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来源类型Working Paper
规范类型报告
DOI10.3386/w23491
来源IDWorking 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
URLhttps://www.nber.org/papers/w23491
来源智库National Bureau of Economic Research (United States)
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资源类型智库出版物
条目标识符http://119.78.100.153/handle/2XGU8XDN/581165
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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.
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