| Province of residence | Unweighted | Weighted |
|---|---|---|
| Percentage | ||
| Newfoundland and Labrador | 18.3 | 18.3 |
| Prince Edward Island | 15.2 | 17.2 |
| Nova Scotia | 28.6 | 27.3 |
| New Brunswick | 28.3 | 26.9 |
| Quebec | 30.0 | 28.7 |
| Ontario | 27.8 | 28.8 |
| Manitoba | 27.9 | 26.4 |
| Saskatchewan | 24.3 | 23.6 |
| Alberta | 27.8 | 27.4 |
| British Columbia | 32.1 | 31.1 |
| Canada | 27.2 | 28.3 |
| Date received in OCS (DD/MM/YYYY) |
Title | Tracking Number | Field |
|---|---|---|---|
| 03/04/2019 | April 5, 2019 Meeting with the Deputy Minister of Health Canada | OCS20190217 | 8 |
| 03/04/2019 | Information on the GC Jobs Transformation Initiative that will be presented at the Deputy Ministers Recruitment Innovation Cluster on April 4, 2019 | OCS20190219 | 3 |
| 03/04/2019 | CANDEV Data Challenge Series Promotional Video | OCS20190220 | 3 |
| 03/04/2019 | MoU between Statistiques Canada and Agriculture and Agri-Food Canada | OCS20190222 | 5 |
| April Quarterly Meeting with Martin Taylor CRDCN | OCS20190232 | 8 | |
| 05/04/2019 | Indigenous Procurement | OCS20190233 | 7 |
| 09/04/2019 | The House of Commons Standing Committee on Transportation Infrastructure and Communities:"Interim Report on Establishing a Canadian Transportation and Logistics Strategy" | OCS20190239 | 5 |
| 11/04/2019 | Survey of Business under Federal Jurisdiction | OCS20190251 | 8 |
| 15/04/2019 | April 16, 2019 Meeting of Deputy Minister Committee on Economic Trends and Policies (DMC-ETP) | OCS20190254 | 5 |
| 15/04/2019 | DMC-ETP:Robot Processing Automation | OCS20190262 | 5 |
| 16/04/2019 | Activity planned as part of the 50th anniversary of the Official Languages Act | OCS20190266 | 3 |
| 16/04/2019 | PSMAC - item #4 on April 18th Agenda - Upcoming Changes to Proactive Disclosure - Bill C-58 | OCS20190267 | 3 |
| 16/04/2019 | PSMAC - item #2 on April 18th Agenda : People Management Policy and Policy on the Management of Executives | OCS20190269 | 3 |
| 23/04/2019 | Investing in Canada Plan (IICP) Deputy Minister's Coodinator Committee Meeting | OCS20190274 | 5 |
| 23/04/2019 | Revisions to Statistics Canada's Instrument of Delegation of Human Resources Authorities | OCS20190275 | 3 |
| 18/04/2019 | 2019-2020 Employment Equity and Diversity Action Plan | OCS20190276 | 3 |
| 18/04/2019 | Meeting with the Governor General | OCS20190280 | 8 |
| 24/04/2019 | Statistics Canada's Scorecard - Many Voices On Mind - A pathway to Reconcilliation | OCS20190284 | 3 |
| 29/04/2019 | DM Network (April 30, 2019): National Managers Community | OCS20190316 | 3 |
| 29/04/2019 | DM Network (April 30, 2019): StatCan Professionals Network 2019-20 Action Plan | OCS20190317 | 3 |
Having recently taken over responsibility for maintaining the National Road Network (NRN), Statistics Canada has been reviewing the NRN's data model.
The NRN contains trusted geospatial data on Canadian road characteristics, and forms a foundational layer of the GeoBase initiative. GeoBase is a federal, provincial and territorial government initiative that is overseen by the Canadian Council on Geomatics. It ensures that access is provided to an up-to-date and maintained base of high-quality geospatial data for all of Canada. Through the GeoBase portal, users can access this information at no cost and with unrestricted use.
In an ongoing effort to improve products and services, Statistics Canada has opened a dialogue with users and partners of this product. As part of this initiative, a consultation was organized to seek input on the NRN's data model, and to ensure that it meets users' needs.
Statistics Canada conducted an online consultation from May to August 2019. Participants were asked a series of questions pertaining to the current quality and future development of the NRN data model.
This consultation is now closed.
