Canadian Centre for Education Statistics

This information is collected under the authority of the Statistics Act, Revised Statutes of Canada, 1985, Chapter S19.

Confidential when completed

Voluntary survey

Although your participation in this survey is voluntary, your cooperation is important so that the information collected will be as accurate and complete as possible.

Survey purpose

Results from this survey allow users a better understanding of the financial position (income and expenditures) of all community colleges and public vocational schools in Canada. Your 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.

Financial Year Ending: Day, Month, Year (2023)

Identification of the institution

• Name of institution
• Address (number and street)
• City
• Province
• Postal code
• Check the appropriate boxes
  • Type
    • Public
    • Private
  • Governing authority
    • Province or territory
    • Board

Identification of the reporting officer

• Name and title of reporting officer
• Address (number and street)
• City
• Province
• Postal code
• Email address
• Telephone number
• Fax number
• Signature of the reporting officer
• Day, Month, Year

Does your institution offer courses at the elementary-secondary level, other than those academic upgrading courses such as Adult Basic Education which should be reported in this questionnaire?

• Yes
• No

If yes, please exclude revenues and expenditures relating to that level of education.

Instructions

1. Please read the guidelines carefully.
2. All amounts should be expressed in thousands of dollars ($'000).
3. Indicate estimated amounts with an asterisk (*).

Affiliated institutions or campuses included in this report

Affiliated institutions or campuses partially included in this report

Affiliated institutions or campuses excluded from this report

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

Table of Contents

1. Introduction
2. Key definitions
   1. Price base date
   2. Volume state date
   3. Residential property
   4. Non-residential property
   5. Properties subject to municipal, provincial, territorial and federal payment-in-lieu
3. Input data
   1. Data sources
   2. Unit reported
4. Auxiliary Data
   1. Multiple Listing Service data
   2. Building permit and investment in construction data
   3. Census of Population
   4. Census of Agriculture
   5. List of CSDs from the Data Integration Infrastructure Division
5. Classification
   1. Geography
   2. Type of Property
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. Residential price index 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 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 2022

1. Introduction

The Property Values Program produces annual estimates of assessment values of properties at current prices across Canada. 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} In order to ensure comparability of the data, a number of adjustments are made, including: coding property categories to a common classification; adjusting to a common price base date and volume state (or stock) date; and imputation of missing property values in some areas. Additionally, other removals and adjustments are carried out in order to produce estimates of assessment values at current price 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 (also called the valuation date) corresponds to a fixed point in time as of when a property is valued.

b. Volume state date

The volume state date is the fixed point in time as of when the stock of properties is recorded, which also corresponds to the date where all properties are represented in an assessment roll data file.

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 lawfully usable 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 lawfully usable for non-residential purposes.

Agricultural properties ^{Footnote 3} (not including farm residences, which are part of residential property) as well as machinery and equipment 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 for receiving municipal services. A payment-in-lieu of taxes is made to compensate a local government for some or all of the tax revenue that it loses because of the nature of the ownership or use of a particular piece of real property. Usually, no property tax is collected for buildings owned by government.

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. See Annex 2.

b. Unit reported

Data are reported either at the municipality level, or at 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 for resale homes and are comprised of dollar volume sales and number of units sold by real estate board. Data are available for all provinces and territories with the exception of Québec and Nunavut.

b. Building Permits and Investment in construction 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 and Investment in Building Construction programs. The data are produced monthly, by jurisdiction.

c. Census of Population

Data from Census of Population are available every five years. Between census years, yearly values, referred to as "intercensal" values, are derived using linear interpolation. ^{Footnote 4} These values are used at various stages of the production cycle such as for the imputation of missing values and for the estimation of farm residences.

d. Census of Agriculture

Similar to the Census of population, data from Census of Agriculture are available every five years. Yearly values ("intercensal" values) are also derived using linear interpolation and used during the production cycle. Census of Agriculture values are used to estimate the values of farm residences in Ontario, Saskatchewan and British Columbia, provinces where such values are embedded in totals or are missing.

e. List of CSDs from the Data Integration Infrastructure Division

The list of Census Subdivisions (CSD) is produced, maintained and updated annually by the Data Integration Infrastructure Division at Statistics Canada.

