Big Data in Spatial Economics
Saturday, Jan. 5, 2019 10:15 AM - 12:15 PM
- Chair: Dave Donaldson, Massachusetts Institute of Technology
The Welfare Consequences of Urban Redevelopment: Evidence From Mumbai’s Textile Mills
AbstractDeveloping country cities are characterized by informal housing—slums—but growing urban populations and incomes will lead governments to pursue a host of policies that promote the construction of modern, formal sector housing. This has the potential to affect entire neighborhoods since the effects are likely to spillover beyond directly targeted locations. In this paper, we ask how large are the spillovers from formal development, and what do these imply for the welfare consequences of pro-formalization policies in developing country cities? We address this question in three steps. First, we exploit a unique natural experiment in Mumbai that led 15% of central city land occupied by the city’s defunct textile mills to come onto the market for redevelopment in the 2000s. Second, we use a “deep learning” approach to measure slums from satellite images, and combine this with administrative sources to construct a uniquely spatially disaggregated dataset spanning the period. Third, we develop a quantitative general equilibrium model of a city featuring formal and informal housing supply to guide our empirical analysis. We find evidence of substantial housing and agglomeration externalities, and provide reduced-form evidence suggestive of both efficiency gains (through increased employment density in central areas) and potential equity losses (through the conversion of slums and gentrification near redeveloped mill sites).
The Geography of Collaboration
AbstractThis paper examines the geography of collaboration using detailed data on patent submissions in Japan. The construction of bridges connecting several of the main Japanese islands as well as the extension of high speed rail links provide a natural experiment to study the role of distance in the formation and continuation of collaboration in innovation.
Efficiency in Decentralized Transport Markets
AbstractWe leverage detailed data on vessel movements and shipping contracts to shed new light on world trade costs and trade flows. The data reveal new facts about shipping patterns, and motivate us to build a framework modeling the behavior of exporters and ships. Our framework has two novel features: (i) trade costs are endogenous and determined jointly with trade flows; as a result, they depend on the entire network of countries; (ii) search frictions between exporters and ships can limit trade. We estimate the model and recover flexibly the matching process between ships and exporters. Endogenous trade costs provide a novel link to understand trade patterns and we showcase this by considering the impact of (i) an improvement in shipping efficiency; (ii) a slow-down in China; (iii) the opening of the Northwest Passage; (iv) search frictions.
- F1 - Trade
- R1 - General Regional Economics