Get data on Ontario gross domestic product (GDP) by manufacturing industries.

The GDP is a monetary measure of the value of all final goods and services produced in a period.

This dataset shows Ontario’s GDP by manufacturing industries, including:

• food, beverage and tobacco
• chemical and petroleum products
• primary metal products
• fabricated metal products
• machinery
• electrical and electronic products
• transportation equipment
• paper products and printing
• plastic and rubber products
• other manufacturing

This statistic shows the gross domestic product (GDP) of Ontario in 2022, by industry. In 2022, the GDP of the construction industry in Ontario was 57.4 billion chained 2012 Canadian dollars.

Get data on the Ontario gross domestic product (GDP) for major agricultural industries in Ontario. The GDP is a monetary measure of the value of all the goods and services produced in a period.

Output by industry, in current dollars, evaluated at basic price for all provinces and territories. These estimates are derived from the provincial Supply and Use Tables.

Get data on the Ontario gross domestic product (GDP) for major agricultural industries in Ontario. The GDP is a monetary measure of the value of all the goods and services produced in a period.

Number of employees by North American Industry Classification System (NAICS) and type of employee, last 5 years.

The Advanced Manufacturing Investment Strategy focused on manufacturing companies that were investing in leading edge technologies and processes to increase their productivity and competitiveness in Ontario. Projects must have had a minimum total project value of $10 million or create/retain 50 or more high value jobs within 5 years. Ontario's Advanced Manufacturing Investment Strategy is no longer accepting applications, but has been very successful to date in meeting its objectives. This data set contains a list of recipients of Advanced Manufacturing Investment Strategy from 2006 to 2012. This list includes the following details: * funding program * name of company * location * fiscal year contract signed * government loan commitment * total project jobs created and retained as in the contract.

The Canadian metal fabrication equipment market, valued at approximately $1.5 billion CAD in 2025, is projected to experience robust growth, exceeding a 6% Compound Annual Growth Rate (CAGR) through 2033. This expansion is driven by several key factors. Firstly, a surge in construction activity, fueled by both residential and commercial projects, significantly boosts demand for metal fabrication equipment. Secondly, the burgeoning oil and gas sector, alongside increasing investments in renewable energy infrastructure, further fuels market growth. The manufacturing sector, a consistent driver of demand, continues to modernize and adopt automation technologies, increasing reliance on advanced metal fabrication equipment like automatic and semi-automatic systems. Finally, government initiatives promoting infrastructure development and industrial growth contribute positively to market expansion. However, several factors could potentially restrain market growth. Fluctuations in commodity prices, particularly steel and aluminum, directly impact production costs and equipment demand. Additionally, increasing labor costs and a skilled labor shortage could pose challenges. The market is segmented by service type (machining and cutting, forming, welding, others), product type (automatic, semi-automatic, manual), and end-user industry (manufacturing, power and utilities, construction, oil and gas, others). Key players, including BTD Manufacturing, Colfax, Komaspec, Matcor Matsu Group Inc, Sandvik Mining and Construction Canada Inc, STANDARD IRON & WIRE WORKS INC, TRUMPF Canada Inc, Atlas Copco, AMADA Canada, and DMG MORI Canada, are actively shaping market dynamics through innovation and technological advancements. This competitive landscape is expected to further accelerate the adoption of advanced equipment, driving overall market growth. This comprehensive report provides a detailed analysis of the Canada metal fabrication equipment industry, covering the period from 2019 to 2033. With a base year of 2025 and an estimated year of 2025, the report offers valuable insights into market size, growth drivers, challenges, and future trends. This in-depth study uses historical data from 2019-2024 and projects the market's trajectory until 2033, providing a crucial resource for businesses, investors, and policymakers navigating this dynamic sector. Key search terms include: Canadian metal fabrication, metal fabrication equipment market Canada, Canadian metalworking machinery, industrial automation Canada, metal forming equipment Canada. Recent developments include: February 2022: Arrow Machine and Fabrication Group of Guelph, Ontario, announced the acquisition of Steelcraft, a Kitchener, Ontario, steel design, engineering, and fabrication firm. This acquisition expands Arrow's global customer base and manufacturing footprint. It also further promotes the company's strategy of partnering with leading operator-run machining and fabrication organizations to leverage their collective capabilities, solve customer problems, and develop deeper supply chain interactions., January 2022: Ag Growth International Inc. (AGI) completed the acquisition of Eastern Fabricators, Prince Edward Island, Canada. Eastern specializes in the engineering, design, fabrication, and installation of stainless-steel equipment and systems for food processors. Eastern operates three facilities in Canada, with two in Prince Edward Island and one in Ontario.. Notable trends are: Construction Industry Offers Immense Demand for the Metal Fabrication Equipment.

