Canada CA: Internally Displaced Persons: New Displacement Associated with Disasters data was reported at 192,000.000 Case in 2024. This records an increase from the previous number of 15,000.000 Case for 2023. Canada CA: Internally Displaced Persons: New Displacement Associated with Disasters data is updated yearly, averaging 33,500.000 Case from Mar 2009 (Median) to 2024, with 12 observations. The data reached an all-time high of 192,000.000 Case in 2024 and a record low of 2,000.000 Case in 2009. Canada CA: Internally Displaced Persons: New Displacement Associated with Disasters data remains active status in CEIC and is reported by World Bank. The data is categorized under Global Database’s Canada – Table CA.World Bank.WDI: Population and Urbanization Statistics. Internally displaced persons are defined according to the 1998 Guiding Principles (http://www.internal-displacement.org/publications/1998/ocha-guiding-principles-on-internal-displacement) as people or groups of people who have been forced or obliged to flee or to leave their homes or places of habitual residence, in particular as a result of armed conflict, or to avoid the effects of armed conflict, situations of generalized violence, violations of human rights, or natural or human-made disasters and who have not crossed an international border. 'New Displacement' refers to the number of new cases or incidents of displacement recorded over the specified year, rather than the number of people displaced. This is done because people may have been displaced more than once.;The Internal Displacement Monitoring Centre (http://www.internal-displacement.org/);Sum;

Battery manufacturers in Canada have experienced strong growth over the past few years. There’s growing demand for batteries in various applications, including portable electronics, electric vehicles and renewable energy storage. This has created opportunities for providers to expand their operations and increase production. In the midst of strong long-term revenue growth, recent economic conditions have induced some volatility in the industry. COVID-19 hindered demand from many businesses and manufacturers because of a large decline in corporate profit. Despite this, revenue still soared in 2020 as working from home increased spending on laptops and other devices, boosting investment in batteries from the household sector. When the economy recovered from the pandemic, commercial sector spending returned, aiding the industry’s performance. Battery manufacturers have avoided major declines in revenue resulting from current economic challenges related to recessionary fears as long-term faith in the Canadian economy from the industry’s customers has kept demand elevated. Overall, revenue for battery manufacturers in Canada is anticipated to soar at a CAGR of 8.0% during the current period, reaching CA$356.3 million in 2024. This includes a 0.5% increase in revenue in that year. Imports are a crucial threat to manufacturers. Imported batteries satisfy nearly all domestic demand and their economic value is much larger than domestic revenue. Because of the high proliferation of imports, companies often compete based on technology, spurring research and development (R&D) spending. Intense price competition and extensive R&D costs have also severely hampered profit, culminating in an industry with most businesses operating at a devastating loss. The industry is expected to perform well during the outlook period, but revenue growth will slow as market saturation increases. The adoption of electric vehicles (EVs) is expected to expand, driving demand for batteries used in EVs. Canadian battery manufacturers will have opportunities to supply batteries to EV manufacturers in the United States and participate in the growth of this market. As more renewable energy sources, such as wind and solar power, come online, batteries will need to store excess energy and provide backup power. Overall, revenue for battery manufacturers in Canada is forecast to expand at a CAGR of 1.6% during the outlook period, reaching $CA386.1 million in 2029.

Correlations between datasets used within the sensitivity analysisa.

For information on economic census geographies, including changes for 2012, see the economic census Help Center..Table NameUtilities: Subject Series: Misc Subjects: Exported Energy to Canada and Mexico for the U.S.: 2012ReleaseScheduleThe data in this file are scheduled for release in June 2016.Key TableInformationSee Methodology. for information on data limitations.UniverseThe universe of this file is selected establishments of firms with payroll in business at any time during 2012 and classified in Utilities (Sector 22).GeographyCoverageThe data are shown at the United States level only.IndustryCoverageThe data are shown for 2012 NAICS codes 2211 and 2212.Data ItemsandOtherIdentifyingRecordsThis file contains data on:.Establishments.Revenue.Revenue from exports.Revenue of establishments responding to exported energy inquiry as a percent of total revenue.FTP DownloadDownload the entire table athttps://www2.census.gov/econ2012/EC/sector22/EC1222SXSB2.zipContactInformation. U.S. Census Bureau, Economy Wide Statistics Division. Data User Outreach and Education Staff. Washington, DC 20233-6900. Tel: (800) 242-2184. Tel: (301) 763-5154. ewd.outreach@census.gov. . .Includes only establishments of firms with payroll. See Table Notes for more information. Data based on the 2012 Economic Census. For information on confidentiality protection, sampling error, nonsampling error, and definitions, see Methodology..Symbols:D - Withheld to avoid disclosing data for individual companies; data are included in higher level totalsN - Not available or not comparableFor a complete list of all economic programs symbols, see the Symbols Glossary.Source: U.S. Census Bureau, 2012 Economic Census.Note: The data in this file are based on the 2012 Economic Census. To maintain confidentiality, the U.S. Census Bureau suppresses data to protect the identity of any business or individual. The census results in this file contain sampling and nonsampling error. Data users who create their own estimates using data from this file should cite the U.S. Census Bureau as the source of the original data only. For the full technical documentation, see Methodology link in above headnote.

