| 来源类型 | Discussion paper |
| 规范类型 | 论文 |
| 来源ID | DP12448 |
| DP12448 Predictably Unequal? The Effects of Machine Learning on Credit Markets | |
| Paul Goldsmith-Pinkham; Ansgar Walther | |
| 发表日期 | 2017-11-19 |
| 出版年 | 2017 |
| 语种 | 英语 |
| 摘要 | Recent innovations in statistical technology, including in evaluating creditworthiness, have sparked concerns about impacts on the fairness of outcomes across categories such as race and gender. We build a simple equilibrium model of credit provision in which to evaluate such impacts. We find that as statistical technology changes, the effects on disparity depend on a combination of the changes in the functional form used to evaluate creditworthiness using underlying borrower characteristics and the cross-category distribution of these characteristics. Employing detailed data on US mortgages and applications, we predict default using a number of popular machine learning techniques, and embed these techniques in our equilibrium model to analyze both extensive margin (exclusion) and intensive margin (rates) impacts on disparity. We propose a basic measure of cross-category disparity, and find that the machine learning models perform worse on this measure than logit models, especially on the intensive margin. We discuss the implications of our findings for mortgage policy. |
| 主题 | Financial Economics |
| 关键词 | Machine learning Credit access Mortgages Statistical discrimination |
| URL | https://cepr.org/publications/dp12448 |
| 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/541259 |
| 推荐引用方式 GB/T 7714 | Paul Goldsmith-Pinkham,Ansgar Walther. DP12448 Predictably Unequal? The Effects of Machine Learning on Credit Markets. 2017. |
| 条目包含的文件 | 条目无相关文件。 | |||||
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