Individuals who wished to obtain more information or to take part in a consultation were requested to contact Statistics Canada by sending an email to statcan.consultations@statcan.gc.ca.
Statistics Canada is committed to respecting the privacy of consultation participants. All personal information created, held or collected by the agency is protected by the Privacy Act. For more information on Statistics Canada's privacy policies, please consult the Privacy notice.
Seven questions were presented to participants, pertaining to both the current state and the future of the NRN.
After our analysis, our recommendations include the following:
The Statistics Canada Training Institute Catalogue presents a compendium of corporate flagship courses and training courses, which mark Statistics Canada as a centre of expertise in the full range of activities required to make a statistical agency function.
The confirmation of inscription letters are sent directly to the candidates by electronic mail.
Employees or managers who wish to cancel or reschedule any course, for which they have received an invitation, are under obligation to notify the Training contact indicated in the confirmation of inscription letter, at least ten (10) working days before the course is scheduled to begin or find a substitute to take the course. The full course fee will be charged if notification of cancellation is received later than 10 working days prior to the course.
Where a cancellation or rescheduling of a course is late or an individual fails to attend without prior notice, a penalty equal to the full course administrative fee will be levied.
Statistics Canada organizes conferences to bring together analysts, researchers, planners, and policy- and decision-makers to network, share insights and discuss topics of interest.
| Province/Territory of Entry | United States | Overseas | ||
|---|---|---|---|---|
| Total Spending ($ 000,000) |
Spending C.V. (%) |
Total Spending ($ 000,000) |
Spending C.V. (%) |
|
| Newfoundland and Labrador | 1.2 | 23.8 | 1.9 | 40.2 |
| Prince Edward Island | 0.0 | 0.0 | 0.0 | 0.0 |
| Nova Scotia | 57.0 | 17.3 | 26.0 | 16.6 |
| New Brunswick | 76.0 | 19.4 | 1.1 | 23.1 |
| Quebec | 301.0 | 6.3 | 363.0 | 6.2 |
| Ontario | 854.0 | 3.7 | 857.0 | 6.0 |
| Manitoba | 28.0 | 15.5 | 1.6 | 62.7 |
| Saskatchewan | 12.0 | 15.7 | 3.8 | 44.0 |
| Alberta | 98.0 | 7.9 | 68.0 | 12.7 |
| British Columbia | 458.0 | 6.3 | 711.0 | 7.4 |
| Yukon | 8.6 | 13.9 | 0.0 | 0.0 |
| Canada | 1,894.0 | 2.7 | 2,033.0 | 3.3 |
The Visitor Travel Survey (VTS) provides a full range of statistics on the volume of international visitors to Canada and detailed characteristics of their trips. In recent years, there has been an increased interest in estimating sub-provincial inbound travel spending. Direct estimates of foreign travel spending can be obtained from the VTS, but they would be reliable only if the sample sizes are large enough. Therefore, a Small Area Estimation (SAE) methodology is now used to improve the quality of sub-provincial estimates, using Payment processors' (acquirer) data provided by Destination Canada. This document briefly describes this methodology.
The VTS was introduced in January 2018 to replace the U.S. and overseas visitors to Canada component of the International Travel Survey (ITS). The objective of the VTS is to provide a full range of statistics on the volume of international visitors to Canada and detailed characteristics of their trips such as expenditures, activities, places visited and length of stay. The target population of the VTS is all U.S. and overseas residents entering Canada. Excluded from the survey's coverage are diplomats and their dependents, refugees, landed immigrants, military, crew and former Canadian residents.
The demand for inbound travel spending estimates at smaller geographical levels has greatly increased in recent years. Standard weighted estimates (or direct estimates) at sub-provincial levels can be obtained from the VTS. However, these direct estimates can be considered reliable as long as the sample size in the area of interest is large enough. To address this issue, a SAE methodology is used to improve the quality of sub-provincial estimates, using Payment processors' data provided by Destination Canada.
