Canada’s economy: stalled, not broken

Stable economic foundation despite early-year softness

Recent data suggest Canada slipped into a shallow technical recession over the turn of the year, with GDP falling at a 1.0% annualized pace in Q4 2025 and edging down a further 0.1% in Q1 2026. Even so, the weakness looks less alarming than the headline implies. Output was distorted by temporary factors, including a surge in gold imports and a temporary slowdown in defence spending, while final domestic demand has been resilient.

GDP growth

Canada’s economy contracted modestly in the first quarter of 2026, with real GDP down 0.1 percent at an annualized rate. That marks a second consecutive quarterly decline and puts Canada in a technical recession. However, the contraction has been shallow, concentrated in trade exposed sectors and regions, and the data does not suggest a material, broad-based slowdown. That distinction matters because the headline GDP figures likely overstate economic weakness. There is meaningful scope for revision, as we saw with the sizeable revisions to 2025 GDP, and the income side of the accounts looked much firmer than the expenditure side in the first quarter. Real gross domestic income actually rose, helped by better terms of trade and stronger energy revenues, even as expenditure GDP slipped.

Recent weakness was driven mainly by softer government spending on defence related outlays and constrained business capital spending under the weight of persistent uncertainty. Residential spending was also notably weak, reflecting the ongoing drag from soft housing markets. Services spending held up reasonably well, but discretionary areas such as autos remained soft. GDP has also been hit by sectoral tariffs, especially in autos and steel, and by the wider chill in business confidence that comes from prolonged trade uncertainty. Exports have been weak in tariff sensitive sectors, especially autos, while imports were distorted by strength in precious metals and gold related flows, making the net trade picture look worse in the quarter.

As shown in Figure 1, GDP data from Q4 2025 to Q1 2026 reveals considerable volatility across key components, including inventories, imports, fixed capital formation, and government spending. With the exception of imports, which has a negative contribution to percentage change in real GDP in both quarters, each component shifted between contraction in one quarter and expansion in another, underscoring the importance of focusing on underlying medium-term trends rather than short-term fluctuations.

Consumers have been the main shock absorber for the economy. Household spending did grow in the first quarter but largely through drawing down savings and leaning on wealth effects from stronger equity markets. The saving rate has fallen to 3.5%, which leaves less cushion going forward. That support is unlikely to be durable, especially with household income growth softening. Consumer spending will likely struggle more in coming quarters as higher gasoline prices erode purchasing power, and retail data already point to signs of pressure in real volumes. The burden is especially heavy for lower income households, even though many of them should benefit from the Canada benefits and essential package, and in our view much of that support will be spent rather than saved. That should provide some offset, but it does not change the fact that consumers have been supporting the economy at a time when underlying income growth remains weak.

There are still reasons not to become too pessimistic. CUSMA remains an important stabilizer and, so far, continues to shield much of Canada’s trade with the United States from a more severe tariff shock. Higher energy prices are also providing a lift to nominal GDP, trade revenues, and national income, even if those gains are unevenly distributed across provinces and households. In addition, stronger energy income, some support from higher real wages, and a likely rebound in trade and monthly GDP in the second quarter should help the economy look somewhat better after a very soft start to the year.

More broadly, the evidence does not yet suggest that AI is delivering measurable productivity gains at the macro level in Canada. Early indicators remain modest, as evident in Figure 2 below, despite broad-based business adoption across most areas (see Figure 3), reinforcing the view that the economy is still in the initial phase of adoption, where investment and integration precede widespread efficiency gains.

Notably, adoption remains weaker in marketing automation. Additionally, Canada’s sector composition differs meaningfully from that of the United States, with a relatively smaller technology footprint, which may further temper the near-term productivity impact of AI, as evident from Figure 1.

As shown in Figure 4, Canada’s GDP per capita has been trending higher despite a modest decline in March. This reflects our earlier expectation that slower population growth, particularly among temporary foreign workers and international students who are often concentrated in lower wage roles, would lift per capita output.

Our overall view is that while Canada is in a technical recession, the economy still looks more stalled than broken.

We have lowered our 2026 GDP forecast to 1.5%, with only a modest improvement to 1.6% in 2027. That outlook, however, remains sensitive to external risks, most notably the outcome of upcoming CUSMA negotiations.

Ashish Dewan CFA, CFP is Senior Investment Strategist at Vanguard Investments Canada. Excerpted from Vanguard’s “Canada 2026 Q3 Outlook: A Softer Start to the Year Masks a Stable Underlying Economic Backdrop.”

Disclaimer

Content © 2026 by Vanguard Group. All rights reserved. Reproduction in whole or in part by any means without prior written permission is prohibited. This article first appeared July 3, 2026, on the “Insights” page of the Vanguard Group, Inc.’s website. Used with permission. All investing is subject to risk, including the possible loss of the money you invest. Be aware that fluctuations in the financial markets and other factors may cause declines in the value of your account. There is no guarantee that any particular asset allocation or mix of funds will meet your investment objectives or provide you with a given level of income. Diversification does not ensure a profit or protect against a loss.

Investments in bonds are subject to interest rate, credit, and inflation risk.

Investments in stocks and bonds issued by non- U.S. companies are subject to risks including country/regional risk and currency risk. These risks are especially high in emerging markets.

IMPORTANT: The projections and other information generated by the Vanguard Capital Markets Model regarding the likelihood of various investment outcomes are hypothetical in nature, do not reflect actual investment results, and are not guarantees of future results. VCMM results will vary with each use and over time.

The VCMM projections are based on a statistical analysis of historical data. Future returns may behave differently from the historical patterns captured in the VCMM. More important, the VCMM may be underestimating extreme negative scenarios unobserved in the historical period on which the model estimation is based.

The Vanguard Capital Markets Model® is a proprietary financial simulation tool developed and maintained by Vanguard’s primary investment research and advice teams. The model forecasts distributions of future returns for a wide array of broad asset classes. Those asset classes include U.S. and international equity markets, several maturities of the U.S. Treasury and corporate fixed income markets, international fixed income markets, U.S. money markets, commodities, and certain alternative investment strategies. The theoretical and empirical foundation for the Vanguard Capital Markets Model is that the returns of various asset classes reflect the compensation investors require for bearing different types of systematic risk (beta). At the core of the model are estimates of the dynamic statistical relationship between risk factors and asset returns, obtained from statistical analysis based on available monthly financial and economic data from as early as 1960. Using a system of estimated equations, the model then applies a Monte Carlo simulation method to project the estimated interrelationships among risk factors and asset classes as well as uncertainty and randomness over time. The model generates a large set of simulated outcomes for each asset class over several time horizons. Forecasts are obtained by computing measures of central tendency in these simulations. Results produced by the tool will vary with each use and over time.

Image: iStock.com/fruttipics