Catalogue number: 892000062024002
Release date: July 16, 2024
This short video explains how the use of disaggregated data can help policymakers to develop more targeted and effective policies by identifying the unique needs and challenges faced by different demographic groups.
- Data journey step
- Foundation
- Data competency
-
- Metadata Creation and Use
- Audience
- Basic
- Suggested prerequisites
- The Importance of Disaggregated Data: An Introduction (part 1)
- Length
- 08:38
- Cost
- Free
Watch the video
The Importance of Disaggregated Data: An Introduction (part 2) - Transcript
Statistics 101: Exploring measures of central tendency - Transcript
(The Statistics Canada symbol and Canada wordmark appear on screen with the title: "The Importance of Disaggregated Data: An Introduction (part 2)".)
What is disaggregated data?
In a country like Canada, one can expect to see diverse pictures of communities, ethnocultural groups, life stages, gender and occupations. These groups, big or small, may experience different socio-economic challenges or outcomes throughout their lives, such as academic success, or labour market trajectories. Some specific groups may even be more vulnerable, and could experience mental health challenges during their lives or even homelessness. For a better understanding, we disaggregate data. That means taking carefully gathered and aggregated data, which is a critical step in ensuring data are kept anonymous, and stepping back to look at the data for various populations by breaking down large-scale datasets into sub-categories such as religion, gender, ethnicity, or a combination of the like.
Where can you find disaggregated data?
Given the potential usefulness of disaggregated data, your next question might be, where can I find the data I need? The truth is, you might not be able to find one single source that will answer all of your questions. To provide a complete picture, you might need to combine data from various sources, and Statistics Canada's many products and services can be a big part of that. Our website provides access to a wide range of research reports and disaggregated data from a growing collection of survey and administrative data sources. Data can also be complemented by valuable insights from people with lived experience, engagements and consultations, and other sources of qualitative information.
Each initiative will require analysis of different disaggregated groups
(Categories revealed one after the other: "Gender and/or Sex", "Age", "Ethnocultural diversity", "Indigenous identity", "Geography", "Education", "Occupation", "Income", "Sexual orientation", "Disability", "Language", "Immigration status", "Employment", "Family status", "And many more!")
Here are some examples of disaggregated groups that are important to consider today. Every day, we are evolving as people and going through challenges related to where we are at in life. Some of us may be running after young children, or caring for our senior parents. We may be starting university or looking to upskill after losing our job. In 2022, Canada welcomed over 1,000,000 immigrants, so many of us are new to the country. Disaggregated data reflect who we are and help inform on our challenges at a particular point in time. Each policy or community initiative will need to consider the right disaggregated groups to focus on. And these groups will evolve over time, as our communities evolve. In Samir's example, he considered geography, age and disability to improve delivery of public transportation in Greendale. Another initiative will need to consider different groups. Essentially, disaggregated data means you can tailor the analysis to your needs.
Likewise for business-oriented initiatives
(Categories revealed one after the other: "Geography", "Sector", "Firm size", "Foreign/ domestic controlled", "Exporter/importer", "Majority ownership by groups (e.g. women, Indigenous, racialized)", "Incorporated", "Age of business", "Employment", "And many more!")
Just like people, businesses are not all the same. For policies or programs that target businesses, it's also important to consider disaggregated groups. Each of these characteristics can influence outcomes and challenges for businesses. Think of a small restaurant business led by a recent immigrant. They will have different challenges than a large food processing factory owned by multinational.
Let's disaggregate: By gender
Here is an example of disaggregated data at work. Let's imagine you are tasked with creating a policy initiative to help people find jobs. Let's start by taking a look at the unemployment rate for Canada to see if there are any major differences between women and men. The unemployment rate primarily reflects people who are looking for a job, expressed as a percentage of all people in the labour force, that is, people who either have a job or are looking for one. For 2023, results look relatively similar by gender, with the unemployment rate reaching 5.3% for women and 5.6% for men, trends that reflect economic and labour market performance in that year. Disaggregating by gender only doesn't seem to be enough to identify notable differences, so let's dig deeper into the data and add other dimensions that are relevant to our understanding of unemployment.
Let's disaggregate: By gender and age
Let's disaggregate by both gender and age. Based on the chart, we can see that youth, those aged 15 to 24 years old, have higher unemployment rates than core-aged and older people. Additionally, young men have a slightly higher unemployment rate than young women. About one in nine young men in the labour force are unemployed. Their unemployment rate in 2023 was 11.6%. In comparison, the rate among young women was 10%. What other identity factors could be considered for this analysis? Well, since we already know that Canada has a very diverse youth population, it may be worthwhile to disaggregate the data by racialized group membership in addition to gender and age.
Let's disaggregate: By gender, age and racialized group membership
In the data we have, we see that racialized group membership is measured using the concept of "visible minorities". So, if we focus on youth aged 15 to 24 and further disaggregate by gender and being a member of a visible minority group, we observe new differences, with young, visible minority men having the highest unemployment rate at 13.4%, followed closely by young visible minority women at 13%. In contrast, young women who are not members of a visible minority group have the lowest rate, at just under 9%. At this point, you may be thinking about ways to help racialized youth find employment. But that is still a broad group, with notable differences in labour market characteristics, so let's disaggregate even further.
Let's disaggregate: By gender, age and specific racialized group membership
Let's look at results for the five largest visible minority groups in Canada. Again, some important differences have emerged through another level of disaggregation. Among young men, Black and Arab youth seem to face higher unemployment than the overall average represented by the blue bar. Among young women, the unemployment rate is somewhat higher than the overall average across most of the racialized groups, while it is lower among young Filipino women. Based on these disaggregated data, you might think about how to ensure your policy initiative can reach certain groups that may be more likely to need supports in finding a job, such as racialized youth in general, and young Black and Arab men in particular.
(The following words are revealed over a funnel shape that starts wider and ends narrower: "Gender", "Age (youth)", "Racialized group membership" and "South Asian, Chinese, Black, Filipino, Arab".)
As we have seen in this example, the use of disaggregated data can help policymakers to develop more targeted and effective policies by identifying the unique needs and challenges faced by different demographic groups. In this example, if we had based our analysis on only the unemployment rate at the national level, or even for women and men, it would not have been enough to ensure our policy intervention would be focused on the most at-risk people. And this is just one example. Depending on your organization or analytical needs, you may disaggregate the data in different ways. Perhaps geography or another characteristic is a relevant factor in your situation. The point is, it often takes multiple layers or levels of disaggregation to get at the full story.
(The Canada Wordmark appears.)
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