Journal of Labor Economics · 2021 · Ian Burn、Patrick Button、Luis Felipe Munguía Corella、David Neumark
中文摘要
我们利用招聘广告文本，以及一项简历（通信研究式）田野实验（Neumark, Burn, and Button, 2019）中年长与年轻求职者获得回复的差异，研究年龄歧视刻板印象（体现为招聘广告中使用的语言）与招聘中年龄歧视之间的关系。我们的分析运用计算语言学和机器学习方法，在田野实验情境下检验可能构成招聘中年龄歧视基础的年龄歧视刻板印象。在此过程中，我们开发了分析文本数据的方法和框架，凸显了各类计算机科学技术对实证经济学研究的用处。我们发现，与年长员工刻板印象相关的语言有时能预测针对年长员工的歧视。就男性而言，我们发现我们所考察的三类年龄刻板印象——健康、性格与技能——均能预测年龄歧视的证据；就女性而言，关于性格的年龄刻板印象能预测年龄歧视。总体而言，年龄刻板印象预测年龄歧视的证据对男性要强得多，而且我们关于男性的结果与产业心理学中关于年龄刻板印象的文献相当一致。
Abstract
We study the relationships between ageist stereotypes - as reflected in the language used in job ads - and age discrimination in hiring, exploiting the text of job ads and differences in callbacks to older and younger job applicants from a resume (correspondence study) field experiment (Neumark, Burn, and Button, 2019). Our analysis uses computational linguistics and machine learning methods to examine, in a field-experiment setting, ageist stereotypes that might underlie age discrimination in hiring. In so doing, we develop methods and a framework for analyzing textual data, highlighting the usefulness of various computer science techniques for empirical economics research. We find evidence that language related to stereotypes of older workers sometimes predicts discrimination against older workers. For men, we find evidence that age stereotypes about all three categories we consider - health, personality, and skill - predict age discrimination, and for women, age stereotypes about personality predict age discrimination. In general, the evidence that age stereotypes predict age discrimination is much stronger for men, and our results for men are quite consistent with the industrial psychology literature on age stereotypes.