Wholesale Trade Survey (monthly): CVs for total sales by geography - August 2025

Wholesale Trade Survey (monthly): CVs for total sales by geography - August 2025
Geography Month
202408 202409 202410 202411 202412 202501 202502 202503 202504 202505 202506 202507 202508
percentage
Canada 1.3 1.3 1.2 1.3 1.2 1.3 1.5 0.9 1.2 0.9 0.4 0.5 0.4
Newfoundland and Labrador 0.8 0.8 1.3 1.5 1.1 1.5 0.8 0.7 1.7 0.3 0.3 0.3 0.2
Prince Edward Island 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0
Nova Scotia 7.9 4.7 5.8 9.1 12.0 7.1 3.8 3.3 6.9 10.4 2.6 2.9 2.2
New Brunswick 2.9 1.9 3.3 2.6 2.4 3.1 1.7 1.3 4.1 1.5 1.0 0.9 1.3
Quebec 4.2 4.8 4.3 4.8 4.6 4.5 5.4 3.7 4.3 3.1 1.3 1.8 1.3
Ontario 2.6 2.4 2.2 2.2 2.4 2.7 3.2 1.7 2.3 1.6 0.7 0.8 0.8
Manitoba 1.9 2.4 2.7 1.9 2.3 1.0 1.1 1.3 1.3 1.1 0.7 0.8 1.1
Saskatchewan 1.8 0.7 1.5 1.0 1.4 1.6 0.7 0.8 1.6 0.5 0.4 0.9 0.6
Alberta 1.5 1.2 1.6 2.2 1.2 1.4 1.2 0.8 0.6 0.7 0.4 0.5 0.5
British Columbia 2.8 3.1 3.1 2.7 2.2 2.6 2.8 1.8 1.8 2.2 0.8 1.2 1.5
Yukon Territory 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0
Northwest Territories 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0
Nunavut 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0

Approaching Generative Artificial Intelligence: Recommendations and Lessons Learned from AgPal Chat

By Andy Fan, Rafael Moraes; Agriculture and Agri-Food Canada

Introduction

Born out of the winning entry of the inaugural Canadian Public Service Data Challenge, we have had the incredible opportunity to build AgPal Chat, a generative Artificial Intelligence (AI) search tool that provides helpful federal, provincial, and territorial agricultural information to Canadians in a conversational manner. It is available on the AgPal.ca website as a new, complementary way to connect users with relevant Canadian agricultural information. It is the result of an incredible cross-functional effort across industry, academia, and other government departments to improve service delivery to Canadians.

In this article, we focus on sharing our lessons learned on the technical and policy aspects of implementation of AgPal Chat. Some of our key findings and recommendations include: the use of Retrieval Augmented Generation (RAG) to enhance AI accuracy, the necessity of guardrails to ensure ethical and safe AI interactions, and the role of strong data governance and policy compliance in creating responsible AI systems.

Prompt Engineering

Prompt engineering is a fascinating and intricate field at the intersection of human expertise and AI. Its central goal is to fine tune queries in a way that elicits the most accurate, unbiased, and relevant responses from AI systems, especially those based on language models. This practice is of utmost importance because, unlike traditional software interfaces, natural language systems rely heavily on the subtleties of human language with all its nuances and complexities. Therefore, designing effective prompts is both an art and a science, one that requires a deep understanding of the underlying AI technology, as well as the particularities of human language and cognition.

It is also a continuous, iterative process that involves testing and refining prompts to ensure AI systems generate accurate, unbiased, and relevant responses. This ongoing adjustment is crucial to avoid introducing unintended biases, as even small changes in wording can significantly impact AI behavior. Regular assessment and careful balancing of technical and linguistic elements help maintain the reliability and impartiality of AI outputs.

It's important to recognize that each large language model (LLM) will have its own optimal prompt that elicits the best performance, as different models may respond differently to the same prompt due to variations in their architecture and training data. However, the process of discovering this optimal prompt remains consistent across models. It involves the same iterative cycle of experimentation, evaluation, and refinement to ensure the prompts guide the AI in producing accurate and unbiased outputs.

Retrieval-Augmented Generation Technique

"Retrieval-Augmented Generation" or RAG is a framework that combines the retrieval of information from a knowledge source (like a database or a collection of documents) with the generative capabilities of a language model. Without it, there is a higher chance even fine-tuned LLMs will output “hallucinations” when asked about topics they have not seen extensively in their training set. To ensure that an AI provides more precise information, RAG must be implemented within the prompt construction process. Figure 1 below showcases an example of the RAG process. Open-source libraries like Langchain or Llama Index, as well as proprietary solutions (such as Azure Cognitive Search) can be employed if you want to use RAG without building it from scratch. In AgPal Chat’s case, we decided to build the RAG pattern ourselves. This led to a more flexible solution that fits our specific needs.