Number of persons in the labour force (employment and unemployment) and unemployment rate, by North American Industry Classification System (NAICS), gender and age group.

The City of Barrie conducted an extensive Business & Employer Data Survey. The business & employer data collected will give better insight into the City’s economic landscape and will help the City make more informed decisions that better support our businesses and residents. This data will allow the City to better monitor existing industry classifications, business sectors, business longevity and employment trends.Why is this survey important?The survey is important as it provides the City of Barrie with comprehensive business data, including:business name;location;employment numbers (both full-time and part-time); andindustry classifications.By collecting this data year to year, the City will be able to:monitor industry trends and levels of employment;inform other City initiatives, such as a review of infrastructure and servicing requirements, and transit planning; anddevelop a business directory of all local businesses.

Get statistical data on the economic impact of Ontario’s agri-food value chain by key commodity sectors. The data shows the impact on Ontario’s gross domestic product and employment numbers. This economic information represents totals for the entire agri-food commodity value chains: * farm * food, beverage, and tobacco manufacturing * retail Statistical data are compiled to serve as a source of agriculture and food statistics for the province of Ontario. Data are prepared primarily by Statistics and Economics staff of the Ministry of Agriculture, Food and Rural Affairs, in co-operation with the Agriculture Division of Statistics Canada and various government departments and farm marketing boards.

Ontario Media Development Corporation (OMDC), an agency of the Ministry of Tourism, Culture and Sport is the central catalyst for the province’s cultural media cluster including book publishing, film and television, interactive digital media, magazine publishing and music industries. OMDC promotes, enhances and leverages investment, jobs, and original content creation by: (a) contributing to the continued expansion of a business environment in Ontario that is advantageous to the growth of the cultural media industry and to the growth of new employment, investment and production opportunities in Ontario; (b) facilitating and supporting innovation, invention and excellence in Ontario ’s cultural media industry by stimulating creative production, format innovation and new models of collaboration among sectors of the cultural media industry; (c) fostering and facilitating co-operation among entities within the cultural media industry and between the public and private sectors to stimulate synergies in product development and the creation of products with original Canadian content; (d) assisting in the promotion and marketing of Ontario’s cultural media industry as a world-class leader; (e) administering provincial tax credit programs and such other programs and initiatives as may be required by legislation or a Minister of the Government of Ontario; and (f) acting as a catalyst for information, research and technological development in the cultural media industry provincially, nationally and internationally. To help Ontario be recognized as a leading global jurisdiction to invest in, create, produce and enjoy original cultural media product. “Culture is our Business”

