Data ethics part 2: Ethical reviews

Catalogue number: 892000062022004

Release date: October 17, 2022

In this video, you will learn the answers to the following questions:

  • What are ethical reviews?
  • How do ethical reviews help Statistics Canada apply data ethics principles throughout the data journey?

Using a case example, this video will show you how Statistics Canada uses ethical reviews to apply data ethics principles throughout the data journey.

Data journey step
Foundation
Data competency
  • Data quality evaluation
  • Data security and governance
  • Data stewardship
Audience
Basic
Suggested prerequisites
Data ethics: An introduction
Length
11:20
Cost
Free

Watch the video

Data ethics part 2: Ethical reviews - Transcript

(The Statistics Canada symbol and Canada wordmark appear on screen with the title: "Data ethics part 2: Ethical reviews - A case study")

Gathering, exploring, analyzing and interpreting data are essential steps in producing information that benefits society, the economy and the environment. In this video, we will discuss the importance of considering data ethics throughout the process of producing statistical information.

Learning goals

(Text on screen: In this video, you will learn the answers to the following questions:

  • What are ethical reviews?
  • How do ethical reviews help Statistics Canada apply data ethics principles throughout the data journey?

Pre-requisite viewing: "Data Ethics: An introduction"" also available in Statistics Canada's Data Literacy Training Learning Catalogue.)

As a pre-requisite to this video, make sure to watch the video titled "Data Ethics: An Introduction" also available in Statistics Canada's data literacy training catalog. In this video, you will learn the answers to the following questions:

  • What are ethical reviews?
  • And how do ethical reviews help Statistics Canada apply data ethics principles throughout the data journey?

Using a case example, this video will show you how Statistics Canada uses ethical reviews to apply data ethics principles throughout the data journey.

Steps of the data journey

(Diagram of the Steps of the data journey: Step 1 - define, find, gather; Step 2 - explore, clean, describe; Step 3 - analyze, model; Step 4 - tell the story. The data journey is supported by a foundation of stewardship, metadata, standards and quality.)

This diagram is a visual representation of the data journey from collecting the data to exploring, cleaning, describing and understanding the data. To analyzing the data and lastly to communicating with others the story the data tell.

Data ethics principles are relevant throughout all steps in the data journey.

What is an ethical review?

(Text on screen:

  • Series of questions, comments and statements that are meant to obtain enough information about a given project to have a rational discussion on its ethical merits.
  • Always conducted by more than one individual. Each member of this ethics committee should have a professional background in science and/or ethics.
  • Aimed at providing ethical guidance to those working on the project.
  • Organized following the 6 guiding principles of Data Ethics at Statistics Canada.)

In part one of this video series on data ethics, we introduced you to the concept of data ethics and why data ethics are important. We also touched on how ethical reviews helped to obtain enough information about a given data acquisition or project in order to have a rational discussion on its ethical merits. This discussion always involves more than one person, and each person should have a professional background in science and or ethics in order to provide ethical guidance to those working on the project.

The ethical review we will use in this case study is organised to follow Statistics Canada's six guiding principles of data ethics.

Six guiding principles

(Text on screen: The six guiding principles of data ethics at Statistics Canada are...

  • Data are used to benefit Canadians
  • Data are used in a secure and private manner
  • Data acquisitions and processing methods are transparent and accountable
  • Data acquisitions and processing methods are trustworthy and sustainable
  • The data themselves are of high quality
  • Any information resulting from the data are reported fairly and do no harm)

At Statistics Canada, the ethical review of a project is generally based on 6 guiding principles:

  • Data are used to benefit Canadians.
  • Data are used in a secure and private manner.
  • Data acquisitions and processing methods are transparent and accountable.
  • Data acquisitions and processing methods are trustworthy and sustainable.
  • The data themselves are of high quality.
  • Any information resulting from the data are reported fairly and do no harm.

Case study: Survey on Narcotics Among Minors

(Text on screen: The Centre for Population Health Data is currently developing a new survey, the 2022 Canadian Survey on Narcotics among Youth (CSNY). Given that youth are a vulnerable population and the subject matter could be considered sensitive, the purpose of the following ethical review is to help make informed decisions with respect to this data acquisition, as well as to assist in completing the documentation required in order to justify its use.

