Statistics Canada released a couple of papers in the last month which unfairly got zero play in the general media, so thought I would pick them up and amplify them here.

The first one, by the ever-excellent Marc Frenette, is called Do Youth From Lower- and Higher-Income Families Benefit Equally From Postsecondary Education? and it’s a pretty important question from a public policy point of view, since a good deal of the rationale for widening access is premised on the fact that low-income students do in fact benefit equally. 

One of the big new tricks in studying Canadian higher education is to measure socio-economic class of students by taking their enrolment data from the Postsecondary Student Information System (PSIS) and linking it to their data in the T1 Family File (a repository of tax returns), then looking at total parental income  based on the incomes of the people residing at the address from which the student filed their returns at the age of 19 (this doesn’t quite cover all students because there are a very few who would not have a parental house as a permanent address for things like tax returns, but it’s the best method we have).  Once each student is thus assigned a socio-economic marker, you can track them forwards through time to see how they do through school and afterwards, and you can compare how students from different income backgrounds fare in terms of income, labour market outcomes, etc.  You can also compare these students to individuals who never went to post-secondary (which you can work out by whether they filed for a tuition tax credit). 

All this Frenette duly does and crunches the data.  The core of his findings are encapsulated in the following chart:

So, the key to this chart is the slopes of each of the three curves.  That they slope upwards tells you that kids from richer backgrounds do better, financially, than those from poorer backgrounds, at any given level of education.  That’s presumably because kids from higher income brackets have parents with better job networks that they can tap.  But – and this is Frenette’s key point – the slope of three lines (no education, college, university) – are all pretty much the same.  In other words, having richer parents gives you an advantage, and having more education gives you an advantage, but those two effects are independent of one another.  Or, more simply, the education effect is similar across all levels of parental income. 

This is good news.

The other paper – which is also good news – is by Katherine Wall, and is entitled Persistence and Representation of Women in STEM programs.  This is a much trickier study than it looks because persistence does not always mean what people think it means.  The paper basically found the following:

• Men in STEM fields are more likely to drop out of PSE than women in STEM fields
• Women in STEM fields are more like to switch into non-STEM fields than men in STEM fields
• Men are more likely than women to switch between STEM fields (eg from engineering to physics), ergo women are likelier to persist in specific STEM fields than men.
• Women from non-STEM fields are likelier to switch into STEM fields than men in non-STEM fields.

You see what I mean about persistence being a tricky subject?  If you stop at that first bullet point, you might get a pretty negative picture.  But when you put all the other pieces together, you find that the percentage of women in STEM is basically exactly the same from first year up to the point of graduation.  It’s still the case that women are drastically under-represented in Engineering (19% of enrolments) and Math/Computer Sciences (27.6%), so we can’t say everything is peachy in these fields.  But this study suggests that the problem lies far more in attractingfemale students than it does in graduating them.

Bon Weekend!