SAE methods attempt to produce reliable estimates when the sample size in the area is small. In this application of the methodology, the small area estimate is a function of two quantities: the direct estimate from the survey data, and a prediction based on a model – sometimes referred to as the indirect, or synthetic estimate. The model involves survey data from the geographical area of interest, but also incorporates data from other areas (as input to the model parameters) and auxiliary data. The auxiliary data must come from a source that is independent of the VTS, and it must be available at the appropriate levels of geography. The SAE model uses the Payment processors' data which includes a portion of credit and debit card payments made by international visitors to Canada, as the auxiliary data. More precisely, the Payment data along with the direct survey estimates, are used to derive the small area estimates. For the smallest areas, the direct estimates are not reliable and the small area estimates are driven mostly by the predictions from the model. However, for the largest areas, this is the opposite and the small area estimates tend to be close to the direct estimates.
There are two types of SAE models: area-level (or aggregate) models that relate small area means to area-specific auxiliary variables, and unit-level models that relate the unit values of the study variable to unit-specific auxiliary variables. The VTS uses an area-level model as the auxiliary information (i.e., Payment data) is aggregated.
Section 2 describes the requirements to produce sub-provincial inbound travel spending estimates. In section 3, diagnostics used for model validation and evaluation of small area estimates are briefly discussed.
The small area estimates were obtained through the use of the small area estimation module of the generalized software G-ESTFootnote 1 version 2.02 (Estevao et al., 2017a, 2017b). For each area i, three inputs need to be provided to the G-EST in order to obtain small area estimates:
i) Direct estimates , which are calculated using survey weights
where represents spending by unit k in domain i, and is the sampling weights assigned to unit k on the VTS sample
ii) Smoothed variance estimates at the domain of interest, which are obtained by applying a piecewise smoothing approach on the variance estimates that are calculated using mean bootstrap weights
iii) Vector of auxiliary variables
For the estimation of inbound travel spending, the domain of interest are defined as: 11 country / country groups × 22 tourism regions / grouped tourism regions
The 11 country / country groups are as follows:
| Group | Country |
|---|---|
| 1 | Australia |
| 2 | China |
| 3 | Japan |
| 4 | South Korea |
| 5 | India |
| 6 | United Kingdom |
| 7 | France |
| 8 | Germany |
| 9 | Mexico |
| 10 | United States |
| 11 | Other countries |
The 84 tourism regions are grouped into 22 domains, as shown in the following table.
| Tourism region / Grouped Tourism Regions | Tourism regions | Province/Territory |
|---|---|---|
| 1000 (Newfoundland & Labrador) | 001, 005, 010, 015, 020, 099Footnote 2 | Newfoundland and Labrador |
| 1100 (Prince Edward Island) | 101 | Prince Edward Island |
| 1200 (Nova Scotia) | 202, 206, 211, 215, 220, 225, 232, 299 | Nova Scotia |
| 1300 (New Brunswick) | 300, 302, 304, 308, 318, 399 | New Brunswick |
| 2400 (Rest of Quebec) | 401, 405, 410, 420, 425, 430, 435, 440, 445, 450, 455, 465, 470, 475, 480, 485, 491, 492, 493, 495, 499 | Quebec |
| 0415 (Quebec) | 415 | |
| 0460 (Montreal | 460 | |
| 3500 (Rest of Ontario) | 502, 511, 516, 526, 531, 536, 541, 551, 556, 560, 565, 570, 599 | Ontario |
| 0506 (Niagara Falls and Wine Country) | 506 | |
| 0521 (Greater Toronto Area) | 521 | |
| 0546 (Ottawa and Countryside) | 546 | |
| 4600 (Manitoba) | 601, 605, 610, 615, 620, 625, 630, 635, 699 | Manitoba |
| 4700 (Saskatchewan) | 701, 705, 710, 715, 720, 725, 730, 799 | Saskatchewan |
| 4800 (Rest of Alberta) | 801, 805, 810, 825, 899 | Alberta |
| 0815 (Canadian Rockies) | 815 | |
| 0820 (Calgary and Area) | 820 | |
| 5900 (Rest of British Columbia) | 901, 910, 920, 925, 999 | British Columbia |
| 0905 (Vancouver, Coast & Mountains) | 905 | |
| 0915 (Kootenay Rockies) | 915 | |
| 6000 (Yukon) | 981 | Yukon |
| 6100 (Northwest Territories) | 991 | Northwest Territories |
| 6200 (Nunavut) | 992 | Nunavut |
It should be mentioned that for the VTS, a modification of the basic area-level model, piecewise area-level model, was used. The piecewise area-level is useful when a single linear model does not provide an adequate explanation on the relationship between the variable of interest and the covariates. The area specific auxiliary variable i.e., spending from the Payment data, is partitioned into intervals and a separate line segment is fit to each interval.