The 2022 Canadian Survey on Narcotics among Youth (CSNY) is a fictitious survey to be used as an example throughout this video.)

The following is a fictitious survey that will be used as an example throughout this video.

The Centre for Population Health Data is currently developing a new survey, the 2022 Canadian Survey on Narcotics among Youth, or CSNY. Given that youth are a vulnerable population and that the subject matter could be considered sensitive, the purpose of the following ethical review is to help make informed decisions with respect to this data acquisition, as well as to assist in completing the documentation required in order to justify its use.

Benefits to Canadians.

(Text on screen: Data should be used to make informed decisions and manage resources effectively, ultimately aiming to clearly benefit the lives of Canadians.

  • Why is this data necessary? Who will it benefit and how?
  • What are some examples of ways in which these data could be used to help Canadians?)

When asking about the benefits to Canadians, the intention of an ethical review is to ensure that the data acquisition is necessary and provide examples of the ways in which these data could be used to help Canadians.

Case Study: Benefits To Society

(Text on screen: Obtaining data on narcotic use among youth is necessary in order to allocate resources and create prevention programs tailored to address the specific variables shown to impact drug use among young Canadians.

  • Example: Survey responses could suggest a link between experiencing bullying at school and drug use. Programs could aim to address bullying.
  • A persistent data gap in this area could have negative consequences.)

In regards to our case study, the justification for collecting data on the use of narcotics among youth, is that data on narcotics or opioids, specifically among minors, was previously unavailable and by collecting these data, there could be an opportunity to allocate resources and create prevention programs tailored to address the specific drivers shown to impact drug use among young Canadians.

For example, should the analysis of these data suggest a link between experiencing bullying at school and narcotic use, such prevention programs could aim to address bullying. Inversely, it might be argued that not going forward with the survey could have negative consequences. Most severely of which being a number of deaths which possibly could have been avoided had the current data gap been filled, and prevention programs put in place.

Privacy and Security.

(Text on screen: There is a fine balance between respecting privacy and producing information. We must ensure that any intrusion our statistical activities may cause is proportional to the requirements identified as absolutely necessary in order to produce that information.

  • Every data point counts. Is all of the information being requested, needed?
  • What measures are being taken in order to protect the privacy of Canadians?)

There is a fine balance between respecting privacy and producing information. We must ensure that any sense of intrusion our statistical activities may cause is proportional to the requirements identified as absolutely necessary in order to produce that information.

  • Remember, every data point counts. So is all of the information being requested actually needed?
  • What measures are being taken in order to protect the privacy of Canadians?

Case Study: Privacy and Security

(Text on screen:

  • Why are we asking for this information?
    • A data gap currently exists regarding use of narcotics among youth. Meaning, no existing sources are available.
  • What measures are being taken in order to protect the privacy of respondents?
    • The questionnaires will be password-protected and have a quick-exit option to help ensure privacy throughout the survey completion process.)

Our fictional survey proposes to ask potentially sensitive questions to youth, therefore we need to explain how the acquisition of these data is proportional to the benefit it aims to achieve. In this case, there is no other reliable data available, and so this survey would become a viable option for programs requiring this kind of information.

The ethical review process also allows us to take a deeper dive into what security measures are being taken to avoid any breach of privacy, as well as any countermeasures in place, should a breach occur. For this particular survey, there are safety mechanisms in place to protect the privacy of respondents who, as miners might feel more comfortable knowing their answers, will remain confidential. We will discuss the specifics of these measures later on in the video.

Transparency and Accountability.

(Text on screen: Statistical organizations have the responsibility to be transparent about where the data come from, how they are used and the steps taken to ensure confidentiality.

  • It is common to make available such details as what strategies they intend to put in place to inform Canadians about this data acquisition.
  • How are we communicating the benefits of the acquisition and the measures taken to protect their privacy?)

Statistical activities have the responsibility to be transparent about where the data comes from, how they are used and the steps taken to ensure confidentiality.

