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 propose a new measure, the contextual intercentrality measure, that generalizes the one proposed by Ballester, Calvo -Armengol, and Zenou (2006) by taking into account the change in contextual effects following the removal of the key player. We also provide an example showing that the key player can be different whether contextual effects are taken into account or not. This means that the planner may target the wrong person if it ignores the effect of the context when removing a criminal from a network.