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The paper studies the market failures associated with land tenure insecurity and information asymmetry in an urban land use model, and analyzes households' responses to mitigate tenure insecurity. When buyers and sellers of land plots can pair along trusted kinship lines whereby deception (the non-disclosure of competing claims on a land plot to a buyer) is socially penalized, information asymmetry is attenuated, but overall participation in the land market is reduced. Alternatively, when owners can make land plots secure by paying to register them in a cadaster, both information asymmetry and tenure insecurity are reduced, but the registration cost limits land market participation at the periphery of the city. The paper then compares the overall surpluses under these trust and registration models and under a hybrid version of the model that reflects the context of today's West African cities where both registration and trusted relationships are simultaneously available to residents. The analysis highlights the substitutability of trusted relationships to costly registration and predicts the gradual evolution of economies towards the socially preferable registration system if registration costs can be sufficiently reduced.
Communities and Human Settlements --- Ethnic Kinship --- Informal Land Use --- Information Assymetry --- Land Administration --- Land Information Systems --- Land Market --- Land Registration --- Land Tenure Insecurity --- Land Use --- Land Use and Policies --- Property Rights
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This paper reviews the emerging big data literature applied to urban transportation issues from the perspective of economic research. It provides a typology of big data sources relevant to transportation analyses and describes how these data can be used to measure mobility, associated externalities, and welfare impacts. As an application, it showcases the use of daily traffic conditions data in various developed and developing country cities to estimate the causal impact of stay-at-home orders during the Covid-19 pandemic on traffic congestion in Bogota, New Dehli, New York, and Paris. In light of the advances in big data analytics, the paper concludes with a discussion on policy opportunities and challenges.
Bayesian Structural Time Series --- Big Data --- Coronavirus --- COVID-19 --- Mobility --- Pandemic Impact --- Science and Technology Development --- Statistical and Mathematical Sciences --- Traffic Congestion --- Transport --- Transport Analysis --- Transport Economics Policy and Planning
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