Bridges

This paper measures the causal effects of land transport infrastructure on economic activity, exploiting quasi-experimental variation in bridge construction over the Mississippi and Ohio Rivers in the United States. The central empirical puzzle motivating the study is a hump-shaped relationship between per capita income and distance to major land transport routes in contemporary U.S. data: income peaks around 5 km from a transport route, with an elasticity of 0.072 closer than 4.1 km and -0.096 at greater distances, so that 85% of Americans live where local income increases with distance to transport routes rather than decreasing. The question is whether this pattern reflects causal effects of infrastructure, selection, or sorting.

The paper develops two complementary identification strategies. The first exploits tributary confluences — where smaller rivers join larger rivers, sharply raising downstream flow rates and bridge construction costs — to generate quasi-random variation in bridge location. Because bridge construction costs increase convexly with river flow (maximum bending moment scales with span length squared), bridges are disproportionately built just upstream of confluences. The median upstream census tract lies 0.7 km from a bridge versus 2.3 km for the median downstream tract, making upstream tracts on average 60% closer to bridges and 27% closer to the nearest major land transport route. This asymmetry dates to at least 1880 and persists to 2010. Despite this persistent connectivity advantage, by 2010 upstream tracts have 13% lower per capita incomes and 63% higher population densities than downstream neighbours. The implied elasticity of per capita income with respect to distance to land transport, scaling the income effect by the distance-to-transport effect, is approximately 0.44. Income density (income per unit area) is higher upstream, though the difference is not statistically significant. Historical placebo tests using pre-bridge-construction data show no asymmetry in land values or population upstream versus downstream, supporting the identification assumption.

The second strategy exploits variation in the timing of bridge construction. Because major bridge projects involve decades of planning, financing, design, and construction — the Wheeling Suspension Bridge was chartered in 1816 but opened in 1849 — the precise opening date is argued to be exogenous to short-run deviations from local growth trends. Using a county-level panel from 1860 to 2010 (432 counties, 14–19 states), the paper estimates event-study regressions around the first time a county experiences a 50% reduction in distance to a bridge. After such a reduction, farm land values (the best available consistent proxy for total economic activity in historical data) rise immediately and cumulatively by approximately 9% over 30 years. Population rises by approximately 5% over the same period. The proportionally larger rise in land values than population implies higher per capita economic activity in better-connected counties after 30 years.

These two sets of results are reconciled through a narrative account of development. Better bridge access drives industrialization — manufacturing employment shares rise in counties experiencing improved connectivity — and urbanization. Cities form around historical transport routes and expand. Richer households then sort away from historical city centres into lower-density suburban areas, while lower-income households remain near or selectively migrate to the historical transport corridors. This within-city sorting produces the observed cross-sectional gradient: areas nearest transport routes end up with higher population density but lower per capita incomes. The negative local income effect of proximity to transport routes is larger in more urbanized areas and areas with higher income inequality, and is concentrated among non-white and low-education populations.

The paper also contributes a new dataset covering every road and rail bridge (237 total) ever constructed over the Mississippi and Ohio Rivers from 1849 to 2010, assembled from the National Bridge Inventory and extensively cross-checked with satellite imagery and historical sources.

Q: What is the motivating empirical puzzle about transport infrastructure and income?

A: In contemporary U.S. census data, per capita income does not monotonically increase with proximity to land transport routes. Instead, the relationship is hump-shaped: income peaks around 5 km from a major transport route, with a positive elasticity of 0.072 within 4.1 km and a negative elasticity of -0.096 beyond that distance. Population density, by contrast, falls monotonically with distance to transport routes. As a result, 85% of Americans live in places where local mean income increases with distance to transport infrastructure rather than decreasing.

Q: How does the tributary confluence identification strategy work?

A: Tributary confluences — where smaller rivers join the main river — cause sharp, localized increases in river flow rates and thus in bridge construction costs, because cost scales convexly with required span length. This makes bridges systematically more likely to be built just upstream of confluences than just downstream. The strategy compares census tracts located upstream versus downstream of the 27 major tributary confluences identified on the Mississippi and Ohio Rivers, controlling for nearest-tributary fixed effects and distance to the confluence.

Q: What is the magnitude of the connectivity difference between upstream and downstream census tracts?

A: Upstream census tracts are approximately 60% closer to a bridge than downstream tracts (coefficient of 0.91 in log distance to bridge, p < 0.01), and consequently 27% closer to the nearest major land transport route (coefficient of 0.32, p < 0.10). This asymmetry is established by 1880 and persists through 2010. The advantage arises approximately equally from proximity to railroads and primary roads.

Q: What are the causal effects of this connectivity advantage on per capita income and population density?

A: Despite being better connected, upstream census tracts have 13% lower per capita incomes (coefficient 0.14 on the downstream indicator in log per capita income, p < 0.05) and 63% higher population densities (coefficient -0.49 on the downstream indicator in log population density, p < 0.05) in 2010. Income density is higher upstream, but the difference is not statistically distinguishable from zero. Scaling the income effect by the effect on distance to land transport implies an elasticity of approximately 0.44.

Q: What pre-bridge-era placebo tests support the identifying assumption for the tributary confluence strategy?

A: Matching modern census tracts to county-level historical data from 1840 and 1850 (before substantive bridge construction began), the paper finds no statistically significant asymmetry in land values or population density upstream versus downstream of tributary confluences. Asymmetric patterns emerge only after bridge construction begins. Ferry crossing locations, traced through place names in the USGS Geographic Names database, also appear equally frequently upstream and downstream, suggesting ferries did not differentially locate upstream.

