Inventaire des pratiques
Primary GCM Objectives
Principes directeurs du Pacte mondial*
Objectifs de développement durable (ODD)
Dates
2021 - Present
Résumé
For years, Statistics Canada has been providing Canadians with big picture statistics on a variety of topics impacting people across the country. However, the big picture can hide key differences in the experiences of specific population groups. The COVID-19 pandemic highlighted how a single event can be experienced differently across various groups, thus revealing pre-existing social and economic inequalities. For pandemic recovery plans and programs to address these differential impacts, Canadians need more detailed data that can be disaggregated, or broken down, into categories such as gender, race, age, income level, or a combination of these and other categories, and at the lowest level of geography possible. The objectives of the Disaggregated Data Action Plan (DDAP) include supporting more representative data collection, enhancing statistics on diverse populations, supporting the government and society’s efforts to address systemic racism and gender discrimination, and bringing fairness and inclusion considerations into decision-making processes. At Statistics Canada, the DDAP operates under five pillars: 1) enhanced engagement and communications, 2) national disaggregated data standards, 3) expanded disaggregated data assets, 4) increased intersectional and longitudinal insights, and 5) access to enhanced disaggregated data. For every Canadian to reach their full potential, we need to properly understand the circumstances in which people live and the barriers they face – we cannot improve what we cannot measure.
Organisations
Principale(s) organisation(s) de mise en œuvre
Government of Canada
Partenaires impliqués
Government of Canada
Bénéfice et impact
The Disaggregated Data Action Plan (DDAP) targets the four Government of Canada employment equity groups: Indigenous peoples, Women, Visible Minorities / Racialized populations, and Persons with disabilities. However, where relevant and possible, disaggregation is extended to other groups (e.g., sexual orientation, children and youth, seniors, official language, immigrants, low-income Canadians).
While it is too early for the full impacts of the DDAP to be observed, some early accomplishments include (and are not limited to):
• studies that examined trends in pay gaps; businesses majority-owned by immigrants, businesses majority-owned by immigrants to Canada and businesses majority-owned by racialized people, and the educational and economic outcomes of lesbian, gay and bisexual people from diverse ethnocultural backgrounds, as well as an article examining variations in immigrants' lower risk of suicide-related behaviours.
• DDAP-funded research on innovative methods, including improving sampling for better representation of diverse population groups and coordinating sampling between surveys to reduce respondent burden, especially for small population groups. Examples include the oversampling of the flagship surveys: Labour Force Survey, Canadian Community Health Survey, and General Social Survey to result in disaggregated data that could offer valuable insights on various groups.
• DDAP funded projects on the disaggregation of web panel surveys, different indicators on demographic, labour market, health, and social, disaggregated data acquisition, statistical and data standards, Canadian Survey on Business Conditions (CSBC) and more. Additional information can be found in this DDAP-funded Gender, Diversity and Inclusion Statistics (GDIS) Hub.
These increased and improved disaggregated statistics will give all levels of government, businesses, policy specialists, data users, non-for-profit organizations, and all Canadians the level of detail they need to make evidence-based decisions. By informing policy decisions, these data will strengthen the government's efforts to address systemic racism and gender gaps and help create a more equitable Canada.
While it is too early for the full impacts of the DDAP to be observed, some early accomplishments include (and are not limited to):
• studies that examined trends in pay gaps; businesses majority-owned by immigrants, businesses majority-owned by immigrants to Canada and businesses majority-owned by racialized people, and the educational and economic outcomes of lesbian, gay and bisexual people from diverse ethnocultural backgrounds, as well as an article examining variations in immigrants' lower risk of suicide-related behaviours.
• DDAP-funded research on innovative methods, including improving sampling for better representation of diverse population groups and coordinating sampling between surveys to reduce respondent burden, especially for small population groups. Examples include the oversampling of the flagship surveys: Labour Force Survey, Canadian Community Health Survey, and General Social Survey to result in disaggregated data that could offer valuable insights on various groups.