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.

CSDs containing First Nations or other autonomous or self-governing areas are out of scope for Fiscal Arrangements purposes (see Annex 1); consequently, estimates are not produced for these CSDs.

b. Type of property

The type of property classification was 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 bodies, 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 bodies later than when the data are required. Missing property assessment values for these municipalities are imputed.

For taxation year 2022, there were 150 jurisdictions with missing data that were imputed, 140 of which were in Newfoundland-and-Labrador, 8 were in Northwest Territories and 2 were in Saskatchewan.

a. Imputation of residential values

The imputed residential value for a CSD is calculated by multiplying the number of private dwellings by the average value of owner-occupied dwellings for the CSD from the intercensal Census of Population file.

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

• 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.

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 of July 1 of the year preceding the taxation year.

a. Choice of source data vintage

In order to minimize price adjustments, the data from the file whose price base date most closely aligns with the target price base date is used to produce the estimates of a given taxation year. In the event that 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 dates:

• Quebec
• Alberta
• British Columbia

c. Residential price adjustment

MLS resale values are used in the reassessment of properties by assessment agencies, however they are not the only information that are used. Other information such as demolition/construction permits, renovation permits, construction costs, 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, but rather favours the use of price indices to price 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 property stock on an annual basis ^{Footnote 13} and (2) they report data for both assessment values and numbers of properties. This level of detail allowed the derivation of the annual non-residential price movements. The conclusion was to use the model coefficient of 0.73336 as a discount factor to the residential series.

The discount factor methodology was satisfactory for several years, while MLS resale values observed a constant 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, where possible.

i. Modelling of non-residential assessment data

Similar to 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 applied to the MLS polynomial trend series to price adjust the non-residential property values. 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 date over the index value for the month of the price base date of the source data. This price adjustment ratio is then applied to the assessment value to yield the adjusted value.

$\mathrm{Price}\mathrm{Adjustment}\mathrm{Ratio}= \frac{\mathrm{Target}\mathrm{Price}\mathrm{Base}\mathrm{Date}\mathrm{INDEX}\mathrm{VALUE}}{\mathrm{Price}\mathrm{Base}\mathrm{Date}\mathrm{INDEX}\mathrm{VALUE}}$

$\mathrm{Price}\mathrm{Adjusted}\mathrm{Value}= \mathrm{Price}\mathrm{Adjustment}\mathrm{Ratio}x\mathrm{Assessment}\mathrm{Value}$

8. Volume adjustments

Volume adjustments ensure that properties reflect a common volume state date 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 account for approximately 2% of total values.

b. Non-residential volume adjustments

As for residential volume adjustments, non-residential investment in construction completion values are used in the calculations of volume adjustments. Non-residential volume adjustments account for approximately 2% of 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 bodies. 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, provincial or municipal government 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, territorial and municipal (PTM) PILT properties are excluded.

When breakdowns of values of PILT properties are not available, as is the case for a number of provinces and territories, these values are estimated. The estimation of PTM-PLT values takes into account the S/C/H values, some of which are also PTM-PILT properties, which have already been removed. Only the "remaining" PILT values are estimated and removed.

Although the estimation methodology using aggregate assessment roll data is successful in estimating the remaining proportion to remove, the arrival of assessment roll microdata allows for a more precise estimation of remaining PILT proportions to remove.

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. Removal of machinery and equipment values in Alberta, Northwest Territories and Nunavut

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

The data received from Northwest Territories and Nunavut contain a sizeable share of M&E components in the non-residential total. They are mainly embedded in the following three non-residential classes: mineral, transmission and hydrocarbon. The M&E components are removed by multiplying the reported improvement values by a deflationary factor for each of the previously mentioned three non-residential classes. These factors are provided yearly by the respondents. This treatment ensures that only real property values are included in final estimates, and that the M&E components are excluded.