A diagram of a software Description automatically generated with medium confidence

Figure 1 source: Next-Gen Large Language Models: The Retrieval-Augmented Generation (RAG) Handbook

Description - Figure 1: Retrieval-Augmented Generation (RAG) process example

This is an image illustrating the Retrieval-Augmented Generation (RAG) process from freecodecamp. The diagram consists of several interconnected components:

  • Input (Query): A question from the user, such as "How do you evaluate the fact that OpenAI's CEO, Sam Altman, went through a sudden dismissal by the board in just three days, and then was rehired by the company, resembling a real-life version of 'Game of Thrones' in terms of power dynamics?"
  • Indexing: The system indexes documents into chunks/vectors using embeddings.
  • Retrieval: Relevant documents are retrieved based on the query. For example:
    • Chunk 1: "Sam Altman Returns to OpenAI as CEO, Silicon Valley Drama Resembles the ‘Zhen Huan’ Comedy."
    • Chunk 2: "The Drama Concludes? Sam Altman to Return as CEO of OpenAI, Board to Undergo Restructuring."
    • Chunk 3: "The Personnel Turmoil at OpenAI Comes to an End: Who Won and Who Lost?"
  • Generation:
    • Without RAG: The system provides a generic response without specific details, such as "I am unable to provide comments on future events. Currently, I do not have any information regarding the dismissal and rehiring of OpenAI’s CEO..."
    • With RAG: The system combines the context from the retrieved documents and prompts to generate a more detailed and relevant response, such as "...This suggests significant internal disagreements within OpenAI regarding the company's future direction and strategic decisions. All of these twists and turns reflect power struggles and corporate governance issues within OpenAI..."
  • Output: The final answer is generated based on the selected retrieval method (with or without RAG), showcasing the difference in detail and accuracy.

Here is how the RAG technique generally works:

  1. Retrieval Step: Given a chat history, the RAG system first retrieves relevant documents or pieces of information from a database or a corpus. This is usually done using a retrieval model or search algorithm optimized to quickly find the most relevant content from large collections of information.
  2. Augmentation: The retrieved documents are then used to augment the input to the generative model. This means that the language model receives both the chat history and the content of the retrieved documents as context.
  3. Generation Step: A generative language model then generates a response based on this augmented input. The model uses the additional information to produce more accurate, detailed, and contextually relevant answers.

RAG frameworks are particularly useful for tasks where a language model needs access to external information or must answer questions based on factual data that may not be stored within its parameters. Examples of such tasks include open-domain question answering and fact-checking. The retrieval step allows the system to pull in up-to-date or specific information that the language model alone would not have access to, based upon its training data.

Guardrails

Guardrails are pre-defined rules or constraints that are put in place to prevent an AI system from generating inappropriate, biased, or unbalanced content. They work by guiding the generation process away from certain topics or phrases and by post-processing the AI's output to remove or revise problematic content. These guardrails are crucial for several reasons:

  1. Content Control: They prevent the generation of inappropriate, offensive, or harmful content. This includes hate speech, sexually explicit material, and other types of content that might not be suitable for all audiences.
  2. Ethical Guidelines: Guardrails help ensure that LLMs and chatbots abide by ethical guidelines. They can prevent the endorsement of illegal activities or those that might cause harm to users or third parties.
  3. Bias Mitigation: Despite best efforts, LLMs can sometimes perpetuate or even amplify biases present in their training data. Guardrails can be designed to identify and mitigate such biases, ensuring more fair and balanced interactions.
  4. Safety: By keeping the AI's behavior within certain limits, guardrails enhance user safety by not allowing the system to provide dangerous or incorrect information. This is particularly important in high-stakes domains like healthcare or legal advice, where incorrect information can have serious consequences.
  5. User Trust and Compliance: Ensuring that the system behaves predictably and within the bounds of socially acceptable norms helps to build user trust. Guardrails also help in compliance with various regulatory standards and legal requirements, which is essential for the deployment of chatbots in different industries.
  6. Prevention of Misuse: Guardrails are also important for preventing users from manipulating or 'tricking' the AI into behaving in unintended ways, such as generating malicious content or participating in deceptive practices.
  7. Maintaining Focus: They help the system stay on topic and relevant to the user's intent, improving user experience by preventing the chatbot from producing irrelevant or nonsensical responses.

To incorporate guardrails effectively, one must first identify the potential vulnerabilities that could instigate the model to have a bias and understand the context in which impartiality might be compromised. For example, if an AI model is generating objective news summaries, it should treat different entities and subjects objectively and not provide an opinion. Guardrails for this scenario could range from eliminating certain opinion laden words to implementing more sophisticated sentiment analysis checks that flag excessively positive or negative language around specific topics. Lastly, ensuring that an AI tool responds solely to pertinent inquiries is a matter of both discrimination and focus within the guardrail system. The AI should discern between questions that it should answer and those that are either irrelevant, inappropriate, or outside the scope of its functionality. Again, guardrails play a crucial role here. By giving clear instructions and examples for what constitutes a pertinent inquiry, the AI can deflect or refuse to answer questions that do not meet those criteria.

