Status of Women Committee on June 8th, 2017

[Sheila Block]

Correct.

[Anita Vandenbeld, Liberal, Ottawa West—Nepean, ON]

So the 2016 data, which was the long-form census, is not available yet. Is that right?

[Sheila Block]

It's going to be available in October, and we're very much looking forward to having reliable data again, and we are finalizing our order to StatsCan.

[Anita Vandenbeld, Liberal, Ottawa West—Nepean, ON]

Thank you. That answers my question.

[Sheila Block]

Okay, I'm sorry about that.

[Anita Vandenbeld, Liberal, Ottawa West—Nepean, ON]

I thought we were talking about the new long-form census. Okay, thank you very much.

I'm struck by what you say about the fact that when you control for age and immigration status, this gap is persisting even into the second and third generations. That means that racism is involved, that there is some form of prejudice and stereotyping that goes beyond some of these control groups. One of the things that the federal public service is piloting right now is name-blind hiring. Is that something you think would help in this regard?

[Prof. Grace-Edward Galabuzi]

That approach has been attempted in other jurisdictions—in Europe for sure, and in Britain. It corrects some of the function of racialization, but not all of it. I think the literature shows that perhaps the most effective way is to acknowledge that racialization has an impact, and address that directly through forms of affirmative action, so that there's a conscious decision around ensuring that the workplace is more representative, as opposed to assuming that the removal of identification leads to that representation.

I think the experience in Britain is that the removal of identification does not lead to representation per se, and that a more deliberate or more intentional approach is more effective in addressing questions of disparity and under-representation.

[Anita Vandenbeld, Liberal, Ottawa West—Nepean, ON]

Thank you.

For my final question, I'm looking at the statistics about occupation. One of the things that I'm noticing—and this is on page 11—is that in natural and applied sciences racialized men are at 22.5% and so are overrepresented, and that for racialized women the second-highest category is business, finance and administration. One of the things that we've been looking at in our committee is how to get more women into STEM professions, into business, into the professions that are primarily higher-income professions. I was a little bit surprised to see those statistics. Could you clarify that?

[Sheila Block]

Those are at a very high level of aggregation, so this isn't identifying the women who are engineers. In particular, in one of those categories you discussed, you could have a lot of clerical workers included in there. That's one of the limitations of this high level of aggregation.

[Prof. Grace-Edward Galabuzi]

All of our classifications are accounted for.

[Anita Vandenbeld, Liberal, Ottawa West—Nepean, ON]

Thank you.

When you're disaggregating the data and looking at gender and race, have you factored in racialized women with disabilities, racialized women who are LGBTQ, and a number of other intersecting identities in any of your studies?

[Prof. Grace-Edward Galabuzi]

Thus far, we have not. We've talked about the need to do that. It's a much more complex request of StatsCan, but we're looking forward to doing more of that analysis. I think we started the study at a fairly basic level in terms of intersection, which is race and gender. With the scope, there is a necessity for us to do that.

The argument we're making is that the more disaggregated data is available, the greater is our ability to do the analysis you are asking us to do.

[Anita Vandenbeld, Liberal, Ottawa West—Nepean, ON]

More data, of course. Thank you.

Finally, when we were looking at pay equity in the special committee on pay equity, we discussed how the formulas can be very complicated, even when you just look at gender. If you start looking at pay equity and factoring in other intersectional groups like race, it can be very difficult even to identify similar groups.

Do you have any recommendations on how one would be able to do that and not overburden the system with complexity?

[Sheila Block]

I think there are moments when it can be helpful to step back. Because racialized women work disproportionately for the minimum wage, if you increase that wage, they will be the major beneficiaries of that change. As Grace-Edward mentioned about affirmative action, if you take positive actions like that, focused on the bottom end of the labour market, they will have a disproportionate impact on that. If you look at broader policies that ask if the workforce reflects the available labour force, that is also important.

There are huge data limitations. Even if Statistics Canada collected every single data point we were interested in, and every variable, there would be limitations. I think when you look at policy, you have to look at how feasible it is, how tractable it is, and how we can move the ball forward.

[Prof. Grace-Edward Galabuzi]

I think it's important when we're dealing with issues of social justice that we not allow complexity to determine what we need to do to address experiences of injustice. I think we are at a level of sophistication right now with regard to pay equity systems that was not available to us 10 or 15 years ago.

I think we just need to do the same with regard to the other intersections, because they have an impact, not unlike gender, on pay equity.

[The Chair, Conservative, Marilyn Gladu]

Very good.

Now we'll go to my colleague, Ms. Vecchio, for seven minutes.

[Karen Vecchio, Conservative, Elgin—Middlesex—London, ON]

Thank you very much.

To start, just for the record, can you give us your definition of “racialized”? What does that include, and what does it not include?

[Sheila Block]

My apologies, because we should probably have started with that.

The definition is based on what we have from Statistics Canada. What we have from Statistics Canada is a variable called “visible minority”, which is based on the federal employment equity legislation. I have a footnote here that will describe it.

Grace-Edward, can you say why we use “racialized” rather than “visible minority”, and then I'll say who's included?

[Prof. Grace-Edward Galabuzi]

Well, say who's included and then—

[Voices]

Oh, oh!

[Sheila Block]

The visible minority status is self-reported and refers to the visible minority group to which the respondent belongs. The Employment Equity Act defines visible minorities as persons other than aboriginal people who are non-Caucasian in race, or non-white in colour. The census respondents are asked if this person is White, Chinese, South Asian, Black, Filipino, Latin American, Southeast Asian, Arab, West Asian, Japanese, Korean, or other, in which case they specify.

We built this analysis on that the question in the census.

[Karen Vecchio, Conservative, Elgin—Middlesex—London, ON]

Okay, thank you very much.

With that, I hear what you're talking about with the StatsCan information. Do we have anything to show the percentage of racialized women compared to non-racialized women? What is the ratio there?

[Sheila Block]

We based all of this analysis on the labour market share. While it's grown since then, as I say, this is based on—

[Karen Vecchio, Conservative, Elgin—Middlesex—London, ON]

Sorry, this is based on the labour market?

But do you have general population information on that?

[Sheila Block]

I have with me general populations from 2006. It's 16.2% of the total population in 2006.

Grace-Edward, do you have the 2011 numbers?