Statistics Canada publishes monthly labour force statistics for all Canadian Census Metropolitan Areas (CMAs) and provinces. In addition, the City of Toronto purchases a special run from Statistics Canada of Labour Force Survey (LFS) data for city of Toronto residents (i.e. separate from the rest of the Toronto CMA). LFS data are collected by place of residence, and therefore city of Toronto's "employment" represents "employed residents" and not "jobs" in the city of Toronto. There are more jobs in the city of Toronto than employed city of Toronto residents. In this LFS database, you will find 22 monthly tables and 28 annual tables. Most of the tables contain data for five geographies: city of Toronto, Toronto CMA, Toronto/Hamilton/Oshawa CMAs, Ontario and Canada ( see attachment Table of Contents below a full description ). LFS data in the IVT tables are not seasonally adjusted. Top level seasonally adjusted LFS data are available in our monthly Toronto Economic Bulletin on Open Data. LFS is based on a monthly sample of approximately 2,800 households in the Toronto CMA, about half of the sample is from the city of Toronto; therefore, estimates will vary from the results of a complete census. LFS follows a rotating panel sample design, in which households remain in the sample for six consecutive months. The total sample consists of six representative sub-samples of panels, and each month a panel is replaced after completing its six month stay in the survey. Outgoing households are replaced by households in the same or similar area. This results in a five-sixths month-to-month sample overlap, which makes the design efficient for estimating month-to-month changes. The rotation after six months prevents undue respondent burden for households that are selected for the survey ( see attachment Guide to the Labour Force Survey for more information). Upon reviewing the data, you will see that at least some cells in the IVT tables have been suppressed. For confidentiality reasons, Statistics Canada suppresses Labour Force Survey data for any cell that corresponds to less than 1,500 persons. At the beginning of 2015, Statistics Canada substantially changed the methodology used to produce LFS population estimates for the city of Toronto. These changes have resulted in large and inexplicable swings in population and related counts, which are not real. However, the unemployment and participation rates for city residents showed very little change in this revision. The red dots in the chart above represents Statistics Canada's Annual Demographics estimates for the populations of the city of Toronto, age 15 and over. These are only estimates, but they are generally accepted as the most accurate estimates for the city's population. (Source: https://www150.statcan.gc.ca/n1/pub/91-214-x/91-214-x2018000-eng.htm). The most recent Statistics Canada population estimate for the city of Toronto is for July 1, 2015; therefore, we have to use projections thereafter. There are several population projections for the city. The projection that EDC staff has chosen to use for rebasing city of Toronto LFS data is the Ontario Ministry of Finance Population Projections 2017-2041 and downloaded June, 2017 from http://www.fin.gov.on.ca/en/economy/demographics/projections/ Please see attachment Rebased Labour Force Survey for City of Toronto below for annual adjustment factors, monthly adjustment factors and an example of how to rebase the absolute numbers for the city of Toronto.

Calculation results across each sensitivity analysis simulation, 90% confidence intervals with median NPVs in brackets, expressed in millions.

