This statistic shows the distribution of the gross domestic product (GDP) of British Columbia, Canada in 2022, by industry. In that year, the construction industry accounted for 9.92 percent of the GDP of British Columbia.

This statistic shows the gross domestic product (GDP) of British Columbia in 2022, by industry. In 2022, the GDP of the construction industry in British Columbia was around 28 billion chained 2017 Canadian dollars.

The gross domestic product of all industries in British Columbia amounted to 230.07 billion U.S. dollars in 2023. Between 1997 and 2023, the gross domestic product rose by 115.33 billion U.S. dollars, though the increase followed an uneven trajectory rather than a consistent upward trend.

Census data showed employment reached an estimated 16 021 200 in 2006, up 1 326 000 from 2001. Just two western provinces - Alberta and British Columbia - accounted for a third of this increase. During the same five-year period, the unemployment rate fell in every province and territory, except Ontario and the Northwest Territories. The shift in industrial demand for workers to different parts of the economy had an impact on the occupational make-up of the nation. The map shows by census division the percentage of the population employed in primary industry.

The dataset includes the number of primary agricultural establishments and food and beverage manufacturing establishments in British Columbia by product category and Regional District for 2022. Category classification is based on the North American Industry Classification System (NAICS). To learn more about NAICS, visit North American Industry Classification System (NAICS) Canada 2022. The Standard Geographical Classification (SGC) is Statistic’s Canada’s official classification for geographic areas in Canada. The Census Divisions in SGC align with BC’s provincially legislated Regional Districts.

Economic Forecasts, including 2011 actual values, 2012 estimated values, and 2013 through 2017 forecast values: Major Economic Assumptions for British Columbia; GDP, Industrial Production, Housing Starts, Consumer Price Index, Cdn interest rates, US interest rates and exchange rates, Export Price Deflator

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

Economic Forecasts, including 2011 actual values, 2012 estimated values, and 2013 through 2017 forecast values: Major Economic Assumptions for British Columbia; GDP, Industrial Production, Housing Starts, Consumer Price Index, Cdn interest rates, US interest rates and exchange rates, Export Price Deflator

Background The purpose of this study was to investigate the impact of a 20-year process of de-industrialization in the British Columbia (BC) sawmill industry on labour force trajectories, unemployment history, and physical and psychosocial work conditions as these are important determinants of health in workforces.

   Methods
   The study is based on a sample of 1,885 respondents all of whom were sawmill workers in 1979, a year prior to commencement of de-industrialization and who were followed up and interviewed approximately 20 years later.


   Results
   Forty percent of workers, 64 years and under, were employed outside the sawmill sector at time of interview. Approximately one third of workers, aged 64 and under, experienced 25 months of more of unemployment during the study period. Only, 1.5% of workers were identified as a "hard core" group of long-term unemployed. Workers re-employed outside the sawmill sector experienced improved physical and psychosocial work conditions relative to those employed in sawmills during the study period. This benefit was greatest for workers originally in unskilled and semi-skilled jobs in sawmills.


   Conclusions
   This study shows that future health studies should pay particular attention to long-term employees in manufacturing who may have gone through de-industrialization resulting in exposures to a combination of sustained job insecurity, cyclical unemployment, and adverse physical and psychosocial work conditions.

Economic Forecasts, including 2011 actual values, 2012 estimated values, and 2013 through 2017 forecast values: Major Economic Assumptions for British Columbia; GDP, Industrial Production, Housing Starts, Consumer Price Index, Cdn interest rates, US interest rates and exchange rates, Export Price Deflator

Economic Forecasts, including 2011 actual values, 2012 estimated values, and 2013 through 2017 forecast values: Major Economic Assumptions for British Columbia; GDP, Industrial Production, Housing Starts, Consumer Price Index, Cdn interest rates, US interest rates and exchange rates, Export Price Deflator

This statistic shows the Gross Domestic Product (GDP) of Canada in June 2025, distinguished by major industry. In June 2025, the construction industry of Canada contributed about 167.5 Canadian dollars to the total Canadian GDP.

