Using Aggregated Relational Data to Feasibly Identify Network Structure Without Network Data

48 Pages Posted: 13 Jun 2017

See all articles by Emily Breza

Emily Breza

Harvard University

Arun G. Chandrasekhar

Stanford University - Department of Economics

Tyler McCormick

University of Washington

Mengjie Pan

University of Washington

Date Written: June 2017

Abstract

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. We propose an inexpensive and feasible strategy for network elicitation using Aggregated Relational Data (ARD) – responses to questions of the form “How many of your social connections have trait k?” Our method uses ARD to recover the parameters of a general network formation model, which in turn, permits the estimation of any arbitrary node- or graph-level statistic. The method works well in simulations and in matching a range of network characteristics in real-world graphs from 75 Indian villages. Moreover, we replicate the results of two field experiments that involved collecting network data. We show that the researchers would have drawn similar conclusions using ARD alone. Finally, using calculations from J-PAL fieldwork, we show that in rural India, for example, ARD surveys are 80% cheaper than full network surveys.

Suggested Citation

Breza, Emily and Chandrasekhar, Arun G. and McCormick, Tyler and Pan, Mengjie, Using Aggregated Relational Data to Feasibly Identify Network Structure Without Network Data (June 2017). NBER Working Paper No. w23491. Available at SSRN: https://ssrn.com/abstract=2984668

Emily Breza (Contact Author)

Harvard University ( email )

1875 Cambridge Street
Cambridge, MA 02138
United States

Arun G. Chandrasekhar

Stanford University - Department of Economics ( email )

Landau Economics Building
579 Serra Mall
Stanford, CA 94305-6072
United States

Tyler McCormick

University of Washington ( email )

Seattle, WA 98195
United States

Mengjie Pan

University of Washington ( email )

Seattle, WA 98195
United States

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