The Canada co-working office space market is experiencing robust growth, projected to reach a market size of $3.24 billion in 2025 and maintain a Compound Annual Growth Rate (CAGR) exceeding 8% through 2033. This expansion is fueled by several key drivers. The increasing adoption of flexible work arrangements and the rise of remote work and hybrid models are significantly boosting demand for co-working spaces. Furthermore, the flourishing tech sector, particularly in major cities like Toronto and Vancouver, contributes substantially to this market's growth. Small and medium-sized enterprises (SMEs) are major users, drawn to the cost-effectiveness and scalability of co-working spaces, avoiding large upfront investments in traditional office leases. The diverse range of services offered, such as high-speed internet, meeting rooms, and networking opportunities, further enhances the attractiveness of this model. While potential restraints include economic downturns affecting business expansion and increased competition among providers, the market's overall trajectory remains optimistic, supported by the evolving work landscape and the continued preference for flexible and collaborative work environments. The market is segmented by end-user (personal, small/large scale companies), office type (flexible managed, serviced), application (IT, legal, BFSI, consulting), and geography (Vancouver, Calgary, Ottawa, Toronto, and the rest of Canada). Toronto and Vancouver, as major economic hubs, are expected to dominate the market share within the Canadian co-working landscape. The competitive landscape is dynamic, featuring both established international players like WeWork and Regus, and a significant number of smaller, local providers catering to niche markets. The success of individual providers hinges on their ability to offer unique value propositions, such as specialized amenities tailored to specific industries, prime locations, and strong community building initiatives. Future growth will depend on adapting to evolving needs, incorporating advanced technologies, and strategically expanding to underserved markets. The market is poised for further consolidation as larger firms acquire smaller competitors. The trend towards sustainable and environmentally conscious office spaces will also play a significant role in shaping future market offerings. Recent developments include: January 2023: Captivate, a leading digital out-of-home video network, announced its strategic partnership with WeWork, a leading global flexible space provider, to transform existing digital screens in WeWork lobbies, elevator banks, and communal workspaces to display the Captivate on-screen content experience. This partnership makes Captivate WeWork's exclusive ad sales representation for the U.S. and Canada., January 2022: In Vancouver, a new space in the Broadway Corridor, dubbed City Link, will bring 40,000 sq. ft. to Mount Pleasant, which IWG has found to be "Vancouver's fastest-growing tech hub". Meanwhile, the new King George Hub will offer even more space, with some 51,500 sq. ft. planned.. Notable trends are: Toronto Region is Providing Ample of Opportunities to Tech Giants and Promoting the Market Growth.

