Credibly Identifying Social Effects: Accounting for Network Formation and Measurement Error

Abstract

Understanding whether and how connections between agents (networks) such as declared friendships in classrooms, transactions between firms, and extended family connections, influence their socio-economic outcomes has been a growing area of research within economics. Early methods developed to identify these social effects assumed that networks had formed exogenously, and were perfectly observed, both of which are unlikely to hold in practice. A more recent literature, both within economics and in other disciplines, develops methods that relax these assumptions. This paper reviews that literature. It starts by providing a general econometric framework for linear models of social effects, and illustrates how network endogeneity and missing data on the network complicate identification of social effects. Thereafter, it discusses methods for overcoming the problems caused by endogenous formation of networks. Finally, it outlines the stark consequences of missing data on measures of the network, and regression parameters, before describing potential solutions.CREDIBLY IDENTIFYING SOCIAL EFFECTS 1017 unobserved dimensions could influence within-group interactions, and through this the actual outcome. Ignoring variation in interactions within such groups can lead to misleading conclusions and policy design, as shown in recent work by Carrell et al. (2013).More recently, a growing body of research within empirical economics uses data which directly measure interactions between pairs of agents (network data hereon) to sidestep these issues. This growth has been spurred by the increasing availability of such data, as well as the development of methods to identify and estimate social effects with such data. Starting with Bramoullé et al. (2009) andDe Giorgi et al. (2010), methods have been developed to overcome the reflection problem. They show how information on network structure can be used to break the simultaneity, and obtain the necessary exclusion restrictions for parameter identification. These methods, reviewed in detail by Advani and Malde (2014), Topa and Zenou (2015) and Boucher and Fortin (2015), impose strong restrictions on the network formation process and the quality of the data.In particular, the network is assumed to be exogenous conditional on observed agent-and network-level characteristics, and to be fully and perfectly observed by the researcher. Both assumptions are unlikely to hold in practice. In a schooling context, for example, personality traits which are rarely observed by a researcher might influence both a child's choice of friends and her schooling performance. Estimates of the influence of a child's friends' outcomes on her outcomes will be biased if her choice of friends is not accounted for. Similarly, accurately collecting fine-grained information on all connections is very costly and logistically challenging, making it rare to observe a complete, perfectly measured network. This has important implications for identification of social effects using restrictions based on the network struct...

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