Questions commonly asked at this stage include a request to provide more detail on what strategies they intend to put in place to inform Canadians about this data acquisition, as well as how the data acquisition benefits Canadians and measures taken to protect their privacy.

Case Study: Transparency and Accountability

(Text on screen:

  • What strategies will be put in place to inform Canadian youth about this survey?
    • Information about the survey will be placed on Statistics Canada's website to be accessed by all interested parties. In the survey questionnaire itself, young respondents will be informed of the goals of the survey.
    • The questionnaire will also inform respondents how the survey plans to benefit society, the measures taken to protect privacy and how their information will remain confidential.)

In our case study, aspects around transparency and accountability will be addressed when the strategies to be put in place to inform Canadian youth about this survey are specified.

Information about the survey will be placed on Statistics Canada's website for all interested parties to view freely. Then, via the survey questionnaire itself, young respondents will be informed of the goals of the survey.

The questionnaire will also inform respondents how the survey plans to benefit them and society as a whole, the measures taken to protect their privacy and how their information will remain confidential even if the data are used by other government agencies for their research and programs purposes. In this case, such agencies might include Health Canada and the Public Health Agency of Canada.

Data Quality

(Text on screen: Acquired data must be as representative and accurate as possible.

  • Have any potential sources of bias for this data source been identified so far? If so, how will they be managed?)

Canadians should expect that the data acquired and statistical information provided by their government be as representative as possible. Maintaining this expectation means ensuring that biases and errors do not compromise the potential benefits of a project. At Statistics Canada, we meet this expectation by applying scientifically proven and statistically rigorous methods in all steps of the data journey.

Case Study: Data Quality

(Text on screen:

  • What strategies will be put in place to inform Canadian youth about this survey?
    • Information about the survey will be placed on Statistics Canada's website to be accessed by all interested parties. In the survey questionnaire itself, young respondents will be informed of the goals of the survey.
    • The questionnaire will also inform respondents how the survey plans to benefit society, the measures taken to protect privacy and how their information will remain confidential.)

A potential threat to data quality is the fact that respondents often live with their parents, and some of them may not benefit from total privacy in order to give honest answers to questions, especially concerning questions on drug use, be these illegal or prescribed. there could be significant bias if respondents do not answer honestly. In this case, the questionnaires will be password protected and have a quick exit option to help preserve privacy, but the potential for bias still exists if the respondent finds the questions too personal.

Fairness and Do No Harm

(Text on screen: When conducting statistical activities, it is necessary to consider all the potential risks that a statistical activity may pose to the well-being of individuals or specific groups.

  • Can you foresee any negative consequences an individual might experience as a result of this data acquisition?
  • Might any part of the data acquisition process cause undue stress to Canadians?)

It is necessary to consider all the potential risks to the well-being of Canadians. How statistical activities are carried out and how the resulting information is communicated must be considered in order to promote equity among all Canadians. To be fair and do no harm means any negative consequences an individual might experience as a result of this data acquisition must be foreseen before any data is collected or obtained. Is it possible that any part of the data acquisition process might cause undue stress to Canadians?

Case study: Fairness and Do No Harm

(Text on screen:

  • What, if any, negative consequences might a respondent experience as a result of answering this survey?
    • Some of the topics covered in the survey can trigger emotional responses: Bullying, mental health, school performance
  • How will you address questions that might be distressing to respondents?
    • Mental health resources will be provided to respondents along with the questionnaire, and interviewers have received training to deal with difficult situations.)

When thinking about potential negative consequences to taking part in our fictional survey, respondents to the Canadian Survey on Narcotics among Youth might have emotional responses triggered by questions on their experiences with bullying, mental health or school performance. In this case, the ethical review committee should have inquired about the steps taken to mitigate the risk and in response, confirmed that mental health resources will be given to respondents throughout the duration of the questionnaire completion process and, afterwards, should they be required.

Trust and Sustainability

(Text on screen: Statistics Canada needs active participation from Canadians to ensure that we can continue our statistical activities going forward.

  • How will we ensure we are able to continue producing high quality information that matters to Canadians while maintaining the trust of the public long-term?)