To ensure respondent confidentiality, estimates below a certain threshold are suppressed. For Canada, Quebec, Ontario, Alberta and British Columbia suppression is applied to all data below 1,500. The threshold level for Newfoundland and Labrador, Nova Scotia, New Brunswick, Manitoba and Saskatchewan is 500, while in Prince Edward Island, estimates under 200 are suppressed. For census metropolitan areas (CMAs) and economic regions (ERs), use their respective provincial suppression levels mentioned above. Estimates are based on smaller sample sizes the more detailed the table becomes, which could result in lower data quality. Fluctuations in economic time series are caused by seasonal, cyclical and irregular movements. A seasonally adjusted series is one from which seasonal movements have been eliminated. Seasonal movements are defined as those which are caused by regular annual events such as climate, holidays, vacation periods and cycles related to crops, production and retail sales associated with Christmas and Easter. It should be noted that the seasonally adjusted series contain irregular as well as longer-term cyclical fluctuations. The seasonal adjustment program is a complicated computer program which differentiates between these seasonal, cyclical and irregular movements in a series over a number of years and, on the basis of past movements, estimates appropriate seasonal factors for current data. On an annual basis, the historic series of seasonally adjusted data are revised in light of the most recent information on changes in seasonality. Number of civilian, non-institutionalized persons 15 years of age and over who, during the reference week, were employed or unemployed. Estimates in thousands, rounded to the nearest hundred. Number of persons who, during the reference week, worked for pay or profit, or performed unpaid family work or had a job but were not at work due to own illness or disability, personal or family responsibilities, labour dispute, vacation, or other reason. Those persons on layoff and persons without work but who had a job to start at a definite date in the future are not considered employed. Estimates in thousands, rounded to the nearest hundred. Number of persons who, during the reference week, were without work, had looked for work in the past four weeks, and were available for work. Those persons on layoff or who had a new job to start in four weeks or less are considered unemployed. Estimates in thousands, rounded to the nearest hundred. The unemployment rate is the number of unemployed persons expressed as a percentage of the labour force. The unemployment rate for a particular group (age, gender, marital status, etc.) is the number unemployed in that group expressed as a percentage of the labour force for that group. Estimates are percentages, rounded to the nearest tenth. Industry refers to the general nature of the business carried out by the employer for whom the respondent works (main job only). Industry estimates in this table are based on the 2022 North American Industry Classification System (NAICS). Formerly Management of companies and administrative and other support services"." This combines the North American Industry Classification System (NAICS) codes 11 to 91. This combines the North American Industry Classification System (NAICS) codes 11 to 33. This combines the North American Industry Classification System (NAICS) codes 41 to 91. Unemployed persons who have never worked before, and those unemployed persons who last worked more than 1 year ago. For more information on seasonal adjustment see Seasonally adjusted data - Frequently asked questions." Labour Force Survey (LFS) North American Industry Classification System (NAICS) code exception: add group 1100 - Farming - not elsewhere classified (nec). When the type of farm activity cannot be distinguished between crop and livestock, (for example: mixed farming). Labour Force Survey (LFS) North American Industry Classification System (NAICS) code exception: add group 2100 - Mining - not elsewhere classified (nec). Whenever the type of mining activity cannot be distinguished. Also referred to as Natural resources. The standard error (SE) of an estimate is an indicator of the variability associated with this estimate, as the estimate is based on a sample rather than the entire population. The SE can be used to construct confidence intervals and calculate coefficients of variation (CVs). The confidence interval can be built by adding the SE to an estimate in order to determine the upper limit of this interval, and by subtracting the same amount from the estimate to determine the lower limit. The CV can be calculated by dividing the SE by the estimate. See Section 7 of the Guide to the Labour Force Survey (opens new window) for more information. The standard errors presented in this table are the average of the standard errors for 12 previous months The standard error (SE) for the month-to-month change is an indicator of the variability associated with the estimate of the change between two consecutive months, because each monthly estimate is based on a sample rather than the entire population. To construct confidence intervals, the SE is added to an estimate in order to determine the upper limit of this interval, and then subtracted from the estimate to determine the lower limit. Using this method, the true value will fall within one SE of the estimate approximately 68% of the time, and within two standard errors approximately 95% of the time. For example, if the estimated employment level increases by 20,000 from one month to another and the associated SE is 29,000, the true value of the employment change has a 68% chance of falling between -9,000 and +49,000. Because such a confidence interval includes zero, the 20,000 change would not be considered statistically significant. However, if the increase is 30,000, the confidence interval would be +1,000 to +59,000, and the 30,000 increase would be considered statistically significant. (Note that 30,000 is above the SE of 29,000, and that the confidence interval does not include zero.) Similarly, if the estimated employment declines by 30,000, then the true value of the decline would fall between -59,000 and -1,000. See Section 7 of the Guide to the Labour Force Survey (opens new window) for more information. The standard errors presented in this table are the average of standard errors for 12 previous months. They are updated twice a year The standard error (SE) for the year-over-year change is an indicator of the variability associated with the estimate of the change between a given month in a given year and the same month of the previous year, because each month's estimate is based on a sample rather than the entire population. The SE can be used to construct confidence intervals: it can be added to an estimate in order to determine the upper limit of this interval, and then subtracted from the same estimate to determine the lower limit. Using this method, the true value will fall within one SE of the estimate, approximately 68% of the time, and within two standard errors, approximately 95% of the time. For example, if the estimated employment level increases by 160,000 over 12 months and the associated SE is 55,000, the true value of the change in employment has approximately a 68% chance of falling between +105,000 and +215,000. This change would be considered statistically significant at the 68% level as the confidence interval excludes zero. However, if the increase is 40,000, the interval would be -15,000 to +95,000, and this increase would not be considered statistically significant since the interval includes zero. See Section 7 of the Guide to the Labour Force Survey (opens new window) for more information. The standard errors presented in this table are the average of standard errors for 12 previous months and are updated twice a year Excluding the territories. Starting in 2006, enhancements to the Labour Force Survey data processing system may have introduced a level shift in some estimates, particularly for less common labour force characteristics. Use caution when comparing estimates before and after 2006. For more information, contact statcan.labour-travail.statcan@statcan.gc.ca

Get statistical data for rural and urban Ontario on key socioeconomic variables.

The data identifies:

• demographic information for rural and urban Ontario by census year (population change and age breakdown, immigrants, visible minorities, educational attainment, average household income)
• economic indicators for rural and urban Ontario (monthly and annual employment by industry, monthly and annual labour force characteristics, number of businesses by industries and employee number)
• rural and urban Ontario census profiles (for all census of population variables starting with 2001)

Find more resources with socioeconomic data and information about Rural Ontario

Number of employees by North American Industry Classification System (NAICS) and data type (seasonally adjusted, trend-cycle and unadjusted), last 5 months. Data are also available for the standard error of the estimate, the standard error of the month-to-month change and the standard error of the year-over-year change.

