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The Urban Political Economy: Why are there so many private gains and so few public gains?

Grant number: 19/09399-0
Support type:Scholarships abroad - Research
Effective date (Start): October 01, 2019
Effective date (End): March 31, 2020
Field of knowledge:Applied Social Sciences - Economics - Regional and Urban Economics
Principal researcher:Ciro Biderman
Grantee:Ciro Biderman
Host: Zegras Christopher P
Home Institution: Escola de Administração de Empresas (EAESP). Fundação Getúlio Vargas (FGV). São Paulo , SP, Brazil
Research place: Massachusetts Institute of Technology (MIT), United States  
Associated research grant:13/15658-1 - Subnational political institutions: a comparative study of Brazilian States, AP.TEM


The way we commute in large cities has been changing significantly in the last five years. The main elements of this shift include the advancement of batteries for motor vehicles; the advancement of the autonomous vehicle; the advent of sharing as a submodality of transport; and the advances in machine-learning techniques associated with "big data" that allow the processing of information in real time. In this project I deal with the impact of these changes on issues that generate positive externalities: public transit, active modes and road safety. Gains have become more concentrated private rather than social gains. Through specific studies this research seeks to understand first the impacts of public transit policies. Second, it seeks to suggest what are the possible governance failures that may have led to this outcome. The governance problem is not due to a lack of managerial capacity. At least part of the problems must be related to a bad political equilibrium. The most general hypothesis we try to test is whether a distinct political equilibrium would be sufficient to generate significant social gains in large and medium-sized cities. The computational advancement, besides its impact on mobility, opens a methodological opportunity to advance in the causal analysis and also that is called "supplementary analyzes" which try to check if the main result is indeed robust. It is part of this project to advance in the knowledge of these new methodologies of big data and machine learning.Keywords: mobility; big data; machine learning; road safety; public transit; governance of innovations; causal methods

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