Category: Data

Why Student Debt Burden is Falling Like a Stone

Everyone talks about “rising student debt burdens” as if they are real.  But they’re not.  In fact, the burden of carrying a student loan has fallen significantly over the past decade. Student loan burden is best measured by looking at the percentage of monthly after-tax income that it takes to service a loan each month.  This figure will therefore be affected by four different factors, namely: the size of student loan debt, interest rates, post-graduation income, and taxes.  Here’s what’s happened

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Student/Faculty Ratios Across Fields of Study

Here’s an intriguing question: what do student/faculty ratios look like across the academy?  No one ever publishes this number.  What you tend to get out of the Statscan data (with a little help from the excellent folks who put out the CAUT Almanac) is a graph of overall student/faculty ratios, such as the one below in Figure 1, which shows that across all institutions and all fields, there has been an increase of about 20% in the faculty/student ratio over the

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Administrative Bloat?

If there’s one common complaint among academic staff it’s that non-academic staff… administrators… are multiplying like weeds, and taking over the university.  Of course, no one can tell if this is actually happening because Canadian universities have never bothered to put together any common statistics on non-academic staff. What we do have, though, is data on non-academic staff compensation – that is, we can see how much non-academic staff were paid in any given year, and track that over time.  We can then

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Unorthodox Dropout Statistics

Every once in awhile, some policy-maker or journalist gets their knickers in a twist about dropout rates.  And whenever that happens, people start looking for data.  Which, in this case, basically doesn’t exist. Institutions have their own non-completion data, but since lots of people switch institutions for one reason or another a non-complete doesn’t equal a “dropout”.  Our national unit-record system – Statistics Canada’s Post-Secondary Student Information System – is supposed to be able to solve this precise problem, but

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Using PIAAC to Measure Value-Added in Higher Ed: US Good, Australia Abysmal

A few weeks ago, when commenting on the PIAAC release, I noted that one could use the results to come up with a very rough-and-ready measure of “value added” in higher education.  PIAAC contains two relevant pieces of data for this: national mean literacy scores for students aged 16-19 completing upper-secondary education, and national mean literacy scores for students aged 16-29 who have completed Tertiary A.  Simply by subtracting the former from the latter, one arrives at a measure of

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