Statistics Canada needs active participation from Canadians to ensure that we can continue our statistical activities going forward. Assuring confidentiality, protecting personal information, producing representative data, and being accountable are all choices Statistics Canada makes in order to show Canadians that their trust is well-placed. This trust is essential if Statistics Canada is to continue producing high quality information that matters to Canadians while maintaining the trust of the public long-term.

Case Study: Trust and Sustainability

(Text on screen:

  • The survey will inform respondents that any information that they provide will not be shared with legal authorities, parents or guardians of any kind.
  • Explaining our statistical business processes, including our ethical reviews, will help maintain the trust of Canadians and, thus, ensure the sustainability of our statistical programs.)

In order to maintain the trust of Canadian youth, we will explain to respondents that any information that they provide will not be shared with legal authorities or their parents. Explaining our statistical business processes, including our ethical reviews, will help maintain the trust of Canadians, their confidence in our processes and our promise of confidentiality. Because, in the absence of such trust, we cannot continue to produce high quality information that benefits society, the economy and the environment.

Recap of Key Points

(Text on screen:

  • Ethical reviews are a series of questions and comments that are meant to obtain enough information about a given project to have a rational discussion on its ethical merits.
  • Ethical reviews are organized following the 6 Guiding Principles of Data Ethics at Statistics Canada.)

In this video entitled Data ethics part 2: Ethical reviews, we learned that ethical reviews are a series of questions and comments that are meant to obtain enough information about a given project to have a rational discussion on its ethical merits.

At Statistics Canada, ethical reviews are organized following the six guiding principles of data ethics.

(The Canada Wordmark appears.)

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2021 Census Webinar Series: Immigration, Ethnocultural and Religious Diversity, and Migration (17220006)

On October 26, Statistics Canada will be releasing the sixth set of results from the 2021 Census of Population.

This release will focus on:

  • Citizenship and immigration in Canada;
  • Ethnocultural and religious composition of the population; and
  • Mobility and migration

The census webinar will provide you with key information including:

  • Concepts and definitions;
  • High-level national, provincial, and territorial findings; and
  • Instructions on how to access data products and resources online

Following the presentation, Statistics Canada officials will be available to answer questions.

Webinar video:

What is the 2021 Census of Population Webinar Series?

The 2021 Census of Population Webinar Series is based on our most recent census, held on May 11, 2021. The census provides a detailed and comprehensive statistical portrait of Canada that is vital to our country. The webinars will be presented after the major data releases scheduled between February 9 and November 30, 2022 and will share information on concepts, data products, and resources available from the 2021 Census.

Stay tuned for webinars on demography, families, Canadian military experience, income, linguistic diversity, indigenous peoples, housing, ethnocultural and religious diversity, immigration and mobility, education, labour and more.

Statistics 101: Statistical Bias

Catalogue number: 892000062022005

Release date: October 17, 2022

In this video, we will explain the concept of statistical bias, which occurs when statistics differ systematically from the reality they are trying to measure because of problems with the way the data were produced.

Data journey step
Foundation
Data competency
  • Data analysis
  • Data quality evaluation
  • Identifying problems using data
Audience
Basic
Suggested prerequisites
N/A
Length
10:38
Cost
Free

Watch the video

Statistics 101: Statistical Bias - Transcript

Statistics 101: Statistical Bias - Transcript

(The Statistics Canada symbol and Canada wordmark appear on screen with the title: "Statistical Bias")

Statistics 101 Statistical Bias

In every-day language, bias refers to how a person's point of view, values or beliefs can influence their judgement or decisions in particular circumstances.

In this video, we will explain the concept of statistical bias, which occurs when statistics differ systematically from the reality they are trying to measure because of problems with the way the data were produced.

Learning Goals

Before we talk about bias, we will begin with a few words about error. Statistics are measurements that describe our society, economic activity, or other aspects of the world around us. While statistics try and estimate the true value as accurately as possible, they can often contain a certain level of error. Statistical bias is the difference between the statistical measure and the true value.