For example, in building AgPal Chat, queries about only Canadian agricultural information in the AgPal system would be pertinent. Guardrails were therefore set up to provide comprehensive and focused responses to agricultural- related questions, while avoiding or redirecting those concerning other unrelated data. A simplified example of the implementation was including a line in the system prompt stating, "Do not answer questions that are not related to data provided by the AgPal system".

In practice, guardrails can take many forms. Based on our experience in building AgPal Chat, we would recommend having at least:

  • Filtering systems that detect and block unwanted types of content.
  • Rate-limiting features to prevent spamming or abuse of the system.
  • Explicit prompting of "do not say" lists or behavior rules.
  • Review processes or human-in-the-loop mechanisms (e.g., logging and monitoring user prompts and answers) Microsoft has a great example of a guardrail prompt (Figure 2):
Screens screenshot of a black screen Description automatically generated
Description - Figure 2: Metaprompt guardrail example

This is an image of a metaprompt guardrail example for an ice cream shop conversational agent from Microsoft.

The metaprompt consists of:

## This is a conversational agent whose code name is Dana:

  • Dana is a conversational agent at Gourmet Ice Cream, Inc.
  • Gourmet Ice Cream’s marketing team uses Dana to help them be more effective at their jobs.
  • Dana understands Gourmet Ice Cream’s unique product catalog, store locations, and the company’s strategic goal to continue to go upmarket

## On Dana’s profile and general capabilities:

  • Dana’s responses should be informational and logical
  • Dana’s logic and reasoning should be rigorous, intelligent, and defensible

## On Dana’s ability to gather and present information:

  • Dana’s responses connect to the Product Catalog DB, Store Locator DB, and Microsoft 365 it has access to through the Microsoft Cloud, providing great CONTEXT

## On safety:

  • Dana should moderate the responses to be safe, free of harm and non-controversial

The prompt consists of:

Write a tagline for our ice cream shop.

The Response consists of:

Scoops of heaven in the heart of Phoenix!

Implementing guardrails in AI systems is crucial but challenging. It requires careful design to ensure they handle diverse inputs while maintaining accuracy. Ongoing maintenance is also necessary to keep guardrails effective as language models and content evolve. Despite these challenges, guardrails are essential for ensuring safe and responsible AI interactions.

Data Management and Data Governance

The output of the retrieval augment generation pattern is directly correlated with quality of the underlying data that is being used. AgPal Chat leverages the result of years of strong data management and governance practices from the AgPal team, who provided a foundation of high quality, well-curated data on Agricultural programs and services on the AgPal website. In this context, having good data management and governance practices can improve the accuracy and relevance of the generated texts by ensuring that the data sources are reliable, consistent, and up to date. Some recommendations to help realize the benefits of data management and governance include:

  1. Establishing a clear and comprehensive data strategy that defines the vision, goals, and principles of data management and governance.
  2. Implementing a robust and flexible data architecture that supports the integration, interoperability, and accessibility of different data sources.
  3. Adopting a data quality framework (see related TBS guidance) that ensures the validity, completeness, timeliness, and accuracy of data sources.
  4. Applying a data security model that protects the confidentiality, integrity, and availability of data sources and generated texts.
  5. Creating a data governance structure that assigns roles, responsibilities, and accountabilities for data management and governance.
  6. Monitoring and evaluating the data management and governance performance and outcomes, and making continuous improvements based on feedback and best practices.

Policy Related Considerations

When building AI applications in a federal public sector context, in addition to existing policies and guidelines (Directive on Automated Decision-making, Scope of the Directive, Guide on the use of Generative AI), some policy considerations should be made to ensure they are built in a responsible and ethical manner. We have found that these considerations were crucial in helping shape our design and approach to developing AgPal Chat and would highly recommend consulting them during the design phase.

Broader policy considerations on this front include:

  • Compliance: Ensure that the chatbot’s design and deployment are in line with existing applicable policies and legislation, and follow any best practices, suggested guidance from other public authorities and industry-specific regulations. Additionally, ensure that all internal or departmental policies and guidelines are adhered to. In addition to compliance, ensure that appropriate measures are in place to mitigate any legal and regulatory risks. This may involve seeking legal advice, implementing compliance processes, and staying up to date with developments in the legal and regulatory landscape.
  • Risk Evaluation: Evaluate and address potential cybersecurity threats, biases, violations of privacy, and the possibility of generating hallucinations or inaccurate information. If the system interfaces with the public, consider the public sentiment or current events that could impact the way that the tool may be perceived.
  • Stakeholder Engagement: Proactively engage with key stakeholders, such as legal counsel, privacy and security experts, Gender-based Analysis (GBA) Plus focal points, Diversity, Equity and Inclusion (DEI) representatives, other partners (e.g., Indigenous Communities), and internal process authorities (e.g., enterprise architecture, project governance) as early as possible to ensure a coordinated, compliant, and holistic approach.
  • Transparency: It is essential to notify users that they are communicating with an AI tool, rather than a human, to avoid any confusion or misunderstanding. Sharing additional information about the system such as a description of how it works, the data it is using, and the steps taken to ensure its quality can also be helpful to increase trust.
  • Bias and Discrimination Monitoring: Monitor the performance of the AI tools in guarding against bias or discrimination, ensuring that the technology is used responsibly and equitably. Capture the user queries and the system responses to review them on a scheduled basis across the lifecycle.
  • Education: Offer clear instructions to users on the optimal way to interact with the chatbot, including guidance on formatting their prompts or queries and what information should be included. Ensure that chatbot developers have access to training and resources to help them become skilled in using the technology and understand its capabilities, limitations, and best practices for its responsible use.
  • Iterative Development: Recognize the need for ongoing iteration to keep pace with regulatory and technological changes. Adopting an agile approach is one potential solution.
  • Sustainability: Ensure the design and implementation of AI tools are guided by a commitment to environmental sustainability to support long-term viability and mitigate any negative impacts on the environment or on the people and communities.

