In many economic and social settings, the behavior or welfare of an individual is directly affected by the presence, characteristics, or behavior of other individuals in an associated reference group. Children might be affected by their classmates, home-owners by their neighbors, or commuters by their fellow motorists, to give just a few examples. In many of these settings, individuals are not randomly assigned to their reference groups but rather actively decide which group to join. This non-random sorting of individuals into groups leads to improper inferences about the influence of other group members both within the group and in the initial choice of group. A community with great amenities, for example, is likely to attract many high-income residents and have high housing prices. If the amenities are not seen in the data, a naive analysis is likely to attribute the high housing prices to the presence of high-income neighbors and suggest that high-income individuals have strong preferences to live with one another. This project develops a methodology for properly identifying the role of social interactions in the sorting of individuals into reference groups. This strategy draws on fact that each individual's choice of reference group is affected by the nature of their alternative options (the set of available neighborhoods in our earlier example). The research begins by developing a methodology that uses this source of variation to identify pure congestion and agglomeration interactions in sorting models. The methodology will then be extended to models with more complex forms of social interactions in which individuals are affected not only by the number but also the attributes of those in the same reference group.
The scope of the currently identified applications of this methodology extends from urban, public, and environmental economics to models of non-price competition in industrial organization. Properly identifying social interactions in these settings is extremely important for a wide variety of policy considerations. We demonstrate the significance of this research with a series of empirical applications designed to both demonstrate the methodology and illustrate its practical importance. The first uses 1990 US Census data for the SF Bay Area to study neighborhood sorting. This analysis provides a complete picture of how households trade-off between important features of neighborhoods (location, schools, crime, socio-demographics, housing, and price), as well as how these trade-offs vary for households with different characteristics including income, race, education, employment, and family structure. Having properly identified the complex set of preferences that underlies the urban housing market, this project conducts a number of simulations designed to uncover the underlying causes of important aggregate urban phenomena such as racial segregation, crime patterns, school compositions, commuting patterns, and the geographic distribution of housing prices. This application will then be extended to focus more specifically on a series of education policy questions. The second set of applications address a very different policy issue: the consequences of global climate change in developing countries. While techniques are well developed to measure the marketed (agricultural) impacts of global warming, non-marketed impacts, such as those on climate amenities, have not been satisfactorily measured with traditional techniques owing to a lack of key data describing inter-location variation in the prices of housing and other geographically non-traded commodities. Specifying an equilibrium model for these commodities and estimating it with observed settlement patterns and our identification strategy yields a full measure of the amenity cost of global warming for Brazil, and its implications for post-Kyoto negotiations over greenhouse gas abatement efforts between Annex I countries and LDC's are discussed. This project will also fund the collection of data for the extension of this analysis to other developing countries in Central and South America.
