interact with each other in general equilibrium. The endogenous information explains why workers in a higher position in the job-ladder benefit more from a referral, which is consistent with empirical findings of this paper. I calibrate the model by matching the observed referral wage premium for different workers. The calibration shows a significant welfare gain 2.42% - 9.01% from referral. The welfare gain is more substantial when underlying match quality is dispersed, or when employers' screening ability is low. The quantitative result indicates that active labor market policy can have significant welfare effects, and the effects are larger in a labor market with high uncertainty.
策略性转介与在职求职均衡
大多数员工在找工作时都会使用推荐,以寻找更好的匹配。为了研究推荐如何以及在多大程度上影响匹配质量和劳动力市场总量,我建立了一个均衡在职搜索模型,其中工人有两种搜索方式:正式申请和推荐,并且关于匹配质量信息的信息通过推荐内生传递。该模型将不同岗位的信息传递程度描述为一种战略博弈的结果,博弈的均衡性和员工的最优搜索在一般均衡中相互作用。内源性信息解释了为什么在工作阶梯中处于较高位置的员工从转介中获益更多,这与本文的实证研究结果一致。我通过匹配观察到的不同工人的推荐工资溢价来校准模型。校准结果显示,从转诊中获得了2.42% - 9.01%的显著福利增益。当潜在匹配质量分散或雇主筛选能力较低时,福利收益更大。量化结果表明,积极的劳动力市场政策可以产生显著的福利效应,且在不确定性较高的劳动力市场中效果更大。
本文章由计算机程序翻译,如有差异,请以英文原文为准。
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