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. We analyze the identified social interactions matrix to provide novel insights into the long-standing 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.

Peer Effects in Networks: A Survey

“…More generally, a promising idea emerging from the recent literature is that peer effects can be identified even with very imperfect knowledge of the actual network of interactions, see in particular Theorems 6 and 7 in Blume et al (2015). In a panel context, peer effects can even be identified with no knowledge of the network under the assumption that the network does not change over time, see Manresa (2016), de Paula, Rasul, andSouza (2018), Rose (2016). In that case, the network of interactions itself can potentially be identified and be recovered from the data.…”

Endogenous Social Connections in Legislatures

“…The establishment of a link g i,j increases the effectiveness of i and of any other legislator who has a direct or indirect link to i: so if j does not have a direct or an indirect link that points to i, then j is indifferent; if j has a direct or indirect link to i, then j strictly prefers that i establishes a link with him/her. 22 It follows that legislator i chooses his links solving:…”

Crime and Networks: 10 Policy Lessons

“…Collecting data and mapping out criminal networks is clearly more problematic, costly, and time consuming than mapping out (for example) a network of co-authorships among economists. 14 While most authors are trying to make their work as visible 14 See de Paula et al (2019) who provide results on the identification of social networks from observational panel data that contains no information on social ties between agents. See, also, Breza et al (2019) who 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?"…”