The accuracy of small area estimates depends on the reliability of the model. It is therefore essential to make a careful assessment of the validity of the model before releasing estimates. For instance, it is important to verify that a linear relationship actually holds between direct estimates from VTS () and payment data (), at least approximately.
For the VTS, diagnostic plots and tests in the G-EST are used to assess the model, and outliers are identified iteratively by examining the standardized residuals from that model.
A concept that is useful to evaluate the gains of efficiency resulting from the use of the small area estimate over the direct estimate is the Mean Square Error (MSE. The MSE is unknown but can be estimated (see Rao and Molina, 2015). Gains of efficiency over the direct estimate are expected when the MSE estimate is smaller than the smoothed variance estimate or the direct variance estimate. In general, the small area estimates in the VTS were significantly more efficient than the direct estimates, especially for the areas with the smallest sample size.
Estevao, V., You, Y., Hidiroglou, M., Beaumont, J.-F. (2017a). Small Area Estimation-Area Level Model with EBLUP Estimation- Description of Function Parameters and User Guide. Statistics Canada document.
Estevao, V., You, Y., Hidiroglou, M., Beaumont, J.-F. and Rubin-Bleuer, S. (2017b). Small Area Estimation-Area Level Model with EBLUP Estimation- Methodology Specifications. Statistics Canada document.
Rao, J.N.K., and Molina, I. (2015). Small Area Estimation. John Wiley & Sons, Inc., Hoboken, New Jersey.
Statistics Canada. (2017). Monthly Labour Force Survey Small Area Estimation- Documentation to accompany small area estimates. Statistics Canada document.
| Geography | Month | ||||||||||||
|---|---|---|---|---|---|---|---|---|---|---|---|---|---|
| 201803 | 201804 | 201805 | 201806 | 201807 | 201808 | 201809 | 201810 | 201811 | 201812 | 201901 | 201902 | 201903 | |
| percentage | |||||||||||||
| Canada | 0.6 | 0.5 | 0.5 | 0.6 | 0.7 | 0.7 | 0.7 | 0.6 | 0.5 | 0.7 | 0.8 | 0.6 | 0.5 |
| Newfoundland and Labrador | 0.4 | 1.0 | 0.4 | 0.5 | 0.5 | 0.3 | 0.2 | 0.5 | 0.4 | 0.3 | 0.6 | 0.5 | 0.2 |
| Prince Edward Island | 0.0 | 0.0 | 0.0 | 0.0 | 0.0 | 0.0 | 0.0 | 0.0 | 0.0 | 0.0 | 0.0 | 0.0 | 0.0 |
| Nova Scotia | 1.5 | 3.5 | 3.4 | 1.2 | 1.8 | 1.9 | 2.4 | 2.5 | 1.8 | 5.4 | 4.6 | 2.3 | 2.0 |
| New Brunswick | 1.7 | 1.0 | 2.4 | 2.0 | 2.0 | 4.9 | 3.0 | 2.4 | 3.3 | 1.3 | 1.1 | 0.8 | 1.0 |
| Quebec | 1.5 | 1.7 | 1.6 | 1.6 | 1.9 | 1.9 | 1.8 | 1.3 | 1.5 | 1.3 | 1.9 | 1.3 | 1.5 |
| Ontario | 0.8 | 0.7 | 0.7 | 0.9 | 0.9 | 1.0 | 0.9 | 0.9 | 0.8 | 1.1 | 1.3 | 0.9 | 0.8 |
| Manitoba | 0.7 | 1.7 | 2.3 | 0.9 | 1.0 | 1.1 | 0.9 | 2.1 | 1.4 | 2.0 | 1.2 | 0.6 | 0.8 |
| Saskatchewan | 0.6 | 0.7 | 0.4 | 0.4 | 0.7 | 0.6 | 0.4 | 0.8 | 0.5 | 0.9 | 0.6 | 0.3 | 0.3 |
| Alberta | 1.7 | 1.1 | 1.4 | 1.3 | 1.7 | 1.7 | 2.1 | 1.4 | 1.7 | 1.6 | 1.0 | 1.2 | 1.2 |
| British Columbia | 1.3 | 1.5 | 1.3 | 1.8 | 2.3 | 1.4 | 1.8 | 1.6 | 1.4 | 1.7 | 2.2 | 1.4 | 1.0 |
| Yukon Territory | 0.0 | 0.0 | 0.0 | 0.0 | 0.0 | 0.0 | 0.0 | 0.0 | 0.0 | 0.0 | 0.0 | 0.0 | 0.0 |
| Northwest Territories | 0.0 | 0.0 | 0.0 | 0.0 | 0.0 | 0.0 | 0.0 | 0.0 | 0.0 | 0.0 | 0.0 | 0.0 | 0.0 |
| Nunavut | 0.0 | 0.0 | 0.0 | 0.0 | 0.0 | 0.0 | 0.0 | 0.0 | 0.0 | 0.0 | 0.0 | 0.0 | 0.0 |
| Geography | Month |
|---|---|
| 201903 | |
| Percent | |
| Canada | 0.7 |
| Newfoundland and Labrador | 0.7 |
| Prince Edward Island | 1.4 |
| Nova Scotia | 2.1 |
| New Brunswick | 1.9 |
| Quebec | 1.2 |
| Ontario | 1.7 |
| Manitoba | 0.8 |
| Saskatchewan | 2.3 |
| Alberta | 1.0 |
| British Columbia | 1.4 |
| Yukon Territory | 0.5 |
| Northwest Territories | 0.4 |
| Nunavut | 0.5 |
