| 来源类型 | Working Paper |
| 规范类型 | 报告 |
| DOI | 10.3386/w25837 |
| 来源ID | Working Paper 25837 |
| Transforming Naturally Occurring Text Data Into Economic Statistics: The Case of Online Job Vacancy Postings | |
| Arthur Turrell; Bradley J. Speigner; Jyldyz Djumalieva; David Copple; James Thurgood | |
| 发表日期 | 2019-05-20 |
| 出版年 | 2019 |
| 语种 | 英语 |
| 摘要 | Using a dataset of 15 million UK job adverts from a recruitment website, we construct new economic statistics measuring labour market demand. These data are ‘naturally occurring’, having originally been posted online by firms. They offer information on two dimensions of vacancies—region and occupation—that firm-based surveys do not usually, and cannot easily, collect. These data do not come with official classification labels so we develop an algorithm which maps the free form text of job descriptions into standard occupational classification codes. The created vacancy statistics give a plausible, granular picture of UK labour demand and permit the analysis of Beveridge curves and mismatch unemployment at the occupational level. |
| 主题 | Econometrics ; Estimation Methods ; Macroeconomics ; Consumption and Investment ; Labor Economics ; Unemployment and Immigration |
| URL | https://www.nber.org/papers/w25837 |
| 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/583510 |
| 推荐引用方式 GB/T 7714 | Arthur Turrell,Bradley J. Speigner,Jyldyz Djumalieva,et al. Transforming Naturally Occurring Text Data Into Economic Statistics: The Case of Online Job Vacancy Postings. 2019. |
| 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 | ||
| w25837.pdf（853KB） | 智库出版物 | 限制开放 | CC BY-NC-SA | 浏览 | ||
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