Using Aggregated Relational Data to Feasibly Identify Network Structure without Network Data
2020/07/28 by Emily Breza, Arun G. Chandrasekhar, Tyler H. McCormick +1 · 110 citations
Engineering · Mathematics · Physics and Astronomy · Social Sciences · #Complex Network Analysis Techniques #Complex network #Computer science #Data mining #Data science #Econometrics #Engineering #Field (mathematics) #Graph #Limiting #Mathematics #Network analysis #Network science #Node (physics) #Relational database #Replicate #Sampling (signal processing) #Social Capital and Networks #Social network (sociolinguistics) #Statistics #Theoretical computer science
paper · open access · doi:10.1257/aer.20170861
published in American Economic Review 110(8), 2454-2484 (American Economic Association)
openalex publication_date 2020/07/28 · openalex created_date 2025/10/10 · openalex updated_date 2026/08/01
Abstract
Social network data are often prohibitively expensive to collect, limiting empirical network research. 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 links have trait k ?" Our method uses ARD to recover parameters of a network formation model, which permits sampling from a distribution over node- or graph-level statistics. We replicate the results of two field experiments that used network data and draw similar conclusions with ARD alone.
Citations
Cited by
- Can Network Theory-Based Targeting Increase Technology Adoption?
- Unveiling the social fabric through a temporal, nation-scale social network and its characteristics
- Party On: The Labor Market Returns to Social Networks in Adolescence
- Changes in Social Network Structure in Response to Exposure to Formal Credit Markets
- The Pick of the Crop: Agricultural Practices and Clustered Networks in Village Economies
- New Data Sources for Demographic Research
- From hidden populations to social structure: Evolution of the Network Scale-Up Method, Aggregated Relational Data, and their applications
- Social Density, Clientelism, and Community Benefits
- Socioeconomic homogeneity in acquaintance networks: Occupational prestige, education, and intergenerational mobility
- Spectral goodness-of-fit tests for complete and partial network data
- Price Regulation with Spillovers
- Estimating Peer Effects Using Partial Network Data
- Name Your Friends, but Only Five? The Importance of Censoring in Peer Effects Estimates Using Social Network Data
- Using Aggregate Relational Data to Infer Social Networks
- Bayesian Modeling for Aggregated Relational Data: A Unified Perspective
- A Nonparametric Test for Cross-Unit Spillovers
- Experimental Design under Network Interference
- Identification of Peer Effects with Miss-specified Peer Groups: Missing Data and Group Uncertainty
- Networks of Conflict and Cooperation
- Thirty Years of The Network Scale-up Method. [europepmc]
- Unveiling the social fabric through a temporal, nation-scale social network and its characteristics. [europepmc]
- Understanding the Personal Networks of People Experiencing Homelessness in King County, WA with Aggregate Relational Data. [europepmc]
- Understanding Post-Pandemic Inflation Dynamics with a Behavioral Macroeconomic Model of the Canadian Economy [europepmc]
- Estimating Temporal Trends using Indirect Surveys [europepmc]
Related