• DDAP funded projects on the disaggregation of web panel surveys, different indicators on demographic, labour market, health, and social, disaggregated data acquisition, statistical and data standards, Canadian Survey on Business Conditions (CSBC) and more. Additional information can be found in this DDAP-funded Gender, Diversity and Inclusion Statistics (GDIS) Hub.
These increased and improved disaggregated statistics will give all levels of government, businesses, policy specialists, data users, non-for-profit organizations, and all Canadians the level of detail they need to make evidence-based decisions. By informing policy decisions, these data will strengthen the government's efforts to address systemic racism and gender gaps and help create a more equitable Canada.
Principales leçons
Disaggregated data can help us understand the needs of the diverse groups in Canadian population and identify the gaps and inequalities in existing policies and services. Some "Do's" and "Don'ts" are provided below.
"DO'S"
1. Understand why it is important to collect, analyze and share disaggregated data. Please refer to the Disaggregated Data Action Plan to learn more about the importance of disaggregated data in addressing systemic inequalities in Canada.
2. Seek out varied sources for disaggregated data. Combine data from various sources, such as stories from people with lived experiences, academic literature, administrative data and statistical datasets (for instance, in Canada, the Gender, Diversity and Inclusion Statistics Hub) to create a complete picture.
3. Include diverse perspectives at every stage of the process: collection, analysis, implementation and reporting. Design data collection tools that resonate with your respondents. Store, analyze and disseminate data in an inclusive and culturally relevant manner.
4. Make disaggregated data collection, analysis, implementation and reporting into an iterative process. Continually seek insights to improve and enrich disaggregated data as you plan and implement your work.
5. Look for differences between various groups as well as within the groups. Ask questions to help you identify groups that still need to be represented. Find differences within common groups and disaggregate them further.
6. Apply empathy and ethical best practices when working with data from different communities. In order to build trust with government, the lived experiences of different communities need to be considered and represented.
7. Ask questions and ask for help. Ask colleagues and learn from training and guides (for instance, the Measuring Impact by Design guide in Canada), to improve your capacity to collect and use disaggregated data.
DON'TS
1. Don’t stop trying to find data. Even if not immediately available, make a wish list and seek out the data that you need for your work. Ask a colleague or experts for help finding it.
2. Don’t consider Gender-based Analysis Plus (GBA+) to be a tick box. Learn about the GBA+ process and integrate it into every stage of your work to ensure inclusivity.
"DO'S"
1. Understand why it is important to collect, analyze and share disaggregated data. Please refer to the Disaggregated Data Action Plan to learn more about the importance of disaggregated data in addressing systemic inequalities in Canada.
2. Seek out varied sources for disaggregated data. Combine data from various sources, such as stories from people with lived experiences, academic literature, administrative data and statistical datasets (for instance, in Canada, the Gender, Diversity and Inclusion Statistics Hub) to create a complete picture.
3. Include diverse perspectives at every stage of the process: collection, analysis, implementation and reporting. Design data collection tools that resonate with your respondents. Store, analyze and disseminate data in an inclusive and culturally relevant manner.
4. Make disaggregated data collection, analysis, implementation and reporting into an iterative process. Continually seek insights to improve and enrich disaggregated data as you plan and implement your work.
5. Look for differences between various groups as well as within the groups. Ask questions to help you identify groups that still need to be represented. Find differences within common groups and disaggregate them further.
6. Apply empathy and ethical best practices when working with data from different communities. In order to build trust with government, the lived experiences of different communities need to be considered and represented.
7. Ask questions and ask for help. Ask colleagues and learn from training and guides (for instance, the Measuring Impact by Design guide in Canada), to improve your capacity to collect and use disaggregated data.
DON'TS
1. Don’t stop trying to find data. Even if not immediately available, make a wish list and seek out the data that you need for your work. Ask a colleague or experts for help finding it.
2. Don’t consider Gender-based Analysis Plus (GBA+) to be a tick box. Learn about the GBA+ process and integrate it into every stage of your work to ensure inclusivity.