In this video, you will learn the answers to the following questions:

  • What are some of the different types of error?
  • What are some of the types of error that lead to statistical bias?
  • And where can errors which lead to statistical bias can occur throughout the data journey?

Steps in the data journey

(Diagram of the Steps of the data journey: Step 1 - define, find, gather; Step 2 - explore, clean, describe; Step 3 - analyze, model; Step 4 - tell the story. The data journey is supported by a foundation of stewardship, metadata, standards and quality.)

This diagram is a visual representation of the data journey from collecting the data to exploring, cleaning, describing and understanding the data, analyzing the data and lastly to communicating with others the story the data tell.

Errors leading to statistical bias can occur at any step throughout the data journey.

What are the different types of error?

When trying to measure and analyze data, some level of error is to be expected, but what exactly do we mean when we say there are different types of error? To accept that errors exist is not necessarily a bad thing, but it is important to understand that not all errors are equal. Two main types of error we will learn about today are random error and systematic error.

Random vs Systematic Error

Random errors introduce variability between separate measurements of the same thing. For example, responses or measurements that are taken at different times can result in response variability, or another randomly selected sample can result in sampling variability. Randomness can also occur in the data processing procedures. Nevertheless, in these cases the measurements still tend to cluster around the true value. Therefore, despite some error, there are still accurate.

On the other hand, systematic errors result in non-random variability that skew or pull the measurement away from the true value, resulting in a measurement that may be smaller, bigger, higher or lower than the true value and can result in incorrect conclusions.

What is statistical bias?

Now that we understand the difference between random and systemic errors, and how systemic errors can lead to inaccurate conclusions, from this point on in the video, we will refer to such inaccurate conclusions as Statistical bias, because when we say Statistical bias, what we really mean is a statistic that differs from the reality it is trying to measure resulting from systemic errors in the way the data were collected, reported, and/or analyzed.

Where to look for statistical bias

Bias statistics can come from any number of data sources, be it survey data, administrative data, big data, etc. As well there are many types of errors that can lead to bias. Today however, we will focus on three particular areas susceptible to systematic errors which can lead to bias statistics. They are: firstt, data collection; second measurement; and third, analytics.

Data collection

Beginning with data collection, bias can be a result of systemic errors in the way the data are collected resulting in data that do not adequately represent the population you are trying to measure.

Some examples of bias include:

  • coverage bias,
  • non-response bias,
  • and self-selection bias.

Coverage bias

Coverage bias occurs when, due to the way in which the data collection process was designed, it excludes or includes groups that are (or are not) part of the target population. The main sources of coverage errors are:

  • Undercoverage, meaning a failure to include all membersof the population that should be included, and
  • Overcoverage, inclusion of members in the population that should not be included.

For example, a survey is trying to measure the daily spending habits of Canadians, but the questionnaire is only available on smartphones. The results of the survey will not include data from people without smartphones. And since the number of people with smartphones is smaller than the target population of all Canadians, there is a coverage bias because part of the population, those without smartphones, is not being covered by the survey.

Non-response bias

Non-response bias occurs when respondents differ from those who choose not to respond.

Some causes of non-response bias include a lack of interest in the topic. For example, people may be less likely to respond to a survey if they feel it does not interest them or benefit them personally. Sensitive topics can also lead to non-response if someone feels the questionnaire is asking questions that are too personal or sensitive.

Self-selection bias

Self-selection bias occurs when individuals who volunteer to provide data or participate in a study differ from those who do not volunteer. You might even say that self-selection bias is the exact opposite of non-response bias, even though they both contribute to inaccurate conclusions.

Measurement

The next area we will explore in our search for sources (or causes) of statistical bias is measurement. Measurement bias occurs when there are systematic errors in the way the concept of interest is measured or reported.

Some examples include:

  • recall bias,
  • social desirability bias,
  • leading questions and
  • faulty measurement tools.

Recall bias

Recall bias occurs when respondents do not remember previous events or experiences accurately or omit details. For example, a respondent may have difficulty remembering how much they paid for gas in the past month. Or, if asked about visits to the doctor in the past year, the respondent might include a visit from 15 months ago, or forget a visit from 10 months ago.