As we chart new paths and methods of collecting data, we want to underline that at Statistics Canada, your privacy is protected every day. It is our obligation to keep your data safe and we take this responsibility seriously. Your trust allows us to do our job. We must continue to provide accurate and timely economic and social data, and insights to enable sound policy decisions.
As part of our commitment to transparency, Statistics Canada invites you to meet our Trust Centre team. If you have any questions, please contact us.
André Loranger is an experienced senior public official, having worked at Statistics Canada for 26 years, leading large and complex statistical programs covering all aspects of the Canadian economy. He has previously served in various senior leadership positions in the Agency including Assistant Chief Statistician for Economic Statistics and Assistant Chief Statistician for Strategic Data Management, Methods and Analysis. In that capacity, Mr. Loranger was also Statistics Canada's Chief Data Officer responsible for the overall stewardship of the organization's information data holdings.
Mr. Loranger represents Statistics Canada on various international expert groups pertaining to economic statistics. He is currently the chair of the United Nations Network of Economic Statisticians and the chair of the United Nations Committee of Experts on Environmental-Economic Accounting. He is also a board member of the Canadian Association for Business Economics and the Conference on Research in Income and Wealth.
Mr. Loranger holds an M.A. (Economics) degree and a Bachelor of Social Sciences (Economics) from the University of Ottawa.
Stéphane Dufour has been with Statistics Canada for 33 years and has held various key positions—mostly in collection and operations—and lead several major transformational initiatives along the way. He became an executive in 2006, taking on the role of Director of Administrative Support Services. In 2008, Stéphane was appointed as Director General of the Collection and Regional Services Branch and, in 2012, he was appointed as Director General of the Human Resources Branch. Later in that same year, he became the Assistant Chief Statistician responsible for Corporate Services and the Chief Financial Officer.
Prior to his current role, Stéphane served as the Assistant Chief Statistician of the Census, Regional Services and Operations Field. Most recently, he has been appointed as a Special Advisor within the agency. Additionally, he is the co-chair of the executive board for the United Nations High-Level Group for the Modernisation of Official Statistics and has been involved in several international cooperation projects over his career.
Stéphane holds a bachelor's degree in business administration (accounting) from Université du Québec à Hull (now Université du Québec en Outaouais) and studied economics at the University of Ottawa. He also holds a graduate certificate in Advanced Public Sector Management from the University of Ottawa and participated in the Advanced Leadership Program of the Canada School of Public Service.
Kathleen Mitchell has been the Assistant Chief Statistician of the Corporate Strategy and Management Field and the Chief Financial Officer at Statistics Canada since September 2022. Kathleen was previously the Director General of the Finance, Planning and Procurement Branch and the Deputy Chief Financial Officer of the agency from April 2018 to September 2022. She began her career at Statistics Canada in 1998 as a student and took on various functions within the former Finance Branch, including eight years as an executive. She previously accepted the position of Director, Resource Management, at the Department of Justice in December 2016 before returning to Statistics Canada as Director General. Kathleen has a keen interest in team leadership and all aspects of human resources. She also has a busy family and appreciates time to read.
