Appendix 1 – PIA Summary

StatsCAN app Privacy Impact Assessment Summary

Introduction

Under the authority of the Statistics Act^{Footnote 1}, Statistics Canada publishes statistical information to inform Canadians about the general activities and condition of the people. To support these dissemination activities, the StatsCAN app was made available in the Apple App Store and Google Play Store on January 31, 2022. This free app lets Canadians tap into expert analysis, fun facts, visuals, short stories and insights that bring together data, tools and publications to provide them with the latest information on Canada's economy, society and environment.

The app provides timely and convenient access to trusted, unbiased facts right from Statistics Canada and provides a personalized browsing journey allowing users to follow subjects of interest to know when the latest publications become available, save publications for reading later, or opt-in to in-app notifications that provide a comprehensive overview of the country's latest statistical news.

The StatsCAN app supports the five pillars of Statistics Canada's modernization agenda^{Footnote 2}, which align with the agency's mission, vision, and values. These pillars respond to the ever-changing data landscape and to users' and stakeholders' requirements for more data, provided faster, and made available in multiple formats and from multiple access points.

The initial StatsCAN app was not intended or built to collect, use, or disclose any user personal information aside from standard aggregate app metrics and Key Performance Indicators (KPI) provided by the respective app stores hosting the app to measure app performance, such as number of downloads, uninstalls, number of active users, etc., and only in-app notifications were available to users. As such, the initial implementation did not require a Privacy Impact Assessment.

However, in Statistics Canada’s efforts to enhance the app and deliver additional features to improve the user experience, some new functionalities (a feedback form, in-app metrics, and push notifications), are being implemented that utilize some user data. As such, this Privacy Impact Assessment (PIA) was created to describe these uses in more detail and analyze potential privacy implications. These new features may be implemented simultaneously or sequentially.

Objective

A privacy impact assessment for StatsCAN app was conducted to determine if there were any privacy, confidentiality or security issues with the new functionalities being introduced to the app and, if so, to make recommendations for their resolution or mitigation.

Description

Feedback form

Purpose

The purpose of the feedback form is to receive feedback from StatsCAN app users regarding their thoughts and opinions about the app, any improvements they would like to see, or as a mechanism to report issues or bugs. This information will assist the StatsCAN app team in decision-making about the app. The advantage of this feedback form mechanism over the current one is that it will allow users to communicate more easily and directly with Statistics Canada’s app team (users may not wish to publicly post on their app store, and may similarly not wish to leave the app to submit feedback). Unlike the generic Contact Us form that exists on the StatCan website, the new feedback form will be specific to the StatsCAN app, making it easier for users to identify any technical bugs, and provide device information to assist the team in resolving the bugs reported. The StatsCAN app feedback form is not intended to replace the Contact Us form where users wish to contact Statistics Canada regarding the agency’s products and services, or to inquire about concepts, methods or data quality of releases.

The form is hosted on the Statistics Canada website but is not linked to the homepage, or any other public-facing pages on the StatCan website. The form has been developed to be ‘hidden’ and will only be accessible from a desktop if the user has a direct link. Although the form will be hosted on the website, it will be directly integrated within the StatsCAN app and accessible through the Settings screen.

Submission of identifiable personal information (email address) is not mandatory. If the user chooses to leave their email address when reporting an issue or sending a comment or suggestion, the app team may follow up with the user (if more details are required). However, if the user does not want to leave contact information, feedback can be submitted anonymously, without direct identifiers.

In-app metrics

Purpose

New in-app metrics (detailed below) will be analyzed by the StatsCAN app team to better understand their users. These metrics will inform what type of content users are interested in and what types of features are being used most often. In-app metrics will enable the app team to continue building a product that meets users’ needs and delivers an ideal user experience.

Google Analytics for Firebase ("Firebase”) will be the technology used to collect and analyze detailed in-app metrics for both Android OS and iOS users. Firebase was selected due to project parameters and budgetary considerations, as the estimated time and effort for implementation by the StatsCAN app IT^{Footnote 3} team was much lower compared to other analytics services for mobile. Firebase was also identified as an industry standard for collecting and analyzing in-app metrics. In-app metrics are collected using an Application Program Interface (API) that is plugged into the back-end of the StatsCAN app allowing it to process certain information in accordance with the relevant Firebase Terms of Service^{Footnote 4}. This functionality will be appropriately assessed to ensure compliance with applicable Canadian legislation and TBS direction before going into live production (operationalization). Any risks or vulnerabilities found within the assessment will be mitigated and approved by the relevant partners.

For more information on how the StatsCAN app’s third-party analytics service provider uses and safeguards user data, please consult:

• Firebase services: Privacy and Security in Firebase
• Google Analytics: Safeguarding your data
• Google: How Google uses information from sites or apps that use our services

Users can learn more and manage their information used by Google services at Privacy Policy – Privacy & Terms – Google.

If users do not want their information to be used by the third-party analytics service provider, they may alternatively access the same published content on Statistics Canada’s website, which adheres to Statistics Canada’s general privacy notice.

Push notifications

Purpose

The purpose of push notifications is to improve the user experience by better notifying users of the availability of StatCan products and releases that interest them. Users will no longer need to access the app directly to be notified of new releases, should they choose to enable push notifications. Notifying users of new content that has been published in the app will also increase the visibility and use of new data, as well as its timeliness. This will, in turn, increase the agency’s relevance and reach to the Canadian public.

Risk Area Identification and Categorization

The PIA identifies the level of potential risk (level 1 is the lowest level of potential risk and level 4 is the highest) associated with the following risk areas:

a) Type of program or activity Risk scale
Program or activity that does not involve a decision about an identifiable individual.	1
b) Type of personal information involved and context
Personal information, with no contextual sensitivities after the time of collection, provided by the individual with consent to also use personal information held by another source.	2
c) Program or activity partners and private sector involvement
Within the institution (among one or more programs within the same institution)	1
d) Duration of the program or activity
Long-term program or activity.	3
e) Program population
* The program’s use of personal information is not for administrative purposes. Information is collected for client information and public communications purposes and will not be used to make a decision about any identifiable individual.	N/A
f) Personal information transmission
The personal information is transmitted using wireless technologies.	4
g) Technology and privacy
To implement in-app metrics, Firebase, a mobile application development platform developed & operated by Google will be collecting and handling personal information. Firebase will collect user behaviour metrics within the StatsCAN mobile application.
h) Potential risk that in the event of a privacy breach, there will be an impact on the individual or employee.
There is a very low risk of a breach of some of the personal information being disclosed without proper authorization. The impact on the individual would be minor.
i) Potential risk that in the event of a privacy breach, there will be an impact on the institution.
There is a very low risk of a breach of some of the personal information being disclosed without proper authorization. The impact on the institution would be minor.

Conclusion

This assessment of the StatsCAN app did not identify any privacy risks that cannot be managed using existing safeguards.

Resources

• Calgary-based Canadian Natural Resources Limited announced it had entered into an agreement to acquire from Chevron Canada Limited its 20% interest in the Athabasca Oil Sands Project and Chevron's 70% operated working interest of light crude oil and liquids rich assets in the Duvernay play in Alberta for an aggregate consideration of USD $6.5 billion. The company said the effective date for these acquisitions is September 1, 2024 and are targeted to close in the fourth quarter of 2024, subject to regulatory approvals.
• Calgary-based Enbridge Inc. announced it will build, own, and operate crude oil and natural gas pipelines in the U.S. Gulf of Mexico for the recently sanctioned Kaskida development, operated by BP Exploration & Production Company. Enbridge said that the Canyon Oil Pipeline System will have a capacity of 200,000 barrels per day and the Canyon Gathering System will have a capacity of 125 million cubic feet per day. Enbridge also said that the pipelines are expected to be operational by 2029 and that the cost of the pipelines will be approximately USD $700 million.
• Vancouver-based Western Forest Products Inc. announced that it plans to reduce lumber production in its British Columbia sawmills by approximately 30 million board feet during the period from October to December, 2024 due to a combination of market challenges including weaker lumber demand and higher U.S. softwood lumber duty rates, and factors relating to the B.C. operating environment creating a lack of available economic log supply at certain saw mills. The company said that for the full year of 2024, total lumber production will be reduced by approximately 90 million board feet.
• Illinois-based Coeur Mining, Inc. and SilverCrest Metals Inc. of Vancouver announced they had entered into a definitive agreement whereby, a wholly-owned subsidiary of Coeur will acquire all of the issued and outstanding shares of SilverCrest for a total implied equity value of approximately USD $1.7 billion. The companies said the transaction is expected to close late in the first quarter of 2025, subject to shareholder, court, and regulatory approvals.

Minimum wage

• Prince Edward Island's minimum wage increased from $15.40 to $16.00 per hour on October 1st.
• Ontario's minimum wage increased from $16.55 to $17.20 per hour on October 1st.
• Manitoba's minimum wage increased from $15.30 to $15.80 per hour on October 1st.
• Saskatchewan's minimum wage increased from $14.00 to $15.00 per hour on October 1st.

Economic and fiscal updates

• The Government of Ontario released its 2024 Ontario Economic Outlook and Fiscal Review on October 30th, which included providing a $200 taxpayer rebate early next year, proposing to further extend the temporary gas tax and fuel tax rate cuts until June 30, 2025, and expanding access for families who are seeking fertility treatment. The Government is projecting a $6.6 billion deficit in 2024-25 and real gross domestic product (GDP) growth of 0.9% in 2024 and 1.7% in 2025.
• The Government of Newfoundland and Labrador delivered its mid-year fiscal and economic update on October 30th. The Government forecasts a $218 million deficit in 2024-2025 and real GDP growth of 3.3% in 2024.

Other news

• The Bank of Canada reduced its target for the overnight rate by 50 basis points to 3.75%. The last change in the target for the overnight rate was a 25 basis points cut in September 2024. The Bank also said it is continuing its policy of balance sheet normalization.
• The Government of Canada announced reforms to one of the Temporary Foreign Worker (TFW) Program streams, and that effective November 8th, the starting hourly wage for workers coming into Canada through the high-wage stream will be increased to 20% higher than its current level. The Government said that as a result, a greater number of jobs are expected to be subject to the stricter rules of the low-wage stream.
• The Government of Canada announced the 2025-2027 Immigration Levels Plan, which includes controlled targets for temporary residents, specifically international students and foreign workers, as well as for permanent residents. The Government said the 2025–2027 Immigration Levels Plan is expected to result in a population decline of 0.2% in both 2025 and 2026, before returning to a population growth of 0.8% in 2027.
• The Government of Ontario announced it was introducing legislation, the Affordable energy Act, that would, if passed, enable the implementation of an integrated energy plan that would prioritize zero-emissions nuclear energy as the province's grid expands, support the government's expansion of energy efficiency programs, help get more electric vehicle (EV) chargers built and reduce "last-mile" connection costs for electricity infrastructure.
• TD Canada Trust, RBC Royal Bank of Canada (RBC), BMO Bank of Montreal, Canadian Imperial Bank of Commerce (CIBC), Scotiabank, and Laurentian Bank of Canada announced they were decreasing their Canadian dollar prime lending rates by 50 basis points from 6.45% to 5.95%, effective October 24th.
• The Port of Montreal announced on October 10th that a partial strike of indefinite duration in which longshoremen would not be working overtime as part of their duties had been initiated. The Port said this could result in processing delays and a backlog of containers waiting to be handled. On October 27th, the Port of Montreal announced a 24-hour strike will take place at the Port of Montreal on Sunday, October 27, in accordance with the notice filed by the Longshoremen's Union CUPE, Local 375 and that this would affect all Port of Montreal container and dry bulk terminals. On October 31st, the Port of Montreal announced that a new strike had been filed by the longshore workers' union providing for an unlimited strike affecting two container terminals and that these two terminals would be closed until further notice.

