Abstract
AbstractUnderstanding 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.
Highlights
Networks – connections between agents – are an ubiquitous part of life
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 structure: for example, the methods proposed by Bramoulleet al. (2009) and De Giorgi et al (2010) rely on information of who is not connected with whom to provide exclusion restrictions
We provide an overview of a range of econometric methods to deal with network endogeneity and measurement error when estimating linear models of social effects
Summary
Networks – connections between agents – are an ubiquitous part of life. Student’s academic achievement is influenced by their friends and classmates; employee productivity by interactions with other team members; individuals learn about new products and opportunities from their acquaintances and friends; firms cooperate and compete with other firms in developing new innovations; and so on. 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. 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 structure: for example, the methods proposed by Bramoulleet al. Due to the sampling method or otherwise, have important consequences for both measurement of statistics of the network, and the parameter estimates of social effect models This is because networks consist of two interrelated objects: agents (nodes) and links.
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