Annual Provincial and Territorial Gross Domestic Product (GDP) at basic prices, by North American Industry Classification aggregates, in chained and current dollars, growth rate.

The Semiconductor and other electronic component manufacturing industry in Canada is experiencing a dynamic period influenced by regional trends and global shifts. Over the past several years, geopolitical forces, such as the tensions between the US and China, have significantly restructured trade lanes. The pressure to prioritize domestic manufacturing has aligned well with Canada's resources however, allowing local firms to maintain profitability despite cheaper labor markets abroad. Canadian semiconductor and electronic component production lags behind global competition, however, and the need for government investment is increasing. A lack of government support has limited industry revenue growth, which has fallen at a CAGR of 0.6% from 2019 to 2024, when it reached $5.4 billion. Despite declines in revenue, Canada's affordable utility costs, particularly in provinces like Quebec and Ontario, sustain a viable manufacturing environment and help elevate profit. Also, immigration assistance and talent support for skilled engineers show a proactive stance toward retaining critical human resources necessary for ongoing innovation and growth. Though government initiatives do not resemble the level of investment included in the CHIPS act, funding for microchip development has boosted the sector, leading to industry revenue increasing 6.3% during 2024. These investments are essential in keeping pace with technological advancements and addressing rising competition, especially from the US and Asian nations. Moving forward, the industry must adeptly navigate international trade conflicts while leveraging AI technology to take advantage of favorable demand conditions. This measured approach will enable Canadian firms to enhance their global competitiveness and secure steady growth in the evolving semiconductor landscape. From 2024 to 2029, industry revenue will increase at a CAGR of 2.7% when revenue will hit $6.1 billion.

In 2023, the city of Toronto, in the Canadian province of Ontario had a workforce of over *** million. The industry that employed the most people was wholesale and retail trade, with over ******* people employed, followed by professional, scientific, and technical services. The industries that employed the fewest people were agriculture, forestry, fishing, mining, quarrying, and oil and gas extraction.

Racism and industrial development across lands and waters in the province of Ontario have played a significant role in decreased access to traditional food for Indigenous peoples. Traditional food access is important for health reasons, as well as cultural and spiritual wellness, and its loss has dire consequences for both people and the environment. In this commentary, we bring together our practices and experiences as settler Canadians in the fields of environmental law and environmental studies to share three short case studies exploring the linkages among traditional food access, racism, and industrial development. Specifically, we discuss how the aerial spraying of forests, mining exploration, and contaminants in fish are impacting traditional food access, and analyze how industry and monetary gains are drivers in these scenarios. For each of these case studies, we provide examples of research and advocacy from our respective fields carried out with Indigenous communities. We conclude by offering our insights for addressing systemic racism in food systems, focusing on a need for policy to prioritize Indigenous sovereignty and rights and opportunities for collaboration spanning different areas of practice and Western and Indigenous knowledge systems.Le racisme et le développement industriel sur les terres et dans les eaux de l’Ontario ont joué un rôle considérable dans la diminution de l’accès à la nourriture traditionnelle pour les peuples autochtones. Or, cet accès est important pour des raisons de santé de même que pour le bien-être culturel et spirituel; cette perte a des conséquences désastreuses pour les personnes et pour l’environnement. Dans ce texte, nous réunissons nos expériences et nos pratiques en tant que personnes canadiennes allochtones dans les champs du droit de l’environnement et des études environnementales pour partager trois courtes études de cas qui explorent les liens entre l’accès à la nourriture traditionnelle, le racisme et le développement industriel. Plus précisément, nous discutons les répercussions de la pulvérisation aérienne des forêts, de l’exploration minière et de la contamination des poissons sur l’accès à la nourriture traditionnelle, et nous analysons comment l’industrie et l’appât du gain sont en cause dans ces scénarios. Pour chacune de ces études de cas, nous fournissons des exemples de recherche et d’argumentation menées avec des communautés autochtones dans nos champs respectifs. Nous concluons avec nos suggestions sur la manière de s’attaquer au racisme systémique dans les systèmes alimentaires, en se concentrant sur la nécessité d’instaurer des politiques qui mettent de l’avant la souveraineté et les droits des Autochtones, en plus de susciter la collaboration entre différents domaines de pratique et entre les systèmes de savoir occidentaux et autochtones.

Counts of Entrants, Incumbents, and Exits by North American Industry Classification System, for each province and territory from the Longitudinal Employment Analysis Program.