General Abstract/Purpose (70 words): Data were collected to assist in cost-benefit analysis of flood mitigation actions that could be taken by the U.S. and Canada to prevent structural damage and associated costs and losses in future flood conditions, including conditions worse than the historical record flooding in spring of 2011. Data were commissioned to revise or fill gaps in estimates from structural damage modeling software commonly used for depth-damage economic assessments of flood impacts. The Summary text that immediately follows this introductory sentence offers overview information, but also includes context and detail that is not present in the Word document ("Principal Indicator Combo SET - REVIEW FINAL v2.docx") that constitutes the main body of this data release, supported by Excel files (that are copied without formatting in csv files for each Excel tab). Lake Champlain is a relatively large lake bordered by New York on the western side and Vermont on the eastern side, whose uppermost region spans the U.S.-Canadian border. The 436 mi^2 (1,130 km^2) lake sits within a 9,277 mi^2 (23,900 km^2) basin, and Champlain’s only drainage point is north into Canada via the Richelieu River into the province of Quebec. About 75% of the Lake Champlain shoreline of New York is within Adirondack State Park, covering all or part of Clinton, Essex, and Washington counties. Of Vermont’s 14 counties, Franklin, Chittenden, and Addison Counties border Lake Champlain, while Grand Isle is surrounded by Champlain and at its northern edge the Canadian border. Development and anthropogenic modifications, especially over the last 50 years, have converted wetlands, changed the timing and flows of water, and increased impervious surface area including new residences in floodplains on both sides of the border. Occasionally there is damaging flooding, with significant economic damages in New York, Vermont, and Quebec. With flood stage at 99.57’ (30.35m) and major flooding from 101.07’ (30.81m) over sea level, a 101.4’ (30.91m) flood in 1993 broke the previous recorded high flood in 1869. Following the third heaviest recorded snow, almost no seasonal snowmelt, then heavy rains, the spring of 2011 brought record flooding more than one foot over the 1993 record to 102.77’ (31.32m), expanding the lake’s area by 66 mi^2 (106.2 km^2, or about 5.8%). From reaching flood stage to peak and then returning to a lake level below flood stage took around six weeks. Wind-to-wave-driven erosion was up to 5 feet (1.5m) above static lake elevation in some areas. The record flood height (102.77’) is often reported as 103.07’ or 103.27’ in Burlington, owing to different vertical and horizontal datums and digital elevation models (DEMs), and some wave action. In a 1976 flood the U.S. side incurred more than 50% of the economic damages, but in 2011, Quebec experienced some 80% of structural and economic damages estimated at $82 million. Tropical Storm Irene hit the area in August of 2011 and did far more damage on the American side, for example spurring $29 million in home and business repair loans for damage across 12 of Vermont’s 14 counties. Co-reporting across the two events for 2011 confounded some data, making it impossible to separately identify spring flooding numbers. Following the Boundary Waters Treaty between the U.S. and Canada in 1909, from 1912 the International Joint Commission (IJC) handles boundary water issues between the two countries. The IJC Lake Champlain Richelieu River (LCRR) Study Project is a bi-national (U.S., Canada) multi-agency effort to assess flood risk and flood mitigation options as they affect potential structural damages and wider non-structural damages that include secondary economic, community, and psychological effects. Key economic parts of the report to the IJC LCRR Study Board are calculated using a new tool developed for the study project, an Integrated Socio-Economic-Environmental (ISEE) model, with forecasting for damages up to 105.57’ flood (105.9’, or 106’ [32.3m] for short, by alternative datum and DEMs, as apply in some of the modeling and estimations herein). There is also a Collaborative Decision Support Tool (CDST) that also processes non-structural economic damages, costs, or losses as inputs. CDST is a pared-down version of ISEE that applies historical estimates but does not project outcomes for higher floods in the future. Outputs from this data release are inputs to the ISEE or the CDST for calculations of the benefit-to-cost ratios projected to follow different structural interventions. For example adding a weir in the Richelieu River yielded a greater-than-one benefit-to-cost ratio in late-stage modeling, whereas a dam on either side, or an entirely new canal on the Canadian side, were never entertained as cost feasible or even appropriate. USGS economists were contracted to supply economic “principal indicators” for potential U.S.-side depth-damage effects from lake-rise flooding. The scope of this analysis is limited by several factors associated with the objectives of the IJC LCRR Study Board. Damages from tributary flooding were defined out of a project focused on joint-management options for mitigating flood effects, as tributary flows would be managed only by the U.S. Uncommonly low Lake Champlain levels were also ultimately considered as a stakeholder concern (the weir option also addressed this concern). It is standard to model economic damages to structures and related economic costs due to flooding using the FEMA-designed Hazus®-MH (Multi-Hazard) Flood Model of structural damages (https://www.fema.gov/flood-maps/products-tools/hazus; the Hazus-MH Technical Manual, 2011, 569pp, which explains definitions and parameterization of the tool rather than use of the tool itself, is a frequently referred source here). “Hazus” (tool) modeling is used in the LCRR Study Board research to estimate structural damages at different flood depths, and the primary work presented in this data release estimates depth-damage values for “Principal Indicators” (PIs) that were defined to supplement or alternatively estimate results from applying Hazus, where gaps exist or where straight Hazus values may be questionable in the LCRR context. A number of Principal Indicators were estimated on the Canadian and U.S. sides, where no PIs include any estimates for repair of structural damage, as those calculations are done separately using the Hazus tool (or the ISEE model application with Hazus outputs as inputs). In the final list, the USGS team produced estimates for six PIs: temporary lodging costs, residential debris clean-up and disposal, damage to roads and bridges, damage to water treatment facilities, income loss from industrial or commercial properties, and separately and specifically recreation sector income loss. So associated with residential damage, the costs of securing emergency and longer-term lodging when a household is displaced by lake-rise flooding are estimated, and the costs of cleaning up and removing and disposing of debris from residential property damage are estimated. In the public sector, costs of clean up and repair of damages to roads and bridges from lake-rise flooding are calculated, as are damages and potential revenue losses from flood mitigation measures and service reductions where public or private water utilities are inundated by lake-rise flooding. In the commercial sector, revenue losses from being closed for business due to flooding are calculated outside of the recreation sector, and then also for the recreation sector as lakeside campgrounds, marinas, and ferry services (where the last is also used for local commercial traffic). All of these PIs are characterized by being little-discussed in the literature. To derive information necessary to bound economic estimates for each of the 6 PIs, consultation with subject-matter experts in New York and Vermont (or at agencies covering these areas) was employed more often than anything in peer-reviewed literature specifically applied. Depth-damage functions that result are not formal mathematical functions, and across the six PIs calculations and results tend to be in increments of one foot or more. Results thus suggest magnitudes of costs that comply with reasonable scenario assumptions for a small but fairly consistent set of flood depths from 99.57’ to 105.57’, where the latter value is almost three feet (1m) above the historic maximum flood. Nothing reported in these estimates is empirically deterministic, or capable of including probabilistic error margins. Simplifying assumptions serve first to actually simplify the calculations and legibility of estimated results, and second to avoid the impression that specifically calibrated empirical estimations are being conducted. This effort offers plausible, logical, reliable, and reproducible magnitudes for estimates, using a method that can be easily modified if better information becomes available for future estimations. Certain worksheets and specific results are withheld to avoid the outright identification of specific businesses (or homes). Facts in this abstract generally attribute to: International Lake Champlain-Richelieu River Study Board, 2019. The Causes and Impacts of Past Floods in the Lake Champlain-Richelieu River Basin – Historical Information on Flooding, A Report to the International Joint Commission, 108pp (https://ijc.org/en/lcrr). Some supplemental factual support is from: Lake Champlain Basin Program, 2013. Flood Resilience in the Lake Champlain Basin and Upper Richelieu River, 93 pp (https://ijc.org/en/lcrr).