United States and other international news

• On October 2nd, United States President Joseph R. Biden, Jr. declared that major disasters exist in the State of Georgia and the Commonwealth of Virginia and ordered Federal aid to supplement Commonwealth and local recovery efforts in the areas affected by Hurricane Helene beginning on September 24, 2024, and continuing.
• On October 7th, U.S. President Joseph R. Biden, Jr. declared that an emergency exists in the State of Florida and ordered Federal assistance to supplement State, tribal, and local response efforts due to the emergency conditions resulting from Hurricane Milton beginning on October 5, 2024, and continuing.
• The Reserve Bank of New Zealand (RBNZ) lowered the Official Cash Rate (OCR), its main policy rate, by 50 basis points to 4.75%. The last change in the OCR was a 25 basis points cut in August 2024.
• The European Central Bank (ECB) lowered its three key interest rates by 25 basis points to 3.25% (deposit facility), 3.40% (main refinancing operations), and 3.65% (marginal lending facility). The last change in the deposit facility was a 25 basis points reduction in September 2024. The ECB said it intends to discontinue reinvestments under the pandemic emergency purchase programme (PEPP) at the end of 2024.
• The Bank of Japan announced it will encourage the uncollateralized overnight call rate to remain at around 0.25%.
• The European Commission announced it had completed its anti-subsidy investigation by imposing definitive countervailing duties on imports of battery electric vehicles (BEVs) from China for a period of five years. The Commission said that sampled Chinese exporting producers will be subject to the following countervailing duties: BYD: 17.0%, Geely: 18.8%, and SAIC: 35.3%. The Commission also said that other cooperating companies would be subject to a duty of 20.7% while all other non-cooperating companies would have a duty of 35.3%.
• On October 1st, the International Longshoremen's Association (ILA) announced that members had launched a strike at all major United States ports on the Atlantic and Gulf Coasts. On October 3rd, the ILA and the United States Maritime Alliance, Ltd. announced they had reached a tentative agreement on wages and had agreed to extend the Master Contract until January 15, 2025 to return to the bargaining table to negotiate all other outstanding issues. The ILA said that effective immediately, all current job actions would cease, and all work covered by the Master Contract would resume.
• Virginia-based Boeing Company announced plans to reduce the size of its total workforce by roughly 10% over the coming months. Boeing said the reductions would include executives, managers and employees.
• Kansas-based Spirit AeroSystems announced employee furloughs in response to the ongoing strike by Boeing employees that began on September 13th. The company said that effective October 28th, it will implement a 21-day furlough for approximately 700 employees working on the 767 and 777 programs and that if the strike continues beyond November, financial pressures may require it to implement additional cost savings measures including layoffs and additional furloughs.
• United Kingdom-based Rio Tinto and Arcadium Lithium plc of Ireland, a lithium chemicals producer, announced a definitive agreement under which Rio Tinto will acquire Arcadium in an all-cash transaction that values Arcadium's diluted share capital at approximately USD $6.7 billion. The companies said the transaction is expected to close in mid-2025, subject to receipt of shareholder and customary regulatory approvals and other closing conditions.
• France-based Airbus SE announced it plans to adapt its Airbus Defence and Space Division's organization and workforce at that this is expected to result in a reduction of up to 2,500 positions within Airbus Defence and Space until mid 2026.
• Germany-based Siemens AG announced an agreement to acquire Altair Engineering Inc. of Michigan, a provider of software in the industrial simulation and analysis market, for an enterprise value of USD $10 billion. Siemens said the transaction is expected to close within the second half of 2025, subject to customary closing conditions.

Financial market news

• West Texas Intermediate crude oil closed at USD $69.26 per barrel on October 31st, up from a closing value of USD $68.17 at the end of September. Western Canadian Select crude oil traded in the USD $55 to $66 per barrel range throughout October. The Canadian dollar closed at 71.86 cents U.S. on October 31st, down from 74.08 cents U.S. at the end of September. The S&P/TSX composite index closed at 24,156.87 on October 31st, up from 24,000.37 at the end of September.

Centre for Production, Distribution and Investment Statistics, Economic Statistics Field

Table of Contents

1. Introduction
2. Key definitions
   1. Price base date (PBD)
   2. Volume state date (VSD)
   3. Residential property (RES)
   4. Non-residential property (NONRES)
   5. Properties subject to municipal, provincial, territorial and federal payment-in-lieu (PIL)
3. Input data
   1. Data sources
   2. Unit reported
4. Auxiliary Data
   1. Multiple Listing Service (MLS) data
   2. Building permits and investment in building construction data
   3. Census of Population
   4. Municipal boundary changes
5. Classification
   1. Geography
   2. Type of Property (TOP)
6. Imputation for missing data
   1. Imputation of residential values
   2. Imputation of non-residential values
7. Price adjustments
   1. Choice of Source Data Vintage
   2. Jurisdictions that are not price adjusted
   3. Residential Price adjustment
      1. Modelling of assessment values
      2. Modelling of MLS monthly resale values
      3. Modelling of donor values (for Nunavut)
   4. Non-residential price adjustment
      1. Modelling of non-residential assessment data
      2. Discount Factor applied to MLS Polynomial Trend series
      3. Discount factor applied to Nunavut price index
   5. Calculating the price adjusted value
8. Volume adjustments
   1. Residential volume adjustments
   2. Non-residential volume adjustments
9. Removals and adjustments in accordance with typical property assessment and taxation practices
   1. Removal of CSDs on account of First Nations and other Aboriginal Groups
   2. Exclusion of exempt residential property
   3. Exclusions of schools, churches and hospitals
   4. Removal of properties subject to provincial-territorial and municipal payments-in-lieu of taxes
   5. Adjustments in the Northwest Territories and Nunavut
   6. Removal of machinery and equipment (M&E) values in Alberta, Northwest Territories and Nunavut
   7. Removal of personal property values in Manitoba
   8. Mixed-use properties
10. Quality control
    Annex 1. List of CSD types representing First Nations and other Aboriginal Groups
    Annex 2. List of provinces and territories with microdata in tax year 2023

1. Introduction

The Property Values Program produces annual estimates of assessment values of properties across Canada. These estimates are produced using a common price date, which corresponds to July 1st of the year preceding the tax year under evaluation. Finance Canada uses these estimates to determine fiscal capacity with respect to property taxes for the Equalization program and the Territorial Formula Financing (TFF) program. ^{Footnote 1} To ensure comparability of the data, a number of adjustments are made. They include: the coding of property categories to a common classification; the adjusting of values to a common price base date and to a common volume state (or stock) date; and the imputation of missing property values. Additionally, other removals and adjustments are carried out to produce estimates of assessment values at a common price date that meet the requirements to determine fiscal capacity.

This document presents these adjustments in more detail.

2. Key definitions

a. Price base date ^{Footnote 2}

The price base date (PBD) is also called the valuation date and corresponds to a fixed point in time when a property is valued by assessment agencies.

The Target Price Base Date (TPBD) serves as the benchmark for price adjustments within the Property Values Program. It is set as July 1st of the year preceding the tax year under assessment. For instance, the TPBD for the tax year 2023 (TY2023) corresponds to July 1st, 2022.

b. Volume state date

The volume state date (VSD) is the fixed point in time when the physical condition of properties is considered for the purpose of assessment.

The Target Volume State Date (TVSD) serves as the benchmark for volume adjustments within the Property Values Program. It is set as December 31st of the year preceding the tax year under assessment. For instance, the TVSD for the tax year 2023 (TY2023) corresponds to December 31, 2021.

c. Residential property

Defined as all types of property categorized as residential for assessment purposes in the majority of provinces and territories. It includes single and multi-unit properties, farm residences, cottages and vacation homes, mobile homes, and vacant lands which are designated for residential purposes.

d. Non-residential property

Defined as all types of property categorized as non-residential for assessment purposes in the majority of provinces and territories. It includes industrial, commercial and institutional properties, engineering construction and mining properties, and vacant lands which are designated for non-residential purposes.

Agricultural properties ^{Footnote 3} (excluding residential dwellings on farm property, which are considered residential property for the Property Values Program) as well as the value of machinery and equipment improvements on properties are excluded from final estimates.

e. Properties subject to municipal, provincial, territorial and federal payment-in-lieu

Defined as municipal, provincial, territorial and federal government-owned property for which owners remit payment-in-lieu of tax to municipal governments or local taxation authorities.

3. Input data

a. Data sources

Assessment data are collected from provincial, territorial and municipal assessment entities and are based on municipal assessment rolls. Data providers agree to provide the data on a regular basis either through formal agreements or responding per data request.

Starting in January 2018, assessment roll microdata is gradually being received from every jurisdiction, to replace the use of assessment roll aggregate data. In 2024, we received assessment roll microdata for the tax year 2023 from 12 provinces and territories, up from six provinces and territories in 2017 providing microdata for the tax year 2016. See Annex 2.

b. Unit reported

Data are reported either at the municipal, property or sub-property level.

4. Auxiliary Data

a. Multiple Listing Service data

Multiple Listing Service (MLS) data are produced by the Canadian Real Estate Association (CREA). The data are obtained via Haver Analytics, a company that is the sole distributer of CREA MLS data. MLS data are aggregate monthly residential sales data reported as dollar volume sales and the number of units sold by real estate board. Data are available at sub-provincial level for all provinces and territories with the exception of only provincial-level data for Québec, and no data available for Nunavut. MLS data files are used for price adjustment.

b. Building Permits and Investment in Building Construction data

Data on the number of residential and non-residential building permits issued, investment in construction completion, by type of work (e.g., new unit, conversion, etc.), is obtained from Statistics Canada's Building Permits (BDP) and Investment in Building Construction (IBC) programs. The data are produced monthly, by jurisdiction. IBC data files are used for volume adjustment.

c. Census of Population

Data from Census of Population are available every five years. Between census years, yearly property values, referred to as "intercensal" values, are derived using linear interpolation. ^{Footnote 4} These values are used for the imputation of missing property values.

d. Municipal boundary changes

Municipal boundary changes are mapped to the 2021 census geography using the “Interim List of Changes to Municipal Boundaries, Status, and Names.”

This report provides a summary of changes to municipal boundaries, status and names. The list is usually produced on an annual basis for changes that occurred during the previous year. A five-year list is produced on Census of population years. Property Values program uses the report for the mapping of new municipalities to the Census 2021 geography during the intercensal period. Upon the publication of the 2026 census, Property Values program will reconcile the intercensal municipal changes to the new census geography.

5. Classification

a. Geography

The municipalities covered by the collected data are assigned to Census Subdivisions (CSDs) updated annually by Statistics Canada's Data Integration Infrastructure Division, using the Standard Geographical Classification system. The assignment of CSDs is revised yearly to reflect changes (municipal amalgamations, legal status changes, etc.) that occur during the year. During the period between censuses, these municipal changes are mapped to their prior census subdivisions, census year 2021. Accumulated intercensal changes are revised to their new CSDs in the year following the publication of the census.

CSDs containing First Nations or other autonomous or self-governing areas are out of scope for Fiscal Arrangements purposes (see Annex 1). As a result, these CSDs are not included in the provincial estimates.

b. Type of property

The type of property classification is reviewed to improve comparability of the data amongst provinces and territories. The classification of properties is more precise when more details are available in the data.

6. Imputation for missing data

There exist municipalities or regions that are not assessed by provincial or territorial assessment agencies, and therefore no property taxes are levied. As a result, assessment values are missing for some jurisdictions, mostly in unorganized areas. ^{Footnote 5} Additionally, on occasion, some municipalities submit their assessment values to assessment agencies later than when the data are required. Missing property assessment values for these municipalities are imputed.

For taxation year 2023, there were 146 jurisdictions with missing data that were imputed, 136 of which were in Newfoundland-and-Labrador, 8 were in Northwest Territories and 2 were in Saskatchewan.

a. Imputation of residential values

The imputation strategy relies on three key assumptions: (1) the reported owner-occupied dwelling values from CSDs in the same province and population group are expected to be similar; (2) the composition of the residential housing mix is consistent between the donor and imputed population group; and (3) property tax assessors would have valued properties similarly in both the donor group and imputed population group.

