Key Player Policies When Contextual Effects Matter
We consider a model where the criminal decision of each individual is affected by not only her own characteristics, but also by the characteristics of her friends (contextual effects). We determine who the key player is, i.e. the criminal who once removed generates the highest reduction in total crime in the network. We generalize the intercentrality measure proposed by Ballester et al. (2006) by taking into account the change in contextual effects following the removal of the key player. We also provide an example that shows how the new formula can be calculated in practice.
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- Sanjeev Goyal, 2007. "Introduction to Connections: An Introduction to the Economics of Networks," Introductory Chapters, in: Connections: An Introduction to the Economics of Networks Princeton University Press.
- Jeffrey R. Kling & Jens Ludwig & Lawrence F. Katz, 2005.
"Neighborhood Effects on Crime for Female and Male Youth: Evidence from a Randomized Housing Voucher Experiment,"
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Oxford University Press, vol. 120(1), pages 87-130.
- Jeffrey R. Kling & Jens Ludwig & Lawrence F. Katz, 2004. "Neighborhood Effects on Crime for Female and Male Youth: Evidence from a Randomized Housing Voucher Experiment," NBER Working Papers 10777, National Bureau of Economic Research, Inc.
- Xu Lin, 2010. "Identifying Peer Effects in Student Academic Achievement by Spatial Autoregressive Models with Group Unobservables," Journal of Labor Economics, University of Chicago Press, vol. 28(4), pages 825-860, October. Full references (including those not matched with items on IDEAS)
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