In May 2025, global inflation rates and central bank interest rates showed significant variation across major economies. Most economies initiated interest rate cuts from mid-2024 due to declining inflationary pressures. The U.S., UK, and EU central banks followed a consistent pattern of regular rate reductions throughout late 2024. In early 2025, Russia maintained the highest interest rate at 20 percent, while Japan retained the lowest at 0.5 percent. Varied inflation rates across major economies The inflation landscape varies considerably among major economies. China had the lowest inflation rate at -0.1 percent in May 2025. In contrast, Russia maintained a high inflation rate of 9.9 percent. These figures align with broader trends observed in early 2025, where China had the lowest inflation rate among major developed and emerging economies, while Russia's rate remained the highest. Central bank responses and economic indicators Central banks globally implemented aggressive rate hikes throughout 2022-23 to combat inflation. The European Central Bank exemplified this trend, raising rates from 0 percent in January 2022 to 4.5 percent by September 2023. A coordinated shift among major central banks began in mid-2024, with the ECB, Bank of England, and Federal Reserve initiating rate cuts, with forecasts suggesting further cuts through 2025 and 2026.

Canada CA: Law Mandates Nondiscrimination Based on Gender in Hiring: 1=Yes; 0=No data was reported at 0.000 NA in 2018. This stayed constant from the previous number of 0.000 NA for 2016. Canada CA: Law Mandates Nondiscrimination Based on Gender in Hiring: 1=Yes; 0=No data is updated yearly, averaging 0.000 NA from Mar 2010 (Median) to 2018, with 5 observations. The data reached an all-time high of 0.000 NA in 2018 and a record low of 0.000 NA in 2018. Canada CA: Law Mandates Nondiscrimination Based on Gender in Hiring: 1=Yes; 0=No data remains active status in CEIC and is reported by World Bank. The data is categorized under Global Database’s Canada – Table CA.World Bank.WDI: Governance: Policy and Institutions. Law mandates nondiscrimination based on gender in hiring is whether the law specifically prevents or penalizes gender-based discrimination in the hiring process; the law may prohibit discrimination in employment on the basis of gender but be silent about whether job applicants are protected from discrimination. Hiring refers to the process of employing a person for wages and making a selection by presenting a candidate with a job offer. Job advertisements, selection criteria and recruitment, although equally important, are not considered “hiring” for purposes of this question.; ; World Bank: Women, Business and the Law.; ;

