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Atlanta Marriott Marquis, International 2
Hosted By:
American Economic Association
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.
Econometric Methods for Endogenous Networks
Paper Session
Sunday, Jan. 6, 2019 1:00 PM - 3:00 PM
- Chair: Alan Griffith, University of Washington
Using Aggregated Relational Data to Feasibly Identify Network Structure Without Network Data
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.
Recovering Social Networks from Panel Data: Identification, Simulations and an Application
Abstract
It is almost self-evident that social interactions can determine economic behavior and outcomes. Yet, information on social ties does not exist in most publicly available and widely used datasets. We present methods to recover information on the entire structure of social networks from observational panel data that contains no information on social ties between individuals. In the context of a canonical social interactions model, we provide sufficient conditions under which the social interactions matrix, endogenous and exogenous social effect parameters are all globally identified. We describe how high-dimensional estimation techniques can be used to estimate the model based on the Adaptive Elastic Net GMM method. We showcase our method in Monte Carlo simulations using two stylized and two real world network structures. Finally, we employ our method to study tax competition across US states. We find the identified network structure of tax competition differs markedly from the common assumption of tax competition between geographically neighboring states: the majority of geographic neighboring states (63%) are found not to be relevant for tax setting. We analyze the identified social interactions matrix to provide novel insights into the longstanding debate on the relative roles of factor mobility and yardstick competition in driving tax setting behavior across states. Most broadly, our method shows how the analysis of social interactions can be usefully extended to economic realms where no network data exists.Random Assignment with Non-Random Peers: A Structural Approach to Counterfactual Treatment Assessment
Abstract
Economists' efforts to leverage peer effects by creative assignment have come up short due, in part, to endogenous peer choice. Even with random assignment, not accounting for agents' choice of peers may bias estimates of peer influence and, in turn, predictions of outcomes under alternative policies. To address this, I build a two-part model: (1) agents form a network by making continuous linking decisions; (2) conditional on the network, outcomes are determined allowing for peer effects and unobserved heterogeneity. I provide a method to recover unobserved heterogeneity in estimating the network formation process, leveraging new theoretical results. I estimate the model using innovative data from a randomized study in Indian schools, then assess the model's predictions against realized outcomes. This paper makes important contributions to the methodology of peer effects estimation, the theory and econometrics of network formation, and provides new links between structural and experimental approaches to policy evaluation.Discussant(s)
Anton Badev
,
Federal Reserve Board
Angelo Mele
,
Johns Hopkins University
Sida Peng
,
Microsoft Research
Xiaodong Liu
,
University of Colorado
JEL Classifications
- C4 - Econometric and Statistical Methods: Special Topics
- C1 - Econometric and Statistical Methods and Methodology: General