In Alberta, property values for the M&E components are reported separately by the data providers and are excluded from the final estimates.

g. Removal of personal property values in Manitoba

The assessment roll in Manitoba includes personal property such as goods and chattels, 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 2. List of provinces and territories with microdata in tax year 2022

Newfoundland, Prince Edward Island, Nova Scotia, New Brunswick, Ontario, Manitoba, Saskatchewan (except Prince Albert), Alberta, British Columbia, Yukon, Northwest Territories, Nunavut.

Universe

CCOFOG data are prepared for all general government sectors: the federal general government sector, the provincial general and territorial government sector, the local general government sector, the colleges and universities sector and the health, school board and Canada and Quebec Pension Plan sectors. Canadian Classification of the Functions of Government (CCOFOG) coding is applied at the program level for the general ledger accounts, specified purpose accounts, special funds and income statements of specific entities, such as colleges and universities. A complete list of government entities is available at Public Sector Universe.

Data composition

The published CCOFOG data represent only expenses but exclude consumption of fixed capital. No estimates of the acquisition of non-financial assets are prepared at this time. Occasionally new data becomes available. However, no provisional adjustments are performed to back periods.

Coding process

The CCOFOG classification has three levels. The highest level is referred to as the division and has 10 separate categories. The second level is referred to as the group and the lowest level is referred to as the class. The classifications are available at Canadian Classification of Functions of Government (CCOFOG) 2014.

The primary mandate of a government's program, together with additional information provided by the Canadian Government Finance Statistics (CGFS) coding, is used to assign the CCOFOG classification. When a program has multiple mandates suggesting multiple CCOFOG codes be used, available information is used to determine the main proportion of the observed expense and the entire expenditure is assigned to that CCOFOG classification. However, in certain cases, a proportion of the expenses are redistributed to other CCOFOG codes to better reflect the nature of the expenditure.

In general, special funds usually have a single function and thus a single CCOFOG code is assigned. For example, a social housing authority would have all expenses coded to 71069 – Housing, other than public debt transactions, which would be assigned to 7017.

General principles

CCOFOG provides a consistent way to compare government expenditures across jurisdictions and through time. The aim is to classify expenditures according to their function, or socioeconomic objective, reflecting the aims the associated government wants to achieve.

The 2014 Government Finance Statistics Manual, published by the International Monetary Fund, provides an overview of the COFOG assignment rules in Chapter 6 and its annex. Canada rigorously adheres to the guidelines described in the manual.

As a practical matter, individual governments, federal, provincial, or local, typically report their expenditures by department or agency and, within these structures, by economic class of expenditure (compensation of employees, use of goods and services, social benefits, etcetera). Each government's organizational structure may change over time and is unlikely to line up well with that of other governments. This issue is important within Canada, and it is especially acute when it comes to comparing government expenditures across different countries. CCOFOG statistics promote comparability by providing a single, purpose-oriented classification for the expenditures of all jurisdictions and holding this classification constant through time.

It is also important, when making inter-governmental comparisons between countries or between governments within Canada, to use consolidated rather than unconsolidated statistics. This is especially true for expenditures on large expense categories such as health and education where inter-governmental transfers are substantial.

The consolidated provincial, territorial and local government (PTLG) estimates are often used for provincial and territorial comparisons. These estimates combine provincial and territorial governments, health and social service institutions, universities and colleges, municipalities and other local public administrations and school boards. Importantly, this aggregation removes interparty transactions. The PTLG aggregation is most often used for comparability purposes since there can be different delineations of responsibilities between provincial and local levels of government across provinces.

The consolidated Canadian general government estimates combine the federal government with PTLG data. They exclude data for the Canada and Quebec Pension Plans as well as those for federal and provincial government business enterprises.

Date: June 2023

Program manager: Director, Centre of Population Health Data
Director General, Health Statistics

Reference to Personal Information Bank (PIB):

Personal information collected and used in the National Cannabis Survey is described in Statistics Canada’s “Health Surveys” Personal Information Bank. The Personal Information Bank refers to personal information that is related to participants of health surveys conducted by Statistics Canada.

The “Health Surveys” Personal Information Bank (Bank number: StatCan PPU 806) is published on the Statistics Canada website under the latest Information about Programs and Information Holdings chapter.

