Structural Analysis of Transportation Policies and Urban Structure
Paper Session
Sunday, Jan. 9, 2022 3:45 PM - 5:45 PM (EST)
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Chairs:
Fernando Ferreira, University of Pennsylvania - Panle Jia Barwick, Cornell University and NBER
Estimating the Economic Value of Zoning Reform
Abstract
We develop a framework to estimate the economic value of housing regulations, and apply the framework to a zoning reform in the city of S˜ao Paulo, which substantially altered maximum permitted constructed area to land area ratios at the block level. Using a spatial regression discontinuity design, we find that developers swiftly reacted to increases in allowable building densities by filing for more multi-family construction permits. We incorporate these microestimates of developer responses to zoning reforms in to an equilibrium model of housingsupply and demand to estimate the long term impact of zoning changes on construction, house
prices, residential location decisions and resident welfare. The model predicts the reform will
produce a 0.6 percent increase in the total housing stock, leading to rather small reductions in prices, and consequently small gains in welfare. Neighborhoods with the largest increases in
permitted construction to land areas receive more construction (5%) and lower prices (2.4%).
College-educated and higher income households gain the most from the reform, because more
of those families can now move from the suburbs to the more central parts of the city. Finally,
welfare gains from price changes increase five fold once we account for changes in the built
environment, as density increases and the age of housing units go down. Counterfactual simulations of more aggressive zoning reforms - e.g. doubling allowed densities - produce much
larger gains in welfare.
The Welfare Costs of Urban Traffic Regulations
Abstract
We compare the short term impacts of alternative transportation policies to reduce road traffic. More specifically, we measure the welfare costs associated with using alternate day travel schemes, a regulation that has the advantage of being easy to implement and has been often used recently. We compare them with the welfare costs of reducing traffic using standard price instruments. To do so, we develop a structural model of transportation mode choice with endogenous road congestion level. The model considers car trip durations are endogenous equilibrium outcomes from transportation mode choices and road congestion technology. We estimate the model using rich survey data on individual transportation decisions combined with data on expected trip durations from Google Maps and TomTom and high frequency traffic data from road sensors for Paris area. Our results suggest that all the policies are costly for individuals: the benefits of relaxing road congestion does not offset the costs of substituting away from cars. The tolls can be a globally welfare improving regulation if the tax revenue is redistributed.Refugee Influx and City Structure: Evidence from Mobile Phone Data in Jordan
Abstract
Forcibly displaced people are an important feature in many developing countries. Today, one out of every 108 people in the world are displaced. Yet we understand very little about the lives of refugees and other displaced persons within their host communities. In this paper, we examine important decisions made by refugees within a host country, such as where to live and work. In terms of high frequency dynamics, what are the patterns of migration (temporary, permanent, circular)? We examine the role of social networks in shaping these decisions. How important are social networks to refugees in deciding on places to live or for finding work? Do these networks evolve over time, and are there features that facilitate or hinder the integration of these networks with the host communities? We investigate several dimensions of equilibrium impacts on cities from a large influx of refugees. What dynamics emerge across neighborhoods in terms of housing prices for both refugees and incumbents? Do long term residents sort into different neighborhoods when refugees arrive, and at what point does this occur? How might employment opportunities evolve?We examine these questions in the context of Amman, Jordan. Jordan’s population grew by 13.3% due to the influx of Syrian refugees. Most refugees live in urban environments, with 35% residing in Amman. To answer high frequency and spatial granularity questions, we analyze the metadata from all transactions (voice, SMS and data activity; i.e. CDR data) on one of the largest mobile phone operators in Jordan from June 2015 to the present. Our dataset is unique in that we are able to merge a panel survey of 4,000 respondents with these high frequency cellphone metadata. We validate the use of CDR data to determine home and work locations based on survey responses. We then classify these locations out of sample for the remainder of the network. Applying machine learning methods, we classify customers as long-term residents or refugees/migrants. These results allow us to look deeply into migration dynamics and interactions between host and refugee communities. Social networks can be traced across time using voice and SMS transactions. Neighborhood characteristics and long-term changes in outcomes, such as housing prices, employment and shares of refugees are calculated from administrative data from before and after the refugee influx. We will fit these various data to a structural model in order to measure the indirect impacts of the refugee influx on the host community and better understand the equilibrium implications for all residents of the city.
JEL Classifications
- R2 - Household Analysis
- R4 - Transportation Economics