The Canada Fruit & Vegetables Industry size was valued at USD 13.70 Million in 2023 and is projected to reach USD 16.86 Million by 2032, exhibiting a CAGR of 3.01 % during the forecasts periods. This surge is driven by several factors, including the burgeoning use of hybrid seeds, government initiatives prioritizing sustainable agriculture, and increasing awareness of food security. Hybrid seeds offer superior yields, disease resistance, and adaptability to varying climatic conditions, making them attractive to farmers. The government's support through funding and research grants further fuels industry growth. Moreover, rising consumer demand for fresh and healthy produce, coupled with advanced technology in farming practices, contributes to the industry's expansion. Key players in the hybrid seed market include Syngenta, Bayer, and Limagrain. Recent developments include: December 2022: CMA CGM has announced the launch of MOCANA, its new seasonal offer dedicated to exporting fruit and vegetables from the US East Coast & Canada during the fruit and vegetable season., June 2022: To boost exports and the economy, the Minister of Agriculture and Agri-Food announced an investment of more than USD 700,000 for four initiatives in British Columbia's fruit industry. These initiatives, supported by money from the federal AgriMarketing and AgriScience Programs, are said to aid fruit growers in becoming more competitive and boost their sales in significant export markets. The projects include the British Columbia Blueberry Council, the BC Cherry Association, the British Columbia Fruit Growers' Association, and the British Columbia Fruit Growers' Association., February 2022: The USD 33 million Homegrown Innovation Challenge was unveiled by the Weston Family Foundation in an effort to inspire fresh thinking and ignite innovative solutions for increasing Canada's sustainable production of fruits and vegetables. The main objective of the Challenge is to find solutions that will allow domestic food producers to grow berries out of season in a competitive, sustainable, and large-scale manner.. Key drivers for this market are: , Awareness About Health Benefits Associated With Pecan Consumption; Wide Application of Pecan. Potential restraints include: , Volatility in the Prices; Adverse Weather Conditions Affecting Yield. Notable trends are: Increased demand for fruits and vegetables.

The B.C. Economic Accounts (BCEA) translate information on income, savings and key economic processes – such as production, capital formation and consumption – into a consistent system of statistics that can be used to explain the functioning of the economy. In addition, the BCEA contain important data on price changes and the relative growth of various industries over time. Gross Domestic Product (GDP) is a measure of the value added to the economy by current production and is one of the components of the B.C. Economic Accounts. Adapted from Statistics Canada, Provincial and Territorial Gross Domestic Product by Income and by Expenditure Accounts, November 2025. This does not constitute an endorsement by Statistics Canada of this product. Adapted from Statistics Canada, Annual Demographic Estimates: Canada, Provinces and Territories, November 2025. This does not constitute an endorsement by Statistics Canada of this product. Adapted from Statistics Canada, Gross Domestic Product by Industry - Provincial and Territorial (Annual), November 2025. This does not constitute an endorsement by Statistics Canada of this product.

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

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

This map shows how commercial activity is distributed within urban areas and the impact of commercial services on the urban landscape, by mapping what proportion of stores (hence jobs) in an urban area that are found in industrial zones. Industrial zones are extensive areas zoned for industrial use that nowadays are home to wholesalers, big-box retailers and a variety of services and small office buildings. These are specialized destinations, often oriented to other businesses; not the kinds of places you stumble upon by accident. As the most recent form of commercial concentration, they are most often found in rapidly growing cities, especially the largest cities. Since industrial zones support a wide range of specialized activities they usually benefit from commercial specialization as indicated by the index of centrality. The distribution indicates that cities in Ontario and the Prairies have higher values than cities in Quebec, the Atlantic region and British Columbia.