Conclusion

Prompt engineering, as a discipline, sits at the critical juncture of ensuring that AI-powered systems provide users with responses that are not only accurate and factually correct but also impartial, ethical, and contextually relevant. The introduction of RAG has marked a significant step forward in achieving this, by providing a mechanism for AI to dynamically access and incorporate external information. This process enhances the reliability and factual basis of the AI's responses, particularly in situations where the AI must draw from a vast and ever-evolving pool of knowledge.

The implementation of ethical guardrails, strong data management practices, and compliance to existing policies, laws and regulations can help AI systems better respect social norms and user trust, contributing to a more beneficial interaction for all parties involved.

Future research and improvements for AgPal Chat could focus on refining prompt engineering to improve contextual relevance, expanding the use of Retrieval-Augmented Generation (RAG) for more dynamic data integration, and enhancing the AI's scalability and efficiency, enhancing service delivery to users while retaining safety and reliability.

As AI continues to advance and become an integral part of personal and professional environments alike, the work put into prompt engineering will undeniably play a pivotal role in shaping the future of human-AI interactions. Ensuring that prompt engineering techniques continue to evolve alongside AI models will be vital in upholding the principles of accuracy, impartiality, and relevance. The proper application of prompt engineering and guardrails will enable AI to reach its full potential as a tool for enhancing human knowledge, decision-making, and productivity, without sacrificing ethical integrity or user trust.

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References

Canadian Housing Statistics Program (CHSP) – Record linkage results per province and territory, 2023

Canadian Housing Statistics Program (CHSP) – Record linkage results per province and territory, 2023Tablenote 1

Record linkage results per province and territory - 2021
Province/Territory Linkage Linkage RateTablenote 2 False Discovery RateTablenote 3 False Negative error RateTablenote 4
%
Prince Edward Island Census (persons) 85.9% <2.0% <2.0%
Tax & Social Insurance Registry (persons) 94.3% <1.5% <2.5%
Business Register (businesses and governments) 93.9% <0.5% <0.5%
Newfoundland and Labrador Census (persons) 86.4% <2.5% <2.0%
Tax & Social Insurance Registry (persons) 94.7% <3.0% <2.5%
Business Register (businesses and governments) 96.8% <1.0% <2.0%
Nova Scotia Census (persons) 87.8% <2.0% <1.0%
Tax & Social Insurance Registry (persons) 95.3% <1.5% <1.0%
Business Register (businesses and governments) 96.0% <0.5% < 2.0%
New Brunswick Census (persons) 87.0% <2.5% <1.5%
Tax & Social Insurance Registry (persons) 95.5% <3.5% <1.5%
Business Register (businesses and governments) 97.2% <1.0% <1.5%
Ontario Census (persons) 93.2% < 0.5% < 0.5%
Tax & Social Insurance Registry (persons) 98.6% < 0.5% < 0.5%
Business Register (businesses and governments) 98.6% < 1.0% <1.0%
Manitoba Census (persons) 91.0% <1.0% <2.0%
Tax & Social Insurance Registry (persons) 96.9% <1.0% <2.0%
Business Register (businesses and governments) 97.3% <1.0% <0.5%
Alberta Census (persons) 88.7% <1.0% <3.0%
Tax & Social Insurance Registry (persons) 94.5% <1.5% <4.0%
Business Register (businesses and governments) 97.9% <0.5% <2.5%
British Columbia Census (persons) 90.6% < 1.5% <1.0%
Tax & Social Insurance Registry (persons) 96.5% < 1.5% <1.5%
Business Register (businesses and governments) 98.9% <0.5% <1.0%
Yukon Census (persons) 82.8% <2.5% <2.0%
Tax & Social Insurance Registry (persons) 91.6% <2.5% <1.0%
Business Register (businesses and governments) 98.8% <0.5% <1.0%
Northwest Territories
 		 Census (persons) 91.7% <0.5% <0.5%
Tax & Social Insurance Registry (persons) 97.2% <0.5% <1.0%
Business Register (businesses and governments) 97.7% <0.5% <0.5%
Nunavut Census (persons) 66.9% <3.0% <1.5%
Tax & Social Insurance Registry (persons) 92.7% <1.5% <1.5%
Business Register (businesses and governments) 99.7% <0.5% <0.5%
Tablenote 1

Canadian Housing Statistics Program (CHSP) – Record linkage results per province and territory, 2022.

