Social and economic network data can be useful for both researchers and
policymakers, but can often be impractical to collect. We propose collecting
Aggregated Relational Data (ARD) using questions that are simple and easy to
add to any survey. These question are of the form "how many of your friends in
the village have trait k?"
We show that by collecting ARD on even a small share of the population,
researchers can recover the likely distribution of statistics from the
underlying network. We provide three empirical examples. We first apply the
technique to the 75 village networks in Karnataka, India, where Banerjee et al.
(2016b) collected near-complete network data. We show that with ARD alone on
even a 29% sample, we can accurately estimate both node-level features (such as
eigenvector centrality, clustering) and network-level features (such as the
maximum eigenvalue, average path length). To further demonstrate the power of
the approach, we apply our technique to two settings analyzed previously by the
authors. We show ARD could have been used to predict how to assign monitors to
savers to increase savings in rural villages (Breza and Chandrasekhar, 2016).
ARD would have led to the same conclusions the authors arrived at when they
used expensive near-complete network data. We then provide an example where
survey ARD was collected, along with some partial network data, and demonstrate
that the same conclusions would have been drawn using only the ARD data, and
that with the ARD, the researchers could more generally measure the impact of
microfinance exposure on social capital in urban slums (Banerjee et al.,