During the intercensal period, an owner-occupied dwelling value (Intercensal OODV) is found from the forward extrapolation in time to the relevant Tax Year, for the CSD, the line that connects the owner-occupied dwelling counts from two prior census values. For Tax Year 2023 those would be the 2016 Census of Population and the 2021 Census of Population.

The concept of owner-occupied dwelling is different from the concept of residential property value. Residential property value in a geography is the sum of all of owner and non-owner-occupied dwellings, vacant dwellings properties and vacant residential land. Let RV be the ratio of owner-occupied dwelling value (Intercensal OODV) to residential property value (RPV). Based on the key assumptions stated above, we assume that RV of the donor CSD is equal to the RV of the imputed CSD:

$\mathrm{<em>RV</em>}=\frac{\mathrm{<em>Intercensal\; OODV\; of\; Donor</em>}}{\mathrm{<em>RPV\; of\; Donor</em>}}=\frac{\mathrm{<em>Intercensal\; OODV\; of\; Imputed</em>}}{\mathrm{<em>IRPV</em>}}$

Therefore, the “imputed residential property value (IRPV)” could be calculated as

$\mathrm{<em>IRPV</em>}=\frac{\mathrm{<em>Intercensal\; OODV\; of\; Imputed</em>}}{\mathrm{<em>RV</em>}}$

In order to produce an imputed value that best reflects the price base date and volume state date:

• the number of private dwellings value is taken from the yearly intercensal file of the same year as the volume state date of the raw file; and
• the average value of owner-occupied dwellings is taken from the yearly intercensal file or derived from assessed values of the same year as the price base date of the raw file.

The resulting imputed values are then processed and adjusted ^{Footnote 6} using the same methodology as for raw values.

b. Imputation of non-residential values

Unlike the imputation for residential property values where dwelling values from intercensal files can be used to estimate the value of residential properties, no similar direct indicator is available for non-residential properties. Therefore, non-residential values are imputed using data of CSDs with similar Census population counts within the same province or territory.

Ratios of the total non-residential values over the total population are calculated using data from CSDs for each population class (see table 1 below) for each province and territory. These ratios ^{Footnote 7} are then applied to the population count of the missing CSD to derive the imputed non-residential value. Most of the missing CSDs are from rural areas.

Table 1 – Population class used for imputation on non-residential values ^{Footnote 8}

Population Class	Description
1	Rural
2	Small Sized Municipalities
3	Medium Sized Municipalities
4	Large Sized Municipalities

7. Price adjustments

Due to differences in assessment practices and frequency of revaluation practices, data received do not always align with the target price base date (TPBD) of July 1 of the year preceding the taxation year.

a. Choice of source data vintage

To minimize price adjustments, the data from the file whose price base date (PBD) most closely aligns with the target price base date (TPBD) is used to produce the estimates of a given taxation year. If two input files have the same time interval between their price base date and the target price base date, the file with the closest volume state date is selected.

b. Jurisdictions that are not price adjusted

The following provinces do not undergo price adjustments since their price base date corresponds to the desired target price base date:

• Quebec
• Alberta
• British Columbia

c. Residential price adjustment

Sale and resale values are used in the reassessment of properties by assessment agencies. Multiple Listing Service (MLS) resale data is a suitable candidate as a proxy for this information. However, sales data are not the only information that are used by assessment agencies in determining assessment values. Other information such as demolition/construction permits, renovation permits, construction costs, physical inspection and other indicators are used in their complex modelling methodology. Also, MLS resale values are a subset of all residential property values as they exclude private sales as well as properties that have not sold in many years. By consequence, although they are a good indicator, MLS resale values do not always closely follow assessment values price movements.

Statistics Canada does not attempt to replicate the complex modelling of assessment agencies. Instead, it favours the use of price indices to adjust assessment values to the target price base date.

i. Modelling of assessment values

For certain provinces, reassessments occur yearly or on a frequent basis and the target price base date is close to the price base date of the data received. To make better use of the assessment data collected since the onset of this program and to improve the quality of estimates, a price index is generated by calculating the polynomial trend^{Footnote 9} of average values by property classes. Using average values excludes the effect due to yearly changes in volume (new construction and demolition) and help isolate price movements. Such an index is called Assessment Roll Trend (AR Trend). This modelling is performed at the provincial level.

This method is used in the following provinces:

• Newfoundland
• Prince Edward Island
• Nova Scotia
• New Brunswick

ii. Modelling of MLS monthly resale values

For remaining provinces and territories (except Nunavut), in order to represent yearly price movements, a price index is generated by calculating the polynomial trend of seasonally adjusted MLS monthly average resale values. These polynomial trend series are calculated by MLS jurisdiction and applied by CSD.

This method is used in the following provinces and territories:

• Ontario
• Manitoba
• Saskatchewan
• Yukon
• Northwest Territories

iii. Residential price index for Nunavut

As resale data do not exist for Nunavut, Statistics Canada uses data for the region of northern Quebec ^{Footnote 10} as a proxy for this territory. ^{Footnote 11} The property assessment data are provided by the provincial government of Quebec.

The Nunavut residential index is calculated using an unweighted average of residential and non-residential property values reported. ^{Footnote 12}

An annual series is generated and converted into a monthly series by adding one twelfth of the dollar difference between two observations to each successive month between observed values (linear interpolation), creating a monthly index. Residential price-adjustments are then applied to Nunavut property values using the same algorithm (for ratios) designed for resale data.

d. Non-residential price adjustment

Unlike residential properties, non-residential properties (more specifically industrial, commercial, and industrial) are not often for sale. It is therefore comparatively more difficult to find appropriate market indicators to use for non-residential price adjustment. To overcome this, the correlation between residential and non-residential price changes was analysed.

A regression analysis was performed, and a model was constructed using assessment data from four provinces: Prince Edward Island, New Brunswick, Quebec, and British Columbia. The reasons for using these specific four provinces are twofold: (1) these provinces evaluate their non-residential property stock on an annual basis ^{Footnote 13} and (2) they report data for both assessment values and numbers of non-residential properties. This level of detail allowed the derivation of the annual non-residential price movements. The conclusion was to use the model coefficient of 0.73 as a discount factor to the residential series.

The discount factor methodology was satisfactory for several years, while MLS resale values observed a consistent behaviour compared to non-residential values. However, over the last 3 years, the correlation between residential and non-residential values became weaker. This combined with the fact that assessment data was collected since 2006, it became realistic to favour the development of the polynomial trend of assessment data (AR Trend) methodology to replace the discount factor methodology, when possible.

i. Modelling of non-residential assessment data

Like the modelling of residential assessment data, non-residential assessment data is modelled using polynomial trend of average values by broad property types.

This method is used in:

• Newfoundland (provincial level)
• Prince Edward Island (provincial level)
• Nova Scotia (provincial level)
• New Brunswick (provincial level)
• Ontario (separate modelling for Toronto and rest of province)
• Manitoba (separate modelling for Winnipeg and rest of province)

ii. Discount factor applied to MLS polynomial trend series

For remaining provinces and territories (except Nunavut), it is not possible to model the assessment data as the reassessments cycle is long and there is not yet enough source data for modelling. In these cases, the discount factor is used to adjust the non-residential property values by applying it to the MLS polynomial trend series of residential properties. In future, it may become possible to update this methodology, as more assessment data is received.

This method is used in:

• Saskatchewan
• Yukon
• Northwest Territories

iii. Discount factor applied to Nunavut price index

Similarly, the discount factor is applied to the Nunavut residential price index.

e. Calculating the price adjusted value

It involves price index preparation, price adjustment ratio and adjusted value calculation.

Price index is generated using polynomial regression model on either data of MLS prices or of assessment averages.

The price adjustment ratio is calculated by taking the value of the index value representing the month of the target price base date (TPBD) over the index value for the month of the price base date (PBD) of the source data. This price adjustment ratio is then applied to the assessment value to yield the adjusted value on the month of the target price base date.

$Rati{o}_{\mathit{P}\mathit{A}\mathit{D}\mathit{J}}\mathit{=}IndexValu{e}_{\mathit{T}\mathit{P}\mathit{B}\mathit{D}}\mathit{ }\mathit{/}\mathit{ }IndexValu{e}_{\mathit{P}\mathit{B}\mathit{D}}\mathit{ }$

$AssessmentValu{e}_{\mathit{T}\mathit{P}\mathit{B}\mathit{D}}\mathit{ }\mathit{=}\mathit{ }Rati{o}_{\mathit{P}\mathit{A}\mathit{D}\mathit{J}}\mathit{ }\mathit{\times }\mathit{ }AssessmentValu{e}_{\mathit{P}\mathit{B}\mathit{D}}$

8. Volume adjustments

Volume adjustments ensure that properties reflect a common volume state date as of January 1st of the taxation year. For assessment data that reflects a volume state date earlier or later than the target volume state date, the value of all completed construction that occurred in the period between the two dates is estimated using Statistics Canada's monthly Building Permits Program or from the Investment in Building Construction Program and then added or subtracted, as the case may be, from the total property values. This methodology is used for both residential and non-residential property values.

a. Residential volume adjustments

For residential properties, the volume adjustment is calculated by estimating the construction that was completed in between the volume state date and the target volume state date using the investment in construction completion values.

Construction completion values represent the total investment in construction available upon completion of construction. Monthly values that fall between the volume state date and the target volume state date are summed for an estimated total volume adjustment for the period. Residential volume adjustments typically account for less than 2% of estimated total values.

b. Non-residential volume adjustments

Same as for residential volume adjustments, non-residential investment in construction completion values are used in the calculations of volume adjustments. Non-residential volume adjustments could slightly exceed 2% of estimated total values.

9. Removals and adjustments in accordance with typical property assessment and taxation practices

a. Removal of CSDs on account of First Nations and other Aboriginal Groups

Census subdivisions containing First Nations reserves, and autonomous or self-governing areas are removed as they are deemed out of scope. Such CSDs are identified based on their CSD type.^{Footnote 14}

b. Exclusion of exempt residential property

In some provinces, certain properties are identified as exempt from property taxes as presented in the input files received from the assessment agencies. Any value associated with these properties are excluded from estimates for the purposes of fiscal arrangements.

c. Exclusions of schools, churches and hospitals

The most important non-residential properties which are generally exempt from property taxes are schools, churches and hospitals (S/C/H).

Some provinces and territories provide detailed breakdowns of S/C/H in their assessment data. For these provinces and territories, the exact proportion of S/C/H is removed from the final estimates.

For provinces and territories where the S/C/H breakdowns are not available, the proportion of the S/C/H assessment values relative to total assessment values for non-residential properties is estimated by calculating and applying the proportion of S/C/H property values from a similar reporting province or territory. It should be noted that values for engineering and mining properties are excluded from the total assessment value for non-residential properties used in the calculation of the S/C/H proportions.

The list of provinces and territories used in the calculation of estimated S/C/H proportion depends on data availability and can change from one year to the next as microdata is received.

d. Removal of properties subject to provincial-territorial and municipal payments-in-lieu of taxes

Instead of regular property taxes, federal and provincial governments usually remit a payment in lieu of taxes (PILT) for their exempt properties. However, only federal PILT property represents fiscal capacity for the consolidated provincial-territorial-municipal-local sector; provincial and territorial PILT properties and municipal institutional properties are excluded.

e. Adjustments in the Northwest Territories and Nunavut

Unlike in provinces and the Yukon, property assessments in the Northwest Territories and Nunavut do not consistently follow market value standards.

Land values within the municipal taxation areas (Iqaluit in Nunavut; Yellowknife, Fort Simpson, Fort Smith, Hay River, Norman Wells and Inuvik in NWT) reflect full market value, while land values in the remainder of the two territories (i.e. in the General Taxation Areas) are, according to the data provider, based on average regional development costs.

