| 来源类型 | Discussion paper |
| 规范类型 | 论文 |
| 来源ID | DP15840 |
| DP15840 The Gender Pay Gap Revisited with Big Data: Do Methodological Choices Matter? | |
| Anthony Strittmatter; Conny Wunsch | |
| 发表日期 | 2021-02-23 |
| 出版年 | 2021 |
| 语种 | 英语 |
| 摘要 | The vast majority of existing studies that estimate the average unexplained gender pay gap use unnecessarily restrictive linear versions of the Blinder-Oaxaca decomposition. Using a notably rich and large data set of 1.7 million employees in Switzerland, we investigate how the methodological improvements made possible by such big data affect estimates of the unexplained gender pay gap. We study the sensitivity of the estimates with regard to i) the availability of observationally comparable men and women, ii) model flexibility when controlling for wage determinants, and iii) the choice of different parametric and semi-parametric estimators, including variants that make use of machine learning methods. We find that these three factors matter greatly. Blinder-Oaxaca estimates of the unexplained gender pay gap decline by up to 39% when we enforce comparability between men and women and use a more flexible specification of the wage equation. Semi-parametric matching yields estimates that when compared with the Blinder-Oaxaca estimates, are up to 50% smaller and also less sensitive to the way wage determinants are included. |
| 主题 | Labour Economics |
| 关键词 | Gender inequality Gender pay gap Common support Model specification Matching estimator Machine learning |
| URL | https://cepr.org/publications/dp15840 |
| 来源智库 | Centre for Economic Policy Research (United Kingdom) |
| 资源类型 | 智库出版物 |
| 条目标识符 | http://119.78.100.153/handle/2XGU8XDN/544836 |
| 推荐引用方式 GB/T 7714 | Anthony Strittmatter,Conny Wunsch. DP15840 The Gender Pay Gap Revisited with Big Data: Do Methodological Choices Matter?. 2021. |
| 条目包含的文件 | 条目无相关文件。 | |||||
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