Abstract
We study the impact of the Panama Canal on the development of Canada’s manufacturing sector from 1900 to 1939. Using newly digitized county-level data from the Census of Manufactures and a market-access approach, we exploit the plausibly exogenous nature of this historical episode to study how changes in transportation costs influence the process of structural transformation and manufacturing productivity. Our reduced-form estimates show that lowered shipping costs increased manufacturing employment as a share of the population by increasing the number of manufacturing establishments, though not their average size, capital intensity, or skilled labor share. Manufacturing revenues grew 9% more in counties with market access gains at the 75th percentile, compared to counties with 25th percentile gains. Productivity grew by 13% more. These effects persist when we consider general equilibrium effects: the closure of the Canal in 1939 would result in economic losses equivalent to 1.86% of GDP, chiefly as a by-product of the restriction of the country’s access to international markets. Altogether, these results suggest that the Canal substantially altered the economic geography of the Western Hemisphere in the first half of the twentieth century.
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See The Economist (2021).
The administrative divisions or districts in Canada that are analogous to counties in the United States are referred to by different names in different Canadian provinces. The term “county” is used in this paper in a generic descriptive sense for ease of reference.
We will use industrialization and structural transformation interchangeably. We refer by structural transformation to the process through which manufacturing becomes more prominent in the economic structure of employment and production, while the role of agriculture decreases Herrendorf et al., 2014.
As Supplementary Table A.8 shows, gains in market access fail to predict changes in differences between the value of marginal products and marginal costs, input wedges which measure the extent of imperfections in the economy. This finding also explains why we those wedges as exogenous to market access in the model we expose.
Though Input - Output Tables are only available for Canada in 1961 (StaisticssCanada, 1987) and for Colombia in 1970 (DANE, 1976), figures for those years show that wheat and lumber in Canada had more forward linkages than coffee in Colombia: 48.5% of the Gross Value of Products of “Wheat, flour, meal and others” was used as an input by other sectors. Similarly, 47.5% of the gross value of products of “Lumber and timber” was used as input by other sectors. In contrast, Colombian data for 1970 shows that only 6.3% of the coffee sector’s output was used as an input by other sectors. Similarly, backward linkages towards the nonprimary sector were much stronger for Canadian staples relative to the Colombian staple. The coffee sector’s Value Added was 48.3% of Gross Output, with 83% of the intermediate input expenditures accruing to the agricultural sector. In contrast, the Forestry sector’s Value Added was 52.5% of Gross Output, with only 23.8% of the intermediate input expenditure accruing to agricultural goods (including forestry products themselves).
Innis (1931), MacIntosh (1939), and Lawrence (1957) emphasize access to export markets as the central driver of the transformations triggered by the Panama Canal. We cannot systematically test for and disentangle a role for greater import competition. Historical data suggests, however, that export flows from Vancouver were significantly higher than respective import flows. For example, Innis points out that Vancouver exported 8.1 tons per imported ton from Europe by 1927. Table 1, which includes data for 1927, shows that wheat and lumber made most of the Eastbound, whereas iron and steel, glass and glassware, paper, and sulfur did so for the Westbound traffic.
Another company, the Compagnie Nouvelle du Canal de Panama was established in 1894 but failed to make material progress towards completion.
The transport network data for the US uses 1890 county boundaries. To use population figures for 1910 and 1920, we match counties in 1910 and 1920 to 1890, assuming that the spatial distribution of the population is homogeneous (Eckert et al., 2020) For Canada, we use 1941 boundaries from that year’s Census of Population, which were the same as those used for the 1939 Census of Manufactures.
We detail the list of countries we cover and the corresponding port to which they are assigned in the Online Appendix. In practice, the only systematically excluded area is Sub-Saharan Africa. Canadian trade with Africa, some of which is covered in our sample, represented between 0.2 and 0.8% of imports and between 1 and 2% of exports over the 1930 s.
Straight-line wagon connections passing over the Great Lakes and the St. Lawrence River basin were not included.
See the Online Appendix B.
These chokepoints are the Suez Canal, Cape Horn, Cape of Good Hope, Singapore, the Strait of Gibraltar and Bishop Rock.
Standard deviation of rates is 0.04 cents per ton-mile. Rates vary by distance: a trip 100 miles longer is associated with a 1.16 cents per ton-mile cheaper rate.
The weights used for the computation of the average are the inverse of distance between county c and destination d, normalized by the sum of all weights, so the total weights add up to 1. Exclusively for distance indices, we use the azimuthal equidistant projection of the world centered around the geographic center of Canada.
Contemporaries acknowledged this fact. The Report on the Influence of the Panama Canal on Trade with Western Canada, June, 1930 (C. 3319) concludes that “in most cases Manitoba points are best reached via Eastern Canadian ports; Saskatchewan is border-line territory, but Alberta and British Columbia destinations can most cheaply be reached by all classes of commodities by means of the Panama Canal route.”