Improvements (i.e. buildings) in both territories are assessed based on depreciated Edmonton construction costs, using Alberta's depreciation schedule. The value so determined for Yellowknife is then multiplied by a factor of 1.35, which is set out in regulations. According to the assessment data provider, this was done to reflect Yellowknife's actual construction costs relative to Edmonton's. Yellowknife's assessed building values therefore approximately reflect market value. ^{Footnote 15}

Outside of Yellowknife, in the two territories, a discount factor of 0.666 has been applied to building values initially assessed at depreciated Edmonton construction costs. This factor is also set out in regulations and, according to the assessment data provider, was introduced to encourage development. Upon data entry, this embedded 0.666 scaling factor is removed from the building values in the Northwest Territories outside of Yellowknife and Nunavut.

f. Machinery and equipment values

Property values for machinery and equipment (M&E) components in the non-residential category are deemed to be out of scope.

g. Removal of personal property values in Manitoba

The assessment roll in Manitoba includes personal property, oil and gas at time of extraction is taxed, which are not considered real property. Such property values are excluded from the estimate.

h. Mixed-use properties

Some properties are used for both residential and non-residential purposes. In cases where no further breakdowns are available, the values of mixed-use properties are redistributed between residential and non-residential property types according to the existing distribution of total residential and non-residential property values by CSD. In cases where further breakdowns are available, mostly in jurisdictions where microdata was received, the values are assigned according to the exact breakdown. Mixed-use residential and non-residential properties that are redistributed represent 0.015% of the total valuation of properties in Canada.

One of the most common cases of mixed-use type properties are of a building consisting of ground level commercial with one or more floors of residential units above.

10. Quality control

Statistics Canada's quality assurance framework requires an assessment of data relevance, accuracy, timeliness, accessibility, interpretability and coherence. The quality of the raw input data collected from provincial, territorial and municipal assessment departments and agencies cannot be evaluated in this framework. However, confrontational analysis is performed to compare the source data to existing statistical programs and public information such as annual reports obtained from provincial websites and assessment agencies. Any irregularities identified are carefully reviewed and analyzed before the official release of the data.

Total adjusted residential estimates, for both taxable and exempt properties, are compared to Statistics Canada's Census of Population. The coherence of the values is examined by census coverage analysis, which compares the source data to private dwelling counts and values found in Statistics Canada's Census of Population.

Annex 1. List of CSD types representing First Nations and other Aboriginal Groups ^{Footnote 16}

The following are the list of CSD types representing First Nations and other Aboriginal groups presented by province and territory.

Annex 1. List of CSD types representing First Nations and other Aboriginal Groups

Province / Territory	CSD Type	CSD Type description	Legal Code	Legal Code description	Number of CSDs
Nova Scotia	IRI	Indian reserve	FL	Federally legislated	2
New Brunswick	IRI	Indian reserve	FL	Federally legislated	3
Ontario	IRI	Indian reserve	FL	Federally legislated	1
Manitoba	IRI	Indian reserve	FL	Federally legislated	9
Manitoba	S-É	Indian settlement	U	Not legal municipality - aboriginal geography	1
Saskatchewan	IRI	Indian reserve	FL	Federally legislated	3
Saskatchewan	S-É	Indian settlement	U	Not legal municipality - aboriginal geography	1
Alberta	IRI	Indian reserve	FL	Federally legislated	1
British Columbia	IGD	Indian government district	PL	Provincially legislated - legal municipality	2
British Columbia	IRI	Indian reserve	FL	Federally legislated	3
British Columbia	NL	Nisga'a land	FL	Federally legislated	1

Annex 2. List of provinces and territories with microdata in tax year 2023

Annex 2. List of provinces and territories with microdata in tax year 2023

Province / Territory	Tax year 2016	Tax year 2023
Newfoundland and Labrador	Yes	Yes
Prince Edward Island	No	Yes
Nova Scotia	Yes	Yes
New Brunswick	No	Yes
Quebec	No	No
Ontario	Yes	Yes
Manitoba	No	Yes
Saskatchewan (except Prince Albert)	No	Yes
Alberta	No	Yes
British Columbia	No	Yes
Yukon	Yes	Yes
Northwest Territories	Yes	Yes
Nunavut	No	Yes
Total number of provinces and territories with microdata	5	12

Canadian Centre for Education Statistics

Postsecondary Student Information System (PSIS)
Record Layout, Files and Data Element Descriptions

For use when reporting data for 2023/2024 and previous academic years

December 2024

Postsecondary Student Information System (PSIS)
Record Layout, Files and Data Element Descriptions
Data Submission Information at a Glance

This information is collected under the authority of the Statistics Act, Revised Statutes of Canada, 1985, Chapter S-19.

Completion of this questionnaire is a legal requirement under this act.

Survey Purpose

The Postsecondary Student Information System (PSIS) is a national survey that enables Statistics Canada to provide detailed information on enrolments and graduates of Canadian public postsecondary institutions in order to meet policy and planning needs in the field of postsecondary education. The information may also be used by Statistics Canada for other statistical and research purposes.

Confidentiality

Statistics Canada is prohibited by law from releasing any information it collects which could identify any person, business, or organization, unless consent has been given by the respondent or as permitted by the Statistics Act. Statistics Canada will use the information from this survey for statistical purposes and research purposes.

Please note that in the following record layout, words designating the masculine gender include the feminine gender.

PSIS Database Structure

The PSIS database holds data at two (2) levels: (1) institution, program and course data; and (2) student data. The institution, program and course data includes a list of all public postsecondary institutions in Canada, and an inventory of all programs and courses offered through these institutions. The student data contains demographic, program, and course information for students registered at these institutions.

The information required to feed the PSIS database is stored in the six (6) PSIS data files, which are transmitted to Statistics Canada. The six (6) files describe either the student, or the institution and its available programs. The files and their interrelationships can be described as follow: The Institution Description file is linked to the Institution Program and Institution Course files whereas the Student Description file is linked to the Student Program and Student Course files. In addition, the Student Program file is linked to the Institution Program file and the Institution Course file is linked to the Student Course file.

Note to user: Data elements not in bold are those required to identify a unique record (each column corresponds to a file). Data elements required to link the files between them are identified by row. For example, the data elements required to link the ID and the SC files are: 1005/1000, 1025, 1035 and 1036.

Six (6) PSIS Input Files and File Description

Listed below are six (6) PSIS input files which you will need to submit in your annual report cycle (due by February 1, 2022). A brief description of each file is also outlined below.

1. Institution Description (ID) file
2. Institution Program (IP) file
3. Institution Course (IC) file
4. Student Description (SD) file
5. Student Program (SP) file
6. Student Course (SC) file

Institution Metadata

1. Institution Description (ID) file

(Postsecondary institution metadata; number of elements = 5; length = 132 bytes)
The Institution Description (ID) file lists and describes the different periods of academic activity by which programs and courses are organized. Specifically, the records contained on the ID file describe how a postsecondary institution divides its year into periods (sessions, terms, or other components) during which courses are commonly offered. Postsecondary institutions provide one (1) ID record for each period that begins or ends during the twelve (12) month report cycle. (For more detailed information, please refer to the Institution Description (ID) file section of this document.)

Please note that the information contained on the ID file is used to validate the Institution Code (element ID1025) provided to all of the six (6) PSIS files. It is also used to validate the period in which student courses are offered (element ID1035). Each period in which courses are reported on the Student Course (SC) file must also be present on the ID file. 

2. Institution Program (IP) file

(Postsecondary institution metadata; number of elements = 11; length = 314 bytes)
The Institution Program (IP) file contains program code and program name (IP2000, IP2020), program duration (IP2070 and IP2071), credential type (IP2010) and other characteristics of each program offered by the postsecondary institution. The IP file is an inventory of the programs offered by the postsecondary institution. It contains one (1) IP record for each program offered during the twelve (12) month cycle.
Please note that there is a logical link between this file and the Student Program (SP) file. The SP file contains one (1) record for each combination of student and program. Each program code reported on the SP file must be present on the IP file. (For more detailed information, please refer to the Institution Program (IP) file section of this document.)

3. Institution Course (IC) file

(Postsecondary institution metadata; number of elements = 7; length = 260 bytes)
The Institution Course (IC) file contains course code and course name (IC3000, IC3020), course credits normally awarded and course credit units (IC3090, IC3091) and other characteristics of each course offered by the postsecondary institution. The IC file is an inventory of the courses offered by the postsecondary institution. One (1) IC record for each of the courses offered during the twelve (12) month cycle should be reported.
Please note that there is a logical link between this file and the Student Course (SC) file. The SC file contains one (1) record for each combination of student and course. Each course code reported on the SC file must be present on the IC file. (For more detailed information, please refer to the Institution Course (IC) file section of this document.)

Student Metadata

4. Student Description (SD) file

(Student metadata; number of elements = 37; length = 1018 bytes)
The Student Description (SD) file contains demographic and other descriptive information about the students attending the various postsecondary institutions. Among others, it contains student name (SD4040 to SD4050), birth date (SD4230), gender (SD4240), Social Insurance Number (SIN) (SD4020), contact information (SD4060 to SD4180), and characteristics such as whether or not the student has self-identified as an Aboriginal person (SD4210).This file contains one (1) record per student per postsecondary institution.

Please note that there is a logical link between this file and the Student Program (SP) file. The SD file contains one (1) record per student enrolled in a program or who has graduated from a program. (For more detailed information, please refer to the Student Description (SD) file section of this document.)

5. Student Program (SP) file

(Student metadata; number of elements = 17; length = 402 bytes)
The Student Program (SP) file contains one (1) record for each program in which the student was enrolled during the reporting cycle.  The student program record includes the original dates in which the student started/ended a program (SP5010, SP5090), student status in program at end of report cycle (SP5100), specialization or major field of study (SP5015 and SP5016), total transfer credits (SP5220), cumulative credits for program (SP5230) and other characteristics of the student’s program as recorded by the postsecondary institution.

Please note that there is a logical link between this file and the Institution Program (IP) file. Each program code reported on the SP file must be present on the IP file. In addition, there is a logical link between this file and the Student Description (SD) file. Each student record reported on the SD file must be associated with at least one (1) program record on the SP file. (For more detailed information, please refer to the Student Program (SP) file section of this document.)

6. Student Course (SC) file

(Student metadata; number of elements = 10; length = 258 bytes)
The Student Course (SC) file contains one (1) record for each course in which the student was enrolled during the reporting cycle. Also, include one (1) course record for students that are registered either in a CO-OP work term, writing a thesis, or performing any other academic activities related to their program but not structured as a course. The student course record includes the dates which the student started/ended the course (SC6020, SC6021) and status in course at end of report cycle (SC6030).

Please note that there is a logical link between this file and the Institution Course (IC) file. Each course code reported on the SC file must be present on the IC file. In addition, there is a logical link between this file and the Student Program (SP) file. Each program in which the student was enrolled (SP file) must be associated with at least one (1) course record on the SC file. The SP record for a student who graduates during the report cycle and for which the student did not have any course registrations during the report cycle (e.g., the student applies for and is granted a credential during the current report cycle for work completed in an earlier cycle) should not have an associated SC record. (For more detailed information, please refer to the Student Course (SC) file section of this document.)

Postsecondary Student Information System (PSIS)
Institution Description (ID) File

The following data elements are required to identify unique records: Start date of report cycle (ID1005), Institution Code (ID1025), Period Code (ID1035), and Sub-period Code (ID1036)

Record Layout, Files and Data Element Descriptions

First, select your start date of report cycle and store it in element ID1005. Please see the description of ID1005 for guidance on how to select your report cycle.

The Institution Description (ID) file lists and describes the different periods of academic activity by which programs and courses are organized. Specifically, the records contained on the ID file describe how a postsecondary institution divides its year into periods (sessions, terms, or other components) during which courses are commonly offered. Postsecondary institutions provide one (1) ID record for each period that begins or ends during the twelve (12) month report cycle. Periods can be of any length (although not many courses span twelve (12) consecutive months or more). Periods include the time allocated for exams. Your periods can begin before your report start date and can extend beyond the end of your report cycle. See data element ID1035, (Period Code) for suggestions on how to report periods.

Institution Code (element 1025 on all six (6) PSIS files)

You will find your code(s) on the list of postsecondary institution codes supplied by Statistics Canada.

