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This paper combines local election results and geo-referenced road construction data over 1993-2012 to investigate political bias in road infrastructure investment in a democratic setting, focusing on the case of Mexico. Using a regression discontinuity design, the paper finds strong evidence of partisan allocation of federally-funded highways to municipalities that voted for the president's party in legislative races, nearly doubling the stock of highways compared to opposition municipalities. The extent of political favoritism in highway provision is stronger under divided government when the president has no majority in the legislature, suggesting political efforts to control the Congress.
Education --- Flood control --- Infrastructure --- Inter-urban roads and passenger transport --- Political favoritism --- Public capital --- Roads and highways --- Roads and highways performance --- Tertiary education --- Transport --- Transportation --- Water resources
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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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