Social desirability bias

Social desirability bias occurs when participants, either consciously or sub-consciously respond to questions in an attempt to present a more positive self-image. For example, someone might over-report what they consider a "good" behavior, like the amount of exercise they do in a day or the amount of fruits and vegetables they eat, or they could under-report more socially and desirable behaviors, like smoking.

Leading questions

Leading questions occur when a survey question prompts, encourages or guides the respondent toward a previously determined or desired answer. For example, the wording "Most people think this is a great restaurant. Do you agree?" May elicit more positive responses than the more neutral alternative. "How would you rate this restaurant?"

Faulty measurement tools

Bias can occur when tools or measures used to collect data are faulty, malfunction or used inaccurately leading to systematically different measurements. For example, measurement tools such as a scale in a doctor's office, that's improperly calibrated, will consistently report incorrect weights.

Analytics

So far we have covered how errors can lead to bias in the data collection and measurement stages, but in this third and final section of the video, we will discuss analytics bias, which occurs when data analysis is conducted using non-representative data or when a model or researcher skews the results of a study towards a specific outcome.

Some examples of analytics bias include:

  • confirmation bias and
  • modelling bias.

Confirmation bias

If analysis is conducted to support a specific point of view or narrative, it may be biased, meaning it could ignore or exclude important elements that do not fit that point of view or narrative. Confirmation bias occurs when data analysts only choose data and results that agree with their hypothesis or beliefs.

Modelling bias

Bias can occur in data modelling when the data used are not representative or when the model, or algorithm, are also biased and do not accurately represent the phenomenon they seek to represent.

One example of training data not being representative is in the use of a company's historical data to staff a new position. If the algorithm is trained on data that shows successful hires and promotions at the company were mostly men, then it will learn to seek out and continue to suggest men be placed in future roles.

An example of a biased algorithm however, would be if the algorithm was programmed to pre-filter any results by excluding candidates with last names that include characters not present in the English alphabet.

Recap of Key Points

To recap what we learned in this video:

  • There are two main types of error: random error and systematic error,
  • Statistical bias refers to differences between an estimate and the true value.
  • And the three particular areas susceptible to errors which can lead to bias include, bias in the population covered by the data, bias in the measurement of the concepts of interest and bias in their analysis or methods used for analysis.

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Wholesale Trade Survey (monthly): CVs for total sales by geography - August 2022

Wholesale Trade Survey (monthly): CVs for total sales by geography - August 2022
Geography Month
202108 202109 202110 202111 202112 202201 202202 202203 202204 202205 202206 202207 202208
percentage
Canada 0.6 0.7 0.7 0.8 1.2 0.8 0.7 0.6 0.8 0.8 0.6 0.7 0.6
Newfoundland and Labrador 0.4 0.4 0.3 0.4 0.4 1.0 0.6 1.5 1.9 0.5 0.3 0.3 0.6
Prince Edward Island 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0
Nova Scotia 2.5 2.8 2.4 2.8 5.9 2.8 1.8 2.5 2.7 3.5 1.6 4.7 2.8
New Brunswick 2.1 2.4 2.2 4.0 1.4 3.2 0.5 1.4 2.9 1.3 1.2 2.1 2.7
Quebec 1.7 1.6 1.6 1.7 1.9 2.2 1.4 1.4 2.5 1.9 1.4 1.5 1.4
Ontario 1.0 1.1 1.1 1.3 2.1 1.3 1.2 1.1 1.2 1.3 1.1 1.1 0.9
Manitoba 1.0 1.1 1.7 1.2 1.5 1.7 1.6 0.6 0.8 1.8 1.7 1.2 1.0
Saskatchewan 1.3 1.6 1.0 0.8 0.5 0.9 0.3 0.4 0.6 0.7 0.7 0.6 1.1
Alberta 1.1 1.0 1.4 2.0 1.0 1.8 1.6 0.8 1.8 1.2 1.2 1.4 1.4
British Columbia 1.4 1.8 1.2 1.7 1.3 1.6 2.3 1.6 1.4 1.6 2.1 1.9 2.0
Yukon Territory 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0
Northwest Territories 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0
Nunavut 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0

Environmental, Social and Governance (ESG) Program consultative engagement

Consultative engagement objective

The landscape in which businesses operate is evolving due to increased awareness of environmental degradation and the importance of diversity and inclusion, changing the expectations for corporate behaviour. Environmental, social and governance (ESG) are three non-financial themes that can be used to inform the long-term risk or return of an investment. The rationale is that industries that are adequately managing their ESG risks will be less vulnerable to changes in regulations or societal expectations and will therefore perform better in the long-run.