Description of statistical activity

Statistics Canada is conducting the National Cannabis Survey (NCS), under the authority of the Statistics Act^{Footnote 1}, on behalf of Health Canada. The National Cannabis Survey was conducted by Statistics Canada in 2018, 2019 and 2020, and was deemed covered under Statistics Canada’s Generic Privacy Impact Assessment. The survey aims to gather detailed information on Canadians' cannabis habits, including their purchasing and use behaviours to provide insights into the types of cannabis products Canadians use, how they access them, and associated impacts on the Canadian economy.

This voluntary household survey collects information from individuals aged 18 years or older living in Canada's ten provinces, who are not members of collective dwellings or living on reserves.

Topics collected in previous cycles of the NCS include cannabis use behaviours, use of different cannabis products, money spent on cannabis products, change in consumption habits due to legalization, symptoms of impaired control over cannabis use, and cannabis use while driving. The 2023 cycle will additionally collect information on cannabis purchasing behaviours from both the legal and illegal markets and growing cannabis at home.

Sociodemographic information such as age, gender, postal code, education, income, general heath, and mental health will continue to be collected, with the additional collection of indigenous identity, population group (racialized population), sexual orientation, and long-term conditions (disability).

A master microdata file will be produced and made available in Statistics Canada’s Research Data Centres (RDC)^{Footnote 2}. A public use microdata file^{Footnote 3} may also be produced, following standard disclosure control processes to mitigate against the risk of reidentification.

Reason for supplement:

While the Generic Privacy Impact Assessment (PIA) addresses most of the privacy and security risks related to statistical activities conducted by Statistics Canada, this supplement was developed to address the sensitive nature of the new information being collected through the 2023 cycle. This includes information about participation in illegal activities and sociodemographic content including indigenous identity, population group, sexual orientation, and long-term conditions (disability). As is the case with all PIAs, Statistics Canada's privacy framework ensures that elements of privacy protection and privacy controls are documented and applied.

Necessity and Proportionality

The collection of personal information for the National Cannabis Survey (NCS) can be justified against Statistics Canada’s Necessity and Proportionality Framework:

1. Necessity: Collecting data on cannabis use and purchasing patterns since 2018 has been essential to understanding the impact of cannabis legalization in Canada. Data collected are critical for monitoring changes in patterns of cannabis use, its effects on health and social outcomes, how Canadians access cannabis, and the effectiveness of harm reduction strategies such as education and prevention programs. Policy makers, health researchers and other Canadians will benefit from this information to inform evidence-based strategies, policies, and programs related to cannabis use in Canada. Failure to collect this data could lead to uninformed policies that could be harmful to Canadians. For example, without data on purchasing patterns, authorities may not be able to adequately regulate the legal market or ensure that consumers have access to safe and quality-controlled cannabis products.

   The inclusion of questions on sexual orientation, disability, racialized population, and Indigenous identity in the National Cannabis Survey (NCS) is important for a comprehensive understanding of cannabis-related behaviors and experiences within diverse population groups. Even before legalization, certain populations were identified as having higher cannabis use rates and being more at-risk of cannabis related harms and addiction. By collecting data on these sociodemographic variables, the NCS can help in designing targeted prevention and intervention programs that address the specific needs and challenges faced by these populations. For example, if the survey data reveals higher rates of cannabis use among certain communities or vulnerable groups, targeted information campaigns can be developed to raise awareness about the potential risks and provide resources for safer use practices. The data can also shed light on the effectiveness of harm reduction strategies, such as educational programs, in reducing cannabis-related harm. Policymakers can use these insights to refine existing policies or develop new ones that are better aligned with the needs and experiences of diverse populations.