Each eight (8)-digit code comprises a two (2)-digit province, a three (3)-digit postsecondary institution and a three (3)-digit campus. The list contains one (1) code for the parent postsecondary institution and one (1) for each campus. The parent institution code has 000 in the last three (3) digits, while the campuses are numbered sequentially beginning with 001. Postsecondary institutions without campuses have only a parent institution code. Examples: Postsecondary institution XXX in province P1 has two (2) campuses and postsecondary institution YYY in province P2 has none. Their codes would appear on the code list as follows: Postsecondary institution XXX includes P1XXX000 for Parent Institution; P1XXX001, Campus 1; P1XXX002; Campus 2; and Postsecondary institution YYY includes P2YYY000 for Parent Institution.

If your postsecondary institution has campuses, you can choose to report your PSIS data at the campus level or at the parent (000) level depending on how you store your postsecondary institution metadata and student metadata on your own administrative systems. We recommend reporting at the lowest level available, as more detailed analysis can potentially be done.

The following combinations of reporting level and file type are valid. Choose one (1) reporting level for all three (3) postsecondary institution metadata files and one (1) for all three (3) student metadata files; (i.e., do not combine parent- and campus-level reporting within the three (3) postsecondary institution metadata files or within the three (3) student metadata files).

Postsecondary institution metadata (ID, IP, IC) Parent Institution and Campus is paired with Student metadata (SD, SP, SC) Parent Institution and Campus.

Postsecondary Student Information System (PSIS)
Institution Program (IP) File

The following data elements are required to identity unique records: Year of Start of Report Cycle (IP1000), Institution Code (IP1025), Program Code (IP2000), and Credential Type (IP2010)

Record Layout, Files and Data Element Descriptions

The Institution Program (IP) file contains program code and program name (IP2000, IP2020), program duration (IP2060 to IP2071), credential type (IP2010, IP2011), educational entrance requirements (IP2150 to IP2155) and other characteristics of each program offered by the postsecondary institution. The IP file is an inventory of the programs offered by the postsecondary institution. It contains one (1) IP record for each program offered during the twelve (12) month cycle.

There is a logical link between this file and the Student Program (SP) file. The SP file contains one (1) record for each combination of student and program. Each program code reported on the SP file must be present on the IP file.

If students are taking courses without registration in a program, create as many non-program records on the IP file for each of the non-program categories appropriate to your institution (see element IP2015). This will involve:

1. putting a program name in element IP2020;
2. putting a program code in element IP2000;
3. putting a value of "98 - Not applicable" in element IP2010;
4. assigning the students to this program in the SP file; and
5. following the instructions in the other elements for the assignment of "Not Applicable" code for this non-program record.

Please refer to the "Program type" and "Non-credit" entries of the Reporting Guide for Program Type and Credential Type for additional information on the non-programs.

Universities that store their program data with separate fields for degree and specialization(s) or major field(s) of study may be able to report just the degree code in element IP2000 and the student’s specialization(s) or major field(s) of study in elements SP5015, SP5016 and SP5017 on the SP file. For example, if all BA programs have the same duration, credit requirement, provincial funding code, entrance requirements, on-the-job training (OJT) components, etc., then you could report only one (1)  IP record for all BA’s, with element IP2000 = "BA", and then show the different majors on the SP records of individual students. But if some of the BA programs have different durations or credit requirements, etc., then you must provide separate IP records for them. In these cases, you could combine the degree code and the specialization(s) or major field(s) of study in element IP2000 of the IP and SP records.

For programs that award two (2) credentials, please refer to the "Joint credential program" entry of the Reporting Guide for Program Type and Credential Type.

For programs that may lead to the option of several qualifications in terms of successful outcomes, such as one program (same code) which leads to a certificate or a diploma, please refer to the "programs with multiple exit options" entry of the Reporting Guide for Program Type and Credential Type.

For apprenticeship programs, provide one (1) IP record for each year or level of the program.

For the entire reporting period, describe all the programs offered during the twelve (12) months beginning on your Report Cycle Start Date.

Postsecondary Student Information System (PSIS)
Institution Course (IC) File

The following data elements are required to identify unique records: Year of Start of Report Cycle (IC1000), Institution Code (IC1025), and Course Code (IC3000)

Record Layout, Files and Data Element Descriptions

The Institution Course (IC) file contains course code and course name (IC3000, IC3020), course duration and course duration units (IC3080, IC3081), course credits normally awarded and course credit units (IC3090, IC3091) and other characteristics of each course offered by the postsecondary institution. The IC file is an inventory of the courses offered by the postsecondary institution. One (1) IC record for each of the courses offered during the twelve (12) month cycle should be reported.

There is a logical link between this file and the Student Course (SC) file. The SC file contains one (1) record for each combination of student and course. Each course code reported on the SC file must be present on the IC file.

For the entire reporting period, describe all the courses offered during the twelve (12) months beginning on your Report Cycle Start Date.

Postsecondary Student Information System (PSIS)
Student Description (SD) File

The following data elements are required to identity unique records: Year of Start of Report Cycle (SD1000), Institution Code (SD1025), and Institution's Student Identifier (SD4000)

Record Layout, Files, and Data Elements Descriptions

The Student Description (SD) file contains demographic and other descriptive information about the students attending the various postsecondary institutions. Among others, it contains student name (SD4040 to SD4050), birth date (SD4230), gender (SD4240), Social Insurance Number (SIN) (SD4020), contact information (SD4060 to SD4180), and characteristics such as whether or not the student has self-identified as an Aboriginal (SD4210). This file contains one (1) record per student per postsecondary institution.

You are requested to provide one (1) record for each student registered at the postsecondary institution at any time between your Start Date of report cycle (ID1005) and the end of your winter term, or the end of your academic year if your postsecondary institution does not have a winter term. Also, include one (1) SD record for a student who graduates during the report cycle, even if the student did not have any course registrations during the report cycle (e.g., the student applies for, and is granted a credential during the current report cycle for work completed in an earlier cycle).

Also, include any students who were last registered in the previous report year and whose status in the program was "Unknown" at the time the previous year's Report was produced. The "Unknown" status refers to element SP5100 on the SP file: Status was unknown (under review or not yet determined or dependent on the completion or grading of courses that normally would have ended by the end of the report cycle). These students should be included in the Report to ensure that Statistics Canada can update their end status.

If the student was registered in more than one (1) program during the report cycle, provide only one (1) SD record and multiple Student Program (SP) records.

There is a logical link between this file and the Student Program (SP) file. The SD file contains one (1) record per student enrolled in a program or who has graduated from a program in the current reporting year.

Postsecondary Student Information System (PSIS)
Student Program (SP) File

The following data elements are required to identity unique records: Year of Start of Report Cycle (SP1000), Institution Code (SP1025), Institution's Student Identifier (SP4000), Student's Program Code (SP2000), Credential Type (SP2010), and Original Start Date in Program (SP5010)

Record Layout, Files, and Data Elements Descriptions

The Student Program (SP) file contains one (1) record for each program in which the student was enrolled during the reporting cycle. The student program record includes the original dates in which the student started/ended a program (SP5010, SP5090), student status in program at end of report cycle (SP5100), specialization or major field of study (SP5015 to SP5021), total transfer credits (SP5220), fees billed (SP5190 to SP5200), cumulative credits for program (SP5230) and other characteristics of the student’s program as recorded by the postsecondary institution.

Report one (1) SP record for each program in which the student is registered at any time during the report cycle. Also, include one (1) SP record for a student who graduates during the report cycle, even if the student did not have any course registrations during the report cycle (e.g., the student applies for and is granted a credential during the current report cycle for work completed in an earlier cycle).

If the student was registered in more than one (1) program during the report cycle, provide only one (1) SD record and multiple Student Program (SP) records.

There is a logical link between this file and the Institution Program (IP) file. Each program code reported on the SP file must be present on the IP file. In addition, there is a logical link between this file and the Student Description (SD) file. Each student record reported on the SD file must be associated with at least one (1) program record on the SP file.

Universities that store their program data with separate fields for degree and specialization(s) or major field(s) of study should report the student's degree in element SP2000 and the student's specialization(s) or major field(s) of study in elements SP5015, SP5016 and SP5017.

For programs that award two (2) credentials, please consult the "Joint credential program" entry of the Reporting Guide for Program Type and Credential Type.

If the student is taking courses without being registered in a program, do not omit the student from the SP file. Create one (1) SP record with a non-program code in element SP2000 to match the non-program record created on the IP file. Follow the instructions in the other elements of the SP file for the assignment of "Not applicable" codes for this non-program record. Please refer to the "Program type" and "Non-credit" entries of the Reporting Guide for Program Type and Credential Type for additional information on the non-program records.

Postsecondary Student Information System (PSIS)
Student Course (SC) File

The following data elements are required to identity unique records: Year of Start of Report Cycle (SC1000), Institution Code (SC1025), Institution's Student Identifier (SC4000), Student's Course Code (SC3000), Date Student Started Course (SC6020), and Number or Code of Student's Course Section (SC6070)

Record Layout, Files, and Data Elements Descriptions

The Student Course (SC) file contains one (1) record for each course in which the student was enrolled during the reporting cycle. Also, include one (1) course record for students that are registered either in a CO-OP work term, writing a thesis, or performing any other academic activities related to their program but not structured as a course. The student course record includes the dates which the student started/ended the course (SC6020, SC6021), status in course at end of report cycle (SC6030), the credits student would receive for course (SC6060), tuition fees billed for course (SC6040) and other characteristics of the student’s course as recorded by the postsecondary institution.

Report one (1) SC record for each course in which the student is registered at any time during the report cycle after the final day for course additions and deletions (as defined by your postsecondary institution: usually about two (2) weeks after classes begin). Exclude courses for which the student is wait listed. Also, exclude courses for which the student was not registered and did not actually attend, even if the student received credit for the course by means of a challenge or by some other administrative method.

There is a logical link between this file and the Institution Course (IC) file. Each course code reported on the SC file must be present on the IC file. In addition, there is a logical link between this file and the Student Program (SP) file. Each program in which the student was enrolled (SP file) must be associated with at least one (1) course record on the SC file. The SP record for a student who graduates during the report cycle and for which the student did not have any course registrations during the report cycle (e.g., the student applies for and is granted a credential during the current report cycle for work completed in an earlier cycle) should not have an associated SC record.

For the 2023/2024 report cycle, the submission deadline is February 3, 2025.

If you have any questions, please contact us by e-mail at statcan.PSIS-SIEP.statcan@statcan.gc.ca

Introduction

The Treasury Board Secretariat (TBS) and Statistics Canada (StatCan) have partnered to administer the Public Service Employee Survey (PSES). Federal public servants will be invited to complete the survey. The PSES will support the development of strategies to meet the needs of public servants and address any issues identified.

Objective

A privacy impact assessment for Public Service Employee Survey (PSES) was conducted to determine if there were any privacy, confidentiality or security issues with this activity and, if so, to make recommendations for their resolution or mitigation.

Description

This voluntary survey is conducted under the authority of the Statistics Act. It is planned that this partnership will continue; with the PSES to be collected every two years, and other data collection activities happening in alternate years. In the past, Statistics Canada conducted the PSES every three years, with no alternate year activities.

The project also includes the acquisition of PSES 2018, 2019 and 2020 data from the Treasury Board Secretariat of Canada (TBS) under the authority of the Statistics Act to ensure all cycles of the PSES are in one data environment. This is essential to supporting the trend analysis needs of PSES data users, researchers and participating departments and agencies.

Risk Area Identification and Categorization

The Risk Area Identification and Categorization remains unchanged from the original.

Conclusion

This assessment of the Public Service Employee Survey did not identify any privacy risks that cannot be managed using existing safeguards.

