NETWORK SEARCH: CLIMBING THE JOB LADDER FASTER
将不规则网络结构引入在职搜索模型，发现通过社交网络找到的工作工资更高，联系更广的工人能更快晋升，并通过校准解释了经验发现中的构成效应而非信息效应。
Abstract We introduce an irregular network structure into a model of frictional, on‐the‐job search in which workers find jobs through their network connections or directly from firms. We show network‐found jobs have higher wages, and thus better‐connected workers climb the job ladder faster. The mean field approach allows us to formulate heterogeneous workers' recursive problem tractably. Our calibration is consistent with several empirical findings because of a composition—not information—effect. We also introduce new model‐consistent evidence: Job‐to‐job switches at higher ladder rungs are more likely to use networks.