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Social network data is often prohibitively expensive to collect, limitingempirical network research. Typical economic network mapping requires (1)enumerating a census, (2) eliciting the names of all network links for eachindividual, (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 asfinancial intermediaries or high-risk groups, proprietary data and privacyconcerns may render (2)-(3) impossible. Both restrict the accessibility ofhigh-quality networks research to investigators with considerable resources.  We propose an inexpensive and feasible strategy for network elicitation usingAggregated Relational Data (ARD) -- responses to questions of the form "Howmany of your social connections have trait k?" Our method uses ARD to recoverthe parameters of a general network formation model, which in turn, permits theestimation of any arbitrary node- or graph-level statistic. The method workswell in simulations and in matching a range of network characteristics inreal-world graphs from 75 Indian villages. Moreover, we replicate the resultsof two field experiments that involved collecting network data. We show thatthe researchers would have drawn similar conclusions using ARD alone. Finally,using calculations from J-PAL fieldwork, we show that in rural India, forexample, ARD surveys are 80% cheaper than full network surveys.

Using Aggregated Relational Data to feasibly identify network structure without network data