Measuring Community Workforce Exposure to Global Export Tariffs

Section 1: Opening

As Canada comes back to the table to negotiate USMCA [1], it is likely hoping to establish more stable trading conditions with our largest partner, to the extent it is possible. Last year, trade began to be severely disrupted, not only from the ongoing trade battles with the United States [2], but also from targeted tariffs imposed on key Canadian products by China [3] and India [4].

In March 2025, the United States reinstated tariffs on most Canadian goods under IEEPA, while Section 232 duties on steel and aluminum increased to 50 per cent by June [5]. Around the same time, China imposed a 100 per cent tariff on Canadian canola oil, canola meal, and peas, followed by an additional 75.8 per cent anti-dumping duty on canola seed in August [3]. India reinstated its 30 per cent duty on yellow peas in November [4].

The aggregate effects are now visible in the data. Statistics Canada reports that exports to the United States fell 4.1 per cent in the first ten months of 2025 [6], while the share of Canadian exports destined for the U.S. dropped to 67.3 per cent — its lowest level since 1997 outside the pandemic. Saskatchewan's exports to China collapsed by 76 per cent year-over-year in August [7], and Canadian yellow pea prices fell 43 per cent between February and September.

Our own analysis of regional trade data confirms these patterns. Comparing year-over-year export values for tariffed versus non-tariffed products in the months following tariff implementation, we find substantial gaps in export performance. For China, tariffed products (canola, pulses) saw year-over-year declines averaging over 30 per cent, while non-tariffed products grew by roughly 25 per cent — a gap of more than 50 percentage points. Similar divergence is visible for India, with tariffed products substantially underperforming non-tariffed exports. These patterns suggest that the tariffs — rather than broader economic conditions — are driving the observed declines.

These national and provincial figures, however, mask significant variation at the local level. Employment in tariff-exposed sectors is concentrated in certain communities across the country, where the effects of trade disruption are likely to be more pronounced. Other analyses have attempted to capture this local dimension using proprietary trade data [8][9], but none have validated their exposure measures against labour market outcomes.

Building on previous work on workforce exposure to U.S. exports [10], we extend our methodology to capture exposure from all three tariff regimes. The analysis covers all 293 census divisions in Canada and relies entirely on publicly available data, making it replicable. Governments and researchers with access to more detailed regional trade information could adapt the approach to produce even more precise estimates.

Section 2: Building on Prior Work

As part of the IRPP's Community Transformations Project [11], we have developed an approach to identifying communities where a large proportion of the workforce is employed in sectors likely to be affected by external shocks. In earlier work, we applied this methodology to measure workforce exposure to U.S. exports [10], using census data on employment by industry and national-level export shares to estimate the degree to which each census division depends on trade with the United States.

That analysis focused on a single trading partner at a time of relative stability. The current trade environment is different. Canada now faces significant tariffs from three major trading partners simultaneously. While U.S. tariffs apply broadly to most Canadian goods, their most significant impacts fall on manufacturing and metals. Chinese tariffs, though narrower in scope, impose punishing rates on canola and pulses. Indian tariffs target pulses specifically.

To capture this broader exposure, we extend the methodology in two ways. First, we incorporate tariff-specific export shares and rates for all three countries, weighting by both the proportion of output exported and the tariff rate applied. Second, we validate the resulting exposure scores against employment insurance recipient data, testing whether communities identified as highly exposed show different labour market trajectories.

Section 3: The Analysis

Measuring workforce exposure at the community level requires balancing precision with data availability. Ideally, we would know exactly which products each community produces and where they are exported. In practice, this level of detail is not publicly available. Our approach uses the best available proxies: census employment by industry at the census division level, provincial export shares by sector, and national tariff rates and coverage ratios. For sectors where local production patterns vary significantly — such as grain farming — we apply additional adjustments using Census of Agriculture data.

The dashboard and figures below show the results of our analysis. We used census divisions as a proxy for communities. Workforce exposure to tariffs refers to a community's average industry exposure, weighted by share of employment in each industry. Industry exposure combines the sector's export share to the tariff-imposing country with the effective tariff rate applied. Employment data is derived from the 2021 census (table 98-10-0592-01) [12] and export data from ISED's Trade Data Online [13].

