No abstract

Social networks can shape many aspects of social and economic activity: migration and trade, job-seeking, innovation, consumer preferences and sentiment, public health, social mobility, and more. In turn, social networks themselves are associated with geographic proximity, historical ties, political boundaries, and other factors. Traditionally, the unavailability of large-scale and representative data on social connectedness between individuals or geographic regions has posed a challenge for empirical research on social networks. More recently, a body of such research has begun to emerge using data on social connectedness from online social networking services such as Facebook, LinkedIn, and Twitter. To date, most of these research projects have been built on anonymized administrative microdata from Facebook, typically by working with coauthor teams that include Facebook employees. However, there is an inherent limit to the number of researchers that will be able to work with social network data through such collaborations. In this paper, we therefore introduce a new measure of social connectedness at the US county level. Our Social Connectedness Index is based on friendship links on Facebook, the global online social networking service. Specifically, the Social Connectedness Index corresponds to the relative frequency of Facebook friendship links between every county-pair in the United States, and between every US county and every foreign country. Given Facebook’s scale as well as the relative representativeness of Facebook’s user body, these data provide the first comprehensive measure of friendship networks at a national level.

Alternative models for confirmatory factor analysis of multitrait-multimethod (MTMM) data were evaluated by varying the number of traits and methods and sample size for 255 MTMM matrices constructed from real data (Study 1), and for 180 MTMM matrices constructed from simulated data (Study 2). The correlated uniqueness model converged to proper solutions for 99% (Study 1) and 96% (Study 2) of the MTMM matrices, whereas the general model typically used converged to proper solutions for only 24% (Study 1) and 22% (Study 2) of the MTMM matrices. The general model was usually ill-defined (100% in Study 1, 90% in Study 2) for small MTMM matrices with small Ns, but performed better when the size of the MTMM matrix and N were larger. Even when both models converged to proper solutions, however, parameter estimates for the correlated uniqueness model were more accurate and precise in relation to known population parameters in Study 2. Index terms: confirmatory factor analysis, construct validity, discriminant validity, LISREL, method effects, multitrait-multimethod analysis, underidentified models.

We show how data from online social networking services can help researchers better understand the effects of social interactions on economic decision making. We combine anonymized data from Facebook, the largest online social network, with housing transaction data and explore both the structure and the effects of social networks. Individuals whose geographically distant friends experienced larger recent house price increases are more likely to transition from renting to owning. They also buy larger houses and pay more for a given house. Survey data show that these relationships are driven by the effects of social interactions on individuals' housing market expectations. This paper was previously circulated as "Social Networks and Housing Markets." For helpful comments, we are grateful to