Q: How does the timing-based identification strategy work, and what is its key assumption?

A: The strategy uses a county-level panel from 1860 to 2010 and estimates event-study regressions around the first time a county experiences a 50% reduction in distance to a bridge. County fixed effects and county-specific quadratic time trends absorb all fixed differences across counties and average changes in trends. The key assumption is that the exact opening date of a bridge is exogenous to short-run deviations from local long-run growth trends — supported by the argument that major bridges involve decades-long planning processes that evolve independently of local economic fluctuations. Pre-trend tests show no significant differences in outcomes before the event.

Q: What are the quantitative effects of a major improvement in bridge access on land values and population?

A: After a county first experiences a 50% reduction in distance to a bridge, farm land values rise immediately and cumulatively by approximately 9% (cumulative effect on log land values of about 0.09) over 30 years, relative to counties with no such change. Population rises by approximately 5% (cumulative log effect of about 0.05) over the same period. The proportionally larger effect on land values than on population implies that per capita economic activity is higher in better-connected counties 30 years after the event. The divergence between land value and population effects grows over time, suggesting productivity advantages accumulate.

Q: Why does the paper use farm land values rather than other income measures in the historical panel?

A: Farm land values — the total value of farm land and buildings — are the best consistently measured proxy for total economic activity available throughout the 1860–2010 census panel. The paper notes explicitly that as the economy industrializes and urbanizes, farm land values increasingly miss urban land values, implying that the estimated effects on farm land values are likely lower bounds on the true effects on total economic activity.

Q: How does the paper address the concern that bridge timing might reflect anticipated local growth?

A: The paper shows that results hold when restricting to counties whose distance to a bridge is only affected by bridges constructed in other counties, addressing the concern that local planners might time construction in anticipation of local growth. The results are also insensitive to controlling for pre-period trends, and outcomes of interest are uncorrelated with future changes in distance to a bridge in preferred specifications.

Q: How does the paper reconcile the negative local income effect (tributary confluence strategy) with the positive aggregate effect (timing strategy)?

A: The reconciliation proceeds through a narrative account combining industrialization, urbanization, and within-city sorting. Better bridge access drives a shift toward manufacturing employment and attracts population, consistent with a productivity advantage enabling exploitation of economies of scale. Cities form around historical transport routes. As cities mature and expand, richer households sort into lower-density suburban areas further from the historical transport corridor, while lower-income households remain near or migrate to the city centre. This within-city sorting produces lower per capita incomes near transport routes even as aggregate economic activity is higher in better-connected areas.

Q: What evidence supports the within-city sorting mechanism specifically?

A: The negative income effect of proximity to transport routes is larger in more urbanized areas and in areas with higher income inequality. The effect is concentrated in areas that were more rapidly urbanizing in the 19th century, and it is stronger for non-white and low-education populations. Upstream census tracts simultaneously show higher manufacturing employment shares and higher population densities, consistent with cities having formed around transport routes, followed by residential sorting away from the core.

Q: What are the two novel identification strategies and their broader applicability?

A: The tributary confluence strategy exploits discontinuities in bridge construction costs generated by sharp increases in river flow rates at confluences; it requires only that bridges are more likely built upstream of confluences than downstream, an asymmetry the paper shows is detectable elsewhere in the world from satellite imagery. The timing strategy exploits the multi-decade planning and construction process for major bridges as a source of near-exogenous variation in opening dates. Both strategies can be applied in other settings where major rivers form substantial barriers to land transport networks.

Q: What does the paper contribute to the debate about whether early U.S. transport infrastructure followed or led economic development?

A: The results support the view that early investments in land transport infrastructure led to meaningful changes in economic geography rather than merely following pre-existing growth patterns. However, the paper finds a moderate level of responsiveness — population density responds to bridge access over several decades, not immediately — consistent with a broader literature documenting sluggish population responses to changes in economic conditions.

Tributary confluence: A location where a smaller river (tributary) joins a larger river, causing a sharp, localized increase in downstream flow rates and therefore a discontinuous increase in bridge construction costs, generating the quasi-experimental variation in bridge location exploited in the paper.

Within-city sorting: The process by which, as cities expand around historical transport routes, richer households differentially relocate to lower-density suburban areas further from the transport corridor while lower-income households remain near or migrate to the historical city centre, reversing the income gradient at small spatial scales.

Income density: The product of population density and per capita income, corresponding to total economic activity per unit area; the paper finds income density is higher in better-connected upstream census tracts even when per capita income is lower, reflecting the dominant effect of higher population density.

Farm land values: The total value of farm land and buildings, used as the best consistently available proxy for total economic activity in the 1860–2010 historical county panel; the paper treats estimated effects on farm land values as lower bounds on effects on total economic activity because farm values increasingly miss urban land as the economy industrializes.

Structural transformation: The shift in the composition of employment away from agriculture and toward manufacturing, which the paper documents occurring in counties that experience improved bridge access, interpreted as evidence that transport infrastructure provides a productivity advantage attracting industrial activity.

Distance to a bridge (as proxy for land transport access): In the study area along the Mississippi and Ohio Rivers, where all land has comparable water access, distance to the nearest bridge strongly predicts distance to the nearest major land transport route (rail or primary road), allowing bridge distance to serve as a consistent measure of transport connectivity throughout the entire study period.

Market access: A measure of economic connectivity that captures both the state of the transport network and the size of accessible markets; the paper notes that log distance to a bridge explains 46% of the variation in market access in 1890 (from Donaldson and Hornbeck’s data) with an elasticity of approximately 0.1, and that halving distance to a bridge increases market access by approximately 7%.