There are gaps with respect to indicators related to ESG and Indigenous Peoples. The objective of engagement was to better understand the data needs of rights holders and stakeholders in relation to ESG and Indigenous Peoples and what role Statistics Canada could play in meeting these needs. The engagement was focused on understanding data gaps and analytical needs with respect to ESG and Indigenous Peoples.

Consultative engagement methods

The majority of organisations contacted about engagement were identified based on their interests in ESG and ESG's alignment with their scope of work (e.g., sustainable finance and ESG, resource development, Indigenous reconciliation and economic reconciliation). Other participants were drawn from earlier phases of project engagement, and some were referrals from current partners working with Statistics Canada. Contacts covered various interests from Federal, Provincial and Territorial Governments, Finance, Resource, and Industry Organisations working with Indigenous communities, and National and Regional Indigenous Organisations.

Some participants received introductory presentations upon request to familiarise them with ESG and the objectives of the engagement process. Others moved directly to the official engagement activities. Close to 100 participants attended 25 discussion group sessions moderated over 2 hours per session.

Results

Seven key findings were derived from the results of this consultative engagement initiative:

  1. ESG frameworks should be standardised and include Indigenous Peoples' values and interests
  2. Data should reflect direct, indirect, and cumulative impacts
  3. Data should reflect the interconnectedness of environmental, social and governance issues
  4. Engagement should be done early and often, on a continuous basis throughout the life-cycle of a project and should encompass consent and capacity building
  5. Data users should have access to a range of data products to suit their different needs
  6. Development of ESG indicators related to Indigenous Peoples should be Indigenous-led
  7. Data should be presented in a way that reflects positively on Indigenous Peoples and does not perpetuate colonial stereotypes

Statistics Canada thanks participants for their contributions to this consultative engagement initiative. Their insights will help guide the agency in providing information to support ESG and Indigenous Peoples.

You can read the entire report at Environmental, Social and Governance Project and Indigenous Peoples Engagement Report.

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Financial Information of Universities – 2021/2022

Canadian Centre for Education Statistics

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

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.

Purpose of the survey

This survey collects financial information (income and expenditures) on all universities and degree-granting colleges 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.

Fax or e-mail transmission disclosure

Statistics Canada advises you that there could be risk of disclosure during the transmission of information by facsimile or e-mail. However, upon receipt, Statistics Canada will provide the guaranteed level of protection afforded all information collected under the authority of the Statistics Act.

Record linkages

To enhance the data from this survey, Statistics Canada may combine it with information from other surveys or from administrative sources.

General information

  • Name of University (or College)
  • Address of preparer
    • Street
    • City
    • Province
    • Postal Code
  • Fiscal year ending: Day Month Year
  • Name and title of preparer
  • Telephone
    • Area code
    • Number
    • Local
  • Fax
    • Area code
    • Number
  • E-mail address
  • Name of Senior Administrative Officer (if different from above)

Instructions

  1. Please read carefully the accompanying Guidelines.
  2. All amounts should be expressed in thousands of dollars ($'000).
  3. In the "Observations and Comments" section, please explain financial data that may not be comparable with the prior year.
  4. Please do not fill in shaded areas. All non-shaded cells should be completed.
    A nil entry should be indicated with a zero.