Release Date: 2021-04-08.Release Schedule:..The data in this file come from the 2017 Economic Census. For information about economic census planned data product releases, see Economic Census: About: 2017 Release Schedules...Key Table Information:..Includes only establishments and firms with payroll..Data may be subject to employment- and/or sales-size minimums that vary by industry...Data Items and Other Identifying Records:. Number of establishments. Sales, value of shipments, or revenue ($1,000). Revenue from exports ($1,000). Response coverage of exported energy inquiry (%). .Geography Coverage:..The data are shown for employer establishments at the US level only. For information about economic census geographies, including changes for 2017, see Economic Census: Economic Geographies...Industry Coverage:..The data are shown for 2017 NAICS codes 2211 and 2212. For information about NAICS, see Economic Census: Technical Documentation: Economic Census Code Lists...Footnotes:..Not applicable..FTP Download:..Download the entire table at: https://www2.census.gov/programs-surveys/economic-census/data/2017/sector22/EC1722EXPNRG.zip..API Information:.Economic census data are housed in the Census Bureau API. For more information, see Explore Data: Developers: Available APIs: Economic Census..Methodology:.To maintain confidentiality, the U.S. Census Bureau suppresses data to protect the identity of any business or individual. The census results in this file contain sampling and/or nonsampling error. Data users who create their own estimates using data from this file should cite the U.S. Census Bureau as the source of the original data only...To comply with disclosure avoidance guidelines, data rows with fewer than three contributing establishments are not presented. Additionally, establishment counts are suppressed when other select statistics in the same row are suppressed. For detailed information about the methods used to collect and produce statistics, including sampling, eligibility, questions, data collection and processing, data quality, review, weighting, estimation, coding operations, confidentiality protection, sampling error, nonsampling error, and more, see Economic Census: Technical Documentation: Methodology...Symbols:.D - Withheld to avoid disclosing data for individual companies; data are included in higher level totals.N - Not available or not comparable.S - Estimate does not meet publication standards because of high sampling variability, poor response quality, or other concerns about the estimate quality. Unpublished estimates derived from this table by subtraction are subject to these same limitations and should not be attributed to the U.S. Census Bureau. For a description of publication standards and the total quantity response rate, see link to program methodology page..X - Not applicable.A - Relative standard error of 100% or more.r - Revised.s - Relative standard error exceeds 40%.For a complete list of symbols, see Economic Census: Technical Documentation: Data Dictionary.. .Source:.U.S. Census Bureau, 2017 Economic Census.For information about the economic census, see Business and Economy: Economic Census...Contact Information:.U.S. Census Bureau.For general inquiries:. (800) 242-2184/ (301) 763-5154. ewd.outreach@census.gov.For specific data questions:. (800) 541-8345.For additional contacts, see Economic Census: About: Contact Us.

Description of the parametrization of the distribution of WildFireSat impacts.

(StatCan Product) Detailed employed labour force by selected industries (Food and Beverage Manufacturing) for Canada, provinces and Alberta's Economic Regions (annual averages). Customization details: This information product has been customized to present information on employed labour force by selected industries (Food and Beverage Manufacturing) for Canada, provinces and Alberta's Economic Regions (ER). A comparison is also made between Food and Beverage Manufacturing industries that includes tobacco manufacturing to the one that does not. The file includes 5 tables: Table 1: Detailed Employed Labour Force by Selected Industries, Canada and Provinces Table 2a: Alberta Employed Labour Force in the Food Related Industries, Canada and Provinces (Food and Beverage Manufacturing Industries Exludes Tobacco Manufacturing) Table 2b: Alberta Employed Labour Force in Food Related Industries, Canada and Provinces (Food and Beverage Manufacturing Industries includes Tobacco Manufacturing. Table 3: Employed Labour Force, Agriculture and Food and Beverage Manufacturing Industries, Alberta and Alberta Economic Regions. Table 4: Detailed Employed Labour Force for All Industries (4-digit NAICS), Alberta 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.

Calculation results across all distributions, 90% confidence intervals with median present values in brackets, in millions ($2019, CAD).

As industry-university collaborations are promoted to commercialize university research and foster economic growth, it is important to understand how companies benefit from these collaborations, and to ensure that resulting academic discoveries are developed for the benefit of all stakeholders: companies, universities and public. Lock up of inventions, and censoring of academic publications, should be avoided if feasible. This case-study analysis of interviews with 90 companies in Canada, Japan, the UK and USA assesses the scope of this challenge and suggests possible resolutions. The participating companies were asked to describe an important interaction with universities, and most described collaborative research. The most frequently cited tensions concerned intellectual property management and publication freedom. IP disagreements were most frequent in the context of narrowly-focused collaborations with American universities. However, in the case of exploratory research, companies accepted the IP management practices of US universities. It might make sense to let companies have an automatic exclusive license to IP from narrowly defined collaborations, but to encourage universities to manage inventions from exploratory collaborations to ensure development incentives. Although Canada, the UK and US have strong publication freedom guarantees, tensions over this issue arose frequently in focused collaborations, though were rare in exploratory collaborations. The UK Lambert Agreements give sponsors the option to control publications in return for paying the full economic cost of a project. This may offer a model for the other three countries. Uniquely among the four countries, Japan enables companies to control exclusively most collaborative inventions and to censor academic publications. Despite this high degree of control, the interviews suggest many companies do not develop university discoveries to their full potential. The steps suggested above may rebalance the situation in Japan. Overall, the interviews reveal the complexity of these issues and the need for flexibility on the part of universities and companies.