Moreover, at the province and national level, only the 40 “leading industries” (around 75% of revenue) are reported in detail.
We use the information on revenues and inputs provided in the Census of Manufactures of 1906, which is discriminated by establishment size at the province level. In practice, we multiply our 1901 and 1911 figures by a factor that captures how much each variable (revenues, employment, etc.) in 5+ employees establishments in 1906 should expand to equate the variable for all establishments in 1906.
The productivity scalar used here is \(\xi =1/[1-\frac{1}{C}\sum _{c}\sum _{k}s_{k,c}]\), where C is the total number of counties.
We also show results for a productivity decomposition that uses national level input cost shares to estimate output elasticities.
These concepts can be illustrated with a hypothetical example: Toronto is a manufacturing center where producers are highly productive, and markets work relatively well, so the differences between marginal output and marginal costs are small. Vancouver is a city where wages and the return to capital are the same as in Toronto, but where wood is much cheaper because forests surround it. Production there, however, is constrained because it is distant from most major markets on the eastern seaboard of North America. Now, let us assume there are two identical carpenters in both cities. Whenever their carpentry skills improved so that they could use less wood to manufacture a table, Toronto and Vancouver’s TFPR would increase. If the Vancouver carpenter started working more hours manufacturing tables while leaving his skills constant, Vancouver’s AE would increase. Note that, in a perfectly competitive economy where the difference between marginal products and marginal costs are equalized across sectors and places - the carpenter’s increased labor in Vancouver would not increase productivity.
We use the interquartile range of the residual (within) variation of our causing variable, which is the one we use for identification and equals 0.0027. The corresponding magnitude for the original causing variable is 0.0045.
Supplementary Figure A.3 shows that our point estimates and standard errors change very little when we use different values of trade elasticity.
This assumption is consistent with the theoretical predictions resulting from the model we expose and motivates our empirical exercise. See Eq. 14.
We prefer to include a county’s area devoted to forest as a measure of pre-existing endowments or lumber potential to measures of baseline lumbering output since output-based measures might be endogenous to market access. We include those measures for completeness in Supplementary Table A.12, while nothing that our conclusions do not change.
This subsection follows Hornbeck and Rotemberg (2021) closely.
The United States was an important source of migration to Canada over the first half of the twentieth century. By 1921, over 4% of the Canadian population and 19% of Canada’s foreign-born population were born in the United States. Furthermore, between 1931 and 1940, the United States was the top country of origin of immigrants to Canada (StatisticsCanada, 2016) .
Where \(\kappa _2 = {\bar{U}}\rho ^{\frac{1+\theta }{\theta }}\) and \(\kappa _{2c} = \frac{\kappa _{1c} + ln\psi _c - \theta \alpha _c^T \ln \frac{\alpha _c^T}{T_c}}{1+\theta \alpha _c^T}\)
We set \(\alpha _c^T=0.1748\) (Caselli & Coleman, 2006) \(\alpha _c^L=0.255\), \(\alpha _c^K=0.0768\), and \(\alpha _c^M=0.669\). These come directly from the data.
Changes in AE might be written as \(d\ln (AE_c)=\frac{P_c Q_c}{Productivity_c}[\sum _k ( \alpha _c^k - s_c^k ) d \ln (X_c^k)]\). See equation (20) in Hornbeck and Rotemberg (2021).
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Acknowledgements
We are grateful to the editor, Oded Galor, and five anonymous referees for their helpful comments and discussions. We thank John Haltiwanger, Rick Hornbeck, Eunhee Lee, Nuno Limao, Noel Maurer, Chris Meissner, Martin Rotemberg, John Shea, John Wallis and participants at the NBER Summer Institute Seminar - DAE, the Annual Meetings of the EHA, the Annual Meetings of the EEA, Uniandes’ Seminario CEDE, RIDGE Historical Development Workshop, the URosario Political Economy Workshop, and the UMD Trade Workshop for their comments and suggestions. Kris Inwood and Ian Keay provided valuable guidance in terms of data sources at an early stage of the paper.
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We are grateful to the editor, Oded Galor, and five anonymous referees for their helpful comments and discussions. We thank John Haltiwanger, Rick Hornbeck, Eunhee Lee, Nuno Limão, Noel Maurer, Chris Meissner, Martin Rotemberg, John Shea, John Wallis and participants at the NBER Summer Institute Seminar - DAE, the Annual Meetings of the EHA, the Annual Meetings of the EEA, Uniandes’ Seminario CEDE, RIDGE Historical Development Workshop, the URosario Political Economy Workshop, and the UMD Trade Workshop for their comments and suggestions. Kris Inwood and Ian Keay provided valuable guidance in terms of data sources at an early stage of the paper.
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Galiani, S., Jaramillo, L.F. & Uribe-Castro, M. Free-riding Yankees: Canada and the Panama Canal. J Econ Growth (2025). https://doi.org/10.1007/s10887-025-09252-8
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DOI: https://doi.org/10.1007/s10887-025-09252-8