   The inclusion of these sociodemographic variables align with the goals of program evaluation related to the Cannabis Act (C-45), including the Cannabis Act Legislative Review. Furthermore, the NCS aligns with the principles of equity, inclusivity, and accurate representation outlined in the Framework for the Legalization and Regulation of Cannabis in Canada. It aims to address the impacts on the health and cannabis consumption habits of Indigenous peoples, racialized communities, women, and other populations facing barriers to participation or at greater risk of harm. The sociodemographic information collected in the NCS align with the principles of surveillance and monitoring highlighted in the Framework for the Legalization and Regulation of Cannabis in Canada. It acknowledges the need for baseline indicators and population-level monitoring to measure the impact of changes resulting from cannabis legalization. By capturing information on these sociodemographic variables, the NCS contributes to best practices with ongoing surveillance and monitoring efforts, providing essential data to inform evidence-based decision-making, policy development, and retrospective evaluation of the Cannabis Act.

   The inclusion of these variables is necessary to enhance the survey's ability to capture a comprehensive picture of cannabis-related behaviours. By including these variables, the NCS aims to provide insights into potential disparities or variations in cannabis use and behaviors across various sociodemographic groups. This information is important for developing targeted interventions, policies, and programs that address specific needs and challenges faced by different populations. Although direct correlations between population sub-groups and cannabis behaviour may not be established, Statistics Canada will continue to approach the analysis of survey findings with caution and sensitivity, ensuring that analytical interpretations align with current research and avoid causing harm or perpetuating stereotypes.

   Including the variable on sexual orientation in the NCS is in line with the practices of established Statistics Canada surveys collecting data on cannabis use, such as the Canadian Community Health Survey, the Canadian Tobacco and Nicotine Survey, and the Canadian Alcohol and Drugs Survey. Additionally, the Canadian Cannabis Survey conducted by Health Canada also includes this variable. By aligning with these existing surveys, the NCS ensures consistency in data collection practices and facilitates comparisons across different health and substance use surveys in Canada over time.

   To ensure a more comprehensive understanding of cannabis use and purchasing patterns in Canada, the NCS has expanded its sample size from 12,000 in previous iterations to 18,200 in the 2023 cycle. This increase in sample size is necessary to ensure the statistical quality^{Footnote 4} of the survey, allowing for more robust analysis and disaggregation of data by subgroups of the population. By having a larger and more representative sample, the NCS can provide more precise estimates and insights into cannabis-related behaviors and their variations across different sociodemographic groups. This enhanced statistical quality will enable researchers, policymakers, and health professionals to better understand the nuances and complexities of cannabis use patterns and make informed decisions based on the findings, while also providing a sufficient number of responses to allow for the publication of aggregate results that effectively protect the personal information of respondents.

2. Effectiveness - Working assumptions: To ensure the effectiveness of the personal information collected and used in the National Cannabis Survey (NCS), rigorous measures have been taken, both in the pre-2023 cycles and in the upcoming 2023 cycle. The questions pertaining to cannabis were sourced from established surveys such as Statistics Canada's Canadian Community Health Survey (CCHS) and the Canadian Tobacco and Nicotine Survey (CTNS). These questions have previously undergone qualitative testing, affirming their effectiveness in capturing relevant information on cannabis-related behaviors. By leveraging questions from well-established surveys, the NCS benefits from their proven track record in accurately capturing data. Using these questions also allows for comparison of the results of the NCS with those of the CCHS and CTNS surveys, enabling improved interpretation and analysis of the data and providing valuable insights into trends and patterns related to cannabis use and behaviors over time.

   In the 2023 cycle of the NCS, additional personal information variables have been included to better capture desired sub-populations in order to gain a deeper understanding of how cannabis-related behaviors may differ among different groups. These personal information variables have also been drawn from existing Statistics Canada surveys. These variables have likewise been carefully reviewed and undergone comprehensive testing and validation processes, ensuring their effectiveness as indicators of key population characteristics and behaviors.

   By employing established and rigorously tested questions and expanding the range of variables in the 2023 cycle, the NCS maintains a robust framework for collecting and utilizing personal information effectively. The inclusion of these new variables aligns with Statistics Canada’s best practices in survey research and enhances the effectiveness of the NCS in addressing the specific needs and experiences of diverse populations. These measures ensure that the NCS is well-equipped to provide reliable and valuable insights into cannabis-related behaviors and their impacts on various population groups.

