| 来源类型 | Working Paper |
| 规范类型 | 报告 |
| DOI | 10.3386/w29105 |
| 来源ID | Working Paper 29105 |
| Using Satellite Imagery and Deep Learning to Evaluate the Impact of Anti-Poverty Programs | |
| Luna Yue Huang; Solomon M. Hsiang; Marco Gonzalez-Navarro | |
| 发表日期 | 2021-08-02 |
| 出版年 | 2021 |
| 语种 | 英语 |
| 摘要 | The rigorous evaluation of anti-poverty programs is key to the fight against global poverty. Traditional approaches rely heavily on repeated in-person field surveys to measure program effects. However, this is costly, time-consuming, and often logistically challenging. Here we provide the first evidence that we can conduct such program evaluations based solely on high-resolution satellite imagery and deep learning methods. Our application estimates changes in household welfare in a recent anti-poverty program in rural Kenya. Leveraging a large literature documenting a reliable relationship between housing quality and household wealth, we infer changes in household wealth based on satellite-derived changes in housing quality and obtain consistent results with the traditional field-survey based approach. Our approach generates inexpensive and timely insights on program effectiveness in international development programs. |
| 主题 | Econometrics ; Data Collection ; Public Economics ; Development and Growth ; Development ; Environmental and Resource Economics ; Regional and Urban Economics |
| URL | https://www.nber.org/papers/w29105 |
| 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/586779 |
| 推荐引用方式 GB/T 7714 | Luna Yue Huang,Solomon M. Hsiang,Marco Gonzalez-Navarro. Using Satellite Imagery and Deep Learning to Evaluate the Impact of Anti-Poverty Programs. 2021. |
| 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 | ||
| w29105.pdf（4048KB） | 智库出版物 | 限制开放 | CC BY-NC-SA | 浏览 | ||
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