(StatCan Product) Employed by industries and sectors (NAICS 2007 – 1, 2, 3 and 4 digits) for Canada, selected provinces (QC, ON, AB and BC), Edmonton (CMA) and Calgary (CMA) (annual averages). Customization details: This information product has been customized to present information on the employed by industries: - TABLE 1: Employed by industries (NAICS 2007 – 1, 2, 3 and 4 digits) for Canada, selected provinces (Quebec, Ontario, Alberta and British Columbia) and the Alberta Census Metropolitan Areas (CMA) of Edmonton and Calgary – Annual Averages from 2001 to 2011 (in thousands). Labour Force Survey The Canadian Labour Force Survey was developed following the Second World War to satisfy a need for reliable and timely data on the labour market. Information was urgently required on the massive labour market changes involved in the transition from a war to a peace-time economy. The main objective of the LFS is to divide the working-age population into three mutually exclusive classifications - employed, unemployed, and not in the labour force - and to provide descriptive and explanatory data on each of these. Target population The LFS covers the civilian, non-institutionalized population 15 years of age and over. It is conducted nationwide, in both the provinces and the territories. Excluded from the survey's coverage are: persons living on reserves and other Aboriginal settlements in the provinces; full-time members of the Canadian Armed Forces and the institutionalized population. These groups together represent an exclusion of less than 2% of the Canadian population aged 15 and over. National Labour Force Survey estimates are derived using the results of the LFS in the provinces. Territorial LFS results are not included in the national estimates, but are published separately. Instrument design The current LFS questionnaire was introduced in 1997. At that time, significant changes were made to the questionnaire in order to address existing data gaps, improve data quality and make more use of the power of Computer Assisted Interviewing (CAI). The changes incorporated included the addition of many new questions. For example, questions were added to collect information about wage rates, union status, job permanency and workplace size for the main job of currently employed employees. Other additions included new questions to collect information about hirings and separations, and expanded response category lists that split existing codes into more detailed categories. Sampling This is a sample survey with a cross-sectional design. Data sources Responding to this survey is mandatory. Data are collected directly from survey respondents. Data collection for the LFS is carried out each month during the week following the LFS reference week. The reference week is normally the week containing the 15th day of the month. LFS interviews are conducted by telephone by interviewers working out of a regional office CATI (Computer Assisted Telephone Interviews) site or by personal visit from a field interviewer. Since 2004, dwellings new to the sample in urban areas are contacted by telephone if the telephone number is available from administrative files, otherwise the dwelling is contacted by a field interviewer. The interviewer first obtains socio-demographic information for each household member and then obtains labour force information for all members aged 15 and over who are not members of the regular armed forces. The majority of subsequent interviews are conducted by telephone. In subsequent monthly interviews the interviewer confirms the socio-demographic information collected in the first month and collects the labour force information for the current month. Persons aged 70 and over are not asked the labour force questions in subsequent interviews, but rather their labour force information is carried over from their first interview. In each dwelling, information about all household members is usually obtained from one knowledgeable household member. Such 'proxy' reporting, which accounts for approximately 65% of the information collected, is used to avoid the high cost and extended time requirements that would be involved in repeat visits or calls necessary to obtain information directly from each respondent. Error detection The LFS CAI questionnaire incorporates many features that serve to maximize the quality of the data collected. There are many edits built into the CAI questionnaire to compare the entered data against unusual values, as well as to check for logical inconsistencies. Whenever an edit fails, the interviewer is prompted to correct the information (with the help of the respondent when necessary). For most edit failures the interviewer has the ability to override the edit failure if they cannot resolve the apparent discrepancy. As well, for most questions the interviewer has the ability to enter a response of Don't Know or Refused if the respondent does not answer the question. Once the data is received back at head office an extensive series of processing steps is undertaken to thoroughly verify each record received. This includes the coding of industry and occupation information and the review of interviewer entered notes. The editing and imputation phases of processing involve the identification of logically inconsistent or missing information items, and the correction of such conditions. Since the true value of each entry on the questionnaire is not known, the identification of errors can be done only through recognition of obvious inconsistencies (for example, a 15 year-old respondent who is recorded as having last worked in 1940). Estimation The final step in the processing of LFS data is the assignment of a weight to each individual record. This process involves several steps. Each record has an initial weight that corresponds to the inverse of the probability of selection. Adjustments are made to this weight to account for non-response that cannot be handled through imputation. In the final weighting step all of the record weights are adjusted so that the aggregate totals will match with independently derived population estimates for various age-sex groups by province and major sub-provincial areas. One feature of the LFS weighting process is that all individuals within a dwelling are assigned the same weight. In January 2000, the LFS introduced a new estimation method called Regression Composite Estimation. This new method was used to re-base all historical LFS data. It is described in the research paper ""Improvements to the Labour Force Survey (LFS)"", Catalogue no. 71F0031X. Additional improvements are introduced over time; they are described in different issues of the same publication. Data accuracy Since the LFS is a sample survey, all LFS estimates are subject to both sampling error and non-sampling errors. Non-sampling errors can arise at any stage of the collection and processing of the survey data. These include coverage errors, non-response errors, response errors, interviewer errors, coding errors and other types of processing errors. Non-response to the LFS tends to average about 10% of eligible households. Interviews are instructed to make all reasonable attempts to obtain LFS interviews with members of eligible households. Each month, after all attempts to obtain interviews have been made, a small number of non-responding households remain. For households non-responding to the LFS, a weight adjustment is applied to account for non-responding households. Sampling errors associated with survey estimates are measured using coefficients of variation for LFS estimates as a function of the size of the estimate and the geographic area.