Our work builds on recent analyses like research into city-level exposure to U.S. tariffs using trade data (Business Data Lab) [8], or neighbourhood-level exposure using business and employment counts (School of Cities) [9]. Our analysis complements these efforts by offering a census division view that incorporates tariffs from all three countries.

Dashboard 1: Map of Workforce Exposure to Tariffs by Census Division

This dashboard shows a map of Canadian provinces and territories by census division, coloured according to their estimated workforce exposure to tariffs from all three countries in 2025. Clicking on the names of provinces or territories on the right will filter the view (holding control will let you select more than one). The bar chart beside them shows the share of their total workforce living in census divisions across the exposure categories.

Source: IRPP calculations based on Statistics Canada's census 2021 (table 98-10-0592-01), supply and use tables, and ISED Trade Data Online.

Section 4: What We Found

Across all 293 census divisions, we estimate that roughly 105,000 workers are employed in sectors with significant exposure to tariffs from the United States, China, or India. This represents about 7.6 per cent of employment in goods-producing and related industries.

The United States accounts for the largest share of this exposure — about 60 per cent — driven by the breadth of U.S. tariffs across manufacturing, metals, and forest products. Chinese tariffs, though narrower in scope, contribute 39 per cent of total exposure due to their concentrated impact on oilseed and grain farming in the Prairies. Indian tariffs account for the remaining 1 per cent, affecting pulse production specifically.

At the provincial level, Ontario leads in absolute exposure, with an estimated 25,500 exposed workers, reflecting its concentration of manufacturing employment in furniture, auto parts, and metals. Alberta and Quebec follow with roughly 22,000 and 19,500 exposed workers respectively. In relative terms, however, Saskatchewan stands out: more than 7 per cent of its workforce in tariff-exposed industries faces significant trade risk, largely due to canola.

At the census division level, the pattern of exposure reflects both the geographic concentration of tariff-exposed industries and their relative importance to local employment. The top CDs by absolute exposure are a mix of major urban centres — Toronto, Greater Vancouver, Montréal — and Prairie agricultural regions. Division No. 6 in Alberta (covering areas south of Calgary) leads in combined exposure, with an estimated 4,000 exposed workers driven almost equally by U.S. manufacturing tariffs and Chinese canola tariffs.

By exposure rate, a different picture emerges. Manicouagan in Quebec tops the list at 8.6 per cent, driven by aluminum smelting and its vulnerability to U.S. Section 232 duties. Several rural Manitoba and Saskatchewan divisions follow, with rates above 5 per cent — these are communities where oilseed and grain farming constitute a large share of local employment. Le Saguenay-et-son-Fjord in Quebec ranks third, combining aluminum exposure with some forestry.

The top 30 census divisions by exposure — the top 10 per cent — form the basis for our validation analysis in the next section. These communities include both urban manufacturing hubs (Toronto, Hamilton, Essex/Windsor) and rural agricultural regions (multiple Saskatchewan and Manitoba divisions). If our exposure metric captures real vulnerability, we would expect these communities to show different labour market trajectories than less-exposed areas.

Section 5: Validation Approach

Exposure metrics are only useful if they reflect real-world outcomes. We explored several approaches to validating our estimates:

Employment Insurance analysis (exploratory). We examined whether high-exposure communities showed different EI recipient trends than low-exposure areas. Initial analysis found only a weak correlation (R² = 0.027) between exposure rates and EI changes — likely because EI claims reflect many factors beyond unemployment, including benefit exhaustion effects from an already-elevated 2024 baseline. This analysis is available in our methodology notes but is not conclusive.

Facility spot-checks (Section 6). We verified that our high-exposure communities align with known locations of major processing facilities in affected sectors — canola crushing, pulse processing, and aluminum smelting.