Return to tablenote 1 referrer

Tablenote 2

Linkage Rate: The linkage rate is calculated as the percentage of owner records with accepted links to the database shown. It is the denominator for the false discovery rate (FDR). While it is not a data quality indicator alone, in addition to the FDR and the false negative error rate (FNR) it provides a complete picture of the overall linkage quality.

Return to tablenote 2 referrer

Tablenote 3

False Discovery Rate (FDR): The FDR is calculated as the percentage of records with false links among records with accepted links (i.e., a record with a false link is a record that was linked incorrectly).

Return to tablenote 3 referrer

Tablenote 4

False Negative error Rate (FNR): The FNR is calculated as the percentage of records with true links which were not found in the linkage process (i.e., records that were not linked when they should have been).

Return to tablenote 4 referrer

Canadian Economic News, September 2025 Edition

This module provides a concise summary of selected Canadian economic events, as well as international and financial market developments by calendar month. It is intended to provide contextual information only to support users of the economic data published by Statistics Canada. In identifying major events or developments, Statistics Canada is not suggesting that these have a material impact on the published economic data in a particular reference month.

All information presented here is obtained from publicly available news and information sources, and does not reflect any protected information provided to Statistics Canada by survey respondents.

Wildfires

  • The Government of Nova Scotia announced on August 29th that the risk of wildfires had lowered enough in some counties, including Cape Breton, Antigonish, and Halifax, for restrictions on travel and activities in the woods to be lifted. The Government said restrictions in other counties would remain in place until October 15th, as would the provincial burn ban. On September 18th, the Government announced that restrictions on travel and activities in the woods were lifted in all remaining counties except Annapolis. On September 26th, the Government announced that the ban on open fires was lifted in all counties except Annapolis.
  • The Government of Newfoundland and Labrador announced on September 8th that the province-wide outdoor fire ban issued on August 5th would be extend. On September 12th, the Government announced the fire ban had been lifted.
  • The Government of Prince Edward Island announced on September 8th it had extended the fire closure order that bans all fires in the province, including campfires, until September 22nd or further notice. On September 22nd, the Government announced that the most recent fire closure order was ending and would not be renewed.

Resources

  • Calgary-based Cenovus Energy Inc. announced it had reached an agreement for the sale of its 50% interest in WRB Refining LP to its joint venture partner Phillips 66 of Texas for approximately $1.9 billion. Cenovus said the transaction is expected to close around the end of the third quarter, subject to the satisfaction of customary closing conditions.
  • Calgary-based Imperial Oil Limited announced plans to restructure and said the transition process is expected to reduce employee roles by approximately 20% by the end of 2027. Imperial said it expects to record a one-time restructuring charge of approximately $330 million before-tax in the third quarter of 2025.
  • Ksi Lisims LNG, a proposed net-zero LNG export facility on British Columbia's northwest coast, co-developed by the Nisg̱a'a Nation, Western LNG of Texas, and Rockies LNG of Calgary, announced it had received an Environmental Assessment Certificate from the Government of British Columbia and a positive Decision Statement from the Government of Canada. Ksi Lisims said the Certificate is a key regulatory milestone required to proceed with construction, which could begin as early as this year.
  • Calgary-based Enbridge Inc. announced it had reached a final investment decision on two Gas Transmission projects: the Algonquin Reliable Affordable Resilient Enhancement project (AGT Enhancement) in the U.S. Northeast and the Eiger Express Pipeline (Eiger) to serve the U.S. Gulf Coast LNG market. Enbridge said it expects to complete the AGT Enhancement in 2029 and the Eiger in 2028.
  • Vancouver-based Teck Resources Limited and Anglo American plc of the United Kingdom announced they had reached an agreement to combine the two companies in a merger of equals to form the Anglo Teck group, with its global headquarters located in Vancouver. The companies said the merger is expected to close within 12-18 months, subject to shareholder approval and to the completion of conditions customary for a transaction of this nature, including approval under the Investment Canada Act and competition and regulatory approvals in various jurisdictions globally.
  • Toronto-based Barrick Mining Corporation announced it had reached an agreement to sell the Hemlo Gold Mine in Ontario to Carcetti Capital Corp. of Vancouver for gross proceeds of up to USD $1.09 billion. Barrick said the transaction is expected to be completed within the fourth quarter of 2025, subject to satisfaction of customary closing conditions and obtaining required regulatory approvals.
  • Burnaby, British Columbia-based Interfor Corporation announced plans to reduce its lumber production by approximately 145 million board feet between September and December of 2025, representing approximately 12% of its normal operating stance. Interfor said the curtailments are expected to impact all of its operating regions, with both the Canadian and U.S. operations expected to reduce their production levels by approximately 12% each.

