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.
Consultation methodology
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.
How participants got involved
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.
Results
Seven questions were presented to participants, pertaining to both the current state and the future of the NRN.
The majority of participants are satisfied with the current annual release interval, with quarterly releases being the next most popular.
Most participants see sufficient value in adding attributes for divided highways and National Highway System (NHS) association, but not for direction of digitization and truck prohibition.
Participants see a need for higher completion rates for address ranges, speed limits and road types.
The majority of participants are satisfied with the current list of road classifications. The most desired expansions include railways, bridges and culverts.
Participants are evenly split on the idea of retiring the junctions point layer.
The majority of participants are satisfied with the current list of structures. The most desired expansion is railway crossings.
Most participants are satisfied with the currently available positional accuracy of 10 metres. However, many participants desire sub-metre accuracy.
After our analysis, our recommendations include the following:
Move toward more timely processing updates, which will improve temporal accuracy.
Identify roads that are a part of the NHS; do not include direction of digitization or indicators of where trucks are prohibited.
Maintain the existing road class list. However, include additional classes that are not currently part of the NRN specification, but that can be found in the released data.
Retire the road junctions as a part of the NRN dataset, but provide ad hoc support as needed.
Maintain the current list of structures where a road may cross, but monitor the number of crossings against other datasets. If there is a significant change in occurrences of roads crossing unaccounted-for structures, then revisit the list.
Adopt a five-metre accuracy standard across the NRN.
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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.
1. Introduction
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.
2. Area-level model
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:
Table 1: Country / country groups
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.
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.
3. Evaluation of small area estimates
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.
References
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.
CVs for Total sales by geography - March 2019
Table summary
This table displays the results of Annual Retail Trade Survey: CVs for Total sales by geography - March 2019. The information is grouped by Geography (appearing as row headers), Month and Percent (appearing as column headers).
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André Loranger, Chief Statistician of Canada
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, Special Advisor
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, Assistant Chief Statistician, Corporate Strategy and Management Field, and Chief Financial Officer
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, Assistant Chief Statistician, Census, Regional Services and Operations Field
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, Assistant Chief Statistician, Social, Health and Labour Statistics Field
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.
Mélanie Scott, Chief Information Officer and Assistant Chief Statistician of the Digital Solutions Field
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, Assistant Chief Statistician, Strategic Data Management, Methods and Analysis Field
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, Acting Assistant Chief Statistician for Economic Statistics
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.
Welcome to the Statistics Canada Training Institute. Today, we will explore producer price indexes— what they are, how they are made and what they are used for.
To understand producer price indexes, you first need to know what producer prices are and what a price index is. Producer prices are the prices at which businesses sell their products or services to others (for example, the government, consumers or other businesses).
Although value-added taxes generally apply to the sale of these products, they are excluded from producer prices. For example, a retailer sells gasoline to a consumer for $1.32 per litre, plus tax. The tax on the litre sold is not part of the amount the retailer receives.
There are two main uses for producer price indexes. One is to adjust business or government contracts to inflation. The other is to calculate the real value of economic output by adjusting for price changes. In other words, price indexes are used to measure real gross domestic product (GDP). This is why there are many producer price indexes, covering a wide range of economic activities, such as manufacturing, construction, professional services, distributive trades and financial services.
Value chain
To illustrate the various producer price indexes present in the production process, let's take a look at the value chain for a house. A value chain is a series of activities that each adds value to the end product or service. These activities include labour, materials, goods, services and technology (or the so-called "factors of production"). Each step in the value chain needs to be measured to assess its macroeconomic impact, which is where producer price indexes come in. When you buy a new house, you (the consumer) see the end product. However, many activities go into planning, building and purchasing this house:
The first step in building a house is to extract the raw materials used to build it. A wide variety of materials are needed, such as lumber, steel, concrete, and glass. Let's take a closer look at the value chain of lumber.
First, logs are harvested by forestry companies. The price for the raw logs is captured in the Raw Materials Price Index. This index includes all costs producers incur to bring a log to the factory gate—for example, transportation charges, customs duties and subsidies, if applicable.
