Using Aggregated Relational Data to Feasibly Identify Network Structure without Network Data
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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.
Document Object Identifier (DOI): 10.3386/w23491
Published: Emily Breza & Arun G. Chandrasekhar & Tyler H. McCormick & Mengjie Pan, 2020. "Using Aggregated Relational Data to Feasibly Identify Network Structure without Network Data," American Economic Review, vol 110(8), pages 2454-2484.
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