Manufacturing

  • Sault Ste. Marie-based Algoma Steel Group Inc. announced the execution of binding term sheets to secure $500 million in liquidity support, comprising $400 million loan facilities from the Government of Canada under the Large Enterprise Tariff Loan facility and $100 million loan facilities from the Province of Ontario. Algoma said the Facilities provide financial flexibility amid prolonged trade uncertainty and position it to advance its ongoing business transformation.
  • Kansas-based INVISTA announced its decision to discontinue production at its site in Maitland, Ontario and relocate production to its Victoria, Texas site over the coming months. INVISTA said approximately 100 roles directly supporting Maitland operations would be impacted.

Minimum wage

  • Nunavut's minimum wage increased from $19.00 to $19.75 per hour on September 1st.
  • The Northwest Territories' minimum wage increased from $16.70 per hour to $16.95 per hour on September 1st.

Other news

  • The Government of Canada announced on September 5th a series of new measures for workers and businesses in those sectors most impacted by U.S. tariffs and trade disruptions, including:
    • a new reskilling package for up to 50,000 workers, make Employment Insurance more flexible, and launch a new digital jobs and training platform;
    • invest $5 billion through a new fund with flexible terms to help firms in all sectors impacted by tariffs;
    • a new Buy Canadian Policy to ensure the federal government buys from Canadian suppliers;
    • expand Business Development Bank of Canada loans for small and medium-sized enterprises (SMEs) to $5 million; provide more flexible financing through the Large Enterprise Tariff Loan Facility; and give the auto sector flexibility by waiving 2026 model year vehicles from Electric Vehicle Availability Standard requirements and by launching an immediate 60-day review to reduce costs;
    • assist Canada's canola and agricultural producers; and
    • expand support to SMEs to $1 billion over three years through the Regional Tariff Response Initiative, with flexible terms, and increase new non-repayable contributions to eligible businesses impacted by tariffs across all affected sectors, including agricultural and seafood.
  • The Government also said it had launched the Major Projects Office (MPO) to fast-track nation-building projects.
  • The Government of Canada announced on September 11th the first series of projects being referred to the Major Projects Office (MPO) for consideration, including:
    • LNG Canada Phase 2 in Kitimat, British Columbia that will double LNG Canada's production of liquified natural gas;
    • Darlington New Nuclear Project in Bowmanville, Ontario that will make Canada the first G7 country to have an operational small modular reactor (SMR);
    • Contrecœur Terminal Container Project in Contrecœur, Québec that will expand the Port of Montreal's capacity by approximately 60%;
    • McIlvenna Bay Foran Copper Mine Project in East-Central Saskatchewan that will supply copper and zinc; and
    • Red Chris Mine expansion in Northwest British Columbia that will extend the lifespan of the mine by over a decade and increase Canada's annual copper production by over 15%.
  • The Government said these projects represent investments of more than $60 billion in the economy.
  • The Government of Canada announced on September 14th it had launched Build Canada Homes – a new federal agency that will build affordable housing at scale. The Government said it would build deeply affordable and community housing for low-income households, and partner with private market developers to build affordable homes for the Canadian middle class.
  • The Government of Canada announced on September 16th that prairie businesses impacted by global trade disruptions could now apply for funding through the Regional Tariff Response Initiative (RTRI).
  • The Bank of Canada lowered its target for the overnight rate by 25 basis points to 2.50%. The last change in the target for the overnight rate was a 25 basis points cut in March 2025.
  • TD Canada Trust, RBC Royal Bank of Canada (RBC), BMO Bank of Montreal, Canadian Imperial Bank of Commerce (CIBC), Scotiabank, and Laurentian Bank of Canada announced they were decreasing their Canadian dollar prime lending rates by 25 basis points from 4.95% to 4.70%, effective September 18th.
  • On September 25th, the Canadian Union of Postal Workers (CUPW) announced that all CUPW members at Canada Post were on a nation-wide strike. Canada Post said that mail and parcels would not be processed or delivered for the duration of the strike, and that some post offices would be closed.
  • Calgary-based WestJet announced an agreement with Boeing for the purchase of 60 737-10 MAX narrowbody aircraft, with options for an additional 25. The company said the order also includes seven 787-9 Dreamliner widebody aircraft with options for four more. WestJet said this order increases its current order book to 123 aircraft and 40 options, while extending its fleet growth plans through 2034.
  • Canada Pension Plan Investment Board (CPP Investments) announced it had entered into a definitive agreement to acquire an approximate 13% indirect equity interest in Sempra Infrastructure Partners of California for approximately USD $3.0 billion, alongside affiliates of New York-based KKR, a global investment firm. CPP Investments said the transaction is expected close in the second to third quarter of 2026, subject to necessary regulatory and other approvals and closing conditions.

