How does the cost of moving people shape the spatial organization of economic activity? While a large literature studies transportation infrastructure through the lens of goods transport costs, modern infrastructure investments—such as high-speed rail (HSR)—primarily reduce the cost of moving people. This paper develops a framework to study how reductions in passenger travel costs affect consumption and production decisions and jointly determine spatial equilibrium outcomes.
We propose a quantitative spatial equilibrium model in which passenger travel costs enter both household utility and firms’ production decisions. On the demand side, workers choose where to live and work based on wages, housing costs, and amenities, while also valuing access to other locations through endogenous travel decisions. These travel decisions capture short-term mobility—such as tourism and intercity visits—that directly affect welfare. On the production side, firms trade intermediate goods in an Eaton–Kortum framework augmented with endogenous search. Building on Bernard et al. (2019), we depart from threshold-based search and instead allow firms to choose continuous search intensity subject to convex costs, whereby greater search effort increases encounters with nonlocal suppliers and the likelihood of identifying higher-productivity ones. Because search requires face-to-face interaction, reductions in travel costs lower the cost of search, potentially increasing search intensity and expanding firms’ effective supplier set, thereby reshaping trade patterns even when goods transport costs remain unchanged.
This structure gives rise to two distinct channels through which passenger travel costs affect the economy. A consumption channel operates through tourism and access to amenities, while a production channel operates through business travel and supplier search. Together, these channels shape migration patterns, intercity flows of people, trade networks, and local prices in general equilibrium.
To take the model to the data, we assemble a novel combination of datasets that jointly capture travel, trade, and mobility at the city-pair level. We construct bilateral passenger travel costs by integrating high-speed rail schedules, conventional rail data, and highway networks, following and extending approaches from Ma and Tang (2024). We combine this with transaction-level data from China’s UnionPay network to construct high-frequency intercity travel flows. These data track the movement of tens of millions of sampled cardholders and allow us to observe daily bilateral mobility patterns across all cities in China. Importantly, we use the timing of transactions to distinguish between workday travel, which proxies for business-related interaction, and non-workday travel, which proxies for tourism and leisure. Finally, we incorporate city-level input–output data to recover bilateral trade shares. Together, these datasets allow us to map observed mobility and trade patterns into the model’s consumption and production margins.
The empirical setting provides a unique opportunity to separately identify the two channels through which passenger travel costs operate. The decomposition of travel flows into workday and non-workday components allows us to discipline the model along the production and consumption margins, respectively, while the staggered expansion of the HSR network provides quasi-experimental variation in bilateral travel costs. This combination enables us to move beyond reduced-form estimates and quantify how reductions in passenger travel costs propagate through multiple layers of the economy.
We use the model to characterize how improvements in connectivity affect the spatial distribution of economic activity and to decompose the resulting welfare effects into consumption and production components. This decomposition provides a unified account of how infrastructure generates gains: through expanded access to consumption opportunities and through more efficient firm-to-firm interactions. It also allows us to assess how these gains are distributed across locations depending on their position in the travel network and their role in production.