| 来源类型 | Working Paper |
| 规范类型 | 报告 |
| DOI | 10.3386/w23534 |
| 来源ID | Working Paper 23534 |
| A Framework for Sharing Confidential Research Data, Applied to Investigating Differential Pay by Race in the U. S. Government | |
| Andrés F. Barrientos; Alexander Bolton; Tom Balmat; Jerome P. Reiter; John M. de Figueiredo; Ashwin Machanavajjhala; Yan Chen; Charles Kneifel; Mark DeLong | |
| 发表日期 | 2017-06-26 |
| 出版年 | 2017 |
| 语种 | 英语 |
| 摘要 | Data stewards seeking to provide access to large-scale social science data face a difficult challenge. They have to share data in ways that protect privacy and confidentiality, are informative for many analyses and purposes, and are relatively straightforward to use by data analysts. We present a framework for addressing this challenge. The framework uses an integrated system that includes fully synthetic data intended for wide access, coupled with means for approved users to access the confidential data via secure remote access solutions, glued together by verification servers that allow users to assess the quality of their analyses with the synthetic data. We apply this framework to data on the careers of employees of the U. S. federal government, studying differentials in pay by race. The integrated system performs as intended, allowing users to explore the synthetic data for potential pay differentials and learn through verifications which findings in the synthetic data hold up in the confidential data and which do not. We find differentials across races; for example, the gap between black and white female federal employees' pay increased over the time period. We present models for generating synthetic careers and differentially private algorithms for verification of regression results. |
| 主题 | Econometrics ; Estimation Methods ; Data Collection ; Labor Economics ; Demography and Aging ; Labor Market Structures |
| URL | https://www.nber.org/papers/w23534 |
| 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/581208 |
| 推荐引用方式 GB/T 7714 | Andrés F. Barrientos,Alexander Bolton,Tom Balmat,et al. A Framework for Sharing Confidential Research Data, Applied to Investigating Differential Pay by Race in the U. S. Government. 2017. |
| 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 | ||
| w23534.pdf（445KB） | 智库出版物 | 限制开放 | CC BY-NC-SA | 浏览 | ||
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