Reserved for Statistics Canada

  • Full-time equivalent
  • Report Status
  • Institution Code: nceYYIII
  • Comments
Table 1
Income by fund
Table summary
This is an empty data table used by respondents to provide data to Statistics Canada. This table contains no data.
Types of income Funds
General operating Special purpose and trust Sponsored research Ancillary Capital Endowment Total funds
Entities consolidated Entities not consolidated Sub-total
(thousands of dollars)
Government departments and agencies - grants and contracts  
Federal  
1. Social Sciences and Humanities Research Council                  
2. Health Canada                  
3. Natural Sciences and Engineering Research Council                  
4. Canadian Institutes of Health Research (CIHR)                  
5. Canada Foundation for Innovation (CFI)                  
6. Canada Research Chairs                  
7. Other federal                  
Other  
8. Provincial                  
9. Municipal                  
10. Other provinces                  
11. Foreign                  
Tuition and other fees  
12. Credit course tuition                  
13. Non-credit tuition                  
14. Other fees                  
Donations, including bequests  
15. Individuals                  
16. Business enterprises                  
17. Not-for-profit organizations                  
Non-government grants and contracts  
18. Individuals                  
19. Business enterprises                  
20. Not-for-profit organizations                  
Investment  
21. Endowment                  
22. Other investment                  
Other  
23. Sale of services and products                  
24. Miscellaneous                  
25. TotalNote 1                  
Note 1

The total for Sponsored research – Entities not consolidated must agree with the total for Sponsored research – Entities not consolidated in Table 2.

Return to note 1 referrer

Observations and comments

  • Description (Fund and type of income)
  • Comments
Table 2
Expenditures by fund
Table summary
This is an empty data table used by respondents to provide data to Statistics Canada. This table contains no data.
Types of expenditures Funds
General operating Special purpose and trust Sponsored research Ancillary Capital Endowment Total funds
Entities consolidated Entities not consolidated Sub-total
(thousands of dollars)
Academic salaries  
1. Academic ranks                  
2. Other instruction and research                  
3. Other salaries and wages                  
4. Benefits                  
5. Travel                  
6. Library acquisitions                  
7. Printing and duplicating                  
8. Materials and supplies                  
9. Communications                  
10. Other operational expenditures                  
11. Utilities                  
12. Renovations and alterations                  
13. Scholarships, bursaries and prizes                  
14. Externally contracted services                  
15. Professional fees                  
16. Cost of goods sold                  
17. Interest                  
18. Furniture and equipment purchase                  
19. Equipment rental and maintenance                  
20. Internal sales and cost recoveriesNote 1                  
21. Sub-total                  
22. Buildings, land and land improvements                  
23. Lump sum payments                  
24. TotalNote 2                  
Note 1

Total funds (calculated) – Internal sales and cost recoveries must net to zero.

Return to note 1 referrer

Note 2

The total for Sponsored research – Entities not consolidated must agree with the total for Sponsored research – Entities not consolidated in Table 1.

Return to note 2 referrer

Observations and comments

  • Description (Fund and type of expenditure)
  • Comments
Table 4
General operating expenditures by function
Table summary
This is an empty data table used by respondents to provide data to Statistics Canada. This table contains no data.
Types of expenditures Functions
Instruction and
non-sponsored
research		 Non-credit
instruction		 Library Computing and
communications		 Administration and academic support Student
services		 Physical
plant		 External
Relations		 Total
functionsNote 1
(thousands of dollars)
Academic salaries  
1. Academic ranks                  
2. Other instruction and research                  
3. Other salaries and wages                  
4. Benefits                  
5. Travel                  
6. Library acquisitions                  
7. Printing and duplicating                  
8. Materials and supplies                  
9. Communications                  
10. Other operational expenditures                  
11. Utilities                  
12. Renovations and alterations                  
13. Scholarships, bursaries and prizes                  
14. Externally contracted services                  
15. Professional fees                  
16. Cost of goods sold                  
17. Interest                  
18. Furniture and equipment purchase                  
19. Equipment rental and maintenance                  
20. Internal sales and cost recoveries                  
21. Sub-total                  
22. Buildings, land and land improvements                  
23. Lump sum payments                  
24. Total                  
Note 1

The amounts in column Total functions should agree with the amounts in column General operating in Table 2.

Return to note 1 referrer

Observations and comments

  • Description (Function and type of expenditure)
  • Comments
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