STATISTICAL DISCRIMINATION AND DURATION DEPENDENCE IN A SEMISTRUCTURAL MODEL
构建了一个包含工人异质性和学习机制的求职模型，利用美国当前人口调查数据估计失业持续时间对就业率的影响，发现消除统计歧视能显著提高长期失业者的就业率。
Abstract This article develops a job‐search model with unobserved worker heterogeneity and learning about worker types from unemployment duration. The model features negative duration dependence that stems from unobserved heterogeneity, skill depreciation, and statistical discrimination. We estimate job‐finding rates implied by our model using microlevel data from the Current Population Survey. We find that removing interview costs counterfactually, thereby eliminating statistical discrimination, substantially increases the job‐finding rates of the long‐term unemployed. The performance of low‐skill workers at the interview stage with discriminating firms plays a key role in explaining our counterfactual result.