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Networks, interactions and equilibrium

In the past 20 years, research on social networks has exploded in a range of fields, including computer science, economics, sociology, physics and mathematics; see e.g. Carrington et al. (2005), Newman et al. (2006), Borgatti et al. (2009) and Jackson (2009). Interactions in social networks affect an array of social and economic outcomes. For example, peer effects affect educational outcomes and choices to engage in behaviours such as smoking and crime. Social contacts affect job-finding probabilities in labour markets. Strategic interactions among firms affect pricing and store location decisions. At the economy-wide level, interactions determine prices, the matching of buyers to sellers and the matching of firms to workers. It is clear that social networks, the interactions that occur within these networks and the global equilibrium outcomes that result from these structures have profound implications for understanding of a wide range of social and economic phenomena. Moreover, new cross-sectional and longitudinal data sources increasingly have information not only on social outcomes but also on network structure and membership. For example, Understanding Society will contain a module with information on social networks and interactions. As a result, there is an opportunity to bring new data to bear to analyse the sophisticated theoretical work that has been done in the past two decades and to enrich it with models of heterogeneity developed by the Centre's research.

Research findings

We have been pursuing several projects that analyse consumer behaviour in supermarkets.
Date started: 01 June 2012
A key consideration when studying government policies towards higher education is how do the decisions of teenagers, the decisions of parents, the general economic environment and government policies interact to determine college enrolment decisions.
Date started: 01 June 2012
It is not uncommon for economic feedback models to produce more than one solution. In some cases, multiplicity can cause identification problems. In others, it can produce more variation in the data. In this work, we show formally that the latter is the case in many models of entry-exit of firms and in many peer effect models.
Date started: 01 June 2012

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