| 来源类型 | Working Paper |
| 规范类型 | 报告 |
| DOI | 10.3386/w24953 |
| 来源ID | Working Paper 24953 |
| Measuring Bias in Consumer Lending | |
| Will Dobbie; Andres Liberman; Daniel Paravisini; Vikram Pathania | |
| 发表日期 | 2018-09-03 |
| 出版年 | 2018 |
| 语种 | 英语 |
| 摘要 | This paper tests for bias in consumer lending decisions using administrative data from a high-cost lender in the United Kingdom. We motivate our analysis using a simple model of bias in lending, which predicts that profits should be identical for loan applicants from different groups at the margin if loan examiners are unbiased. We identify the profitability of marginal loan applicants by exploiting variation from the quasi-random assignment of loan examiners. We find significant bias against both immigrant and older loan applicants when using the firm's preferred measure of long-run profits. In contrast, there is no evidence of bias when using a short-run measure used to evaluate examiner performance, suggesting that the bias in our setting is due to the misalignment of firm and examiner incentives. We conclude by showing that a decision rule based on machine learning predictions of long-run profitability can simultaneously increase profits and eliminate bias. |
| 主题 | Financial Economics ; Behavioral Finance ; Labor Economics ; Demography and Aging |
| URL | https://www.nber.org/papers/w24953 |
| 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/582627 |
| 推荐引用方式 GB/T 7714 | Will Dobbie,Andres Liberman,Daniel Paravisini,et al. Measuring Bias in Consumer Lending. 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 | ||
| w24953.pdf（2573KB） | 智库出版物 | 限制开放 | CC BY-NC-SA | 浏览 | ||
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