Recommandations(if the practice is to be replicated)
These four guiding principles of the Disaggregated Data Action Plan (DDAP) should be applied:
1) Data and analyses should be disaggregated at the lowest level of population detail possible, while respecting quality and confidentiality,
2) Analyses should focus on intersectionality, as opposed to binary interactions,
3) Data should be available at the lowest level of geography possible,
4) Confidentiality and privacy must be protected according to relevant standards. In Canada, for example, confidentiality and privacy are protected under the Statistics Act, the Access to Information Act and the Privacy Act.
1) Data and analyses should be disaggregated at the lowest level of population detail possible, while respecting quality and confidentiality,
2) Analyses should focus on intersectionality, as opposed to binary interactions,
3) Data should be available at the lowest level of geography possible,
4) Confidentiality and privacy must be protected according to relevant standards. In Canada, for example, confidentiality and privacy are protected under the Statistics Act, the Access to Information Act and the Privacy Act.
Innovation
For years, Statistics Canada has been providing Canadians with big picture statistics on a variety of topics impacting people across the country. However, the big picture can hide key differences in the experiences of specific population groups.
The COVID-19 pandemic highlighted how a single event can be experienced differently across various groups, revealing uneven social and economic realities. To address how different groups have different lived experiences, more detailed data is needed, which can be broken down, or disaggregated, into sub-categories according to gender, ethnocultural characteristics, age, sexual orientation, disability – or intersections of these and other sub-categories. Data also needs to be broken down to the lowest possible level of geography, since events impact people differently depending on where they geographically live.
These increased and improved disaggregated statistics will give all levels of government, businesses, policy specialists, data users, non-for-profit organizations, and all Canadians the level of detail they need to make evidence-based decisions. By informing policy decisions, these data will strengthen the government's efforts to address systemic racism and gender gaps and help create a more equitable Canada.
The DDAP targets the four employment equity groups in Canada:
• Indigenous peoples
• Women
• Visible Minorities / Racialized populations
• Persons with disabilities
However, where relevant and possible, disaggregation is extended to other groups (e.g., sexual orientation, children and youth, seniors, official language, immigrants, low-income Canadians).
Use of acronyms 2SLGBTQI+ and 2SLGBTQ+
In August 2022, the Government of Canada adopted the use of the acronym 2SLGBTQI+ to refer to Two-Spirit, lesbian, gay, bisexual, transgender, queer and intersex people, and those who use other terms related to gender and sexual diversity.
The COVID-19 pandemic highlighted how a single event can be experienced differently across various groups, revealing uneven social and economic realities. To address how different groups have different lived experiences, more detailed data is needed, which can be broken down, or disaggregated, into sub-categories according to gender, ethnocultural characteristics, age, sexual orientation, disability – or intersections of these and other sub-categories. Data also needs to be broken down to the lowest possible level of geography, since events impact people differently depending on where they geographically live.
These increased and improved disaggregated statistics will give all levels of government, businesses, policy specialists, data users, non-for-profit organizations, and all Canadians the level of detail they need to make evidence-based decisions. By informing policy decisions, these data will strengthen the government's efforts to address systemic racism and gender gaps and help create a more equitable Canada.
The DDAP targets the four employment equity groups in Canada:
• Indigenous peoples
• Women
• Visible Minorities / Racialized populations
• Persons with disabilities
However, where relevant and possible, disaggregation is extended to other groups (e.g., sexual orientation, children and youth, seniors, official language, immigrants, low-income Canadians).
Use of acronyms 2SLGBTQI+ and 2SLGBTQ+
In August 2022, the Government of Canada adopted the use of the acronym 2SLGBTQI+ to refer to Two-Spirit, lesbian, gay, bisexual, transgender, queer and intersex people, and those who use other terms related to gender and sexual diversity.
Ressources additionnelles
Média
Date de soumission:
16 octobre 2024
*Toutes les références au Kosovo doivent être comprises dans le contexte de la résolution 1244 (1999) du Conseil de sécurité des Nations Unies.