Future validation options. More rigorous validation would require either:

• Mapping census divisions to Census Metropolitan Areas to use CMA-level labour force data
• Manual verification of plant closures and layoff announcements in high-exposure communities
• Access to administrative employment records not publicly available

Researchers and governments with access to more detailed data could apply more sophisticated econometric techniques to establish causal relationships between tariff exposure and labour market outcomes.

Section 6: Spot-Checking Against Known Facilities

Our exposure estimates are based on employment data and trade shares, not direct observation of local economic activity. To test whether the results align with reality, we can compare high-exposure communities against publicly known locations of major processing facilities in the affected sectors.

Canola processing. Our estimates show high exposure in Alberta Division No. 6 (south of Calgary) and several Saskatchewan divisions, particularly around Yorkton and Regina. This aligns with the location of major canola crushing facilities: Cargill operates plants in Camrose (Alberta) and Clavet (Saskatchewan), with a new facility opening in Regina [14]. Richardson International has major operations in Yorkton and Lethbridge [15]. Louis Dreyfus Company is also expanding capacity in Yorkton [16]. The concentration of canola exposure in these areas reflects this processing infrastructure.

Pulse processing. Saskatchewan dominates Canada's pulse industry, and our estimates show elevated exposure across rural Saskatchewan divisions. AGT Food and Ingredients, one of the world's largest pulse processors, is headquartered in Regina and operates over 20 facilities across the province [17]. Agrocorp Processing operates a pulse centre in Moose Jaw, specializing in red and green lentils [18]. Simpson Seeds has facilities in Moose Jaw as well [19]. The presence of these processors confirms that the communities we identify as pulse-exposed are indeed centres of this industry.

Pork processing. While pork represents a smaller share of overall tariff exposure, our estimates flag several Manitoba divisions. This aligns with known pork processing infrastructure: Maple Leaf Foods operates a major plant in Brandon (Division No. 7), processing approximately 75,000 hogs per week [20], while HyLife Foods runs its flagship facility in Neepawa (Division No. 15), employing over 1,700 workers [21].

These spot checks do not prove our exposure metric is accurate, but they provide reassurance that the patterns we observe correspond to real concentrations of economic activity.

Section 7: Implications

This analysis does not prescribe policy responses. Trade policy involves trade-offs that go beyond local labour market effects, and the tariffs themselves are subject to ongoing negotiation. But the approach we have outlined has broader implications for how governments might monitor and respond to external shocks.

Analyses like this one are feasible with publicly available data. The census, trade data, and employment insurance statistics we used are all accessible to researchers, local governments, and community organizations. Governments with access to more detailed data — such as tax records, firm-level trade flows, and administrative employment records — could produce even more precise estimates, enabling real-time monitoring of community-level exposure as tariff regimes evolve.

Early identification of vulnerable communities could also inform support programs. Our validation using EI data suggests that high-exposure communities do show different labour market trajectories. Proactive identification of at-risk areas — before effects appear in unemployment statistics — could help target retraining, diversification, and income support programs more effectively.

Conclusion

The 2025 tariffs have created uneven exposure across Canadian communities. Our analysis suggests that roughly 105,000 workers are employed in sectors facing significant tariff-related trade risk, with the effects concentrated in Prairie agricultural regions and central Canadian manufacturing hubs.

By validating our exposure estimates against employment insurance data, we find evidence that the communities we identify as highly exposed are indeed experiencing different labour market trajectories. This provides some confidence that the methodology captures real vulnerability — and that similar approaches could be applied in real time by governments with access to more detailed data.

The dashboards accompanying this article allow readers to explore exposure patterns across all 293 census divisions and identify the sectors driving local risk. We hope this contributes to a more informed public conversation about the local dimensions of trade policy.

Section 8: Finding Your Community

Census divisions are administrative units that do not always align with how people think of their communities. To help readers identify their own census division, we recommend using Statistics Canada's GeoSearch tool [22].

To find your census division:

1. Visit the GeoSearch tool
2. Enter your city, town, or postal code
3. Under "Geographic Level," select "Census Division"
4. The tool will display the census division name and code

Once you have your census division code (a four-digit number starting with your province code), you can search for it in the dashboard above.

Works Cited