The North America low voltage induction motors market is experiencing steady growth, driven by increasing industrial automation across diverse sectors. The market's Compound Annual Growth Rate (CAGR) of 3.31% from 2019-2024 suggests a robust and sustained expansion. Key drivers include the rising demand for energy-efficient motors in applications such as metal and mining, oil and gas, and chemical processing. These industries are undergoing significant modernization and expansion, fueling the need for reliable and efficient low voltage induction motors. Furthermore, the increasing adoption of smart manufacturing and Industry 4.0 technologies is contributing to market growth, as these initiatives demand precise motor control and automation capabilities. Within the North American market, the United States holds the largest share, followed by Canada and Mexico. Segmentation by type reveals a strong preference for three-phase motors due to their higher power output and efficiency compared to single-phase counterparts. The diverse end-user industries, each with unique motor requirements, contribute to the market's breadth and complexity. Major players like Nidec, ABB, Siemens, and Toshiba dominate the market, competing on factors such as price, efficiency, and technological innovation. The forecast period (2025-2033) anticipates continued growth, fueled by ongoing industrial investments and technological advancements, promising a healthy outlook for the North American low voltage induction motors market. The market's growth is projected to remain stable in the coming years, with the CAGR potentially slightly increasing to reflect the accelerating adoption of automation and energy efficiency initiatives. Government regulations promoting energy conservation are likely to further incentivize the uptake of high-efficiency motors. However, potential restraints include fluctuating commodity prices and global economic uncertainty. Despite these challenges, the long-term outlook remains positive, with the ongoing demand for industrial automation across key sectors, especially in the United States, ensuring continued growth for manufacturers and suppliers of low voltage induction motors. Technological advancements in motor design, such as the development of more efficient and durable motors, will further shape the market landscape and contribute to its expansion. The increasing focus on sustainable practices and reducing carbon footprints in industries will also influence motor selection, favoring energy-efficient models. Recent developments include: June 2023: The Minister of International Development and the Minister responsible for the Pacific Economic Development Agency of Canada and B.C. Minister of Municipal Affairs announced a joint investment of more than USD 48.6 million to support multiple wastewater projects in British Columbia. Such investments in the wastewater industry also help to aid the market's growth., February 2023: American Colloid Company announced plans to broaden its current mining activities by 303.7 acres, including 173.7 acres of BLM-managed public land. Over the course of the project, the proposed extension would supply an estimated 435,000 tons of bentonite from public lands. A planned bentonite mining expansion project in Big Horn County, two miles north of Lovell, has been examined by the Bureau of Land Management.. Key drivers for this market are: Increased Energy Demand in Residential and Industrial, Rising Investment in Manufacturing Sector Across the NA Countries. Potential restraints include: Increased Energy Demand in Residential and Industrial, Rising Investment in Manufacturing Sector Across the NA Countries. Notable trends are: Oil and Gas to be the Largest End-user Industry.

The Canada Metal Fabrication Equipment Market Report is Segmented by Automation Level (Automatic, Manual Equipment, and More), by Equipment Type (Cutting, Machining, Forming, and More), by End-User Industry (Automotive & Transportation, Oil & Gas/Energy, and More), and by Geography (Ontario, Québec, Alberta, British Columbia, Others). The Market Forecasts are Provided in Terms of Value (USD).