JSTOR is a not-for-profit service that helps scholars, researchers, and students discover, use, and build upon a wide range of content in a trusted digital archive. We use information technology and tools to increase productivity and facilitate new forms of scholarship. For more information about JSTOR, please contact support@jstor.org. Stability and Competitive Equilibrium in Trading Networks John William HatfieldUniversity of Texas at Austin Scott Duke KominersHarvard University and University of Chicago Alexandru Nichifor University of St Andrews Michael OstrovskyStanford University Alexander Westkamp Maastricht UniversityWe introduce a model in which agents in a network can trade via bilateral contracts. We find that when continuous transfers are allowed and utilities are quasi-linear, the full substitutability of preferences is sufficient to guarantee the existence of stable outcomes for any underlying network structure. Furthermore, the set of stable outcomes is essentially equivalent to the set of competitive equilibria, and all stable outcomes are in the core and are efficient. By contrast, for any domain of preferences strictly larger than that of full substitutability, the existence of stable outcomes and competitive equilibria cannot be guaranteed.

Which neighborhoods experience physical improvements? In this paper, we introduce a computer vision method to measure changes in the physical appearances of neighborhoods from time-series street-level imagery. We connect changes in the physical appearance of five US cities with economic and demographic data and find three factors that predict neighborhood improvement. First, neighborhoods that are densely populated by college-educated adults are more likely to experience physical improvements-an observation that is compatible with the economic literature linking human capital and local success. Second, neighborhoods with better initial appearances experience, on average, larger positive improvements-an observation that is consistent with "tipping" theories of urban change. Third, neighborhood improvement correlates positively with physical proximity to the central business district and to other physically attractive neighborhoods-an observation that is consistent with the "invasion" theories of urban sociology. Together, our results provide support for three classical theories of urban change and illustrate the value of using computer vision methods and street-level imagery to understand the physical dynamics of cities.urban economics | gentrification | urban studies | computer vision | neighborhood effects F or more than a century, urban planners, economists, sociologists, and architects have advanced theories connecting the dynamics of a neighborhood's physical appearance to its location, demographics, and built infrastructure.The tipping theory of Schelling (1) and Grodzins (2) suggests that neighborhoods in bad physical condition will get progressively worse, whereas nicer areas will get better. Economic theories of urban change at the city level often emphasize population density and education (3-6), and it is natural to hypothesize that agglomeration of human capital will predict neighborhood-level improvements as well. Theories from urban sociology, such as the invasion theory of Burgess (7), however, emphasize locations and social networks, predicting that improvements in a city's appearance should be spatially clustered, and that improvements should occur both near the central business districts (CBDs) and near other physically attractive neighborhoods.To test theories of physical neighborhood change, we need to quantify neighborhood appearance at different points in time. Historically, however, methods to quantify neighborhood appearance have not been scalable. The empirical literature on urban appearance, which was pioneered by urban planners such as Lynch (8), Rapoport (9), and Nasar (10), as well as by psychologists such as Milgram (11), has relied on interviews, lowthroughput visual perception surveys, and manual evaluation of images. Those methods, however, can only be used to collect data on a few neighborhoods and have limited spatial resolution. In the past decade, new data on urban appearance have emerged in the form of "street view" imagery (12). As of 2016, Google Street View has photographe...