Victor Couture

Assistant Professor

My research is in urban economics and transportation. Current projects investigate the consequences of gentrification, the potential for e-commerce to reduce spatial inequality, the efficiency of urban transportation systems, and preferences for social interactions. I received my PhD from the University of Toronto in 2013, and worked as an assistant professor of real estate at UC Berkeley before joining UBC in 2020.

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Tracking human activity in real time and at fine spatial scale is particularly valuable during episodes such as the COVID-19 pandemic. In this paper, we discuss the suitability of smartphone data for quantifying movement and social contact. These data cover broad sections of the US population and exhibit pre-pandemic patterns similar to conventional survey data.
We develop and make publicly available a location exposure index that summarizes county-to-county movements and a device exposure index that quantifies social contact within venues. We also investigate the reliability of smartphone movement data during the pandemic.

Repository of smartphone movement data for Covid19 research: PlaceIQ County-level Human Exposure Indexes

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This paper estimates the impact of the first nation-wide e-commerce expansion program on rural households. To do so, we combine a randomized control trial with new survey and administrative microdata. In contrast to existing case studies, we find little evidence for income gains to rural producers and workers. Instead, the gains are driven by a reduction in cost of living for a minority of rural households who tend to be younger, richer and in more remote markets. These effects are mainly due to overcoming logistical barriers to e-commerce, rather than additional investments to adapt e-commerce to the rural population.

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This paper documents and explains the striking rise in the proclivity of college-educated individuals to reside near city centers. We show that this recent urban revival is driven entirely by younger cohorts in larger cities. With a residential choice model, we quantify the role of jobs, amenities, and house prices in explaining this trend. We find that changing preferences of young college graduates for non-tradable service amenities like restaurants, bars, gyms, and personal services account for more than 50 percent of their growth near city centers. Complementary datasets confirm that the young and college-educated are indeed spending more on and taking more trips to non-tradable service establishments. Our investigation into the causes of rising preferences for non-tradable services highlights their expanding role in generating socializing opportunities with other young college graduates, but also indicates roles played by delayed family formation, rising incomes, and improvements in the quality and diversity of non-tradable services.

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We investigate the determinants of driving speed in large US cities. We first estimate city level supply functions for travel in an econometric framework where both the supply and demand for travel are explicit. These estimations allow us to calculate a city level index of driving speed and to rank cities by driving speed. Our data suggest that a congestion tax of, on average, about 3.5 cents per kilometer yields welfare gains of about 30 billion dollars per year, that centralized cities are slower, that cities with ring roads are faster, and that the provision of automobile travel in cities is subject to decreasing returns to scale.

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This paper proposes a micro-foundation for knowledge spillovers. I model a city in which free knowledge transfers are bids by experts to entrepreneurs who auction jobs. These knowledge bids resemble a consultant’s pitch to a potential client. Two fundamental properties of knowledge underlie the model: First, it is often necessary to reveal some knowledge to demonstrate its value. Second, knowledge is freely reproducible. Larger cities generate more meetings between experts and entrepreneurs, resulting in more learning and better matches. Larger cities also foster competition for jobs, which motivates experts to raise their knowledge bids. These results demonstrate how competitive behavior can be a source of agglomeration economies, and contribute to explain the higher productivity of urban workers.

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We develop a methodology to estimate robust city-level vehicular speed indices, exactly decomposable into uncongested speed and congestion. We apply it to 180 Indian cities using 57 million simulated trips measured by a web mapping service. We verify the reliability of our simulated trips using a number of alternative data sources, including data on actual trips. We find wide variation in speed across cities that is driven more by differences in uncongested speed than congestion. Denser and more populated cities are slower, only in part because of congestion. Urban economic development is correlated with faster speed despite worse congestion.

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We explore the link between rising nominal incomes at the top of the income distribution, within-city spatial sorting, and real income inequality. We develop and quantify a spatial model of a city with heterogeneous agents and non-homothetic preferences for endogenous differentiated private neighborhood amenities (e.g., restaurants and entertainment). As the rich get richer, their increased demand for such luxury amenities drives housing prices up in downtown areas, where amenity development is fueled by economies of density. The poor are made worse off, either being displaced or paying higher rents for amenities that they do not value as much. Using our model, we find that the neighborhood change within urban areas during the last two decades increased the welfare of richer households relative to that of poorer households by an additional two percentage points above and beyond the differential income growth. We conclude that welfare estimates of increased income inequality are understated if within-city spatial sorting responses are ignored.

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Despite growing interest in urban consumption amenities, little is known about their origin and importance. This paper estimates the consumption value of urban density by combining travel microdata with Google’s local business data. This dataset allows to integrate travel costs into a discrete choice model for restaurants. I find that in high density areas, consumers enjoy large benefits from visiting places that they prefer, and relatively smaller gains from shorter trip time. These results demonstrate the importance of non-tradable consumption in explaining the value of cities, and represent the first estimates of the gains from variety in the service sector.

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Big Data in Spatial Economics I - Overview of Current and Future Research

Big Data in Spatial Economics II - Working with Big Data



Quantifying Social Interactions Using Smartphone Data, with Jonathan Dingel (Chicago), Allison Green (Princeton), and Jessie Handbury (Wharton)

The Gains from Online Integration: Theory and Evidence from China, with Ben Faber (UC Berkeley), Cecile Gaubert (UC Berkeley), and Yizhen Gu (Jinan)

Accessibility in Urban India, with Prottoy Akbar (Pittsburgh), Gilles Duranton (Wharton), and Adam Storeygard (Tufts)

Winter 2021

ECON494 Seminar in Applied International Economics Sections

Focus on a particular aspect of applied international economics. Independent empirical research project required. Registration restricted to students in the Bachelor of International Economics Program.

Winter 2021

ECON555 International Trade Sections