| 来源类型 | Working Paper |
| 规范类型 | 报告 |
| DOI | 10.3386/w28328 |
| 来源ID | Working Paper 28328 |
| Machine Learning and Perceived Age Stereotypes in Job Ads: Evidence from an Experiment | |
| Ian Burn; Daniel Firoozi; Daniel Ladd; David Neumark | |
| 发表日期 | 2021-01-11 |
| 出版年 | 2021 |
| 语种 | 英语 |
| 摘要 | We explore whether ageist stereotypes in job ads are detectable using machine learning methods measuring the linguistic similarity of job-ad language to ageist stereotypes identified by industrial psychologists. We then conduct an experiment to evaluate whether this language is perceived as biased against older workers. We find that language classified by the machine learning algorithm as closely related to ageist stereotypes is perceived as ageist by experimental subjects. The scores assigned to the language related to ageist stereotypes are larger when responses are incentivized by rewarding participants for guessing how other respondents rated the language. These methods could potentially help enforce anti-discrimination laws by using job ads to predict or identify employers more likely to be engaging in age discrimination. |
| 主题 | Labor Economics ; Demography and Aging ; Labor Discrimination ; Other ; Law and Economics |
| URL | https://www.nber.org/papers/w28328 |
| 来源智库 | National Bureau of Economic Research (United States) |
| 引用统计 | |
| 资源类型 | 智库出版物 |
| 条目标识符 | http://119.78.100.153/handle/2XGU8XDN/586001 |
| 推荐引用方式 GB/T 7714 | Ian Burn,Daniel Firoozi,Daniel Ladd,et al. Machine Learning and Perceived Age Stereotypes in Job Ads: Evidence from an Experiment. 2021. |
| 条目包含的文件 | ||||||
| 文件名称/大小 | 资源类型 | 版本类型 | 开放类型 | 使用许可 | ||
| w28328.pdf（4017KB） | 智库出版物 | 限制开放 | CC BY-NC-SA | 浏览 | ||
除非特别说明，本系统中所有内容都受版权保护，并保留所有权利。
