| 来源类型 | Working Paper |
| 规范类型 | 报告 |
| DOI | 10.3386/w24951 |
| 来源ID | Working Paper 24951 |
| Occupational Classifications: A Machine Learning Approach | |
| Akina Ikudo; Julia Lane; Joseph Staudt; Bruce Weinberg | |
| 发表日期 | 2018-09-03 |
| 出版年 | 2018 |
| 语种 | 英语 |
| 摘要 | Characterizing the work that people do on their jobs is a longstanding and core issue in labor economics. Traditionally, classification has been done manually. If it were possible to combine new computational tools and administrative wage records to generate an automated crosswalk between job titles and occupations, millions of dollars could be saved in labor costs, data processing could be sped up, data could become more consistent, and it might be possible to generate, without a lag, current information about the changing occupational composition of the labor market. This paper examines the potential to assign occupations to job titles contained in administrative data using automated, machine-learning approaches. We use a new extraordinarily rich and detailed set of data on transactional HR records of large firms (universities) in a relatively narrowly defined industry (public institutions of higher education) to identify the potential for machine-learning approaches to classify occupations. |
| 主题 | Econometrics ; Data Collection ; Labor Economics ; Labor Supply and Demand |
| URL | https://www.nber.org/papers/w24951 |
| 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/582625 |
| 推荐引用方式 GB/T 7714 | Akina Ikudo,Julia Lane,Joseph Staudt,et al. Occupational Classifications: A Machine Learning Approach. 2018. |
| 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 | ||
| w24951.pdf（438KB） | 智库出版物 | 限制开放 | CC BY-NC-SA | 浏览 | ||
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