Social Networks and the Aggregation on Individual Decisions

Abstract

This paper analyzes individual decisions to participate in an activity and the aggregation of those decisions when individuals gather information about the outcomes and choices of (a few) others in their social network. In this environment, aggregate participation rates are generally inefficient. Increasing the size of social networks does not necessarily increase efficiency and can lead to less efficient long-run outcomes. Both subsidies for participation and penalties for non-participation can increase participation rates, though not necessarily by the same amount. Punishing non-participation has much greater effects on participation rates than rewarding participation when current rates are very low. A program that provides youth with mentors who have participated themselves can increase participation rates, especially when those rates are low. Finally, communities plagued by the flight of successful participants will experience lower short-and long-run participation rates.

Individual Perceptions of the Criminal Justice System

“…Banerjee, 1992, Bikhchandani, Hirshleifer, andWelsh, 1992) has shown that the aggregation of individual decisions can lead to informational cascades and conformity when individuals possess idiosyncratic information and gather information from others. Furthermore, Heavner and Lochner (2002) show that policies like anti-gang initiatives or mentor programs will have heterogeneous impacts on neighborhoods that differ in the current level of gang and criminal activity. More generally, the way in which individuals acquire information and develop expectations is important in determining outcomes and policy effects in any environment; yet, little is actually known about these processes.…”

Thieves, thugs, and neighborhood poverty

“…The first is that of neighborhood effects, where individual behavior is directly affected by the characteristics and/or actions of his neighbors. For example, preferences for peer conformity may alter an individual's taste for engaging in crime (Glaeser et al, 1996;Brock and Durlauf, 2001), or an individual's information about payoffs to crime may evolve differently depending on the number of criminals in his neighborhood (Heavner and Lochner, 2002;Calvo-Armengol and Zenou, 2004;Calvo-Armengol et al, 2007;Patacchini and Zenou, 2008). Somewhat relatedly, one individual's criminal behavior may create a positive externality for other potential criminals in that one person's criminal behavior taxes fixed police resources, which in turn lowers the probability of detecting/arresting other criminals (Ferrer, 2010), or similarly, one neighborhoods' level of private policing may affect the relative payoffs to crime in another neighborhood (Helsley and Strange, 2005).…”

“…Note that this means that this model explicitly considers an environment where individual's preferences and/or judicial costs for committing or not committing crime are not directly influenced by their neighbors' preferences or neighborhood characteristics, which has been the focus of much of the previous literature on neighborhood effects and crime (e.g. Glaeser et al, 1996;Brock and Durlauf, 2001;Heavner and Lochner, 2002;Calvo-Armengol and Zenou, 2004;Calvo-Armengol et al, 2007;Patacchini and Zenou, 2008;Ferrer, 2010;Helsley and Strange, 2005;Wilson, 1987;Krivo and Peterson, 1996).…”

Thieves, Thugs, and Neighborhood Poverty

“…Therefore, it is also possible that as crime increases, a city becomes somewhat less economically segregated than it would be otherwise, meaning the OLS results discussed previously could also be downwardly biased. 13…”