Association for Public Policy Analysis & Management

A “social mix” policy is a policy aiming to create socio-economically diverse community. For decades, in the housing and community policy fields, social mix policy has been perceived as an instrument to contribute social cohesion by constructing broad positive social networks, and to avoid the problems from poverty concentration including neighborhood stigmatization, deterioration of social service and infrastructure, and occurrence of violence and anti-social behavior. However, on the research side, the effects of social mix policy have been continuously disputed. While some scholars confirm positive influences of social mix policy, including improvement of individual opportunities of the disadvantaged in mixed communities, and enhancement of social interaction among various groups, others strongly question whether these really happen. Some researcher finds that social mixing does not bring about social contacts, or causes conflicts rather than cohesion in neighborhoods.

This strongly implies that we need more knowledge about the unexpected "disintegration" mechanism from social mix policies. This research tries to explore how the social integration and disintegration mechanisms emerge from a social mix policy. For this purpose, we adopt agent-based modeling. Agent-based modeling allows randomness in initial locations of people, so let their emergent behaviors from how far the agents are away from each other, and how far away they are from parks possible. This is what linear models cannot deal with. In addition, ABM reveals inside mechanism in microscopic level regarding how each factor works. This modeling can also help participatory planning process, by offering visual presentations of simulation results.

Using ABM, this research explores how social interaction occurs, more specifically, what conditions and factors promote or discourage interactions in a community, and finally influence the social outcome, unemployment rates which is a proxy for individual opportunities. By simulating four scenarios with different conditions, this research investigates how social interaction changes according to first, the nature of the initial mixture of race, second, spatial proximity, and third, public physical facilities which are represented with a park here. Based on the assumption that as interaction intensifies, the unemployment rate goes down, we examined how the unemployment rate changes from four scenarios of social mix.

This research shows that initial racial composition influences on interactions, thus on unemployment rates, but did not show huge differences in aggregated unemployment rate results. It also shows how parks can increases interactions, therefore on unemployment rates, in addition to other factors. These results help us build empirical evidence that shows how each factor in a social mix policy work in these mechanisms, and which policy design contributes to attaining the expected policy outcomes. These findings can be used for designing more sophisticated and effective social mix policy.