Table of contents

• Message from the Canadian Statistics Advisory Council
• Recommendation 1
• Recommendation 2
• Recommendation 3
• Recommendation 4
• 1. High-quality data are central to Canada's economic success and well-being
  • 1.1 Misinformation and disinformation undermine trust in quality data
  • 1.2 Leadership is key to the development of data across government
• 2. Canada's societal issues demand greater collaboration and data sharing
• 3. Harness technology and skills to move forward and lead the way and not be left behind
  • 3.1 Performance and security with cloud technology
  • 3.2 Role of artificial intelligence in statistics
  • 3.3 Attracting and retaining data science and modern analytics skills
• 4. Keeping the momentum
• Endnotes

Message from the Canadian Statistics Advisory Council

The world has witnessed profound economic, social and technological change over the last decade, and Canada has been no exception. In such a dynamic environment, it is paramount that the Government of Canada and Statistics Canada keep pace with change.

Good data and statistics are essential to support economic growth and ensure Canada's prosperity and well-being. Poor data lead to bad decisions with costly consequences.

To assure that Canada has trusted data and high-quality statistics, it is important that Statistics Canada continues its modernization efforts to keep pace with technological and methodological change. Their data and statistics need to reflect a rapidly changing economy and evolving society. This requires modern data science that responsibly adopts the latest technologies, analytical skills that draw on multiple sectors, and coordinated and intersectional data. Programs must be funded appropriately to keep pace with social and technological change. In the long run, decisions made with timely good data are more insightful, effective and save costs.

Official statistics produced by Statistics Canada and other data producers in the national statistical system are increasingly called into question through misinformation and disinformation. This puts their authority, legitimacy and critical role in society at risk. Leaders and experts in the public, private, academic and media sectors all have a role to play in ensuring the health of the national statistical system, including calling out the misinformation and disinformation that comes with incorrect or misleading statistics.

Clear leadership and stability are key to a strong statistical system. The Council is grateful to André Loranger, the Acting Chief Statistician of Canada (who is an ex officio member of the Council), and his excellent team for responding to our requests for information with both written and oral presentations. A timely permanent appointment is essential to ensure long-term leadership and stability as the agency responds to the changing needs of society. We offer particular thanks to Étienne Saint-Pierre, Gaëlle Miollan and Sam Ndayishimye of the Canadian Statistics Advisory Council Secretariat for their advice and assistance. We are also grateful to former Chief Statistician, Anil Arora, who offered invaluable insight and support to the Council.

Signed:

The Canadian Statistics Advisory Council

• Dr. Howard Ramos, Chairperson
• Dr. Anke Kessler
• Annette Hester
• Dr. Benoit Dostie
• Dr. Catherine Beaudry
• David Chaundy
• Jan Kestle
• Dr. Stephen Tapp
• Vinamra Mathur

Recommendation 1:
Foster trust and data literacy

Good data and statistics are essential to support economic growth and to ensuring Canada's prosperity and well-being. Poor data can lead to bad decisions with costly consequences. This is true for everyone, including for individual Canadians, for businesses and for the public sector.

Official statistics produced by Statistics Canada and other data producers in the national statistical system are increasingly called into question through misinformation and disinformation, which puts their authority, legitimacy and critical role in society at risk.

Leaders and experts in the public, private, academic and media sectors all have a role to play in calling out misinformation and disinformation that come with incorrect or misleading statistics and charts, or analyses that misinterpret them.

The Chief Statistician of Canada should

1. foster informed public dialogue about the importance of quality data based on robust methods for effective decision making
2. raise awareness of the principles of official statistics including relevance, impartiality, professional standards and transparency across traditional media and social media
3. promote and enhance Statistics Canada's comprehensive data literacy program to enable Canadians and decision makers to assess and use statistical data more effectively.

The Minister of Innovation, Science and Industry should

4. promote the importance of official statistics produced within the national statistical system, including those produced by Statistics Canada, to support economic growth and ensure Canada's prosperity and well-being.

Recommendation 2:
Lead effective national data strategies

Effective national data strategies start with a common understanding among Canadians and governments of priority issues and data required to inform these priorities. Data needs and cost assessments must be conducted for every stage of proposed projects or programs, from planning and implementation to evaluation of results, as well as identifying lessons learned and best practices. When data are shared across jurisdictions, there is a dramatic increase in the ability to plan and evaluate the benefits of programs.

The Chief Statistician of Canada should continue to

1. provide leadership in identifying data gaps and data-sharing opportunities across jurisdictions
2. establish and lead partnerships for developing and coordinating statistical data flows at the federal level
3. set data and statistical methodology standards nationwide.

The Minister of Innovation, Science and Industry should

4. recognize and support Statistics Canada's leadership role in the development of national statistics and national statistical data standards as part of the 2023-2026 Data Strategy for the Federal Public Service.

Recommendation 3:
Invest in technology and data skills

It is paramount that Statistics Canada continues its modernization efforts to keep pace with technological and methodological change. Their data and statistics need to reflect a fast-paced economy and changing society. This requires modern data science and analytical skills, including data coordination, data interpretation, data visualization, geospatial analysis and computational modeling. These investments not only keep pace with change, but importantly increase efficiency and save costs in the long run. This, in turn, better meets Canada's data needs.

The Chief Statistician of Canada should continue to

1. invest in training Statistics Canada staff and in internships to foster data science skills for developing and using new methods and data sources.

The Minister of Innovation, Science and Industry should

2. support the federal government's 2023–2026 Data Strategy for the Federal Public Service with a whole-of-government approach to technology including cloud computing, modern data methods and responsible use of artificial intelligence
3. support ongoing investments in Statistics Canada's cloud infrastructure platform to ensure that its statistical programs effectively meet the needs and expectations of Canadians
4. support a whole of government approach to the hiring and retention of people with specialized data skills. This includes drawing from the many Canadian universities and colleges that offer programs specializing in data science, big data, artificial intelligence and machine learning.

Recommendation 4:
Effectively use artificial intelligence

The federal government is investing billions of dollars to leverage artificial intelligence to support and increase Canadian productivity through enhanced computing capabilities and technological infrastructure. For Statistics Canada, artificial intelligence is not new. The agency has been using linguistic models since the early 1990s in support of coding activities.

The decision to use machine learning and other forms of artificial intelligence for statistical purposes must consider the benefits and risks associated with security, data quality and efficiency. Not all technologies are suitable for producing statistics, and methodology should never be trumped by technology.

The Chief Statistician of Canada should

1. continue to explore new artificial intelligence technologies to improve the production of quality data in areas such as data imaging, data visualization and basic tabular analysis
2. engage with Canadians about the role of artificial intelligence in statistical organizations and the safeguards that have been, and need to be, put in place.

The Minister of Innovation, Science and Industry should

3. recognize and support Statistics Canada's statistical leadership in helping the Government of Canada develop its artificial intelligence strategy.

1. High quality data are central to Canada's economic success and well-being

As the world is changing rapidly and societal problems become more complicated, governments and businesses need crosscutting information that better integrates economic, social, cultural and environmental perspectives and that speaks to Canada's diverse communities. Understanding the interrelationships between societal issues leads to more effective solutions that ensure a vibrant economy and a healthy population.

High-quality data and analyses play an important role in public debate helping government policy makers address pressing problems such as economic growth, the high costs of living, access to affordable housing and social inclusion. Canada's economic growth has slowed down over recent years with a sharp downturn during the pandemic. More disconcerting is the declines in gross domestic product (GDP) per capita since the pandemic that represent a departure from the long-term trend in per capita growth. Much public debate has also emerged over the interrelationships between high levels of temporary migration, housing and the economy. Canadians and policy makers must also be better able to anticipate and respond to the impacts of climate change and devastating environmental events, such as wildfires and flooding.

Canada's weak productivity performance has sparked much concern because, historically, much of the long-term growth in GDP per capita has reflected sustained improvements in labour productivity. Recent studies^{Endnote 1Endnote 2Endnote 3Endnote 4} highlight the negative implications of weaker GDP per capita for living standards and wage growth.

Economic, socio-demographic and environmental concerns are not new, but have become more complex in a technologically fast-changing and global society. Analysts need real-time and granular local data to better understand the factors impacting these issues and determine which Canadians are most affected.

High-quality data are essential for developing and implementing effective policies that support businesses and innovation, which drive economic growth. Public and private sector policies must be grounded on a strong national statistical system that Canadians can trust. This system can only be maintained if Statistics Canada and the federal government keep pace with new technologies and methods. They need to move forward and lead the way and not be left behind.

1.1 Misinformation and disinformation undermine trust in quality data

Quality statistical information is one of Canada's most valuable resources. However, it cannot be taken for granted. Good data and statistics in areas such as productivity, inflation, housing and the environment are essential to support economic growth and ensure Canada's prosperity and well-being. Poor data can lead to bad decisions with costly consequences. This is true for individual Canadians, for businesses and for the public sector.

Official statistics produced by Statistics Canada are increasingly challenged by misinformation with little consideration of the United Nations Fundamental Principles of Official Statistics that include relevance, impartiality, professional standards and transparency. Criticism of the agency and its data in social media and newsfeeds increasingly draws on unsubstantiated sources using methods that are poorly conceived.

Even more disconcerting are inaccurate statements and allegations in the media about Statistics Canada and its data, which, if unopposed, will lead to an erosion of trust in the agency and its leadership. Recent examples include criticisms of the methods used by Statistics Canada to determine the price of consumer goods^{Endnote 5} and the estimates of the number of temporary migrants in Canada^{Endnote 6}. In both cases, the ensuing debates suggest methodological flaws in these challenges to the agency's methods and statistics.^{Endnote 7Endnote 8}

In some cases, these criticisms involve misinformation, where Statistics Canada's data and statistical methods are questioned in good faith. This requires proactive strategies by the agency to respond and correct the record.

More troubling are cases of disinformation, involving deliberate attempts to mislead. Organized disinformation, when it involves official statistics, can undermine confidence in evidence-based decision making and is a real threat to Canadian democracy over time.

Fostering trust and countering misinformation and disinformation

Misinformation and disinformation are best countered through public dialogue that distinguishes good data from poor or misleading data, and which emphasizes how good data supports community well-being. This message cannot come from Statistics Canada alone.

Statistical experts and opinion leaders in the public, private and academic sectors, as well as the media, have a role to play in calling out the misinformation and disinformation. They can raise awareness about the critical role of official statistics in a democracy and attest to how these statistics are based on high standards of impartiality, data quality, robust scientific methods and transparency.

Statistics Canada has a central role to play in helping Canadians find their way through the plethora of competing and conflicting statistics from social networks and traditional media. The agency needs to adapt the way it publishes social and economic indicators in ways that new and younger audiences can relate to and trust. The agency must ensure quality statistical information on current topics is readily available to prevent misinformation from taking hold. The agency must also be proactive and transparent with Canadians about changes to statistical programs and methods, and indicate how they are subject to external methodological review. Within the public service, the federal government has published Countering Disinformation: A Guidebook for Public Servants on its portal on democratic institutions. The guidebook offers a pathway for engaging these challenges.

Experts within the agency should also continue to be proactive in countering misinformation through dialogue with publishers and media who put out erroneous or misleading statistics and charts, or analyses that misinterpret them. Collaborating with sources that have erred in good faith ensures a more effective and credible dissemination of accurate information and official statistics in the long run.

The motives behind disinformation are devious and more difficult to counter. The actions are often meant to undermine trust in democratic institutions. Simply countering with factual official statistics is not sufficient because these are readily rebuffed by the proponents of disinformation. Statistical information that does not abide by professional statistical standards should be publicly dismissed.

Fostering data literacy

Data literacy is key to recognizing and appreciating high-quality and impartial data. The agency needs to do even more outreach with its Data Literacy Training Initiative. This comprehensive program is tailored to different levels of expertise within government departments and the broader public. The program also provides insight on the importance of data relevance, impartiality, professional standards, confidentiality and transparency.

The agency is already very active with schools, providing teachers with educational resources which showcase the use of analytical and data visualization tools to explore topics such as population, housing, food and transportation. These tools provide a basis for the younger generation to build essential skills for the workplace and to appreciate the value of data as they progress through life.