Geoff Bowlby has been with Statistics Canada for 29 years, with a background as an economist and project manager.
Prior to taking on the role of Assistant Chief Statistician in 2024, Geoff was Canada’s Census Manager and steered the agency’s most extensive and complex statistical program through the impact of a global pandemic. He also served as the Director General of Collection and Regional Services Branch, the director responsible for the monthly Labour Force Survey, as well as the director of Special Surveys.
Geoff is currently running two UN task forces focussed on the revision of the international manuals being developed for the next worldwide round of population and housing censuses.
Outside of work, Geoff is active with his family. For years, he coached his son’s hockey team, and with his wife, attended figure skating to cheer on their two daughters.
Geoff has a bachelor’s degree in Economics from Glendon College at York University and a master’s in Economics from the University of Waterloo. In addition, he graduated from the University of Ottawa’s Certificate Program in Public Sector Leadership in 2020.
Josée Bégin has a master's degree in mathematics and statistics (MSc) from the University of Ottawa. She started her career at the Canada Revenue Agency in 1994 before joining Statistics Canada in 2002, where she gained experience in overseeing large and complex statistical programs. Josée became the Assistant Chief Statistician of the Social, Health and Labour Statistics Field in January 2023.
The Social, Health and Labour Statistics Field provides accurate, timely and relevant information across a range of social topics to decision makers at all levels of government, non-governmental organizations, researchers and the Canadian public. Its portfolio includes a number of large survey and administrative data programs, such as the Centre for Population Health Data; the Canadian Centre for Justice and Community Safety Statistics; the Centre for Gender, Diversity and Inclusion Statistics; and the Centre for Labour Market Information. This field is also home to Canadian census content expertise.
Her favourite hobbies include yoga and reading.
Committed to creating a diverse and bilingual and inclusive work environment throughout her career, Mélanie previously served as the Assistant Deputy Minister of the Digital Services Branch at Shared Services Canada. Prior to that, she provided leadership as an executive in IT security, data management business relationships and partnerships, and IT operations in various senior roles at the Communications Security Establishment Canada, and Library and Archives Canada.
A recipient of the Governor General's Academic Medal of Canada, Mélanie holds a Bachelor of Computer Science from the University of Sherbrooke and a Master of Business Administration EMBA (Executive MBA Program) from the University of Ottawa. A busy mother of three who enjoys running and skiing in her free time, Mélanie is currently completing the Certificate in Public Leadership and Governance with the University of Ottawa.
Eric Rancourt is the Assistant Chief Statistician for the Strategic Data Management, Methods and Analysis Field at Statistics Canada. He is also Statistics Canada's Chief Data Officer.
He has worked at Statistics Canada for 33 years and has occupied several roles, such as Director General of the Modern Statistical Methods and Data Science Branch, Director General of Strategic Data Management, Director of International Cooperation, Director of Corporate Planning, Head of research, Production manager of Survey Methodology Journal, and Researcher.
His main areas of work have been on the treatment of non-response, estimation, and the use of administrative and alternative data in statistical programs. Recently, he has worked on frameworks for optimizing privacy and information, data ethics, and modern statistical designs.
He holds a BSc in Statistics from University Laval, BAs in Arts (Ancient Studies; Medieval and Renaissance Studies), as well as a BA in philosophy from the University of Ottawa focusing on data ethics. He is Chair of the Board for the Canadian Statistical Sciences Institute (CANSSI). He has been involved in many professional associations, including the International Association of Survey Statisticians (IASS) for which he is Vice-President, and the International Association for Official Statistics (IAOS), and is an elected member of the International Statistical Institute (ISI). He is Chair-Elect for the Survey Research Methods Section of the American Statistical Association (ASA) and he is also a member of the Statistical Society of Canada (SSC).
Jennifer Withington has been working at Statistics Canada for 27 years. As an executive since 2016, Jennifer has held various roles including Director General of the Macroeconomic Accounts, and Director of the International Accounts and Trade Division. She is responsible for key economic indicators such as the Consumer Price Index, Gross Domestic Product, and International Merchandise Trade.
Jennifer represents Statistics Canada on several International Committees including the United Nations (UN) Advisory Expert Group on National Accounts and the Group of Experts on National Accounts.
Jennifer holds a bachelor’s degree in economics and political Science as well as a master’s degree in economics from McGill University.