3. Proportionality: The National Cannabis Survey is an essential tool for monitoring changes in cannabis use patterns since the legalization of cannabis in Canada and understanding its impact on health, social outcomes, and the Canadian economy. In the 2023 cycle of the National Cannabis Survey (NCS), several adjustments will be made to accommodate the inclusion of new variables. As part of this process, certain questions from previous NCS cycles will be removed, proportional to what will be added. Specifically, questions related to the impact of legislation on the decision to try cannabis or modify consumption habits, impaired control in relation to cannabis use, and the use of cannabis while operating motor vehicles will no longer be included as the focus of the 2023 cycle is primarily on gathering data related to cannabis use and purchasing patterns, as well as socio-sociodemographic characteristics; the decision to remove these specific questions was made to streamline the survey and allocate more resources towards obtaining detailed information on these key areas.

   Two other questions will be removed – one regarding marital status and one inquiring about the respondent's main activity, such as employment or studying – to streamline the survey and ensure that the new variables introduced in 2023 could be effectively incorporated.

   The sample size of 18,200 people living in Canada's ten provinces has been assessed as the minimum required to meet Statistics Canada’s quality guidelines. This increase in the sample size is considered proportional to get publishable results on the new content, and to disaggregate results by different population subgroups. Careful consideration was made to ensure that each question would accurately respond to the research questions and help inform future decisions related to cannabis use policies.

   The adjustments made to the NCS 2023 cycle reflect the evolving nature of the survey and the ongoing commitment to collecting relevant and meaningful data. These modifications help to optimize the survey's focus and ensure the efficient use of respondents' time, while still providing valuable insights into cannabis use patterns and their associated factors. The findings are expected to support evidence-based strategies, policies, and programs related to cannabis use in Canada. As mentioned above, without this data policymakers would lack valuable information about cannabis use, which could lead to uninformed policies that could be harmful to Canadians. As such, the benefit to be derived from the National Cannabis Survey by Canadians can be considered proportional to its privacy intrusiveness.

4. Alternatives: Consultations with internal and external partners were conducted on existing administrative data and other surveys on cannabis use behaviors among Canadians, such as the Canadian Community Health Survey (CCHS) and the Canadian Tobacco and Nicotine Survey (CTNS). While other sources of data were considered, none would provide the combination of sociodemographic, health and cannabis-specific indicators required to fulfill the survey’s primary objective of obtaining detailed information about the habits of people who purchase and use cannabis.

Mitigation factors:

Some questions contained in the National Cannabis Survey are considered sensitive as they relate to substance use and illegal purchasing of cannabis among various sociodemographic subgroups. The overall risk of harm to the survey respondents has been deemed manageable with existing Statistics Canada safeguards that are described in Statistics Canada’s Generic Privacy Impact Assessment, which include the following measures:

• Transparency
  Prior to participating in the voluntary survey, respondents will be informed of the survey purpose and topics, allowing them to assess whether they wish to participate. This information will be provided via invitation and reminder letters and will be reiterated at the beginning of the questionnaire. Respondents will also be informed, in both invitation and reminder letters as well as in the questionnaire itself, that their participation is voluntary before being asked any questions. Confidentiality reminders have also been placed before questions that may be perceived as more sensitive. Information about the survey, as well as the survey questionnaire, will also be available on Statistics Canada's website.

• Confidentiality
  To mitigate against the risk of re-identification, individual responses will be grouped with those of others when reporting results. Individual responses and results for very small groups will never be published or shared with government departments or agencies. Additionally, careful analysis of the data and consideration will be given prior to the release of aggregate data to ensure that marginalized and vulnerable communities are not disproportionally impacted.

Conclusion:

This assessment concludes that, with the existing Statistics Canada safeguards and additional mitigation factors listed above, any remaining risks are such that Statistics Canada is prepared to accept and manage the risk.

The following table provides an overview of the most recent analytical products published alongside the Consumer Price Index (CPI). These documents are intended for a varied audience, ranging from users interested in general information to those requiring more technical or conceptual details related to the CPI. The list will be updated periodically to include latest releases.

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