(with Shanjun Li, Panle Barwick, and Yuanning Liang)
This paper measures the impact of housing price changes on household consumption at the city level using the universe of credit and debit card transactions in China from 2011 to 2013. In sharp contrast to the literature on the U.S. housing market, our analysis shows a large and negative housing price elasticity of consumption: a 10% increase in housing prices would lead to a 9.1% reduction in non-housing spending. We argue that the negative elasticity is driven by the combination of a strong investment incentive in housing and heavy borrowing constraints faced by households. This is corroborated by the finding that households increase their savings as housing prices increase. Relative to the existing literature, our analysis helps to better explain how low- and middle-income households adapt to the limited investment venues and surging housing prices in the context of persistently high savings rates.
Emissions in the Stream: Estimating the Greenhouse Gas Impacts of an Oil and Gas Boom (Accepted at Environmental Research Letters)
(with Achmad Khomaini, Benjamin Leibowicz and Sheila Olmstead)
We compile a detailed inventory of projected upstream oil and gas production expansions as well as recently and soon-to-be built midstream and downstream facilities within the region. Using data from emissions permits, emissions factors, and facility capacities, we estimate expected GHG emissions at the facility level for facilities that have recently been constructed or are soon to be constructed. Our central estimate suggests that the total annual emissions impact of the regional oil and gas infrastructure buildout may reach 541 million tons of CO2 equivalent (CO2e) by 2030, which is more than 8% of total US GHG emissions in 2017 and roughly equivalent to the emissions of 131 coal-fired power plants. Researchers have largely focused on upstream emissions such as fugitive methane (CH4) associated with new US production; our findings reveal the potentially greater prominence of midstream and downstream sources in the studied region.
(with Antonio Bento and Kevin Roth)
Taking advantage of a program that allows solo-drivers to enter ExpressLanes upon a payment of a toll, we provide the first estimates of commuters’ value of urgency, defined as a discrete amount to avoid failing on-time arrival. We provide evidence that, because commuters are schedule constrained, preferences for urgency explain about 70 percent of drivers’ willingness to pay to access these ExpressLanes. Earlier theoretical models that ignore preferences for urgency fail to fit the data and explain important empirical regularities. While the value of time and value of reliability have been commonly used for infrastructure project evaluation, our results show that the value of urgency is the critical parameter for evaluation of congestible infrastructure projects where pricing is possible.
Transportation Policies and Equilibrium Sorting: Evidence from Beijing
(with Shanjun Li, Panle Barwick, and Jing Wu)
Air pollution and traffic congestion are two of the most pressing urban challenges in many fast growing economies. Various transportation policies from both the demand and supply sides including congestion pricing, driving restrictions, the gasoline tax, and the expansion of public transit have been adopted to address these issues. We develop and estimate a residential location sorting model to examine the interactions of transportation policies and household sorting. The sorting model incorporate commuting decisions and generates equilibrium predictions of household locations under different transportation policies. We estimate the model parameters using a large household travel survey and rich housing transaction data in Beijing. The analysis illustrates the importance of incorporating travel mode choices in household location decisionsand the importance of understanding sorting behavior in designing effective transportation policies.
The Long Road to Work: Divergent Effects of Transportation Policies by Worker Skill in a Locational Sorting Model
This paper examines the effect of transportation policies directed at reducing travel times for commuting on inequality in urban labor markets. I consider the choice of city of residence by workers with and without a college degree to model the effect of changes in commuting patterns on economic inequality. Behavioral and supply parameters are estimated in a sorting model that controls for the effect on location decisions vis-à-vis wages, rents and amenities. Compelling findings emerging from these estimates include the fact that while cities with the most productive skilled workers tend to have shorter commutes, the opposite relationship holds for unskilled workers commuting from suburbs, yet these workers seem to benefit from living in cities that have more efficient transportation systems. This would suggest that availability of better transportation infrastructure may not be the constraining factor for productive low skilled workers. I also find that these relationships seem to hold for public transit usage, where more productive cities tend to have higher rates of commuting via public transit by skilled workers, and less productive workers higher rates of usage by unskilled workers. Based on these estimates, policy simulations consider the effect of public transit expansion to lower travel times to work financed by a head tax, congestion pricing or fare increase. Overall the paper documents limited benefits to workers without a college education from the set of proposed policies relative to those with a degree. These results have important distributional consequences for national and state-level policies intended to mitigate congestion externalities and spatial mismatch of workers in urban labor markets, suggesting that alternative policies that reallocate workers in space might better serve equity concerns.