In universities and colleges, more detailed and complex data sets are being made available to students through public use files and Research Data Centres. When instructors use these data, students can learn to discern the quality of data and analyses using more advanced statistical methods. While instructors and students also have access to Statistics Canada's data literacy training program that is publicly available on the agency's website, it is underused by the academic sector. There should be more partnerships between Statistics Canada and academia to co-develop a curriculum that keeps pace with the changing data science and analytical skills needed within the agency and government. Statistics Canada is currently working with the Canadian Research Data Centre Network to determine how data training may be useful in areas such as disaggregated data analysis.

In the Canadian Statistics Advisory Council's 2023 Annual Report, the Council felt that, given the important role of data in federal programs, the data and analysis training offered to federal program managers and analysts should be mandatory. The data literacy training program should also be made available to influencers, journalists, publishers and owners of both social and traditional media.

1.2 Leadership is key to the development of data across government

Statistics Canada has a key leadership role in creating environments where various data sources can effectively and securely be collected and integrated for statistical analysis. The agency is well placed to set national standards for statistical concepts, definitions and classifications, given its internationally recognized expertise in statistical methods and data standards.

The federal government's 2023–2026 Data Strategy for the Federal Public Service addresses many challenges that the Council has highlighted over the past several years. These include the need for a whole-of-government approach to program design and data stewardship for management of statistical data and data standards; embedding and appropriately resourcing data needs up front and throughout the development, delivery, monitoring and evaluation phases; and improving data literacy and digital skills across government.

These federal initiatives are ambitious and will take time to implement. They will also require adequate resources and new funding models. It will be important for Statistics Canada to play a leadership role, considering its expertise in statistical methods and in large-scale transformative management. The work is supported by the Disaggregated Data Action Plan, that was created to support Statistics Canada's efforts to continually identify and fill data and knowledge gaps across programs.

Intersectional approaches are needed

Too often, Canadian research on social and economic inequalities is done in silos, missing the cross-cutting issues, or intersectionality,^{Footnote A} needed to truly address the country's most pressing problems.^{Endnote 9Endnote 10}

For example, when studying environmental concerns, it is a challenge to obtain the information needed to understand what lands and which populations and businesses are most vulnerable. When disasters strike, timely data are required to support immediate relief and the longer-term recovery of communities. Accurate assessments of wildfires require not only satellite data to identify where they occur, but also information on the remote and rural communities affected, including First Nations reserves. This includes social and economic profiles, and data on businesses and assets to enable the proper resources can be mobilized to mitigate damages. These data cannot be looked at in isolation. Looking at them jointly requires modernized data infrastructures, data visualization and creative analytical techniques.

Housing affordability and projected housing stock are other areas where there is demand for more intersectional information. It is important to understand the combination of factors that put certain Canadians at risk of being unable to obtain or afford adequate housing. Decisions by policy makers that are based on solid statistics and analyses are much more likely to successfully support Canadians who are most in need. This requires different jurisdictions to work with Canada Mortgage and Housing Corporation and Statistics Canada to expand the national housing database by securely linking individual records from different data sources and jurisdictions. Private sector firms, such as those in the real estate and construction industries also have valuable data holdings that could be leveraged.

Several years ago, Statistics Canada developed innovative uses of existing data through data linkage environments. The Social Data Linkage Environment allows for the integration of existing census, survey and administrative data files in areas such as population demographics, health, justice, education and income. The Business - Linkable File Environment enables analyses such as on the factors for firm productivity, by linking business microdata from administrative and survey sources. The agency has also developed the Education and Labour Market Longitudinal Platform (ELMLP) in collaboration with federal, provincial, territorial and other stakeholders. The ELMLP allows longitudinal integration of administrative data related to education, enabling a greater understanding of student and apprenticeship pathways and transitions to the labour market and outcomes over time. These linkage environments are powerful tools to support the increased opportunities for cross-cutting and longitudinal data analytics.

Fostering data quality and statistical data standards

Statistics Canada needs to promote the importance of quality data and data standards to both data providers and users as it sets national statistical standards. Its Quality Assurance Framework is leading edge and forms the basis for those developed by international organisations such as the United Nations and the Organisation for Economic Co-operation and Development.

In its quality assurance framework, Statistics Canada defines quality or fitness for use of statistical information in terms of six dimensions: relevance, accuracy, timeliness, accessibility, interpretability, and coherence. Some users might appreciate a single quality rating. However, this is not practical, given that an assessment of quality depends on what the user needs the data for, and they may prioritize one of the six dimensions over another. For example, annual federal transfer payments to provinces and territories amounting to billions of dollars require economic and social indicators of the highest accuracy, while during the pandemic, Statistics Canada published more timely flash economic and employment indicators that were not as accurate as its regular monthly estimates.

Footnotes for section 1.2

Footnote A

Intersectionality relates to the combination of factors that results in how groups and individuals face discrimination and privilege. These factors include age, gender identity, sexual orientation, race, ethnicity, religion and disability.

Return to footnote A referrer

2. Canada's societal issues demand greater collaboration and data sharing

An effective national statistical system is built on mutual relations and the sharing of information and expertise. Statistics Canada cannot and should not do it alone. Collaboration is needed across the public and private sectors to share data for public good. This includes partnerships across provinces, territories, municipalities, business sectors, non-profit sectors, academia, and Indigenous organizations and communities.

The potential cost to Canadians for programs based on poor or incomplete data is enormous. Even in fiscally difficult times, it remains cost-effective to modernize statistical and technical infrastructures and promote data flows that are the foundation for understanding and tackling important issues. In a context of restraint, adaptable and creative funding is possible, especially when based on the whole of government approach advocated in the federal Budget 2024.

When data are shared across jurisdictions, the ability to plan and evaluate the benefits of programs increases dramatically. This requires strong governance and data stewardship models that are trusted by Canadians, that ensure their personal data are secure and that effectively produce the quality detailed data that are required.

Effective national data strategies must be grounded in shared statistical and methodological objectives. This starts with a dialogue across sectors and jurisdictions, led by Statistics Canada and other government leaders, to gain a common understanding of pressing societal issues and the data needed to address them. Data needs, data sharing opportunities and costs assessments must be conducted for every stage of proposed projects or programs, from planning and implementation to evaluation of results and post-mortems. Concepts, definitions and representativeness of data should be examined for consistency with national standards. Methodological adjustments ensure that data across jurisdictions are comparable.

Some noteworthy collaboration initiatives:

Multijurisdictional data on drugs and substance abuse

The Canadian Drugs and Substances Strategy is a good example of multiple jurisdictions coming together to address substance use and the overdose crisis in Canada. Federal, provincial, territorial, community and Indigenous organizations collaborate with professional and regulatory bodies and health care providers. Statistics Canada is partnering with Health Canada and the Public Health Agency of Canada to develop a secure, virtual analytical environment with integrated health and socioeconomic data from across jurisdictions and diverse data providers. This initiative aims to fill critical information gaps about risk and protective factors, as well as populations most at risk for harms related to the ongoing drug and overdose crisis.

Provincial and territorial health data

Integrating data at the provincial and territorial levels adds complexity when jurisdictions become siloed, and legislation and policies create barriers to data sharing. A critical lack of integrated national health data and the need for a pan-Canadian Health Data Strategy have been highlighted by an expert advisory panel reporting to the Public Health Agency of Canada.

The Council was encouraged by the 2023 Canada Health Transfer agreements, which included support for improving data flows and developing national indicators on health care and health care workers. Statistics Canada is contributing to the federal, provincial and territorial Shared Health Priorities by developing new data that will enable annual reporting by provinces and territories on key health indicators for both children and adults. These health indicators will be disaggregated as much as possible by age, gender, urban or rural status, and income.

The potential of these data is demonstrated through data hub initiatives created by health research organizations that work with Statistics Canada, and with provincial and territorial health authorities. Linking health, socioeconomic and environmental data across jurisdictions using national standard definitions and data categories helps researchers understand the complex interplay of influences on human health, well-being and development. The Data Access Support Hub of the Health Data Research Network and the Strategy for Patient-Oriented Research of the Canadian Institutes of Health Research are examples of good practices.

Municipalities data hub

The Centre for Municipal and Local Data has been cited in previous reports as a model for successful collaboration with Statistics Canada. It is the result of an ongoing partnership with the Federation of Canadian Municipalities. Additional funding was announced in the 2024 Budget to continue and expand the data hub. This should include working to address important data gaps such as the costs of maintaining and repairing municipal infrastructures, particularly in light of devastating environmental events such as wildfires and flooding.

Municipalities are also increasingly involved in federal data strategies such as the Canadian Drugs and Substances Strategy and the Housing Statistics Portal cited by the Council in this and previous reports.

Businesses data lab

The Business Data Lab has been cited in previous reports as a model for successful collaboration with Statistics Canada. Since 2022, the Canadian Chamber of Commerce has collaborated with Statistics Canada to generate real-time information on business conditions and analytical insights on various topics related to the economy, particularly for small and medium-sized businesses. In the 2024 Budget, the Canadian Chamber of Commerce received funds to continue and expand the data lab. Statistics Canada will continue to provide the required infrastructure to support the work of the Chamber and collaborate with them on joint initiatives.

It is important that Statistics Canada continues to explore partnership opportunities within the private sector. Establishing partnerships with large private sector organizations to share their big data holdings is challenging because of administrative, legal and fiscal factors, which are not expected to improve in the foreseeable future.

At the same time, these data have great potential to shed significant light on several issues. For example, the real estate sector holds real time data on housing that can offer granular insights to fill gaps that would otherwise be missed, satellite data held by private sector companies can offer new information on rural communities, and partnerships between the telecom sector and Statistics Canada that integrate mobility phone data with census tract information can help to better understand commuting patterns and remote work practices.

Non-profit sector engagement

Statistics Canada has introduced a module for non-profit organizations (NGO) in their Canadian Survey of Business Conditions. The agency has benefited from advice from community, business and non-profit organizations. Funding has also been available through the agency's Disaggregated Data Action Plan. The results highlight the profound impact that NGOs have on Canadians' lives as well as the ongoing significance of these organizations as vital players in a diverse society and a dynamic economy.

Recently Statistics Canada has been collaborating with the NGO Maple Leaf Centre for Food Security to analyse the relationship between poverty and food insecurity. Empowering NGOs to do this kind of work is an important outcome of fostering intersectional data. The resulting article, Food insecurity among Canadian families, expands the current body of knowledge on food security, showing that income alone cannot explain food insecurity. The study, which used a variety of Statistics Canada data sources, found that food insecurity stems from the interplay of various factors, including the stability of income, assets and debt, access to family and social supports, and the cost of living.

First Nations Consumer Price Index

Statistics Canada is providing advice and expertise to the First Nations Tax Commission (FNTC),in collaboration with the Bank of Canada and Indigenous Services Canada. They offer capacity-building support to the FNTC as it explores the measurement of price inflation in diverse and often remote Indigenous communities. This research integrates information across data sources and communities to draw initial insights and estimates of inflationary pressures. Part of the First Nations Data Governance Strategy, this initiative fills an important data gap as Reserves are not included in the national Consumer Price Index.

Indigenous-led data strategies are integral to a national data system. The Council in previous reports , has presented how First Nations, Inuit and Métis communities and organizations have been developing capacity, infrastructure and strategic frameworks to support data governance and data collection processes at both national and regional levels. The First Nations Information Governance Centre (FNIGC) and its regional partners play a leadership role in developing and implementing the First Nations Governance Data Strategy. This strategy reflects priorities for establishing a First Nations-led network of fully functioning, interconnected data and statistical service centres, or Regional Information Governance Centres. Surveys administered by FNIGC, in coordination with regional partners^{Endnote 12} cover topics such as health, early education, employment and communities. Statistics Canada provides data capacity-building support to many of these initiatives and is improving the visibility of Indigenous Peoples in Canada's national statistics.

3. Harness technology and skills to move forward and lead the way and not be left behind

It is paramount that Statistics Canada continues its modernization efforts to keep pace with rapid technological and methodological change. With new technologies, there are opportunities to access and share untapped data resources that provide needed perspectives to the issues Canadians face.