United States and other international news

  • The U.S. Federal Open Market Committee (FOMC) lowered the target range for the federal funds rate by 25 basis points to 4.00% to 4.25%. The last change in the target range was a 25 basis points cut in December 2024. The Committee also said that it would continue reducing its holdings of Treasury securities and agency debt and agency mortgage-backed securities.
  • The European Central Bank (ECB) left its three key interest rates unchanged at 2.00% (deposit facility), 2.15% (main refinancing operations), and 2.40% (marginal lending facility). The last change in these rates was a 25 basis points reduction in June 2025.
  • The Bank of England's Monetary Policy Committee (MPC) voted to maintain the Bank Rate at 4.0%. The last change in the Bank Rate was a 25 basis points cut in August 2025.
  • The Monetary Policy and Financial Stability Committee of Norway's Norges Bank reduced the policy rate by 25 basis points to 4.00%. The last change in the policy rate was a 25 basis points decrease in June 2025.
  • The Bank of Japan (BoJ) announced it will encourage the uncollateralized overnight call rate to remain at around 0.50%. The last change in the uncollateralized overnight call rate was a 25 basis points increase in January 2025. The BoJ also said it had decided to sell its holdings of exchange-traded funds (ETFs) and Japan real estate investment  trusts (J-REITs) to the market in accordance with the fundamental principles for their disposal which include the principle to avoid inducing destabilizing effects on the financial markets.
  • The Executive Board of Sweden's Riksbank lowered the repo rate by 25 basis points to 1.75%. The last change in the repo rate was a 25 basis points reduction in June 2025.
  • The Reserve Bank of Australia (RBA) left the cash rate target unchanged at 3.60%. The last change in the cash rate target was a 25 basis points cut in August 2025.
  • The eight participating OPEC+ countries - Saudi Arabia, Russia, Iraq, UAE, Kuwait, Kazakhstan, Algeria, and Oman - announced they would implement a production adjustment of 137 thousand barrels per day from the 1.65 million barrels per day additional voluntary adjustments announced in April 2023. The members said this adjustment would be implemented in October 2025.
  • Pennsylvania and Illinois-based The Kraft Heinz Company announced that its Board of Directors had unanimously approved a plan to separate the Company into two independent, publicly traded companies. Kraft Heinz said it expects the transaction to close in the second half of 2026.
  • Washington State-based Starbucks announced that overall company-operated coffeehouses in North America would decline by about 1% in fiscal year 2025 after accounting for both openings and closures. Starbucks also said it would eliminate approximately 900 current non-retail partner roles and close many open positions.
  • California-based Electronic Arts Inc. (EA) announced it had entered into a definitive agreement to be acquired by an investor consortium comprised of PIF of Saudi Arabia, Silver Lake of California, and Affinity Partners of Florida in an all-cash transaction that values EA at an enterprise value of approximately $55 billion. EA said the transaction is expected to close in the first quarter of fiscal year 2027, subject to customary closing conditions, including receipt of required regulatory approvals and approval by EA stockholders.
  • Germany-based Bosch announced a reduction of around 13,000 jobs, particularly at its Mobility locations in Germany. Bosch said the time frames for the necessary adjustments vary and extend until the end of 2030.

Financial market news

  • West Texas Intermediate crude oil closed at USD $62.37 per barrel on September 30th, down from a closing value of USD $64.01 at the end of August. Western Canadian Select crude oil traded in the USD $49.00 to $54.00 per barrel range throughout September. The Canadian dollar closed at 71.83 cents U.S. on September 29th, down from 72.77 cents U.S. at the end of August. The S&P/TSX composite index closed at 30,022.81 on September 30th, up from 28,564.45 at the end of August.

Canadian Housing Statistics Program (CHSP) – Reference years of the property stock and assessment values, by province and territory, 2023

Canadian Housing Statistics Program (CHSP) – Reference years of the property stock and assessment values, by province and territory, 2023Footnotes 1

Reference years of the property stock and assessment values, by province and territory – 2023
Province/Territory CHSP reference year Property stock date Assessment value year
Prince Edward Island 2023 January 2023 2023
Newfoundland and Labrador: St. John's, City – census subdivision (CSD) 2023 January 2023 2020
Newfoundland and Labrador: Outside St. John's, City 2023 January 2023 2021
Nova Scotia 2023 December 2022 2022
New Brunswick 2023 January 2023 2022
Ontario 2023 January 2023 2016
Manitoba: Outside WinnipegFootnotes 2 2023 January 2023 2021
Manitoba: Winnipeg - census subdivision (CSD) 2023 January 2023 2018
Saskatchewan: Flin Flon - census subdivision (CSD) 2023 January 2023 2021
Saskatchewan: Lloydminster - census subdivision (CSD) 2023 January 2023 2022
Saskatchewan: Outside Lloydminster and Flin Flon - census subdivisions (CSD) 2023 January 2023 2019
Alberta 2023 January 2023 2022
British Columbia 2023 October 2022 2022
YukonFootnotes 2: Whitehorse - census agglomeration (CA) 2023 November 2022 2021
Yukon: Outside census agglomeration (CA) 2023 November 2022 2022
Northwest Territories: Yellowknife - census agglomeration (CA) 2023 October 2022 2017
Nunavut: Iqaluit - census subdivision (CSD) 2023 October 2022 2012
Nunavut: Outside of Iqaluit - census subdivision (CSD) 2023 October 2022 2011

Footnotes

Footnote 1

Canadian Housing Statistics Program (CHSP) – Reference years of the property stock and assessment values, by province and territory, 2022

Return to footnote 1 referrer

Footnote 2

Assessment boundaries and Statistics Canada Standard Geographical Classification (SGC) boundaries generally correspond in Manitoba and Yukon, although not perfectly.

