| 来源类型 | Working Paper |
| 规范类型 | 报告 |
| DOI | 10.3386/w23491 |
| 来源ID | Working Paper 23491 |
| Using Aggregated Relational Data to Feasibly Identify Network Structure without Network Data | |
| Emily Breza; Arun G. Chandrasekhar; Tyler H. McCormick; Mengjie Pan | |
| 发表日期 | 2017-06-12 |
| 出版年 | 2017 |
| 语种 | 英语 |
| 摘要 | Social network data is often prohibitively expensive to collect, limiting empirical network research. Typical economic network mapping requires (1) enumerating a census, (2) eliciting the names of all network links for each individual, (3) matching the list of social connections to the census, and (4) repeating (1)-(3) across many networks. In settings requiring field surveys, steps (2)-(3) can be very expensive. In other network populations such as financial intermediaries or high-risk groups, proprietary data and privacy concerns may render (2)-(3) impossible. Both restrict the accessibility of high-quality networks research to investigators with considerable resources. |
| 主题 | Econometrics ; Data Collection ; Microeconomics ; Economics of Information ; Industrial Organization ; Market Structure and Firm Performance |
| URL | https://www.nber.org/papers/w23491 |
| 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/581165 |
| 推荐引用方式 GB/T 7714 | Emily Breza,Arun G. Chandrasekhar,Tyler H. McCormick,et al. Using Aggregated Relational Data to Feasibly Identify Network Structure without Network Data. 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 | ||
| w23491.pdf（878KB） | 智库出版物 | 限制开放 | CC BY-NC-SA | 浏览 | ||
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