The next step in this value chain is the manufacturing process, during which the raw logs are turned into lumber. The price of this activity is captured in the Industrial Product Price Index and is the price of goods sold at the factory gate. As a result, the prices covered by this index are not what a purchaser pays, but what the producer receives. This excludes all indirect taxes, such as sales taxes and tariffs, as these do not go towards the factors of production.
The finished lumber leaves the factory and needs to be transported to the wholesaler. The transportation process is an economic activity that creates a service within the value chain. If the lumber is transported by truck, the price for this transportation service is captured in the For-hire Motor Carrier Freight Services Price Index. This industry is a vital part of the Canadian economy and the services it provides are crucial to the effective and efficient flow of goods.
Wholesalers store and distribute the lumber to retail stores or directly to the end user. The price for this service is captured in the Wholesale Services Price Index. The price of the wholesale service is defined as the margin price of what the wholesaler receives for their service. The margin price is calculated as the difference between the selling price and the purchase price of the wholesale service being measured.
Now it's time to transform the lumber into a house. Before construction can begin, the house needs to be designed by an architect and structural engineer. The architects and engineers who do this work charge for their services. The Architectural, Engineering and Related Services Price Index captures information on the prices of architectural, landscape architecture, engineering, and geophysical and non-geophysical surveying and mapping services. In this case, an architect would price out a standard contract for their services.
Once the house plans and specifications have been drawn up, a general contractor is selected to build the house. The changes in the price the contractor charges are measured in the Residential Building Construction Price Index. This type of index measures the change over time in the prices contractors charge for building various types of residences (for example, bungalows, two-storey houses, townhouses, high-rise and low-rise apartment buildings). The contractor's price includes the value of all materials, labour, equipment, overhead overhead and profit involved in the construction of a new building.
Towards the end of the value chain is the final product: a brand new house. The contractor or builder sells the house to a consumer. The change in this sale price is captured in the New Housing Price Index. This index measures changes over time in the contractors' sale prices of new residential houses, where detailed specifications pertaining to each house remain the same between two consecutive months.
House purchases are normally financed by a mortgage. The change in the price of this service is measured in the New Lending Services Price Index. This index measures monthly price changes over time for new lending services. Prices represent the difference between annual percentage rates for new loan products and weighted averages of yields on financial market instruments.
Index creation
Now that we have demonstrated how producer prices span the entire economy and talked about some uses of these indexes, let's discuss how indexes are built. Much like our brand new house, indexes have many layers and components.
To build an index, we need to identify a basket of goods and services that businesses in Canada might sell. We do this because it's impossible to capture the prices of every good and service a business produces. Instead, we track the selling prices of their most representative goods and services over time. To measure pure price change, it is important to keep the contents of the basket fixed over time. The basket differs depending on which part of the value chain is being measured. For example, the For-hire Motor Carrier Freight Services Price Index's basket contains the type of shipment and route travelled, whereas the basket for a residential building construction price index contains components such as building materials, labour and overhead.
Each item in the basket receives a relative importance, or basket weight, that represents its share in the total value of all goods or services in that particular sector of the economy. For example, motorized and recreational vehicles represent a larger share of the total value than meat, fish and dairy products; therefore, motorized and recreational vehicles receive a larger weight than meat, fish and dairy products in the Industrial Product Price Index basket.
Basket contents and their weights represent the production patterns of Canadian businesses, which change over time. For example, years ago manufacturers produced cars with cassette players. Today, they produce cars with Bluetooth or even electric cars. Updating the basket regularly ensures that the index remains representative of the range of goods and services produced in Canada.
Collection
With a practically infinite selection of goods and services, evaluating the basket price requires a large-scale coordinated effort by Statistics Canada. Most of the price quotes used to calculate producer price indexes are collected using surveys of Canadian producers. Other prices are obtained from non-survey sources, such as the Internet.
Price change measurement
Now that we have obtained prices for our basket of goods and services, we can measure the change in price from one period to another. We want to measure pure price change, which is the change in price from inflation alone, so the price is collected for the identical product or service over time. Changes in the quantity or quality of collected products or services are taken into account. For example, say a business produced a pivot chair with no arms and sold it for $100, then started producing only pivot chairs with arms that sold for $120. Even though the selling price has increased, adjustments are made for these quality changes to show no change.
Conclusion
Thank you for joining us on this tutorial of Value Chains and Producer Price Indexes