Return to footnote 2 referrer

Monthly Survey of Manufacturing: National Level CVs by Characteristic - August 2025

National Level CVs by Characteristic, August 2025
Table summary
This table displays the results of Monthly Survey of Manufacturing: National Level CVs by Characteristic. The information is grouped by Month (appearing as row headers), and Sales of goods manufactured, Raw materials and components inventories, Goods / work in process inventories, Finished goods manufactured inventories and Unfilled Orders, calculated in percentage (appearing as column headers).
Month Sales of goods manufactured Raw materials and components inventories Goods / work in process inventories Finished goods manufactured inventories Unfilled Orders
%
August 2024 0.70 1.10 1.86 1.23 1.56
September 2024 0.73 1.12 1.95 1.30 1.53
October 2024 0.76 1.11 1.87 1.25 1.52
November 2024 0.70 1.11 1.81 1.25 1.64
December 2024 0.63 1.06 1.89 1.26 1.45
January 2025 0.67 1.11 1.71 1.25 1.45
February 2025 0.72 1.14 1.85 1.33 1.46
March 2025 0.72 1.18 1.77 1.38 1.49
April 2025 0.75 1.16 1.78 1.41 1.52
May 2025 0.78 1.20 1.87 1.45 1.51
June 2025 0.81 1.20 1.77 1.43 1.43
July 2025 0.74 1.21 1.82 1.41 1.46
August 2025 0.75 1.24 1.84 1.37 1.46

Employment Insurance Coverage Survey: CVs for eligibility of the unemployed for employment insurance benefits, by province - 2024

Employment Insurance Coverage Survey: CVs for eligibility of the unemployed for employment insurance benefits, by province - 2024
Province Eligibility Rate Coefficient of Variation (C.V.)
Percentage (%)
Canada 83.1 2.3
Newfoundland 98.9 1.4
Prince Edward IslandFootnotes 1 100 0.0
Nova Scotia 85.3 7.5
New Brunswick 89.6 6.3
Quebec 83.4 5.3
Ontario 80.3 4.2
Manitoba 74.4 11.5
Saskatchewan 72.4 11.6
Alberta 85.8 6.0
British Columbia 85.3 6.5

Footnotes

Footnote 1

The 100% eligibility rate observed in Prince Edward Island is based on a small survey sample in which all respondents reported being eligible for regular Employment Insurance (EI) benefits. This result is not necessarily representative of all EI contributors in the province.

Return to footnote 1 referrer

Retail Commodity Survey: CVs for Total Sales (July 2025)

Retail Commodity Survey: CVs for Total Sales (July 2025)
Table summary
This table displays the results of Retail Commodity Survey: CVs for Total Sales (July 2025). The information is grouped by NAPCS-CANADA (appearing as row headers), and Month (appearing as column headers).
NAPCS-CANADA Month
202504 202505 202506 202507
Total commodities, retail trade commissions and miscellaneous services 0.60 0.53 0.54 0.57
Retail Services (except commissions) [561] 0.59 0.53 0.53 0.57
Food and beverages at retail [56111] 0.44 0.38 0.33 0.34
Cannabis products, at retail [56113] 0.00 0.00 0.00 0.00
Clothing at retail [56121] 0.57 0.81 0.55 0.63
Jewellery and watches, luggage and briefcases, at retail [56123] 1.87 `2.47 2.12 1.95
Footwear at retail [56124] 1.29 1.31 1.13 1.09
Home furniture, furnishings, housewares, appliances and electronics, at retail [56131] 0.88 0.94 0.79 0.76
Sporting and leisure products (except publications, audio and video recordings, and game software), at retail [56141] 2.58 2.50 2.16 3.03
Publications at retail [56142] 7.94 8.32 8.65 8.47
Audio and video recordings, and game software, at retail [56143] 4.30 3.31 3.05 4.06
Motor vehicles at retail [56151] 1.81 1.72 1.84 1.92
Recreational vehicles at retail [56152] 4.04 3.75 3.15 3.56
Motor vehicle parts, accessories and supplies, at retail [56153] 1.32 1.36 1.35 1.26
Automotive and household fuels, at retail [56161] 1.45 1.38 1.37 1.35
Home health products at retail [56171] 2.94 2.55 2.68 3.00
Infant care, personal and beauty products, at retail [56172] 2.47 2.59 2.57 2.61
Hardware, tools, renovation and lawn and garden products, at retail [56181] 1.82 1.70 2.04 1.98
Miscellaneous products at retail [56191] 2.73 3.93 3.14 2.61
Retail trade commissions [562] 1.83 1.60 1.63 1.55
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