This cannot not be done without a strong commitment to funding such infrastructure and promoting a whole-of-government approach to technology including cloud computing, modern data methods and use of artificial intelligence.

3.1 Performance and security with cloud technology

Residing within a secure public cloud data centre, the agency's cloud platform stores statistical information and applications for processing as well as Statistics Canada's official data holdings. It has become a backbone for creating cross-cutting and longitudinal data sets to inform and address the country's problems. Leveraging cloud infrastructure, Statistics Canada has also created a collaboration platform that provides opportunities to coordinate information across federal departments and facilitate collaborations with provinces and territories, municipalities, private and academic sectors and Indigenous governments and organizations.

Use of the cloud has huge potential for developing new methodologies and managing increasingly complex data. The cloud infrastructure is replacing an outdated system infrastructure centred on physical data centres. Several legacy statistical applications need modernization to fully leverage the modern cloud infrastructure in support of a new age of big data and powerful data analytics.

As cloud computing becomes more central to Statistics Canada's work, safeguarding personal information remains a top priority. Statistics Canada uses a scaled approach for authorizing access to its microdata holdings based on their sensitivity. The agency has also aligned itself with mandatory safeguards published by the Canadian Centre for Cyber Security, Treasury Board Secretariat and Shared Services Canada.^{Endnote 13}

The cloud infrastructure platform requires ongoing investments if it is to effectively meet the needs and expectations of Canadians in a sustainable way. Developing and using statistical data today comes with significant costs. This not only includes evergreening and optimizing the cloud platform, but also investments to optimize Statistics Canada's systems, applications and data management infrastructure. Because Statistics Canada's microdata holdings are becoming more analytically powerful, there is a requirement for new levels of computing power. All of these costs must be planned and resourced within new models of procurement.

Remote access to microdata

Cloud computing offers enormous potential for allowing authorized researchers remote access to Statistics Canada's microdata holdings from approved office or home workspaces. A greater network of researchers creates opportunities for collaboration and data integration, leading to more in-depth statistical findings. It also broadens the community of experts able to push back on the misinformation and disinformation discussed earlier. In expanding access, the agency should ensure strategic synergies between the different modes of microdata access as presented in its Continuum of Data Access.

The agency has recently been able to provide virtual access for government researchers, and the academics they collaborate with, within the federal government cloud infrastructure. Appropriate governance and safeguards ensure protection of the confidentiality of the microdata in a virtual environment.

Offering this to academic researchers more generally has added complexity. Establishing a sustainable, secure non-government network requires new partnerships, funding and cloud computing approaches. The Canadian Research Data Centre Network and Statistics Canada are working with their university partners to build a virtual research data centre.

There is also a demand from private sector researchers and non-profit think tanks to have access to the agency's microdata holdings for statistical research purposes. Their research is important to support business innovation and productivity in Canada, as well as intersectional research on issues such as housing, immigration and poverty. As Statistics Canada expands remote access to a broader set of users, additional governance and accountability structures are needed to ensure the safeguarding of personal information.

Improving the scope of census data

The Census of Population and the Census of Agriculture are Canada's primary sources of national local-area data that can be compared across the country. To meet the increased demand for more granular and integrated data, the Census of Population has replaced census questions with administrative data on income and immigration. The Census of Agriculture has also used administrative data to replace questions on revenue, expenses and operating arrangements. In 2021, census data on cannabis farms were produced entirely based on administrative data. Statistical models ensure census data consistency and quality.

The agency is currently researching an increased use of administrative data for the Census of Population. Referred to as a combined census, administrative data already provided to other government departments could, under certain conditions, be used for the purpose of enumerating the population. At the same time, alternate data sources are being explored to replace questions on maple taps, labour, operator characteristics and greenhouses in the Census of Agriculture.

Leveraging innovative modelling techniques and robust administrative data linkage methods would lessen the burden of participation for Canadians and contribute to the long-term efficiency and sustainability of the censuses. It would also enable contingency strategies when faced with collection challenges such as natural disasters.

Addressing declining response rates

Statistics Canada's surveys provide important demographic and socioeconomic characteristics and lived experiences that are not available from other data sources including the census. While the census continues to have a very high response rate (98%), the long-term decline in social survey response rates was exacerbated by the pandemic.

The low response rates for voluntary surveys are particularly disconcerting, as collection response rates for the Canadian Social Survey, the Canadian Community Health Survey and the National Travel Survey are all below 50%. Canadians have become much harder to reach. There is also a fatigue from a continuous stream of surveys from both the private and public sectors. Statistical agencies around the world, as well as private research and analytics firms, are facing this challenge.

As downward pressure on survey responses rates is not likely to ease in the future, Statistics Canada must find ways to account for this in its survey designs and preserve the valuable information that only these surveys provide.

For the mandatory Labour Force Survey, the agency is taking advantage of modern technologies and platforms which can be used to better reach Canadians and provide them with options for responding online.

For its voluntary surveys, Statistics Canada is developing leading edge methods that integrate the agency's census, administrative data and web-based statistical data with survey data. The benefits are numerous. Survey output on communities and on vulnerable populations is becoming timelier and more detailed, and there is reduced respondent burden.

In this welcome groundswell of modernization, it is important to ensure that technology is there to support and not drive methodological design.

3.2 Role of artificial intelligence in statistics

Canada is considered a world leader^{Endnote 14} in a technology that is gaining attention globally—the development and use of artificial intelligence.^{Footnote BEndnote 15Endnote 16} According to the International Monetary Fund (IMF)^{Endnote 17} artificial intelligence can increase productivity, boost economic growth, and lift incomes. However, it can also eliminate jobs and widen inequality. Canada ranks high in the IMF's AI Preparedness Index of countries based on their digital infrastructure, human capital, labor policies, innovation, integration and regulation.

At the same time, without sustained investment in this technology, Canada runs the risk of quickly falling behind.^{Endnote 18} In Budget 2024, the federal government announced investments of $2.4 billion to build computing capabilities and technological infrastructure for Canada's world-leading artificial intelligence researchers and to help businesses increase their productivity by leveraging artificial intelligence solutions. The government is holding public consultations^{Endnote 19} this year to seek input on proposed artificial intelligence initiatives.

The decision to use machine learning and other forms of artificial intelligence for statistical purposes must always consider the benefits and risks with regard to security, data quality and efficiencies. Statistical data produced using artificial intelligence is only as good as the information it uses. Not all technologies are suitable for the production of statistics, and methodology should never be trumped by technology.

Improper use of artificial intelligence when collecting data can lead to an erosion of trust and disengagement of citizens. As this touches the heart of Statistics Canada's core values of trust and privacy of information, the agency has been very active in advocating for the proper use of artificial intelligence by government when dealing with data.

Statistics Canada assesses artificial intelligence from two perspectives. The first is ensuring that their economic, social and environmental indices, data and analyses consider the impact of artificial intelligence. The agency has several initiatives underway including measuring the value of data in the System of National Accounts. The second is leveraging the technology in the production of official statistics. For Statistics Canada and other national statistical offices, modelling and artificial intelligence are not new. The agency has been using linguistic models since the early 1990s in support of coding activities as computer algorithms and statistical models perform automatic and interactive coding tasks. This has led to more efficient data processing and an improvement in data quality.

More recently, Statistics Canada is leveraging new technologies and cloud platforms to use machine learning and large language models at various stages of data visualization and data production. They are being used to predict crop yields, perform basic tabular analysis and engage with Canadians through chatbots. The agency does not use artificial intelligence to infer statistical estimates as the technology has not proven to produce quality statistics.

A notable initiative is the Canadian Coroner and Medical Examiner Database. Machine learning is being used to organize data collected from provinces and territories into coherent datasets, as each jurisdiction has its own method for classifying data. This has improved the national data available to analysts to better detect trends in mortality over time and allow medical examiners and coroners to understand growing hazards.

Statistics Canada should continue to help the Government of Canada develop its artificial intelligence strategy. The Agency plays a key role in the AI and Data Governance Standardization Collaborative, an initiative led by the Standards Council of Canada that brings together industry, government, Indigenous organizations, civil society, academic and research bodies, pan-Canadian organizations, and standards development organizations. On the international front, the agency leads big data and data science expert groups that are identifying and addressing common challenges encountered when incorporating machine learning into the production processes of organizations.

The agency should share its expertise with Canadians on the role of artificial intelligence in statistical organisations and the safeguards that have been and need to be put in place. The dialogue with Canadians would enhance trust in the agency and inform future use of artificial intelligence technology.

Footnotes for section 3.2

Footnote B

Artificial intelligence is a technology that enables computers to simulate human intelligence and problem-solving capabilities. The Organisation for Economic Co-operation and Development defines an artificial intelligence system as a machine-based system that infers, from the input it receives, how to generate outputs such as predictions, content, recommendations or decisions that can influence physical or virtual environments.

Return to footnote B referrer

3.3 Attracting and retaining data science and modern analytics skills

Keeping pace with changing technology and statistical methods requires modern data science and analytical skills, including data coordination, data interpretation, data visualization, geospatial analysis and computational modelling.

Statistics Canada needs data scientists, methodologists, economists, sociologists and environmentalists with these skills to gather, analyze and report on large amounts of data. Applying data visualization in the early stages of survey design and development introduces novel concepts and dimensions to the statistical outputs. As the agency uses more unstructured data forms, they are required to develop statistical formulas and computer algorithms that transform unprocessed data into quality statistical information. The agency's subject matter experts must have strong multidisciplinary training that integrates mathematics, statistical concepts, computer science and data visualization to develop statistical measures that best inform societal issues. In addition, machine learning requires more advanced statistical and methodology probability theory, computer algorithms and neural networks.

These skills are in high demand, and it is a challenge for Statistics Canada to keep up with other departments and the private sector in attracting and retaining workers with these competencies. As part of the 2023–2026 Data Strategy for the Federal Public Service, there should be a whole-of-government approach to the hiring and retention of specialized data skills. This includes drawing from the many Canadian universities and colleges who are now offering programs that specialise in data science, big data, artificial intelligence and machine learning. This also means partnering with universities, so their programs keep pace with the agency's specific needs. To be effective, incentives to attract new and experienced specialists could also be considered by Statistics Canada and the public service.

There are too few partnerships and internships between Statistics Canada and the private sector. More could be considered, though this would require additional funding. Disparities in compensation between the public and private sectors may also present challenges.

Successful cutting edge data science tools are user-focused and accessible across different devices and operating systems. Natural Resources Canada's web portal on Canadian minerals and metal statistics is an example of the power of data visualization to introduce and facilitate understanding of a topic. The portal complies with government accessibility standards and is functional across user platforms. Statistics Canada has created interactive dashboards and data hubs such as the International trade monthly interactive dashboard and the Gender Diversity and Inclusion Hub which have improved access to detailed and multi-sourced data. The agency needs to invest more in this direction to improve on the design, usability, interactivity and performance of data products across user platforms, leveraging the power of visualization tools. Government of Canada budget constraints and hiring ceilings are a challenge that hinder these objectives.

4. Keeping the momentum

Over the past few years, the agency has made important investments in cloud technologies, data analytics and remote access to its data holdings. Its methodologists and analysts are expanding their expertise to work with new types of data, such as satellite imagery, web-based data, bio-specimens and water waste.

Continuing this work is not possible without ongoing investments by the federal government and recognition of Statistics Canada's leadership role that are in line with the 2023–2026 Data Strategy for the Federal Public Service.

The importance of data is echoed in the federal Budget 2024 where Statistics Canada data are cited in the analysis of issues Canadians face, including housing, health, food and crime. The budget proposes new monies for continuing the work of Statistics Canada, in partnership with Canada Mortgage and Housing Corporation and with Canadian Chamber of Commerce in creating the data hubs described as best practices in the Council's 2023 Annual Report. There are also funds to support disaggregated statistics to highlight the diverse lived experiences of different groups, including women, Indigenous People and racialized groups.

The incoming Chief Statistician will have the important task of running Statistics Canada and maintaining the agency's momentum on pursuing modernization, partnerships and trusted statistical leadership.